A brainstem integrator for self-location memory and positional homeostasis in zebrafish.

To track and control self-location, animals integrate their movements through space. Representations of self-location are observed in the mammalian hippocampal formation, but it is unknown if positional representations exist in more ancient brain regions, how they arise from integrated self-motion, and by what pathways they control locomotion. Here, in a head-fixed, fictive-swimming, virtual-reality preparation, we exposed larval zebrafish to a variety of involuntary displacements. They tracked these displacements and, many seconds later, moved toward their earlier location through corrective swimming ("positional homeostasis"). Whole-brain functional imaging revealed a network in the medulla that stores a memory of location and induces an error signal in the inferior olive to drive future corrective swimming. Optogenetically manipulating medullary integrator cells evoked displacement-memory behavior. Ablating them, or downstream olivary neurons, abolished displacement corrections. These results reveal a multiregional hindbrain circuit in vertebrates that integrates self-motion and stores self-location to control locomotor behavior.

Thermal robustness of signaling in bacterial chemotaxis.

Temperature is a global factor that affects the performance of all intracellular networks. Robustness against temperature variations is thus expected to be an essential network property, particularly in organisms without inherent temperature control. Here, we combine experimental analyses with computational modeling to investigate thermal robustness of signaling in chemotaxis of Escherichia coli, a relatively simple and well-established model for systems biology. We show that steady-state and kinetic pathway parameters that are essential for chemotactic performance are indeed temperature-compensated in the entire physiological range. Thermal robustness of steady-state pathway output is ensured at several levels by mutual compensation of temperature effects on activities of individual pathway components. Moreover, the effect of temperature on adaptation kinetics is counterbalanced by preprogrammed temperature dependence of enzyme synthesis and stability to achieve nearly optimal performance at the growth temperature. Similar compensatory mechanisms are expected to ensure thermal robustness in other systems.

CNN-based fully automatic wrist cartilage volume quantification in MR images: A comparative analysis between different CNN architectures.

PURPOSE: Automatic measurement of wrist cartilage volume in MR images. METHODS: We assessed the performance of four manually optimized variants of the U-Net architecture, nnU-Net and Mask R-CNN frameworks for the segmentation of wrist cartilage. The results were compared to those from a patch-based convolutional neural network (CNN) we previously designed. The segmentation quality was assessed on the basis of a comparative analysis with manual segmentation. The best networks were compared using a cross-validation approach on a dataset of 33 3D VIBE images of mostly healthy volunteers. Influence of some image parameters on the segmentation reproducibility was assessed. RESULTS: The U-Net-based networks outperformed the patch-based CNN in terms of segmentation homogeneity and quality, while Mask R-CNN did not show an acceptable performance. The median 3D DSC value computed with the U-Net_AL (0.817) was significantly larger than DSC values computed with the other networks. In addition, the U-Net_AL provided the lowest mean volume error (17%) and the highest Pearson correlation coefficient (0.765) with respect to the ground truth values. Of interest, the reproducibility computed using U-Net_AL was larger than the reproducibility of the manual segmentation. Moreover, the results indicate that the MRI-based wrist cartilage volume is strongly affected by the image resolution. CONCLUSIONS: U-Net CNN with attention layers provided the best wrist cartilage segmentation performance. In order to be used in clinical conditions, the trained network can be fine-tuned on a dataset representing a group of specific patients. The error of cartilage volume measurement should be assessed independently using a non-MRI method.

Molecular MRI-Based Monitoring of Cancer Immunotherapy Treatment Response.

Immunotherapy constitutes a paradigm shift in cancer treatment. Its FDA approval for several indications has yielded improved prognosis for cases where traditional therapy has shown limited efficiency. However, many patients still fail to benefit from this treatment modality, and the exact mechanisms responsible for tumor response are unknown. Noninvasive treatment monitoring is crucial for longitudinal tumor characterization and the early detection of non-responders. While various medical imaging techniques can provide a morphological picture of the lesion and its surrounding tissue, a molecular-oriented imaging approach holds the key to unraveling biological effects that occur much earlier in the immunotherapy timeline. Magnetic resonance imaging (MRI) is a highly versatile imaging modality, where the image contrast can be tailored to emphasize a particular biophysical property of interest using advanced engineering of the imaging pipeline. In this review, recent advances in molecular-MRI based cancer immunotherapy monitoring are described. Next, the presentation of the underlying physics, computational, and biological features are complemented by a critical analysis of the results obtained in preclinical and clinical studies. Finally, emerging artificial intelligence (AI)-based strategies to further distill, quantify, and interpret the image-based molecular MRI information are discussed in terms of perspectives for the future.

Brain-wide circuit interrogation at the cellular level guided by online analysis of neuronal function.

Whole-brain imaging allows for comprehensive functional mapping of distributed neural pathways, but neuronal perturbation experiments are usually limited to targeting predefined regions or genetically identifiable cell types. To complement whole-brain measures of activity with brain-wide manipulations for testing causal interactions, we introduce a system that uses measured activity patterns to guide optical perturbations of any subset of neurons in the same fictively behaving larval zebrafish. First, a light-sheet microscope collects whole-brain data that are rapidly analyzed by a distributed computing system to generate functional brain maps. On the basis of these maps, the experimenter can then optically ablate neurons and image activity changes across the brain. We applied this method to characterize contributions of behaviorally tuned populations to the optomotor response. We extended the system to optogenetically stimulate arbitrary subsets of neurons during whole-brain imaging. These open-source methods enable delineating the contributions of neurons to brain-wide circuit dynamics and behavior in individual animals.

Mapping brain activity at scale with cluster computing.

Understanding brain function requires monitoring and interpreting the activity of large networks of neurons during behavior. Advances in recording technology are greatly increasing the size and complexity of neural data. Analyzing such data will pose a fundamental bottleneck for neuroscience. We present a library of analytical tools called Thunder built on the open-source Apache Spark platform for large-scale distributed computing. The library implements a variety of univariate and multivariate analyses with a modular, extendable structure well-suited to interactive exploration and analysis development. We demonstrate how these analyses find structure in large-scale neural data, including whole-brain light-sheet imaging data from fictively behaving larval zebrafish, and two-photon imaging data from behaving mouse. The analyses relate neuronal responses to sensory input and behavior, run in minutes or less and can be used on a private cluster or in the cloud. Our open-source framework thus holds promise for turning brain activity mapping efforts into biological insights.

Calcium imaging of neural circuits with extended depth-of-field light-sheet microscopy.

Increasing the volumetric imaging speed of light-sheet microscopy will improve its ability to detect fast changes in neural activity. Here, a system is introduced for brain-wide imaging of neural activity in the larval zebrafish by coupling structured illumination with cubic phase extended depth-of-field (EDoF) pupil encoding. This microscope enables faster light-sheet imaging and facilitates arbitrary plane scanning-removing constraints on acquisition speed, alignment tolerances, and physical motion near the sample. The usefulness of this method is demonstrated by performing multi-plane calcium imaging in the fish brain with a 416×832×160 µm field of view at 33 Hz. The optomotor response behavior of the zebrafish is monitored at high speeds, and time-locked correlations of neuronal activity are resolved across its brain.

Gap junction networks can generate both ripple-like and fast ripple-like oscillations.

Fast ripples (FRs) are network oscillations, defined variously as having frequencies of > 150 to > 250 Hz, with a controversial mechanism. FRs appear to indicate a propensity of cortical tissue to originate seizures. Here, we demonstrate field oscillations, at up to 400 Hz, in spontaneously epileptic human cortical tissue in vitro, and present a network model that could explain FRs themselves, and their relation to 'ordinary' (slower) ripples. We performed network simulations with model pyramidal neurons, having axons electrically coupled. Ripples (< 250 Hz) were favored when conduction of action potentials, axon to axon, was reliable. Whereas ripple population activity was periodic, firing of individual axons varied in relative phase. A switch from ripples to FRs took place when an ectopic spike occurred in a cell coupled to another cell, itself multiply coupled to others. Propagation could then start in one direction only, a condition suitable for re-entry. The resulting oscillations were > 250 Hz, were sustained or interrupted, and had little jitter in the firing of individual axons. The form of model FR was similar to spontaneously occurring FRs in excised human epileptic tissue. In vitro, FRs were suppressed by a gap junction blocker. Our data suggest that a given network can produce ripples, FRs, or both, via gap junctions, and that FRs are favored by clusters of axonal gap junctions. If axonal gap junctions indeed occur in epileptic tissue, and are mediated by connexin 26 (recently shown to mediate coupling between immature neocortical pyramidal cells), then this prediction is testable.

Benchtop mesoSPIM: a next-generation open-source light-sheet microscope for cleared samples.

In 2015, we launched the mesoSPIM initiative, an open-source project for making light-sheet microscopy of large cleared tissues more accessible. Meanwhile, the demand for imaging larger samples at higher speed and resolution has increased, requiring major improvements in the capabilities of such microscopes. Here, we introduce the next-generation mesoSPIM ("Benchtop") with a significantly increased field of view, improved resolution, higher throughput, more affordable cost, and simpler assembly compared to the original version. We develop an optical method for testing detection objectives that enables us to select objectives optimal for light-sheet imaging with large-sensor cameras. The improved mesoSPIM achieves high spatial resolution (1.5 µm laterally, 3.3 µm axially) across the entire field of view, magnification up to 20×, and supports sample sizes ranging from sub-mm up to several centimeters while being compatible with multiple clearing techniques. The microscope serves a broad range of applications in neuroscience, developmental biology, pathology, and even physics.

Synaptic gating at axonal branches, and sharp-wave ripples with replay: a simulation study.

Mechanisms of place cell replay occurring during sharp-wave ripples (SPW-Rs) remain obscure due to the fact that ripples in vitro depend on non-synaptic mechanisms, presumably via axo-axonal gap junctions between pyramidal cells. We suggest a model of in vivo SPW-Rs in which synaptic excitatory post-synaptic potentials (EPSPs) control the axonal spiking of cells in SPW-Rs: ripple activity remains hidden in the network of axonal collaterals (connected by gap junctions) due to conduction failures, unless there is a sufficient dendritic EPSP. The EPSP brings the axonal branching point to threshold, and action potentials from the collateral start to propagate to the soma and to the distal axon. The model coherently explains multiple experimental data on SPW-Rs, both in vitro and in vivo. The mechanism of synaptic gating leads to the following implication: a sequence of pyramidal cells can be replayed at ripple frequency by the superposition of subthreshold dendritic EPSPs and ripple activity in the axonal plexus. Replay is demonstrated in both forward and reverse directions. We discuss several testable predictions. In general, the mechanism of synaptic gating suggests that pyramidal cells under certain conditions can act like a transistor.

Dynamic and rapid deep synthesis of chemical exchange saturation transfer and semisolid magnetization transfer MRI signals.

Model-driven analysis of biophysical phenomena is gaining increased attention and utility for medical imaging applications. In magnetic resonance imaging (MRI), the availability of well-established models for describing the relations between the nuclear magnetization, tissue properties, and the externally applied magnetic fields has enabled the prediction of image contrast and served as a powerful tool for designing the imaging protocols that are now routinely used in the clinic. Recently, various advanced imaging techniques have relied on these models for image reconstruction, quantitative tissue parameter extraction, and automatic optimization of acquisition protocols. In molecular MRI, however, the increased complexity of the imaging scenario, where the signals from various chemical compounds and multiple proton pools must be accounted for, results in exceedingly long model simulation times, severely hindering the progress of this approach and its dissemination for various clinical applications. Here, we show that a deep-learning-based system can capture the nonlinear relations embedded in the molecular MRI Bloch-McConnell model, enabling a rapid and accurate generation of biologically realistic synthetic data. The applicability of this simulated data for in-silico, in-vitro, and in-vivo imaging applications is then demonstrated for chemical exchange saturation transfer (CEST) and semisolid macromolecule magnetization transfer (MT) analysis and quantification. The proposed approach yielded 63-99% acceleration in data synthesis time while retaining excellent agreement with the ground truth (Pearson's r > 0.99, p < 0.0001, normalized root mean square error < 3%).

Prox2 and Runx3 vagal sensory neurons regulate esophageal motility.

Vagal sensory neurons monitor mechanical and chemical stimuli in the gastrointestinal tract. Major efforts are underway to assign physiological functions to the many distinct subtypes of vagal sensory neurons. Here, we use genetically guided anatomical tracing, optogenetics, and electrophysiology to identify and characterize vagal sensory neuron subtypes expressing Prox2 and Runx3 in mice. We show that three of these neuronal subtypes innervate the esophagus and stomach in regionalized patterns, where they form intraganglionic laminar endings. Electrophysiological analysis revealed that they are low-threshold mechanoreceptors but possess different adaptation properties. Lastly, genetic ablation of Prox2 and Runx3 neurons demonstrated their essential roles for esophageal peristalsis in freely behaving mice. Our work defines the identity and function of the vagal neurons that provide mechanosensory feedback from the esophagus to the brain and could lead to better understanding and treatment of esophageal motility disorders.

The Benchtop mesoSPIM: a next-generation open-source light-sheet microscope for large cleared samples.

In 2015, we launched the mesoSPIM initiative (www.mesospim.org), an open-source project for making light-sheet microscopy of large cleared tissues more accessible. Meanwhile, the demand for imaging larger samples at higher speed and resolution has increased, requiring major improvements in the capabilities of light-sheet microscopy. Here, we introduce the next-generation mesoSPIM ("Benchtop") with significantly increased field of view, improved resolution, higher throughput, more affordable cost and simpler assembly compared to the original version. We developed a new method for testing objectives, enabling us to select detection objectives optimal for light-sheet imaging with large-sensor sCMOS cameras. The new mesoSPIM achieves high spatial resolution (1.5 µm laterally, 3.3 µm axially) across the entire field of view, a magnification up to 20x, and supports sample sizes ranging from sub-mm up to several centimetres, while being compatible with multiple clearing techniques. The new microscope serves a broad range of applications in neuroscience, developmental biology, and even physics.

Role of translational coupling in robustness of bacterial chemotaxis pathway.

Chemotaxis allows bacteria to colonize their environment more efficiently and to find optimal growth conditions, and is consequently under strong evolutionary selection. Theoretical and experimental analyses of bacterial chemotaxis suggested that the pathway has been evolutionarily optimized to produce robust output under conditions of such physiological perturbations as stochastic intercellular variations in protein levels while at the same time minimizing complexity and cost of protein expression. Pathway topology in Escherichia coli apparently evolved to produce an invariant output under concerted variations in protein levels, consistent with experimentally observed transcriptional coupling of chemotaxis genes. Here, we show that the pathway robustness is further enhanced through the pairwise translational coupling of adjacent genes. Computer simulations predicted that the robustness of the pathway against the uncorrelated variations in protein levels can be enhanced by a selective pairwise coupling of individual chemotaxis genes on one mRNA, with the order of genes in E. coli ranking among the best in terms of noise compensation. Translational coupling between chemotaxis genes was experimentally confirmed, and coupled expression of these genes was shown to improve chemotaxis. Bioinformatics analysis further revealed that E. coli gene order corresponds to consensus in sequenced bacterial genomes, confirming evolutionary selection for noise reduction. Since polycistronic gene organization is common in bacteria, translational coupling between adjacent genes may provide a general mechanism to enhance robustness of their signaling and metabolic networks. Moreover, coupling between expression of neighboring genes is also present in eukaryotes, and similar principles of noise reduction might thus apply to all cellular networks.

Pregnancy-induced maternal microchimerism shapes neurodevelopment and behavior in mice.

Life-long brain function and mental health are critically determined by developmental processes occurring before birth. During mammalian pregnancy, maternal cells are transferred to the fetus. They are referred to as maternal microchimeric cells (MMc). Among other organs, MMc seed into the fetal brain, where their function is unknown. Here, we show that, in the offspring's developing brain in mice, MMc express a unique signature of sensome markers, control microglia homeostasis and prevent excessive presynaptic elimination. Further, MMc facilitate the oscillatory entrainment of developing prefrontal-hippocampal circuits and support the maturation of behavioral abilities. Our findings highlight that MMc are not a mere placental leak out, but rather a functional mechanism that shapes optimal conditions for healthy brain function later in life.

Predicted auxiliary navigation mechanism of peritrichously flagellated chemotactic bacteria.

Chemotactic movement of Escherichia coli is one of the most thoroughly studied paradigms of simple behavior. Due to significant competitive advantage conferred by chemotaxis and to high evolution rates in bacteria, the chemotaxis system is expected to be strongly optimized. Bacteria follow gradients by performing temporal comparisons of chemoeffector concentrations along their runs, a strategy which is most efficient given their size and swimming speed. Concentration differences are detected by a sensory system and transmitted to modulate rotation of flagellar motors, decreasing the probability of a tumble and reorientation if the perceived concentration change during a run is positive. Such regulation of tumble probability is of itself sufficient to explain chemotactic drift of a population up the gradient, and is commonly assumed to be the only navigation mechanism of chemotactic E. coli. Here we use computer simulations to predict existence of an additional mechanism of gradient navigation in E. coli. Based on the experimentally observed dependence of cell tumbling angle on the number of switching motors, we suggest that not only the tumbling probability but also the degree of reorientation during a tumble depend on the swimming direction along the gradient. Although the difference in mean tumbling angles up and down the gradient predicted by our model is small, it results in a dramatic enhancement of the cellular drift velocity along the gradient. We thus demonstrate a new level of optimization in E. coli chemotaxis, which arises from the switching of several flagellar motors and a resulting fine tuning of tumbling angle. Similar strategy is likely to be used by other peritrichously flagellated bacteria, and indicates yet another level of evolutionary development of bacterial chemotaxis.

Dual-view light-sheet imaging through a tilted glass interface using a deformable mirror.

Light-sheet microscopy has become indispensable for imaging developing organisms, and imaging from multiple directions (views) is essential to improve its spatial resolution. We combine multi-view light-sheet microscopy with microfluidics using adaptive optics (deformable mirror) which corrects aberrations introduced by the 45o-tilted glass coverslip. The optimal shape of the deformable mirror is computed by an iterative algorithm that optimizes the point-spread function in two orthogonal views. Simultaneous correction in two optical arms is achieved via a knife-edge mirror that splits the excitation path and combines the detection paths. Our design allows multi-view light-sheet microscopy with microfluidic devices for precisely controlled experiments and high-content screening.

Chemotaxis: how bacteria use memory.

Bacterial chemotaxis represents one of the simplest and best studied examples of unicellular behavior. Chemotaxis allows swimming bacterial cells to follow chemical gradients in the environment by performing temporal comparisons of ligand concentrations. The process of chemotaxis in the model bacterium Escherichia coli has been studied in great molecular detail over the past 40 years, using a large range of experimental tools to investigate physiology, genetics and biochemistry of the system. The abundance of quantitative experimental data enabled detailed computational modeling of the pathway and theoretical analyses of such properties as robustness and signal amplification. Because of the temporal mode of gradient sensing in bacterial chemotaxis, molecular memory is an essential component of the chemotaxis pathway. Recent studies suggest that the memory time scale has been evolutionary optimized to perform optimal comparisons of stimuli while swimming in the gradient. Moreover, noise in the adaptation system, which results from variations of the adaptation rate both over time and among cells, might be beneficial for the overall chemotactic performance of the population.