Massively parallel digital transcriptional profiling of single cells

Nature Communications, Год журнала: 2017, Номер 8(1)

Опубликована: Янв. 16, 2017

Abstract Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3′ mRNA counting tens thousands single per sample. Cell encapsulation, up 8 samples at time, takes place in ∼6 min, with ∼50% cell capture efficiency. To demonstrate system’s technical performance, we collected data from ∼250k across 29 samples. validated sensitivity and its ability detect rare populations using lines synthetic RNAs. profiled 68k peripheral blood mononuclear characterize large immune populations. Finally, used sequence variation determine host donor chimerism single-cell resolution bone marrow isolated transplant patients.

Язык: Английский

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Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq

Science, Год журнала: 2016, Номер 352(6282), С. 189 - 196

Опубликована: Апрель 7, 2016

To explore the distinct genotypic and phenotypic states of melanoma tumors, we applied single-cell RNA sequencing (RNA-seq) to 4645 single cells isolated from 19 patients, profiling malignant, immune, stromal, endothelial cells. Malignant within same tumor displayed transcriptional heterogeneity associated with cell cycle, spatial context, a drug-resistance program. In particular, all tumors harbored malignant two states, such that characterized by high levels MITF transcription factor also contained low elevated AXL kinase. Single-cell analyses suggested microenvironmental patterns, including cell-to-cell interactions. Analysis tumor-infiltrating T revealed exhaustion programs, their connection activation clonal expansion, variability across patients. Overall, begin unravel cellular ecosystem how genomics offers insights implications for both targeted immune therapies.

Язык: Английский

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Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis

Cell, Год журнала: 2015, Номер 162(1), С. 184 - 197

Опубликована: Июнь 18, 2015

Язык: Английский

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Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer

Cell, Год журнала: 2017, Номер 171(7), С. 1611 - 1624.e24

Опубликована: Ноя. 30, 2017

Язык: Английский

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Single-cell chromatin accessibility reveals principles of regulatory variation

Nature, Год журнала: 2015, Номер 523(7561), С. 486 - 490

Опубликована: Июнь 17, 2015

Язык: Английский

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The history and advances in cancer immunotherapy: understanding the characteristics of tumor-infiltrating immune cells and their therapeutic implications

Cellular and Molecular Immunology, Год журнала: 2020, Номер 17(8), С. 807 - 821

Опубликована: Июль 1, 2020

Abstract Immunotherapy has revolutionized cancer treatment and rejuvenated the field of tumor immunology. Several types immunotherapy, including adoptive cell transfer (ACT) immune checkpoint inhibitors (ICIs), have obtained durable clinical responses, but their efficacies vary, only subsets patients can benefit from them. Immune infiltrates in microenvironment (TME) been shown to play a key role development will affect outcomes patients. Comprehensive profiling tumor-infiltrating cells would shed light on mechanisms cancer–immune evasion, thus providing opportunities for novel therapeutic strategies. However, highly heterogeneous dynamic nature TME impedes precise dissection intratumoral cells. With recent advances single-cell technologies such as RNA sequencing (scRNA-seq) mass cytometry, systematic interrogation is feasible provide insights into functional diversities In this review, we outline progress particularly by focusing landmark studies characterization tumor-associated cells, summarize phenotypic connections with immunotherapy. We believe review could strengthen our understanding facilitate elucidation modulation progression, guide immunotherapies treatment.

Язык: Английский

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An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma

Cell, Год журнала: 2019, Номер 178(4), С. 835 - 849.e21

Опубликована: Июль 18, 2019

Язык: Английский

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Deep generative modeling for single-cell transcriptomics

Nature Methods, Год журнала: 2018, Номер 15(12), С. 1053 - 1058

Опубликована: Ноя. 21, 2018

Язык: Английский

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Metastatic colonization by circulating tumour cells

Nature, Год журнала: 2016, Номер 529(7586), С. 298 - 306

Опубликована: Янв. 1, 2016

Язык: Английский

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Current best practices in single‐cell RNA‐seq analysis: a tutorial

Molecular Systems Biology, Год журнала: 2019, Номер 15(6)

Опубликована: Июнь 1, 2019

Single-cell RNA-seq has enabled gene expression to be studied at an unprecedented resolution. The promise of this technology is attracting a growing user base for single-cell analysis methods. As more tools are becoming available, it increasingly difficult navigate landscape and produce up-to-date workflow analyse one's data. Here, we detail the steps typical analysis, including pre-processing (quality control, normalization, data correction, feature selection, dimensionality reduction) cell- gene-level downstream analysis. We formulate current best-practice recommendations these based on independent comparison studies. have integrated into workflow, which apply public dataset further illustrate how work in practice. Our documented case study can found https://www.github.com/theislab/single-cell-tutorial This review will serve as tutorial new entrants field, help established users update their pipelines.

Язык: Английский

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