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1.
Genome Biol ; 20(1): 193, 2019 09 09.
Artigo em Inglês | MEDLINE | ID: mdl-31500668

RESUMO

Technical variation in feature measurements, such as gene expression and locus accessibility, is a key challenge of large-scale single-cell genomic datasets. We show that this technical variation in both scRNA-seq and scATAC-seq datasets can be mitigated by analyzing feature detection patterns alone and ignoring feature quantification measurements. This result holds when datasets have low detection noise relative to quantification noise. We demonstrate state-of-the-art performance of detection pattern models using our new framework, scBFA, for both cell type identification and trajectory inference. Performance gains can also be realized in one line of R code in existing pipelines.


Assuntos
Genômica/métodos , Análise de Célula Única/métodos , Perfilação da Expressão Gênica , Modelos Genéticos , Análise de Sequência de RNA , Software
2.
BMC Bioinformatics ; 20(1): 448, 2019 Sep 02.
Artigo em Inglês | MEDLINE | ID: mdl-31477013

RESUMO

BACKGROUND: Multiplexed in-situ fluorescent imaging offers several advantages over single-cell assays that do not preserve the spatial characteristics of biological samples. This spatial information, in addition to morphological properties and extensive intracellular or surface marker profiling, comprise promising avenues for rapid advancements in the understanding of disease progression and diagnosis. As protocols for conducting such imaging experiments continue to improve, it is the intent of this study to provide and validate software for processing the large quantity of associated data in kind. RESULTS: Cytokit offers (i) an end-to-end, GPU-accelerated image processing pipeline; (ii) efficient input/output (I/O) strategies for operations specific to high dimensional microscopy; and (iii) an interactive user interface for cross filtering of spatial, graphical, expression, and morphological cell properties within the 100+ GB image datasets common to multiplexed immunofluorescence. Image processing operations supported in Cytokit are generally sourced from existing deep learning models or are at least in part adapted from open source packages to run in a single or multi-GPU environment. The efficacy of these operations is demonstrated through several imaging experiments that pair Cytokit results with those from an independent but comparable assay. A further validation also demonstrates that previously published results can be reproduced from a publicly available multiplexed image dataset. CONCLUSION: Cytokit is a collection of open source tools for quantifying and analyzing properties of individual cells in large fluorescent microscopy datasets that are often, but not necessarily, generated from multiplexed antibody labeling protocols over many fields of view or time periods. This project is best suited to bioinformaticians or other technical users that wish to analyze such data in a batch-oriented, high-throughput setting. All source code, documentation, and data generated for this article are available under the Apache License 2.0 at https://github.com/hammerlab/cytokit .


Assuntos
Biomarcadores/metabolismo , Processamento de Imagem Assistida por Computador/métodos , Microscopia de Fluorescência/métodos , Análise de Célula Única/métodos , Software , Linfócitos T/metabolismo , Tamanho Celular , Células Cultivadas , Humanos , Linfócitos T/citologia
3.
Genome Biol ; 20(1): 194, 2019 09 09.
Artigo em Inglês | MEDLINE | ID: mdl-31500660

RESUMO

BACKGROUND: Single-cell transcriptomics is rapidly advancing our understanding of the cellular composition of complex tissues and organisms. A major limitation in most analysis pipelines is the reliance on manual annotations to determine cell identities, which are time-consuming and irreproducible. The exponential growth in the number of cells and samples has prompted the adaptation and development of supervised classification methods for automatic cell identification. RESULTS: Here, we benchmarked 22 classification methods that automatically assign cell identities including single-cell-specific and general-purpose classifiers. The performance of the methods is evaluated using 27 publicly available single-cell RNA sequencing datasets of different sizes, technologies, species, and levels of complexity. We use 2 experimental setups to evaluate the performance of each method for within dataset predictions (intra-dataset) and across datasets (inter-dataset) based on accuracy, percentage of unclassified cells, and computation time. We further evaluate the methods' sensitivity to the input features, number of cells per population, and their performance across different annotation levels and datasets. We find that most classifiers perform well on a variety of datasets with decreased accuracy for complex datasets with overlapping classes or deep annotations. The general-purpose support vector machine classifier has overall the best performance across the different experiments. CONCLUSIONS: We present a comprehensive evaluation of automatic cell identification methods for single-cell RNA sequencing data. All the code used for the evaluation is available on GitHub ( https://github.com/tabdelaal/scRNAseq_Benchmark ). Additionally, we provide a Snakemake workflow to facilitate the benchmarking and to support the extension of new methods and new datasets.


Assuntos
Perfilação da Expressão Gênica/métodos , Análise de Sequência de RNA/métodos , Análise de Célula Única/métodos , Máquina de Vetores de Suporte
4.
Nat Methods ; 16(9): 875-878, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31471617

RESUMO

Single-cell RNA sequencing (scRNA-seq) data are noisy and sparse. Here, we show that transfer learning across datasets remarkably improves data quality. By coupling a deep autoencoder with a Bayesian model, SAVER-X extracts transferable gene-gene relationships across data from different labs, varying conditions and divergent species, to denoise new target datasets.


Assuntos
Neoplasias da Mama/metabolismo , Biologia Computacional/métodos , Leucócitos Mononucleares/metabolismo , Análise de Sequência de RNA/normas , Análise de Célula Única/métodos , Linfócitos T/metabolismo , Transcriptoma , Animais , Teorema de Bayes , Feminino , Perfilação da Expressão Gênica , Regulação da Expressão Gênica , Humanos , Camundongos , Análise de Sequência de RNA/métodos
5.
Chem Commun (Camb) ; 55(67): 9967-9970, 2019 Aug 15.
Artigo em Inglês | MEDLINE | ID: mdl-31367705

RESUMO

Herein, a seesaw ratiometric (SR) probe is designed which integrates fluorescence and surface enhanced Raman scattering (SERS) technology. Fluorescence imaging enables tracking of the spatiotemporal dynamic behaviour of telomerase. Meanwhile, SERS reverse ratiometric measurement can enable sensitive detection of telomerase activity in single living cells.


Assuntos
Corantes/química , Imagem Óptica/métodos , Análise de Célula Única/métodos , Análise Espectral Raman/métodos , Telomerase/metabolismo , DNA/química , Transferência Ressonante de Energia de Fluorescência/métodos , Ouro/química , Humanos , Células MCF-7 , Nanopartículas Metálicas/química
6.
Anticancer Res ; 39(8): 4011-4017, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31366482

RESUMO

BACKGROUND/AIM: Genotoxicity is the capacity of an agent to induce damage to DNA. Given the close relationship between genotoxicity and carcinogenesis, several assays have been developed for detecting genetic damage. Among them, the single-cell gel (comet) assay plays an important role for evaluating DNA damage in mammalian cells, including those of the oral cavity. The purpose of this article was to provide a critical review of the application of single-cell gel comet assay to buccal cells. MATERIAL AND METHODS: A search of the scientific literature was conducted of published studies available on single-cell gel comet assay and oral cells. RESULTS: The results showed that the majority of studies were conducted on humans, whereas few were designed for use in rodents and in vitro. CONCLUSION: Further studies within the field are relevant for better understanding the underlying mechanisms of genotoxicity in oral cells, especially since the use of humans is quite complicated due to issues of ethics.


Assuntos
Carcinogênese/genética , Dano ao DNA/genética , Boca/efeitos dos fármacos , Análise de Célula Única/métodos , Ensaio Cometa/métodos , DNA/genética , Humanos , Boca/patologia , Mucosa Bucal/metabolismo , Mucosa Bucal/patologia , Mutagênicos
7.
Genome Biol ; 20(1): 155, 2019 08 06.
Artigo em Inglês | MEDLINE | ID: mdl-31387612

RESUMO

We describe a highly sensitive, quantitative, and inexpensive technique for targeted sequencing of transcript cohorts or genomic regions from thousands of bulk samples or single cells in parallel. Multiplexing is based on a simple method that produces extensive matrices of diverse DNA barcodes attached to invariant primer sets, which are all pre-selected and optimized in silico. By applying the matrices in a novel workflow named Barcode Assembly foR Targeted Sequencing (BART-Seq), we analyze developmental states of thousands of single human pluripotent stem cells, either in different maintenance media or upon Wnt/ß-catenin pathway activation, which identifies the mechanisms of differentiation induction. Moreover, we apply BART-Seq to the genetic screening of breast cancer patients and identify BRCA mutations with very high precision. The processing of thousands of samples and dynamic range measurements that outperform global transcriptomics techniques makes BART-Seq first targeted sequencing technique suitable for numerous research applications.


Assuntos
Perfilação da Expressão Gênica/métodos , Genômica/métodos , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Análise de Sequência de RNA/métodos , Neoplasias da Mama/genética , Análise Custo-Benefício , Células-Tronco Embrionárias/metabolismo , Feminino , Perfilação da Expressão Gênica/economia , Genômica/economia , Sequenciamento de Nucleotídeos em Larga Escala/economia , Humanos , Células-Tronco Pluripotentes/metabolismo , Análise de Sequência de RNA/economia , Análise de Célula Única/economia , Análise de Célula Única/métodos , Via de Sinalização Wnt , Fluxo de Trabalho
8.
Genome Biol ; 20(1): 165, 2019 08 12.
Artigo em Inglês | MEDLINE | ID: mdl-31405383

RESUMO

To fully utilize the power of single-cell RNA sequencing (scRNA-seq) technologies for identifying cell lineages and bona fide transcriptional signals, it is necessary to combine data from multiple experiments. We present BERMUDA (Batch Effect ReMoval Using Deep Autoencoders), a novel transfer-learning-based method for batch effect correction in scRNA-seq data. BERMUDA effectively combines different batches of scRNA-seq data with vastly different cell population compositions and amplifies biological signals by transferring information among batches. We demonstrate that BERMUDA outperforms existing methods for removing batch effects and distinguishing cell types in multiple simulated and real scRNA-seq datasets.


Assuntos
Aprendizado Profundo , Perfilação da Expressão Gênica/métodos , Análise de Sequência de RNA/métodos , Algoritmos , Humanos , Leucócitos Mononucleares/metabolismo , Pâncreas/citologia , Pâncreas/metabolismo , Análise de Célula Única/métodos , Linfócitos T/metabolismo
9.
Nat Methods ; 16(9): 809-812, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31406385
10.
Nat Methods ; 16(8): 715-721, 2019 08.
Artigo em Inglês | MEDLINE | ID: mdl-31363220

RESUMO

Accurately modeling cellular response to perturbations is a central goal of computational biology. While such modeling has been based on statistical, mechanistic and machine learning models in specific settings, no generalization of predictions to phenomena absent from training data (out-of-sample) has yet been demonstrated. Here, we present scGen (https://github.com/theislab/scgen), a model combining variational autoencoders and latent space vector arithmetics for high-dimensional single-cell gene expression data. We show that scGen accurately models perturbation and infection response of cells across cell types, studies and species. In particular, we demonstrate that scGen learns cell-type and species-specific responses implying that it captures features that distinguish responding from non-responding genes and cells. With the upcoming availability of large-scale atlases of organs in a healthy state, we envision scGen to become a tool for experimental design through in silico screening of perturbation response in the context of disease and drug treatment.


Assuntos
Algoritmos , Biologia Computacional/métodos , Leucócitos Mononucleares/metabolismo , Aprendizado de Máquina , Fagócitos/metabolismo , Análise de Célula Única/métodos , Transcriptoma , Animais , Simulação por Computador , Perfilação da Expressão Gênica , Humanos , Leucócitos Mononucleares/citologia , Camundongos , Fagócitos/citologia , Especificidade da Espécie
11.
Nat Methods ; 16(8): 750-756, 2019 08.
Artigo em Inglês | MEDLINE | ID: mdl-31363221

RESUMO

The Drosophila wing disc has been a fundamental model system for the discovery of key signaling pathways and for our understanding of developmental processes. However, a complete map of gene expression in this tissue is lacking. To obtain a gene expression atlas in the wing disc, we employed single cell RNA sequencing (scRNA-seq) and developed a method for analyzing scRNA-seq data based on gene expression correlations rather than cell mapping. This enables us to compute expression maps for all detected genes in the wing disc and to discover 824 genes with spatially restricted expression patterns. This approach identifies clusters of genes with similar expression patterns and functional relevance. As proof of concept, we characterize the previously unstudied gene CG5151 and show that it regulates Wnt signaling. Our method will enable the leveraging of scRNA-seq data for generating expression atlases of undifferentiated tissues during development.


Assuntos
Proteínas de Drosophila/genética , Drosophila/genética , Embrião não Mamífero/metabolismo , Regulação da Expressão Gênica no Desenvolvimento , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Análise de Célula Única/métodos , Asas de Animais/metabolismo , Algoritmos , Animais , Drosophila/embriologia , Embrião não Mamífero/citologia , Feminino , Perfilação da Expressão Gênica , Análise de Sequência de RNA , Asas de Animais/embriologia
12.
Nat Biotechnol ; 37(8): 925-936, 2019 08.
Artigo em Inglês | MEDLINE | ID: mdl-31375813

RESUMO

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 for mapping transposase-accessible chromatin in single cells using sequencing (scATAC-seq). We apply scATAC-seq to obtain chromatin profiles of more than 200,000 single cells in human blood and basal cell carcinoma. In blood, application of scATAC-seq enables marker-free identification of cell type-specific cis- and trans-regulatory elements, mapping of disease-associated enhancer activity and reconstruction of trajectories of cellular differentiation. In basal cell carcinoma, application of scATAC-seq reveals regulatory networks in malignant, stromal and immune cells in the tumor microenvironment. Analysis of scATAC-seq profiles from serial tumor biopsies before and after programmed cell death protein 1 blockade identifies chromatin regulators of therapy-responsive T cell subsets and reveals a shared regulatory program that governs intratumoral CD8+ T cell exhaustion and CD4+ T follicular helper cell development. We anticipate that scATAC-seq will enable the unbiased discovery of gene regulatory factors across diverse biological systems.


Assuntos
Células da Medula Óssea/metabolismo , Cromatina/química , Análise de Célula Única/métodos , Linfócitos T/metabolismo , Linhagem Celular , Simulação por Computador , Regulação da Expressão Gênica , Hematopoese , Sequenciamento de Nucleotídeos em Larga Escala , Humanos , Leucócitos Mononucleares , Fatores de Transcrição/metabolismo
13.
BMC Bioinformatics ; 20(1): 388, 2019 Jul 12.
Artigo em Inglês | MEDLINE | ID: mdl-31299886

RESUMO

BACKGROUND: Single-cell RNA-sequencing technologies provide a powerful tool for systematic dissection of cellular heterogeneity. However, the prevalence of dropout events imposes complications during data analysis and, despite numerous efforts from the community, this challenge has yet to be solved. RESULTS: Here we present a computational method, called RESCUE, to mitigate the dropout problem by imputing gene expression levels using information from other cells with similar patterns. Unlike existing methods, we use an ensemble-based approach to minimize the feature selection bias on imputation. By comparative analysis of simulated and real single-cell RNA-seq datasets, we show that RESCUE outperforms existing methods in terms of imputation accuracy which leads to more precise cell-type identification. CONCLUSIONS: Taken together, these results suggest that RESCUE is a useful tool for mitigating dropouts in single-cell RNA-seq data. RESCUE is implemented in R and available at https://github.com/seasamgo/rescue .


Assuntos
Análise de Sequência de RNA/métodos , Análise de Célula Única/métodos , Software , Viés , Células/metabolismo , Simulação por Computador , Regulação da Expressão Gênica , Humanos , RNA/genética , RNA/metabolismo
14.
J Phys Chem Lett ; 10(15): 4374-4381, 2019 Aug 01.
Artigo em Inglês | MEDLINE | ID: mdl-31313926

RESUMO

Voltage imaging allows mapping of the membrane potential in living cells. Yet, current intensity-based imaging approaches are limited to relative membrane potential changes, missing important information conveyed by the absolute value of the membrane voltage. This challenge arises from various factors affecting the signal intensity, such as concentration, illumination intensity, and photobleaching. Here, we demonstrate electronic preresonance hyperspectral stimulated Raman scattering (EPR-hSRS) for spectroscopic detection of the membrane voltage using a near-infrared-absorbing microbial rhodopsin expressed in E. coli. This newly developed near-infrared active microbial rhodopsin enables electronic preresonance SRS imaging at high sensitivity. By spectral profiling, we identified voltage-sensitive SRS peaks in the fingerprint region in single E. coli cells. These spectral signatures offer a new approach for quantitation of the absolute membrane voltage in living cells.


Assuntos
Rodopsinas Microbianas/química , Análise Espectral Raman/métodos , Escherichia coli/metabolismo , Concentração de Íons de Hidrogênio , Raios Infravermelhos , Potenciais da Membrana , Mutação , Rodopsinas Microbianas/genética , Rodopsinas Microbianas/metabolismo , Análise de Célula Única/métodos
15.
Genome Biol ; 20(1): 142, 2019 07 17.
Artigo em Inglês | MEDLINE | ID: mdl-31315641

RESUMO

We develop CellSIUS (Cell Subtype Identification from Upregulated gene Sets) to fill a methodology gap for rare cell population identification for scRNA-seq data. CellSIUS outperforms existing algorithms for specificity and selectivity for rare cell types and their transcriptomic signature identification in synthetic and complex biological data. Characterization of a human pluripotent cell differentiation protocol recapitulating deep-layer corticogenesis using CellSIUS reveals unrecognized complexity in human stem cell-derived cellular populations. CellSIUS enables identification of novel rare cell populations and their signature genes providing the means to study those populations in vitro in light of their role in health and disease.


Assuntos
Análise de Célula Única/métodos , Transcriptoma , Algoritmos , Linhagem Celular , Humanos , Neurônios/citologia
16.
Nat Commun ; 10(1): 2907, 2019 07 02.
Artigo em Inglês | MEDLINE | ID: mdl-31266958

RESUMO

Single-nucleus RNA-seq (snRNA-seq) enables the interrogation of cellular states in complex tissues that are challenging to dissociate or are frozen, and opens the way to human genetics studies, clinical trials, and precise cell atlases of large organs. However, such applications are currently limited by batch effects, processing, and costs. Here, we present an approach for multiplexing snRNA-seq, using sample-barcoded antibodies to uniquely label nuclei from distinct samples. Comparing human brain cortex samples profiled with or without hashing antibodies, we demonstrate that nucleus hashing does not significantly alter recovered profiles. We develop DemuxEM, a computational tool that detects inter-sample multiplets and assigns singlets to their sample of origin, and validate its accuracy using sex-specific gene expression, species-mixing and natural genetic variation. Our approach will facilitate tissue atlases of isogenic model organisms or from multiple biopsies or longitudinal samples of one donor, and large-scale perturbation screens.


Assuntos
Anticorpos/análise , Núcleo Celular/genética , Genômica/métodos , Análise de Célula Única/métodos , Idoso , Idoso de 80 Anos ou mais , Animais , Núcleo Celular/química , Núcleo Celular/metabolismo , DNA/genética , Feminino , Humanos , Masculino , Camundongos , Camundongos Endogâmicos C57BL , Neurônios/química , Neurônios/citologia , Neurônios/metabolismo , Córtex Pré-Frontal/química , Córtex Pré-Frontal/metabolismo , RNA Mensageiro/genética , RNA Mensageiro/metabolismo
17.
Nat Protoc ; 14(8): 2370-2415, 2019 08.
Artigo em Inglês | MEDLINE | ID: mdl-31278398

RESUMO

Intelligent image-activated cell sorting (iIACS) is a machine-intelligence technology that performs real-time intelligent image-based sorting of single cells with high throughput. iIACS extends beyond the capabilities of fluorescence-activated cell sorting (FACS) from fluorescence intensity profiles of cells to multidimensional images, thereby enabling high-content sorting of cells or cell clusters with unique spatial chemical and morphological traits. Therefore, iIACS serves as an integral part of holistic single-cell analysis by enabling direct links between population-level analysis (flow cytometry), cell-level analysis (microscopy), and gene-level analysis (sequencing). Specifically, iIACS is based on a seamless integration of high-throughput cell microscopy (e.g., multicolor fluorescence imaging, bright-field imaging), cell focusing, cell sorting, and deep learning on a hybrid software-hardware data management infrastructure, enabling real-time automated operation for data acquisition, data processing, intelligent decision making, and actuation. Here, we provide a practical guide to iIACS that describes how to design, build, characterize, and use an iIACS machine. The guide includes the consideration of several important design parameters, such as throughput, sensitivity, dynamic range, image quality, sort purity, and sort yield; the development and integration of optical, microfluidic, electrical, computational, and mechanical components; and the characterization and practical usage of the integrated system. Assuming that all components are readily available, a team of several researchers experienced in optics, electronics, digital signal processing, microfluidics, mechatronics, and flow cytometry can complete this protocol in ~3 months.


Assuntos
Citometria de Fluxo/métodos , Processamento de Imagem Assistida por Computador/métodos , Redes Neurais (Computação) , Análise de Célula Única/métodos , Células Cultivadas , Humanos , Dispositivos Lab-On-A-Chip , Microalgas/citologia , Processamento de Sinais Assistido por Computador , Software
18.
Nat Protoc ; 14(8): 2571-2594, 2019 08.
Artigo em Inglês | MEDLINE | ID: mdl-31341290

RESUMO

RNase H-dependent PCR-enabled T-cell receptor sequencing (rhTCRseq) can be used to determine paired alpha/beta T-cell receptor (TCR) clonotypes in single cells or perform alpha and beta TCR repertoire analysis in bulk RNA samples. With the enhanced specificity of RNase H-dependent PCR (rhPCR), it achieves TCR-specific amplification and addition of dual-index barcodes in a single PCR step. For single cells, the protocol includes sorting of single cells into plates, generation of cDNA libraries, a TCR-specific amplification step, a second PCR on pooled sample to generate a sequencing library, and sequencing. In the bulk method, sorting and cDNA library steps are replaced with a reverse-transcriptase (RT) reaction that adds a unique molecular identifier (UMI) to each cDNA molecule to improve the accuracy of repertoire-frequency measurements. Compared to other methods for TCR sequencing, rhTCRseq has a streamlined workflow and the ability to analyze single cells in 384-well plates. Compared to TCR reconstruction from single-cell transcriptome sequencing data, it improves the success rate for obtaining paired alpha/beta information and ensures recovery of complete complementarity-determining region 3 (CDR3) sequences, a prerequisite for cloning/expression of discovered TCRs. Although it has lower throughput than droplet-based methods, rhTCRseq is well-suited to analysis of small sorted populations, especially when analysis of 96 or 384 single cells is sufficient to identify predominant T-cell clones. For single cells, sorting typically requires 2-4 h and can be performed days, or even months, before library construction and data processing, which takes ~4 d; the bulk RNA protocol takes ~3 d.


Assuntos
Reação em Cadeia da Polimerase/métodos , RNA Mensageiro/genética , Receptores de Antígenos de Linfócitos T/genética , Análise de Sequência de RNA/métodos , Análise de Célula Única/métodos , Células Cultivadas , Clonagem Molecular , Humanos , RNA Mensageiro/metabolismo , Receptores de Antígenos de Linfócitos T/metabolismo , Ribonuclease H/metabolismo , Linfócitos T/química , Linfócitos T/citologia
19.
Nat Med ; 25(8): 1280-1289, 2019 08.
Artigo em Inglês | MEDLINE | ID: mdl-31359001

RESUMO

In response to various stimuli, vascular smooth muscle cells (SMCs) can de-differentiate, proliferate and migrate in a process known as phenotypic modulation. However, the phenotype of modulated SMCs in vivo during atherosclerosis and the influence of this process on coronary artery disease (CAD) risk have not been clearly established. Using single-cell RNA sequencing, we comprehensively characterized the transcriptomic phenotype of modulated SMCs in vivo in atherosclerotic lesions of both mouse and human arteries and found that these cells transform into unique fibroblast-like cells, termed 'fibromyocytes', rather than into a classical macrophage phenotype. SMC-specific knockout of TCF21-a causal CAD gene-markedly inhibited SMC phenotypic modulation in mice, leading to the presence of fewer fibromyocytes within lesions as well as within the protective fibrous cap of the lesions. Moreover, TCF21 expression was strongly associated with SMC phenotypic modulation in diseased human coronary arteries, and higher levels of TCF21 expression were associated with decreased CAD risk in human CAD-relevant tissues. These results establish a protective role for both TCF21 and SMC phenotypic modulation in this disease.


Assuntos
Fatores de Transcrição Hélice-Alça-Hélice Básicos/genética , Doença da Artéria Coronariana/prevenção & controle , Miócitos de Músculo Liso/fisiologia , Análise de Célula Única/métodos , Animais , Fatores de Transcrição Hélice-Alça-Hélice Básicos/fisiologia , Células Cultivadas , Humanos , Camundongos , Camundongos Endogâmicos C57BL , Osteoprotegerina/genética , Fenótipo , Polimorfismo de Nucleotídeo Único , Análise de Sequência de RNA
20.
Nature ; 571(7765): 355-360, 2019 07.
Artigo em Inglês | MEDLINE | ID: mdl-31270458

RESUMO

Defining the transcriptomic identity of malignant cells is challenging in the absence of surface markers that distinguish cancer clones from one another, or from admixed non-neoplastic cells. To address this challenge, here we developed Genotyping of Transcriptomes (GoT), a method to integrate genotyping with high-throughput droplet-based single-cell RNA sequencing. We apply GoT to profile 38,290 CD34+ cells from patients with CALR-mutated myeloproliferative neoplasms to study how somatic mutations corrupt the complex process of human haematopoiesis. High-resolution mapping of malignant versus normal haematopoietic progenitors revealed an increasing fitness advantage with myeloid differentiation of cells with mutated CALR. We identified the unfolded protein response as a predominant outcome of CALR mutations, with a considerable dependency on cell identity, as well as upregulation of the NF-κB pathway specifically in uncommitted stem cells. We further extended the GoT toolkit to genotype multiple targets and loci that are distant from transcript ends. Together, these findings reveal that the transcriptional output of somatic mutations in myeloproliferative neoplasms is dependent on the native cell identity.


Assuntos
Genótipo , Mutação , Transtornos Mieloproliferativos/genética , Transtornos Mieloproliferativos/patologia , Neoplasias/genética , Neoplasias/patologia , Transcriptoma/genética , Animais , Antígenos CD34/metabolismo , Calreticulina/genética , Linhagem Celular , Proliferação de Células , Células Clonais/classificação , Células Clonais/metabolismo , Células Clonais/patologia , Endorribonucleases/metabolismo , Hematopoese/genética , Células-Tronco Hematopoéticas/classificação , Células-Tronco Hematopoéticas/metabolismo , Células-Tronco Hematopoéticas/patologia , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Humanos , Camundongos , Modelos Moleculares , Transtornos Mieloproliferativos/classificação , NF-kappa B/metabolismo , Neoplasias/classificação , Células-Tronco Neoplásicas/citologia , Células-Tronco Neoplásicas/metabolismo , Células-Tronco Neoplásicas/patologia , Mielofibrose Primária/genética , Mielofibrose Primária/patologia , Proteínas Serina-Treonina Quinases/metabolismo , Análise de Sequência de RNA/métodos , Análise de Célula Única/métodos , Resposta a Proteínas não Dobradas/genética
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