Robust Approximate Characterization of Single-Cell Heterogeneity in Microbial Growth

TL;DR

This paper introduces a method to approximate microbial cell cycle time distributions from microscopy data without tracking, using Bayesian Synthetic Likelihood, effective even at low temporal resolution where traditional tracking fails.

Contribution

It presents a novel approach to estimate cell cycle distributions without tracking, leveraging stochastic modeling and Bayesian inference at low temporal resolution.

Findings

High-quality approximation at low temporal resolution

Effective inference without cell tracking

Robust to reduced data sampling

Abstract

Live-cell microscopy allows to go beyond measuring average features of cellular populations to observe, quantify and explain biological heterogeneity. Deep Learning-based instance segmentation and cell tracking form the gold standard analysis tools to process the microscopy data collected, but tracking in particular suffers severely from low temporal resolution. In this work, we show that approximating cell cycle time distributions in microbial colonies of C. glutamicum is possible without performing tracking, even at low temporal resolution. To this end, we infer the parameters of a stochastic multi-stage birth process model using the Bayesian Synthetic Likelihood method at varying temporal resolutions by subsampling microscopy sequences, for which ground truth tracking is available. Our results indicate, that the proposed approach yields high quality approximations even at very low temporal resolution, where tracking fails to yield reasonable results.