Image-Based Single Cell Profiling: High-Throughput Processing of Mother Machine Experiments

Microfluidic lab-on-chip technology combined with live-cell imaging has enabled the observation of single cells in their spatio-temporal context. The mother machine (MM) cultivation system is particularly attractive for the long-term investigation of rod-shaped bacteria since it facilitates continuo...

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Bibliographic Details
Published in:PloS one 2016-09, Vol.11 (9), p.e0163453-e0163453
Main Authors: Sachs, Christian Carsten, Grünberger, Alexander, Helfrich, Stefan, Probst, Christopher, Wiechert, Wolfgang, Kohlheyer, Dietrich, Nöh, Katharina
Format: Article
Language:English
Subjects:
Algorithms
Biology and Life Sciences
Biotechnology
Computer and Information Sciences
Computer programs
Corynebacterium glutamicum
Cultivation
Data processing
Dehydrogenases
Engineering and Technology
Experiments
Growth rate
Image analysis
Image detection
Image processing
Image registration
Inventors
Lab-on-a-chip
Microfluidics
Open source software
Parallel processing
Research and Analysis Methods
Science Policy
Software
Stacks
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Summary:Microfluidic lab-on-chip technology combined with live-cell imaging has enabled the observation of single cells in their spatio-temporal context. The mother machine (MM) cultivation system is particularly attractive for the long-term investigation of rod-shaped bacteria since it facilitates continuous cultivation and observation of individual cells over many generations in a highly parallelized manner. To date, the lack of fully automated image analysis software limits the practical applicability of the MM as a phenotypic screening tool. We present an image analysis pipeline for the automated processing of MM time lapse image stacks. The pipeline supports all analysis steps, i.e., image registration, orientation correction, channel/cell detection, cell tracking, and result visualization. Tailored algorithms account for the specialized MM layout to enable a robust automated analysis. Image data generated in a two-day growth study (≈ 90 GB) is analyzed in ≈ 30 min with negligible differences in growth rate between automated and manual evaluation quality. The proposed methods are implemented in the software molyso (MOther machine AnaLYsis SOftware) that provides a new profiling tool to analyze unbiasedly hitherto inaccessible large-scale MM image stacks. Presented is the software molyso, a ready-to-use open source software (BSD-licensed) for the unsupervised analysis of MM time-lapse image stacks. molyso source code and user manual are available at https://github.com/modsim/molyso.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0163453