Biology subjects

Groppi, A.

Publications and source records attributed to Groppi, A..

3 recordsLinked to original sources

bioRxiv2026

SpaceBlocks: an expert-supervised workflow for cohort-level spatial transcriptomics analysis

Spatial transcriptomics (ST) has become a central tool in tissue biology, with multiple technologies addressing the issue from a different angle. Each platform imposes its own raw data format conventions, and few workflows address reproducible, scalable, cohort-level analyses while integrating expert-supervised decisions to strengthen computational and biological provenance. In this work, we present SpaceBlocks, a Snakemake workflow for reproducible cohort-level analysis of single-cell resolution-ST data that prioritizes expert supervision over full automation. SpaceBlocks homogenizes heterogeneous platform outputs into a common data format, whose structure is validated to ensure downstream processing. Comprehensive expert supervision is facilitated through per sample quality control and cell type annotation diagnostic visualizations, after which downstream analyses automatically follow. SpaceBlocks also allows the ingestion of technology-agnostic externally formatted data and provides interoperability with existing tools and technologies. We demonstrate the use of SpaceBlocks through the reanalysis of 37 samples from public Visium HD, Xenium 5K and MERSCOPE datasets. SpaceBlocks exhibited processing scalability across the three platforms. This reanalysis identified previously missed cell types in Visium HD and heterogeneous marker gene expression in MERSCOPE, highlighting the need for expert-supervised annotations. Moreover, SpaceBlocks facilitated the identification of differentially expressed genes associated with previously defined multiple sclerosis (MS) spatial domains in MERSCOPE, and transcriptional differences between healthy and malignant colorectal tissues in Visium HD. Together, these findings establish SpaceBlocks as a flexible and reproducible workflow for expert-supervised spatial transcriptomics analysis across technologies and biological cohorts.

bioinformatics↗

bioRxiv2026

Spatial and Bulk Transcriptomic Profiling Defines the Molecular Evolution of Cutaneous Squamous Cell Carcinoma and Reveals Stage-Specific Biomarkers of Clinical Relevance

Cutaneous squamous cell carcinoma (cSCC) is a common skin cancer associated with substantial morbidity and mortality in advanced stages. Despite its well-described stepwise progression from actinic keratosis to invasive disease, robust molecular markers for stage discrimination and clinical decision-making remain limited. We sought to define the transcriptional continuum underlying cSCC progression, identify stage-associated biomarkers, and assess the broader relevance of these programs across human malignancies. Bulk RNA sequencing (HTG EdgeSeq) and spatial transcriptomics (GeoMx) were performed on biopsies from eight patients, each presenting multiple disease stages (healthy skin, premalignant lesion, tumor core, and invasive front) within the same lesion field, enabling within-patient analysis of progression. Spatial transcriptomic analyses identified more than 2,000 differentially expressed genes whose expression varied across disease stages. These genes were organized into 18 coordinated expression programs reflecting progressive biological rewiring during tumor evolution. Proliferation, extracellular matrix remodeling, inflammation, and stress-response pathways were progressively upregulated, whereas epithelial differentiation and metabolic processes, including lipid and amino acid metabolism, were downregulated. Macrophages exhibited distinct metabolic reprogramming, with increased purine metabolism, glycolysis, and pyruvate metabolism across progression. To evaluate the broader clinical relevance of these progression-associated programs, we developed a reproducible Snakemake pipeline to systematically screen 32 solid and hematologic malignancies from The Cancer Genome Atlas (TCGA). A combined cSCC-progression signature was significantly associated with poor overall survival (P < 0.05) in 10 additional cancer types. Finally, we identified 12 stage-informative biomarkers, whose spatially restricted expression patterns were validated using Visium HD. This study provides a spatially resolved and stage-aware transcriptomic map of cSCC progression, identifies coordinated gene programs underlying disease evolution, and defines progression-associated signatures with prognostic relevance across multiple cancers, highlighting their potential translational value.

cancer biology↗

bioRxiv2025

Building pangenomes for domesticated and wild tree species: genomic complexity and strategies

Long-read sequencing and pangenomics are revolutionizing crop research by providing more complete genome information and revealing crucial structural variations linked to important agricultural traits. Building on recent advances in intraspecific pangenome construction, this study addresses the challenge of creating broader, cross-taxon pangenomes, using the Armeniaca taxonomic section as a model. Leveraging a diverse panel of genome assemblies, we constructed a pangenome graph and cataloged associated single nucleotide polymorphisms (SNPs) and structural variants. We characterized the diversity of these variants and assessed the extent to which different taxa contribute to overall pangenome expansion. Additionally, we evaluated the performance of low-depth sample mapping to the graph-based reference, highlighting key technical limitations that may affect the quality of downstream analyses. We further identified specific subsets of SVs that exhibit associations with particular classes of transposable elements. As a case study illustrating the potential functional and phenotypic relevance of graph-derived SVs, we examined the genomic configuration of the DAM locus within the Armeniaca pangenome.

bioinformatics↗