Cell uptake 2 channels coloc
Uptake assay with two spot channels and brightfield cell.
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Uptake assay with two spot channels and brightfield cell.
Contributed by: Melanie Schuerz (University of Salzburg)
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Ready-made project templates and AI models you can drop straight into EVAnalyzer.
Project Templates
Section titled “Project Templates”Full .evapt project templates - class definitions, plate settings, and pipelines - ready to open directly via the Open button, or via File → New from Template. See Saving a Project as a Template for how these work.
Cell uptake 3 channels coloc
Uptake assay with two spot channels and brightfield cell.
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Uptake assay with two spot channels and brightfield cell.
Contributed by: Melanie Schuerz (University of Salzburg)
Coloc 2 channels
Spot colocalization with two image channels.
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Spot colocalization with two image channels.
Contributed by: Melanie Schuerz (University of Salzburg)
Coloc 3 channels
Spot colocalization with three image channels.
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Spot colocalization with three image channels.
Contributed by: Melanie Schuerz (University of Salzburg)
EV uptake in cells
Methods of Cell Biology course
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Methods of Cell Biology course
Contributed by: Melanie Schuerz (LBI NVPM)
Example Colocalization
Ported from CellProfiler ExampleColocalization pipeline
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Two-channel spot colocalization (Cy3-like / Cy5-like nucleosome staining). Ported from the CellProfiler ExampleColocalization.cppipe example. Channel 0 = OrigStain1 (files *_N_R), Channel 1 = OrigStain2 (files *_N_G), merged into one 2-channel TIFF per site via Fiji. Object counts run ~10-15% off CP and coloc% ~5pt high, mainly because evanalyzer’s watershed declumping (prominence-based) splits touching spots differently than CP’s fixed-radius local-maxima method, and evanalyzer’s illumination correction averages over blocks before fitting (more robust to bright spots) instead of fitting the raw pixels directly like CP does.
Contributed by: Jeff Reifenberger, Brad Berstein’s (Massachusetts General Hospital)
About the example data
Measuring the colocalization between fluorescently labeled molecules is a widely used approach to measure the degree of spatial coincidence and potential interactions among subcellular species (e.g., proteins). This example shows how the object identifcation and RelateObjects modules are used to measure the degree of overlap between two fluorescent channels.
About these images:
Fluoresecent images of a histone-modified nucleosome that is labeled with a Cy3-like dye and an antibody labeled with a Cy5-like dye that is sensitive to to the histone modifications.
These images were contributed by Jeff Reifenberger of Brad Berstein’s Lab at Massachusetts General Hospital, March 2012
Example Comet Assay
Ported from CellProfiler ExampleCometAssay pipeline
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Single-channel comet assay (DNA damage electrophoresis). Ported from the CellProfiler ExampleCometAssay.cppipe example. Comet = whole cell blob (head+tail), CometHead = dense head sub-region, CometTail = Comet minus CometHead. CP masks the image to Comet before re-thresholding for CometHead (MaskImage) and has no direct evanalyzer equivalent for image-level masking by object class; approximated here with an intersection filter (classifyObjects overlappingWith comet@whole) instead, applied after thresholding the full image. CP’s ‘Robust Background’ threshold method also has no evanalyzer equivalent; approximated with Otsu. Illumination correction’s median-smoothing radius needed a much larger value (20 block-grid units) than a naive unit conversion from CP’s settings suggested, otherwise residual block-boundary noise fragmented each comet into many tiny pieces instead of one blob. Result: CometTails.tif matches CP exactly (6/6/6 whole/head/tail); NoTails.tif is close but not exact (5/5/5 vs CP’s 6/4/6) - one faint comet isn’t detected, and because head-detection here runs on the full image rather than a CP-style pre-masked one, all 5 found comets get a head where CP only assigned heads to 4 of its 6.
Contributed by: Scott Floyd, Michael Pacold, Jorge Ernesto González (Centro de Protección e Higiene de Las Radiaciones)
About the example data
The fluorescent comet images were donated by Scott Floyd and Michael Pacold.
The silver-stained comets were contributed by Jorge Ernesto González from the Centro de Protección e Higiene de Las Radiaciones (CPHR).
Spot count
Count the number of spots in an image channel.
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Count the number of spots in an image channel.
Contributed by: Melanie Schuerz (University of Salzburg)
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Pipeline Templates
Section titled “Pipeline Templates”Single .evapipe pipeline templates - a preconfigured set of steps you can insert into a project’s pipeline via the Template section of the command selection dialog. See Pipeline templates for how these work.
Cell brightfield
Cell brightfield
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Segments the overall cell area from a brightfield image using Sobel edge detection, blurring and an automatic threshold. The output object class can be remapped after import (default class id used during conversion: 1).
Contributed by: Melanie Schuerz (University of Salzburg)
EV detection
EV detection
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Detects extracellular vesicles (EVs) in a fluorescence channel using rolling ball background subtraction, blurring and Li thresholding. The output object class can be remapped after import (default class id used during conversion: 1).
Contributed by: Melanie Schuerz (University of Salzburg)
EV in Cell
EV in cell detection
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Detects extracellular vesicles inside cells using median background subtraction, blurring, Li thresholding and watershed splitting. The output object class can be remapped after import (default class id used during conversion: 1).
Contributed by: Melanie Schuerz (University of Salzburg)
Nuclei detection
Nuclei detection
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Detects cell nuclei using rolling ball background subtraction, blurring, Triangle thresholding and watershed splitting. The output object class can be remapped after import (default class id used during conversion: 1).
Contributed by: Melanie Schuerz (University of Salzburg)
Yeast cells brightfield
Yeast cells brightfield
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Segments yeast cells from a brightfield image using contrast enhancement, Gaussian blur, Canny edge detection, manual thresholding and a Voronoi tessellation around detected cell centers. The output object class can be remapped after import (default class id used during conversion: 4). Approximation notes: Voronoi centers reference ‘M01’ (a Hough-transform output label) which has no equivalent class; centers set to UNSET and should be remapped manually $houghTransform has no equivalent in the new schema and was approximated with ConnectedComponents
Contributed by: Melanie Schuerz (University of Salzburg)
AI Models
Section titled “AI Models”Pre-trained models for the AI Segmentation and AI Object Classifier commands.
Coming soon.