RESUMEN
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.
RESUMEN
Deep tissue injuries occur in muscle tissues around bony prominences under mechanical loading leading to severe pressure ulcers. Tissue compression can potentially compromise lymphatic transport and cause accumulation of metabolic biowastes, which may cause further cell damage under continuous mechanical loading. In this study, we hypothesized that biowastes released by mechanically damaged muscle cells could be toxic to the surrounding muscle cells and could compromise the capability of the surrounding muscle cells to withstand further mechanical loadings. In vitro, we applied prolonged low compressive stress (PLCS) and short-term high compressive stress to myoblasts to cause cell damage and collected the biowastes released by the damaged cells under the respective loading scenarios. In silico, we used COMSOL to simulate the compressive stress distribution and the diffusion of biowastes in a semi-3D buttock finite element model. In vitro results showed that biowastes collected from cells damaged under PLCS were more toxic and could compromise the capability of normal myoblasts to resist compressive damage. In silico results showed that higher biowastes diffusion coefficient, higher biowastes release rate, lower biowastes tolerance threshold and earlier timeline of releasing biowastes would cause faster propagation of tissue damage. This study highlighted the importance of biowastes in the development of deep tissue injury to clinical pressure ulcers under prolonged skeletal compression.
