A high-content platform for physiological profiling and unbiased classification of individual neurons

Summary: High-throughput physiological assays lose single-cell resolution, precluding subtype-specific analyses of activation mechanism and drug effects. We demonstrate APPOINT (automated physiological phenotyping of individual neuronal types), a physiological assay platform combining calcium imaging, robotic liquid handling, and automated analysis to generate physiological activation profiles of single neurons at large scale. Using unbiased techniques, we quantify responses to sequential stimuli, enabling subgroup identification by physiology and probing of distinct mechanisms of neuronal activation within subgroups. Using APPOINT, we quantify primary sensory neuron activation by metabotropic receptor agonists and identify potential contributors to pain signaling. We expand the role of neuroimmune interactions by showing that human serum directly activates sensory neurons, elucidating a new potential pain mechanism. Finally, we apply APPOINT to develop a high-throughput, all-optical approach for quantification of activation threshold and pharmacologically validate contributions of ion channel families to optical activation. Motivation: Physiological assays are typically small scale, such as patch clamp and traditional calcium imaging, whereas larger-scale techniques lose cellular resolution and thus have limited value in heterogeneous neuronal populations. Applications that require analysis of large cell numbers, including screens or experiments that address neuronal diversity, require larger-scale physiological characterization at single-cell resolution. In addition, small-scale tools are limited by experimental and analytical biases, and the desired platform would reduce these biases. We combine high-content longitudinal calcium imaging with liquid handling and an unbiased machine-learning-based analysis pipeline to generate a tool for larger-scale granular physiological investigation