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Related Concept Videos

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
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Single-Cell Sequencing Methodologies: From Transcriptome to Multi-Dimensional Measurement.

Yingwen Chen1, Jia Song2, Qingyu Ruan1

  • 1The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, The Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China.

Small Methods
|December 20, 2021
PubMed
Summary
This summary is machine-generated.

This review covers advanced single-cell sequencing methods, including transcriptome, spatial, and multi-omics approaches. It highlights the importance of integrative analysis and computational tools for understanding cellular diversity and disease.

Keywords:
multi-dimensional sequencingsingle cellssingle-cell RNA sequencing

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Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • Cells are fundamental biological units with unique molecular characteristics and developmental paths.
  • Studying single cells is crucial for understanding cellular diversity, disease mechanisms, and organismal organization.
  • Single-cell RNA sequencing (scRNA-seq) offers high-resolution insights into gene expression and cellular dynamics.

Purpose of the Study:

  • To provide a comprehensive review of cutting-edge single-cell sequencing technologies.
  • To discuss the necessity of integrative single-cell analysis across multiple dimensions (genome, epigenome, transcriptome, spatial, temporal).
  • To highlight the role of bioinformatics in interpreting single-cell data and revealing differential gene expression.

Main Methods:

  • Review of single-cell transcriptome sequencing (scRNA-seq) methodologies.
  • Discussion of spatial and temporal transcriptome profiling techniques.
  • Exploration of multi-omics sequencing approaches.
  • Summary of computational methods for scRNA-seq data analysis.

Main Results:

  • Single-cell analysis provides unprecedented resolution for dissecting cellular heterogeneity.
  • Cellular heterogeneity exists across multiple dimensions, necessitating integrative approaches.
  • Bioinformatic tools are critical for accurate interpretation of complex single-cell sequencing data.
  • The review summarizes current methodologies and their applications.

Conclusions:

  • Integrative single-cell analysis is essential for a holistic understanding of biological systems.
  • Advancements in sequencing and computational methods are driving progress in the field.
  • Addressing current challenges and future perspectives are crucial for the advancement of single-cell research.