Advanced imaging, and computational flow cytometry: Integrating AI, high-content systems and emerging technologies in clinical and translational immunology

Flow cytometry has long served as a foundational pillar in immunology, enabling rapid, multi-parametric single-cell analysis. As highlighted at the 1st Mediterranean Meeting on Flow Cytometry in Granada, the field is undergoing a massive evolutionary leap. Traditional boundaries are expanding due to breakthroughs in spectral cytometry, imaging cytometry, automated computational workflows, and nanocytometry. These technological advancements are reshaping how we characterize complex cellular populations, moving the discipline from purely qualitative observations to high-dimensional, single-cell multi-omics tracking and spatial morphology. To maximize the clinical impact of these innovations, a concerted effort is required to standardize high-content data analysis, validate image-based cellular metrics, and establish robust quality control frameworks across collaborative regional networks.

The primary objective of this Research Topic is to address the data bottleneck and technical hurdles introduced by advanced cytometry platforms. While high-dimensional, spectral, and imaging flow cytometry allow researchers to detect dozens of parameters alongside detailed visual data simultaneously, manually analyzing this data has become a major roadblock to reproducibility and translational speed. To bridge this gap, this collection highlights recent developments in artificial intelligence and machine learning for automated gating, image classification, and pattern recognition, high-content single-cell processing pipelines, and specialized nanocytometry protocols designed to investigate subcellular and extracellular particles. By compiling state-of-the-art methodology and cross-disciplinary studies, this topic aims to fast-track the integration of complex single-cell data and morphological profiling with functional clinical outcomes and precision medicine.

This Research Topic welcomes submissions that address cutting-edge developments, clinical translations, and rigorous standardization methodologies in cytometry. We especially encourage contributions aligned with the core scientific focus areas of the Mediterranean cytometry community.


Specific themes contributors are invited to address include:

-Advanced Applications in Clinical Disciplines focusing on novel approaches in immunology

-Emerging Technologies and Imaging covering progress in spectral cytometry, high-throughput imaging cytometry, spatial cellular features, and nanocytometry

-Computational Cytometry regarding the deployment of AI, deep learning for image analysis, automated data-processing pipelines, and dimension reduction.

-Translational Medicine exploring cell therapy, functional assays, and biomarker discovery

-Standardization to establish quality control frameworks and multi-center data integration strategies.

We welcome several manuscript types, including Original Research, Reviews, Methods, Mini-Reviews, and Technology and Code reports.



Please note that submissions must be grounded in immune biology and present a substantial experimental methods advance supported by strong experimental validation and benchmarking.