Background High-throughput time-lapse microscopy has allowed researchers to monitor individual cells as they grow into colonies and react to treatments, but a deeper understanding of the data obtained after image analysis is still lacking. This is in part due to the biological and computational challenges related to long-running experiments and single-cell tracking. Methods Clonal Variability Simulator (CloVarS) is a Python tool for generating synthetic data of single-cell lineage trees to model time-lapse microscopy experiments. After colony initialization, each individual cell is simulated for a given number of simulation frames. During simulation, cells can migrate, enter mitosis (divide), and enter apoptosis (die). These events are determined by distributions of cell division and death times, which can be inferred from and fit to experimental data. Colonies have an adjustable mother-daughter (MD) and sister-sister (SisSis) fitness memory (<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44330_2025_33_Article_IEq1.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="23" /> </InlineMediaObject> <EquationSource Format="TEX">\(f_m\)</EquationSource> </InlineEquation>), meaning that cell fitness can range from equal to the fitness of their parent or sibling (<InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44330_2025_33_Article_IEq1.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="23" /> </InlineMediaObject> <EquationSource Format="TEX">\(f_m\)</EquationSource> </InlineEquation> =1), non-related (<InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44330_2025_33_Article_IEq1.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="23" /> </InlineMediaObject> <EquationSource Format="TEX">\(f_m\)</EquationSource> </InlineEquation> =0) to anti-correlated (<InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44330_2025_33_Article_IEq1.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="23" /> </InlineMediaObject> <EquationSource Format="TEX">\(f_m\)</EquationSource> </InlineEquation> =-1). Arbitrary treatments can be delivered to colonies at any time, modifying the division and death distributions. Results We show examples of trees with different division and death curves and the resulting number of cells per tree. Values of <InlineEquation ID="IEq5"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44330_2025_33_Article_IEq1.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="23" /> </InlineMediaObject> <EquationSource Format="TEX">\(f_m\)</EquationSource> </InlineEquation> from -1 to 1 generated SisSis and MD Pearson correlations that were fit to correlations observed in different experimental data from normal and cancer cells. Discussion CloVarS is an important asset for quickly exploring colony fitness dynamics, its heritability, testing biological hypotheses, benchmarking cell tracking algorithms, and ultimately improving our understanding of single-cell lineage data.

CloVarS: a simulation of single-cell clonal variability

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2025年6月16日

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Juliano L. Faccioni,
Frederico Kraemer-Mattos,
Karine R. Begnini,
Julieti H. Buss,
Daphne Torgo,
Camilla Brückmann de Mattos,
Camila B. Cassel,
Sophie Seidel,
Leonardo G. Brunnet,
Manuel M. Oliveira,
Guido Lenz

摘要

Background

High-throughput time-lapse microscopy has allowed researchers to monitor individual cells as they grow into colonies and react to treatments, but a deeper understanding of the data obtained after image analysis is still lacking. This is in part due to the biological and computational challenges related to long-running experiments and single-cell tracking.

Methods

Clonal Variability Simulator (CloVarS) is a Python tool for generating synthetic data of single-cell lineage trees to model time-lapse microscopy experiments. After colony initialization, each individual cell is simulated for a given number of simulation frames. During simulation, cells can migrate, enter mitosis (divide), and enter apoptosis (die). These events are determined by distributions of cell division and death times, which can be inferred from and fit to experimental data. Colonies have an adjustable mother-daughter (MD) and sister-sister (SisSis) fitness memory ( \(f_m\) ), meaning that cell fitness can range from equal to the fitness of their parent or sibling ( \(f_m\) =1), non-related ( \(f_m\) =0) to anti-correlated ( \(f_m\) =-1). Arbitrary treatments can be delivered to colonies at any time, modifying the division and death distributions.

Results

We show examples of trees with different division and death curves and the resulting number of cells per tree. Values of \(f_m\) from -1 to 1 generated SisSis and MD Pearson correlations that were fit to correlations observed in different experimental data from normal and cancer cells.

Discussion

CloVarS is an important asset for quickly exploring colony fitness dynamics, its heritability, testing biological hypotheses, benchmarking cell tracking algorithms, and ultimately improving our understanding of single-cell lineage data.