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High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression

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CellSium: versatile cell simulator for microcolony ground truth generation

Christian Carsten Sachs¹, Karina Ruzaeva^1,2, Johannes Seiffarth^1,3

¹Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany.

Bioinformatics Advances

|January 26, 2023

Abstract

Summary:

To train deep learning-based segmentation models, large ground truth datasets are needed. To address this need in microfluidic live-cell imaging, we present CellSium, a flexibly configurable cell simulator built to synthesize realistic image sequences of bacterial microcolonies growing in monolayers. We illustrate that the simulated images are suitable for training neural networks. Synthetic time-lapse videos with and without fluorescence, using programmable cell growth models, and simulation-ready 3D colony geometries for computational fluid dynamics are also supported.

Availability And Implementation:

CellSium is free and open source software under the BSD license, implemented in Python, available at github.com/modsim/cellsium (DOI: 10.5281/zenodo.6193033), along with documentation, usage examples and Docker images.

Supplementary Information:

Supplementary data are available at Bioinformatics Advances online.

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