التفاصيل البيبلوغرافية
| العنوان: |
Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign. |
| المؤلفون: |
Sisi Chen, Rivaud, Paul, Park, Jong H., Tiffany Tsou, Charles, Emeric, Haliburton, John R., Pichiorri, Flavia, Thomson, Matt |
| المصدر: |
Proceedings of the National Academy of Sciences of the United States of America; 11/17/2020, Vol. 117 Issue 46, p28784-28794, 11p |
| مصطلحات موضوعية: |
CELL populations, POPULATION, DNA probes, MULTIPLE myeloma, DISEASE progression |
| مستخلص: |
Single-cell measurement techniques can now probe gene expression in heterogeneous cell populations from the human body across a range of environmental and physiological conditions. However, new mathematical and computational methods are required to represent and analyze gene-expression changes that occur in complex mixtures of single cells as they respond to signals, drugs, or disease states. Here, we introduce a mathematical modeling platform, PopAlign, that automatically identifies subpopulations of cells within a heterogeneous mixture and tracks gene-expression and cell-abundance changes across subpopulations by constructing and comparing probabilistic models. Probabilistic models provide a low-error, compressed representation of single-cell data that enables efficient large-scale computations. We apply PopAlign to analyze the impact of 40 different immunomodulatory compounds on a heterogeneous population of donor-derived human immune cells as well as patient-specific disease signatures in multiple myeloma. PopAlign scales to comparisons involving tens to hundreds of samples, enabling largescale studies of natural and engineered cell populations as they respond to drugs, signals, or physiological change. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
