Underrepresented Student Internship Program

The Underrepresented Student Internship Program provides the opportunity for undergraduate students from US colleges and universities to receive mentorship and training from the NIH Human Biomolecular Atlas Program (HuBMAP) labs over the summer.
 
 
 
 

 2023 Internships - applications opening soon!

Twenty-two HuBMAP labs are accepting interns in Summer 2023.

In the descriptions of the internship opportunities below, we identify which experiences would be computational and which would be experimental. Students can indicate their preference for computational or experimental internships on the application.

Due to the ongoing uncertainty of the COVID-19 pandemic, in-person experiences are not guaranteed.

Questions? Email internshipapply@hubmapconsortium.org

 

Important Dates

Applications Open: Dec 1, 2022

Applications Close: Feb 1, 2023

Acceptance Notifications Sent Out: Mid-March 2023

Performance Period: ~10-12 weeks between June 5 - Aug 18, 2023

 
 

Information for 2023 Applicants

Eligibility

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 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.

 

Application

All applicants will be required to submit an application form, transcript, personal statement, and 2 letters of recommendation. All materials should be submitted via the application form, by February 1, 2023.

Here is a sample application form for reference.

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 to the application committee from your references, by the application due date of February 1, 2023. Letters of recommendation should be sent to internshipapply@hubmapconsortium.org

Requirements & Expectations

Accepted students are required to:

  • Conduct your own small research project or work on part of an ongoing research project.
  • Attend a weekly virtual seminar series that will introduce you to rapidly progressing medical and basic research areas.
  • Complete all Compliance Training, Conflict of Interest Forms and/or any other training deemed necessary for your 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 your experience. The date of the presentation will be determined by the NIH HuBMAP program.
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

 

Funding

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. Due to the ongoing uncertainty of the COVID-19 pandemic, in-person experiences are not guaranteed.

See details on the 2023 internship opportunities

 
 

See what HuBMAP interns have accomplished

 
Research achievements

In 2022, we offered in person, remote, and hybrid opportunities, and our 13 interns had a rewarding, engaging experience across 13 HuBMAP labs. The 2022 interns had the opportunity to present their research findings to the HuBMAP and broader community. You can view the full playlist or view the individual presentations below.

  • 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 Karli’s final presentation 
  • Camryn Pettenger-Willey worked at PNNL to optimize a protocol for imaging different versions of proteins within tissues. Watch Camryn’s final presentation
  • 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 Marielena’s final presentation
  • Anusha Thaniana performed single-cell RNA-sequencing to determine the activity levels of genes within the heart to study Fontan Associated Liver Disease. Watch Anusha’s final presentation
  • 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 Mohamed’s final presentation
  • 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 Lin’s final presentation
  • Genna Mahabeer mapped proteins within the red and white pulp regions of the spleen. Watch Genna’s final presentation
  • 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 Sam’s final presentation
  • M.J. Hopkins determined the optimal concentration of antibodies necessary to characterize the cells from the airway in single-cell techniques.Watch M.J.’s final presentation
  • Fransiskus (Frans) 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 Frans’ final presentation
  • Gabrielle LeNoir analyzed specific cell populations in the female reproductive system to better understand the stages of the menstrual cycle. Watch Gabrielle’s final presentation 
  • Tram Nguyen worked on the user interface for the HuBMAP Data Portal by making graphs of existing metadata using the R programming language. Watch Tram’s final presentation
  • 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 Lesley’s final presentation

Interested in viewing the 2021 presentations? You can watch them here.

 
 

2023 Internship Program Opportunities

  1. Please check back as more opportunities will be added.
Bendall Lab, Stanford University (Computational and Experimental)

Bendall:

Our lab, @Bendall_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.

Our lab, @Bendall_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.

What the goals are:

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 2022 we are recruiting students for a research experience involving infrastructure that will help make HuBMAP data more accessible to the community. The research experience will provide the opportunity to:

  • Develop or extend Python packages to interface with the HuBMAP APIs to automate and facilitate processing of metadata for NIH Common Fund Data Ecosystem (CFDE) and NIH database of Genotypes and Phenotypes (dbGaP) submissions
  • Develop or implement novel algorithms to optimize pre-processing/post-processing data workflows of large datasets
  • Develop novel analysis pipelines for Apache Airflow and Jupyter notebooks

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 in programming with Python as well as experience with containerization.

 

 
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 (S&T) and the communication of results via static and interactive maps of science (learn more at 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, this lab 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

Lab Website

 
Gehlenborg Lab, Harvard Medical School (Computational)

The Gehlenborg 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. For the Summer of 2022 we are hoping to recruit a student to work on a project that will make HuBMAP data more accessible to the community, such as:

  • Develop or extend R and/or Python packages to interface with the HuBMAP Portal APIs to enable direct access in RStudio or Jupyter Notebooks
  • Create example visualizations with Vitessce (http://vitessce.io) that explain single-cell and tissue biology to lay audiences
  • Contribute new visualization types to Vitessce, e.g., interactive statistical graphs or specialized visualizations for HuBMAP data types
  • Extend the diversity data summary page developed by our 2021 HuBMAP Diversity Summer Intern: https://portal.hubmapconsortium.org/diversity
  • Projects that make communicating biomedical discoveries from single cell biology easier for lay audiences by exploring different visualizations, design principles etc.

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.

Lab Website

 
Ginty Lab, GE Research (Experimental and Computational)

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 understand 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 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: 

1)          Human Digital Twin: Automated Cell Type Distance Computation and 3D Atlas Construction in Multiplexed Skin Biopsies | bioRxiv 

2)          Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue - PubMed (nih.gov)

3)          An atlas of inter- and intra-tumor heterogeneity of apoptosis competency in colorectal cancer tissue at single-cell resolution - PubMed (nih.gov)

4)          Characterizing the heterogeneity of tumor tissues from spatially resolved molecular measures - PubMed (nih.gov)

5)          Advances in skin science enable the development of a COVID-19 vaccine (nih.gov)

 

Gregory Lab, University of Pennsylvania (Computational and Experimental)

Led by Brian Gregory, this lab works on basic RNA biology using genomic approaches and we are mainly interested in how RNA is regulated in cells. Our research: This lab, in its work for HuBMAP and in partnership with the other Penn labs, is generating a single cell molecular map of the human female reproductive system. We are obtaining whole human reproductive systems (ovaries, Fallopian tubes, uterus) from donors and systematically dissecting them to create spatial samples for single cell molecular assays. In addition, the organs are computationally modelled to geometrically characterize their anatomy. The research involves single cell genomic assays and technology development.

Experiences: Each intern will be advised by both the PI of the lab and a graduate student or a postdoc. The students will have hands-on experience in laboratory and computational research. The students will also participate in structured mentoring activity as part of Penn’s greater biomedical summer internship program. This includes attending seminars, career development guidance, and access to national seminars.

Pre-requisites: For computational research, proficiency with a programming language. For experimental research, molecular biology laboratory experience.

Lab Website

 
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.

 
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 integrating 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 papers: 

1)        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.

2)        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

3)        See biosketch

Lab Website

 
Kim Lab, University of Pennsylvania (Computational and Experimental)

Led by Junhyong Kim, this lab works at the interface of genomic technologies, computational biology, and evolutionary biology. This lab, in its work for HuBMAP and in partnership with the other Penn labs, is generating a single cell molecular map of the human female reproductive system. We are obtaining whole human reproductive systems (ovaries, Fallopian tubes, uterus) from donors and systematically dissecting them to create spatial samples for single cell molecular assays. In addition, the organs are computationally modeled to geometrically characterize their anatomy. The research involves single cell genomic assays and technology development in partnership with the O'Neill and Gregory labs, and data analysis and informatics.

The interns will be advised by both the PI of the lab and a graduate student or a postdoc. The students will have hands-on experience in laboratory and computational research. The students will also participate in structured mentoring activity as part of Penn’s greater biomedical summer internship program. This includes attending seminars, career development guidance, and access to national seminars.

Pre-requisites: For computational research, proficiency with a programming language. For experimental research, molecular biology laboratory experience.

Lab Website

 
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.

 
O’Neill Lab, University of Pennsylvania (Experimental)

Led by Kate O’Neill, this lab focuses on endometrial regeneration and factors impacting the stem cell niche in the endometrium. Our research: This lab, in its work for HuBMAP and in partnership with the other Penn labs, is generating a single cell molecular map of the human female reproductive system. We are obtaining whole human reproductive systems (ovaries, Fallopian tubes, uterus) from donors and systematically dissecting them to create spatial samples for single cell molecular assays. In addition, the organs are computationally modelled to geometrically characterize their anatomy. The research involves clinical tissue sampling, bio-banking, and pathology and single cell genomic assays and technology development.

Each intern will be advised by both the PI of the lab and a graduate student or a postdoc. The students will have hands-on experience in laboratory and clinical research. The students will also participate in structured mentoring activity as part of Penn’s greater biomedical summer internship program. This includes attending seminars, career development guidance, and access to national seminars.

Pre-requisites: Molecular biology or clinical laboratory experience.

Lab Website

 
Pasa-Tolic Lab, Pacific Northwest National Laboratory (PNNL) (Computational)

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 computational modeling and statistics for 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 be computational-focused with little to no wet lab experience.

 
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”

Lab Website

‘The future of research benefits from diversity:’ Faculty Spotlight With Liming Pei, PhD

 
Pouch and Gee lab, Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania (Computational)

Led by James Gee, PhD, and Alison Pouch, PhD, the PICSL focuses on computational methods of studying anatomy in nature, and making cutting-edge image analysis methods and tools publicly available. This lab, in its work for HuBMAP and in partnership with the other Penn labs, is generating a single cell molecular map of the human female reproductive system. We are obtaining whole human reproductive systems (ovaries, Fallopian tubes, uterus) from donors and systematically dissecting them to create spatial samples for single cell molecular assays. In addition, the organs are computationally modelled to geometrically characterize their anatomy. The research involves 3-D computational modeling of the organs.

Each intern will be advised by both the PI of the lab and a graduate student or a postdoc. The students will have hands-on experience in computational research. The students will also participate in structured mentoring activity as part of Penn’s greater biomedical summer internship program. This includes attending seminars, career development guidance, and access to national seminars.

Pre-requisites: Proficiency with a programming language.

Lab Website

 
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, contribute to publications, attend scholarly meetings and talks, and obtain 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 with 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.

Lab Website

 

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 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.

Lab Website

 

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.

Lab website

 

Tan Lab, Children’s Hospital of Philadelphia and University of Pennsylvania Lab (Experimental)

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. 

Lab Website

 

Van Valen Lab, California Institute of Technology

The Van Valen lab has put together a human-in-the-loop AI software platform for large-scale labeling of spatial datasets. A HuBMAP intern would assist the Van Valen lab in labeling cells and cell types in spatial proteomic and spatial transcriptomic datasets.</p

Lab website

 

Please check back as more opportunities will be added.


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