| Hauptseite > Publikationsdatenbank > Image-Based Single Cell Profiling: High-Throughput Processing of Mother Machine Experiments |
| Typ | Amount | VAT | Currency | Share | Status | Cost centre |
| APC | 1355.96 | 0.00 | EUR | 100.00 % | (Deposit) | ZB |
| Sum | 1355.96 | 0.00 | EUR | |||
| Total | 1355.96 |
| Journal Article | FZJ-2016-05127 |
; ; ; ; ; ;
2016
PLoS
Lawrence, Kan.
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Please use a persistent id in citations: http://hdl.handle.net/2128/12459 doi:10.1371/journal.pone.0163453
Abstract: BackgroundMicrofluidic 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.ResultsWe 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.
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