Comment
https://doi.org/10.1038/s41592-024-02307-5

Realization of cellomics to dive into thewhole-body or whole-organ cell cloud
Tomoki T. Mitani, Etsuo A. Susaki, Katsuhiko Matsumoto & Hiroki R. Ueda

Tissues, organs and organ systems arecomposed of interacting cells (the cellome).
We discuss the emergence of an omics
approach that we refer to as cellomics. It
enables cellome-wide analysis in whole-organ
or whole-body specimens, based on advanced
three-dimensional imaging and image analysis
technology. We think that cellomics will pave
the way for the incorporation of cellular,intercellular and spatial information acrossmillions of cells in our body.
Since Robert Hooke’s discovery of cells in cork, biological research
has focused on the idea that living organisms are composed of cells as
their primary unit of tissues and organs. Cellular networks consist of
tissues, organs and organ systems, with homeostatic maintenance anddisease traits attributed to multicellular systems and their constituents.
To encompass spatial information on all cells in multicellular systems,
a distinct -ome representation, called the cellome, is desirable. This
Comment introduces the concept, methods and applications of cellomics, an omics workflow for analyzing the cellome.

Concept and workflow of cellomics
We defined cellomics as a part of omics technology, as it targets thecellome — the entirety of cells in an organ or body. Established omics
approaches, such as genomics, examine the entire genome at the levelof its discrete units (in the case of genomics, individual nucleotides).
Similarly, cellomics investigates the entire cellome at the level of individual cells to link information on a specific trait to the cellular andintercellular information (Figs. 1a and 2a), which has already been practiced in Caenorhabditis elegans1. The cellome unit can be represented
as each cell coordinate (the centroid of the cell body or nucleus) in the
microscopy image covering a whole organ or a body. The coordinates
may be associated with molecular information such as cell type, activity marker, protein expression (for example, of neuronal or immune
cell type-related molecules, neuronal c-Fos, proliferation, cell death,inflammation or senescence-related molecules). In molecular-levelomics such as proteomics or transcriptomics, the primary units (protein
or RNA molecules) are perceived as discrete (that is, segmented anddefined), but various modifications, such as phosphorylation, acetylation and methylation, make it clear that not all modified protein or RNA
molecules are fully known. In this regard, cellomics also follows theprinciple of established omics in that cells are discrete entities, yet theirproperties are not fully known (for example, the definition of cell type).

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We also propose cellomics as an approach to analyzing spatial context information, such as cell position, morphology, number, density,
distributions and cell-to-cell interactions, across an organ or body. Thisapproach differs from conventional omics technologies that focus on
molecular contents within cells (Fig. 1b). Cellomics comprehensively
analyzes spatial context information with adequate speed and coverage, focusing on minimal molecular content information. In thiscontext, we view cellomics as a counterpart to single-cell sequencing
technology, which is dedicated to the rapid and thorough analysis ofmolecular contents. While recent developments in spatial transcriptomics2 have expanded molecular-targeting omics to include spatial
context information, current spatial omics (for example, Visium) arebased on processes for limited area of 2D sections (they collect molecular information with limited spatial information) and, as a trade-off, cannot cover the whole cellome across the three-dimensional (3D) organ.
We assume that cellomics is used for population-level studies, which isin contrast to some current spatial omics projects covering a volume
of a single sample at large cost (for example, MERFISH or Visium for amouse brain hemisphere). Because of such complementary features,
we believe that the significance of cellomics will increase as a result of
the trend toward integrating the omics axes.
A standard omics workflow involves high-throughput collection
and analysis of unit information, preparation and maintenance ofreference data, and visualization and sharing of data. We illustratecorresponding cellomics procedures (Fig. 1c). Cells are labeled using
genetic and histological techniques based on cell type or activity
marker gene expression, or cell-to-cell interaction. Optical microscopy
is used for data collection, with a micrometer-to-submicrometer resolution suitable for detecting cell-size objects. The position of labeledcells is calculated and aligned with reference data for multisample
comparison. The analyzed data can be shared by integrating them intoa single-cell-resolution cellome reference database.
Rapid imaging with sufficient resolution is crucial for collecting
whole-cellome data. Light-sheet microscopy in combination with tissue clearing technologies can image an entire organ’s cellome in a few
hours3. Serial sectioning tomography systems, which combine physicalserial sectioning with line-scan or spinning disc confocal microscopy,
can achieve high speed and resolution4,5. Nevertheless, limitations inaxial resolution pose a challenge, particularly in regions with high celldensities. Combining light-sheet microscopy with expansion microscopy could be a promising approach, achieving a practical axial resolution of ~5 μm. The detection of cellular nuclei in the granule cell layerof the cerebellum with sufficient accuracy has been demonstratedusing this approach3,6. This technical specification supports precise
cell detection in most organs, as most have cell densities similar to orless than that of the cerebellum. Efforts to form thinner light sheets forillumination, along with the combination with expansion microscopy
protocols, push axial resolutions close to around 3 μm (refs. 7–9).
This axial resolution could potentially approach or even surpass theComment

resolutions required for imaging exceptionally dense organs such as
lymph nodes, which have two to four times the cell density of the cerebellar granule cell layer10,11. We anticipate that these next-generation
3D imaging technologies, which will have satisfactory imaging speed
and resolution, will meet the requirement for omics-scale multisample
data acquisition.
For subsequent data analysis, cellomics requires whole-organ
or whole-body image processing, wherein cell positions are computationally digitized and aligned to corresponding cell coordinates in a whole-cellome reference (reference representing all cell
coordinates within a specific organ or body) at both the global (theorgan shape) and local (each cell coordinate) level. We see this process as analogous to genome-wide sequence data analysis with thecorresponding reference genome, at both the global (the wholegenome) and the local (the single base) levels12. The cell coordinate
calculation (cell digitization) also requires substantial throughput
to handle the entire cellome. Methods employing filter processing
and point detection are fast and scalable3, but may be less adept at
intricate cell shapes. Learning-based algorithms are more versatile indetecting diverse cell morphologies, although they can be prone tooverfitting and require extensive training13. Despite these challenges,
recent software tool developments have advanced cell digitization,
propelling cellomics forward.
Multisample comparison in cellomics can be achieved using
image-based anatomical references with volumetric boundaries14
or cellome references composed of cell coordinates6. The CUBIC
single-cell resolution atlas (CUBIC-Atlas) for the whole mouse brain
was an initial attempt to create the latter reference6. Digitized cell
coordinates in the sample data are mapped to the CUBIC-Atlas
globally (the step to register and align the entire organ image onto
the reference image) and locally (the step to match each cell coordinate in the sample with a corresponding reference cell). This
single-cell-resolution atlas has enabled the identification of cellularsubgroups independent of human-defined anatomical regions6.
This type of atlas, together with associated software, can also offer
a database feature, allowing researchers to transfer and incorporate
individual collected cellular information on cell type, cell activity andcell-to-cell interactions into the single-cell-resolution white map15,
a blank 3D point cloud that initially contains only coordinates of
all cells without any annotations and can accommodate additional
information related to each cell. We consider this function analogous
to a whole-genome database such as the UCSC genome browser(https://genome.ucsc.edu/). Such databases integrate and distributedata among the research community to facilitate sharing, viewing,reanalysis and mining of the data collected by the corresponding
omics scheme.

Applications of cellomics in large-scale comparative
studiesFig. 1 | The concept and possible workflow of cellomics. a, Conceptual
analogies of genomics and cellomics. Both omics approaches examine omicsscale information at the unit level (single base or single cell). They can identify
the relationship between the characteristics of omics units (such as a single-basemutation or single-cell functional change) and a particular trait (such as a diseasephenotype) when the omics-scale data are obtained from a large-scale population.
b, Left: a graphical representation of the biological information contained withintissues and organs. Biological systems include both spatial contexts and molecular
contents. Cellomics targets the former information while established omics focus
primarily on the latter category of information. Right: a diagram depicting twoaxes of biological information and their respective omics approaches. c, A possible

cellomics workflow. Cell labeling using genetic and histological methods enables
the visualization of specific cells. Next-generation 3D imaging (NGI) enables datacollection from entire specimens with single-cell resolution. High-throughput
serial sectioning and advanced light-sheet fluorescence microscopy with clearing
and expansion microscopy techniques meet this requirement. Cell digitizationis the process of converting image data into a 3D point cloud representing
the coordinates of each cell, via image segmentation using filters or machinelearning. Point-to-point cell mapping entails the registration and alignment of theentire organ image onto a reference, followed by the transfer of the sample cellcharacteristics (for example, genetically or histologically labeled cell type, cell
activity and cell-to-cell interactions) to the corresponding reference cell.

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We foresee cellomics across an organ or a body enabling cellome-wide
comparison and classification from various samples, eventually at a population scale (Fig. 2a). Here we consider examples for
population-level cellome analysis in drug response16–18, 3D pathology19
and cross-species comparative studies.
Integrating a drug–phenotype correlation panel (a collectionof phenotypes induced by each drug) and a cellome–drug correlation panel (a collection of cell groups reacting to a specific drug
administration; that is, a drug response cellome) from multiple
drug administration experiments could yield information about
a mutual cell population involved in the expression of a specificphenotype (a drug phenotype-specific cellome) (Fig. 2b). When
conducting this type of study at a population scale, cellomics can
support data-driven drug discovery to predict the drug’s main andside effects and add biological meaning to the induced phenotype.
The cellomics-based sensitive drug evaluation might be particularly
helpful to select a more promising drug development pipeline during preclinical stages.
In digital pathology, the utility of 3D pathology in AI-based diagnosis has been investigated20. We also anticipate a future population-scale
approach incorporating cellomics in pathology. This data-driven
pathology can potentially contribute to revealing the association
between a particular disease phenotype (for example, symptom andstage) and a particular cell group by integrating a patient–phenotype
panel and a cellome–patient panel (Fig. 2c). This approach shouldbe particularly beneficial for identifying cells and their functional
alterations that lead to refractory diseases, such as neuropsychiatricand systemic autoimmune disorders. The cellomics pathology may
thus provide evidence for developing new therapeutics that target
disease-related cell populations.
Comparative genomics is a field of study that compares the
genomic sequences of various species, supporting the discoveryof evolutionarily relevant genes in the primate brain, for instance21.
Similarly, comparative cellomics could be used to investigate cells
and cell circuits that diverged during evolution in various species. In
contrast to molecular networks, the structure and function of multicellular networks can directly implement mechanisms of organ- andorganism-level phenotypes, such as sensing and cognition processes
in the brain. As in the preceding examples, combining the species–
phenotype and cellome–species panels facilitates population-scalecellomics. This approach can lead to the discovery of evolutionary
associations between interspecies differences in a specific biological function and a unique multicellular circuit represented in thecellome (Fig. 2d).

Comment

Phenotypes

Subjects

Units

Variants

Association

Population

Whole genome

Single-base level

Omics-scale big data

Comprehensive analysis=

×

Population

Single-cell level

Whole organ(whole cellome)

・・・

Genomics

AGC T A×

SamplesaVariations insequence information

Whole-genome sequences

Omics-scale big data

Comprehensive analysis

=

×

・
・
・

×

Samples

Cellomics

Z

Y
X

Variations in cellularspatial informationb

Whole-organ cell distribution

102 plex

1–4 plex
Spatial informationof tissue architecture(spatial contexts)

Organ and tissue

Molecular-level
information inside cells(molecular contents)> 103 plex

Cellomics

Whole organ

Spatial contexts(e.g., cellome)

In situ sequencing

AAGGGTTCCTTTG…
GGAATCCATTAAG…
TTCGAAAGGCTTA…
CGTAAGCTTAGGC…
GCTAATGCGTACC…
TAGCGTAACTGGA…
CTAGGCTAATCGT…

Spatialtranscriptome

Sliced tissue

Single-cell sequencing

Single cell

Molecular contents(e.g., transcriptome)cCell labeling

3D imaging (NGI)

Cell digitization

Point-to-point cell mapping
Integrate into
reference cellome

Z

Z
Y
X
Structure
Featurenature methods

Y
X

Comment

Spatial cell-to-cellinteractioncComparison methods of sample population

Drug-response
cellomeDrug-response
cellomePatient–phenotype
panel

Cellome–patientpanel

Phenotype-associatedcell profiles

Time coursed=Patients

Disease-relatedcellome

Disease-relatedcellomeSpecies–phenotype
panel

Cellome–speciespanelPhenotype-associatedcell profiles

Time
Phenotypes

Control Disease Disease(mild) (severe)

Species××

Species=Species-associatedcellomePhenotypes

Condition

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Drugs

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Spatial cellularpattern×

Phenotype-associatedcell profiles

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Phenotypes

Drug–phenotype
panel

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Cell morphology

Phenotypes

Cell activity

Patientsb

Cellular information
Cell typeSpeciesaSpecies-associatedcellomeFig. 2 | Application of cellomics. a, Various forms of information associatedwith cellomics data and their usage in a large-scale cellomics study. Cellomics
enables the addition or deciphering of cellular and intercellular information toand from the cell coordinates across the specimen’s space. Cell type and activity
can typically be visualized by a marker gene or protein expression. Cell activity
markers include molecules involved in activation, proliferation, inflammation,
stress or cellular senescence. Spatial cell-to-cell interactions range from directcontact (for example, synaptic connections or cell–cell adhesion) and ligand–receptor binding to signaling via secreted proteins. Cellomics can be expanded
to population-scale samples to compare and classify the cellome-associatedinformation. b, A cellomics application in drug discovery and development.
A group of systemic phenotypes for each drug administration form a drug–
phenotype correlation panel. A drug–cell response correlation panel is createdby combining various drug classes with whole-organ cell populations that react
to various drugs (the drug-related cellome), each labeled with a cell activity

marker. Combining these correlation panels from various drug administration
studies can reveal a set of cell profiles associated with a particular phenotype.
c, A cellomics application in 3D pathology. Each patient’s phenotypes (forexample, symptoms and stage) are gathered to create a patient–phenotype
correlation panel. The cell populations of each patient marked by disease-related
molecules, such as inflammation or cell death markers, are combined to create apatient–disease cell correlation panel. An integrated panel can offer phenotypeassociated cell profiles for a particular disease symptom and progression
stage. d, A cellomics application in a cross-species comparative study. A groupof phenotypes from each species assembles to form a species–phenotype
correlation panel. Gathering cellomes from various species (species-associatedcellomes) produces a cross-species cellome correlation panel. Phenotypeassociated cell profiles that express interspecies consistency or variation
regarding a particular physiological function and its relevance to the cellome canbe created by putting these correlation panels together.

Current limitations of cellomics

organ. The primary objective of global alignment is to minimize thecomputational workload. Nevertheless, the application of globalalignment enables quantitative comparisons of cells that do not possess fixed positions, including cancer and immune cells, across different specimens. Despite the difficulties associated with achievinga precise local alignment of these cells in the coordinate system of anorgan (that is, cellome atlas), it is possible to align these cell populations on a global scale and compare them across samples. This is due
to discernible variations in cell populations within a given organ,
enabling quantitative assessments of sample cellomes from normal

Cellomics has allowed comprehensive comparisons of whole organs
across multiple specimens. Yet cell populations such as immune cells
and cancer cells may not have a standardized place in a cellome reference. However, alterations in the distribution of these cells within aparticular organs, where a cellome atlas can be defined, are comparable in the same global space. This comparability is made possible bycellome-wide data analysis, which involves sequential registrations
and alignments at two distinct scales: locally, at the coordinate levelof each cell; and globally, at the anatomical structure level of thenature methods

Comment

and diseased organs. In vertebrates, however, the variation in cell position and type between individuals and species is an obvious obstacle
when considering a more precise cell-to-cell comparison. Therefore,
practical cell-to-cell alignment methods that take variability into
account should be pursued5,22. Examples include the use of global
optimization approaches, in which the spatial coordinate information and molecular expression profile of each cell are integrated as
Bayesian probability distributions. The objective is to minimize thecost function, which evaluates the discrepancies or differences incell alignment, as exemplified by studies on C. elegans22. Variability
can arise from biological diversity but also from a lack of technicalreproducibility. A standardized platform is needed for source data,code, user instructions, parameter values and methods for data compatibility and software reproducibility evaluation20.
How cellomics could be applied to soft organs (for example, intestine) or multicellular samples (for example, organoids) without a fixedatlas is another potential issue. A possible solution is to extend thescheme to the atlas-free approach by extracting multidimensional
data on cellular spatial distribution features. This strategy facilitatescellome-based multisample clustering by using hidden patterns in 3D
cell distribution, similarly to dimension reduction methods in single-celltranscriptomics. While this approach can even be adapted to a limited
tissue area, careful consideration of the observation volume (that is, thecoverage size of the cellome) to accurately reflect each sample’s uniquespatial characteristics and variance would be required. In particular, forheterogeneous samples such as tumor masses or organoids, analyzing
the entire sample is crucial for standardized comparison. However, thelarge 3D point clouds from cellome-scale coordinates may be more complicated to interpret than a simple 1D analysis of DNA, RNA or protein
sequences. Point cloud informatics can decipher these intricate patterns and extract feature information effectively23. The field is currently
focused on point clouds on object surfaces in engineering, while thefuture holds the promise of expanded interdisciplinary collaborations.
Cellomics may drive the development of new computational tools as
research into mammalian cellomes advances.

3
Department of Neurology, Graduate School of Medicine, Osaka
University, Osaka, Japan. 4Department of Biochemistry and Systems
Biomedicine, Juntendo University Graduate School of Medicine,Tokyo, Japan. 5Nakatani Biomedical Spatialomics Hub, Juntendo
University Graduate School of Medicine, Tokyo, Japan. 6Department
of Systems Pharmacology, Graduate School of Medicine, Universityof Tokyo, Tokyo, Japan. 7These authors contributed equally:
Tomoki T. Mitani, Etsuo A. Susaki.

Conclusion and perspective

Acknowledgements

We envision cellomics to resolve spatial context information fromorgan- or body-scale single-cell resolution data. We believe that thisnew omics has the potential to serve researchers examining the 100
million cells in the mouse brain2, 200 billion cells in the human brain
and 30 trillion cells in the human body as an integrated platform of
spatial context information. The platform aspires to eventually support
the integration of molecular content information from all cells in an
organ and a body, potentially using large-scale resources such as the
Human Cell Atlas24. Population-scale cellomics studies could assist in
spatially exploring important cells in physiological and pathologicalconditions, potentially enabling broader investigations. The numberof analyzed molecules is a current limitation; this limitation would bepartially overcome by the application of multiplexing technologies25,26.
We expect cellomics to substantially impact numerous biomedical
disciplines, similarly to other established omics.
Tomoki T. Mitani1,2,3,7, Etsuo A. Susaki 1,4,5,7, Katsuhiko Matsumoto1,6& Hiroki R. Ueda 1,6
1
Laboratory for Synthetic Biology, RIKEN Center for Biosystems
Dynamics Research, Osaka, Japan. 2Department of Systems Biology,
Graduate School of Medicine, Osaka University, Osaka, Japan.

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e-mail: uedah-tky@umin.ac.jp

Published online: xx xx xxxx
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We thank T. Mano (OIST) and the Ueda lab members, particularly S. Harada, K. Yamaura
and F. Kinoshita, for their advice in enhancing the manuscript. This study was supported
by grants from the JST ERATO JPMJER2001 (to H.R.U.); JST CREST JPMJCR23B7 (to E.A.S.);
MEXT QLEAP JPMXS0120330644 (to H.R.U.); JST Moonshot R&D JPMJMS2023 (to K.M.);
AMED/MEXT JPMJER2001 (to H.R.U.); JP22ama221517, JP20gm6210027, JP21ak0101181 and
JP21wm0425003 (to E.A.S.); the JSPS KAKENHI JP18H05270 (to H.R.U.), 20K06885 (to K.M.),
22H02824, 22H04926 and 23K20044 (to E.A.S.), and 20K16498 (to T.T.M.); grants-in-aid
from the Human Frontier Science Program (to H.R.U.);Operating Costs Subsidies for Private
Universities (to E.A.S.); Takeda Science Foundation, UTEC-UTokyo, Uehara Memorial
Foundation and Nakatani Foundation (to E.A.S.).

Author contributions

E.A.S. and H.R.U. came up with the concept of cellomics. T.T.M. and E.A.S. prepared
the manuscript and figures. K.M. and H.R.U. were involved in revising the content
and manuscript.

Competing interests

CUBICStars Inc., which H.R.U. founded and where T.T.M., K.M. and E.A.S. are employed,
has filed patents regarding CUBIC-HV and CUBIC-Cloud. The company also provides
CUBIC-Cloud web services. H.R.U., E.A.S., T.T.M. and K.M. are co-inventors on patents andpatent applications owned or filed by RIKEN covering the CUBIC reagents and/or MOVIE.
E.A.S. receives collaboration funding from Kantum Ushikata Co., LTD., that is related to thedevelopment of devices for cellomics.

Additional information

Peer review information Nature Methods thanks Raju Tomer and the other, anonymous,
reviewer(s) for their contribution to the peer review of this work.