Matrix Inversion and Subset Selection (MISS): A pipeline for mapping of diverse cell types across the murine brain. Academic Article uri icon

Overview

abstract

  • The advent of increasingly sophisticated imaging platforms has allowed for the visualization of the murine nervous system at single-cell resolution. However, current experimental approaches have not yet produced whole-brain maps of a comprehensive set of neuronal and nonneuronal types that approaches the cellular diversity of the mammalian cortex. Here, we aim to fill in this gap in knowledge with an open-source computational pipeline, Matrix Inversion and Subset Selection (MISS), that can infer quantitatively validated distributions of diverse collections of neural cell types at 200-μm resolution using a combination of single-cell RNA sequencing (RNAseq) and in situ hybridization datasets. We rigorously demonstrate the accuracy of MISS against literature expectations. Importantly, we show that gene subset selection, a procedure by which we filter out low-information genes prior to performing deconvolution, is a critical preprocessing step that distinguishes MISS from its predecessors and facilitates the production of cell-type maps with significantly higher accuracy. We also show that MISS is generalizable by generating high-quality cell-type maps from a second independently curated single-cell RNAseq dataset. Together, our results illustrate the viability of computational approaches for determining the spatial distributions of a wide variety of cell types from genetic data alone.

publication date

  • April 1, 2022

Research

keywords

  • Brain
  • Brain Mapping
  • Neurons

Identity

PubMed Central ID

  • PMC9168512

Scopus Document Identifier

  • 85127423668

Digital Object Identifier (DOI)

  • 10.1073/pnas.2111786119

PubMed ID

  • 35363567

Additional Document Info

volume

  • 119

issue

  • 14