Identifying Cell Types from Spatially Referenced Single-Cell Expression Datasets

Complex tissues, such as the brain, are composed of multiple different cell types, each of which have distinct and important roles, for example in neural function. Moreover, it has recently been appreciated that the cells that make up these sub-cell types themselves harbour significant cell-to-cell...

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Main Authors:	Pettit, Jean-Baptiste, Tomer, Raju, Achim, Kaia, Richardson, Sylvia, Azizi, Lamiae, Marioni, John
Format:	Online
Language:	English
Published:	Public Library of Science 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4177667/

id	pubmed-4177667
recordtype	oai_dc
spelling	pubmed-41776672014-10-02 Identifying Cell Types from Spatially Referenced Single-Cell Expression Datasets Pettit, Jean-Baptiste Tomer, Raju Achim, Kaia Richardson, Sylvia Azizi, Lamiae Marioni, John Research Article Complex tissues, such as the brain, are composed of multiple different cell types, each of which have distinct and important roles, for example in neural function. Moreover, it has recently been appreciated that the cells that make up these sub-cell types themselves harbour significant cell-to-cell heterogeneity, in particular at the level of gene expression. The ability to study this heterogeneity has been revolutionised by advances in experimental technology, such as Wholemount in Situ Hybridizations (WiSH) and single-cell RNA-sequencing. Consequently, it is now possible to study gene expression levels in thousands of cells from the same tissue type. After generating such data one of the key goals is to cluster the cells into groups that correspond to both known and putatively novel cell types. Whilst many clustering algorithms exist, they are typically unable to incorporate information about the spatial dependence between cells within the tissue under study. When such information exists it provides important insights that should be directly included in the clustering scheme. To this end we have developed a clustering method that uses a Hidden Markov Random Field (HMRF) model to exploit both quantitative measures of expression and spatial information. To accurately reflect the underlying biology, we extend current HMRF approaches by allowing the degree of spatial coherency to differ between clusters. We demonstrate the utility of our method using simulated data before applying it to cluster single cell gene expression data generated by applying WiSH to study expression patterns in the brain of the marine annelid Platynereis dumereilii. Our approach allows known cell types to be identified as well as revealing new, previously unexplored cell types within the brain of this important model system. Public Library of Science 2014-09-25 /pmc/articles/PMC4177667/ /pubmed/25254363 http://dx.doi.org/10.1371/journal.pcbi.1003824 Text en © 2014 Pettit et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Pettit, Jean-Baptiste Tomer, Raju Achim, Kaia Richardson, Sylvia Azizi, Lamiae Marioni, John
spellingShingle	Pettit, Jean-Baptiste Tomer, Raju Achim, Kaia Richardson, Sylvia Azizi, Lamiae Marioni, John Identifying Cell Types from Spatially Referenced Single-Cell Expression Datasets
author_facet	Pettit, Jean-Baptiste Tomer, Raju Achim, Kaia Richardson, Sylvia Azizi, Lamiae Marioni, John
author_sort	Pettit, Jean-Baptiste
title	Identifying Cell Types from Spatially Referenced Single-Cell Expression Datasets
title_short	Identifying Cell Types from Spatially Referenced Single-Cell Expression Datasets
title_full	Identifying Cell Types from Spatially Referenced Single-Cell Expression Datasets
title_fullStr	Identifying Cell Types from Spatially Referenced Single-Cell Expression Datasets
title_full_unstemmed	Identifying Cell Types from Spatially Referenced Single-Cell Expression Datasets
title_sort	identifying cell types from spatially referenced single-cell expression datasets
description	Complex tissues, such as the brain, are composed of multiple different cell types, each of which have distinct and important roles, for example in neural function. Moreover, it has recently been appreciated that the cells that make up these sub-cell types themselves harbour significant cell-to-cell heterogeneity, in particular at the level of gene expression. The ability to study this heterogeneity has been revolutionised by advances in experimental technology, such as Wholemount in Situ Hybridizations (WiSH) and single-cell RNA-sequencing. Consequently, it is now possible to study gene expression levels in thousands of cells from the same tissue type. After generating such data one of the key goals is to cluster the cells into groups that correspond to both known and putatively novel cell types. Whilst many clustering algorithms exist, they are typically unable to incorporate information about the spatial dependence between cells within the tissue under study. When such information exists it provides important insights that should be directly included in the clustering scheme. To this end we have developed a clustering method that uses a Hidden Markov Random Field (HMRF) model to exploit both quantitative measures of expression and spatial information. To accurately reflect the underlying biology, we extend current HMRF approaches by allowing the degree of spatial coherency to differ between clusters. We demonstrate the utility of our method using simulated data before applying it to cluster single cell gene expression data generated by applying WiSH to study expression patterns in the brain of the marine annelid Platynereis dumereilii. Our approach allows known cell types to be identified as well as revealing new, previously unexplored cell types within the brain of this important model system.
publisher	Public Library of Science
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4177667/
_version_	1613138194615762944

Identifying Cell Types from Spatially Referenced Single-Cell Expression Datasets

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