Single-Cell Transcriptomics of Human and Mouse Lung Cancers Reveals Conserved Myeloid Populations across Individuals and Species

Žilionis, Rapolas; Engblom, Camilla; Pfirschke, Christina; Savova, Virginia; Zemmour, David; Saatcioglu, Hatice D.; Krishnan, Indira; Maroni, Giorgia; Meyerovitz, Claire; Kerwin, Clara M.; Choi, Sun Ok; Richards, William G.; Rienzo, Assunta De; Tenen, Daniel G.; Bueno, Raphael; Levantini, Elena; Pittet, Mikaël J.; Klein, Allon M.

doi:10.1016/j.immuni.2019.03.009

Cited by 1,088 publications

(1,306 citation statements)

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“…We identified about 25% of total immune cells infiltrating BCs as CD14 + TAMs that express variable levels of CD163, underlying the phenotypic heterogeneity in TAMs. Through high‐dimensional analysis approaches, several recent reports have uncovered TAM heterogeneity at the single‐cell level in distinct tumor types, including BC, clear cell renal cell carcinoma, melanoma and lung adenocarcinoma . These studies revealed a great variety of distinct phenotypic TAM subsets that co‐exist by sharing the expression of well‐established M1 and M2 markers.…”

Section: Discussionmentioning

confidence: 99%

“…Through highdimensional analysis approaches, several recent reports have uncovered TAM heterogeneity at the single-cell level in distinct tumor types, including BC, 9,29 clear cell renal cell carcinoma, 10 melanoma 30 and lung adenocarcinoma. 11,31 These studies revealed a great variety of distinct phenotypic TAM subsets that co-exist by sharing the expression of well-established M1 and M2 markers. In agreement with our present work, these emerging studies suggest that TAMs may adapt to a variety of tumor microenvironmental clues during tumor development, by acquiring a large spectrum of states.…”

Section: Discussionmentioning

confidence: 99%

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CD163⁺ tumor‐associated macrophage accumulation in breast cancer patients reflects both local differentiation signals and systemic skewing of monocytes

et al. 2020

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Objectives The accumulation of tumor‐associated macrophages (TAMs) is correlated with poor clinical outcome, but the mechanisms governing their differentiation from circulating monocytes remain unclear in humans. Methods Using multicolor flow cytometry, we evaluated TAMs phenotype in 93 breast cancer (BC) patients. Furthermore, monocytes from healthy donors were cultured in the presence of supernatants from dilacerated primary tumors to investigate their differentiation into macrophages (MΦ) in vitro. Additionally, we used transcriptomic analysis to evaluate BC patients’ blood monocytes profiles. Results We observed that high intra‐tumor CD163‐expressing TAM density is predictive of reduced survival in BC patients. In vitro, M‐CSF, TGF‐β and VEGF from primary tumor supernatants skewed the differentiation of healthy donor blood monocytes towards CD163highCD86lowIL‐10high M2‐like MΦ that strongly suppressed CD4+ T‐cell expansion via PD‐L1 and IL‐10. In addition, blood monocytes from about 40% of BC patients displayed an altered response to in vitro stimulation, being refractory to type‐1 MΦ (M1‐MΦ) differentiation and secreting higher amounts of immunosuppressive, metastatic‐related and angiogenic cytokines. Aside from showing that monocyte transcriptome is significantly altered by the presence of BC, we also demonstrated an overall metabolic de‐activation in refractory monocytes of BC patients. In contrast, monocytes from sensitive BC patients undergoing normal M1‐MΦ differentiation showed up‐regulation of IFN‐response genes and had no signs of metabolic alteration. Conclusion Altogether, our results suggest that systemic factors skew BC patient blood monocytes towards a pro‐metastatic profile, resulting in the accumulation of further polarised CD163high TAMs resembling type‐2 MΦ (M2‐MΦ) in the local BC microenvironment. These data indicate that monitoring circulating monocytes in BC patients may provide an indication of early systemic alterations induced by cancer and, thus, be instrumental in the development of improved personalised immunotherapeutic interventions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

CD163⁺ tumor‐associated macrophage accumulation in breast cancer patients reflects both local differentiation signals and systemic skewing of monocytes

et al. 2020

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“…7b). We next examined four dendritic cell (DC) subsets collected from human lung cancers 22 and sequenced with inDrop platform. Specially, tumor-infiltrating DC2 cells have been proven to be highly heterogeneous populations 23,24 and deviated substantially from the other homogeneous cell types including DC1, LAMP3+ DC and pDC ( Fig.…”

Section: The Rogue Accurately Assesses the Purity Of Cell Populationsmentioning

confidence: 99%

ROGUE: an entropy-based universal metric for assessing the purity of single cell population

Liu

et al. 2019

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Single-cell RNA sequencing (scRNA-seq) is a versatile tool for discovering and annotating cell types and states, but the determination and annotation of cell subtypes is often subjective and arbitrary. Often, it is not even clear whether a given cluster is uniform. Here we present an entropy-based statistic, ROGUE, to accurately quantify the purity of identified cell clusters. We demonstrated that our ROGUE metric is generalizable across datasets, and enables accurate, sensitive and robust assessment of cluster purity on a wide range of simulated and real datasets. Applying this metric to fibroblast and B cell datasets, we identified additional subtypes and demonstrated the application of ROGUE-guided analyses to detect true signals in specific subpopulations. ROGUE can be applied to all tested scRNA-seq datasets, and has important implications for evaluating the quality of putative clusters, discovering pure cell subtypes and constructing comprehensive, detailed and standardized single cell atlas. Z.Z. conceived this study. C.L. and B.L. designed the -model. B.L. introduced the algorithm of ROGUE, performed the benchmark testing, analyzed the data and developed the R package. Z.L. and X.R. assisted with method development. B.L., C.L. and Z.Z. wrote the manuscript with all the authors' inputs.

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“…These would enable new cell types, which have different combinations of pathways expressed, to be compared. Since there are unique cell types to particular patient groups in the Zilionis study 10 , we trained on one patient set ( Fig. 8A) and projected cell types that the network was not trained on into the SNN-derived embedded space (Fig.…”

Section: Siamese Derived Embedding Space Retains Capacity To Distingumentioning

confidence: 99%

MapCell: Learning a comparative cell type distance metric with Siamese neural nets with applications towards cell-types identification across experimental datasets

Koh

Hoon

2019

Preprint

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Large collections of annotated single-cell RNA sequencing (scRNA-seq) experiments are being generated across different organs, conditions and organisms on different platforms.Transferring annotations from this growing database of single cell expression data to a new unannotated experimental dataset can accelerate insights into the underlying biology.There have been many approaches towards aligning and unifying such heterogeneous datasets. In our work, we recognized the need for a robust data driven distance metric to map annotation across datasets. Towards this aim, we applied a one-shot training approach, Siamese Neural Networks (SNN), to learn a distance metric that can differentiate between known annotated single cell types. Requiring only a small training set, we demonstrated that the SNN can perform predictions across different scRNAseq platforms, identify novel cell types and transfer annotations across samples.

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Single-Cell Transcriptomics of Human and Mouse Lung Cancers Reveals Conserved Myeloid Populations across Individuals and Species

Cited by 1,088 publications

References 50 publications

CD163⁺ tumor‐associated macrophage accumulation in breast cancer patients reflects both local differentiation signals and systemic skewing of monocytes

CD163⁺ tumor‐associated macrophage accumulation in breast cancer patients reflects both local differentiation signals and systemic skewing of monocytes

ROGUE: an entropy-based universal metric for assessing the purity of single cell population

MapCell: Learning a comparative cell type distance metric with Siamese neural nets with applications towards cell-types identification across experimental datasets

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Single-Cell Transcriptomics of Human and Mouse Lung Cancers Reveals Conserved Myeloid Populations across Individuals and Species

Cited by 1,088 publications

References 50 publications

CD163+ tumor‐associated macrophage accumulation in breast cancer patients reflects both local differentiation signals and systemic skewing of monocytes

CD163+ tumor‐associated macrophage accumulation in breast cancer patients reflects both local differentiation signals and systemic skewing of monocytes

ROGUE: an entropy-based universal metric for assessing the purity of single cell population

MapCell: Learning a comparative cell type distance metric with Siamese neural nets with applications towards cell-types identification across experimental datasets

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Product

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CD163⁺ tumor‐associated macrophage accumulation in breast cancer patients reflects both local differentiation signals and systemic skewing of monocytes

CD163⁺ tumor‐associated macrophage accumulation in breast cancer patients reflects both local differentiation signals and systemic skewing of monocytes