Editing Transcriptome (section)

===Single-cell transcriptomics===
{{Main|Single-cell transcriptomics}}
Transcription can also be studied at the level of individual cells by [[single-cell transcriptomics]]. Single-cell RNA sequencing (scRNA-seq) is a recently developed technique that allows the analysis of the transcriptome of single cells, including [[bacteria]].<ref name="Toledo-Arana">{{cite journal |vauthors=Toledo-Arana A, Lasa I |title=Advances in bacterial transcriptome understanding: From overlapping transcription to the excludon concept |journal=Mol Microbiol |volume=113 |issue=3 |pages=593–602 |date=March 2020 |pmid=32185833 |pmc=7154746 |doi=10.1111/mmi.14456 |url=}}</ref> With single-cell transcriptomics, subpopulations of cell types that constitute the tissue of interest are also taken into consideration.<ref>{{cite journal|last1=Kanter|first1=Itamar|last2=Kalisky|first2=Tomer|title=Single Cell Transcriptomics: Methods and Applications|journal=[[Frontiers in Oncology]]|date=10 March 2015|volume=5|pages=53|doi=10.3389/fonc.2015.00053|pmid=25806353|pmc=4354386|issn=2234-943X|doi-access=free}}</ref> This approach allows to identify whether changes in experimental samples are due to phenotypic cellular changes as opposed to proliferation, with which a specific cell type might be overexpressed in the sample.<ref>{{cite journal|url=https://www.nature.com/articles/nrg3833|title=Computational and analytical challenges in single-cell transcriptomics|journal=[[Nature Reviews Genetics]]|first1=Oliver|last1=Stegle|first2=Sarah|last2=A. Teichmann|first3=John|last3=C. Marioni|year=2015 |volume=16|issue=3|pages=133–45|doi=10.1038/nrg3833|pmid=25628217|s2cid=205486032|url-access=subscription}}</ref> Additionally, when assessing cellular progression through [[cellular differentiation|differentiation]], average expression profiles are only able to order cells by time rather than their stage of development and are consequently unable to show trends in gene expression levels specific to certain stages.<ref>{{cite journal|last1=Trapnell|first1=Cole|title=Defining cell types and states with single-cell genomics|journal=[[Genome Research]]|date=1 October 2015|volume=25|issue=10|pages=1491–1498|doi=10.1101/gr.190595.115|pmid=26430159|issn=1088-9051|pmc=4579334}}</ref> Single-cell trarnscriptomic techniques have been used to characterize rare cell populations such as [[circulating tumor cell]]s, cancer stem cells in solid tumors, and [[embryonic stem cells]] (ESCs) in mammalian [[blastocyst]]s.<ref name="kanter">{{cite journal|title=Single Cell Transcriptomics: Methods and Applications|journal=Frontiers in Oncology|year=2015|volume=5|issue=13|doi=10.3389/fonc.2015.00053|pmid=25806353|first1=Itamar|last1=Kanter|first2=Tomer|last2=Kalisky|page=53|pmc = 4354386|doi-access=free}}</ref>

Although there are no standardized techniques for single-cell transcriptomics, several steps need to be undertaken. The first step includes cell isolation, which can be performed using low- and high-throughput techniques. This is followed by a qPCR step and then single-cell RNAseq where the RNA of interest is converted into cDNA. Newer developments in single-cell transcriptomics allow for tissue and sub-cellular localization preservation through cryo-sectioning thin slices of tissues and sequencing the transcriptome in each slice. Another technique allows the visualization of single transcripts under a microscope while preserving the spatial information of each individual cell where they are expressed.<ref name="kanter" />