Project description:Benchmarking Proteomics Quantitation in DIA-type data using real patient material to create a benchmark dataset comprising inter-patient heterogeneity
Project description:Background: Macrophage-based immune dysregulation plays a critical role in development of delayed gastric emptying in animal models of diabetes. Human studies have also revealed loss of anti-inflammatory macrophages and increased expression of genes associated with pro-inflammatory macrophages in full thickness gastric biopsies from gastroparesis patients. Aim: We aimed to determine broader protein expression (proteomics) and protein-based signaling pathways in full thickness gastric biopsies of diabetic (DG) and idiopathic gastroparesis (IG) patients. Additionally, we determined correlations between protein expressions, gastric emptying and symptoms. Methods: Full-thickness gastric antrum biopsies were obtained from nine DG, seven IG patients and five non-diabetic controls. Aptamer-based SomaLogic tissue scan that quantitatively identifies 1300 human proteins was used. Protein fold changes were computed, and differential expressions were calculated using Limma. Ingenuity Pathway Analysis and correlations were carried out. Multiple-testing corrected p-values <0.05 were considered statistically significant. Results: 73 proteins were differentially expressed in DG, 132 proteins in IG and 40 proteins were common to DG and IG. In both DG and IG, “Role of Macrophages, Fibroblasts and Endothelial Cells” was the most statistically significant altered pathway (DG FDR: 7.9x10-9; IG FDR: 6.3x10-12). In DG, properdin expression correlated with GCSI-bloating (r: -0.99, FDR: 0.02) and expressions of prostaglandin G/H synthase 2, protein kinase C zeta type and complement C2 correlated with 4 hr gastric retention (r: -0.97, FDR: 0.03 for all). No correlations were found between proteins and symptoms or gastric emptying in IG. Conclusions: Protein expression changes suggest a central role of macrophage-driven immune dysregulation and complement activation in gastroparesis.
Project description:We performed data independent acquisition (DIA)-based proteomics to characterize the proteomes of 67 PDAC resection specimens. Patients received either neoadjuvant chemotherapy or neoadjuvant combined chemo-radiation therapy. We employed DIA, yielding a proteome coverage in excess of 3,500 proteins. The two neoadjuvant therapies yielded highly distinguishable proteome profiles of the residual tumor mass.
Project description:Multiple primary cancers (MPC) refer to the occurrence of two or more independent primary malignant tumors in the same patient, either simultaneously or metachronously. Their clinical diagnosis and differential diagnosis are challenging, treatment strategies are complex, and the incidence has been increasing in recent years. However, there is still a lack of potential biomarkers that can be used for early identification and prognosis assessment, which has become a key bottleneck in current research. This study employed Oxford Nanopore Technologies (ONT) long-read transcriptome sequencing and data-independent acquisition (DIA) proteomics to perform integrated multi-omics analysis of Whole blood samples from patients with only primary lung cancer (OPLC), lung cancer with MPC, and healthy controls (HC), systematically characterizing the molecular features of MPC at both transcript and protein levels. The results showed that MPC patients exhibited significantly increased transcript complexity, with the numbers of differentially expressed genes (DEGs), differentially expressed transcripts (DETs), and differential transcript usage (DTU) events being substantially higher than those in OPLC and HC groups. Multiple genes displayed rich isoform diversity. Functional enrichment analysis indicated that MPC-specific molecules were significantly enriched in immune- and inflammation-related pathways such as NF-κB, NOD-like receptor, Toll-like receptor, and TNF signaling. By integrating gene, transcript, and protein expression profiles, several core molecules (e.g., ATP6AP2, APMAP, CIAO2A, and ITGB2) with consistent expression across multiple regulatory levels were identified, all showing significant and consistent alterations in MPC. This study reveals the molecular characteristics of transcript isoform complexity in the peripheral blood of MPC patients through long-read sequencing combined with proteomics, providing important theoretical insights for understanding the mechanisms of MPC and identifying potential targets.