The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index.
Spatial CRISPR genomics identifies regulators of the tumor microenvironment
Open accessAbstract
SUMMARY While CRISPR screens are helping uncover genes regulating many cell-intrinsic processes, existing approaches are suboptimal for identifying extracellular gene functions, particularly in the tissue context. Here, we developed an approach for spatial functional genomics called Perturb-map. We applied Perturb-map to knock out 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. Moreover, we paired Perturb-map and spatial transcriptomics for unbiased analysis of CRISPR-edited tumors. We found that in Tgfbr2 knockout tumors, the tumor microenvironment (TME) was converted to a fibro-mucinous state, and T cells excluded, concomitant with upregulated TGFβ and TGFβ-mediated fibroblast activation, indicating that TGFβ-receptor loss on cancer cells increased TGFβ bioavailability and its immunosuppressive effects on the TME. These studies establish Perturb-map for functional genomics within the tissue at single-cell resolution with spatial architecture preserved and provide insight into how TGFβ responsiveness of cancer cells can affect the TME.
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Fiji: an open-source platform for biological-image analysis
Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2019 · Cited by 66,890
Integrated analysis of multimodal single-cell data
Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zagar, M., et al. (2020) · doi:10.1101/2020.10.12.335331 · Cited by 15,508
Complex heatmaps reveal patterns and correlations in multidimensional genomic data
Gu, Z., Eils, R., & Schlesner, M. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw313 · Cited by 10,034
QuPath: Open source software for digital pathology image analysis
Bankhead, P., Loughrey, M. B., Fernández, J. A., Dombrowski, Y., McArt, D. G., Dunne, P. D., McQuaid, S., Gray, R. T., Murray, L. J., Coleman, H. G., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-17204-5 · Cited by 8,908
Understanding the tumor immune microenvironment (TIME) for effective therapy
Binnewies, M., Roberts, E. W., Kersten, K., Chan, V., Fearon, D. F., Merad, M., Coussens, L. M., Gabrilovich, D. I., Ostrand-Rosenberg, S., Hedrick, C. C., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0014-x · Cited by 6,145
From Louvain to Leiden: guaranteeing well-connected communities
Traag, V. A., Waltman, L., & van Eck, N. J. (2019) · Scientific Reports · doi:10.1038/s41598-019-41695-z · Cited by 5,635
TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells
Mariathasan, S., Turley, S. J., Nickles, D., Castiglioni, A., Yuen, K., Wang, Y., Kadel III, E. E., Koeppen, H., Astarita, J. L., Cubas, R., et al. (2018) · Nature · doi:10.1038/nature25501 · Cited by 5,400
Simple Features for R: Standardized Support for Spatial Vector Data
Pebesma, E. (2018) · The R Journal · doi:10.32614/rj-2018-009 · Cited by 4,701
Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9
Doench, J. G., Fusi, N., Sullender, M., Hegde, M., Vaimberg, E. W., Donovan, K. F., Smith, I., Tothova, Z., Wilen, C., Orchard, R., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3437 · Cited by 4,637
Cited by
Best practices for single-cell analysis across modalities
Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051
“…2 diabetes mellitus regulatory signatures 13 , dysregulated response of the innate 14 and adaptive 15 immune system against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and better understanding of immunosuppressive effects of the tumour microenvironment at spatial resolution 16 .”
BackgroundGerstberger, S., Jiang, Q., & Ganesh, K. (2023) · Cell · doi:10.1016/j.cell.2023.03.003 · Cited by 913
Past, present, and future of CRISPR genome editing technologies
Pacesa, M., Pelea, O., & Jinek, M. (2024) · Cell · doi:10.1016/j.cell.2024.01.042 · Cited by 609
Rood, J. E., Hupalowska, A., & Regev, A. (2024) · Cell · doi:10.1016/j.cell.2024.07.035 · Cited by 139
Sánchez-Paulete, A. R., Mateus-Tique, J., Mollaoglu, G., Nielsen, S. R., Marks, A., Lakshmi, A., Khan, J. A., Wilk, C. M., Pia, L., Baccarini, A., et al. (2022) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-21-1075 · Cited by 98
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Metadata
- Authors
- Dhainaut, M., Rose, S. A., Akturk, G., Wroblewska, A., Nielsen, S. R., Park, E. S., Buckup, M., Roudko, V., Pia, L., Sweeney, R., Le Berichel, J., Wilk, C. M., Bektesevic, A., Lee, B. H., Bhardwaj, N., Rahman, A. H., Baccarini, A., Gnjatic, S., Pe’er, D., Merad, M., & Brown, B. D.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2022-03
- Volume
- 185
- Issue
- 7
- Pages
- 1223-1239.e20
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W4220765071
- Cited by
- 281
- References
- 71
- FWCI
- 14.43
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Dhainaut, M., Rose, S. A., Akturk, G., Wroblewska, A., Nielsen, S. R., Park, E. S., Buckup, M., Roudko, V., Pia, L., Sweeney, R., Le Berichel, J., Wilk, C. M., Bektesevic, A., Lee, B. H., Bhardwaj, N., Rahman, A. H., Baccarini, A., Gnjatic, S., Pe’er, D., . . . Brown, B. D. (2022). Spatial CRISPR genomics identifies regulators of the tumor microenvironment. Cell. https://doi.org/10.1016/j.cell.2022.02.015
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