Back

Establishing label-free quantitative X-ray dose-response profiling by Holo-Tomographic Flow Cytometry

Pirone, D.; de Vita, C.; Mottareale, R.; Giugliano, G.; Giordano, G.; Grilli, S.; Bianco, V.; Miccio, L.; Memmolo, P.; Durante, M.; Pugliese, M.; Ferraro, P.

2026-01-12 biophysics
10.64898/2026.01.12.699031 bioRxiv
Show abstract

Flow cytometry (FC) offers multiparametric analysis capabilities that can quantify cellular damage after exposure to cytotoxic agents. Here, we present a comprehensive study establishing a label-free quantitative X-ray dose-response profiling using a novel FC modality based on 3D Quantitative Phase Imaging, termed Holo-Tomographic Flow Cytometry (HTFC). This approach enables fully label-free 3D refractive index (RI) measurements, allowing detailed and quantitative characterization of the biophysical properties and morphology of living cells exposed to X-rays. By analyzing datasets of 3D RI tomograms from cells irradiated at graded doses, we identify intracellular biophysical markers that define a robust X-ray dose-response curve. Validation against standard clonogenic survival assays on three model cancer cell lines reveal a high correlation (>90%). HTFC not only eliminates labeling and operator bias but also markedly reduces experimental time from 1-2 weeks to 24 hours, offering a fully automated and objective readout. While clonogenic survival remains the benchmark for radiosensitivity assessment, our findings establish HTFC as a powerful label-free platform for fast assessment of radiation damage. This technology paves the way for predictive biosensors that can capture patient-specific responses, thereby supporting the transition from conventional, uniform radiotherapy protocols to personalized treatment strategies.

Published in Sensors and Actuators B: Chemical · not in our set (fewer than 10 published preprints to learn from) · training set

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.