Biology subjects

Pulliam, T.

Publications and source records attributed to Pulliam, T..

4 recordsLinked to original sources

bioRxiv2026

Homeostatic fibroblast niche collapse in clinically uninvolved skin marks a pre-fibrotic stage of systemic sclerosis

Systemic sclerosis, or scleroderma, is a chronic autoimmune disease characterized by vasculopathy, immune dysregulation, and progressive fibrosis of the skin and internal organs. Heterogeneous clinical presentation and polygenic risk complicate our understanding of disease mechanisms and the path to effective therapy. Single-cell analyses have revealed substantial transcriptional diversity in skin, but disease-associated reprogramming remains poorly defined. To address this, we performed high-resolution spatial transcriptomic profiling of paired clinically uninvolved and clinically affected scleroderma donor skin alongside healthy controls. We identified a continuum of cellular, molecular, and spatial changes to the stroma that were already evident in clinically uninvolved tissue yet still distinct from the broader and more advanced changes in paired fibrotic samples. Uninvolved skin was comparable to healthy tissue by clinical and histopathological assessment, but distinguishable by the numerical reduction, transcriptional reprogramming, and spatial isolation of LGR5+SCARA5+PI16+ progenitor fibroblasts. In parallel, the dermal vascular smooth muscle compartment shifted from a contractile to a synthetic, activated state, suggesting reprogramming that begins before overt fibrosis. Cell neighborhood analysis revealed that progenitor fibroblasts occupy dense stromal niches in healthy skin that collapse in clinically uninvolved tissue, replaced by isolated, disease-associated cellular networks. These data indicate that clinically uninvolved skin occupies an intermediate stage along the pathologic continuum of scleroderma, establishing a framework for strategies to preserve homeostatic fibroblast niches before tissue remodeling and fibrotic commitment.

immunology↗

bioRxiv2025

B cells specific for polyomavirus-derived oncoprotein are predictive of Merkel cell carcinoma progression

Merkel cell carcinomas typically arise from clonal integration of the Merkel cell polyomavirus. Immunogenic viral oncoproteins then lead to tumorigenesis. Oncoprotein-specific T cells are essential for anti-MCC immunity, but it is unclear whether B cells promote tumor control. Here, we analyzed the frequency and phenotype of viral oncoprotein-specific and total B cells in 47 blood samples and 19 unmatched tumors from MCC patients-- of which 8 out 19 progressed. The phenotype of blood B cells did not correlate with MCC patient outcomes. In contrast, all 11 patients with robust oncoprotein-specific antibody-secreting and/or germinal center B cells in tumors experienced long-term MCC control. In vitro, B cells engineered to be specific for viral oncoproteins increased the sensitivity of oncoprotein-specific CD4+ T cells by over 50-fold. Together, our findings suggest that cancer-specific B cells promote anti-tumor immunity via increased T cell responses and that cancer-specific B cell augmentation could be therapeutically relevant. Statement of SignificanceThe link between cancer-specific B cells in anti-tumor immunity and clinical outcomes remains poorly defined. Here, we show that tumor-associated B cells specific for a viral oncoprotein expressed in MCC patient tumors predict disease control with remarkable accuracy, establishing their potential as active participants in tumor immunity.

immunology↗

bioRxiv2023

Tempered IL-2 Signals Program PD-1 Checkpoint Blockade-Responsive Stem-like Exhausted T Cells During Priming

Stem-like progenitor exhausted CD8 T cells are critical for maintaining long-term resistance during chronic infections and cancer, and represent an important checkpoint blockade immunotherapy target for functional reinvigoration and disease control. Hence, there is vigorous interest in understanding the ontogenesis of TCF-1Hi stem-like exhausted CD8 T cells, and in defining the signals that promote their development. Here, we show that virus-specific TCF1HiGzmBLo CD8 T cells develop during early stages of chronic viral infection, and are progressively deprogrammed from the stem-like lineage towards terminal differentiation by strong IL-2 signals. In vivo fate-tracking studies show that strong T cell-intrinsic IL-2 signals through the high affinity heterotrimeric IL-2R/{beta}/{gamma} receptor drive skewed development of terminally differentiated TCF-1LoGzmBHi cells, which largely die in chronic antigenic environment. In contrast, tempered IL-2 signals through the intermediate affinity IL-2R{beta}/{gamma} heterodimer, or delayed priming in diminished IL-2 milieu support preferential development of TCF-1Hi stem-like exhausted CD8 T cells, capable of long-term persistence and potent responsiveness to PD-1 therapy in later stages of chronic viral infection. In human tumors as well, single cell RNA-seq analyses of tumor infiltrating lymphocytes from melanoma, human papillomavirus+ head and neck cancer and lung cancer patients revealed an inverse relationship between IL-2 signaling signature and T cell stemness. Moreover, melanoma patients with enriched IL-2 signaling signature showed poor responses to checkpoint blockade immunotherapy. Collectively, these findings bear relevance to clinical immunotherapies of chronic viral infections and cancers, and support exogenous IL-2 signal manipulation prior to therapy as a strategy for augmenting long-term clinical outcomes. ONE SENTENCE SUMMARYProgramming of exhausted T cell fates by differential IL-2

immunology↗

bioRxiv2020

BayesSpace enables the robust characterization of spatial gene expression architecture in tissue sections at increased resolution

Recently developed spatial gene expression technologies such as the Spatial Transcriptomics and Visium platforms allow for comprehensive measurement of transcriptomic profiles while retaining spatial context. However, existing methods for analyzing spatial gene expression data often do not efficiently leverage the spatial information and fail to address the limited resolution of the technology. Here, we introduce BayesSpace, a fully Bayesian statistical method for clustering analysis and resolution enhancement of spatial transcriptomics data that seamlessly integrates into current transcriptomics analysis workflows. We show that BayesSpace improves the identification of transcriptionally distinct tissues from spatial transcriptomics samples of the brain, of melanoma, and of squamous cell carcinoma. In particular, BayesSpaces improved resolution allows the identification of tissue structure that is not detectable at the original resolution and thus not recovered by other methods. Using an in silico dataset constructed from scRNA-seq, we demonstrate that BayesSpace can spatially resolve expression patterns to near single-cell resolution without the need for external single-cell sequencing data. In all, our results illustrate the utility BayesSpace has in facilitating the discovery of biological insights from a variety of spatial transcriptomics datasets.

bioinformatics↗