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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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288 Y. Mimori-Kiyosue et al.
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Fig. 16.4 Example of fluorescence multidimensional image data analysis (A) Schematic representation of automated phenotyping of an RNAi screen [
data analysis modules for the post-implantation mouse embryo [
with a convolutional neural network. Figure reproduced from Reference [
permission [license number: 5665130789171] (A))
]. (B) Overview of image processing and
115
96]. Cell division was predicted
96]. (Reprinted with
16 Potential of High-Spatiotemporal Resolution Live Cell Imaging … 289
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16.2.3.4 Introduction of AI and Machine Learning Technologies
for Image Analysis
With the advent of big data and the availability of computational resources since the
2000s, AI and machine learning began to have a significant effect on drug discovery
based on live cell imaging [
rithms and models to analyze large imaging datasets and extract meaningful features.
In 2006, Neumann et al. developed an automated platform for high-content, genomewide RNAi screening by time-lapse fluorescence microscopy [
all steps, including printing transfection-ready siRNA microarrays, fluorescence
imaging, and computational phenotyping of digital images using machine learning to
detect cell states, in a high-throughput workflow. Subsequently, Held et al. developed
CellCognition, a supervised machine learning method that combines morphology
classification with hidden Markov modeling for annotation of the progression through
morphologically distinct biological states in high-throughput live cell imaging of
HeLa cells expressing histon H2B-mCherry, a nucleus/chromosome marker, and
mEGFP-fused markers [
The wide applicability and excellent generalization properties of machine learning
algorithms have been exploited by a variety of image analysis software such as
Microscopy Image Browser, which offers multiple preprocessing and region selection options, along with superpixel-based segmentation [
enables large-scale web-based collaborative image processing [
8, 106, 120, 121]. Researchers started developing algo-
122]. They automated
123].
124
]; Cytomine, which
125
]; and ilastik,
generic features, powerful non-linear classifiers, probabilistic graphical models and
solvers, all wrapped into workflows with a convenient user interface for fast interactive training and post-processing of segmentation, tracking and counting algorithms
].
126
[
16.2.3.5 Introduction of Deep Learning Technologies for Image
Analysis
Deep learning emerged as a game-changing development in the field of image analysis shortly after 2010. Deep learning is a subset of machine learning that uses
artificial neural networks, particularly deep neural networks with multiple layers, to
perform sophisticated pattern recognition and feature extraction from data. These
techniques were initially applied to medical imaging and clinical specimens such
as paraffin-embedded pathological tissues [
rapidly to optical imaging [
proposed in 2015, is an encoder-decoder type network, specialized for semantic
segmentation (the task of classifying each pixel in an image into different classes).
It is specialized for semantic segmentation (the task of classifying each pixel in an
image into different classes) and has been very useful in areas such as biomedical
image analysis [
are widely used as an important method for deep learning image segmentation [135].
134]. Since then, various derived networks have been proposed and
131–133
]. U-net, a deep learning network architecture
127–130
] and subsequently extended
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Deep learning has also been successfully used in the context of image restoration algorithms for fluorescence images in CARE (content-aware image restoration)
networks to transform input images into output images with an improved signal-tonoise ratio and near isotropic resolution [
strate remarkable capabilities in quantitative image analysis tasks, such as object
detection, segmentation, object tracking, and lineage tracing [
In developmental biology, to address the challenges of four-dimensional (3D +
time) fluorescent time-lapse datasets, McDole et al. started using deep learning to
recognize cell divisions during the process of tracking and reconstructing all cells in
early mouse development using light-sheet microscopy in 2018 (Fig.
ELEPHANT, an interactive platform for 3D cell tracking, employed deep learning
for cell detection and per-frame linking in light-sheet datasets with diverse cell
appearances and movements during the embryonic development of Caenorhabditis
elegans [
term light-sheet imaging of intestinal organoids into “digital organoids” by data
processing using deep learning techniques to segment single organoids, their lumen,
cells, and nuclei in 3D over long periods of time [
is visualized by linking lineage trees with corresponding 3D segmentation meshes,
which allows a combined understanding of the multivariate and multiscale data.
More recently, a method has been reported to automatically identify and track
nuclei in light-sheet microscopy recordings of entire developing mouse embryos by
combining deep learning and global optimization in 2023 [
139]. de Medeiros et al. presented a framework that converted long-
136]. Deep learning models also demon-
137, 138].
16.4B) [96].
140]. The extracted information
141
]. These approaches
embryos, tissues, and organs through automated reconstruction of the entire cell
lineage. This technology is expected to be valuable in fields such as regenerative
medicine, particularly in studies related to the control of iPSC differentiation.
16.2.3.6 Image-Based Phenotypic Screening and Multi-Omics
Integration
Image-based screens routinely contain millions of cells and produce hundreds or even
thousands of features to describe each cell. Interpreting this many parameters cannot
reasonably be expected of humans, but machine learning methods seek to exploit
the inherent data structure to infer models that are used to conduct versatile data
analysis tasks [
Such machine learning-based phenotypic profiling has become increasingly popular
in recent years [
These techniques enabled automated analysis of phenotypic features, identification of relevant cellular signatures, and prioritization of promising drug candi-
7] and facilitated the adoption of image-based phenotypic screening in drug
dates [
discovery [
content imaging of two-dimensional (2D) and 3D disease models, such as cancer
organoids and human-iPSC-derived cells, for drug discovery, together with attempts
6]. The trained models can even predict unknown cell phenotypes.
].
14
8, 84]. This type of technology is also beginning to be used in high-
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to fully automate experimental systems using robotics [4, 17, 18, 142, 143]. A laboratory automation system has the ability to perform clinically and physiologically
relevant cell-based assays from remote locations, which facilitates global research
collaboration and accelerates the discovery of novel drug candidates [
A particularly important issue currently being addressed is the advancement of
multi-omics integration through informatics [
various data modalities, such as genomics, proteomics, and transcriptomics [
researchers gain a more comprehensive understanding of cellular responses to drugs.
The integration of genomic, transcriptome, and proteomic data with spatial information by pseudotime analysis, which involves ordering individual cells along a
continuous trajectory representing their hypothetical developmental or biological
progression, has expanded the capabilities of data-driven drug discovery [
While numerous computational methods have been developed to perform pseudotime analysis, these methods may not always provide accurate results for lineage
tracing. Integrating information from live cell imaging with molecular data could
potentially offer researchers a more comprehensive understanding of cellular mechanisms, facilitate the identification of potential drug targets, and enable the prediction
of drug responses based on specific cellular profiles.
144]. By combining information from
18].
145],
146, 147].
16.2.4 Limitations in Current Live Cell Imaging-Based Drug
Discovery and Development
Live cell imaging has greatly contributed to drug development, but researchers face
several limitations in this field. Some of the main limitations are listed below.
1. Spatiotemporal resolution: Achieving high spatial and temporal resolution
simultaneously remains a challenge. Balancing the need for detailed cellular information with the requirement for high temporal resolution is particularly challenging when studying dynamic cellular processes or rapid drug responses. However,
capturing subtle changes in cellular morphology and organelle dynamics in response
to drugs is crucial for gaining deeper and more accurate insight, making it a
particularly challenging aspect that requires improvement.
2. Phototoxicity: The use of fluorophores and intense light sources in live cell
imaging causes phototoxicity, leading to cellular damage or altered behaviors, which
limits observation periods.
3. Compatibility with 3D and tissue-level imaging: Studying cellular behavior
in complex 3D environments and tissue-level imaging poses additional challenges.
Techniques such as light-sheet and multiphoton microscopy have emerged to address
these limitations, but further advancements are needed to fully capture the intricacies
of cellular dynamics in physiologically relevant contexts.
4. Complexity of biological systems: Live cell imaging data are inherently
complex, which capture intricate cellular processes and behaviors. Additionally,
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live cell imaging data are subject to biological variability, including cellular heterogeneity and changes in cellular behavior over time. Incorporating such complexity
and variability into data science models and interpreting the outputs while considering these variations can be even more challenging for multidimensional live
imaging data[
science methods tailored to the unique characteristics of live cell imaging data and
interdisciplinary collaborations are required to overcome these hurdles.
147]. For continued advancements in imaging technologies, data
16.3 Emergence of High-Spatiotemporal Resolution Live
Cell Imaging Technology Using an Ultra-Thin Light
Sheet
16.3.1 Principles and Performance of High-Resolution
Light-Sheet Microscopy Using Ultra-Thin Light Sheet
16.3.1.1 The Development of Bessel Beam Microscopy
As mentioned above, light-sheet microscopy is suitable for volume live imaging
of large 3D specimens. Initially, light-sheet microscopy was optimized to image
relatively large samples, such as early embryos and organoid, using light sheets with
a thickness of a few microns to achieve cellular-level resolution suitable for large
volume imaging [
fine internal structures of cells, leading to techniques employing ultra-thin light sheets
using Bessel beam technology to achieve higher resolution.
Since about 2010, in the laboratory of Dr. Eric Betzig at the Janelia Research
Campus of the Howard Hughes Medical Institute, who had previously developed
super-resolution microscopy techniques [
light-sheet microscopy using ultra-thin light sheets began with the application of
Bessel beams to light-sheet generation [
diffracting beams generated through self-interference when a beam is crossed by an
axicon lens or similar device [
in a designated region, maintaining the same intensity distribution, even over long
distances, and propagating beyond obstacles because of their self-healing properties
152]. Ideally, Bessel beams generate a uniform light sheet in the sample. A Gaussian
[
beam has a full width at half maximum of approximately 2.8 microns, whereas a
Bessel beam generates a main beam of approximately 0.5 microns [
a sample with this thin beam to create a virtual ultra-thin light sheet, it is possible to
improve the axial resolution and achieve comparable resolution in xyz directions.
Bessel beams, however, also generate significant side lobes around the main beam
that increase background light through unwanted illumination, leading to a decrease
in resolution and an increase in phototoxicity (Fig. 16.5A, B). To mitigate the influence of side lobes, a structured illumination approach using multiple images is
87]. However, the micron-level resolution was unsuitable to image
71], efforts to achieve a high resolution in
] (Fig. 16.3A, D). Bessel beams are non-
148
149–153] (Fig. 16.5A). They continuously generate
148
]. By scanning
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Fig. 16.5 Generation of Bessel Beam and 2D Lattice (A) Generation of Bessel beams. (B) Generation of 2D lattice using multiple Bessel beams [
to Fig.
16.3D for the PSFs of each modality. Scale bars, 1.0 µm(B);5 µm (C). (Reprinted with
permission [license number: 5607081260689] (B, C))
]. (C) Comparison of beam shapes [9]. Refer
9
294 Y. Mimori-Kiyosue et al.
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employed, surpassing the diffraction limit and achieving a resolution of 0.27 microns
in the axial direction [
to suppress side lobe generation, a light sheet with a full width at half maximum
of approximately 0.5 microns is generated, enabling 3D scanning at spatial resolutions of 0.12 × 0.12 × 0.15 microns with a time interval of approximately 12s
148]. However, the former method has a slow imaging speed and increased photo-
[
toxicity due to multiple illuminations, whereas the latter, although faster than the
structured illumination mode, falls short of the desired time resolution of at least
1s per volume to study cellular dynamics. Additionally, it presents challenges for
multicolor imaging.
16.3.1.2 Development of Lattice Light-Sheet Microscopy
To improve imaging speed, researchers explored multi-beam approaches, leading to
the discovery that arranging beams at optimal intervals generates a lattice light sheet
with reduced side lobes because of interference between beams in 2014 [
The right panel in Fig.
beams arranged in a row have varying intervals. As the beam intervals become closer,
the beams interfere with each other, causing the side lobes to spread or narrow (2D
lattice). There is a trade-off between the intensity and pattern of the main beam
and side lobes. While it is impossible to completely eliminate side lobes because
reducing the main beam width enhances side lobes, the pattern with the smallest
ratio of side lobe width and intensity to the main beam (shown in Fig.
was adopted as the most optimal. Subsequently, in 2023, a new type of harmonically
balanced lattice light sheet was proposed that improves performance at all spatial
frequencies within its 3D resolution limits and maintains this performance over
lengthened propagation distances, allowing for expanded fields of view [
beams shorten the scanning distance and enable faster image acquisition (Fig.
bottom).
In this lattice light-sheet microscopy (LLSM), such complex excitation light
patterns are generated using a spatial light modulator. The beams shaped by the
spatial light modulator pass through a mask to remove unwanted light and then enter
the illumination objective lens. At the tip of the lens, Bessel beams are generated
16.3A, D). The left panel in Fig. 16.5C shows the pattern at the entrance pupil
(Fig.
of the illuminating light and on the sample plane. Galvo mirrors are used to scan
the beams and generate the light sheet, while the sample is moved. The imaging
is performed through an observation objective lens perpendicular to the light-sheet
plane.
The high signal-to-noise ratio of the images formed by non-backgroundgenerating ultra-thin light sheet excitation is remarkable. Depending on the
fluorescence intensity of the sample, high-contrast images are obtained with an
exposure time of 1–10 ms per slice image. This allows the acquisition of 100–200
slice images within a second while conducting 3D scanning. In the high-speed
scanning mode with the lattice light sheet, the spatial resolutions are approximately
148] (Fig. 16.3D). Furthermore, using two-photon excitation
9, 154].
16.5C shows the interference pattern when multiple Bessel
16.5C bottom)
155]. Multi-
16.5B
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0.23 × 0.23 × 0.37 microns, and when structured illumination is applied, resolutions
of 0.15 × 0.15 × 0.28 microns are achieved. By improving the z-axis resolution,
images of equivalent quality can be observed from any direction by rotating the
images on a computer (Fig.
A limitation of LLSM is the restriction on the achievable beam length, which
limits the maximum volume that can be scanned at once to approximately 100 × 80
× 40 microns. Therefore, in a model presented in 2018, improvements were made
to the design to accommodate multivolume imaging to capture larger volumes [
16.6A). Additionally, when imaging thick samples, adaptive optics (AO) [157]
(Fig.
are used on both sides of the sample to correct for distortions in both excitation
and fluorescence light caused by differences in the refractive index (AO-LLSM).
The distortion is measured by illuminating the sample with a t wo-photon excitation
light sheet and using a Shack–Hartmann wavefront sensor. The time required for
detection and correction is approximately 70 ms, resulting in a minimal effect on
imaging speed. The effect of optical compensation is shown in Fig.
16.1C).
156]
16.6B.
16.3.2 Examples of Time-Series Volume Images Obtained
by LLSM
16.3.2.1 Multicellular Systems
Capturing cellular aspects in multicellular systems is currently the most important
issue in cell biology, as described above, and light-sheet microscopy is a promising
tool for this purpose [
morphology and distribution of cells in tissues can be measured at the organelle
level to reveal individual cell differences and dynamic characteristics. Figure
visualizes the morphology and internal organelle distribution of individual cells that
comprise the eye of a zebrafish embryo. To allow individual cells in a crowded
multicellular environment of intact organisms to be studied individually, a method
was developed to computationally separate and isolate individual cell images by clear
delineation of the cell membrane (Fig.
Figure 16.6B illustrates the detailed morphology of immune cells moving within
the interior of zebrafish embryos, while actively engulfing artificially injected foreign
particles, dextran particles. Observing the movements of immune cells in detail is
essential for elucidating the mechanisms of immune responses. Various movements
of tumor cells in the blood vessels of zebrafish embryos have also been studied:
tumor cells exhibit rolling while extending long, adhesive microvilli and crawling
while conforming to the shape of a vessel, and, finally, transendothelial extravasation
156].
[
Furthermore, in combination with the expansion microscopy technique
(ExLLSM), the application of this methodology to the mouse cerebral cortex and
Drosophila whole brain has been reported [
2, 94]. Using LLSM with high spatiotemporal resolution, the
16.6A
16.5A, C).
76
]. The morphological parameters of
296 Y. Mimori-Kiyosue et al.
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Fig. 16.6 Examples of AO-LLSM images in vivo (A) Diversity of cellular and organelle
morphology across the zebrafish eye visualizing plasma membrane (cyan), trans-Golgi (green),
ER (magenta), and mitochondria (brown) [
a developing zebrafish embryo. (b) A 128-µm by 150-µm by 75-µm volume assembled from 48
subvolumes. (c) Slice image highlighting cell divisions (white and green arrowheads) at the apical
surface of the retinal neuroepithelium and mitochondria (orange arrowheads) present from the apical
to the basal surface in one dividing cell. (d) Computationally separated cells across the eye, with
the organelles colored as indicated. Scale bars, 30 µm (a, c, d); 20 µm (b). (B) The perilymphatic
space of the inner ear of transgenic zebrafish embryos expressing plasma membrane-targeted Citrine
[
156]. (a) A representative view before and after AO correction plus deconvolution. (b) Changing
morphologies of two immune cells migrating in the inner ear (top and bottom rows), one showing
internalized dextran particles (blue). Scale bars, 10 µm(a) 5 µm (b). (Reprinted with permission
[license number: 5607080175990] (A, B))
156]. (a) Array of 4 by 4 by 3 tiles across the eye of
approximately 1500 dendritic spines have been measured in the mouse cerebral
cortex, and in the Drosophila whole brain, all dopaminergic neurons across the
brain have been imaged and their clusters traced to their terminals to determine their
respective cell types.
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16.3.2.2 Imaging of Cellular and Organelle Morphological Changes
The ability of LLSM to rapidly capture volume images with excellent 3D resolution makes it particularly ideal for tracking microstructural movements, such as shape
changes in cell membranes with 3D motion. Cell membranes form various structures
such as ruffling, filopodia, and lamellipodia. Precisely measuring membrane structures of less than 1 micron in thickness is difficult with conventional fluorescence
microscopy, which has particularly low resolution in the z-axis direction. Therefore,
electron microscopy has been necessary to accurately determine 3D morphology
158]. Using a Bessel beam microscope, the predecessor to LLSM, the rippling
[
motion of individual ruffled membranes was captured for the first time in 3D live
images using optical microscopy [
In the observation of the cell’s interior, system-level analysis of the organelle
interactome among six different membranous organelles (endoplasmic reticulum,
Golgi, lysosome, peroxisome, mitochondria, and lipid droplets) has demonstrated
that each organelle exhibits a characteristic distribution and dispersion pattern in
3D space and a reproducible pattern of contacts among the six organelles that are
affected by microtubules and the cell nutrient status [
is applicable under normal conditions and when cells are exposed to stimuli such as
drugs, pathogens, and stress, offers powerful analytical tools to reveal the response
of cells to drugs in terms of their organization and dynamics.
148] (Fig. 16.7A).
159]. Such methodology, which
16.3.2.3 Tracking of Microstructures
High-speed 3D imaging is also ideal for tracking objects moving in and out of a
cell, which cannot be tracked by conventional microscopes. Figure
an example of a cell process that is cleaved to become an extracellular vesicle and
floats in the culture medium (yellow arrows and arrowheads) [
be tracked in Brownian motion in the medium under conditions where the density
of objects is sufficiently low and multiple migration trajectories do not intersect
16.6C, red arrowheads).
(Fig.
Using human embryonic stem cell-derived intestinal epithelial organoids and
analytical pipelines to hand and analyze terabytes of high-content imaging data,
Schöneberg et al. tracked clathrin-mediated endocytosis events [
tracks were recorded in ∼35 cells simultaneously, resulting in ∼4,000 processed
tracks per movie, revealing that the endocytosis dynamics are unexpectedly similar
at apical, lateral, and basal regions, despite reported differences in membrane tension.
Figure 16.7C and D provide a detailed visualization of the process of cell division.
The top right image in Fig.
spindle,” derived from a 3D video of an anaphase cell [
chromosomes is rendered (in orange), and the growth trajectories of microtubules
tracked using the microtubule growth marker EB1-GFP [
lines and spheres [
analyze the mechanisms that control chromosome segregation in living cells and
9]. Figure 16.7D shows that in vivo karyotyping can be used to
16.7C is a reconstructed image, referred to as the “digital
160]. Vesicles can also
11]. The surface of the
161] are represented with
16.7Bshows
]. Endocytosis
10
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