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MicrobeJ, a tool for high throughput bacterial cell detection and quantitative analysis.
Ducret, Adrien; Quardokus, Ellen M; Brun, Yves V.
Afiliação
  • Ducret A; Department of Biology, Indiana University, 1001 E 3rd Street, Bloomington, Indiana 47405, USA.
  • Quardokus EM; Department of Biology, Indiana University, 1001 E 3rd Street, Bloomington, Indiana 47405, USA.
  • Brun YV; Department of Biology, Indiana University, 1001 E 3rd Street, Bloomington, Indiana 47405, USA.
Nat Microbiol ; 1(7): 16077, 2016 06 20.
Article em En | MEDLINE | ID: mdl-27572972
ABSTRACT
Single-cell analysis of bacteria and subcellular protein localization dynamics has shown that bacteria have elaborate life cycles, cytoskeletal protein networks and complex signal transduction pathways driven by localized proteins. The volume of multidimensional images generated in such experiments and the computation time required to detect, associate and track cells and subcellular features pose considerable challenges, especially for high-throughput experiments. There is therefore a need for a versatile, computationally efficient image analysis tool capable of extracting the desired relationships from images in a meaningful and unbiased way. Here, we present MicrobeJ, a plug-in for the open-source platform ImageJ(1). MicrobeJ provides a comprehensive framework to process images derived from a wide variety of microscopy experiments with special emphasis on large image sets. It performs the most common intensity and morphology measurements as well as customized detection of poles, septa, fluorescent foci and organelles, determines their subcellular localization with subpixel resolution, and tracks them over time. Because a dynamic link is maintained between the images, measurements and all data representations derived from them, the editor and suite of advanced data presentation tools facilitates the image analysis process and provides a robust way to verify the accuracy and veracity of the data.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Bactérias / Processamento de Imagem Assistida por Computador / Software / Técnicas Bacteriológicas Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Revista: Nat Microbiol Ano de publicação: 2016 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Bactérias / Processamento de Imagem Assistida por Computador / Software / Técnicas Bacteriológicas Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Revista: Nat Microbiol Ano de publicação: 2016 Tipo de documento: Article