BTLA interacting proteins in mouse CD4+ effector T cells - Part2
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ABSTRACT: Identifying BTLA interacting proteins in mouse CD4+ effector T cells expressing BTLA at endogenous levels and after stimulation with pervanadate.
Project description:Identifying PD-1 interacting proteins in mouse CD4+ effector T cells expressing PD-1 at endogenous levels and after stimulation with pervanadate.
Project description:The pathophysiology underlying the autoimmune disease type 1 diabetes (T1D) is poorly understood. Obtaining an accurate proteomic profile of the T helper cell population is essential for understanding the pathogenesis of T1D. Here, we performed in-depth proteomic profiling of peripheral CD4+ T cells in a pediatric cohort in order to identify cellular signatures associated with the onset of T1D. Using only 250,000 CD4+ T cells per patient, isolated from biobanked PBMC samples, we identified nearly 6,000 proteins using deep-proteome profiling with LC-MS/MS data-independent acquisition. Our analysis revealed an inflammatory signature in patients with T1D; this signature is characterized by circulating mediators of neutrophils, platelets, and the complement system. This signature likely reflects the inflammatory extracellular milieu, suggesting that activation of the innate immune system plays an important role in disease onset. Our results emphasize the potential value of using high-resolution LC-MS/MS to investigate limited quantities of biobanked samples in order to identify disease-relevant proteomic patterns.
Project description:To be able to reliably generate theoretical libraries that can be used in SWATH experiments, we developed a prediction framework, deep-learning for SWATH analysis (dpSWATH), to improve the sensitivity and specificity of data generated by Q-TOF mass spectrometers. The theoretical library built by dpSWATH allowed us to increase the identification rate of proteins and peptides compared to traditional or library-free methods. Especially, the in-silico library built based on the transcriptome scale identified the most proteins while kept a similar FDR as DDA library. Based on our analysis we conclude that dpSWATH is superior in predicting libraries that can be used for SWATH-MS measurements compared to other algorithms that are based on Orbitrap data.
Project description:SWATH analysis of Yeast Proteome over time in response to Osmotic Stress. We sampled cell cultures in biological triplicates at six time points following the application of osmotic stress and acquired single injection DIA datasets on a high-resolution 5600 tripleTOF instrument operated in SWATH mode. Proteins were quantified by the targeted extraction and signal integration from the SWATH-MS datasets of peak groups representing proteotypic peptides for specific yeast proteins.