Ultra-fast label-free quantification and comprehensive proteome coverage with narrow-window data-independent acquisition

Host-derived protein profiles of human neonatal meconium across gestational ages

Meconium, a non-invasive biomaterial reflecting prenatal substance accumulation, could provide valuable insights into neonatal health. However, the comprehensive protein profile of meconium across gestational ages remains unclear. Here, we conducted an extensive proteomic analysis of first meconium from 259 newborns across varied gestational ages to delineate protein composition and elucidate its relevance to neonatal diseases. The first meconium samples were collected, with the majority obtained before feeding, and the mean time for the first meconium passage from the anus was 11.9 ± 9.47 h. Our analysis revealed 5370 host-derived meconium proteins, which varied depending on sex and gestational age. Specifically, meconium from preterm infants exhibited elevated concentrations of proteins associated with the extracellular matrix. Additionally, the protein profiles of meconium also exhibited unique variations depending on both specific diseases, including gastrointestinal diseases, congenital heart diseases, and maternal conditions. Furthermore, we developed a machine learning model to predict gestational ages using meconium proteins. Our model suggests that newborns with gastrointestinal diseases and congenital heart diseases may have immature gastrointestinal systems. These findings highlight the intricate relationship between clinical parameters and meconium protein composition, offering potential for a novel approach to assess neonatal gastrointestinal health.

Repurposed 3D Printer Allows Economical and Programmable Fraction Collection for Proteomics of Nanogram Scale Samples

In this work, we describe the construction and application of a repurposed 3D-printer as a fraction collector. We utilize a nano-LC to ensure minimal volumes and surfaces although any LC can be coupled. 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 60% signal recovery at the peptide level and, more importantly, we observed improved protein level intensity coverage, 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 nearly 8000 proteins to be identified from 50 ng of the material. The utility of the 2D system was further investigated for phosphoproteomics (>21 000 phosphosites from 50 μg starting material) and pull-down type experiments and showed substantial improvements over single-shot experiments. We show that the 2D RP-RP system is a highly versatile and powerful tool for many proteomics workflows.

Rapid Plasma Proteome Profiling via Nanoparticle Protein Corona and Direct Infusion Mass Spectrometry

Noninvasive detection of protein biomarkers in plasma is crucial for clinical purposes. Liquid chromatography-mass spectrometry (LC-MS) is the gold standard technique for plasma proteome analysis, but despite recent advances, it remains limited by throughput, cost, and coverage. Here, we introduce a new hybrid method that integrates direct infusion shotgun proteome analysis (DISPA) with nanoparticle (NP) protein corona enrichment for high-throughput and efficient plasma proteomic profiling. We realized over 280 protein identifications in 1.4 min collection time, which enables a potential throughput of approximately 1000 samples daily. The identified proteins are involved in valuable pathways, and 44 of the proteins are FDA-approved biomarkers. The robustness and quantitative accuracy of this method were evaluated across multiple NPs and concentrations with a mean coefficient of variation of 17%. Moreover, different protein corona profiles were observed among various NPs based on their distinct surface modifications, and all NP protein profiles exhibited deeper coverage and better quantification than neat plasma. Our streamlined workflow merges coverage and throughput with precise quantification, leveraging both DISPA and NP protein corona enrichment. This underscores the significant potential of DISPA when paired with NP sample preparation techniques for plasma proteome studies.

Quantitative protein mass spectrometry for multiplex measurement of coagulation and fibrinolytic proteins towards clinical application: What, why and how?

Plasma proteins involved in coagulation and fibrinolysis are essential to hemostasis. Consequently, their circulating levels and functionality are critical in bleeding and thrombosis development. Well-established laboratory tests to assess these are available; however, said tests do not allow high multiplicity, require large volumes of plasma and are often costly. A novel technology to quantify plasma proteins is quantitative protein mass spectrometry (QPMS). Aided by stable isotope-labeled internal standards a large number of proteins can be quantified in one single analytical run requiring <30 μL of plasma. This provides an opportunity to improve insight in the etiology and prognosis of bleeding and thrombotic disorders, in which the balance between different proteins plays a crucial role. This manuscript aims to give an overview of the QPMS potential applications in thrombosis and hemostasis research (quantifying the 38 proteins assigned to coagulation and fibrinolysis by the KEGG database), but also to explore the potential and hurdles if designed for clinical practice. Advantages and limitations of QPMS are described and strategies for improved analysis are proposed, using as an example the test requirements for antithrombin. Application of this technology in the future could represent a step towards individualized patient care.

Improving Proteomic Identification Using Narrow Isolation Windows with Zeno SWATH Data-Independent Acquisition

Data-independent acquisition (DIA) techniques such as sequential window acquisition of all theoretical mass spectra (SWATH) acquisition have emerged as the preferred strategies for proteomic analyses. Our study optimized the SWATH-DIA method using a narrow isolation window placement approach, improving its proteomic performance. We optimized the acquisition parameter combinations of narrow isolation windows with different widths (1.9 and 2.9 Da) on a ZenoTOF 7600 (Sciex); the acquired data were analyzed using DIA-NN (version 1.8.1). Narrow SWATH (nSWATH) identified 5916 and 7719 protein groups on the digested peptides, corresponding to 400 ng of protein from mouse liver and HEK293T cells, respectively, improving identification by 7.52 and 4.99%, respectively, compared to conventional SWATH. The median coefficient of variation of the quantified values was less than 6%. We further analyzed 200 ng of benchmark samples comprising peptides from known ratios ofEscherichia coli, yeast, and human peptides using nSWATH. Consequently, it achieved accuracy and precision comparable to those of conventional SWATH, identifying an average of 95,456 precursors and 9342 protein groups across three benchmark samples, representing 12.6 and 9.63% improved identification compared to conventional SWATH. The nSWATH method improved identification at various loading amounts of benchmark samples, identifying 40.7% more protein groups at 25 ng. These results demonstrate the improved performance of nSWATH, contributing to the acquisition of deeper proteomic data from complex biological samples.

High-Throughput Proteomics and Phosphoproteomics of Rat Tissues Using Microflow Zeno SWATH

High-throughput tissue proteomics has great potential in the advancement of precision medicine. Here, we investigated the combined sensitivity of trap-elute microflow liquid chromatography with a ZenoTOF for DIA proteomics and phosphoproteomics. Method optimization was conducted on HEK293T cell lines to determine the optimal variable window size, MS2 accumulation time and gradient length. The ZenoTOF 7600 was then compared to the previous generation TripleTOF 6600 using eight rat organs, finding up to 23% more proteins using a fifth of the sample load and a third of the instrument time. Spectral reference libraries generated from Zeno SWATH data in FragPipe (MSFragger-DIA/DIA-NN) contained 4 times more fragment ions than the DIA-NN only library and quantified more proteins. Replicate single-shot phosphopeptide enrichments of 50-100 μg of rat tryptic peptide were analyzed by microflow HPLC using Zeno SWATH without fractionation. Using Spectronaut we quantified a shallow phosphoproteome containing 1000-3000 phosphoprecursors per organ. Promisingly, clear hierarchical clustering of organs was observed with high Pearson correlation coefficients >0.95 between replicate enrichments and median CV of 20%. The combined sensitivity of microflow HPLC with Zeno SWATH allows for the high-throughput quantitation of an extensive proteome and shallow phosphoproteome from small tissue samples.

Reanalysis of DIA Data Demonstrates the Capabilities of MS/MS-Free Proteomics to Reveal New Biological Insights in Disease-Related Samples

Data-independent acquisition (DIA) at the shortened data acquisition time is becoming a method of choice for quantitative proteomic applications requiring high throughput analysis of large cohorts of samples. With the advent of the combination of high resolution mass spectrometry with an asymmetric track lossless analyzer, these DIA capabilities were further extended with the recent demonstration of quantitative analyses at the speed of up to hundreds of samples per day. In particular, the proteomic data for the brain samples related to multiple system atrophy disease were acquired using 7 and 28 min chromatography gradients (Guzman et al., Nat. Biotech. 2024). In this work, we applied the recently introduced DirectMS1 method to reanalysis of these data using only MS1 spectra. Both DirectMS1 and DIA results were matched against long gradient DDA analysis from the earlier study of the same sample cohort. While the quantitation efficiency of DirectMS1 was comparable with DIA on the same data sets, we found an additional five proteins of biological significance relevant to the analyzed tissue samples. Among the findings, DirectMS1 was able to detect decreased caspase activity for Vimentin protein in the multiple system atrophy samples missed by the MS/MS-based quantitation methods. Our study suggests that DirectMS1 can be an efficient MS1-only addition to the analysis of DIA data in high-throughput quantitative proteomic studies.

Development of an efficient, effective, and economical technology for proteome analysis

We present an efficient, effective, and economical approach, named E3technology, for proteomics sample preparation. By immobilizing silica microparticles into the polytetrafluoroethylene matrix, we develop a robust membrane medium, which could serve as a reliable platform to generate proteomics-friendly samples in a rapid and low-cost fashion. We benchmark its performance using different formats and demonstrate them with a variety of sample types of varied complexity, quantity, and volume. Our data suggest that E3technology provides proteome-wide identification and quantitation performance equivalent or superior to many existing methods. We further propose an enhanced single-vessel approach, named E4technology, which performs on-filter in-cell digestion with minimal sample loss and high sensitivity, enabling low-input and low-cell proteomics. Lastly, we utilized the above technologies to investigate RNA-binding proteins and profile the intact bacterial cell proteome.

The Future of Proteomics is Up in the Air: Can Ion Mobility Replace Liquid Chromatography for High Throughput Proteomics?

The coevolution of liquid chromatography (LC) with mass spectrometry (MS) has shaped contemporary proteomics. LC hyphenated to MS now enables quantification of more than 10,000 proteins in a single injection, a number that likely represents most proteins in specific human cells or tissues. Separations by ion mobility spectrometry (IMS) have recently emerged to complement LC and further improve the depth of proteomics. Given the theoretical advantages in speed and robustness of IMS in comparison to LC, we envision that ongoing improvements to IMS paired with MS may eventually make LC obsolete, especially when combined with targeted or simplified analyses, such as rapid clinical proteomics analysis of defined biomarker panels. In this perspective, we describe the need for faster analysis that might drive this transition, the current state of direct infusion proteomics, and discuss some technical challenges that must be overcome to fully complete the transition to entirely gas phase proteomics.

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.

Blood to Biomarker Quantitation in Under One Hour with Rapid Proteomics using a Hyperthermoacidic Protease

Multi-step multi-hour tryptic proteolysis has limited the utility of bottom-up proteomics for cases that require immediate quantitative information. The recently available hyperthermoacidic (HTA) protease "Krakatoa" digests samples in a single 5 to 30-minute step at pH 3 and >80 °C; conditions that disrupt most cells and tissues, denature proteins, and block disulfide reformation. The combination of quick single-step sample preparation with high throughput dual trapping column single analytical column (DTSC) liquid chromatography-mass spectrometry (LC-MS) achieves "Rapid Proteomics" in which the time from sample collection to actionable data is less than 1 hour. The presented development and systematic evaluation of this methodology found reproducible quantitation of over 160 proteins from just 1 microliter of whole blood. Furthermore, the preference of the HTA-protease for intact proteins over peptides allows for sensitive targeted quantitation of the Angiotensin I and II bioactive peptides in under half an hour. With these methods we analyzed serum and plasma from 53 individuals and quantified Angiotensin and proteins that were not detected with trypsin. This assessment of Rapid Proteomics suggests that concentration of circulating protein and peptide biomarkers could be measured in almost real-time by LC-MS.

TOC Figure

Rapid proteomics enables near real-time monitoring of circulating blood biomarkers. One microliter of blood is collected every 8 minutes, digested for 20 minutes, and then analyzed by targeted mass spectrometry for 8 minutes. This results in a 30-minute delay with datapoints every 8 minutes.

Fully Automated Workflow for Integrated Sample Digestion and Evotip Loading Enabling High-Throughput Clinical Proteomics

Protein identification and quantification is an important tool for biomarker discovery. With the increased sensitivity and speed of modern mass spectrometers, sample preparation remains a bottleneck for studying large cohorts. To address this issue, we prepared and evaluated a simple and efficient workflow on the Opentrons OT-2 robot that combines sample digestion, cleanup, and loading on Evotips in a fully automated manner, allowing the processing of up to 192 samples in 6 h. Analysis of 192 automated HeLa cell sample preparations consistently identified ∼8000 protein groups and ∼130,000 peptide precursors with an 11.5 min active liquid chromatography gradient with the Evosep One and narrow-window data-independent acquisition (nDIA) with the Orbitrap Astral mass spectrometer providing a throughput of 100 samples per day. Our results demonstrate a highly sensitive workflow yielding both reproducibility and stability at low sample inputs. The workflow is optimized for minimal sample starting amount to reduce the costs for reagents needed for sample preparation, which is critical when analyzing large biological cohorts. Building on the digesting workflow, we incorporated an automated phosphopeptide enrichment step using magnetic titanium-immobilized metal ion affinity chromatography beads. This allows for a fully automated proteome and phosphoproteome sample preparation in a single step with high sensitivity. Using the integrated digestion and Evotip loading workflow, we evaluated the effects of cancer immune therapy on the plasma proteome in metastatic melanoma patients.

Boosting the Sensitivity of Quantitative Single-Cell Proteomics with Infrared-Tandem Mass Tags

Single-cell proteomics is a powerful approach to precisely profile protein landscapes within individual cells toward a comprehensive understanding of proteomic functions and tissue and cellular states. The inherent challenges associated with limited starting material demand heightened analytical sensitivity. Just as advances in sample preparation maximize the amount of material that makes it from the cell to the mass spectrometer, we strive to maximize the number of ions that make it from ion source to the detector. In isobaric tagging experiments, limited reporter ion generation limits quantitative accuracy and precision. The combination of infrared photoactivation and ion parking circumvents the m/z dependence inherent in HCD, maximizing reporter generation and avoiding unintended degradation of TMT reporter molecules in infrared-tandem mass tags (IR-TMT). The method was applied to single-cell human proteomes using 18-plex TMTpro, resulting in 4-5-fold increases in reporter signal compared to conventional SPS-MS3 approaches. IR-TMT enables faster duty cycles, higher throughput, and increased peptide identification and quantification. Comparative experiments showcase 4-5-fold lower injection times for IR-TMT, providing superior sensitivity without compromising accuracy. In all, IR-TMT enhances the dynamic range of proteomic experiments and is compatible with gas-phase fractionation and real-time searching, promising increased gains in the study of cellular heterogeneity.

Metabolomic and proteomic applications to exercise biomedicine

Objectives

'OMICs encapsulates study of scaled data acquisition, at the levels of DNA, RNA, protein, and metabolite species. The broad objectives of OMICs in biomedical exercise research are multifarious, but commonly relate to biomarker development and understanding features of exercise adaptation in health, ageing and metabolic diseases.

Methods

This field is one of exponential technical (i.e., depth of feature coverage) and scientific (i.e., in health, metabolic conditions and ageing, multi-OMICs) progress adopting targeted and untargeted approaches.

Results

Key findings in exercise biomedicine have led to the identification of OMIC features linking to heritability or adaptive responses to exercise e.g., the forging of GWAS/proteome/metabolome links to cardiovascular fitness and metabolic health adaptations. The recent addition of stable isotope tracing to proteomics ('dynamic proteomics') and metabolomics ('fluxomics') represents the next phase of state-of-the-art in 'OMICS.

Conclusions

These methods overcome limitations associated with point-in-time 'OMICs and can be achieved using substrate-specific tracers or deuterium oxide (D2O), depending on the question; these methods could help identify how individual protein turnover and metabolite flux may explain exercise responses. We contend application of these methods will shed new light in translational exercise biomedicine.

The One Hour Human Proteome

We describe deep analysis of the human proteome in less than 1 h. We achieve this expedited proteome characterization by leveraging state-of-the-art sample preparation, chromatographic separations, and data analysis tools, and by using the new Orbitrap Astral mass spectrometer equipped with a quadrupole mass filter, a high-field Orbitrap mass analyzer, and an asymmetric track lossless (Astral) mass analyzer. The system offers high tandem mass spectrometry acquisition speed of 200 Hz and detects hundreds of peptide sequences per second within data-independent acquisition or data-dependent acquisition modes of operation. The fast-switching capabilities of the new quadrupole complement the sensitivity and fast ion scanning of the Astral analyzer to enable narrow-bin data-independent analysis methods. Over a 30-min active chromatographic method consuming a total analysis time of 56 min, the Q-Orbitrap-Astral hybrid MS collects an average of 4319 MS1 scans and 438,062 tandem mass spectrometry scans per run, producing 235,916 peptide sequences (1% false discovery rate). On average, each 30-min analysis achieved detection of 10,411 protein groups (1% false discovery rate). We conclude, with these results and alongside other recent reports, that the 1-h human proteome is within reach.

Systematic Optimization of Automated Phosphopeptide Enrichment for High-Sensitivity Phosphoproteomics

Improving coverage, robustness, and sensitivity is crucial for routine phosphoproteomics analysis by single-shot liquid chromatography-tandem mass spectrometry (LC-MS/MS) from minimal peptide inputs. Here, we systematically optimized key experimental parameters for automated on-bead phosphoproteomics sample preparation with a focus on low-input samples. Assessing the number of identified phosphopeptides, enrichment efficiency, site localization scores, and relative enrichment of multiply-phosphorylated peptides pinpointed critical variables influencing the resulting phosphoproteome. Optimizing glycolic acid concentration in the loading buffer, percentage of ammonium hydroxide in the elution buffer, peptide-to-beads ratio, binding time, sample, and loading buffer volumes allowed us to confidently identify >16,000 phosphopeptides in half-an-hour LC-MS/MS on an Orbitrap Exploris 480 using 30 μg of peptides as starting material. Furthermore, we evaluated how sequential enrichment can boost phosphoproteome coverage and showed that pooling fractions into a single LC-MS/MS analysis increased the depth. We also present an alternative phosphopeptide enrichment strategy based on stepwise addition of beads thereby boosting phosphoproteome coverage by 20%. Finally, we applied our optimized strategy to evaluate phosphoproteome depth with the Orbitrap Astral MS using a cell dilution series and were able to identify >32,000 phosphopeptides from 0.5 million HeLa cells in half-an-hour LC-MS/MS using narrow-window data-independent acquisition (nDIA).

An inflection point in high-throughput proteomics with Orbitrap Astral: analysis of biofluids, cells, and tissues

This technical note presents a comprehensive proteomics workflow for the new combination of Orbitrap and Astral mass analyzers across biofluids, cells, and tissues. Central to our workflow is the integration of Adaptive Focused Acoustics (AFA) technology for cells and tissue lysis, to ensure robust and reproducible sample preparation in a high-throughput manner. Furthermore, we automated the detergent-compatible single-pot, solid-phase-enhanced sample Preparation (SP3) method for protein digestion, a technique that streamlines the process by combining purification and digestion steps, thereby reducing sample loss and improving efficiency. The synergy of these advanced methodologies facilitates a robust and high-throughput approach for cells and tissue analysis, an important consideration in translational research. This work disseminates our platform workflow, analyzes the effectiveness, demonstrates reproducibility of the results, and highlights the potential of these technologies in biomarker discovery and disease pathology. For cells and tissues (heart, liver, lung, and intestine) proteomics analysis by data-independent acquisition mode, identifications exceeding 10,000 proteins can be achieved with a 24-minute active gradient. In 200ng injections of HeLa digest across multiple gradients, an average of more than 80% of proteins have a CV less than 20%, and a 45-minute run covers ~90% of the expressed proteome. In plasma samples including naive, depleted, perchloric acid precipitated, and Seer nanoparticle captured, all with a 24-minute gradient length, we identified 87, 108, 96 and 137 out of 216 FDA approved circulating protein biomarkers, respectively. This complete workflow allows for large swaths of the proteome to be identified and is compatible across diverse sample types.

LC-MS/DIA-based strategy for comprehensive flavonoid profiling: an Ocotea spp. applicability case

We introduce a liquid chromatography - mass spectrometry with data-independent acquisition (LC-MS/DIA)-based strategy, specifically tailored to achieve comprehensive and reliable glycosylated flavonoid profiling. This approach facilitates in-depth and simultaneous exploration of all detected precursors and fragments during data processing, employing the widely-used open-source MZmine 3 software. It was applied to a dataset of six Ocotea plant species. This framework suggested 49 flavonoids potentially newly described for these plant species, alongside 45 known features within the genus. Flavonols kaempferol and quercetin, both exhibiting O-glycosylation patterns, were particularly prevalent. Gas-phase fragmentation reactions further supported these findings. For the first time, the apigenin flavone backbone was also annotated in most of the examined Ocotea species. Apigenin derivatives were found mainly in the C-glycoside form, with O. porosa displaying the highest flavone : flavonol ratio. The approach also allowed an unprecedented detection of kaempferol and quercetin in O. porosa species, and it has underscored the untapped potential of LC-MS/DIA data for broad and reliable flavonoid profiling. Our study annotated more than 50 flavonoid backbones in each species, surpassing the current literature.

Paradigm shift in biomarker translation: a pipeline to generate clinical grade biomarker candidates from DIA-MS discovery

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.

One-Tip enables comprehensive proteome coverage in minimal cells and single zygotes

Mass spectrometry (MS)-based proteomics workflows typically involve complex, multi-step processes, presenting challenges with sample losses, reproducibility, requiring substantial time and financial investments, and specialized skills. Here we introduce One-Tip, a proteomics methodology that seamlessly integrates efficient, one-pot sample preparation with precise, narrow-window data-independent acquisition (nDIA) analysis. One-Tip substantially simplifies sample processing, enabling the reproducible identification of >9000 proteins from ~1000 HeLa cells. The versatility of One-Tip is highlighted by nDIA identification of ~6000 proteins in single cells from early mouse embryos. Additionally, the study incorporates the Uno Single Cell Dispenser™, demonstrating the capability of One-Tip in single-cell proteomics with >3000 proteins identified per HeLa cell. We also extend One-Tip workflow to analysis of extracellular vesicles (EVs) extracted from blood plasma, demonstrating its high sensitivity by identifying >3000 proteins from 16 ng EV preparation. One-Tip expands capabilities of proteomics, offering greater depth and throughput across a range of sample types.