cell type identification, example
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.
Robust Inference of Cell-to-Cell Expression Variations from Single- and K-Cell Profiling
"Environment- or signal-induced changes in gene expression have largely been assessed by measuring alterations in average expression using a population of cells. However, measuring the average alone could miss changes in cell-to-cell heterogeneity. Assessing such changes in a statistically rigorous manner has been challenging, in part due to difficulties in disentangling technical vs. biological variations in single-cell measurements. To help overcome these challenges, we have developed a flexible computational method for inferring CHPs within a cell population or across two cell populations using single-cell, k-cell, or both data types simultaneously, by obtaining measures of statistical uncertainty around the inferred heterogeneity parameters so that the significance of an observed change can be evaluated. " "Different cells can make different amounts of biomolecules such as RNA transcripts of genes. New technologies are emerging to measure the transcript level of many genes in single cells. However, accurate quantification of the biological variation from cell to cell can be challenging due to the low transcript level of many genes and the presence of substantial measurement noise. Here we present a flexible, novel computational approach to quantify biological cell-to-cell variation that can use different types of data, namely "
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).
" Advances in microfluidics have made it possible to isolate a large number of cells, and along with improvements in RNA isolation and amplification methods, it is now possible to profile the transcriptome of individual cells using next-generation sequencing technologies. Technological developments have advanced at a breathtaking speed. The first single-cell RNA sequencing (scRNA-seq) experiment was published in 2009, and the authors profiled only eight cells
1. Only 7 years later, 10X Genomics released a data set of more than 1.3 million cells
2. Thus, we are now in an era where large volumes of scRNA-seq data make it possible to provide detailed catalogues of the cells found in a sample. "
ageing, microenvironmental perturbations, 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 (), gene organization in the chromosomes, transcription factors association (), , _______ . 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; Rutherford & Lindquist, 1998; Queitsch et al, 2002; Raj et al, 2010; Folta et al, 2014; Schaefer et al, 2017, Cortijo 2019), 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 ().
The true biological variability should be readily estimated regardless what technology is used.
%%%% 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.
B-cell function related paper