Creating cell-specific computational models of stem cell-derived cardiomyocytes using optical experiments.

Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) have gained traction as a powerful model in cardiac disease and therapeutics research, since iPSCs are self-renewing and can be derived from healthy and diseased patients without invasive surgery. However, current iPSC-CM differentiation methods produce cardiomyocytes with immature, fetal-like electrophysiological phenotypes, and the variety of maturation protocols in the literature results in phenotypic differences between labs. Heterogeneity of iPSC donor genetic backgrounds contributes to additional phenotypic variability. Several mathematical models of iPSC-CM electrophysiology have been developed to help to predict cell responses, but these models individually do not capture the phenotypic variability observed in iPSC-CMs. Here, we tackle these limitations by developing a computational pipeline to calibrate cell preparation-specific iPSC-CM electrophysiological parameters. We used the genetic algorithm (GA), a heuristic parameter calibration method, to tune ion channel parameters in a mathematical model of iPSC-CM physiology. To systematically optimize an experimental protocol that generates sufficient data for parameter calibration, we created in silico datasets by simulating various protocols applied to a population of models with known conductance variations, and then fitted parameters to those datasets. We found that calibrating to voltage and calcium transient data under 3 varied experimental conditions, including electrical pacing combined with ion channel blockade and changing buffer ion concentrations, improved model parameter estimates and model predictions of unseen channel block responses. This observation also held when the fitted data were normalized, suggesting that normalized fluorescence recordings, which are more accessible and higher throughput than patch clamp recordings, could sufficiently inform conductance parameters. Therefore, this computational pipeline can be applied to different iPSC-CM preparations to determine cell line-specific ion channel properties and understand the mechanisms behind variability in perturbation responses.

Creating cell-specific computational models of stem cell-derived cardiomyocytes using optical experiments.

Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) have gained traction as a powerful model in cardiac disease and therapeutics research, since iPSCs are self-renewing and can be derived from healthy and diseased patients without invasive surgery. However, current iPSC-CM differentiation methods produce cardiomyocytes with immature, fetal-like electrophysiological phenotypes, and the variety of maturation protocols in the literature results in phenotypic differences between labs. Heterogeneity of iPSC donor genetic backgrounds contributes to additional phenotypic variability. Several mathematical models of iPSC-CM electrophysiology have been developed to help understand the ionic underpinnings of, and to simulate, various cell responses, but these models individually do not capture the phenotypic variability observed in iPSC-CMs. Here, we tackle these limitations by developing a computational pipeline to calibrate cell preparation-specific iPSC-CM electrophysiological parameters. We used the genetic algorithm (GA), a heuristic parameter calibration method, to tune ion channel parameters in a mathematical model of iPSC-CM physiology. To systematically optimize an experimental protocol that generates sufficient data for parameter calibration, we created simulated datasets by applying various protocols to a population of in silico cells with known conductance variations, and we fitted to those datasets. We found that calibrating models to voltage and calcium transient data under 3 varied experimental conditions, including electrical pacing combined with ion channel blockade and changing buffer ion concentrations, improved model parameter estimates and model predictions of unseen channel block responses. This observation held regardless of whether the fitted data were normalized, suggesting that normalized fluorescence recordings, which are more accessible and higher throughput than patch clamp recordings, could sufficiently inform conductance parameters. Therefore, this computational pipeline can be applied to different iPSC-CM preparations to determine cell line-specific ion channel properties and understand the mechanisms behind variability in perturbation responses.

Using AI and computer vision to analyze technical proficiency in robotic surgery.

BACKGROUND: Intraoperative skills assessment is time-consuming and subjective; an efficient and objective computer vision-based approach for feedback is desired. In this work, we aim to design and validate an interpretable automated method to evaluate technical proficiency using colorectal robotic surgery videos with artificial intelligence. METHODS: 92 curated clips of peritoneal closure were characterized by both board-certified surgeons and a computer vision AI algorithm to compare the measures of surgical skill. For human ratings, six surgeons graded clips according to the GEARS assessment tool; for AI assessment, deep learning computer vision algorithms for surgical tool detection and tracking were developed and implemented. RESULTS: For the GEARS category of efficiency, we observe a positive correlation between human expert ratings of technical efficiency and AI-determined total tool movement (r = - 0.72). Additionally, we show that more proficient surgeons perform closure with significantly less tool movement compared to less proficient surgeons (p < 0.001). For the GEARS category of bimanual dexterity, a positive correlation between expert ratings of bimanual dexterity and the AI model's calculated measure of bimanual movement based on simultaneous tool movement (r = 0.48) was also observed. On average, we also find that higher skill clips have significantly more simultaneous movement in both hands compared to lower skill clips (p < 0.001). CONCLUSIONS: In this study, measurements of technical proficiency extracted from AI algorithms are shown to correlate with those given by expert surgeons. Although we target measurements of efficiency and bimanual dexterity, this work suggests that artificial intelligence through computer vision holds promise for efficiently standardizing grading of surgical technique, which may help in surgical skills training.

Multiplex Immuno-Liquid Chromatography-Mass Spectrometry-Parallel Reaction Monitoring (LC-MS-PRM) Quantitation of CD8A, CD4, LAG3, PD1, PD-L1, and PD-L2 in Frozen Human Tissues.

The immune status of tumors critically influences their responsiveness to PD1 blockades and other immune-based therapies. Programmed death ligand 1 (PD-L1) immunohistochemistry (IHC) is a clinically validated predictive biomarker of response to checkpoint-inhibitor therapy in a limited number of clinical settings but is poorly predictive in most. With emerging evidence that multiple pathways and immune-checkpoint proteins may coordinately contribute to the adaptive immune resistance, the identification and quantitation of multiple immune markers in tumor tissue could help identify the controlling pathways in a given patient, guide the selection of optimal therapy, and monitor response to treatment. We developed and validated a sensitive and robust immuno-liquid chromatography-parallel reaction monitoring assay to simultaneously quantify the expression levels of six immune markers (CD8A, CD4, LAG3, PD1, PD-L1, and PD-L2) using as little as 1-2 mg of fresh frozen tissue. The lower limit of quantitation ranged from 0.07 ng/mg protein for PD1 to 1.0 ng/mg protein for CD4. The intrabatch accuracy was within -16.6% to 15.0% for all proteins at all concentrations, and the variation ranged from 0.8% to 14.7%, while interbatch accuracy was within -6.3% to 8.6%, and the variation ranged from 1.3% to 12.8%. The validated assay was then applied to quantify all six biomarkers in different tissues and was confirmed to have sufficient sensitivity (0.07-1.00 ng/mg protein) and reproducibility (variation ranged from 4.3 to 12.0%). In an analysis of 26 cervical tumors, CD8A and CD4 were detected in all tumors, followed by PD-L1 in 85%, LAG-3 in 65%, PD1 in 50%, and PD-L2 in 35%. The strongest correlations were observed between CD8A and CD4 ( r = 0.88) and CD8A and LAG-3 ( r = 0.86). PD1 was not significantly correlated with any of the other proteins tested. This method can be applied to survey the immune signatures across tumor types and tailored to incorporate additional markers as needed.