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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.

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.

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.

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/

To measure the activity of neurons using whole-brain activity imaging, precise detection of each neuron or its nucleus is required. In the head region of the nematode C. elegans, the neuronal cell bodies are distributed densely in three-dimensional (3D) space. However, no existing computational methods of image analysis can separate them with sufficient accuracy. Here we propose a highly accurate segmentation method based on the curvatures of the iso-intensity surfaces. To obtain accurate positions of nuclei, we also developed a new procedure for least squares fitting with a Gaussian mixture model. Combining these methods enables accurate detection of densely distributed cell nuclei in a 3D space. The proposed method was implemented as a graphical user interface program that allows visualization and correction of the results of automatic detection. Additionally, the proposed method was applied to time-lapse 3D calcium imaging data, and most of the nuclei in the images were successfully tracked and measured., Author Summary To reach the ultimate goal of neuroscience to understanding how each neuron functions in the brain, whole-brain activity imaging techniques with single-cell resolution have been intensively developed. There are many neurons in the whole-brain images and manual detection of the neurons is very time-consuming. However, the neurons are often packed densely in the 3D space and existing automatic methods fail to correctly split the clumps. In fact, in previous reports of whole-brain activity imaging of C. elegans, the number of detected neurons were less than expected. Such scarcity may be a cause of measurement errors and misidentification of neuron classes. Here we developed a highly accurate automatic cell detection method for densely-packed cells. The proposed method successfully detected almost all neurons in whole-brain images of the nematode. Our method can be used to track multi-objects and enables automatic measurements of the neuronal activities from whole-brain activity imaging data. We also developed a visualization and correction tool that is helpful for experimenters. Additionally, the proposed method can be a fundamental technique for other applications such as making wiring diagram of neurons or establishing a cell lineage in embryonic development. Thus our framework supports effective and accurate bio-image analyses.

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.