Exponentiating Single-Cell Sequencing

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United States Simons Institute for the Theory of Computing
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Published on ● Video Link: https://www.youtube.com/watch?v=WTvDwb4YAcQ


Duration: 34:57
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8

Jimmie Ye (UCSF)
https://simons.berkeley.edu/talks/tbd-444
From Algorithms to Discovery in Genome-Scale Biology and Medicine

The integration of single cell and spatial transcriptomics provides a new approach to profile human disease pathology in situ. Here, I will introduce our work on dissecting lung alveolar damage in severe COVID-19 using a new single cell atlas and transcriptome wide spatial profiling of post-mortem lung tissue. First, we generated a comprehensive single-cell lung cell atlas through integration of multiple healthy and COVID-19 datasets. Second, we generated a spatially resolved transcriptomic dataset of diffuse alveolar damage (DAD) across different stages of pathology using the Nanostring WTA technology. To resolve changes in cell type abundance across progressive pathology, we integrated our single cell and spatial transcriptomic datasets. We identified dynamic sets of immune and stromal cells and tissue microenvironments that distinguish early (exudative) and late (organised) alveolar damage. Finally, we could re-map pathological phenotypes in our single-cell transcriptomic reference using pathology biomarkers identified from spatial data. Our work identifies candidate molecular and cellular targets of novel therapies for COVID-19 in the respiratory system.



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Tags:
Simons Institute
theoretical computer science
UC Berkeley
Computer Science
Theory of Computation
Theory of Computing
From Algorithms to Discovery in Genome-Scale Biology and Medicine
Jimmie Ye