Biophysical Reviews (2021) 13:1113–1126
https://doi.org/10.1007/s12551-021-00859-w

REVIEW

Towards organism‑level systems biology by next‑generation genetics
and whole‑organ cell profiling
Yoichi Minami1 · Yufei Yuan1 · Hiroki R. Ueda1,2
Received: 16 August 2021 / Accepted: 18 October 2021 / Published online: 18 November 2021
© International Union for Pure and Applied Biophysics (IUPAB) and Springer-Verlag GmbH Germany, part of Springer Nature 2021

Abstract
The system-level identification and analysis of molecular and cellular networks in mammals can be accelerated by “nextgeneration” genetics, which is defined as genetics that can achieve desired genetic makeup in a single generation without
any animal crossing. We recently established a highly efficient procedure for producing knock-out (KO) mice using the“Triple-CRISPR” method, which targets a single gene by triple gRNAs in the CRISPR/Cas9 system. This procedure achieved
an almost perfect KO efficiency (96–100%). We also established a highly efficient procedure, the “ES-mouse” method,
for producing knock-in (KI) mice within a single generation. In this method, ES cells were treated with three inhibitors tokeep their potency and then injected into 8-cell-stage embryos. These procedures dramatically shortened the time required
to produce KO or KI mice from years down to about 3 months. The produced KO and KI mice can also be systematicallyprofiled at a single-cell resolution by the “whole-organ cell profiling,” which was realized by tissue-clearing methods, suchas CUBIC, and an advanced light-sheet microscopy. The review describes the establishment and application of these technologies above in analyzing the three states (NREM sleep, REM sleep, and awake) of mammalian brains. It also discusses
the role of calcium and muscarinic receptors in these states as well as the current challenges and future opportunities in thenext-generation mammalian genetics and whole-organ cell profiling for organism-level systems biology.
Keywords Systems biology · Next-generation genetics · Triple CRISPR · ES mouse · Whole-organ cell profiling · Tissue
clearing

Introduction
A life system is a system consisting of multiple layers of
biological components (e.g., genes, transcripts, and proteins), and the elements between these layers interact witheach other (e.g., the central dogma of molecular biology)(Cobb 2017). Living systems are indeed far too complex
to be understood based on a linear chain of cause andeffect. Systems biology promises to speed up the process
Yoichi Minami and Yufei Yuan contributed equally to this article* Hiroki R. Ueda
uedah-tky@umin.ac.jp
1

Department of Systems Pharmacology, Graduate Schoolof Medicine, the University of Tokyo, Hongo 7‑3‑1,
Bunkyo‑ku, Tokyo 113‑0033, Japan

2

Laboratory for Synthetic Biology, RIKEN Center
for Biosystems Dynamics Research, 1‑3 Yamadaoka, Suita,
Osaka 565‑0871, Japanof research by bypassing the classical methods and using aholistic approach (Kellenberger 2004). Systems biology isa multidisciplinary field of research, useful for understanding the target biological system. It typically includes four
steps: identifying factors (systems identification), analyzing relationships (systems analysis), controlling systems
behavior (systems control), and designing and rebuilding
systems (systems design) (Kitano 2000, 2002; Ukai andUeda, 2010). The latter two are categorized as synthetic
biology in some cases. About systems biology, many good
textbooks and reviews have been published (Ideker et al.
2001; Alon 2019; Davis et al. 2019; Nijhout et al. 2019;
Tyson and Novak, 2020). Focusing on oscillators (e.g., cell
cycle, somite segmentation clock, and the circadian clock),examples of successful systems biology approaches can befound in the following articles (Ueda et al. 2005; Masamizu
et al. 2006; Novak and Tyson, 2008; Ukai-Tadenuma et al.
2008; Tigges et al. 2009; Ferrell et al. 2011; Ukai-Tadenuma
et al. 2011; Ode et al. 2017).

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This review explains the possibility of performing
organism-level systems biology research on mammals,
especially systems identification, which can be attributed tohigh-throughput quantitative analysis and next-generation
genetics. We further discuss the whole-organ cell profiling,which is realized by tissue-clearing methods and light-sheet
microscopy. Furthermore, the challenge of achieving systems biology on an organismal level in sleep studies using
mice will be presented, and finally, systems biology inhumans will be discussed.

Organism‑level systems biology
Systems identification
Systems biology focuses on the relationships among players
rather than the players themselves. Systems identification isa step towards identifying the players in the target biologicalphenomenon. The keywords in systems identification are“comprehensiveness” and “quantitative analysis.” In orderto get a complete picture of the target and understand therelationships among players, it is necessary to identify theplayers comprehensively. Uniform and high-throughput
quantitative analyses support measuring the behavior of
the system and analyzing players’ weights on the system’s
behavior (Susaki et al. 2017).

ComprehensivenessMany important findings in life science have been reported
in the past 20 years. The human genome project was a successful pioneer of the big science in life science, and its
output, decoded genome sequence, changed researchers’
way of thinking, planning, and performing experiments.
Nowadays, not only species’ genomes (e.g., humans, mice,and rats) (Lander et al. 2001; Venter et al. 2001; Mouse
Genome Sequencing et al. 2002; Gibbs et al. 2004; International Human Genome Sequencing 2004; Zoonomia 2020)
but also personal genomes are available (Gonzaga-Jauregui
et al. 2012; Lupski 2016).
Systems identification is supported by omics approaches.
The suffix “-ome” (completion) is used in a variety of biological layers, such as gene (genome), RNA (transcriptome),
and protein (proteome) (Joyce and Palsson, 2006). The omics
allow researchers to perform non-biased and comprehensive
analyses and achieve the real nature of their target biologicalsystems. Previously, researchers were constrained to choose
study targets based on existing reports and their inspiration
without knowing the whole picture. However, through omics
approaches, researchers can choose targets from a known
number of candidates. Further, we can test “all” candidates
listed (Joyce and Palsson, 2006). Multi-omics is a kind of

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challenge that elucidates the fundamental features of organisms by integrating various omics data (Hasin et al. 2017).
Employing data and models produced from multi-omics
methods allows researchers to obtain a comprehensive viewof their target molecular pathways, organisms, or diseases,
etc. (Nielsen 2017; Pinu et al. 2019). In 2020, Wang et al.
discovered a circadian kinome using multi-omics data. The
team employed fly heads to perform phosphoproteomics
jointly with transcriptomic and proteomic profiling andfound 789 phosphorylation sites that displayed circadianoscillations. They also predicted 27 kinases that were potentially involved in the phosphorylation of these sites (Wanget al. 2020).
The understanding of cellular systems was brought bydecoding and understanding the perfect set of genes in somerespects. The understanding of multicellular units, such as
tissues and organisms, is more complicated because tissues
and organisms are composed from heterogeneous cell types.
Although the understanding of functional gene networks intissues has been achieved through knockout (KO) mousestudies, there are bottlenecks to raise throughput of thegene modified animal studies. In this sense, we introduce
next-generation genetics as mentioned in “Next-generation
genetics.” To understand components behavior involving thesystems, we have to have a “cell catalogue” of the target tissues or organism. In this sense, the methods for figuring out
the whole cells in one tissue are needed. One of such key
technologies is the tissue clearing as mentioned in “Wholeorgan cell profiling.”

Next‑generation genetics
The bottlenecks
Although investigating mammalian systems biology on anorganism-level appears ideal, two bottlenecks exist in thegenetic modification and the crossing steps. Traditionally,
the process of generating a knock-in (KI) or KO model
mouse is arduous and prolonged. Researchers need to first
produce their mutations of interest by means of gene targeting, whose efficiency depends on the prolonged rate of
spontaneous homologous recombination, and then introduce these mutations to the embryonic stem cells (ESCs)of mice (Capecchi 2005). Another bottleneck occurs in the
crossing step. After obtaining the gene-modified ESCs,
researchers need to inject these into the blastocysts of thewild-type mice. As a result, chimera mice have both cells
derived from the ESCs and wild-type embryos. Typically,
two generations are required for these chimera mice (F0)to give rise to the homozygous mutants (F2), which carry
two copies of the mutated genes of interest. Such a crossing
procedure proceeds slowly and typically demands a period

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of over 9 months (3 months/generation × 3 generations) for
it to be completed (Fig. 1a).
Furthermore, rapid quantitative phenotyping methods arenecessary. In sleep research, we need expertise in surgical
techniques and analysis skills to investigate sleep staging.
Thus, it is essential to accelerate the process of sleep analysis through quantitative analysis and other innovations.

Quantitative phenotype analysis
To achieve a complete understanding of biological systems
at an organism level, establishing quantification analyticalmethods of the target biological systems’ behavior is essential. In chronobiology, the identifications of the period genein fruit flies and the Clock gene in mice are great successes
brought by the forward genetic mutants screening. These
identifications were achieved by counting the infra-red beaminterruption (flies) or wheel revolutions (mice) to analyze thecircadian rhythm phenotypes of the subjects (Konopka andBenzer, 1971; Reddy et al. 1984; Zehring et al. 1984; Vitaterna et al. 1994; King et al. 1997). Regarding sleep research,
Yanagisawa’s group performed a forward genetic screening
on mice harboring ethyl nitrosourea–derived mutations.
Through electroencephalography and electromyographystaging, they found two interesting mutants, Sleepy (Sik3)
and Dreamless (Nalcn) (Funato et al. 2016). International
Mouse Phenotype Consortium published their phenotyping
results from studying more than 7,500 knockout mice with

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standardized phenotyping methods. Zhang et al. screened
750 mutant mice lines with the indirect calorimetry datasets
and identified five novel genes involved in circadian misalignment (Zhang et al. 2020). Our team developed Snappy
Sleep Stager (SSS), a high-performance, non-invasive, andrespiration-based fully automated sleep phenotyping system(Sunagawa et al. 2016). Now sleep–wake patterns of 1,152
mice can be tested weekly in our facility.

Triple CRISPROver the past decade, efforts have been made to overcome
the aforementioned genetic modification and crossing issues.
The CRISPR/Cas9 system, which contains a Cas9 DNA
endonuclease, a CRISPR RNA (crRNA), and a trans-activating crRNA (tracrRNA), emerged as one of such efforts.
The crRNA and tracrRNA were further edited and fused to
become a synthetic single-guide RNA (sgRNA) (Deltcheva
et al. 2011; Jinek et al. 2012). This technology had effectively raised the efficiency of genetic modification and enabled researchers to cross the bottleneck in genetic modification (Wang et al. 2013; Yang et al. 2013). More advanced
than the traditional CRISPR/Cas9 system, the TripleCRISPR method concurrently utilizes three types of sgRNAfor targeting one gene. Our study has revealed that thisinvention allows the Triple-CRISPR technology to achievea biallelic KO efficiency of about 100%, which is considerably higher than the efficiency attained by the conventional

Fig. 1  The next-generation
mammalian genetics. a Conventional methods with crossing.
By conventional methods,
more than 1 year is required toobtain enough gene-modified
mice and test the hypothesis.
This is because crossing isrequired for preparing mice. b
The next-generation genetics
without crossing. The nextgeneration mammalian genetics
is genetics without crossing. We
can test hypotheses after about
3 months because crossing is
unnecessary. These are basedon the Triple CRISPR and ES
mouse methods. Details of the
Triple CRISPR (lower left)
and ES mouse (lower right) are
mentioned in the text

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single-targeted CRISPR (Sunagawa et al. 2016). Attributed
to such an almost perfect biallelic KO efficiency from the
Triple CRISPR, researchers can now address the crossing
issue, as they are able to obtain the homozygous mutants
at the F0 generation (Fig. 1b). Promisingly, a triple-target
CRISPR screen identified Slc38a4/SNAT4 as a critical aminoacid transporter for placental development in mice (Matoba
et al. 2019). The group also succeeded in generating micewith spermatozoa fully derived from ES mice using thismethod (Miura et al. 2021).

ES mouse
The ES mouse is almost entirely ESC-derived and carries
mutations throughout the body in its F0 generation. ESCs
are commonly cultured with leukemia inhibitory factor (LIF)
and serum to maintain their potency and preserve them atan undifferentiated stage. There are three inhibitors (3i), theFGF receptor inhibitor, the GSK3 inhibitor, and the ERK
inhibitor, which also support the undifferentiated stages of
the ESCs (Ying et al. 2008). After being treated with the
3i, these ESCs are then injected into 8-cell stage embryos
at high potency, which increases the contribution of ESCderived cells in the born mice (Kiyonari et al. 2010). Generating diverse kinds of KI ESCs in parallel with the ES mousetechnology, Ukai et al. further enhanced the productivity of
the KI ESCs. They accomplished a systematic production ofvarious types of the KI ES mice in 2 to 3 months (Ode et al.
2017; Ukai et al. 2017) (Fig. 1b).

Whole‑organ cell profiling
Establishing tissue‑clearing methods
Tissue opacity results from light scattering and light absorption. The former originates from the heterogeneity of refractive index (RI) in components of biological tissues, such as
water, lipids, and proteins. The latter comes from chromophores, such as hemoglobin. There is a hundred-year historyof developing chemical reagents to unify RI and decolorize
the native pigments (Susaki and Ueda, 2016; Ueda et al.
2020a, 2020b; Tian et al. 2021). The German anatomist
Werner Spalteholz was a pioneer of the field. In the 1910s,
he developed a method for making human tissue translucent
by hydrophobic reagents. Another hydrophobic tissue clearing method, BABB, was reported after decades of silence(Dent et al. 1989). Hydrophilic tissue-clearing methods werereported by Chiang et al. and Tuchin et al. (Chiang et al.
2001; Tuchin et al. 2002). The great success in 3D-fluorescent imaging using clearized tissues by Dodt et al. andHama et al. (Dodt et al. 2007; Hama et al. 2011) triggered
extensive development of several types of tissue clearing

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methods in the 2010s, such as the hydrophobic 3DISCO andthe hydrogel-based CLARITY (Erturk et al. 2012; Chunget al. 2013; Chung et al. 2013). We established hydrophilic
CUBIC methods (Susaki et al. 2014; Tainaka et al. 2014).
Boyden’s group reported Expansion microscopy, which
employs a swollen hydrogel to expand tissue isotropically(Chen et al. 2015).
A light-sheet microscope is an appropriate tool to obtaina single-cell resolution image of a whole target organ on arealistic time scale. This microscope uses sheet light illuminations to illuminate the specimen from the side. Researchers can then capture 2D stacks and reconstruct a 3D image
in silico (Dodt et al. 2007; Keller and Dodt, 2012; Murakami
et al. 2018; Matsumoto et al. 2019; Mano et al. 2021).

Cell profiling at a whole‑organ level
Whole-cell profiling in the brain is one of scientist’s longlasting dreams, and tissue clearing methods make this dreamcome true (Fig. 2). Here, we briefly introduced examples
of cell profiling at a whole-organ level. The methods ofthese cell profiling technologies, including those not mentioned here, are well reviewed elsewhere (Susaki and Ueda
2016; Gradinaru et al. 2018; Mano et al. 2018; Wassie et al.
2019; Parra-Damas and Saura, 2020; Ueda et al. 2020a,
2020b; Molbay et al. 2021; Tian et al. 2021).
Dott et al. first observed mouse brains at a single-cellresolution using organic solvent benzyl alcohol and benzyl
benzoate (BABB) with light-sheet microscopy (Dodt et al.
2007). Ertürk et al. also observed the whole mouse brain ata single-cell resolution by developing the 3DISCO (Erturk
et al. 2012). Pan et al. clearized tissues from the brain to
the spinal cord using uDISCO in mice and rats and imaged
neuronal connections and vasculature for the subjects fromhead to toe for over 7 cm (Pan et al. 2016). Samples treatedwith hydrophobic reagents shrink in size. The advantageof the hydrophobic method is its rapid and robust clearing,but the disadvantage of this method is its rapid bleachingof endogenously expressed fluorescent proteins. In thisregard, uDISCO can preserve fluorescent proteins for several
months and make intact organs and rodent bodies transparent (Erturk et al. 2012; Pan et al. 2016). Ke et al. utilized
SeeDB to describe the near-complete wiring diagram ofsister mitral cells associated with a common glomerulus
in the mouse olfactory bulb (Ke et al. 2013). Susaki et al.
observed not only mice but also common marmoset’s brainsusing CUBIC methods (Susaki et al. 2014, 2015). Murakami
et al. drew a whole-brain atlas at one single-cell resolution
using CUBIC-X and generated the CUBIC Atlas, an analytic platform for whole-brain cell profiling (Murakami et al.
2018). This method has been further developed into CUBICCloud (Mano et al. 2021). SeeDB, CUBIC, and CUBIC-X
are hydrophilic methods that feature biocompatibility andBiophysical Reviews (2021) 13:1113–1126

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Fig. 2  Whole-organ cell profiling by tissue-clearing. a Tissue
opacity resulted from lightscattering and light absorption.
Tissue clearing can be achievedby setting an appropriate refractive index (left) and decolorization of pigments (right). b Byhydrophilic (e.g., Scale, SeeDB,
CUBIC), hydrophobic (e.g.,
BABB, 3DISCO, uDISO), orhydro-gel-based tissue-clearing
methods (e.g., CLARITY,
Expansion Microscopy), fixedorgans become translucent.
Examples of before and after
CUBIC-treated brains areshown. Photos are taken by
Mr. Shigeta (The University ofTokyo). c We can reconstructorgans in silico at a singlecell resolution. As examples,
snapshots of a mouse brain
reconstructed in silico (CUBICCloud) are shown; the wholebrain (left) and an enlarged partof the brain (right). Each color
dot represents a single cell. The
snapshots are kind gifts from
Drs. Matsumoto and Mitani(RIKEN BDR)

biosafety, and these methods require relatively long incubation time for tissue clearization. SeeDB focuses on RI
matching, while CUBICs and CUBIC-X use a combinationof delipid and RI matching for tissue clearing. In contrast to
CUBICs, CUBIC-X has a hydration process, which expands
tissues. Inoue et al. succeeded in human brain clearing
by rapid delipidation of human brain tissues (Inoue et al.
2019). Recently, Zhao et al. reported tissue clearing of thehuman brain using SHANEL (Zhao et al. 2020). Hydrophilic
SHANEL can be applied to primates, especially humans.
The hydrogel-based method, CLARITY, was employed inmouse brain tissue clearing (Chung et al. 2013). SHIELD isa technology from the same family as CLARITY that can beused to accomplish transparency in human tissues, including
the brain (Park et al. 2019). CLARITY is an original hydrogel-based tissue clearing method, and SHIELD preserves thefluorescence and antigenicity of proteins as well as the architecture of transcripts and tissues under harsh conditions.
Besides brains, tissue clearing can also be applied toother tissues. In our work, CUBIC and its derivatives were
employed to investigate organs, such as the kidney and heart,and the whole body of mice and marmosets (Tainaka et al.
2014; Susaki and Ueda, 2016; Susaki et al. 2020). Belleet al. succeeded in their 3D visualization and analysis ofearly human development by transparentization of humanembryos (gestation weeks (GW) 6 to 8) and human fetuses(GW8.5 to 14). They utilized a combination of whole-mountimmunostaining, 3DISCO clearing, and light-sheet imaging
during their research (Belle et al. 2017). These suggest thatcell profiling at a whole-organ level is now realized not onlyin animal models but also in humans.

Application in mouse sleep research
Calcium makes mice sleep
By combining SSS with the Triple-CRISPR technology, wewere able to perform sleep phenotyping of KO mice covering almost all gene families. A comprehensive KO study
through one gene family is now achievable (Sunagawa et al.
2016; Tatsuki et al. 2016; Niwa et al. 2018; Yoshida et al.
2018) (Fig. 3).
Using a simulation approach, Tatsuki et al. identified
that ­Ca2+ was a key player for regulating sleep in mice. Ourgroup has developed a computational model based on a

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Fig. 3  Applications of nextgeneration genetics and cell
profiling methods. Based on aworking hypothesis, researchers genetically perturb miceefficiently using the nextgeneration genetics. Throughhigh-throughput analysis on
their models’ phenotypes,
researchers are able to test theirworking hypothesis. Whole-cell
profiling may help researchers
verify their hypothesis, andspatio-temporal information
obtained from cell profiling
could improve their hypothesis.
In our sleep research, we first
hypothesized the critical role ofcalcium in sleep and then tested
our hypothesis by generating a series of KO mice. We
systematically analyzed theirphenotypes by respiratory-based
sleep analyzer afterward­ a2+-dependent sleep duration hypothesis, and we identified
C
multiple genes that could be potentially involved in the act of
sleep. We then decided to screen for key molecules related tosleep generation by testing 21 gene KO mice using the SSS
and the Triple-CRISPR methods. Our research discoveredthat dysfunctional C­ a2+-dependent K + channels (Kcnn2,
Kcnn3), voltage-gated C­ a2+ channels (Cacna1g, Cacna1h),2+and ­Ca /calmodulin-dependent kinases (Camk2a, Camk2b)
reduced the duration of sleep in mice. In contrast, dysfunctional plasma membrane C­ a2+ ATPase (Atp2b3) lengthened
the sleep time in mice (Tatsuki et al. 2016). In addition, awhole neuron behavioral change by NMDA receptor antagonist (MK-801) was studied using the CUBIC analysis (Tatsuki et al. 2016). We were able to reveal that the impaired
NMDA receptors decreased sleep duration while elevatingneural excitability in mice. The research above suggests thatcalcium promotes sleep in mice (Tatsuki et al. 2016). Further
studies have indicated the involvement of sleep-promoting
kinase phosphorylation, like CaMK2 phosphorylation,
which is downstream from the calcium pathway (Ode andUeda, 2020).

Acetylcholine receptors are needed for REM
and NREM sleep
The regulatory role that the cholinergic system plays inmammalian sleep has been widely investigated (Brown
et al. 2012). Our research team systematically generated
acetylcholine receptors KO mice (11 nicotinic acetylcholine
receptors and 5 muscarinic acetylcholine receptors) using

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the Triple CRISPR and confirmed phenotypes using the SSS
method (Niwa et al. 2018). The results of our study suggest that muscarinic acetylcholine receptors M1 and M3 areessential for the regulation of sleep. The KO of Chrm1 shortened and fragmented REM sleep, while the KO of Chrm3
reduced NERM sleep durations of the subjects. Strikingly,
the Chrm1 and Chrm3 double KO mice illustrated a REMless phenotype. This phenotype was also confirmed by investigating the ES mouse base Chrm1/Chrm3 double KO mice
(Niwa et al. 2018). Overall, the next-generation genetics has
raised the throughput of in vivo analyses involving genetically modified animals, which has enabled a more efficientsystem identification in mammalian organism-level systems
biology (Fig. 3).

Future directions for organism‑level systems
biology
From correlation to causality
Humans are the most important and fascinating model
animals. However, due to many limitations, systems biological approaches cannot be conducted on humans as wehave been able to do on mice (Ukai et al. 2019). Ethically,
experimental-motivated genetic modification is not allowed
to be conducted on humans (Lanphier et al. 2015; Munsie
and Gyngell, 2018). Technically, we cannot perform genetically controlled experiments on humans because of ourgenetic diversity (Frazer et al. 2009; di Iulio et al. 2018).

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Furthermore, life-long observations are difficult to perform
as humans have relatively long-life spans (Fig. 4).
Today, we endeavor to establish a complete picture ofhuman systems biology. To accomplish this, it is importantto gather multiple layers of research: quantitative behavioral
analysis in humans and genome-wide analysis of associatedgenes (Seshadri et al. 2010; Jansen et al. 2019; Uffelmann
et al. 2021). This is in addition to high-throughput model
animal phenotyping studies as well as further analysis of key
regulators of the target systems at a molecular level in vitro(Fig. 4). Specifically, it is necessary to initially observe andinvestigate the correlations between human genotypes andphenotypes through large-scale quantitative analyses inhumans. Particularly, these analyses have revealed interesting topics worthy of investigation. For example, a genomewide analysis of insomnia disorder revealed heritability as asignificant factor. The research also found a significant locus
on Chr 7 (q11.22) among the three ancestral groups studied(European American, African American, and Latino American) and a significant gene-based association for RFX3 on
Chr 9 in European American population (Stein et al. 2018).
Notably, biologists are able to employ various genome-wide

analyses to explore the potential gene components of diseases of their interest such as Alzheimer’s disease, HIVdisease, stroke, coronary artery disease (CAD), type 2 diabetes, and breast cancer (Stein et al. 2010; Wu et al. 2011;
Dichgans et al. 2014; Xue et al. 2018; Ferreira et al. 2019).
Additionally, associations found in genome-wide analysis
studies could help scientists in the process of selecting genes
for further investigation. However, since such correlations
observed from human data do not indicate causal relations
directly, it is imperative for researchers to conduct rigorous experiments using animal models to further explore thecausality between the selected genes and the target diseases
of interest (Tam et al. 2019). Moreover, when envisioning
the possible genotype–phenotype relationships that contribute to their target systems, biologists from this new era canutilize the various high-throughput next-generation genetic
methods to perform phenotyping studies on mice models(Susaki et al. 2017; Ukai et al. 2019). Such research will
be completed in vitro and in silico to analyze the identified
candidates’ molecular characteristics (Ukai et al. 2019).
Identification of the potential regulators of the systems
can also guide the process of assessing the target systems.

Fig. 4  Systems biology in humans. a The difference between humans
and mice when being employed as subjects for various biologicalresearch and analyses. Regarding the symbols, check marks suggest
methods that can be completed on the subjects or advantages whenemploying the subjects. Triangles stand for methods that can be completed with risks or caution. Crosses indicate methods that cannot
be completed on the subjects or disadvantages when employing the

subjects. b Multiple layers of studies to achieve a system-level understanding of humans. Here, we demonstrate examples of large-scalequantitative research in humans, genome-wide association studies,verification by mouse phenotyping studies, and identification of regulators of the biological systems of interest. Details are shown in the
text

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Through accumulating these various studies and promoting
a comprehensive understanding of this field, biologists will
eventually have a broader understanding of their research
topic at a systems level (Chuang et al. 2010; Kohl et al.
2010). Such inspiring insights would ultimately contributeto advanced drug discovery and systems-based medicine.

Nature vs. nurture
Systems behavior is defined as both “nature” and “nurture.”“Nature” is defined as an individual’s genomic information,
while “nurture” is defined as the environmental exposure
history of the individual (Coll et al. 2003). Such environmental information can accumulate in the body as epigenetic changes or immunological factors, etc. (Tammen et al.
2013).
The methods we have described so far have only revealed
the “natural” aspect of the human biological system. Is there
any way to reveal the “nurtural” aspect? Epigenome analysis
using next-generation sequencers can provide comprehensive information on gene silencing/activation, allowing us toinvestigate acquired changes in our genome (Ku et al. 2011;
Kimura 2013). Also, if we are able to identify the epitopesof all the antibodies present in a single person, we can thenlearn about the antigen exposure history of the person. This
process will also reveal the history of the environments towhich the person has been exposed to. Although the development of such a technology is challenging, it is worth
future investigation.

Conclusions
Systems biology is an interdisciplinary area of research thataims to comprehensively investigate biological systems. For
this review, we employed various examples to illustrate thatthe “next-generation” genetics could accelerate the processof systems-level identification and analysis. We further
explained the challenges that organism-level systems biology research had faced and the general concepts of the “Triple-CRISPR” and ES mouse methods. These two methods
have significantly enhanced the efficiency of organism-levelsystems biology studies. In addition, the roles of calcium andmuscarinic receptors in the three states (NREM sleep, REM
sleep, and awake) of mammalian brains were investigatedusing these technologies. We also described ways that thetissue-clearing method, CUBIC, and advanced light-sheet
microscopy made cell profiling at a whole-organ level a
reality. Although systems biology research in mice is now
realizable, such research conducted on humans remains challenging due to ethical and technical issues. Future efforts incombining personal genomics with human systems behavior analysis could shed light on systems biology in humans

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and systems-based medicine, which would benefit all living
beings.
Acknowledgements We thank all the lab members at the Universityof Tokyo and RIKEN BDR, especially Drs. Rikuhiro G Yamada, Koji
L Ode, Katsuhiko Matsumoto, Tomoki Mitani, and Mr. Daichi Shigeta for creative suggestions, preparing figure materials, and offering
constructive criticism.
Author contribution This review is written by YM, YY, and UHR.
Funding This work was supported by grants from the Brain /MINDS
JP21dm0207049, Science and Technology Directorate Platform Program for Advanced Biological Medicine JP21am0401011, AMEDCREST JP21gm0610006 (Japan Agency for Medical Research andDevelopment/Ministry of Education, Culture, Sports, Science andTechnology) (H.R.U.), Grant-in-Aid for Scientific Research (S)JP25221004 (Japan Society for the Promotion of Science KAKENHI)(H.R.U.), MEXT Quantum Leap Flagship Program (MEXT QLEAP)
Grant Number JPMXS0120330644, and Scientific Research (C)JP20K07282 (Japan Society for the Promotion of Science KAKENHI)(Y.M.), HFSP Research Grant Program RGP0019/2018 (Human Frontier Science Program) (H.R.U.), ERATO JPMJER2001 (Japan Scienceand Technology Agency) (H.R.U.), and an intramural Grant-in-Aid
from the RIKEN BDR (H.R.U.).
Availability of data and material Not applicable.
Code availability Not applicable.

Declarations
Ethics approval Not applicable.
Consent to participate Not applicable.
Consent for publication All authors agree for a publication.
Additional declarations for articles in life science journals that report
the results of studies involving humans and/or animals Not applicable.
Conflict of interest H.R.U. is a co-inventor on a patent covering Triple
CRISPR (PCT/JP2015/086259 (pending), patent applicant is RIKEN,
other co-inventors are K. Sumiyama, G.A. Sunagawa, M. Ukai-Tadenuma, D. Perrin) and patent applications covering the CUBIC reagents(PCT/JP2014/070618 (pending), patent applicant is RIKEN, other coinventors are E. A. Susaki and K. Tainaka; PCT/JP2017/016410 (pending), patent applicant is RIKEN, other co-inventors are K. Tainaka andT. Murakami) and a co-founder of CUBICStars Inc.

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