Analytical Chemistry

Infrared Matrix-Assisted Laser Desorption Electrospray Ionization (IR-MALDESI) mass spectrometry is an ambient-direct sampling method being developed for high-throughput, label-free, biochemical screening of large-scale compound libraries. Here, we report the development of an ultrahigh-throughput continuous motion IR-MALDESI sampling approach capable of acquiring data at rates up to 22.7 samples per second in a 384-well microtiter plate. At top speed, less than 1% analyte carryover is observed from well-to-well and signal intensity relative standard deviations (RSD) of 11.5% and 20.9% for 3 M 1-hydroxymidazolam and 12 M dextrorphan, respectively, are achieved. The ability to perform parallel kinetics studies on 384 samples with ~30s time resolution using an isocitrate dehydrogenase 1 (IDH1) enzyme assay is shown. Finally, we demonstrate the repeatability and throughput of our approach by measuring 115,200 samples from 300 microtiter plate reads consecutively over 5.54 hours with RSDs under 8.14% for each freshly introduced plate. Taken together, these results demonstrate the use of IR-MALDESI at sample acquisition rates that surpass other currently reported direct sampling mass spectrometry approaches used for high throughput compound screening. For Table of Contents Only O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=125 SRC="FIGDIR/small/465730v3_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@5f916corg.highwire.dtl.DTLVardef@4473b4org.highwire.dtl.DTLVardef@82e8forg.highwire.dtl.DTLVardef@128b57b_HPS_FORMAT_FIGEXP M_FIG C_FIG

High-performance liquid chromatography (HPLC) is a common medium-throughput technique to quantify the components of often complex mixtures like those typically obtained from biological tissue extracts. However, analysis of HPLC data from complex multianalyte samples is hampered by a lack of tools to accurately determine the precise analyte quantities on a level of precision equivalent to mass-spectrometry approaches. To address this problem, we developed a tool we call PeakClimber, that uses a sum of exponential Gaussian functions to accurately deconvolve overlapping, multianalyte peaks in HPLC traces. Here we analytically show that HPLC peaks are well-fit by an exponential Gaussian function, that PeakClimber more accurately quantifies known peak areas than standard industry software for both HPLC and mass spectrometry applications, and that PeakClimber accurately quantifies differences in triglyceride abundances between colonized and germ-free fruit flies.

Native electrospray ionization mass spectrometry (ESI-MS) using nano ESI or desorption electrospray ionization (DESI) has been widely used to study interactions between macromolecules and ligands, usually protein-metabolite interactions (PMIs). In MS spectra the charge state distributions (CSD) of proteins differ between native and non-native conditions and based on this, we report a method that can differentiate specific protein-metabolite interactions from non-specific binding. Our approach is based on a 3D-printed open port probe electrospray system (gOPP-ESI) using mobile phase gradients (aqueous to methanol) for the ionization of protein/protein-metabolite complex. Notably, we found that a true protein-metabolite complex is more resistant to the denaturing effect of methanol compared to the free protein. This is corroborated by the observation that forming high charge states of protein-metabolite complexes requires higher proportions of methanol than free protein while, for non-specific complexes, there is no obvious difference in the CSD. Therefore, by comparing the changes in the CSD of free protein and protein-metabolite complex versus the increase of methanol, we can distinguish metabolites that specifically interact with the target protein. The approach is evaluated with well-characterized protein-ligand pairs, and we confirmed that cytidine phosphates, N, N', N''-triacetylchitotriose, and fluvastatin are specific ligands for ribonuclease A, lysozyme, and beta-lactoglobulin respectively. However, cytidine-5-triphosphate (CTP) interacts non-specifically with lysozyme and beta-lactoglobulin. We believe that after first-round native-MS assays to identify which metabolites cause mass shifts to the free protein, the gOPP-ESI-MS could be used as a quick second-round check to exclude non-specific binding and discover metabolites truly interacting with the protein of interest, reducing the number of candidates for subsequent validation experiments. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/583904v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@e2aad7org.highwire.dtl.DTLVardef@13e287borg.highwire.dtl.DTLVardef@1d3c6c0org.highwire.dtl.DTLVardef@9da2a8_HPS_FORMAT_FIGEXP M_FIG C_FIG

Recent advances in mass spectrometry-based single-cell proteomics (SCP) technologies have revolutionized the SCP field for comprehensive characterization of cellular heterogeneity. However, most of the current SCP approaches employ sub-{micro}L to 1 {micro}L processing volume for effective single-cell sample preparation using either ultralow-volume specialized devices or a 384-well plate by frequently adding water to the plate well to compensate water evaporation, which greatly limits their broad accessibility and analytical robustness. Here we report a robust convenient SCP method termed iSOP (improved Surfactant-assisted One-Pot processing) for processing of single cells at the low {micro}L processing volume using the 384-well plate with tight sealing to avoid sample drying loss. This iSOP SCP method was built upon our previously developed SOP method by the replacement of a PCR tube or 96-well plate with the low-volume 384-well plate and systematic optimization of the single-cell processing conditions. After optimization, 3 {micro}L was selected as the processing volume with a mixture of 2 ng trypsin and 2 ng Lys-C enzymes in terms of robustness, detection sensitivity, and operation convenience. With a commonly accessible LC-MS platform, iSOP-MS can detect and quantify [~]1,200-1,800 protein groups from single HeLa or MCF7 cells. Application of iSOP-MS to two neuroblastoma cell lines has demonstrated that iSOP-MS enabled reliable identification of an average of [~]1,700 and [~]2,050 protein groups from single BE2-C and SK-N-SH cells, respectively, and precise characterization of cellular heterogeneity between the two distinct cell types and within the same cell type. When compared to other available SCP methods, iSOP-MS is more robust and convenient for routine, cost-effective quantitative SCP analysis.

Untargeted lipidomics allows analysis of a broader range of lipids than targeted methods and permits discovery of unknown compounds. Previous ring trials have evaluated the reproducibility of targeted lipidomics methods, but inter-laboratory comparison of compound identification and unknown feature detection in untargeted lipidomics has not been attempted. To address this gap, five laboratories analyzed a set of mammalian tissue and biofluid reference samples using both their own untargeted lipidomics procedures and a common chromatographic and data analysis method. While both methods yielded informative data, the common method improved chromatographic reproducibility and resulted in detection of more shared features between labs. Spectral search against the LipidBlast in silico library enabled identification of over 2,000 unique lipids. Further examination of LC-MS/MS and ion mobility data, aided by hybrid search and spectral networking analysis, revealed spectral and chromatographic patterns useful for classification of unknown features, a subset of which were highly reproducible between labs. Overall, our method offers enhanced compound identification performance compared to targeted lipidomics, demonstrates the potential of harmonized methods to improve inter-site reproducibility for quantitation and feature alignment, and can serve as a reference to aid future annotation of untargeted lipidomics data.

To date, collision-induced dissociation and methods based on electron transfer dissociation are considered the standard approaches for the mass spectrometry analysis of N- and O-glycopeptides, respectively, allowing for identification of both peptide and glycan compositions. In recent years, alternative fragmentation methods such as ultraviolet photodissociation (UVPD) and more energetic versions of electron-based techniques (such as electron ionisation dissociation, EID) have been shown to be useful for the analysis of glycopeptides, producing rich information on the glycopeptide structure, including types of glycosidic linkages. We evaluated ultraviolet photodissociation (UVPD), electron ionization dissociation (EID), electron capture dissociation (ECD), and activated-ion ECD (AI-ECD) using an Orbitrap-Omnitrap hybrid for LC-MS analysis of complex N-glycopeptides. Both UVPD and EID generated extensive peptide, glycosidic, and cross-ring fragments, enabling detailed structural characterization. While ECD alone produced few glycopeptide identifications, AI-ECD significantly improved yields through supplemental vibrational activation. UVPD and EID achieved comparable identification efficiencies to stepped collisional dissociation and provided additional linkage information. These results establish the Omnitrap as a powerful platform for comprehensive glycoproteomic analysis and highlight the need for enhanced computational tools to interpret complex UVPD and EID spectra.

Mass spectrometry (MS)-based nucleic acid analysis provides direct chemical evidence for oligonucleotide sequence, composition, and modifications. However, oligonucleotide LC-MS analysis commonly relies on ion-pairing reversed-phase liquid chromatography (IP-RPLC). Although IP-RPLC provides strong retention and high-resolution separation of highly charged nucleic acids, ion-pairing reagents can contaminate LC-MS systems, suppress electrospray ionization, require extensive system cleaning, and limit the use of high-end MS platforms that are primarily dedicated to proteomics or metabolomics. Here, we developed and evaluated an ion-pair-free capillary hydrophilic interaction liquid chromatography mass spectrometry (capillary HILIC-MS) workflow for RNA modification mapping. To enable robust analysis of biologically relevant samples, we optimized sample preparation, high-organic loading conditions, chromatographic parameters, and MS source settings to overcome key challenges associated with capillary HILIC, including limited sample volume, solvent compatibility, and solvent breakthrough during injection. The optimized capillary HILIC-MS method provided effective separation of oligonucleotides below 30 nt and enabled sensitive detection of RNA modifications in the populations of tRNAs and rRNAs in biological samples. Importantly, the ion-pair-free workflow also allowed switching between nucleic acid analysis and proteomics on the same LC-MS platform without the need for extensive system decontamination. Together, this workflow provides a sensitive, robust, and MS-compatible approach for nucleic acid analysis, expanding the utility of high-end LC-MS systems for both therapeutic oligonucleotide characterization and biological RNA modification profiling.

NanoHILIC/MS/MS provides high sensitivity for low-input peptide analysis, yet its use in bottom-up proteomics has been constrained by a persistent solvent mismatch: tryptic peptides exhibit poor solubility in the [≥]95% acetonitrile (ACN) required for nanoHILIC injection. Our previously reported two-step solubilization method [Anal Chem 2025, 97 (19), 10227-10235] alleviated this issue but required large dilution volumes, limiting the amount of sample that could be injected. Here, we introduce DiReCT (Dissolution from Reverse-Phase Chromatography Tips), a StageTip-based workflow that integrates peptide solubilization, desalting, and nanoHILIC-compatible elution into a single operation. During elution from RP-StageTips, residual water on the stationary phase is rapidly displaced by a small volume of high-ACN solvent, generating a transient mid-ACN environment that maximizes peptide solubility without drying. This mechanism enables high-recovery peptide concentration and allows direct injection of the entire eluate onto nanoHILIC/MS/MS. Using [~]0.25 ng of HeLa digest, DiReCT/nanoHILIC/MS/MS identified 1177 peptides and 410 proteins, representing 8.9- and 6.7-fold increases over nanoRPLC/MS/MS, respectively.

Despite advances in high-resolution mass spectrometry (HRMS), confident lipid annotation remains challenging due to the extensive chemical diversity of the lipidome and the prevalence of isomeric species. Ion mobility collision cross section (CCS) measurements provide structural information that complements HRMS; however, not all HRMS platforms can perform these measurements, necessitating a trade-off among mass resolution, accuracy, and robustness. Here, we introduce a method to infer lipid CCS values directly from liquid chromatography (LC)-Orbitrap MS experiments (Orbi). We show that Orbitrap mass analyzer pressure readings, and therefore CCS values, are influenced by the LC gradient solvent composition, requiring correction using isotopically labeled internal standards injected post-column. We also show that hundreds of lipid features can be assigned OrbiCCS values in a single LC run, with average precision better than 1% and an accuracy of 1-2% relative to reference DTCCS and TIMSCCS values. This excellent CCS accuracy not only enables more reliable annotation of lipid species in complex mixtures by matching OrbiCCS values to reference databases but also accelerates lipid structural elucidation based on the unknown's position in Orbi-retention time-m/z space.

We describe an instrument configuration based on the Orbitrap Exploris 480 mass spectrometer that has been coupled to an Omnitrap platform. The Omnitrap possesses three distinct ion-activation regions, that can be used to perform resonant based collision induced dissociation, several forms of electron associated fragmentation, and ultraviolet photodissociation. Each section can also be combined with infrared multiphoton dissociation. In this work, we demonstrate all these modes of operation on a range of peptides and proteins. The results show that this instrument configuration produces similar data to previous implementations of each activation technique and at similar efficiency levels. We demonstrate that this unique instrument configuration is extremely versatile for the interrogation of polypeptides.

Capillary zone electrophoresis (CZE)-mass spectrometry (MS) has been proposed as a powerful analytical tool for bottom-up, top-down, and native proteomics (multi-level proteomics) decades ago to analyze complex biological samples at the levels of peptides (bottom-up), proteoforms (top-down), and complexoforms (native). However, its broad adoption has been impeded by the limited robustness and reproducibility. Here, we present multi-level proteomics data from nearly 170 CZE-MS runs ([~]170 hours of instrument time), demonstrating qualitatively (i.e., the number of identified peptides and proteoforms, the number of detected complexoforms, and their migration time) and quantitatively (i.e., peptide, proteoform, and complexoform intensity) reproducible measurement of complex samples with varying levels of complexity, i.e., Escherichia coli cells, HeLa cells, and human plasma. CZE-MS-based native proteomics enabled the detection of hundreds of complexoforms up to 800 kDa from the complex systems via consuming only nanograms of protein material. The results indicate that CZE-MS is sensitive and reproducible enough for broad adoption for multi-level proteomics-based biomedical research.

Sphingolipids are diverse lipids with sphingobases and N-acyl fatty acids as the hydrophobic moieties. While the importance of the in-depth elucidation of hydrophobic structures is widely recognized in lipid biology, mass spectrometry-based annotation of ceramides in the commonly used protonated form is often hindered by in-source dehydration during electrospray ionization in the heated state and variable water losses in the product ion spectrum. In this study, we investigated the sodium ion form and its product ions in ceramides with the use of electron-activated dissociation tandem mass spectrometry (EAD MS/MS) in addition to collision-induced dissociation to facilitate indepth structural elucidation. While dehydrated ions from the protonated form were frequently observed, the sodium adduct ions remained stable because of their higher activation energy compared with the protonated form, which was validated using quantum chemical calculations. Using the three adduct forms under optimized conditions increased confidence in annotating the ceramide peaks through retention-time matching. Furthermore, EAD MS/MS of the sodium adduct ions facilitated the positional determination of double bonds and hydroxyl groups in the ceramide hydrophobic moiety. Our approach is showcased by the annotation of phytoceramides with N-acyl 2- and 3-hydroxyl groups in mouse feces and ceramides with N-acyl n-6 very long-chain polyunsaturated 2-hydroxy fatty acids in mouse testis.

Machine learning (ML)-assisted Raman spectroscopy has become a powerful analytical tool for the classification and identification of analytes; however, technical challenges impacting its detection accuracy have not been investigated. This study explores experimental factors affecting classification performance. Among the evaluated ML models, ML algorithms show minimal impacts on classification accuracy. Instead, experimental factors, including spectral similarity between tested samples and the data quality, dominate detection performance. Increases in spectral noises and spectral similarity significantly reduce classification accuracy. In well-controlled samples with low experimental noise, ML-assisted Raman spectroscopy can discriminate lipid mixtures with a composition difference of 1.85 mol%. To assess the effect of biological heterogeneity, we analyzed single-cell Raman spectra from Saccharomyces cerevisiae strains carrying single, double, or triple gene mutations. Intrinsic cell-to-cell variability introduced substantial spectral differences, severely reducing the accuracy of multiclass classification of these genetically similar strains at the single-cell level. Averaging Raman spectra across multiple cells improved classification accuracy by reducing this spectral variability. We also assess the effectiveness of transfer learning across different Raman spectrometers, specifically by applying a ML model trained on one instrument to another Raman spectrometer. Transfer learning can be improved with proper instrument calibration, highlighting the importance of instrument standardization. Overall, our results demonstrate that data quality and spectral similarity are the primary bottlenecks in ML-assisted Raman spectroscopy. Careful attention to sample preparation, data acquisition, measurement conditions, and instrument calibration is critical to achieving robust and reliable classification performance.

Advances in Electrostatic Linear Ion Trap (ELIT) Charge Detection Mass Spectrometry (CDMS) over the past 10 years have revolutionized its use for analyzing very high-molecular-weight species such as protein complexes, viral vectors, vaccines, viruses, and amyloid fibrils. Nonetheless, ELIT-based CDMS has remained confined to a small number of specialized instrumentation groups, predominantly in academia, where large and complex home-built instruments are operated by highly skilled scientists in dedicated facilities. In this report, we discuss the primary challenges addressed in the design of a benchtop ELIT-based CDMS instrument. We highlight key design aspects of the hardware, acquisition modes, and control software, and we present important performance metrics (mass range, resolution and sensitivity) demonstrated using samples representative of the technology's key application areas.

Ribonucleic acid (RNA) modifications, with over 170 identified types, play diverse roles in cellular processes. The past decade has witnessed surging demand for accurate identification and localization of RNA modifications in both endogenous and synthetic therapeutic RNAs. With accurate spectral annotation for RNA, tandem mass spectrometry (MS/MS) can meet this demand. Here we present RNabel, a user-friendly software tool for in-depth annotation of MS/MS spectra of RNA oligonucleotides. RNabel considers a full set of backbone-cleavage ions (a, b, c, d, a-B, w, x, y, z) in which the ribonucleotide unit could be A, U, C, G, Y (pseudouridine), or I (Inosine). Additionally, RNabel considers 196 modifications on the base, the phosphoribose linkage, the 5' or the 3' terminus, or detachment of a sub-nucleotide fragment as a neutral or charged group. Users can create new components if needed, including ribonucleotides, modifications, neutral or charged groups that could detach from a ribonucleotide. RNabel efficiently processes large datasets in four acceptable formats including .mgf, .raw, .txt from msConvert, and RNabel batch files. Multiple statistical metrics are provided for quality assessment of spectral annotation. To accelerate RNA modification analysis, RNabel is made freely available for Mac and Windows users at https://github.com/songge1111/RNabel/releases. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=116 SRC="FIGDIR/small/733900v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@8ccae5org.highwire.dtl.DTLVardef@15c8cfaorg.highwire.dtl.DTLVardef@12b93a2org.highwire.dtl.DTLVardef@1e9aab9_HPS_FORMAT_FIGEXP M_FIG C_FIG

In this work we describe a low loss fractionation system comprised of a reconfigured Evosep One LC system for the first dimension and a repurposed 3D-printer as a fraction collector. The setup operates as a high-pH fractionation system capable of effectively working with nanogram scales of lysate digests. The 2D RP-RP system demonstrated superior proteome coverage over single-shot data-dependent acquisition (DDA) analysis using only 5 ng of human cell lysate digest with performance increasing with increasing amounts of material. We found that the fractionation system allowed over 70% signal recovery at the peptide level and, more importantly, we observed over 30% increase on protein level intensity which indicates the complexity reduction afforded by the system outweighs the sample losses endured. The application of data-independent acquisition (DIA) and wide window acquisition (WWA) to fractionated samples allowed more than 8,000 proteins to be identified from 50 ng of material. The utility of the 2D system was further investigated for phosphoproteomics (>21,000 phosphosites from 50 g starting material) and pull-down type experiments and showed substantial improvements over single-shot experiments. We show that the 2D RP-RP system is highly versatile and powerful tool for many proteomics workflows.

High-resolution binding site mapping is important for in-depth activity assessment of new therapeutics including AI-designed antibodies. However, complex protein targets such as glycosylated antigens are challenging for many methods including crystallography. PD1 is a highly glycosylated antigen, and with the traditional HDX-MS method, only 51% sequence coverage could be obtained with multiple epitope residues undetected for Pembrolizumab. By implementing glyco-peptide detection, subzero temperature LC-MS and electron based MSMS fragmentation, the new HDX FineMapping methodology enabled 100% sequence coverage and complete epitope characterization for the Pembrolizumab-PD1 system, with amino acid level resolution. Furthermore, HDX FineMapping detects binding epitopes directly in solution, without any mutation or modification to either the antigen or the antibody. The amino acid level resolution combined with low cost, minimal sample consumption, fast turnaround time, and no need of mutant library or crystallization makes it a competitive methodology for binding mode validation of AI-designed therapeutics.

Mass spectrometry imaging has emerged as a pivotal tool in spatial metabolomics, yet its reliance on the imzML format poses critical challenges in data storage, transmission, and computational efficiency. While imzML ensures cross-platform compatibility, its lower compressed binary architecture results in large file sizes and high parsing overhead, hindering cloud-based analysis and real-time visualization. This study introduces an enhanced Aird compression format optimized for spatial metabolomics through two innovations: (1) a dynamic combinatorial compression algorithm for integer-based encoding of m/z and intensity data; (2) a coordinate-separation storage strategy for rapid spatial indexing. Experimental validation on 47 public datasets demonstrated significant performance gains. Compared to imzML, Aird achieved a 70% reduction in storage footprint (mean compression ratio: 30.03%) while maintaining near-lossless data precision (F1-score = 99.26% at 0.1 ppm m/z tolerance). For high-precision-controlled datasets, Aird accelerated loading speeds by 15-fold in MZmine. The Aird format overcomes crucial bottlenecks in spatial metabolomics by harmonizing storage efficiency, computational speed, and analytical precision, reducing I/O latency for large cohorts. By achieving near-native feature detection accuracy, Aird establishes a robust infrastructure for translational applications, including disease biomarker discovery and pharmacokinetic imaging.

With the increased demand of large-cohort proteomic analysis, fast and reproducible sample preparation has become the critical issue that needs to be solved. Herein, we developed a fully automated and integrated proteomics sample preparation workflow (autoSISPROT), enabling the simultaneous processing of 96 samples in less than 2.5 hours. Benefiting from its 96-channel all-in-tip operation, protein digestion, peptide desalting, and TMT labeling could be achieved in a fully automated manner. The autoSISPROT demonstrated good sample preparation performances, including >94% of digestion efficiency, nearly 100% of alkylation efficiency, >98% of TMT labeling efficiency, and >0.9 of intra- and inter-batch Pearson correlation coefficients. Furthermore, by combining with cellular thermal shift assay-coupled to mass spectrometry (CETSA-MS), the autoSISPROT was able to process and TMT-label 40 samples automatically and accurately identify the known target of methotrexate. Importantly, taking advantage of the data independent acquisition and isothermal CETSA-MS, the autoSISPROT was well applied for identifying known targets and potential off-targets of 20 kinase inhibitors by automatedly processing 87 samples, affording over a 10-fold improvement in throughput when compared to classical CETSA-MS. Collectively, we developed a fully automated and integrated workflow for high-throughput proteomics sample preparation and drug target identification.

Automated data pre-processing (DPP) forms the basis of any liquid chromatography-high resolution mass spectrometry-driven non-targeted metabolomics experiment. However, current strategies for quality control of this important step have rarely been investigated or even discussed. We exemplified how reliable benchmark peak lists could be generated for eleven publicly available datasets acquired across different instrumental platforms. Moreover, we demonstrated how these benchmarks can be utilized to derive performance metrics for DPP and tested whether these metrics can be generalized for entire datasets. Relying on this principle, we cross-validated different strategies for quality assurance of DPP, including manual parameter adjustment, variance of replicate injection-based metrics, unsupervised clustering performance, automated parameter optimization, and deep learning-based classification of chromatographic peaks. Overall, we want to highlight the importance of assessing DPP performance on a regular basis.