Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise

Frontiers in Cell and Developmental Biology

Cell and Developmental Biology

Luis U. Aguilera¹  Huy D. Vo¹  Linda S. Forero-Quintero¹  Brian Munsky² 

[1] Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO, United States;Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO, United States;School of Biomedical Engineering, Colorado State University, Fort Collins, CO, United States;

关键词: single-cell;    in situ;    stochastic gene expression;    experiment design;    Fisher Information matrix;    measurement noise;    model inference;   

DOI  :  10.3389/fcell.2023.1133994

 received in 2022-12-29, accepted in 2023-05-10,  发布年份 2023

来源: Frontiers

【 摘 要 】

Introduction: Despite continued technological improvements, measurement errors always reduce or distort the information that any real experiment can provide to quantify cellular dynamics. This problem is particularly serious for cell signaling studies to quantify heterogeneity in single-cell gene regulation, where important RNA and protein copy numbers are themselves subject to the inherently random fluctuations of biochemical reactions. Until now, it has not been clear how measurement noise should be managed in addition to other experiment design variables (e.g., sampling size, measurement times, or perturbation levels) to ensure that collected data will provide useful insights on signaling or gene expression mechanisms of interest.Methods: We propose a computational framework that takes explicit consideration of measurement errors to analyze single-cell observations, and we derive Fisher Information Matrix (FIM)-based criteria to quantify the information value of distorted experiments.Results and Discussion: We apply this framework to analyze multiple models in the context of simulated and experimental single-cell data for a reporter gene controlled by an HIV promoter. We show that the proposed approach quantitatively predicts how different types of measurement distortions affect the accuracy and precision of model identification, and we demonstrate that the effects of these distortions can be mitigated through explicit consideration during model inference. We conclude that this reformulation of the FIM could be used effectively to design single-cell experiments to optimally harvest fluctuation information while mitigating the effects of image distortion.

【 授权许可】

Unknown   
Copyright © 2023 Vo, Forero-Quintero, Aguilera and Munsky.