The course's emphasis will be on teaching image analysis in model studies. The course will include the preparation of slides, selection, application of microscopic techniques, and the analysis of images from wide-field, confocal, light-sheet, super-resolution (SIM, STORM / PALM) microscopic systems. During the course, students will get acquainted with a variety of analytical programs (e.g. FIJI, Arivis, FairSIM, ThunderSTORM, ILASTIC, Matlab) used in image analysis. Case studies will also include data management, statistics, and the preparation of a pictorial appendix for publication purposes.

1. Introduction to Microscopic Systems and Software: methods, principles, theoretical background, Image formation in Fluorescence Microscopy, Basics of Image Analysis, Image analysis software overview.

2. Data Management, Preparation of Images, Graphs, Tables for publication, Ethical codex in Image processing and publication.

3. Customizing Fiji/ImageJ with ImageJ Macro, hands-on part

4. Theoretical introduction, an overview of graphical formats Bioformats, PSF, Nyquist, calculation, data export, shading correction, chromatic correction,, aligning, stitching, deconvolution, Segmentation (thresholding, watershed, object detection), Colocalization. Pearson Statistics, Manderson Statistics, Statistical tests, FIJI, Macros, Plug-ins

Practical part: a) Filters, Segmentation (threshold, WEKA), Deep Learning models (STARDist, Noise2Void ), Quantification.Colocalization, Macros, Workflow

5. Labeling techniques in biomedical research, advantages, disadvantages, How to test my hypothesis using fluorescence microscopy? Practical use and drawbacks of indirect immunofluorescence; fluorophore properties; choice of fluorophores., Organic fluorophores / intercalating dyes for DNA (such as DAPI, Hoechst, etc.) and lipids (such as DOPE, Laurdan, fluorescent lipid analogs, etc.), Genetically encoded fluorescent tags (GFP palette variants), photoactivatable and photoconvertible fluorophores, Self-labeling protein tags (HaloTag, SNAP, CLIP)

6. Quantification and Processing data from Fluorescence Microscopes (Nis-elements, Image J)

7. Quantification and Processing data from Axioscan microscope (case study)

8. Quantification and Processing data from Lightsheet Microscope, Arivis, ImageJ (case study) Arivis Vision4D: Introduction to Arivis Vision4D, Overview of biological applications, batch processing, Practical demonstration ZEN and Arivis Volume Fusion with Arivis Vision4D, Importing images with Arivis Vision4D, Importing Complex Images with Arivis Vision4D, Channel Colors, Color handling, visualization settings manual stitching and alignment using the tile sorter, Split view mode, projection gallery, and info viewer, Create movies with the storyboard and video export, New Analysis Pipeline User Interface with Arivis Vision4D, segmentation, filters, custom features
, Basics of Parent-Child Analysis, Tracking, Membrane Based Segmenter, Working with Results,

9. Fluorescence Lifetime Imaging (FLIM) of Cellular Metabolism, Becker and Hickel software, ImageJ (case study), TCSPC FLIM principle, hardware, FLIM Acquisition, FLIM fitting, SPCImage

10 Imaging membrane protein structure and function using polarization-resolved fluorescence microscopy (case study). The lecture will cover the design, properties, and applications of genetically encoded fluorescent probes for polarization-resolved fluorescence microscopy. The lecture will also discuss topics related to the directionality of optical properties of fluorescent molecules, respective advantages of single-photon and two-photon excitation, and data quantitation and interpretation in terms of protein structure.

11, ThunderStorm plugin in Image J (case study)
ThunderSTORM: a comprehensive ImageJ plug-in for SMLM data analysis and super-resolution imaging https://zitmen.github.io/thunderstorm/
Single Molecule Localisation (briefly), Workflow - localization, filtering, rendering, Simulation engine, 3D STORM - astigmatism method
Scientific lecture Case Study Study Methods for the quantitative analyses of SMLM data, Coordinate-based colocalization / Nearest Neighbor Distance (NND) analysis in ThunderSTORM, Practical Part ThunderSTORM hands-on sessions: Individual work with ThunderSTORM software, Quantitative analysis of Spatial and Organisational organization of cells, Distance analysis; overlap analysis; clustering analysis.

12. Imaris software introduction and case study