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Garry P. Nolan
Researcher at Stanford University
Publications - 519
Citations - 54521
Garry P. Nolan is an academic researcher from Stanford University. The author has contributed to research in topics: Immune system & T cell. The author has an hindex of 104, co-authored 474 publications receiving 46025 citations. Previous affiliations of Garry P. Nolan include Massachusetts Institute of Technology & New York University.
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Tissue schematics map the specialization of immune tissue motifs and their appropriation by tumors
TL;DR: In this article, a computational approach for constructing tissue schematics (TSs) from high-parameter imaging data and a biological model for interpreting them is presented, which maps the spatial assembly of cellular neighborhoods into tissue motifs, whose modular composition enables the generation of complex outputs.
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Transcription and the broken heart
TL;DR: Two groups independently disrupted theNF-ATcgene in mice, and found that the resultant embryos showed severe defects in cardiac valve and septum formation, leading to death after 14-15 days' gestation.
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The road ahead: Implementing mass cytometry in clinical studies, one cell at a time
TL;DR: The necessary steps to transform mass cytometry from a technological tour-de-force to a valuable clinical platform are explored and six studies that illustrate recent progress are highlighted.
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Immunologic timeline of Ebola virus disease and recovery in humans
Anita K. McElroy,Anita K. McElroy,Rama Akondy,David R. Mcllwain,Han Chen,Zach Bjornson-Hooper,Nilanjan Mukherjee,Aneesh K. Mehta,Garry P. Nolan,Stuart T. Nichol,Christina F. Spiropoulou +10 more
TL;DR: Cryopreserved PBMCs from 4 patients who survived Ebola virus disease are evaluated using an established mass cytometry antibody panel to characterize various cell populations during both the acute and convalescent phases, providing insights into the human immune response during EVD.
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Identification of cell types in multiplexed in situ images by combining protein expression and spatial information using CELESTA
Weiruo Zhang,Irene Li,Nathan E. Reticker-Flynn,Zina Good,Serena S. Chang,Nikolay Samusik,Saumyaa Saumyaa,Yuanyuan Li,Xin Zhou,Rachel Liang,Christina S. Kong,Quynh-Thu Le,Andrew J. Gentles,John B. Sunwoo,Garry P. Nolan,Edgar G. Engleman,Sylvia K. Plevritis +16 more
TL;DR: In this paper , an unsupervised machine learning algorithm, CELESTA, was developed to identify the cell type of each cell, individually, using the cell's marker expression profile and, when needed, its spatial information.