4EU+ Image analysis and data processing in superresolution microscopy: fairSIM and ThunderSTORM open source systems

Annotation

The course is an introduction to superresolution microscopy techniques STORM and SIM. The theoretical background will be complemented with many practical presentations. Experts and scientists from the field teach the lectures and practical sessions. The two-day theoretical course with practical demonstrations and exercises is intensely devoted to modern methodologies of super-resolution light microscopy (SIM, STORM). The priority is the practical demonstration of the image analysis software by the authors themselves. After completing the course, the participants will be able to determine what is appropriate microscopy technique used to answer the research questions, including sample preparation and data processing for publication. The course will be taught in English.

Information about the course

 Title – Image analysis and data processing in superresolution microscopy: fairSIM and ThunderSTORM open source systems

- Code – MB100P04

 Guarantor – Msc. Zuzana Burdíková, Ph.D

 All lecturers – Msc. Zuzana Burdíková, Ph.D, Msc. Zdeněk Švindrych, Ing. Martin Schätz, Ph.D. , MSc. Ondřej Šebesta, MSc. Peter Hohoth, Ph.D., Msc. Marian Novotny, MD. Robert Haase, Ph.D., Msc. Karel Stepka

 Faculty, department – Faculty of Science, Laboratory of Fluorescent and Confocal Microscopy, Charles University

 Credits – 02 ECTS

 Language of instruction - English

 Flagship and/or transversal skills – Flagship 4, Critical thinking

 Capacity - 15

 Examination – project

 Minimal requirements, prerequisites, conditions for selection, and enrolment of students: Basic knowledge of Image J is required. The course aims to explain the workflow in Image Analysis, and processing, and it is assumed that the student is interested in Image Analysis.

 Virtual mobility - yes

 How the course will be taught (one week) and the starting date –block; on august 23. -25. of the SUMMER semester of 2022,

Syllabus

Day 1

Introduction to superresolution microscopy: methods, principles, theoretical background image formation in Fluorescence Microscopy

Resolution and Noise

Super-resolution Localization Microscopy (STORM, PALM, DNA-PAINT, …)

Structured Illumination Microscopy (SIM)

Introduction to image processing in FIJI, ImageJ

Two-channel colocalization (mitochondrial and membrane labeling) p-Value of colocalization, data filtering

Quantitative analysis

Quantitative data (filtering, thresholding, background separation)

Filter on photon count

Data visualization, 3D visualization

Histogram, measurement of different parameters

Pseudo-colors, pixel size, rendering mode, multicolor image

Image export

Image reconstruction of superresolution SIM data in ImageJ: fairSIM Super-resolved structured illumination microscopy (SR-SIM) illumination patterns

SR-SIM image reconstruction algorithms, access to the plugin, and source code fairSIM, ImageJ plugin that provides SR-SIM reconstructions

FairSIM reconstruction of data sets

Automated reconstruction parameter estimation for data sets of adequate quality

Practical part: ImageJ, fairSIM hands-on

Day 2

ThunderSTORM: a comprehensive ImageJ plug-in for SMLM data analysis and super-resolution imaging https://zitmen.github.io/thunderstorm/

Single Molecule Localisation (briefly)

The idea behind ThunderSTORM

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

Voronoi tesselation in Coloc-Tesseler software quantitative evaluation of the spatial organization in the cell nucleus

Practical Part ThunderSTORM hands-on sessions: Individual work with ThunderSTORM software

Day 3

Customizing Fiji/ImageJ with ImageJ Macro, Hands-on

Interactive Design of GPU-accelerated Image Data Flow Graphs in Fiji

Introduction to Data Management and FAIR principles

Pixel Classification Using ILASTIK

Object Detection Using StarDist

Practical part : ILASTIK or StarDist Hands-on