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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
Discover what biological imaging is able to accomplish in this up-to-date textbook
One of the fundamental goals of biology is to understand how living organisms establish and maintain their spatiotemporal organization of the biochemical, cell biological and developmental biology processes that sustain life. Biological systems are inherently complex with a large number of components needed to sustain cellular function. In order to understand any complex system, one must determine its composition by identifying the components it is made of, how each of these components function and carry out their specific task, and how they interact with one another to function together. To grasp the link of such changes to physiological cell and tissue function and/or pathogenesis/disease progression, we need to understand how modifications alter macromolecular function, macromolecular interactions, and/or spatiotemporal distribution and overall supramolecular structural organization. Biological imaging holds the key to understanding spatiotemporal organization, and will thus be increasingly important for the next generations of biological and biochemical researchers.
Introduction to Biological Imaging provides the first comprehensive textbook surveying this subject. It elucidates the fundamental principles underlying the capture and production of bioimages, the requirements of image analysis and interpretation, and some key problems and solutions in bioimaging. It includes everything experimental biologists need to incorporate appropriate bioimaging solutions into their work.
Introduction to Biological Imaging readers will also find:
- Coverage of all major types of biological imaging, including medical imaging, cellular imaging, macromolecular imaging, and more
- Advice on preparing samples for various imaging methods
- Specific examples in each chapter connecting bioimaging process to the production of real experimental data
Introduction to Biological Imaging is a valuable introduction for undergraduate or graduate students in courses relating to bioimaging, as well as scientists and researchers in the biological and medical fields who want a one-stop reference for the full range of imaging techniques.
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Veröffentlichungsjahr: 2024
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Table of Contents
Cover
Table of Contents
Title Page
Copyright Page
Dedication Page
Preface
1 Introduction – A Brief History of Imaging
1.1 Spatiotemporally Organization of Biological Processes
1.2 Why Imaging?
2 Basics of Atomic and Molecular Physics
2.1 Matter and Energy
2.2 Electromagnetic Spectrum
2.3 Interaction of Radiation with Matter
3 Basics of Optics
3.1 Properties of Light
3.2 Classical Optics
3.3 Interaction of Light with Matter
3.4 Lenses
3.5 Numerical Aperture
3.6 Scattering
3.7 Interference
3.8 Diffraction
3.9 Airy Disk
3.10 Point‐Spread Function
3.11 Resolution
3.12 Aberrations
3.13 Polarization
3.14 Photoelectric Effect
3.15 Valence Electron Transitions
3.16 Vibrational and Rotational Transitions
3.17 Stimulated Emission
4 Basics of Fourier “Math”
5 Basics of Imaging and Image Formation
5.1 Goals of Imaging
5.2 Source of Radiation
5.3 Beam Collimation
5.4 Lenses
5.5 Lens‐Free Imaging/Near Field Imaging
5.6 Signal Detection and Detectors
5.7 Modulation‐, Contrast‐, and Optical‐Transfer Function
5.8 Field of View, Coverage
5.9 Spot‐Scan Versus Flood‐Beam Imaging
5.10 Radiation Damage
5.11 Spectroscopic Imaging
5.12 Multidimensional and Multi‐channel Imaging
5.13 Sample Preparation Requirements
5.14 Labels and Labeling
5.15 Label‐Free Imaging
5.16 2D Versus 3D Imaging
5.17 Structural Versus Functional Imaging
5.18 Correlative Imaging
6 Basics of Image Processing and Image Analysis
6.1 Overview
6.2 Characteristics of Digital Images
6.3 Compression of Image Data
6.4 Metadata
6.5 Spatial, Temporal and Spectral Resolution
6.6 Image Intensity Values
6.7 Raster Graphics Versus Vector Graphics
6.8 Simple Image Processing Operations
6.9 Image Restoration
6.10 Image Enhancement
6.11 Image Reconstruction
6.12 Visualization
6.13 Image Segmentation
6.14 Making Changes to Object Selection: Dilation, Erosion, Closing, Filling Holes
6.15 Measurements/Quantitative Analysis
7 Techniques – Macromolecular Structure Determination
7.1 X‐Ray Crystallography
7.2 Macromolecular Cryo‐Electron Microscopy
7.3 NMR Spectroscopy
7.4 Small Angle Scattering – SAXS and SANS
7.5 Atomic Force Microscopy (AFM)
8 Techniques – Cell and Tissue Architectural Imaging
8.1 Overview
8.2 Historical Perspective/Milestones
8.3 Theoretical Background
8.4 Sample Preparation
8.5 Instrumentation
8.6 Data Collection and 3D Reconstruction
8.7 Data Visualization and Analysis
9 Techniques – Cell and Tissue Characterization and Localization Imaging
9.1 Overview
9.2 Historical Perspective/Milestones
9.3 Theoretical Background
9.4 Instrumentation/Experimental Setup
9.5 Data Collection
9.6 Fluorophore Considerations
9.7 Genetically Encoded Fluorescent Proteins
9.8 RNA/DNA – Fluorescent Probes
9.9 Fluorescent Imaging Types
9.10 Detangling the Many Fluorescence Imaging Approaches
9.11 Improve Axial Resolution and Reduce Phototoxicity
9.12 Superresolution Imaging – Breaking the Resolution Barrier
9.13 Superresolution Imaging
9.14 Near‐Field Scanning Microscopy
9.15 Molecular Dynamics Study of Macromolecules
9.16 Visualization
9.17 Data Analysis
10 Techniques – Chemical Imaging at the Cell and Tissue Level
10.1 X‐Ray Microanalysis: X‐Ray Fluorescence Microscopy (XFM), Energy‐Dispersive X‐Ray Spectroscopy (EDX), Wavelength‐Dispersive X‐ray Spectroscopy (WDS)
10.2 Raman Imaging
10.3 Fourier‐Transform Infrared (FTIR) Imaging
10.4 Mass Spectrometry
11 Techniques – Tissue and Organ Medical Imaging
11.1 Positron Emission Tomography
11.2 Computed (Axial) Tomography
11.3 Magnetic Resonance Imaging (MRI)
11.4 Medical Ultrasound
11.5 Bioluminescence
11.6 Optical Coherence Tomography
11.7 Histology/Histopathology
12 The Future of Bioimaging
12.1 Future of Macromolecular Imaging
12.2 Future of (Sub)Cellular and Tissue Imaging
12.3 Future of Chemical Imaging
12.4 Future of Medical Imaging
12.5 Future of Image Processing and Analysis
12.6 Future of Multiscale Imaging
12.7 Future of Multimodal Imaging – Correlative Imaging
12.8 Future of Information Integration – Multiscale, Multimodal, and Integrated
Index
End User License Agreement
List of Tables
Chapter 7
Table 7.1 Nobel Prizes involving X‐rays and crystallography.
Table 7.2 Nobel Prizes involving macromolecular electron microscopy.
Table 7.3 Nobel Prizes involving nuclear magnetic resonance spectroscopy.
Table 7.4 Nobel Prize involving neutron scattering.
Table 7.5 Nobel Prize involving atomic force microscopy.
Chapter 8
Table 8.1 Nobel Prizes involving cell and tissue architecture.
Chapter 9
Table 9.1 Nobel Prizes involving cell and tissue characterization and local...
Chapter 10
Table 10.1 Nobel Prizes involving chemical imaging.
Chapter 11
Table 11.1 Nobel Prizes involving medical imaging.
List of Illustrations
Chapter 2
Figure 2.1 Protons and neutrons are the results of different combinations of...
Figure 2.2 Role of baryons, ions, leptons, and bosons for different imaging ...
Figure 2.3 Structure of an atom with nucleus and electron cloud. Left: Typic...
Figure 2.4 Atomic nuclei – Organization and element identity. Protons and ne...
Figure 2.5 Bohr's atom model with electrons circulating the nucleus on discr...
Figure 2.6 Electron cloud model/quantum mechanical model of the hydrogen ato...
Figure 2.7 Maximal electron occupancy of shells in atoms and electron transi...
Figure 2.8 Atomic orbitals typical for biologically relevant elements such a...
Figure 2.9 Electron configurations for several selected elements. The hydrog...
Figure 2.10 Periodic table of elements (excluding transition elements and ra...
Figure 2.11 Atomic absorption and emission spectra. Top: Solar spectrum with...
Figure 2.12 Molecular orbitals of carbon: sp
3
‐, sp
2
‐, sp‐hybridization. In t...
Figure 2.13 Covalent bonds – Sigma and pi‐bonds. Covalent sigma‐bonds are fo...
Figure 2.14 Molecular bonds in organic chemistry and conjugated pi‐systems. ...
Figure 2.15 Non‐polar, polar, hydrogen‐, and ionic bonds. Carbon–carbon and ...
Figure 2.16 Electromagnetic spectrum. The electromagnetic spectrum ranges fr...
Figure 2.17 Use of particle radiation and photons in bioimaging. One disting...
Figure 2.18 Interaction of electromagnetic radiation with matter. Gamma‐ray ...
Figure 2.19 Macroscopic description of radiation interacting with matter. Us...
Figure 2.20 Behavior of a radiation wave front at a medium boundary. An inco...
Figure 2.21 Penetration/absorption of alpha‐particles (helium nuclei), beta‐...
Figure 2.22 Scattering. In scattering, incoming particle or photon radiation...
Figure 2.23 Scattering of X‐ray by single electron. In this highly simplifie...
Figure 2.24 Diffraction: Single obstacle and single slit. Waves, encounterin...
Chapter 3
Figure 3.1 Properties of an electromagnetic wave. Electromagnetic waves are ...
Figure 3.2 Reflection. When light rays encounter a mirror surface the angle ...
Figure 3.3 Refraction. Light at the boundary of two different media is eithe...
Figure 3.4 Dispersion. White light (which is a mixture of light of different...
Figure 3.5 Lenses. Lenses with a positive focal length (converging lenses) a...
Figure 3.6 Ray tracing/geometric optics. There are a three rules that guide ...
Figure 3.7 Lens magnification. The magnification of an object is given by th...
Figure 3.8 Numerical aperture. The numerical aperture (NA) reflects a lens' ...
Figure 3.9 Constructive and destructive interference. Two waves with the sam...
Figure 3.10 Single‐slit, double‐slit, and multi‐slit wave interference. Left...
Figure 3.11 Bragg diffraction. In a crystal, atoms are organized into a peri...
Figure 3.12 Airy disk. Light passing through an aperture (e.g., in a microsc...
Figure 3.13 Point‐spread function. The point‐spread function indicates how l...
Figure 3.14 Raleigh criterion in resolution. The Raleigh criterion states th...
Figure 3.15 Spherical and chromatic aberration. In the case of spherical abe...
Figure 3.16 Birefringence. Calcite crystals show birefringence, a phenomenon...
Figure 3.17 Photoelectric effect and inner photoelectric effect. Photons wit...
Figure 3.18 Jablonski diagram. The Jablonski diagram depicts the various way...
Figure 3.19 Vibrational and rotational states. Molecules can exist in vibrat...
Figure 3.20 Stimulated emission. For stimulated emission to occur the system...
Chapter 4
Figure 4.1 Waves: Amplitude and frequency. Sine waves are characterized by t...
Figure 4.2 Superposition of waves: Role of amplitude and frequency. Superpos...
Figure 4.3 Superposition of waves: Role of phase shift, amplitude, and frequ...
Figure 4.4 Constructive and destructive interference depends on the phase sh...
Figure 4.5 Approximation of a step function by adding sinusoid wave function...
Figure 4.6 Description of a vector as a complex number. A vector can be desc...
Figure 4.7 Fourier–Bessel series. Fourier–Bessel series are a special case o...
Chapter 5
Figure 5.1 Goals of bioimaging. There are different goals in biological imag...
Figure 5.2 Source of electromagnetic radiation and application in bioimaging...
Figure 5.3 Source of particle radiation and application in bioimaging. Accel...
Figure 5.4 Beam collimation. Radiation can be either collimated by lenses or...
Figure 5.5 Ray tracing in geometric optics of lenses. There are three guidin...
Figure 5.6 Lens magnification. The magnification
M
(
h
i
/
h
o
) is given by the r...
Figure 5.7 Far‐field and near‐field illumination. Left: Far‐field illuminati...
Figure 5.8 Detectors – Choice of detectors. A variety of factors need to be ...
Figure 5.9 Types of radiation and detection principles. There are a variety ...
Figure 5.10 Electron microscopy and helium ion microscopy. In scanning elect...
Figure 5.11 Particle radiation detection by gas ionization or scintillation....
Figure 5.12 Detection of electromagnetic radiation. Depending on their energ...
Figure 5.13 Detection of fluorescence photon signal by the inner photoelectr...
Figure 5.14 Detection of radio waves by a radio frequency (RF) detector elec...
Figure 5.15 Photomultiplier tubes (PMT). Photomultiplier tubes (PMT) are sen...
Figure 5.16 Photographic film processing. When a silver halide crystal emuls...
Figure 5.17 Photostimulable phosphor plates. High energy photons oxidize a E...
Figure 5.18 Charge‐Coupled Device. Charge‐Coupled Devices (CCD) contain a ph...
Figure 5.19 Complementary metal‐oxide semiconductor. Complementary metal‐oxi...
Figure 5.20 Modulation transfer function. The modulation transfer function (...
Figure 5.21 Contrast transfer function (CTF) in electron microscopy. In tran...
Figure 5.22 Field of view/coverage – Large scale heterogeneity and difficult...
Figure 5.23 Field of view/coverage – Detection of rare events. In a 45 μm × ...
Figure 5.24 Field of view/coverage – Cell‐to‐cell heterogeneity and new cell...
Figure 5.25 Spot scan versus flood beam illumination. In spot scan illuminat...
Figure 5.26 Radiation damage. Ionizing radiation can damage biological macro...
Figure 5.27 FTIR spectroscopic imaging. In FTIR imaging one uses a Michelson...
Figure 5.28 Multidimensional imaging. A typical 2D image is a gray value as ...
Figure 5.29 Multichannel imaging versus multi‐dimensional imaging. Color ima...
Figure 5.30 Sample preparation for macromolecular imaging. The key objective...
Figure 5.31 Sample preparation for 3D architecture determination. Sample pre...
Figure 5.32 Sample preparation for localization of macromolecules. In genera...
Figure 5.33 Sample preparation for determining the chemical inventory of cel...
Figure 5.34 Sample preparation for medical imaging of tissues and organs. Ma...
Figure 5.35 Affinity‐targeted labels and endogenously expressed fusion prote...
Figure 5.36 2D versus 3D imaging. 3D volume of mammary gland cells cultured ...
Figure 5.37 Optical sectioning and physical sectioning. Optically transparen...
Figure 5.38 Example of 2D versus 3D imaging. Top and middle: Two non‐adjacen...
Figure 5.39 Tomographic 3D reconstructions by back projections. For imaging ...
Chapter 6
Figure 6.1 Image processing and analysis. Acquired digital images undergo im...
Figure 6.2 Digital image: An image is a rectangular array of pixels (picture...
Figure 6.3 Characteristics of a 3D volume image. For three‐dimensional data,...
Figure 6.4 1‐bit, 2‐bit, 4‐bit, 8‐bit, 16‐bit. 1‐bit encoding means that the...
Figure 6.5 File structure including a header and a data block. An image typi...
Figure 6.6 Example of an image header: MRC‐format. This example of an image ...
Figure 6.7 Image data block: Fast, medium, and slow axes. The fast axis in i...
Figure 6.8 Grayscale and color raster images. Images are composed of a raste...
Figure 6.9 RGB color images: Intensity of the red, green, and blue channels....
Figure 6.10 Color spaces: Additive (RGB) and subtractive (CMYK). Depending o...
Figure 6.11 Color spaces: RGB, HSV/HSB, HSL. Apart from the above‐mentioned ...
Figure 6.12 File formats: Raster graphics, vector graphics, video. The main ...
Figure 6.13 2D, 3D, 4D, and 5D images. A single image is two‐dimensional, wi...
Figure 6.14 JPEG‐compression of image data. The first step of JPEG compressi...
Figure 6.15 Quantization. Quantization starts with a discrete cosine transfo...
Figure 6.16 Video compression. For video compression there are different typ...
Figure 6.17 Metadata. Metadata typically contain information about the sampl...
Figure 6.18 Spatial resolution, temporal resolution, spectral resolution. Sp...
Figure 6.19 Contrast adjustment. Density values in images often do not cover...
Figure 6.20 Histogram‐based 16‐bit to 8‐bit conversion. The entire 16‐bit im...
Figure 6.21 False color representation, look‐up tables (LUT). 8‐bit screens ...
Figure 6.22 Pixel‐based raster image versus vector‐graphics and anti‐aliasin...
Figure 6.23 Resizing images and binning. When resizing images, one needs to ...
Figure 6.24 Contrast inversion. Contrast in a scanning electron micrograph o...
Figure 6.25 Non‐uniform illumination correction. Non‐uniform illumination co...
Figure 6.26 Noise reduction – Smoothing filters. Top row shows the entire im...
Figure 6.27 Noise reduction in Fourier space – Bandpass filter. Bandpass fil...
Figure 6.28 Noise reduction in real space – Gaussian filter. Applying a Gaus...
Figure 6.29 Noise reduction in real space – Mean filter. When applying the m...
Figure 6.30 Noise reduction in real space – Median filter. The median filter...
Figure 6.31 Noise reduction in real space – Non‐linear anisotropic diffusion...
Figure 6.32 Image registration – Serial section transmission electron microg...
Figure 6.33 Alignment of tilt series image for 3D reconstruction. As shown o...
Figure 6.34 Famous examples of scientific visualization. Top: Flow map of Na...
Figure 6.35 Macromolecular density maps are visualized by volume rendering, ...
Figure 6.36 Subcellular region of a cell imaged as 3D volume by Focused Ion ...
Figure 6.37 Visualizing 3D (sub)cellular data. Left: Mesh rendering of a cel...
Figure 6.38 Contour levels for visualizing macromolecular structures. Projec...
Figure 6.39 Visualizing macromolecular structures. Examples of different rep...
Figure 6.40 Visualizing cellular imaging data – Scale and complexity. Cellul...
Figure 6.41 Thresholding. In this case, a cellular volume was recorded by du...
Figure 6.42 Histogram region selection and thresholding. Top row: Different ...
Figure 6.43 Image segmentation – Manual painting combined with watershed seg...
Figure 6.44 Classical automated image segmentation. There are a variety of c...
Figure 6.45 Clustering methods. Left: Raman spectrum of pure compounds known...
Figure 6.46 Region growing: Graph cut. Top row, left panels: Schematic depic...
Figure 6.47 Edge detection. Edges are regions in an image where there is a s...
Figure 6.48 Active contours. Active contours are curves being placed somewha...
Figure 6.49 Level set method. In the level set method, a user identifies see...
Figure 6.50 Watershed segmentation. Watershed segmentation starts with the h...
Figure 6.51 Boundary segmentation. Prior to boundary segmentation on needs t...
Figure 6.52 Machine learning – Large volume of a
Desulfovibrio vulgaris
(bac...
Figure 6.53 Machine learning – Orientation classification model. By providin...
Figure 6.54 Machine learning and deep learning. Machine learning refers to t...
Figure 6.55 Deep learning – U‐net based segmentation of large cellular 3D vo...
Figure 6.56 Making changes to the object selection. Top: Thresholding an ima...
Figure 6.57 Model building and quantitative analysis. Quantification of some...
Figure 6.58 Skeletonization. After segmentation, skeletons of filamentous fe...
Chapter 7
Figure 7.1 Macromolecular 3D structure. X‐ray crystallography, single partic...
Figure 7.2 X‐ray – Overview. X‐ray crystallography relies on the formation o...
Figure 7.3 Crystal lattices. There are seven crystal systems that differ in ...
Figure 7.4 X‐ray – Bragg diffraction and Fourier methods. A crystal is a reg...
Figure 7.5 X‐ray diffraction pattern. The diffraction pattern of fumarate re...
Figure 7.6 Isomorphous replacement – Harker diagram. For each
hkl
‐index ther...
Figure 7.7 3D crystals. Fumarate reductase grows as 3D crystals under slight...
Figure 7.8 X‐ray generation. Accelerated electrons hit a rotating copper ano...
Figure 7.9 Synchrotron radiation. Synchrotron radiation is given off by high...
Figure 7.10 Detectors for X‐ray crystallography – CCD and CMOS. CCD detector...
Figure 7.11 Data collection. For data collection in X‐ray crystallography, t...
Figure 7.12 Multi‐wavelength anomalous diffraction (MAD) phasing. For MAD ph...
Figure 7.13 Atomic model building. Atomic models can be placed into the expe...
Figure 7.14 Overview – Cryo‐EM. Thin films of different kind of samples such...
Figure 7.15 Cryo‐EM: Challenges and solutions. A variety of challenges need ...
Figure 7.16 Macromolecular EM: Biological samples with icosahedral, helical,...
Figure 7.17 Prominent examples of specimen types for 3D‐EM. There are severa...
Figure 7.18 Sample preparation for macromolecular electron microscopy. Macro...
Figure 7.19 Sample preparation for cryo‐EM. Plunge‐freezing of a thin biolog...
Figure 7.20 The projection theorem. The projection theorem states that a 3D ...
Figure 7.21 Helical reconstruction – Microtubule. After straight portions of...
Figure 7.22 2D crystalline symmetry – P‐type H
+
– ATPase. 2D crystals of the...
Figure 7.23 3D structure of particles with no internal symmetry. For single ...
Figure 7.24 Contrast formation in cryo‐EM (TEM). Electrons traveling through...
Figure 7.25 3D volumes from 2D images – Particles in random orientation. Zer...
Figure 7.26 3D volumes from 2D images – Tilt series data collection and back...
Figure 7.27 Electron guns. Electron guns consist of an electron source resid...
Figure 7.28 Electron optics – System of lenses for transmission (TEM) and sc...
Figure 7.29 Transmission electron microscopy (TEM) detectors. Typical area d...
Figure 7.30 Cryo‐EM – Sample preparation. EM grids with their metal bars sup...
Figure 7.31 Cryo‐EM – Data collection. Objects with helical symmetry or icos...
Figure 7.32 Data analysis – 2D crystals. Images of planar 2D crystals result...
Figure 7.33 Data analysis – Tubular crystals and filaments with helical symm...
Figure 7.34 Data analysis – Viruses with icosahedral symmetry. Images of vir...
Figure 7.35 Data analysis – Single particles without internal symmetry. Sing...
Figure 7.36 Data analysis – Electron tomography with subvolume/subtomogram a...
Figure 7.37 Electron diffraction – Thin 3D crystals. An electron beam irradi...
Figure 7.38 Resolution estimates – Fourier shell correlation. The Fourier sh...
Figure 7.39 Factors limiting resolution in the electron microscope. There ar...
Figure 7.40 NMR overview. In
1
H‐NMR spectroscopy, a macromolecular sample at...
Figure 7.41 NMR – Spin alignment and relaxation. In the absence of a strong ...
Figure 7.42 NMR – Longitudinal (T1) spin relaxation. The nuclear spins under...
Figure 7.43 NMR – Transverse (T2) spin relaxation. Application of an appropr...
Figure 7.44 NMR – RF‐pulse excitation and spin relaxation. In a constant str...
Figure 7.45 NMR – Chemical shift and peak assignment. The NMR signal detecte...
Figure 7.46 2D NMR spectroscopy. Compared to one‐dimensional NMR spectroscop...
Figure 7.47 Small angle X‐ray scattering. In small angle X‐ray scattering (S...
Figure 7.48 Small‐angle neutron scattering (SANS). In small angle neutron sc...
Figure 7.49 Atomic force microscopy. An atomic force microscopy (AFM) probe ...
Figure 7.50 Atomic force microscopy – Schematic imaging result. Atomic force...
Chapter 8
Figure 8.1 Cell and tissue architecture imaging. The key objective of subcel...
Figure 8.2 Ultrastructure of muscle tissue. These images show at different s...
Figure 8.3 Cellular ultrastructure – Organelles and supramolecular assemblie...
Figure 8.4 Conventional SEM, TEM electron tomography and Focused Ion Beam SE...
Figure 8.5 Transmission electron microscopy (TEM) imaging. In TEM imaging, a...
Figure 8.6 Scanning electron microscopy (SEM). Upon irradiation of the surfa...
Figure 8.7 Helium ion microscopy (HeIM). Helium ion microscopy (HeIM) is lik...
Figure 8.8 X‐ray microscopy tomography (XRMT). In X‐ray microscopy tomograph...
Figure 8.9 Fluorescence, optical, and electron microscopy – Examples. Left c...
Figure 8.10 TEM contrast generation in heavy‐metal stained samples. Unlike p...
Figure 8.11 Contrast generation in the SEM. The main mechanisms of contrast ...
Figure 8.12 Contrast generation in the helium ion microscope (HeIM). The mai...
Figure 8.13 Contrast generation in X‐ray microscopy tomography (XRMT). As X‐...
Figure 8.14 3D reconstruction from tilt series – Tomographic imaging. Shown ...
Figure 8.15 Cryogenic sample preparation for TEM – Thinning. Biological samp...
Figure 8.16 Sample preparation for TEM – Conventional processing versus high...
Figure 8.17 Instrumentation: Optical microscopy, transmission and scanning e...
Figure 8.18 Detectors used in TEM, SEM, HeIM, and XRMT. CCD and CMOS detecto...
Figure 8.19 Specimen stage: Side entry grid holder for TEM. Side entry sampl...
Figure 8.20 Specimen stage: Sample holder for SEM/HeIM, and XRMT. Compared t...
Figure 8.21 TEM tomography – 3D reconstruction from tilt series. In electron...
Figure 8.22 Serial section TEM. In serial section TEM, the resin block (with...
Figure 8.23 Array tomography/Automated Tape‐collecting UltraMicrotome (ATUM)...
Figure 8.24 Serial Block Face SEM. For Serial Block Face SEM a diamond knife...
Figure 8.25 Focused Ion Beam (dual beam) SEM. In Focused Ion Beam (FIB) SEM ...
Figure 8.26 X‐ray microscopy tomography. The samples in X‐ray microscopy tom...
Figure 8.27 TEM tomography data visualization of original and denoised data....
Figure 8.28 Visualization and manual segmentation of cells. Top: Single slic...
Figure 8.29 Segmentation (feature extraction) of large cellular volumes by D...
Figure 8.30 Volumetric model building and quantitative analysis. Segmented v...
Figure 8.31 Deriving biological meaning from ultrastructural analysis. In th...
Chapter 9
Figure 9.1 Cell and tissue characterization and dynamic localization of macr...
Figure 9.2 Microscope design over the last few centuries. A selection of mic...
Figure 9.3 Applications of microscopy to biological samples.In 1665 in h...
Figure 9.4 Examples of white light and fluorescence microscopy applications....
Figure 9.5 Fluorescence light microscopy – Overview. In fluorescence microsc...
Figure 9.6 Theoretical background of fluorescence. For fluorescence to occur...
Figure 9.7 Upright versus inverted microscope for trans‐illumination. For wh...
Figure 9.8 Fluorescent light microscopy in epi‐illumination. A typical setup...
Figure 9.9 Emission spectrum of different types of light sources. Different ...
Figure 9.10 Spherical and chromatic aberrations. Among the various aberratio...
Figure 9.11 Optical/fluorescence microscopy detectors. The most prominently ...
Figure 9.12 How to choose the right kind of microscopy. The best choice for ...
Figure 9.13 White light imaging of tissue paper.Tissue paper consists of...
Figure 9.14 Point spread function for fluorescence microscopy. In fluorescen...
Figure 9.15 Fluorophore considerations – Overview. Labels are typically divi...
Figure 9.16 Synthetic organic fluorescent dyes. Organic fluorescent dyes are...
Figure 9.17 Quantum dots (Q‐dots) fluorophores. Quantum dots are made of a c...
Figure 9.18 Intrinsically fluorescent proteins. Intrinsically fluorescent pr...
Figure 9.19 Extrinsically fluorescent proteins. Extrinsically fluorescent pr...
Figure 9.20 Fluorescent probes for DNA or RNA labeling. Fluorescence in‐situ...
Figure 9.21 Different fluorescent imaging approaches: Epifluorescence, confo...
Figure 9.22 Deconvolution microscopy. Any point source in a microscope gets ...
Figure 9.23 Confocal laser scanning microscopy. In a classical confocal micr...
Figure 9.24 Dual spinning disk confocal microscopy. Spinning disk confocal m...
Figure 9.25 4Pi microscopy. In 4Pi microscopy, a sample is illuminated by a ...
Figure 9.26 Two‐photon microscopy. Two‐photon microscopy uses a pulsed Ti:sa...
Figure 9.27 Single plane illumination microscopy. Single plane illumination ...
Figure 9.28 Total internal reflection fluorescence microscopy. In total inte...
Figure 9.29 Photoactivation. In photoactivation, at any given time only a sm...
Figure 9.30 Photoconversion. Like in photoactivation, in photoconversion onl...
Figure 9.31 Photoswitching. In stochastic photoswitching, only a small fract...
Figure 9.32 Single molecule localization microscopy – PALM. In single molecu...
Figure 9.33 Patterned illumination microscopy – STED. Stimulated emission de...
Figure 9.34 Patterned illumination microscopy – SIM. Structured illumination...
Figure 9.35 Fluorescence recovery after photobleaching microscopy. In fluore...
Figure 9.36 Fluorescence loss in photobleaching microscopy. In fluorescence ...
Figure 9.37 Fluorescence speckle microscopy. In fluorescence speckle microsc...
Figure 9.38 Förster resonance energy transfer microscopy. For Förster resona...
Figure 9.39 Data analysis of high‐throughput imaging data. For high‐throughp...
Chapter 10
Figure 10.1 Chemical imaging – Principles. The main techniques in chemical i...
Figure 10.2 Imaging the chemical inventory of cells and tissues. The key obj...
Figure 10.3 X‐ray microanalysis: Energy‐dispersive and wavelength‐dispersive...
Figure 10.4 Energy‐disperse X‐ray spectroscopy imaging – Examples. This figu...
Figure 10.5 Vibrational states. Each electronic state has vibrational ground...
Figure 10.6 Raman scattering – Theoretical background. As vibrational modes ...
Figure 10.7 Spontaneous Stokes and Anti‐Stokes Raman scattering. In spontane...
Figure 10.8 Coherent anti‐Stokes Raman scattering. Coherent anti‐Stokes Rama...
Figure 10.9 Stimulated Raman scattering. In stimulated Raman scattering (SRS...
Figure 10.10 Tip‐enhanced Raman scattering and surface‐enhanced Raman scatte...
Figure 10.11 Polarized Raman microspectroscopy of plant cell wall. In this e...
Figure 10.12 Analysis of Raman images. In Raman imaging, every pixel contain...
Figure 10.13 Fourier Transform Infrared Imaging. In Fourier transform infrar...
Figure 10.14 Mass spectrometry – Overview. In mass spectrometry there are di...
Figure 10.15 Mass spectrometry imaging. Mass spectrometry imaging (MSI) requ...
Figure 10.16 Mass spectrometry ionization. The ionization process is an impo...
Figure 10.17 Time‐of‐flight and quadrupole mass analyzers. Time‐of‐flight (T...
Figure 10.18 Mass analyzer characteristics. The different mass analyzers dif...
Figure 10.19 Tandem mass spectrometry. In tandem mass spectrometry, there ar...
Figure 10.20 Mass spectrometry detectors. MS detectors can be based on very ...
Figure 10.21 Analyzing mass spectrometry data. Different mass spectrometry t...
Chapter 11
Figure 11.1 Overview of medical imaging of tissues and organs. Among the med...
Figure 11.2 Positron Emission Tomography.In positron emission tomography...
Figure 11.3 Computed tomography of a human brain and torso.Computed Tomo...
Figure 11.4 Magnetic Resonance Imaging.Cross‐sectional T1‐weighted and T...
Figure 11.5 Ultrasound.Ultrasound imaging is frequently used to image fe...
Figure 11.6 Bioluminescence.Bioluminescence is based on the oxidation of...
Figure 11.7 Optical coherence tomography.This image shows a cross‐sectio...
Figure 11.8 Histopathology.These images showing H&E‐stained sections of ...
Chapter 12
Figure 12.1 Seeing only part of the picture – Six blind men examining an ele...
Figure 12.2 Exploded view of a mechanical machine in the renaissance.The...
Figure 12.3 Multiscale imaging: Hair cell, mammary gland tissue, and zebrafi...
Figure 12.4 Multiscale, multimodal imaging of mouse cochlea. A heavy metal‐s...
Figure 12.5 Correlative light and electron microscopy imaging. The goal of C...
Figure 12.6 Multiscale, multimodal integrated bioimaging. The scientific com...
Guide
Cover Page
Table of Contents
Title Page
Copyright Page
Dedication Page
Preface
Begin Reading
Index
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Introduction to Biological Imaging
Manfred Auer, PhD
Department of Biomedical Engineering
Southeast University
Nanjing, Jiangsu
China
This edition first published 2024© 2024 John Wiley & Sons Ltd
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Library of Congress Cataloging‐in‐Publication DataNames: Auer, Manfred, author.Title: Introduction to biological imaging / Manfred Auer.Description: Hoboken, NJ : Wiley, 2024. | Includes index.Identifiers: LCCN 2023052798 (print) | LCCN 2023052799 (ebook) | ISBN 9781119705949 (paperback) | ISBN 9781119705970 (adobe pdf) | ISBN 9781119705956 (epub)Subjects: MESH: Diagnostic Imaging | Biological Phenomena | Diagnostic Techniques and Procedures | Image Processing, Computer‐Assisted–methodsClassification: LCC RC78.7.D53 (print) | LCC RC78.7.D53 (ebook) | NLM WN 180 | DDC 616.07/54–dc23/eng/20231220LC record available at https://lccn.loc.gov/2023052798LC ebook record available at https://lccn.loc.gov/2023052799
Cover Design: WileyCover Images: Courtesy of Manfred Auer; Courtesy of Ke Xu
To my children, my parents and Ulla. I am very grateful for your love and support.
Preface
Among the more ambitious goals in modern biology is our quest to find a link between (patho‐) physiological state of an organism and the role that key molecules play in it, be that the process of tissue and organ development, the differentiation of individual cells, or the manifestation of disease.
However, biological systems are inherently complex with many components needed to sustain cellular function. In order to understand any complex system, one must determine its composition, i.e., identify the components it is made of, how each of these components carry out their specific task, and how they interact with one another to function together as an assembly of components. In other words, we need to determine the specific mechanism of function and its regulation for each relevant individual macromolecule, but we must also determine its interaction with other macromolecules. Therefore, detailed information is needed at different levels of scale and complexity, ranging from three‐dimensional (3D) structure and function of individual macromolecules, which allows the determination of the reaction or interaction mechanisms at atomic scale, over the structural organization of such individual components into macromolecular and/or supramolecular complexes, as well as their spatiotemporal distribution and kinetics within organelles, cells, and tissues.
Furthermore, it is important to see how genetic modification or pharmacological intervention alters such macromolecular function, macromolecular interactions, and/or spatiotemporal distribution, and thus the overall supramolecular structural organization of organelles, cells, and tissues. Only when we understand holistically 3D structure, function, kinetics, and regulation of such macromolecules, molecular machines, and supramolecular complexes in their organelle‐, cell‐, and tissue‐context will we be able to understand how such changes lead to physiological cell and tissue function and/or pathogenesis/disease progression.
The list of systems components can be determined by the various “‐omics” approaches, with genomics and transcriptomics determining the nucleic acid sequences of genes and their transcribed messenger RNA, respectively, and thus link gene sequences and expression profiles to cellular behavior. Proteomics probes the translated gene products, i.e., proteins along with possible posttranslational modifications. Lipidomics studies cellular lipids and is a subset of metabolomics that aims to study all small molecules that are synthesized or broken down in any of the primary and secondary metabolic pathways of a cell. The presence and abundance of inorganic, small organic, and macromolecular components in cells and tissues can be detected by mass spectrometry approaches. Many of these aforementioned approaches are easily scalable, and in many cases the information can be obtained through the service of large regional or national centers, enabled by commercial vendors.
The “‐omics” approaches often are nontargeted, collecting a vast amount of information about all present components. Targeted approaches that focus on the presence of a small subset of components are typically based on affinity probes with a reporter molecule that can be easily read out, e.g., with an optical light detector. Therefore, data collection and analysis is straightforward and lends itself to automation of the process and hence high throughput.
Not too surprisingly, the aforementioned tools of “‐omics” for identification of components are widely used and can provide a large amount of information about a biological system, resulting in an ever‐increasing parts list. However, knowing what components constitute such complex systems only gets one so far in understanding the inner workings of the system. A deeper understanding requires knowledge on how the parts fit together, in other words what their spatial and temporal relationship is, which components interact with other, and whether there are additional organizational levels that transcend the individual components. As an analogy, any complex machinery such as a car is more than the sum of all its constituent components (e.g., engine, wheels, brakes, a steering wheel, and power transmission). Thus, higher‐level 3D organization of individual components can lead to new function, and 3D organization manifests itself at different levels. Naturally, it is not enough to understand the combustion engine; we also need to understand how this engine is connected to the wheels. Hence, we need to understand organization at different levels of scale and complexity, ranging from pistons and valves in the engine to the level of the entire car.
The biological equivalent is the anatomy of a living organism, which describes the system at the organ level, whereas histology examines the various tissue types that constitute the organs, and cell biology examines the complex system at the level of individual cells (and possibly a small group of individual cells), subcellular level of organelles, and supramolecular assemblies like the cytoskeleton. Determining the 3D structure of individual macromolecules (e.g., proteins and nucleic acids) and macromolecular machines (e.g., ribosomes and transcription machinery) typically falls into the repertoire of structural biology, which aims for atomic‐resolution insight into the protein architecture of, e.g., an enzyme, and thus a chemical understanding of its enzymatic activity. There are good arguments to consider structural biology a part of bioimaging, and therefore the major techniques for structure determination are discussed in this volume, but a purist may point out that strictly speaking, techniques like NMR‐spectroscopy and X‐ray crystallography (and other scattering techniques) are not imaging techniques, at least not in a stricter sense of the word. Similarly, mass spectrometry in itself is not an imaging technique; however, images can be created from the information that is being recorded.
Layout of the Book
The layout of this book is guided by the idea that basic concepts, which typically have their home in disciplines like physics, (bio‐)chemistry, biology, or computer science/mathematics need to be conquered first to truly appreciate how the different imaging disciplines are built on these basic concepts. We then examine how these basics concepts are used for each imaging approach.
A desired outcome would be for the reader to realize the many commonalities of different imaging techniques, which are often only separated from each other by the type of radiation that is used (e.g., particle radiation versus photons) or by which narrow band of the electromagnetic spectrum is being used for imaging. Ideally, the reader would also realize the common challenges that many imaging techniques are trying to overcome in their own way, be that with respect to signal detection, scale, complexity, and resolution.
Another important aspect in the layout of this book is the realization that different imaging techniques have substantially different goals. There are imaging techniques that provide information about the exact shape of individual macromolecules and supramolecular assemblies, where we aim for high‐resolution 3D structures, whereas other approaches may not at all be interested in shape. Instead, they may be interested in spatiotemporal organization and macromolecular vicinity of such macromolecules.
Also, different levels of complexity exist: the most fundamental level of cellular function consists of small inorganic and organic molecules (e.g., metabolites) and macromolecules (e.g., proteins, nucleic acids, carbohydrates, and lipids). Thus, knowledge about their identity, presence, abundance, location, spatiotemporal distribution, and kinetics as well as regulation stands at the center of focus. 3D structural information may yield atomic‐level insight about mechanisms of function.
The next higher level of organization are macromolecular machines and supramolecular complexes that typically underlie defined cellular function such as metabolism, replication, transcription and translation of genetic information, cell division, cell differentiation, signaling, and cell death, to name a few important cell biological phenomena.
Yet another level up; such processes often occur in defined cellular compartments. Hence, such processes ideally need to be studied in their native subcellular region and organelle context. However, such environments are often ill‐defined with respect to the presence, abundance, and/or location of other macromolecules. For this reason, much progress of such cell biological processes has been made in reconstituted in vitro systems, where different parameters (such as composition and concentration) can be more easily controlled.
For some processes, e.g., cell–cell adhesion (to name an example), one may assume the presence of certain macromolecules based on the cell biological models that have been established for cell adhesion, but the presence of certain macromolecules known to be involved adhesion does not guarantee an intact, functional cell–cell adhesion complex. Hence, both the spatiotemporal localization of suspected cell–cell adhesion proteins to cell–cell adhesion sites and the actual ultrastructural organization of a cell–cell adhesion site need to be studied.
Therefore, regarding the layout of this book, the sequence of the chapters discussing the different imaging techniques also reflects differences in complexity when going from the level of macromolecular complexes to the levels of cellular organelles, cells, and ultimately tissues. We will briefly touch upon even higher organization of cells and tissues, when we acknowledge the presence of organs, yet the study of organs typically requires yet another set of bioimaging techniques, typically referred to as medical imaging. While medical imaging approaches also make an important contribution to the well‐being of higher vertebrates (e.g., in veterinary medicine or vertebrate model systems for human health), it is typically not categorized as a bioimaging approach, but it is a category of imaging on its own. However, as will be discussed briefly, medical imaging techniques are based on the same foundational principles that are discussed in the beginning and deserve at least some space for discussion, even though they are the subject of their own specialized literature. The book ends with an outlook on where each of the disciplines may be heading to and a plea for the necessity and a possible path for a multiscale, multimodal integrated bioimaging future, perhaps building on the success that cosmology enjoyed, when combining and integrating various imaging techniques.
The reader may experience redundancy in terms of information, which to a large degree is intended, as first it allows a reader to read each chapter on their own, without the necessity of starting at the very beginning and read until the very end in a chronological order, but also because the desired effect is that the reader will come across information he/she already has learned in previous/other chapters and by coming across the same material possibly from a slightly different angle will hopefully reinforce the learning effect. At least that is the intention of the redundancy of various aspects of bioimaging.
This textbook aims for a broad readership ranging from the novice on bioimaging, who wants to get an overview, to professionals in bioimaging, who may have in‐depth knowledge about their own discipline but may not be as well versed in other (neighboring) imaging techniques.
1Introduction – A Brief History of Imaging
The 2011 News Focus section of one the leading science journals (Science 334: 1046–1051) identified fundamental questions remaining in biology. More than a decade later, many of these questions remain unsolved. The following bullet points list questions that will require imaging techniques as the key for answers:
Arranging the basic building blocks
Does a gene's location in the nucleus matter? Genes on different parts of the chromosome and perhaps even on different chromosomes loop together in domains of active transcription. However, are there levels of organization beyond this?
How does a cell position its proteins? With a typical cell making ~10,000 different types of proteins amounting to over a billion proteins altogether, protein distribution is far from random. How does the cell know where to position these proteins, and while eukaryotic cells employ organelles to compartmentalize metabolic pathways, and proteins often have sorting signals that will guide them to their final destination, “many proteins operate outside organelles and still need to find specific homes or molecular partners within the relatively vast spaces of a cell.” mRNA is often non‐stochastically distributed, and this observation may explain some of the protein's localization pattern but simply shifts the problem to how mRNA knows where to go.
Are there specialized domains (e.g., lipid rafts) in cell membranes that allow the specific segregation of membrane proteins (e.g., into organized signaling platforms)?
Controlling cell differentiation
What changes in cellular organization accompany cell differentiation?
How do cells detect and migrate in response to tiny concentration gradients of chemicals in their environment?
How do groups of cells migrate together (e.g., during development of the organism)?
How does a cell or a tissue know its size and maintain it within the normal range? While cells can range in size from small to huge, for each cell type, a narrow range of dimensions is observed. So, while obviously small and large volumes are compatible with cellular function, how does each cell know when to stop growing? This question can be extended to tissues and organs. How is size so tightly controlled, and do these mechanisms act independently of each other?
Managing complex biological functions
Can cells communicate over long distances (e.g., via membrane nanotubes)?
How does the malfunction of a single protein lead to complex pathological phenotypes (e.g., cancer, neurodegenerative diseases, psychiatric diseases, organ failure, death)?
Can we mathematically predict the behavior of cells and tissues/microbial communities in response to environmental challenges?
Does the complexity of spatiotemporal organization of cells in the brain and the interplay of the separate components necessarily lead to conscience?
Do microbial cells in a biofilm community coordinate behavior and how do they work together toward a sustainable lifestyle?
Other questions
How do highly specialized cells and tissues know exactly where they are in the complex multicellular organism to behave appropriately? What is the underlying mechanism of their cellular GPS?
What is the molecular mechanism of evolution? How does the mutation of single traits/protein sequences result in novel species?
What did the first cell look like and how did it function? How was it possible for life as we know it to occur in the first place?
As mentioned above, these questions have been formulated well over a decade ago, yet they essentially are still unanswered. Many questions point to the enigma of how biological processes are organized in space and time: it seems at this point unlikely that the sophisticated and often robust biological responses to any perturbations of the system are just the results of stochastic encounters of the constituent components. Hence, life is highly organized and structured, and it is these structural patterns that modern biology will need to elucidate.
1.1 Spatiotemporally Organization of Biological Processes
As is evident from the above‐posed questions, one of the fundamental goals of biology is to understand how living organisms establish and maintain their spatiotemporal organization of the biochemical, cell biological and developmental biology processes that sustain life. Discussed below are a few examples of such spatiotemporal organization, such as the compartmentalization of cellular energy metabolism as well as the development and maintenance of tissues with the underlying differentiation of cells. Examples of cellular differentiation are the establishment of apical‐basolateral polarity in epithelial cells or the unique organization of the cytoskeleton in such specialized cells such as muscle cells or neurons.
1.1.1 Metabolism
Long gone are the times in biology when cells were viewed as bags of enzymes. We now understand that cellular metabolism is organized by spatial compartmentalization of metabolic enzymes into specific cellular spaces. Different parts of the metabolic pathway take place in different organelles: Oxidative phosphorylation occurs in mitochondria, whereas most chemical reactions of glycolysis take place in the cytoplasmic space. Furthermore, enzymatic activity belonging to the same metabolic pathway often reside in close spatial proximity, which ensures highly efficient metabolism. Such chemical reactions are catalyzed by macromolecules (typically proteins), with many of these chemical reactions requiring a chemical energy source, such as adenosine‐tri‐phosphate (ATP). The phosphorylation of the substrate ADP to obtain ATP is catalyzed by the so‐called F‐type ATPase, a multi‐protein complex that uses as energy a proton‐gradient over the inner mitochondrial membrane, which is established by proteins of the respiratory chain. ATP generation occurs in mitochondria cristae, a three‐dimensional membrane network continuous with the inner mitochondrial membrane. But even within this highly compartmentalized space of the mitochondria cristae, F‐ATPases are not distributed randomly inside the cristae membrane but are found at locations of high membrane curvature at the end of mitochondria cristae.
1.1.2 Developmental Biology
Arguably one of the most intriguing examples of nature's ability for self‐assembly and spatiotemporal pattern formation is the development of an organism starting with a single cell, the fertilized egg, to build the highly complex body of a vertebrate like zebrafish larvae or the human body. While developmental biologists have successfully determined the many transcription factors necessary for turning on the processes of tissue patterning and cell differentiation (e.g., into nerve, muscle, endothelial or epithelial cells), we do not have much information regarding the changes in cellular macromolecule abundance and distribution as well as changes in supramolecular 3D architecture inside cells that underlie morphogenesis and the formation of different tissue types. Highly specialized cells emerge from precursor stem cells, carrying out highly specialized functions. For example, vertebrate inner ear hair cells, the cells that underlie our senses of hearing and balance, display a highly prominent feature, known as the hair bundle, an F‐actin‐rich organelle, protruding from the apical surface of hair cells in the sensory epithelia. There are over 100 proteins that when mutated lead to loss of our senses of hearing and balance. Many gene products of known deafness genes localize to the hair bundle and play a role in either hair bundle formation, maintenance, or degeneration. This is a good example of where we stand for many topics in current biology, we have a reasonable idea of who is involved in a biological process, so we have a kind of parts list, but we do not know how the parts exactly fit together to sustain cellular function. Specifically, for the case of the hair bundle we do not know exactly how these over 100 proteins work together in a spatiotemporal pattern to form stereocilia, which are actin bundle‐filled, finger‐like membrane protrusions from the apical hair cell surface that collectively make up the hair bundle, the organelle of mechanoelectrical transduction. All we know is that genetic mutations in these over 100 deafness genes lead to hair bundle degeneration and thus hearing loss.
1.1.3 Disease Mechanisms
To truly understand pathogenesis and disease progression, it is not sufficient to identify a culprit mutation that alters the protein sequence of a key protein. Instead, we need to gain in‐depth insight into what changes occur with respect to the spatiotemporal distribution of key proteins, and what are the resulting consequences for the overall subcellular morphology, behavior, and underlying 3D architecture. If cellular health is dependent on the presence and abundance of certain proteins, as well as their correct subcellular localization, we need to determine what constitutes a healthy range of abundance and distribution for each protein in a typical cell. Such parameters are largely unknown, as is the number, size and shape of organelles, cell–cell or cell–matrix junctions, the cytoskeleton, other key cellular machineries. Also, largely unknown is how the lack of expression or the malfunction at the level of a single protein can affect the rest of the cell, as disease typically manifests itself at the cell and tissue level.
1.2 Why Imaging?
From the sections above it should be obvious that completing a parts list, as is done by the various omics‐efforts (e.g., genomics, transcriptomics, proteomics, and metabolomics), is only the starting point to understand biological processes. Imaging can turn a parts list into a wiring diagram by visualizing protein–protein interactions and thus to understand cellular function. From the very first days of cell biology, imaging played a major role in understanding cellular processes. Images of cells, first obtained by optical light microscopy and then later by electron microscopy, underscored the notion that cells are highly organized and contain many subcellular structures, such as the above‐mentioned cellular powerhouses, also known as mitochondria. The nucleus stores and faithfully replicates the genetic information, transcribes DNA sequences into mRNA, some of which is transported into the ribosome‐studded rough endoplasmic reticulum, where the mRNA sequence is translated by ribosomes into amino acid sequences that fold in 3D into functional proteins and protein complexes, some of which are then post‐translationally modified, and ultimately secreted into the extracellular space via the Golgi apparatus. Other mRNA sequences are transported via the nuclear membrane into the cytoplasm, where they are translated into proteins by ribosomes, with the proteins being retained in the cytoplasm. It is increasingly clear that the high level of organization and the spatiotemporal separation of biochemical and cell biological processes are key to the proper function of cells, and thus ultimately to the health and survival of organisms.
1.2.1 A Very Brief History of Cellular Biological Imaging
A great deal of research over the last half century has been invested into studying spatiotemporally well‐defined processes that underlie cellular function and the pathogenic consequences of a breakdown of this well‐orchestrated macromolecular symphony. Apart from advances in cell culturing, cell sorting and cell fractionation, imaging arguably has played the most crucial role in cell biology by identifying the spatial location of subcellular components and their temporal changes of localization patterns. This is exemplified by the detailed description of cell division and the associated cytoskeletal changes. Microscopy marked the beginning of cell biological studies when Robert Hooke in 1655 looked at thin layer of cork and coined the word “cell,” and when Anton van Leeuwenhoek in 1674 was the first to examine living cells, specifically green algae. They were helped by the intrinsic contrast of the cell wall compared to the empty cell interior of dead plant cells and the colored chloroplasts populating the cytoplasm, respectively. Hence the origin and infancy of cell biology was a search for structure and thus mostly descriptive, with thorough observation and description in form of manual drawings. Among the most famous cell biologists that used microscopy were Santiago Ramon y Cajal and Camillo Golgi, who were co‐awarded the Nobel Prize in Physiology or Medicine in 1906 “in recognition of their work on the structure of the nervous system.” Their discoveries relied on the development of microscopes based on the knowledge gathered by Joseph von Fraunhofer, Friedrich Adolph Norbert, and Ernst Abbe, operating close to the diffraction limit of light, as well as the development of staining protocols to enhance contrast, first introduced by Francois‐Vincent Raspail in the middle of the 19th century.
Such stains, which included silver and gold salts, allowed the neuroanatomical drawings of neurons and their intercellular connections by Cajal and Golgi, and led to the discovery of the Golgi apparatus inside cells. However, the need for stains also meant that cells could not be studied in vivo but needed to be fixed and exposed to a variety of chemical reagents, limiting what could be accomplished by optical light microscopy. Around the middle of the 20th century, a new technology using electrons instead of photons emerged that had a two order of magnitude increased resolving power. The fact that electron microscopy revolutionized both material science and cell biology was recognized by the 1986 Nobel Prize in Physics, awarded to Dr. Ernst Ruska “for his fundamental work in electron optics, and the design of the first electron microscope.”
Due to the strong interactions of electrons with matter, cells needed to be imaged in the vacuum of the electron microscope, and only very thin slices (typically 50–100 nm in thickness) could be imaged. This required the development of sample preparation techniques that allowed the cutting of vacuum‐resistant ultra‐thin sections, which led to tissue embedment into a plastic resin. The necessity for embedding cells and tissues into a plastic resin for the longest time resulted in sample preparation artifacts, such as aggregation and extraction, due to the prolonged exposure of biological material to organic solvents and the hydrophobic plastic resin needed to infiltrate cells prior to resin polymerization. Such sample preparation artifacts gave cellular electron microscopy (after its initial big success) a bad reputation, as it was unclear whether imaging results could be trusted, particularly if they showed unexpected imaging results. This skepticism has been partially overcome only in the two decades by cryogenic sample preparation techniques like high‐pressure freezing and freeze‐substitution, where the water‐solvent is exchanged with an organic solvent well below 0 °C. While cryogenic sample preparation techniques have led to a significantly improved faithful preservation of cells and tissues, many researchers still will not trust the studying cellular volumes if any form of chemical fixation, staining, dehydration, or resin‐embedding is used. They only accept the study of samples in their unstained, frozen‐hydrated (vitreous) state, using cryo electron microscopy. This is widely considered as the gold standard of cellular imaging. However, this latest round of skepticism may be undeserved, as the use of cryogenic approaches such as high‐pressure freezing followed by freeze‐substitution and resin‐based cellular electron microscopy yields excellent preservation. Furthermore, minor alterations of the macromolecular ultrastructure can be tolerated as cellular imaging does not aim for precise shape of macromolecules but examine larger‐scaler organization that is not affected by heavy‐metal staining.
Cellular resin‐EM has yielded an enormous wealth of information regarding cellular architecture, which was recognized in the 1974 Nobel Prize of Physiology or Medicine for Albert Claude, Christian de Duve, and George Palade “for their discoveries concerning the structural and functional organization of the cell.” Throughout the 1970s, 1980s, and early 1990s cellular electron microscopy remained a major tool in cell biology, however, most researchers used traditional resin sample preparation, that are known to lead to poor sample preservation, and only very few engaged in cryogenic approaches such as high‐pressure freezing/freeze‐substitution. From this era stems the often‐undeserved reputation that resin‐embedded samples are artifact‐prone, and that only fully hydrated samples can be trusted.
