Spatially and functionally distinct subclasses of breast cancer-associated fibroblasts revealed by single cell RNA sequencing

Bartoschek, Michael; Oskolkov, Nikolay; Bocci, Matteo; Lövrot, John; Larsson, Christer; Sommarin, Mikael; Madsen, Chris D.; Lindgren, David; Pekár, Gyula; Karlsson, Göran; Ringnér, Markus; Bergh, Jonas; Björklund, Åsa K.; Pietras, Kristian

doi:10.1038/s41467-018-07582-3

Cited by 597 publications

(686 citation statements)

References 51 publications

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“…In breast tumors, four subset of CAFs were captured in MMTV-PyMT tumors, namely vascular CAFs (vCAF), matrix CAFs (mCAF), cycling CAFs (cCAF), and developmental CAFs (dCAF), representing a perivascular origin (vCAF and cCAF), a resident fibroblast origin (mCAF), and an EMT origin (dCAF). 186 In pancreatic cancer in contrast to normal pancreas, distinct CAFs subpopulations include a population associated with insulin-like growth factor signaling (FB1), a population expressing Ly6a/c1 (lymphocyte antigen complex locus and locus c1) and Pi16 (peptidase inhibitor 16) (FB2), and a population with mesothelial markers, including Msln (mesothelin), and Cav1, Cdh11 (cadherin 11), and Gas6 (growth arrest-specific 6) (FB3).187 Interestingly, only FB1 and FB3 persisted in pancreatic ductal adenocarcinoma (PDAC), with FB2 cells merging toward an FB1 transcriptome, and FB3 contain Acta2+ myofibroblasts as well as MHCII+ cells,187 suggesting an immune modulatory function. Similarly, CAF diversity in PDAC was reported as iCAF, myCAF, and apCAF subpopulations, with iCAF (inflammatory) characterized by interleukins and chemokines expression, myCAF (myofibroblasts) expressing Acta2 as well as other contractile and ECM remodeling proteins, and an apCAF (antigen-presenting) with MHCII expression and the capacity to present antigen to T cells in vitro.188 scRNA seq suggests there may be novel properties of CAFs reinforcing their role in immune control of cancer.…”

Section: Of Fibroblastsmentioning

confidence: 99%

Origin and functional heterogeneity of fibroblasts

2020

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The inherent plasticity and resiliency of fibroblasts make this cell type a conventional tool for basic research. But where do they come from, are all fibroblasts the same, and how do they function in disease? The first fibroblast lineages in mammalian development emerge from the ooze of primary mesenchyme during gastrulation. They are cells that efficiently create and negotiate the extracellular matrix of the mesoderm in order to migrate and meet their developmental fate. Mature fibroblasts in epithelial tissues live in the interstitial spaces between basement membranes that spatially delimit complex organ structures. While the function of resident fibroblasts in healthy tissues is largely conjecture, the accumulation of fibroblasts in pathologic lesions offers insight into biologic mechanisms that control their function; fibroblasts are poised to coordinate fibrogenesis in tissue injury, neoplasia, and aging. Here, we examine the developmental origin and plasticity of fibroblasts, their molecular and functional definitions, the epigenetic control underlying their identity and activation, and the evolution of their immune regulatory functions. These topics are reviewed through the lens of fate mapping using genetically engineered mouse models and from the perspective of single‐cell RNA sequencing. Recent observations suggest dynamic and heterogeneous functions for fibroblasts that underscore their complex molecular signatures and utility in injured tissues.

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Section: Of Fibroblastsmentioning

confidence: 99%

Origin and functional heterogeneity of fibroblasts

2020

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“…Recent studies have utilized RNA-sequencing approaches to characterize the tumor microenvironment in different types of cancer 10,14,[29][30][31][32] . In pancreatic cancer, two spatially separated and reversible subtypes of CAFs have been identified by bulk RNAseq -myofibroblasts (myCAF), located immediately adjacent to the cancer cells, and inflammatory fibroblasts (iCAF), located further away within the dense pancreatic tumor stroma 30 .…”

Section: Discussionmentioning

confidence: 99%

“…1h), suggesting that they have further diverged from NMF (as compared to pCAFs), or perhaps have a different origin altogether. Cancer cells that have undergone EMT were recently shown to contribute to the stromal milieu in mice 14 . In order to exclude their potential contribution to the sCAF population, we analyzed the bulk RNA sequencing data for lineage traces of 4T1 cancer cells (transfected plasmid reads; see Methods section for details).…”

Section: Comprehensive Mapping Of Breast Cafs Reveals Subpopulationsmentioning

confidence: 99%

“…CAFs promote cancer phenotypes including proliferation, invasion, extracellular matrix (ECM) remodeling and inflammation [4][5][6][7] , as well as chemoresistance 8 and immunosuppression 9 . Over the years different cell surface markers were shown to identify unique subpopulations of CAFs, and different origins have been suggested for CAFs, including tissue resident fibroblasts, myofibroblasts, bone-marrow (BM) derived mesenchymal stem cells (MSC), and adipocytes [10][11][12][13][14] . The indispensable roles that CAFs play in promoting malignancy and the evident existence of unique subpopulations accentuate the urgency to better understand stromal heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

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Cancer-associated fibroblast compositions change with breast-cancer progression linking S100A4 and PDPN ratios with clinical outcome

Friedman

Levi-Galibov

David

et al. 2020

Preprint

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Tumors are supported by cancer-associated fibroblasts (CAFs). CAFs are heterogeneous and carry out distinct cancer-associated functions. Understanding the full repertoire of CAFs and their dynamic changes could improve the precision of cancer treatment. CAFs are usually analyzed at a single time-point using specific markers, and it is therefore unclear whether CAFs display plasticity as tumors evolve.Here, we analyze thousands of CAFs using index and transcriptional single-cell sorting, at several time-points along breast tumor progression in mice, uncovering distinct subpopulations. Strikingly, the transcriptional programs of these subpopulations change over time and in metastases, transitioning from an immune-regulatory program to wound healing and antigen-presentation programs, indicating that CAFs and their functions are dynamic. Two main CAF subpopulations are also found in human breast tumors, where their ratio is associated with disease outcome across subtypes, and is particularly correlated with BRCA mutations in triple-negative breast cancer. These findings indicate that the repertoire of CAFs changes over time in breast cancer progression, with direct clinical implications.

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“…It demonstrates robust efficacy in capturing transcriptome-wide cell-to-cell heterogeneity with high resolution (2)(3)(4)(5). With meta information such as time series or patient histology, scRNA-seq has the potential to decipher the underlying patterns in cell cycles (6)(7)(8), complex diseases (9)(10)(11), and cancers (8,(12)(13)(14)(15)(16). A count matrix captures expression profiles with genes as rows and cells as columns, and the measurements of count as the matrix entries.…”

Section: Introductionmentioning

confidence: 99%

I-Impute: a self-consistent method to impute single cell RNA sequencing data

Feng

Chen

Wang

et al. 2019

Preprint

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Single-cell RNA-sequencing (scRNA-seq) is essential for the study of cell-specific transcriptome landscapes. The scRNA-seq techniques capture merely a small fraction of the gene due to "dropout" events. When analyzing with scRNA-seq data, the dropout events receive intensive attentions. Imputation tools are proposed to estimate the values of the dropout events and de-noise the data. To evaluate the imputation tools, researchers have developed different clustering criteria by incorporating the ground-truth cell subgroup labels. There lack measurements without cell subgroup knowledge. A reliable imputation tool should follow the "self-consistency" principle; that is, the tool reports the results only if it finds no further errors or dropouts from the data. Here, we propose "self-consistency" as an explicit evaluation criterion; also, we propose I-Impute, a "selfconsistent" method, to impute scRNA-seq data. I-Impute leverages continuous similarities and dropout probabilities and refines the data iteratively to make the final output self-consistent. On the in silico data sets, I-Impute exhibited the highest Pearson correlations for different dropout rates consistently compared with the state-of-art methods SAVER and scImpute. On the datasets of 90.87%, 70.98% and 56.65% zero rates, I-Impute exhibited the correlations as 0.78, 0.90, and 0.94, respectively, between ground truth entries and predicted values, while SAVER exhibited the correlations as 0.58, 0.79 and 0.88, respectively and scImpute exhibited correlations as 0.65, 0.86, and 0.93, respectively. Furthermore, we collected three wetlab datasets, mouse bladder cells dataset, embryonic stem cells dataset, and aortic leukocyte cells dataset, to evaluate the tools. I-Impute exhibited feasible cell subpopulation discovery efficacy on all the three datasets. It achieves the highest clustering accuracy compared with SAVER and scImpute; that is, I-Impute displayed the adjusted Rand indices of the three datasets as 0.61, 0.7, 0.52, which improved the indices of SAVER by 0.01 to 0.17, and improved the indices of scImpute by 0.19 to 0.4. Also, I-impute promoted normalized mutual information of the three datasets by 0.01 to 0.09 comparing with SAVER, and by 0.15 to 0.34 comparing with scImpute. I-Impute exhibits robust imputation ability and follows the "self-consistency" principle. It offers perspicacity to uncover the underlying cell subtypes in real scRNA-Seq data. Source code of I-Impute can be accessed at https://github.com/xikanfeng2/I-Impute.

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Spatially and functionally distinct subclasses of breast cancer-associated fibroblasts revealed by single cell RNA sequencing

Cited by 597 publications

References 51 publications

Origin and functional heterogeneity of fibroblasts

Origin and functional heterogeneity of fibroblasts

Cancer-associated fibroblast compositions change with breast-cancer progression linking S100A4 and PDPN ratios with clinical outcome

I-Impute: a self-consistent method to impute single cell RNA sequencing data

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