Challenges in measuring and understanding biological noise

Abstract

Biochemical reactions are intrinsically stochastic, leading to variation in the production of mRNAs and proteins within cells. In the scientific literature, this source of variation is typically referred to as 'noise'. The observed variability in molecular phenotypes arises from a combination of processes that amplify and attenuate noise. Our ability to quantify cell-to-cell variability in numerous biological contexts has been revolutionized by recent advances in single-cell technology, from imaging approaches through to 'omics' strategies. However, defining, accurately measuring and disentangling the stochastic and deterministic components of cell-to-cell variability is challenging. In this Review, we discuss the sources, impact and function of molecular phenotypic variability and highlight future directions to understand its role.

Fig. 1. Regulatory features controlling noise

Promoter sequence, number of transcription factor (TF) binding sites (TFBS), number of transcriptional start sites (TSS), enhancer elements, RNA polymerase II (RNAPII) loading, DNA methylation, nucleosome positioning, histone modifications, Polycomb repressive complex binding, miRNAs, nuclear export of mRNA and ribosome binding are features that modulate noise.

Fig. 2. Regulation of noise forms single gene and coupled variability.

Left hand side: Noise and regulatory mechanisms that control noise lead to molecular phenotypic variability in mRNA and protein abundance. Right hand side: Structured variability can be detected across multiple levels of co-variation between genes.

Fig. 3. Variability versus mean expression relationship.

Gene expression was profiled in serum grown mESCs using (A) scRNA-Seq and (B) smFISH of selected genes. The blue line indicates the variability versus mean expression relationship as expected from a Poisson generative process. The red points in (A) represent gene-specific variability and mean expression measures calculated across single mESCs. Black points indicate these measures calculated across pool-and-split technical control samples, where variability is purely technical. Variability is plotted versus mean expression using a log-log scale. While genes in the technical samples approximately follow a Poisson trend (black points), biological cell-to-cell variability induces over-dispersion in the single-cell samples (red points). The measures of variability are: variance (first column), Fano factor (variance/mean expression, second column) and CV² (variance/mean expression squared, third column).

Fig. 4. The role of biological noise in cellular systems

(Top) From left to right: schematic of mouse embryonic development from the 4-cell stage to E4.5. Cell colours indicate gene expression strength. Heterogeneous expression at the 4-cell stage induces commitment to form extra-embryonic lineages or pluripotent cells. These pluripotent cells at E3.5 show high expression heterogeneity forming the inner cell mass (ICM). Cells rearrange to form the epiblast and primitive endoderm at E4.5. (Middle) Within a population of immune cells (e.g. dendritic cells, Th cells), a sub-population either shows higher response strength or induces the production of cytokines such as Il2 or Ifnβ. These early responders induce activation of surrounding cells via paracrine signalling and self-stimulation via autocrine signalling. (Bottom) Stochasticity in expression introduces non-genetic heterogeneity that supports the adaptation of cancerous cells. Cancer progresses to form a collection of cells with divergent expression patterns. This phenotypic heterogeneity leads to fractional killing during treatment and cancer recurrence.