ArticleBMC bioinformatics2014
BactImAS: a platform for processing and analysis of bacterial time-lapse microscopy movies.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Deciphering cell to cell spatial relationship for pathology images using SpatialQPFs.Scientific reports · 2024Article
- Quantitative microbiology with widefield microscopy: navigating optical artefacts for accurate interpretations.Npj imaging · 2024Article
- Advances and opportunities in image analysis of bacterial cells and communities.FEMS microbiology reviews · 2021Article
- High-throughput detection and tracking of cells and intracellular spots in mother machine experiments.Nature protocols · 2019Article
- Single molecule super-resolution imaging of bacterial cell pole proteins with high-throughput quantitative analysis pipeline.Scientific reports · 2019Article
- A Novel Methodology for Characterizing Cell Subpopulations in Automated Time-lapse Microscopy.Frontiers in bioengineering and biotechnology · 2018Article
- ViCAR: An Adaptive and Landmark-Free Registration of Time Lapse Image Data from Microfluidics Experiments.Frontiers in genetics · 2017Article
- MicrobeJ, a tool for high throughput bacterial cell detection and quantitative analysis.Nature microbiology · 2016Article
- A Tool for Alignment and Averaging of Sparse Fluorescence Signals in Rod-Shaped Bacteria.Biophysical journal · 2016Article
- Single-cell tracking reveals antibiotic-induced changes in mycobacterial energy metabolism.mBio · 2015Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe software available to date for analyzing image sequences from time-lapse microscopy works only for certain bacteria and under limited conditions. These programs, mostly MATLAB-based, fail for microbes with irregular shape, indistinct cell division sites, or that grow in closely packed microcolonies. Unfortunately, many organisms of interest have these characteristics, and analyzing their image sequences has been limited to time consuming manual processing.
resultsHere we describe BactImAS - a modular, multi-platform, open-source, Java-based software delivered both as a standalone program and as a plugin for Icy. The software is designed for extracting and visualizing quantitative data from bacterial time-lapse movies. BactImAS uses a semi-automated approach where the user defines initial cells, identifies cell division events, and, if necessary, manually corrects cell segmentation with the help of user-friendly GUI and incorporated ImageJ application. The program segments and tracks cells using a newly-developed algorithm designed for movies with difficult-to-segment cells that exhibit small frame-to-frame differences. Measurements are extracted from images in a configurable, automated fashion and an SQLite database is used to store, retrieve, and exchange all acquired data. Finally, the BactImAS can generate configurable lineage tree visualizations and export data as CSV files. We tested BactImAS on time-lapse movies of Mycobacterium smegmatis and achieved at least 10-fold reduction of processing time compared to manual analysis. We illustrate the power of the visualization tool by showing heterogeneity of both icl expression and cell growth atop of a lineage tree.
conclusionsThe presented software simplifies quantitative analysis of time-lapse movies overall and is currently the only available software for the analysis of mycobacteria-like cells. It will be of interest to the community of both end-users and developers of time-lapse microscopy software.
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