Journal of the American Society for Mass Spectrometry

The advancement of crosslinking mass spectrometry (CLMS) has significantly enhanced the ability to study protein-protein interactions and complex biological systems. This study evaluates the performance of the Orbitrap Astral and Eclipse mass spectrometers in CLMS workflows, focusing on the identification of low-abundance crosslinked peptides. The comparison employed consistent liquid chromatography setups and experimental conditions, using Cas9 crosslinked with PhoX and DSSO as quality control samples. Results demonstrated that the Astral analyzer outperformed the Eclipse, achieving over 40% more unique residue pairs (URP) due to its superior sensitivity and dynamic range, attributed to its multi-reflection time-of-flight analyzer and nearly lossless ion transmission. Additionally, the study revealed that single higher-energy collisional dissociation (HCD) fragmentation methods significantly outperformed stepped HCD methods on the Astral, while the Eclipse maintained similar performance across both approaches. Gradient optimization experiments further highlighted the impact of separation times on crosslink identifications, with longer gradients yielding higher identification rates. Collectively, this work underscores the importance of instrumentation choice, fragmentation strategies, and method optimization in maximizing CLMS performance for protein interaction studies.

Post-translational modifications (PTMs) contribute greatly to the diversity of the human proteome by affecting protein structure, function, interactions, stability, localization, and more. The study of PTMs is essential to understand various cellular functions, disease mechanisms, and aid in the development of biomarkers and design of therapeutic targets. Owing to their diversity, dynamic nature, and low stoichiometry compared to unmodified proteome counterparts, the analysis of PTMs remains challenging. In this study, immunoaffinity enrichment of PTM peptides was combined with analysis using Data Dependent Acquisition (DDA) and narrow window Data Independent Acquisition (nDIA) on the Orbitrap Astral mass spectrometer (MS) as well as comparative analysis using the Orbitrap Fusion Lumos MS for Ubiquitination, Phosphorylation, Acetylation, Succinylation, and Methylation. Human cell line and mouse tissue samples at various input peptide amounts were immuno-enriched and mass spectrometry data was acquired on both instruments to assess depth of coverage and number of novel sites identified. The study identified a total of 88,731 unique ubiquitin sites, 64,397 phosphorylation sites (43,721 phosphoserine, 8,414 phosphothreonine and 12,262 phosphotyrosine), 11,629 acetylation, 5,272 succinylation and 1,461 mono-methylation sites. In half the acquisition time, nDIA analysis of immuno-enriched samples on Orbitrap Astral MS provided much greater depth of coverage for all PTMs compared to DDA analysis on Orbitrap Fusion Lumos MS, with up to 33-fold more PTM peptides identified and quantified. Overall, the data presented in this study demonstrates the need for enrichment for PTM detection and the utility of combining antibody-based peptide capture and nDIA on the Orbitrap Astral MS as powerful tools for discovery and profiling of protein post-translational modifications in cells and tissues.

BackgroundMachine learning (ML) technologies, especially deep learning (DL), have gained increasing attention in predictive mass spectrometry (MS) for enhancing the data processing pipeline from raw data analysis to end-user predictions and re-scoring. ML models need large-scale datasets for training and re-purposing, which can be obtained from a range of public data repositories. However, applying ML to public MS datasets on larger scales is challenging, as they vary widely in terms of data acquisition methods, biological systems, and experimental designs. ResultsWe aim to facilitate ML efforts in MS data by conducting a systematic analysis of the potential sources of variance in public MS repositories. We also examine how these factors affect ML performance and perform a comprehensive transfer learning to evaluate the benefits of current best practice methods in the field for transfer learning. ConclusionsOur findings show significantly higher levels of homogeneity within a project than between projects, which indicates that its important to construct datasets most closely resembling future test cases, as transferability is severely limited for unseen datasets. We also found that transfer learning, although it did increase model performance, did not increase model performance compared to a non-pre-trained model.

A comprehensive understanding of drug metabolism is crucial for advancements in drug development. Automation has improved various stages of this process, from compound procurement to data analysis, supporting small molecules, peptides, and oligonucleotides. However, challenges remain, particularly in the time-consuming analysis of samples for metabolite identification. This article introduces new algorithms for automated Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS) data applicable to both small and macromolecules. While methodologies for small molecules are well established, adapting them for macromolecules presents challenges, including computational demands, peak detection complexities, and visualization issues. A data analysis employing diverse algorithms in the data preprocessing step was conducted across six datasets, ranging from small/medium linear or macrocyclic peptides to oligonucleotides with natural and unnatural monomers. Two peak detection approaches were evaluated: using the monoisotopic mass versus the most abundant isotope for mass calculation. Additionally, an exploration of two distinct structure visualization options was conducted for one of the datasets. Furthermore, data obtained through two different acquisition modes was processed. The computational time required for data processing was recorded throughout, ranging from 5 minutes to 2 hours per experiment. The results have been compared against prior studies, revealing substantial reductions in processing time, consistent identification of degradation products, and improved visualization techniques, thereby enhancing result interpretation. A comprehensive identification of 970 metabolites was achieved under varied incubation conditions across the six datasets, showcasing the workflows efficiency in managing experimental data within a molecular range from 700 to 7630 Daltons (Da). Particularly in larger molecules, the most abundant mass algorithm demonstrated higher scores and a greater number of matches, instilling greater confidence in the accurate prediction of metabolite structures. It has been illustrated how the visualization algorithm for macromolecules allows the combination of monomer and atom/bond notation, facilitating a clear depiction of metabolic changes in the molecular structure.

Ion mobility (IM) spectrometry provides semi-orthogonal data to mass spectrometry (MS), showing promise for identifying unknown metabolites in complex non-targeted metabolomics datasets. While current literature has showcased IM-MS for identifying unknowns under near ideal circumstances, less work has been conducted to evaluate the performance of this approach in metabolomics studies involving highly complex samples with difficult matrices. Here, we present a workflow incorporating de novo molecular formula annotation and MS/MS structure elucidation using SIRIUS 4 with experimental IM collision cross-section (CCS) measurements and machine learning CCS predictions to identify differential unknown metabolites in mutant strains of Caenorhabditis elegans. For many of those ion features this workflow enabled the successful filtering of candidate structures generated by in silico MS/MS predictions, though in some cases annotations were challenged by significant hurdles in instrumentation performance and data analysis. While for 37% of differential features we were able to successfully collect both MS/MS and CCS data, fewer than half of these features benefited from a reduction in the number of possible candidate structures using CCS filtering due to poor matching of the machine learning training sets, limited accuracy of experimental and predicted CCS values, and lack of candidate structures resulting from the MS/MS data. When using a CCS error cutoff of {+/-}3%, an average 28% of candidate structures could be successfully filtered. Herein, we identify and describe the bottlenecks and limitations associated with the identification of unknowns in non-targeted metabolomics using IM-MS to focus and provide insight on areas requiring further improvement.

We have created synthetic peptide libraries to benchmark crosslinking mass spectrometry search engines for different types of crosslinker. The unique benefit of using a library is knowing which identified crosslinks are true and which are false. Here we have used mass spectrometry data generated from measurement of the peptide libraries to evaluate the most frequently applied search algorithms in crosslinking mass-spectrometry. When filtered to an estimated false discovery rate of 5%, false crosslink identification ranged from 5.2% to 11.3% for search engines with inbuilt validation strategies for error estimation. When different external validation strategies were applied to one single search output, false crosslink identification ranged from 2.4% to a surprising 32%, despite being filtered to an estimated 5% false discovery rate. Remarkably, the use of MS-cleavable crosslinkers did not reduce the false discovery rate compared to non-cleavable crosslinkers, results from which have far-reaching implications in structural biology. We anticipate that the datasets acquired during this research will further drive optimisation and development of search engines and novel data-interpretation technologies, thereby advancing our understanding of vital biological interactions.

N-Homocysteinylation has been shown to induce immunogenic, thrombogenic, and amyloidogenic properties of proteins. Although very important to gain insight into the mechanisms of homocysteine (Hcy) toxicity, proteome-wide studies of the effects of Hcy-thiolactone (HTL) protein modification remain challenging due to the low abundance of N-Hcy-proteins. High field asymmetric waveform ion mobility spectrometry (FAIMS) has been shown to improve the identification of other PTMs, we therefore expected it to facilitate the characterization of N-homocysteinylated proteins (N-Hcy-proteins) and help gain insight into their role in human disease. After extensive measurement optimization, we compared the yield of N-Hcy-protein/peptide identification across mouse liver and brain samples, either native or modified in vitro with HTL. Additionally, we examined the influence of different reduction and alkylation agents, namely DTT/IAA and TCEP/MMTS, on the number of identified N-Hcy-sites. FAIMS increased the number of N-Hcy-Lys-peptides and N-Hcy-proteins by 1.3-7-fold and 1.1-14-fold, respectively, regardless of alkylation method. We have identified 69 and 1,198 in vivo and in vitro N-Hcy-proteins, respectively. KEGG pathway term enrichment analysis showed that among in vitro N-Hcy-proteins, ten top KEGG pathways were Parkinson disease, prion disease, Huntington disease, oxidative phosphorylation, amyotrophic lateral sclerosis, pathways of neurodegeneration - multiple diseases, carbon metabolism, carcinogenesis - reactive oxygen species, Alzheimer disease, and diabetic cardiomyopathy. We conclude that FAIMS is a valuable addition to N-Hcy-proteome analysis workflow and facilitates the mapping of N-Hcy-sites. Data are available via ProteomeXchange with identifier PXD062860.

Lung cancer is the second most diagnosed cancer in the world. Non-small cell lung cancer is the most common type of lung cancer in the United States. Tissue biopsy is the gold standard for detecting lung cancer but is highly invasive as it necessitates the extraction of a sample of tissue for histologic analysis. It also carries risks of bleeding and/or infection and is inconvenient from a patient perspective. The development of a minimally invasive test, such as one utilizing a blood or urine sample, and capable of providing accurate results for lung cancer detection and/or subtyping, would significantly enhance the clinical landscape and streamline patient care. In this study we utilize A549 and H1299 human lung cancer cell lines, differing in cell type, location within the lung, and genetic composition (Kras & p53 status), and employ diaPASEF for global quantitative proteomic analysis. We demonstrate that extracellular vesicle protein content provides enhanced resolution to differentiate between these two cell lines relative to protein lysate content and reveals alterations in signaling. Protein clusters are identified showing enrichment for distinct biological processes representing multiple gene ontology categories unique to each lung cancer subtype-oxidative phosphorylation, apical junction, and epithelial-mesenchymal transition. We subsequently delineate a short list of urine-detectable protein candidates that is prognostic in a second cohort of lung cancer patients. This list of protein candidates may be useful for the development of a non-invasive test to distinguish between these two subtypes of human lung cancer.

Although liver transplantation (LT) is an effective therapy for cirrhosis, the risk of post-LT NASH is alarmingly high and is associated with accelerated progression to fibrosis/cirrhosis, cardiovascular disease, and decreased survival. Lack of risk stratification strategies hamper liver undergoes significant remodeling during inflammatory injury. During such remodeling, degraded peptide fragments (i.e., degradome) of the ECM and other proteins increase in plasma, making it a useful diagnostic/prognostic tool in chronic liver disease. To investigate whether inflammatory liver injury caused by post-LT NASH would yield a unique degradome profile, predictive of severe post-LT NASH fibrosis, we performed a retrospective analysis of 22 biobanked samples from the Starzl Transplantation Institute (12 with post-LT NASH after 5 years and 10 without). Total plasma peptides were isolated and analyzed by 1D-LC-MS/MS analysis using a Proxeon EASY-nLC 1000 UHPLC and nanoelectrospray ionization into an Orbitrap Elite mass spectrometer. Qualitative and quantitative peptide features data were developed from MSn datasets using PEAKS Studio X (v10). LC-MS/MS yielded [~]2700 identifiable peptide features based on the results from Peaks Studio analysis. Several peptides were significantly altered in patients that later developed fibrosis and heatmap analysis of the top 25 most significantly-changed peptides, most of which were ECM-derived, clustered the 2 patient groups well. Supervised modeling of the dataset indicated that a fraction of the total peptide signal ([~]15%) could explain the differences between the groups, indicating a strong potential for representative biomarker selection. A similar degradome profile was observed when the plasma degradome patterns were compared being obesity sensitive (C57Bl6/J) and insensitive (AJ) mouse strains. Both The plasma degradome profile of post-LT patients yields stark difference based on later development of post-LT NASH fibrosis. This approach could yield new "fingerprints" that can serve as minimally-invasive biomarkers of negative outcomes post-LT.

Hydrogen/deuterium exchange mass spectrometry (HDX-MS) can provide precise analysis of a proteins conformational dynamics across varied states, such as heat-denatured vs. native protein structures, localizing regions that are specifically affected by such conditional changes. Maximizing protein sequence coverage provides high confidence that regions of interest were located by HDX-MS, but one challenge for complete sequence coverage is N-glycosylation sites. The deuteration of glycopeptides has not always been identified in previous reports of HDX-MS analyses, causing significant sequence coverage gaps in heavily glycosylated proteins and uncertainty in structural dynamics in many regions throughout a glycoprotein. We report HDX-MS analysis of the SARS-CoV-2 spike protein ectodomain in its trimeric pre-fusion form, which has 22 predicted N-glycosylation sites per monomer, with and without heat treatment. We identified glycopeptides and calculated their isotopic mass shifts from deuteration. Inclusion of the deu-terated glycopeptides increased sequence coverage of spike ectodomain from 76% to 84%, demonstrated that glycopeptides had been deuterated, and improved confidence in results localizing structural re-arrangements. Inclusion of deuterated glycopeptides improves the analysis of the conformational dynamics of glycoproteins such as viral surface antigens and cellular receptors. Abstract Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=196 SRC="FIGDIR/small/544985v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@1d8e1f6org.highwire.dtl.DTLVardef@1db0774org.highwire.dtl.DTLVardef@c68d1eorg.highwire.dtl.DTLVardef@15aed98_HPS_FORMAT_FIGEXP M_FIG C_FIG

Spontaneous chemical modifications in long-lived proteins can potentially change protein structure in ways that impact proteostasis and cellular health. For example, isomerization of aspartic acid interferes with protein turnover and is anticorrelated with cognitive acuity in Alzheimers disease. However, few isomerization rates have been determined for Asp residues in intact proteins. To remedy this deficiency, we used protein extracts from SH-SY5Y neuroblastoma cells as a source of a complex, brain-relevant proteome with no baseline isomerization. Cell lysates were aged in vitro to generate isomers, and extracted proteins were analyzed by data-independent acquisition (DIA) liquid chromatography-mass spectrometry (LC-MS). Although no Asp isomers were detected at Day 0, isomerization increased across time and was quantifiable for 105 proteins by Day 50. Data analysis revealed that isomerization rate is influenced by both primary sequence and secondary structure, suggesting that steric hindrance and backbone rigidity modulate isomerization. Additionally, we examined lysates extracted under gentle conditions to preserve protein complexes and found that protein-protein interactions often slow isomerization. Base catalysis was explored as a means to accelerate Asp isomerization due to findings of accelerated asparagine deamidation. However, no substantial rate enhancement was found for isomerization, suggesting fundamental differences in acid-base chemistry. With an enhanced understanding of Asp isomerization in proteins in general, we next sought to better understand Asp isomerization in tau. In vitro aging of monomeric and aggregated recombinant tau revealed that tau isomerizes significantly faster than any similar protein within our dataset, which is likely related to its correlation with cognition in Alzheimers disease. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/626870v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@1f57a5borg.highwire.dtl.DTLVardef@13405fcorg.highwire.dtl.DTLVardef@751a60org.highwire.dtl.DTLVardef@16a753_HPS_FORMAT_FIGEXP M_FIG C_FIG

Protein barcoding has emerged as a powerful tool for the multiplexed identification and characterization of proteins, providing a mechanism for precise tracking of protein affinity, location, and expression. In this study, we describe the development of a protein barcoding workflow for use with single-molecule Next-Generation Protein Sequencing (NGPS) on the benchtop Platinum(R) instrument. We present data on the validation of eight peptide barcodes, each designed to minimize detection bias and maximize sensitivity across various experimental conditions. We have also optimized the design of expression constructs to decrease both the hands-on time and input requirements of the workflow. In this workflow, affinity-tagged proteins are expressed with unique peptide barcodes. Following experimental selection or treatments, the proteins are purified, and the peptide barcodes are cleaved and sequenced on the Platinum instrument. We demonstrate that we can detect barcodes at 400 fmol of sample input concentration within the eight-plex mixture, and at 50 fmol of sample input for individual barcodes. We also show the capacity of this barcoding approach to achieve a ten-fold dynamic range, underscoring its sensitivity in recovering variants with low abundance. Through the combination of protein barcoding and NGPS, we lay the groundwork for future studies aimed at characterizing protein interactions and improving targeted drug delivery strategies. MotivationProtein barcoding is an emerging tool for the multiplexed selection, analysis, and tracking of proteins. The motivation for this study was to address the limitations of existing protein barcode detection tools, such as mass spectrometry, which can have drawbacks related to quantification, cost, and accessibility. By integrating a protein barcoding workflow with the benchtop Platinum(R) protein sequencer, this work offers a sensitive and accessible approach for protein barcoding in applications ranging from protein engineering to nucleic acid therapy development.

Accurate pathological assessment of tissue samples is key for diagnosis and optimal treatment decisions. Traditional pathology techniques suffer from subjectivity resulting in inter-observer variability, and limitations in identifying subtle molecular changes. Omics approaches provide both molecular evidence and unbiased classification, which increases the quality and reliability of final tissue assessment. Here, we focus on mass spectrometry (MS)-based proteomics as a method to reveal biopsy tissue differences. For MS data to be useful, molecular information collected from formalin fixed paraffin embedding (FFPE) biopsy tissues needs to be consistent and quantitatively accurate and contain sufficient clinically relevant molecular information. Therefore, we developed an MS-based workflow and assessed the analytical repeatability on 36 kidney biopsies, ultimately analysing molecular differences and similarities of over 5000 proteins per biopsy. Additional 301 transplant biopsies were analysed to understand other physical parameters including effects of tissue size, standing time in autosampler, and the effect on clinical validation. MS data were acquired using Data-Independent Acquisition (DIA) which provides gigabytes of data per sample in the form of high proteome (and genome) representation, at exquisitely high quantitative accuracy. The FFPE-based method optimised here provides a coefficient of variation below 20%, analysing more than 5000 proteins per sample in parallel. We also observed that tissue thickness does affect the outcome of the data quality: 5 m sections show more variation in the same sample than 10 m sections. Notably, our data reveals an excellent agreement for the relative abundance of known protein biomarkers with kidney transplantation lesion scores used in clinical pathological diagnostics. The findings presented here demonstrate the ease, speed, and robustness of the MS-based method, where a wealth of molecular data from minute tissue sections can be used to assist and expand pathology, and possibly reduce the inter-observer variability.

Many proteoforms can be produced from a gene due to genetic mutations, alternative splicing, post-translational modifications (PTMs), and other variations. PTMs in proteoforms play critical roles in cell signaling, protein degradation, and other biological processes. Mass spectrometry (MS) is the primary technique for investigating PTMs in proteoforms, and two alternative MS approaches, top-down and bottom-up, have complementary strengths. The combination of the two approaches has the potential to increase the sensitivity and accuracy in PTM identification and characterization. In addition, protein and PTM knowledgebases, such as UniProt, provide valuable information for PTM characterization and validation. Here, we present a software pipeline called PTM-TBA (PTM characterization by Top-down, Bottom-up MS and Annotations) for identifying and localizing PTMs in proteoforms by integrating top-down and bottom-up MS as well as UniProt annotations. We identified 1,662 mass shifts from a top-down MS data set of SW480 cells, 545 (33%) of which were matched to 12 common PTMs, and 351 of which were localized. PTM-TBA validated 346 of the 1,662 mass shifts using UniProt annotations or a bottom-up MS data set of SW480 cells.

Insulin is an essential hormone made by the pancreatic beta-cells in the islets of Langerhans. Beta-cells produce more insulin protein than virtually all other cellular proteins combined. Dysfunction in the process of insulin synthesis can lead to disease, including rare forms of monogenic diabetes. Specifically, aberrant intra-insulin and inter-insulin disulphide bonds have been implicated in the pathology of type 1 diabetes and type 2 diabetes, respectively. In type 1 diabetes, misprocessed insulin isoforms may be neoepitopes that kick-start and/or exacerbate the auto-immune response. In type 2 diabetes, aberrant disulphides form insulin dimers that can clog the endoplasmic reticulum and contribute to beta cell dysfunction. To facilitate the study of novel and known insulin neoepitopes and dimers, we present an unbiased and rapid technique for identifying insulin disulphide patterns from pancreatic islet extracts. The basis of this method is the cleavage between insulins cysteine residues with the metalloprotease, thermolysin, and subsequent identification of cysteine containing fragments and their partner peptides by LC-MS/MS. Using this technique, we identify 6 aberrant disulphide bonded insulin species, including a previously described type 1 diabetes neoepitope, as well as inter-chain disulphide bonded insulin dimers. Furthermore, using the endoplasmic stress inducer, thapsigargin, we observe increased disulphide errors in a patient donor sample. This approach lays foundations to identify the scope and cause of aberrant insulin disulphide formation in health and disease.

High-field asymmetric waveform ion mobility spectrometry (FAIMS) coupled to liquid chromatography-mass spectrometry (LC-MS) has been shown to increase peptide and protein detections compared to LC-MS/MS alone. However, FAIMS has not been compared to other methods of gas-phase fractionation, such as quadrupole gas-phase fractionation, which could increase our understanding of the mechanisms of improvement. The goal of this work was to assess whether FAIMS improves peptide identifications because 1) gas-phase fractionation enables the analysis of less abundant signals by excluding more abundant precursors from filling the ion trap, 2) the use of FAIMS reduces co-isolation of peptides during the MS/MS process resulting in a reduction of chimeric spectra, or 3) a combination of both. To investigate these hypotheses, pooled human brain tissue samples were measured in triplicate using FAIMS gas-phase fractionation, quadrupole gas-phase fractionation, or no gas-phase fractionation on two Thermo Eclipse Tribrid Mass Spectrometers. On both instruments, our data confirmed prior observations that FAIMS increased the number of peptides identified. We further demonstrated that the main benefit of FAIMS is due to the reduced co-isolation of persistent peptide precursor ions, which results in a decrease in chimeric spectra.

Recombinant adeno-associated viruses (rAAVs) are the leading platform for in vivo delivery of gene therapies, with several already approved for clinical use. However, the heterogeneity and structural complexity of these viral particles render them challenging targets to characterize. Orbitrap-based native mass spectrometry (MS) is a method capable of directly characterizing intact megadalton protein assemblies. Here we used such an approach to characterize four different preparations of rAAV8 (two empty and two filled) differing in both their transgene and relative capsid protein isoform (i.e. VP1, VP2 and VP3) content. Interestingly, in native MS measurements of these samples, we observe complicated, unusual, and dramatically different spectral appearances between the four rAAV preparations that cannot be rationalized or interpreted using conventional approaches (i.e. charge state deconvolution). By combining high-resolution native MS, single particle charge detection MS, and spectral simulations, we reveal that these unexpected features result from a combination of stochastic assembly-induced heterogeneity and divergent gas phase charging behaviour between the four rAAV preparations. Our results stress the often-neglected heterogeneity of rAAVs, but also highlight the pitfalls of standard high-resolution mass analysis for such particles. Finally, we show that charge detection MS and spectral simulations can be used to tackle these challenges.

Mass spectrometry imaging (MSI) has gained increasing popularity for tissue-based diagnostics due to its ability to identify and visualize molecular characteristics unique to different phenotypes within heterogeneous samples. Data from MSI experiments are often visualized using single ion images and further analyzed using machine learning and multivariate statistics to identify m/z features of interest and create predictive models for phenotypic classification. However, often only a single molecule or m/z feature is visualized per ion image, and mainly categorical classifications are provided from the predictive models. As an alternative approach, we developed an aggregated molecular phenotype (AMP) scoring system. AMP scores are generated using an ensemble machine learning approach to first select features differentiating phenotypes, weight the features using logistic regression, and combine the weights and feature abundances. AMP scores are then scaled between 0 and 1, with lower values generally corresponding to class 1 phenotypes (typically control) and higher scores relating to class 2 phenotypes. AMP scores therefore allow the evaluation of multiple features simultaneously and showcase the degree to which these features correlate with various phenotypes, leading to high diagnostic accuracy and interpretability of predictive models. Here, AMP score performance was evaluated using metabolomic data collected from desorption electrospray ionization (DESI) MSI. Initial comparisons of cancerous human tissues to normal or benign counterparts illustrated that AMP scores distinguished phenotypes with high accuracy, sensitivity, and specificity. Furthermore, when combined with spatial coordinates, AMP scores allow visualization of tissue sections in one map with distinguished phenotypic borders, highlighting their diagnostic utility.

Clinical biomarker development has been stymied by inaccurate protein quantification from mass spectrometry (MS) discovery data and a prolonged validation process. To mitigate these issues, we created the Targeted Extraction Assessment of Quantification (TEAQ) software package. This innovative tool uses the discovery cohort analysis to select precursors, peptides, and proteins that adhere to established targeted assay criteria. TEAQ was applied to Data-Independent Acquisition MS data from plasma samples acquired on an Orbitrap Astral MS. Identified precursors were evaluated for linearity, specificity, repeatability, reproducibility, and intra-protein correlation from 11-point loading curves under three throughputs, to develop a resource for clinical-grade targeted assays. From a clinical cohort of individuals with inflammatory bowel disease (n=492), TEAQ successfully identified 1116 signature peptides for 327 quantifiable proteins from 1180 identified proteins. Embedding stringent selection criteria adaptable to targeted assay development into the analysis of discovery data will streamline the transition to validation and clinical studies.

XL-MS provides low-resolution structural information of proteins in cells and tissues. Combined with quantitation, it can identify changes in the interactome between samples, for example, control and drug-treated cells, or young and old mice. A difference can originate from protein conformational changes altering the solvent-accessible distance separating the cross-linked residues. Alternatively, a difference can result from conformational changes localized to the cross-linked residues, for example, altering the solvent exposure or reactivity of those residues or post-translational modifications on the cross-linked peptides. In this manner, cross-linking is sensitive to a variety of protein conformational features. Dead-end peptides are cross-links attached only at one end to a protein, the other terminus being hydrolyzed. As a result, changes in their abundance reflect only conformational changes localized to the attached residue. For this reason, analyzing both quantified cross-links and their corresponding dead-end peptides can help elucidate the likely conformational changes giving rise to observed differences of cross-link abundance. We describe analysis of dead-end peptides in the XLinkDB public cross-link database and, with quantified mitochondrial data isolated from failing heart versus healthy mice, show how a comparison of abundance ratios between cross-links and their corresponding dead-end peptides can be leveraged to reveal possible conformational explanations.