Matrix Inversion and Subset Selection (MISS): A novel pipeline for mapping of diverse cell types across the murine brain

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 with Subset Selection (MISS), that can infer quantitatively validated distributions of diverse collections of neural cell types at 200μm resolution using a combination of single-cell 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 pre-processing 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. INTRODUCTION Characterizing whole-brain distributions of neural cell types is a topic of keen interest in modern neuroanatomy, with many applications to both basic and clinical neuroscience research (Arneson et al., 2018; Fu et al., 2018, 2019; Muratore et al., 2017; Rama Rao et al., 2018; Skene et al., 2018). Advances in molecular methods for quantifying gene expression and data analytic cell clustering techniques based on morphologic or genetic profiles are enabling the mapping of mesoand microscale neuronal and nonneuronal cell type architecture at a whole-brain level (Codeluppi et al., 2018; Erö et al., 2018; Kim et al., 2017; Moffitt et al., 2018; Murakami et al., 2018; Tasic et al., 2018; Wang et al., 2018; Zeisel et al., 2018). Mammalian whole-brain cell type mapping has historically focused on neuromodulatory systems, largely because the identification of catecholamine-producing subpopulations using molecular markers is rather straightforward (Beier et al., 2015; Björklund and Dunnett, 2007; Pazos et al., 1985; Vincent and Kimura, 1992) . More recently, serial two-photon tomography (STPT) imaging of cells expressing individual cell type markers genetically tagged to green fluorescent protein (GFP) successfully mapped three important subpopulations of inhibitory GABAergic interneurons (Kim et al., 2017) and cholinergic neurons (Li et al., 2017) across the murine brain. Although the above animal laboratory techniques cover the entire brain, they are expensive and time-consuming to apply to mice, impractical to apply to larger-brained model organisms, and impossible to apply to human subjects. Recent computational work demonstrates the feasibility of using existing datasets of cell type gene expression or cell markers for mapping cells in space across the vertebrate brain. Recent work produced whole-brain maps of broad classes of neuronal and glial subpopulations at single-cell resolution using purely computational methods (Erö et al., 2018), with the limitation that the mapped cells were classed into large meta-groups, such as GABAergic versus glutamatergic neurons, rather than more specific cell types. Pioneering work mapping single-cell RNA sequencing (scRNAseq) data from aquatic flatworms and zebrafish onto in situ hybridization (ISH) expression provided a plausible route to mapping highly specified cell types in mammalian nervous systems (Achim et al., 2015; Satija et al., 2015). Others have inferred the spatial distribution of cell types by deconvolving type-specific microarray expression profiles from spatially realized ISH expression data (Grange et al., 2014). In theory, using a matrix-inversion-based approach provides a better estimate of cell density than correlation-based mapping, because cell types with highly similar gene expression profiles will necessarily have highly correlated spatial profiles. However, the original work pioneering matrix inversion for cell type mapping provided no external quantitative validation of their maps. We hypothesized that, with several modifications, the approach of deconvolving ISH data into cell type densities using recently available scRNAseq data could create voxel-level cell type maps that could be externally validated both qualitatively and quantitatively. We present an information-theoretic computational pipeline that can supply per-voxel estimates of cell densities of distinct and highly specified cell types across the whole murine brain, which is the best characterized mammalian nervous system at a molecular level. After deriving matrices of type-specific expression profiles from scRNAseq data (AIBS, 2018; Tasic et al., 2018; Zeisel et al., 2018) and the matrix of spatial gene expression information from the Allen Institute for Brain Science (AIBS) ISH atlas (Lein et al., 2007), we then solve a linear system of equations for cell type density per voxel using a nonnegative least-squares algorithm, like prior approaches (Grange et al., 2014). Two key methodological innovations make the present maps possible. First, we hypothesize mapping accuracy will improve dramatically if we filter out all “low-information” genes prior to matrix inversion. Although using only a subset of the genes runs counter to similar prior approaches (Andersson et al., 2020; Grange et al., 2014), we suspected the inclusion of genes that are lowly expressed or poorly differential across cell types would deteriorate the quality of the resulting maps. Therefore, we developed a novel information-theoretic algorithm, Minimal Redundancy – Maximum Relevance – Minimum Residual (MRx3), selecting an optimal subset of genes to capture cell densities across the mouse brain (see Methods for details) and used it to perform subset selection prior to matrix inversion. Second, we formulated several objective metrics to assess the biological accuracy of cell type maps, relying in part upon regional quantification of individual cell types where available (Kim et al., 2017). We further evaluated the generalizability of our approach by mapping a larger, more widely sampled, and independently collected scRNAseq dataset (Zeisel et al., 2018). Overall, we demonstrate that both MRx3-based subset selection and matrix inversion are necessary to achieve superior cell type maps as compared with correlation-based methods and deconvolution/inversion methods without subsetting (Grange et al., 2014). All cell-type-specific maps across both scRNAseq datasets are available for download along with this methodological pipeline, which we call Matrix Inversion with Subset Selection (MISS). The MISS pipeline is designed to project any arbitrary single-cell expression data onto any arbitrary spatial expression atlas and can be applied to other brains, such as those from macaques and humans. The aim of the current paper is to demonstrate the accuracy of the present maps and the importance of gene subset selection with data-driven approaches such as our MRx3 algorithm using mouse data where quality assessments are tractable.