Fractional proliferation: a method to deconvolve cell population dynamics from single-cell data

Abstract

We present an integrated method that exploits extended time-lapse automated imaging to quantify dynamics of cell proliferation. Cell counts are fit with a Quiescence-Growth model that estimates rates of cell division, entry into quiescence and death. The model is constrained with rates extracted experimentally from the behavior of tracked single cells over time. We visualize the output of the analysis in Fractional Proliferation graphs, which deconvolve dynamic proliferative responses to perturbations into the relative contributions of dividing, quiescent (non-dividing) and dead cells. The method reveals that the response of “oncogene-addicted” human cancer cells to tyrosine kinase inhibitors is a composite of altered rates of division, death and entry into quiescence, challenging the notion that such cells simply ‘die’ in response to oncogene-targeted therapy.

Increased Culture Density Is Linked to Decelerated Proliferation, Prolonged G1 Phase, and Enhanced Propensity for Differentiation of Self-Renewing Human Pluripotent Stem Cells

“…The PBE framework is especially suitable for capturing single-cell events and traits (eg, division, gene expression, position in the cell cycle) and their effects on the asynchronous hPSC population. Ensemble-wide (eg, ordinary differential equation-based) models are simpler than PBEs [54,55], but do not afford high enough resolution to reflect stem cell heterogeneity. Gene regulatory network modules, which focus on single-cell phenotypic changes and not on population-level changes [56], can be embedded in the PBEs [57].…”

Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles

“…Àθ Á þ c logðmkÞ, [7] whereθ is the vector of inferred parameters, c is the number of inferred parameters in the model, m is the number of transcripts in the cluster, and k is the number of n-cell random samples for each transcript. The best model predicted two distinct regulatory states with the lowest BIC score (SI Appendix, Table S1).…”

Quantitative Approaches to Heterogeneity and Growth Variability in Cell Populations

“…Researchers noted that distribution of subclone growth rates did not significantly differ from that of the parent population, supporting the conjecture that growth variability has an epigenetic origin [18]. Such variability in growth rates may be amenable to further quantitative analysis of population dynamics with analytic tools developed in Tyson et al [19].…”

“…Variability of growth rates, among other indicators of heterogeneity in growth kinetics of individual tumours, has long been detected, but precision in quantification may have been made possible only in the past few years by methods developed by, among others, Quaranta and his group (see [12,19]). For instance, a team from Verona, Italy, quantified growth variability of tumour cell clones from a human leukaemia cell line, by cloning Molt3 cells, and measuring the growth of 201 clonal populations by microplate spectrophotometry.…”

“…Intermitotic time encompasses hours from the end of one cell division to the start of the successive one. Single cells were tracked by automated confocal microscopy collecting images at regular intervals by automated microscopy as described [19]. Intermitotic times were calculated as described [19] and fitted to an exponentially modified Gaussian (EMG) distribution http://en.wikipedia.org/wiki/Exponentially_modified_Gaussian_ distribution.…”