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

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