What We Did on our Summer Vacation - Learning the Ins and Outs of Single-Cell Research
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
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 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
scMEP: A Matchmaker using Single-Cell Profiling
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.
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 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.
Research reported here was supported by the National Institutes of Health under award number 3UH3CA246594-02S1.
The Human BioMolecular Atlas Program (HuBMAP) Presents Its First Data Release
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.
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.
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
Do You Know Where Your Proteins Are?
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.
Amplifying the Light with Immuno-SABER
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.
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.
This page last reviewed on September 27, 2021