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

Description

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. 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