Definitive Screening Designs to Optimize Library-Free DIA-MS Identification and Quantification of Neuropeptides

Abstract

Method optimization is crucial for successful mass spectrometry (MS) analysis. However, extensive method assessments, altering various parameters individually, are rarely performed due to practical limitations regarding time and sample quantity. To maximize sample space for optimization while maintaining reasonable instrumentation requirements, a definitive screening design (DSD) is leveraged for systematic optimization of data-independent acquisition (DIA) parameters to maximize crustacean neuropeptide identifications. While DSDs require several injections, a library-free methodology enables surrogate sample usage for comprehensive optimization of MS parameters to assess biomolecules from limited samples. We identified several parameters contributing significant first- or second-order effects to method performance, and the DSD model predicted ideal values to implement. These increased reproducibility and detection capabilities enabled the identification of 461 peptides, compared to 375 and 262 peptides identified through data-dependent acquisition (DDA) and a published DIA method for crustacean neuropeptides, respectively. Herein, we demonstrate a DSD optimization workflow, using standard material, not reliant on spectral libraries for the analysis of any low abundance molecules from previous samples of limited availability. This extends the DIA method to low abundance isoforms dysregulated or only detectable in disease samples, thus improving characterization of previously inaccessible biomolecules, such as neuropeptides. Data are available via ProteomeXchange with identifier PXD038520.

EndoGenius: Optimized Neuropeptide Identification from Mass Spectrometry Datasets

“…A definitive screening design (DSD) was generated and interpreted with JMP Pro 15.0.0, as outlined previously. 41 A series of continuous attributes were selected to represent levels of spectrum−peptide match quality. These terms included average fragment error, precursor error, number of consecutive b-ions, number of consecutive y-ions, average number of annotations per fragment ion, average number of annotations per fragment ion that were not associated with a neutral loss of water or ammonia, hyperscore, 35 and motif score.…”

“…Perhaps the simplest method has been to increase the FDR threshold to 5% and exercise more caution to manually verify results. 41 Alternatively, non-FDR thresholds have been suggested, such as in PEAKS, where it is recommended to use a −10log P, their proprietary scoring method, of 20. 13 We noted in our work that this value routinely corresponds to approximately 3−5% FDR.…”

MotifQuest: An Automated Pipeline for Motif Database Creation to Improve Peptidomics Database Searching Programs

“…Results from each platform were exported at a value of 5% false-discovery rate (FDR), a common practice for peptidomics searches with less than one-hundred anticipated identifications. 38 Additionally, results from EndoGenius were also exported at an EndoGenius score threshold of greater than or equal to 1000, and PEAKS results were exported at a −10lgP greater than or equal to 20. Results from EndoGenius searches of NPs using manual and automated motif databases are provided in Supporting Information, Files 8 and 9, respectively.…”

Assessing fecal metaproteomics workflow and small protein recovery using DDA and DIA PASEF mass spectrometry

“…In a DDA mode, the most abundant ions from MS 1 scan will be selected and fragmented during tandem MS scans, however, this data acquisition mode can risk losing information on the other less abundant peptides particularly in complex samples such as microbiomes, which limits the depth, sensitivity, and reproducibility of metaproteomic data 13,14 . Contrastingly, DIA can sample all the peptides within the selected mass range and therefore theoretically can detect lower-abundance peptides in complex samples 15 . In the past few years, the application of DIA-MS based proteomics was profoundly expanded due to these advantages, the advancement of bioinformatics tools, such as DIA-NN, and advanced instrumental developments, such as DIA-PASEF and Astral MS analyzer 16,17 .…”