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

TitleImage-Based Single Cell Profiling: High-Throughput Processing of Mother Machine Experiments.
Publication TypeJournal Article
Year of Publication2016
AuthorsSachs, CC, Grünberger, A, Helfrich, S, Probst, C, Wiechert, W, Kohlheyer, D, Nöh, K
JournalPLoS One
Volume11
Issue9
Paginatione0163453
Date Published2016
ISSN1932-6203
Abstract

BACKGROUND: 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.

RESULTS: 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.

CONCLUSION: 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.

DOI10.1371/journal.pone.0163453
Alternate JournalPLoS ONE