BenTaieb, A. | bioRxiv Science

bioRxiv2026

Spatial Decoding of Tertiary Lymphoid Structure Maturation in Non-Small Cell Lung Cancer Using Deep Neural Networks

Understanding the role of tertiary lymphoid structures (TLS) is crucial in non-small cell lung cancer (NSCLC), as they are associated with patient prognosis and treatment outcomes. Specific cellular ecosystems that originate anti-tumor activity or predict immunotherapy response remain poorly characterized. To this end, we developed a high-resolution, multimodal spatial atlas jointly profiling transcriptomics, proteomics, and histology to characterize TLS maturation in NSCLC alongside secondary lymph organs as a baseline. Using this atlas, we proposed a pathologist-in-the-loop framework that combines a variational graph autoencoder (VGAE) with diffusion pseudotime to refine human expert annotations and characterize TLS maturation. These spatial molecular representations were extended to H&E whole-slide images via a vision transformer-based foundation model. Next, we resolved cellular composition, spatial organization, and cell-cell interactions within these data and defined two divergent spatial ecosystems. Clinical evidence suggests that these ecosystems are associated with distinct patient outcomes: a mature germinal center niche with favorable prognoses, and a tumor-macrophage-fibroblast niche with unfavorable prognoses. In summary, our work decodes key components of TLS heterogeneity, identifies hallmark spatial patterns involved in NSCLC adaptive immunity, and provides a framework for translating spatial omics insights into clinical applications.

genomics↗

bioRxiv2025

Foundation Model Attributions Reveal Shared Inflammatory Program Across Diseases

Determining a genes functional significance within a cellular context has long been a challenge, as absolute expression level is an unreliable indicator. We introduce SIGnature, a framework for scoring gene importance by leveraging attributions derived from single-cell RNA-sequencing (scRNA-seq) foundation models. Attribution scores reduce technical noise, emphasize regulatory genes, and facilitate cross-dataset comparison - a core challenge for scRNA-seq analyses. We developed the SIGnature package as a tool for generating and querying attributions, enabling rapid gene set searches across massive scRNA-seq atlases. We demonstrated its utility using the MS1 monocyte signature, a poorly understood gene program activated in severe COVID-19 and sepsis. Searching 400 studies revealed novel associations between the MS1 signature and multiple hyperinflammatory conditions, including Kawasaki disease. Experimental validation confirmed Kawasaki disease patient serum induces the MS1 phenotype. These findings highlight that SIGnature can uncover shared mechanisms across conditions, demonstrating its power for large-scale signature scoring and cross-disease analysis.

bioinformatics↗