Clinical Proteomics

BackgroundMatrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) could aid the diagnosis of acute respiratory infections (ARI) owing to its affordability and high-throughput capacity. MALDI-MS has been proposed for use on commonly available respiratory samples, without specialized sample preparation, making this technology especially attractive for implementation in low-resource regions. Here, we assessed the utility of MALDI-MS in differentiating SARS-CoV-2 versus non-COVID acute respiratory infections (NCARI) in a clinical lab setting of Kazakhstan. MethodsNasopharyngeal swabs were collected from in- and outpatients with respiratory symptoms and from asymptomatic controls (AC) in 2020-2022. PCR was used to differentiate SARS-CoV-2+ and NCARI cases. MALDI-MS spectra were obtained for a total of 252 samples (115 SARS-CoV-2+, 98 NCARI and 39 AC) without specialized sample preparation. In our first sub-analysis, we followed a published protocol for peak preprocessing and Machine Learning (ML), trained on publicly available spectra from South American SARS-CoV-2+ and NCARI samples. In our second sub-analysis, we trained ML models on a peak intensity matrix representative of both South American (SA) and Kazakhstan (Kaz) samples. ResultsApplying the established MALDI-MS pipeline "as is" resulted in a high detection rate for SARS-CoV-2+ samples (91.0%), but low accuracy for NCARI (48.0%) and AC (67.0%) by the top-performing random forest model. After re-training of the ML algorithms on the SA-Kaz peak intensity matrix, the accuracy of detection by the top-performing Support Vector Machine with radial basis function kernel model was at 88.0, 95.0 and 78% for the Kazakhstan SARS-CoV-2+, NCARI, and AC subjects, respectively with a SARS-CoV-2 vs. rest ROC AUC of 0.983 [0.958, 0.987]; a high differentiation accuracy was maintained for the South American SARS-CoV-2 and NCARI. ConclusionsMALDI-MS/ML is a feasible approach for the differentiation of ARI without a specialized sample preparation. The implementation of MALDI-MS/ML in a real clinical lab setting will necessitate continuous optimization to keep up with the rapidly evolving landscape of ARI.

Viral infections are commonly diagnosed by the detection of viral genome fragments or proteins using targeted methods such as PCR and immunoassays. In contrast, metagenomics enables the untargeted identification of viral genomes, expanding its applicability across a broader spectrum. In this study, we introduce proteomics as a complementary approach for the untargeted identification of human-pathogenic viruses from patient samples. The viral proteomics workflow (vPro-MS) is based on an in-silico derived peptide library covering the human virome in UniProtKB (331 viruses, 20,386 genomes, 121,977 peptides), which was especially designed for diagnostic purposes. A scoring algorithm (vProID score) was developed to assess the confidence of virus identification from proteomics data (https://github.com/RKI-ZBS/vPro-MS). In combination with high-throughput diaPASEF-based data acquisition, this workflow enables the analysis of up to 60 samples per day. The specificity was determined to be > 99,9 % in an analysis of 221 plasma, swab and cell culture samples covering 18 different viruses (e.g. SARS, MERS, EBOV, MPXV). The sensitivity of this approach for the detection of SARS-CoV-2 in nasopharyngeal swabs corresponds to a PCR cycle threshold of 27 with comparable quantitative accuracy to metagenomics. vPro-MS enables the integration of untargeted virus identification in large-scale proteomic studies of biofluids such as human plasma to detect previously undiscovered virus infections in patient specimens.

INTRODUCTIONThe pandemic readiness toolbox needs to be extended, providing diagnostic tools that target different biomolecules, using orthogonal experimental setups and fit-for-purpose specification of detection. Here we build on a previous Cov-MS effort that used liquid chromatography-mass spectrometry (LC-MS) and describe a method that allows accurate, high throughput measurement of SARS-CoV-2 nucleocapsid (N) protein. MATERIALS and METHODSWe used Stable Isotope Standards and Capture by Anti-Peptide Antibodies (SISCAPA) technology to enrich and quantify proteotypic peptides of the N protein from trypsin-digested samples from COVID-19 patients. RESULTSThe Cov2MS assay was shown to be compatible with a variety of sample matrices including nasopharyngeal swabs, saliva and blood plasma and increased the sensitivity into the attomole range, up to a 1000-fold increase compared to direct detection in matrix. In addition, a strong positive correlation was observed between the SISCAPA antigen assay and qPCR detection beyond a quantification cycle (Cq) of 30-31, the level where no live virus can be cultured from patients. The automatable "addition only" sample preparation, digestion protocol, peptide enrichment and subsequent reduced dependency upon LC allow analysis of up to 500 samples per day per MS instrument. Importantly, peptide enrichment allowed detection of N protein in a pooled sample containing a single PCR positive sample mixed with 31 PCR negative samples, without loss in sensitivity. MS can easily be multiplexed and we also propose target peptides for Influenza A and B virus detection. CONCLUSIONSThe Cov2MS assay described is agnostic with respect to the sample matrix or pooling strategy used for increasing throughput and can be easily multiplexed. Additionally, the assay eliminates interferences due to protein-protein interactions including those caused by anti-virus antibodies. The assay can be adapted to test for many different pathogens and could provide a tool enabling longitudinal epidemiological monitoring of large numbers of pathogens within a population, applied as an early warning system.

Clear cell renal cell carcinoma (ccRCC) is the kidney malignancy with the highest incidence and mortality rates. Despite the high patient burden, there are no biomarkers for rapid diagnosis and public health surveillance. Urine would be an ideal source of ccRCC biomarkers due to the low invasiveness, easy accessibility, and the kidneys intrinsic role in filtering urine. In the present work, by combining proteomics, lipidomics and metabolomics, we detected urogenital metabolic dysregulation in ccRCC patients with increased lipid metabolism, altered mitochondrial respiration signatures and increased urinary lipid content. Importantly, we identify three early-stage diagnostic biomarkers for ccRCC in urine samples: Serum amyloid A1 (SAA1), Haptoglobin (HP) and Lipocalin 15 (LCN15). We further implemented a parallel reaction monitoring mass spectrometry protocol for rapid and sensitive detection of SAA1, HP and LCN15 and combined all three proteins into a diagnostic UrineScore. In our discovery cohort, this score had a performance accuracy of 96% in receiver operating characteristic curve (ROC) analysis for classification of ccRCC versus control cases. Our data identifies tractable and highly efficacious urinary biomarkers for ccRCC diagnosis and serve as a first step towards the development of more rapid and accessible urinary diagnostic platforms.

Ductal carcinoma in situ (DCIS) is the most common type (80%) of noninvasive breast lesions. The lack of validated prognostic markers, limited patient numbers and variable tissue quality significantly impact diagnosis, risk stratification, patient enrolment, and results of clinical studies. We performed label-free quantitative proteomics on 50 clinical formalin-fixed, paraffin embedded biopsies, validating 22 putative biomarkers from independent genetic studies. Our comprehensive proteomic phenotyping reveals more than 380 differentially expressed proteins and metabolic vulnerabilities, that can inform new therapeutic strategies for DCIS and IDC. Due to the readily druggable nature of proteins and metabolites, this study is of high interest for clinical research and pharmaceutical industry. To further evaluate our findings, and to promote the clinical translation of our study, we developed a highly multiplexed targeted proteomics assay for 90 proteins associated with cancer metabolism, RNA regulation and signature cancer pathways, such as Pi3K/AKT/mTOR and EGFR/RAS/RAF.

Ambient Ionisation Mass Spectrometry techniques: Desorption Electrospray Ionisation (DESI) and Laser Desorption - Rapid Evaporative Ionisation Mass Spectrometry (LD-REIMS) were used to detect the SARS-CoV-2 in dry nasal swabs. 45 patients were studied from samples collected between April - June 2020 in a clinical feasibility study. Diagnostic accuracy was calculated as 86.7% and 84% for DESI and LD-REIMS respectively. Results can be acquired in seconds providing robust and quick analysis of COVID-19 status which could be carried out without the need for a centralised laboratory. This technology has the potential to provide an alternative to population testing and enable the track and trace objectives set by governments and curtail the effects of a second surge in COVID-19 positive cases. In contrast to current PCR testing, using this technique there is no requirement of specific reagents which can cause devastating delays upon breakdowns of supply chains, thus providing a promising alternative testing method.

Massive formalin-fixed, paraffin-embedded (FFPE) tissue archives exist worldwide, representing a potential gold mine for clinical proteomics research. However, current protocols for FFPE proteomics lack standardization, efficiency, reproducibility, and scalability. Here we present High-Yield Protein Extraction and Recovery by direct SOLubilization (HYPERsol), an optimized workflow using adaptive-focused acoustics (AFA) ultrasonication and S-Trap sample processing that enables proteome coverage and quantification from FFPE samples comparable to that achieved from flash-frozen tissue (average R = 0.936).

The ability to identify spatially resolved proteomes has advanced markedly in recent years, yet integrating definitive protein identification with precise spatial localization in a single workflow remains a challenge. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) enables antibody-free mapping of proteins directly in tissue sections, but its capacity for unambiguous protein identification is limited. Here, we present a combined MALDI-MSI and liquid chromatography-tandem mass spectrometry (LC-MS/MS) approach, to map protein localization, and track spatial changes in murine thymus during chemotherapy-induced involution and regeneration. Our workflow incorporates a scoring algorithm (pepBridge) that aligns MALDI-MSI molecular signals with LC-MS/MS identifications, enabling confident assignment of proteins that are critical to thymic function. Using this pipeline, we reveal spatiotemporal changes in proteins involved in cell migration, cytoskeletal remodeling, and endogenous thymic regeneration. Notably, we identify distinct spatial shifts in Nucleoprotein TPR and Tubulin-associated chaperone A (TBCA), corresponding to chemotherapy-driven architectural remodeling. From a translational perspective, these findings highlight pathways and candidate targets to promote immune recovery in pediatric cancer patients undergoing cytoreductive therapy. Analytically, this framework advances spatial proteomics by enabling high-confidence protein identification in lymphoid and other tissues, broadening the potential of translational proteomic research.

Clinical proteomics has the potential to add a valuable data layer to genomic and histopathological analyses in precision oncology, but its application to limited clinical material remains challenging. Here, we demonstrate that state-of-the-art mass spectrometry-based proteomics enables in-depth proteomic profiling of formalin-fixed paraffin-embedded (FFPE), including small biopsies and single tissue sections mounted on glass slides. Despite minimal input material, single-slide analyses of clinical lung tumor specimens yielded biologically and clinically informative data, supporting detection of actionable proteins, immune-related signatures, and multivariate biomarkers. These results establish the feasibility of proteomics for retrospective FFPE studies and routine clinical practice, expanding opportunities for biomarker discovery and precision medicine from scarce tissue material.

Advancements in deep plasma proteomics are enabling high-resolution measurement of plasma proteoforms, which may reveal a rich source of novel biomarkers previously concealed by aggregated protein methods. Here, we analyze 188 plasma proteomes from non-small cell lung cancer subjects (NSCLC) and controls to identify NSCLC-associated protein isoforms by examining differentially abundant peptides as a proxy for isoform-specific exon usage. We find four proteins comprised of peptides with opposite patterns of abundance between cancer and control subjects. One of these proteins, BMP1, has known isoforms that can explain this differential pattern, for which the abundance of the NSCLC-associated isoform increases with stage of NSCLC progression. The presence of cancer and control-associated isoforms suggests differential regulation of BMP1 isoforms. The identified BMP1 isoforms have known functional differences, which may reveal insights into mechanisms impacting NSCLC disease progression.

We performed quantitative proteomics on 61 human-derived breast cancer cell lines to a depth of ~13,000 proteins. The resulting high-throughput datasets were assessed for quality and reproducibility. We used the datasets to identify and characterize the subtypes of breast cancer and showed that they conform to known transcriptional subtypes, revealing that molecular subtypes are preserved even in under-sampled protein feature sets. All datasets are freely available as public resources on the LINCS portal. We anticipate that these datasets, either in isolation or in combination with complimentary measurements such as genomics, transcriptomics and phosphoproteomics, can be mined for the purpose of predicting drug response, informing cell line specific context in models of signalling pathways, and identifying markers of sensitivity or resistance to therapeutics.

Formalin-fixed, paraffin-embedded (FFPE) tissues constitute the primary material for diagnostic pathology and retrospective clinical research, yet their use in metabolomics remains limited due to molecular cross-linking and analyte degradation. Here, we establish a cost-efficient molecular pathology workflow that integrates ultra-high-performance liquid chromatography mass spectrometry (UHPLC-MS) with matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to quantify and spatially map nucleosides in FFPE breast cancer tissues. Optimized extraction using methanol yielded nucleoside profiles comparable to fresh-frozen tissues, while MALDI-MSI enabled the spatial visualization of nine nucleosides across distinct histological regions. Several nucleosides including deoxyadenosine and 5-formylcytosine showed strong discriminatory power between tumor stages, revealing progressive metabolic rewiring during breast cancer progression. Finally, spatial nucleoside patterns observed in a murine model were recapitulated in patient-derived FFPE tissues, underscoring the translational potential of nucleoside-based spatial metabolomics for clinical research and biomarker discovery. Together, this workflow establishes MALDI-MSI as a powerful and scalable spatial molecular pathology tool for interrogating nucleoside biology in archival breast cancer samples. Following MALDI-MSI, the same FFPE tissue sections can undergo laser capture microdissection, enabling genomic, proteomic, or targeted metabolomic profiling of MSI-defined tumor niches and microenvironmental regions. This integration directly links spatial nucleoside signatures to molecular alterations relevant to precision oncology in future.

Changes in the human proteome caused by disease before, during and after medical care is phenotype-dependent, so the proteome of each individual at any time point is a snapshot of the bodys response to disease and to disease treatment. Here, we introduce a new concept named differential Personal Pathway index (dPPi). This tool extracts and summates comprehensive disease-specific information contained within an individuals proteome as a holistic way to follow the response to disease and medical care over time. We demonstrate the principle of the dPPi algorithm on proteins found in urine from patients suffering from neoplasia of the bladder. The relevance of the dPPi results to the individual clinical cases is described. The dPPi concept can be extended to other malignant and non-malignant diseases, and to other types of biopsies, such as plasma, serum or saliva. We envision the dPPi as a tool for clinical decision-making in precision medicine.

Reliable large-scale testing for respiratory viruses, including influenza viruses, coronaviruses, such as SARS-CoV-2, and respiratory syncytial virus (RSV) is essential for both endemic surveillance and pandemic response. While RT-qPCR remains the current gold standard, the COVID-19 pandemic highlighted the need for alternative, scalable detection platforms. Although alternative approaches like liquid chromatography coupled with mass spectrometry (LC-MS) are well-suited for viral multiplexing, they have yet to undergo clinical validation. Here, we present a quantitative immuno-multiple reaction monitoring (iMRM) assay, called Winterplex, for the simultaneous detection of influenza A, influenza B, RSV, and SARS-CoV-2, each targeted via two proteotypic peptides per virus. The method was validated according to ISO standards for in vitro diagnostics and benchmarked against RT-qPCR. To enhance future pandemic preparedness, urgent investment is needed to translate and expand multiplexed MS-based diagnostic methodologies, which offer the flexibility to incorporate a wide range of targets, making them ideally suited for rapid and adaptable responses to emerging viral threats.

Urine based biomarker discovery employing proteomics platform has been successfully attempted for multiple diseases. Urine is an excellent source of biomarker discovery but its potential is not fully tapped in tuberculosis (TB) diagnostics. In the present study, proteomic profiling of urine samples from thirty five subjects (Mean age=41 years (15-76), M/F=28/7) belonging to active TB, latent TB, lung cancer, chronic obstructive pulmonary disorders (COPD) and healthy subjects were carried out employing a robust multiplex technique. We identified 131 proteins out of which 16 molecules showed at least two-fold change in TB. The study identified a signature of three putative markers, leucine-rich alpha-2-glycoprotein (up-regulated), roundabout homolog 4 and isoform 2 of prostatic acid phosphatase (down-regulated) that could differentiate active TB from other pulmonary diseases. Besides, we investigated whether a network based approach can be efficiently used to expand dynamic coverage, gain a comprehensive view of underlying perturbed functions during the infection and to discover potential biomarkers. While comparing the functionally associated sub-networks of active TB with healthy urine proteome, we identified 54 proteins from the discriminative TB sub-network, some of which are known to be involved in the infection process. Few examples in this study like serpin peptidase inhibitor and catenin that has not been identified in the experiment but detected in the difference network demonstrate that proteomic profiling when integrated with network biology method could be a holistic approach to expand the dynamic range and identify potential candidate biomarkers and also provide a broad overview of perturbed functions during the infection.

Lung tissue is composed of various functional units, each essential for maintaining the intricate functions of the lung. Disruptions in the molecular and cellular mechanisms in the lung can cause tissue fibrosis, inflammation, and severe breathing difficulties, which are common in conditions such as bronchopulmonary dysplasia (BPD). BPDs molecular changes are not well understood, which hinders effective diagnosis and treatment. Here, we present a new multimodal imaging workflow for detailed molecular and metabolic characterization of tissues at multiple spatial scales. We applied a combined imaging approach using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and ultrafast focused light-based imaging & photonics platform (U-FLIP) that included two-photon fluorescence (TPF), second harmonic generation (SHG), and stimulated Raman scattering (SRS). We also developed a hierarchical multimodal registration network (HiMReg) for the precise co-registration of each modality. This approach revealed previously unknown metabolic changes in distinct functional tissue units affected by BPD, including altered lipid distributions, reduced optical redox states, and specific collagen remodeling in bronchioles. Our findings evidenced alterations in lipid composition and metabolism of BPD-affected alveoli compared to healthy tissue, providing novel insights into disease pathophysiology. Our findings elucidate the intricate spatial and molecular complexity of BPD, building on prior research that did not provide the spatial resolution necessary to capture the nuances of metabolic alterations. This multimodal approach offers exceptional insights into disease exploration and could transform the way we study spatially heterogeneous conditions. By providing detailed maps of the metabolic shifts occurring in distinct tissue microanatomical features, the methods developed here could enable the discovery of new therapeutic avenues, making it highly attractive for the field of biomedical research.

Metabolomics has emerged as a mainstream approach for investigating complex metabolic phenotypes but has yet to be integrated into routine clinical diagnostics. Metabolomics-based diagnosis of urinary tract infections (UTIs) is a logical application of this technology since microbial waste products are concentrated in the bladder and thus could be suitable markers of infection. We conducted an untargeted metabolomics screen of clinical specimens from patients with suspected UTIs and identified two metabolites, agmatine and N6-methyladenine, that are predictive of culture positive samples. We developed a 3.2-minute LC-MS assay to quantify these metabolites and showed that agmatine and N6-methyladenine correctly identify UTIs caused by 13 Enterobacterales species and 3 non-Enterobacterales species, accounting for over 90% of infections (agmatine AUC > 0.95; N6-methyladenine AUC > 0.89). These markers were robust predictors across two blinded cohorts totaling 1,629 patient samples. These findings demonstrate the potential utility of metabolomics in clinical diagnostics for rapidly detecting UTIs.

We present here a novel surface mass spectrometry strategy to perform untargeted metabolite profiling of formalin-fixed paraffin-embedded (FFPE) pediatric ependymoma archives. Sequential Orbitrap secondary ion mass spectrometry (3D OrbiSIMS) and liquid extraction surface analysis-tandem MS (LESA-MS/MS) permitted the detection of 887 metabolites (163 chemical classes) from pediatric ependymoma tumor tissue microarrays (diameter <1 mm; thickness: 4 m). From these 163 classes, 60 classes were detected with both techniques, whilst LESA-MS/MS and 3D OrbiSIMS individually allowed the detection of another 83 and 20 unique metabolite classes, respectively. Through data fusion and multivariate analysis, we were able to identify key metabolites and corresponding pathways predictive of tumor relapse which were retrospectively confirmed using gene expression analysis with publicly available data. Altogether, this sequential mass spectrometry strategy has shown to be a versatile tool to perform high throughput metabolite profiling on sample-limited tissue archives. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC="FIGDIR/small/182071v3_ufig1.gif" ALT="Figure 1"> View larger version (11K): org.highwire.dtl.DTLVardef@1f9c850org.highwire.dtl.DTLVardef@1ce0568org.highwire.dtl.DTLVardef@c5023borg.highwire.dtl.DTLVardef@15ad6e_HPS_FORMAT_FIGEXP M_FIG For Table of Contents Only C_FIG

Identification of biomarkers of early-stage disease typically requires analysis of very large cohorts which can only be reasonably achieved using high-throughput methods. We have developed and optimised novel methods for whole blood analysis using volumetric absorptive microsampling devices to produce over 3000 protein identifications by LCMS on a Q Exactive HF-X Orbitrap. These methods were tested using a set of whole blood samples from lung cancer patients and matching healthy controls finding 455 differentially expressed proteins, using a mid-throughput method enabling analysis of 18 samples per day. To increase throughput for larger clinical cohorts, a 60-sample per day method was tested on a Sciex ZenoTOF 7600. The high-throughput method produced 1.5-fold fewer protein identifications and a higher overall % CV compared to the mid-throughput method. Despite the lower numbers, it produced a set of 36 disease-relevant and discriminatory differentially expressed proteins that, using a machine learning model, could differentiate between the disease and control samples with an area under the ROC curve (AUC) of 88.9% using random forest algorithms. These data support the use of high-throughput mass spectrometry methods to screen large cohorts for diagnostic biomarkers that can then be followed up with more targeted analyses.

ObjectiveA catheter-free, non-radiolabeled method that permits in vivo measurement of tissue-specific glucose uptake does not exist. To address this gap, we sought to develop and validate a new, higher throughput mass spectrometry (MS)-based method that combines an injection of insulin with a non-radiolabeled glucose tracer, 2-fluoro-2-deoxyglucose (2FDG), to determine insulin-stimulated tissue-specific glucose clearance in conscious, unrestrained mice. MethodsInjections of saline or insulin with 2FDG were coupled with LC-Q Exactive Hybrid Quadrupole-Orbitrap (LC) MS-based measures of plasma 2FDG and tissue (2-fluoro-2-deoxyglucose-6-phosphate) 2FDGP to determine glucose clearance in mice under several different conditions. ResultsThe newly developed method was first applied to a dose response experiment in mice. Next, the ability of this method to quantify changes in glucose clearance in response to an insulin stimulus was assessed, and glucose clearance was compared between chow and high fat fed mice. Results from these studies showed that insulin-stimulated skeletal muscle and heart glucose clearance can be estimated following a bolus injection of tracer, and these fluxes are blunted in diet-induced obese mice. The broad applicability of this approach was then demonstrated by assessing glucose clearance in a mouse model with anticipated changes in insulin-stimulated skeletal muscle glucose metabolism. ConclusionsThe results validated a new LC-MS method to quantify insulin-stimulated tissue-specific glucose clearance in vivo without the use of catheters or radiolabeled tracers. The method offers great potential because it is designed for application to pre-clinical studies seeking high throughput tests and/or assays that can be coupled with discovery technologies such as genomics, proteomics and metabolomics. HIGHLIGHTSO_LIIn vivo glucose clearance can be estimated by a new non-radiolabeled method. C_LIO_LIThe plasma tracer to tracee ratio is required to determine tissue tracer phosphorylation. C_LIO_LIMeasures of plasma glucose and tracer kinetics are critical for data interpretation. C_LIO_LIThe new method can be combined with omics technologies such as metabolomics. C_LI