Contents

Protein array is used to analyze protein expressions by screening simultaneously several protein-molecule interactions such as protein-protein and protein-DNA interactions. In most cases, the detection of interactions leads to an image containing numerous lines of spots that will be analyzed by comparing tables of intensity values.

Datasets originate from internal test runs at ACQUIFER on the Imaging Machine (see also application note on "The ACQUIFER PlateViewer: A tool for visualizing high content screening data and supervised feedback microscopy")

102 hpf medaka embryos in 96 well plate (4 embryo/well) - brightfield - 2X magnification - ACQUIFER Imaging Machine

Gierten, Jakob, et al. "Automated high-throughput heart rate measurement in medaka and zebrafish embryos under physiological conditions." bioRxiv (2019): 548594.

Used as benchmark dataset for Multi-Template-Matching by Thomas and Gehrig 

See implementation in Fiji https://github.com/LauLauThom/MultipleTemplateMatching

Multi-template matching can be used to localize multiple objects using one or a set of template images.

Contrary to previous implementations that allow to use only one template, here a set of templates can be used or the initial template(s) can be transformed by rotation/flipping.

Multiple objects detection without redundant detections is possible thanks to a Non-Maxima Supression relying on the degree of overlap between detections.

FPBioimage is a volumetric visualization tool which runs in all modern web browsers. Try the tool yourself at our example site here.

The PYthon Microscopy Environment is an open-source package providing image acquisition and data analysis functionality for a number of microscopy applications, but with a particular emphasis on single molecule localisation microscopy (PALM/STORM/PAINT etc ...). The package is multi platform, running on Windows, Linux, and OSX.

It comes with 3 main modules:

• PYMEAcquire - Instrument control and simulation
• dh5view - Image Data Analysis and Viewing
• VisGUI - Visualising Localization Data Sets

MATLAB, for advanced analysts. Slides, Scripts, Reproduction of results & deconstruction. Uniquely designed analtical approaches towards understanding cell cluster movements, based on stress / cell migration analysis.

Fiji plugin for detecting, tracking and quantifying filopodia

ImageJ & R, for advanced analysts. Slides, Scripts, Reproduction of results & deconstruction. Segmentation of filopodia (thin filamentous structure) and analysis of their shape and movements to characterize phenotypes.

MATLAB, CellProfiler, Advanced Cell Classifier, for advanced analysts. Slides, Scripts, Reproduction of results & deconstruction. We aim to implement an image-based analysis of a biological system by profiling the phenotipic response of a cancer cell line following perturbation with a small-molecule compound library

ImageJ, for those with GUI knowledge but without scripting knowledge

ImageJ, for those with GUI knowledge but without scripting knowledge

ImageJ, GUI training for beginners, Count and Measure 2D nuclei,Count number of foci on 3D image , compare 2D, 2.5D, 3D results (simple workflows)

We take an example image data of microtubule binding protein EB1, and will study how to automatically track those signals and how to analyze the tracking results. We use ImageJ for measuring the temporal changes in signal positions, and will feed the tracking results for analyzing their dynamics using Matlab in the following session.

In this session we will use a 3D multichannel reconstruction of zebrafish larva to explore the visualization capabilities of Matlab. We will start from extracting and inspecting single slices and will continue with combining multiple channels, finally generating a surface rendering for visual colocalization analysis.During the process we will review methods for manipulating multidimensional arrays, including resizing, reshaping and conditional selection.

This session will focus on the exploration of MATLAB functions and methodologies for image processing. We will cover aspects like importing images and metadata, as well as saving processed images and csv data files. Techniques such as image filtering, segmentation and morphological operations will be addressed. To promote a comparison with ImageJ, a simple workflow for object counting and properties extraction will be coded, using as input data the Embryos sample image.

Matlab is a commercial software for numerical computing and statistical analysis that can be used to process and analyze multidimensional images. Within this session you will get familiar with the Matlab environment and its programming language. Among the topics addressed: matrix manipulation and advanced indexing, access to files, data inspection and basic plotting. This session will provide you the foundations that you need for the following modules, where you will manipulate images, extract measurements, and statistically analyze and visualize results.

ImageJ, for those with GUI knowledge but without scripting knowledge. 

ImageJ, for those with GUI knowledge but without scripting knowledge

ImageJ, for those with GUI knowledge but without scripting knowledge, workflow: MIP, change LUT, merge channels