An AI-assisted integrated, scalable, single-cell phenomic-transcriptomic platform to elucidate intratumor heterogeneity against immune response.

We present a novel framework combining single-cell phenotypic data with single-cell transcriptomic analysis to identify factors underpinning heterogeneity in antitumor immune response. We developed a pairwise, tumor-immune discretized interaction assay between natural killer (NK-92MI) cells and patient-derived head and neck squamous cell carcinoma (HNSCC) cell lines on a microfluidic cell-trapping platform. Furthermore we generated a deep-learning computer vision algorithm that is capable of automating the acquisition and analysis of a large, live-cell imaging data set (>1 million) of paired tumor-immune interactions spanning a time course of 24 h across multiple HNSCC lines (n = 10). Finally, we combined the response data measured by Kaplan-Meier survival analysis against NK-mediated killing with downstream single-cell transcriptomic analysis to interrogate molecular signatures associated with NK-effector response. As proof-of-concept for the proposed framework, we efficiently identified MHC class I-driven cytotoxic resistance as a key mechanism for immune evasion in nonresponders, while enhanced expression of cell adhesion molecules was found to be correlated with sensitivity against NK-mediated cytotoxicity. We conclude that this integrated, data-driven phenotypic approach holds tremendous promise in advancing the rapid identification of new mechanisms and therapeutic targets related to immune evasion and response.

Fluid mechanics of human fetal right ventricles from image-based computational fluid dynamics using 4D clinical ultrasound scans.

There are 0.6-1.9% of US children who were born with congenital heart malformations. Clinical and animal studies suggest that abnormal blood flow forces might play a role in causing these malformation, highlighting the importance of understanding the fetal cardiovascular fluid mechanics. We performed computational fluid dynamics simulations of the right ventricles, based on four-dimensional ultrasound scans of three 20-wk-old normal human fetuses, to characterize their flow and energy dynamics. Peak intraventricular pressure gradients were found to be 0.2-0.9 mmHg during systole, and 0.1-0.2 mmHg during diastole. Diastolic wall shear stresses were found to be around 1 Pa, which could elevate to 2-4 Pa during systole in the outflow tract. Fetal right ventricles have complex flow patterns featuring two interacting diastolic vortex rings, formed during diastolic E wave and A wave. These rings persisted through the end of systole and elevated wall shear stresses in their proximity. They were observed to conserve ∼25.0% of peak diastolic kinetic energy to be carried over into the subsequent systole. However, this carried-over kinetic energy did not significantly alter the work done by the heart for ejection. Thus, while diastolic vortexes played a significant role in determining spatial patterns and magnitudes of diastolic wall shear stresses, they did not have significant influence on systolic ejection. Our results can serve as a baseline for future comparison with diseased hearts.