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Open AccessJournal ArticleDOI

Single‐cell RNA sequencing technologies and applications: A brief overview

TLDR
In this article , the authors provide a concise overview about the scRNA-seq technology, experimental and computational procedures for transforming the biological and molecular processes into computational and statistical data, and highlight a few examples on how scRNAseq can provide unique information for better understanding health and diseases.
Abstract
Single-cell RNA sequencing (scRNA-seq) technology has become the state-of-the-art approach for unravelling the heterogeneity and complexity of RNA transcripts within individual cells, as well as revealing the composition of different cell types and functions within highly organized tissues/organs/organisms. Since its first discovery in 2009, studies based on scRNA-seq provide massive information across different fields making exciting new discoveries in better understanding the composition and interaction of cells within humans, model animals and plants. In this review, we provide a concise overview about the scRNA-seq technology, experimental and computational procedures for transforming the biological and molecular processes into computational and statistical data. We also provide an explanation of the key technological steps in implementing the technology. We highlight a few examples on how scRNA-seq can provide unique information for better understanding health and diseases. One important application of the scRNA-seq technology is to build a better and high-resolution catalogue of cells in all living organism, commonly known as atlas, which is key resource to better understand and provide a solution in treating diseases. While great promises have been demonstrated with the technology in all areas, we further highlight a few remaining challenges to be overcome and its great potentials in transforming current protocols in disease diagnosis and treatment.

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Citations
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Ushering in a new era of single-cell transcriptomics in bacteria

TL;DR: A short review of bacterial scRNA-seq approaches can be found in this article , where a spatial transcriptomics approach based on multiplexed in situ hybridization (parseqFISH) is proposed.
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The Multi-Dimensional Biomarker Landscape in Cancer Immunotherapy

TL;DR: How some of the most widely used conventional technologies and biomarkers may be useful for the purpose of predicting immunotherapy outcomes in patients are summarized, and their shortcomings are discussed.
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The Analysis of the Human Megakaryocyte and Platelet Coding Transcriptome in Healthy and Diseased Subjects

TL;DR: Bulk and single-cell RNA-seq studies that have aimed to characterize the coding transcriptome of healthy megakaryocytes and platelets in humans, and how these methods can be applied in the field of inherited platelet disorders for gene discovery and for unraveling novel disease mechanisms using RNA from platelets and megakARYocytes and rare disease bioinformatics are illustrated.
Journal ArticleDOI

Research progress of single-cell transcriptome sequencing in autoimmune diseases and autoinflammatory disease: A review.

TL;DR: Wang et al. as mentioned in this paper described the principles of single-cell transcriptome sequencing (scRNA-seq) and introduced common sequencing platforms and practical procedures, and focused on the progress of scRNAseq in 41 autoimmune diseases, which include 9 systemic autoimmune diseases and autoinflammatory diseases (rheumatoid arthritis, systemic lupus erythematosus, etc.) and 32 organ-specific autoimmune diseases (5 Skin diseases, 3 Nervous system diseases, 4 Eye diseases, 2 Respiratory system disease, 2 Circulatory system diseases).
References
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Journal ArticleDOI

Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

TL;DR: The survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
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Comprehensive Integration of Single-Cell Data.

TL;DR: A strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities.
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GSVA: gene set variation analysis for microarray and RNA-seq data.

TL;DR: This work introduces Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner and constitutes a starting point to build pathway-centric models of biology.
Journal ArticleDOI

In situ click chemistry generation of cyclooxygenase-2 inhibitors

TL;DR: In situ click chemistry is used to develop COX-2 specific inhibitors with high in vivo anti-inflammatory activity, significantly higher than that of widely used selective cyclooxygenase-2 inhibitors.
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What are the applications of scRNA?

The applications of scRNA-seq include unraveling the heterogeneity and complexity of RNA transcripts within individual cells, revealing the composition of different cell types and functions, and building a high-resolution catalogue of cells in all living organisms.