Spatial Atlas of the Mouse Central Nervous System at Molecular Resolution

Abstract

Spatially charting molecular cell types at single-cell resolution across the three-dimensional (3D) volume of the brain is critical for illustrating the molecular basis of the brain anatomy and functions. Single-cell RNA sequencing (scRNA-seq) has profiled molecular cell types in the mouse brain, but cannot capture their spatial organization. Here, we employed an in situ sequencing technique, STARmap PLUS, to map more than one million high-quality cells across the whole adult mouse brain and the spinal cord, profiling 1,022 genes at subcellular resolution with a voxel size of 194 X 194 X 345 nm in 3D. We developed computational pipelines to segment, cluster, and annotate molecularly defined cell types and tissue regions with single-cell resolution. To create a transcriptome-wide spatial atlas, we further integrated the STARmap PLUS measurements with a published scRNA-seq atlas, imputing 11,844 genes at the single-cell level. Finally, we engineered a highly expressed RNA barcoding system to delineate the tropism of a brain-wide transgene delivery tool, AAV-PHP.eB, revealing its single-cell resolved transduction efficiency across the molecular cell types and tissue regions of the whole mouse brain. Together, our datasets and annotations provide a comprehensive, high-resolution single-cell resource that integrates spatial molecular atlas, cell taxonomy, brain anatomy, and genetic manipulation accessibility of the mammalian central nervous system (CNS).

Optimizing Xenium In Situ data utility by quality assessment and best practice analysis workflows

“…Mouse brain has been extensively characterized by a wide range of classical and contemporary spatial techniques and for that reason is often used to evaluate technical performance of SRT methods. The cellular composition of the mouse brain has been elucidated through many studies using scRNA-seq 13–17 and various SRTs 7–9,18–23 , which makes it an excellent tissue for benchmarking SRT methods. We therefore set out to benchmark Xenium against available datasets from the same general region of the mouse brain, primarily isocortex.…”

“…We therefore set out to benchmark Xenium against available datasets from the same general region of the mouse brain, primarily isocortex. The imaging-based SRT datasets included the recently published high sensitivity in situ sequencing (HS-ISS) 24 , STARmap PLUS 23 , sequential smFISH and MERFISH from the Allen Institute 25 , ExSeq 22 , BaristaSeq 26 , osmFISH 18 and the Vizgen MERFISH Mouse Brain Receptor Map. For the sequencing-based SRT, we had three different publicly available Visium datasets.…”

Spatial Transcriptomics: Technical Aspects of Recent Developments and Their Applications in Neuroscience and Cancer Research

“…Manno et al [38] demonstrated the potential to create a spatial atlas of developing mouse embryos by integrating scRNA-seq data with in situ sequencing of transcripts using HybISS applying a deep-learning-based method. [88] STARmap PLUS established a spatial molecular atlas with single-cell resolution by successful segmentation from transcript annotation, [114] from which the imputed gene expression pattern of a brain section was found to be comparable to the in situ hybridization (ISH) database of Allen Mouse Brain Atlas. [115] STARmap PLUS also enables the study of engineered recombinant adeno-associated virus (rAAV) tropism across whole mouse brain regions by capturing transcripts packaged in AAVs.…”

Insights into Neurodegeneration in Parkinson's Disease from Single‐Cell and Spatial Genomics

“…78 To date, both approaches have revealed a census of cells in complex regions within the rodent and human brains, thereby revealing previously known and, as-of-yet, unexplored gradients and molecular patterns. [79][80][81][82] More recent work has aimed at defining spatially variable changes that might result from a tissuewide perturbation, such as in response to traumatic brain injury 77 and aging 83 or in the vicinity of local amyloid plaque buildup. 84 The additional advances made in computational tools to investigate and understand these data have allowed for the better assignment of cell-cell interactions, tissue organization, and differential expression within and between cell types across experimental conditions.…”

“…New “‐omic” spatial technologies broadly fall within two categories: capture‐based methods that rely on a spatially defined indexed surface to locally capture mRNA from tissue and probe‐based technologies in which molecules are tagged with fluorescent probes based on hybridization in situ 78 . To date, both approaches have revealed a census of cells in complex regions within the rodent and human brains, thereby revealing previously known and, as‐of‐yet, unexplored gradients and molecular patterns 79‐82 . More recent work has aimed at defining spatially variable changes that might result from a tissue‐wide perturbation, such as in response to traumatic brain injury 77 and aging 83 or in the vicinity of local amyloid plaque buildup 84 .…”