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Multiscale topology classifies and quantifies cell types in subcellular spatial transcriptomics [stereo-seq]

ABSTRACT: Spatial transcriptomics has the potential to transform our understanding of RNA expression in tissues. Classical array-based technologies produce multiple-cell-scale measurements requiring deconvolution to recover single cell information. However, rapid advances in subcellular measurement of RNA expression at whole-transcriptome depth necessitate a fundamentally different approach. To integrate single-cell RNA-seq data with nanoscale spatial transcriptomics, we present a topological method for automatic cell type identification (TopACT). Unlike popular decomposition approaches to multicellular resolution data, TopACT is able to pinpoint the spatial locations of individual sparsely dispersed cells without prior knowledge of cell boundaries. In extant mouse brain data, TopACT locates previously undetectable macrophages. Pairing TopACT with multiparameter persistent homology landscapes predicts immune cells forming a peripheral ring structure within kidney glomeruli in a murine model of lupus nephritis, which we experimentally validate with multiplex imaging. The proposed topological data analysis unifies multiple biological scales, from subcellular gene expression to multicellular tissue organization.

ORGANISM(S): Mus musculus

PROVIDER: GSE264321 | GEO | 2024/04/23

REPOSITORIES: GEO

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Project description:Aging significantly elevates the risk for Alzheimer’s disease (AD), contributing to the accumulation of AD pathologies, such as amyloid-β (Aβ), inflammation, and oxidative stress. The human prefrontal cortex (PFC) is highly vulnerable to the impacts of both aging and AD. Unveiling and understanding the molecular alterations in PFC associated with normal aging (NA) and AD is essential for elucidating the mechanisms of AD progression and developing novel therapeutics for this devastating disease. In this study, for the first time, we employed a cutting-edge spatial transcriptome platform, STOmics® SpaTial Enhanced Resolution Omics-sequencing (Stereo-seq), to generate the first comprehensive, subcellular resolution spatial transcriptome atlas of the human PFC from six AD cases at various neuropathological stages and six age, sex, and ethnicity matched controls. Our analyses revealed distinct transcriptional alterations across six neocortex layers, highlighted the AD-associated disruptions in laminar architecture, and identified changes in layer-to-layer interactions as AD progresses. Further, throughout the progression from NA to various stages of AD, we discovered specific genes that were significantly upregulated in neurons experiencing high stress and in nearby non-neuronal cells, compared to cells distant from the source of stress. Notably, the cell-cell interactions between the neurons under the high stress and adjacent glial cells that promote Aβ clearance and neuroprotection were diminished in AD in response to stressors compared to NA. Through cell-type specific gene co-expression analysis, we identified three modules in excitatory and inhibitory neurons associated with neuronal protection, protein dephosphorylation, and negative regulation of Aβ plaque formation. These modules negatively correlated with AD progression, indicating a reduced capacity for toxic substance clearance in AD subject samples. Moreover, we have discovered a novel transcription factor, ZNF460, that regulates all three modules, establishing it as a potential new therapeutic target for AD. Overall, utilizing the latest spatial transcriptome platform, our study developed the first transcriptome-wide atlas with subcellular resolution for assessing the molecular alterations in the human PFC due to AD. This atlas sheds light on the potential mechanisms underlying the progression from NA to AD.

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Multiscale topology classifies and quantifies cell types in subcellular spatial transcriptomics [stereo-seq]

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