More on Transcriptional Noise in Aging

As a companion piece to a recent article questioning whether transcriptional noise actually exists as envisaged, this review paper covers what is known and unknown in this part of the field. Transcriptional noise is random variation in the first stage of gene expression, and it is thought to increase with age. It seems likely to be a consequence of the broad variety of changes and dysfunctions that occur in cellular biochemistry in old tissues, an accompaniment to faltering quality control of protein synthesis and altered epigenetics. While easily defined at the high level, transcriptional noise is challenging to measure in a defensible way, and hence there is a good deal of debate over quite fundamental questions relating to this topic.

Increasing stochasticity is a key feature in the aging process. At the molecular level, in addition to genome instability, a well-recognized hallmark of aging, cell-to-cell variation in gene expression was first identified in mouse hearts. With the technological breakthrough in single-cell RNA sequencing, most studies performed in recent years have demonstrated a positive correlation between cell-to-cell variation and age in human pancreatic cells, as well as mouse lymphocytes, lung cells, and muscle stem cells during senescence in vitro. This phenomenon is known as the "transcriptional noise" of aging.

In addition to the increasing evidence in experimental observations, progress also has been made to better define transcriptional noise. Traditionally, transcriptional noise is measured using simple statistical measurements, such as the coefficient of variation, Fano factor, and correlation coefficient. Recently, multiple novel methods have been proposed, e.g., global coordination level analysis, to define transcriptional noise based on network analysis of gene-to-gene coordination. However, remaining challenges include a limited number of wet-lab observations, technical noise in single-cell RNA sequencing, and the lack of a standard and/or optimal data analytical measurement of transcriptional noise. Here, we review the recent technological progress, current knowledge, and challenges to better understand transcriptional noise in aging.


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