Poslední úprava: Mgr. Zuzana Burdíková, Ph.D. (05.10.2021)
Kurz předpokládá základní znalosti světelné fluorescenční mikroskopie, včetně přípravy preparátů a snímání obrazu, základy algoritmizace, programování a statistiky. Důraz kurzu bude kladen na výuku analýzy obrazu v modelových studiích. Součástí kurzu bude příprava preparátů, výběr a aplikace mikroskopických technik a výsledná analýza obrazu z wide-field, konfokálních, light-sheet, super-rezolučních (SIM, STORM/PALM) mikroskopických systémů. V průběhu výuky se studenti seznámí s paletou analytických programů (např. FIJI, Arivis,FairSIM, ThunderSTORM ,ILASTIC, Matlab)ˇ, využívaných při analýze časového a prostorového uspořádání buněčných objektů. Příkladové studie budou obsahovat také data management, statistiku a přípravu obrazové přílohy pro publikační účely. Kurz bude vyučován v angličtině.
Poslední úprava: RNDr. Veronika Sacherová, Ph.D. (14.01.2022)
Syllabus
The emphasis of the course will be on teaching image analysis in model studies. The course will include 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 the data management, statistics and the preparation of a pictorial appendix for publication purposes.
Prerequisites
Basic knowledge of Fluorescent and Confocal Microscopy, Image Analysis, and Image J, basics of programming, and statistics are required. The course is aimed at explaining the workflow in Image Analysis, processing and it is assumed that the student is interested in Image Analysis.
Introduction to Microscopic Systems and Softwares : methods, principles, theoretical background, Image formation in Fluorescence Microscopy, Basics of Image Analysis, Image analysis software overview
Labelling techniques in biomedical research, advantages, disadvantages, ImageJ
Quantification and Processing data from Fluorescence Microscopes (Nis-elements, Image J)
Quantification and Processing data from Axioscan microscope (case study)
Quantification and Processing data from Lightsheet Microscope , Arivis, Imagej (case study)
Two-photon polarization microscopy reveals protein structure and function (case study)
Fluorescence Lifetime Imaging (FLIM) of Cellular Metabolism, Becker and Hickel software, ImageJ (case study)
ThunderStorm plugin in Image J (case study)
Quantitative analysis of Spatial and Organisational organisation of cells by ThunderStorm plug-in, Coloc-Tesseler , Interaction Factor
News in microscopy industry - hardware and image analysis development
SIM, Statistics
Matlab, Python,Statistics
Požadavky ke zkoušce - angličtina
Poslední úprava: RNDr. Veronika Sacherová, Ph.D. (14.01.2022)
Semestral project
Sylabus - angličtina
Poslední úprava: Mgr. Zuzana Burdíková, Ph.D. (19.01.2023)
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
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,
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.
