The HuBMAP JumpStart program provides junior investigators the opportunity to undertake independent, scientific research projects within HuBMAP labs.
We hope that this program will:
- Foster collaborations between HuBMAP projects with the aim of strengthening the overall scientific goals of HuBMAP
- Increase career development opportunities for junior investigators (JIs) and build their expertise through scientific leadership, increased responsibilities, and using different technologies and developing new skills in another lab
- Expand JI's professional networks through valuable mentoring and networking opportunities
JumpStart Program Applications
The application deadline for this year's program has passed.
Information for the next round of applications will be made available at a later date. Check back for updates.
Questions? Email us at: JumpStart@hubmapconsortium.org
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Applications Due: Dec. 16, 2022
Application Review Period: February 2023
Award Notification: April 2023
-Foster collaborations between HuBMAP projects
-Enhance career development opportunities Provide mentorship and networking opportunities
Project must be relevant to HuBMAP and not overlap with work currently being supported by NIH funded awards. Examples of responsive projects include: -Integrating data from two technologies Assessing inter-institution data reproducibility
-Generating new data types that enhance existing reference datasets or 3D maps
-Creating new tools for visualizing or modeling HuBMAP data alongside other sources
-Enhancing the speed, accuracy, or reproducibility of computational pipelines
-Must involve a partnership between 2 or more JIs from at least two different institutions. A partnership can include external JIs not funded by HuBMAP, but working on related atlasing projects; the number of external JIs cannot exceed the number of JIs supported by HuBMAP awards. -PI/PD support from each institution is needed.
Number of Awards:
3-4 projects per year, with 2-3 JIs per project
-Project length can be up to 2 years. Project cannot exceed the duration of the parent award.
-Budget maximum of $10,000 per institution per year (total cost), up to a total maximum of $30,000 per year (i.e. a collaboration of up to 3 JIs from different institutions can be proposed). A partnership can include external JIs not funded by HuBMAP, though the number of external JIs cannot exceed the number of internal JIs.
-Funds can be used for supplies, reagents, core resources, etc.; funds cannot be used for personnel costs.
-Budget support can include travel for cross-training purposes and may include a stay of up to four weeks if scientifically justified.
-Post docs or graduate students (JIs) working on and already supported on HuBMAP funded awards.
-Must be at a US institution that is able to receive a subaward from CMU.
-½ page Abstract
-Proposed Aim, Significance /Innovation/Anticipated Results (1 page)
-1 page appendix (figures and references) -Mentoring Plan (1 page)
-Personal Statement from the JIs: How will this help their career? (½ page)and include a venue where regular progress update presentations will be made.
-PI letter of support for the JI and specific project (from each institution)
-Detailed Budget - salary costs are not allowed
-Link to Template
-How to submit: send application documents to JumpStart@hubmapconsortium.org
-Applications will be reviewed and scored by HuBMAP-supported PIs who do not have a conflict of interest.
-Final funding decisions will be made based on HuBMAP priorities.
-JIs are required to present progress at least once a year in an appropriate HuBMAP meeting forum, such as the Annual Investigator Meeting
-JIs are expected to abide by Consortium policies and to acknowledge awards in presentations / publications
-A series of networking and career development meetings will be organized for awardees throughout the duration of their awards
2021 JumpStart Grant Award Winners
Hang Hu, Purdue University
Hang's project is entitled: Self-supervised Mass Spectrometry Imaging Clustering with Convolutional Neural Network and Contrastive Learning. He 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: I am a 4th year Ph.D. candidate in Dr. Julia Laskin's research group at Purdue University. I investigate nano-DESI mass spectrometry imaging (MSI) and participates in the Computational Interdisciplinary Graduate Program. I am interested in the application of machine learning, computer vision and lab automation for MSI. Outside the lab, I usually run 15 miles a week, and I enjoy cooking!
Angela Kruse, Vanderbilt University
Angela's project is entitled: 3-D Multimodal Analysis of Eye and Pancreas Blocks Using Light Sheet Microscopy and Imaging Mass Spectrometry. Here is Angela's description of her project:
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
Yang's project is entitled: 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.