EconPapers    
Economics at your fingertips  
 

Gene expression cartography

Mor Nitzan, Nikos Karaiskos, Nir Friedman () and Nikolaus Rajewsky ()
Additional contact information
Mor Nitzan: Harvard University
Nikos Karaiskos: Max Delbrück Center for Molecular Medicine in the Helmholtz Association
Nir Friedman: The Hebrew University of Jerusalem
Nikolaus Rajewsky: Max Delbrück Center for Molecular Medicine in the Helmholtz Association

Nature, 2019, vol. 576, issue 7785, 132-137

Abstract: Abstract 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.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
https://www.nature.com/articles/s41586-019-1773-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:576:y:2019:i:7785:d:10.1038_s41586-019-1773-3

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/s41586-019-1773-3

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-19
Handle: RePEc:nat:nature:v:576:y:2019:i:7785:d:10.1038_s41586-019-1773-3