Proteomics increasingly contributes to our understanding of the roles that proteins play in biology and a wide range of applications including the microbiome, bioremediation, 1 and diseases such as cancer, 2 Alzheimer's, 3 diabetes, 4 cardiovascular disease, 5 obesity, 6 and many aspects of human health. 7,8 The size of the human genome, ~20300 protein coding genes, results in an estimated three billion proteoforms 9 that potentially exist in biological samples subject to proteomic analysis. The traditional approach of bottom-up proteomics requires digestion of proteins into peptides which further increases sample complexity. Despite this added complexity, however, peptides that "fly" well into the mass spectrometer are easily fragmented and detected and can be sequenced routinely using numerous data acquisition and analysis pipelines. To provide proteome depth across a dynamic range of 10-12 orders of magnitude requires sophisticated analytical instrumentation and extensive sample fractionation techniques. Quite impressively, this level of analyte multiplexing in single experiments has been taken advantage of in numerous studies over the last 15 years. Many biological questions of interest, however, seek to determine differences in protein concentrations across two or more conditions, multiple time points, and in various tissues. This leads to a desire to increase the overall sample throughput in bottom-up proteomics. Mass spectrometry (MS)-based proteomics and protein microarray technology have made high throughput protein quantification possible. Microarray-based technology for proteomics includes full-length protein, peptide, antibody, reverse-phase, and tissue arrays 10 that detect tens to thousands of proteins with fluorescent detection. Array-based approaches have advantages of multiplexing samples to simultaneously screen interactions among several biomolecules with linearity. 11 Despite the advantages array-based approaches offer, they also suffer from intense experimental design, chip customization, protein immobilization in native state, normalization, nonspecific binding, cross reactivity, 12 lack of *

Plasma proteomics identified proteins in various immune pathways that may contribute to racial/ethnic disparities in sepsis survival outcomes.

We have introduced a high throughput quantitative proteomics workflow, combined precursor isotopic labeling and isobaric tagging (cPILOT) capable of multiplexing up to 22 or 24 samples with tandem mass tags or isobaric N,N-dimethyl leucine isobaric tags, respectively, in a single experiment. This enhanced sample multiplexing considerably reduces mass spectrometry acquisition times and increases the utility of the expensive commercial isobaric reagents. However, the manual process of sample handling and pipetting steps in the strategy can be labor intensive, time consuming, and introduce sample loss and quantitative error. These limitations can be overcome through the incorporation of automation. Here we transferred the manual cPILOT protocol to an automated liquid handling device that can prepare large sample numbers (i.e., 96 samples) in parallel. Overall, automation increases feasibility and reproducibility of cPILOT and allows for broad usage by other researchers with comparable automation devices. as blood serum/plasma, proximal fluids, and tissues 1 , 2 .Proteomics biomarker discovery and validation have recently gained significant consideration due to the power of sample multiplexing strategies 3 , 4 . Sample multiplexing is a technique that enables simultaneous comparison and quantification of two or more sample conditions within a single MS injection 5 , 6 . Sample multiplexing is achieved by barcoding peptides or proteins from multiple samples with chemical, enzymatic, or metabolic tags and obtaining MS information from all samples in a single MS or MS/MS experiment. Among the available isobaric tags are isobaric tagging reagents (iTRAQ), commercial tandem mass tags (TMT), and in house

Objectives We compared the proteomic signatures of the hippocampal lesion induced in three different animal models of mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE+HS): the systemic pilocarpine model (PILO), the intracerebroventricular kainic acid model (KA), and the perforant pathway stimulation model (PPS). Methods We used shotgun proteomics to analyze the proteomes and find enriched biological pathways of the dorsal and ventral dentate gyrus (DG) isolated from the hippocampi of the three animal models. We also compared the proteomes obtained in the animal models to that from the DG of patients with pharmacoresistant MTLE+HS. Results We found that each animal model presents specific profiles of proteomic changes. The PILO model showed responses predominantly related to neuronal excitatory imbalance. The KA model revealed alterations mainly in synaptic activity. The PPS model displayed abnormalities in metabolism and oxidative stress. We also identified common biological pathways enriched in all three models, such as inflammation and immune response, which were also observed in tissue from patients. However, none of the models could recapitulate the profile of molecular changes observed in tissue from patients. Significance Our results indicate that each model has its own set of biological responses leading to epilepsy. Thus, it seems that only using a combination of the three models may one replicate more closely the mechanisms underlying MTLE+HS as seen in patients.

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