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E-raamat: Standard and Super-Resolution Bioimaging Data Analysis: A Primer

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A comprehensive guide to the art and science of bioimaging data acquisition, processing and analysis

Standard and Super-Resolution Bioimaging Data Analysis gets newcomers to bioimage data analysis quickly up to speed on the mathematics, statistics, computing hardware and acquisition technologies required to correctly process and document data.

The past quarter century has seen remarkable progress in the field of light microscopy for biomedical science, with new imaging technologies coming on the market at an almost annual basis. Most of the data generated by these systems is image-based, and there is a significant increase in the content and throughput of these imaging systems. This, in turn, has resulted in a shift in the literature on biomedical research from descriptive to highly-quantitative. Standard and Super-Resolution Bioimaging Data Analysis satisfies the demand among students and research scientists for introductory guides to the tools for parsing and processing image data. Extremely well illustrated and including numerous examples, it clearly and accessibly explains what image data is and how to process and document it, as well as the current resources and standards in the field.

  • A comprehensive guide to the tools for parsing and processing image data and the resources and industry standards for the biological and biomedical sciences
  • Takes a practical approach to image analysis to assist scientists in ensuring scientific data are robust and reliable
  • Covers fundamental principles in such a way as to give beginners a sound scientific base upon which to build
  • Ideally suited for advanced students having only limited knowledge of the mathematics, statistics and computing required for image data analysis

An entry-level text written for students and practitioners in the bioscience community, Standard and Super-Resolution Bioimaging Data Analysis de-mythologises the vast array of image analysis modalities which have come online over the past decade while schooling beginners in bioimaging principles, mathematics, technologies and standards. 

List of Contributors
xi
Foreword xiii
1 Digital Microscopy: Nature to Numbers
1(30)
Ann Wheeler
1.1 Acquisition
4(7)
1.1.1 First Principles: How Can Images Be Quantified?
4(2)
1.1.2 Representing Images as a Numerical Matrix Using a Scientific Camera
6(2)
1.1.3 Controlling Pixel Size in Cameras
8(3)
1.2 Initialisation
11(10)
1.2.1 The Sample
12(1)
1.2.2 Pre-Processing
12(1)
1.2.3 Denoising
12(2)
1.2.4 Filtering Images
14(2)
1.2.5 Deconvolution
16(3)
1.2.6 Registration and Calibration
19(2)
1.3 Measurement
21(2)
1.4 Interpretation
23(6)
1.5 References
29(2)
2 Quantification of Image Data
31(16)
Jean-Yves Tinevez
2.1 Making Sense of Images
31(4)
2.1.1 The Magritte Pipe
31(2)
2.1.2 Quantification of Image Data Via Computers
33(2)
2.2 Quantifiable Information
35(10)
2.2.1 Measuring and Comparing Intensities
35(1)
2.2.2 Quantifying Shape
36(5)
2.2.3 Spatial Arrangement of Objects
41(4)
2.3 Wrapping Up
45(1)
2.4 References
46(1)
3 Segmentation in Bioimaging
47(36)
Jean-Yves Tinevez
3.1 Segmentation and Information Condensation
47(5)
3.1.1 A Priori Knowledge
48(1)
3.1.2 An Intuitive Approach
49(2)
3.1.3 A Strategic Approach
51(1)
3.2 Extracting Objects
52(22)
3.2.1 Detecting and Counting Objects
52(8)
3.2.2 Automated Segmentation of Objects
60(14)
3.3 Wrapping Up
74(5)
3.4 References
79(4)
4 Measuring Molecular Dynamics and Interactions by Forster Resonance Energy Transfer (FRET)
83(16)
Aliaksandr Halavatyi
Stefan Terjung
4.1 FRET-Based Techniques
83(6)
4.1.1 Ratiometric Imaging
84(1)
4.1.2 Acceptor Photobleaching
85(1)
4.1.3 Other FRET Measurement Techniques
85(2)
4.1.4 Alternative Methods to Measure Interactions
87(2)
4.2 Experimental Design
89(3)
4.2.1 Ratiometric Imaging of FRET-Based Sensors
90(1)
4.2.2 Acceptor Photobleaching
91(1)
4.3 FRET Data Analysis
92(2)
4.3.1 Ratiometric Imaging
92(1)
4.3.2 Acceptor Photobleaching
93(1)
4.3.3 Data Averaging and Statistical Analysis
93(1)
4.4 Computational Aspects of Data Processing
94(1)
4.4.1 Software Tools
94(1)
4.4.2 FRET Data Analysis with Fiji
94(1)
4.5 Concluding Remarks
95(1)
4.6 References
96(3)
5 FRAP and Other Photoperturbation Techniques
99(44)
Aliaksandr Halavatyi
Stefan Terjung
5.1 Photoperturbation Techniques in Cell Biology
99(7)
5.1.1 Scientific Principles Underpinning FRAP
100(3)
5.1.2 Other Photoperturbation Techniques
103(3)
5.2 FRAP Experiments
106(3)
5.2.1 Selecting Fluorescent Tags
107(1)
5.2.2 Optimisation of FRAP Experiments
107(2)
5.2.3 Storage of Experimental Data
109(1)
5.3 FRAP Data Analysis
109(18)
5.3.1 Quantification of FRAP Intensities
112(1)
5.3.2 Normalisation
113(2)
5.3.3 In Silico Modelling of FRAP Data
115(5)
5.3.4 Fitting Recovery Curves
120(1)
5.3.5 Evaluating the Quality of FRAP Data and Analysis Results
121(1)
5.3.6 Data Averaging and Statistical Analysis
122(1)
5.3.7 Software for FRAP Data Processing
123(4)
5.4 Procedures for Quantitative FRAP Analysis with Freeware Software Tools
127(3)
5.4.1 Quantification of Intensity Traces with Fiji
127(1)
5.4.2 Processing FRAP Recovery Curves with FRAPAnalyser
128(2)
5.5 Notes
130(1)
5.6 Concluding Remarks
131(1)
5.7 References
132(11)
5A Case Study: Analysing COPII Turnover During ER Exit
135(1)
5A.1 Quantitative FRAP Analysis of ER-Exit Sites
135(3)
5A.2 Mechanistic Insight into COPII Coat Kinetics with FRAP
138(2)
5A.3 Automated FRAP at ERESs
140(1)
5A.4 References
141(2)
6 Co-Localisation and Correlation in Fluorescence Microscopy Data
143(30)
Dylan Owen
George Ashdown
Juliette Griffie
Michael Shannon
6.1 Introduction
143(2)
6.2 Co-Localisation for Conventional Microscopy Images
145(19)
6.2.1 Co-Localisation in Super-Resolution Localisation Microscopy
151(5)
6.2.2 Fluorescence Correlation Spectroscopy
156(5)
6.2.3 Image Correlation Spectroscopy
161(3)
6.3 Conclusion
164(1)
6.4 Acknowledgments
165(1)
6.5 References
165(8)
7 Live Cell Imaging: Tracking Cell Movement
173(28)
Mario De Piano
Gareth E. Jones
Claire M. Wells
7.1 Introduction
173(1)
7.2 Setting up a Movie for Time-Lapse Imaging
174(1)
7.3 Overview of Automated and Manual Cell Tracking Software
175(9)
7.3.1 Automatic Tracking
176(4)
7.3.2 Manual Tracking
180(1)
7.3.3 Comparison Between Automated and Manual Tracking
181(3)
7.4 Instructions for Using ImageJ Tracking
184(5)
7.5 Post-Tracking Analysis Using the Dunn Mathematica Software
189(9)
7.6 Summary and Future Direction
198(1)
7.7 References
198(3)
8 Super-Resolution Data Analysis
201(26)
Debora Keller
Nicolas Olivier
Thomas Pengo
Graeme Ball
8.1 Introduction to Super-Resolution Microscopy
201(1)
8.2 Processing Structured Illumination Microscopy Data
202(8)
8.2.1 SIM Reconstruction Theory
203(1)
8.2.2 Parameter Fitting and Corrections
204(1)
8.2.3 SIM Quality Control
205(1)
8.2.4 Checking System Calibration
205(1)
8.2.5 Checking Raw Data
205(3)
8.2.6 Checking Reconstructed Data
208(1)
8.2.7 SIM Data Analysis
208(2)
8.3 Quantifying Single Molecule Localisation Microscopy Data
210(10)
8.3.1 SMLMS Pre-Processing
210(1)
8.3.2 Localisation: Finding Molecule Positions
210(1)
8.3.3 Fitting Molecules
210(2)
8.3.4 Problem of Multiple Emissions Per Molecule
212(1)
8.3.5 Sieving and Quality Control and Drift Correction
213(5)
8.3.6 How Far Can I Trust the SMLM Data?
218(2)
8.4 Reconstruction Summary
220(1)
8.5 Image Analysis on Localisation Data
220(3)
8.5.1 Cluster Analysis
221(1)
8.5.2 Stoichiometry and Counting
222(1)
8.5.3 Fitting and Particle Averaging
223(1)
8.5.4 Tracing
223(1)
8.6 Summary and Available Tools
223(1)
8.7 References
224(3)
9 Big Data and Bio-Image Informatics: A Review of Software Technologies Available for Quantifying Large Datasets in Light-Microscopy
227(22)
Ahmed Fetit
9.1 Introduction
227(1)
9.2 What Is Big Data Anyway?
228(3)
9.3 The Open-Source Bioimage Informatics Community
231(12)
9.3.1 ImageJ for Small-Scale Projects
231(4)
9.3.2 CellProfiler, Large-Scale Projects and the Need for Complex Infrastructure
235(3)
9.3.3 Technical Notes - Setting Up CellProfiler for Use on a Linux HPC
238(4)
9.3.4 Icy, Towards Reproducible Image Informatics
242(1)
9.4 Commercial Solutions for Bioimage Informatics
243(4)
9.4.1 Imaris Bitplane
243(1)
9.4.2 Definiens and Using Machine-Learning on Complex Datasets
244(3)
9.5 Summary
247(1)
9.6 Acknowledgments
247(1)
9.7 References
248(1)
10 Presenting and Storing Data for Publication
249(20)
Ann Wheeler
Sebastien Besson
10.1 How to Make Scientific Figures
249(7)
10.1.1 General Guidelines for Making Any Microscopy Figure
250(1)
10.1.2 Do's and Don'ts: Preparation of Figures for Publication
251(2)
10.1.3 Restoration, Revelation or Manipulation
253(3)
10.2 Presenting, Documenting and Storing Bioimage Data
256(11)
10.2.1 Metadata Matters
257(1)
10.2.2 The Open Microscopy Project
258(1)
10.2.3 OME and Bio-Formats, Supporting Interoperability in Bioimaging Data
259(1)
10.2.4 Long-Term Data Storage
260(2)
10.2.5 USB Drives Friend or Foe?
262(1)
10.2.6 Beyond the (USB) Drive Limit
262(1)
10.2.7 Servers and Storage Area Networks
263(2)
10.2.8 OMERO Scalable Data Management for Biologists
265(2)
10.3 Summary
267(1)
10.4 References
268(1)
11 Epilogue: A Framework for Bioimage Analysis
269(16)
Kota Miura
Sebastien Tosi
11.1 Workflows for Bioimage Analysis
270(7)
11.1.1 Components
270(2)
11.1.2 Workflows
272(1)
11.1.3 Types of Workflows
273(3)
11.1.4 Types of Component
276(1)
11.2 Resources for Designing Workflows and Supporting Bioimage Analysis
277(5)
11.2.1 A Brief History
278(1)
11.2.2 A Network for Bioimage Analysis
279(1)
11.2.3 Additional Textbooks
279(1)
11.2.4 Training Schools
280(1)
11.2.5 Database of Components and Workflows
280(2)
11.2.6 Benchmarking Platform
282(1)
11.3 Conclusion
282(1)
11.4 References
283(2)
Index 285
ANN WHEELER, PhD, is Head of the Advanced Imaging Resource at the MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, UK.

RICARDO HENRIQUES, PhD, is Head of the Quantitative Imaging and NanoBioPhysics research group at the MRC Laboratory for Molecular Cell Biology, University College London, UK.
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