TY  - JOUR
AB  - **Background**<br /><br />

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.<br />
**Results**<br /><br />

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.<br />
**Conclusion**<br /><br />

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.
DA  - 2016
DO  - 10.1371/journal.pone.0163453
LA  - eng
IS  - 9
PY  - 2016
SN  - 1932-6203
T2  - PLoS one
TI  - Image-Based Single Cell Profiling: High-Throughput Processing of Mother Machine Experiments
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-29125228
Y2  - 2024-11-22T07:45:21
ER  -