Blobs and curves: object-based colocalisation for plant cells

Nelson, Carl J.; Duckney, Patrick; Hawkins, Timothy J.; Deeks, Michael J.; Laissue, Pierre Philippe; Hussey, Patrick J.; Obara, Bogusław

doi:10.1071/fp14047

Cited by 5 publications

(9 citation statements)

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“…GFP and FM4-64 objects and their centroid positions were identified using the same method as SPRAP. Objects in each channel were paired using a Hungarian matching-algorithm [ 53 , 54 ]. We defined that positive co-localization had occurred when the paired centroid distance was less than 200 nm, a value below the predicted Abbe limit.…”

Section: Methodsmentioning

confidence: 99%

An Immune-Responsive Cytoskeletal-Plasma Membrane Feedback Loop in Plants

et al. 2018

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SummaryCell wall appositions (CWAs) are produced reactively by the plant immune system to arrest microbial invasion through the local inversion of plant cell growth. This process requires the controlled invagination of the plasma membrane (PM) in coordination with the export of barrier material to the volume between the plant PM and cell wall. Plant actin dynamics are essential to this response, but it remains unclear how exocytosis and the cytoskeleton are linked in space and time to form functional CWAs. Here, we show that actin-dependent trafficking to immune response sites of Arabidopsis thaliana delivers membrane-integrated FORMIN4, which in turn contributes to local cytoskeletal dynamics. Total internal reflection fluorescence (TIRF) microscopy combined with controlled induction of FORMIN4-GFP expression reveals a dynamic population of vesicular bodies that accumulate to form clusters at the PM through an actin-dependent process. Deactivation of FORMIN4 and its close homologs partially compromises subsequent defense and alters filamentous actin (F-actin) distribution at mature CWAs. The localization of FORMIN4 is stable and segregated from the dynamic traffic of the endosomal network. Moreover, the tessellation of FORMIN4 at the PM with meso-domains of PEN3 reveals a fine spatial segregation of destinations for actin-dependent immunity cargo. Together, our data suggest a model where FORMIN4 is a spatial feedback element in a multi-layered, temporally defined sequence of cytoskeletal response. This positional feedback makes a significant contribution to the distribution of actin filaments at the dynamic CWA boundary and to the outcomes of pre-invasion defense.

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Section: Methodsmentioning

confidence: 99%

An Immune-Responsive Cytoskeletal-Plasma Membrane Feedback Loop in Plants

et al. 2018

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“…As the feature‐set found in images is universal, the strategies for measuring these features and algorithms to automate such tasks are transferable between different contexts and even between different fields of science. For instance, the detection of ‘blob‐like’ features within images can be exploited to track microscopic cellular organelles (Nelson et al ., ) or, equally, suspicious human behaviour at railway stations (Elhamod & Levine, ). It should be noted however that each algorithmic approach must be carefully optimised to new contexts.…”

Section: Removing Boundaries (And Finding Them Again)mentioning

confidence: 99%

“…In the case of blob-like organelles, segmentation approaches have progressed from simple intensity-thresholding, through adaptive thresholding, pattern-matching templates to multifeature classifiers (Nelson et al, 2015). The advantage of object-detection over pixel-based classifiers for colocalisation studies (e.g.…”

Section: Subcellular Object Detectionmentioning

confidence: 99%

“…JACoP plugin for ImageJ, Bolte & Cordelieres, 2008), is a quantitative estimate of the distribution of distances between objects, rather than single statistical measure from the whole image. Distance metrics are also more relevant to analysis of spatial associations, such as organelles travelling on the cytoskeleton, than colocalisation indices (Nelson et al, 2015).…”

Section: Subcellular Object Detectionmentioning

confidence: 99%

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Making microscopy count: quantitative light microscopy of dynamic processes in living plants

Fricker

Moger

Littlejohn

et al. 2016

Journal of Microscopy

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Summary Cell theory has officially reached 350 years of age as the first use of the word ‘cell’ in a biological context can be traced to a description of plant material by Robert Hooke in his historic publication ‘Micrographia: or some physiological definitions of minute bodies’. The 2015 Royal Microscopical Society Botanical Microscopy meeting was a celebration of the streams of investigation initiated by Hooke to understand at the subcellular scale how plant cell function and form arises. Much of the work presented, and Honorary Fellowships awarded, reflected the advanced application of bioimaging informatics to extract quantitative data from micrographs that reveal dynamic molecular processes driving cell growth and physiology. The field has progressed from collecting many pixels in multiple modes to associating these measurements with objects or features that are meaningful biologically. The additional complexity involves object identification that draws on a different type of expertise from computer science and statistics that is often impenetrable to biologists. There are many useful tools and approaches being developed, but we now need more interdisciplinary exchange to use them effectively. In this review we show how this quiet revolution has provided tools available to any personal computer user. We also discuss the oft‐neglected issue of quantifying algorithm robustness and the exciting possibilities offered through the integration of physiological information generated by biosensors with object detection and tracking.

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“…One difficult aspect of this work is the evaluation; and here the authors are thorough, comparing to artificial data, plant roots washed from soil, and to other root-extraction software. Nelson et al (2015) compare an object-based (computervision style) method to a more traditional pixel-based (imageprocessing style) measure, when quantifying the colocalisation of labels in fluorescent microscopy images of NET1A with plasmodesmata. Through identifying biological phenomena as objects, computationally speaking, they are able to better model change, and to extract distance information.…”

Section: Introductionmentioning

confidence: 99%

From image processing to computer vision: plant imaging grows up

Dee

French

2015

Functional Plant Biol.

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Abstract. Image analysis is a field of research which, combined with novel methods of capturing images, can help to bridge the genotype-phenotype gap, where our understanding of the genotype has until now been leaps and bounds ahead of our ability to work with the phenotype. Methods of automating image capture in plant science research have increased in usage recently, as has the need to provide objective and highly accurate measures on large image datasets, thereby bringing the phenotype back to the centre of interest. In this special issue of Functional Plant Biology, we present some recent advances in the field of image analysis, and look at examples of different kinds of image processing and computer vision, which is occurring with increasing frequency in the plant sciences.

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Blobs and curves: object-based colocalisation for plant cells

Cited by 5 publications

References 46 publications

An Immune-Responsive Cytoskeletal-Plasma Membrane Feedback Loop in Plants

An Immune-Responsive Cytoskeletal-Plasma Membrane Feedback Loop in Plants

Making microscopy count: quantitative light microscopy of dynamic processes in living plants

From image processing to computer vision: plant imaging grows up

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