Single-cell transcriptional variability also known as gene expression noise is assumed advantage in adaptive response to environmental changes (Freddolino 2018, Cortijo 2019). Several studies have reported high values of gene expression noise for several genes linked with multiple pathways in prokaryotes, plants and mammalian cells (_____, Yin et al, 2009; Mantsoki et al, 2016; Riddle et al, 2018); nevertheless, the origin and function of this betweenâcell transcriptional variability is unknown.
Gene expression is one of the most regularly measured quantitative phenotypes of cells; it is generally reported as a bulk level average of the cells that belong to the same tissue with a high degree of accuracy (
https://www.nature.com/articles/nature02797,
https://www.nature.com/articles/ng1036z). In humans, bulk-level gene expression profiles have allowed characterizing the transcriptome variation between populations and identifying an association between the transcriptome and the genomic information; that association enabled to construct an extensive catalog of potential gene-expression variability causative variants (vQTL) (Durbin, 2010; Sabeti, 2007; Sajantila, 2013; Hulse, 2013).
Nowadays, the development of new single-cell RNA-seq (scRNA-seq) methods based in droplets that allow measuring the gene expression profile of thousands of cells with an unprecedented resolution (Zhang, 2018).
Gene expression noise have been associated with non-genetic founders such ageing, microenvironmental perturbations, and stochastic factors at the cellular level such the local cell density, cell size, shape and rate of proliferation (Snijder, 2009; Mitchell, 2018), as well as with genetic factors such as the promoter sequence (Jones ,2014; Murphy, 2007), gene organization in the chromosomes, transcription factors association (Sanchez, 2013), , _______ . Moreover, an outburst of the gene expression noise during development (Wernet et al, 2006; Chang et al, 2008; Pare et al, 2009; Meyer et al, 2017; Faure, 2017,Chang, Hemberg et al. 2008), and the identification of mutants in which the transcriptional variability is increased put forward that gene expression noise may be being regulated by genetic factors (Rutherford & Lindquist, 1998; Queitsch et al, 2002; Raj et al, 2010; Folta et al, 2014; Schaefer et al, 2017, Cortijo 2019).
However, these previous measurements of gene expression noise were made under several technical limitations that restricted the number of measurable cells and genes by experiment, banning the identification of subpopulations driven by non-genetic factors (Mitchel, 2018). Now, by using scRNA-seq our understanding of the origin, global distribution, and functional consequences of gene expression noise can be improved (___). scRNA-seq allow to identify cell-to-cell variation between the same type of cells, this variation often indicates a diversity of hidden functional capacities that facilitate collective behavior in tissue function and normal development, and the change of this functional diversity was suggested to be associated with disease development (Chang, Hemberg et al. 2008, Bahar Halpern, Tanami et al. 2015, Richard, Boullu et al. 2016, Mohammed, Hernando-Herraez et al. 2017).
%%%% This has not followed a specific reference, just joining ideas
- The origin and function of the gene expression noise in a highly homogeneous population of cells are unknown.
- Why identify the origin and function of the gene expression noise is important
- There are several biological and technical difficulties in estimating the 'true' gene expression variability across cells.
- What others did before
- With the new techniques of sequencing-based in droplets, it is possible to overcome these difficulties. What we did
- What we got
During multicellular organisms development, differentiation of cell types generates specific gene expression patterns for each cellular lineage; when gene expression profiles at the single-cell level are sequenced within the same tissue, these cell-type-specific genes are commonly identified as high variable genes. Research associated with the identification of highly variable genes
allowing to identify subpopulations of cells by dimensionality reduction methods.