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268 S. Fuchigami and S. Takada
https://t.me/med1917
improved apparatus is less invasive. With a faster feedback loop, the probe tip can
avoid colliding strongly to the target-tethered surface, reducing the chance to perturb
the specimen protein complex.
15.6 Application to Molecular Cell Biology
Nowadays, HS-AFM has been commonly used to characterize the structural
dynamics of proteins and other biomolecules. Therefore, we cannot cover the entire
applications that used HS-AFM measurement. Here, we only briefly illustrate a few
cases reported in the last few years.
Two decades of study elucidated that a significant port of proteins are disordered
and that such intrinsically disordered proteins (IDP) are functional. Structural characterization of IDP is rather challenging since many structural biology approaches, such
as cryo-electron microscopy and X-ray crystallography, are not applicable to IDP.
Kodera et al. recently reported that HS-AFM measurement can be used to characterize IDPs [
dynamic disorder–order transitions can be characterized by HS-AFM (Fig.
Since many of the drug target proteins do contain many disordered regions, the
applicability to the IDP would be one of the important advantages of HS-AFM.
41]. Interestingly, not only disordered regions but also regions that show
15.7A).
Fig. 15.7 A Temporal HS-AFM images of Atg13, an autophagy protein (Reprinted with permission
from [
41]). The Atg13 images suggest dynamic formation and disruption of small globules. B HS-
AFM images of yeast condensin with various hinge angles (left) with the flexible fitting modeling
of the hinge region (right) (Modified and reprinted with permission from [
44])
15 Data Assimilation to Integrate High-Speed Atomic Force Microscopy … 269
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The structural maintenance of chromosomes (SMC) proteins including condensin
and cohesin are key proteins broadly working to organize the chromosome structures.
As such, malfunction of SMC proteins has been implicated to be lethal or result in
severe disease. The SMC proteins have very characteristic ring-shaped architecture,
which are highly conserved across three domains of life. Among many, a function seemingly shared by most SMC proteins is the DNA loop extrusion; the SMC
creates and then expands a DNA loop. Molecular mechanisms to realize the DNA
loop extrusion are currently under intensive study. AFM has been repeatedly used
to characterize the structural dynamics and loop extrusion events of condensin by
Dekker’s group [
the flexible fitting method described in Sect.
of DNA binding to the open hinge conformation [
42, 43]. Koide et al. used HS-AFM images to model structures by
15.2, which is followed by the analysis
44] (Fig. 15.7B).
The innate immune system, one of the two major immune systems in human,
serves as the main defense system for infections that were not previously experienced.
In the innate immune signaling cascade, Myd88 plays a key role as an adaptor protein
that connects the infection signal with downstream network although its precise
role remains elusive. Uno et al. observed intriguing conformational dynamics of
monomeric Myd88 with HS-AFM [
45]. MyD88 contains two functional domains,
the death domain (DD) and the Toll/interleukin-1 receptor domain (TIR), which are
connected by a flexible linker. The HS-AFM measurement of the monomeric Myd88
revealed the existence of two distinct states. In the open state, the DD and TIR are
separated, whereas they look like one coalesced globule in the other closed state.
Site-specific mutation analysis uncovered key residues to stabilize the closed state.
The DD has been suggested to form oligomers with other DDs in the downstream
signaling molecules. The TIR also tends to form oligomers with other TIRs involved
in the upstream signals. The open state should make these interactions possible
serving as an adaptor protein, whereas the closed state cannot form such oligomers,
suggesting that the closed state could be a self-inhibitory state. Oligomeric states of
DD and TIR would be other challenging targets of HS-AFM.
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Chapter 16
https://t.me/med1917
Potential of High-Spatiotemporal
Resolution Live Cell Imaging for Drug
Discovery and Development
Yuko Mimori-Kiyosue, Tomonobu Koizumi, and Takashi Washio
16.1 Introduction
The use of live cell imaging techniques in drug development has revolutionized the
field by enabling spatiotemporal visualization and analysis of the dynamic nature
of cellular processes, such as cell division, migration, signaling, and responses
]. It provides valuable insights into target identification, mechanisms
to stimuli [
of action, pharmacokinetics, and treatment responses, enabling more efficient and
effective development of novel therapeutics. The integration of live cell imaging
with high-throughput screening platforms and automated image analysis systems
has revolutionized drug development to screen large compound libraries. Current
research needs have focused on capturing cellular behavior in more physiologically
relevant situations [
the study of cellular responses and drug effects in organ-like microenvironments
4, 5]. In recent years, a growing trend toward the development of technologies has
[
1
2, 3]. Organoid culture and organ-on-a-chip technologies enable
Y. Mimori-Kiyosue (B)
Department of Molecular Genetics, Institute of Biomedical Science, Kansai Medical University,
2-5-1 Shinmachi, Hirakata 573-1010, Osaka, Japan
e-mail: kiyosuey@hirakata.kmu.ac.jp
RIKEN Center for Biosystems Dynamics Research, 2-2-3 Minatojima-Minamimachi, Chuo-Ku,
Kobe 650-0047, Japan
T. Koi zumi
RIKEN Program for Drug Discovery and Medical Technology Platforms, 2-1 Hirosawa,
Wako-Shi, Saitama 351-0198, Japan
e-mail: tomonobu.koizumi@riken.jp
T. Washi o
The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka,
Ibaraki 567-0047, Osaka, Japan
e-mail: washio@ar.sanken.osaka-u.ac.jp
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024
H. Satoh et al. (eds.), Drug Development Supported by Informatics,
https://doi.org/10.1007/978-981-97-4828-0_16
273
274 Y. Mimori-Kiyosue et al.
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emerged to extract information from images using data science, including artificial
intelligence (AI) and machine learning [
Advancements in imaging technologies have further propelled the field, enabling
high-spatiotemporal resolution live cell imaging represented by lattice light-sheet
microscopy (LLSM) that offers unprecedented detail and clarity [
nologies generate massive amounts of data through high-resolution multidimensional imaging, fast acquisition rates, and extended time-lapse recordings, which
also present several challenges in terms of data storage, processing, and analysis
10–12]. Because the overall development of imaging and image processing may
[
further advancement drug development, solving the data size problem is an urgent
issue. Addressing these challenges requires a multidisciplinary approach combining
expertise in imaging technology, computational methods, data management, and
algorithm development.
This chapter starts by providing an overview of the history of live cell imaging
and its applications in drug development, specifically focusing on highlighting the
importance of data science in this context. Next, we introduce the current highspatiotemporal resolution live cell imaging technology, LLSM, and discuss the
challenges associated with its application in drug discovery. Lastly, we discuss the
potential effect that can be expected in the advancement of drug development upon
addressing these challenges.
6–8].
9]. These tech-
16.2 Current Drug Discovery Enabled by Live Cell Imaging
16.2.1 Applications of Live Cell Imaging in Drug Discovery
and Development
Initially, drug discovery heavily relied on traditional biochemical assays and animal
models [
and their responses to drugs. However, with the introduction of live cell imaging
(Fig.
processes in real time, revolutionizing the field of drug discovery [
processes, has become a powerful tool at various stages of the drug discovery process
1
[
and characterize cellular targets, aiding in the selection of viable drug targets. In
drug screening and lead compound optimization, live cell imaging facilitates the
assessment of drug potency, selectivity, and toxicity, leading to the identification of
promising candidates for further development.
researchers to investigate disease mechanisms, evaluate drug efficacy, and monitor
cellular responses to treatment. By capturing real-time data on cellular behaviors and
13], which provided limited insights into the dynamic behavior of cells
16.1A–C), researchers gained the ability to directly observe and analyze cellular
1].
Live cell imaging, which provides detailed and dynamic information about cellular
]. During target identification and validation, it enables researchers to visualize
Live cell imaging is also instrumental in studying disease models, enabling
16 Potential of High-Spatiotemporal Resolution Live Cell Imaging … 275
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276 Y. Mimori-Kiyosue et al.
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◄Fig. 16.1 Examples of time-lapse live cell imaging and image analysis workflow (A) HeLa cells
expressing microtubule plus-end marker EB1-GFP (green) [
H2B-EGFP (red). (B, C) Mouse intestinal cell organoids expressing the nucleus/chromosome
marker H2B-EGFP (magenta) and the plasma membrane marker 2xLyn-TagRFP-T (green) were
observed by phase-contrast microscopy (B) and LLSM (C). In (B), yellow arrowheads indicate the
tips of protrusions that elongate as the stem cells proliferate. (C) shows a cross-sectional image of
the organoid projection in the upper panel and a planar image in the lower panel. The position where
the cross-sectional image was created is indicated by the yellow arrow on the left. Four mitotic cells
are observed in (A) and two in (C) (arrows and numbers). The direction of the observation plane
is indicated by the arrows. Scale bars, 40 µm (A); 200 µm(B);20 µm (C). (D) Flow of image
analysis. Image and data analysis constitute an interdisciplinary process involving multiple steps.
Tasks at each step are described for only a few cases. In recent years, machine learning has played
a crucial role in processes such as image enhancement, object recognition, segmentation, tracking,
and information extraction from images and data
160] and nucleus/chromosome marker
interactions, this technique provides a more comprehensive understanding of disease
progression and the effect of potential therapeutics.
By integrating live cell imaging with advanced image analysis algorithms, highthroughput screening, and other technologies such as AI and machine learning, the
potential for drug discovery is further enhanced [
8, 14–16]. Data science brings
powerful computational and analytical tools to live cell imaging, enabling the extraction, integration, and interpretation of information from large and complex datasets
16.1D). It has also contributed to the automation of microscopy systems and
(Fig.
17, 18
the introduction of robotics for laboratory automation [
]. By leveraging data
science techniques, researchers can gain deeper insights into cellular processes, drug
responses, and phenotypic changes, leading to more efficient and effective drug
discovery and development.
Recent advances in novel high-resolution live cell imaging technologies have
potential f or drug discovery innovation. However, there are technical challenges in
using the vast amount of image data captured, mainly from a data science perspective.
To understand how live cell imaging technology has progressed and how it can be
applied to drug discovery, we first review the history of the development of live cell
imaging and its contribution to drug discovery (Fig.
16.2, Table 16.1).
16.2.2 History of Live Cell Imaging for Drug Discovery
and Development
16.2.2.1 Early Live Cell Imaging and the Rise of Cell Biology
The foundation of bioimaging can be traced back to the invention of the microscope
in the seventeenth century by Antonie van Leeuwenhoek, which allowed scientists
to observe and describe microscopic structures [
Hooke published “Micrographia,” which included detailed illustrations of cells in
cork, marking the first recorded observation of “cells” [
19] (Fig. 16.2). In 1665, Robert
20].
16 Potential of High-Spatiotemporal Resolution Live Cell Imaging … 277
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Fig. 16.2 Timeline of microscope development for live cell imaging, data analysis, and achievements attained in cell biology
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