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Quantitative Bioimaging: An Introduction to Biology, Instrumentation, Experiments, and Data Analysis for Scientists and Engineers [Kõva köide]

(Texas A & M University, Texas, USA), , (Texas A & M University, Texas, USA)
  • Formaat: Hardback, 503 pages, kõrgus x laius: 254x178 mm, kaal: 1360 g, 1 Tables, black and white; 234 Illustrations, color
  • Ilmumisaeg: 16-Dec-2020
  • Kirjastus: CRC Press
  • ISBN-10: 1138598984
  • ISBN-13: 9781138598980
Teised raamatud teemal:
Quantitative Bioimaging: An Introduction to Biology, Instrumentation, Experiments, and Data Analysis for Scientists and EngineersSuurem pilt
  • Formaat: Hardback, 503 pages, kõrgus x laius: 254x178 mm, kaal: 1360 g, 1 Tables, black and white; 234 Illustrations, color
  • Ilmumisaeg: 16-Dec-2020
  • Kirjastus: CRC Press
  • ISBN-10: 1138598984
  • ISBN-13: 9781138598980
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781138598980.html
Märksõnad:
An Introduction to Biology Instrumentation Experiments and Data Analysis for Scientists and Enginee. This textbook provides a truly unique introduction that integrates the concepts and methods of optics, molecular and cellular biology, image analysis, and bioengineering. The coverage spans from essential aspects of molecular and cellular biology to a detailed treatment of practical aspects.

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.

Preface ix
Acknowledgments xi
I Introduction
1(40)
Overview
3(2)
1 Then and Now
5(2)
2 Introduction to Two Problems in Cellular Biology
7(10)
2.1 Antibody trafficking
7(2)
2.2 Localization experiments
9(1)
2.3 Association experiments
9(3)
2.4 Dynamic studies
12(1)
2.5 Iron transport, transferrin, and the transferrin receptor
13(4)
3 Basics of Microscopy Techniques
17(12)
3.1 Optical microscopy for cell biology
17(1)
3.2 Transmitted light microscopy
17(1)
3.3 Fluorescence microscopy
18(4)
3.3.1 Fluorescence
20(1)
3.3.2 Layout of an epifluorescence widefield microscope
21(1)
3.4 Inverted versus upright microscope
22(1)
3.5 Components of commercial microscopes
23(4)
3.5.1 Light sources
24(2)
3.5.2 Objectives
26(1)
3.6 Fixed and live cell experiments
27(1)
3.7 Sample preparation
27(1)
3.8 A note regarding safety
28(1)
4 Introduction to Image Formation and Analysis
29(12)
4.1 Image formation and point spread functions
29(2)
4.2 Resolution: an elementary introduction
31(3)
4.3 Modeling and analyzing the data
34(3)
Notes
37(2)
Exercises
39(2)
II Biology and Chemistry
41(86)
Overview
43(2)
5 From genes to proteins
45(20)
5.1 Bonds
45(1)
5.2 DNA and genes
46(2)
5.3 How are proteins made?
48(5)
5.4 Structures of proteins
53(5)
5.5 Protein structure determination
58(7)
6 Antibodies
65(12)
6.1 Structure of antibodies
65(2)
6.2 Variable regions and binding activity
67(2)
6.3 Constant regions
69(2)
6.4 Antibody production for laboratory and clinical use
71(2)
6.4.1 The classical method: hybridoma technology
71(2)
6.5 Diagnostic techniques using antibody detection methods
73(4)
6.5.1 Enzyme-linked immunosorbent assay
73(2)
6.5.2 Surface plasmon resonance for the quantitation of the affinity of an interaction
75(2)
7 Cloning of genes for protein expression
77(14)
7.1 Features of expression constructs
77(1)
7.2 Methods for generating expression plasmids
78(8)
7.2.1 Restriction enzymes
78(1)
7.2.2 Polymerase chain reaction
79(1)
7.2.3 Details of approaches for generating expression plasmids
79(7)
7.2.4 Transfection of mammalian cells for expression
86(1)
7.3 Antibody engineering
86(5)
7.3.1 Chimeric antibodies
87(1)
7.3.2 Humanized antibodies
88(1)
7.3.3 Isolation of V regions
88(3)
8 Principles of Fluorescence
91(16)
8.1 Wave and particle description of light
91(1)
8.2 Jablonski diagram
91(1)
8.3 Stokes shift
92(1)
8.4 Photobleaching
93(1)
8.5 Photophysical characterization of fluorophores
94(2)
8.5.1 Quantum yield
94(1)
8.5.2 Beer-Lambert law, effective absorption cross section and molar extinction coefficient
95(1)
8.5.3 Brightness of a fluorophore
96(1)
8.6 Excitation and emission spectra
96(1)
8.7 Fluorophores
97(10)
8.7.1 Chemical fluorescent dyes
97(1)
8.7.1.1 Labeling of proteins via cysteine or lysine residues
98(1)
8.7.1.2 Labeling of proteins with fluorophore-conjugated strepta-vidin
98(2)
8.7.1.3 In situ labeling of proteins in cells using peptide tags
100(1)
8.7.2 Quantum dots
100(1)
8.7.2.1 Labeling of proteins with quantum dots
101(1)
8.7.3 Fluorescent proteins
102(3)
8.7.4 Photoactivatable and photoswitchable fluorescent probes
105(1)
8.7.5 Other labeling modalities
105(2)
9 Cells
107(20)
9.1 Cellular structure
107(4)
9.2 Receptors
111(1)
9.3 Typical biological systems
112(1)
9.3.1 Subcellular trafficking of the Fc receptor, FcRn
112(1)
9.3.2 Subcellular trafficking of the transferrin receptor
112(1)
9.4 Sample preparation
112(9)
9.4.1 Labeling of proteins in fixed cells
113(1)
9.4.2 Sample preparation for typical fixed cell experiments
114(1)
9.4.3 Sample preparation for typical live cell imaging experiments
115(6)
Notes
121(4)
Exercises
125(2)
III Optics and Microscopy
127(136)
Overview
129(2)
10 Microscope Designs
131(24)
10.1 Light path for widefield fluorescence microscopy
131(1)
10.1.1 Infinity-corrected light path
131(1)
10.2 Imaging in three dimensions
132(1)
10.2.1 Focus control and acquisition of z-stacks
132(1)
10.2.2 Multifocal plane microscopy
133(1)
10.3 Imaging of multiple colors
133(2)
10.4 Light path for confocal microscopy
135(2)
10.5 Two-photon excitation microscopy
137(3)
10.6 Objectives
140(2)
10.6.1 Numerical aperture and immersion medium
140(1)
10.6.2 Corrections
141(1)
10.6.3 Transmission efficiency
141(1)
10.7 Optical filters
142(9)
10.7.1 Example: a filter set for a GFP-labeled protein
145(2)
10.7.2 Imaging of multiple fluorophores
147(4)
10.8 Transmitted light microscopy
151(4)
11 Microscopy Experiments
155(32)
11.1 Fixed cell experiments
155(4)
11.1.1 Localization of FcRn
155(1)
11.1.2 Association experiments with FcRn, EEA1, LAMP1, and transferrin
156(2)
11.1.3 Pulse-chase verification of fate of mutated IgG
158(1)
11.2 Imaging a 3D sample
159(3)
11.2.1 Acquisition of z-stacks
160(1)
11.2.2 Out-of-focus haze
160(2)
11.3 Live cell experiments
162(2)
11.3.1 Example: FcRn-mediated IgG trafficking
163(1)
11.4 Total internal reflection fluorescence microscopy (TIRFM)
164(3)
11.4.1 Objective-based total internal reflection fluorescence microscopy
164(3)
11.4.2 Exocytosis imaged by total internal reflection fluorescence microscopy
167(1)
11.5 pH measurement and ratiometric imaging
167(3)
11.6 Single molecule microscopy
170(7)
11.6.1 Bulk versus single molecule experiments
170(2)
11.6.2 Single molecule tracking experiments
172(1)
11.6.3 Localization-based super-resolution microscopy
173(2)
11.6.3.1 Photophysics of the stochastic excitation of organic fluorophores
175(2)
11.6.4 A localization-based super-resolution experiment
177(1)
11.7 Multifocal plane microscopy
177(10)
11.7.1 Focal plane spacing and magnification
179(1)
11.7.2 Transferrin trafficking in epithelial cells
180(1)
11.7.3 Imaging the pathway preceding exocytosis
181(6)
12 Detectors
187(18)
12.1 Photoelectric effect
187(1)
12.2 Point detectors
188(1)
12.3 Image detectors
188(5)
12.3.1 Charge-coupled device (CCD) detectors
190(1)
12.3.2 Complementary metal-oxide-semiconductor (CMOS) detectors
191(1)
12.3.3 Electron-multiplying charge-coupled device (EMCCD) detectors
192(1)
12.4 Randomness of photon detection and detector noise sources
193(1)
12.5 Grayscale and color cameras
194(2)
12.6 Specifications of image detectors
196(3)
12.7 Measurements of detector specifications
199(6)
12.7.1 Determination of CCD and CMOS detector specifications
199(1)
12.7.1.1 Data model
199(1)
12.7.1.2 Linearity of the response
200(1)
12.7.1.3 Estimation of electron-count-to-DU conversion factor
201(1)
12.7.1.4 Estimation of readout noise mean and variance
202(1)
12.7.1.5 Estimation of mean of dark current
202(1)
12.7.2 Determination of EMCCD detector specifications
202(1)
12.7.2.1 Data model
202(1)
12.7.2.2 Estimation of electron-count-to-DU conversion factor
203(1)
12.7.2.3 Estimation of readout noise mean and variance
204(1)
13 Geometrical Optics
205(18)
13.1 Reflection and refraction
205(4)
13.1.1 Reflection
205(1)
13.1.2 Refractive index
205(1)
13.1.3 Snell's law
206(1)
13.1.4 Total internal reflection
207(1)
13.1.5 Extreme rays in microscopy optics
207(2)
13.2 Lenses
209(4)
13.2.1 Focal points and focal planes
210(1)
13.2.2 Image formation
211(1)
13.2.3 Lensmaker's formula and lens formula
211(2)
13.3 Magnification
213(5)
13.3.1 Lateral magnification
213(3)
13.3.2 Axial magnification
216(2)
13.3.3 Dependence of lateral magnification on axial position
218(1)
13.4 Applications to microscopy
218(5)
14 Diffraction
223(40)
14.1 Wave description of light
223(6)
14.1.1 Plane waves
223(1)
14.1.1.1 Planes of identical phase
224(1)
14.1.1.2 Speed of wave propagation
225(1)
14.1.1.3 Wave number and wavelength
226(1)
14.1.1.4 Propagation in different media
226(1)
14.1.1.5 Optical path length
227(1)
14.1.2 Spherical waves
227(1)
14.1.2.1 Converging and diverging spherical waves
228(1)
14.1.3 Spatial part of a wave
228(1)
14.2 What does a camera detect?
229(1)
14.3 Effect of a thin lens on waves
230(4)
14.4 Huygens-Fresnel principle and Fresnel integral
234(3)
14.4.1 Huygens-Fresnel principle
235(2)
14.5 Imaging through a thin lens
237(14)
14.5.1 Amplitude point spread function
239(1)
14.5.2 Convolution description
240(1)
14.5.3 Relationship to geometrical optics
241(1)
14.5.4 Point spread function and Fourier transformation
241(2)
14.5.4.1 In-focus point spread function
243(1)
14.5.5 Imaging with defocus and the 3D point spread function
244(3)
14.5.5.1 3D point spread function evaluated on the optical axis
247(2)
14.5.5.2 Depth of field and depth of focus
249(2)
14.5.5.3 Heuristic 3D resolution criterion
251(1)
14.6 Convolution for intensity profiles
251(4)
Notes
255(4)
Exercises
259(4)
IV Data Analysis
263(2)
Overview
265(2)
15 From Photons to Image: Data Models
267(1)
15.1 Accounting for each photon: fundamental data model
267(1)
15.1.1 Temporal component of photon detection -- Poisson process
268(2)
15.1.1.1 Mean number of detected photons
270(2)
15.1.2 Spatial component of photon detection -- spatial density function
272(1)
15.1.2.1 Translational invariance and image function
273(3)
15.1.3 Background component
276(1)
15.1.4 Examples
277(1)
15.2 Practical data models
278(15)
15.2.1 Poisson data model
279(5)
15.2.2 CCD/CMOS data model
284(1)
15.2.3 Deterministic data model
285(1)
15.2.3.1 Gaussian approximation for the CCD/CMOS data model
286(1)
15.2.4 EMCCD data model
286(2)
15.2.4.1 High gain approximation for the EMCCD data model
288(1)
15.2.4.2 Gaussian approximation for the EMCCD data model
289(4)
16 Parameter Estimation
293(20)
16.1 Maximum likelihood estimation
293(4)
16.1.1 Example 1: mean of a Poisson random variable
294(1)
16.1.2 Example 2: mean of a Gaussian random variable
295(2)
16.2 Log-likelihood functions for the image data models
297(5)
16.2.1 Log-likelihood function for the fundamental data model
297(2)
16.2.1.1 Example 3: Localization of an object with a 2D Gaussian image profile
299(1)
16.2.2 Log-likelihood functions for the practical data models
300(2)
16.3 Obtaining the maximum likelihood estimate
302(1)
16.4 Maximum likelihood estimation and least squares estimation
303(1)
16.5 Unbiased estimator
304(4)
16.5.1 Example 1: sample mean
304(1)
16.5.2 Example 2: sample variance
304(2)
16.5.3 Example 3: center of mass as an object location estimator under the fundamental data model
306(2)
16.6 Variance of an estimator
308(5)
16.6.1 Example 1: mean of a Poisson and a Gaussian random variable
308(1)
16.6.2 Example 2: center of mass as an object location estimator under the fundamental data model
309(2)
16.6.3 Example 3: center of mass as an object location estimator under a fixed photon count data model
311(2)
17 Fisher Information and Cramer-Rao Lower Bound
313(18)
17.1 Cramer-Rao inequality
313(5)
17.1.1 Sketch of derivation of Cramer-Rao lower bound
314(2)
17.1.2 Multivariate Cramer-Rao lower bound
316(1)
17.1.3 Example 1: mean of a Poisson random variable
317(1)
17.1.4 Example 2: mean of a Gaussian random variable
318(1)
17.2 Fisher information for the fundamental data model
318(4)
17.2.1 Example: known photon detection rate
320(1)
17.2.2 Example: known photon distribution profile
321(1)
17.3 Fisher information for the practical data models
322(5)
17.3.1 Noise coefficient and the Fisher information
323(4)
17.4 Noise coefficient analysis of the pixel signal level
327(3)
17.4.1 Noise coefficient -- an in-depth look
327(1)
17.4.2 Noise coefficient for CCD/CMOS detectors
327(1)
17.4.3 EMCCD detectors as low-light detectors
328(1)
17.4.4 Comparison of CCD/CMOS and EMCCD detectors
329(1)
17.5 Fisher information for multi-image data
330(1)
18 Localizing Objects and Single Molecules in Two Dimensions
331(40)
18.1 Object localization as a parameter estimation problem
331(2)
18.2 Example: estimating the location of a single molecule
333(2)
18.3 How well can the location of an object be estimated?
335(5)
18.3.1 Bias of location estimation
335(3)
18.3.1.1 Bias of the center of mass as a location estimator under the practical data models
338(1)
18.3.2 Variance of location estimation
338(2)
18.4 Estimation of other parameters
340(1)
18.5 Cramer-Rao lower bound for location estimation -- fundamental data model
340(8)
18.5.1 Cramer-Rao lower bound for the Airy image function
344(2)
18.5.2 Cramer-Rao lower bound for the 2D Gaussian image function
346(1)
18.5.3 Extensions to further experimental situations
347(1)
18.6 Cramer-Rao lower bound for location estimation -- practical data model
348(8)
18.6.1 Poisson data model -- effects of pixelation, finite image size, and background noise
349(2)
18.6.2 Localizing objects from CCD/CMOS and EMCCD images
351(3)
18.6.3 Object location makes a difference
354(2)
18.7 Efficiency of estimators: how well is the behavior of estimators described by the Cramer-Rao lower bound?
356(2)
18.7.1 Fundamental data model
356(1)
18.7.2 Practical data models
357(1)
18.8 Approximations
358(2)
18.8.1 Gaussian approximations for the CCD/CMOS and EMCCD data models
359(1)
18.8.2 Inverse square root approximation of the dependence on the photon count
360(1)
18.9 Lower bound as a tool for the design of data analysis
360(3)
18.9.1 Choosing the region of interest
361(1)
18.9.2 Improving estimation performance by adding images
362(1)
18.10 Example: single molecule localization from experimentally acquired images
363(8)
18.10.1 Choice of data model based on the detector used
365(1)
18.10.2 Modeling the image of the molecule and the background component
365(1)
18.10.3 Determining the "known" parameters
366(1)
18.10.4 Location estimates
367(1)
18.10.5 Initial values
367(1)
18.10.6 Assessing the standard deviation of the localization
368(3)
19 Localizing Objects and Single Molecules in Three Dimensions
371(24)
19.1 Parameter estimation for object localization in three dimensions
371(1)
19.2 Cramer-Rao lower bound for 3D location estimation -- fundamental data
372(6)
19.2.1 3D localization of a point source
374(4)
19.3 Cramer-Rao lower bound for 3D location estimation -- practical data
378(1)
19.4 Depth discrimination problem
379(2)
19.5 Dependence of lateral location estimation on the axial position
381(1)
19.6 Multifocal plane microscopy
382(13)
19.6.1 Estimating the axial location from MUM data
384(2)
19.6.2 Experimental example
386(1)
19.6.3 Maximum likelihood localization with simulated data
386(3)
19.6.4 Overcoming the depth discrimination problem
389(2)
19.6.5 Zero Fisher information and the depth discrimination problem
391(1)
19.6.6 Experimental design: finding appropriate focal plane spacings
392(1)
19.6.7 Further approaches to address the depth discrimination problem
393(2)
20 Resolution
395(16)
20.1 Resolution as a parameter estimation problem
395(1)
20.2 Cramer-Rao lower bound for distance estimation -- fundamental data
395(2)
20.3 Two in-focus objects: an information-theoretic Rayleigh's criterion
397(3)
20.4 Two objects in 3D space
400(5)
20.5 Cramer-Rao lower bound for distance estimation -- practical data
405(6)
21 Deconvolution
411(16)
21.1 The deconvolution problem
411(2)
21.2 Discretization
413(1)
21.2.1 Linear algebra formulation
414(1)
21.3 Linear least squares algorithm
414(5)
21.3.1 Condition number of a matrix
415(1)
21.3.1.1 Example of an ill-conditioned least squares problem
416(1)
21.3.2 Regularization of the least squares problem
416(1)
21.3.2.1 Example continued: regularization of the ill-conditioned least squares problem
417(1)
21.3.3 A Fourier transform approach
418(1)
21.4 Maximum likelihood formulation
419(2)
21.4.1 Expectation maximization algorithm
419(2)
21.5 Positron emission tomography
421(6)
21.5.1 Deconvolution for the Poisson data model
424(1)
21.5.2 An illustrative example
425(2)
22 Spatial Statistics
427(1)
22.1 Formal definitions
427(1)
22.1.1 Spatial Poisson processes
428(1)
22.2 Intensity functions of spatial processes
429(1)
22.2.1 Computing the intensity functions
430(1)
22.2.2 Stationary point processes
431(1)
22.3 K function and L function
432(7)
22.3.1 An example of an inhibition process
433(2)
22.3.2 Estimating the K function
435(4)
Notes
439(6)
Exercises
445
Online Appendix -- Mathematical Background /www.routledge.com/9781138598980
1(1)
A Probability and Statistics
1(8)
A.1 Tutorial: Poisson and Gaussian random variables
1(4)
A.1.1 Poisson random variables
3(1)
A.1.1.1 Additivity of Poisson random variables
4(1)
A.1.2 Gaussian random variables
4(1)
A.2 Expectation of a Poisson random variable given a sum of Poisson random variables
5(2)
A.3 Additivity of Fisher information matrices
7(2)
B Analysis
9(2)
B.1 Delta function
9(1)
B.2 Taylor series approximation
9(1)
B.3 Change of variables theorem
9(2)
B.3.1 Change of Cartesian coordinates to polar coordinates
10(1)
C Fourier Transform
11(6)
C.1 Fourier transform
11(1)
C.1.1 Fourier transform of circularly symmetric functions
12(1)
C.2 Discrete Fourier transform
12(2)
C.3 Multidimensional discrete Fourier transform
14(3)
D Least Squares Minimization
17(4)
D.1 Least squares minimization problem
17(2)
D.2 Linear least squares in the Fourier domain
19(2)
E Fisher Information for the Fundamental Data Model
21(8)
F Fisher Information for the Deterministic Data Models
29(2)
F.1 Simple form of the deterministic data model
29(1)
F.2 Gaussian approximation for the CCD/CMOS data model
30(1)
F.3 Gaussian approximation for the EMCCD data model
30(1)
G Models of EMCCD Electron Multiplication
31(12)
G.1 Geometric multiplication-based EMCCD probability mass function
31(6)
G.1.1 Branching processes
31(1)
G.1.2 Electron multiplication as a branching process
32(1)
G.1.3 Geometric distribution of offspring electrons
32(1)
G.1.4 Probability generating function of output electron count given one initial electron
32(1)
G.1.5 Probability generating function of output electron count given initial electrons
33(1)
G.1.6 Probability mass function of output electron count given initial electrons
33(3)
G.1.7 Probability mass function of output electron count
36(1)
G.2 Exponential multiplication-based EMCCD probability density function
37(1)
G.3 Geometric multiplication-based EMCCD noise coefficient
38(2)
G.4 Exponential multiplication-based EMCCD noise coefficient
40(3)
Notes 43(2)
Exercises 45(406)
Figure Credits 451(6)
Bibliography 457(8)
List of Symbols 465(4)
Index of Names 469(2)
Index 471
Raimund J. Ober, E. Sally Ward, Jerry Chao