JumpStart Fellowship

The HuBMAP JumpStart fellowship offers junior investigators working on human atlasing projects the opportunity to take a leadership role in conducting synergistic, collaborative  research projects within the HuBMAP consortium. These projects will build on work that already exists in HuBMAP by either extending aims of current  awards or as a new but related research project. The goal of this program is two-fold: 1) create a mentored, career development opportunity for promising junior researchers,  and 2) promote collaboration among HuBMAP and the atlas building community.

JumpStart Fellowship Applications

Application process

Technical Assistance webinar

Interested researchers should plan to attend the Technical Assistance webinar to be held on November 8, 2023, at 11 am Eastern time. This informational event and Q&A session will explain the details of the program, application, and review process, and provide extra context for applicants outside the HuBMAP consortium.

This event has passed, but you can still submit a letter of intent by November 28 to be invited to the Pitch event.

Letter of intent

Interested applicants should submit a letter of intent to apply to JumpStart@hubmapconsortium.org by November 28, 2023. Those applicants will invited to attend the Pitch event.

Pitch event

Interested applicants will be invited to pitch their ideas during a HuBMAP pitch event on December 5, 2023.  Each applicant will be asked to summarize their proposal in a 10 minute presentation and will receive feedback from NIH staff after the event. 

Application period

Applications will be accepted at JumpStart@hubmapconsortium.org from January 2 to January 26, 2024. See below for expected format and content of the application.

More Information

Program details

Project Scope
Project must be relevant to the NIH Common Fund HuBMAP Program and not overlap with work currently being supported by funded awards.

Project length can be up to 12 months. Project start date should be in early summer 2024. Earliest possible start date is June 2024.

Projects may request up to $50,000 total costs. Funds can be used for travel, supplies, reagents, core resources, or personnel costs.

Open to students, fellows, staff scientists and researchers within or outside of the HuBMAP consortium who have not previously received an independent NIH research grant over $100,000 direct costs. Must be at a U.S. institution that is able to receive a subaward from Carnegie Mellon University.
Application Format: Applications should be PDFs and contain:
  • Proposed Aim(s), Significance, Innovation, Anticipated Results (1 page)
  • Personal Statement (1 page)
    • A short, compelling statement why you are well-suited for carrying out this project, how it relates to your future career plans and details of mentoring and equipment that will support your work.
  • Detailed Budget, Budget Justification (1 page)
  • Letter of support for applicant and proposed project from a mentor (1 page)

Application should be submitted as a PDF via email to JumpStart@hubmapconsortim.org.

Review process
Written applications (see format above) will be reviewed in February 2024, and scored by HuBMAP-supported PIs and Program Consultants who do not have a conflict of interest. Final funding decisions will be made based on HuBMAP priorities and announced in April - May 2024.
Award management
The earliest possible start date for awards is June 2024. Successful projects will be asked to present their work in an appropriate HuBMAP meeting forum, such as the Annual Investigator Meeting, and submit a progress report at the end of the award.

2022 - 2023 JumpStart Fellowship Award Winners

Andreas Bueckle, Indiana University and Lu Chen, Stony Brook University

Quality Control/Quality Assurance for the Human Reference Atlas and On-Ramping to the HuBMAP Portal Using the HRA Organ Gallery in Virtual Reality

Abstract: We present our plans for the Human Reference Atlas (HRA) Organ Gallery, a virtual reality (VR) application where we want to display the 3D organ models and cell types in the Common Coordinate Framework (CCF) and make them interactive in their true scale. Our VR application will allow users to immerse themselves in a virtual environment where they can learn about human organs and cell type distribution in a dynamic and interactive manner, built with HuBMAP data. The HRA Organ Gallery aims to procure an immersive window into the data already in the HRA.

Concretely, we will support two use cases: on-ramping for the HuBMAP Data Portal, where users can pick tissue blocks of interest in VR and ‘store’ them for later analysis on a 2D monitor using the HuBMAP Data Portal, and quality assurance/quality control (QA/QC), where users can identify wrong tissue block registrations in VR and correct them either in the moment or subsequently. 

About the researchers

Andreas Bueckle is the Research Lead in the Cyberinfrastructure for Network Science Center at the Luddy School of Informatics, Computing, and Engineering at Indiana University Bloomington. His research interest is interactive information visualization in virtual reality (VR), augmented reality (AR), and other immersive techniques. Born and raised in Germany, Andreas holds a B.A. in Media Studies from Eberhard Karls University in Tuebingen, an M.A. in Communications from Berlin University of the Arts, and a Ph.D. in Information Science from Indiana University. From early on, he developed a deep interest in digital artifacts, most notably videography and photography. After working as a video journalist and cameraman on projects in Germany, France, India, and the US, Andreas decided to switch to a more technical education and started to pursue and finish his Ph.D. in Information Science, working with Dr. Katy Börner at Indiana University Bloomington. He has a TEDx talk titled “Living and Learning in the Metaverse,” which you can view on YouTube and on the TED website.

Andreas is also serving as co-chair of the planning committee and as lead of the Workshop Working Group for the NIH Junior Investigators Meeting in Bethesda, MD, March 17-19, 2024 . Previously, he served on the organizing committee for the NIH Junior Investigators Meeting in NYC, March 1-3, 2023.

Lu Chen is a Ph.D. student in the Computer Science Department at Stony Brook University, SUNY. She works in the Data Management and Biomedical Data Analytics Lab (BMIDB) under the supervision of Professor Fusheng Wang. Her research interests are spatial databases, 3D big data management, and analytics. She received an M.S. and B.S. in Information and Communication Engineering from Zhejiang University, China.

Angela Kruse, Vanderbilt University and John Hickey, Stanford University

Linking metabolomic profiles to cellular neighborhoods via integration of MALDI imaging mass spectrometry and CODEX multiplexed immunofluorescence microscopy

Abstract: We aim to develop an integrated analytical pipeline using CODEX and MALDI IMS to link lipidomic/metabolomic profiles to specific cell types and neighborhoods in the intestine. This collaboration will provide unprecedented insight into the molecular and cellular organization of this metabolically essential organ. The pairing of Dr. John Hickey, an expert in CODEX multiplexed imaging, with Dr. Angela Kruse, an expert in MALDI IMS, will enable the exchange of expertise, resources, and samples to ensure the success of this study. We will also establish a new computational workflow for discovery of molecular markers (detected by MALDI IMS) associated with unique cellular neighborhoods (defined by CODEX) that leverages the Stanford TMC’s algorithms for identifying multi-hierarchical cell neighborhoods and the Vanderbilt TMC’s multimodal image co- registration and mining tools. This project will be completed in three phases: generating unique datasets from the intestine, computational algorithms, training across the Stanford and Vanderbilt TMCs, and insight into how metabolism is regulated across cell types and cellular neighborhoods in the intestine. This will also provide an example of a highly coordinated study between two TMCs and lay the foundation for future research toward HuBMAP’s goal of an enhanced molecular understanding of human biology.

About the researchers

John Hickey is a postdoctoral fellow in Garry Nolan's lab at Stanford University, where he uses and develops systems biology tools to analyze spatial relationships among cells in tissues. Prior to that, he pursued his PhD in Biomedical Engineering at Johns Hopkins University under the guidance of Dr. Jonathan Schneck and Hai-Quan Mao. During his doctoral studies, he developed biomaterials for T cell therapy. John has been recognized by a number of organizations for his work, including receiving: NSF Graduate Research Fellowship, ARCS Scholar, Siebel Scholar, NCI Postdoctoral Fellowship, and American Cancer Society Postdoctoral Fellowship. John will open his own lab in 2024 at Duke University as an Assistant Professor in the Biomedical Engineering Department with secondary appointments in the Cell Biology and Biostatistics & Bioinformatics Departments of the School of Medicine.

Angela Kruse is research faculty in the Department of Cell and Developmental Biology and the Mass Spectrometry Research Center at Vanderbilt University. She received her B.S. in Genetics and Plant Biology from the University of California, Berkeley and her Ph.D. in Plant Pathology with a focus in Biochemistry from Cornell University. Her current research goal is to better understand how diabetes affects the molecular environment in the pancreas, kidney, and eye using a combination of imaging mass spectrometry, proteomics, biochemistry, and microscopy. She hopes to spend her career applying and integrating cutting edge technologies to address important challenges facing our society and environment.


2021 JumpStart Fellowship Award Winners

Hang Hu, Purdue University

Self-supervised Mass Spectrometry Imaging Clustering with Convolutional Neural Network and Contrastive Learning

Hang aims to develop a novel self-supervised learning approach for efficient classification of mass spectrometry imaging data. Using this tool, the goal is to be able to cluster more than 1000 ion images in half an hour without any manual user annotation. 

About the researcher: Hang is a 4th year Ph.D. candidate in Dr. Julia Laskin's research group at Purdue University. He investigates nano-DESI mass spectrometry imaging (MSI) and participates in the Computational Interdisciplinary Graduate Program. Hang is interested in the application of machine learning, computer vision and lab automation for MSI. Outside the lab, he usually runs 15 miles a week, and enjoys cooking!

Angela Kruse, Vanderbilt University

3-D Multimodal Analysis of Eye and Pancreas Blocks Using Light Sheet Microscopy and Imaging Mass Spectrometry

As technology develops, scientists are able to study thousands of molecules such as proteins or lipids from increasingly small tissue samples. One technology used by HuBMAP scientists is imaging mass spectrometry (IMS) which can be used to create a map showing the location of molecules in thin tissue sections. These maps can help improve our understanding of human biology, but it can be challenging or impossible to relate the information from a small tissue sample to an intact organ. To address this challenge, I will combine IMS with another technology called light sheet microscopy (LSM). LSM can be used to visualize specific proteins in very thick pieces of tissue. Using LSM, I will make a 3-dimensional (3-D) map of several major structures such as veins and islets in thick blocks of pancreas and eye tissue. Next, I will divide these blocks into thin sections and use IMS to map the peptides and lipids in these 2-D samples. Finally, I will combine each data type to reconstruct the pancreas and eye tissue blocks with all the molecular information we can gain through IMS inside the 3-D map made by LSM. This study is expected to help us better understand how molecular data from small samples relates to an intact human organ. Adding this organ-level context can help future scientists use our data most effectively. 

About the researcher: Angela Kruse is a Postdoctoral Research Fellow in the Mass Spectrometry Research Center at Vanderbilt University. She received her B.S. in Genetics and Plant Biology from the University of California, Berkeley and her Ph.D. in Plant Pathology with a focus in Biochemistry from Cornell University. Her current research goal is to better understand the molecular environment of human retinal, lens, and pancreatic tissues using a combination of imaging mass spectrometry, proteomics, biochemistry, and bioinformatics. She hopes to spend her career applying and integrating cutting edge technologies to address important challenges facing our society and planet. When not in the lab, Angela enjoys hiking with her dog Ginger and tending to her overlarge collection of houseplants. She comes from a large family with six older siblings, and is a classical flute and piccolo player.

Yang Liu, Yale University

Spatial multi-omics profiling of human kidney tissue using DBiT-seq

The human kidney is a structurally complex organ composed of different cell types. To understand how the kidney functions, we need to know not only what cell types are there, but where they are located and how they interact with their neighbors and environments. This project will study the human kidney using a newly built spatial omics sequencing tool, and explore the molecular basis of kidney functions. Especially, it will further contrast the spatial biomolecular atlas of kidney in young and old adults to investigate the effect of aging. 

About the researcher: Dr. Yang Liu is a third year Postdoc working at Dr. Rong Fan’s lab, interested in building novel tools for spatial omics sequencing. He developed a high spatial resolution multi-omics sequencing technique, named DBiT-seq, which can achieve near single cell spatial resolution (10 µm) sequencing of RNA and protein on the same tissue section. His ongoing work includes developing DBiT-seq V2.0, building 3D human healthy heart Atlas and studying development of human tumor tissues. During PhD training, he worked primarily as an analytical chemist and toxicologist, focusing on the developments of a variety of highly sensitive analytical methods for the quantification of post-translational modifications. He is also highly interested in applying cutting-edge bioinformatic tools to understand spatial omics data.


Questions? Contact us at JumpStart@hubmapconsortium.org