Geometry of the Gene Expression Space of Individual Cells.

There is a revolution in the ability to analyze gene expression of single cells in a tissue. To understand this data we must comprehend how cells are distributed in a high-dimensional gene expression space. One open question is whether cell types form discrete clusters or whether gene expression for...

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Main Authors: Yael Korem, Pablo Szekely, Yuval Hart, Hila Sheftel, Jean Hausser, Avi Mayo, Michael E Rothenberg, Tomer Kalisky, Uri Alon
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2015-07-01
Series:PLoS Computational Biology
Online Access:http://europepmc.org/articles/PMC4498931?pdf=render
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author Yael Korem
Pablo Szekely
Yuval Hart
Hila Sheftel
Jean Hausser
Avi Mayo
Michael E Rothenberg
Tomer Kalisky
Uri Alon
author_facet Yael Korem
Pablo Szekely
Yuval Hart
Hila Sheftel
Jean Hausser
Avi Mayo
Michael E Rothenberg
Tomer Kalisky
Uri Alon
author_sort Yael Korem
collection DOAJ
description There is a revolution in the ability to analyze gene expression of single cells in a tissue. To understand this data we must comprehend how cells are distributed in a high-dimensional gene expression space. One open question is whether cell types form discrete clusters or whether gene expression forms a continuum of states. If such a continuum exists, what is its geometry? Recent theory on evolutionary trade-offs suggests that cells that need to perform multiple tasks are arranged in a polygon or polyhedron (line, triangle, tetrahedron and so on, generally called polytopes) in gene expression space, whose vertices are the expression profiles optimal for each task. Here, we analyze single-cell data from human and mouse tissues profiled using a variety of single-cell technologies. We fit the data to shapes with different numbers of vertices, compute their statistical significance, and infer their tasks. We find cases in which single cells fill out a continuum of expression states within a polyhedron. This occurs in intestinal progenitor cells, which fill out a tetrahedron in gene expression space. The four vertices of this tetrahedron are each enriched with genes for a specific task related to stemness and early differentiation. A polyhedral continuum of states is also found in spleen dendritic cells, known to perform multiple immune tasks: cells fill out a tetrahedron whose vertices correspond to key tasks related to maturation, pathogen sensing and communication with lymphocytes. A mixture of continuum-like distributions and discrete clusters is found in other cell types, including bone marrow and differentiated intestinal crypt cells. This approach can be used to understand the geometry and biological tasks of a wide range of single-cell datasets. The present results suggest that the concept of cell type may be expanded. In addition to discreet clusters in gene-expression space, we suggest a new possibility: a continuum of states within a polyhedron, in which the vertices represent specialists at key tasks.
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spelling doaj.art-b9bb13d9f239484fa6c52d902336c2032022-12-22T03:43:57ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582015-07-01117e100422410.1371/journal.pcbi.1004224Geometry of the Gene Expression Space of Individual Cells.Yael KoremPablo SzekelyYuval HartHila SheftelJean HausserAvi MayoMichael E RothenbergTomer KaliskyUri AlonThere is a revolution in the ability to analyze gene expression of single cells in a tissue. To understand this data we must comprehend how cells are distributed in a high-dimensional gene expression space. One open question is whether cell types form discrete clusters or whether gene expression forms a continuum of states. If such a continuum exists, what is its geometry? Recent theory on evolutionary trade-offs suggests that cells that need to perform multiple tasks are arranged in a polygon or polyhedron (line, triangle, tetrahedron and so on, generally called polytopes) in gene expression space, whose vertices are the expression profiles optimal for each task. Here, we analyze single-cell data from human and mouse tissues profiled using a variety of single-cell technologies. We fit the data to shapes with different numbers of vertices, compute their statistical significance, and infer their tasks. We find cases in which single cells fill out a continuum of expression states within a polyhedron. This occurs in intestinal progenitor cells, which fill out a tetrahedron in gene expression space. The four vertices of this tetrahedron are each enriched with genes for a specific task related to stemness and early differentiation. A polyhedral continuum of states is also found in spleen dendritic cells, known to perform multiple immune tasks: cells fill out a tetrahedron whose vertices correspond to key tasks related to maturation, pathogen sensing and communication with lymphocytes. A mixture of continuum-like distributions and discrete clusters is found in other cell types, including bone marrow and differentiated intestinal crypt cells. This approach can be used to understand the geometry and biological tasks of a wide range of single-cell datasets. The present results suggest that the concept of cell type may be expanded. In addition to discreet clusters in gene-expression space, we suggest a new possibility: a continuum of states within a polyhedron, in which the vertices represent specialists at key tasks.http://europepmc.org/articles/PMC4498931?pdf=render
spellingShingle Yael Korem
Pablo Szekely
Yuval Hart
Hila Sheftel
Jean Hausser
Avi Mayo
Michael E Rothenberg
Tomer Kalisky
Uri Alon
Geometry of the Gene Expression Space of Individual Cells.
PLoS Computational Biology
title Geometry of the Gene Expression Space of Individual Cells.
title_full Geometry of the Gene Expression Space of Individual Cells.
title_fullStr Geometry of the Gene Expression Space of Individual Cells.
title_full_unstemmed Geometry of the Gene Expression Space of Individual Cells.
title_short Geometry of the Gene Expression Space of Individual Cells.
title_sort geometry of the gene expression space of individual cells
url http://europepmc.org/articles/PMC4498931?pdf=render
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