Undergraduate Student Internship Program
The Undergraduate Student Internship Program provides the opportunity for undergraduate students from US colleges and universities to receive mentorship and training from NIH Human Biomolecular Atlas Program (HuBMAP) labs over the summer.
Quotes from our interns
2025 Internship Information
Applicants who have been selected for the 2025 internship program have been contacted. If you have not heard from our team please take this as indication that you were not selected for this year's program. Thank you to all of the applicants - this year was our most competitive applicant pool yet!
Important dates
Applications open: November 25, 2024
Applications close: February 1, 2025
Acceptance notifications sent: March 2025
Performance period: ~10-12 weeks between May 19th - Aug 15, 2025. Specific start and end dates will be discussed with accepted students.
2025 Opportunities
Nineteen HuBMAP labs are accepting interns in Summer 2025. Each description indicates if the lab is experimental or computational. Read on for more information about each opportunity.
Anderton Lab, Pacific Northwest National Laboratory (Experimental and Computational)
Pacific Northwest National Laboratory (PNNL) seeks an outstanding intern candidate to utilize the matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) instrumentation and methods for elucidating the spatial lipidome of the human lung. The candidate will also use complementary optical microscopy methods to correlate our findings with physiological and morphological tissue features. In particular, the candidate will generate molecular maps of the human pulmonary tissues in health and disease. Additionally, the candidate may also participate in the generation of bulk and spatially resolved proteomics and lipidomics using a variety of state-of-the-art and innovative approaches.
Börner Lab, The Cyberinfrastructure for Network Science Center, Indiana University (Computational)
Our mission is to advance datasets, tools, and services for the study of biomedical, social and behavioral science, physics, and other networks. A specific focus is research on the structure and evolution of science and technology and the communication of results via static and interactive maps of science, see https://scimaps.org.
We develop custom interactive data visualization applications for both public and private sector clients in need of innovative ways to interpret and utilize their data.
As part of the HuBMAP HIVE Mapping Component at Indiana University, our center welcomes applications by undergraduate students with a strong background in biology to work on the construction and usage of a Human Reference Atlas, see this article. Specifically, the student will work on data visualizations of anatomy and single-cell data in close collaboration with visualization experts and in the context of HuBMAP.
Qualifications:
- Demonstrated strong data visualization expertise is required.
- Have a basic knowledge in human anatomy, histology, or biomedical field.
- Experience with vector image editing software, such as Adobe Illustrator and Inkscape.
- Experience in cross-disciplinary teams and strong collaboration skills
- A can-do work ethic
Fan Lab, Yale University (Experimental)
Dr. Fan’s laboratory at Yale University is supported by the NIH HuBMAP consortium to develop spatially resolved high-plex protein and whole transcriptome co-profiling technology named spatial-CITE-seq and further incorporate a panel of extracellular matrix (ECM) proteins in the assay to map not only cell atlases but also extracellular components in relation to cellular niches and function. Dr. Rong Fan is the Harold Hodgkinson Professor of Biomedical Engineering and of Pathology at Yale University. He received his Ph.D. in Chemistry from the University of California at Berkeley in 2006 and completed postdoctoral research at Caltech in 2009 before starting his own laboratory at Yale in 2010. Recently, his laboratory developed a microfluidic approach for high-spatial-resolution multi-omics atlas sequencing via deterministic barcoding tissue (DBiT-seq) (Liu et al., Cell, 2020) and further developed the first and only spatial epigenome sequencing technologies such as spatial-ATAC-seq (Deng Y. et al, Nature 2022) and spatial-CUT&Tag (Deng Y., et al., Science 2022). Dr. Fan is the recipient of the NSF Early-Stage Faculty Career Development (CAREER) Award and the Packard Fellowship for Science and Engineering. He has been elected to the American Institute of Medical and Biological Engineering (AIMBE), Connecticut Academy of Science and Engineering (CASE), the National Academy of Inventors (NAI). His laboratory will host a summer intern student in a project related to spatial multi-omics profiling of human heart, skin, or lymphoid tissues.
Hagood Lab, University of North Carolina, Chapel Hill (Experimental)
Our lab studies mesenchymal cell phenotype in the context of lung development, homeostasis and remodeling (building, maintaining and repairing the lung). Our “LAPMAP” (Lung airway and parenchymal molecular atlas program) Tissue Mapping Center has generated gene expression (RNA-seq) and chromatin accessibility (ATAC-seq) data on hundreds of thousands of single cells from adult human lungs. These studies have identified known and novel subsets of mesenchymal cells that require validation and spatial localization within lung tissues using immunofluorescence (IF) and RNAscope imaging.
Internship program students can learn IF and RNAscope labeling of fixed human lung tissues and microscopic imaging. Students would also get to learn about and observe other ongoing projects in the lab, and will be encouraged to participate in HuBMAP and related consortium seminars occurring during their internship.
Prerequisites include coursework in biology and basic laboratory experience.
HIDIVE Lab, Harvard Medical School (Computational)
The HIDIVE Lab in the Department of Biomedical Informatics at Harvard Medical School is a group of data scientists and software developers who are passionate about driving biomedical discovery by creating efficient and effective visual interfaces between analysts and data. We focus on the development of visual analysis tools for the whole spectrum of biomedical data to address challenges in basic and applied research. We are particularly interested in applying our approaches in cancer genomics, epigenomics, and chromosome conformation studies. The HIDIVE Lab is directed by Nils Gehlenborg, PhD, Associate Professor of Biomedical Informatics at Harvard Medical School.
Some previous experience with programming and software engineering will be useful in most projects but is not essential. An interest and experience in data visualization and visual design would be desirable but not required. However, we would also welcome students with more extensive background and experience in programming with R, Python, or Javascript.
Jain Lab, Washington University in St. Louis (Experimental and Computational)
The Jain Lab is interested in understanding the cellular and molecular organization of every cell type in the healthy and diseased human kidney so we can find better treatments, prevention strategies and disease markers and improve human health. To this end the Jain lab and collaborators are making several molecular maps and integrate them to construct a human kidney single cell and spatial atlas of genes, proteins and metabolites in the kidney. The goal is that a user will be able to query these molecules and find where and when they are expressed in healthy and diseased kidneys, identify pathways that instruct cells to recover from injury and repair the damaged cells or make the healthy cells more resilient to injury.The atlas project allows exciting opportunities at various steps that are critical to meet the goals ranging from experience in patient enrolment, tissue processing, preservation and preparation for molecular assays, 3D high resolution imaging to define anatomical organization and neurovascular patterning, single cell analysis of RNA expression and regulation and defining cellular diversity in the kidney.
Opportunities can be in wet lab or computational learning basics of single cells analysis.
Wet lab: Dissecting human or mouse kidneys, tissue handling, preservation for various molecular and genomics assays, processing, histological techniques, immunological assays for confocal and lightsheet (3D anatomical imaging) microscopy, nuclear isolation for single cell assays
Dry lab:
- a) Learning different computer software for microscopy image analysis including annotations of structure, nerve and vessel tracing, quantifying different types of cells and determining how these structures are organized in different regions of the kidney that may allude to its proper function.
- b) Learning the basics of single cell gene expression analysis, quality control and common tools for pathway analysis. Learn how to use the R software for single cell analysis.
The student will be expected to execute the tasks that are assigned, enhance the experience by self-learning and reading relevant papers in the field about methods being used. This will be a full time assignment. The student will keep a daily journal of experiments that could be in a form of an e-note book.
Relevant readings:
- El-Achkar et al. Physiological Genomics 2021 Vol. 53, No. 1. A Multimodal and Integrated Approach to Interrogate Human Kidney Biopsies with Rigor and Reproducibility. doi: 10.1152/physiolgenomics.00104.2020.
- Lake et al. An atlas of healthy and injured cell states and niches in the human kidney. bioRxiv 2021.07.28.454201 [Preprint]; doi: 10.1101/2021.07.28.454201. Invited as a flagship paper, pending editorial formatting for final acceptance, Nature
- See biosketch
Laurent Lab, University of California, San Diego (Computational and Experimental)
The UCSD/JAX HuBMAP Female Reproductive Tissue Mapping Center is focused on generating and integrating bulk, single-nucleus, and spatial omics data to build 3D maps of the placenta. Students will have the opportunity to perform "wet-bench" experiments or "dry lab" data analysis, depending on their prior experiences and interests. Those interested in data generation should have theoretical and practical experience in basic molecular biology techniques, while those interested in data analysis should have at least classroom exposure to programming and next-generation sequencing data analysis.
Naba Lab, University of Illinois at Chicago (Computational)
The Naba lab studies the role of the extracellular matrix (ECM) in development, health, and disease, with a particular focus on cancer. To do so, we utilize classical molecular, cellular, and developmental biology approaches in combination with cutting-edge proteomics and computational analyses. Our goal is to better understand how the ECM contributes to diseases so that we can exploit it to develop novel diagnostic and therapeutic strategies.The Naba lab is a highly collaborative and dynamic group that strives for scientific excellence, and offers a stimulating and inclusive working environment conducive to learning and professional development. Interns joining us for the summer will have the opportunity to use newly developed interfaces called MatriCom and MatriSpace to interrogate scRNA-Seq and spatial transcriptomic datasets generated by HuBMAP’s tissue mapping centers. The goal of the project will be to identify the cell populations expressing ECM and ECM receptor gene transcripts for all organs. This will help us answer fundamental questions for the field of ECM biology such as: to what extent do cell types expressing ECM genes also express the genes encoding for ECM receptors? Do similar cell types express similar ECM gene sets across organs (e.g., endothelial cells, pericytes, fibroblasts)?
Key readings (optional):
- Textbook chapter: The Extracellular Matrix of Animals in Molecular Biology of the Cell by Alberts et al. https://www.ncbi.nlm.nih.gov/books/NBK26810/
- Review article: Naba A et al., The extracellular matrix in the “omics” era. Matrix Biology, 2016, pii: S0945-053X(15)00121-3. http://dx.doi.org/10.1016/j.matbio.2015.06.003
Pei Lab, Children’s Hospital of Pennsylvania and University of Pennsylvania (Computational and Experimental)
The overall theme of my lab is to understand how different organs react to energy cues and communicate with each other to maintain whole-organism homeostasis in both physiological and pathological contexts. Related to our HuBMAP projects, we are using single-cell genomics technology to map a multidimensional atlas of the human heart, and study human mitochondrial genome variations in aging. The summer internship will be in-person and a combination of both computational and experimental research. Please see below links to my website and a recent interview of “the future of research benefits from diversity”.
For students: “You bring your passion and dedication, and we bring everything else.”
Faculty Spotlight With Liming Pei, PhD: ‘The future of research benefits from diversity.’
Okuda Lab, University of North Carolina Chapel Hill (Experimental or Computational)
Every day, we inhale about 10,000 liters of air to supply oxygen to our bodies. Along with it, a vast array of pathogens, chemical pollutants, and other irritants enter our airways, each carrying potential life-threatening risks to our lung health. However, our lungs are protected by mucociliary clearance (MCC), a vital innate defense mechanism essential for maintaining lung health.
The Okuda lab’s overarching research goal is to understand how the MCC system is regulated to maintain lung homeostasis and what leads to its breakdown in muco-obstructive lung diseases, including cystic fibrosis (CF), asthma, and chronic obstructive lung disease (COPD). Our previous work has mapped the regional expression patterns of major airway secretory mucins, MUC5AC and MUC5B, and CFTR and ionocytes inboth normal and CF human airways (Am J Respir Crit Care Med, 2019, 2021). These works underscore the small airway region (< 2 mm in diameter) as a critical site for the pathogenesis of muco-obstructive lung diseases.
In pursuit of deeper insights, we have developed microdissection techniques specifically for human small airways and have established in vitro and explant cultures of small airway epithelial cells. By integrating these culture systems with single-cell omics and gene-editing technologies, we aim to elucidate the cellular biology and physiology of the human small airways.
As a Tissue Mapping Center within the Human Biomolecular Atlas Program (HuBMAP), the Okuda lab contributes to a nationwide effort to create a cellular-level map of the human body. Our responsibilities in HuBMAP include precise lung dissection, distribution of tissue samples to collaborating institutions for multimodal assays, and conducting spatial transcriptomics analysis on lung tissue sections. Since 2023, we have hosted HuBMAP summer internship students, each of whom has developed fundamental lab and bioinformatic skills under the mentorship of a dedicated team designed to support individual research interests and needs.
Pryhuber Lab, University of Rochester Medical Center (Experimental)
The HuBMAP Tissue Mapping Center for Lung (HuBMAP-Lung) is provided as a collaboration between labs at UCSD, PNNL, UNC, Seattle Children’s, with its organizational hub at the University of Rochester Medical Center (URMC). We will be happily accepting up to 4 HuBMAP summer interns! For UCSD, PNNL and UNC, please see lab summaries from Drs. Shi, Anderton and Hagood. Here in Rochester, at URMC, the Pryhuber lab is in our 7th year in the NIH Developmental Lung Molecular Atlas Program (LungMAP) and 5th year in HuBMAP. Students working in our URMC lab will undertake primarily an Experimental Lab experience with their choice in focus being in 1) hands-on human tissue dissection, preservation and organization supporting the BioRepository for Investigation of Diseases of the Lung (BRINDL) for the MAP programs, 2) highly multiplexed immunofluorescent photomicroscopy (co-detection by indexing) of cell types and sub-types in normal and disease lung to include image visualization and spatial analysis techniques, and/or 3) preparation and multi-photon imaging of thick lung sections for 3D visualization. Interns will also have exposure to physician-scientists with particular expertise and interest in childhood lung diseases as well as to lab scientists, computational biologists and informaticians working on analyses of large, human lung cell protein, lipid and transcript-‘omics datasets in collaboration across our HuBMAP TMC and with the HIVE. The students will also have the opportunity to participate in the structured Strong Children’s Research Center summer program of seminars and faculty sessions.
Ruffalo Lab, Carnegie Melon University (Computational)
Sarder Lab, University of Florida (Computational)
The Computational Image Analysis Platform (CIMAP) for HuBMAP project welcomes applications for 2024 Summer Undergraduate Research Internship. Interns will form an interdisciplinary team and will receive mentoring from Drs. Sarder (AI and machine learning), Tomaszewski (computational pathology and cell biology), Jain (spatial omics), and Levites Strekalova (health services and research translation). CIMAP focuses on large digital microscopy image data analysis and data fusion of image and molecular omics data with significant impact in human health. See a demo of our tool at >https://bit.ly/3qz83qk
Research interns will be responsible for conducting research in the area of Computational Image Science, contributing to publications, attending scholarly meetings and talks, and obtaining guidance on next step career development.
This internship is best suited for juniors and seniors who are currently enrolled in the STEM (Science, Technology, Engineering, and Mathematics) fields with interests and prior coursework in biology, engineering, digital imaging, or quantitative health.
MATLAP and/or Python skills will be considered a strong asset.
This lab develops tools for the analysis and integration of sequencing datasets. We have a particular interest in applying these technologies to study single cells, a field known as single cell genomics, and utilizing this to better understand human biology. In HuBMAP, we are working on building reference maps of healthy human tissues, and generating a 'parts list' of the individual components that comprise a human being.
Students will receive introductory training in the R programming language, as well as introductory techniques for analyzing single-cell genomics research. They will conduct a research project working closely with lab members to construct and update a reference map for one of the tissues in the HuBMAP project, such as the lung, kidney, or skin.
Prerequisites: Students should have an interest in pursuing a career in research, and in applying to graduate a program in Biology, Computational Biology, or Genomics.
Satija Lab, New York Genome Center (Computational)
This lab develops tools for the analysis and integration of sequencing datasets. We have a particular interest in applying these technologies to study single cells, a field known as single cell genomics, and utilizing this to better understand human biology. In HuBMAP, we are working on building reference maps of healthy human tissues, and generating a 'parts list' of the individual components that comprise a human being.
Students will receive introductory training in the R programming language, as well as introductory techniques for analyzing data generated in single-cell genomics research. They will conduct a research project working closely with lab members to construct and update a reference map for one of the tissues in the HuBMAP project, such as the lung, kidney, or skin.
Prerequisites: Students should have an interest in pursuing a career in research, and in applying to a graduate program in Biology, Computational Biology, or Genomics.
Segrè Lab, Mass Eye and Ear / Harvard University (Computational)
The Segrè lab is located in the Ocular Genomics Institute and Department of Ophthalmology at Massachusetts Eye and Ear, Harvard Medical School, affiliated with the Broad Institute of Harvard and MIT, and consists of medical and graduate students and computational biologists. It focuses on developing and applying computational and statistical methods that combine functional and single cell genomics data with large-scale human genome-wide association studies (GWAS) to uncover novel causal regulatory mechanisms, genes, pathways and cell types that lead to common diseases, with a focus on retina- and vascular-related diseases. The lab has recently developed a method called ECLIPSER that integrates single cell expression and genetic regulation (eQTL) data with GWAS loci to identify key pathogenic cell types that affect disease risk, that they have applied to a range of diseases (Eraslan et al., Science 2022, Hamel et al., medRxiv 2022).
We are interested to host a summer intern undergraduate student who will work on a computational project related to the following goals of our HuBMAP project (co-PI: Dr. Rajat Gupta, HMS):
- Harmonize single cell expression data across over 40,000 vascular cells from different human tissues in HuBMAP using available machine learning software and identify organotypic features of vascular cells.
- Apply the computational method, ECLIPSER to the vascular single cell data from HuBMAP and genetic association data compiled from existing databases for vascular diseases, such as coronary artery disease, stroke and migraine, to identify disease-causal cell populations and functional gene modules/pathways.
The student should have some experience with programming ideally in python or R, basic knowledge in genetics, and be excited to contribute to biomedical discoveries for disease. Experience with large-scale data analysis or working with sequencing data is a plus, but not necessary.
Snyder Lab, Stanford Medicine (Experimental or Computational)
The small and large intestine are organs critical for maintaining homeostasis of the human body by mediating nutritional absorption upon the ingestion of food. Both organs are extensive in length, with known diDerences in function and cellular heterogeneity within diDerent portions of each. A cross section anywhere in the bowel reveals a complex layering of components involved in absorption and secretion, motility of gut contents, circulation, and immunity. We are building, 3D, single-cell resolution atlases for 8 intestinal regions. We collect tissues from deceased organ donors with explicit consent for distribution among the HuBMAP consortium and broad access genome data sharing (GDS).
The tissue collection is performed by our collaborator at Washington University. Each organ region is profiled by three technologies. These assays are done in our lab at Stanford and would be where an intern would contribute.
- CODEX (CO-Detection by indEXing) multiplexed spatial immunoassay: We serially section the tissue section into 30 sections and assay 58 antibodies to label the major cell types. We use computational tools to annotate the cell types in each image based on the antibody labeling; perform neighborhood analysis to analyze the organization of cells into tissue functional units; and digitally re-assemble the serial sections into a computed 3D map.
- Spatial transcriptomics: We use the Xenium platform to spatially profile with single-cell resolution the location of 480 target genes on slides from the same tissue blocks as used for the CODEX. Analysis tasks are similar to CODEX except that we do not have serial sections.
- Multiome assay: single-nuclei RNA-sequencing and ATAC-sequencing (open chromatin) on the same nuclei. This is done on a neighboring chunk of tissue as used for CODEX an Xenium. It does not have single-cell resolution, but we are able to measure more genes than in CODEX or Xenium. Analysis includes processing the sequencing data to determine ATAC-seq peaks and gene expression respectively and clustering individual nuclei into similar populations of cells and annotating those cell types by marker genes. We then use those annotations to provide greater detail to the expression patterns of the annotated cells in our CODEX and Xenium maps.
In addition to these assays, we also have analysis efforts for how to integrate these datasets, and wet and dry lab efforts to compare spatial omics platforms.
Spraggins Lab, Vanderbilt University (Experimental)
The Spraggins laboratory in the Mass Spectrometry Research Center at Vanderbilt University is looking for students interested in learning about and working with next-generation molecular imaging technologies, biocomputational tools, and applications that bring together imaging mass spectrometry, highly multiplexed immunofluorescence microscopy, and spatial omics. We have several projects ongoing focused on creating comprehensive molecular atlases of the kidney, pancreas, and eye as part of the HuBMAP program.
We are seeking candidates that are interested in learning about the various analytical platforms implemented in our group and wanting an immersive research experience to better grasp the day-to-day workings of a research program. The Spraggins lab endeavors to create a dynamic workplace environment by embracing a diverse array of researchers from broad scientific, cultural, and educational backgrounds.
Candidates with a background in cell biology, biochemistry, or chemistry would be qualified. Those proficient in computational programs such as Python or R, as well as those with experience in multimodal analytical modalities such as microscopy, transcriptomics, or mass spectrometry would be well positioned to excel. This position is considered experimental, although aspects of computational modeling and analysis will be applied.
Tan Lab, Children’s Hospital of Philadelphia and University of Pennsylvania Lab (Computational)
Gene regulation plays a fundamental role in cellular development and cancer. Our lab uses genomics and systems biology approaches to understand the gene regulatory factors underlying cellular processes. We take snapshots of the regulatory systems using bulk and single-cell omics and imaging assays and develop computational algorithms to integrate data and generate testable models of gene regulatory pathways in model systems. Research projects in the lab are organized into three major themes: 1) Understanding the molecular basis of therapeutic resistance in cancer; 2) Unraveling the gene regulatory factors involved in cellular development; 3) Development of computational methods to interpret high-dimensional and single-cell transcriptomics, proteomics, and epigenomics data.
This will be a computational project. The student will participate in analyzing multi-omic and molecular imaging data generated via our HubMAP project. Through the project, the student will further develop programming, statistical, and bioinformatics skills. The student will also gain knowledge about cutting-edge single-cell experimental technologies and human bone marrow and heart biology. Ideal candidates will have previous coursework in computer programming, statistics, and molecular/cell biology. Research experience in bioinformatics is a plus.
Vlachos Lab, Beth Israel Deaconess Medical Center / Harvard University (Computational)
Spatial profiling technologies have promised to deliver unparalleled resolution by unlocking a new dimension of gene expression, facilitating new discoveries and gleaning new insight that can be leveraged in translational research. However, spatial technologies is a field that still has not developed gold standards for analyses and the data is often not utilized to its full potential. We focus on developing novel methods, as well as applying techniques already used in other fields to unlock the hidden potential of spatial data. With a laser focus on translation, our major interests are how to go from spatial data to actionable medical insight. We're part of the Beth Israel Deaconess Medical Center Lymphatic Vasculature Tissue Mapping Center (BIDMC Lymphatic TMC) in Longwood, Boston, where we use single-cell transcriptomic and proteomic approaches to profile the body's lymphatic vasculature. We continuously develop protocols to process harder samples, such as miniscule, transparent samples, as well as samples with low amounts of cells and RNA.
Calling all current and former interns!
If you ever participated in HuBMAP research as an undergraduate, we invite you to join the HuBMAP Intern Networking Group on LinkedIn. PIs and mentors are also encouraged to join.
2023 HuBMAP Intern Accomplishments
In 2023, we offered in-person and remote opportunities, and our 22 interns had a rewarding, engaging experience across 19 HuBMAP labs. At the end of the internship, the interns presented their research findings to the HuBMAP and broader community.
Barera Ajaz
Kim, O'Neill, and Gregory labs at the University of Pennsylvania
Ella Barton
Jain lab at Washington University in St. Louis School of Medicine
Esonica Charles
Hagood lab at the University of Rochester Medical Center
Charles Godinez
Laurent lab at the University of California, San Diego
Jada Harvey
Pryhuber lab at the University of Rochester Medical Center
Harley Hernandez
Kim, O'Neill, and Gregory labs at the University of Pennsylvania
Sofia Martin
Jain lab at the Washington University School of Medicine
Rosemary McKerley
Shi lab at the University of California, San Diego
Liya Mooradian
Pei lab at Children's Hospital of Philadelphia/ University of Pennsylvania
Antonio Moore
Anderton lab at the Pacific Northwest National Laboratory
Favour Olushola
Pasa-Tolic lab at the Pacific Northwest National Laboratory
Arianne Parvaresh-Rizi
Tan lab at Children's Hospital of Philadelphia/ University of Pennsylvania
Interested in viewing presentations from earlier years?
2022 intern presentations
2021 intern presentations