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==Methods of construction== {{Main|Transcriptomics technologies}} Transcriptomics is the quantitative science that encompasses the assignment of a list of strings ("reads") to the object ("transcripts" in the genome). To calculate the expression strength, the density of reads corresponding to each object is counted.<ref name="cellerinopre" /> Initially, transcriptomes were analyzed and studied using [[expressed sequence tags]] libraries and serial and cap analysis of gene expression (SAGE). Currently, the two main [[Transcriptomics technologies|transcriptomics techniques]] include [[DNA microarray]]s and [[RNA-Seq]]. Both techniques require RNA isolation through [[RNA extraction]] techniques, followed by its separation from other cellular components and enrichment of mRNA.<ref name="#9664454">{{cite book | vauthors = Bryant S, Manning DL | title = RNA Isolation and Characterization Protocols | chapter = Isolation of messenger RNA | series = Methods in Molecular Biology | volume = 86 | pages = 61β4 | date = 1998 | pmid = 9664454 | doi = 10.1385/0-89603-494-1:61 | isbn = 978-0-89603-494-5 }}</ref><ref name="#2440339">{{cite journal | vauthors = Chomczynski P, Sacchi N | title = Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction | journal = Analytical Biochemistry | volume = 162 | issue = 1 | pages = 156β9 | date = April 1987 | pmid = 2440339 | doi = 10.1016/0003-2697(87)90021-2 }}</ref> There are two general methods of inferring transcriptome sequences. One approach maps sequence reads onto a reference genome, either of the organism itself (whose transcriptome is being studied) or of a closely related species. The other approach, [[de novo transcriptome assembly|''de novo'' transcriptome assembly]], uses software to infer transcripts directly from short sequence reads and is used in organisms with genomes that are not sequenced.<ref name="scimag" /> === DNA microarrays === {{main|DNA microarray}} [[File:Affymetrix-microarray.jpg|thumb|[[DNA microarray]] used to detect gene expression in human (''left'') and mouse (''right'') samples]] The first transcriptome studies were based on [[microarray]] techniques (also known as DNA chips). Microarrays consist of thin glass layers with spots on which [[oligonucleotide]]s, known as "probes" are arrayed; each spot contains a known DNA sequence.<ref>{{Cite journal|title=Quantitative monitoring of gene expression patterns with a complementary DNA microarray|last1=Schena|first1=M.|last2=Shalon|first2=D.|date=20 October 1995|journal=Science|location=New York, N.Y. |volume=270|number=5235|pages=467β470|issn=0036-8075|pmid=7569999|last3=Davis|first3=R. W.|last4=Brown|first4=P. O.|doi = 10.1126/science.270.5235.467|bibcode = 1995Sci...270..467S|s2cid=6720459}}</ref> When performing microarray analyses, mRNA is collected from a control and an experimental sample, the latter usually representative of a disease. The RNA of interest is converted to cDNA to increase its stability and marked with [[fluorophore]]s of two colors, usually green and red, for the two groups. The cDNA is spread onto the surface of the microarray where it hybridizes with oligonucleotides on the chip and a laser is used to scan. The fluorescence intensity on each spot of the microarray corresponds to the level of gene expression and based on the color of the fluorophores selected, it can be determined which of the samples exhibits higher levels of the mRNA of interest.<ref name="microarrays" /> One microarray usually contains enough oligonucleotides to represent all known genes; however, data obtained using microarrays does not provide information about unknown genes. During the 2010s, microarrays were almost completely replaced by next-generation techniques that are based on DNA sequencing. ===RNA sequencing=== {{Main|RNA-Seq}} RNA sequencing is a [[next-generation sequencing]] technology; as such it requires only a small amount of RNA and no previous knowledge of the genome.<ref name="etymology" /> It allows for both qualitative and quantitative analysis of RNA transcripts, the former allowing discovery of new transcripts and the latter a measure of relative quantities for transcripts in a sample.<ref name="cellerino12" /> The three main steps of sequencing transcriptomes of any biological samples include RNA purification, the synthesis of an RNA or cDNA library and sequencing the library.<ref name="cellerino12">{{Harvnb|Cellerino|Sanguanini|2018|p=12}}</ref> The RNA purification process is different for short and long RNAs.<ref name="cellerino12" /> This step is usually followed by an assessment of RNA quality, with the purpose of avoiding contaminants such as DNA or technical contaminants related to sample processing. RNA quality is measured using UV spectrometry with an absorbance peak of 260 nm.<ref name="cellerino13">{{Harvnb|Cellerino|Sanguanini|2018|p=13}}</ref> RNA integrity can also be analyzed quantitatively comparing the ratio and intensity of [[28S RNA]] to [[18S RNA]] reported in the RNA Integrity Number (RIN) score.<ref name="cellerino13" /> Since mRNA is the species of interest and it represents only 3% of its total content, the RNA sample should be treated to remove rRNA and tRNA and tissue-specific RNA transcripts.<ref name="cellerino13" /> The step of library preparation with the aim of producing short cDNA fragments, begins with RNA fragmentation to transcripts in length between 50 and 300 [[base pair]]s. Fragmentation can be enzymatic (RNA [[endonuclease]]s), chemical (trismagnesium salt buffer, [[Hydrolysis|chemical hydrolysis]]) or mechanical ([[sonication]], nebulisation).<ref name="cellerino18">{{Harvnb|Cellerino|Sanguanini|2018|p=18}}</ref> [[Reverse transcription]] is used to convert the RNA templates into cDNA and three priming methods can be used to achieve it, including oligo-DT, using random primers or ligating special adaptor oligos. ===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=593β602 |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=133β45|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=1491β1498|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" />
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