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

Lu, Shaina, Ortiz, Cantin, Fürth, Daniel, Fischer, Stephan, Meletis, Konstantinos, Zador, Anthony, Gillis, Jesse (July 2021) Assessing the replicability of spatial gene expression using atlas data from the adult mouse brain. PLoS Biology, 19 (7). e3001341. ISSN 1545-7885

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URL: https://www.ncbi.nlm.nih.gov/pubmed/34280183
DOI: 10.1371/journal.pbio.3001341

Abstract

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.

Item Type: Paper
Subjects: Investigative techniques and equipment > brain atlas
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > genes, structure and function > gene expression
organism description > animal > mammal > rodent > mouse
neurobiology > neuroanatomy
CSHL Authors:
Communities: CSHL labs > Gillis Lab
CSHL labs > Zador lab
SWORD Depositor: CSHL Elements
Depositing User: CSHL Elements
Date: 19 July 2021
Date Deposited: 20 Jul 2021 15:18
Last Modified: 02 Aug 2021 18:47
PMCID: PMC8321401
URI: https://repository.cshl.edu/id/eprint/40296

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