A drug-selectable acoustic reporter gene system for human cell ultrasound imaging.

A promising new field of genetically encoded ultrasound contrast agents in the form of gas vesicles has recently emerged, which could extend the specificity of medical ultrasound imaging. However, given the delicate genetic nature of how these genes are integrated and expressed, current methods of producing gas vesicle-expressing mammalian cell lines requires significant cell processing time to establish a clonal/polyclonal line that robustly expresses the gas vesicles sufficiently enough for ultrasound contrast. Here, we describe an inducible and drug-selectable acoustic reporter gene system that can enable gas vesicle expression in mammalian cell lines, which we demonstrate using HEK293T cells. Our drug-selectable construct design increases the stability and proportion of cells that successfully integrate all plasmids into their genome, thus reducing the amount of cell processing time required. Additionally, we demonstrate that our drug-selectable strategy forgoes the need for single-cell cloning and fluorescence-activated cell sorting, and that a drug-selected mixed population is sufficient to generate robust ultrasound contrast. Successful gas vesicle expression was optically and ultrasonically verified, with cells expressing gas vesicles exhibiting an 80% greater signal-to-noise ratio compared to negative controls and a 500% greater signal-to-noise ratio compared to wild-type HEK293T cells. This technology presents a new reporter gene paradigm by which ultrasound can be harnessed to visualize specific cell types for applications including cellular reporting and cell therapies.

A biologically validated mathematical model for decoding epithelial apical, basolateral, and paracellular electrical properties.

Epithelial tissues form selective barriers to ions, nutrients, waste products, and infectious agents throughout the body. Damage to these barriers is associated with conditions such as celiac disease, cystic fibrosis, diabetes, and age-related macular degeneration. Conventional electrophysiology measurements like transepithelial resistance can quantify epithelial tissue maturity and barrier integrity but are limited in differentiating between apical, basolateral, and paracellular transport pathways. To overcome this limitation, a combination of mathematical modeling, stem cell biology, and cell physiology led to the development of 3 P-EIS, a novel mathematical model and measurement technique. 3 P-EIS employs an intracellular pipette and extracellular electrochemical impedance spectroscopy to accurately measure membrane-specific properties of epithelia, without the constraints of prior models. 3 P-EIS was validated using electronic circuit models of epithelia with known resistances and capacitances, confirming a median error of 19% (interquartile range: 14%-26%) for paracellular and transcellular resistances and capacitances (n = 5). Patient stem cell-derived retinal pigment epithelium tissues were measured using 3 P-EIS, successfully isolating the cellular responses to adenosine triphosphate. 3 P-EIS enhances quality control in epithelial cell therapies and has extensive applicability in drug testing and disease modeling, marking a significant advance in epithelial physiology.NEW & NOTEWORTHY This interdisciplinary paper integrates mathematics, biology, and physiology to measure epithelial tissue's apical, basolateral, and paracellular transport pathways. A key advancement is the inclusion of intracellular voltage recordings using a sharp pipette, enabling precise quantification of relative impedance changes between apical and basolateral membranes. This enhanced electrochemical impedance spectroscopy technique offers insights into epithelial transport dynamics, advancing disease understanding, drug interactions, and cell therapies. Its broad applicability contributes significantly to epithelial physiology research.

Electrophysiological and morphological characterization of single neurons in intact human brain organoids.

Brain organoids represent a new model system for studying developmental human neurophysiology. Methods for studying the electrophysiology and morphology of single neurons in organoids require acute slices or dissociated cultures. While these methods have advantages (e.g., visual access, ease of experimentation), they risk damaging cells and circuits present in the intact organoid. To access single cells within intact organoid circuits, we have demonstrated a method for fixturing and performing whole cell patch clamp recording from intact brain organoids using both manual and automated tools. We demonstrate applied electrophysiology methods development followed by an integration of electrophysiology with reconstructing the morphology of the neurons within the brain organoid using dye filling and tissue clearing. We found that whole cell patch clamp recordings could be achieved both on the surface and within the interior of intact human brain organoids using both manual and automated methods. Manual experiments were higher yield (53 % whole cell success rate manual, 9 % whole cell success rate automated), but automated experiments were more efficient (30 patch attempts per day automated, 10 patch attempts per day manual). Using these methods, we performed an unbiased survey of cells within human brain organoids between 90 and 120 days in vitro (DIV) and present preliminary data on morphological and electrical diversity in human brain organoids. The further development of intact brain organoid patch clamp methods could be broadly applicable to studies of cellular, synaptic, and circuit-level function in the developing human brain.

Sensitivity optimization of a rhodopsin-based fluorescent voltage indicator.

The ability to optically image cellular transmembrane voltages at millisecond-timescale resolutions can offer unprecedented insight into the function of living brains in behaving animals. Here, we present a point mutation that increases the sensitivity of Ace2 opsin-based voltage indicators. We use the mutation to develop Voltron2, an improved chemigeneic voltage indicator that has a 65% higher sensitivity to single APs and 3-fold higher sensitivity to subthreshold potentials than Voltron. Voltron2 retained the sub-millisecond kinetics and photostability of its predecessor, although with lower baseline fluorescence. In multiple in vitro and in vivo comparisons with its predecessor across multiple species, we found Voltron2 to be more sensitive to APs and subthreshold fluctuations. Finally, we used Voltron2 to study and evaluate the possible mechanisms of interneuron synchronization in the mouse hippocampus. Overall, we have discovered a generalizable mutation that significantly increases the sensitivity of Ace2 rhodopsin-based sensors, improving their voltage reporting capability.

Quantitative assessment of automated purification and concentration of E. coli bacteria.

Automated methods for rapidly purifying and concentrating bacteria from environmental interferents are needed in next-generation applications for anything from water purification to biological weapons detection. Though previous work has been performed by other researchers in this area, there is still a need to create an automated system that can both purify and concentrate target pathogens in a timely manner with readily available and replaceable components that could be easily integrated with a detection mechanism. Thus, the objective of this work was to design, build, and demonstrate the effectiveness of an automated system, the Automated Dual-filter method for Applied Recovery, or aDARE. aDARE uses a custom LABVIEW program that guides the flow of bacterial samples through a pair of size-based separation membranes to capture and elute the target bacteria. Using aDARE, we eliminated 95% of the interfering beads of a 5 mL-sample volume containing 107 CFU/mL of E. coli contaminated with 2 µm and 10 µm polystyrene beads at 106 beads/mL concentration., The target bacteria were concentrated to more than twice the initial concentration in 900 µL of eluent, resulting in an enrichment ratio for the target bacteria of 42 ± 13 in 5.5 min. These results show the feasibility and effectiveness of using size-based filtration membranes to purify and concentrate a target bacterium, in this case E. coli, in an automated system.

Perfluorocarbon nanodroplet size, acoustic vaporization, and inertial cavitation affected by lipid shell composition in vitro.

Perfluorocarbon nanodroplets (PFCnDs) are ultrasound contrast agents that phase-transition from liquid nanodroplets to gas microbubbles when activated by laser irradiation or insonated with an ultrasound pulse. The dynamics of PFCnDs can vary drastically depending on the nanodroplet composition, including the lipid shell properties. In this paper, we investigate the effect of varying the ratio of PEGylated to non-PEGylated phospholipids in the outer shell of PFCnDs on the acoustic nanodroplet vaporization (liquid to gas phase transition) and inertial cavitation (rapid collapse of the vaporized nanodroplets) dynamics in vitro when insonated with focused ultrasound. Nanodroplets with a high concentration of PEGylated lipids had larger diameters and exhibited greater variance in size distribution compared to nanodroplets with lower proportions of PEGylated lipids in the lipid shell. PFCnDs with a lipid shell composed of 50:50 PEGylated to non-PEGylated lipids yielded the highest B-mode image intensity and duration, as well as the greatest pressure difference between acoustic droplet vaporization onset and inertial cavitation onset. We demonstrate that slight changes in lipid shell composition of PFCnDs can significantly impact droplet phase transitioning and inertial cavitation dynamics. These findings can help guide researchers to fabricate PFCnDs with optimized compositions for their specific applications.

Method for Rapid Enzymatic Cleaning for Reuse of Patch Clamp Pipettes: Increasing Throughput by Eliminating Manual Pipette Replacement between Patch Clamp Attempts.

The whole-cell patch-clamp method is a gold standard for single-cell analysis of electrical activity, cellular morphology, and gene expression. Prior to our discovery that patch-clamp pipettes could be cleaned and reused, experimental throughput and automation were limited by the need to replace pipettes manually after each experiment. This article presents an optimized protocol for pipette cleaning, which enables it to be performed quickly (< 30 s), resulting in a high yield of whole-cell recording success rate (> 90%) for over 100 reuses of a single pipette. For most patch-clamp experiments (< 30 whole-cell recordings per day), this method enables a single pipette to be used for an entire day of experiments. In addition, we describe easily implementable hardware and software as well as troubleshooting tips to help other labs implement this method in their own experiments. Pipette cleaning enables patch-clamp experiments to be performed with higher throughput, whether manually or in an automated fashion, by eliminating the tedious and skillful task of replacing pipettes. From our experience with numerous electrophysiology laboratories, pipette cleaning can be integrated into existing patch-clamp setups in approximately one day using the hardware and software described in this article. Graphic abstract: Rapid enzymatic cleaning for reuse of patch-clamp pipettes.

Machine Learning-Based Pipette Positional Correction for Automatic Patch Clamp In Vitro.

Patch clamp electrophysiology is a common technique used in neuroscience to understand individual neuron behavior, allowing one to record current and voltage changes with superior spatiotemporal resolution compared with most electrophysiology methods. While patch clamp experiments produce high fidelity electrophysiology data, the technique is onerous and labor intensive. Despite the emergence of patch clamp systems that automate key stages in the typical patch clamp procedure, full automation remains elusive. Patch clamp pipettes can miss the target cell during automated experiments because of positioning errors in the robotic manipulators, which can easily exceed the diameter of a neuron. Further, when patching in acute brain slices, the inherent light scattering from non-uniform brain tissue can complicate pipette tip identification. We present a convolutional neural network (CNN), based on ResNet101, to identify and correct pipette positioning errors before each patch clamp attempt, thereby preventing the deleterious effects of and accumulation of positioning errors. This deep-learning-based pipette detection method enabled superior localization of the pipette within 0.62 ± 0.58 µm, resulting in improved cell detection success rate and whole-cell patch clamp success rates by 71% and 59%, respectively, compared with the state-of-the-art cross-correlation method. Furthermore, this technique reduced the average time for pipette correction by 81%. This technique enables real-time correction of pipette position during patch clamp experiments with similar accuracy and quality of recording to manual patch clamp, making notable progress toward full human-out-of-the-loop automation for patch clamp electrophysiology.

Rapid Cortical Adaptation and the Role of Thalamic Synchrony during Wakefulness.

Rapid sensory adaptation is observed across all sensory systems, and strongly shapes sensory percepts in complex sensory environments. Yet despite its ubiquity and likely necessity for survival, the mechanistic basis is poorly understood. A wide range of primarily in vitro and anesthetized studies have demonstrated the emergence of adaptation at the level of primary sensory cortex, with only modest signatures in earlier stages of processing. The nature of rapid adaptation and how it shapes sensory representations during wakefulness, and thus the potential role in perceptual adaptation, is underexplored, as are the mechanisms that underlie this phenomenon. To address these knowledge gaps, we recorded spiking activity in primary somatosensory cortex (S1) and the upstream ventral posteromedial (VPm) thalamic nucleus in the vibrissa pathway of awake male and female mice, and quantified responses to whisker stimuli delivered in isolation and embedded in an adapting sensory background. We found that cortical sensory responses were indeed adapted by persistent sensory stimulation; putative excitatory neurons were profoundly adapted, and inhibitory neurons only modestly so. Further optogenetic manipulation experiments and network modeling suggest this largely reflects adaptive changes in synchronous thalamic firing combined with robust engagement of feedforward inhibition, with little contribution from synaptic depression. Taken together, these results suggest that cortical adaptation in the regime explored here results from changes in the timing of thalamic input, and the way in which this differentially impacts cortical excitation and feedforward inhibition, pointing to a prominent role of thalamic gating in rapid adaptation of primary sensory cortex.SIGNIFICANCE STATEMENT Rapid adaptation of sensory activity strongly shapes representations of sensory inputs across all sensory pathways over the timescale of seconds, and has profound effects on sensory perception. Despite its ubiquity and theoretical role in the efficient encoding of complex sensory environments, the mechanistic basis is poorly understood, particularly during wakefulness. In this study in the vibrissa pathway of awake mice, we show that cortical representations of sensory inputs are strongly shaped by rapid adaptation, and that this is mediated primarily by adaptive gating of the thalamic inputs to primary sensory cortex and the differential way in which these inputs engage cortical subpopulations of neurons.

Automated Intracellular Pharmacological Electrophysiology for Ligand-Gated Ionotropic Receptor and Pharmacology Screening.

Communication between neuronal cells, which is central to brain function, is performed by several classes of ligand-gated ionotropic receptors. The gold-standard technique for measuring rapid receptor response to agonist is manual patch-clamp electrophysiology, capable of the highest temporal resolution of any current electrophysiology technique. We report an automated high-precision patch-clamp system that substantially improves the throughput of these time-consuming pharmacological experiments. The patcherBotPharma enables recording from cells expressing receptors of interest and manipulation of them to enable millisecond solution exchange to activate ligand-gated ionotropic receptors. The solution-handling control allows for autonomous pharmacological concentration-response experimentation on adherent cells, lifted cells, or excised outside-out patches. The system can perform typical ligand-gated ionotropic receptor experimentation protocols autonomously, possessing a high success rate in completing experiments and up to a 10-fold reduction in research effort over the duration of the experiment. Using it, we could rapidly replicate previous data sets, reducing the time it took to produce an eight-point concentration-response curve of the effect of propofol on GABA type A receptor deactivation from likely weeks of recording to ∼13 hours of recording. On average, the rate of data collection of the patcherBotPharma was a data point every 2.1 minutes that the operator spent interacting with the patcherBotPharma The patcherBotPharma provides the ability to conduct complex and comprehensive experimentation that yields data sets not normally within reach of conventional systems that rely on constant human control. This technical advance can contribute to accelerating the examination of the complex function of ion channels and the pharmacological agents that act on them. SIGNIFICANCE STATEMENT: This work presents an automated intracellular pharmacological electrophysiology robot, patcherBotPharma, that substantially improves throughput and reduces human time requirement in pharmacological patch-clamp experiments. The robotic system includes millisecond fluid exchange handling and can perform highly efficient ligand-gated ionotropic receptor experiments. The patcherBotPharma is built using a conventional patch-clamp rig, and the technical advances shown in this work greatly accelerate the ability to conduct high-fidelity pharmacological electrophysiology.

Inferring thalamocortical monosynaptic connectivity in vivo.

As the tools to simultaneously record electrophysiological signals from large numbers of neurons within and across brain regions become increasingly available, this opens up for the first time the possibility of establishing the details of causal relationships between monosynaptically connected neurons and the patterns of neural activation that underlie perception and behavior. Although recorded activity across synaptically connected neurons has served as the cornerstone for much of what we know about synaptic transmission and plasticity, this has largely been relegated to ex vivo preparations that enable precise targeting under relatively well-controlled conditions. Analogous studies in vivo, where image-guided targeting is often not yet possible, rely on indirect, data-driven measures, and as a result such studies have been sparse and the dependence upon important experimental parameters has not been well studied. Here, using in vivo extracellular single-unit recordings in the topographically aligned rodent thalamocortical pathway, we sought to establish a general experimental and computational framework for inferring synaptic connectivity. Specifically, attacking this problem within a statistical signal detection framework utilizing experimentally recorded data in the ventral-posterior medial (VPm) region of the thalamus and the homologous region in layer 4 of primary somatosensory cortex (S1) revealed a trade-off between network activity levels needed for the data-driven inference and synchronization of nearby neurons within the population that results in masking of synaptic relationships. Here, we provide a framework for establishing connectivity in multisite, multielectrode recordings based on statistical inference, setting the stage for large-scale assessment of synaptic connectivity within and across brain structures.NEW & NOTEWORTHY Despite the fact that all brain function relies on the long-range transfer of information across different regions, the tools enabling us to measure connectivity across brain structures are lacking. Here, we provide a statistical framework for identifying and assessing potential monosynaptic connectivity across neuronal circuits from population spiking activity that generalizes to large-scale recording technologies that will help us to better understand the signaling within networks that underlies perception and behavior.

Deep learning-based real-time detection of neurons in brain slices for in vitro physiology.

A common electrophysiology technique used in neuroscience is patch clamp: a method in which a glass pipette electrode facilitates single cell electrical recordings from neurons. Typically, patch clamp is done manually in which an electrophysiologist views a brain slice under a microscope, visually selects a neuron to patch, and moves the pipette into close proximity to the cell to break through and seal its membrane. While recent advances in the field of patch clamping have enabled partial automation, the task of detecting a healthy neuronal soma in acute brain tissue slices is still a critical step that is commonly done manually, often presenting challenges for novices in electrophysiology. To overcome this obstacle and progress towards full automation of patch clamp, we combined the differential interference microscopy optical technique with an object detection-based convolutional neural network (CNN) to detect healthy neurons in acute slice. Utilizing the YOLOv3 convolutional neural network architecture, we achieved a 98% reduction in training times to 18 min, compared to previously published attempts. We also compared networks trained on unaltered and enhanced images, achieving up to 77% and 72% mean average precision, respectively. This novel, deep learning-based method accomplishes automated neuronal detection in brain slice at 18 frames per second with a small data set of 1138 annotated neurons, rapid training time, and high precision. Lastly, we verified the health of the identified neurons with a patch clamp experiment where the average access resistance was 29.25 M[Formula: see text] (n = 9). The addition of this technology during live-cell imaging for patch clamp experiments can not only improve manual patch clamping by reducing the neuroscience expertise required to select healthy cells, but also help achieve full automation of patch clamping by nominating cells without human assistance.

Compensation of physiological motion enables high-yield whole-cell recording in vivo.

BACKGROUND: Whole-cell patch-clamp recording in vivo is the gold-standard method for measuring subthreshold electrophysiology from single cells during behavioural tasks, sensory stimulations, and optogenetic manipulation. However, these recordings require a tight, gigaohm resistance, seal between a glass pipette electrode's aperture and a cell's membrane. These seals are difficult to form, especially in vivo, in part because of a strong dependence on the distance between the pipette aperture and cell membrane. NEW METHOD: We elucidate and utilize this dependency to develop an autonomous method for placement and synchronization of pipette's tip aperture to the membrane of a nearby, moving neuron, which enables high-yield seal formation and subsequent recordings deep in the brain of the living mouse. RESULTS: This synchronization procedure nearly doubles the reported gigaseal yield in the thalamus (>3 mm below the pial surface) from 26 % (n = 17/64) to 48 % (n = 32/66). Whole-cell recording yield improved from 10 % (n = 9/88) to 24 % (n = 18/76) when motion compensation was used during the gigaseal formation. As an example of its application, we utilized this system to investigate the role of the sensory environment and ventral posterior medial region (VPM) projection synchrony on intracellular dynamics in the barrel cortex. COMPARISON WITH EXISTING METHOD(S): Current methods of in vivo whole-cell patch clamping do not synchronize the position of the pipette to motion of the cell. CONCLUSIONS: This method results in substantially greater subcortical whole-cell recording yield than previously reported and thus makes pan-brain whole-cell electrophysiology practical in the living mouse brain.

Capillary-Based and Stokes-Based Trapping of Serial Sections for Scalable 3D-EM Connectomics.

Serial section electron microscopy (ssEM), a technique where volumes of tissue can be anatomically reconstructed by imaging consecutive tissue slices, has proven to be a powerful tool for the investigation of brain anatomy. Between the process of cutting the slices, or "sections," and imaging them, however, handling 10°-106 delicate sections remains a bottleneck in ssEM, especially for batches in the "mesoscale" regime, i.e., 102-103 sections. We present a tissue section handling device that transports and positions sections, accurately and repeatability, for automated, robotic section pick-up and placement onto an imaging substrate. The device interfaces with a conventional ultramicrotomy diamond knife, accomplishing in-line, exact-constraint trapping of sections with 100-µm repeatability. An associated mathematical model includes capillary-based and Stokes-based forces, accurately describing observed behavior and fundamentally extends the modeling of water-air interface forces. Using the device, we demonstrate and describe the limits of reliable handling of hundreds of slices onto a variety of electron and light microscopy substrates without significant defects (n = 8 datasets composed of 126 serial sections in an automated fashion with an average loss rate and throughput of 0.50% and 63 s/section, respectively. In total, this work represents an automated mesoscale serial sectioning system for scalable 3D-EM connectomics.

High-yield, automated intracellular electrophysiology in retinal pigment epithelia.

BACKGROUND: Recent advancements with induced pluripotent stem cell-derived (iPSC) retinal pigment epithelium (RPE) have made disease modeling and cell therapy for macular degeneration feasible. However, current techniques for intracellular electrophysiology - used to validate epithelial function - are painstaking and require manual skill; limiting experimental throughput. NEW METHOD: A five-stage algorithm, leveraging advances in automated patch clamping, systematically derived and optimized, improves yield and reduces skill when compared to conventional, manual techniques. RESULTS: The automated algorithm improves yield per attempt from 17% (manually, n = 23) to 22% (automated, n = 120) (chi-squared, p = 0.004). Specifically for RPE, depressing the local cell membrane by 6 µm and electroporating (buzzing) just prior to this depth (5 µm) maximized yield. COMPARISON WITH EXISTING METHOD: Conventionally, intracellular epithelial electrophysiology is performed by manually lowering a pipette with a micromanipulator, blindly, towards a monolayer of cells and spontaneously stopping when the magnitude of the instantaneous measured membrane potential decreased below a predetermined threshold. The new method automatically measures the pipette tip resistance during the descent, detects the cell surface, indents the cell membrane, and briefly buzzes to electroporate the membrane while descending, overall achieving a higher yield than conventional methods. CONCLUSIONS: This paper presents an algorithm for high-yield, automated intracellular electrophysiology in epithelia; optimized for human RPE. Automation reduces required user skill and training while, simultaneously, improving yield. This algorithm could enable large-scale exploration of drug toxicity and physiological function verification for numerous kinds of epithelia.

PatcherBot: a single-cell electrophysiology robot for adherent cells and brain slices.

OBJECTIVE: Intracellular patch-clamp electrophysiology, one of the most ubiquitous, high-fidelity techniques in biophysics, remains laborious and low-throughput. While previous efforts have succeeded at automating some steps of the technique, here we demonstrate a robotic 'PatcherBot' system that can perform many patch-clamp recordings sequentially, fully unattended. APPROACH: Comprehensive automation is accomplished by outfitting the robot with machine vision, and cleaning pipettes instead of manually exchanging them. MAIN RESULTS: the PatcherBot can obtain data at a rate of 16 cells per hour and work with no human intervention for up to 3 h. We demonstrate the broad applicability and scalability of this system by performing hundreds of recordings in tissue culture cells and mouse brain slices with no human supervision. Using the PatcherBot, we also discovered that pipette cleaning can be improved by a factor of three. SIGNIFICANCE: The system is potentially transformative for applications that depend on many high-quality measurements of single cells, such as drug screening, protein functional characterization, and multimodal cell type investigations.

Autonomous patch-clamp robot for functional characterization of neurons in vivo: development and application to mouse visual cortex.

Patch clamping is the gold standard measurement technique for cell-type characterization in vivo, but it has low throughput, is difficult to scale, and requires highly skilled operation. We developed an autonomous robot that can acquire multiple consecutive patch-clamp recordings in vivo. In practice, 40 pipettes loaded into a carousel are sequentially filled and inserted into the brain, localized to a cell, used for patch clamping, and disposed. Automated visual stimulation and electrophysiology software enables functional cell-type classification of whole cell-patched cells, as we show for 37 cells in the anesthetized mouse in visual cortex (V1) layer 5. We achieved 9% yield, with 5.3 min per attempt over hundreds of trials. The highly variable and low-yield nature of in vivo patch-clamp recordings will benefit from such a standardized, automated, quantitative approach, allowing development of optimal algorithms and enabling scaling required for large-scale studies and integration with complementary techniques. NEW & NOTEWORTHY In vivo patch-clamp is the gold standard for intracellular recordings, but it is a very manual and highly skilled technique. The robot in this work demonstrates the most automated in vivo patch-clamp experiment to date, by enabling production of multiple, serial intracellular recordings without human intervention. The robot automates pipette filling, wire threading, pipette positioning, neuron hunting, break-in, delivering sensory stimulus, and recording quality control, enabling in vivo cell-type characterization.

Automated Assessment of Loss of Consciousness Using Whisker And Paw Movements During Anesthetic Dosing in Head-Fixed Rodents.

The precise identification of loss of consciousness (LOC) is key to studying the effects of anesthetic drugs in neural systems. The standard behavioral assay for identifying LOC in rodents is the Loss of Righting Reflex (LORR), assessed by placing the animal in the supine position every minute until it fails to right itself. However, this assay cannot be used when the rodents are head-fixed, which limits the use of powerful techniques such as multi-electrode recordings, in vivo patch clamp, and neuronal imaging. In these situations, an alternative way to assess LOC is needed. We propose that loss of movement (LOM) in whiskers and paws of head-fixed animals can be used as an alternative behavioral assay in head-fixed animals. Unlike LORR, LOM in whiskers and paws is much harder to detect by visual inspection. Therefore, we developed a method to automatically assess for LOM of whiskers and paws in head fixed rodents during in vivo patch clamp recordings. Our method uses an algorithm based on optical flow and point-process filtering which can be run on images acquired on regular cameras at low frame-rates. We show that the algorithm can achieve at least comparable accuracy in detecting LOC when compared with consensus among human observers, as well as improved precision when compared with individual observers. In the future, we aim to to expand the method to detect more behavioral end-points during anesthesia such as paradoxical excitation. Eventually, we hope to enable multi-modal anesthesia studies, which incorporates behavioral and neurophysiological data.

Large-scale neuroanatomy using LASSO: Loop-based Automated Serial Sectioning Operation.

Serial section transmission electron microscopy (ssTEM) is the most promising tool for investigating the three-dimensional anatomy of the brain with nanometer resolution. Yet as the field progresses to larger volumes of brain tissue, new methods for high-yield, low-cost, and high-throughput serial sectioning are required. Here, we introduce LASSO (Loop-based Automated Serial Sectioning Operation), in which serial sections are processed in "batches." Batches are quantized groups of individual sections that, in LASSO, are cut with a diamond knife, picked up from an attached waterboat, and placed onto microfabricated TEM substrates using rapid, accurate, and repeatable robotic tools. Additionally, we introduce mathematical models for ssTEM with respect to yield, throughput, and cost to access ssTEM scalability. To validate the method experimentally, we processed 729 serial sections of human brain tissue (~40 nm x 1 mm x 1 mm). Section yield was 727/729 (99.7%). Sections were placed accurately and repeatably (x-direction: -20 ± 110 µm (1 s.d.), y-direction: 60 ± 150 µm (1 s.d.)) with a mean cycle time of 43 s ± 12 s (1 s.d.). High-magnification (2.5 nm/px) TEM imaging was conducted to measure the image quality. We report no significant distortion, information loss, or substrate-derived artifacts in the TEM images. Quantitatively, the edge spread function across vesicle edges and image contrast were comparable, suggesting that LASSO does not negatively affect image quality. In total, LASSO compares favorably with traditional serial sectioning methods with respect to throughput, yield, and cost for large-scale experiments, and represents a flexible, scalable, and accessible technology platform to enable the next generation of neuroanatomical studies.

Transport and trapping of nanosheets via hydrodynamic forces and curvature-induced capillary quadrupolar interactions.

HYPOTHESIS: The manipulation of nanosheets on a fluid-fluid interface remains a significant challenge. At this interface, hydrodynamic forces can be used for long-range transport (>1× capillary length) but are difficult to utilize for accurate and repeatable positioning. While capillary multipole interactions have been used for particle trapping, how these interactions manifest on large but thin objects, i.e., nanosheets, remains an open question. Hence, we posit hydrodynamic forces in conjunction with capillary multipole interactions can be used for nanosheet transport and trapping. EXPERIMENTS: We designed and characterized a fluidic device for transporting and trapping nanosheets on the water-air interface. Analytical models were compared against optical measurements of the nanosheet behavior to investigate capillary multipole interactions. Energy-based modeling and dimensional analysis were used to study trapping stability. FINDINGS: Hydrodynamic forces and capillary interactions successfully transported and trapped nanosheets at a designated trapping location with a repeatability of 10% of the nanosheet's length and 12% of its width (length = 1500 µm, width = 1000 µm) and an accuracy of 20% of their length and width. Additionally, this is the first report that surface tension forces acting upon nanoscale-thick objects manifest as capillary quadrupolar interactions and can be used for precision manipulation of nanosheets.