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

Sachs CC, Grünberger A, Helfrich S, Probst C, Wiechert W, Kohlheyer D, Nöh K (2016)
PLoS one 11(9): e0163453 -.

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Zeitschriftenaufsatz | Veröffentlicht | Englisch
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Abstract / Bemerkung
**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.
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Zeitschriftentitel
PLoS one
Band
11
Zeitschriftennummer
9
Artikelnummer
e0163453 -
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Sachs CC, Grünberger A, Helfrich S, et al. Image-Based Single Cell Profiling: High-Throughput Processing of Mother Machine Experiments. PLoS one. 2016;11(9): e0163453 -.
Sachs, C. C., Grünberger, A., Helfrich, S., Probst, C., Wiechert, W., Kohlheyer, D., & Nöh, K. (2016). Image-Based Single Cell Profiling: High-Throughput Processing of Mother Machine Experiments. PLoS one, 11(9), e0163453 -. doi:10.1371/journal.pone.0163453
Sachs, C. C., Grünberger, A., Helfrich, S., Probst, C., Wiechert, W., Kohlheyer, D., and Nöh, K. (2016). Image-Based Single Cell Profiling: High-Throughput Processing of Mother Machine Experiments. PLoS one 11:e0163453 -.
Sachs, C.C., et al., 2016. Image-Based Single Cell Profiling: High-Throughput Processing of Mother Machine Experiments. PLoS one, 11(9): e0163453 -.
C.C. Sachs, et al., “Image-Based Single Cell Profiling: High-Throughput Processing of Mother Machine Experiments”, PLoS one, vol. 11, 2016, : e0163453 -.
Sachs, C.C., Grünberger, A., Helfrich, S., Probst, C., Wiechert, W., Kohlheyer, D., Nöh, K.: Image-Based Single Cell Profiling: High-Throughput Processing of Mother Machine Experiments. PLoS one. 11, : e0163453 - (2016).
Sachs, Christian Carsten, Grünberger, Alexander, Helfrich, Stefan, Probst, Christopher, Wiechert, Wolfgang, Kohlheyer, Dietrich, and Nöh, Katharina. “Image-Based Single Cell Profiling: High-Throughput Processing of Mother Machine Experiments”. PLoS one 11.9 (2016): e0163453 -.
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2017-07-11T10:07:33Z

3 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

Laboratory-scale photobiotechnology-current trends and future perspectives.
Morschett H, Loomba V, Huber G, Wiechert W, von Lieres E, Oldiges M., FEMS Microbiol Lett 365(1), 2018
PMID: 29126108
Beyond the bulk: disclosing the life of single microbial cells.
Rosenthal K, Oehling V, Dusny C, Schmid A., FEMS Microbiol Rev 41(6), 2017
PMID: 29029257

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