High Resolution Slide-seqV2 Spatial Transcriptomics Enables Discovery of Disease-Specific Cell Neighborhoods and Pathways
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA785857 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR17115500.fastq.gz | Fastqsanger.gz | |||
| SRR17115501.fastq.gz | Fastqsanger.gz | |||
| SRR17115502.fastq.gz | Fastqsanger.gz | |||
| SRR17115503.fastq.gz | Fastqsanger.gz | |||
| SRR17115504.fastq.gz | Fastqsanger.gz |
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Project description:High resolution spatial transcriptomics is a transformative technology that enables mapping of RNA expression directly from intact tissue sections; however, its utility for the elucidation of disease processes and therapeutically actionable pathways remain largely unexplored. Here we applied Slide-seqV2 to mouse and human kidneys, in healthy and in distinct disease paradigms. First, we established the feasibility of Slide-seqV2 in human kidney by analyzing tissue from 9 distinct donors, which revealed a cell neighborhood centered around a population of LYVE1+ macrophages. Second, in a mouse model of diabetic kidney disease, we detected changes in the cellular organization of the spatially-restricted kidney filter and blood flow regulating apparatus. Third, in a mouse model of a toxic proteinopathy, we identified previously unknown, disease-specific cell neighborhoods centered around macrophages. In a spatially-restricted subpopulation of epithelial cells, we also found perturbations in 77 genes associated with the unfolded protein response (UPR), including Tmed9. Treatment with a TMED9-targeting compound showed efficient removal of toxic mutant proteins and reversal of the UPR. Our studies illustrate and experimentally validate the utility of Slide-seqV2 for the discovery of disease-specific cell neighborhoods and actionable targets.
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Project description:High-resolution spatial transcriptomics enables mapping of RNA expression directly from intact tissue sections; however, its utility for the elucidation of disease processes and therapeutically actionable pathways remains unexplored. We applied Slide-seqV2 to mouse and human kidneys, in healthy and distinct disease paradigms. First, we established the feasibility of Slide-seqV2 in tissue from nine distinct human kidneys, which revealed a cell neighborhood centered around a population of LYVE1+ macrophages. Second, in a mouse model of diabetic kidney disease, we detected changes in the cellular organization of the spatially restricted kidney filter and blood-flow-regulating apparatus. Third, in a mouse model of a toxic proteinopathy, we identified previously unknown, disease-specific cell neighborhoods centered around macrophages. In a spatially restricted subpopulation of epithelial cells, we discovered perturbations in 77 genes associated with the unfolded protein response. Our studies illustrate and experimentally validate the utility of Slide-seqV2 for the discovery of disease-specific cell neighborhoods.
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Project description:While advances in single cell genomics have helped to chart the cellular components of tumor ecosystems, it has been more challenging to characterize their specific spatial organization and functional interactions. Here, we combine single cell RNA-seq, spatial transcriptomics by Slide-seq, and in situ multiplex RNA analysis, to create a detailed spatial map of healthy and dysplastic colon cellular ecosystems and their association with disease progression. We profiled inducible genetic CRC mouse models that recapitulate key features of human CRC, assigned cell types and epithelial expression programs to spatial tissue locations in tumors, and computationally used them to identify the regional features spanning different cells in the same spatial niche. We find that tumors were organized in cellular neighborhoods, each with a distinct composition of cell subtypes, expression programs, and local cellular interactions. Comparing to scRNA-seq and Slide-seq data from human CRC, we find that both cell composition and layout features were conserved between the species, with mouse neighborhoods correlating with malignancy and clinical outcome in human patient tumors, highlighting the relevance of our findings to human disease. Our work offers a comprehensive framework that is applicable across various tissues, tumors, and disease conditions, with tools for the extrapolation of findings from experimental mouse models to human diseases.
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Project description:The utility of deep neural nets has been demonstrated for mapping hematoxylin-and-eosin (H&E) stained image features to expression of individual genes. However, these models have not been employed to discover clinically relevant spatial biomarkers. Here we develop MOSBY (Multi-Omic translation of whole slide images for Spatial Biomarker discoverY) that leverages contrastive self-supervised pretraining to extract improved H&E whole slide images features, learns a mapping between image and bulk omic profiles (RNA, DNA, and protein), and utilizes tile-level information to discover spatial biomarkers. We validate MOSBY gene and gene set predictions with spatial transcriptomic and serially-sectioned CD8 IHC image data. We demonstrate that MOSBY-inferred colocalization features have survival-predictive power orthogonal to gene expression, and enable concordance indices highly competitive with survival-trained multimodal networks. We identify and validate (1) an ER stress-associated colocalization feature as a chemotherapy-specific risk factor in lung adenocarcinoma, and (2) the colocalization of T effector cell vs cysteine signatures as a negative prognostic factor in multiple cancer indications. The discovery of clinically relevant biologically interpretable spatial biomarkers showcases the utility of the model in unraveling novel insights in cancer biology as well as informing clinical decision-making.
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Project description:[Oryza sativa Genome Oligo Set Version 1.0 was designed by Beijing Genomics Institute (BGI) and contain 60,727 70mer oligos representing both indica and japonica genomes. All oligos were designed from cDNAs, expreseed sequence tag (EST) sequences, predicted genes of BGI rice genome build and other public resources. Mapping to TIGR rice genome pseudommolecues release 2 is based on the BLAST results, if a oligo has greater than 97% identity to a gene sequence from TIGR pseudomolecules, the oligo represents that gene. Rice 30K Operon Version 1.0 -70 mer oligos. The array is in 48 pin conformation. It has 25 columns and 25 rows in each of 4 x 12 subarrays. This is the slideA of the two slide sets. Platform_catalog_number: XDEN-A Platform_coating: poly L lysine Platform_manufacture_protocol: Omnigrid 100 contact printer; TeleChem Stealth SMP3 split pins; See http://keck.med.yale.edu/dnaarrays/printing Platform_manufacturer: Keck Biotechnology Resource Lab at Yale Platform_support: glass slide Platform_technology: spotted oligonucleotide ] Series_sample_order: Sample 1-15 Slide A; Sample 16-30 Slide B Keywords: other
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Project description:Recent technological innovations have enabled the high-throughput quantification of gene expression and epigenetic regulation within individual cells, transforming our understanding of how complex tissues are constructed. Missing from these measurements, however, is the ability to routinely and easily spatially localise these profiled cells. We developed a strategy, Slide-tags, in which single nuclei within an intact tissue section are ‘tagged’ with spatial barcode oligonucleotides derived from DNA-barcoded beads with known positions. These tagged nuclei can then be used as input into a wide variety of single-nucleus profiling assays. We used Slide-tags to profile two different stages of development in the mouse brain.
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Project description:The goal of spatial transcriptome analysis is to profile the glomeruli position representing different olfactory receptors. 20 OB sections were obtained with equal spacing (130 μm) along the anterior-posterior axis. In total, two sets of 20 slides were obtained from different animals. All of the Slide-seqV2 procedures were performed as previously described
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Project description:[Oryza sativa Genome Oligo Set Version 1.0 was designed by Beijing Genomics Institute (BGI) and contain 60,727 70mer oligos representing both indica and japonica genomes. All oligos were designed from cDNAs, expreseed sequence tag (EST) sequences, predicted genes of BGI rice genome build and other public resources. Mapping to TIGR rice genome pseudommolecues release 2 is based on the BLAST results, if a oligo has greater than 97% identity to a gene sequence from TIGR pseudomolecules, the oligo represents that gene. Rice 30K Operon Version 1.0 -70 mer oligos. The array is in 48 pin conformation. It has 25 columns and 25 rows in each of 4 x 12 subarrays. This is the slideA of the two slide sets. Platform_catalog_number: XDEN-A Platform_coating: poly L lysine Platform_manufacture_protocol: Omnigrid 100 contact printer; TeleChem Stealth SMP3 split pins; See http://keck.med.yale.edu/dnaarrays/printing Platform_manufacturer: Keck Biotechnology Resource Lab at Yale Platform_support: glass slide Platform_technology: spotted oligonucleotide ] Series_sample_order: Sample 1-15 Slide A; Sample 16-30 Slide B
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