The human body is made up of trillions of cells, each having a specialized role, and each affected by its environment and neighboring cells. The NIH Common Fund’s Human BioMolecular Atlas Program (HuBMAP) is a consortium of over 300 researchers using cutting-edge techniques to study the spatial organization of cells within tissues to better understand normal function of organs in health and disease. One kind of these techniques is called “proteomics” which studies the proteins within each of the cells that make up the body. By knowing which proteins are within specific cell types, researchers can better determine the types of cells that make up an organ, and where those cells are within that organ, which will assist researchers in finding and treating disease. While studying proteins is a good way to identify cells within a tissue, proteins can have different types of molecules attached to them that change their function and activity. When these molecules are added to a protein, it becomes a “proteoform,” and one protein can have many proteoforms, depending on the types of molecules that have been added. Since the proteoforms are different versions of the same protein, they are a more specific marker of cell type and function, and thus it is vital for researchers to develop new technologies that can identify the multitudes of proteoforms within a tissue section. To this end, Neil Kelleher, Ph.D., and his group at Northwestern published “Highly multiplexed, label-free proteoform imaging of tissues by individual ion mass spectrometry” in the August 2022 issue of Science Advances, describing the use of Proteoform Image Mass Spectrometry (PiMS) to identify and image proteoforms within slices of human kidney. PiMS combines two different types of high-throughput techniques called proteomics to determine the mass of the proteoform, and to create an image of that proteoform within the tissue. With the information gathered from these methods, the researchers then searched a custom-made database of 1000 proteoforms that are known to be in the kidney to identify the proteoforms they found. Once they knew what proteoforms they were looking at, they were able to make one single composite image of the location of many proteoforms within the same section of kidney, like the one below. PiMS is able to determine the types of proteoforms within the kidney section at a spatial resolution of 80 micrometers, allowing researchers insights into the proteins that comprise human tissues at a level not seen before. PiMS can identify and reveal the location of proteoforms within an organ, making it a promising new application for tissue mapping efforts, biomarker discovery, and disease diagnostics, as well as being able to unveil what was once unseen.

Highly multiplexed, label-free proteoform imaging of tissues by individual ion mass spectrometry
Pei Su 1, John P McGee 1, Kenneth R Durbin 1, Michael A R Hollas 1, Manxi Yang 2, Elizabeth K Neumann 3, Jamie L Allen 3, Bryon S Drown 1, Fatma Ayaloglu Butun 4, Joseph B Greer 1, Bryan P Early 1, Ryan T Fellers 1, Jeffrey M Spraggins 3 5, Julia Laskin 2, Jeannie M Camarillo 1 4, Jared O Kafader 1 4, Neil L Kelleher 1 4 6

Sci Adv. 2022 Aug 12;8(32):eabp9929. doi: 10.1126/sciadv.abp9929. Epub 2022 Aug 10.
PMID: 35947651 PMCID: PMC9365283 DOI: 10.1126/sciadv.abp9929
This work is supported by NIH Common Fund grant UH3CA246635.

In the summer of 2022, the Human BioMolecular Atlas Program (HuBMAP) launched its second iteration of the Underrepresented Student Internship Program which enables undergraduate students to work with HuBMAP researchers for the summer to learn cutting-edge single-cell technologies, 3D model making, and software building. Thirteen students were chosen by HuBMAP researchers at ten institutions to work in their labs over the summer to learn these technologies. At the end of the internship, each student gave a presentation about their experience. See below to learn more about their exciting work! 

HuBMAP Researcher: Alison Pouch, Ph.D. and James Gee, Ph.D, Ph.D. at the University of Pennsylvania
Intern project: Karli Prather worked at the Penn Image Computer and Science (PICSL) Lab to develop an interactive and customizable digital representation of the ovary. She built a computer model of the pelvis to give users a point of reference for where the ovary is situated in the body. 

Watch the presentation - here

HuBMAP Researcher: Ljiljana Paša-Tolić, Ph.D. at Pacific Northwest National Laboratory (PNNL)
Intern project: Camryn Pettenger-Willey worked at PNNL to optimize a protocol for imaging different versions of proteins within tissues. 

Watch the presentation - here

HuBMAP Researcher: Kate O'Neill, MD, MTR at the University of Pennsylvania
Intern project: Marielena Grijalva optimized the protocol for isolating nuclei from uterine tissue for Singulator, a machine that dissociates tissue into single-cell or nuclei suspensions. 

Watch the presentation - here

HuBMAP Researcher: Liming Pei, Ph.D. at the Children’s Hospital of Philadelphia
Intern project: Anusha Thaniana performed single-cell RNA-sequencing to determine the activity levels of genes within the heart to study Fontan Associated Liver Disease.

Watch the presentation - here

HuBMAP Researcher: Brian Gregory, Ph.D. at the University of Pennsylvania
Intern project: Mohamed El-Sadec optimized the High-throughput Analysis of Modified Ribonucleoties (HAMR) software which makes notes of nucleotides in RNA sequencing data that have been modified after transcription.
 
Watch the presentation - here

HuBMAP Researcher: Jeff Spraggins, Ph.D. at Vanderbilt University
Intern project: Lin Xu helped to develop a program that automatically adds different colors to images of tissue slices. By colorizing these images, people are more easily able to understand the structure of the cells and tissues that they see. 

Watch the presentation - here

HuBMAP Researcher: Huiping Liu, Ph.D. at Northwestern University
Intern project: Genna Mahabeer mapped proteins within the red and white pulp regions of the spleen. 

Watch the presentation - here

HuBMAP Researcher: Katy Börner, Ph.D at Indiana University
Intern project: Sangmyung (Sam) Lee compared Tabula sapiens to HuBMAP’s Human Reference Atlas (HRA). Tabula sapiens is a molecular reference atlas for more than 400 cell types of the human body, and ASCT+B tables are a data framework built by HuBMAP researchers to capture naming terms for anatomical human body parts and spatial reference objects. By comparing the two, the HRA can determine what’s missing, and what needs to be added to improve.

Watch the presentation - here

HuBMAP Researcher: James Hagood, MD at the University of North Carolina
Intern project: M.J. Hopkins determined the optimal concentration of antibodies necessary to characterize the cells from the airway in single-cell techniques.

Watch the presentation - here

HuBMAP Researcher: Phil Blood, Ph.D. at the Pittsburgh Supercomputing Center (PSC)
Intern project: Fransiskus Agapa developed a computer program that automatically extracted, prepared, and submitted data and metadata from HuBMAP and the NIH database of Genotypes and Phenotypes (dbGap) to be ingested by the Common Fund Data Ecosystem (CFDE).

Watch the presentation - here

HuBMAP Researcher: Kate O'Neill, MD, MTR at the University of Pennsylvania
Intern project: Gabrielle LeNoir analyzed specific cell populations in the female reproductive system to better understand the stages of the menstrual cycle.

Watch the presentation - here

HuBMAP Researcher: Nils Gehlenborg, Ph.D. at Harvard University
Intern project: Tram Nguyen worked on the user interface for the HuBMAP Data Portal by making graphs of existing metadata using the R programming language.

Watch the presentation - here

HuBMAP Researcher: Rahul Satija, Ph.D. at the New York Genome Center (NYGC)
Intern project: Lesley Aguilar-Salceda used single-cell RNA sequencing to study different cell types in the motor cortex of the brain in mice, ferrets, and pigs to find genes that have been conserved throughout evolution.

Watch the presentation - here

If you would like to see more about the work of the these talented students, or the HuBMAP Consortium itself, please go to the HuBMAP YouTube channel or visit us at HuBMAP Consortium or NIH Common Fund HuBMAP. 
 

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

The human body is made up of trillions of cells, each having a specialized role, and each affected by its environment and neighboring cells. The NIH Common Fund’s Human BioMolecular Atlas Program (HuBMAP) is a consortium of over 300 researchers studying the spatial organization of cells within tissues to better understand normal function of organs in health and disease. As part of that effort, HuBMAP is developing and optimizing new technologies to study cells within their native states in different tissues, such as “multiplex antibody-based imaging.” In methods like this one, antibodies are used for their ability to bind to proteins that only appear in a certain kind of cell. Attached to one end of the antibody is a molecule that serves as a marker, making the location of the antibody visible to imaging equipment like high-tech microscopes. Scientists can look for these markers and identify the types of cells in a tissue sample based on the proteins recognized by the antibodies. Many different antibodies are used to recognize different kinds of proteins, which is called a multiplex approach. Recently, different components of HuBMAP came together to write a primer, or instruction manual, to describe the resources, considerations for obtaining high-quality imaging data, and expert knowledge needed to assist other researchers in choosing the best multiplexed antibody-based imaging method to map proteins in tissues. The primer includes advice on choosing antibodies and experimental workflows, as well as suggestions for large dataset management for these new methods. Additionally, the authors highlighted the need to integrate multiplexed imaging data with other biomolecule data types for more robust analyses. Such analyses that combine different types of data to form a more complete picture, will allow researchers to measure many different biomolecule types, like RNA and protein, within the same sample. This will give a better understanding of internal cellular functions, as well as the influence of neighboring cells on a particular cell in a certain location.

The primer, which appears as a perspective paper in the journal Nature Methods, outlines the challenges and advancements involved in multiplexed antibody imaging. It is hoped its guidance and other advances in HuBMAP will contribute to the development of tissue maps that aid in understanding normal tissue and organ functioning, disease progression, and response to treatment in different tissues. 

Spatial Mapping of Protein Composition and Tissue Organization: A Primer for Multiplexed Antibody-Based Imaging. John W Hickey, Elizabeth K Neumann, Andrea J Radtke, Jeannie M Camarillo, Rebecca T Beuschel, Alexandre Albanese, Elizabeth McDonough, Julia Hatler, Anne E Wiblin, Jeremy Fisher, Josh Croteau, Eliza C Small, Anup Sood, Richard M Caprioli, R Michael Angelo, Garry P Nolan, Kwanghun Chung, Stephen M Hewitt, Ronald N Germain, Jeffrey M Spraggins, Emma Lundberg, Michael P Snyder, Neil L Kelleher, Sinem K Saka. Nat Methods. 2022 Mar;19(3):284-295. doi: 10.1038/s41592-021-01316-y. Epub 2021 Nov 22. PMID: 34811556 DOI: 10.1038/s41592-021-01316-y
This work is supported by U54 HG010426, U54 DK120058, UH3 CA246635, UH3 CA246594, UH3 CA246633, NIAID (Andrea Radtke and Ron Germain), NCI (Stephen Hewitt)

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 ChannelHuBMAP YouTube Channel  or read about them on the HuBMAP Consortium websiteHuBMAP Consortium website.

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

All 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.

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 the goal of the NIH Common Fund Human BioMolecular Atlas Program (HuBMAP)  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.
 

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 https://portal.hubmapconsortium.org/. 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.

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

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 - http://deltarho.org/docs-trelliscope/). 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.

Until recently, scientists had to be satisfied with dissecting a population of cells from a specific tissue containing many different cells to draw conclusions about single cell types. With the advent of single-cell analysis techniques, scientists can now identify and study individual cells without worrying about interference from other cell types. Dr. Peng Yin and colleagues at Harvard University, members of the Human BioMolecular Atlas Program (HuBMAP) Consortium, published details of a new single-cell analysis technique called Immunostaining with Signal Amplification By Exchange Reaction (Immuno-SABER). Immuno-SABER allows researchers to simultaneously visualize many proteins in the same tissue sample by combining recognition of proteins by antibodies with signal amplification using DNA as a tool.

Immuno-SABER addresses one of the key challenges in identifying and amplifying specific biomolecules in a milieu of many others. DNA-barcodes act as ‘docking-sites’ for different colored fluorescent molecules bearing complementary DNA, and by varying the number and sequence of the barcodes a wider range of different protein targets can be imaged. With different DNA-barcodes attached to antibodies, the signal can be amplified and multiplexed. Yin and collaborators showed they could amplify the signal, as well as image ten protein targets simultaneously within either human tonsils, or mouse retinal cells. 

Immuno-SABER is open-source, economical, and designed to be compatible with standard workflows. Yin and his colleagues predict this novel technique could be useful for tissue atlas projects, biomarker screening, or as a complement to another high throughput technique called single-cell RNA-seq analysis that is commonly used to study individual cells. 

Reference:

Immuno-SABER enables highly multiplexed and amplified protein imaging in tissues. Saka SK, et al.  Nature Biotechnology. 2019 Sep Epub 2019 Aug 19. Vol 37(9):1080-1090. doi: 10.1038/s41587-019-0207-y.