In Silico Synchronization of Cellular Populations Through Expression Data Deconvolution

TL;DR

This paper introduces a deconvolution method to extract true single-cell cycle behaviors from heterogeneous population data, improving the analysis of cell-cycle-dependent processes.

Contribution

It presents a new, simple deconvolution technique that enhances biological fidelity and aids in parameter estimation for gene regulation models.

Findings

Method successfully removes asynchronous variability effects.

Improved accuracy in estimating single-cell gene regulation parameters.

Validated with preliminary experimental data.

Abstract

Cellular populations are typically heterogenous collections of cells at different points in their respective cell cycles, each with a cell cycle time that varies from individual to individual. As a result, true single-cell behavior, particularly that which is cell-cycle--dependent, is often obscured in population-level (averaged) measurements. We have developed a simple deconvolution method that can be used to remove the effects of asynchronous variability from population-level time-series data. In this paper, we summarize some recent progress in the development and application of our approach, and provide technical updates that result in increased biological fidelity. We also explore several preliminary validation results and discuss several ongoing applications that highlight the method's usefulness for estimating parameters in differential equation models of single-cell gene regulation.