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- Sprache: Englisch
Super Resolution Optical Imaging and Microscopy
Extremely comprehensive resource containing cutting-edge and practical knowledge of super-resolution optical imaging
This book covers both the basic principles and specific technical details of super-resolution microscopy techniques. It covers the criteria to choose different fluorophores for various SRM methods and critically assesses the nitty-gritty of associated problems that are often encountered in practical applications. A progressive guide to designing the next generation of advanced fluorophores to meet the goal of advanced SR imaging studies is also put forward.
Written by two well-qualified authors, the book contains exclusive content to enhance readers’ understanding on innovation of newer SRM technologies. Sample topics covered in the book include:
- Optical techniques, fluorescent probe design, and algorithm development
- Recent highlight and breakthroughs in biology using SRM methods
- The overall success of SRM in biological inventions
- The future direction and scope of the field
This book is an invaluable resource for chemists and researchers/scientists involved in designing newer fluorescent materials for SRM studies. It can also assist biologists engaged in advanced biological studies using SRM by guiding them through sample preparation, image processing, and precautions to be taken in practical imaging studies.
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Seitenzahl: 439
Veröffentlichungsjahr: 2023
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Table of Contents
Cover
Table of Contents
Title Page
Copyright
Preface
1 Super‐Resolution Microscopy (SRM): Brief Introduction
1.1 Optical Microscopy
1.2 Specialized Optical Microscopes
1.3 Optical Diffraction Limit
1.4 Super‐Resolution Microscopy: Overcoming the Diffraction Limit
1.5 Near‐Field Scanning Optical Microscopy
1.6 Far‐Field Super‐Resolution Microscopy
1.7 Fluorescent Probes for Super‐Resolution Microscopy
1.8 Image Analysis Algorithms
1.9 Applications
1.10 Outline of the Content of Succeeding Chapters
Acknowledgment
References
2 Point Spread Function Engineering SRM
2.1 Stimulated Emission Depletion Microscopy (STED)
2.2 Ground State Depletion (GSD) Microscopy
2.3 Reversible Saturable Optical Fluorescence Transition Microscopy
2.4 Conclusion
Acknowledgment
References
3 Single‐Molecule Localization Microscopy (SMLM)
3.1 Main Idea of SMLM
3.2 Stochastic Optical Reconstruction Microscopy (STORM)
3.3 Photo‐Activated Localization Microscopy (PALM)
3.4 Point Accumulation for Imaging in Nanoscale Topography (PAINT)
3.5 Single‐Molecule Localization Algorithms
3.6 MINFLUX
3.7 Conclusion
Acknowledgment
References
4 Fluorescence Fluctuation‐Based Super‐Resolution Imaging
4.1 Stochastic Optical Fluctuation Imaging (SOFI)
4.2 Other Techniques
4.3 Applications of Fluorescence Fluctuation‐Based SRM Methods
4.4 Conclusion
Acknowledgment
References
5 Structured Illumination Microscopy
5.1 Introduction
5.2 Wide‐field SIM
5.3 Point‐Scanning SIM
5.4 Conclusions and Future Prospects
Acknowledgement
References
6 Deep Learning‐Based SR Microscopy
6.1 Introduction
6.2 Fundamentals of Deep Networks
6.3 Deep Learning for SR Image Reconstruction
6.4 Challenges of Deep Learning‐Based Methods
6.5 Conclusion
References
7 Fluorescent Materials for Super‐Resolution Imaging
7.1 Fluorescent Probes for Super‐Resolution Imaging
7.2 Fluorescent Proteins
7.3 Small‐Molecule Fluorescent Probes
7.4 Fluorescent Metal Complexes for SRM
7.5 Fluorescent Nanomaterials (Nanoparticles/Quantum Dots/Carbon Nanotubes/Carbon Dots (CDs)/Polymers Dots) for SRM
Acknowledgment
References
8 Conclusion and Future Perspectives
Index
End User License Agreement
List of Tables
Chapter 4
Table 4.1 Comparison of fluorescence fluctuation‐based SRM. Adapted from [19...
Chapter 7
Table 7.1 Properties of some fluorescent proteins, frequently used for STED ...
Table 7.2 Properties of some reversibly photoswitchable fluorescent proteins...
Table 7.3 Properties of several photoactivable/photoconvertible/photoswitcha...
List of Illustrations
Chapter 1
Figure 1.1 Images of antiquated (a) and modern (b) optical microscopes.
Figure 1.2 Schematic diagram of Köhler illumination.
Figure 1.3 Schematic representation of a confocal microscope.
Figure 1.4 The schematic of Abbe's diffraction limit.
Chapter 2
Figure 2.1 Schematic diagram of STED. (a): Schematic diagram of stimulated e...
Figure 2.2 Schematic of the 3D‐STED imaging system with incoherent wavefront...
Figure 2.3 Structured illumination system. (a) Schematic diagram of the stru...
Figure 2.4 Design principles of a pulsed 2P‐STED microscope. (a) Simplified ...
Figure 2.5 Schematic of the dual‐color STED microscope [12].
Figure 2.6 Time‐gated STED imaging. (a) Schematic of the fast‐gated STED ima...
Figure 2.7 Phasor‐plot analysis based on STED‐FLIM improves resolution. (a) ...
Figure 2.8 Fluorescent bead imaging results using segmentation and pSTED‐SPL...
Figure 2.9 Schematic diagram of upconversion. (a) Optical emission depletion...
Figure 2.10 Illustration of FED theory [34]. (a): PSF of confocal excitation...
Figure 2.11 Schematic principle of DE‐STED [35]. (a) A sketch of a universal...
Figure 2.12 Scheme diagram of the modulated STED system. (a) Laser pulse and...
Figure 2.13 Confocal and super‐resolution images of rat heart tissue at diff...
Figure 2.14 Dynamic STED super‐resolution imaging of mitochondria. (a) Dynam...
Figure 2.15 Experimental setup of the
in vivo
STED microscopy technique. (a)...
Figure 2.16 Energy states of a typical fluorophore in GSD:
S
0
is the singlet...
Figure 2.17 Illustration of photo‐switching in RESOLFT based on various tran...
Figure 2.18 Principle of STED, GSD, and RESOLFT microscopy.
Figure 2.19 The green reversibly switchable fluorescent proteins for
τ
‐...
Figure 2.20 Schematic of reversible photoswitching in GFP probes. (a) Schema...
Figure 2.21 Imaging of ubiquitously expressed rsEGFP2‐a‐tubulin in
Drosophil
...
Figure 2.22 Spin coherence time measurements of two NV defects can only be r...
Chapter 3
Figure 3.1 Schematic illustration of SMLM.
Figure 3.2 Typical system setup for STORM. A custom‐designed system can be s...
Figure 3.3 Overview of labeling approaches for STORM imaging. Labeling probe...
Figure 3.4 The PSF of a single emitter imaged on a wide‐field microscope can...
Figure 3.5 Effect of varying (a) photons per PSF, (b) on/off DC, and (c) loc...
Figure 3.6 Multi‐color STORM images taken with dye pairs and different dyes....
Figure 3.7 prSTORM applied to multi‐color STORM imaging. (a) Design of the a...
Figure 3.8 Simulated
x
–
y
(left) and
x
–
z
(right) slices of the 3D‐PSF of an e...
Figure 3.9 Illustration of 3D localization of individual fluorophores using ...
Figure 3.10 Extended‐depth 3D prSTORM imaging of the ER in COS‐7 cells expre...
Figure 3.11 Various experimentally measured PSFs for 3D localization microsc...
Figure 3.12 3D nanoscale imaging in fixed mammalian cells. The microtubules ...
Figure 3.13 (a) Raw image of lamina‐Cy3b on eukaryotic cell nuclear membrane...
Figure 3.14 (a) Experimental setup of the spatial filter SMLM (SFSMLM) metho...
Figure 3.15 Live‐cell STORM imaging. ER dynamics labeled by BODIPY TR in liv...
Figure 3.16 PALM images of PA‐FPs on a glass coverslip. Measured positions o...
Figure 3.17 Applications of sptPALM for mapping protein dynamics. (a) Two‐co...
Figure 3.18 Image of the supported bilayer on glass using Nile red. (a) Conv...
Figure 3.19 (a) Long rectangular DNA origami (LRO) structures (nominally 291...
Chapter 4
Figure 4.1 Principle of SOFI,Panel (a) shows the emitter distribution in...
Figure 4.2 Different steps involved in calculating the cross‐cumulant SOFI u...
Figure 4.3 Flowchart illustrating the distinct steps of the bSOFI algorithm....
Figure 4.4 Algorithm for Fourier interpolation.
Figure 4.5 Schematic diagram of the S‐SOFI algorithm. (a) Dynamic speckle pa...
Figure 4.6 VISion analysis. (a) N‐cycles of variance images for a single par...
Figure 4.7 SRRF algorithm. (a) Spatial analysis. (b) Temporal analysis.
Figure 4.8 Image formation process in ESI. (a) Pixels of the original image....
Figure 4.9 Flowchart of multiple signal classification algorithm (MUSICAL)....
Chapter 5
Figure 5.1 Principle, instrumentation, and imaging results of SIM. (a) Schem...
Figure 5.2 OS using SIM: OS is achieved by shifting the structured illuminat...
Figure 5.3 Frequency spectrum extension and SR imaging of SIM. (a) Spectrum‐...
Figure 5.4 Schematic diagram of 3D‐SIM. Laser light from the multimode fiber...
Figure 5.5 Representation of various steps of simulated speckle blind‐SIM. A...
Figure 5.6 Numerical experiment analysis of the resolution of a reconstructe...
Figure 5.7 Experimental analysis of data obtained using blind SIM. The sampl...
Figure 5.8 Schematic setup of grazing incidence SIM: M: mirror; AOTF: acoust...
Figure 5.9 Schematic diagram of the Hessian‐SIM and polarization rotator [33...
Figure 5.10 Optical path diagram of the confocal microscopy system.
Figure 5.11 (a) Optical step of the ISM system. (b) Comparison of the imagin...
Figure 5.12 (a) Point spread function of the confocal microscope under a CCD...
Figure 5.13 System optical path of MSIM.
Figure 5.14 (a) RCM system light path diagram. (b) RCM imaging schematic dia...
Figure 5.15 (a) Optical setup of OPRA. (b) OPRA imaging results of fluoresce...
Figure 5.16 (a) 2P ISIM imaging of the microtubule structure, (b) image comp...
Figure 5.17 (a) Setup of the EISM microscope. (b) EISM imaging results for A...
Figure 5.18 (a) Optical path design of spectral detection ISM. (b) Biologica...
Figure 5.19 Inherent trade‐offs in PS‐SIM [89]. Source: [89]/Springer Nature...
Chapter 6
Figure 6.1 Structure of a single perceptron.
Figure 6.2 Framework of a multilayer neural network.
Figure 6.3 Sigmoid activation function.
Figure 6.4 Rectified linear unit (ReLU) activation function.
Figure 6.5 Example of a CNN architecture.
Figure 6.6 Convolution operation.
Figure 6.7 Deep‐STORM CNN architecture details.
Figure 6.8 GAN model architecture.
Figure 6.9 ANNA‐PALM reconstructions and error maps for different mismatch l...
Chapter 7
Figure 7.1 Live‐cell/in vivo STED imaging performed with FPs: (a) vimentin a...
Scheme 7.1 Different reversible photoswitching modes of RFSPs with example....
Figure 7.2 (a) τ‐RESOLFT imaging of neurons, co‐transfected with LifeAct‐Dro...
Figure 7.3 (a, b) Three‐color FPALM images using Dendra2 (cyan), PAmCherry1‐...
Figure 7.4 Left: Live Cos7 cells transfected with UnaG‐Sec61β showing wide‐f...
Figure 7.5 Structure of rhodamine‐based dyes 510R‐CTX, 580CP‐LTX, and SiR‐CT...
Figure 7.6 (a) Structure of the HIDE probe, DiI‐SiR, and its mechanism of st...
Figure 7.7 (a) Structures of the rhodamine‐based cell‐permeable tubulin, DNA...
Figure 7.8 Designing strategy for the SiR‐binding RNA aptamer and its use in...
Figure 7.9 (a) Structures of the RNA‐binding nuclear staining probe, SL‐Neu,...
Figure 7.10 (a) Designing the probe QDV for STED imaging of the protein and ...
Figure 7.11 (a) Structures of the phosphole‐based fluorescent probes MitoPB ...
Scheme 7.2 Schematic representation of photoswitching in rhodamine dyes via ...
Figure 7.12 (a) Switchable SiR dye for SMLM. (b) Photoswitchable/photoactiva...
Figure 7.13 Structure and design of the sulforhodamine dye, HoeSR, which was...
Figure 7.14 Structure and design principle of the bioorthogonal HM‐SiR probe...
Figure 7.15 (a) Structure and design of acid‐resistant rhodamine probes depi...
Figure 7.16 Spontaneous blinking in the xanthene fluorophore for SMLM imagin...
Figure 7.17 Photoswitching of Cy5 is represented in a schematic diagram wher...
Figure 7.18 Multi‐color SMLM images of the microtubules and mitochondria usi...
Figure 7.19 (I) Two‐color STORM imaging of microtubules with Alexa 647 and A...
Figure 7.20 Two‐color live cell 3D STORM imaging showing CCPs labeled with A...
Figure 7.21 (a) VDP‐targeting Cy5 probe for live‐cell STORM imaging of the m...
Figure 7.22 (i) Photoactivatable BODIPY–oxazine conjugated for PALM imaging ...
Figure 7.23 BODIPY‐based fluorescent probes ER‐Tracker Red and LysoTracker R...
Figure 7.24 (a) Equilibrium between the spiro‐closed spiropyran form (SP) an...
Figure 7.25 (a) Photoswitching of PMI–DTE.(b) DCDHF‐based fluorophore, i...
Figure 7.26 (a) Structure of some frequently used fluorophores in SIM imagin...
Figure 7.27 (a) SIM imaging using CLIP or SNAP tags of different cell‐imperm...
Figure 7.28 Fluorescent probes (a) HD‐Br.(b) symmetrical Γ‐aryl substitu...
Figure 7.29 Structure of different fluorescent probes utilized to monitor in...
Figure 7.30 (a) Ru‐complexes Ru–ER for STED imaging of ER and Ru‐complex Ru–...
Figure 7.31 (a) Photoactivation of Zn–salen complex. (b) Ir‐complex for live...
Figure 7.32 (a) Organosilica nanohybrids with eased SOC‐induced ISC process ...
Figure 7.33 (a) Fabrication of the AIE NPs (TTF@SiO2 NPs) from the AIE limin...
Figure 7.34 (a) Structure of the fluorescent dye DBTBT‐4C8 and the
in vitro
(...
Figure 7.35 (a) Cationic carbon quantum dot (cQD) for live‐cell STED imaging...
Figure 7.36 (a) CdSe@ZnS QDs for STED nanoscopic imaging of lysosomes.. ...
Figure 7.37 (a) STED nanoscopic imaging using CsPbBr3 NPs with extremely low...
Figure 7.38 Stochastic “blueing” of QDs and their utilization in single‐colo...
Figure 7.39 (a) Graphene quantum dots for SMLM.(b) Cy3 and Cy5 encapsula...
Figure 7.40 (a) Structure of PFO, CNPPV, coumarin 6, and NIR695 and their ut...
Figure 7.41 BODIPY‐based polymer dots (Pdots) for super‐resolution SOFI imag...
Guide
Cover
Table of Contents
Title Page
Copyright
Preface
Begin Reading
Index
End User License Agreement
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Super Resolution Optical Imaging and Microscopy
Methods, Algorithms, and Applications
Edited by Junle Qu and Zhigang Yang
Editors
Dr. Junle QuShenzhen University10th Gaoxin Nan RoadShenzhenChina
Dr. Zhigang YangShenzhen University10th Gaoxin Nan RoadShenzhenChina
Cover: © nopparit/Getty Images
All books published by WILEY‐VCH are carefully produced. Nevertheless, authors, editors, and publisher do not warrant the information contained in these books, including this book, to be free of errors. Readers are advised to keep in mind that statements, data, illustrations, procedural details or other items may inadvertently be inaccurate.
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Bibliographic information published by the Deutsche NationalbibliothekThe Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the Internet at <http://dnb.d-nb.de>.
© 2024 WILEY‐VCH GmbH, Boschstraße 12, 69469 Weinheim, Germany
All rights reserved (including those of translation into other languages). No part of this book may be reproduced in any form – by photoprinting, microfilm, or any other means – nor transmitted or translated into a machine language without written permission from the publishers. Registered names, trademarks, etc. used in this book, even when not specifically marked as such, are not to be considered unprotected by law.
Print ISBN: 978‐3‐527‐34986‐9ePDF ISBN: 978‐3‐527‐83551‐5ePub ISBN: 978‐3‐527‐83552‐2oBook ISBN: 978‐3‐527‐83553‐9
Preface
Super‐resolution microscopy (SRM) is a rapidly expanding field that has significantly increased our capacity to investigate the nanoscale world of biological structures and processes. SRM techniques can effectively circumvent the diffraction limit of the conventional optical microscopy to enable the visualization of cellular and molecular details with an unprecedented resolution. This book provides a comprehensive overview of the principles, techniques, and applications of SRM to the biologists, biochemists, and biophysicists. This book consists of eight chapters, each of which covers a different aspect of SRM.
Chapter 1 lays the groundwork by providing an introduction to SRM and its significance in biological research. It outlines the historic evolution of the optical microscope with briefly highlighting the specialized modern optical microscopes including confocal and inverted microscopes. It also describes diffraction limit as the primary limitation in conventional optical microscopy and briefly defines the origin of different SRM techniques toward circumventing the diffraction limit. This chapter also briefly outlines the contents of succeeding chapters.
Chapter 2 explores the point spread function (PSF) engineering‐based super‐resolution imaging techniques including stimulated emission depletion (STED), ground state depletion (GSD), and reversible saturated optical fluorescence transition (RESOLFT) microscopy. This chapter includes detailed overview of these SRM techniques to assess their applicability in studying the subcellular structures and functions at nanoscale level.
In Chapter 3, the single molecule localization microscopy (SMLM)‐based super‐resolution imaging methods are described in depth. The specific role of individual fluorophores in acquiring the high‐resolution diffraction‐unlimited images in different SMLM methods such as STORM, PALM, and PAINT is mechanistically elucidated in this chapter along with the relevant optical setups and fluorophore labeling strategies. This chapter also discusses different localization algorithms with clear references to their actual performances.
Details of the fluorescence fluctuation‐based SRM have been introduced in Chapter 4, which includes super‐resolution optical fluctuation imaging (SOFI), variance imaging for super‐resolution (VISion), Bayesian analysis of blinking and bleaching (3B), super‐resolution radial fluctuation (SRRF), entropy‐based super‐resolution imaging (ESI), and multiple signal classification algorithm. (MUSICAL). The significance of several reconstruction algorithms to improve the spatial resolution and image quality in these techniques is also discussed briefly.
Chapter 5 introduces structured illumination microscopy (SIM), a versatile technique that can be used for optical sectioning, super‐resolution imaging, surface profiling, and quantitative phase imaging. Wide‐field SIM projects a precise pattern onto the sample to visualize structures beyond the diffraction limit. This chapter includes an in‐depth overview of different SIM techniques that offer superior imaging resolution.
Chapter 6 examines the role of deep learning in super‐resolution fluorescence microscopy. It highlights the significance of deep networks, multilayer perceptrons, and activation functions in super‐resolution image reconstruction, as well as the application of deep learning in super‐resolution image reconstruction. This chapter emphasizes the significance of reliability, robustness, and interpretability in deep learning applications, delving into network architectures, training data, loss functions, and the difficulties associated with using deep learning to analyze fluorescence microscopic data.
Chapter 7 describes how super‐resolution fluorescence imaging techniques, such as STED, SMLM, and SIM, have revolutionized biological research with the use of dedicated fluorescent materials. The chapter contains a detailed insight into the advancements in developing fluorescent probes including fluorescent proteins, organic dyes, metal complexes, and nanoparticles with special photophysical properties for various SRM techniques.
Chapter 8 provides a brief summary of the book and offers genuine perspectives on the possible future developments in SRM. It underlines that the apt combination of new optical innovations, advanced fluorescent probes, and improved image analysis algorithms is essential to perform progressive live‐cell super‐resolution imaging studies. It also highlights that designing optimized new fluorescent probes for SRM and expanding the scope of using SRM for direct in vivo and clinical applications are the two important areas for future developments.
Even though this book is a comprehensive guide to the principles, techniques, and applications of SRM, it is essential to remember that this field is still developing. Day‐by‐day, the SRM is expected to become more appealing to scientists for acquiring in‐depth nanoscale information of living samples. This book is intended to serve as a resource for academics interested in exploring the immense potential of SRM and advancing its ongoing development.
The authors of various chapters in this book are employees of the Center for Biomedical Photonics (CBMP) at Shenzhen University who have made significant contributions to the field of SRM. All the authors with their acknowledged expertise in different facets of SRM techniques are currently engaged in SRM‐related biological research. As the book's editors, we would like to express our gratitude to all the contributors for their hard work and dedication. We would also like to thank the book reviewers, whose insightful criticism and suggestions significantly enhanced this book's quality. We also acknowledge the unconditional love and support of our families (of all coworkers) to complete this book on time. Finally, the editors appreciate the financial and instrumental support provided to our Center for Biomedical Photonics (CBMP) at Shenzhen University by the National Key R&D Program, National Natural Science Foundation of China, Natural Science Foundation of Guangdong Province, and Shenzhen Science and Technology Innovation Commission.
We sincerely hope that this book will spur further advances in understanding the complex nanoscale world of life, potentially leading to novel scientific discoveries.
Junle Qu
Zhigang Yang
1Super‐Resolution Microscopy (SRM): Brief Introduction
Zhigang Yang, Soham Samanta, and Yingchao Liu
Center for Biomedical Photonics (CBMP) & Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, 518060, China
1.1 Optical Microscopy
There is a well‐known saying “Seeing is believing.” Undoubtedly, visualization is one of the most trusted means of perceiving an object. Hence, bioimaging using various optical microscopy techniques constitutes the foundation of biological research.
To improve the performance of microscopes, scientists have realized significant technological advancements over the last few centuries to achieve the present epoch of biological research. The quest for improving the performance of microscopes resulted in the discovery of newer optical microscopic methods, since its first invention. In the sixteenth and seventeenth centuries, the earliest functional microscopes were introduced to overcome the limitation of the human eyes, which revolutionized the study of the natural world from a new perspective, even though a low magnification of the object was achieved. Therefore, to appreciate the current quest for developing more advanced optical systems, it is important to first briefly review the history of the evolution of the development of microscopes.
1.1.1 History and Background
Direct observation of objects using the unaided eyes was the only means of perceiving the outside world until the emergence of the first optical microscope. Initially, lenses were significant components of any microscopic system and performed a function similar to that of the lens of the eye. Since the invention of the first compound microscope in the late sixteenth century, which consisted of an eyepiece, objective lens, wooden tube covered in fish skin, and cardboard, [1] it has become increasingly popular. In 1663, Robert Hooke initially employed his self‐made compound microscope to observe microscopic objects, and 2 years later, Hooke obtained microscopic images of many familiar objects using his microscope. The term “cell” was first coined by him, which was later recognized as the basic building block of every living organism [2, 3]. In the 1670s, Antonie van Leeuwenhoek pioneered hand‐made stamp‐sized microscopes to observe freshwater microorganisms at a magnification of approximately 300. In 1674, with the assistance of the simple microscope, Leeuwenhoek effectively launched a new area of research— microbiology. Nevertheless, single‐lens microscopes remained popular until the 1850s [4]. Figure 1.1 depicts images of an antiquated (left) and a modern (right) optical microscope.
Figure 1.1 Images of antiquated (a) and modern (b) optical microscopes.
The resolution of a simple microscope is limited owing to the constraints of using a single lens, which is improved in a compound microscope with an objective lens and an eyepiece. The focal length, numerical aperture (NA), and field of view (FOV) are the common features of an objective lens. In a compound microscope, the objective lens possesses a short focal length and is therefore placed close to the specimen. It enables the formation of a real image in the front focal plane of the second lens, whereas the eyepiece can form a magnified virtual image at the observing end. The total magnification of a microscope is the product of the magnification associated with the objective and eyepiece lenses. In addition, a compound microscope can be constructed using a converging lens, a body tube, and illumination sources. Typically, the aim of the converging lens is to focus the image of the light source onto a sample. However, in an alternative setup, the source image is focused onto the condenser so that it could ultimately be focused onto the entrance pupil of the microscope objective lens. This is known as Köhler illumination, which typically offers the advantage of averaging the nonuniformities of the source in the imaging process (Figure 1.2) [5].
For modern microscopes, the objective lens enables a standard magnification ranging from 2 to 100 folds. In the imaging process, the objective lens collects rays from each target point, which are imaged at the front focal plane of the eyepiece. The common rules of ray tracing are used in image formation. Without consideration of aberration, geometric rays from each object point form a point image. However, in the presence of aberrations, each fine object point is replaced by a blur spot. The eyepiece is designed to observe the relayed image at a distance that is convenient for the viewer. In this system, the brightness of an image is determined by the size of the lens aperture and the pupil of the eye. By adjusting the focal length, the resultant magnification of the objective can be modulated according to the requirements for viewing the object through the eyepiece with a suitable resolution. The light source imaged in the focal plane suffers from diffraction and other effects in the imaging system, which complicates the image formation process in the microscope. The German physicist Ernst Abbe is regarded as the founder of the modern theory of image formation using a microscope. In 1873, Abbe investigated microscopes in which objects in the focal plane were illuminated by focusing light from a condenser. The convergent light can be thought of as a collection of many plane waves, propagating in specific directions, which form the incident illumination via superimposition. Each of these effective plane waves is diffracted by features in the object plane, which implies that the smaller the objective features, the larger the diffraction angle [6].
Figure 1.2 Schematic diagram of Köhler illumination.
Source: Adapted from Education in Microscopy and Digital Imaging; http://zeiss-campus.magnet.fsu.edu/articles/basics/kohler.html.
1.2 Specialized Optical Microscopes
Gradual improvements in optical microscopy with different adaptations have been espoused for specific purposes. In 1958, the most successful microscope was invented by the British doctor John McArthur, and since then, different “McArthur” microscopes with slight modifications have been manufactured by different manufacturers. To meet the needs of professional researchers, different specialized microscopes (e.g. inverted, phase‐contrast, and confocal) have been subsequently developed for modern optical applications. In this chapter, the inverted and confocal microscopes will be primarily discussed, given that these two categories of microscopes form the basis of super‐resolution microscopy (SRM).
1.2.1 Inverted Microscopes
In 1850, the first inverted microscope was invented by John Lawrence Smith at Tulane University. Inverted microscopes are specifically designed for various life science, material science, and industrial applications, wherein images are mounted upside down, unlike an upright microscope. In inverted microscopes, the light source and condenser are positioned at the topmost part and point downward toward the stage, the objective lens is set below the stage pointing upward, and the eyepieces are angled upward to facilitate the observation of specimens. Inverted microscopes are crucial for various types of biological and medical research, considering their vast application in cell biology and biomedicine [7, 8].
1.2.2 Confocal Microscopes
In the 1950s, Marvin Minsky [9, 10] first conceived the idea of eliminating out‐of‐focus light to improve the image quality of a microscope so that only light in the focal plane can reach the detector. Based on this principle, the confocal microscope was constructed, wherein a pinhole was inserted in the image plane to restrict out‐of‐focus light from reaching the detector (Figure 1.3). A focused spot of illumination is generated in confocal microscopy via spatial filtering, which must be raster scanned across the sample to generate an image. The confocal microscope is equipped with pinhole apparatuses at the illuminator and the detector sides in a conjugate image plane in front of the detector, which is called “confocal.” The construction of a confocal microscope includes the positioning of a pinhole in front of a light source (zirconium arc lamp) to generate a point of light, which is focused on the sample by an objective lens. A second objective lens is used to focus the illuminated specimen onto the second pinhole put in front of the detector. In this arrangement, the out‐of‐focus rays from the illuminated sample are successfully removed using this “double focusing” system since they cannot reach the detector (photomultiplier, PMT, or avalanche photodiode, APD).
Figure 1.3 Schematic representation of a confocal microscope.
Therefore, a confocal microscope only detects the clear structures in the focal point, and the out‐of‐focus light is filtered. Since only the light from the focal point contributes to forming the final image in the confocal imaging system, this system can facilitate the three‐dimensional visualization of fine structures of biological specimens. Essentially, the commonly used laser scanning confocal microscope (LSCM) can acquire two‐dimensional images at different focal depths, wherein the focal point of the laser is scanned across a specimen to obtain two‐dimensional optical sections. Three‐dimensional images can be constructed by overlapping a series of two‐dimensional images at different focal depths of the specimen (called a z‐series). Moreover, a confocal microscope can be adapted with a two‐photon or multi‐photon optical system using argon and krypton/argon lasers or synthetic titanium–sapphire lasers to enable single‐ or multi‐color imaging of specimens, either stained with fluorescent probes or exhibiting auto‐fluorescence. It is important to note that multi‐color imaging methods are useful for the study of the inter‐organelle dynamics of living cells.
Although confocal optical microscopy is frequently utilized to study biological samples, given that it is highly effective for distinguishing the morphology of objects, it cannot be applied in the study of the morphology of gels. It can be directly used to observe and study amorphous and crystalline materials with structural diversity. In a confocal microscope, a tunable laser is usually used as the excitation source to excite fluorescent dyes with different spectral characteristics and to identify different labeled intracellular targets. To separate and detect the emission light, generally, specific filters and dichroic mirrors are included in the optical system.
For scientific researchers with a background in biology, chemistry, and materials science, commercial confocal laser fluorescence microscopes are among the most common and useful means of studying a diverse array of objects. In recent years, confocal fluorescence microscopy has been successfully used to gain insights into intracellular functions, such as intracellular localization, interactions, and subcellular events, occurring in live systems with excellent spatial resolution. However, even the best‐performing conventional confocal microscope that uses an objective lens to collect the fluorescence signal can only achieve a resolution of c. 200 nm at best using visible light. Therefore, the focus of research on the development of advanced microscopes has shifted toward the development of new strategies to overcome the optical diffraction limit.
1.3 Optical Diffraction Limit
Without any external assistance, the human eye can only identify objects with a size larger than 0.1 mm. The evolution of the optical microscope has resulted in the improvement of the resolving power to directly observe specimens at the nanoscale. However, in 1873, Ernst Abbe predicted that a conventional optical microscope cannot achieve a resolution better than ∼200 nm in the lateral plane (x–y plane), which was defined as the optical diffraction limit [11]. Since then, for more than 100 years, the optical diffraction limit remained a major obstacle. Essentially, Abbe revealed that the physical limit of resolution is proportional to the sine of half the angular aperture. Therefore, the lens plays a key role in Abbe's experiment (Figure 1.4). Lenses are an integral part of optical systems. Depending on their function, lenses may exhibit a characteristic semi‐angular aperture (α) for a wavelength of illuminating light (λ) and a refractive index of the medium (n). Abbe used the term numerical aperture, which is calculated using the formula NA = nsinα. The spatial resolution (d) can be interpreted as the minimum distance between two target objects that can be distinguished as separate entities in the x, y, and z direction (alongside the optical axis) image plane and can be denoted as
Figure 1.4 The schematic of Abbe's diffraction limit.
Generally, the excitation wavelength (λ) is chosen in the range of c. 380–740 nm, which is visible to the human eye. Practically, the illumination light cannot be focused to an infinitesimal spot, and the refractive index is different when using a glass lens in air (n = 1), water (n = 1.33), or immersion oil (n = 1.52). In traditional optical microscopy, the best lateral spatial resolution in the x–y plane is calculated to be 200–250 nm, where objects at a vicinity (distance less than 200 nm) cannot be discriminated.
1.4 Super‐Resolution Microscopy: Overcoming the Diffraction Limit
It is theoretically impossible to break the optical diffraction limit,which restricts the best attainable resolution in a conventional microscope. This diffraction barrier can be circumvented to achieve image resolution beyond the diffraction limit via extra‐information analysis in the image forming and reconstructing stages, which constitute the basic concept of super‐resolution microscopy. The term super‐resolution microscopy was first used by Giuliano Toraldo di Francia in his academic publications in the early 1950s. Approximately 10 years later, Charles W. McCutchen proposed the theoretical basis for building a super‐resolution optical system to distinguish spatial details beyond the diffraction limit. Since then, different theoretical methods based on special illumination patterns and mathematical algorithms have been devised to complement the super‐resolution concept.
Existing optical techniques with complex hardware and software setups are not suitable for integration with super‐resolution methods for substantially overcoming the diffraction barrier since these systems are based on the use of lenses and visible light. Most of the early theoretical methods only facilitated marginal circumvention of the diffraction limit. In recent years, imaging resolution has been considered to be part of information theory, rather than a fundamental limit wherein an image can be produced using analytical processes for a time series depending on localization precision and shape of the illumination using new detection modalities. In 1994, Stefan W. Hell and others first proposed the theory of stimulated emission depletion microscopy as an advanced super‐resolution imaging technique that was practically achieved in 2007 [12–14]. Thereafter, several super‐resolution imaging techniques have been developed to overcome the optical diffraction limit. In several SRM techniques, the theoretical spatial resolution can be unlimited, despite the utilization of lenses and visible light. However, improving the spatial resolution in SRM techniques has become more reliant on the choice of optical probes and sample preparation than on the optical methods. For their pioneering contribution to the development of super‐resolution fluorescence microscopy, the 2014 Nobel Prize in Chemistry was awarded to Eric Betzig, Stefan W. Hell, and William E. Moerner.
In a biological system, owing to the limitation of spatial resolution, a large number of fundamental processes pertaining to chemical biology that occur inside cells at a scale of tens to hundreds of nanometers and over a timescale of a few milliseconds can be observed using conventional microscopy. Therefore, the development of new fluorescence imaging approaches that are capable of breaking the diffraction barrier to resolve the complex structure and functional details of biological systems is in high demand. Therefore, Abbe's diffraction limit can be theoretically and experimentally overcome under special conditions, which introduced a new era of microscopy: super‐resolution microscopy/optical nanoscopy. Thus, an optical microscope could be designed to facilitate imaging, even at the nanoscale level.
1.5 Near‐Field Scanning Optical Microscopy
In conventional optical microscopes, the optimal spatial resolution is limited mainly by the wavelength of the incident light and the NA of the objective lens. In 1928, Edward H. Synge first proposed the concept of near‐field scanning optical microscopy (NSOM), also usually called scanning near‐field optical microscopy (SNOM). This technique is capable of circumventing the diffraction limit to achieve ultra‐high resolution. NSOM has been successfully employed to obtain ultra‐highspatial resolution images and was developed based on atomic force microscopy (AFM) and optical fluorescence microscopy. Technically, the efficacy of NSOM is largely dependent on the size of the aperture tip [15]. Additionally, several technical issues must be addressed during the acquisition of images in NSOM. To acquire super‐high spatial resolution images through NSOM, it is important to ensure an extremely short narrow‐aperture probe to sample distance (almost 0), a longer scanning time, and low incident light intensity. The fabricated aperture size should be smaller than the wavelength of light, especially for scanning optically flat samples with feature sizes that are smaller than the aperture size. As a result, the evanescent light in the near‐field region is not diffraction‐limited, which allows for nanometer‐scale spatial resolution.
In NSOM, the spatial resolution is markedly improved by 10–20 folds compared to conventional optical microscopes. The aperture is kept sufficiently close to the specimen so that the shear force can be detected as feedback. The tip experiences a constant force while horizontally raster scanning the surface of the sample. Generally, a piezoelectric device is used to induce the oscillation of the probe to allow the tip to oscillate. The shear force produces a reduced amplitude or oscillation frequency when the tip approaches the sample to generate a feedback signal that is used for the reconstruction of topographic images. The acquired images have a very high resolution of up to 10 nm. Since 1992, NSOM has evolved as a scientific tool and represents one of the promising techniques for overcoming Abbe's diffraction barrier.
NSOM is particularly useful in nano‐science research, especially for the study of a broad range of materials with high spatial resolution. However, significant improvements are necessary to expand the scope of this instrument, especially in terms of more sensitive feedback. Additionally, NSOM has some crucial limitations in terms of scanning depth and acquisition time, apart from fundamental issues related to the study of soft materials, which need to be addressed in the future.
1.6 Far‐Field Super‐Resolution Microscopy
Traditional optical microscopic techniques, such as wide‐field optical microscopy and confocal laser scanning microscopy, are examples of far‐field optical microscopy techniques. They are advantageous compared to conventional optical microscopy in terms of ease of implementation and their broad applications. However, the resolution of far‐field optical microscopy is limited by the diffraction of light. Conventional microscopy does not allow ultra‐fine objects to be resolved beyond the optical diffraction limit. Traditional optical microscopy can generate images with a spatial resolution limited by the wavelength of the incident light and the NAs of the objective lens. For an optical microscope, reducing the wavelength of light and increasing the NA are important in increasing the resolution. However, irrespective of the type of lens that is used, an optical microscope cannot resolve objects smaller than half the wavelength of light (visible light: 400–700 nm).
In conventional fluorescence microscopy, fluorescent signals are collected by the objective lens of a microscope. For a microscope working in air with an NA of c. 1, the maximum resolution is approximately 0.61 λ, although some resolution improvements can be achieved by imaging in an immersion media of high refractive index. Thus, owing to the limitation of spatial resolution, various fundamental processes pertaining to chemical biology that occur inside living cells in the range of tens to hundreds of nanometers and within a timescale of a few milliseconds are difficult to resolve using conventional optical microscopy. Moreover, new imaging procedures that allow complex structures in biological cells, with a size less than 200 nm, to be resolved are in high demand.
To improve spatial resolution, several super‐resolution imaging techniques have been developed in recent years, and the contribution to this area was recognized with the 2014 Nobel Prize to Eric Betzig, Stefan W. Hell, and William E. Moerner. The most recently developed prominent super‐resolution microscopy techniques mainly involve point spread function engineering, single‐molecule localization, and moiré fringe effect‐based frequency modulation. Among the frequently used SRM techniques that typically facilitate far‐field observations are stimulated emission depletion microscopy (STED), reversible saturable optical fluorescence transitions (RESOLFT), photoactivated localization microscopy (PALM), stochastic optical reconstruction microscopy (STORM), structured illumination microscopy (SIM), points accumulation for imaging in nanoscale topography (PAINT), super‐resolution optical fluctuation imaging microscopy (SOFI), and the newly developed minimal photon fluxes (MINFLUX) super‐resolution microscopy [16–19]. The prevalent utilization of these far‐field SRM techniques can be ascribed to their comparatively low complexity, ease of implementation, and a wider FOV, compared to the NSOM.
1.7 Fluorescent Probes for Super‐Resolution Microscopy
Fluorescent probes play key roles in super‐resolution microscopy to achieve super‐resolution imaging and are of significant interest to researchers in chemistry, biochemistry, and related areas. The fluorescent probes for STED microscopy must exhibit a good fluorescence quantum yield and brightness, in addition to very high photostability to withstand the high STED laser illumination and resist photobleaching. However, for PALM/STORM microscopy, fluorescent probes should be reversibly or irreversibly photo‐switchable so that a super‐resolution image can be reconstructed from the collected thousands of frames of raw data images. Similarly, in SOFI, the fluorescence intensity of the probes should fluctuate rapidly under laser illumination, and super‐resolution images are then reconstructed from the raw data using specific algorithms. To date, many outstanding fluorescent probes, including fluorescent proteins, organic fluorescent dyes, and nanoparticles, have been developed that enabled the visualization of subcellular features in unprecedented detail using various super‐resolution imaging techniques. However, several label‐free fluorescent super‐resolution microscopy techniques have also been recently developed.
1.8 Image Analysis Algorithms
The robustness of an optical system and the aptness of fluorescent probes are the two key factors that fundamentally determine the performance of a specific super‐resolution imaging technique. However, even if the steps for data collection are properly followed, super‐resolution imaging can be hindered by the inappropriate analysis of image data. In particular, during the image reconstruction stage, post‐processing algorithms are critical to data analysis, which is important in the extraction of useful information from the images. Different super‐resolution imaging methods require specific algorithms to appropriately analyze the raw image data. In point‐spread‐function–based super‐resolution imaging techniques such as STED microscopy, simple deconvolution algorithms are typically used to process the raw data. However, the efficacy of single‐molecule localization microscopy (SMLM) techniques is highly dependent on post‐processing algorithms. Therefore, different algorithms have been developed to reconstruct super‐resolution images with a gradual improvement in image quality. Although initial single‐molecule localization algorithms used for PALM/STORM were appropriate for reconstruction of fine structures in fixed cells, the quest for new algorithms that apply to densely labeled sample analysis and the requirement of fewer frames during image reconstruction are pertinent to the improvement of the temporal resolution of live cells in super‐resolution imaging experiments. In recent years, different algorithms have been developed to accomplish robust live‐cell PALM/STORM imaging, which has been complemented by the advancement of new fluorescent probes. Different reconstruction algorithms, including compressive sensing, multiple Gaussian fitting, fast and unbiased localization reconstruction (Falcon), and neural network–based reconstruction methods, have been developed for the reconstruction of SMLM images with enhanced temporal/spatial resolution. The technical complexity of SIM, in addition to its sensitivity to optical aberrations and background fluorescence, means that it is challenging to use common image reconstruction algorithms for this technique, especially for thick samples. To address this issue, different new progressive image reconstruction algorithms, such as spot‐scanning SIM, have been designed that can enhance spatial resolution without compromising SNR and sensitivity and facilitate 3D imaging of thick samples. Moreover, the newly developed spot‐scanning SIM technique is readily compatible with many other microscopy techniques, such as multi‐photon fluorescence microscopy and fluorescence lifetime microscopy. Apart from the traditional analytical image algorithms, artificial intelligence algorithms, such as deep learning and metrics learning neural networks, have become increasingly popular and are widely applied in image optimization, especially in super‐resolution microscopy.
1.9 Applications
The exploration of biological events using the rapidly developing super‐resolution microscopy techniques has revealed new areas of study in biological research. A detailed understanding of the biological events that occur in cellular organelles, which is extremely significant for understanding the origin of critical physiological disorders, is now possible using SRM. In particular, the study of ultra‐small subcellular organelles, such as mitochondria, lysosome, and endoplasmic reticulum, at the nanoscale using SRM methods is instrumental to the discovery of important insights in cell biology, which can facilitate the discovery of new drugs. Super‐resolution imaging techniques, which are being applied to both live and fixed cell imaging to obtain intra‐cellular details with unprecedented high resolution, are contributing to the study of fundamental processes in cell biology such as inter‐organelle communication, in a subcellular context.
1.10 Outline of the Content of Succeeding Chapters
The preceding discussions present a brief account of the systematic evolution of microscopy techniques toward the visualization of specimens with high resolution. From confocal microscopy to SRM, there have been notable improvements that have benefited research in biology pertaining to the discovery of new subcellular pathways. In particular, SRM facilitates the study of intracellular dynamics at nanoscale resolution, which has the potential to augment the advancement of biomedical research in terms of finding a cure for complex chronic ailments. However, unlike confocal microscopy, SRM has started to flourish, and significant effort has been invested in this area not only to improve the performance of SRM methods but also to expand their practical application in real‐time sample analysis. In this regard, it is important to provide a detailed account of the status of these SRM techniques in modern biological research.
Therefore, in the succeeding chapters, we will present a chronological overview of the technical details and the working principle of different SRM methods, along with the scope of further technological advancements. Moreover, these chapters will highlight the effort in developing new algorithms and computational methods toward the enhancement of image quality. A chapter is dedicated to highlighting the progress in designing newer fluorescent probes, including FPs, organic probes, metal complexes, and nanoparticles, for various SRM methods, in addition to their specific pros and cons. These chapters have been comprehensively written to satisfy not only a general readership but also to answer specific queries of the advanced scientific audience.
Acknowledgment
This work is supported by the National Natural Science Foundation of China (61875131).
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2Point Spread Function Engineering SRM
Wei Yan, Luwei Wang, Yinru Zhu, Jialin Wang, and Ruijie Xiang
Center for Biomedical Photonics (CBMP) & Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, 518060, China
In conventional optical imaging systems, the image spot formed by a point light source after passing through the imaging system is called the point spread function (PSF) of the optical imaging system. The PSF describes the resolution ability of an imaging system and is an important basis for determining the resolution of an optical imaging system. Super‐resolution imaging based on the modulation of the PSF is called PSF engineering for super‐resolution imaging.
2.1 Stimulated Emission Depletion Microscopy (STED)
2.1.1 Principles of STED
Diffraction is a consequence of the wave characteristics of light. This phenomenon occurs when light passes through an aperture of a certain size (e.g. a lens), which causes an infinitely small point in the object plane to become a diffraction spot of finite size in the image plane. Therefore, when two or more object points are too close to each other, their diffraction spots are superimposed to form a blurred image spot. At this time, they cannot be distinguished in the image. Thus, the diffraction of light after it passes through a lens limits the resolving power of an optical system, inducing an inherent optical diffraction limit. A point object forms an image spot after passing through an optical microscope. According to Abbe's optical diffraction theory, the resolution of optical microscopy imaging cannot surpass the optical diffraction limit. However, in optical research, extracting the ultra‐fine structural information of samples beyond the diffraction limit is a key objective. To break the diffraction limit, in 1994, the German scientist Stefan W. Hell first proposed the stimulated emission depletion (STED) technique, which can theoretically compress the effective PSF and circumvent the limitation of optical diffraction to achieve super‐resolution imaging [1]. STED is a technology based on laser point scanning microscopic imaging. The basic structure of the system is similar to that of a confocal scanning microscope: the laser scanning device is used to scan the beam point‐by‐point on the sample to complete the imaging of the region of interest. Based on Einstein's radiation theory, Stefan W. Hell found that a long‐wavelength doughnut‐shaped high‐energy laser can be used to perform targeted stimulated radiation of fluorescent molecules in an excited state to compress the size of the excitation light spot, which is equivalent to compression full width at half maximum of the PSF. A schematic diagram of the principle of STED technology is shown in Figure 2.1.
Figure 2.1 Schematic diagram of STED. (a): Schematic diagram of stimulated emission. (b): Overlapping of beams.
Source: Wang et al. [2]. Reproduced with permission of © 2020 Acta Phys. Sin.
