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Previous benchmarking studies have demonstrated the importance of instrument acquisition methodology and statistical analysis on quantitative performance in label-free proteomics. However, the effects of these parameters in combination with replicate number and false discovery rate (FDR) corrections are not known. Using a benchmarking standard, we systematically evaluated the combined impact of acquisition methodology, replicate number, statistical approach, and FDR corrections. These analyses reveal a complex interaction between these parameters that greatly impacts the quantitative fidelity of protein- and peptide-level quantification. At a high replicate number ( n = 8), both data-dependent acquisition (DDA) and data-independent acquisition (DIA) methodologies yield accurate protein quantification across statistical approaches. However, at a low replicate number (n = 4), only DIA in combination with linear models for microarrays (LIMMA) and reproducibility-optimized test statistic (ROTS) produced a high level of quantitative fidelity. Quantitative accuracy at low replicates is also greatly impacted by FDR corrections, with Benjamini-Hochberg and Storey corrections yielding variable true positive rates for DDA workflows. For peptide quantification, replicate number and acquisition methodology are even more critical. A higher number of replicates in combination with DIA and LIMMA produce high quantitative fidelity, while DDA performs poorly regardless of replicate number or statistical approach. These results underscore the importance of pairing instrument acquisition methodology with the appropriate replicate number and statistical approach for optimal quantification performance., Competing Interests: The authors declare no competing financial interest., (Not subject to U.S. Copyright. Published 2021 by American Chemical Society.)

Histone post-translational modifications (PTMs) are important regulators of chromatin structure and gene expression. Quantitative analysis of histone PTMs by mass spectrometry remains extremely challenging due to the complex and combinatorial nature of histone PTMs. The most commonly used mass spectrometry-based method for high-throughput histone PTM analysis is data-dependent acquisition (DDA). However, stochastic precursor selection and dependence on MS1 ions for quantification impede comprehensive interrogation of histone PTM states using DDA methods. To overcome these limitations, we utilized a data-independent acquisition (DIA) workflow that provides superior run-to-run consistency and postacquisition flexibility in comparison to DDA methods. In addition, we developed a novel DIA-based methodology to quantify isobaric, co-eluting histone peptides that lack unique MS2 transitions. Our method enabled deconvolution and quantification of histone PTMs that are otherwise refractory to quantitation, including the heavily acetylated tail of histone H4. Using this workflow, we investigated the effects of the histone deacetylase inhibitor SAHA (suberoylanilide hydroxamic acid) on the global histone PTM state of human breast cancer MCF7 cells. A total of 62 unique histone PTMs were quantified, revealing novel SAHA-induced changes in acetylation and methylation of histones H3 and H4.

Astrocytes are important regulators of normal brain function in mammals, including roles in synaptic signaling, synapse formation, and neuronal health and survival. Many of these functions are executed via secreted proteins. To analyze the astrocyte secretome, a combination of shotgun proteomics and bioinformatics was employed to analyze conditioned media from primary murine astrocyte cultures. Both two- and one-dimensional LC-MS/MS were used to analyze astrocyte secreted proteins, resulting in the identification of over 420 proteins. To refine our results, the intracellular protein contaminants were removed in silico using a cytoplasmic control. In additional rounds of refinement, putative secreted proteins were subjected to analysis by SignalP, SecretomeP, and gene ontology analysis, yielding a refined list of 187 secreted proteins. In conclusion, the use of shotgun proteomics combined with multiple rounds of data refinement produced a high quality catalog of astrocyte secreted proteins.

Two-dimensional (2D) fractionation is a commonly used tool to increase dynamic range and proteome coverage for bottom-up, shotgun proteomics. However, there are few reports comparing the relative separation efficiencies of 2D methodologies using low-microgram sample quantities. In order to systematically evaluate 2D separation techniques, we fractionated microgram quantities of E. coli protein extract by seven different methods. The first dimension of separation was performed with either reversed-phase high-pressure liquid chromatography (RP-HPLC), gel electrophoresis (SDS-PAGE), or strong cation exchange (SCX-HPLC). The second dimension consisted of a standard reversed-phase capillary HPLC coupled to an electrospray ionization quadrupole time-of-flight mass spectrometer for tandem mass spectrometric analysis. The overall performance and relative fractionation efficiencies of each technique were assessed by comparing the total number of proteins identified by each method. The protein-level RP-HPLC and the high-pH RP-HPLC peptide-level separations performed the best, identifying 281 and 266 proteins, respectively. The online pH variance SCX and the SDS-PAGE returned modest performances with 178 and 139 proteins identified, respectively. The offline SCX had the worst performance with 81 proteins identified. We also examined various chromatographic factors that contribute to separation efficiency, including resolving power, orthogonality, and sample loss.

Recently developed sample preparation techniques employing microwave irradiation have enabled the comprehensive study of endogenous mammalian neuropeptides. These methods reduce interference from post-mortem protein degradation by deactivating proteases via heat denaturation. Alternatively, we have developed a protocol using cryostat dissection and a boiling extraction buffer to achieve a similar effect. This novel methodology greatly reduces post-mortem protein contamination and increases neuropeptide identification without the use of specialized equipment. In addition, a 2D HPLC scheme employing differential pH selectivity in the first and second dimensions has been used to enhance neuropeptidome coverage. By using our novel dissection protocol in tandem with 2D RP-RP HPLC, we were able to identify a total of 56 peptides from known neuropeptide precursors, including 17 previously unidentified peptides. The use of cryostat dissection and two-dimensional RP-RP HPLC enhances the detection of novel neuropeptides by deactivating proteases and reducing sample complexity.