Untargeted Methods Research Articles

Reference materials for food authentication.

The global food industry faces significant challenges in ensuring the safety and authenticity of food products. Economic adulteration and counterfeiting of food are estimated to cost the industry US$30-40 billion annually. Analytical testing plays a vital role in detecting food fraud. For ensuring the metrological traceability and comparability of testing results, the use of reference materials (RMs) is crucial. The article describes the role of RMs in food authenticity testing, including their applications in method validation, calibration, quality control, and the definition of conventional measurement scales. It also reviews the availability of RMs that can be used in measurement procedures to authenticate food. Furthermore, the applications of RMs in targeted adulterant detection methods, for compositional parameters used to authenticate foods and food supplements, isotopic measurements, untargeted food authenticity testing methods, and detection and quantification of genetically modified organisms (GMOs), are explored. The document concludes by recommending the development of research grade test materials or representative test materials to harmonise untargeted testing methods and improve comparability of results across laboratories and over time.

Exploring the potential of MRM-IDA-EPI method in mass spectrometry for exposomic analysis: a commentary.

Exposomics is a field that studies environmental exposures and their impact on human health. The MRM-IDA-EPI method, which combines targeted and untargeted mass spectrometry methods, is useful for identifying and quantifying biomarkers in various biological matrices. The method's accuracy and precision in forensic toxicological screening suggest potential applications for detecting low-level environmental exposures. It can help detect and understand environmental exposures, explain their metabolic processes, and assess their impact on human health more effectively.

Validation and quantification of peptide antigens presented on MHCs using SureQuant.

Vaccines and immunotherapies that target peptide-major histocompatibility complexes (peptide-MHCs) have the potential to address multiple unmet medical needs in cancer and infectious disease. Designing vaccines and immunotherapies to target peptide-MHCs requires accurate identification of target peptides in infected or cancerous cells or tissue, and may require absolute or relative quantification to identify abundant targets and measure changes in presentation under different treatment conditions. Internal standard parallel reaction monitoring (also known as 'SureQuant') can be used to validate and/or quantify MHC peptides previously identified by using untargeted methods such as data-dependent acquisition. SureQuant MHC has three main use cases: (i) conclusive confirmation of the identities of putative MHC peptides via comparison with an internal synthetic stable isotope labeled (SIL) peptide standard; (ii) accurate relative quantification by using pre-formed heavy isotope-labeled peptide-MHC complexes (hipMHCs) containing SIL peptides as internal controls for technical variation; and (iii) absolute quantification of each target peptide by using different amounts of hipMHCs loaded with synthetic peptides containing one, two or three SIL amino acids to provide an internal standard curve. Absolute quantification can help determine whether the abundance of a peptide-MHC is sufficient for certain therapeutic modalities. SureQuant MHC therefore provides unique advantages for immunologists seeking to confidently validate antigenic targets and understand the dynamics of the MHC repertoire. After synthetic standards are ordered (3-4 weeks), this protocol can be carried out in 3-4 days and is suitable for individuals with mass spectrometry experience who are comfortable with customizing instrument methods.

PathMHC: a workflow to selectively target pathogen-derived MHC peptides in discovery immunopeptidomics experiments for vaccine target identification.

Vaccine-elicited T cell responses can contribute to immune protection against emerging infectious disease risks such as antimicrobials-resistant (AMR) microbial pathogens and viruses with pandemic potential, but rapidly identifying appropriate targets for T cell priming vaccines remains challenging. Mass spectrometry (MS) analysis of peptides presented on major histocompatibility complexes (MHCs) can identify potential targets for protective T cell responses in a proteome-wide manner. However, pathogen-derived peptides are outnumbered by self peptides in the MHC repertoire and may be missed in untargeted MS analyses. Here we present a novel approach, termed PathMHC, that uses computational analysis of untargeted MS data followed by targeted MS to discover novel pathogen-derived MHC peptides more efficiently than untargeted methods alone. We applied this workflow to identify MHC peptides derived from multiple microbes, including potential vaccine targets presented on MHC-I by human dendritic cells infected with Mycobacterium tuberculosis . PathMHC will facilitate antigen discovery campaigns for vaccine development.

P21-45 Development of LC-HRMS untargeted analysis methods for neurological extracellular vesicles into environmental exposure science

P21-45 Development of LC-HRMS untargeted analysis methods for neurological extracellular vesicles into environmental exposure science

Untargeted and targeted metabolomics analysis of CO poisoning and mechanical asphyxia postmortem interval biomarkers in rat and human plasma by GC[sbnd]MS

Accurate and objective estimation of the postmortem interval (PMI) is crucial in forensic practice. This study aimed to infer PMI through equations based on the relationship between PMI and metabolomics biomarkers.Rats were subjected to models representing various temperatures and causes of death, with blood collected at different intervals. Untargeted gas chromatographymass spectrometry metabolomics detection methods were developed, and candidate biomarkers were chosen as co-differentially expressed metabolites in four models. A targeted method was then developed for quantitatively determining candidate biomarkers. Animal tests and human cadaver samples with clearly documented causes of death and time were used to verify the reliability of the regression equation.Results: Unique differential metabolites for CO poisoning deaths included 2,3-butanediol, hypoxanthine, and dehydrated hexanol, while those for mechanical asphyxia deaths comprised propylamine, 1,3-propylene glycol, phosphoric acid, and sorbitol. Pyruvate, glycerol and isoleucine were identified as candidate biomarkers. Human case results demonstrated the method's potential (error rate < 20 %). The findings of this study may offer reference points for estimating PMI and causes of death in forensic practice.

Controlling glycolysis to generate characteristic volatile organic compounds of lung cancer cells

Characteristic volatile organic compounds (VOCs) are anticipated to be used for the identification of lung cancer cells. However, to date, consistent biomarkers of VOCs in lung cancer cells have not been obtained through direct comparison between cancer and healthy groups. In this study, we regulated the glycolysis, a common metabolic process in cancer cells, and employed solid phase microextraction gas chromatography mass spectrometry (SPME–GC–MS) combined with untargeted analysis to identify the characteristic VOCs shared by cancer cells. The VOCs released by three types of lung cancer cells (A549, PC-9, NCI-H460) and one normal lung epithelial cell (BEAS-2B) were detected using SPME–GC–MS, both in their resting state and after treatment with glycolysis inhibitors (2-Deoxy-d-glucose, 2-DG/3-Bromopyruvic acid, 3-BrPA). Untargeted analysis methods were employed to compare the VOC profiles between each type of cancer cell and normal cells before and after glycolysis regulation. Our findings revealed that compared to normal cells, the three types of lung cancer cells exhibited three common differential VOCs in their resting state: ethyl propionate, acetoin, and 3-decen-5-one. Furthermore, under glycolysis control, a single common differential VOC—acetoin was identified. Notably, acetoin levels increased by 2.60–3.29-fold in all three lung cancer cell lines upon the application of glycolysis inhibitors while remaining relatively stable in normal cells. To further elucidate the formation mechanism of acetoin, we investigated its production by blocking glutaminolysis. This interdisciplinary approach combining metabolic biochemistry with MS analysis through interventional synthetic VOCs holds great potential for revolutionizing the identification of lung cancer cells and paving the way for novel cytological examination techniques.

A global perspective on the status of clinical metabolomics in laboratory medicine- a survey by the IFCC metabolomics working group.

Metabolomics aims for comprehensive characterization and measurement of small molecule metabolites (<1700 Da) in complex biological matrices. This study sought to assess the current understanding and usage of metabolomics in laboratory medicine globally and evaluate the perception of its promise and future implementation. A survey was conducted by the IFCC metabolomics working group that queried 400 professionals from 79 countries. Participants provided insights into their experience levels, knowledge, and usage of metabolomics approaches, along with detailing the applications and methodologies employed. Findings revealed a varying level of experience among respondents, with varying degrees of familiarity and utilization of metabolomics techniques. Targeted approaches dominated the field, particularly liquid chromatography coupled to a triple quadrupole mass spectrometer, with untargeted methods also receiving significant usage. Applications spanned clinical research, epidemiological studies, clinical diagnostics, patient monitoring, and prognostics across various medical domains, including metabolic diseases, endocrinology, oncology, cardiometabolic risk, neurodegeneration and clinical toxicology. Despite optimism for the future of clinical metabolomics, challenges such as technical complexity, standardization issues, and financial constraints remain significant hurdles. The study underscores the promising yet intricate landscape of metabolomics in clinical practice, emphasizing the need for continued efforts to overcome barriers and realize its full potential in patient care and precision medicine.

An integrated LC-MS/MS platform for noninvasive urinary nucleus acid adductomics: A pilot study for tobacco exposure

Tobacco smoke exposure significantly increases the level of global nucleoside damage. To evaluate all aspects of nucleic acid (NA) modifications, NA adductomics analyzes DNA, RNA and nucleobase adducts and provides comprehensive data. Liquid chromatography-tandem triple quadrupole mass spectrometry (LC-QQQ-MS/MS) and LC-Zeno-TOF-MS/MS were employed to screen for DNA, RNA and nucleobase adducts, as part of the analytical platform that was designed to combine high sensitivity and high resolution detection. We identified and distinguished urine nucleoside adducts via precursor ion and neutral loss scanning. A total of 245 potential adducts were detected, of which 28 were known adducts. The smoking group had significantly higher concentrations of nucleoside adducts in rat urine than the control group, based on MRM scanning, which was then used to perform relative quantitative analysis of these adducts. Urine nucleoside adducts were further confirmed using LC-Zeno-TOF-MS/MS. This highlights the potential of untargeted detection methods to provide comprehensive data on both known and unknown adducts. These approaches can be used to investigate the interactions among oxidative and alkylation stresses, and epigenetic modifications caused by exposure to tobacco smoke.

Transporter Protein Expression of Corneal Epithelium in Rabbit and Porcine: Evaluation of Models for Ocular Drug Transport Study.

The transcorneal route is the main entry route for drugs to the intraocular parts, after topical administration. The outer surface, the corneal epithelium (CE), forms the rate-limiting barrier for drug permeability. Information about the role and protein expression of drug and amino acid transporter proteins in the CE is sparse and lacking. The aim of our study was to characterize transporter protein expression in rabbit and porcine CE to better understand potential drug and nutrient absorption after topical administration. Proteins, mainly Abc and Slc transporters, were characterized with quantitative targeted absolute proteomics and global untargeted proteomics methods. In the rabbit CE, 24 of 48 proteins were detected in the targeted approach, and 21 of these were quantified. In the porcine CE, 26 of 58 proteins were detected in the targeted approach, and 20 of these were quantified. Among these, 15 proteins were quantified in both animals: 4f2hc (Slc3a2), Aqp0, Asct1 (Slc1a4), Asct2 (Slc1a5), Glut1 (Slc2a1), Hmit (Slc2a13), Insr, Lat1 (Slc7a5), Mct1 (Slc16a1), Mct2 (Slc16a7), Mct4 (Slc16a3), Mrp 4 (Abcc4), Na+/K+-ATPase, Oatp3a1 (Slco3a1), and Snat2 (Slc38a2). Overall, the global proteomics results supported the targeted proteomics results. Organic anion transporting polypeptide Oatp3a1 was detected and quantified for the first time in both rabbit (1.4 ± 0.4 fmol/cm2) and porcine (11.1 ± 5.3 fmol/cm2) CE. High expression levels were observed for L-type amino acid transporter, Lat1, which was quantified with newly selected extracellular domain peptides in rabbit (48.9 ± 11.8 fmol/cm2) and porcine (37.6 ± 11.5 fmol/cm2) CE. The knowledge of transporter protein expression in ocular barriers is a key factor in the successful design of new ocular drugs, pharmacokinetic modeling, understanding ocular diseases, and the translation to human.

WTV2.0: A high-coverage plant volatilomics method with a comprehensive selective ion monitoring acquisition mode

Volatilomics is essential for understanding the biological functions and fragrance contributions of plant volatiles. However, the annotation coverage achieved using current untargeted and widely targeted volatomics (WTV) methods has been limited by low sensitivity and/or low acquisition coverage. Here, we introduce WTV 2.0, which enabled the construction of a high-coverage library containing 2111 plant volatiles, and report the development of a comprehensive selective ion monitoring (cSIM) acquisition method, including the selection of characteristic qualitative ions with the minimal ion number for each compound and an optimized segmentation method, that can acquire the smallest but sufficient number of ions for most plant volatiles, as well as the automatic qualitative and semi-quantitative analysis of cSIM data. Importantly, the library and acquisition method we developed can be self-expanded by incorporating compounds not present in the library, utilizing the obtained cSIM data. We showed that WTV 2.0 increases the median signal-to-noise ratio by 7.6-fold compared with the untargeted method, doubled the annotation coverage compared with the untargeted and WTV 1.0 methods in tomato fruit, and led to the discovery of menthofuran as a novel flavor compound in passion fruit. WTV 2.0 is a Python library with a user-friendly interface and is applicable to profiling of volatiles and primary metabolites in any species.

Metabolic characterization of sphere-derived prostate cancer stem cells reveals aberrant urea cycle in stemness maintenance.

Alteration of cell metabolism is one of the essential characteristics of tumor growth. Cancer stem cells (CSCs) are the initiating cells of tumorigenesis, proliferation, recurrence, and other processes, and play an important role in therapeutic resistance and metastasis. Thus, identification of the metabolic profiles in prostate cancer stem cells (PCSCs) is critical to understanding prostate cancer progression. Using untargeted metabolomics and lipidomics methods, we show distinct metabolic differences between prostate cancer cells and PCSCs. Urea cycle is the most significantly altered metabolic pathway in PCSCs, the key metabolites arginine and proline are evidently elevated. Proline promotes cancer stem-like characteristics via the JAK2/STAT3 signaling pathway. Meanwhile, the enzyme pyrroline-5-carboxylate reductase 1 (PYCR1), which catalyzes the conversion of pyrroline-5-carboxylic acid to proline, is highly expressed in PCSCs, and the inhibition of PYCR1 suppresses the stem-like characteristics of prostate cancer cells and tumor growth. In addition, carnitine and free fatty acid levels are significantly increased, indicating reprogramming of fatty acid metabolism in PCSCs. Reduced sphingolipid levels and increased triglyceride levels are also observed. Collectively, our data illustrate the comprehensive landscape of the metabolic reprogramming of PCSCs and provide potential therapeutic strategies for prostate cancer.

Screening for Forensically Relevant Drugs Using Data-Independent High-Resolution Mass Spectrometry.

Forensic and clinical laboratories are expected to provide a rapid screening of samples for a wide range of analytes; however, the ever-changing landscape of illicit substances makes analysis complicated. There is a great need for untargeted methods that can aid these laboratories in broad-scope drug screening. Liquid chromatography hyphenated with high-resolution mass spectrometry (LC-HRMS) has become a popular technique for untargeted screening and presumptive identification of drugs of abuse due to its superior sensitivity and detection capabilities in complex matrices. An untargeted extraction and data acquisition method was evaluated for the broad screening of high-priority drugs of abuse in whole blood. A total of 35 forensically relevant target analytes were identified and extracted at biologically relevant low and high (10× low) concentrations from whole blood using supported liquid extraction. Data-independent acquisition was accomplished using ultraperformance liquid chromatography and a quadrupole time-of-flight mass spectrometry. Results were acceptable for screening assays, with limits of detection at or below the recommended low-concentration cutoffs for most analytes. Analyte ionization varied from 30.1 to 267.6% (average: 110.5%) at low concentrations and from 8.6 to 383.5% (average: 93.6%) at high concentrations. Extraction recovery ranged from 8.5 to 330.5% (average: 105.3%) at low concentrations and from 9.4 to 127.5% (average: 82.7%) at high concentrations. This variability was also captured as precision, ranging from 4.7 to 135.2% (average: 36.5%) at low concentrations and from 0.9 to 59.0% (average: 21.7%) at high concentrations. The method described in this work is efficient and effective for qualitative forensic toxicology screening, as demonstrated by analysis of 166 authentic suspected impaired driver and postmortem specimens. That said, it is critical that laboratories establishing untargeted LC-HRMS screening assays be aware of the strengths and limitations across diverse drug categories and chemical structures.

An Efficient Workflow for Quality Control Marker Screening and Metabolite Discovery in Dietary Herbs by LC-Orbitrap-MS/MS and Chemometric Methods: A Case Study of Chrysanthemum Flowers.

LC-MS is widely utilized in identifying and tracing plant-derived food varieties but quality control markers screening and accurate identification remain challenging. The adulteration and confusion of Chrysanthemum flowers highlight the need for robust quality control markers. This study established an efficient workflow by integrating UHPLC-Orbitrap-MS/MS with Compound Discoverer and chemometrics. This workflow enabled the systematic screening of 21 markers from 10,540 molecular features, which effectively discriminated Chrysanthemum flowers of different species and cultivars. The workflow incorporated targeted and untargeted methods by employing diagnostic product ions, fragmentation patterns, mzCloud, mzVault, and in-house databases to identify 206 compounds in the flowers, including 17 screened markers. This approach improved identification accuracy by reducing false positives, eliminating in-source fragmentation interference, and incorporating partial verification utilizing our established compound bank. Practically, this workflow can be instrumental in quality control, geolocation determination, and varietal tracing of Chrysanthemum flowers, offering prospective use in other plant-derived foods.

Development of an Untargeted LC-MS Metabolomics Method with Postcolumn Infusion for Matrix Effect Monitoring in Plasma and Feces.

Untargeted metabolomics based on reverse phase LC-MS (RPLC-MS) plays a crucial role in biomarker discovery across physiological and disease states. Standardizing the development process of untargeted methods requires paying attention to critical factors that are under discussed or easily overlooked, such as injection parameters, performance assessment, and matrix effect evaluation. In this study, we developed an untargeted metabolomics method for plasma and fecal samples with the optimization and evaluation of these factors. Our results showed that optimizing the reconstitution solvent and sample injection amount was critical for achieving the balance between metabolites coverage and signal linearity. Method validation with representative stable isotopically labeled standards (SILs) provided insights into the analytical performance evaluation of our method. To tackle the issue of the matrix effect, we implemented a postcolumn infusion (PCI) approach to monitor the overall absolute matrix effect (AME) and relative matrix effect (RME). The monitoring revealed distinct AME and RME profiles in plasma and feces. Comparing RME data obtained for SILs through postextraction spiking with those monitored using PCI compounds demonstrated the comparability of these two methods for RME assessment. Therefore, we applied the PCI approach to predict the RME of 305 target compounds covered in our in-house library and found that targets detected in the negative polarity were more vulnerable to the RME, regardless of the sample matrix. Given the value of this PCI approach in identifying the strengths and weaknesses of our method in terms of the matrix effect, we recommend implementing a PCI approach during method development and applying it routinely in untargeted metabolomics.

LCMS: An R package for automated semitargeted analysis in lipidomics

AbstractWhile nontargeted analysis aims to profile and report the relative distributions of a wide range of molecules from different lipid classes/subclasses, its major challenge is the annotation and identification of the molecules. Semitargeted analysis circumvents the problem by establishing a (potentially large) list of molecules to be targeted in the samples that are identified before the analysis. This approach is particularly adapted for lipid analysis to help with the automation of lipid annotation and identification. However, the manual extraction of peaks for many molecules and many samples is time consuming. Consequently, an automation of these extractions is deeply required. This paper presents a free R package for the automation of semitargeted analysis for lipid analysis. From raw files collected with LC‐MS device and a list of molecules to target (containing their class), it automatically returns Excel files containing the intensities for each targeted molecule and each sample. This package allows a fast computation of the intensities. Furthermore, it guarantees the reproducibility of the results and is freely available and user‐friendly.Practical Applications: With the help of the R package presented in this paper, the use of semitargeted lipidomics as an alternative to untargeted analysis should be investigated by more labs. Work on the comparisons between the approaches could be conducted. While untargeted methods are mostly used, they require long pretreatments and identification of molecules of interest. On the contrary, in semitargeted analysis, once the integration table and retention time are obtained, the results are fast and directly interpretable. An idea for lipidomics would be to use untargeted lipidomics to compute the integration table and retention table, then use semitargeted analysis for a fast computation of well identified molecules.

Lipid composition, caloric content, and novel oxidation products from microbial communities within seasonal pack ice cores

Sea ice is a critical feature of polar environments with importance for coastal ecosystems. Along the West Antarctic Peninsula (WAP), climate change is causing decreases in seasonal sea ice duration and extent. Organic carbon within sea ice is an important aspect of southern ocean carbon cycling but less studied than water column or sedimentary carbon reservoirs. Though conditions within sea ice can be extreme, phytoplankton are able to flourish and serve as a key food source for higher trophic levels, especially in the spring and fall. A portion of this sea ice phytoplankton biomass is composed of lipids, which are both calorically dense and useful as tracers of biological processes within the sea ice. To better understand both the trophic value and the diversity of lipids present within sea ice we employed high-resolution accurate-mass mass spectrometry to analyze ice core samples from six sites collected along the peninsula in November 2018. Using untargeted methods we annotated 1,173 intact lipid species across 14 classes of intact polar lipids, triacylglycerols, and pigments. We compared lipids’ physical distribution within sea ice cores and found they are highly geographically and physically heterogenous within sites. Ratios between intact polar lipid classes show little signs of phytoplankton nutrient stress; a finding consistent with internally nutrient replete sea ice brines. We found key differences in the composition between pack ice versus fast ice and observed chlorophyll a to be inconsistently correlated with triacylglycerol content. We determined the mean caloric content of lipids within pack ice (4.45 ± 3.47 kJ m−2) and found that caloric densities from the lipids alone were in the range of total water column energy content. Lastly, we show evidence of fatty acid hydroxy fatty acid triacylglycerols within the ice matrix and discuss possible biochemical sources of these novel biomarkers in an ocean system. These results shed light on the chemical diversity of a dynamic and ecosystem relevant carbon pool.

Comparison of metagenomic and targeted methods for sequencing human pathogenic viruses from wastewater.

Most public health initiatives that monitor viruses in wastewater have utilized quantitative polymerase chain reaction (PCR) and whole genome PCR sequencing, mirroring techniques used for viral epidemiology in individuals. These techniques require prior knowledge of the target viral genome and are limited to monitoring individual or small groups of viruses. Metagenomic sequencing may offer an alternative strategy for monitoring a broad spectrum of viruses in wastewater, including novel and emerging pathogens. In this study, while amplicon sequencing gave high viral genome coverage, untargeted shotgun sequencing of total nucleic acid samples was unable to detect human pathogenic viruses with enough sensitivity for use in genomic epidemiology. Enrichment of shotgun libraries for respiratory viruses using hybrid-capture technology provided genotypic information on a range of viruses simultaneously, indicating strong potential for wastewater surveillance. This type of targeted metagenomics could be used for monitoring diverse targets, such as pathogens or antimicrobial resistance genes, in environmental samples.

Specific detection of protein carbonylation sites by 473nm photodissociation mass spectrometry.

The study of protein oxidation remains a challenge despite the biomedical interest in reliable biomarkers of oxidative stress. This is particularly true for carbonylations although, recently, liquid chromatography-mass spectrometry techniques (LC-MS) have been proposed to detect this non-enzymatic and poorly distributed oxidative modification of proteins using untargeted or carbonyl-reactive probe methods. These methods proved to be feasible but could not preserve the dynamic range of the protein sample, making it impossible to quantify oxidatively modified proteoforms compared with native proteoforms. Here, we propose an innovative method based on the implementation of a reactive carbonyl probe conjugated with a laser-sensitive chromophore, dabcyl-aminooxy, which confers optical specificity to the LC-MS approach. In addition, our protein carbonyl detection method allows usto localize individual carbonylation sites by observing fragments of derivatized oxidized peptides. Two model proteins, alpha-synuclein and beta-lactoglobulin, were oxidized and carbonylation sites were detected, resulting in the identification of respectively 34 and 77 different carbonylated amino acids. Thus, we demonstrated the application of a direct and sensitive method for studying protein carbonylation sites in complex protein extracts.

A Paratope-Enhanced Method to Determine Breadth and Depth TCR Clonal Metrics of the Private Human T-Cell Vaccine Response after SARS-CoV-2 Vaccination.

Quantitative metrics for vaccine-induced T-cell responses are an important need for developing correlates of protection and their use in vaccine-based medical management and population health. Molecular TCR analysis is an appealing strategy but currently requires a targeted methodology involving complex integration of ex vivo data (antigen-specific functional T-cell cytokine responses and TCR molecular responses) that uncover only public antigen-specific metrics. Here, we describe an untargeted private TCR method that measures breadth and depth metrics of the T-cell response to vaccine challenge using a simple pre- and post-vaccine subject sampling, TCR immunoseq analysis, and a bioinformatic approach using self-organizing maps and GLIPH2. Among 515 subjects undergoing SARS-CoV-2 mRNA vaccination, we found that breadth and depth metrics were moderately correlated between the targeted public TCR response and untargeted private TCR response methods. The untargeted private TCR method was sufficiently sensitive to distinguish subgroups of potential clinical significance also observed using public TCR methods (the reduced T-cell vaccine response with age and the paradoxically elevated T-cell vaccine response of patients on anti-TNF immunotherapy). These observations suggest the promise of this untargeted private TCR method to produce T-cell vaccine-response metrics in an antigen-agnostic and individual-autonomous context.