Featured HuBMAP Publications
Spatial mapping of protein composition and tissue organization: a primer for multiplexed antibody-based imaging
Hickey JW, Neumann EK, Radtke AJ, Camarillo JM, Beuschel RT, Albanese A, McDonough E, Hatler J, Wiblin AE, Fisher J, Croteau J, Small EC, Sood A, Caprioli RM, Angelo RM, Nolan GP, Chung K, Hewitt SM, Germain RN, Spraggins JM, Lundberg E, Snyder MP, Kelleher NL, Saka SK.
Published In Nature Methods, November 2021.
This paper was born out of consortium meetings where many stakeholders in the field, including technology developers, users, vendors, funders, and publishers, were able to exchange perspectives and knowledge.
Andrea J. Radtke, firstname.lastname@example.org or Sinem K. Saka, email@example.com.
- Tissue Mapping Center, Vanderbilt University (kidney)
- Tissue Mapping Center, Vanderbilt University (pancreas and eye)
- Tissue Mapping Center, Stanford University
- Transformative Technology Development, Harvard University
- Rapid Technology Implementation, Stanford University
- Rapid Technology Implementation, Northwestern University
- Rapid Technology Implementation, General Electric
NIH Award Nos. 1U54 DK120058-01, U54 EY032442, U54 HG010426-01, UG3 HL145600-01, UH3 CA246633-01, and UH3 CA246635-01
Missarova A, Jain J, Butler A, Ghazanfar S, Stuart T, Brusko M, Wasserfall C, Nick H, Brusko T, Atkinson M, Satija R, Marioni JC.
Published in Genome Biology, December 2021
scRNA-seq datasets are increasingly used to identify gene panels that can be probed using alternative technologies, such as spatial transcriptomics, where choosing the best subset of genes is vital. Existing methods are limited by a reliance on pre-existing cell type labels or by difficulties in identifying markers of rare cells. We introduce an iterative approach, geneBasis, for selecting an optimal gene panel, where each newly added gene captures the maximum distance between the true manifold and the manifold constructed using the currently selected gene panel. Our approach outperforms existing strategies and can resolve cell types and subtle cell state differences.
scRNA-seq pancreas dataset
Obtained from HuBMAP's Azimuth portal.