Perturb-map coupled with spatial transcriptomics identifies mutation associated gene signatures in a mouse model of lung adenocarcinoma(Dataset ID: STDS0000129)

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1,051
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657
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Spots: 19,968
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Genes: 32,289
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PMID: 35290801
Summary
Visualization
Data
Analysis results
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Dataset information
Summary:
The cellular architecture of a tumor has a major impact on cancer outcome, and thus there is interest in identifying genes controlling the tumor microenvironment (TME). While CRISPR screens are helping uncover genes regulating many cell-intrinsic processes, existing approaches are suboptimal for identifying gene functions operating extracellularly or within a tissue context. To address this, we developed an approach for spatial functional genomics called Perturb-map, which utilizes protein barcodes (Pro-Code) to enable spatial detection of barcoded cells within tissue. We applied Perturb-map to knockout dozens of genes in parallel in a mouse model of lung cancer and simultaneously assessed how each knockout influenced tumor growth, histopathology, and immune composition. Additionally, we paired Perturb-map and spatial transcriptomics for unbiased molecular analysis of Pro-Code/CRISPR lesions. Our studies found in Tgfbr2 knockout lesions, the TME was converted to a fibro-mucinous state and T-cells excluded, concomitant with upregulated TGFb and TGFb-mediated stroma activation, suggesting Tgfbr2 loss on lung cancer cells increased TGFb bioavailability and enhanced its suppressive effects on the TME. These studies establish Perturb-map for functional genomics within a tissue at single cell-resolution with spatial architecture preserved.
Overall design:
Male C57BL/6J mice were injected intravenously with KP (Kras G12D, p53 deleted) cells transduced with a Pro-Code/CRISPR library targeting 35 different genes associated with tumor-immune interaction. 10X Visium spatial transcriptomics profiling was performed on 4 separate sections of mouse lung from 3 different mice. Annotations for specific genes targeted in visium spots capturing tumor cells were generated by parallel hyperion imaging on a serial section to identify the linked protein barcode.
Technology:
10x Visium
Platform:
GPL24247
Species:
Mus musculus(mm10)
Tissues:
Lung
Sex:
Male
Submission date: 2022-01-11Update date: 2022-01-17
Sample number: 4Section number: 4
DOI: To be continue
Contributors
Rose, Samuel A,Dhainaut, Maxime,Brown, Brian D
Contact: brian.brown@mssm.edu
Accessions
GEO Series Accessions: GSE193460
How to cite
  • Cite database of STOmicsDB:
    [1] Xu, Zhicheng et al. "STOmicsDB: a comprehensive database for spatial transcriptomics data sharing, analysis and visualization." Nucleic acids research vol. 52,D1 (2024): D1053-D1061. doi: 10.1093/nar/gkad933'
  • Cite visualization dataset:
    [2] Rose, Samuel A,Dhainaut, Maxime,Brown, Brian D. Perturb-map coupled with spatial transcriptomics identifies mutation associated gene signatures in a mouse model of lung adenocarcinoma[DS/OL]. STOmicsDB, 2022[2022-01-11]. https://db.cngb.org/stomics/datasets/STDS0000129/. doi: xxxxxx
    #Format: {contributors}. {title}[DS/OL]. STOmicsDB, {the year of submission data}[{submission data}]. {dataset link}. doi: {doi ID}
  • Cite original data article:
    Citation: Dhainaut, Maxime et al. “Spatial CRISPR genomics identifies regulators of the tumor microenvironment.” Cell vol. 185,7 (2022): 1223-1239.e20. doi:10.1016/j.cell.2022.02.015