Chalasani Lab

Ultrasound can safely penetrate deep into biological tissues, which has allowed this modality to be extensively used in clinical applications including diagnostic imaging, thermal ablation and lithotripsy. Recent identification of ultrasound-sensitive molecular actuators has led to the development of sonogenetics, a new and rapidly growing field. Here, these actuators are expressed in target cells, sensitizing these cells to the mechanical and/or thermal bioeffects of ultrasound, allowing them to be selectively manipulated using non-invasive ultrasound stimulation with high spatial and temporal precision. In this review, we will outline current sonogenetic approaches to manipulate both neurons and immune cells in vitro and in vivo, along with some practical considerations associated with designing these studies. Additionally, we highlight some exciting ongoing avenues for research and provide perspectives on the future of sonogenetics in the lab and the clinic. We suggest that by combining cell type resolution with the non-invasive nature of ultrasound stimulation, sonogenetics has the potential to revolutionize both scientific research and clinical practice.

Decision-making is a ubiquitous component of animal behavior that is often studied in the context of foraging. Foragers make a series of decisions while locating food (food search), choosing between food types (diet or patch choice), and allocating time spent within patches of food (patch-leaving). Here, we introduce a framework for investigating foraging decisions using detailed analysis of individual behavior and quantitative modeling in the nematode . We demonstrate that make patch choice decisions upon encounter with food. Specifically, we show that when foraging among small, dispersed, and dilute patches of bacteria, initially several bacterial patches, opting to prioritize exploration of the environment, before switching to a more exploitatory foraging strategy during subsequent encounters. Observed across a range of bacterial patch densities, sizes, and distributions, we use a quantitative model to show that this decision to or is guided by available sensory information, internal satiety signals, and learned environmental statistics related to the bacterial density of recently encountered and patches. We behaviorally validated model predictions on animals that had been food-deprived, animals foraging in environments with multiple patch densities, and null mutants with defective sensory modalities. Broadly, we present a framework to study ecologically relevant foraging decisions that could guide future investigations into the cellular and molecular mechanisms underlying decision-making.

Animals with small nervous systems have a limited number of sensory neurons that must encode information from a changing environment. This problem is particularly exacerbated in nematodes that populate a wide variety of distinct ecological niches but only have a few sensory neurons available to encode multiple modalities. How does sensory diversity prevail within this constraint in neuron number? To identify the genetic basis for patterning different nervous systems, we demonstrate that sensory neurons in respond to various salt sensory cues in a manner that is partially distinct from that of the distantly related nematode . Previously we showed that likely lacked bilateral asymmetry (Hong et al., 2019). Here, we show that by visualizing neuronal activity patterns, contrary to previous expectations based on its genome sequence, the salt responses of are encoded in a left/right asymmetric manner in the bilateral ASE neuron pair. Our study illustrates patterns of evolutionary stability and change in the gustatory system of nematodes.

Decision-making is a ubiquitous component of animal behavior that is often studied in the context of foraging. Foragers make a series of decisions while locating food (food search), choosing between food types (diet or patch choice), and allocating time spent within patches of food (patch-leaving). Here, we introduce a framework for investigating foraging decisions using detailed analysis of individual behavior and quantitative modeling in the nematode . We demonstrate that make patch choice decisions upon encounter with food. Specifically, we show that when foraging amongst small, dispersed, and dilute patches of bacteria, initially several bacterial patches, opting to prioritize exploration of the environment, before switching to a more exploitatory foraging strategy during subsequent encounters. Observed across a range of bacterial patch densities, sizes, and distributions, we use a quantitative model to show that this decision to or is guided by available sensory information, internal satiety signals, and learned environmental statistics related to the bacterial density of recently encountered and patches. We behaviorally validated model predictions on animals that had been food-deprived, animals foraging in environments with multiple patch densities, and null mutants with defective sensory modalities. Broadly, we present a framework to study ecologically relevant foraging decisions that could guide future investigations into the cellular and molecular mechanisms underlying decision-making.

High-throughput, intracellular electrophysiology is crucial for advancing the understanding of neuronal processing and network dynamics. Nanoelectrode arrays (NEA) offer a promising approach by directly capturing intracellular signals across sub-neuronal compartments, including action potentials, postsynaptic potentials, and low-frequency membrane fluctuations. However, the complexity of NEA datasets, characterized by multiscale events of varying amplitude and duration, demands novel analytical strategies. In this work, a dynamic spike sorting pipeline is introduced and designed to isolate, extract, and sort these diverse electrical signals within a landscape of spontaneous electrical behavior. It is obtained estimates of signal attenuation and distortion using a bespoke biophysical circuit simulation designed to match the specific nanoelectrode interface. Based on these observations, bounds are set for filtering and extracting multiscale waveforms, and validated their isolation using pharmacological data. Finally, it is shown that multiscale analysis of spontaneous electrical recordings reveals interrelationships between high frequency events such as action potentials, and low frequency membrane potential fluctuations which may inform models of neuronal network excitability. Advanced sorting algorithms tailored for nanoelectrode array recordings are essential for unlocking the full potential of next generation, high throughput neuroelectronic devices and achieving a deeper understanding of neuronal dynamics.