Underrepresented Student Internship Program
We are happy to announce the 2024 HuBMAP Underrepresented Student Internship Program!
Read the details below.
The Underrepresented 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
2024 Internship Information
Applications will be accepted from November 27, 2023 until February 1, 2024.
Internship experiences can be computational or experimental. The lab descriptions below identify which experiences will be computational and which will be experimental. Applicants can indicate their preference for computational or experimental internships on the application.
Accepted students will receive a stipend of $6,000. Additional funding to cover living and travel expenses for in-person experiences will be determined on a lab by lab basis.
Questions? Email us at: internshipapply@hubmapconsortium.org
Important dates
Applications open: November 27, 2023
Applications close: February 1, 2024
Acceptance notifications sent: March 2024
Performance period: ~10-12 weeks between May 20th - Aug 16, 2024. Specific start and end dates will be discussed with accepted students.
Eligibility criteria
Eligible students will:
- Be an undergraduate student attending a US institution at the time of application.
- Be college undergraduates in the United States majoring in any of the STEM fields with a minimum of 3.0 grade point average.
- Be 17 years of age or older.
- Be a member of an underrepresented group (as defined by NIH). See the NIH criteria.
Preference will be given to students who do not have ready access to biomedical (and/or single cell biology) research opportunities in their home institutions.
Successful applicants will have:
- An interest in and passion for studying biological or computational sciences, or related STEM fields
- At least a 3.0 GPA
- A demonstrated strong work ethic
- Some experience with programming, if applying to a computational program
- Some experience with lab work, if applying to an experimental program
- A personal statement which outlines their interests and experience
Application process
All applicants will be required to submit an application form, transcript, personal statement, and 2 letters of recommendation. All materials should be submitted by February 1, 2024.
Transcript: Unofficial transcripts are acceptable. Please submit transcripts as an attachment to your application form, as a PDF.
Personal Statement: Please submit a personal statement as an attachment to your application form, briefly describing your background, interest in participating in this summer undergraduate research program in single cell science, and your career goals. Please attach your personal statement in PDF format. Personal statements should be no longer than 2 pages single spaced, font size 11, Times New Roman.
Letters of recommendation: Two letters of recommendation should be sent via this form to the application committee from your references by the application due date of February 1, 2024.
Requirements and expectations
Accepted students are required to:
- Conduct their own small research project or work on part of an ongoing research project.
- Attend a weekly virtual seminar series that will introduce rapidly progressing medical and basic research areas.
- Complete all Compliance Training, Conflict of Interest Forms and/or any other training deemed necessary for the internship as soon as it is received.
- Report to work on time as designated by the mentor to work on assigned research project. Students are expected to work no more than 40 hours a week.
- Present a virtual poster at the end of the project about their experience. The date of the presentation will be determined by the NIH HuBMAP program.
2024 Internship Opportunities
Positions for 2024
Internship opportunities for 2024 are listed below. Each entry indicates if the internship will be computational or experimental. Clicking on a lab name will take you to detailed information on that opportunity.
Children's Hospital of Philadelphia and University of Pennsylvania, Pei Lab (Computational and Experimental)
Children’s Hospital of Philadelphia and University of Pennsylvania, Tan Lab (Experimental)
GE Research, Ginty Lab (Experimental and Computational)
Harvard Medical School, HIDIVE Lab (Computational)
Harvard University, Singhal Lab (Experimental)
Harvard University, Vlachos Lab (Computational)
Indiana University Cyberinfrastructure for Network Science Center, Börner Lab (Computational)
Mass Eye & Ear / Harvard University, Segrè Lab (Computational)
New York Genome Center, Satija Lab (Computational)
Pacific Northwest National Laboratory, Anderton Lab (Experimental and Computational)
Pacific Northwest National Laboratory, Pasa-Tolic Lab (Computational and Experimental)
Pittsburgh Supercomputing Center, HuBMAP Infrastructure and Engagement, Blood Lab (Computational)
Stanford University, Angelo Lab (Computational and Experimental)
University of California, San Diego, Laurent Lab (Computational and Experimental )
University of California San Diego, Shi Lab (Experimental)
University of Florida, Sarder Lab (Computational)
University of Illinois, Chicago, Naba Lab (Computational)
University of North Carolina Chapel Hill, Hagood Lab (Experimental)
University of Rochester Medical Center, Pryhuber Lab (Experimental)
Vanderbilt University, Spraggins Lab (Experimental)
Washington University School of Medicine, Jain Lab (Computational and Experimental)
Yale University, Fan Lab (Experimental)
Available Opportunities
24 HuBMAP labs are accepting interns in Summer 2024. Each description indicates if the lab is experimental or computational. Read on for more information about each.
Please check back as additional research opportunities may be added to this page.
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.
Angelo Lab, Stanford University (Computational and Experimental)
The Angelo lab is looking for a talented undergraduate to join us for a summer research apprenticeship. The goal of this internship program is to broaden and enrich our group by assisting outstanding students from diverse cultural, ethnic, and socioeconomic backgrounds who have diverse talents, interests, and life experiences and whose educational pursuits align with our research. The student will collaborate closely with one of the HuBMAP TMC bone marrow lead scientists.
The primary goal of this project is to comprehensively map the cellular composition and structure of human bone marrow and to understand how this relationship is affected by age, gender, and race. We have been using technologies that enable spatial quantification of RNA, protein, and N-glycans for identifying specific cellular identities and cell states of the bone marrow. The undergraduate student will be trained specifically in Multiplexed Ion Beam Imaging by Time of Flight (MIBI-TOF), a technique that uses mass spectrometry and metal labeled antibodies to visualize up to 40 proteins at once at subcellular resolution.
During this internship you will learn the workflow of the MIBI-TOF which includes the development of wet lab skills. Below are a few examples of the kind of work you’ll gain exposure to:
- preparation of buffers
- Antibody selection for MIBI
- Antibody screening - IHC
- Antibody labeling
- MIBI staining
Blood Lab, HuBMAP Infrastructure and Engagement Component (IEC) at Pittsburgh Supercomputing Center (Computational)
The Pittsburgh Supercomputing Center is a joint computational research center with Carnegie Mellon University and the University of Pittsburgh. PSC provides university, government and industrial researchers with access to several of the most powerful systems for high-performance computing, communications and data storage available to scientists and engineers nationwide for unclassified research. PSC advances the state of the art in high-performance computing, communications and data analytics and offers a flexible environment for solving the largest and most challenging problems in research.
For the Summer of 2024 we are recruiting students for a research experience involving infrastructure that will help make HuBMAP data more accessible to the community. As a part of the internship, students will gain exposure to state-of-the-art infrastructure technologies and will be exposed to multiple aspects of the development processes, including software sprints, technical documentation, databases, networking and APIs. Examples of potential research projects include:
- Develop or extend Python packages to interface with public data and metadata through the HuBMAP APIs to automate and facilitate data reuse and increase FAIRness.
- Develop tools to optimize pre-processing/post-processing data workflows of large datasets.
- Develop analysis pipelines in Jupyter notebooks that use HuBMAP data.
Some previous experience with programming and software engineering will be useful in most projects but is not essential. However, we would also welcome students with more extensive background and experience with Python programming.
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.
Ginty Lab, University of Pennsylvania (Computational and Experimental)
GE Research and Department of Dermatology, University of Pittsburgh (U. Pitt) are co-leads for the skin tissue mapping center (TMC). As part of this project, an in-person internship will be available for 10 weeks in multi-modal data integration at GE Research Center, Niskayuna and in collaboration with University of Pittsburgh.
Skin is composed of over 20 different cell types and a vast microenvironment of glandular structures, hair follicles, vasculature, and the immune system. As part of our TMC, we are building a multi-modality and multi-resolution image dataset of normal skin tissue samples from across different age groups, different skin color, different anatomical locations with different exposures to UV. The goals of this internship are to gain experience in building a skin atlas, including understanding the data output and format (multiplexed images and single cell RNAseq), assembling the single cell data. They will learn about single cell data analysis and help develop new ways to combine this data with spatially intact 2D data. The work will be computational but with the opportunity to be exposed to the experimental and pathology workflows. The student will learn about experimental design, data QC, single cell segmentation methods, methods for spatial cell analysis and work with the team and broader HuBMAP to develop new ways to combine and visualize different types of single cell data.
Relevant publications:
- Human Digital Twin: Automated Cell Type Distance Computation and 3D Atlas Construction in Multiplexed Skin Biopsies | bioRxiv
- Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue - PubMed (nih.gov)
- An atlas of inter- and intra-tumor heterogeneity of apoptosis competency in colorectal cancer tissue at single-cell resolution - PubMed (nih.gov)
- Characterizing the heterogeneity of tumor tissues from spatially resolved molecular measures - PubMed (nih.gov)
- Advances in skin science enable the development of a COVID-19 vaccine (nih.gov)
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
Pasa-Tolic Lab, Pacific Northwest National Laboratory (PNNL) (Computational and experimental)
Our lab at the Pacific Northwest National Laboratory is developing methods for characterization of proteins in localized tissue regions and in single cells to assist in building 3D maps of human tissues and identifying molecular processes underpinning cellular function. Our approach incorporates innovations in microfluidic sample preparation, proteomics, mass spectrometry imaging, and multimodal data integration and visualization. Students can expect to gain experience in tissue processing, protein measurements using state of the art mass spectrometry, data analysis and visualization. This would be a fantastic opportunity for students interested in expanding their background while working in an interdisciplinary team-based environment. We are looking for enthusiastic students who are passionate about science and thrive in a learning culture to join our team! General lab experience or familiarity with Python or R basics would be beneficial. The internship will include a mix of wet bench and dry lab analysis.
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.’
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.
Sarder Lab, University of Florida (Computational)
The Computational Image Analysis Platform (CIMAP) for HuBMAP project welcomes applications for 2023 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.
Shi Lab, University of California, San Diego (Experimental)
The Shi Lab at UC San Diego is developing and applying laser scanning multimodal microscopy and spectroscopic technologies -- this includes stimulated Raman scattering (SRS) spectroscopy, multiphoton fluorescence microscopy (MPF), and second harmonic generation (SHG) for studying metabolic dynamics during aging and diseases. The imaging platform is used to map in situ cellular metabolic activities, and visualize the spatial distribution of newly synthesized molecules such as protein, lipid, DNA/RNA, and carbohydrates, which is important for studying aging processes, immunosenescence, as well as diseases including neuronal degeneration, diabetes, and cancer. The Shi group is integrating bioorthogonal labeled SRS microscopy the multimodal microscopy, which can automatically image co-registered SRS/MPF/SHG/DO-SRS for directly imaging complex molecular events in various tissues at sub-cellular scale. These approaches represent powerful tools for disease detection, diagnosis and treatment, as well as for mechanistic understanding of scientific fundamentals.
For the Summer of 2024 we are hosting summer students for a research experience involving infrastructure that will help make HuBMAP multimodal imaging data more accessible to the community. The research experience will provide the opportunity to:
- Develop or extend Python packages facilitate imaging processing of metadata for NIH Common Fund Data Ecosystem (CFDE) and novel algorithms to optimize pre-processing/post-processing multimodal co-registered imaging data workflows of large datasets.
- Development of novel analysis pipelines which can be useful in most projects but is not essential.
This opportunity is preferred for but not limited to students with more extensive background and experience in programming with Python or imaging analysis.
Singhal Lab, Beth Israel Deaconess Medical Center / Harvard University (Experimental)
Normal anatomic variations of the upper extremity lymphatic system may protect or predispose certain women to developing BCRL after breast cancer treatment. Notably, the major compensatory lymphatic pathway of the arm, the Mascagni-Sappey (MS) pathway, is variably present in cadaver studies and drains to lymph nodes that are usually spared during breast cancer treatment. Cadaver studies have also determined that the MS pathway has variable anatomic connections which can impact its ability to drain the arm effectively. Our lab utilizes modern lymphatic imaging techniques to define the anatomy of the MS pathway and its variations in both normal women and in women who have undergone high risk breast cancer treatment but did not develop lymphedema. By describing anatomic variations of the upper extremity lymphatic system, we aim to predict which variations predispose women to developing lymphedema. Finally, we are developing a non-invasive intraoperative optical imaging technique to assess the function of the MS pathway during breast cancer operations to predict the patient’s risk of developing lymphedema. Importantly, the ability to evaluate real-time lymphatic function would allow cancer teams to implement preventative interventions in high risk patients. As the most common cause of lymphedema in the United States is secondary to cancer procedures, this model of lymphedema prevention could be widely applied to the treatment of gynecologic cancers, urologic cancers, skin cancers, and sarcomas.
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