Four-dimensional lipidomics profiling in X-linked adrenoleukodystrophy using trapped ion mobility mass spectrometry.

Lipids play pivotal roles in an extensive range of metabolic and physiological processes. In recent years, the convergence of trapped ion mobility spectrometry and MS has enabled 4D-lipidomics, a highly promising technology for comprehensive lipid analysis. 4D-lipidomics assesses lipid annotations across four distinct dimensions-retention time, collisional cross section, m/z (mass-to-charge ratio), and MS/MS spectra-providing a heightened level of confidence in lipid annotation. These advantages prove particularly valuable when investigating complex disorders involving lipid metabolism, such as adrenoleukodystrophy (ALD). ALD is characterized by the accumulation of very-long-chain fatty acids (VLCFAs) due to pathogenic variants in the ABCD1 gene. A comprehensive 4D-lipidomics strategy of ALD fibroblasts demonstrated significant elevations of various lipids from multiple classes. This indicates that the changes observed in ALD are not confined to a single lipid class and likely impacts a broad spectrum of lipid-mediated physiological processes. Our findings highlight the incorporation of mainly saturated and monounsaturated VLCFA variants into a range of lipid classes, encompassing phosphatidylcholines, triacylglycerols, and cholesterol esters. These include ultra-long-chain fatty acids with a length of up to thirty carbon atoms. Lipid species containing C26:0 and C26:1 were the most frequently detected VLCFA lipids in our study. Furthermore, we report a panel of 121 new candidate biomarkers in fibroblasts, exhibiting significant differentiation between controls and individuals with ALD. In summary, this study demonstrates the capabilities of a 4D-lipid profiling workflow in unraveling novel insights into the intricate lipid modifications associated with metabolic disorders like ALD.

Exploiting ion-mobility mass spectrometry for unraveling proteome complexity.

Ion mobility spectrometry-mass spectrometry (IMS-MS) is experiencing rapid growth in proteomic studies, driven by its enhancements in dynamic range and throughput, increasing the quantitation precision, and the depth of proteome coverage. The core principle of ion mobility spectrometry is to separate ions in an inert gas under the influence of an electric field based on differences in drift time. This minireview provides an introduction to IMS operation modes and a description of advantages and limitations is presented. Moreover, the principles of trapped IMS-MS (TIMS-MS), including parallel accumulation-serial fragmentation are discussed. Finally, emerging applications linked to TIMS focusing on sample throughput (in clinical proteomics) and sensitivity (single-cell proteomics) are reviewed, and the possibilities of intact protein analysis are discussed.

Increasing taxonomic and functional characterization of host-microbiome interactions by DIA-PASEF metaproteomics.

Introduction: Metaproteomics is a rapidly advancing field that offers unique insights into the taxonomic composition and the functional activity of microbial communities, and their effects on host physiology. Classically, data-dependent acquisition (DDA) mass spectrometry (MS) has been applied for peptide identification and quantification in metaproteomics. However, DDA-MS exhibits well-known limitations in terms of depth, sensitivity, and reproducibility. Consequently, methodological improvements are required to better characterize the protein landscape of microbiomes and their interactions with the host. Methods: We present an optimized proteomic workflow that utilizes the information captured by Parallel Accumulation-Serial Fragmentation (PASEF) MS for comprehensive metaproteomic studies in complex fecal samples of mice. Results and discussion: We show that implementing PASEF using a DDA acquisition scheme (DDA-PASEF) increased peptide quantification up to 5 times and reached higher accuracy and reproducibility compared to previously published classical DDA and data-independent acquisition (DIA) methods. Furthermore, we demonstrate that the combination of DIA, PASEF, and neuronal-network-based data analysis, was superior to DDA-PASEF in all mentioned parameters. Importantly, DIA-PASEF expanded the dynamic range towards low-abundant proteins and it doubled the quantification of proteins with unknown or uncharacterized functions. Compared to previous classical DDA metaproteomic studies, DIA-PASEF resulted in the quantification of up to 4 times more taxonomic units using 16 times less injected peptides and 4 times shorter chromatography gradients. Moreover, 131 additional functional pathways distributed across more and even uniquely identified taxa were profiled as revealed by a peptide-centric taxonomic-functional analysis. We tested our workflow on a validated preclinical mouse model of neuropathic pain to assess longitudinal changes in host-gut microbiome interactions associated with pain - an unexplored topic for metaproteomics. We uncovered the significant enrichment of two bacterial classes upon pain, and, in addition, the upregulation of metabolic activities previously linked to chronic pain as well as various hitherto unknown ones. Furthermore, our data revealed pain-associated dynamics of proteome complexes implicated in the crosstalk between the host immune system and the gut microbiome. In conclusion, the DIA-PASEF metaproteomic workflow presented here provides a stepping stone towards a deeper understanding of microbial ecosystems across the breadth of biomedical and biotechnological fields.

Mobility-resolved broadband dissociation and parallel reaction monitoring for laser desorption/ionization-mass spectrometry - Tattoo pigment identification supported by trapped ion mobility spectrometry.

In this work, trapped ion mobility spectrometry (TIMS) was introduced to facilitate tandem mass spectrometry (MS2) experiments for laser desorption/ionization-mass spectrometry (LDI-MS) as mobility-resolved fragmentation. The mobility separation of desorbed ions was followed by subsequent fragmentation using data-independent broadband collision-induced dissociation (bbCID) or targeted fragmentation through a prototypic version of parallel reaction monitoring-parallel accumulation serial fragmentation (prm-PASEF) for LDI. Both mobility-resolved fragmentation options, TIMS-bbCID and prm-PASEF, were applied to LDI point measurements to identify organic pigments in tattoo inks. Furthermore, the prototypic prm-PASEF algorithm was used in imaging applications to increase confidence in annotating organic tattoo pigments in skin samples with adverse reactions. Due to less complex spectra in matrix-free LDI, both fragmentation methods yielded fast and reliable MS2 identification workflows. TIMS-bbCID was especially beneficial for the rapid acquisition of multiple fragment spectra. For the targeted prm-PASEF approach, analytes' mobilities needed to be collected prior to simplified fragmentation. Therefore, a reference list for 14 pigments was created. The possible number of experiments per thin section and the associated savings in analysis time compared to conventional MS2 were particularly suitable for the imaging application. Furthermore, the mobility dimension enabled a new orthogonal identification parameter, increasing the annotation confidence of tattoo pigments through compound specific mobilities.

Proteomic datasets of HeLa and SiHa cell lines acquired by DDA-PASEF and diaPASEF.

We present four datasets on proteomics profiling of HeLa and SiHa cell lines associated with the research described in the paper "PROTREC: A probability-based approach for recovering missing proteins based on biological networks" [1]. Proteins in each cell line were acquired by two different data acquisition methods. The first was Data Dependent Acquisition-Parallel Accumulation Serial Fragmentation (DDA-PASEF) and the second was Parallel Accumulation-Serial Fragmentation combined with data-independent acquisition (diaPASEF) [2], [3]. Protein assembly was performed following search against the Swiss-Prot Human database using Peaks Studio for DDA datasets and Spectronaut for DIA datasets. The assembled result contains identified PSMs, peptides and proteins that are above threshold for each HeLa and SiHa sample. Coverage-wise, for DDA-PASEF, approximately 6,090 and 7,298 proteins were quantified for HeLa and SiHA sample, while13,339 and 8,773 proteins were quantified by diaPASEF for HeLa for SiHa sample, respectively. Consistency-wise, diaPASEF has fewer missing values (∼ 2%) compared to its DDA counterparts (∼5-7%). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the iProX partner repository [4] with the dataset identifier PXD029773.

Use of PASEF for Accelerated Protein Sequence Confirmation and De Novo Sequencing with High Data Quality.

Biopharmaceutical sequences can be well confirmed by multiple protease digests-e.g., trypsin, elastase, and chymotrypsin-followed by LC-MS/MS data analysis. High quality data can be used for de novo sequencing as well. PASEF (Parallel Accumulation and Serial Fragmentation) on the timsTOF instrument has been used to accelerate proteome and protein sequence studies and increase sequence coverage concomitantly.Here we describe the protein chemical and LC-MS methods in detail to generate high quality samples for sequence characterization from only 3 digests. We applied PASEF to generate exhaustive protein sequence coverage maps by combination of results from the three enzyme digests using a short LC gradient. The data quality obtained was high and adequate for determining antibody sequences de novo.Nivolumab and dulaglutide were digested by 3 enzymes individually. For nivolumab, 94/94/90% sequence coverage and 86/84/85% fragment coverage were obtained from the individual digest analysis with trypsin/chymotrypsin/elastase, respectively. For dulaglutide, 96/100/90% sequence coverage and 92/90/83% fragment coverage were obtained. The merged peptide map from the 3 digests for nivolumab resulted in ∼550 peptides; enough to safely confirm the full sequences and to determine the nivolumab sequence de novo.