Cell-to-cell variability in organelle abundance reveals mechanisms of organelle biogenesis

TL;DR

This study develops a stochastic modeling framework to analyze cell-to-cell variability in organelle numbers, enabling discrimination between biogenesis mechanisms and providing insights into organelle regulation.

Contribution

It introduces an exact analytical approach to distinguish different organelle biogenesis mechanisms using single-cell variability data.

Findings

Different biogenesis mechanisms produce distinct organelle number distributions.

The widely accepted model of peroxisome biogenesis is inconsistent with experimental data.

The model predicts bimodal distributions under certain conditions.

Abstract

How cells regulate the number of organelles is a fundamental question in cell biology. While decades of experimental work have uncovered four fundamental processes that regulate organelle biogenesis, namely, de novo synthesis, fission, fusion and decay, a comprehensive understanding of how these processes together control organelle abundance remains elusive. Recent fluorescence microscopy experiments allow for the counting of organelles at the single-cell level. These measurements provide information about the cell-to-cell variability in organelle abundance in addition to the mean level. Motivated by such measurements, we build upon a recent study and analyze a general stochastic model of organelle biogenesis. We compute the exact analytical expressions for the probability distribution of organelle numbers, their mean, and variance across a population of single cells. It is shown that different mechanisms of organelle biogenesis lead to distinct signatures in the distribution of organelle numbers which allows us to discriminate between these various mechanisms. By comparing our theory against published data for peroxisome abundance measurements in yeast, we show that a widely believed model of peroxisome biogenesis that involves de novo synthesis, fission, and decay is inadequate in explaining the data. Also, our theory predicts bimodality in certain limits of the model. Overall, the framework developed here can be harnessed to gain mechanistic insights into the process of organelle biogenesis.