Assessing the replicability of spatial gene expression using atlas data from the adult mouse brain.

High-throughput, spatially resolved gene expression techniques are poised to be transformative across biology by overcoming a central limitation in single-cell biology: the lack of information on relationships that organize the cells into the functional groupings characteristic of tissues in complex...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Shaina Lu, Cantin Ortiz, Daniel Fürth, Stephan Fischer, Konstantinos Meletis, Anthony Zador, Jesse Gillis
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/647bfa0ef7344221b9f7d06814d96bc6
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:647bfa0ef7344221b9f7d06814d96bc6
record_format	dspace
spelling	oai:doaj.org-article:647bfa0ef7344221b9f7d06814d96bc62021-12-02T19:54:23ZAssessing the replicability of spatial gene expression using atlas data from the adult mouse brain.1544-91731545-788510.1371/journal.pbio.3001341https://doaj.org/article/647bfa0ef7344221b9f7d06814d96bc62021-07-01T00:00:00Zhttps://doi.org/10.1371/journal.pbio.3001341https://doaj.org/toc/1544-9173https://doaj.org/toc/1545-7885High-throughput, spatially resolved gene expression techniques are poised to be transformative across biology by overcoming a central limitation in single-cell biology: the lack of information on relationships that organize the cells into the functional groupings characteristic of tissues in complex multicellular organisms. Spatial expression is particularly interesting in the mammalian brain, which has a highly defined structure, strong spatial constraint in its organization, and detailed multimodal phenotypes for cells and ensembles of cells that can be linked to mesoscale properties such as projection patterns, and from there, to circuits generating behavior. However, as with any type of expression data, cross-dataset benchmarking of spatial data is a crucial first step. Here, we assess the replicability, with reference to canonical brain subdivisions, between the Allen Institute's in situ hybridization data from the adult mouse brain (Allen Brain Atlas (ABA)) and a similar dataset collected using spatial transcriptomics (ST). With the advent of tractable spatial techniques, for the first time, we are able to benchmark the Allen Institute's whole-brain, whole-transcriptome spatial expression dataset with a second independent dataset that similarly spans the whole brain and transcriptome. We use regularized linear regression (LASSO), linear regression, and correlation-based feature selection in a supervised learning framework to classify expression samples relative to their assayed location. We show that Allen Reference Atlas labels are classifiable using transcription in both data sets, but that performance is higher in the ABA than in ST. Furthermore, models trained in one dataset and tested in the opposite dataset do not reproduce classification performance bidirectionally. While an identifying expression profile can be found for a given brain area, it does not generalize to the opposite dataset. In general, we found that canonical brain area labels are classifiable in gene expression space within dataset and that our observed performance is not merely reflecting physical distance in the brain. However, we also show that cross-platform classification is not robust. Emerging spatial datasets from the mouse brain will allow further characterization of cross-dataset replicability ultimately providing a valuable reference set for understanding the cell biology of the brain.Shaina LuCantin OrtizDaniel FürthStephan FischerKonstantinos MeletisAnthony ZadorJesse GillisPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Biology, Vol 19, Iss 7, p e3001341 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Shaina Lu Cantin Ortiz Daniel Fürth Stephan Fischer Konstantinos Meletis Anthony Zador Jesse Gillis Assessing the replicability of spatial gene expression using atlas data from the adult mouse brain.
description	High-throughput, spatially resolved gene expression techniques are poised to be transformative across biology by overcoming a central limitation in single-cell biology: the lack of information on relationships that organize the cells into the functional groupings characteristic of tissues in complex multicellular organisms. Spatial expression is particularly interesting in the mammalian brain, which has a highly defined structure, strong spatial constraint in its organization, and detailed multimodal phenotypes for cells and ensembles of cells that can be linked to mesoscale properties such as projection patterns, and from there, to circuits generating behavior. However, as with any type of expression data, cross-dataset benchmarking of spatial data is a crucial first step. Here, we assess the replicability, with reference to canonical brain subdivisions, between the Allen Institute's in situ hybridization data from the adult mouse brain (Allen Brain Atlas (ABA)) and a similar dataset collected using spatial transcriptomics (ST). With the advent of tractable spatial techniques, for the first time, we are able to benchmark the Allen Institute's whole-brain, whole-transcriptome spatial expression dataset with a second independent dataset that similarly spans the whole brain and transcriptome. We use regularized linear regression (LASSO), linear regression, and correlation-based feature selection in a supervised learning framework to classify expression samples relative to their assayed location. We show that Allen Reference Atlas labels are classifiable using transcription in both data sets, but that performance is higher in the ABA than in ST. Furthermore, models trained in one dataset and tested in the opposite dataset do not reproduce classification performance bidirectionally. While an identifying expression profile can be found for a given brain area, it does not generalize to the opposite dataset. In general, we found that canonical brain area labels are classifiable in gene expression space within dataset and that our observed performance is not merely reflecting physical distance in the brain. However, we also show that cross-platform classification is not robust. Emerging spatial datasets from the mouse brain will allow further characterization of cross-dataset replicability ultimately providing a valuable reference set for understanding the cell biology of the brain.
format	article
author	Shaina Lu Cantin Ortiz Daniel Fürth Stephan Fischer Konstantinos Meletis Anthony Zador Jesse Gillis
author_facet	Shaina Lu Cantin Ortiz Daniel Fürth Stephan Fischer Konstantinos Meletis Anthony Zador Jesse Gillis
author_sort	Shaina Lu
title	Assessing the replicability of spatial gene expression using atlas data from the adult mouse brain.
title_short	Assessing the replicability of spatial gene expression using atlas data from the adult mouse brain.
title_full	Assessing the replicability of spatial gene expression using atlas data from the adult mouse brain.
title_fullStr	Assessing the replicability of spatial gene expression using atlas data from the adult mouse brain.
title_full_unstemmed	Assessing the replicability of spatial gene expression using atlas data from the adult mouse brain.
title_sort	assessing the replicability of spatial gene expression using atlas data from the adult mouse brain.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/647bfa0ef7344221b9f7d06814d96bc6
work_keys_str_mv	AT shainalu assessingthereplicabilityofspatialgeneexpressionusingatlasdatafromtheadultmousebrain AT cantinortiz assessingthereplicabilityofspatialgeneexpressionusingatlasdatafromtheadultmousebrain AT danielfurth assessingthereplicabilityofspatialgeneexpressionusingatlasdatafromtheadultmousebrain AT stephanfischer assessingthereplicabilityofspatialgeneexpressionusingatlasdatafromtheadultmousebrain AT konstantinosmeletis assessingthereplicabilityofspatialgeneexpressionusingatlasdatafromtheadultmousebrain AT anthonyzador assessingthereplicabilityofspatialgeneexpressionusingatlasdatafromtheadultmousebrain AT jessegillis assessingthereplicabilityofspatialgeneexpressionusingatlasdatafromtheadultmousebrain
_version_	1718375903547883520

Assessing the replicability of spatial gene expression using atlas data from the adult mouse brain.

Ejemplares similares