Proteins secreted by cells are of the highest biomedical relevance since they play a significant role in the progression of numerous diseases. However, characterization of the proteins specifically secreted in response to precise stimuli is challenging, since these proteins are contaminated by cellular byproducts. Here we present a method to characterize a dynamic secretome and demonstrate its utility by performing the deepest quantitative analysis to date of proteins secreted by lymphoid Jurkat T-cells upon activation. Cell-free supernatant proteins were analyzed by using an optimized protocol for differential (18)O/(16)O-labeling and LC-MS/MS, followed by statistical analysis using a random-effects model. More than 4000 unique peptides belonging to 1288 proteins were identified and a large proportion could be quantified. To determine the proteins whose secretion was up-regulated upon T-cell activation, protein variance of the null hypothesis was estimated after protein classification in terms of secretion and ontology using bioinformatic tools. 62 proteins showed a statistically significant change in abundance upon cell activation and most of them (49 proteins) were up-regulated. These proteins were functionally involved mainly in inflammatory response, signal transduction, cell growth and differentiation and cell redox homeostasis. Our approach provides a promising technology for the high-throughput quantitative study of dynamic secretomes.
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