TY - JOUR TI - Pathway dynamics can delineate the sources of transcriptional noise in gene expression AU - Ham, Lucy AU - Jackson, Marcel AU - Stumpf, Michael PH A2 - Grima, Ramon A2 - Wittkopp, Patricia J A2 - Grima, Ramon VL - 10 PY - 2021 DA - 2021/10/12 SP - e69324 C1 - eLife 2021;10:e69324 DO - 10.7554/eLife.69324 UR - https://doi.org/10.7554/eLife.69324 AB - Single-cell expression profiling opens up new vistas on cellular processes. Extensive cell-to-cell variability at the transcriptomic and proteomic level has been one of the stand-out observations. Because most experimental analyses are destructive we only have access to snapshot data of cellular states. This loss of temporal information presents significant challenges for inferring dynamics, as well as causes of cell-to-cell variability. In particular, we typically cannot separate dynamic variability from within cells (‘intrinsic noise’) from variability across the population (‘extrinsic noise’). Here, we make this non-identifiability mathematically precise, allowing us to identify new experimental set-ups that can assist in resolving this non-identifiability. We show that multiple generic reporters from the same biochemical pathways (e.g. mRNA and protein) can infer magnitudes of intrinsic and extrinsic transcriptional noise, identifying sources of heterogeneity. Stochastic simulations support our theory, and demonstrate that ‘pathway-reporters’ compare favourably to the well-known, but often difficult to implement, dual-reporter method. KW - gene expression KW - stochasticity KW - extrinsic noise KW - noise decomposition JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -