TY - JOUR
T1 - Gene expression cartography
AU - Nitzan, Mor
AU - Karaiskos, Nikos
AU - Friedman, Nir
AU - Rajewsky, Nikolaus
N1 - Publisher Copyright:
© 2019, The Author(s), under exclusive licence to Springer Nature Limited.
PY - 2019/12/5
Y1 - 2019/12/5
N2 - Multiplexed RNA sequencing in individual cells is transforming basic and clinical life sciences1–4. Often, however, tissues must first be dissociated, and crucial information about spatial relationships and communication between cells is thus lost. Existing approaches to reconstruct tissues assign spatial positions to each cell, independently of other cells, by using spatial patterns of expression of marker genes5,6—which often do not exist. Here we reconstruct spatial positions with little or no prior knowledge, by searching for spatial arrangements of sequenced cells in which nearby cells have transcriptional profiles that are often (but not always) more similar than cells that are farther apart. We formulate this task as a generalized optimal-transport problem for probabilistic embedding and derive an efficient iterative algorithm to solve it. We reconstruct the spatial expression of genes in mammalian liver and intestinal epithelium, fly and zebrafish embryos, sections from the mammalian cerebellum and whole kidney, and use the reconstructed tissues to identify genes that are spatially informative. Thus, we identify an organization principle for the spatial expression of genes in animal tissues, which can be exploited to infer meaningful probabilities of spatial position for individual cells. Our framework (‘novoSpaRc’) can incorporate prior spatial information and is compatible with any single-cell technology. Additional principles that underlie the cartography of gene expression can be tested using our approach.
AB - Multiplexed RNA sequencing in individual cells is transforming basic and clinical life sciences1–4. Often, however, tissues must first be dissociated, and crucial information about spatial relationships and communication between cells is thus lost. Existing approaches to reconstruct tissues assign spatial positions to each cell, independently of other cells, by using spatial patterns of expression of marker genes5,6—which often do not exist. Here we reconstruct spatial positions with little or no prior knowledge, by searching for spatial arrangements of sequenced cells in which nearby cells have transcriptional profiles that are often (but not always) more similar than cells that are farther apart. We formulate this task as a generalized optimal-transport problem for probabilistic embedding and derive an efficient iterative algorithm to solve it. We reconstruct the spatial expression of genes in mammalian liver and intestinal epithelium, fly and zebrafish embryos, sections from the mammalian cerebellum and whole kidney, and use the reconstructed tissues to identify genes that are spatially informative. Thus, we identify an organization principle for the spatial expression of genes in animal tissues, which can be exploited to infer meaningful probabilities of spatial position for individual cells. Our framework (‘novoSpaRc’) can incorporate prior spatial information and is compatible with any single-cell technology. Additional principles that underlie the cartography of gene expression can be tested using our approach.
UR - http://www.scopus.com/inward/record.url?scp=85075376819&partnerID=8YFLogxK
U2 - 10.1038/s41586-019-1773-3
DO - 10.1038/s41586-019-1773-3
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C2 - 31748748
AN - SCOPUS:85075376819
SN - 0028-0836
VL - 576
SP - 132
EP - 137
JO - Nature
JF - Nature
IS - 7785
ER -