Author

Sarah R Chapin

Cold Spring Harbor LaboratoryORCID 0000-0002-7775-3380Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Signature Assays, Organisms & Methods

Click to filter · or open tag

Organism:human· 2 Approach:statistical model· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:host-pathogen & immune response· 1 Assay:scRNA-seq· 1

Frequent Co-Authors

Hannah V. MeyeriD· 2 shared Rishvanth K. PrabakariD· 2 shared Adam SiepeliD· 1 shared Alexander GroshiD· 1 shared Alexandra J KiedrowskiiD· 1 shared Amitava BanerjeeiD· 1 shared Carl KingsfordiD· 1 shared Claire ReganiD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersImmunology

Hannah V. MeyerORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

Gene regulatory logic underlying the differentiation of mixed lineages in human thymic epithelial cells

Rishvanth K Prabakar, Sarah R Chapin, Yong Lin, Stephen J Staklinski, Deena M Netz, Madison E Lapine, Dominic Pruss, Todor Cvetanovic, Salome L Carcy, Joshua Torres, Alexander Grosh, Alexandra J Kiedrowski, Harvey I Pass, Ralph Mosca, Claire Regan, , Hannah V Meyer

The paper identifies 17 human thymic epithelial cell subsets using single-cell data and shows they share a common ancestor and regulatory logic, with some conservation in mice.

bioRxiv · Nov 2, 2026 · PreprintImmunology

Sparse, random sampling is sufficient for central tolerance

Hannah V. Meyer, Sanjoy Dasgupta, Amitava Banerjee, Yong Lin, Rishvanth K. Prabakar, Sarah R. Chapin, Carl Kingsford, Saket Navlakha

The paper shows that sparse, random sampling of self peptides in the thymus is sufficient for T cell tolerance to most peripheral self peptides, supported by experimental data and machine learning analysis.

bioRxiv · Dec 12, 2025 · PreprintImmunology