When scientists (typically) determine “liver gene transcription, mRNA or protein level, or enzyme activity” –– the entire organ is homogenized and analyzed. Same with lung, kidney, brain, mammary or prostate gland, etc. However, each of these organs comprises multiple cell-types, many of which might not be expressing the “gene under study.” To make things even more complicated, it is very likely (in each organ) the 100 million cells of the type being studied are not behaving identically or synchronously. Human gene expression is the ultimate example of a complex system, with more than 20,000 genes orchestrating the functions of human tissues. Until now, however, we have lacked the tools to measure how genes vary in expression within individual cells of one organ or tissue over time.
Authors [see attached article] describe an intriguing method that enables the level and rate of change of expression (“RNA velocity”) to be estimated simultaneously for each gene in a single cell. This approach has considerable implications for studying cellular dynamics –– especially during disease progression and in complex processes such as embryonic development. Biologists face operational problems when trying to understand dynamic changes in gene expression that occur as cells age, differentiate, or become diseased: [a] Techniques that enable researchers to broadly measure the expression of all genes in a given cell involve destroying the cell of interest (this prohibits analysis over time and thus provides only a snapshot of gene expression). [b] Techniques that enable long-term measurements of gene expression in living cells can be used to track only a limited number of genes.
Authors show that RNA velocity — the time derivative of the gene-expression state — can be directly estimated by distinguishing between unspliced and spliced messenger RNAs (mRNAs) in common single-cell RNA-sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a time-scale of hours. Authors [see attached] validate the accuracy of RNA velocity in the neural crest lineage, (during embryonic development). They demonstrate its use on multiple published datasets and technical platforms, reveal the branching lineage tree of the developing mouse hippocampus, and examine the kinetics of transcription in human embryonic brain. It is anticipated that RNA velocity will greatly aid the analysis of developmental lineages and cellular dynamics –– particularly in humans (e.g. progression of clinical diseases)..!!
Nature 23 Aug 2o18; 560: 434–435 [editorial]
COMMENT:Thank you, Dan, for giving everyone in GEITP this heads-up. I have already asked my students to apply this new technique to a developmental model and to a disease model.