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ALARM

DOI

Computational analysis of a conserved immune-cell recruitment program across transplantation, infection and inflammatory disease.

This repository contains the analysis code accompanying the ALARM study:

Sinha D, Laurent T, Broquet A, et al.
A gene-expression module identifies circulating immune cells with enhanced recruitment to sites of inflammation.
iScience. 2026;29(1):114227.
DOI: 10.1016/j.isci.2025.114227

Study overview

Circulating immune cells must transition from blood into inflamed tissues, but the transcriptional programs associated with this recruitment are not fully understood. Using longitudinal single-cell transcriptomics from kidney transplant recipients, the study identified a conserved gene-expression module termed ALARM, enriched for transcriptional regulators, homing receptors and early activation markers.

ALARM-high immune cells were reduced in peripheral blood during rejection while being enriched in rejecting graft tissue and in a large-animal transplantation model, consistent with preferential recruitment to inflammatory sites. The module includes CXCR4, and functional experiments showed that CXCR4–CXCL12 signalling promotes T-cell migration, early activation and a metabolic shift toward glycolysis.

Across external datasets, ALARM was also associated with immune-cell redistribution during infection and showed predictive value across multiple immune-mediated diseases, with additional validation in a pneumonia mouse model.

Computational scope

The repository captures several complementary layers of analysis:

  • longitudinal single-cell RNA-seq analysis;
  • unsupervised gene-program discovery with cNMF;
  • cell-type and module-usage association analyses;
  • cross-species validation;
  • bulk-transcriptomic validation;
  • migration-related single-cell analysis;
  • ligand–receptor / CXCR4–CXCL12 biology;
  • metabolic modelling with COMPASS;
  • disease classification and predictive modelling.

Repository structure

The manuscript code is organised largely by figure and analytical objective:

ALARM/
└── Manuscript/
    └── codes/
        ├── figure2_mod_discovery/
        ├── figure3_pig/
        ├── figure4_bulk/
        ├── figure6_transwell_scRNA/
        ├── figure7_transwell_compass/
        └── figure10_classification/

Key analysis blocks

Directory Main purpose
figure2_mod_discovery ALARM module discovery and cell-type association; includes cNMF workflow
figure3_pig Cross-species validation in the pig transplantation model
figure4_bulk Validation across bulk transcriptomic datasets
figure6_transwell_scRNA Single-cell analysis of migration / transwell experiments
figure7_transwell_compass Metabolic-state analysis using COMPASS
figure10_classification Disease classification and predictive analyses

For example, the module-discovery workflow includes data preparation, cNMF execution, definition of module genes and cell-type/module-usage analyses.

Biological interpretation

The main result is not simply a disease-associated expression signature. ALARM represents a shared immune-cell state associated with recruitment from circulation into inflamed tissue, connecting transcriptional regulation, chemotaxis, activation and cellular metabolism.

This makes the project an example of computational immunology moving from single-cell pattern discovery through cross-cohort and cross-species validation to experimentally supported biological interpretation.

Publication

The final peer-reviewed article is available from iScience:

Citation

If you use this code or build on the ALARM framework, please cite:

Sinha D, Laurent T, Broquet A, et al. A gene-expression module identifies circulating immune cells with enhanced recruitment to sites of inflammation. iScience. 2026;29(1):114227. https://doi.org/10.1016/j.isci.2025.114227

Contact

Debajyoti Sinha
Nantes Université · CHU Nantes · Inserm
Computational biology · single-cell transcriptomics · computational immunology

About

A gene-expression module in circulating immune cells is associated with cell migration during immune diseases

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