Original Research

Exp. Biol. Med.

Sec. Artificial Intelligence/Machine Learning Applications to Biomedical Research

Volume 250 - 2025 | doi: 10.3389/ebm.2025.10445

This article is part of the IssueProceedings of the 10th Annual Conference of the Arkansas Bioinformatics Consortium (AR-BIC) - Real-World Impact of AIView all 8 articles

Optimal Transport Reveals Immune Perturbation and Fingerprints Over Time in COVID-19 Vaccination

Zexuan  WangZexuan WangJiong  ChenJiong ChenMatei  IonitaMatei IonitaQipeng  ZhanQipeng ZhanZhuoping  ZhouZhuoping ZhouLi  ShenLi Shen*
  • University of Pennsylvania, Philadelphia, United States

The final, formatted version of the article will be published soon.

Mass cytometry enables high-throughput characterization of heterogeneous cell populations at single-cell resolution, using metal isotopes to capture cellular signals and avoiding the spectral overlap common in flow cytometry. Despite advancements, conventional data analysis often focuses on manual gating or clustering within specific samples, overlooking disparities across subjects or biological samples. To address this gap, we propose a novel framework that treats the cell-by-protein matrix as a high-dimensional distribution, using Quantized Optimal Transport (QOT) to quantify distances between samples based on their cellular protein expression profiles.This approach allows for a direct comparison of distributions without relying on predefined gating strategies, capturing subtle variations in the data. We validated our method through two experiments using real-world time-series Coronavirus Disease 2019 (COVID-19) cytometry data.First, we conducted a leave-one-out analysis to identify immunologically unstable proteins over time, revealing CD3 and CD45 as the proteins changing the most during the vaccine response.Second, we aimed to capture individual immune fingerprints over time by calculating pairwise Wasserstein distances between samples and applying hierarchical clustering. Using silhouette scores to evaluate clustering e↵ectiveness, we identified optimal combinations of immunological markers that e↵ectively grouped samples from the same participant across di↵erent time points.Our findings demonstrate that the QOT framework provides a robust and flexible tool for cohortlevel analysis of mass cytometry data, enabling the identification of unstable immunological markers and capturing immune response heterogeneity among vaccinated cohorts.

Keywords: Optimal Transport, COVID-19 vaccination, Immunity, Mass Cytometry, Fingerprint

Received: 22 Nov 2024; Accepted: 23 Apr 2025.

Copyright: © 2025 Wang, Chen, Ionita, Zhan, Zhou and Shen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Li Shen, University of Pennsylvania, Philadelphia, United States

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