SCIP: a single-cell image processor toolbox

Leonardo Martins, Ramakanth Neeli-Venkata, Samuel M. D. Oliveira, Antti Häkkinen, André S. Ribeiro, José M. Fonseca

Research output: Contribution to journalArticlepeer-review

Abstract

Summary: Each cell is a phenotypically unique individual that is influenced by internal and external processes, operating in parallel. To characterize the dynamics of cellular processes one needs to observe many individual cells from multiple points of view and over time, so as to identify commonalities and variability. With this aim, we engineered a software, 'SCIP', to analyze multi-modal, multi-process, time-lapse microscopy morphological and functional images. SCIP is capable of automatic and/or manually corrected segmentation of cells and lineages, automatic alignment of different microscopy channels, as well as detect, count and characterize fluorescent spots (such as RNA tagged by MS2-GFP), nucleoids, Z rings, Min system, inclusion bodies, undefined structures, etc. The results can be exported into *mat files and all results can be jointly analyzed, to allow studying not only each feature and process individually, but also find potential relationships. While we exemplify its use on Escherichia coli, many of its functionalities are expected to be of use in analyzing other prokaryotes and eukaryotic cells as well. We expect SCIP to facilitate the finding of relationships between cellular processes, from small-scale (e.g. gene expression) to large-scale (e.g. cell division), in single cells and cell lineages. Availability and implementation:

Original languageEnglish
Pages (from-to)4318-4320
Number of pages3
JournalBioinformatics (Oxford, England)
Volume34
Issue number24
DOIs
Publication statusPublished - 15 Dec 2018

Fingerprint Dive into the research topics of 'SCIP: a single-cell image processor toolbox'. Together they form a unique fingerprint.

Cite this