Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion

Satpathy, Ansuman T.; Granja, Jeffrey M.; Yost, Kathryn E.; Qi, Yanyan; Meschi, Francesca; McDermott, Geoffrey P.; Olsen, Brett N.; Mumbach, Maxwell R.; Pierce, Sarah E.; Corces, M. Ryan; Shah, Preyas; Bell, Jason C.; Jhutty, Darisha; Nemec, Corey M.; Wang, Jean; Wang, Li; Yin, Yifeng; Giresi, Paul G.; Chang, Anne Lynn S.; Zheng, Grace; Greenleaf, William J.; Chang, Howard Y.

doi:10.1038/s41587-019-0206-z

Cited by 673 publications

(554 citation statements)

References 85 publications

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“…Sequenced cells are from bone marrow, cerebellum, heart, kidney, large intestine, liver, lung, prefrontal cortex, small intestine, spleen, testes, thymus, and whole brain, generated using a sci-ATAC-seq protocol [28]. • The PBMCs dataset is produced by the 10x Genomics Chromium (10xG) droplet-based scATAC platform and comprises 5335 cells from human peripheral blood mononuclear cells (PBMCs) [29]. There are no true cell type labels for PBMCs cells.…”

Section: Discussionmentioning

confidence: 99%

“…There are no true cell type labels for PBMCs cells. However, we used 10x Genomics Cell Ranger ATAC's [29,30] clustering labels as ground truth and performed simulation on each cluster. Although existing labels are not perfect, we included this data to evaluate how simATAC mimics features of a group of cells with similar biological characteristics from a droplet-based platform [21].…”

Section: Discussionmentioning

confidence: 99%

“…All datasets used in this study are available from NIH GEO: (1) 63 10xG sample groups from GSE129785 [29], (2) GSE99172 [8], (3) GSE74310 [38], (4) GSE65360 [39], (5) GSE68103 (GSM1647122) [12], (6) GSE68103 (GSM1647123) [12], (7) GSE112091 (series GSE112245) [40], and (8) GSE100033 (GSM2668124) [41]. The datasets used for assessing and supporting the findings of this study are available publicly: (1) a dataset of sorted human bone marrow hematopoietic cells (Buenrostro2018) [27], (2) a subset of 13 sci-ATAC-seq adult mouse tissues (Cusanovich2018) [26,28] provided by [21], and (3) human hematopoiesis II (PBMCs) [29] provided by 10xG, which are described in the Evaluation section in detail. The detailed information of all samples with cell groups and numbers are provided in the Additional file 1: Table S5.…”

Section: Methods Simatac Statistical Modellingmentioning

confidence: 99%

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simATAC: a single-cell ATAC-seq simulation framework

Ghaziani

Zhang

Wang

2020

Preprint

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Single-cell Assay for Transposase-Accessible Chromatin sequencing (scATAC-seq) identifies regulated chromatin accessibility modules at the single-cell resolution. Robust evaluation is critical to the development of scATAC-seq pipelines, which calls for reproducible datasets for benchmarking. We hereby present the simATAC framework, an R package that generates a scATAC-seq count matrix, highly resembling real scATAC-seq datasets in library size, sparsity, and averaged chromatin accessibility signals. simATAC deploys statistical functions derived from analyzing 90 real scATAC-seq cell groups to model read distributions. simATAC provides a robust and systematic approach to generate in silico scATAC-seq samples with cell labels for a comprehensive tool assessment.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methods Simatac Statistical Modellingmentioning

confidence: 99%

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simATAC: a single-cell ATAC-seq simulation framework

Ghaziani

Zhang

Wang

2020

Preprint

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“…ATAC-seq identifies accessible DNA regions by probing accessible chromatin with hyperactive mutant Tn5 transposase that inserts sequencing adapters into open regions of the genome (90). Single cell ATAC-seq (scATACseq) measures chromatin accessibility enabling marker-free identification of cell type-specific cis-and trans-regulatory elements and mapping of disease-associated enhancer activity and reconstruction of trajectories of cellular differentiation, and has been used to map gene regulation in cell-to-cell variability and rare cell phenotypes, including in healthy and malignant immune cells (61,63).…”

Section: Single-cell Atac-seqmentioning

confidence: 99%

Single-Cell Approaches to Profile the Response to Immune Checkpoint Inhibitors

et al. 2020

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Novel treatments based upon the use of immune checkpoint inhibitors have an impressive efficacy in different types of cancer. Unfortunately, most patients do not derive benefit or lasting responses, and the reasons for the lack of therapeutic success are not known. Over the past two decades, a pressing need to deeply profile either the tumor microenvironment or cells responsible for the immune response has led investigators to integrate data obtained from traditional approaches with those obtained with new, more sophisticated, single-cell technologies, including high parameter flow cytometry, single-cell sequencing and high resolution imaging. The introduction and use of these technologies had, and still have a prominent impact in the field of cancer immunotherapy, allowing delving deeper into the molecular and cellular crosstalk between cancer and immune system, and fostering the identification of predictive biomarkers of response. In this review, besides the molecular and cellular cancer-immune system interactions, we are discussing how cutting-edge single-cell approaches are helping to point out the heterogeneity of immune cells in the tumor microenvironment and in blood.

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“…marks duplicated reads and identifies transposase cut sites. Cells were filtered by TSS enrichment and unique fragments, as previously described 58 . This approach allows the detection of TSS sites without having a defined peak set.…”

Section: /37mentioning

confidence: 99%

Heme induces innate immune memory

Jentho

Novakovic

Ruiz-Moreno

et al. 2019

Preprint

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Trained immunity defines long-lasting adaptive responses of innate immunity, mediated by transcriptional and epigenetic modifications of myeloid cells. Here, we report that labile heme, an alarmin released extracellularily upon tissue damage and hemolysis, induces trained immunity in human and murine primary cells and in mice. Heme-trainning in monocytes is associated with epigenetic and transcriptional profiles that overlap to some extent with those seen in β-glucan-training, the prototype agonist of trained immunity. In sharp contrast to β -glucan training however, heme-training relied on activation of the Syk/JNK-pathway. Heme training in mice was associated with long-lasting expansion of myeloid-biased progenitor cells as well as with altered chromatin accessibility in hematopoietic stem and progenitor cells. Finally, heme-induced training was protective against bacterial sepsis in mice.In conclusion, we reveal that heme, a bona fide alarmin, induces innate immune memory regulating host response to infection.Jentho et al. 3/37

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Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion

Abstract: Understanding complex tissues requires single-cell deconstruction of gene regulation with precision and scale. Here, we assess the performance of a massively parallel droplet-based method Reprints and permissions information is available at www.nature.com/reprints.

Cited by 673 publications

References 85 publications

simATAC: a single-cell ATAC-seq simulation framework

simATAC: a single-cell ATAC-seq simulation framework

Single-Cell Approaches to Profile the Response to Immune Checkpoint Inhibitors

Heme induces innate immune memory

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