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Single-cell RNA Sequencing Analysis
- October 25, 2022
- Posted by: rasa
- Category: Next Generation Sequencing
RNA sequencing (RNA-seq) is a genomic approach for the detection and quantitative
analysis of messenger RNA molecules in a biological sample and is useful for studying
cellular responses. scRNA-seq can describe RNA molecules in individual cells with high
resolution and on a genomic scale. scRNA-seq is a relatively new technology first introduced
by Tang et al. in 2009, but the cost of sequencing and limited number of protocols at the time
meant that it did not get widespread popularity until 2014.
The first, and most important, step in conducting scRNA-seq has been the effective
isolation of viable, single cells from the tissue of interest. After that isolated individual cells
are lysed to allow capture of as many RNA molecules as possible. In order to specifically
analyse polyadenylated mRNA molecules, and to avoid capturing ribosomal RNAs, poly[T]-
primers are commonly used. Analysis of non-polyadenylated mRNAs is typically more
challenging and requires specialized protocols. Next, poly[T]-primed mRNA is converted to
complementary DNA (cDNA) by a reverse transcriptase. Depending on the scRNA-seq
protocol, the reverse-transcription primers will also have other nucleotide sequences added to
them, such as adaptor sequences for detection on NGS platforms, unique molecular
identifiers to mark unequivocally a single mRNA molecule, as well as sequences to preserve
information on cellular origin. Then, amplified and tagged cDNA from every cell is pooled
and sequenced by NGS, using library preparation techniques, sequencing platforms and
genomic-alignment tools similar to those used for bulk samples.
Workflow includes most of the following steps: 1) Isolation of single cells, 2) Cell
lysis while preserving mRNA, 3) mRNA capture, 4) Reverse transcription of primed RNA
into complementary DNA (cDNA), 5) cDNA amplification, 6) Preparation of cDNA
sequencing library, 7) Pooling of sequence libraries, 8) Use of bio-informatic tools to assess
quality and variability, and 9) use of specialized tools to analyse and present the data.
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