A simple approach to biophysical profiling of blood cells in extranodal NK/T cell lymphoma patients using deep learning-integrated image cytometry

Abstract

Extranodal NK/T cell lymphoma (ENKTL) poses significant challenges in efficient treatment processes due to its aggressive nature and high recurrence rates. There is a critical need to develop a robust statistical model to predict treatment efficacy by dynamically quantifying biomarkers tailored to various stages of lymphoma. Recent analytics such as sequencing and microbiome tests have only been utilized to understand lymphoma progression and treatment response in clinical settings. However, these methods are limited by their quantitative analysis capabilities, long turnaround times, and lack of single-cell resolution, which are essential for understanding the heterogeneous nature of lymphoma. In this study, we developed a deep learning-enhanced image cytometry (DLIC) to investigate biophysical heterogeneities in peripheral blood mononuclear cells (PBMCs) from newly diagnosed (ND) ENKTL patients. We established a substantial cohort of 23 ND ENKTL patients, categorizing them into interim of treatment (n = 21) and end of treatment (n = 19) stages along their serial treatment timelines. Using a basic optical microscope and a commercial microchip, we analyzed over 270,000 single PBMCs in high-throughput, profiling their size, eccentricity, and refractive index in a completely label-free and quantified manner through AI-based nanophotonic computation. We observed distinct heterogeneity variations in these three biophysical indicators across treatment stages and relapse statuses, revealing solid mechanistic correlations among the phenotypes. We established a three-dimensional single-cell distribution map for ENKTL patients and created a standard for quantifying the change in occupational volume. Leveraging this extensive database, DLIC offers on-site analytics in clinical settings, facilitating treatment assessment and prognosis prediction through label-free biophysical analysis of patient PBMCs, extracted directly without additional sample preparation.

Keywords

deep learning / ENKTL / image cytometry / lymphoma / single cell analysis

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