diaTracer enables spectrum-centric analysis of diaPASEF proteomics data.

Data-independent acquisition (DIA) has become a widely used strategy for peptide and protein quantification in mass spectrometry-based proteomics studies. The integration of ion mobility separation into DIA analysis, such as the diaPASEF technology available on Bruker's timsTOF platform, further improves the quantification accuracy and protein depth achievable using DIA. We introduce diaTracer, a new spectrum-centric computational tool optimized for diaPASEF data. diaTracer performs three-dimensional (m/z, retention time, ion mobility) peak tracing and feature detection to generate precursor-resolved "pseudo-MS/MS" spectra, facilitating direct ("spectral-library free") peptide identification and quantification from diaPASEF data. diaTracer is available as a stand-alone tool and is fully integrated into the widely used FragPipe computational platform. We demonstrate the performance of diaTracer and FragPipe using diaPASEF data from cerebrospinal fluid (CSF) and plasma samples, data from phosphoproteomics and HLA immunopeptidomics experiments, and low-input data from a spatial proteomics study. We also show that diaTracer enables unrestricted identification of post-translational modifications from diaPASEF data using open/mass offset searches.

Koina: Democratizing machine learning for proteomics research.

Recent developments in machine-learning (ML) and deep-learning (DL) have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML/DL models for various applications and peptide properties are frequently published, the rate at which these models are adopted by the community is slow, which is mostly due to technical challenges. We believe that, for the community to make better use of state-of-the-art models, more attention should be spent on making models easy to use and accessible by the community. To facilitate this, we developed Koina, an open-source containerized, decentralized and online-accessible high-performance prediction service that enables ML/DL model usage in any pipeline. Using the widely used FragPipe computational platform as example, we show how Koina can be easily integrated with existing proteomics software tools and how these integrations improve data analysis.

MSBooster: improving peptide identification rates using deep learning-based features.

Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, for rescoring peptide-to-spectrum matches using additional features incorporating deep learning-based predictions of peptide properties, such as LC retention time, ion mobility, and MS/MS spectra. We demonstrate the utility of MSBooster, in tandem with MSFragger and Percolator, in several different workflows, including nonspecific searches (immunopeptidomics), direct identification of peptides from data independent acquisition data, single-cell proteomics, and data generated on an ion mobility separation-enabled timsTOF MS platform. MSBooster is fast, robust, and fully integrated into the widely used FragPipe computational platform.

Huntington's disease genotype suppresses global manganese-responsive processes in pre-manifest and manifest YAC128 mice.

Manganese (Mn) is an essential micronutrient required for the proper function of several enzymes. Accumulating evidence demonstrates a selective decrease of bioavailable Mn in vulnerable cell types of Huntington's Disease (HD), an inherited progressive neurodegenerative disorder with no cure. Amelioration of underlying pathophysiology, such as alterations in Mn-dependent biology, may be therapeutic. We therefore sought to investigate global Mn-dependent and Mn-responsive biology following various Mn exposures in a mouse model of HD. YAC128 and wildtype (WT) littermate control mice received one of three different Mn exposure paradigms by subcutaneous injection of 50 mg kg-1 MnCl2·4(H2O) across two distinct HD disease stages. "Pre-manifest" (12-week old mice) mice received either a single (1 injection) or week-long (3 injections) exposure of Mn or vehicle (H2O) and were sacrificed at the pre-manifest stage. "Manifest" (32-week old) mice were sacrificed following either a week-long Mn or vehicle exposure during the manifest stage, or a 20-week-long chronic (2× weekly injections) exposure that began in the pre-manifest stage. Tissue Mn, mRNA, protein, and metabolites were measured in the striatum, the brain region most sensitive to neurodegeneration in HD. Across all Mn exposure paradigms, pre-manifest YAC128 mice exhibited a suppressed response to transcriptional and protein changes and manifest YAC128 mice showed a suppressed metabolic response, despite equivalent elevations in whole striatal Mn. We conclude that YAC128 mice respond differentially to Mn compared to WT as measured by global transcriptional, translational, and metabolomic changes, suggesting an impairment in Mn homeostasis across two different disease stages in YAC128 mice.