MSACL 2026 Abstract

Profiler and IonScell: An Integrated Open-Source Ecosystem for Omics Analysis and Single-Cell Mass Spectrometry Imaging

Yanis Zirem, Léa Ledoux, Laurine Lagache, Julie Defrance, Isabelle Fournier, Michel Salzet
PRISM U1192

Yanis Zirem (Presenter) Laboratory Prism Inserm U1192

Presenter Bio: Yanis Zirem develops open-source bioinformatics tools at the interface of mass spectrometry, spatial omics and artificial intelligence. His work aims to democratize advanced omics data analysis for the research and clinical community.

Relevant Financial Disclosures (within past 24 months, reported on Apr 22, 2026) No relevant financial relationship(s) to disclose.

INTRODUCTION:
The emergence of high-dimensional omics technologies, from bulk proteomics and metabolomics to spatially-resolved Mass Spectrometry Imaging (MSI), demands accessible, reproducible, and integrative analytical frameworks. Here we present two complementary open-source tools: Profiler, a unified web platform for multi-omics analysis and IonScell, a dedicated pipeline for single-cell spectral extraction from MSI data.

METHODS:
Profiler (https://prism-profiler.univ-lille.fr) is a web-based platform supporting proteomics, metabolomics, lipidomics, transcriptomics, and Mass Spectrometry Imaging. It provides an end-to-end analytical pipeline encompassing: (i) data import from 12+ software parsers (MaxQuant, DIA-NN, Spectronaut, DESeq2, etc.), (ii) QC and preprocessing (imputation, batch correction, normalization), (iii) interactive visualization (PCA, UMAP, t-SNE, hierarchical clustering), (iv) differential analysis (v) AI-driven classification and regression with SHAP/LIME explainability, (vi) ORA and GSEA pathway enrichment across 100+ databases, (vii) survival and longitudinal analysis and (viii) one-click self-contained HTML report generation. A companion desktop application (Windows/macOS/Linux) enables offline analysis without upload size limitations. MSI2Profiler extends the platform to MALDI-MSI and DESI-MSI workflows via imzML preprocessing and ROI definition. IonScell is a microscopy-free, open-source pipeline for automated single-cell spectrum extraction from MSI data. The workflow comprises: (i) spectral data cube construction, (ii) TIC image generation, (iii) adaptive thresholding, (iv) watershed segmentation, and (v) morphological filtering. For each segmented cell, IonScell extracts both molecular spectra and geometric features and identifies clonal subpopulations through soft clustering and UMAP embedding. Quality control is integrated at each step, computing spectral quality metrics (SNR, sparsity, dynamic range, entropy, peak count) and automatically filtering low-quality cells. Clone quality is further assessed based on clustering confidence, spectral homogeneity, and separability. Results are exported as annotated CSV/imzML files.

RESULTS:
Profiler has been validated across multiple omics datasets, delivering publication-ready figures, reproducible statistical analyses, and AI models exportable for real-time inference on new samples. The platform is freely available for academic and educational use and has been published in Bioinformatics (Oxford, 2026). IonScell has been validated on breast cancer and glioblastoma (GBM) cell lines, demonstrating accurate isolation of single-cell regions and their unique molecular signatures, enabling robust characterization of intra-tumoral cellular heterogeneity without the need for optical microscopy.

CONCLUSION:
Together, Profiler and IonScell form an integrated bioinformatics ecosystem that bridges bulk multi-omics analysis and spatially-resolved single-cell mass spectrometry. These tools lower the barrier to advanced omics data analysis, supporting translational research in clinical mass spectrometry and enabling new insights into tumor biology and cellular heterogeneity.