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Background In the field of neuroscience, neural modules and circuits that control biological functions have been found throughout entire neural networks. Correlations in neural activity can be used to identify such neural modules. Recent technological advances enable us to measure whole-brain neural activity with single-cell resolution in several species including Caenorhabditis elegans. Because current neural activity data in C. elegans contain many missing data points, it is necessary to merge results from as many animals as possible to obtain more reliable functional modules. Results In this work, we developed a new time-series clustering method, WormTensor, to identify functional modules using whole-brain activity data from C. elegans. WormTensor uses a distance measure, modified shape-based distance to account for the time delay and the mutual inhibition of cell-cell interactions and applies the tensor decomposition algorithm multi-view clustering based on matrix integration using the higher orthogonal iteration of tensors (HOOI) algorithm (MC-MI-HOOI), which can estimate both the weight to account for the reliability of data from each animal and the clusters that are common across animals. Conclusions We applied the method to 24 individual C. elegans and successfully found some known functional modules. Compared with a widely used consensus clustering method to aggregate multiple clustering results, WormTensor showed higher silhouette coefficients. Our simulation also showed that WormTensor is robust to contamination from noisy data. WormTensor is freely available as an R/CRAN package https://cran.r-project.org/web/packages/WormTensor.

Recent development of large-scale activity imaging of neuronal ensembles provides opportunities for understanding how activity patterns are generated in the brain and how information is transmitted between neurons or neuronal ensembles. However, methodologies for extracting the component properties that generate overall dynamics are still limited. In this study, the results of time-lapse 3D imaging (4D imaging) of head neurons of the nematodeC. eleganswere analyzed by hitherto unemployed methodologies.By combining time-delay embedding with independent component analysis, the whole-brain activities were decomposed to a small number of component dynamics. Results from multiple samples, where different subsets of neurons were observed, were further combined by matrix factorization, revealing common dynamics from neuronal activities that are apparently divergent across sampled animals. By this analysis, we could identify components that show common relationships across different samples and those that show relationships distinct between individual samples.We also constructed a network model building on time-lagged prediction models of synaptic communications. This was achieved by dimension reduction of 4D imaging data using the general framework gKDR (gradient kernel dimension reduction). The model is able to decompose basal dynamics of the network. We further extended the model by incorporating probabilistic distribution, resulting in models that we call gKDR-GMM and gKDR-GP. The models capture the overall relationships of neural activities and reproduce the stochastic but coordinated dynamics in the neural network simulation. By virtual manipulation of individual neurons and synaptic contacts in this model, information flow could be estimated from whole-brain imaging results.

Memory-related neuronal responses are often elicited by sensory stimuli that recapitulate previous experience. Despite the importance of this sensory input processing, its underlying mechanisms remain poorly understood. Caenorhabditis elegans chemotax towards salt concentrations experienced in the presence of food. The amphid sensory neurons ASE-left and ASE-right respond to increases and decreases of ambient salt concentration in opposite manners. AIA, AIB and AIY interneurons are post-synaptic to the ASE pair and are thought to be involved in the processing of salt information transmitted from ASE. However, it remains elusive how the responses of these interneurons are regulated by stimulus patterns. Here we show that AIY interneurons display an experience-dependent response to gradual salt concentration changes but not to abrupt stepwise concentration changes. Animals with AIY intact (but AIA and AIB ablated) chemotax towards low salt concentrations similarly to wild-type animals after cultivation with low salt. ASE neurons transmit salt information about the environment through glutamatergic signaling, directing the activity of the interneurons AIY that promote movement towards favorable conditions.

Animals demonstrate flexible behaviors through associative learning based on their experiences. Deciphering the neural mechanisms for sensing and integrating multiple types of sensory information is critical for understanding such behavioral controls. The soil nematode Caenorhabditis elegans avoids salt concentrations it has previously experienced under starvation conditions. Here, we identify a pair of sensory neurons, the ASG neuron pair, which in cooperation with the ASER salt-sensing neuron generate starvation-dependent salt avoidance. Animals whose sensory input is restricted to only ASER failed to show learned avoidance due to inappropriately directed navigation behaviors. However, their navigation through a salt concentration gradient was improved by allowing sensory inputs to ASG in addition to ASER. Detailed behavioral analyses of these animals revealed that input from ASG neurons is required not only for controlling the frequency of initiating a set of sharp turns (called pirouettes) based on detected ambient salt concentrations but also adjusting the migration direction during pirouettes. Optogenetic activation of ASER by ChR2 induced turning behaviors in a salt concentration-dependent manner where presence of intact ASG was important for the starvation-dependent responses. Calcium imaging of the activity of ASG neurons in freely moving worms revealed that ASG is activated upon turning behavior. Our results indicate that ASG neurons cooperate with the ASER neuron to generate destination-directed reorientation in starvation-associated salt concentration avoidance.

It is one of the ultimate goals of ethology to understand the generative process of animal behavior, and the ability to reproduce and control behavior is an important step in this field. However, it is not easy to achieve this goal in systems with complex and stochastic dynamics such as animal behavior. In this study, we have shown that MDN-RNN,a type of probabilistic deep generative model, is able to reproduce stochastic animal behavior with high accuracy by modeling the behavior of C. elegans. Furthermore, we found that the model learns different dynamics in a disentangled representation as a time-evolving Gaussian mixture. Finally, by combining the model and reinforcement learning, we were able to extract a behavioral policy of goal-directed behavior in silico, and showed that it can be used for regulating the behavior of real animals. This set of methods will be applicable not only to animal behavior but also to broader areas such as neuroscience and robotics.

BackgroundAnnotation of cell identity is an essential process in neuroscience that allows comparison of cells, including that of neural activities across different animals. InCaenorhabditis elegans, although unique identities have been assigned to all neurons, the number of annotatable neurons in an intact animal has been limited due to the lack of quantitative information on the location and identity of neurons.ResultsHere, we present a dataset that facilitates the annotation of neuronal identities, and demonstrate its application in a comprehensive analysis of whole-brain imaging. We systematically identified neurons in the head region of 311 adult worms using 35 cell-specific promoters and created a dataset of the expression patterns and the positions of the neurons. We found large positional variations that illustrated the difficulty of the annotation task. We investigated multiple combinations of cell-specific promoters driving distinct fluorescence and generated optimal strains for the annotation of most head neurons in an animal. We also developed an automatic annotation method with human interaction functionality that facilitates annotations needed for whole-brain imaging.ConclusionOur neuron ID dataset and optimal fluorescent strains enable the annotation of most neurons in the head region of adultC. elegans, both in full-automated fashion and a semi-automated version that includes human interaction functionalities. Our method can potentially be applied to model species used in research other thanC. elegans, where the number of available cell-type-specific promoters and their variety will be an important consideration.

Significance Processing and integrating multiple sensory inputs are essential for generating appropriate behavioral output, where sensory information of food is particularly important. The soil nematode Caenorhabditis elegans remembers salt concentrations that they have experienced under starvation conditions and learn to avoid them by monitoring current salt concentrations with the salt-sensory neuron called ASER. Here, we described how a sensory neuron pair that was not previously implicated, called ASG, plays a pivotal role in driving food-related navigation; sensory input to these 2 sensory neurons is required for navigation to avoid salt concentrations associated with starvation. Our results show how integrated sensory information determines the bias of turning behaviors to efficiently navigate toward food in starved worms., Animals demonstrate flexible behaviors through associative learning based on their experiences. Deciphering the neural mechanisms for sensing and integrating multiple types of sensory information is critical for understanding such behavioral controls. The soil nematode Caenorhabditis elegans avoids salt concentrations it has previously experienced under starvation conditions. Here, we identify a pair of sensory neurons, the ASG neuron pair, which in cooperation with the ASER salt-sensing neuron generate starvation-dependent salt avoidance. Animals whose sensory input is restricted to only ASER failed to show learned avoidance due to inappropriately directed navigation behaviors. However, their navigation through a salt concentration gradient was improved by allowing sensory inputs to ASG in addition to ASER. Detailed behavioral analyses of these animals revealed that input from ASG neurons is required not only for controlling the frequency of initiating a set of sharp turns (called pirouettes) based on detected ambient salt concentrations but also adjusting the migration direction during pirouettes. Optogenetic activation of ASER by ChR2 induced turning behaviors in a salt concentration-dependent manner where presence of intact ASG was important for the starvation-dependent responses. Calcium imaging of the activity of ASG neurons in freely moving worms revealed that ASG is activated upon turning behavior. Our results indicate that ASG neurons cooperate with the ASER neuron to generate destination-directed reorientation in starvation-associated salt concentration avoidance.

Annotation of cell identity is an essential process in neuroscience that allows for comparing neural activities across different animals. InC. elegans, although unique identities have been assigned to all neurons, the number of annotatable neurons in an intact animal is limited in practice and comprehensive methods for cell annotation are required. Here we propose an efficient annotation method that can be integrated with the whole-brain imaging technique. We systematically identified neurons in the head region of 311 adult worms using 35 cell-specific promoters and created a dataset of the expression patterns and the positions of the neurons. The large positional variations illustrated the difficulty of the annotation task. We investigated multiple combinations of cell-specific promoters to tackle this problem. We also developed an automatic annotation method with human interaction functionality that facilitates annotation for whole-brain imaging.

Tracking many cells in time-lapse 3D image sequences is an important challenging task of bioimage informatics. Motivated by a study of brain-wide 4D imaging of neural activity in C. elegans, we present a new method of multi-cell tracking. Data types to which the method is applicable are characterized as follows: (i) cells are imaged as globular-like objects, (ii) it is difficult to distinguish cells based only on shape and size, (iii) the number of imaged cells ranges in several hundreds, (iv) moves of nearly-located cells are strongly correlated and (v) cells do not divide. We developed a tracking software suite which we call SPF-CellTracker. Incorporating dependency on cells' moves into prediction model is the key to reduce the tracking errors: cell-switching and coalescence of tracked positions. We model target cells' correlated moves as a Markov random field and we also derive a fast computation algorithm, which we call spatial particle filter. With the live-imaging data of nuclei of C. elegans neurons in which approximately 120 nuclei of neurons are imaged, we demonstrate an advantage of the proposed method over the standard particle filter and a method developed by Tokunaga et al. (2014)., Comment: 14 pages, 6 figures

Shifting from individual neuron analysis to whole-brain neural network analysis opens up new research opportunities for Caenorhabditis elegans (C. elegans). An automated data processing pipeline, including neuron detection, segmentation, tracking and annotation, will significantly improve the efficiency of analyzing whole-brain C. elegans imaging. The resulting large data sets may motivate new scientific discovery by exploiting many promising analysis tools for big data. In this study, we focus on the development of an automated annotation procedure. With only around 180 neurons in the central nervous system of a C. elegans, the annotation of each individual neuron still remains a major challenge because of the high density in space, similarity in neuron shape, unpredictable distortion of the worm’s head during motion, intrinsic variations during worm development, etc. We use an ensemble learning approach to achieve around 25% error for a test based on real experimental data. Also, we demonstrate the importance of exploring extra source of information for annotation other than the neuron positions.

Sensory receptor neurons match their dynamic range to ecologically relevant stimulus intensities. How this tuning is achieved is poorly understood in most receptors. The roundworm Caenorhabditis elegans avoids 21% O2 and hypoxia and prefers intermediate O2 concentrations. We show how this O2 preference is sculpted by the antagonistic action of a neuroglobin and an O2-binding soluble guanylate cyclase. These putative molecular O2 sensors confer a sigmoidal O2 response curve in the URX neurons that has highest slope between 15 and 19% O2 and approaches saturation when O2 reaches 21%. In the absence of the neuroglobin, the response curve is shifted to lower O2 values and approaches saturation at 14% O2. In behavioral terms, neuroglobin signaling broadens the O2 preference of Caenorhabditis elegans while maintaining avoidance of 21% O2. A computational model of aerotaxis suggests the relationship between GLB-5–modulated URX responses and reversal behavior is sufficient to broaden O2 preference. In summary, we show that a neuroglobin can shift neural information coding leading to altered behavior. Antagonistically acting molecular sensors may represent a common mechanism to sharpen tuning of sensory neurons.

UTokyo Research掲載「細胞内のビッグデータから大規模ネットワークの再構築に成功」URI: http://www.u-tokyo.ac.jp/ja/utokyo-research/research-news/reconstruction-of-molecular-network-from-cellular-big-data/, UTokyo Research "Reconstruction of molecular network from cellular big data" URI: http://www.u-tokyo.ac.jp/en/utokyo-research/research-news/reconstruction-of-molecular-network-from-cellular-big-data/

Motivation: Automated fluorescence microscopes produce massive amounts of images observing cells, often in four dimensions of space and time. This study addresses two tasks of time-lapse imaging analyses; detection and tracking of the many imaged cells, and it is especially intended for 4D live-cell imaging of neuronal nuclei of Caenorhabditis elegans. The cells of interest appear as slightly deformed ellipsoidal forms. They are densely distributed, and move rapidly in a series of 3D images. Thus, existing tracking methods often fail because more than one tracker will follow the same target or a tracker transits from one to other of different targets during rapid moves. Results: The present method begins by performing the kernel density estimation in order to convert each 3D image into a smooth, continuous function. The cell bodies in the image are assumed to lie in the regions near the multiple local maxima of the density function. The tasks of detecting and tracking the cells are then addressed with two hill-climbing algorithms. The positions of the trackers are initialized by applying the cell-detection method to an image in the first frame. The tracking method keeps attacking them to near the local maxima in each subsequent image. To prevent the tracker from following multiple cells, we use a Markov random field (MRF) to model the spatial and temporal covariation of the cells and to maximize the image forces and the MRF-induced constraint on the trackers. The tracking procedure is demonstrated with dynamic 3D images that each contain >100 neurons of C.elegans. Availability: http://daweb.ism.ac.jp/yoshidalab/crest/ismb2014 Supplementary information: Supplementary data are available at http://daweb.ism.ac.jp/yoshidalab/crest/ismb2014 Contact: yoshidar@ism.ac.jp

One of the unique characteristics of cellular signaling pathways is that a common signaling pathway can selectively regulate multiple cellular functions of a hormone; however, this selective downstream control through a common signaling pathway is poorly understood. Here we show that the insulin-dependent AKT pathway uses temporal patterns multiplexing for selective regulation of downstream molecules. Pulse and sustained insulin stimulations were simultaneously encoded into transient and sustained AKT phosphorylation, respectively. The downstream molecules, including ribosomal protein S6 kinase (S6K), glucose-6-phosphatase (G6Pase), and glycogen synthase kinase-3β (GSK3β) selectively decoded transient, sustained, and both transient and sustained AKT phosphorylation, respectively. Selective downstream decoding is mediated by the molecules' network structures and kinetics. Our results demonstrate that the AKT pathway can multiplex distinct patterns of blood insulin, such as pulse-like additional and sustained-like basal secretions, and the downstream molecules selectively decode secretion patterns of insulin.

Homeostatic control of blood glucose is regulated by a complex feedback loop between glucose and insulin, of which failure leads to diabetes mellitus. However, physiological and pathological nature of the feedback loop is not fully understood. We made a mathematical model of the feedback loop between glucose and insulin using time course of blood glucose and insulin during consecutive hyperglycemic and hyperinsulinemic-euglycemic clamps in 113 subjects with variety of glucose tolerance including normal glucose tolerance (NGT), impaired glucose tolerance (IGT) and type 2 diabetes mellitus (T2DM). We analyzed the correlation of the parameters in the model with the progression of glucose intolerance and the conserved relationship between parameters. The model parameters of insulin sensitivity and insulin secretion significantly declined from NGT to IGT, and from IGT to T2DM, respectively, consistent with previous clinical observations. Importantly, insulin clearance, an insulin degradation rate, significantly declined from NGT, IGT to T2DM along the progression of glucose intolerance in the mathematical model. Insulin clearance was positively correlated with a product of insulin sensitivity and secretion assessed by the clamp analysis or determined with the mathematical model. Insulin clearance was correlated negatively with postprandial glucose at 2h after oral glucose tolerance test. We also inferred a square-law between the rate constant of insulin clearance and a product of rate constants of insulin sensitivity and secretion in the model, which is also conserved among NGT, IGT and T2DM subjects. Insulin clearance shows a conserved relationship with the capacity of glucose disposal among the NGT, IGT and T2DM subjects. The decrease of insulin clearance predicts the progression of glucose intolerance.

The regulation of glucose metabolism by pulse stimulations of insulin is compared with the effect of ramp stimulations. Specific network motifs mediate the differential response to these temporal patterns of stimulations that mimic in vivo patterns of insulin secretion., Temporal patterns and absolute concentration of insulin selectively control glycolysis, gluconeogenesis and glycogenesis through the different network motif in FAO hepatoma cells. Step stimulation of insulin induces the transient responses and adaptations of glycolysis (via F16P) and glycogenesis through a feedforward with substrate depletion and though an incoherent feedforward loop, respectively, and induces the sustained response of gluconeogenesis (via PEPCK) through a feedforward inhibition. Pulse stimulation of insulin, like additional secretory pattern in vivo, induces responses of glycolysis (via F16P), gluconeogenesis (via PEPCK) and glycogenesis. Ramp stimulation of insulin, like basal secretory pattern in vivo, induces only the response of gluconeogenesis (via PEPCK), but not the responses of glycolysis (via F16P) and glycogenesis., Insulin governs systemic glucose metabolism, including glycolysis, gluconeogenesis and glycogenesis, through temporal change and absolute concentration. However, how insulin-signalling pathway selectively regulates glycolysis, gluconeogenesis and glycogenesis remains to be elucidated. To address this issue, we experimentally measured metabolites in glucose metabolism in response to insulin. Step stimulation of insulin induced transient response of glycolysis and glycogenesis, and sustained response of gluconeogenesis and extracellular glucose concentration (GLCex). Based on the experimental results, we constructed a simple computational model that characterises response of insulin-signalling-dependent glucose metabolism. The model revealed that the network motifs of glycolysis and glycogenesis pathways constitute a feedforward (FF) with substrate depletion and incoherent feedforward loop (iFFL), respectively, enabling glycolysis and glycogenesis responsive to temporal changes of insulin rather than its absolute concentration. In contrast, the network motifs of gluconeogenesis pathway constituted a FF inhibition, enabling gluconeogenesis responsive to absolute concentration of insulin regardless of its temporal patterns. GLCex was regulated by gluconeogenesis and glycolysis. These results demonstrate the selective control mechanism of glucose metabolism by temporal patterns of insulin.

Sensitivity is one of the hallmarks of biological and pharmacological responses. However, the principle of controlling sensitivity remains unclear. Here we theoretically analyse a simple biochemical reaction and find that the signal transfer efficiency of the transient peak amplitude attenuates depending on the strength of negative regulation. We experimentally find that many signalling pathways in various cell lines, including the Akt and ERK pathways, can be approximated by simple biochemical reactions and that the same property of the attenuation of signal transfer efficiency was observed for such pathways. Because of this property, a downstream molecule should show higher sensitivity to an activator and lower sensitivity to an inhibitor than an upstream molecule. Indeed, we experimentally verify that S6, which lies downstream of Akt, shows lower sensitivity to an epidermal growth factor receptor inhibitor than Akt. Thus, cells can control downstream sensitivity through the attenuation of signal transfer efficiency by changing the expression level of negative regulators.

In cellular signal transduction, the information in an external stimulus is encoded in temporal patterns in the activities of signaling molecules; for example, pulses of a stimulus may produce an increasing response or may produce pulsatile responses in the signaling molecules. Here, we show how the Akt pathway, which is involved in cell growth, specifically transmits temporal information contained in upstream signals to downstream effectors. We modeled the epidermal growth factor (EGF)-dependent Akt pathway in PC12 cells on the basis of experimental results. We obtained counterintuitive results indicating that the sizes of the peak amplitudes of receptor and downstream effector phosphorylation were decoupled; weak, sustained EGF receptor (EGFR) phosphorylation, rather than strong, transient phosphorylation, strongly induced phosphorylation of the ribosomal protein S6, a molecule downstream of Akt. Using frequency response analysis, we found that a three-component Akt pathway exhibited the property of a low-pass filter and that this property could explain decoupling of the peak amplitudes of receptor phosphorylation and that of downstream effectors. Furthermore, we found that lapatinib, an EGFR inhibitor used as an anticancer drug, converted strong, transient Akt phosphorylation into weak, sustained Akt phosphorylation, and, because of the low-pass filter characteristics of the Akt pathway, this led to stronger S6 phosphorylation than occurred in the absence of the inhibitor. Thus, an EGFR inhibitor can potentially act as a downstream activator of some effectors.