TRACE

Tue, 18 Aug 2026 00:00:00 +0000

TRACE is an integrated experimental–computational framework for untargeted LC–MS metabolomics. It uses complete ¹³C/¹⁵N labeling to validate biological metabolites, then maps how competing unlabeled nutrients enter metabolism by isotopic dilution rather than label enrichment.

The work is published in Analytical Chemistry (doi:10.1021/acs.analchem.6c02292). Software documentation is at drruili.github.io/TRACE. TRACE runs on MSdev objects and xcms peak tables.

Why TRACE

Untargeted LC–MS can survey microbial metabolism at scale, but two problems remain tightly coupled:

Which peaks are biological? Modern instruments report tens of thousands of features. Many are adducts, in-source fragments, or chemical contaminants rather than metabolites made by the cell.
Which nutrients are used when several are present? Classical isotope tracing follows one labeled substrate at a time. It does not ask how cells choose among competing carbon and nitrogen sources.

Existing tools, including our earlier single-peak method PAVE, validate features in isolation. Fixed m/z and retention-time tolerances then generate large numbers of false connections as sensitivity increases, and they struggle to group the many MS forms of one metabolite.

TRACE addresses both problems with one design: fully label cells with ¹³C-glucose and ¹⁵N-ammonium, treat isotope-confirmed peaks as network seeds, calibrate error tolerances from those seeds, then map unlabeled nutrient fate by isotopic dilution.

Experimental design

Saccharomyces cerevisiae CEN.PK was grown for more than ten generations in minimal medium with [U-¹³C₆]-glucose and [U-¹⁵N₂]-(NH₄)₂SO₄ as the sole carbon and nitrogen sources, so de novo metabolites carry predictable CN mass shifts.

Four tracer groups define the seeds:

Sample	Label	Role
Unlabeled	C0N0	Light carbon and nitrogen
¹⁵N-ammonium	C0Ny	Light carbon, heavy nitrogen
¹³C-glucose	CxN0	Heavy carbon, light nitrogen
Dual label	CxNy	Heavy carbon and nitrogen

For nutrient competition, unlabeled supplements (leucine, threonine, tryptophan, adenine, uracil, acetate, or a 14-compound mixture) were added on this labeled background. Incorporation of an unlabeled nutrient dilutes the ¹³C/¹⁵N labeling of downstream metabolites.

Figure 1. TRACE workflow. Isotope-confirmed peaks seed a network of adduct, fragment, and isotope connections; dynamic m/z and RT tolerances are learned from true CN links.

From seeds to a calibrated network

TRACE first keeps coeluting peak pairs whose m/z differences match theoretical ¹³C/¹⁵N shifts, then requires a complete unlabeled / ¹⁵N / ¹³C / dual-label CN bundle whose intensities match the expected 1:1:1:1 pattern (Pearson ρ > 0.75).

In a representative positive-mode data set this collapsed ~25,000 features and hundreds of millions of pairs to 1,098 high-confidence bundles. True CN links cluster tightly around zero m/z error and RT shift. TRACE fits that cluster as a Gaussian signal against a nonparametric background and uses the central 99% of the signal as dynamic tolerances, then propagates adduct, fragment, and isotope edges only inside those bounds.

Figure 2. Network construction and dynamic filtering. High-ρ CN bundles define data-driven m/z and RT tolerances that suppress implausible isotopes and rare fragment forms.

Global assignment and annotation

Edges are typed as isotope, fragment, or adduct and scored as chemical transformations (for example [M+H]⁺ → [M+Na]⁺ is H₋₁Na₁). TRACE then partitions seeds into compatible subnetworks so that one metabolite and its MS forms share a seed, instead of resolving conflicting pairwise assignments.

Against an authentic-standard library, TRACE removed PAVE hits with large m/z or RT error, corrected adduct mis-assignments caused by pairwise conflicts, and recovered features that PAVE had missed. About 70% of TRACE-annotated peaks matched HMDB, YMDB, KEGG, or an in-house database; about 27% of candidates matched in-house RT standards.

Figure 3. Network assignment. TRACE groups adducts, fragments, and isotopologues of one metabolite and annotates a representative seed.

More sensitivity is not more biology

The same labeled yeast extract was analyzed on four Orbitrap platforms (Q Exactive Plus, Exploris 480, Astral, Excedion Pro) and at several resolutions. Total features rose by 331% from QE Plus to Excedion Pro, while TRACE-validated metabolites rose only 117%. The share of high-confidence biological annotations fell from 2.94% to 1.48%. Extra peaks are mostly non-biological unless annotation is specific.

Figure 4. Instrument comparison. Feature count grows faster than TRACE-validated metabolites as sensitivity and resolution increase.

Nutrient fate under competition

With formulas and CN networks fixed from the four tracer groups, TRACE maps nutrient conditions onto that network. For each metabolite, the labeling fraction is the abundance-weighted ¹³C or ¹⁵N atom count; the absorption fraction is the drop in labeling relative to the fully labeled control.

The complete mixture contributed carbon and nitrogen across sulfur, purine, and amino-acid metabolism. Individual supplements were pathway-selective: tryptophan into tryptophan metabolism, adenine into purine metabolism, threonine into serine/glycine metabolism, acetate as C₂/C₄/C₆ units.

Two molecules test the method at opposite extremes of pathway architecture:

Glutathione (GSH) is a linear tripeptide. Its isotopologues tracked glutamate, glycine, and cysteine, including acetate-derived carbon in the glutamate moiety and threonine-derived C₂N₁ in glycine.
NAD⁺ is a hub (adenine + two riboses + nicotinamide). Unlabeled adenine produced both salvage (C₅N₀) and ongoing de novo (C₁₀N₅) forms; unlabeled tryptophan diluted nicotinamide via kynurenine; a C₀N₁ nicotinamide form indicated glutamine nitrogen entering independently of the carbon backbone.

Figure 5. Nutrient-source attribution. Isotopic dilution maps competing unlabeled nutrients into pathways; GSH and NAD⁺ illustrate linear assembly versus convergent biosynthesis.

Software

A typical analysis loads a pre-processed MSdev object (xcms peaks with sample.type set to the four isotope groups), runs the pipeline, and exports tables:

library(MSdev)
library(TRACE)

obj <- TRACE_workflow(
 obj,
 rt.tol = 10,
 ppm = 5,
 cpdb = "path/to/trace.cp.db.xlsx"
)

TRACE_export(obj, file = "TRACE_results.xlsx")

Version	Role
`v1.0.0`	Code used for the published article
`main` (1.1.0+)	Ongoing development

Install the article snapshot with devtools::install_github("DrRuiLi/TRACE", ref = "v1.0.0"). See Get started for the full workflow.

Scope

TRACE is built for systems in which complete isotopic labeling (the 1:1:1:1 CN bundle) is achievable, as in yeast grown for many generations on labeled glucose and ammonium. It is not a drop-in seed detector for mammalian cells. High-confidence TRACE annotations can still be transferred by running mammalian samples in the same batch as fully labeled microbial references and aligning features.

Mass Spectrometry based Isotopomer Parsing

Mon, 17 Mar 2025 00:00:00 +0000

Gout Biomarkers

Sun, 16 Mar 2025 00:00:00 +0000

To discover differential metabolites and pathways underlying infrequent gout flares (InGF) and frequent gout flares (FrGF) using metabolomics and establish a predictive model by machine learning (ML) algorithms.

Serum samples from a discovery cohort with 163 InGF and 239 FrGF patients were analyzed by mass spectrometry-based untargeted metabolomics to profile differential metabolites and explore dysregulated metabolic pathways using pathway enrichment analysis and network propagation-based algorithms. ML algorithms were performed to establish a predictive model based on selected metabolites, which was further optimized by a quantitative targeted metabolomics method and validated in an independent validation cohort with 97 participants with InGF and 139 participants with FrGF.

Differential metabolites between InGF and FrGF groups were identified. Top dysregulated pathways included carbohydrates, amino acids, bile acids, and nucleotide metabolism.

Subnetworks with maximum disturbances in the global metabolic networks featured cross-talk between purine metabolism and caffeine metabolism, as well as interactions among pathways involving primary bile acid biosynthesis, taurine and hypotaurine metabolism, alanine, aspartate and glutamate metabolism, suggesting epigenetic modifications and gut microbiome in metabolic alterations underlying InGF and FrGF.

Potential metabolite biomarkers were identified using ML-based multivariable selection and further validated by targeted metabolomics. Area under receiver operating characteristics curve for differentiating InGF and FrGF achieved 0.88 and 0.67 for the discovery and validation cohorts, respectively.

In summary, Systematic metabolic alterations underlie InGF and FrGF, and distinct profiles are associated with differences in gout flare frequencies. Predictive modeling based on selected metabolites from metabolomics can differentiate InGF and FrGF.

MSdev

Wed, 27 Apr 2016 00:00:00 +0000

The lack of a universal analytical platform is one of the key factors limiting the biological application and development of metabolomics methodologies. We developed a metabolomics data analysis and method development platform, MSdev. MSdev integrates and develops various core tools and databases, including mass spectrometry data analysis, chemical computations, metabolomics databases, and molecular graph structures. This platform not only facilitates our routine metabolomics and metabolic flux analysis but also supports the development of advanced methods such as MSIP and MFNA.

Projects | Li Rui - Shanghaitech University