M
Mark M. Davis
Researcher at Stanford University
Publications - 623
Citations - 84251
Mark M. Davis is an academic researcher from Stanford University. The author has contributed to research in topics: T cell & T-cell receptor. The author has an hindex of 144, co-authored 581 publications receiving 74358 citations. Previous affiliations of Mark M. Davis include Washington University in St. Louis & University of Chicago.
Papers
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Journal ArticleDOI
How αβ T-cell receptors ‘see’ peptide/MHC complexes
Yueh-hsiu Chien,Mark M. Davis +1 more
TL;DR: It is found that the V(D)J junction or ‘CDR3' portion of TCRα and β seem most important in contacting peptides bound to MHC molecules, consistent with previous predictions.
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Interrogating the repertoire: broadening the scope of peptide–MHC multimer analysis
TL;DR: A number of recent developments in this technology have made these multimers much easier to make and use in large numbers, and enrichment techniques have provided a greatly increased sensitivity that allows the analysis of the naive T cell repertoire directly.
Journal ArticleDOI
CD4 Augments the Response of a T Cell to Agonist but Not to Antagonist Ligands
TL;DR: Data suggest that CD4 engagement occurs after a TCR-peptide/MHC complex has formed and that it requires a certain minimal half-life of the ternary complex to be fully engaged in signaling.
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Transcript-indexed ATAC-seq for precision immune profiling.
Ansuman T. Satpathy,Naresha Saligrama,Jason D. Buenrostro,Jason D. Buenrostro,Yuning Wei,Beijing Wu,Adam J. Rubin,Jeffrey M. Granja,Caleb A. Lareau,Rui Li,Yanyan Qi,Kevin R. Parker,Maxwell R. Mumbach,William S. Serratelli,David Gennert,Alicia N. Schep,M. Ryan Corces,Michael S. Khodadoust,Youn H. Kim,Paul A. Khavari,William J. Greenleaf,Mark M. Davis,Howard Y. Chang +22 more
TL;DR: T-ATAC-seq is a new tool that enables analysis of epigenomic landscapes in clonal T cells and should be valuable for studies of T cell malignancy, immunity and immunotherapy.
Journal ArticleDOI
Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases.
Francesco Vallania,Andrew Tam,Shane Lofgren,Steven Schaffert,Tej D. Azad,Erika Bongen,Winston A. Haynes,Meia Alsup,Michael N. Alonso,Mark M. Davis,Edgar G. Engleman,Purvesh Khatri +11 more
TL;DR: In silico quantification of cell proportions from mixed-cell transcriptomics data (deconvolution) requires a reference expression matrix, called basis matrix, and introduces immunoStates, a basis matrix built using 6160 samples with different disease states across 42 microarray platforms that significantly reduces biological and technical biases.