Biology subjects

Putze, P.

Publications and source records attributed to Putze, P..

5 recordsLinked to original sources

bioRxiv2026

Microbial Invasion and Immunosuppression Drives Adenoma Progression in Early Colorectal Cancer Development

Several inherited predispositions to colorectal cancer exist, most notably familial adenomatous polyposis (FAP), which typically involves a germline loss-of-function mutation in one APC allele. A second hit in APC or related loci leads to aberrant Wnt signalling, triggering adenoma formation with near-complete penetrance. Yet, substantial variability in disease onset and severity, even among siblings with identical APC germline mutations, implicates environmental modifiers. Emerging evidence points to the gut microbiome as a critical regulator of adenoma initiation and progression, particularly in early-onset CRC. Here, we show that bacterial invasion is associated with neutrophil immunosuppression, T-cell exclusion and adenoma progression in a porcine model of FAP. Longitudinal mapping of progressing and regressing polyps using single-cell RNA sequencing, spatial transcriptomics and integrated microbiome profiling resolved the cellular and microbial architecture underlying these divergent lesion states. Single-cell RNA-seq identified 35 cell subpopulations and showed that regressing polyps are enriched for central-memory CD4+ T cells, cytotoxic CD8+ T cells and inflammatory neutrophils, whereas progressing polyps contain immunosuppressive/PD-L1-associated neutrophils, regulatory and dysfunctional T cells. Spatial transcriptomics with a custom host panel and targeted bacterial probes linked bacterial-high adenomatous regions to immunosuppressive neutrophil niches and T cell exclusion, while regressing lesions showed predominantly lumen-adjacent bacterial localization and spatially organized immune surveillance. These results provide a spatially resolved map of cellular and microbial organisation in early colorectal polyp progression and regression and reveal a microbiome-driven, neutrophil-mediated mechanism of early tumour progression with implications for CRC interception. Grant support: This study was funded by the German Research Foundation (DFG) through SFB1371 "Microbiome signature" (Project no. 395357507; TF, DS, FE, DH) and by the German Ministry for Research and Education (BMBF) through Mi-EOCRC consortium project (Project no. 01KD2102D; KF and DH).

cancer biology↗

bioRxiv2026

Modeling pancreatic cancer tumor stroma co-evolution in an in ovo model

Pancreatic ductal adenocarcinoma (PDAC) is characterized by a dense, desmoplastic microenvironment that drives disease progression, yet conventional models fail to capture this complex tumor-stroma coevolution. Here, we utilize the chick chorioallantoic membrane (CAM) platform to investigate tumor-stroma interactions using murine PDAC cell lines and patient-derived organoids (PDOs). Integrating single-cell RNA sequencing and spatial transcriptomics, we show that the CAM microenvironment supports the emergence of complex tumor ecosystems while preserving patient-specific characteristics. Within five days, in ovo tumors faithfully recapitulated the structural and molecular features of parental tumors. Histological analysis revealed the rapid recruitment and spatial organization of heterogeneous host cancer-associated fibroblast (CAF) populations, showcasing distinct myofibroblastic and inflammatory stromal states. Crucially, the model preserved intrinsic tumor heterogeneity and permitted functional interrogation of subtype-specific extracellular matrix remodeling and metastatic dissemination. Together, our findings demonstrate that the CAM provides a highly permissive niche for tumor-stroma coevolution. As a rapid, scalable, and biologically relevant platform, this in ovo model offers a powerful approach for studying stromal composition, metastatic progression, and patient-specific tumor biology in pancreatic cancer.

cancer biology↗

bioRxiv2026

Cellpin enables reference-based imputation and denoising of spatial transcriptomes

Spatially resolved transcriptomics enables gene expression profiling within tissue architecture, but targeted panels leave much of the transcriptome unmeasured and spatial artifacts such as RNA diffusion and segmentation errors introduce technical noise. These limitations necessitate computational imputation and denoising, yet existing methods typically incorporate spatial measurements during training, limiting scalability and risking the embedding of technology-specific artifacts into learned representations. To address this, we present cellpin, a variational autoencoder trained exclusively on single-cell RNA sequencing data, using teacher-student latent distillation and noise-simulating augmentations to jointly impute unmeasured genes and denoise spatial profiles without requiring cross-modality alignment. Benchmarked against six methods across multiple paired datasets, cellpin achieves superior held-out gene prediction while scaling efficiently to atlas-size references and multi-sample cohorts. In full-transcriptome Atera data, cellpin reduces residual spatial noise and improves cell-state resolution, providing a scalable and principled foundation for biological discovery from spatial transcriptomics data.

bioinformatics↗

bioRxiv2026

Pan-cancer virtual spatial transcriptomics from routine histology with Phoenix

Spatial transcriptomics links gene expression to tissue architecture, providing a mechanistic view of cellular organization. Yet existing datasets cover few donors and miss the complexity of human disease. Experimental costs remain prohibitive, and large-scale profiling is impractically slow for population-level studies. Accurate computational methods are urgently needed. Predicting gene expression from standard histology, however, remains an open problem, as current approaches transfer poorly to unseen cohorts and diseases. Here, we present Phoenix, a (latent) flow matching generative model that infers pan-cancer spatially resolved single-cell gene expression with high accuracy. Phoenix analyzes treatment response in silico: Applied to 763 head and neck cancer patients, it identified three new spatial biomarkers that we validated across two cancers (breast cancer, n = 84; ovarian cancer, n = 157) and treatment regimens (platinum, trastuzumab). Phoenix generalizes beyond carcinomas: In a large sarcoma cohort (802 tissue microarray cores), it accurately predicted cell-type-specific signatures in held-out samples and captured chemotherapy-induced immune remodeling. Phoenix also extends across species: In a mouse model, it accurately predicted the expression of pancreatic cancer lineage markers and the mutant mKras^G12D allele in silico. In total, we evaluated Phoenix on over 10,000 patients. Our results establish virtual spatial transcriptomics as a scalable framework for studying tissue organization, therapeutic response, and disease mechanisms.

bioinformatics↗

bioRxiv2026

Cross-species single-cell atlases chart progression, therapy-driven remodelling and immune evasion in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is typically diagnosed at advanced stages, yet single-cell datasets that capture late-stage and treated disease remain sparse, hindering progress in understanding tumour heterogeneity and therapy resistance. Here, we have generated integrated single-cell transcriptomic atlases of human and mouse PDAC to define the cellular and molecular landscape of the disease, from early to advanced and metastatic stages, including post-treatment disease, and to enable direct cross-species comparison. Using scANVI to harmonize 16 human studies comprising 257 donors and representative mouse models (101 tumours), we compiled over 1.6 million cells and established a four-level hierarchical taxonomy of more than 60 distinct cell states spanning malignant, stromal, immune, endothelial, adipose, exocrine and endocrine compartments. We resolve ten malignant programmes linked to progression and uncover rare immune phenotypes, including CD4CD8 double-positive T cells that remain poorly characterized in PDAC. Notably, we show that radiotherapy (RT) exposure is associated with enrichment of an EMT-persistent malignant state and an immunosuppressive microenvironment characterized by expansion of tumour-associated endothelium, depletion of intratumoral T cells and heightened laminin-CD44 signalling, with RT-associated genes linked to adverse prognosis in independent cohorts. Cross-species mapping reveals that orthotopic syngeneic allografts more faithfully recapitulate the cellular diversity and EMT-enriched states of advanced human PDAC, underrepresented in autochthonous genetically engineered models, with differences driven primarily by cell-type composition rather than pathway divergence. Together, these atlases and pretrained models provide a broadly accessible reference for benchmarking PDAC model fidelity and for interrogating mechanisms of tumour progression, microenvironmental remodelling and therapy response and resistance.

cancer biology↗