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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5580_Библиотеки_им_академика_М_И_Перельмана
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278 Y. Mimori-Kiyosue et al.
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(continued)
Application to HTS
Applicable to a wide
range of assays
Applicable to a wide
range of assays
range of assays
assays
assays
Invasiveness/
Phototoxicity
Very high
(critical)
(severe)
Medium Applicable to a wide
Low ~ medium Applicable to specific
Low Applicable to specific
High Limited applicability
*3
Penetration depth
Very l ow
(~50 µm)
*2
Low
(~50 µm)
(≥1 mm)
Shallow
(≤ 200 nm)
(≤ 50 µm)
Imaging speed
Medium (≥ 5 frames/
sec)
*1
z(axial)
resolution
Poor
(≤ 0.6 µm)
*1
resolution
Moderate (≤
0.2 µm)
*4
Modality xy (lateral)
Table 16.1 Comparison of fluorescence microscopy modalities for live cell imaging
Wide-field
Low ~ medium Moderate (~150 µm) High
Medium ~ high (≥ 10
Moderate (≤ 0.5
Moderate (≤ 0.5
µm)
Moderate (≤
0.2 µm)
Moderate (≤
Confocal,
laser scan
Confocal,
frames/sec)
Fair (≤ 1 µm) Low ~ medium High
µm)
0.2 µm)
0.3 µm)
spining disk
Multiphoton Moderate (≤
High (>100 frames/
sec)
Low ~ medium Low
Excellent
(~ 0.2 µm)
Moderate
0.2 µm)
Excellent (≥
*5
TIRF Moderate (≤
Super-resolution
(≥ 0.5 µm)
0.2 µm)
16 Potential of High-Spatiotemporal Resolution Live Cell Imaging … 279
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Application to HTS
Invasiveness/
Phototoxicity
*3
Penetration depth
*2
Imaging speed
*1
Less applicable
Low Less applicable
Low
High
(≥ 300 µm)
Medium
High (>100 frames/
sec)
High (>100 frames/
73] and others
(minimal)
(≤ 100 µm)
sec)
z(axial)
resolution
Fair
(≤ 1.5 µm)
*1
(≤ 0.4 µm)
resolution
Modality xy (lateral)
Table 16.1 (continued)
Light sheet Fair
Good
(≥ 0.4 µm)
(≥ 0.3 µm)
Lattice light-sheet Good
1. Resolution depends significantly not only on the performance and magnification of the objective lens but also on the transparency and depth of the observed
sample. In the case of multiphoton microscopy or light-sheet microscopy designed for observing deeper regions of large samples, lower magnification objective
lenses are used to correspond to this purpose, resulting in a natural decrease in resolution. Therefore, if the performance is superior compared to using the
same magnification objective lens with other techniques, it is rated as “Fair.” 2. Generally, the imaging speed depends on factors such as the fluorescence
intensity of specimens, the sensitivity of systems, and signal-to-noise ratio (S/N), making accurate comparisons between different modalities difficult. In the
case of scanning modalities like confocal microscopy and multiphoton microscopy, the imaging speed also depends on the scanning area. Scanning confocal
microscopes and multiphoton microscopes may include a resonant mode for high-speed scanning and high-sensitivity detectors, enabling rapid imaging of
samples with high signal intensity. The speed required to image a sample at average fluorescence intensity is shown here, rather than the highest performance of
the microscope system. 3. With the exception of TIRF, which illuminates only shallow areas, the penetration depth depends on the transmittance of the sample
rather than the performance of the imaging systems. Multiphoton microscopy using highly transmissive wavelengths has an advantage. Light-sheet microscopy
using two-photon excitation has also been developed. 4. Deconvolution methods are effective for eliminating optical blur. They are particularly effective in
wide-field microscopy, which often contains significant optical blur. 5. Super-resolution modalities are diverse, and the resolution and imaging speed vary greatly
*
depending on the imaging method. There are methods that have improved z-resolution. For details, please refer to references [
280 Y. Mimori-Kiyosue et al.
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In 1838, Schleiden proposed that all plants are composed of cells, and in 1839,
Schwann extended this idea to animals, stating that all organisms are composed of
cells (cell theory) [
cell division that occurs in eukaryotic cells, occurred in 1882. The German biologist
Walther Flemming published his observations of cell division, including mitosis, in
his groundbreaking study “Zellsubstanz, Kern und Zelltheilung” (Cell substance,
nucleus, and cell division) [
visualize chromosomes and described the process of mitosis in detail. His work laid
the foundation to study cell division and established the significance of imaging
techniques in understanding cellular processes.
The establishment of cell culture techniques in the early-to-mid twentieth century
was a significant milestone in cell biology. In 1907, Ross Granville Harrison, who was
an American biologist, successfully cultured frog nerve cells on a piece of frog cartilage in a saline solution [
grow cells outside of an organism. George Gay of Johns Hopkins University collected
cervical tumor cells from an African American woman, Henrietta Lacks, and cultured
25]. These cells became the first immortal human cell line, HeLa. HeLa cells
them [
became widely used in biomedical research and played a crucial role in numerous
scientific breakthroughs. In modern times, starting around the year 2000, techniques
such as organoid culture, enabling cells to be cultured in environments similar to
those within tissues, have advanced [
establishing induced pluripotent stem cells (iPSCs), capable of reprogramming cells
into pluripotent stem cells, was established [
from iPSCs have contributed to the understanding of human disease pathobiology
and have also been utilized for drug screening.
As a means of analyzing cellular functions, it is important to emphasize that cell
function editing technologies, such as RNA interference (RNAi) in 1998 [
genome editing (CRISPRi/a) in 2012 [
can temporarily activate molecular functions by light stimulation [
essential technologies in biology and drug discovery, especially when combined with
imaging.
In the advancement of microscopy technology, phase-contrast microscopy was
invented by Frits Zernike in the 1930s [
This technique allowed visualization of transparent and unstained live cells, which
greatly expanded the possibilities of studying dynamic cellular processes. Around
1950, time-lapse imaging using brightfield and transmission light microscopy began to be performed. The time-lapse recording device cinemicroscope, which was
assembled from the inverted microscope, motion picture film camera, incubation
chamber, and other parts for live cell imaging, was invented [
to observations of cell surface ruffling and lamellipodia structures [
early 1950s, Shinya Inoué tuned up a polarizing microscope originally designed
for minerals, capable of detecting subtle birefringence, to observe the structures
within living cells (Shinya-scope). Through this innovation, he observed mitotic
21, 22]. The first documented imaging of “mitosis,” a process of
23]. Flemming used histological staining techniques to
24]. This experiment marked the first successful attempt to
26
]. Additionally, in 2006, the technology for
]. The differentiated cells derived
27, 28
] and
36
37
], as well as optogenetics in 2005, which
38], have become
29, 30
] (Nobel Prize in Physics in 1953).
31
] and contributed
]. In the
32, 33
16 Potential of High-Spatiotemporal Resolution Live Cell Imaging … 281
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spindles and chromosomes, which had long been skeptically regarded as artifacts
resulting from specimen fixation, and visually demonstrated the separation of actual
chromosomes [
16.2.2.2 Fluorescence Imaging
Following the invention of histological staining, which led to discoveries such as
detailed chromosome movement [
39], the discovery and application of fluorescent dyes and vital stains revolu-
1898 [
tionized live cell imaging. The development of fluorescence microscopy techniques in
the early twentieth century [
structures, proteins, and organelles in live cells [
propidium iodide, and fluorescein diacetate, became essential tools to assess cell
viability, membrane integrity, and metabolic activity [
cent probes for many specific cellular molecules were available. Around 1980, the
development of chemical fluorescent probes, including calcium ion indicators such
as Fura-2, began [
To detect molecular interactions, techniques such as FRET (Fluorescence Resonance Energy Transfer) and FLIM (Fluorescence Lifetime Imaging Microscopy)
have also been developed [
fluorophores (donor and acceptor) when they are in close proximity within a range
of less than10 nm [
reveal protein–protein interactions and molecular conformation changes. Temporal
resolution of protein–protein interactions can be achieved using FLIM, which monitors localized changes in probe fluorescence lifetime and is well suited for analysis
of dynamic changes within living cells [
with FRET can provide strong evidence for the physical interactions between two or
more proteins with very high spatial and temporal resolution [
The discovery and application of green fluorescent protein (GFP) by Roger Y.
Tsien, Osamu Shimomura, and Martin Chalfie in the 1990s marked a significant
breakthrough in live cell imaging (the Nobel Prize in Chemistry 2008). GFP, which
is a naturally occurring fluorescent protein in the jellyfish Aequorea victoria, is genetically engineered to tag specific proteins or organelles in live cells. This revolutionary
technique, known as GFP tagging, allowed researchers to visualize and track the
dynamics of specific molecules in real time, providing unprecedented insights into
cellular processes [
of sensors, including FRET-based calcium sensors called “Chameleon” [
high-affinity calcium probes composed of a single GFP called “G-CaMP” [
Very recently, the Nobel Prize in Chemistry 2023 was awarded for the discovery
and development of quantum dots. Quantum dots are nanometer-sized semiconductor particles that exhibit unique optical and electronic properties due to quantum
mechanics [
excited by an energy source, such as light, they emit light at a specific wavelength with
34, 35].
23] and the identification of the Golgi apparatus in
40] enabled specific labeling and visualization of cellular
41]. Vital stains, such as Hoechst,
42, 43]. By the 1980s, fluores-
44, 45].
46
]. FRET relies on the transfer of energy between two
]. By measuring changes in fluorescence signals, FRET can
47
]. The application of FLIM in parallel
48
49].
50, 51]. The GFP technology also contributed to the development
52] and
53, 54
55
] and are used in display technologies such as TVs and LEDs. When
].
282 Y. Mimori-Kiyosue et al.
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high brightness and stability. This property is useful not only for biological imaging,
but also for a variety of applications including drug delivery, medical diagnostics,
and numerous other biomedical applications [
16.2.2.3 Digital Imaging and Quantitative Analysis
The advent of digital imaging and image analysis technologies further propelled
live cell imaging in drug development since about 1970 [
advanced microscopy systems, and computer-based image analysis software enabled
the acquisition, processing, and quantification of live cell imaging data [
Digitization of images has been crucial for their quantitative analysis and applications in data science. Digitization enables the conversion of analog image data
into digital data represented by pixels or voxels, which are processed and analyzed
by computational algorithms. This digital format facilitates quantitative analysis
because it allows precise measurements, feature extraction, and mathematical operations on the image data. Data science techniques, such as image processing algorithms, machine learning, and deep learning models, are applied to digitized images
to extract meaningful information and patterns [
16.2.2.4 Advanced Live Cell Imaging Techniques
56].
57]. Digital cameras,
58].
59].
In the early stages of fluorescence microscopy development, wide-field microscopy
techniques were commonly employed for fluorescence imaging (Fig. 16.3A). Widefield fluorescence microscopy is based on the principle of epi-fluorescence, where the
excitation light and emitted fluorescence pass through the same objective lens [
In this method, both the excitation light and emitted fluorescence reach the detector,
leading to reduced contrast and increased background signals, inducing phototoxicity
in the specimens. According to the Abbe diffraction limit, the maximum achievable
xy resolution of an optical microscope (Fig.
]. Since the shortest wavelength within the visible light spectrum used
length [
in microscopy is around 400 nm, the resolution limit cannot be improved beyond
approximately 0.2 µm as long as visible light is employed. Additionally, the epiillumination system has significant optical anisotropy with lower resolution in the
z-axis direction compared with the xy plane because of factors such as diffraction
and the focal depth of the objective lens (Fig.
high-resolution three-dimensional (3D) information.
with the introduction of confocal microscopy in the 1950s and the development of
multiphoton microscopy in the 1990s [
sectioning and reduced phototoxicity, enabling clearer imaging of thick specimens
and longer-term live cell imaging.
the microscope, Agard and Sedat developed the deconvolution technique in 1983,
61
Advancements in microscopy techniques continued in the twentieth century
As an image processing method to reverse the optical distortion introduced by
16.3B) is approximately half the wave-
16.3C). This makes it difficult to obtain
62–64]. These techniques improved optical
60
].
16 Potential of High-Spatiotemporal Resolution Live Cell Imaging … 283
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Fig. 16.3 Comparison of modalities and resolution in fluorescence microscopy (A) Comparison
of illumination modalities in fluorescence microscopy. The thickness of the excitation light illumination is shown. (B) Definition of resolution. The resolution of a microscope is defined as the
minimum distance at which two small objects can be seen as separate objects. (C) Image formation and point spread function (PSF). (D) Comparison of PSF in different light-sheet microscopy
techniques [
(D))
]. Scale bar, 0.2 µm. (Reprinted with permission [license number: 5607081260689]
9
which enables the reconstruction of high-resolution images by iteratively estimating
the true object properties from the raw image using knowledge of the microscope’s
optical properties [
65]. They obtained the first high-resolution images of the structure
and organization of fluorescently labeled chromatin in the nuclei of Drosophila (fly)
salivary glands. Numerous deconvolution algorithms have been developed according
].
to the imaging techniques [
66
In recent years, the field of live cell imaging has undergone significant advancements in terms of high-resolution techniques. Total internal reflection fluorescence
microscopy (TIRF) allows highly sensitive imaging by excluding background light
because of its shallow depth of transmission (≤200 nm) (Fig.
16.3A), making it
particularly suitable for imaging processes occurring at or near the cell membrane
]. Different approaches to
interface and even at the single molecule level [
67, 68
super-resolution microscopy, which began to be realized in the mid-1990s, have
surpassed the resolution limits of optical microscopes, approaching resolutions of
]. However,
around 10 nm, and received the Nobel Prize in Chemistry in 2014 [
69–73
super-resolution imaging, which often requires long acquisition times, was not ideal
for live cell imaging. Additionally, while in vitro studies have provided detailed
insights into the operational principles of molecular mechanisms, current cell biology
284 Y. Mimori-Kiyosue et al.
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studies are attempting to re-examine them in multicellular environments using live
organisms and systems that mimic live organisms, such as organoids.
Furthermore, as techniques to complement imaging, tissue-clearing techniques
[
74] and expansion microscopy [75] have also been developed. By combining
these, it has become possible to obtain images beyond the capabilities of traditional
microscopy [
samples.
16.2.2.5 High-Throughput Screening
The integration of live cell imaging with high-throughput screening (HTS) platforms and automated image analysis systems has revolutionized drug development,
allowing researchers to quickly conduct millions of chemical, genetic, and pharmacological tests to rapidly identify active compounds, antibodies, or genes that
affect a particular biological process [
high-throughput drug screening possible in the 1980s [
opments toward automated high-throughput microscopy, such as autofocus, sample
positioning, multiwell plates, and high-density formats, have made HTS a practical
strategy [
Initially, there was a debate between isolated target versus cell-based assays, and
cell-based screens were considered “black-box” assays, fraught with variability, and
there were concerns about the time and cost involved in unraveling the mechanism of
action of a hit [
results become more reliable, there was an increasing tendency to use HTS to identify
hit compounds and optimize lead compounds as a complement to biochemical assays
because appropriate cellular assays are more physiologically relevant [
The data generated from HTS have provided new opportunities for data-driven
approaches in drug discovery. However, readouts obtained from HTS are typically
univariate, experiments are often limited to the well level, and many experiments are
not suitable for traditional HTS techniques.
76], although the application of these techniques is limited to fixed
77–79]. Advances in automation made early
80]. Years later, crucial devel-
81
].
82]. However, as imaging and data analysis techniques advance and the
79, 80, 83
].
16.2.2.6 Imaging of Organoid and Organ-On-A-Chip Technologies
Recent advancements in live cell imaging have focused on capturing cellular behavior
in more physiologically relevant contexts [
chip technologies are used for such a purpose. Early development of these technologies took place in the late 2000s and early 2010s, with a variety of models being
developed as they gained attention in the 2010s.
Organoids (miniature organs) derived from pluripotent stem cells or tissue-specific
stem cells self-organize into complex structures and are 3D cell cultures that closely
mimic the structure and function of specific organs or tissues (see Fig.
example). Organ-on-a-chip technologies recreate the microenvironment of specific
organs in a controlled manner [
3, 84
]. Organoid culture and organ-on-a-
16.1Basan
5, 85]. These technologies provide valuable platforms
16 Potential of High-Spatiotemporal Resolution Live Cell Imaging … 285
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to study cellular behavior and drug responses in more relevant and physiological
contexts. However, when using live cell imaging to observe complex specimens, the
imaging depth of live cell imaging is limited by light scattering and absorption in the
sample. Furthermore, achieving high spatial resolution throughout the entire depth
of a complex specimen can be challenging. Optical aberrations, a limited numerical
aperture, and light diffraction reduce the resolution in the z-axis compared with the
xy plane. Because of such limitations, high-resolution live cell imaging in a complex
sample has had limited applicability.
16.2.2.7 Advantages of Light-Sheet Optics Over Epi-Illumination
Optics
The fluorescent light-sheet method [86, 87], also known as SPIM (selective plane
illumination microscopy) or its advanced version, digital scanned laser light-sheet
fluorescence microscopy (DSLM) [
Siedentopf and Zsigmondy in 1902 as “ultramicroscope” to visualize scattering subdiffractive particles [
use as fluorescence microscopy in the early 2000s, offers distinct advantages for live
cell imaging, especially in complex 3D specimens such as embryos and organoids
16.1B, C). The most commonly used optical system for microscopes is the
(Fig.
coaxial epi-illumination system, which performs excitation and observation in the
same light path (Fig.
efficiency, the coaxial epi-illumination system has been adopted in most biological
microscopes, including confocal microscopes. However, as mentioned above, this
system suffers from poor 3D resolution, making it challenging to obtain precise threedimensional information (Fig.
background light and increased phototoxicity. Furthermore, confocal microscopes
are used to obtain three-dimensional images, but scanning-based systems require a
long time for acquisition, and spinning disk high-speed confocal systems [
limitations in imaging thick samples due to pinhole crosstalk [
Optical systems that use light in a sheet form, known as a light sheet, capture
images using an objective lens positioned perpendicular to the light sheet [
16.3A, C). This method has low invasiveness because it only illuminates
(Fig.
the imaging plane with excitation light, resulting in images with a high signal-tonoise ratio by minimizing background light generation. Additionally, because the
light-sheet plane is captured in a single shot using a digital imaging device such
as an sCMOS (scientific complementary metal–oxide–semiconductor) camera, it
allows for simple high-speed imaging. Such performance is well suited for long-term
imaging of live cells, whole organisms, and developmental processes [
This microscopy technique is employed for imaging all cells in early embryos of
mice, zebrafish, and Drosophila, among others [
ization and movement in entire zebrafish embryos during the first 24 h of development, providing “digital embryos” that are comprehensive databases of cell positions,
divisions, and migratory tracks [
91] (The Nobel Prize in Chemistry, 1925), put into practical
16.3A). Because of its advantages in operability and space
88–90], whose prototype was first introduced by
16.3B, C). Additionally, they often experience high
92]have
93
].
87
2, 87, 94
90, 95, 96]. It records nucleus local-
90]. Additionally, it obtains a comprehensive view
].
]
286 Y. Mimori-Kiyosue et al.
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of pathological specimens [97] and whole tissues by combining tissue-clearing techniques [
and organs [
ical activities. However, at present, while a large amount of data can be acquired
from individual samples, the throughput in terms of the number of samples is limited
to low-throughput observations.
98] to enable comprehensive cancer cell profiling of the whole mouse body
99]. These techniques provide a detailed analysis of cellular and biolog-
16.2.3 Incorporating Data Science into Live Cell Imaging
for Drug Discovery and Development
16.2.3.1 Early Stages of Data Science Introduction to Image Analysis
In the late twentieth century, live cell imaging techniques began to be applied to drug
discovery. Initially, imaging was primarily used to visualize cellular processes and
study the effects of drugs on cells. However, the potential for data analysis and the
application of computational methods were not fully realized at this stage.
Algorithms have been developed to analyze biological images using computers
1]. ImageJ, an open-source software platform inherited from NIH Image, created
[
by Wayne Rasband of the National Institutes of Health and first released in 1997,
provides easy installation on any platform and a simple user interface [
it an essential tool for image processing in the biological sciences. ImageJ’s functionality is easily extended with plugins (software components that are separately
installed to add functionality). Because ImageJ’s architecture does not follow modern
software-engineering principles, Fiji was developed to facilitate the transfer of new
algorithms into ImageJ by combining software libraries with a broad range of
scripting languages, providing a platform for collaboration between computer science
and biology [
market in 1993.
101]. A commercial product such as Imaris was also introduced to the
100], making
16.2.3.2 Automated Image Analysis
Starting around 2006, several open-source advanced image analysis software solutions were released [
GUI and wealth of implemented methods allows users to combine modules to create
their own customized image analysis pipelines to address a variety of biological questions quantitatively, including standard assays (e.g., cell count, size, per-cell protein
levels) and complex morphological assays (e.g. cell/organelle shape or subcellular
patterns of DNA or protein staining) [
it made flexible and powerful high-content image analysis available to researchers
without programming skills.
102, 103]. The software package CellProfiler with an intuitive
104, 105]. It quickly became popular because
16 Potential of High-Spatiotemporal Resolution Live Cell Imaging … 287
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Subsequently, various automated and semi-automated image analysis software
have been developed, enabling cell recognition, segmentation at the tissue and
organismal levels, and lineage tracing using time-series multidimensional image
106]. Lineage tracing seeks to trace the ancestral relationships and develop-
data [
mental paths of individual cells by reconstructing the trajectory of cells through their
developmental processes [
mated cell lineage tracing during Caenorhabditis elegans (worm) embryogenesis in
2006, which can trace the lineage up to the 350-cell stage [
achieved digital reconstruction and lineage tracing of plant whole organs in 2010
109], based on time-lapse 3D fluorescence imaging and automated image analysis.
[
16.2.3.3 High-Content Screening
High-content screening (HCS) is the combination of HTS and high-content analysis,
offering richer data, higher throughput, and increased flexibility to reduce bottlenecks at target validation and lead compound optimization [
1990s and early 2000s, HCS platforms were developed by combining automated
live cell imaging with image analysis capabilities, reconstituted cell-based target
validation, secondary screening, lead compound optimization, and structure–activity
relationships [
advanced significantly, capable of recording tens of thousands of images or more
per day (approximately 100 gigabytes per day) for weeks at a time. The number
of images acquired during high-throughput (HT)-HCS experiments, amounting to
hundreds of thousands of images, required fully automated image analysis methods
113].
[
A rapid and fully automated algorithm for cell segmentation in fluorescence
microscopy data for HTS/HCS has been developed [
screening using RNAi, aiming to selectively downregulate the expression of specific
genes using small RNA molecules, Fuchs et al. used multiparametric phenotypic
profiles of RNAi screening data to cluster genes and to discover novel gene func-
115] (Fig. 16.4A). Open-source image analysis platforms such as CellProfiler
tions [
contributed to the automation of t he HCS [
software is an alternative solution that is often provided with HCS s ystems from
microscope manufacturers [
routine HCS experiments, they are difficult to customize for specific assays.
Importantly, starting from the mid-2000s, Graphics Processing Units (GPUs),
originally developed for graphics processing, began to be applied to general computations due to their high computational power [
automated image analysis of cells and tissues that required complex calculations.
These advancements in imaging technology and computing power paved the way for
the introduction of AI and machine learning technologies. The integration of AI and
machine learning marked a revolutionary turning point in image analysis.
15, 112
107]. For instance, Bao et al. developed a system for auto-
108] and Fernandez et al.
15, 110, 111]. In the
]. By around 2010, automated fluorescence microscopes had
114
]. In a genome-wide
106, 117
106, 116
]. Although these tools are sufficient for certain
]. Commercial image analysis
118, 119
]. This enabled rapid and
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