Multi-omics identification of activated T cells and spatial PD-1/PD-L1 signaling as biomarkers of diabetic foot ulcer healing
Bilik, S. M.; Dodson, C.; Rivas, K.; Balukoff, N.; Burgess, J. L.; Griswold, A. J.; Sawaya, A.; Pastar, I.; Strbo, N.; Morasso, M. I.; Tomic-Canic, M.; Stone, R. C.
Show abstract
Diabetic foot ulcers (DFUs) are a common and debilitating complication of diabetes, and amputations from non-healing ulcers carry high morbidity and mortality. A critical need exists for biomarkers that can identify healing potential early and guide targeted interventions. To address this, we applied an integrated multi-omics approach across four patient cohorts comprising 51 DFUs (29 Healing, 22 Non-healing). Bulk RNA-sequencing revealed marked activation of Th1 and Th2 pathways (activation z-score +4.8, p = 3.8x10-{superscript 1}), and immune cell deconvolution predicted higher proportions of T cell populations in Healers. Spatial proteomics in a second cohort identified elevated CD3 T cell density and selective enrichment of PD-1 and PD-L1 expression in vascular niches of the papillary dermis in Healers (p < 0.001). Flow cytometry in a third cohort further demonstrated higher proportions of CD3PD-1 and CD3PD-L1 T cells in Healers compared with Non-healers. Single-cell RNA-sequencing from a fourth cohort showed upregulation of PD-1 and PD-L1 within CD4 T cells from Healers. Complementary immunofluorescence and serological profiling confirmed that both PD-1 and PD-L1 are elevated in tissue and circulating serum of healing DFUs, supporting their potential use as systemic biomarkers. Taken together, vascular-enriched PD-1/PD-L1 signaling and T cell activation were observed in association with healing DFUs, supporting PD-1/PD-L1 as candidate biomarkers in both tissue and blood with potential translational relevance for predicting DFU outcomes and informing precision therapies.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Targeting SERCA2 in organotypic epidermis reveals MEK inhibition as a therapeutic strategy for Darier disease. 93%
- ERK hyperactivation in epidermal keratinocytes impairs intercellular adhesion and drives Grover disease pathology 93%
- Coordinated immune dysregulation in Juvenile Dermatomyositis revealed by single-cell genomics 93%
Similar papers in this journal
- Deciphering the molecular landscape of human peripheral nerves: implications for diabetic peripheral neuropathy 94%
- An endothelial SOX18-mevalonate pathway axis enables repurposing of statins for infantile hemangioma 92%
- Angiopoietin-like protein 2 mediates vasculopathy driven fibrogenesis in a mouse model of systemic sclerosis 92%
Similar papers in this journal
- HIF1α gates tendon response to overload and drives tendinopathy independently of vascular recruitment 94%
- Loss of TDP-43 function and rimmed vacuoles persist after T cell depletion in a xenograft model of sporadic inclusion body myositis 92%
- Transient receptor potential canonical 5 (TRPC5) mediates inflammatory mechanical pain 92%
Similar papers in this journal
- Overriding defective FPR chemotaxis signaling in diabetic neutrophil stimulates infection control in diabetic wound 94%
- Integrative small and long RNA-omics analysis of human healing and non-healing wounds discovers cooperating microRNAs as therapeutic targets 94%
- Transcriptome network analysis implicates CX3CR1-positive type 3 dendritic cells in non-infectious uveitis 94%
Similar papers in this journal
- Quantitative multiplex immunohistochemistry reveals inter- and intra-patient lymphovascular and immune heterogeneity in primary cutaneous melanoma 94%
- Abnormal thrombosis and neutrophil activation increases the risk of hospital-acquired sacral pressure injuries and morbidity in patients with COVID-19 92%
- Extratubular polymerized uromodulin induces leukocyte recruitment and inflammation in vivo 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.