![]() ![]() High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization. Spatially resolved, highly multiplexed RNA profiling in single cells. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Dynamics and spatial genomics of the nascent transcriptome by intron seqFISH. High-definition spatial transcriptomics for in situ tissue profiling. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Single-cell RNA-seq reveals cellular hierarchies and impaired developmental trajectories in pediatric ependymoma. Single-cell topological RNA-seq analysis reveals insights into cellular differentiation and development. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Image-based multivariate profiling of drug responses from single cells. Multidimensional drug profiling by automated microscopy. Immunofluorescence images (Abeta, GFAP, NeuN and DAPI staining) that correspond to spatial transcriptomics data were downloaded from the ‘’ page of the project ( !Synapse:syn22153884/wiki/603937) on 31 October 2020. Intestine dataset: 10x Visium spatial transcriptomics were downloaded from the GEO database with accession number GSE158328.ĪD dataset: Raw and normalized count matrix of the spatial transcriptomics were downloaded from the GEO database of the project (accession number GSE152506). PDAC dataset: Both spatial transcriptomics (including gene expressions and H&E images) and scRNA-seq datasets were downloaded from the Gene Expression Omnibus (GEO) database with accession number GSE111672. Transcript profiles and cell segmentation masks were extracted from data using the Python pipeline provided by the authors at. STARmap mouse cortex dataset: Raw data were downloaded from the project page ( ) on 2 July 2019. Nissl and DAPI stained images were provided by the authors of the seqFISH+ paper. SeqFISH+ mouse cortex dataset: Transcript data were downloaded from the GitHub page of the seqFISH+ project ( ) on 1 August 2019. MUSE enables the integration of multi-modal data to provide insights into the states, functions and organization of cells in complex biological tissues. In diseased tissues, MUSE revealed gene biomarkers for proximity to tumor region and heterogeneity of amyloid precursor protein processing across Alzheimer brain regions. MUSE identified biologically meaningful tissue subpopulations and stereotyped spatial patterning in healthy brain cortex and intestinal tissues. ![]() We apply MUSE to diverse datasets containing spatial transcriptomics (seqFISH+, STARmap or Visium) and imaging (hematoxylin and eosin or fluorescence microscopy) modalities. We demonstrate that MUSE can discover tissue subpopulations missed by either modality as well as compensate for modality-specific noise. Here we present multi-modal structured embedding (MUSE), an approach to characterize cells and tissue regions by integrating morphological and spatially resolved transcriptional data. Spatial transcriptomics enables the simultaneous measurement of morphological features and transcriptional profiles of the same cells or regions in tissues.
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