mRNA-Seq whole-transcriptome analysis of a single cell

Tang, Fuchou; Bárbácioru, Cátálin; Wang, Yangzhou; Nordman, Ellen; Lee, Clarence; Xu, Nanlan; Wang, Xiaohui; Bodeau, John; Tuch, Brian B.; Siddiqui, Asim; Lao, Kaiqin; Surani, M. Azim

doi:10.1038/nmeth.1315

Cited by 3,049 publications

(2,350 citation statements)

References 29 publications

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“…This field has been propelled by remarkable technological advances for efficiently isolating single cells 28, 29, and the reduction of the detection levels of protocols for profiling gene expression 30, 31, histone marks 25, 26, or chromatin accessibility 32, 33 in individual cells. These techniques allow researchers to overcome the limitations of population studies, in which averaging the measurements over a heterogeneous population of cells masks the variability at the epigenetics and transcriptional levels among cells within a culture or tissue 26, 34, 35.…”

Section: Single‐cell Profiling Is Key For Studying Pluripotent State mentioning

confidence: 99%

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

Angarica

Sol

2016

BioEssays

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Pluripotency can be considered a functional characteristic of pluripotent stem cells (PSCs) populations and their niches, rather than a property of individual cells. In this view, individual cells within the population independently adopt a variety of different expression states, maintained by different signaling, transcriptional, and epigenetics regulatory networks. In this review, we propose that generation of integrative network models from single cell data will be essential for getting a better understanding of the regulation of self‐renewal and differentiation. In particular, we suggest that the identification of network stability determinants in these integrative models will provide important insights into the mechanisms mediating the transduction of signals from the niche, and how these signals can trigger differentiation. In this regard, the differential use of these stability determinants in subpopulation‐specific regulatory networks would mediate differentiation into different cell fates. We suggest that this approach could offer a promising avenue for the development of novel strategies for increasing the efficiency and fidelity of differentiation, which could have a strong impact on regenerative medicine.

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Section: Single‐cell Profiling Is Key For Studying Pluripotent State mentioning

confidence: 99%

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

Angarica

Sol

2016

BioEssays

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“…In the past few years a wealth of techniques have been developed to study genome-wide transcriptional output at the single-cell level [1][2][3][4][5][6][7]. In contrast to methods relying on sequencing or PCR, image-based transcriptomics visualizes single transcripts in a population of single cells in situ.…”

Section: Image-based Transcriptomics Is Unique In Several Waysmentioning

confidence: 99%

Computer vision for image-based transcriptomics

Stoeger

Battich

Herrmann

et al. 2015

Methods

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Single-cell transcriptomics has recently emerged as one of the most promising tools for understanding the diversity of the transcriptome among single cells. Image-based transcriptomics is unique compared to other methods as it does not require conversion of RNA to cDNA prior to signal amplification and transcript quantification. Thus, its efficiency in transcript detection is unmatched by other methods. In addition, image-based transcriptomics allows the study of the spatial organization of the transcriptome in single cells at single-molecule, and, when combined with superresolution microscopy, nanometer resolution. However, in order to unlock the full power of image-based transcriptomics, robust computer vision of single molecules and cells is required. Here, we shortly discuss the setup of the experimental pipeline for image-based transcriptomics, and then describe in detail the algorithms that we developed to extract, at high-throughput, robust multivariate feature sets of transcript molecule abundance, localization and patterning in tens of thousands of single cells across the transcriptome. These computer vision algorithms and pipelines can be downloaded from: https://github.com/pelkmanslab/ImageBasedTranscriptomics. AbstractSingle-cell transcriptomics has recently emerged as one of the most promising tools for

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“…In the past 6 years, five main methods have been developed and optimized to reverse transcribe the mRNA and amplify the cDNA from one single cell to achieve a better coverage and a lower cost per cell6, 7, 8, 9, 10, 11, 12, 13, 14 (Table 2). A parallel development of multiple algorithms has taken place in order to deal with the huge amount of data these new experiments have produced 15.…”

Section: Recent Development Of Single‐cell Techniquesmentioning

confidence: 99%

Single‐cell technologies to study the immune system

Proserpio

Mahata

2015

Immunology

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SummaryThe immune system is composed of a variety of cells that act in a coordinated fashion to protect the organism against a multitude of different pathogens. The great variability of existing pathogens corresponds to a similar high heterogeneity of the immune cells. The study of individual immune cells, the fundamental unit of immunity, has recently transformed from a qualitative microscopic imaging to a nearly complete quantitative transcriptomic analysis. This shift has been driven by the rapid development of multiple single‐cell technologies. These new advances are expected to boost the detection of less frequent cell types and transient or intermediate cell states. They will highlight the individuality of each single cell and greatly expand the resolution of current available classifications and differentiation trajectories. In this review we discuss the recent advancement and application of single‐cell technologies, their limitations and future applications to study the immune system.

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mRNA-Seq whole-transcriptome analysis of a single cell

Cited by 3,049 publications

References 29 publications

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

Computer vision for image-based transcriptomics

Single‐cell technologies to study the immune system

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