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Recovering single-cell expression profiles from spatial transcriptomics with scResolve

delete2024-10-01
delete0
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OA
AI
H
Hao Chen
Y
Young Je Lee
J
José Antonio Ovando-Ricárdez
L
Lorena Rosas
M
Mauricio Rojas
A
Ana L. Mora
Z
Ziv Bar‐Joseph
J
Jose Lugo-Martinez *
DOI：10.1016/j.crmeth.2024.100864delete
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摘要

摘要

En 中文
Many popular spatial transcriptomics techniques lack single-cell resolution. Instead, these methods measure the collective gene expression for each location from a mixture of cells, potentially containing multiple cell types. Here, we developed scResolve, a method for recovering single-cell expression profiles from spatial transcriptomics measurements at multi-cellular resolution. scResolve accurately restores expression profiles of individual cells at their locations, which is unattainable with cell type deconvolution. Applications of scResolve on human breast cancer data and human lung disease data demonstrate that scResolve enables cell-type-specific differential gene expression analysis between different tissue contexts and accurate identification of rare cell populations. The spatially resolved cellular-level expression profiles obtained through scResolve facilitate more flexible and precise spatial analysis that complements raw multi-cellular level analysis.
Keyword:
GENE-EXPRESSION
SENESCENT CELLS
CANCER
ATLAS
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Cell Reports Methods 封面图
Cell Reports Methods
IF:
4.5
论文数:
953
被引数:
2.0K

机构

U
University System of Ohio
学者数:
15.5W
论文数: 13.0W
被引数: 200
C
Carnegie Mellon University
学者数:
1.4W
论文数: 1.4W
被引数: 2.7W
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