Buechler, M. | bioRxiv Science

bioRxiv2026

Pi16+ fibroblast-derived Csf1 shapes skin topography

Peptidase inhibitor 16 (Pi16)-expressing fibroblasts are found across tissues and species, but their functional role is unclear. As fibroblasts and macrophages have been proposed to exist in a reciprocal circuit, we hypothesized Pi16+ fibroblasts may regulate macrophage homeostasis. Flow cytometry revealed [~]80% of skin fibroblasts express Pi16, leading us to investigate the role of these cells in maintaining a macrophage niche in this tissue. We generated an in vivo system where fibroblast-derived Colony Stimulating Factor 1 (Csf1) was constitutively eliminated in Pi16+ fibroblasts by crossing animals with a Csf1fl/fl allele to mice in which the gene Pi16 drives an IresCre cassette. Deletion of Csf1 in Pi16+ fibroblasts resulted in significant diminishment of CD64+ and CD11c+ macrophages alongside expansion of PDPN+YFP+ fibroblasts. Alterations in cell population dynamics coincided with thickening of both the dermis and fascial compartments of the skin. Deletion of Csf1 in Pi16+ fibroblasts delayed early wound healing in a unsplinted mouse model. Loss of PI16+ fibroblasts was observed in individuals with limited (lSSc) and diffuse (dSSc) systemic Scleroderma compared to healthy controls. These findings suggest that loss of Csf1 in Pi16+ fibroblasts elicit changes in the population dynamics of skin macrophages and modifications to tissue architecture.

immunology↗

bioRxiv2024

Adversarial learning enables unbiased organism-wide cross-species alignment of single-cell RNA data

Todays single-cell RNA (scRNA) datasets remain siloed, due to significant challenges associated with their integration at scale. Moreover, most scRNA analysis tools that operate at scale leverage supervised techniques that are insufficient for cell-type identification and discovery. Here, we demonstrate that the alignment of scRNA data using unsupervised models is accurate at an organism-wide scale and between species. To do this, we show adversarial training of a deep-learning model we term batch-adversarial single-cell variational inference (BA-scVI) can be employed to align standardized benchmark datasets comprising dozens of scRNA studies spanning tissues in humans and mice. In the aligned space, we analyze cell types that span tissues in both species and find prevalent complement expressing macrophages and fibroblasts. We provide access to the tools presented via an online interface for atlas exploration and reference-based drag-and-drop alignment of new data.

genetics↗

bioRxiv2021

GATA4 and GATA6 loss-of-expression is associated with extinction of the classical programme and poor outcome in pancreatic ductal adenocarcinoma

ObjectiveGATA6 is a master regulator of pancreatic differentiation and a key regulator of the classical phenotype in pancreatic ductal adenocarcinoma (PDAC). Low GATA6 expression is associated with poor patient outcome. GATA4 is the second most expressed GATA factor in the pancreas. The aim was to assess whether, and how, GATA4 contributes to PDAC phenotype and to analyze the association of expression with clinical outcome. DesignWe analyzed PDAC transcriptomic data, stratifying cases according to GATA4 and GATA6 expression, and identified differentially expressed genes and pathways. A multicenter TMA study to assess GATA4 and GATA6 expression in PDAC samples (n=745) from patients undergoing tumour resection was performed using immunohistochemistry with antibodies of validated specificity. GATA4 and GATA6 levels were dichotomized into high/low categorical variables; association with outcome was assessed using univariable and multivariable Cox regression models. ResultsSubtype classification using transcriptomic data revealed that GATA4 mRNA is enriched in classical, compared to basal-like tumours. We classified samples in 4 groups as high/low for GATA4 and GATA6. Reduced expression of GATA4 did not have a major transcriptional impact. However, concomitant low expression of GATA4 enhanced the transcriptomic effects of GATA6 low expression. Reduced expression of both proteins in tumours was associated with the worst patient survival. GATA4 and GATA6 expression significantly decreased in metastases and negatively correlated with basal markers. ConclusionsOur analyses uncover a cooperative interaction between GATA4 and GATA6 to maintain the classical PDAC phenotype and provide compelling clinical rationale for assessing their expression as biomarkers of poor prognosis. SUMMARY BOXO_ST_ABSWhat is already known about this subject?C_ST_ABS- Patients with classical-type PDAC have a better outcome - Retrospective analyses suggest that classical-type PDAC is more sensitive to 5-FU-based chemotherapy - GATA6 is a surrogate biomarker of classical tumours and its expression is associated with better survival - GATA4 and GATA6 have overlapping and unique functions during pancreatic and gastrointestinal development What are the new findings?- Tumours displaying only low GATA4 expression have a transcriptomic profile similar to those with preserved expression of both transcription factors - Combined low expression of GATA4 and GATA6 has the highest transcriptomic impact - In a large multicenter tissue microarray study, patients with tumours showing low expression of both GATA4 and GATA6 have the worst overall survival - Low expression of GATA4 and GATA6 is an independent predictor of survival in patients with resectable PDAC - GATA4 levels are down-regulated in liver metastases and are negatively correlated with basal markers such as KRT5/6, KRT14, and TP63 How might it impact on clinical practice in the foreseeable future?- The combined assessment of GATA4 and GATA6 expression may improve prognostic stratification of patients with PDAC - Prospective studies should confirm whether GATA4 and GATA6 expression is also predictive of response to chemotherapy in PDAC patients

cancer biology↗