Quantitative Bioimaging: An Introduction to Biology, Instrumentation, Experiments, and Data Analysis for Scientists and Engineers (Textbook Series in Physical Sc)

✍ Scribed by Raimund J. Ober, E. Sally Ward, Jerry Chao

• Publisher: CRC Press
• Year: 2020
• Tongue: English
• Leaves: 504
• Edition: 1
• Category: Library

✦ Synopsis

Quantitative bioimaging is a broad interdisciplinary field that exploits tools from biology, chemistry, optics, and statistical data analysis for the design and implementation of investigations of biological processes. Instead of adopting the traditional approach of focusing on just one of the component disciplines, this textbook provides a unique introduction to quantitative bioimaging that presents all of the disciplines in an integrated manner. The wide range of topics covered include basic concepts in molecular and cellular biology, relevant aspects of antibody technology, instrumentation and experimental design in fluorescence microscopy, introductory geometrical optics and diffraction theory, and parameter estimation and information theory for the analysis of stochastic data.

Key Features:

• Comprises four parts, the first of which provides an overview of the topics that are developed from fundamental principles to more advanced levels in the other parts.
• Presents in the second part an in-depth introduction to the relevant background in molecular and cellular biology and in physical chemistry, which should be particularly useful for students without a formal background in these subjects.
• Provides in the third part a detailed treatment of microscopy techniques and optics, again starting from basic principles.
• Introduces in the fourth part modern statistical approaches to the determination of parameters of interest from microscopy data, in particular data generated by single molecule microscopy experiments.
• Uses two topics related to protein trafficking (transferrin trafficking and FcRn-mediated antibody trafficking) throughout the text to motivate and illustrate microscopy techniques.

An online appendix providing the background and derivations for various mathematical results presented or used in the text is available at http://www.routledge.com/9781138598980.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
Preface
Acknowledgments
I. Introduction
Overview
1. Then and Now
2. Introduction to Two Problems in Cellular Biology
2.1. Antibody tra cking
2.2. Localization experiments
2.3. Association experiments
2.4. Dynamic studies
2.5. Iron transport, transferrin, and the transferrin receptor
3. Basics of Microscopy Techniques
3.1. Optical microscopy for cell biology
3.2. Transmitted light microscopy
3.3. Fluorescence microscopy
3.3.1. Fluorescence
3.3.2. Layout of an epifluorescence widefield microscope
3.4. Inverted versus upright microscope
3.5. Components of commercial microscopes
3.5.1. Light sources
3.5.2. Objectives
3.6. Fixed and live cell experiments
3.7. Sample preparation
3.8. A note regarding safety
4. Introduction to Image Formation and Analysis
4.1. Image formation and point spread functions
4.2. Resolution: an elementary introduction
4.3. Modeling and analyzing the data
Notes
Exercises
II. Biology and Chemistry
Overview
5. From genes to proteins
5.1. Bonds
5.2. DNA and genes
5.3. How are proteins made?
5.4. Structures of proteins
5.5. Protein structure determination
6. Antibodies
6.1. Structure of antibodies
6.2. Variable regions and binding activity
6.3. Constant regions
6.4. Antibody production for laboratory and clinical use
6.4.1. The classical method: hybridoma technology
6.5. Diagnostic techniques using antibody detection methods
6.5.1. Enzyme-linked immunosorbent assay
6.5.2. Surface plasmon resonance for the quantitation of the affinity of
7. Cloning of genes for protein expression
7.1. Features of expression constructs
7.2. Methods for generating expression plasmids
7.2.1. Restriction enzymes
7.2.2. Polymerase chain reaction
7.2.3. Details of approaches for generating expression plasmids
7.2.4. Transfection of mammalian cells for expression
7.3. Antibody engineering
7.3.1. Chimeric antibodies
7.3.2. Humanized antibodies
7.3.3. Isolation of V regions
8. Principles of Fluorescence
8.1. Wave and particle description of light
8.2. Jablonski diagram
8.3. Stokes shift
8.4. Photobleaching
8.5. Photophysical characterization of fluorophores
8.5.1. Quantum yield
8.5.2. Beer-Lambert law, effective absorption cross section and molar
8.5.3. Brightness of a uorophore
8.6. Excitation and emission spectra
8.7. Fluorophores
8.7.1. Chemical fluorescent dyes
8.7.1.1. Labeling of proteins via cysteine or lysine residues
8.7.1.2. Labeling of proteins with fluorophore-conjugated streptavidin
8.7.1.3. In situ labeling of proteins in cells using peptide tags
8.7.2. Quantum dots
8.7.2.1. Labeling of proteins with quantum dots
8.7.3. Fluorescent proteins
8.7.4. Photoactivatable and photoswitchable uorescent probes
8.7.5. Other labeling modalities
9. Cells
9.1. Cellular structure
9.2. Receptors
9.3. Typical biological systems
9.3.1. Subcellular tra cking of the Fc receptor, FcRn
9.3.2. Subcellular tra cking of the transferrin receptor
9.4. Sample preparation
9.4.1. Labeling of proteins in fixed cells
9.4.2. Sample preparation for typical fixed cell experiments
9.4.3. Sample preparation for typical live cell imaging experiments
Notes
Exercises
III. Optics and Microscopy
Overview
10. Microscope Designs
10.1. Light path for wide eld uorescence microscopy
10.1.1. In nity-corrected light path
10.2. Imaging in three dimensions
10.2.1. Focus control and acquisition of z-stacks
10.2.2. Multifocal plane microscopy
10.3. Imaging of multiple colors
10.4. Light path for confocal microscopy
10.5. Two-photon excitation microscopy
10.6. Objectives
10.6.1. Numerical aperture and immersion medium
10.6.2. Corrections
10.6.3. Transmission efficiency
10.7. Optical filters
10.7.1. Example: a filter set for a GFP-labeled protein
10.7.2. Imaging of multiple uorophores
10.8. Transmitted light microscopy
11. Microscopy Experiments
11.1. Fixed cell experiments
11.1.1. Localization of FcRn
11.1.2. Association experiments with FcRn, EEA1, LAMP1, and trans-
11.1.3. Pulse-chase veri cation of fate of mutated IgG
11.2. Imaging a 3D sample
11.2.1. Acquisition of z-stacks
11.2.2. Out-of-focus haze
11.3. Live cell experiments
11.3.1. Example: FcRn-mediated IgG tra cking
11.4. Total internal re ection uorescence microscopy (TIRFM)
11.4.1. Objective-based total internal re ection uorescence microscopy
11.4.2. Exocytosis imaged by total internal re ection uorescence mi-
11.5. pH measurement and ratiometric imaging
11.6. Single molecule microscopy
11.6.1. Bulk versus single molecule experiments
11.6.2. Single molecule tracking experiments
11.6.3. Localization-based super-resolution microscopy
11.6.3.1. Photophysics of the stochastic excitation of organic uorophores
11.6.4. A localization-based super-resolution experiment
11.7. Multifocal plane microscopy
11.7.1. Focal plane spacing and magni cation
11.7.2. Transferrin trafficking in epithelial cells
11.7.3. Imaging the pathway preceding exocytosis
12. Detectors
12.1. Photoelectric e ect
12.2. Point detectors
12.3. Image detectors
12.3.1. Charge-coupled device (CCD) detectors
12.3.2. Complementary metal-oxide-semiconductor (CMOS) detectors
12.3.3. Electron-multiplying charge-coupled device (EMCCD) detectors
12.4. Randomness of photon detection and detector noise sources
12.5. Grayscale and color cameras
12.6. Specifications of image detectors
12.7. Measurements of detector speci cations
12.7.1. Determination of CCD and CMOS detector speci cations
12.7.1.1. Data model
12.7.1.2. Linearity of the response
12.7.1.3. Estimation of electron-count-to-DU conversion factor
12.7.1.4. Estimation of readout noise mean and variance
12.7.1.5. Estimation of mean of dark current
12.7.2. Determination of EMCCD detector speci cations
12.7.2.1. Data model
12.7.2.2. Estimation of electron-count-to-DU conversion factor
12.7.2.3. Estimation of readout noise mean and variance
13. Geometrical Optics
13.1. Re ection and refraction
13.1.1. Re ection
13.1.2. Refractive index
13.1.3. Snell’s law
13.1.4. Total internal reflection
13.1.5. Extreme rays in microscopy optics
13.2. Lenses
13.2.1. Focal points and focal planes
13.2.2. Image formation
13.2.3. Lensmaker’s formula and lens formula
13.3. Magnification
13.3.1. Lateral magnification
13.3.2. Axial magnification
13.3.3. Dependence of lateral magni cation on axial position
13.4. Applications to microscopy
14. Diffraction
14.1. Wave description of light
14.1.1. Plane waves
14.1.1.1. Planes of identical phase
14.1.1.2. Speed of wave propagation
14.1.1.3. Wave number and wavelength
14.1.1.4. Propagation in di erent media
14.1.1.5. Optical path length
14.1.2. Spherical waves
14.1.2.1. Converging and diverging spherical waves
14.1.3. Spatial part of a wave
14.2. What does a camera detect?
14.3. Effect of a thin lens on waves
14.4. Huygens-Fresnel principle and Fresnel integral
14.4.1. Huygens-Fresnel principle
14.5. Imaging through a thin lens
14.5.1. Amplitude point spread function
14.5.2. Convolution description
14.5.3. Relationship to geometrical optics
14.5.4. Point spread function and Fourier transformation
14.5.4.1. In-focus point spread function
14.5.5. Imaging with defocus and the 3D point spread function
14.5.5.1. 3D point spread function evaluated on the optical axis
14.5.5.2. Depth of eld and depth of focus
14.5.5.3. Heuristic 3D resolution criterion
14.6. Convolution for intensity pro les
Notes
Exercises
IV. Data Analysis
Overview
15. From Photons to Image: Data Models
15.1. Accounting for each photon: fundamental data model
15.1.1. Temporal component of photon detection — Poisson process
15.1.1.1. Mean number of detected photons
15.1.2. Spatial component of photon detection — spatial density function
15.1.2.1. Translational invariance and image function
15.1.3. Background component
15.1.4. Examples
15.2. Practical data models
15.2.1. Poisson data model
15.2.2. CCD/CMOS data model
15.2.3. Deterministic data model
15.2.3.1. Gaussian approximation for the CCD/CMOS data model
15.2.4. EMCCD data model
15.2.4.1. High gain approximation for the EMCCD data model
15.2.4.2. Gaussian approximation for the EMCCD data model
16. Parameter Estimation
16.1. Maximum likelihood estimation
16.1.1. Example 1: mean of a Poisson random variable
16.1.2. Example 2: mean of a Gaussian random variable
16.2. Log-likelihood functions for the image data models
16.2.1. Log-likelihood function for the fundamental data model
16.2.1.1. Example 3: Localization of an object with a 2D Gaussian image pro le
16.2.2. Log-likelihood functions for the practical data models
16.3. Obtaining the maximum likelihood estimate
16.4. Maximum likelihood estimation and least squares estimation
16.5. Unbiased estimator
16.5.1. Example 1: sample mean
16.5.2. Example 2: sample variance
16.5.3. Example 3: center of mass as an object location estimator under
16.6. Variance of an estimator
16.6.1. Example 1: mean of a Poisson and a Gaussian random variable
16.6.2. Example 2: center of mass as an object location estimator under
16.6.3. Example 3: center of mass as an object location estimator under
17. Fisher Information and Cramér-Rao Lower Bound
17.1. Cram er-Rao inequality
17.1.1. Sketch of derivation of Cram er-Rao lower bound
17.1.2. Multivariate Cram er-Rao lower bound
17.1.3. Example 1: mean of a Poisson random variable
17.1.4. Example 2: mean of a Gaussian random variable
17.2. Fisher information for the fundamental data model
17.2.1. Example: known photon detection rate
17.2.2. Example: known photon distribution pro le
17.3. Fisher information for the practical data models
17.3.1. Noise coe cient and the Fisher information
17.4. Noise coe cient analysis of the pixel signal level
17.4.1. Noise coe cient | an in-depth look
17.4.2. Noise coe cient for CCD/CMOS detectors
17.4.3. EMCCD detectors as low-light detectors
17.4.4. Comparison of CCD/CMOS and EMCCD detectors
17.5. Fisher information for multi-image data
18. Localizing Objects and Single Molecules in Two Dimensions
18.1. Object localization as a parameter estimation problem
18.2. Example: estimating the location of a single molecule
18.3. How well can the location of an object be estimated?
18.3.1. Bias of location estimation
18.3.1.1. Bias of the center of mass as a location estimator under the practical
18.3.2. Variance of location estimation
18.4. Estimation of other parameters
18.5. Cram er-Rao lower bound for location estimation | funda-
18.5.1. Cram er-Rao lower bound for the Airy image function
18.5.2. Cram´er-Rao lower bound for the 2D Gaussian image function
18.5.3. Extensions to further experimental situations
18.6. Cram er-Rao lower bound for location estimation | practical
18.6.1. Poisson data model | e ects of pixelation, nite image size, and
18.6.2. Localizing objects from CCD/CMOS and EMCCD images
18.6.3. Object location makes a di erence
18.7. E ciency of estimators: how well is the behavior of estimators
18.7.1. Fundamental data model
18.7.2. Practical data models
18.8. Approximations
18.8.1. Gaussian approximations for the CCD/CMOS and EMCCD
18.8.2. Inverse square root approximation of the dependence on the
18.9. Lower bound as a tool for the design of data analysis
18.9.1. Choosing the region of interest
18.9.2. Improving estimation performance by adding images
18.10. Example: single molecule localization from experimentally
18.10.1. Choice of data model based on the detector used
18.10.2. Modeling the image of the molecule and the background component
18.10.3. Determining the \known" parameters
18.10.4. Location estimates
18.10.5. Initial values
18.10.6. Assessing the standard deviation of the localization
19. Localizing Objects and Single Molecules in Three Dimensions
19.1. Parameter estimation for object localization in three dimen-
19.2. Cram er-Rao lower bound for 3D location estimation | fun-
19.2.1. 3D localization of a point source
19.3. Cram er-Rao lower bound for 3D location estimation | prac-
19.4. Depth discrimination problem
19.5. Dependence of lateral location estimation on the axial posi-
19.6. Multifocal plane microscopy
19.6.1. Estimating the axial location from MUM data
19.6.2. Experimental example
19.6.3. Maximum likelihood localization with simulated data
19.6.4. Overcoming the depth discrimination problem
19.6.5. Zero Fisher information and the depth discrimination problem
19.6.6. Experimental design: nding appropriate focal plane spacings
19.6.7. Further approaches to address the depth discrimination problem
20. Resolution
20.1. Resolution as a parameter estimation problem
20.2. Cram er-Rao lower bound for distance estimation | funda-
20.3. Two in-focus objects: an information-theoretic Rayleigh’s criterion
20.4. Two objects in 3D space
20.5. Cram er-Rao lower bound for distance estimation | practical
21. Deconvolution
21.1. The deconvolution problem
21.2. Discretization
21.2.1. Linear algebra formulation
21.3. Linear least squares algorithm
21.3.1. Condition number of a matrix
21.3.1.1. Example of an ill-conditioned least squares problem
21.3.2. Regularization of the least squares problem
21.3.2.1. Example continued: regularization of the ill-conditioned least squares
21.3.3. A Fourier transform approach
21.4. Maximum likelihood formulation
21.4.1. Expectation maximization algorithm
21.5. Positron emission tomography
21.5.1. Deconvolution for the Poisson data model
21.5.2. An illustrative example
22. Spatial Statistics
22.1. Formal de nitions
22.1.1. Spatial Poisson processes
22.2. Intensity functions of spatial processes
22.2.1. Computing the intensity functions
22.2.2. Stationary point processes
22.3. K function and L function
22.3.1. An example of an inhibition process
22.3.2. Estimating the
Notes
Exercises
Figure Credits
Bibliography
List of Symbols
Index of Names