Common Fund Highlights

Birthing Biomarkers: Researchers Discover Metabolites associated with Pregnancy Complications

Pregnancy complications, defined as health problems that arise during pregnancy, can have detrimental effects on both infants and pregnant women. Hypertensive disorders of pregnancy, for example, occur when a pregnant woman has high blood pressure. This type of complication affects about 10% of US pregnancies and can lead to poor birth outcomes, including infants being born small for their gestational age. Preterm birth, another type of pregnancy complication, occurs when a baby is born before 37 weeks of pregnancy. Babies born preterm are at increased risk of health challenges such as vision problems or developmental delay.

Identifying biomarkers associated with pregnancy complications could help diagnose or treat these conditions and, ideally, lead to fewer health problems for pregnant women and infants. Though there are some biomarkers associated with hypertensive disorders of pregnancy and preterm birth, researchers are still searching for biomarkers that can help clinicians diagnose or manage these complications.

Metabolites, which are molecules produced as a result of the chemical reactions that occur in our cells, can serve as disease biomarkers. A goal of the Common Fund’s Metabolomics program is to facilitate new ways to diagnose or treat diseases by improving the identification and analysis of metabolites. In research supported by the Metabolomics program, Dr. Susan Sumner and colleagues identified hundreds of metabolites associated with either hypertensive disorders of pregnancy or preterm birth. By analyzing first trimester serum samples from 213 women, the researchers found that several of the metabolites they identified significantly improved the ability of computational models to predict the risk of pregnancy complications. Though more research is needed to determine whether these metabolites will help in the management of pregnancy complications, this work paves the way for the identification of biomarkers that could improve the care of women at risk for hypertensive disorders of pregnancy or preterm birth.


Untargeted analysis of first trimester serum to reveal biomarkers of pregnancy complications: a case-control discovery phase study. E.W. Harville, Y.-Y. Li, K. Pan, S. McRitchie, W. Pathmasiri, and S. Sumner. Scientific Reports. 2021 Feb 10; 11(1):3468.

From Form to Function: Investigating the Structure of SARS-CoV-2

Since its emergence in 2019, the SARS-CoV-2 coronavirus has led to a public health crisis impacting the lives of people worldwide. The development of treatments to combat COVID-19 (the disease caused by SARS-Cov-2) requires an understanding of the structure and function of different components of the virus. For example, SARS-CoV-2 has spike proteins on its surface that enable the virus to invade human cells and cause infection. The spike proteins of SARS-CoV-2 are covered in sugar molecules called glycans that impact how the virus functions. Though research has revealed a lot about the functions of spike proteins, extensive knowledge of how glycans are arranged on spike proteins is needed to provide new insights into how SARS-Cov-2 functions and how it can be treated.

The goal of the Common Fund’s Glycoscience program is to develop resources that make the study of glycans more accessible to the research community. In research supported by the Common Fund, Dr. Radoslav Goldman and colleagues developed methods to quantify the different glycan structures attached to specific positions on proteins. The researchers then applied these methods to develop a nuanced, in-depth portrait of the structure of glycans on SARS-CoV-2 spike proteins. By analyzing spike proteins expressed in lab-grown human cells, the researchers identified eight novel glycopeptides (peptides with glycans attached to them), showing the specific locations of the sugars on SARS-CoV-2 spike proteins in great detail. This work considerably expands knowledge of the structure of SARS-CoV-2, paving the way for deeper insights into how glycans impact virus function.


Low Collision Energy Fragmentation in Structure-Specific Glycoproteomics Analysis. Miloslav Sanda, Julius Benicky, Radoslav Goldman. Anal Chem. 2020 Jun 16; 92(12):8262-8267.

N- and O-Glycosylation of the SARS-CoV-2 Spike Protein. Miloslav Sanda, Lindsay Morrison, Radoslav Goldman. Anal Chem. 2021 Feb 2; 93(4):2003-2009.

Cognitive Behavioral Therapy Can Reduce Chronic Pain and Pain-Related Disability

A group of adult men and women are indoors at a yoga class.

The Collaborative Care for Chronic Pain in Primary Care (PPACT) study, an NIH Collaboratory Demonstration Project, published the primary results from their trial. The study was conducted under real-world conditions in Kaiser Permanente primary care clinics across three US regions (Georgia, Hawaii, and Northwest) to determine the effectiveness of a group-based cognitive behavioral therapy (CBT) intervention for chronic pain and functional impairment in patients receiving long-term opioid therapy. Participants were randomly assigned to an intervention or to standard care. The study showed that patients who participated in the intervention as part of their regular care for chronic pain showed improved function and reduced pain compared to standard treatment, but did not reduce their use of opioid medication. The CBT intervention consisted of (1) tailoring intervention goals to each patient (based on patients’ specific circumstances and preferences), and (2) twelve weekly group sessions focused on providing specific core skills (i.e., training in muscle relaxation techniques) and a yoga-based adaptive movement training. Learn more about this work: PPACT Study Finds Benefits of Cognitive Behavioral Therapy in Reducing Chronic Pain and Pain-Related Disability.


A Primary Care–Based Cognitive Behavioral Therapy Intervention for Long-Term Opioid Users With Chronic Pain. . DeBar L, Mayhew M, Benes L, Bonifay A, Deyo RA, Elder CR, Keefe FJ, Leo MC, McMullen C, Owen-Smith A, Smith DH, Trinacty CM, Vollmer WM. Ann Intern Med. 2021 Nov 2. doi: 10.7326/M21-1436.


The ASCENT of New Technology to Advance Bioelectronic Medicine

SPARC Portal Community SpotlightPeripheral nerves are spread throughout the body, sending signals from our limbs and organs back to the brain and spinal cord allowing us to feel stimuli, such as the warmth from the sun, and to move our limbs to accomplish our daily activities. Bioelectronic medicine sends similar electrical signals to nerves in our body to treat a wide variety of diseases, such as epilepsy, heart failure, rheumatoid arthritis, and obesity. However, more research is needed to discover how much electrical stimulation is needed, where to apply it, and how to best use this technology for each individual patient and condition. Researchers from the NIH Common Fund’s Stimulating Peripheral Activity to Relieve Conditions (SPARC) program have created new digital technology to better understand the effects of nerve stimulation, as well as to aid in designing, building, and running next generation computational models to advance the field of bioelectronic medicine.

This research team at Duke University, led by Dr. Nicole A. Pelot, has developed the Automated Simulations to Characterize Electrical Nerve Thresholds (ASCENT) pipeline, which builds 3D models of nerves using cuff electrodes to simulate individual neuron responses to electrical signals. This technology has been used to compare existing models of individual neurons, as well as new models of neurons in peripheral tissues of the body, that reproduce previously published laboratory experimental results using computational methods. The team at Duke University has used ASCENT technology in collaboration with researchers at the University of Wisconsin-Madison to reveal key pathways for side effects of vagus nerve stimulation, a bioelectronic medicine treatment used to treat epilepsy. These results can serve as a guide to avoid side effects of epilepsy treatment and increase the therapeutic effects of bioelectronic medicine in the treatment of this disease. The ASCENT pipeline has the potential to advance the field of bioelectronic medicine by streamlining the creation of new reproducible computational models of nerve stimulations that can be used to treat a variety of conditions, while factoring in the individuality of specific individuals and details of their unique nervous system makeup. 

Learn more about this work on the SPARC Portal Community Spotlight page.

ASCENT (Automated Simulations to Characterize Electrical Nerve Thresholds): A pipeline for sample-specific computational modeling of electrical stimulation of peripheral nerves. Musselman E, Cariello J, Grill W, Pelot N. PLOS Computational Biology. 2021 August 26. DOI: 10.1371/journal.pcbi.1009285


Building a Healthier Ecosystem: CFDE Expands with New Data Coordination Centers and Partnerships

The Common Fund Data Ecosystem (CFDE) program aims to enable broad use of the data generated by its many programs by creating a data ecosystem—the management infrastructure, analytics, applications, and user interfaces needed to work within and across existing Common Fund data sets.  Continuing to develop and grow this ecosystem, the CFDE has now funded two more Common Fund Data Coordinating Centers (DCCs) as well as funded additional partnership projects among DCCs to help build this functional ecosystem.  

Data Coordination Centers newly engaged with CFDE

The new DCCs  joining the CFDE represent the Glycoscience and 4DN programs.  

  • The Glycoscience DCC will work to integrate glycoscience data from its knowledgebase GlyGen into the CFDE.  GlyGen, incorporates and harmonizes data for glycans, proteins, and glycoproteins from many sources aiming to map connections between glycans and genes and proteins.  
  • The 4D Nucleome  (4DN)  DCC further expands the CFDE by adding multimodal data including sequencing-based and imaging-data, that aim to understand how three-dimensional chromosomal interactions affect long-range gene regulation, chromosomal dynamics under perturbation, and non-coding variants in the genome.   

The addition of these DCCs and datasets will help expand CFDE by contributing a wealth of information about nuclear organization and about roles that glycans play in organisms while increasing the diversity of data types included within CFDE. They join eight DCCs including Extracellular RNA Communication (ExRNA), Gabriella Miller Kids First (Kids First), Genotype-Tissue Expression (GTEx), The Human BioMolecular Atlas Program (HuBMAP), Illuminating the Druggable Genome (IDG), Library of Integrated Network-based Cellular Signatures (LINCS), Metabolomics, and Stimulating Peripheral Activity to Relieve Conditions (SPARC) programs that were initially funded in FY20.  Together with the CFDE-Coordination Center (CFDE-CC), these awardee teams are continuing to advance development of processes for harmonizing basic metadata elements, providing data sets for the CFDE Portal,  forming a culture of sharing insight and knowledge across DCCs, and contributing to CFDE-wide training and outreach efforts.    

New Partnerships among Data Coordination Centers 

Six new DCC partnership projects have also been funded by the CFDE. These collaborative projects will develop approaches and tools to harmonize data and workflows from multiple Common Fund programs enabling cross-dataset analysis. These partnerships are meant to enhance DCC-DCC interactions. In addition, these partnerships aim to demonstrate the utility of their data integration tools and approaches for CF datasets to the broader scientific community. These projects and DCCs include: 

  • Anatomical Interoperation of Resources: Partnering DCCs: SPARC, HuBMAP  

This project will compare the spatial distribution of gene expression in the heart across different developmental stages, health, and disease states. This is critical to improve understanding cardiac pathologies. This will involve data from the SPARC and HuBMAP program and registering tissue architecture, neural and/or vascular tracings, RNA-seq, and other data types against a common coordinate cardiac spatial scaffold.   

  • Gene Burden Testing: Partnering DCCs: Kids First, HuBMAP 

This project will enhance the capabilities of the HuBMAP Knowledge Graph. The aim is to enable HuBMAP and Kids First workflows to run seamlessly on both HuBMAP and Kids First infrastructure and establish standards and solutions that point the way to broader workflow interoperability within the CFDE. The Knowledge Graph will enable finding and accessing the data sets relevant to the queries such as “do children with congenital disabilities have an overabundance of variants in genes that are expressed in specific cell types in tissues of interest?”  

  • CFDE Gene Centric Prototype Dashboard: Partnering DCCs: ExRNA, Glycoscience, GTEx, HuBMAP, IDG, Kids First, LINCS, and Metabolomics 

This project will develop methods to harmonize gene, protein, and RNA identifiers and generate a cloud workspace that pools gene information from DCCs for use cases. This will involve development of standards for gene landing pages and gene centered API and development of a prototype dashboard for gene cards from the DCCs and other resources.   

  • CLinical Observations and Vocabularies (CLOVoc): Partnering DCCs: Kids First, Metabolomics, SPARC

This project will build FAIR metadata about human clinical data and facilitate interoperability amongst these datasets.  This effort will develop minimal clinical metadata framework and APIs to facilitate the discoverability/interoperability and develop FHIR profiles of clinical metadata across partnering DCCs. The goals are to improve the ability to query across CF datasets for a given disease/phenotype or a clinical profile and integrate different datasets so that they are interoperable and reusable for secondary analyses.  

  • Aggregation and Sharing of Variant-centric Information: Partnering DCCs: ExRNA, GTEx, and Kids First 

This project aims to make CFDE variant data FAIR by establishing a framework to derive information about specific variants and regulatory elements from the high-volume -omics profiling datasets to interpret such non-coding variants. 

  • Toxicology Screening Pipeline: Partnering DCCs: IDG, Kids First, LINCS, and SPARC 

This project will develop a pipeline infrastructure that will tag CFDE Portal records for genes, their products, and small-molecule xenobiotics with labels of toxicity potential for reproductive and developmental processes. 

Learn more about these awards by visiting the CFDE Funded Research page.

What We Did on our Summer Vacation - Learning the Ins and Outs of Single-Cell Research

Summer Students from HuBMAP's Underrepresented Student Internship ProgramInterns from HuBMAP's Underrepresented Student Internship Program

In Summer 2021, the Human BioMolecular Atlas Program launched its first Underrepresented Student Internship Program for undergraduate students to work with HuBMAP researchers for the summer to learn cutting-edge single-cell technologies, 3D model making, and software building. Eight students were chosen by researchers at three institutions –Harvard University, Stanford University, and University of Pennsylvania

Harvard University- 
HuBMAP Researcher: Nils Gehlenborg, PhD

Roselkis Morla Adames created a webpage for the HuBMAP portal which allows users to visualize data about the HuBMAP tissue donors, such as sex, race, age, ethnicity, and other factors.  

Stanford University – 
HuBMAP Researcher: Garry Nolan, PhD

Injyil Gates used CODEX imaging, a technique that fluorescently stains proteins in each cell, on samples from 8 sites in the small bowel and colon.  

University of Pennsylvania -
HuBMAP Researcher: Brian Gregory, PhD
Stephanie Bobadilla-Regalado used single-cell RNA sequencing to study immunoglobulin gene expression in tissue samples from a patient undergoing female-to-male sex reassignment. There were five upregulated immunoglobulin genes in these samples, possibly in response to the high-testosterone hormone treatments of the procedure and could represent a possible shift to “male” expression.  

Tatiana Gonzalez studied the effect of hormone therapy on gene expression at the single cell level in cervical tissues. She found three genes (MIR31HG, MUC16, and RHEX) which had increased expression levels during hormone therapy. These genes are involved in cell growth and might be involved in cancer progression.  

HuBMAP Researcher: Junhyong Kim, PhD

Oluwafolajinmi Olugbodi devised ways to retrieve biologically relevant metadata from HuBMAP’s data collections more easily. Using this framework, researchers will be able to input metadata with minimal additional effort.  

HuBMAP Researcher: Kate O'Neill, MD, MTR

Ogechukwu Etuazim used RNA-sequencing to study the differences in gene expression between successful versus ectopic implantations of embryos. 

Casey Henson worked with the Penn Image Computer and Science Lab to learn how to use the ParaView visualization tool with open-source ITK-SNAP software to create animated sectioning of uterine MRI images.  

Kate da Silva worked with the Penn Image Computer and Science lab to learn how to use 3D printing techniques to create a mold of a human ovary out of plastic acrylonitrile butadiene styrene, providing the model with strength not usually seen in more standard models.  

If you would like to see more about the work of these talented students, please watch their presentations on the HuBMAP YouTube Channel or read about them on the HuBMAP Consortium website.

HuBMAP Underrepresented Student Internship Program was funded by 1OT2OD026675-01

Identifying Targets for Pain Medication with Artificial Intelligence

Chronic pain, a condition where pain persists or recurs over a long period of time, is common in the US, affecting an estimated 1 in 5 adults. Experiencing chronic pain significantly impacts patients’ lives by interfering with day-to-day activities and increasing risk of depression and substance abuse. There are different causes of pain, including pain that arises from inflammation, nervous system injury, or tissue damage. The way the body senses these different types of pain varies. Despite this variety, current pain relief medications focus on relatively few drug targets (defined as molecules in the body that interact with or are modified by a drug). As a result, current medications may not be effective at treating some types of chronic pain.

A major goal of the Common Fund’s Illuminating the Druggable Genome program is to discover new targets for medications. In research supported by the Illuminating the Druggable Genome program, Dr. Avi Ma’ayan and colleagues identified new drug targets for pain treatment. The researchers used machine learning, a type of artificial intelligence where computer algorithms make predictions from data. By combining data on genes, proteins, and RNA molecules from 14 databases and publications, the computer algorithms prioritized targets for human genes associated with 17 unique types of pain. Using this approach, the researchers identified 13 potential drug targets for migraine drug development and four for rheumatoid arthritis. This work has the potential to accelerate research on the identified drug targets, paving the way for more treatment options for chronic pain.


Prioritizing Pain-Associated Targets with Machine Learning. Minji Jeon, Kathleen M. Jagodnik, Eryk Kropiwnicki, Daniel J. Stein, Avi Ma’ayan. Biochemistry. 2021 May 11; 60(18):1430-1446.


A Time to Prepare for Careers

The NIH Common Fund supported the “Strengthening the Biomedical Research Workforce” program to enhance opportunities for early career scientists and to help prepare them for a variety of career options within the dynamic biomedical workforce landscape. The program funded 17 “Broadening Experiences in Scientific Training” (aka BEST) awards designed to broaden graduate and postdoctoral training opportunities by creating training programs that reflect a range of potential career options. One important consideration of preparing trainees for careers is understanding any potential unintended consequences, such as slowing student research progress or increasing the time it takes to earn a graduate degree. 

Many biomedical research institutions now offer some type of professional training that enables career exploration and development of a broad set of skills critical to various research-related career paths. In an analysis from ten institutions with BEST programs, data on length of time to earn a doctoral degree were compared with data on student participation in career and professional development activities. Across these institutions there were varying strategies and activities for career preparation. The activities ranged from single events to multi-part workshop series or coursework, as well as experiential learning activities, such as site visits, internships, and individual training sessions. Despite these differences in approaches across the different programs there was no difference in time to degree for doctoral students who participated in career and professional development activities during their academic training. This was the case even when looking specifically at students who spent the most hours in these activities, like internships. Furthermore, even the number of publications produced by students in the training programs was not negatively impacted by participation, suggesting that research productivity is not reduced by participation in career exploration activities. This is part of a growing literature demonstrating that participation in career and professional development opportunities should be encouraged to ensure student preparedness for a variety of diverse and important biomedical research careers and does not negatively impact overall research training or productivity.

Read More in Science Careers:

Worried your Ph.D. adviser won’t support career development activities? Show them this


A cross-institutional analysis of the effects of broadening trainee professional development on research productivity
Brandt PD, Sturzenegger Varvayanis S, Baas T, Bolgioni AF, Alder J, et al. (2021) A cross-institutional analysis of the effects of broadening trainee professional development on research productivity. PLOS Biology 19(7): e3000956., P. D., S. Sturzenegger Varvayanis, T. Baas, A. F. Bolgioni, J. Alder, K. A. Petrie, I. Dominguez, A. M. Brown, C. A. Stayart, H. Singh, A. Van Wart, C. S. Chow, A. Mathur, B. M. Schreiber, D. A. Fruman, B. Bowden, C. A. Wiesen, Y. M. Golightly, C. E. Holmquist, D. Arneman, J. D. Hall, L. E. Hyman, K. L. Gould, R. Chalkley, P. J. Brennwald and R. L. Layton (2021). "A cross-institutional analysis of the effects of broadening trainee professional development on research productivity." PLOS Biology 19(7): e3000956.


The Destiny in our Bones: Density

The NIH Common Fund Knockout Mouse Phenotyping Program (KOMP2) is the flagship partner of  the International Mouse Phenotyping Consortium (IMPC)  to knockout (remove) and characterize all protein-coding genes in the mouse genome.  Overall, this project helps scientists explain the genetic basis of many different types of diseases that occur in both mice and humans, including under-studied rare diseases and common chronic conditions. This collaborative effort has been a powerful resource for describing genes with previously unknown function in hearing, vision, and metabolism, for example. Now, KOMP2 researchers gained insights into the genetics of bone diseases and conditions.Osteoporosis is characterized by increased chance of fracturing bones even in the course of normal activity. Bone Mineral Density (BMD) is a measure that is often changed in a range of bone diseases and conditions, including osteoporosis. The genetic factors involved with changes in BMD, such as in osteoporosis, are not well characterized. A comprehensive and large-scale effort from the KOMP2 aims to change that, by identifying new genes involved in bone formation and health. 

Phenotyping, or analyzing the characteristics of, the many different knockout mice includes skeletal exams of bone parameters: bone area (BA), bone mineral content (BMC), and the resulting calculated BMD. Using IMPC phenotyping data from 3,823 knockout mice, along with bioinformatics, and an advanced model of bone formation, researchers identified 200 genes which regulate BMD. Of these 200 genes, 141 genes were previously not known to affect BMD. This finding greatly adds to researchers’ understanding of the biology of BMD maintenance and identified novel skeletal candidate genes, including Arl4d, Ncald, and Rab3ip, for further investigation. These newly identified genes may also point to potential targets for treatments of bone disorders in humans, like osteoporosis. 
Explore the phenotypic data here.


Mouse mutant phenotyping at scale reveals novel genes controlling bone mineral density(link is external).Swan AL, Schütt C, Rozman J, Del Mar Muñiz Moreno M, Brandmaier S, Simon M, Leuchtenberger S, Griffiths M, Brommage R, Keskivali-Bond P, Grallert H, Werner T, Teperino R, Becker L, Miller G, Moshiri A, Seavitt JR, Cissell DD, Meehan TF, Acar EF, Lelliott CJ, Flenniken AM, Champy MF, Sorg T, Ayadi A, Braun RE, Cater H, Dickinson ME, Flicek P, Gallegos J, Ghirardello EJ, Heaney JD, Jacquot S, Lally C, Logan JG, Teboul L, Mason J, Spielmann N, McKerlie C, Murray SA, Nutter LMJ, Odfalk KF, Parkinson H, Prochazka J, Reynolds CL, Selloum M, Spoutil F, Svenson KL, Vales TS, Wells SE, White JK, Sedlacek R, Wurst W, Lloyd KCK, Croucher PI, Fuchs H, Williams GR, Bassett JHD, Gailus-Durner V, Herault Y, Mallon AM, Brown SDM, Mayer-Kuckuk P, Hrabe de Angelis M; IMPC Consortium. PLoS Genet. 2020 Dec 28;16(12):e1009190. doi: 10.1371/journal.pgen.1009190. PMID: 33370286; PMCID: PMC7822523

CEPT: A recipe for a small-molecule cocktail that enhances the survival of stem cells

In the left and center panels, Caspase 3/7 (green), a marker for dead cells. In contrast, the right panel shows that all cells survived in the presence of the CEPT cocktail. Credit: Chen et al., Nature Methods

The combination of Chroman 1, Emricasan, Polyamines, Trans-ISRIB (CEPT), is a new resource that can help bring induced pluripotent stem cell technology closer to clinical applications. 

When you hear “small cocktail,” stem cell research may be the furthest thing from your mind. In stem cell research, especially induced pluripotent stem cells (iPSCs) technology, small-molecule cocktails can be critical to improve cell survival.  iPSCs are a special type of stem cell derived from skin or blood cells that have been reprogrammed into a stem cell-like state where they can become any cell type in the body. iPSCs-based therapy is already being tested in clinical trials to treat retinal degeneration, heart failure, and neurological diseases [1] and have the potential to treat chronic wounds [2]. However, the development of  iPSC-based therapy can be difficult, partly due to cell-culture induced stress that affects the quality of cells. The lack of high quality iPSCs can limit production of the large amounts of cells needed for disease research, tissue engineering, drug development and regenerative medicine. To address technical challenges that impede the use of iPSC technology for clinical applications, NIH launched the Stem Cell Translation Laboratory (SCTL), part of the NIH Common Fund's Regenerative Medicine Program and housed within the National Center for Advancing Translational Sciences (NCATS). 

A team of researchers at SCTL, led by Dr. Ilyas Singeҫ, tested more than 15,000 U.S. Food and Drug Administration-approved drugs and compounds that led to identification of small-molecule cocktails that would protect iPSCs from cellular stress and improve cell survival. They found CEPT, a four-factor drug combination – or cocktail – made up of chroman 1, emricasan, polyamines, and trans-ISRIB that preserves cell survival through processes including protection from DNA damage and lowering of oxidative stress. Additionally, the team demonstrated that treating iPSCs with CEPT makes establishing new types of iPSCs more feasible and producing larger quantities possible. Thus, the CEPT cocktail has the potential to support up-scaling of iPSCs for clinical use. 

Findings from this work are presented in Nature Methods [3] and indicate that CEPT is a safeguard for iPSCs. Though additional research is needed, using the small molecule cocktail of CEPT treatment may become a standard method to efficiently generate iPSCs and move the field of stem-cell research forward. This new approach is a building block to develop advanced strategies for drug discovery and accelerate breakthroughs in the development of stem cell-based therapies for complex diseases. 

Read more about this work in new articles: 

1. Deinsberger J, Reisinger D, Weber B. Global trends in clinical trials involving pluripotent stem cells: a systematic multi-database analysis. NPJ Regen Med. 2020 Sep 11; 5:15.

2. Gorecka J, Kostiuk V, Fereydooni A, Gonzalez L, Luo J, Dash B, Isaji T, Ono S, Liu S, Lee SR, Xu J, Liu J, Taniguchi R, Yastula B, Hsia HC, Qyang Y, Dardik A. The potential and limitations of induced pluripotent stem cells to achieve wound healing. Stem Cell Res Ther. 2019 Mar 12;10(1):87.

3. Chen Y, Tristan CA, Chen L, Jovanovic VM, Malley C, Chu PH, Ryu S, Deng T, Ormanoglu P, Tao D, Fang Y, Slamecka J, Hong H, LeClair CA, Michael S, Austin CP, Simeonov A, Singeç I.  A versatile polypharmacology platform promotes cytoprotection and viability of human pluripotent and differentiated cells. Nat Methods. 2021 May 3. 

Let’s Talk About Shapes: Viral Characteristics That Make It Successful In Human Cell Infection

Piecing Together the Requirements to Tackle Antiviral Drug Resistance and Future Pandemics

Viruses are a major threat to human health and given that they spread through social interactions have costly consequences. In the United States during the 2019 – 2020 influenza season, an average of 38.2 million cases, 404,646 hospitalizations, and 21,909 influenza related deaths occurred1. The economic burden of influenza in the United States has been estimated at $5.8 billion annually2. Non-influenza-related viruses affecting humans such as measles virus, Ebola virus and chickenpox virus have been similarly devastating and have resulted in deadly epidemics in history and continue to be a major threat against humanity3. Researchers have historically investigated the characteristics that make a virus able to successfully infect a human cell. However, determination of the role of viral shape in cell infection has been challenging due to the shape only being partially genetically encoded and so not amenable to typical genetic experiments.
Now, a team lead by New Innovator Awardee Dr. Tijana Ivanovic at Brandies University has uncovered that the shape of the virus particles determines the efficiency of the virus attaching and fusing to the human cell when viral activity is compromised. Viral particles that are filamentous have an advantage over viral particles that are spherical when there are neutralizing pressures from the human immune response on the viral cell entry machinery. Filamentous particles have been shown to directly resist cell-entry pressures and are less sensitive to inhibition activities by the immune system antibodies. They are able to fuse to the human cell membrane more efficiently at high neutralizing pressure and longer filaments are better able to resist extreme inactivation efforts by human antibodies. Although this is a promising discovery, further studies are needed to determine additional individual specific requirements that are also necessary for successful viral infection. However, this discovery and further studies may potentially lead to the development of antiviral treatments that directly target long filamentous virus particles to possibly curb pandemics and overcome antiviral drug resistance.

The shape of pleomorphic virions determines resistance to cell-entry pressure
Li T, Li Z, Deans EE, Mittler E, Liu M, Chandran K, and Ivanovic T. Nature Microbiology. 2021 Mar 18. doi: 10.1038/s41564-021-00877-0. PMID: 33737748

1. Centers for Disease Control and Prevention, Estimated Influenza Illnesses, Medical visits, Hospitalizations, and Deaths in the United States — 2019–2020 Influenza Season. Retrieved from:
2. Ozawa S., et al. Modeling the economic burden of adult vaccine-preventable diseases in the United States. Health Aff. (Millwood) 35, 2124–2132 (2016).
3. Muñoz LS, Garcia MA, Gordon-Lipkin E, Parra B, Pardo CA. Emerging Viral Infections and Their Impact on the Global Burden of Neurological Disease. Semin Neurol. 2018 Apr;38(2):163-175. doi: 10.1055/s-0038-1647247. Epub 2018 May 23. PMID: 29791942

Collaborative Care Intervention Can Reduce PTSD in Trauma Survivors

Notebook page with text PTSD on a table with a stethoscope and Kushniarou

The primary results of the Trauma Survivors Outcomes and Support (TSOS) trial, an NIH Collaboratory Demonstration Project, have been published. The study showed that a collaborative care intervention for injured patients at trauma centers can reduce symptoms of posttraumatic stress disorder (PTSD). The collaborative care consisted of evidence-based medication, cognitive behavioral therapy, and case management.

Learn more about this work: TSOS Study Intervention Reduces PTSD Symptoms in Injured Patients at Level I Trauma Centers.


Stepped Collaborative Care Targeting Posttraumatic Stress Disorder Symptoms and Comorbidity for US Trauma Care Systems: A Randomized Clinical Trial. Zatzick D, Jurkovich G, Heagerty P, Russo J, Darnell D, Parker L, Roberts MK, Moodliar R, Engstrom A, Wang J, Bulger E, Whiteside L, Nehra D, Palinkas LA, Moloney K, Maier R. JAMA Surg. 2021 Mar 10. doi: 10.1001/jamasurg.2021.0131

scMEP: A Matchmaker using Single-Cell Profiling

scMEP - showing immune cells as they surround colorectal tumor cells.Image of immune cells surrounding colorectal tumor cells. Magenta cells are displaying enriched expression in immune cells, while cyan cells are showing decreased expression in immune cells within the border. The white area is the tumor-immune border.

Things that live are composed of cells, whether it be a single-celled organism like a bacterium, or something made of trillions of cells like a human. For beings that are made of trillions of cells, populations of cells are consolidated into organs or tissues, and work together to make the proteins and other biomolecules that are needed to keep that being alive. However, each of those types of cells has a specific role to play in maintaining the life of that being – for example, only B cells will make antibodies, so if a scientist finds a cell that is making antibodies, they can conclude that this cell is a B cell. Because of this, researchers funded by the NIH Common Fund Human BioMolecular Atlas Program (HuBMAP), are generating molecular profiles of proteins which can identify certain kinds of cells, and then use those profiles to predict where the cells are in relationship to each other in healthy and tumor samples. 
HuBMAP researchers Drs. Michael Angelo, Sean Bendall, and colleagues at Stanford University developed a computational method called “single-cell metabolic regulome profiling” or scMEP. scMEP measures and identifies the proteins involved in performing the functions of cells, as well as where the cells are in relationship to each other within a sample using computational methods to analyze the proteins found by a technique called mass cytometry. In scMEP, mass cytometry is used to identify cells by attaching heavy metal ions to antibodies. Antibodies are very specialized and thus will only bind to specific proteins made by specific cell types. For this reason, researchers can identify proteins and cell types by designing antibodies to bind to proteins and using imaging methods to see where the attached heavy metal ions are in a sample. Once the researchers know what proteins are made by which type of cells, they can build metabolic profiles of those cell types and give that information to scMEP. scMEP can then use these profiles to predict the identity of unknown cell types in samples from either healthy people or patients with colorectal cancer. Once the unknown cells are identified, researchers can then use imaging methods to see where tumor and immune cells are in relationship to each other in a sample from a person with colorectal cancer. 
 scMEP allows researchers to identify the type of cell, and what metabolic processes that cell is performing at a specific moment. Because it uses antibody-based methods for identification, scMEP can be incorporated into any protein-based approach. The researchers hope that by incorporating scMEP into clinical workflows, scientists will be able to better predict how patients respond to immunotherapy, or perhaps find new biomarkers to allow earlier diagnoses of disease, or possible therapeutic targets. They believe that scMEP will give researchers a deeper understanding of cellular metabolism, and thus a greater understanding of the processes that affect human disease. 

Single-cell metabolic profiling of human cytotoxic T cells. Hartmann FJ, Mrdjen D, McCaffrey E, Glass DR, Greenwald NF, Bharadwaj A, Khair Z, Verberk SGS, Baranski A, Baskar R, Graf W, Van Valen D, Van den Bossche J, Angelo M, Bendall SC. Nat Biotechnol. 2020 Aug 31. doi: 10.1038/s41587-020-0651-8. Online ahead of print. PMID: 3286913
This work is supported by NIH grant # UH3 CA246633-02.


Following the Leader


The biological molecule RNA,  sometimes in the form of extracellular RNA or “exRNA,” can be released by cells and transported through the body, with the potential to influence a recipient cell. The NIH Common Fund Extracellular RNA Communication (ERC)  is  addressing major barriers to our understanding of exRNA biology and harnessing its therapeutic potential.   In its second stage, the program is focusing on developing tools and technologies to provide greater understanding of large complexes, like Extracellular Vesicles (EVs), that carry exRNA through the human body. Currently, much of what is known about EVs and a subset of EVs called exosomes comes from research that only provides “snapshot” images or measurements.  However, understanding how EVs move through the body or transmit signals from cells to cells is a dynamic process that needs to be understood in real time rather than as snapshots. Now researchers have developed a system of visualizing exosomes more dynamically and provide insight into their pathfinding and migration.
The exRNA researchers Dr. Alissa Weaver and colleagues designed a system built on their previous technologies that uses a stable and bright pH-sensitive reporter molecule that gives off light observable under a microscope . This provides live imaging of exosomes fusing together or with cells and cellular interactions with extracellular exosomes. Using this improved system, they visualize exosomes generated from lab-grown cells in 3D culture and in living organisms. They observed that exosomes are secreted towards the front of migrating cells and left behind in exosome trails. They described this as exosomes promoting “leader–follower” behavior in 2D and 3D migration. Finally, they also modified the reporter system to allow observation of not only exosome secretion but also internal trafficking events from one cell to another. This live visualization system will be a useful tool for understanding and visualizing exosomes in real time and gaining insight into their roles in both normal processes and the pathogenesis of diverse diseases.

Sung, B. H., von Lersner, A., Guerrero, J., Krystofiak, E. S., Inman, D., Pelletier, R., Zijlstra, A., Ponik, S. M., & Weaver, A. M. A live cell reporter of exosome secretion and uptake reveals pathfinding behavior of migrating cells. (2020). Nature communications, 11(1), 2092.

Getting in Sync

Many human health conditions, such as sleep disorders, cardiovascular diseases, metabolic disorders, and even cancers can be the result of problems with circadian rhythm, our natural sleep-wake cycle. The circadian rhythm is one of the best-characterized mechanisms that mediates environmental signals on molecular, physiological and behavioral activities. But the process by which this rhythm gets in sync, or alignment, with regular daily light and dark cycles is not understood. Researchers from the NIH Common Fund Knockout Mouse Phenotyping Program (KOMP2)  are now shedding light on how mice align their circadian rhythms to these cycles.

The NIH Common Fund Knockout Mouse Phenotyping Program (KOMP2) is part of the International Mouse Phenotyping Consortium (IMPC). This is a global effort to generate "knockout" mice for every protein coding gene in the mouse genome and then carry out a range of tests to understand each gene’s biological function. Studying the energy use of normal and different knockout mice in a well-controlled light and dark setting has now provided clues to some genetic underpinnings of circadian alignment. By collecting and studying indirect calorimetry (IC) data, a measure of energy use and activity levels, from more than 2000 normal mice, the researchers showed that onset time of peak activity and food intake rhythms are reliable parameters for screening defects of circadian alignment. Using a machine learning approach to look at the vast amount of data collected, they developed an algorithm for recognizing normal circadian parameters in mice. The algorithm was developed and validated and is available to use for future analysis of datasets. They then used this machine learning approach to look at a subset of 750 different knockout mice. They found five genes (Slc7a11, Rhbdl1, Spop, Ctc1 and Oxtr) potentially associated with altered patterns of activity or food intake, giving new insight into genes involved in circadian alignment. Because the IMPC researchers are still generating and phenotyping new knockout mice, this approach lays the foundation for a future larger and more comprehensive study of circadian behavior to uncover even more genes that help control circadian rhythm and its effects on health and disease.


Zhang T, Xie P, Dong Y, Liu Z, Zhou F, Pan D, Huang Z, Zhai Q, Gu Y, Wu Q, Tanaka N, Obata Y, Bradley A, Lelliott CJ; Sanger Institute Mouse Genetics Project, Nutter LMJ, McKerlie C, Flenniken AM, Champy MF, Sorg T, Herault Y, Angelis MH, Durner VG, Mallon AM, Brown SDM, Meehan T, Parkinson HE, Smedley D, Lloyd KCK, Yan J, Gao X, Seong JK, Wang CL, Sedlacek R, Liu Y, Rozman J, Yang L, Xu Y. High-throughput discovery of genetic determinants of circadian misalignment. PLoS Genet. 2020 Jan 13;16(1):e1008577. doi: 10.1371/journal.pgen.1008577. PMID: 31929527; PMCID: PMC6980734.

Singled Out: New Technology for Analyzing Single Extracellular Vesicles (EVs)

The Common Fund Extracellular RNA Communication (ERC) program aims to understand the fundamental biology of extracellular RNA (exRNA), as well as accelerate development of exRNAs as potential therapeutics and diagnostics.  RNA, sometimes in the form of extracellular RNA or “exRNA,” can be released by cells and transported through the body, with the potential to influence a recipient cell. The current and final second stage of the ERC program focuses on tool and technology development to understand biological containers like Extracellular Vesicles (EVs) that carry exRNAs and other cargo through the body. Researchers from the ERC program recently piloted a new method called ‘Single Extracellular Vesicle Protein Analysis Using Immuno-Droplet Digital Polymerase Chain Reaction (PCR) Amplification.’ This method can ultra-sensitively detect rare proteins of interest carried by single EVs. Finding rare proteins in body fluids may be critical to detecting diseases much earlier than currently possible. This method could allow clinicians to identify rare but highly predictive tumor cell derived EVs (“tEVs”) and improve the performance of current EV cancer diagnostics. 

Because EV populations are highly varied, even more so than the cells from which they are derived, more research is needed to better understand them and their cargo. Detecting rare proteins contained in EVs – those that are in low quantities or in only select EVs – can be informative for determining if a disease is in very early stages. However, detecting rare proteins in EVs can be problematic because direct detection results in very low level signals that could be uninterpretable. The new method developed by ERC researchers uses a signal amplification step to overcome previous limitations of direct detection. This novel method also pairs signal amplification with a technique for isolating single EVs, letting researchers identify the protein cargo of individual EVs. Using multiple chemical tags to identify different proteins allows researchers to look for more than one protein at a time, which may offer insight into protein signatures contained in EVs. 

To verify the method, researchers first examined EVs containing the well characterized protein PD-L1. PD‐L1 levels can be predictive of response to immunotherapy. Using their new method coupled with flow cytometry, a technique to simultaneously analyze specific characteristics of thousands of individual cells, they directly determined the quantity and fraction of PD‐L1 containing EVs released by tumor cells. Better understanding of EV variation and of the RNA and non-RNA cargo they carry is necessary to determine which and how these carriers send messages to other cells. Not only is this important for understanding the role of EVs in intercellular signaling, but it also increases the translational potential to diagnose and treat diseases.

Ko, J., Wang, Y., Carlson, J. C. T., Marquard, A., Gungabeesoon, J., Charest, A., Weitz, D., Pittet, M. J., Weissleder, R., Single Extracellular Vesicle Protein Analysis Using Immuno‐Droplet Digital Polymerase Chain Reaction Amplification. Adv. Biosys. 2020, 1900307.

Working to Improve Reporting in Extracellular Vesicle Research


The Common Fund Extracellular RNA Communication (ERC) program is focusing on development of tools and technologies to address major barriers to fully understanding and harnessing the therapeutic potential of extracellular RNA molecules (exRNAs), a type of RNA molecule that exists outside of the cell that produced it. This includes focusing on a greater understanding of the structures like Extracellular Vesicles (EVs) that carry exRNAs through the human body. Currently, a lack of uniform methods to purify and characterize EVs and the molecules they carry, like RNA, remain challenging. EVs are very small and come in many varieties and therefore they can be difficult to collect and study. Furthermore, even if they use the same equipment, many labs study EVs using different methods, making it difficult to compare and reliably reproduce results. Researchers from the NIH Common Fund ExRNA program, working with partners in the scientific societies including the International Society for Extracellular Vesicles (ISEV), the International Society for Advancement of Cytometry (ISAC) and the International Society on Thrombosis and Haemostasis (ISTH), have developed a consensus framework that outlines the minimum information that should be included in publications so that certain experimental results from EVs can be consistently compared and reproduced. This consensus framework called: MIFlowCyt-EV (Minimum Information about a Flow Cytometry Experiment-Extracellular Vesicles) was developed for a specific type of experiment called flow cytometry that has been adapted to study EVs. Flow cytometry was originally designed to study individual cells, and its application to the analysis of EVs and other tiny particles has presented many challenges, and sometimes controversial results. This is in part due to limitations of the instruments used, lack of robust methods, and ambiguities in how data should be interpreted. It is also due to the fact that most flow cytometry equipment was made to sort cells, not the much smaller EVs produced by cells. The MIFlowCyt-EV framework incorporates an understanding of these limitations in the reporting requirements in an effort to fully capture meaningful data. As more and more researchers adhere to these standards in reporting EV flow cytometry studies, the ability to accurately compare results from different research labs will increase and measurement and analysis of EVs will improve.


Welsh JA, Van Der Pol E, Arkesteijn GJA, Bremer M, Brisson A, Coumans F, Dignat-George F, Duggan E, Ghiran I, Giebel B, Görgens A, Hendrix A, Lacroix R, Lannigan J, Libregts SFWM, Lozano-Andrés E, Morales-Kastresana A, Robert S, De Rond L, Tertel T, Tigges J, De Wever O, Yan X, Nieuwland R, Wauben MHM, Nolan JP, Jones JC. MIFlowCyt-EV: a framework for standardized reporting of extracellular vesicle flow cytometry experiments. J Extracellular Vesicles. 2020 Feb 3;9(1):1713526. doi: 10.1080/20013078.2020.1713526.

Little Mice, Big Data

Analyzing large amounts of data can be a daunting task in any research field; this includes applying appropriate statistical methods to ensure robust conclusions. With many areas of biomedical research now generating massive datasets, there is a growing need for easy to use and freely available statistical tools. Increasingly, researchers are working toward making their research data and analyses follow the “FAIR” principles—findable, accessible, interoperable, and reusable. 

The NIH Common Fund Knockout Mouse Phenotyping Program (KOMP2),  as part of the International Mouse Phenotyping Consortium (IMPC), is leading the way in understanding different biological processes and diseases, and making their data FAIR. The researchers are part of an ambitious project to genetically silence – or “knockout” – and characterize all genes that code for proteins in the mouse genome. This effort to generate "knockout mice" for every protein-coding gene is the first step before systematically carrying out a range of tests to understand each gene’s biological function, or “phenotype.” 

The IMPC has developed a software package called “OpenStats,” specifically designed for the type of high throughput data generated by large research programs like the IMPC. But, it can also be tailored for smaller scale projects. The software package has been tested and implemented by the IMPC, which is increasingly focused on reproducibility, studying both sexes, and using appropriate statistical tools and methodologies. OpenStats builds on the current IMPC statistical computing software called PhenStat . However, when compared to PhenStat, it used far less computing time and obtained consistently similar results. One important way OpenStats contributes to FAIR data is by assessing input data for completeness, redundancy, and other mismatched variables or formatting. It also provides automated ways to consistently label commonly used sex and gender terms as a single term (“sex”) to promote interoperability and reusability of data. Importantly, OpenStats is freely available (, allowing any researcher to reproduce and reuse analyses from others’ research while ensuring their own analysis is FAIR.


OpenStats: A robust and scalable software package for reproducible analysis of high-throughput phenotypic data. Haselimashhadi H, Mason JC, Mallon AM, Smedley D, Meehan TF, et al. (2020) PLOS ONE 15(12): e0242933.

A New Genetic Landscape of the Immune System Defined

The NIH Common Fund Knockout Mouse Phenotyping Program (KOMP2) collaborates with the International Mouse Phenotyping Consortium (IMPC) to knockout (remove) and characterize all protein-coding genes in the mouse genome.  Overall, this project helps scientists explain the genetic basis of many different types of diseases that occur in both mice and humans, including under-studied rare diseases and common chronic diseases that affect much of the human population. This collaborative effort has been a powerful resource for describing genes with previously unknown function in hearing, embryonic development, and metabolism, for example. Now, KOMP2 researchers gained new insights on the genetics of the immune system. In addition to a standardized phenotyping of each knockout, or looking at the physical characteristics of the mice, researchers in this study carried out extra tests to more closely investigate the immune system in knockout mice. This included adding specialized tests to look at immune cell activity in the lymphatic system, the organs in the body that control the immune system, as well as testing how exposure to viral, bacterial, and parasite pathogens alters immune responses. 

In this study, 25% of the 530 genes studied revealed observable differences in immune cell phenotypes. Furthermore, more than half of the genes with these immune phenotypes had no previous known link to immune system function, indicating important new discovery opportunities made possible by using these approaches. In this analysis, even well-studied genes, for example Bach2, had new phenotypes that may contribute to disease mechanisms. Collectively, these findings illustrate the value of a large-scale immune system screen to identify previously unrecognized components of immune system development and regulation, as well as uncover additional roles for known genes. Importantly, most of the mouse genes identified had a human counterpart gene that was known to be important, but not necessarily recognized to be involved in immune system regulation. This is consistent with the potential that further exploration of the genes identified will have meaningful relevance to human health.


High-throughput phenotyping reveals expansive genetic and structural underpinnings of immune variation. Abeler-Dörner L, Laing AG, Lorenc A, Ushakov DS, Clare S, Speak AO, Duque-Correa MA, White JK, Ramirez-Solis R, Saran N, Bull KR. Nat Immunol 21, 86–100 (2020). 

Collaborating on Coronavirus: Discovering the Role of Lung Cells in Coronavirus infection

While scientists continue to develop vaccines and therapies for the coronavirus disease (COVID-19), it is also vital to understand how the coronavirus infects cells and which types of cells it attacks upon entering the body. This area of research aligns with NIH Common Fund Human BioMolecular Atlas Program's goal to study how cells in the human body influence biological processes such as aging and disease progression.

Drs. Fiona Ginty, PhD of GE Research, and Gloria Pryhuber, MD of University of Rochester Medical Center, two HuBMAP members (Dr. Pryhuber is also a member of LungMAP), have been studying cell-to-cell interactions to find interventions to prevent the coronavirus from entering cells. Patients with COVID-19 experience a wide range of symptoms, which may exist because of several factors, such as the make-up and activation of neighboring cells, the organization of cells in space, and the types of neighboring cells that are activated.

Drs. Ginty and Pryhuber will use protein analyzing methods to measure cell surface proteins that interact with the coronavirus and allow it to enter cells. Using a cutting-edge technique called immunofluorescence microscopy, they will be able to see how cells of the upper and lower respiratory tract interact with the coronavirus. The hope is that once they identify the proteins expressed by infected cells, they may find molecular targets to promote patient recovery and lead to more effective treatments against COVID-19.

GE Researchers Conducting Lung Study to Understand Most Severe Cases Leading to COVID-19 Fatalities

Research reported here was supported by the National Institutes of Health under award number 3UH3CA246594-02S1.

A Large-scale Genetic Analysis of African Populations Reveals New Insights in Human Migration and Health



Africa is the geographic origin of modern human populations and their migration across the world. Populations in Africa harbor more genetic diversity compared to non-African populations, and yet individuals of African descent are poorly represented in most genetic studies. Examining the breadth and depth of genetic diversity across African populations is important for a more complete understanding of the human genome, human migration, and helping to identify individuals and populations at risk for developing specific diseases. The Common Fund’s Human Heredity and Health in Africa (H3Africa) program is generating unique data to help fill significant gaps in knowledge of the diversity within human genomes.

A study published in Nature, and conducted through the H3Africa consortium, features sequenced DNA samples from 426 individuals that represent 50 distinct groups of people from 13 African countries, including previously unstudied populations. The researchers uncovered over three million novel genetic variants (very small changes in DNA sequence) adding to a greater understanding of the breadth of genetic diversity in Africa. Studying these new variants may help to explain differences in disease prevalence associated with specific populations and may eventually guide targeted treatments.

This research has also shed light on human migration by examining the timing of the Bantu migration to southern Africa. The routes and events of this migration have been debated in both the fields of genetics and linguistics. Data from this study provided evidence to support that Bantu populations from Central West Africa likely migrated into modern Zambia before migrating to East and South Africa. This finding supports a theory that Bantu populations migrated into Central Africa at a later point in time (~2,000 years ago) than previously thought. Understanding the Bantu migration and other migrations can help define the demographic event of African genetic diversity. 

In terms of clinically important variants, the team studied HLA-B*570, a variant associated with an allergic reaction to the antiretroviral drug Abacavir. This variant was previously only found in North East African and Kenyan populations, as well as people of European and Asian Ancestry. Here, new data showed HLA-B*570 was present in other African populations such as Bantu populations from Zambia, Ugandan Nilo-Saharan, and Xhosa populations. The data are relevant to HIV patients of African descent who are at risk of an adverse reaction to Abacavir and may help inform better treatment options. 

This work also showcases infrastructure developed through the H3Africa program to enhance genomics research in African institutions. Large genomics studies like this require huge computing capacity, and initiatives such as H3Africa provide both infrastructure and skill development of local researchers to help support large-scale genomics on the continent. The H3Africa consortium will continue to use this infrastructure for further studies of human genetic variation and capitalize on the promise of genomics for better understanding health and disease. 

Watch a video on H3Africa researchers explaining findings for this study:
Read news articles about this work at: NIH Director’s Blog, Nature, The Scientist.


High-depth African genomes inform human migration and health. Choudhury, Ananyo,Aron, Shaun,Botigué, Laura R,Sengupta, Dhriti,Botha, Gerrit,Bensellak, Taoufik,Wells, Gordon,Kumuthini, Judit,Shriner, Daniel,Fakim, Yasmina J,Ghoorah, Anisah W,Dareng, Eileen,Odia, Trust,Falola, Oluwadamilare,Adebiyi, Ezekiel,Hazelhurst, Scott,Mazandu, Gaston,Nyangiri, Oscar A,Mbiyavanga, Mamana,Benkahla. Nature.2020 Oct;586(7831):741-748


Signatures for Success: Reducing Side Effects of Cancer Drugs

Image of medication and a stethoscope on top of papers listing side

The right drug can help stop cancer in its tracks by preventing cell growth and blocking the formation of new blood vessels—cutting off the supply of nutrients that the cancer needs in order to survive and grow. However, sometimes cancer drugs can also cause unwanted side effects, like damage to the heart (cardiotoxicity), which can impair blood flow throughout the body. The Library of Integrated Network-based Cellular Signatures (LINCS) Drug Toxicity Signature Generation Center (DToxS) used a technique called transcriptomic profiling combined with clinical data and drug structure data to develop a new way to predict if cancer drugs might cause cardiotoxicity in patients.

LINCS researchers used human heart cells (cardiomyocytes) to test 23 different FDA-approved cancer drugs, collecting thousands of data points using the transcriptomic profiling technique to show how each different drug changed the types and amounts of molecules produced by the heart cells. This analysis allowed the scientists to pinpoint the specific combinations of molecular changes that might be related to cardiotoxicity. In parallel, they examined the structures of the cancer drugs and analyzed clinical data to see if the molecular changes found in the heart cells could also be found in cancer patients treated with these medications. Taking all this together, they found that testing cancer drugs in human heart cells along with an analysis of the structure of the drug could be combined with clinical data to create a drug ‘signature’ to help predict unwanted side effects like cardiotoxicity. This drug signature approach developed by the LINCS research team could be a useful way to assess safety early in the drug development process, potentially identifying the medications that have a lower chance of causing unwanted side effects.

The protocols and data sets generated by this research are available in the DToxS Data Repository and are freely available for research use.

Reference: Transcriptomic profiling of human cardiac cells predicts protein kinase inhibitor-associated cardiotoxicity. van Hasselt JGC, Rahman R, Hansen J, Stern A, Shim JV, Xiong Y, Pickard A, Jayaraman G, Hu B, Mahajan M, Gallo JM, Goldfarb J, Sobie EA, Birtwistle MR, Schlessinger A, Azeloglu EU, Iyengar R. Nat Commun. 2020 Sep 23;11(1):4809.


A Personalized Approach to Clinical Care Leads to Diagnoses

Rare diseases are a significant health care concern with almost 7,000 rare diseases, affecting more than 25 million Americans and their families. It can take several years for people suffering from rare or unknown conditions to arrive at a diagnosis. Often times, resources provided in standard clinical settings are not sufficient to diagnose patients. Several factors including limited technology, time, financial constraints, and medical coverage are barriers to solving some of the most complex medical mysteries. The Undiagnosed Diseases Network (UDN) has pioneered and optimized diagnostic and research strategies to overcome these constraints. This nationwide network of clinicians and researchers is improving the level of diagnosis of rare and undiagnosed conditions by applying personalized clinical and research evaluations.

Since 70-80% of undiagnosed diseases are due to rare genetic disorders, evaluations typically require scanning a patient’s DNA through a process called sequencing. While certain types of sequencing are considered standard practice, patients often obtain results that cannot be further resolved through clinical means. In addition, differences in the interpretation of sequencing between the ordering clinician and testing laboratory occur, and gene variants not currently associated with disease may not be reported, leading to missed opportunities for clinicians to solve cases.

In a UDN study, Dr. Vandana Shashi and colleagues analyzed data from four UDN clinical sites from 2015 to 2019 to assess the number of diagnoses, new disease gene discoveries, and underlying investigative methods required to make the diagnoses. Of the 791 people evaluated at the UDN sites, 231 received diagnoses and 17 new genetic diseases were identified. About 35% of these diagnoses were straightforward and obtained with DNA sequencing. However, many of these straightforward cases were not previously resolved in a standard clinical setting due to financial constraints and limited medical coverage. This study also showed that the majority of the cases were complex and required personalized clinical evaluations and research tools beyond DNA sequencing. Sixty-five percent of the diagnoses were made due to the UDN’s unique diagnostic and research paradigms that surpass standard diagnostic processes.

Clinical sites of the Undiagnosed Diseases Network: Unique contributions to genomic medicine and science. Kelly Schoch, Cecilia Esteves, Anna BicanRebecca Spillmann, Heidi Cope, Allyn McConkie-Rosell, Nicole Walley, Liliana Fernandez, Jennefer N Kohler, Devon Bonner, Chloe Reuter, Nicholas Stong, John J. Mulvihill, Donna Novacic, Lynne Wolfe, Ayat Abdelbaki, Camilo Toro, Cyndi Tifft, May Malicdan, William Gahl, Pengfei Liu, John Newman, David B. Goldstein, Jason Hom, Jacinda Sampson, Matthew T. Wheeler, Undiagnosed Diseases Network, Joy Cogan, Jonathan A. Bernstein, David R. Adams, Alexa T. McCray, Vandana Shashi. Genetics in Medicine, 2020 Oct 23. 

In the news:
Diagnoses of Rare Diseases Enhanced through the Teamwork of National Network, Duke Health

Common Fund Data Ecosystem: A New Frontier in Biomedical Research

Data cloud

Innovative collaborations will create useful tools for scientific discovery

The Common Fund Data Ecosystem (CFDE) aims to enable new ways of doing science by creating an ecosystem—the data management infrastructure, analytics, applications, and user interfaces needed to work within and across existing Common Fund data sets. The CFDE took a major step toward creating this resource by launching a set of collaborative projects that bring together eight Common Fund Data Coordinating Centers (DCCs) to help build this functional ecosystem for answering important biological questions, such as uncovering new molecular pathways and illuminating disease mechanisms.

The Common Fund DCCs will contribute a wealth of diverse data sets, spanning basic biology to clinical research, and will work towards making their data more useful alone and in combination with other data sets. The participating DCCs include Extracellular RNA Communication (ExRNA), Gabriella Miller Kids First (Kids First), Genotype-Tissue Expression (GTEx), The Human BioMolecular Atlas Program (HuBMAP), Illuminating the Druggable Genome (IDG), Library of Integrated Network-based Cellular Signatures (LINCS), Metabolomics, and Stimulating Peripheral Activity to Relieve Conditions (SPARC) programs. Their collaborative projects will tackle important challenges in biomedical research and human health, including (but not limited to):

  • Innovative strategies for data-driven treatment planning—coupling drug and small molecule predictions with patient gene activity data to uncover key molecular pathways and help with developing effective treatment strategies, predicting drug responses, identifying the best candidate drugs for specific patients, and tracking disease progression and recovery. Participating DCCs: GTEx, IDG, Kids First, LINCS, Metabolomics
  • New drug targets for pediatric cancer treatments—identifying new potential therapeutic targets for specific types of pediatric cancers by comparing the gene activity differences between tumors and healthy organ tissue. Participating DCCs: GTEx, Kids First, LINCS
  • Novel insights into complex conditions—generating multi-layered organ maps that will incorporate genetic mutations, structural birth defects, and gene activity changes during development, to create a powerful tool for studying complex conditions like Down syndrome. Participating DCCs: ExRNA, HuBMAP, Kids First, SPARC
  • Solutions for working with data in the cloud—exploring new ways to combine data sets and discover solutions for working across independent cloud-based platforms. Participating DCCs: ExRNA, GTEx, HuBMAP, IDG, Kids First, LINCS, Metabolomics, SPARC

Demonstrating the value of these data sets, particularly in combination, will help the research community see what kinds of new research questions can be asked of and answered by the data. CFDE will also make the data more accessible through a cloud-based public web portal. As these exciting projects begin, they hold the potential for opening new doors to scientific discovery and informing innovative approaches to improving human health.

Learn more about these CFDE engagement awards by visiting the CFDE Funded Research page.


Expanding Our View of The Genomic Landscape Using the Genotype-Tissue Expression (GTEx) Data Set

DNA Helix/GTExistock/ktsimage

The study of human genetics can help us find answers to questions about what makes us unique and how various diseases develop. The understanding of changes and differences in our DNA, known as genetic variation, can help explain some differences in risk of disease and how people respond to drug treatment. Although there has been some success in identifying genetic variants linked to diseases, there remains a challenge of explaining the function of millions of genetic variants across the human genome. To help overcome this challenge, the NIH Common Fund’s Genotype-Tissue Expression (GTEx) Program developed a reference data set for studying how genetic variation impacts the way a gene behaves in various cell types, tissues, and across individuals. 

A group of researchers supported by the GTEx program analyzed the eighth version (V8) of the GTEx data set that includes genetic data from 17,382 samples from 54 tissues of 948 post-mortem donors, to catalogue genetic variants that influence the activity, or expression, of almost all genes. These variants can control how a gene behaves in a cell, like a power button can turn an electronic device on and off. The researchers found many examples of how individual genetic variation among GTEx donors affected gene expression. Part of what makes these findings valuable is that they account for individual genetic variation, but they do so in context of specific cell types, like brain or liver, where different genes play different roles in cell function. Also, the breadth of GTEx cell types allows researchers to ask a variety of questions about many different topics in health and disease. For example, GTEx researchers used the V8 data to see how a person’s sex affects gene expression, to find better ways to identify rare genetic variants, to better link a genetic variant to disease, to study how multiple genetic variants are connected in complex diseases, and to account for natural genetic variation among diverse populations in studies linking genetics to a specific trait. 

In the study to uncover genetic variants that affect gene expression based on sex and population, the researchers identified a genetic variant that increased the expression of the gene AURKA in skeletal muscle in males but not in females. This gene has been widely studied as a risk factor for several cancers. They also identified a variant that decreased the expression of the gene SLC44A5 in the esophagus of individuals of European ancestry, but the expression of this gene was lower in African Americans. The SLC44A5 gene has been linked to Alzheimer’s disease in prior large genetic studies. Findings that link genetics to specific traits, like disease risk, could inform efforts to make personalized medicine a reality.

Studies like these shows how the GTEx V8 data are stimulating new discoveries. The data are available to the research community, so scientists can use them to dissect the effect of genetic variation and gene expression, and to improve our understanding of the role of genetic variation in most human diseases.

The GTEx V8 data are now available to view at

Read news articles about GTEx V8 findings at: New York Genome, The Broad Institution and Science.  

The GTEx Consortium atlas of genetic regulatory effects across human tissues. The GTEx Consortium. Science. 2020 Sept. 11. 
*access to full text may require institutional permission. 


The Human BioMolecular Atlas Program (HuBMAP) Presents Its First Data Release

seqFISH image of a piece of a heart tissue from CalTech Tissue Mapping Center using Vitessce visualization software

An adult human body is made up of trillions of cells. How those cells interact with each other and arrange into tissues and organs directly impacts our health. A new Common Fund program – The Human BioMolecular Atlas Program (HuBMAP) – is creating cutting edge tools to collect molecular and imaging data, enabling the generation of 3D tissue maps, as well as the construction of an atlas which will display the relationships among cells in the human body. Together, the maps and atlas could lead researchers to a better understanding of how the relationships among our cells influence health.

HuBMAP researchers form 18 different collaborative research teams across the United States and Europe and work closely with other researchers around the world. They recently issued an initial data release, which includes data at the level of individual cells from microscopy, mass spectrometry, and sequencing assays from seven organ types – heart, kidney, large and small intestines, lymph nodes, spleen, thymus. These datasets could be used by researchers in cell and tissue anatomy, pharmaceutical companies developing therapies, or even parents showing their children how amazing the human body is.

The tools and maps generated by HuBMAP researchers are openly available and can be found at The current release is just the beginning. HuBMAP aims to continually release new datasets to serve as a foundation for future applications of anatomical data to diagnose, study, and treat disease.

A Knife to the Heart: Mapping the Intracardiac Nervous System

SPARC1 projects are mapping the innervation of organs and tissues, and identifying new targets for future neuromodulation therapies—treatments that directly alter nerve function

Rotating 3D Heart

Cardiac function is tightly controlled to ensure the heart never skips a beat. At the center of this ability is the intracardiac nervous system (ICN), the heart’s own internal nervous system that helps regulate functions like rhythm and blood flow. The neurons that comprise the ICN are found in clusters within the heart, but their exact positions and functions have never been comprehensively mapped. The NIH Common Fund’s Stimulating Peripheral Activity to Relieve Conditions (SPARC) program supports efforts to map nerve structures like the ICN so they can be targeted more specifically by neuromodulation therapies.

Now a team at SPARC awardee institution Thomas Jefferson University, led by Dr. James S. Schwaber, has illuminated the form and function of the rat ICN at single cell resolution within the whole heart. They used a technique called knife-edge scanning microscopy (KESM) along with single cell gene activity data to create a comprehensive map of the heart. As the name suggests, KESM collects images from deep within tissues by scanning along a knife-edge as it sequentially removes one layer of tissue at a time. The 3D structure is then rebuilt from the scanned images. In a parallel process, individual neurons are laser-cut out of the tissue, profiled for active genes, and then oriented to their correct position within the 3D map of the heart. The resulting data set reveals a high level of complexity and diversity of ICN neuron anatomy and function that will stimulate additional research, including identification of potential therapeutic targets, both surgical and neuromodulatory. The reproducible techniques used in this study can be applied to other organs and systems, opening up a new pipeline for high-resolution mapping of neurons within organ systems. 

Read more about this work in these news reports: Science News, Engadget, STAT, BBC Science Focus, Interesting Engineering, Cardiovascular Business, News Medical, Report Door, Cosmos Magazine, Neuro Central, and Slash Gear.

The raw data supporting the study’s conclusions are available on the SPARC Portal.


A Comprehensive Integrated Anatomical and Molecular Atlas of Rat Intrinsic Cardiac Nervous System. Achanta S, Gorky J, Leung C, Moss A, Robbins S, Eisenman L, Chen J, Tappan S, Heal M, Farahani N, Huffman T, England S, Cheng ZJ, Vadigepalli R, Schwaber JS iScience. 2020 May 23.


A “Micro” Technology Yields Big Details

The nucleus is the control center of the cell, packed with 6 linear feet of DNA in a space narrower than the width of a human hair. Since it began, research supported by the 4D Nucleome (4DN) program has improved our understanding of how DNA is packaged and organized in the crowded space of the nucleus. Advancements by the program have prompted scientists to develop new methods to get an even clearer picture of how the DNA is organized. These methods are beginning to give us a good grasp on how large areas of DNA are packaged into different compartments, where portions of DNA that would be far apart in a piece of DNA stretched out long, are actually located close to each other in the nucleus, similar to if you scrunched up a long piece of string and placed it in a ball. Understanding how DNA is organized in three dimensions has informed scientists how that organization can alter which genes are turned on or off, an important factor in many diseases such as cancer. We can now see how large portions of DNA are situated, but can more information be gathered by looking at how smaller regions of DNA in the nucleus are organized?

A team of researchers led by Drs. Xavier Darzacq and Job Dekker, whose research is supported by the 4DN program, developed a new method, called Micro-C, to provide a better image of how smaller regions of DNA are organized in three dimensional space. Looking at the DNA in a mouse model system, the team demonstrated that smaller-than-previously-thought regions of DNA can control gene activity and that the closeness of these small regions is important in recruiting proteins to control how genes are turned on or off. A companion study mirrored these studies demonstrating that Micro-C is suitable for human cells as well. The development of this new method will help strengthen our understanding of how genes are spatially controlled in the crowded nucleus and may provide opportunities for developing future treatments as we learn more about gene activity regulation in healthy and diseased cells.


Resolving the 3D Landscape of Transcription-Linked Mammalian Chromatin Folding. Tsung-Han S Hsieh, Claudia Cattoglio, Elena Slobodyanyuk, Anders S Hansen, Oliver J Rando, Robert Tjian, Xavier Darzacq. Molecular Cell, 2020 May 7. 78, 539-553

Ultrastructural Details of Mammalian Chromosome Architecture. Nils Krietenstein, Sameer Abraham, Sergey V Venev, Nezar Abdennur, Johan Gibcus, Tsung-Han S Hsieh, Krishna Mohan Parsi, Liyan Yang, René Maehr, Leonid A Mirny, Job Dekker, Oliver J Rando. Molecular Cell, 2020 May 7. 78, 554-565


Anchoring in a Sea of Data

The NIH Common Fund Human BioMolecular Atlas Program (HuBMAP) brings together molecular and cellular biologists, pathologists, and bioinformaticians to create a framework for mapping the human body at cellular resolution.  These scientists not only need to develop the tools necessary to study cells and tissues, but also must be able to integrate those data together into a comprehensive atlas.

Rahul Satija, PhD, and colleagues at the New York Genome Center, developed a process that connects DNA, RNA, chromatin, and protein data from separate experiments.  It takes data from different types of experiments and looks for information that the data were generated from the same kind of cell.  Once a match is identified, the algorithm ‘anchors’ the data together, generating links between two datasets.  This anchoring allows the researchers to identify known or unexpected types of cells in a tissue.

Using this method on data from mouse brain tissue, as well as human blood cells, researchers were able to 1) separate out four different types of neurons in one area of a mouse’s brain and find a region on a specific chromosome that instructs cells to become neurons, 2) find blood cells in different of developmental stages, and 3) identify different immune cells in a population by their cell surface proteins. 

By joining these data together, this new computational method has given researchers a novel tool to help build more complete biological atlases, leading the way to more discoveries about the intricacies of human cells and tissues.


Comprehensive Integration of Single-Cell Data.

Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM 3rd, Hao Y, Stoeckius M, Smibert P, Satija R.  Cell. 2019 Jun 13;177(7):1888-1902.e21. doi: 10.1016/j.cell.2019.05.031. Epub 2019 Jun 6.  PMID: 31178118


Genetic Experts Discover A New Gene Region Linked to The Risk of Orofacial Clefts


Orofacial clefts (OFCs) are structural birth defects which occur when a baby’s lip or mouth does not form properly during pregnancy and are some of the most common birth defects. Children with OFCs often have hearing loss, dental problems, and difficulties with eating and speaking. Identifying genetic factors contributing to OFCs could help improve diagnostics, treatments, and outcomes of OFCs.

Children with OFCs also have an increased risk of developing certain types of cancers (like breast, brain, and colon cancers), suggesting a shared genetic pathway between these disorders. The NIH Common Fund’s Gabriella Miller Kids First Pediatric Research (Kids First) Program is helping scientists improve our understanding of the connection between structural birth defects and childhood  cancer by developing a large-scale database of genetic and clinical data from patients and their families with these conditions.                       
Scientists participating in the Kids First Program used genetic data accessible through the Gabriella Miller Kids First Data Resource Portal, to pinpoint a new gene region linked to the risk of OFCs on chromosome 21, near genes involved in facial development. This genetic study of OFCs is the first to involve large enough numbers of patients and their relatives to allow meaningful results.   

The researchers analyzed genetic data from 580 OFCs patients and their families of European and Colombian descent. The newly identified region on chromosome 21 is part of a larger region that had been previously reported in studies of people of European descent, but not those of Latin American descent, suggesting that genetic differences within a population could influence the risk of development of OFCs.

Identifying risk genes is often the first step to understanding a disease and allows for the development of novel therapeutic approaches. Further studies are needed to determine the biological impact of this newly identified gene region. Findings like these could help guide scientists to other genes that play a role in the relationship between structural birth defects and childhood cancer, as well as lead to the discovery of shared genetic pathways between these disorders. 


Mukhopadhyay, N., Bishop, M., Mortillo, M. et al. Whole genome sequencing of orofacial cleft trios from the Gabriella Miller Kids First Pediatric Research Consortium identifies a new locus on chromosome 21. Hum Genet 139, 215–226 (2020).


Finding the Lanes on the Vagus Highway

Illustration of vagus nerve stimulation (VNS)

SPARC2 projects are developing next generation tools and technologies to inform future neuromodulation therapies

The vagus nerve is a bioelectronic highway that carries two-way electrical signals between the brain and many other organs of the body. The “lanes” of this highway are composed of over a hundred thousand nerve fibers. The use of a device to initiate or modify electrical signals in the vagus nerve, called vagus nerve stimulation (VNS), is under study as a therapeutic strategy for a wide range of conditions, including heart failure and inflammatory diseases. This type of therapeutic approach to modify electrical activity in nerves is broadly called neuromodulation. Current VNS technology stimulates the entire nerve, hitting all the fibers in the hope of activating the right ones. The therapy could be improved by selective stimulation of specific fibers to induce the desired therapeutic response, while avoiding or minimizing side effects. To do this, we need more information about how the vagus nerve fibers are organized. The NIH Common Fund’s Stimulating Peripheral Activity to Relieve Conditions (SPARC) program supports efforts to map these fibers so they can be targeted more specifically by neuromodulation therapies like VNS.

A SPARC-supported research team led by Dr. Warren Grill at Duke University is building simulations that predict which fiber lanes on the vagus nerve highway will be activated by different patterns of electrical pulses. Image of a section through a pig vagus nerveTo work well, these models need to be fed anatomical data such as size, number, and location of the fibers within the vagus nerves in humans and in appropriate animal models. In a newly published study, lead author Megan Settell and colleagues from Dr. Kip Ludwig’s group at the University of Wisconsin-Madison, along with Dr. Warren Grill’s group at Duke, used careful dissection, labeling, and analysis to chart the different lanes of fibers along the vagus nerve highway in pigs. They even identified areas along the vagus nerve highway that may contribute to unwanted side effects when electrodes are placed there. Their findings are helping improve predictive VNS simulations that may pave the way to new devices, placement strategies, or stimulation protocols, adding up to more effective VNS therapies in the future. 

Read more about this work in a news article from the University of Wisconsin-Madison.


Functional vagotopy in the cervical vagus nerve of the domestic pig: Implications for the study of vagus nerve stimulation. Settell M, Pelot NA, Knudsen BE, Dingle AM, McConico AL, Nicolai EN, Trevathan JK, Ezzell JA, Ross EK, Gustafson KJ, Shoffstall A, Williams JC, Zeng W, Poore SO, Populin LC, Suminski A, Grill WM, Ludwig K. J Neural Eng. 2020 Feb 27.


Something Fishy in the State of Glycoscience

Glycans (sugars) are biological molecules that are attached to proteins through a process called glycosylation. They play a very important role in most biological processes in our bodies. Glycans help our complex organs develop, help cells talk to each other, and help cells stick to certain parts of the body or move throughout our body. In addition, the wrong type of glycosylation is associated with cancerous cells and may aid cancer progression and metastasis. Despite their known importance, it remains difficult for researchers to study glycans and glycobiology. This is in part because glycans are very complex in how they are built and attached to proteins.

A goal of the Common Fund Glycoscience program is to make the study of glycans more accessible for all researchers, even those who are not experts in the field. To do this, the program is developing new technologies that improve our ability to identify and study glycans and glycoproteins across different experiments. A paper from the lab of Glycoscience researcher Dr. Richard Cummings explains how his lab uses a jawless fish, called the lamprey, to generate a panel of completely new antibodies they call smart anti-glycan reagents (SAGRs). Lampreys produce a different kind of antibody from other animals, like mice or humans, that may be more specific in recognizing different glycans. The researchers took advantage of this by injecting lampreys with different types of glycans or glycoproteins and collecting the antibodies the animals produced in response to the glycans or glycoproteins. The researchers determined the DNA sequences that coded for the lamprey antibodies, and they can now use that information to generate large quantities of these antibodies in the lab. Knowing the DNA sequences also allowed the researchers to fuse the lamprey antibody proteins with another protein commonly used in laboratory assays, resulting in the SAGRs described above. Researchers can now use these SAGRs to better study glycans in a variety of cells or tissues relevant to biomedical science.

This discovery will help the biomedical research community identify and study more complex glycans and glycoproteins, leading to innovative biomedical discoveries and the potential to develop new treatments for disease. In addition to this publication, more information on the SAGRs technology can be found in this video from Dr. Cummings.


Development of smart anti-glycan reagents using immunized lampreys. McKitrick TR, Goth CK, Rosenberg CS, Nakahara H, Heimburg-Molinaro J, McQuillan AM, Falco R, Rivers NJ, Herrin BR, Cooper MD, Cummings RD. Communications Biology, 2020 Feb 28;3(1):91.


Feeling sleepy? New ways to understand how our bodies rest

While there are many factors that help animals sleep, a driving component is known as the circadian rhythm. This rhythm is a set of physical, mental, and behavioral changes that follow the daily light and dark cycle. During nighttime, a chemical called melatonin is released to help synchronize our circadian rhythm and prepare us for sleep by interacting with two proteins called MT1 and MT2. Changes to our circadian rhythm leads to insomnia or other sleep defects. Gaining insight into how the MT1 and MT2 proteins work, could allow researchers to develop new therapeutics to help treat circadian rhythm disorders.

The goal of the Illuminating the Druggable Genome (IDG) program is to identify and provide information on proteins that are currently not well studied within commonly drug-targeted protein families, including G-protein coupled receptors (GCPRs), to which MT1 and MT2 belong. Research supported by the IDG program, and published in the journal Nature, used advanced computational methods to model how over 150 million different chemicals may interact with the MT1 protein. The study identified two brand new molecules that changed how the MT1 protein functioned and shifted a mouse’s sleep cycle by up to 1.5 hours. These two molecules bound to regions of the protein unidentified before and opened a new pathway for changing MT1 activity. This study illustrates opportunities for controlling melatonin biology via specific MT1 binders. It also shows the value of screening large libraries of known chemicals for the discovery of new, active binding sites on proteins of interest. By finding new chemicals that interact with these proteins, we will be able to better understand the biology behind sleep and may be able to develop new therapies for those who suffer from sleep disorders.


Virtual discovery of melatonin receptor ligands to modulate circadian rhythms. Reed Stein, Hye Jin Kang, John McCorvy, Grant C. Glatfelter, Anthony J. Jones, Tao Che, Samuel Slocum, Xi-Ping Huang, Olena Savych, Yurii S. Moroz, Benjamin Stauch, Linda C. Johansson, Vadim Cherezov, Terry Kenakin, John J. Irwin, Brian K. Shoichet, Bryan L. Roth & Margarita L. Dubocovich. Nature, February 2020,

Opening a 'soft window' on large datasets

IMPC imageResearchers from the NIH Common Fund Knockout Mouse Phenotyping Program (KOMP2) are generating a massive amount of useful data from thousands of mice. KOMP2 is part of the International Mouse Phenotyping Consortium (IMPC) effort to generate "knockout mice" for every protein coding gene in the mouse genome – which then carries out a range of tests to understand each gene’s biological function. 

Experimental data from the knockout mice must be carefully compared to control data from normal mice and then appropriately analyzed to be meaningful. By nature of the high-throughput, large-scale study design, more control data are generated over time than data from each unique knockout group tested. While these ever-growing control data can help make analyses more powerful, they can also add complications because of larger variation over time with “batch” effects. Batch effects are unintended influences of variables like seasons, different personnel performing tests, and different reagent lots that can affect data. To account for this unintended variability, KOMP2 researchers developed a “soft windowing” method designed to select a time window that would include the best control data to use. The approach uses an adaptive window, meaning data from control mice measured most concurrently to the knockouts are given the strongest weight of all the control data, while data collected earlier or later had less weight. When validating their soft windowing approach, KOMP2 researchers found that the rate of false positive discovery went down. A false positive result is one that is unlikely to be biologically meaningful and most likely happened by chance. By lowering this sampling “noise,” the researchers were able to establish more associations between genes and function than with traditional methods and therefore to provide a clearer picture of the biological function of many more genes. The method is freely available in the R package SmoothWin and is intended to be generalizable and benefit large-scale human projects like the UK Biobank and All of Us.


Soft Windowing Application to Improve Analysis of High-throughput Phenotyping Data. Haselimashhadi, H., J. C. Mason, V. Munoz-Fuentes, F. Lopez-Gomez, K. Babalola, E. F. Acar, V. Kumar, J. White, A. M. Flenniken, R. King, E. Straiton, J. R. Seavitt, A. Gaspero, A. Garza, A. E. Christianson, C. W. Hsu, C. L. Reynolds, D. G. Lanza, I. Lorenzo, J. R. Green, J. J. Gallegos, R. Bohat, R. C. Samaco, S. Veeraragavan, J. K. Kim, G. Miller, H. Fuchs, L. Garrett, L. Becker, Y. K. Kang, D. Clary, S. Y. Cho, M. Tamura, N. Tanaka, K. D. Soo, A. Bezginov, G. B. About, M. F. Champy, L. Vasseur, S. Leblanc, H. Meziane, M. Selloum, P. T. Reilly, N. Spielmann, H. Maier, V. Gailus-Durner, T. Sorg, M. Hiroshi, O. Yuichi, J. D. Heaney, M. E. Dickinson, W. Wolfgang, G. P. Tocchini-Valentini, K. C. K. Lloyd, C. McKerlie, J. K. Seong, H. Yann, M. H. de Angelis, S. D. M. Brown, D. Smedley, P. Flicek, A. M. Mallon, H. Parkinson and T. F. Meehan 2019 Oct 8;btz744. doi: 10.1093/bioinformatics/btz744. [Epub ahead of print]. PMID: 31591642.

Is that Gene 'Essential'?

Gene “essentiality” is the requirement of a gene for an organism’s survival. Understanding how “essential” a gene is can expand our understanding of diseases and problems related to when something goes wrong with those genes. Researchers  from the Common Fund’s Knockout Mouse Phenotyping Program (KOMP2), part of the International Mouse Phenotyping Consortium (IMPC), developed a new way to determine gene “essentiality.” It is designed to offer researchers, including clinical researchers, an easy-to-use method to assist in human disease gene discovery.

The IMPC is “knocking out” all protein-coding genes in the mouse genome in an attempt to understand their function. As part of this effort, they developed and validated a “Full Spectrum of Intolerance to Loss-of-function” (FUSIL) system. The system categorizes genes based on mouse phenotyping data they are generating combined with human cell data from other research efforts. The resulting system is a “gene essentiality” resource that can be used for both animals and humans. FUSIL consists of five mutually exclusive categories, ranging from those necessary for survival to those where a gene deletion has no noticeable effect. Interestingly, known human disease genes, especially disorders that start early in life, were overrepresented in FUSIL’s “developmental lethal” bin. Taking advantage of this finding, this categorization was applied to datasets from screened developmental disorder cases from three independent disease sequencing studies in humans. The researchers were then able to predict novel candidate genes for some developmental disorders in unsolved clinical cases. Overall, the new FUSIL binning system offers clinical researchers an easy-to-use method when assessing candidate disease genes for patients.

Cacheiro, P., V. Muñoz-Fuentes, S. A. Murray, M. E. Dickinson, M. Bucan, L. M. J. Nutter, K. A. Peterson, H. Haselimashhadi, A. M. Flenniken, H. Morgan, H. Westerberg, T. Konopka, C.-W. Hsu, A. Christiansen, D. G. Lanza, A. L. Beaudet, J. D. Heaney, H. Fuchs, V. Gailus-Durner, T. Sorg, J. Prochazka, V. Novosadova, C. J. Lelliott, H. Wardle-Jones, S. Wells, L. Teboul, H. Cater, M. Stewart, T. Hough, W. Wurst, R. Sedlacek, D. J. Adams, J. R. Seavitt, G. Tocchini-Valentini, F. Mammano, R. E. Braun, C. McKerlie, Y. Herault, M. H. de Angelis, A.-M. Mallon, K. C. K. Lloyd, S. D. M. Brown, H. Parkinson, T. F. Meehan and D. Smedley. Human and mouse essentiality screens as a resource for disease gene discovery. Nat Communications (2020) 11, 655. 

See Also:
Pinpointing rare disease mutations


Do You Know Where Your Proteins Are?

mouse uterine tissue as seen by TrelliscopeImages of mouse uterus cell types (stroma, glandular epithelium, luminal epithelium) generated by Trelliscope

Multicellular organisms are composed of many different cell types, each having a specialized role in the organism’s survival. In order to specialize, cells produce certain proteins with specific functions that ensure the health and well-being of the organism. Cell mapping projects, such as the NIH Common Fund Human BioMolecular Atlas Program (HuBMAP), are developing technologies that will allow researchers to map proteins to distinct cell types within tissue samples. By mapping their proteins, researchers will be able to find various cell types in the body and thus will better understand what makes a normal cell “healthy.”

HuBMAP researcher Dr. Kristin Burnum-Johnson helped develop Nanodroplet Processing in One Pot for Trace Samples (nanoPOTS), a platform that prepares tissue samples for Matrix-Assisted Laser Desorption/Ionization imaging mass spectrometry (MALDI-IMS) (Kelly, R, et al. 2019). MALDI-IMS is used to see where particular proteins and other biomolecules are located in cells. Following this, she and colleagues at Pacific Northwest National Laboratories (PNNL) developed an automated sample collection platform combining nanoPOTS with a cell isolation technique that harvests certain types of cells. Dr. Burnum-Johnson and her team used this novel platform to map more than 2000 proteins in mouse uterine tissue during the process of preparing for embryo implantation. The researchers used uterine tissue because there are three easily distinguishable cell types in the uterine cavity. These cell types - luminal epithelial cells, stromal cells, and glandular epithelial cells – each have a unique set of proteins involved in embryo implantation and make a good test case for mapping. The combination of the automated sample collection platform with MALDI-IMS imaging allows researchers to quickly collect data about many more proteins within a particular tissue sample than ever before. Once protein data are captured, molecular maps are generated by a data visualization tool developed by PNNL, called Trelliscope (more information at - The resulting images show where the different cells are in relation to each other.  

This cutting-edge technique will allow researchers to find the locations of proteins in cells, giving a clearer understanding of where the proteins in your cells are, and how they are keeping your cells healthy.

Video from Pacific Northwest National Laboratory about nanoPOTS here

Tutorial for using Trelliscope to analyze and visualize large complex data in R here

Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution. Piehowski PD, Zhu Y, Bramer LM, Stratton KG, Zhao R, Orton DJ, Moore RJ, Yuan J, Mitchell HD, Gao Y, Webb-Robertson BM, Dey SK, Kelly RT, Burnum-Johnson KE. Nat Commun. 2020 Jan 7;11(1):8. doi: 10.1038/s41467-019-13858-z.


A Change of Heart? Uncovering Sex Differences in Cardiac Neurochemistry

Drawing of a human outline and

SPARC1 projects are mapping the innervation of organs and tissues, and identifying new targets for future neuromodulation therapies

Although cardiovascular disease is a leading cause of death in women in the US, most research studies have only focused on male animals. One team of researchers in the NIH Common Fund’s Stimulating Peripheral Activity to Relieve Conditions (SPARC) program is helping to change this by including female animals in their cardiovascular research studies.

The team at SPARC awardee institution Oregon Health & Science University, led by Dr. Beth Habecker, generated data from both female and male rats to help map cardiac neurochemistry—the chemical signals produced by nerves to influence heart function—across different tissues. They measured gene expression levels (measurements of molecules encoded by genes) in the stellate ganglion nerve cells that send electrical signals to the heart, and the levels of neurotransmitters (chemical signals transmitted by nerves) within the heart. Establishing these baseline values was critical to understanding how female and male hearts were different, and how they were the same.

The researchers discovered a number of differences between females and males. For example, stellate ganglia in female rats had lower expression of a gene that helps decrease heart rate and blood pressure. Surprisingly, female hearts had higher levels of the neurotransmitter norepinephrine than male hearts, despite identical levels of the molecules that control production of norepinephrine in female and male stellate ganglia. Norepinephrine increases heart rate and blood pressure, and higher levels in female hearts could contribute to development of cardiovascular disease, which is increasingly understood to differ between women and men. These findings highlight the continued need to include both sexes in research studies, and to carefully analyze both the target organs and the nerves that carry signals to-and-from them to develop a more complete understanding of health and disease.

Learn more about the NIH Policy on Sex as a Biological Variable.

ReferenceSex differences in sympathetic gene expression and cardiac neurochemistry in Wistar Kyoto rats. Bayles RG, Tran J, Olivas A, Woodward WR, Fei SS, Gao L, Habecker BA. PLoS One. 2019 Jun 13; 14(6):e0218133.


Where is the butter? Connecting fats and Alzheimer’s disease

image shows butter on a plate with a

Patients with Alzheimer’s disease (AD) often also have symptoms of other diseases, such as diabetes or heart disease. Many of these conditions are heavily impacted by how our bodies metabolize (process or use) the fats, or lipids, our bodies need for everyday functions. In addition, many of the more than 20 genetic risk factors linked to AD can also impact how our bodies use or modify lipids. These findings support the idea that changes in the way our bodies metabolize lipids may contribute to not only the development of AD, but also to how the disease develops in different patients.

Research supported by the Common Fund Metabolomics program aimed to test the idea that distinct lipids are altered both in early and late phases of AD. Metabolomics is a way to identify and measure biological molecules, like lipids, that are created, processed, or consumed during cell metabolism. By identifying key metabolomic changes at different stages in AD development, researchers may be able to pinpoint critical steps in how the disease begins and advances. The researchers used metabolomic experiments to test samples (collected by the Alzheimer’s Disease Neuroimaging Initiative(link is external)) for differences in AD-related lipids in the blood of people with different stages of AD. Depending on the AD stage, they found significant alterations in lipids that led to biochemical changes. For example, in earlier stages of AD, one type of lipid was associated with the accumulation of a hallmark AD protein, tau; however, this lipid was not found in later phases of AD even as tau continues to accumulate. Findings such as these present researchers with new ideas for therapeutic strategies to target different biochemical and metabolomic pathways in our bodies. While promising, future studies are needed to determine the full effect of genetic mutations, diet, and lifestyle on these pathways and how they can be used to predict or treat AD.


Sets of coregulated serum lipids are associated with Alzheimer’s disease pathophysiology. Barupal DK, Baillie R, Fan S, Saykin AJ, Meikle PJ, Arnold M, Nho K, Fiehn O, Kaddurah-Daouk R, & Alzheimer Disease Metabolomics Consortium. Alzheimers & Dementia, 11 (2019) 619-627.


Are There Cellular Checkpoints on the Road to Cancer?

image shows a toll bar with a stop

One of the key events during cancer metastasis is a process called the epithelial-to-mesenchymal transition (EMT). EMT occurs when genetic changes allow tumor cells to break away and move into different body regions, potentially leading to new tumors in the body. Previous experiments identified specific “stages” of EMT based on the activity of a small number of genes. However, several new studies indicate these stages may not be as distinct as previously thought. Understanding how EMT occurs is a fundamental goal of cancer biology, as it may lead to new cancer treatment options. The Common Fund 4D Nucleome (4DN) program is primed to help by providing scientists with the tools and resources to monitor the changes in gene activity from groups of cells in tumors as well as small changes in individual cells.

4DN program researchers used advanced techniques to look at gene activity over time in single cells. Instead of finding distinct stages linked to EMT, they showed that EMT is actually a continuum of changes within the cell, with multiple genes getting turned on or off at different times. Many of these genes control how our cells make specific proteins that help hold cells in place. By turning these genes off, it may allow tumor cells to break free and move to new regions within the body. The findings also showed why previous experiments indicated EMT occurred in discrete stages. The researchers discovered “checkpoints” within the continuum where different factors regulate how a cell proceeds through the EMT. If any regulatory factors were disrupted, the progress through EMT could stall, making it look as though there were discrete stages to EMT. It was only by looking at single cells over time that the 4DN researchers could explore the continuum of changes and the factors regulating them without being confused by combined results from large groups of cells. These findings begin to shed light on the genetic changes that govern not only EMT in cancer, but diverse biological processes in development and disease. By learning more about the driving forces behind EMT, researchers can start to explore new cancer treatments that prevent or slow metastasis.


A pooled single-cell genetic screen identifies regulatory checkpoints in the continuum of the epithelial-to-mesenchymal transition. McFaline-Figueroa JL, Hikk AJ, Qui X, Jackson D, Shendure J, Trapnell C. Nature Genetics, 51, 2019, 1389-1398.


A New Way to Spell Life?

building blocks spell life All life on earth contains DNA, which houses the chemical building blocks (or nucleic acids) adenine (A), cytosine (C), thymine (T) and guanine (G). These four nucleic acids store information within the DNA, but whether systems with different numbers or types of building blocks can also support life is an open question. Transformative Research Awardee, Dr. Steven Benner, and his team have, for the first time, built a DNA code out of eight letters, challenging traditional ideas about the nature of life and creating new possibilities for information storage.

To explore whether the DNA alphabet could be expanded, researchers used synthetic biology to design four additional nucleic acids that behaved like the original A, C, T and G. When all eight building blocks were combined, they formed a new construct the researchers termed “hachimoji DNA,” based on Japanese words for ‘eight’ (“hachi”) and ‘letters’ (“moji”) which appropriately describe this novel genetic code. The team then tested whether hachimoji DNA adhered to basic principles necessary to support life. First, they established that the set of bases faithfully bound to each other in a predictable pattern, demonstrating they could act as an information storage system. Second, they showed the overall DNA structure was retained even when the bases were reordered, proving the structure was both stable and changeable. Finally, the team confirmed the synthetic DNA could be copied into a functional RNA molecule, a process necessary for transmitting information. Taken together, the experiments demonstrated an eight-letter genetic code could potentially support life as we know it.

These initial studies are only a starting point, and more work is necessary to determine how similar this synthetic DNA is to natural DNA. For example, researchers still need to confirm whether hachimoji DNA can be replicated like natural DNA. However, the hachimoji DNA was able to accurately store and transmit information, showing the technology has potential applications for the storage and retrieval of digital data. Ultimately, the study is a first step in envisioning other types of “living” structures that might exist outside of earth.

ReferenceHachimoji DNA and RNA: A genetic system with eight building blocks. Hoshika S, Leal NA, Kim MJ, Kim MS, Karalkar NB, Kim HJ, Bates AM, Watkins NE Jr, SantaLucia HA, Meyer AJ, DasGupta S, Piccirilli JA, Ellington AD, SantaLucia J Jr, Georgiadis MM, & Benner SA. Science 2019 Feb 22;363(6429):884-887.

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Slide-seq: A Novel Method to Map Tissue Cells

kidney histology In the same way the world's continents are made up of countries that each contain multiple cities, your body's organs are made up of tissues which each contain multiple cell types. Tissue functions are influenced by the different cell types and their organization, gene activity patterns, and the molecular pathways they use to communicate with other cells. In recent years, detailed explorations of tissue function have been limited by an inability to map where and how individual cells function within the tissue. To address these limitations, Early Independence Awardee, Dr. Fei Chen, and New Innovator Awardee, Dr. Evan Macosko, teamed up to develop Slide-seq, a new method for mapping detailed data on which genes are turned on or off within individual cells.

The technology works like this: First, a surface is packed with DNA-barcoded beads. Next, fresh-frozen tissue is transferred on top of this surface and any RNA released is captured by the beads. RNA is used to measure gene expression which genes are active in a cell. Scientists can use patterns of gene expression to tell the difference between cell types. Finally, by combining gene expression data with position information from the individual beads, the scientists created a high-resolution map of the spatial position of different cell types. The technique was validated with multiple tissues including those from the brain, liver, and kidney. The team confirmed that RNA expression measurements using Slide-seq agreed with other widely-used methods. To demonstrate its potential, the researchers used Slide-seq to map previously unknown gene expression patterns in an area of the mouse brain that had not been detected using existing technologies. They also used their novel technique to quantify different cellular responses in a mouse model of traumatic brain injury (TBI) over hours, days, and weeks. Their results suggested that TBIs can affect neural activity in a large area surrounding the injury for days or weeks post trauma.

Slide-seq technology may offer a better understanding of how different cell types organize themselves in space. This could allow us to map cells that are important after TBIs and other medically important events. However, more work is required to determine the significance of this organizational structure and how it changes over time. As part of this work, the NIH Common Fund's Human Biomolecular Atlas Program (HuBMAP) is developing an open, global research platform to map cells in healthy human bodies. Dr. Macosko joined the HuBMAP team in September of 2019 and will use Slide-seq to help create maps of the cell types and tissues that make up our bodies' "cities" and "countries."

ReferenceSlide-seq: A Scalable Technology for Measuring Genome-Wide Expression at High Spatial Resolution.. Rodriques SG, Stickels RR, Goeva A, Martin CA, Murray E, Vanderburg CR, Welch J, Chen LM, Chen F, & Macosko EZ. Science. 2019 Mar 29;363(6434):1463-1467


How Much Does It Hurt (Exactly)?

Nanosensors Contacting CellsLeft untreated, pain can have major negative effects on sufferers’ quality of life. However, quantifying pain is complicated because it is a subjective feeling that can reflect both actual and perceived biological damage. Dr. Alexander Niculescu, a NIH New Innovator Awardee, is working on an approach to objectively evaluate pain perception using a simple blood draw. He and his team set out to determine if biological indicators of pain exist—pain biomarkers. More specifically, he was interested in those detectable in the blood. The researchers focused their efforts on a cohort of psychiatric patients; as a group, psychiatric patients are at higher risk for pain disorders and often have an increased perception of pain.

During each of approximately six visits, Dr. Niculescu and his team asked participants to rate their pain as high or low. They also drew the participants’ blood and measured the activity levels of different genes. By associating differences in gene activity levels with high-pain or low-pain over time, the researchers identified 65 potential pain biomarkers. After prioritizing and validating potential biomarkers, the team analyzed their ability to predict a high pain state in a second cohort of psychiatric patients. They found (when separated by gender and diagnosis) individual biomarkers were better predictors than the full 65 biomarker panel. The researchers suggest that for the approach to be most useful, biomarkers may need to be personalized for each individual to accurately determine their pain level..

While this study exclusively focused on participants with psychiatric disorders, the researchers hope that its findings could be generalizable to other populations. Although we are still a long way from being able to accurately quantify pain, this approach demonstrates the potential of precision medicine for its diagnosis and treatment.

ReferenceTowards precision medicine for pain: diagnostic biomarkers and repurposed drugs. Niculescu AB, Le-Niculescu H, Levey DF, Roseberry K, Soe KC, Rogers J, Khan F, Jones T, Judd S, McCormick MA, Wessel AR, Williams A, Kurian SM, & White FA. Mol Psychiatry. 2019 Apr;24(4):501-522.


Scientists Discuss Recommendations for Returning Individual genetic research results in African genomic research

There is an ongoing discussion among the scientific community, policymakers, and research participants about how to return a finding concerning an individual research participant’s health that was discovered in the course of conducting genetic research. 
In most African countries, where resources are limited, the option to return individual research results (IRRs) to research participants is not always feasible. Furthermore, African populations are more genetically diverse than other populations, and this diversity remains to be fully understood, which poses limitations on the identification of disease-related DNA changes that are more likely to be found in populations of African descent. 

In a commentary published in Nature Genetics, Human Heredity and Health in Africa (H3Africa) researchers discuss strategies and challenges that should be addressed to develop policies and practices for returning IRRs in Africa.The researchers propose that there is a need to develop a priority list of genes and mutations that are clinically relevant in African populations. For example, known mutations that cause Sickle Cell Disease, an inheritable disorder where red blood cells to have a distorted shape like a sickle or banana, should be considered a reportable finding in countries where the frequency of the disease is high, and genetic testing is not routinely performed. In some instances, providing an individual with this level of information could inform important personal decisions around marriage and reproduction. The team also proposed several ways to overcome challenges around returning IRRs in Africa, such as increasing the number of healthcare workers with experience in medical genetics. The H3Africa Consortium, supported by the NIH Common Fund, is taking steps to help resolve this issue by offering basic genetic counselling training to nurses across the continent. This commentary adds to the conversation around guidelines for returning IRRs from genomics research and approaches to improve healthcare in Africa.  


Returning incidental findings in African genomics research. Wonkam A, de Vries J. Nat Genet. 2019 Nov 25. doi: 10.1038/s41588-019-0542-4


Signature Improvement: Decoding Breast Cancer Drug Targets

Cancer drug knocking out a cancer

A study from the NIH Common Fund’s Library of Integrated Network-based Cellular Signatures (LINCS) program has uncovered why some metastatic breast cancer drugs may work better than others. Metastatic breast cancer, also called ‘Stage 4’ breast cancer, occurs when the cancer has spread to other parts of the body such as the lungs, liver, bones, or brain. A team at LINCS awardee institution Harvard Medical School, led by Dr. Peter Sorger, and their collaborators analyzed three drugs (palbociclib, robociclib, and abemaciclib) that work by blocking a cancer cell’s ability to grow and multiply. All three drugs were thought to function in a similar way, but it was unclear whether each drug may also have unique differences in how they block cancer cells that might affect which drug should be chosen to treat different patients.

The researchers used LINCS tools (including Growth Rate Inhibition metrics, CMap, and Enrichr) and analyses at the RNA, protein, and cellular levels to generate a composite ‘drug signature’ for how each of the drugs affected the cancer cells—like a snapshot of how the cells responded to treatment. Interestingly, they found that one of the drugs (abemaciclib) had a broader range of molecular targets that may make it a good candidate for treating breast cancers that have become resistant to therapy. Further, they showed that abemaciclib treatment of one patient’s drug-resistant metastatic breast cancer decreased the size of a cancer lesion in the patient’s liver. This research highlights how drug signatures may help tailor treatment to improve patient outcomes and identifies targets for developing effective and longer lasting treatments for drug-resistant metastatic breast cancers.

The data sets generated by this research are available in the HMS LINCS Database and are freely available for research use.

ReferenceMultiomics Profiling Establishes the Polypharmacology of FDA-Approved CDK4/6 Inhibitors and the Potential for Differential Clinical Activity. Hafner M, Mills CE, Subramanian K, Chen C, Chung M, Boswell SA, Everley RA, Liu C, Walmsley CS, Juric D, Sorger PK. Cell Chem Biol. 2019 Aug 15; 26(8):1067-1080.


Identifying Zip Codes and Contents of Molecular Packages

Extracellular vesicles (EVs) produced by cancer cells are particularly variable in size and contents, which makes understanding their function in intercellular communication or usage as clinical biomarkers challenging. Researchers supported by the Common Fund program recently studied the contents of different size EVs generated from mouse cancer cells and non-cancer cells to learn more. They used membranes with different sized pores to separate large-, medium-, and small-sized vesicle samples, and an even smaller non-vesicle complexes fraction from different cell types. They found that different sized vesicles carried distinct types of RNA. They also found similarities in the protein content based on the size of the vesicle versus the type of cell it came from. Using special techniques to examine single vesicles they also found that specific surface proteins were only present on EVs of certain sizes.

This study offers a glimpse of the complicated molecular and physical landscape of EVs, but higher resolution and better methods are needed. The work also suggests that by combining vesicle size and content information, it may be possible to know more about their cellular origin. The Extracellular RNA Communication program, now in a second stage, will build upon this knowledge to develop improved approaches to sort, separate, and isolate individual carriers.

Physical and Molecular Landscapes of Mouse Glioma Extracellular Vesicles Define Heterogeneity. Gyuris, A., J. Navarrete-Perea, A. Jo, S. Cristea, S. Zhou, K. Fraser, Z. Wei, A. M. Krichevsky, R. Weissleder, H. Lee, S. P. Gygi and A. Charest (2019). Cell Rep 27(13): 3972-3987 e3976.



Is Music a Universal Language?

Globe listening to

Music has been hailed as “the universal language of mankind.” But while scholars acknowledge that people from all around the world make music, the immense variability of songs from different cultures draws into question what exactly about music is “universal.”  Are specific behaviors, like dance or courtship, linked to song across cultures? Can musical features in songs like rhythm, melody, or emotional effect predict how a song might be used? NIH Director’s Early Independence Awardee Dr. Samuel Mehr carried out a systematic analysis of vocal music from across the globe to quantitatively evaluate these questions. He and his team built a comprehensive database they termed the Natural History of Song, composed of both texts describing 4,709 song performances from 60 traditionally-living societies and a smaller collection of 118 recorded songs from 86 societies. They analyzed four types of songs drawn from this database: dance, lullaby, healing and love.

Using a unique combination of computational analysis, statistics, social science and humanistic data, Dr. Mehr and his team investigated the different contexts in which songs were performed and the variation in musical content across genres. Music was indeed a part of every culture studied, supporting its universality. Although substantial variation in the complexity of melodies and rhythms existed across cultures, three main factors—formality, arousal (whether the music was lively or calming), and religiosity— captured variation in the performance and use of songs across societies. Further, the researchers found that songs varied more within societies than between them. Music was systematically associated with behavior; each culture’s songs for dance, healing, lullaby and love shared similar properties, and listeners were able to successfully classify songs from foreign cultures by genre.

The researchers acknowledge that biases exist, since songs were characterized and analyzed through the lens of Western scholars and listeners. But by combining a rich dataset of human song with computational and statistical methods that attempt to correct for these biases, the researchers demonstrate a novel way to quantify the features that make music universal. These findings advance our understanding of how we perceive, create, and engage with music, and one day may be leveraged to improve health outcomes in infancy and adulthood.

ReferenceUniversality and Diversity in Human Song. Mehr SA, Singh M, Knox D, Ketter DM, Pickens-Jones D, Atwood S, Lucas C, Jacoby N, Egner AA, Hopkins EJ, Howard RM, Hartshorne JK, Jennings MV, Simson J, Bainbridge CM, Pinker S, O’Donnell TJ, Krasnow MM, Glowacki L. Science 366, eaax0868 (2019). DOI: 10.1126/science.aax0868


Saying "No" to Pay-As-You-Go?

Credit Card On Hospital

Health care spending is growing, and multiple efforts have been proposed to curb it. Some suggested solutions focus on changing how insurers pay provider organizations (e.g. hospitals and clinical practices) for their care. A common practice requires insurers to pay a fee for each service provided, a model which can incur exaggerated costs due to a desire for more services, even when the benefit is unclear (fee-for-service model). However, a competing model sets an annual budget for clinical practices for a group of patients, which can promote more preventative care and discourage unnecessary services, lowering spending (Global Payment Model).

Unfortunately, evidence supporting this model is often short-term, mixed, and offers limited insights on health care outcomes. To address this issue, Early Independence Awardee, Dr. Zirui Song conducted a study focused on what could be the most expansive and longest-running private-payer contract of a population-based global payment model.

Dr. Song’s team used insurance claims data from 2006 to 2016 within the Blue Cross Blue Shield of Massachusetts database. The longest-term data is from the first cohort of providers, which joined the plan in 2009. Over 2009 to 2016, average annual spending on claims had slower growth for the first cohort of people in the global payment model-based contract. They saved nearly 12% ($461 per member, per year) on medical claims, compared with control patients, likely enrolled in traditional, fee-for-service plans in northeastern states. Other cohorts with fewer years in the Global payment model had comparable or smaller savings on claims. Patients also received improved quality of care for chronic diseases and conditions (e.g. diabetes and high blood pressure) and experienced fewer emergency room visits than patients in comparable states.

There are some limitations, including the locality and Massachusetts-specific policies, which may hinder how well the study’s findings apply to other locations. However, the expansive and detailed nature of this research is an important step in building evidence for measures that preserve high-quality of care while addressing ballooning health care costs.

Reference: Health Care Spending, Utilization, and Quality 8 Years into Global Payment. Song, Z., Ji, Y., Safran, D. G., & Chernew, M. E.The New England Journal of Medicine, (2019).381, 252-263.



Safety First: Developing Tools and Biomarkers to Prevent Nerve Injury

SPARC2 projects are developing next generation tools and technologies to detect and monitor nerve structure changes in real-time 

Bioelectronic medicine, the use of therapeutic devices to modulate electrical activity in nerves and improve organ function, has the potential to treat a wide variety of diseases and conditions, such as heart arrhythmias, gastrointestinal disorders, and type II diabetes. The nerves that connect the brain and spinal cord to organs in the body are composed of long cable-like bundles of fibers called axons, wrapped in a supportive layer of connective tissue that contains blood vessels essential for nerve health and function. As with any therapeutic, safety is enhanced by understanding and reducing potential unintended effects, including tissue damage to stimulated nerves.

A team at SPARC awardee institution Massachusetts General Hospital, led by Dr. Benjamin Vakoc, worked with scientists in the medical device laboratories at the Food and Drug Administration (FDA) to develop a novel approach for predicting and preventing nerve injury caused by electrical stimulation in real time. In order to capture a clear image of tissue damage, they created a custom 3D-printed device to simultaneously stimulate the nerve while also stabilizing it to prevent blurry images caused by breathing and muscle movement. They used optical coherence tomography (OCT), an imaging approach that measures the quantity and direction of light deflected and reflected by tissue, to monitor blood flow and blood vessel size during electrical stimulation of the sciatic nerve. They then compared OCT observations during stimulation with established analyses of nerve function and tissue structure to correlate the changes with nerve injury. This approach points to new real-time biomarkers that could be used to calibrate bioelectronic medicine therapies in order to predict and prevent nerve injury.

ReferenceToward optical coherence tomography angiography-based biomarkers to assess the safety of peripheral nerve electrostimulation. Vasudevan S, Vo J, Shafer B, Nam, Ahhyun S, Vakoc B, Hammer D. J Neural Eng. 2019 Jun.


Don’t Sweat It: Harnessing the Power of Brown Fat to Burn Calories

SPARC1 projects are mapping the innervation of organs and tissues, identifying new targets for future neuromodulation therapies

What if you could burn extra calories without breaking a sweat? It may sound counterintuitive, but a specific type of fat – called brown fat – has the potential to do just that. Brown fat is abundant in infants, where it helps regulate body temperature by converting energy stored in fat and sugar into heat. For decades scientists thought that brown fat didn’t have an active role later in life, but that view has changed in recent years with the discovery of brown fat deposits in adults that are activated by drops in temperature. When faced with cold temperatures, the nervous system sends electrical signals that tell brown fat cells to burn more calories, which helps raise the body’s core temperature to a more comfortable level. If scientists could figure out a way to regulate the intricate network of nerves that send these signals in order to harness brown fat’s calorie-burning potential this knowledge could be applied in a variety of ways – from treatments for health conditions related to energy regulation, such as diabetes, to enhancing the healthy effects of exercise.

To do this, we would need to know more about the locations of the nerves by finding a way to see through brown fat to map the neurons that innervate it. A team at SPARC awardee institution Louisiana State University, led by Dr. Heike Münzberg, used a combination of mouse genetics and fluorescent tracers to light up the neurons. They then made them visible using a recently developed chemical process for “clearing” the intact tissue containing the nerves. This process enables high-resolution imaging and 3D reconstruction of the specific neurons that innervate brown fat, without disturbing their location in the tissue. Improved understanding of brown fat innervation patterns could lead to neuromodulation strategies to precisely control the metabolic activity of brown fat by regulating the nerves’ electrical signals. These new tissue-clearing and circuit-tracing methods are being used to visualize and map innervation of other organs and tissues, bringing us closer to identifying precise targets for neuromodulation therapies that could be used to treat a variety of diseases and conditions.

ReferenceSympathetic innervation of the interscapular brown adipose tissue in mouse. François M, Torres H, Huesing C, Zhang R1, Saurage C, Lee N, Qualls-Creekmore E, Yu S, Morrison CD, Burk D, Berthoud HR, Münzberg H. Ann N Y Acad Sci. 2019 Jun 11.


Mouse Database Guides New Understanding of Skin and Eye Health in Humans

Researchers mined a publicly available database containing data generated and continuously updated by the International Mouse Phenotyping Consortium (IMPC) to identify mouse genes associated with eye and skin abnormalities. The IMPC database is a resource not only for the scientific community, but also for clinicians who may want to find clues from mice on the genetic basis of certain human diseases. Mouse genes often are helpful for identifying and understanding equivalent genes in humans. The NIH Common Fund Knockout Mouse Phenotyping Program (KOMP2) is part of this global effort to develop this database.

There are many skin and eye disorders resulting from the same single gene mutations in both mice and humans. For example, albinism is a disorder affecting both the skin and eyes. While there are some genes linked to some forms of albinism, other cases, such as Oculocutaneous albinism, have no clear genetic cause.

The researchers used the database to search for “knockout” mice with skin, hair, or pigmentation abnormalities. They found 307 different knockout mice with skin abnormalities. Of these, 52 also had eye abnormalities. For 17 of these 52 knockout mice, there was existing literature so the researchers already knew these gene knocks would have eye or skin problems. However, 35 of the 52 knockouts revealed new genes to study with the potential to better understand albinism. These newly identified genes may also point to potential targets for gene or drug therapy in humans.


Moore, B. A., A. M. Flenniken, D. Clary, A. S. Moshiri, L. M. J. Nutter, Z. Berberovic, C. Owen, S. Newbigging, H. Adissu, M. Eskandarian, C. McKerlie, S. Brown, S. Wells, A.-M. Mallon, A. L. Beaudet, M. H. de Angelis, N. Karp, B. Braun, Y. Herault, X. Gao, Y. Obata, P. Flicek, T. Meehan, H. Parkinson, D. Smedley, J. K. Seong, G. Tocchini-Valentini, F. Mammano, S. M. Thomasy, K. C. K. Lloyd, C. J. Murphy, A. Moshiri and C. International Mouse Phenotyping (2019). "Genome-wide screening of mouse knockouts reveals novel genes required for normal integumentary and oculocutaneous structure and function." Scientific Reports 9(1): 11211.

See Also:

Knockout Mice Are Guide to New Genes for Eye and Skin Disorders


Synthesizing Sugars is No Piece of Cake?

Glycans are sugars that have many important biological and medical applications, such as use in clinical diagnostics and in glycan-based vaccines. However, it is difficult to obtain large enough quantities of specific glycans from nature and current methods of synthesizing glycans in the lab are time consuming and labor intensive. Current synthesis methods require skilled researchers in highly specialized laboratories and can involve as many as 100 steps. Even skilled researchers can have a very hard time making enough glycans for biomedical studies. The Glycoscience Program is developing new tools, techniques, and methods to create large amounts of glycans by simpler methods, which will make the study of glycans more accessible to the biomedical research community. 

Glycoscience Program-funded investigator Dr. Geert Jan-Boons and his research team have developed a new “stop and go” approach to glycan synthesis that involves 10 or fewer steps and is designed to mimic natural glycan synthesis in the body. A process of modifying proteins by adding glycans, called “N-glycosylation,” is one of the most diverse and complex types of protein modifications. N-glycosylation is involved in many biological processes, such as neuron development, fertilization, immune regulation, and in many disease processes. This new “stop and go” strategy allows for synthesis of useful amounts of a variety of complex N-glycans without the need for special synthesis skills, providing glycans for biomedical research. 

Another investigator with the Glycoscience program, Dr. Peng George Wang and his research team, developed a fully automated system to efficiently synthesize glycans using enzymatic reactions, a temperature dependent polymer, and repurposing a commercially available peptide synthesizer. Several important glycans, such as blood group antigens (i.e. the sugars on the surface of blood cells that designate A, B, and O blood types), were synthesized using this system. These experiments illustrate the important ways in which the Glycoscience Program is simplifying glycan synthesis while expanding the diversity of glycans available for biomedical research.  

Machine-Driven Enzymatic Oligosaccharide Synthesis by Using a Peptide Synthesizer. Zhang J, Chen C, Gadi MR, Gibbons C, Guo Y, Cao X, Edmunds G, Wang S, Liu D, Yu J, Wen L, and Wang PG. 2018. Angew Chem 130: 16880-16884. 
Streamlining the chemoenzymatic synthesis of complex N-glycans by a stop and go strategy. Liu L, Prudden AR, Capicciotti CJ, Bosman GP, Yang JY, Chapla DG, Moreman KW, and Boons GJ. 2018. Nature Chemistry: DOI:10.1038/s41557-018-0188-3.


Modeling Mutations to Better Predict Breast Cancer Outcomes

Scientists Using ComputersAlthough the disease breast cancer has one general name, the tumors that define it often contain different genetic mutations. In fact, a single tumor can have groups of cells with different mutations that arise naturally as a tumor grows or that occur after exposure to cancer treatments. This is important because certain mutations allow cancer cells to resist therapies and make the cancer more likely to recur. Dr. Christina Curtis, an NIH Director’s Pioneer Awardee, published two papers in Nature Communications and Nature where she applies computer modeling to genetic data from tumors and clinical follow-up data from patients to get a better understanding of the extent of genetic variability in breast cancer tumors before and after treatment.

In the first study, Dr. Curtis used computer models to investigate differences in genetic mutations between a tumor sample taken at diagnosis to one taken after surgery. Patients in this study had untreated tumors prior to surgery or received pre-surgery chemotherapy. Dr. Curtis’s computer modeling showed that genetic differences in breast cancer tumors may be present even before treatment, and in greater numbers than expected. Their data led them to suggest at least two areas of the same tumor should be sampled to accurately identify treatment-induced mutations. They also used the data to find evolutionary factors associated with tumor treatment-resistance, which could lead to better predictions of tumor behavior.

In the second report, Dr. Curtis and colleagues obtained data from over 3,000 breast cancer patients and used findings from her past research to group the cancer patients based on the molecular make-up of their tumors, including their genetic make-up. In doing this, she created the largest breast cancer cohort with both molecular characteristics and long-term follow up data to date. From this cohort, Dr. Curtis’s team discovered four groups of patient tumors that were prone to recurring later in life, up to 20 years after diagnosis. Collectively, these recurrent tumors made up about 26% of the tumors assessed. On the other end of the spectrum, they uncovered a group of breast cancers that rarely recur after five years. These molecular subtypes may help to improve predictions of late stage relapses beyond what is generally possible today, and someday guide treatment recommendations.


Clonal replacement and heterogeneity in breast tumors treated with neoadjuvant HER2-targeted therapy. Caswell-Jin, J. L., McNamara, K., Reiter, J. G., Sun, R., Hu, Z., Ma, Z., Ding, J., Suarez, C. J., Tilk, S., Raghavendra, A., Forte, V., Chin, S-F., Bardwell, H., Provenzano, E., Caldas, C., Lang, J., West, R., Tripathy, D., Press, M. F., Curtis, C., Nature communications (2019) 565(7739), 361.

Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups. Rueda, O. D., Sammut, S-J., Seoane, J. A., Suet-Feung, C., Caswell-Jin, J. L., Callari, M., Batra, R., Pereira, B., Bruna, A., Ali, H. R., Provenzano, E., Liu, B., Parisien, M., Gillett, C., McKinney, S., Green, A. R., Murphy, L., Purushotham, A., Ellis, I. O., Pharoah, P. D., Rueda, C., Aparicio, S., Caldas, C., & Curtis, C. Nature (2019).

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Rejection Detection (of Transplants)

Nanosensors Contacting CellsSuccessful organ transplants are important to the quality of life of patients with end-stage organ failure. Early detection of acute transplant rejection can increase a patient’s chances for survival, by allowing clinicians to intervene and use anti-rejection drugs to save the graft or organ. Unfortunately, current detection approaches, including tissue biopsies, can involve additional surgery, result in sampling errors, or can lead to additional health issues and disease.

The early stages of organ or graft rejection (when the host’s immune cells attack the new transplant) is linked to increases in some biomolecules. One that has been correlated with this stage is Granzyme B, which acts a signal for cell death. New Innovator Awardee Dr. Gabriel Kwong has developed a groundbreaking nanosensor that detects the early transplant rejection marker, Granzyme B, and activates a fluorescent indicator that is passed in the patient’s urine—no additional surgery required.

Dr. Kwong and his team injected the nanosensor into rodent skin graft models and found the sensors could successfully detect early graft failure. Although rigorous testing is needed before it is ready for use in humans, future developments of this technology could allow for better, and less invasive, detection of transplantation failures to give doctors a versatile tool to manage patient health.

ReferenceNon-invasive early detection of acute transplant rejection via nanosensors of granzyme B activity(note: full text may require institutional access). Mac, Q. D., Matthews, D. V., Kahla, J. A., Stoffers, C. M., Delmas, O. M., Holt, A.H., Adams, B. A., & Kwong, G. A. Nature Biomedical Engineering (2019).

In the News:

Urine Test Detects Organ Transplant Rejection, Could Replace Needle Biopsies
Georgia Institute of Technology


Antiseptic Bathing Reduces Infections in Hospital Patients

Doctors with

Hospital associated infections result in billions of dollars of healthcare costs and thousands of deaths every year. The Active Bathing to Eliminate Infection (ABATE) Trial, led by Dr. Susan Huang from the University of California, Irvine, collected real world data on bathing hospital patients with an antiseptic soap, chlorhexidine, during inpatient hospital stays. A previous study from Dr. Huang and colleagues showed that using this type of bathing as routine care in the intensive care unit (ICU) reduced bloodstream infections by up to 44 percent. Given those exciting results, the ABATE study was designed to test whether bathing with chlorhexidine as well as targeted use of a nasal antibiotic, mupirocin, would also reduce infections in non-ICU patients.

The ABATE study was conducted in 53 hospitals and compared routine bathing to bathing with chlorhexidine in over 330,000 non-ICU patients. In this large and diverse population of patients, there was not a statistically significant difference in the number of infections that occurred between the control and antiseptic bathing, suggesting that switching to this type of bathing as routine practice for all patients would not be an effective method for reducing infections. However, when considering only patients with a medical device, such as a central venous catheter or a lumbar drain, there was a reduction in bloodstream infections of over 30 percent when patients were assigned to the antiseptic bathing. Therefore, this bathing practice should be valuable for this higher risk subset of hospital patients. One of the health care systems in which this study took place already adopted this form of bathing for these patients because of the results from the ABATE Trial.

You can learn more about this study in the NIH press release that accompanied the study publication and by exploring the study page on the Collaboratory website.

ReferenceChlorhexidine versus routine bathing to prevent multidrug-resistant organisms and all-cause bloodstream infections in general medical and surgical units (ABATE Infection trial): a cluster-randomised trial. Huang SS, Septimus E, Kleinman K, Moody J, Hickok J, Heim L, Gombosev A, Avery TR, Haffenreffer K, Shimelman L, Hayden MK, Weinstein RA, Spencer-Smith C, Kaganov RE, Murphy MV, Forehand T, Lankiewicz J, Coady MH, Portillo L, Sarup-Patel J, Jernigan JA, Perlin JB, Platt R, ABATE Infection trial team. Lancet. 2019 Mar 4. doi: 10.1016/S0140-6736(18)32593-5.


What's the Matter Bladder?

Stethoscope next to a model of a

Overactive bladder is a condition that affects millions of people and causes a frequent need to urinate, incontinence, and an increase in bladder voiding. A common treatment uses low levels of electricity to stimulate nerves controlling the bladder. Unfortunately, it’s not precise and can lead to off-target effects and pain. SPARC program awardee Dr. Aaron Mickle developed an innovative approach to this problem by creating a miniature implanted device that can sense and control bladder function in rats. This self-adjusting coordination of devices, that manipulate nerves to control organs, is called a closed-loop system.

Instead of activating nerves using electricity, Dr. Mickle and his team used a technique called optogenetics where genetically modified rat nerve cells could be activated using light. In rats, Mickle’s team inserted a stretch sensor that measured changes in bladder expansion over time. The sensor was connected to LEDs. Both were connected by wires to a flexible base-station device implanted in the abdomen. The bladder-stretch sensor communicated data to the base station, which wirelessly transmitted this information to an external device that recorded and monitored bladder function. Rats received the molecule cyclophosamide, which leads to frequent bladder emptying. When the external device detected this abnormal bladder function (i.e. signs of overactive bladder), it transmitted a wireless signal causing light-driven inhibition of nerves affecting bladder emptying—preventing abnormal frequency of urination.

Although the animal model of overactive bladder shows promise for this technology, questions remain about whether this approach could be used in humans or to treat other diseases. Additionally, the body’s long-term response to the stretchable sensor is unknown and there are other concerns about possible tissue damage or unintended adhesion to the tissue that affect the device’s function. However, if it ultimately proves fruitful, this work could correct organ dysfunction and manage pain.

ReferenceA wireless closed-loop system for optogenetic peripheral neuromodulation. Mickle, A. D., Won, S. M., Noh, K. N., Yoon, J., Meacham, K. W., Xue, Y., Mcllvried, A. L., Copits, B. A., Samineni, V. K., Crawford, K. E., Kim, D. H., Srivastava, P., Kim, B. H., Min, S., Shiuan, Y., Yun, Y., Payne, M. A., Zhang, J., Jang, H., Li, Y., Lai, H. H., Huang, Y., Park, S., Gereau IV, R. W., & Rogers, J. A. Nature (2019) 565(7739), 361.

In the News:

Implanted device enables responsive bladder control, Nature


Faculty Support Career Development of Their Trainees

University building with studentsFaculty mentors are critical to the research training of graduate students and postdoctoral scientists, but there are little data about how faculty members value the time and effort their trainees spend on career development. To address this gap, seven NIH Broadening Experiences in Scientific Training (BEST) institutions conducted surveys of over 700 faculty at their institutions on faculty perceptions of professional and career development for trainees. BEST institutions are conscious that having faculty support activities for trainees is essential and learning more about what faculty believe and value will inform their program development.

Results from these surveys suggest that faculty are generally aware that a majority of biomedical trainees will ultimately pursue career paths outside of academia. In general, they also report that while they actively mentor trainees about non-academic career opportunities, the majority do not believe they have a good understanding of skills needed in non-academic career opportunities or the non-academic contacts to help their mentees. Faculty also responded that time spent on training for non-academic careers, like in BEST activities, was acceptable to them and valuable to both graduate and postdoctoral trainees. Interestingly, while the majority of faculty stated they talk with both graduate and postdoctoral scientists about a myriad of professional non-academic careers, most faculty were less sure that their fellow colleagues provided the same kind of discussion, resources, and opportunities. Overall, these findings support the need for BEST and BEST-like programs to help trainees explore expanded career opportunities and support faculty mentors that are not fully prepared to offer this training on their own.


Faculty perceptions and knowledge of career development of trainees in biomedical science: what do we (think we) know? Watts SW et al. PLoS One. 2019 Jan 30.

​Archived Highlights:​​

Stem Cell-Based Treatment Used to Prevent Blindness In Animal Models of Retinal Degeneration

Age-related macular degeneration (AMD) is the most common cause of blindness among the elderly. With limited treatment options for AMD patients, scientists turned to regenerative medicine approaches, pursuing an induced pluripotent stem cell (iPSC) based therapy for AMD. IPSCs are cells taken from patient tissues and coaxed into a stem cell-like state where they can be become any cell type in the body. In a study led by Dr. Kapil Bharti, a Regenerative Medicine Program-funded investigator, dying retinal pigment epithelial (RPE) cells in pigs and rats with retinal deterioration were replaced with human iPSC-derived RPE cells. The replacement cells helped nurture photoreceptors, the light-sensing cells in the retina. Both healthy RPE and photoreceptor cells are necessary to maintain vision. With both cell types now in place, the investigators observed that the iPSC-based therapy successfully prevented blindness in the animals. The findings from this study will inform a first-in-human clinical trial to treat AMD. 

Watch Dr. Bharti explain his research: 

Image: This is a scanning electron microscope image of an RPE monolayer growing on top of a biodegradable scaffold (blue, left side of image). The polarity of the RPE cells is clearly visible (brown, bottom of cells in middle of image; green, top of cells on right side of image).

Clinical-grade stem cell–derived retinal pigment epithelium patch rescues retinal degeneration in rodents and pigs. Sharma R, Khristov V, Rising A, Jha BS, Dejene R, Hotaling N, Li Y, Stoddard J, Stankewicz C, Wan Q, Zhang C, Campos MM, Miyagishima KJ, McGaughey D, Vilasmil R, Mattapallil M, Stanzel B, Qian H, Wong W, Chase L, Charles S, McGill T, Miller S, Maminishikis A, Amaral J, Bharti K. Science Translational Medicine. January 16, 2019 (Note: full text of the article may require institutional access)

In the news:


Broadening Career Knowledge and Awareness in the Future Biomedical Research Workforce

Historically, biomedical research training focused on preparing all trainees for academic positions: either PIs in a research setting or teaching professors. Increasingly, a shift toward preparation for a wider range of career options has emerged. As part of this shift, the NIH Common Fund issued “Broadening Experiences in Scientific Training” (BEST) awards to 17 institutions to develop innovative approaches for their trainees to prepare them for a broader expanse of careers in the biomedical research enterprise. Overall, the purpose of BEST awards was to expose trainees to the breadth of career paths available and provide them with information, or a working knowledge, to enter the dynamic biomedical research workforce landscape and ultimately strengthen the biomedical research enterprise. A cross-site evaluation was set up to assess the impact of BEST activities on 1) trainee confidence and knowledge to make career decisions, 2) influence of BEST activities to time in training, and 3) ability of the institutions to sustain activities deemed to be beneficial. BEST awardee institutions made great strides in collecting data on their activities and tracking participation for thousands of trainees. Using some of these data, the paper, The NIH “BEST” programs: Institutional programs, the program evaluation, and early data offers insight into the BEST programs.

The manuscript reports high levels of participation in a broad array of BEST activities. Survey data indicated that most graduate students and postdoctoral scientists had a high awareness of biomedical career options beyond traditional academic research-intensive positions. Interestingly, “Research in Industry,” was most strongly considered as a prospective career path by graduate students. 

As NIH Common Fund support ended after 5 years, all BEST programs were successful in either securing funding for their BEST activities and associated staff, or integrating the activities into their graduate programs. While most BEST graduate students and postdoctoral scientists are still in training, participation in BEST activities did not increase training time for those who have completed training. Analysis of additional data over a longer-term will be critical to understanding career outcomes more fully, particularly for training time. By summer 2020, a dataset from the full cross-site evaluation will be made available to the research community allowing researchers to delve further into understanding BEST programs. Check the NIH Common Fund’s Strengthening the Biomedical Research Workforce webpage for updates on the dataset.



Lenzi, R. N., Korn, S. J., Wallace, M., Desmond, N. L., & Labosky, P. A. (2020). The NIH "BEST" programs: Institutional programs, the program evaluation, and early dataFASEB journal : official publication of the Federation of American Societies for Experimental Biology, 10.1096/fj.201902064. Advance online publication. doi:10.1096/fj.201902064

This page last reviewed on December 3, 2021