Spatial charting of single-cell transcriptomes in tissues.
Journal
Nature biotechnology
ISSN: 1546-1696
Titre abrégé: Nat Biotechnol
Pays: United States
ID NLM: 9604648
Informations de publication
Date de publication:
08 2022
08 2022
Historique:
received:
03
08
2021
accepted:
25
01
2022
revised:
24
01
2022
pubmed:
23
3
2022
medline:
16
8
2022
entrez:
22
3
2022
Statut:
ppublish
Résumé
Single-cell RNA sequencing methods can profile the transcriptomes of single cells but cannot preserve spatial information. Conversely, spatial transcriptomics assays can profile spatial regions in tissue sections, but do not have single-cell resolution. Here, we developed a computational method called CellTrek that combines these two datasets to achieve single-cell spatial mapping through coembedding and metric learning approaches. We benchmarked CellTrek using simulation and in situ hybridization datasets, which demonstrated its accuracy and robustness. We then applied CellTrek to existing mouse brain and kidney datasets and showed that CellTrek can detect topological patterns of different cell types and cell states. We performed single-cell RNA sequencing and spatial transcriptomics experiments on two ductal carcinoma in situ tissues and applied CellTrek to identify tumor subclones that were restricted to different ducts, and specific T cell states adjacent to the tumor areas. Our data show that CellTrek can accurately map single cells in diverse tissue types to resolve their spatial organization.
Identifiants
pubmed: 35314812
doi: 10.1038/s41587-022-01233-1
pii: 10.1038/s41587-022-01233-1
pmc: PMC9673606
mid: NIHMS1849763
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
1190-1199Subventions
Organisme : NCI NIH HHS
ID : P30 CA016672
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA236864
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA240526
Pays : United States
Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.
Références
Lim, B., Lin, Y. & Navin, N. Advancing cancer research and medicine with single-cell genomics. Cancer Cell 37, 456–470 (2020).
pubmed: 32289270
pmcid: 7899145
doi: 10.1016/j.ccell.2020.03.008
Tanay, A. & Regev, A. Scaling single-cell genomics from phenomenology to mechanism. Nature 541, 331–338 (2017).
pubmed: 28102262
pmcid: 5438464
doi: 10.1038/nature21350
Regev, A. et al. The human cell atlas. eLife 6, e27041 (2017).
pubmed: 29206104
pmcid: 5762154
doi: 10.7554/eLife.27041
Wang, Y. & Navin, N. E. Advances and applications of single-cell sequencing technologies. Mol. Cell 58, 598–609 (2015).
pubmed: 26000845
pmcid: 4441954
doi: 10.1016/j.molcel.2015.05.005
Papalexi, E. & Satija, R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat. Rev. Immunol. 18, 35–45 (2018).
pubmed: 28787399
doi: 10.1038/nri.2017.76
Longo, S. K., Guo, M. G., Ji, A. L., Khavari, P. A. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nat. Rev. Genet. 22, 627–644 (2021).
pubmed: 34145435
doi: 10.1038/s41576-021-00370-8
Lee, J. H. Quantitative approaches for investigating the spatial context of gene expression. Wiley Interdiscip. Rev. Syst. Biol. Med. 9, e1369 (2017).
doi: 10.1002/wsbm.1369
Janiszewska, M. The microcosmos of intratumor heterogeneity: the space-time of cancer evolution. Oncogene 39, 2031–2039 (2020).
pubmed: 31784650
doi: 10.1038/s41388-019-1127-5
Stahl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).
pubmed: 27365449
doi: 10.1126/science.aaf2403
Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).
pubmed: 30923225
pmcid: 6927209
doi: 10.1126/science.aaw1219
Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-00830-w (2021).
Maaskola, J. et al. Charting tissue expression anatomy by spatial transcriptome decomposition. Preprint at bioRxiv, 362624 (2018).
Andersson, A. et al. Spatial mapping of cell types by integration of transcriptomics data. Preprint at bioRxiv, 2019.2012.2013.874495 (2019).
Andersson, A. et al. Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography. Commun Biol. 3, 565 (2020).
pubmed: 33037292
pmcid: 7547664
doi: 10.1038/s42003-020-01247-y
Kleshchevnikov, V. et al. Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomics. Preprint at bioRxiv, 2020.2011.2015.378125 (2020).
Moncada, R. et al. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat. Biotechnol. 38, 333–342 (2020).
pubmed: 31932730
doi: 10.1038/s41587-019-0392-8
Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016).
pubmed: 26727548
pmcid: 4985242
doi: 10.1038/nn.4216
Ransick, A. et al. Single-cell profiling reveals sex, lineage, and regional diversity in the mouse kidney. Dev. Cell 51, 399–413 e397 (2019).
pubmed: 31689386
pmcid: 6948019
doi: 10.1016/j.devcel.2019.10.005
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
doi: 10.1023/A:1010933404324
Nitzan, M., Karaiskos, N., Friedman, N. & Rajewsky, N. Gene expression cartography. Nature 576, 132 (2019).
pubmed: 31748748
doi: 10.1038/s41586-019-1773-3
Wilkerson, M. D. & Hayes, D. N. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 26, 1572–1573 (2010).
pubmed: 20427518
pmcid: 2881355
doi: 10.1093/bioinformatics/btq170
Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).
pubmed: 19114008
pmcid: 2631488
doi: 10.1186/1471-2105-9-559
Lohoff, T. Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis. Nat. Biotechnol. 40, 74–85 (2022).
pubmed: 34489600
doi: 10.1038/s41587-021-01006-2
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888 (2019).
pubmed: 31178118
pmcid: 6687398
doi: 10.1016/j.cell.2019.05.031
Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol 32, 381–386 (2014).
pubmed: 24658644
pmcid: 4122333
doi: 10.1038/nbt.2859
Qiu, X. J. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979 (2017) .
pubmed: 28825705
pmcid: 5764547
doi: 10.1038/nmeth.4402
Fawkner-Corbett, D. et al. Spatiotemporal analysis of human intestinal development at single-cell resolution. Cell 184, 810–826. e823 (2021).
pubmed: 33406409
pmcid: 7864098
doi: 10.1016/j.cell.2020.12.016
Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018).
pubmed: 30382198
pmcid: 6456269
doi: 10.1038/s41586-018-0654-5
Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313–319 (2021).
pubmed: 33288904
doi: 10.1038/s41587-020-0739-1
Saunders, A. et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell 174, 1015–1030.e1016 (2018).
pubmed: 30096299
pmcid: 6447408
doi: 10.1016/j.cell.2018.07.028
Lake, B. B. et al. A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys. Nat. Commun. 10, 2832 (2019).
pubmed: 31249312
pmcid: 6597610
doi: 10.1038/s41467-019-10861-2
Janosevic, D. et al. The orchestrated cellular and molecular responses of the kidney to endotoxin define a precise sepsis timeline. eLife 10, e62270 (2021).
pubmed: 33448928
pmcid: 7810465
doi: 10.7554/eLife.62270
Naray-Fejes-Toth, A., Snyder, P. M. & Fejes-Toth, G. The kidney-specific WNK1 isoform is induced by aldosterone and stimulates epithelial sodium channel-mediated Na+ transport. Proc. Natl Acad. Sci. USA 101, 17434–17439 (2004).
pubmed: 15583131
pmcid: 536044
doi: 10.1073/pnas.0408146101
Park, J. et al. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science 360, 758–763 (2018).
pubmed: 29622724
pmcid: 6188645
doi: 10.1126/science.aar2131
Jin, S. et al. Inference and analysis of cell-cell communication using CellChat. Nat. Commun. 12, 1088 (2021).
pubmed: 33597522
pmcid: 7889871
doi: 10.1038/s41467-021-21246-9
Zhao, Y. et al. Isolation and epithelial co-culture of mouse renal peritubular endothelial cells. BMC Cell Biol. 15, 40 (2014).
pubmed: 25433516
pmcid: 4260259
doi: 10.1186/s12860-014-0040-6
Miao, Z. et al. Single cell regulatory landscape of the mouse kidney highlights cellular differentiation programs and disease targets. Nat. Commun. 12, 2277 (2021).
pubmed: 33859189
pmcid: 8050063
doi: 10.1038/s41467-021-22266-1
Mantovani, A. et al. The chemokine system in diverse forms of macrophage activation and polarization. Trends Immunol. 25, 677–686 (2004).
pubmed: 15530839
doi: 10.1016/j.it.2004.09.015
Quinn, K. E., Mackie, D. I. & Caron, K. M. Emerging roles of atypical chemokine receptor 3 (ACKR3) in normal development and physiology. Cytokine 109, 17–23 (2018).
pubmed: 29903572
pmcid: 6005205
doi: 10.1016/j.cyto.2018.02.024
Gao, R. et al. Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes. Nat. Biotechnol. 39, 599–608 (2021).
pubmed: 33462507
pmcid: 8122019
doi: 10.1038/s41587-020-00795-2
Korotkevich, G. et al. Fast gene set enrichment analysis. Preprint at bioRxiv, 060012 (2021).
Casasent, A. K. et al. Multiclonal invasion in breast tumors identified by topographic single cell sequencing. Cell 172, 205 (2018) .
pubmed: 29307488
pmcid: 5766405
doi: 10.1016/j.cell.2017.12.007
Prabhakaran, S. et al. Evaluation of invasive breast cancer samples using a 12-chemokine gene expression score: correlation with clinical outcomes. Breast Cancer Res. 19, 71 (2017).
pubmed: 28629479
pmcid: 5477261
doi: 10.1186/s13058-017-0864-z
Sautes-Fridman, C., Petitprez, F., Calderaro, J. & Fridman, W. H. Tertiary lymphoid structures in the era of cancer immunotherapy. Nat. Rev. Cancer 19, 307–325 (2019).
pubmed: 31092904
doi: 10.1038/s41568-019-0144-6
Sinha, V. C. & Piwnica-Worms, H. Intratumoral heterogeneity in ductal carcinoma in situ: chaos and consequence. J Mammary Gland Biol. Neoplasia 23, 191–205 (2018).
pubmed: 30194658
pmcid: 6934090
doi: 10.1007/s10911-018-9410-6
Marusyk, A., Janiszewska, M. & Polyak, K. Intratumor heterogeneity: the Rosetta Stone of therapy resistance. Cancer Cell 37, 471–484 (2020).
pubmed: 32289271
pmcid: 7181408
doi: 10.1016/j.ccell.2020.03.007
Lennington, W. J., Jensen, R. A., Dalton, L. W. & Page, D. L. Ductal carcinoma in-situ of the breast—heterogeneity of individual lesions. Cancer 73, 118–124 (1994).
pubmed: 8275415
doi: 10.1002/1097-0142(19940101)73:1<118::AID-CNCR2820730121>3.0.CO;2-R
Ishwaran, H., Kogalur, U. B., Blackstone, E. H. & Lauer, M. S. Random survival forests. Ann. Appl. Stat. 2, 841–860 (2008).
doi: 10.1214/08-AOAS169
Hahsler, M., Piekenbrock, M. & Doran, D. dbscan: fast density-based clustering with R. J. Stat. Softw. 91, 1–30 (2019).
doi: 10.18637/jss.v091.i01
Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).
pubmed: 27207943
doi: 10.1093/bioinformatics/btw313
Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).
pubmed: 30016406
doi: 10.1093/bioinformatics/bty633
Zappia, L., Phipson, B. & Oshlack, A. Splatter: simulation of single-cell RNA sequencing data. Genome Biol. 18, 174 (2017).
pubmed: 28899397
pmcid: 5596896
doi: 10.1186/s13059-017-1305-0
Schuhmacher, D. et al. transport: computation of optimal transport plans and Wasserstein distances. R package v.0.12-2, https://cran.r-project.org/package=transport (2020).
Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).
pubmed: 22455463
pmcid: 3339379
doi: 10.1089/omi.2011.0118
Scrucca, L., Fop, M., Murphy, T. B. & Raftery, A. E. mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R. J. 8, 289–317 (2016).
pubmed: 27818791
pmcid: 5096736
doi: 10.32614/RJ-2016-021