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◄Fig. 16.7 Examples of LLSM images showing subcellular structures (A) An African green monkey
kidney cell (COS-7) expressing mEmerald–c-Src (a), showing retrograde flow of membrane ruffles
(b) and vacuole formation by macropinocytosis (c; arrowheads) in time-series slice images of the
area shown in color in the cell above [
intervals. Scale bar, 10 µm. (B) Cleavage of cell protrusions of HEK 293 cells expressing MIM IBAR-GFP [
cleavage and floating in the medium (bottom). The projection where cleavage occurred is indicated
by yellow arrows, the fragment generated after cleavage is indicated by yellow arrowheads, and
another fragment floating in the medium is indicated by red arrowheads. Scale bars: 5 µm (top) and
2 µm (bottom). Time stamps: min:s:ms. (C) A cell in anaphase (top), showing chromosomes (orange)
and 3D tracks of growing microtubule ends measured using EB1-GFP (line and balls), color-coded
by velocity [
interphase, averaged across 9 to 12 cells. (D) Identification of individual chromosomes in cells using
Bessel Plane SR-SIM mode [
imaging of chromosomes (green) and kinetochores (red) at six time points from prometaphase to
metaphase (a). Three specific chromosomes (colored magenta, brown, and yellow) were identified by
shape throughout the observation period. Karyotyping by manual segmentation of all chromosomes
at the initial time point (b). The chromosomes were identified using the position of kinetochore pairs.
The cell exhibits extreme polyploidy, with 76 diploids and one possible triploid (red arrow). (c–h)
Chromosomes i n mitotic cells at the surface of a living syncytial embryo of Drosophila (c). Enlarged
view of the boxed chromosome in (c), in which individual chromosomes are differently colored
(d). The sex chromosomes isolated from that shown in (d) identified the embryo as female (e),
and the autosomes 2, 3, and 4 can be determined (f–h). Scale bars: 5 µm (a, c); 2 µm(b),1 µm
(d–h). (E) Decomposition and classification of growth trajectories of spindle microtubules [
Microtubule growth during different phases of cell division (metaphase, anaphase, telophase [a])
taken 40 times at 1.510-s intervals was 3D tracked and overlaid with color-coded lines according
to velocity [b]. In [c], the growth velocity data were divided into bins of 0.1 µm/s increments, and
the range from 0.2 to 0.7 µm/s was plotted in 3-D space for each bin. The spatial distribution of
microtubules with different growth speeds is shown to change as the phase progresses. Scale bars:
5 µm. Figures are based on data from Reference [
5607731207269] (A), [license number: 5607081260689] (B), [license number: 5607071341051]
(C), [license number: 5607800943880] (D), [license number: 1414976–1] (E))
160]. Time-series images of the cell tip (top) and the process of the process of the cell tip
9]. The graph shows the distribution of growth rates at different phases of mitosis and
148]. All data were extracted from 73 image stacks taken at 12-s
]. (a, b) Karyotyping in living cultured U2OS cells. Dual-color
162
162]. (Reprinted with permission [license number:
163].
organisms [162]. Figure 16.7E illustrates examples of microtubule classification at
different phases of mitosis. It allows visualization of how microtubules with varying
growth speeds alter their spatial distribution as cell division progresses [
techniques can have broad implications across the life sciences, offering valuable
insights into fundamental biological processes, diseases, regenerative medicine, and
potential therapeutic interventions.
163]. These
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16.3.3 Potential and Challenges of Using High-Resolution
Live Cell Imaging for Drug Discovery
and Development
16.3.3.1 Expected Benefits
As demonstrated in the various examples above, the improved performance of live
cell imaging provided by LLSM enables researchers to visualize dynamic cellular
processes with greater clarity and detail as mentioned above. It will also make an
important contribution to the emerging field of drug discovery research, such as
“phase separation,” which is recognized as a novel non-membrane organelle and
is being found to be involved in disease [
intracellular organizing analysis of iPS cells [
allows observation of subtle changes and dynamic events induced by drugs that may
be missed by conventional observation, and identification of novel cellular signatures, providing deeper insights into time-dependent cellular responses and valuable
kinetic information on drug effects. Such precise information supports objective
decision-making.
Overall, the improved performance of LLSM in live cell imaging enables a
more detailed and comprehensive analysis of cellular behavior, drug effects, and
mechanisms of action. Additionally, the integration of genomic, transcriptomic,
metabolomic, and proteomic data with imaging data through a multidisciplinary
approach may expand the capabilities of data-driven drug discovery. These aspects
will enhance the efficiency and accuracy of drug discovery procedures, leading to the
identification of promising drug candidates, a better understanding of drug responses,
and improved development of targeted and personalized therapeutic interventions.
164, 165], and quality control through
166]. This enhanced visualization
16.3.3.2 Challenges in Using LLSM with Data Science for Drug
Discovery and Development
Improved performance of live cell imaging offers numerous advantages for drug
discovery, but it also brings certain challenges, especially in conjunction with data
science, primarily because of the large amount of complex data acquisition. The
challenges that may arise as the performance of live cell imaging improves include
the following.
1. Storage and data management: LLSM generates more than several terabytes of
data per day, and a single multidimensional dataset exceeds several hundred gigabytes, which is several hundred to one thousand times the amount of data obtained
by conventional techniques. Storing and managing vast amounts of image data is
a significant challenge. As the volume of image data increases, storage requirements become more demanding, requiring scalable and efficient storage solutions
16 Potential of High-Spatiotemporal Resolution Live Cell Imaging … 301
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[167]. Moreover, data management, including data retrieval, indexing, and annotation, becomes more complex as the volume of image data increases [
easy access to relevant data and maintaining data integrity is a considerable task.
2. Visualization and interpretation: visualizing high-dimensional data becomes
increasingly challenging [
information are difficult for humans to visually explore and understand complex data
structures. Interpreting the relationships between variables becomes more complex
as the number of dimensions increases.
3. Computational complexity and multiplicity: analyzing high-dimensional data
requires more computational resources and time [
tructure is necessary. However, even with adequate hardware, algorithms, and techniques that work well in lower dimensional spaces may struggle or become computationally expensive in high-dimensional spaces. Efficient algorithms and scalable
computational infrastructure are required to handle the computational complexity.
4. Interpretability and explainability: interpreting and explaining models become
more difficult as the complexity increases. Understanding the underlying relationships and variables that contribute to a model’s predictions or decisions becomes
more complex, leading to challenges in providing meaningful explanations.
5. Imaging throughput: the advancement of imaging systems makes it particularly challenging to achieve high throughput both in device improvements and
data handling. Even in the current low-throughput state, there are critical challenges,
especially in data handling. Therefore, without an overall next-generation computing
environment, these issues are difficult to address.
169]. Complex image data with a very large amount of
10, 163]. Adequate hardware infras-
168]. Ensuring
16.3.3.3 What is the Solution?
Addressing these challenges requires advancements in hardware capabilities, innovative approaches to data management and analysis, and algorithm optimization. In
the field of high-resolution imaging, throughput is not easy to improve, as discussed
above. Instead, advances in data analysis techniques fully leverage the potential of
large-scale image datasets and provide deeper insights into biological functions and
drug mechanisms of action. Some important solutions to overcome these challenges
are as follows.
1. Data management and infrastructure: implementing effective data management
strategies and building robust computational infrastructure are essential. This
includes efficient storage solutions for large imaging datasets, high-performance
computing resources for data processing, and scalable architectures to handle the
increasing data volume. Leveraging cloud computing and distributed computing
approaches provide scalable solutions to manage and analyze imaging data.
2. Advanced image analysis algorithms: developing and refining sophisticated
image analysis algorithms are crucial to efficiently process and extract meaningful information from large-scale imaging datasets. These algorithms should
be capable of handling data variability, automating analysis workflows, and
302 Y. Mimori-Kiyosue et al.
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providing accurate and reliable results. Continued research and innovation in
this field are required to contribute to more efficient and robust image analysis
solutions.
3. Explainable AI (XAI) technology: the i ntroduction of explainable AI (XAI) tech-
nology into the field of live cell imaging data analysis is a valuable solution.
XAI aims to increase the transparency, interpretability, and explainability of AI
models and provide insights into how models make predictions and decisions
170]. Particularly in the medical field, a high degree of accountability and thus
[
transparency are required. However, the interpretability and explainability of AI
models in the analysis of live cell imaging data are challenging because of the
complexity of biological systems and the limitations of current AI technology.
Ongoing research on XAI is essential to develop specific approaches and methodologies tailored to the unique characteristics of live cell imaging data and facilitate
effective integration of XAI into this field.
4. Integration with domain knowledge: XAI techniques integrate AI-driven anal-
ysis with existing domain knowledge and biological expertise. By combining
AI predictions with known biological principles, researchers identify potential biases, limitations, and errors in models, and thus derive meaningful interpretations and insights from live cell imaging data, enabling more equitable
and precise predictions. Efforts in developing domain-specific explainability
methods, model-agnostic approaches, and visualizations that capture t he intricacies of biological systems help to address these challenges and enable effective
integration of XAI in this field.
5. Collaboration and interdisciplinary approaches: overcoming the challenges of
live cell imaging in drug discovery requires collaboration among researchers
from diverse fields, including biologists, engineers, computational scientists,
and clinicians. This is also necessary to integrate imaging data with genomic
and proteomic data to expand the capacity of data-driven drug discovery. Interdisciplinary approaches foster innovation, enable knowledge exchange and lead
to comprehensive solutions that integrate imaging technologies with other areas
of research.
6.
Regulatory considerations: the imaging field has been slower to adopt data
science than other fields, in part because of the complexity of data and the diversity of imaging modalities. There is a perceived lack of standardized, robust image
analysis systems needed to process these diverse datasets. As live cell imaging
techniques are increasingly applied in drug discovery, the research community
needs to provide guidelines and frameworks to ensure the quality, reproducibility,
and reliability of imaging-based data. Establishing guidelines and best practices specific to live cell imaging may help streamline its implementation and
integration into drug discovery.
Notably, many of the problems described above are common to high-resolution
live cell imaging and other modalities that produce large amounts of complex data.
Regarding such imaging techniques, although high throughput is currently challenging because of the complexity of hardware and abundance of data, obtaining
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deep insights that have not been achieved before may be best achieved by carefully
analyzing each dataset. However, these challenges may eventually be overcome
by combining these solutions and fostering a collaborative and multidisciplinary
environment, leading to more reliable and effective contributions to drug discovery
efforts.
16.4 Future Perspectives
Once the challenges associated with improved performance of high-resolution live
cell imaging in drug discovery are effectively addressed, it may open up a range
of exciting possibilities and shape the future of drug discovery. The resolution of
challenges in live cell imaging may enable researchers to delve deeper into cellular
processes and observe dynamic cellular processes and drug effects at high resolution,
which may uncover important molecular targets and pathways involved in disease
progression. This understanding may allow the development of innovative treatments
that target specific cellular components or processes.
It may also enable a more personalized approach to drug discovery. By combining
live cell imaging data with other omics data and advanced analytics, researchers can
tailor treatments to an individual’s unique cellular characteristics and drug responses.
This personalized approach may lead to more efficient and targeted drug discovery,
increasing the probability of developing effective therapies.
In summary, addressing the challenges associated with high-resolution live cell
imaging performance in drug discovery may have far-reaching implications. It may
revolutionize the way we understand diseases, improve the safety and efficacy
of drug development, and enable personalized treatment approaches that significantly improve patient outcomes. High-resolution live cell imaging represented by
LLSM provides valuable insights into cellular processes and has the potential to
shape the future of medicine by enabling the discovery and delivery of therapeutic
interventions.
Acknowledgements We thank Drs Sehyung Lee and Shin Ishii (Kyoto University) and Drs. Shiro
Suetsugu and Yoshinobu Sato (Nara Institute of Science and Technology) for valuable discussions.
We also thank Dr Yoshihiro Kawasaki and Mses Tomoko Hamaji, Akiko Hayashi, and Naoko
Tokushige (RIKEN BDR) for preparing the culture cells and organoid samples. A lattice light-sheet
microscope was built in the Mimori-Kiyosue laboratory following the design of the Betzig laboratory
[9] under a research license agreement with the Howard Hughes Medical Institute. T his work was
supported by grants from the Promotion of Science-NEXT program (No. LS128), Grants-in-Aid
for Challenging Exploratory Research (Kakenhi No. 20K20379), Uehara Memorial Foundation,
Takeda Science Foundation, JST CREST (No. JPMJCR1863), RIKEN Center for Life Science
Technologies and RIKEN Center for Biosystems Dynamics Research to Y.M-K. and JST CREST
(No. JPMJCR1666) to T.W.
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