MainCells across an organism must continuously coordinate with one another to sustain life and adapt to changing external environments and alterations within the body. Homeostasis is maintained through a complex network of dynamic interactions. Disease can arise from breakdowns in these intercellular feedback mechanisms. Although biomedicine has identified key interactions, such as stress responses mediated by neuroendocrine signalling or neural pathways between the brain and the gut, a vast number of mechanisms of whole-organism function remain unknown3. Our understanding is limited by the challenges of accessing body-wide cellular dynamics, driving the need for technologies to measure, analyse and model body-wide control mechanisms at the cellular level.Modern synergies between imaging technology and protein engineering allow for time-varying molecular signals to be recorded in tissues. This has caused revolutions in fields such as neuroscience through the imaging of calcium—a fast, universal intracellular messenger involved in a wide range of cellular processes, including neuronal action potentials1—and other signals including voltage and neuromodulators across many neurons simultaneously4. However, time-varying activity patterns of most cell types in the body have not yet been recorded.For most vertebrate models, optical access to large, opaque tissues poses a currently insurmountable challenge to whole-body imaging. Transparent vertebrate animals such as young zebrafish and adult Danionella cerebrum5 overcome this barrier, making them uniquely suited as models for in vivo studies of cellular dynamics across the body, offering unparalleled access to the inner workings of evolutionarily conserved organs such as the liver, pancreas, gut, brain, and the immune and cardiovascular systems.This study introduces WHOLISTIC, a platform for in vivo imaging of cellular calcium dynamics, generalizable to other molecular dynamics, across nearly all cells of transparent vertebrates, such as the young zebrafish. By extending and integrating pancellular transgenic lines expressing genetically encoded calcium indicators in almost all cells in the body, high-speed volumetric fluorescence imaging, and a suite of computational methods for registration and cell population analysis, along with whole-body expansion microscopy (WB-ExM), we captured, analysed and interpreted cellular activity across tissues and organ systems.Body-wide cellular calcium imagingTo image dynamic fluctuations of intracellular calcium across cell types of the body of young zebrafish (Fig. 1a), we engineered a pancellular transgenic zebrafish line that expresses the genetically encoded calcium indicator GCaMP7f under the control of the ubiquitin promoter6,7, Tg(ubi:tTA; TRE:GCaMP7f) (Fig. 1b,c). To avoid transient or sparse expression8, we used the binary expression system tTA–TRE to enhance the concentration of GCaMP7f9, which resulted in extensive and sustained pancellular expression with no observed pathological manifestations (Fig. 1b and Extended Data Fig. 1a,b). Here we chose to focus on intracellular calcium, given its broad involvement in numerous cellular processes across all cell types1. The approach generalizes to other sensors of voltage, membrane tension and other molecules.Fig. 1: WHOLISTIC imaging of intracellular calcium dynamics across the body.a, Schematic of WHOLISTIC. b, Transgenic zebrafish (7 days post-fertilization) expressing GCaMP7f pancellularly (Tg(ubi:tTA;TRE:GCaMP7f)), imaged with spinning-disk confocal microscopy; samples were mounted laterally for visceral access. c, Annotated 3D anatomical model of young zebrafish derived from whole-body expansion microscopy (WB-ExM) data. d, Single fluorescence channel enables identification of organs and tissues via distinct visual textures (enlarged examples). e, Volumetric imaging acquisition paradigm with typical acquisition parameters. Fluorescence intensity variations reflect changes in intracellular calcium. Images covering an area of 800 μm × 600 μm are acquired in steps of approximately 9 μm, spanning a depth of approximately 130 μm using a ×20 0.75 NA objective. f, Registration workflow using multiscale iterative estimation of local motion fields. g, Registration results. Overlay of recording at the start (green) and 1 h into the experiment (magenta; top). The insets show myocytes and gut epithelium before (bottom left) and after (bottom right) registration. h, Dual-colour imaging (pancellular GCaMP plus membrane-targeted RFP; left), and example tissue (right), showing a 3D model and an enlarged view of the anterior part of pronephric kidney (glomerulus (G) and proximal tubules: descending limb (DL) and ascending limb (AL)). i, Cellular-scale segmentation (constrained non-negative matrix factorization). The inset shows an enlarged view of the segmentation results for the kidney nephron (1,265 cellular fragments). j, Coherence-based spectral clustering. The inset shows an enlarged view of clustering results for the kidney nephron (11 functional tissue ensembles). k, Nephron clusters ordered by time to first calcium activity peak within a burst (left), and raster of cellular activity within a single nephric burst, grouped by functional compartment and ordered within compartments using Rastermap (right)68. a.u., arbitrary units. l, Body-wide cellular activity raster ordered using Rastermap. On the right, a spectral cluster assignment is shown for some of the major organ groups. For visualization purposes, a random subset of 50 cells per cluster was included in the plot to reduce density of the figure, which was then ordered by Rastermap. m, Population-level dynamics. Mean activity of example functional tissue ensembles showing distinct dynamic profiles: high-frequency (for example, neurons and muscles), slower-evolving (for example, nephric tissue), tonic bistable (for example, sympathetic ganglion) and oscillatory (for example, enteric tissue) activity.The utility of pancellular imaging is contingent on the ability to discern organs and cell types within densely labelled tissue. The ubiquitous expression of GCaMP7f in tightly packed cells might have led to a conglomeration of indistinguishable cells; however, we found that major organs and tissue types could be distinguished by their distinct visual textures arising from variations in cell size, morphology, subcellular structure, baseline calcium and sensor expression level (Fig. 1d). Together, this enables the recognition of bodily tissues, including muscle, liver, kidney, brain and intestine, as well as smaller cellular populations such as the pineal gland and the endocrine pancreas (Fig. 1d), subsequently verified at a more detailed scale using WB-ExM (Methods). Furthermore, we developed a pancellular Danionella cerebrum GCaMP transgenic line to adapt WHOLISTIC to adult transparent vertebrates (Extended Data Fig. 1c,d). Thus, the expression of calcium sensors under the ubiquitin promoter has the potential to enable the monitoring of dynamic cellular signals throughout the organism, both developing and mature, while also affording the identification of tissue types, with or without the addition of cell-type-specific markers.To acquire and process spatiotemporal WHOLISTIC data and extract interpretable cellular calcium activity time series that can be related to the tissues of origin, we developed a data collection and analysis workflow (Methods and Fig. 1e–j). Acquiring high-quality data across the body was more challenging than imaging the brain due to greater tissue inhomogeneities and complex geometry that increase scattering and diffraction, such that light-sheet microscopy10,11 was unsuitable. Among the microscopy techniques evaluated, spinning-disk confocal microscopy proved most robust to tissue scattering and diffraction (Extended Data Fig. 1e). As most major internal organs in zebrafish are located in the anterior third of the body, we opted to primarily image this anterior region from the side (Fig. 1e and Supplementary Videos 1 and 2).Smooth and skeletal muscle contractions throughout the body result in the displacement of cells (Supplementary Video 3), such that accurately extracting the activity of individual cells over time requires accounting for this cellular displacement. Although motion in the brain is relatively rigid, motion in the viscera is much less constrained, making standard registration algorithms ineffective or slow. To overcome this challenge, we first performed dual-colour imaging of calcium dynamics concurrently with an anatomical reference channel to aid alignment (Extended Data Fig. 1f,g). Next, we developed a registration algorithm based on iterative optical flow estimation12 that estimates motion patch-wise (11 × 11 pixels) and combines smoothness regularization, signal-to-noise weighting of gradients and multi-resolution registration to favour convergence to an optimal tissue alignment for each time point (Methods and Fig. 1f). This achieved high-quality alignment, and most cells were accurately registered throughout the experiments (Fig. 1g, Extended Data Fig. 2a–c and Supplementary Video 4). Subsequent analysis allowed for the identification of single-cell responses to externally applied stimuli, including previously unknown responses of cartilage chondrocytes to cold temperatures (Extended Data Fig. 3 and Supplementary Video 5) and responses to ketamine by cells located at the meningeal boundary of the brain (Extended Data Fig. 4). Thus, WHOLISTIC enables the analysis of spontaneous and stimulus-driven responses across the animal at the single-cell level.Identifying functional tissue ensemblesTo extract cellular-scale calcium activity traces from registered imaging data (Fig. 1h), we used a segmentation method based on constrained non-negative matrix factorization (Voluseg)13 (Methods and Fig. 1i). We tuned this approach conservatively to minimize false mergers, over-parcellating the data into subcellular segments, that can later be aggregated.We found that the majority of cells exhibit measurable, time-varying calcium dynamics even at rest (Fig. 1j–m and Supplementary Video 1). Although consistent with the known importance of calcium signalling, to our knowledge, it has never been demonstrated that most cell types across disparate organs exhibit measurable second-scale calcium fluctuations even in the absence of specific stimuli, nor that the dynamic range of GCaMP7f is sufficiently broad to capture them.The utility of pancellular imaging increases with the ability to readily assign cells to their organs and cell types of origin. Although imaged volumes can be manually annotated with the identity of tissue types based solely on anatomical features (Fig. 1d), it is laborious, especially for sparse or highly distributed populations, and misses finer delineations within cellular populations that are defined not by their appearance but by their calcium activity patterns. We thus explored whether a more data-driven methodology, centred on the aggregation of cells with related activity patterns, could more effectively delineate functional anatomical boundaries within and across tissues. This would also provide a more compact, dimensionally reduced and interpretable representation of the data. To this end, we implemented spectral clustering14 and used a coherence-based similarity metric. Unlike correlation, coherence is invariant to phase lags and thus groups cells engaged in temporally structured activity such as travelling waves (Methods and Fig. 1j).Examining the anatomical footprint of the spectral clusters, we found that they predominantly corresponded to sub-regions of individual tissue types, such as the kidney, gallbladder, liver, gut and muscle (Fig. 1j,k,m and Extended Data Figs. 5 and 6). This can be leveraged to organize the data into interpretable and readily identifiable clusters at the tissue level (Fig. 1j,k), which we denote ‘functional tissue ensembles’. These can then be annotated with tentative tissue-type identities when discernible. As an example, the WHOLISTIC workflow parcellates the pronephric kidney nephron into contiguous functional tissue ensembles, each exhibiting distinct dynamics, that together couple into travelling waves that occur in bursts (Fig. 1j,k). The spatial propagation and inter-burst quiescence suggest that kidney filtration and/or intraluminal flow—conceptualized as a continuous or continuously oscillating process15,16—may in fact be intermittent and pulsatile on shorter timescales, warranting follow-up experiments. Clusters across the body exhibited a range of calcium activity, showcasing diverse temporal spectra, including oscillatory, pulsatile and bistable activity (Fig. 1l,m).Collectively, these findings demonstrate how the ubiquitous expression of molecular sensors, in conjunction with advanced motion-corrected spinning-disk confocal microscopy and the computational workflow for cellular and tissue segmentation, together comprising WHOLISTIC, facilitate the study of cellular interactions across tissues of the body.Optogenetic perturbation of inter-organ couplingBody-wide skeletal muscle coordinationTo exemplify the use of WHOLISTIC to study coupling of calcium activity within tissue types, we began by inspecting functional tissue ensembles corresponding to skeletal muscle, in which the rise of intracellular calcium induces contraction. Muscle ensembles were classified based on their distinctive cellular morphology (Fig. 1d), stereotyped spatial location (Fig. 2a) and characteristic activity waveform. Next, the ensembles were organized through hierarchical clustering of their calcium activity patterns and visualized by projecting their spatial footprint back into anatomical space (Methods and Fig. 2b). This approach readily enabled the identification of the primary epaxial and hypaxial trunk muscles, which exhibited correlated activity patterns (as represented by the union of the blue and green clusters in Fig. 2b), underscoring their propensity to contract synchronously, as documented in other species17. This method also identified the abdominal (also known as hypaxialis), sternohyoid, pectoral fin and branchial muscles18 (Extended Data Fig. 7a). More surprisingly, the anterodorsal portion of the epaxial muscle grouped in a distinct functional tissue ensemble (‘cervical epaxial muscle’), with calcium activity correlated with the abdominal muscle (Fig. 2c, right), a finding that, to our knowledge, has not been previously reported. These results can be visualized within the raw data by examining time points at which specific muscle groups are selectively activated (Fig. 2c, left).Fig. 2: Identification and causal test of intra-organ and inter-organ coupling through WHOLISTIC.a, Model of zebrafish muscle anatomy. b, Cross-correlation matrix of muscle functional ensembles hierarchically ordered with dendrogram (left). Spatial footprints of muscle clusters (coloured as in the dendrogram) on anatomical reference (grey); the abdominal muscle and cervical epaxial (‘C-epax’) muscle are part of the same functional cluster (orange; top right). Average calcium activity traces from muscle clusters (bottom right). c, Time points highlighting co-activation of distinct muscle groups (maximum intensity projection (ΔF); left). Correlation (Corrr) between C-epax and abdominal muscles versus hypaxial muscles (right; five animals, one-sided Wilcoxon signed-rank test, P = 0.03125). d, Reconstruction of anterior motor nerve tracts: spino-occipital nerves innervating the cervical portion of the epaxial muscle and sternohyoid muscles (top), and spinal nerves innervating post-c-epaxial and hypaxial muscles (bottom). e, Revised muscle anatomy model. f, Lag-regression model of tissue motor responsiveness. g, Motor response kernels and threefold cross-validated variance explained (cell averaged). h, Example of tissue activity 6 s pre-swim and 6 s post-swim and ΔF (grey). From top to bottom: (1) ventral fin skin epithelium, (2) midline cells in the spinal cord (later identified as ependyma), (3) notochord sheath cells, (4) gills, and (5) neurons in the hindbrain (here 3-s post-swim activity is shown as activity is short lived). Arrows point to cells of the aforementioned tissue. i, Strategy to validate ependymal cell motor responsiveness (top). Sagittal section of the line expressing GCaMP7f in ependymal cells and red calcium indicator pancellularly, with the inset of the hindbrain and spinal cord (bottom). j, Motor response kernel of ependymal cells fit using independent channels shown in panel i from Tg(foxj1a:GCaMP7f); Tg(ubi:tTA;TRE:jRGECO1b). k, Scatter plot of muscle power versus ependymal cell activity power for individual motor events. The dashed line denotes the linear fit capturing 80.0% of variance. l, Location of the hindbrain and the gastropharyngeal (GP) sphincter (left). Maximum projection of the brain, alpha weighted by the variance explained by sphincter calcium activity (R2) in held-out data, overlaid onto anatomical reference (grey); the gastropharyngeal sphincter is in flat red (right). m, Strategy for optogenetic stimulation of motor vagal cells during WHOLISTIC imaging (left). Stimulus-triggered average responses overlaid with anatomical reference (number of trials = 10); stimulation site and full motor vagus outline are indicated (right). n, Gastropharyngeal sphincter calcium activity showing dose-dependent response to optogenetic activation of caudal motor vagal neurons. o, Change in fluorescence at the gastric sphincter following stimulation of caudal motor vagus or control location along the spinal cord (three fish, more than nine trials per fish). p, Optogenetic tiling across the motor vagus (top). Trial-triggered averages, pixels weighted and colour coded according to the most-activating region of interest inducing the most activity (10 trials per location), overlaid with anatomical reference (grey). Responsive tissue volume (activation density) along the anteroposterior axis of the gastrointestinal tract (bottom).To ascertain the neural underpinning of the independence of activity of the cervical epaxial muscle from the primary epaxial muscle, we traced the anterior motor nerve tracts using a transgenic line labelling motor neurons, Tg(VAChTa:eGFP) (Methods). This revealed that, although the spinal nerves predominantly innervate the majority of the epaxial and hypaxial musculature, the spino-occipital nerve—known to innervate the ventral sternohyoid and pectoral fin muscles19—extends axons dorsally to innervate the cervical portion of the epaxial muscle (Fig. 2d and Extended Data Fig. 7b). This dual innervation supports the independent regional activation, consistent with previous studies in other species demonstrating regional activation20. Together, this shows how WHOLISTIC can uncover, even within extensively studied tissue dynamics—muscles—new functional units, as well as reveal previously unidentified synergies and independencies among muscle groups (Fig. 2e).Muscle-coupled dynamicsTo demonstrate the ability of WHOLISTIC to identify inter-organ interactions, we next studied the coupling between skeletal muscle and other bodily tissues. Beyond moving the animal, skeletal muscles generate internal mechanical forces detected by mechanosensitive cells body wide21. Connections through the nervous, vascular and other systems can additionally induce coupling between muscles and other organs. Yet, the acute effects of muscle activity on non-muscle tissue types and vice versa remain largely uncharted.We thus asked how motor activity acutely affects organ and tissue dynamics throughout the body. We examined the temporal relationships between the activation of skeletal muscles and calcium signalling in other cells of the body (Fig. 2f–h) by fitting a lag-regression model to all cells within the imaged volume, in which cellular calcium activity is modelled as the convolution of muscle activity and a cell-specific motor-response kernel (Methods, Fig. 2f and Extended Data Fig. 7c,d; we note that this model does not purport to infer causal direction). We quantified motor-related cellular activity throughout the imaged volume using statistical parametric mapping, which highlighted responses in various tissues, including the brain, skin epithelium, notochord sheath cells and gills (Fig. 2g,h). This coupling exhibited distinct, tissue-specific responses, often enduring beyond the timescale of muscle activity, except in the brain, where a cluster of brainstem neurons showed short coupling timescales (Fig. 2g,h, row 4).Within the brain, beyond motor-coupled neurons, a cohort of cells in the midline of the hindbrain and spinal cord exhibited temporally extended responses to muscular activity, enduring significantly longer than the neuronal responses (approximately 15 s; Fig. 2g,h, row 2). On the basis of their anatomical position and morphology, we hypothesized that these were ependymal cells, glia-like cells that line the ventricles and the central canal22. To test this hypothesis, we generated a transgenic line expressing GCaMP7f specifically in ependymal cells (Tg(foxj1a:GCaMP7f)), as well as a new pancellular transgenic line expressing a red fluorescent calcium sensor23, Tg(ubi:tTA;TRE:jRGECO1b). Crossing these two lines enabled the simultaneous recording of definitive ependymal cell calcium activity, as well as comprehensive whole-body dynamics in independent channels (Fig. 2i) and confirmed that ependymal cells exhibit Ca2+ transients subsequent to motor activity, with response amplitudes that are proportional to the intensity of motor events (Fig. 2j,k and Extended Data Fig. 7e). This suggests that ependymal cells integrate body movements into their cellular state, potentially contributing to ventricular and cerebrospinal fluid homeostasis.Revealing this coupling between ependymal calcium and muscle activity illustrates the sensitivity of WHOLISTIC for discovering functional properties even amid sparse and distributed cell populations.Optogenetic test of brain–body couplingTo expand the analysis to include visceral organs as regressors, we considered relationships between cellular and smooth muscle activity, which exist in tissues forming the gut, the lining of blood vessels, the gallbladder and others, by incorporating smooth muscle ensembles into the lag-regression model. Among the various relationships identified, the model captured a relationship between the caudal hindbrain and smooth muscle of the gastropharyngeal sphincter. Indeed, this regressor accounted for the largest fraction of variance of a small group of cells within the caudal hindbrain, specifically within the region of the motor vagal nuclei (Fig. 2l and Extended Data Fig. 7f). As the model is agnostic to causal direction, this is consistent with these motor vagal neurons driving the sphincter calcium activation via the vagus nerve. Given the established connection between the motor vagus and the gut across species24, this finding represents the identification of a conserved vagus–gut circuit from an unbiased WHOLISTIC analysis.We sought to probe whether the relationship between this motor vagal area and the gastropharyngeal sphincter was causal. To do this, we combined WHOLISTIC with optogenetics by crossing a transgenic line expressing channelrhodopsin in the motor vagus with the pancellular genetically encoded calcium indicator line (Tg(VAChTa:gal4;UAS:CoChR-eGFP); Tg(ubi:tTA;TRE:jRGECO1b); Extended Data Fig. 7g). We found that activation of neurons within the most caudal segment of the motor vagus induces a dose-dependent calcium activation across the gastric sphincter (Fig. 2m–o), recapitulating the observed coupling identified by the model. Moreover, examination of the entire motor vagus through the successive activation of subsets of neighbouring vagal neurons revealed a topological mapping between the motor vagus and visceral tissues along the anteroposterior axis (Fig. 2p), with more anterior motor vagal neurons controlling more anterior pharyngeal muscles25. We noted that neural activity exerted control of smooth muscle up to the anterior gut but not beyond. To assess whether there was an anatomical underpinning for this spatially restricted control, we visualized the distribution of motor vagal axons along the gastrointestinal tract using WB-ExM (Methods and Extended Data Fig. 7h,i), revealing a significant drop in innervation density at the level of the anterior gut, paralleling the functional results and offering a potential anatomical basis for the functional findings. This combination of WHOLISTIC, optogenetics and ExM-based anatomical mapping underscores its capacity to rapidly uncover and mechanistically examine brain–body circuits.Ultraslow oscillations during motor quiescenceAs a demonstration of the broad applicability of WHOLISTIC, we next considered the converse of movement-coupled modulation of tissue activity: processes that occur during extended periods of motor quiescence. In between phases of sustained motor activity, animals engage in equally important periods of muscle inactivity associated with rest, sleep and tonic immobility. These quiescent states are active phases of physiological regulation, yet although the neural substrate during them is extensively studied, the contributions of other cell types remain less characterized.We therefore aimed to identify cells—whether neuronal or non-neuronal, within or outside the brain—that exhibited heightened activity during motor-quiescent periods. We analysed the variance in cellular calcium activity during episodes of extended muscle inactivity (more than 10 min) compared with periods of regular motor activity. This analysis highlighted a region in the central nervous system that exhibited increased activity during rest (Extended Data Fig. 8a). These signals were identifiable along the midline of the hindbrain and spinal cord, displaying high-amplitude, ultraslow oscillatory activity during motor-quiescent phases, with a periodicity of 3–7 min (Fig. 3a and Extended Data Fig. 8b,c). By contrast, during periods of motor activity, faster and abrupt muscle-locked activity was observed in these cells, and the slow oscillations were either absent or significantly attenuated (Fig. 3b–d). Imaging neuronal and astrocyte-specific lines (Tg(elavl3:H2B-GCaMP7f) and Tg(gfap:jRGECO1b)) failed to recover such dynamics, leading to the hypothesis that these slow signals may originate from ependymal cells. Ependymal cells have been associated with sleep and alterations in cerebrospinal fluid flow26,27 and are juxtaventricular cells, with their cell bodies residing along the central canal and ventricles22. However, the oscillations were primarily observed at the base of the hindbrain (Fig. 3e), above the notochord—an area far from the ventricles or central canal, where ependymal cells are typically assumed to reside (Extended Data Fig. 8d)—bringing into question whether the oscillations originate from ependymal cells. To resolve this discrepancy, we sought to visualize the detailed morphology of ependymal cells that reside deep within the brain.Fig. 3: Discovery of motor-quiescence-coupled ultraslow oscillations and cell type of origin.a, Midline cells in the hindbrain and spinal cord display high-amplitude ultraslow oscillations during periods of extended motor quiescence. Heatmap of oscillatory power (periodicity band: 1–5 min), overlaid on anatomical reference (grey). b, Traces during the two activity states: active period, coupled muscle and ependymal cell activity (left), and motor-quiescent period, ependymal oscillatory dynamics (right). c, Spectral power of oscillatory cells and muscle activity over a 3-h window showing anticorrelated dynamics (periodicity band: 1–5 min and 6–20 s for the midline population and muscles, respectively). d, Quantification of motor activity during oscillatory and non-oscillatory periods, pooled from fish showing both motor states (six fish, one-sided Wilcoxon signed-rank test, P = 0.016). e, Propagation of the Ca2+ wave along the base of the hindbrain. Sequential time points and ΔF overlaid onto anatomical reference (grey) are shown. f, Outline of the WB-ExM protocol. IF, immunofluorescence; HCR, hybridization chain reaction. g, Ependymal cell location. The sagittal and dorsal sections of the double transgenic sample labelling the ventricular and vascular systems (Tg(foxj1a:eGFP) × Tg(flk1:dsRED-CAAX)), stained against eGFP (magenta) and dsRED (green), with total protein stain, Alexa488-NHS (grey; WB-ExM-IF, 10 days post-fertilization, expanded approximately 2×). h, Schematic of individual hindbrain ependymal cells. i, Genetic strategy for imaging ependymal cells through the use of the foxj1a promoter. Sagittal section of the foxj1a-transgenic line (maximum intensity projection). j, Kymograph of ependymal cell activity along the hindbrain and spinal cord showing local travelling waves and occasional coupling to sparse motor activity over the course of approximately 30 min (top). Data were derived from a cell-type-specific transgenic line (Tg(foxj1a:GCaMP7f)). A normalized motor activity trace showing two motor events during the recording (bottom). k, Cross-coherence matrix derived from the kymograph data in panel j, showing a block-diagonal structure indicative of local coherence clusters (left). Coherence-based k-means clusters projected onto the anatomical space, showing spatial continuity (right). l, Periodogram of ependymal cell activity (left), with the population mean (dark) and individual cells (light) from an example specimen shown. Population distribution of peak oscillatory frequency is also shown (right).Whole-body expansion microscopyDespite the relative transparency of the zebrafish, the accumulation of refractive index variations across the body results in light scattering, making it difficult to visualize the fine anatomy of such midline structures. Existing clearing and expansion methods are either only applicable to younger samples (5 days or less post-fertilization), achieve clearing at the expense of extensive proteolytic digestion28,29 or require extensive wash steps to clear adequately without expansion30. To overcome these limitations, we developed an advanced enzyme-free, rapid and robust WB-ExM protocol2 (Fig. 3f) that enables high-quality clearing and up to 5× uniform expansion, along with the acquisition of molecular information at subcellular resolution (Extended Data Fig. 9a–i) throughout entire larval and juvenile zebrafish (Extended Data Fig. 9j), as well as mature Danionella cerebrum (Extended Data Fig. 9k). This protocol combines high-temperature disruption with a multi-round embedding strategy, gradually reinforcing and expanding the sample to ensure homogeneous expansion (Methods). WB-ExM retains immunofluorescence signals (WB-ExM-IF; Methods, Extended Data Fig. 9a,b and Supplementary Video 6) and, with modifications, is compatible with in situ hybridization (WB-ExM-FISH; Methods; Extended Data Fig. 9e–g). In addition, the use of total protein stains yields rich anatomical information (WB-ExM-Histo) with which to contextualize the immunofluorescence and FISH signals31 (Extended Data Fig. 9h and Supplementary Video 7). To quantitatively assess expansion quality across the sample, we developed PhotoMap, an unbiased method for extracting the deformation field of the gel (Extended Data Fig. 10), inspired by GelMap32. Expansion was found to be largely uniform (Extended Data Fig. 10f,g), except in regions of mineralizing bone, where tissue pinching was observed but remained spatially confined (Extended Data Fig. 10c,d). These tools provide a high-throughput route to comprehensive whole-body anatomical and molecular data, complementing histological, X-ray and electron microscopy approaches, and a vital basis for interpreting and validating WHOLISTIC findings.We used WB-ExM-IF to characterize the fine morphological features of ependymal cells (Fig. 3g and Extended Data Fig. 8e–g) and found that this population of hindbrain ependymal cells, typically considered to be small cuboidal cells33, actually extends long and dense ventral projections (approximately 120 μm), forming a sheet-like structure that runs along the entire midline of the hindbrain, reaching the floor of the brain (Fig. 3g,h and Supplementary Video 8), thereby explaining the spatial distribution of the oscillatory signal. The morphology of these ependymal cells echoes a class of ependymal cells, including tanycytes, that retain morphological features of their progenitor, radial glia cells22, and are widely present in the adult mammalian brain, suggesting an evolutionarily conserved lineage34. These cells also displayed previously undescribed morphological features (Extended Data Fig. 8f–k): processes ensheathing commissural fibre crossings approximately 25 μm below the central canal, ensheathment of passing arteries and evenly spaced lateral projections surrounding neuronal tracts exiting the spinal cord echoing those recently identified in mice26,35. Such findings, not readily observable in non-expanded samples, show how WB-ExM resolves the cellular and extracellular provenance of functional signals, supporting hypothesis generation.Cell-type-specific imaging of ependymal cells recovered the motor quiescent-locked ultraslow oscillatory dynamics, confirming the ependymal cell origin of the ultraslow oscillations (Fig. 3i–l). To understand the spatiotemporal structure of ependymal activity, we performed cross-coherence analysis to assess the presence of coupling between the oscillatory dynamics of cells (Methods). This uncovered a spatial organization manifesting as local waves along the anteroposterior axis of the fish, encompassing both the hindbrain and the spinal cord (Fig. 3j), forming a block-like structure in the cross-coherence matrix with a length scale of approximately 75 μm. To quantitatively evaluate this, we derived a method for utilizing coherence as a metric in k-means clustering (Methods); the algorithm, blind to the anatomical location of the cells, recovered spatially continuous clusters (Fig. 3k). Ependymal cells are interconnected via gap junctions36, probably accounting for the localized propagation of the signal. Calcium within ependymal cells has been associated with F-actin-mediated cellular shrinkage, resulting in increased paracellular space between ependymal cells, thereby facilitating efflux through perineuronal routes26. These findings support the hypothesis that these cells form an interconnected network across the brain and spinal cord, capable of integrating and modulating neural activity, vascular signals, and cerebrospinal fluid composition. Moreover, it suggests that the observed state-dependent calcium oscillatory modes of ependymal cell activity may correspond to prolonged periods of altered cerebrospinal fluid exchange during extended motor quiescence.Together, these analyses show that WHOLISTIC can identify state-dependent sparse cellular signals within densely labelled tissue that can subsequently be validated and followed up using cell-type-specific transgenics and WB-ExM, and highlights previously overlooked populations active during specific behavioural states.Brain–body responses to hypoxiaTo demonstrate the power of WHOLISTIC for studying organism-wide responses to physiological stress, in addition to spontaneous activity (Figs. 2 and 3), we examined how animals adapt to hypoxia: a condition of reduced oxygen availability that engages systemic responses across tissues and organs. Hypoxia is a fundamental physiological challenge that arises in contexts ranging from high-altitude adaptation to stroke and heart failure, and triggers genetic, metabolic, cellular and behavioural adaptations that maintain oxygen homeostasis and prioritize energy allocation to critical tissues37. However, the dynamic interplay between single-cell activity changes and whole-organism physiology during hypoxia remains poorly understood, largely due to the technical challenges of simultaneously monitoring multiple tissues in real time.To systematically evaluate the systemic effects of hypoxia across the body, animals were exposed to alternating periods of normoxia (normal O2 levels, 21%, approximately 15 min) and hypoxia (reduced O2 levels, 10% O2, approximately 15 min; Fig. 4a). We observed widespread changes in baseline levels of calcium, quantified by computing the difference in average baseline calcium levels between normoxic and hypoxic phases, referred to as the ‘oxygen modulation score’ (Methods and Fig. 4b). Of note, gastrointestinal tissues showed a systematic increase in baseline calcium levels during hypoxia (Fig. 4c), potentially reflecting reduced calcium buffering capacity of the cells as a result of diminished oxygen availability38. Other organs also showed changes in baseline calcium levels, although the gastrointestinal tract, at the average tissue level, showed among the most notable initial increase in baseline calcium levels (Extended Data Fig. 11a). Given that the arterial blood supply to the brain and gut would contain similar oxygen levels, we sought to understand these disparities in responses.Fig. 4: Uncovering of a body-wide circuit engaged in response to stress.a, Hypoxia paradigm. Cyclic normoxia (21% O2) and hypoxia (10% O2) periods. The black line denotes measured oxygen levels within the water-submerged agarose, approximating exponential kinetics (τ ≈ 1.2 min). b, Whole-body map of cellular oxygen modulation score (maximum intensity projection). The gut, liver and kidney show increased baseline Ca2+ levels during hypoxia. c, Raster of baseline Ca2+ levels across visceral tissues, ordered by peak time (left). Average hepatic baseline calcium levels in response to hypoxia (right; dark denotes the mean response, and light indicates individual animals; n = 4 fish). d, Strategy to image and estimate blood vessel diameter (Tg(flk1:dsRED-CAAX)). The mesenteric artery during normoxia (top right) and hypoxia (bottom right) shows a narrower diameter (maximum intensity projection). e, Diameter of the mesenteric artery during the experiment (three trials for the same animal). f, Mesenteric artery diameter during normoxia and hypoxia (three animals, three or more trials per animal, one-sided Wilcoxon signed-rank test, P = 0.00097). g, Strategy for optogenetic silencing of the brain while imaging blood flow; a line expressing an inhibitory opsin in all neurons (Tg(elavl3:gtACR2-eYFP)) is crossed to a line labelling red blood cells (Tg(gata1:dsRED)). h, Mesenteric blood flow decreases during hypoxia. A sagittal section of an animal, averaged during 10 s, during normoxia (left) and hypoxia (right). i, Optogenetic hindbrain inhibition restores blood flow to the gut. Blood flow in mesenteric (top) and cerebral (bottom) arteries in response to hypoxia and to optogenetic neural inhibition is shown. Optogenetic inactivation of the hindbrain is in red, and control light stimulus on the heart is in blue. j, Blood flow to the mesenteric artery and to the brain under normoxic and hypoxic conditions (four fish, three trials per fish, four vessels per fish, independent two-sided Student’s t-test, hindbrain inhibition: P = 2.0 × 10−9, control: P = 0.56; horizontal dashed line marks zero for visual reference; horizontal lines mark median and data extrema). k, Midline compact cell population shows acute and sustained response to hypoxia, probably being the sympathetic ganglion. Same as panel b with the location of the population shown (top). Enlarged views of the population during normoxia (bottom left) and hypoxia (bottom right) are also shown. l, Raster of a functional tissue cluster that maps to midline population (top left), and mean and s.d. of the raster (bottom left). Average population calcium levels during hypoxia versus normoxia (right) are also shown (five animals, one-sided Wilcoxon signed-rank test, P = 0.03125). m, Strategy to specifically image the sympathetic ganglia (Tg(th:gal4;UAS:GCaMP6f)). Response dynamics of the sympathetic ganglion to hypoxia. Individual cells (blue) and population average (black) are shown. n, Localization of the sympathetic ganglion. A sagittal section of the double transgenic sample labelling the autonomic nervous system and vascular systems (Tg(phox2bb:eGFP) × Tg(flk1:dsRED-CAAX)), stained against eGFP (magenta) and dsRED (blue), with total protein stain, Alexa488-NHS (green; WB-ExM-IF, 10 days post-fertilization, expanded approximately 2×); sympathetic neurons line the cardinal vein. o, Juxtaposition of sympathetic fibres (green) along the mesenteric artery (magenta). Same sample as in panel n. The vascular system (magenta), autonomic nervous system (green) and total protein stain (dark gold) are shown.While considering possible causes for the differential modulation of calcium in the visceral organs compared with the brain, we noticed that the mesenteric artery, the primary artery supplying the visceral organs, appeared constricted during episodes of hypoxia (in unregistered imaging data where tissue motion is visible). This suggests a reallocation of oxygen across the body by rerouting blood flow during physiological stress, a conserved response39 that has never been imaged in real time. To assess this arterial constriction in more detail, we imaged a transgenic line that selectively labels blood vessels, Tg(flk1:dsRED-CAAX) (Fig. 4d) and confirmed the constriction of the main mesenteric artery during hypoxia (Methods, Fig. 4e,f). As the diameter of a blood vessel is an indirect indicator of blood flow, we imaged a transgenic line labelling red blood cells, Tg(gata1:dsRED) (Extended Data Fig. 11b), and found a near-complete cessation of blood flow to the gut during hypoxic conditions, which recovers upon returning to normoxic conditions (Extended Data Fig. 11c and Supplementary Video 9). By contrast, blood flow to the brain and muscle remained largely unaltered, potentially explaining the difference in baseline modulation between these tissues. Thus, consistent with mammals and certain aquatic animals39,40, WHOLISTIC shows that hypoxia induces a rapid redistribution of blood away from the gut, and reveals the full temporal dynamics of the surprisingly fast redirection of oxygen as well as its spatial distribution, and concurrent activity changes in other cells across the body.To test whether the reduction in gastric blood flow was neurally regulated, we inhibited the nervous system using the anaesthetic tricaine and observed that visceral blood flow was no longer diminished during hypoxia (Extended Data Fig. 11c), suggesting that the reduction in visceral blood flow is mediated by neurons, and showing that this brain–body circuit is already developed at 7 days post-fertilization. To more directly test this, we optogenetically inhibited the hindbrain during hypoxia while measuring blood flow (Fig. 4g and Extended Data Fig. 11d). We found that inhibiting the hindbrain induced, within a few seconds, the return of blood flow to the gut (Fig. 4h,i). In spite of the hypoxic condition, near-normal blood flow was maintained for as long as the hindbrain was inhibited, with no corresponding effect observed when directing the stimulation light to a control location (Fig. 4j). Thus, hindbrain activity is necessary for the constriction of the mesenteric artery during hypoxia, which we speculate serves to redistribute oxygen, thereby preserving energy for the brain and other tissues whose activity must be prioritized, and may also be a control mechanism to decrease enteric cellular activity.During hypoxia, we also observed a set of cells in the medial plane, below the notochord, that showed a systematic and sustained increase in activity during hypoxic periods (Fig. 4k,l). Given their location, and that sympathetic neurons induce vasoconstriction via noradrenergic activation of vascular smooth muscle cells41, we hypothesized that this cluster represented the sympathetic ganglion. Using cell-type-specific imaging (Tg(th:gal4; UAS:GCaMP6f))42, along with WB-ExM-IF and WB-ExM-FISH, we showed that this group of cells indeed corresponds to sympathetic neurons and that they send axons along the mesenteric artery (Fig. 4m–o and Extended Data Fig. 11e). Together, these results establish a brain-to-mesenteric artery pathway already functional at 7 days post-fertilization. Combined with optogenetic perturbation, WHOLISTIC can thus causally dissect cell-type-specific brain–body pathways engaged during physiological stress. This work, moreover, establishes the young zebrafish as a powerful model for studying the consequences of stress-induced blood shunting and, more generally, organism-wide feedback loops engaged by stress.DiscussionThe organism is a cohesive entity, comprising networks of cells that communicate and coordinate across multiple scales43. Feedback loops within these networks enable animals to respond to, predict and prepare for both internal and external challenges. Although many regulatory pathways—such as those governing hunger, glucose balance and immune responses—are well characterized44, the complete picture of organism-wide control systems remains a formidable challenge. Given the interdependence of cellular interactions, understanding the organism as an integrated whole is essential3,45,46,47. Yet, methods that probe the entire vertebrate organism while retaining cellular-level access have remained elusive. This study introduces a novel approach for imaging and analysing genetically encoded sensors expressed ubiquitously across cells, providing unprecedented insights into cellular interactions and organism-wide control systems.Future implementations of WHOLISTIC in freely behaving animals will enable the study of body-wide cellular control systems in naturalistic settings, rather than an embedded preparation. This will facilitate investigation of the interplay between physiological states and behavioural strategies, such as goal-driven navigation and sleep–wake transitions. A parallel route will involve experimental systems incorporating multimodal virtual-reality environments48 composed of visual, thermal, chemical, mechanical and other stimuli that respond to the behaviour of an animal. Further advances in protein engineering and microscopy that improve closed-loop monitoring and activation of cellular signals across the body4 will open new avenues of scientific inquiry.Calcium sensors6,23 provide an effective proxy for cellular activity due to the ubiquitous role of calcium in cellular processes1. The finding that most cells exhibit calcium-activity dynamics within the range of standard sensors has enabled extraction of many cellular dynamics. However, this remains a limited view of signalling within and between cells, which occurs via a multitude of molecular, voltage and mechanical pathways. Measuring additional molecules and physical cellular properties will provide deeper insights. WHOLISTIC can be extended to co-image signals within the rapidly expanding repertoire of molecular, voltage and mechanical sensors49,50,51,52,53,54, providing access to additional information channels, including metabolic states, hormones, ATP and neuromodulators that mediate intercellular communication.Given the scale and complexity of multicellular interactions, uncovering higher-order interactions will require advanced analytical and modelling frameworks leveraging modern artificial intelligence, including graph neural networks55, large-scale biophysical models and model-free predictive methods such as empirical dynamic modelling56.Platform automation57 and artificial intelligence-in-the-loop systems, combining measurements of whole-body cellular dynamics with online cell-targeted perturbation, will support gaining fundamental biological insights as well as prototyping real-time medical interventions57,58,59.The fact that all findings presented here—pertaining to different cell types, in different parts of the body, under different physiological assays and screens—arose from the same WHOLISTIC methodology underscores its broad applicability to fundamental biology and medical research. The methodology can serve as a discovery tool, whereas validation and deeper insight can be gained using more specific molecular techniques, as performed here to understand phenomena such as the brainstem’s control of blood flow redistribution during physiological stress and the molecular identification of the quiescence-related signals as originating from ependymal cells.Cellular function is determined by molecular and biophysical properties, tissue environment and connectivity to other cells60,61. Combining organism-wide cellular activity imaging with whole-body expansion microscopy offers a means to bridge function and mechanism at scale, but will require the development of body-wide registration methods, capable of matching cells between in vivo and ex vivo expanded data cell by cell.Much information throughout the body flows through the central and peripheral nervous systems, whose connectivity is becoming increasingly accessible through light-based connectomics62 and machine learning algorithms for connectome reconstruction63. This work will seed an atlas of the mechanistic and molecular basis of functional coupling at a cellular level across the organism.Beyond fundamental biology, WHOLISTIC enables disease modelling that tracks the effects of pathologies and treatments across spatial scales, from cells to the whole body, and temporal scales, from seconds to days. Most drug-screening workflows examine selected aspects of drug action, such as molecular binding64, effects on individual cells or tissue subsets65, or behaviour66,67. WHOLISTIC offers a complementary lens through which to observe the effect of drugs and genetic interventions on the entire body, revealing off-target effects and system-wide interactions that would otherwise be missed when studying cells, tissues or organs in isolation.In conclusion, WHOLISTIC bridges cellular and organismal physiology, systems neuroscience and behaviour, opening a new frontier in systems biology. By embracing organismal complexity while retaining the individual cell as a fundamental unit of analysis, this work provides a holistic yet mechanistic approach to unravelling the cellular interactions governing health and disease across organism-wide networks.MethodsExperimental model and subject detailsZebrafish husbandryZebrafish were reared at 28.5 °C in 14–10-h light–dark cycles (conductivity of 1,000 μS, adjusted via Instant Ocean Sea Salt (approximately 30 g l−1), pH 7.0, adjusted using sodium bicarbonate)69. Zebrafish from 5 to 14 days post-fertilization were fed rotifers and used for experiments. All experiments complied with protocols approved by the Institutional Animal Care and Use Committee of Janelia Research Campus. Zebrafish sex cannot be determined until approximately 4 weeks post-fertilization70, so the sex of the experimental animals was unknown. Where relevant, fish were randomized across conditions.No blinding was used in either data collection or analysis. Blinding during data collection was not possible because the experimental condition determined the acquisition protocol and was therefore necessarily known to the experimenter at the microscope. Blinding during analysis was not applied because all reported quantities were extracted by automated pipelines using identical parameters across conditions.