Metabolic Heterogeneity and Niche Rewiring in Plasma Cells are Associated with Progression from MGUS to Multiple Myeloma

TL;DR

This study uses advanced imaging and metabolomics to show how metabolic changes in plasma cells and their environment contribute to the progression from MGUS to multiple myeloma.

Contribution

The study introduces spatial metabolomics of biopsies to dissect intramedullary metabolic heterogeneity in plasma-cell disorders.

Findings

MM niches show elevated 3-hydroxykynurenine and altered tryptophan–kynurenine flux.

Some MGUS samples have MM-like metabolic niches not detectable in plasma alone.

Cross-compartment integration reveals conserved and redistributed metabolite signatures during progression.

Abstract

Progression from monoclonal gammopathy of undetermined significance (MGUS) to multiple myeloma (MM) is driven by coordinated metabolic reprogramming within clonal plasma cells and the bone marrow microenvironment. We applied high-resolution MALDI–FT-ICR mass spectrometry imaging (MSI) to archived FFPE bone-marrow biopsies, integrated with matched bone marrow plasma metabolomics, to map spatial and systemic metabolic alterations. Spatial clustering delineated plasma-cell–rich niches, while Hill-based diversity and β-diversity metrics quantified intra- and inter-compartment heterogeneity. MM niches exhibited elevated 3-hydroxykynurenine, rewired tryptophan–kynurenine flux, and increased nucleotide and bioactive lipid metabolism associated with proliferation. Notably, some MGUS-like samples displayed MM-like metabolic niches undetectable in bone marrow plasma alone, underscoring spatial heterogeneity. Cross-compartment integration revealed conserved metabolic signatures and systemic redistribution of key metabolites, consistent with ecological reorganization and niche divergence during progression. These findings establish spatial metabolomics of biopsies as a framework to dissect intramedullary metabolic heterogeneity and enable metabolite-based risk stratification in plasma-cell disorders.

Figures

A](#F2)). This clustering method, which efficiently captures intratumoral metabolic heterogeneity in MSI datasets^[14](#R14)^, delineated discrete tissue regions in each sample. Notably, it segregated high-purity plasma cell–rich regions out from the remainder of the bone marrow microenvironment in both MGUS and MM samples, indicating a successful metabolic separation of plasma cells in different precursor and malignant compartments ([Fig. 2B](#F2)). To validate the robustness of this unsupervised segmentation across patients, we projected the data using UMAP dimensionality reduction. The UMAP

A](#F3)). In a SHAP-based classification model of the bone marrow plasma metabolome, 3-HK emerged as the top-ranked discriminatory feature separating MGUS from MM ([Fig. 3B](#F3), left), whereas it was absent among the top predictors in the analogous model for tissue metabolites ([Fig. 3B](#F3), right). Nonetheless, univariate analysis confirmed that 3-HK levels were significantly higher in MM than in MGUS in both bone marrow plasma and tissue (p < 0.05 for each; [Fig. 3C](#F3)).

D](#F4), top), whereas in MM these same pathways shift to predominantly positive correlations (red bars in [Fig. 4D](#F4), bottom). This transition suggests that, in the indolent pre-malignant MGUS state, higher proliferation in plasma cells is associated with lower steady-state levels of nucleotides – possibly reflecting consumption or a restrained de novo synthesis capacity – but in MM, proliferating plasma cells actively accumulate or maintain elevated nucleotide pools, indicating an upregulation of nucleotide biosynthesis to meet increased DNA/RNA demands^[22](#R22),[23](#R23)^. Consistent

C](#F5), left) showed that lipid and amino-acid super-pathways contained the widest log_2_FC distributions, while nucleotide species tended to decrease in both compartments. Extending the analysis to all tissue and bone marrow plasma metabolites ([Fig. 5C](#F5), centre and right) confirmed a broader dynamic range in tissue and highlighted pathway-specific biases: nucleotides and carbohydrates skewed towards MM in tissue, whereas amino-acid and lipid metabolites contributed most of the bone marrow plasma signal.

G](#F6)), we identify two complementary features of progression. First, local metabolic dominance: evenness within plasma-cell–rich regions was reduced in MM relative to MGUS and declined with increasing marrow plasma-cell burden, linking tumor expansion to a narrowing of the active metabolic repertoire. Second, regional decoupling: paired β diversity between plasma-cell cores and surrounding microenvironment increased in MM, indicating progressive metabolic divergence between tumor and niche compartments. These findings parallel spatial-immunology studies showing compartmentalization of immun

Full text

Introduction

Multiple myeloma (MM) represents the malignant phase of a clonal plasma-cell continuum, invariably preceded by monoclonal gammopathy of undetermined significance (MGUS) and often an intermediate smouldering phase^1^. Reliable biomarkers capable of predicting MGUS progression remain unavailable^2^. Given the complexity of the bone marrow microenvironment, disease evolution likely involves both clonal plasma cells and surrounding microenvironmental components^3^. Characterizing metabolic profiles of clonal plasma cells within their bone marrow niche may illuminate mechanisms of disease progression.

Metabolic reprogramming is a hallmark of cancer, enabling cells to meet increased biosynthetic and energetic demands associated with rapid proliferation^4^. In MM, pathways related to fatty acid, glucose, and amino acid metabolism are notably upregulated to facilitate nucleotide and protein synthesis, as well as ATP production^5,6^. However, conventional bulk metabolomics analyses typically use homogenized tissue or plasma samples, lacking spatial resolution and thus failing to localize metabolic signals to specific anatomical sites^7,8^.

Recent advances in spatial metabolomics, particularly MSI, now enable in situ visualization of metabolite distributions within tissue sections^9^. Unlike bulk approaches, spatial metabolomics can directly map metabolites to tumor, stromal, and immune cells within their native microenvironment, revealing spatial heterogeneity^10^.

Few studies have directly investigated metabolic changes associated with MGUS-to-MM progression using bone marrow or plasma metabolomics. Prior work identified substantial metabolic differences between MGUS and MM patients in bone marrow plasma, including decreased branched-chain amino acids and elevated kynurenine-pathway metabolism leading to increased 3-hydroxykynurenine (3-HK) production in MM^11^. Additionally, serum markers linked to tryptophan metabolism correlate with MM progression^12^. However, previous studies have primarily focused on bulk-level differences without integrating tissue-localized and systemic metabolomics or achieving sub-tissue resolution^13^. Notably, spatial metabolomics using high-mass-resolution MALDI–FT-ICR-MSI has not previously been applied systematically to diagnostic FFPE bone marrow biopsies, representing a methodological and clinical innovation that allows interrogation of plasma cell metabolism within its native niche context.

Here, we apply high-mass-resolution MALDI-FT-ICR-MSI systematically to archival FFPE bone marrow biopsies, integrated with matched extracellular bone marrow plasma metabolomics, to map spatially resolved metabolic profiles. By quantifying intra- and inter-compartment heterogeneity and identifying conserved metabolic signatures across plasma cells and microenvironmental niches, this study provides a holistic framework for understanding metabolic evolution during MGUS-to-MM progression. These insights establish a foundation for metabolite-based risk stratification and the identification of targetable metabolic pathways in pre-malignant plasma cell disorders.

Methods

Patient Cohort

This retrospective observational study was approved by the Mayo Clinic Institutional Review Board (IRB#: 15–005140) and conducted in accordance with the Declaration of Helsinki. Clinical residual bone marrow plasma and trephine biopsy samples were obtained from consecutive patients with newly diagnosed multiple myeloma (NDMM) or MGUS-like bone marrows undergoing routine diagnostic evaluation, with informed consent for research use. Diagnoses were confirmed according to International Myeloma Working Group (IMWG) criteria. Relevant clinical and laboratory parameters were collected and are summarized in Table 1. Additional cohort details are provided in the Supplemental Methods.

LC-MS assessment of bone marrow plasma

Bone marrow plasma samples were subjected to global untargeted metabolomic profiling by Metabolon, Inc. using ultrahigh-performance liquid chromatography–tandem mass spectrometry (UPLC–MS/MS), as previously described. Analyses were performed in both positive and negative ion modes, and metabolites were identified by comparison to authenticated reference standards.

High-mass-resolution MALDI-FT-ICR-MSI analysis

MALDI-FT-ICR-MSI was performed on archived FFPE bone-marrow biopsy sections from Mayo Clinic. Imaging was conducted in negative ion mode with a spatial resolution of 50 μm over a mass range of m/z 75–1000. Following MSI acquisition, sections were subjected to H&E staining for histological annotation.

Data Acquisition and Processing

MALDI-MSI data were processed and annotated using established software pipelines, including spectral normalization and accurate-mass-based metabolite annotation against curated databases.

Statistical analysis

Statistical analyses were performed using GraphPad Prism (v9.4.1), R (v4.4.3), and Python (v3.13). Twogroup comparisons were conducted using two-sided Student’s t-tests, with Welch’s correction applied when variances were unequal; nonparametric Mann–Whitney U tests were used for non-normally distributed data. Correlations between continuous variables were assessed using two-tailed Pearson or Spearman correlation coefficients, as appropriate. Unsupervised multivariate analyses were applied to explore metabolic patterns and group differences. All tests were two-sided, with significance defined as P < 0.05. Additional details about methods and analyses are provided in the Supplemental Methods.

Results

Study Design

We analyzed paired bone marrow plasma samples and formalin-fixed paraffin-embedded (FFPE) bone marrow core biopsies from 10 patients with symptomatic, newly diagnosed multiple myeloma (NDMM) and 10 patients with MGUS-like bone marrows(Fig. 1). The MGUS-like cohort, hereby labelled as “MGUS” moving forward, was defined by bone marrow biopsies showing less than 10% clonal plasma cells and included individuals with true MGUS (n = 7) as well as those with solitary bone plasmacytoma with minimal marrow involvement (SBPmm; n = 3). Core biopsies from SBPmm patients were intentionally included because their histologic appearance and degree of clonal plasma cell infiltration are indistinguishable from true MGUS marrows, yet their risk of progression to MM is substantially higher. This design allowed us to assess whether metabolomic profiling could identify metabolic features in MGUS-like marrows, particularly those from SBPmm, that are biologically more similar to NDMM. The clinical characteristics of these patients are listed in Table 1.

Metabolomics-Driven Spatial Clustering Reveals Metabolic Heterogeneity of Bone Marrow Plasma Cells in MGUS and Multiple Myeloma

To interrogate metabolic remodeling where myeloma emerges, we first established a tissue-centric map by segmenting bone-marrow sections into plasma-cell–rich niches using MSI.

Using high-resolution MALDI-FT-ICR MSI data, we performed unsupervised bisecting k-means clustering to partition the metabolic landscapes of 20 FFPE bone marrow specimens(Fig. 2A). This clustering method, which efficiently captures intratumoral metabolic heterogeneity in MSI datasets^14^, delineated discrete tissue regions in each sample. Notably, it segregated high-purity plasma cell–rich regions out from the remainder of the bone marrow microenvironment in both MGUS and MM samples, indicating a successful metabolic separation of plasma cells in different precursor and malignant compartments (Fig. 2B). To validate the robustness of this unsupervised segmentation across patients, we projected the data using UMAP dimensionality reduction. The UMAP embeddings demonstrated a clear bifurcation between plasma cell-rich and non-plasma cell rich metabolic profiles (Fig. 2C).

We next assessed the biological fidelity of the metabolomic clusters by comparing them to standard pathology metrics. After appropriate transformation to satisfy statistical assumptions, the fraction of pixels classified as “tumor region” by MSI in each bone marrow sample strongly correlated with the percentage of plasma cells determined by conventional morphological evaluation (Pearson R = 0.91, p < 0.001; Fig. 2D). Consistent results were obtained using Spearman’s rank correlation on the original scale (ρ = 0.89, p < 1×10^−6^).

Out of 1,338 ion features detected by MALDI-MSI in the bone marrow sections, we curated a subset of 123 high-confidence endogenous metabolites as the basis for subsequent analyses. A heatmap of these metabolites within the plasma cell–rich regions (blue cluster) revealed clear separation of MGUS and MM and pathway-level differences annotated by super-pathway categories (nucleotide, amino acid, lipid, carbohydrate) (Fig. 2E). MM plasma cell rich regions showed broad upregulation across multiple pathways, consistent with increased glycolytic, nucleotide and amino-acid metabolism in malignant states compared to those plasma cell regions in MGUS^15^. Collectively, spatial metabolomic clustering segregated plasma cell–rich regions with distinct metabolic states in MGUS and MM.

Rewiring of tryptophan metabolism distinguishes MGUS from MM progression

To identify key metabolic features associated with the malignant transformation from MGUS to MM, we performed comprehensive, untargeted metabolite profiling of FFPE bone marrow tissue using high-resolution MALDI-FT-ICR MSI and their matched bone marrow plasma by an untargeted ultra-performance liquid chromatography tandem mass spectrometer (UPLC-MS/MS). Differential analysis via volcano plots revealed numerous dysregulated compounds, notably a robust and consistent elevation of 3-HK in MM compared with MGUS across both bone marrow plasma and tissue compartments (Fig. 3A). In a SHAP-based classification model of the bone marrow plasma metabolome, 3-HK emerged as the top-ranked discriminatory feature separating MGUS from MM (Fig. 3B, left), whereas it was absent among the top predictors in the analogous model for tissue metabolites (Fig. 3B, right). Nonetheless, univariate analysis confirmed that 3-HK levels were significantly higher in MM than in MGUS in both bone marrow plasma and tissue (p < 0.05 for each; Fig. 3C).

Given the prominence of 3-HK, we next examined its position within the tryptophan degradation pathway. Five of the six key intermediates in this pathway were detectable, and three (Tryptophan, L-kynurenine and 3-HK) were significantly atered between MGUS and MM; in contrast, in the bone marrow tissue, both tryptophan and 3-HK were elevated in the plasma cells from MM comapred to those from MGUS—indicating an increased flux of extracellular tryptophan into plasma cells through the kynurenine arm during malignant progression from MGUS to MM^16^(Fig. 3D). This pattern is consistent with enhanced indoleamine-2,3-dioxygenase (IDO)-mediated catabolism of tryptophan in MM clonal cells^17^. Imaging mass spectrometry further localized this rewiring: in a representative MM section, 3-HK mapped predominantly to malignant regions consisting of plasma cell clusters, while tryptophan accumulated in adjacent areas devoid of plasma cell clusters (Fig. 3E).

To place 3-HK within a broader pathway context, we correlated 3-HK intensity with all detected metabolites in bone marrow plasma and tissue and then tested positively (r > 0.30) and negatively (r < − 0.30) correlated sets for KEGG pathway over-representation. In bone marrow plasma, positively correlated partners of 3-HK were enriched for fatty-acid/dicarboxylate, endocannabinoid and tryptophan metabolism, whereas negatively correlated partners highlighted carnitine metabolism and amino-acid catabolic routes (Fig. 3F, top). In tissue, positively correlated sets were dominated by pyrimidine and pentose/inositol-phosphate pathways, while negatively correlated sets included lysine and phenylalanine metabolism (Fig. 3F, bottom). These enrichments highlight compartment-specific pathway associations of 3-HK.

Finally, a complementary pathway-level correlation analysis across all tryptophan-pathway metabolites (Fig. 3G) showed that with progression from MGUS to MM their connectivity shifts: correlations with nucleotide and carbohydrate metabolism strengthen, whereas links to lipid metabolism weaken. This reorganization reflects broader metabolic remodeling in MM—one that favors nucleotide biosynthesis and glycolytic energy production over lipid pathways^18^.

Collectively, these results link tryptophan–kynurenine rewiring in MM plasma cells to distinctive metabolite signatures, exemplified by elevated 3-HK. Therefore, we quantified how metabolite levels couple to proliferation across disease stages.

Metabolite–S-phase Proliferation Correlations Reveal Stage-Specific Drivers of Plasma Cell Growth

We correlated all endogenous metabolites within plasma-cell rich regions in MGUS and MM bone marrow tissue with their paired proliferation score of their corresponding clonal plasma cells, revealing stage-specific drivers^19^. The triangular heat map (right half of Fig. 4A) showed tightly co-regulated blocks (glycolysis, nucleotide sugars, bioactive lipids) with dense within-block and sparse cross-block correlations, consistent with partly independent metabolic circuits.

Overlaying proliferation (left half of Fig. 4A) shows that the strength—and even the sign—of metabolite–proliferation coupling shifts with disease stage. In plasma cell clusters in MGUS, sulfate exhibits the highest positive correlation with proliferation (r = 0.79), followed by fructose-2,6-bisphosphate and two UDP-linked nucleotide sugars, hinting that redox buffering and anabolic carbohydrate flux support limited plasma-cell expansion at the premalignant MGUS stage (Fig. 4B, top figure). By contrast, in plasma cell clusters in MM the dominant correlates switch to the eicosanoid arachidonic acid (r = 0.91), Sphingosine 1-phosphate and Leukotriene A4—lipid mediators implicated in mitogenic signalling and NF-κB activation—together with purine derivatives such as inosine (Fig. 4B, bottom figure). This lipid-centric signature aligns with reports that sphingolipid and arachidonate pathways fuel aggressive myeloma proliferation and confer drug resistance^20,21^.

Comparing segmented plasma-cell regions (blue) with whole-marrow averages (grey) confirmed that spatially resolved profiling greatly amplifies the metabolite–proliferation signal (Fig. 4C); most blue points lie above their grey counterparts, with adenine showing the largest gains (Δ|ρ| > 0.25). Focusing on plasma cell-rich niches markedly strengthened the metabolite–proliferation correlations.

Finally, we observe a reversal in the behavior of nucleotide metabolism pathways between MGUS and MM. In MGUS, metabolites involved in purine and pyrimidine metabolism show predominantly negative correlations with plasma cell proliferation (blue bars in Fig. 4D, top), whereas in MM these same pathways shift to predominantly positive correlations (red bars in Fig. 4D, bottom). This transition suggests that, in the indolent pre-malignant MGUS state, higher proliferation in plasma cells is associated with lower steady-state levels of nucleotides – possibly reflecting consumption or a restrained de novo synthesis capacity – but in MM, proliferating plasma cells actively accumulate or maintain elevated nucleotide pools, indicating an upregulation of nucleotide biosynthesis to meet increased DNA/RNA demands^22,23^. Consistently, prior studies have noted altered nucleotide metabolism as a distinguishing feature between MGUS and MM^13^, and high expression of nucleotide biosynthetic enzymes (e.g. cytidine triphosphate synthase 1, CTPS1) correlates with aggressive MM behavior^24^. Beyond nucleotides, other pathways display stage-specific shifts as well: for example, amino sugar metabolism and glycerolipid metabolism show modest positive correlations with proliferation already in MGUS, which further intensify in MM, whereas pathways like taurine/hypotaurine metabolism (e.g. 5-L-glutamyl-taurine) remain uncorrelated in MGUS but become positively linked to proliferation in MM^25,26^.

Having identified plasma cell tissue-intrinsic metabolic drivers of proliferation, we next examined which signals are mirrored systemically by integrating matched bone marrow plasma profiles.

A shared metabolic core links clonal plasma cells with their extracellular environment

Metabolic rewiring accompanies the evolution from MGUS to MM, yet how these alterations partition between the intracellular and extracellular bone marrow micro-environment is unknown^26,27^. To capture both dimensions, we combined high-resolution MALDI-FT-ICR imaging of FFPE biopsies across the entire bone marrow region with the matched bone marrow plasma LC-MS metabolite profiling. A total of 114 metabolites were quantified in both matrices, providing a shared analytical denominator (Fig. 5A, left).

Plotting the MM-to-MGUS log_2_-fold change (log_2_FC) in tissue (y-axis) against plasma (x-axis) stratified each metabolite into eight directional classes (Fig. 5A, right). Concordant shifts were prominent: 17 metabolites rose in both compartments (“Both up”) and five declined (“Both down”). The “Both up” quadrant was dominated by the kynurenine arm of tryptophan catabolism—3-hydroxy-kynurenine and kynurenate—as well as cytidine monophosphate (CMP), highlighting a programme that is simultaneously imprinted across the entire bone marrow of MM and their corresponding bone marrow plasma.

Compartment-restricted changes, whether only in the bone marrow tissue or only in the bone marrow plasma, provided additional biological resolution. Twenty-two metabolites increased only in tissue, including arachidonic acid and unmetabolised tryptophan, pointing to intrinsic or intracellular eicosanoid signalling and substrate accumulation. Conversely, eight metabolites were elevated solely in bone marrow plasma, predominantly long-chain acyl-carnitines, consistent with systemic lipid mobilisation. Offset behaviour (opposite directions) was comparatively rare but informative: 2′-deoxyuridine accumulated in tissue while falling in bone marrow plasma (tissue↑/plasma↓), suggesting local nucleotide salvage, whereas L-cystathionine decreased in tissue but rose in plasma (tissue↓/plasma↑), hinting at differential trans-sulphuration flux. Hypoxanthine, a purine-degradation product, was the most prominent tissue-specific decrease, implying enhanced nucleotide turnover within the malignant niche. Together, these data define a compartment-shared metabolic core with matrix-specific features.

To further dissect the pathway context of these directional classes, a Sankey diagram (Fig. 5B) mapped how each group of metabolites funnels into six major metabolic super-pathways. Notably, nucleotide metabolism dominated the “Both up” category, while amino-acid and lipid metabolism prevailed among tissue- and plasma-specific increases, highlighting functional divergence in metabolic reprogramming between intracellular and extracellular compartments^28^.

Beeswarm plots of all shared metabolites (Fig. 5C, left) showed that lipid and amino-acid super-pathways contained the widest log_2_FC distributions, while nucleotide species tended to decrease in both compartments. Extending the analysis to all tissue and bone marrow plasma metabolites (Fig. 5C, centre and right) confirmed a broader dynamic range in tissue and highlighted pathway-specific biases: nucleotides and carbohydrates skewed towards MM in tissue, whereas amino-acid and lipid metabolites contributed most of the bone marrow plasma signal.

We next asked whether these tissue-resolved and systemic signatures distinguish higher-risk MGUS-like marrows (e.g., SBPmm) from true MGUS cases.

Metabolic profiling distinguishes progressive MGUS and reveals precursor heterogeneity

Applying the above framework to MGUS revealed pronounced precursor heterogeneity and uncovered subsets whose tissue metabolomes already resembled MM, even when the extracellular bone marrow plasma remained non-informative.

Principal-component analysis (PCA) of all detected metabolites demonstrated marked metabolic heterogeneity among MGUS bone-marrow samples (Fig. 6A). Whole-marrow regions from MGUS patients (yellow triangles), encompassing both plasma cell and non–plasma cell compartments, were widely dispersed across the first two principal components. In contrast, plasma-cell–enriched regions identified by unsupervised clustering formed substantially tighter groupings—red circles for MGUS and blue squares for MM—indicating that data-driven segmentation isolates metabolically coherent plasma-cell niches from an otherwise diffuse precursor landscape. Consistently, PERMANOVA confirmed that these region types were metabolically distinct (R2 = 0.885, p = 0.001). Thus, although MGUS bone marrow is heterogeneous at the whole-tissue level, it contains discrete plasma-cell niches with homogeneous metabolic signatures, some of which converge toward an MM-like state.

Restricting the analysis to MGUS samples, Fig. 6B further illustrates substantial intraprecursor heterogeneity. Six spatial clusters (K1–K6) intermingled differently across patients, and Simpson’s diversity indices varied widely (≈ 0.4–0.8), indicating that the MGUS niche is not metabolically uniform. Together, Figs. 6A and 6B establish MGUS as a metabolically diverse precursor state, consistent with reported heterogeneity in immune surveillance and clonal biology that may contribute to divergent risks of progression^29^.

To further delineate this heterogeneity, we performed cluster-level analysis of endogenous metabolites. Six distinct metabolic regions were identified, and metabolites were ranked within each cluster by variable importance in projection (VIP) scores. Figure 6C shows the top 20 metabolites per cluster, with the top four labeled. Several metabolites recurred as high-ranking discriminators across multiple clusters (highlighted in orange), indicating shared metabolic features, whereas others were cluster-specific, reflecting localized metabolic specialization. This analysis highlights the coexistence of common and region-restricted metabolic programs within MGUS/MM bone marrow.

We next examined three SBPmm patients within the MGUS cohort who subsequently progressed to MM. In PCA of bone marrow plasma metabolomes (Fig. 6D, left), these cases overlapped with the MGUS distribution and did not consistently shift toward MM. In contrast, PCA of MSI-defined plasma-cell–enriched tissue regions (Fig. 6D, right) revealed clear stage-related structure: MGUS17 and MGUS18 clustered within the MM ellipse, whereas MGUS20 remained closer to the MGUS group. Thus, spatially resolved tissue metabolomics captured MM-like metabolic features in clonal plasma cells of a subset of MGUS-like marrows that were not evident from bone marrow plasma alone.

Notably, the time to progression among these SBPmm cases varied substantially, ranging from approximately 24 months (MGUS17) to 7 months (MGUS18) and 2 months (MGUS20). This clinical variability was mirrored by differences in metabolic organization. MGUS17, the slowest progressor, exhibited the lowest Simpson index (0.59), whereas MGUS18 and MGUS20 showed higher indices (0.80 and 0.76), consistent with stronger metabolic dominance. These observations suggest that early emergence of a dominant metabolic program—rather than increased heterogeneity per se—may be associated with more rapid progression from MGUS to MM.

In addition to these multivariate patterns, we identified metabolites that increased monotonically across true MGUS, MGUS-like SBPmm, and MM (Fig. 6E). Among the top five were cytidine 2′,3′-cyclic phosphate and 3-HK. Cytidine 2′,3′-cyclic phosphate showed a stepwise increase from non-progressive MGUS to progressive MGUS and MM, indicating that some myeloma-associated metabolic alterations are already present at the precursor stage. Similarly, rising 3-HK levels underscore activation of the kynurenine pathway during progression. Although mechanistic dissection is beyond the scope of this analysis, these consistent trajectories indicate that progressive MGUS harbors metabolic features bridging precursor and malignant states.

To assess how this heterogeneity is organized spatially, we segmented each section into plasma-cell–rich and microenvironmental compartments and quantified diversity using Hill numbers (q = 2) (Fig. 6G). Here, “evenness” (q = 2) quantifies how balanced metabolite abundances are within a region; lower values indicate dominance by a few features and should not be conflated with spatial homogeneity across compartments. This framework links within-region evenness, between-region differentiation, and whole-section heterogeneity in a single system. Within the plasma-cell-rich compartment of each bone marrow sample, evenness was lower in MM than in MGUS (p = 0.014; Fig. 6G, left, panel i). The evenness also showed a strong inverse association with marrow plasma-cell percentage (slope β = −0.996, HC3 p = 8.7 × 10–4; Fig. 6G, left, panel ii). These findings indicate thatregions with higher plasma cell infiltration exhibit more metabolically dominated and less evenly distributed metabolite profiles — consistent with local metabolic simplification during progression.. We next compared the two compartments, plasma cell rich region vs. non-plasma cell region within each patient. The paired two-community β diversity increased in MM (p = 0.004; Cliff’s δ = −0.78, large; Fig. 6G, right). Thus, the metabolic composition of the plasma cell core and the surrounding microenvironment in the bone marrow diverges during progression from MGUS to MM. The microenvironment does not simply mirror the plasma cell-rich region; rather, it drifts away from it.

Together, Fig. 6G closes the loop opened in Fig. 6A–F. Early, MGUS appears heterogeneous in the whole marrow but contains tight, metabolically coherent plasma-cell niches. As disease advances, the plasma cell-rich niche becomes more monoclonal and the microenvironment decouples from it^30^. This scale-aware view reconciles local clonality with global diversity and points to niche separation as a key ecological feature of MGUS-to-MM transition.

Discussion

By integrating high-resolution MALDI-FT-ICR imaging of FFPE bone-marrow biopsies with matched bone marrow plasma metabolite profiling by LC–MS in MGUS and MM patients, we delineate how coordinated changes in local clonal plasma cell metabolism and the systemic metabolic milieu accompany progression from precursor to malignant states. Across compartments, we identify a conserved metabolic core characterized by enhanced tryptophan–kynurenine flux and rewired nucleotide metabolism, together with compartment-specific lipid remodeling. Spatial clustering isolates plasma-cell niches, reveals marked precursor heterogeneity, and identifies MGUS subsets whose tissue metabolomes already resemble MM—even when bone marrow plasma profiles remain non-informative. Coupling analyses further link proliferative activity to stage-dependent shifts in lipid mediators and nucleotide biosynthesis, providing a spatial–systemic framework for understanding MGUS-to-MM evolution and nominating metabolite signatures for future risk-oriented stratification.

A central feature of progression is coordinated reprogramming of the tryptophan–kynurenine axis and nucleotide metabolism, with stage-dependent engagement of lipid signaling. MM patients exhibited depleted tryptophan and elevated 3-HK relative to MGUS (Fig. 3), consistent with prior studies of bone marrow fluid and plasma^11^. The increase in 3-HK reflects enhanced flux through the kynurenine pathway, likely driven by upregulated IDO1 activity^31^. Rather than acting in isolation, this pathway-level shift integrates with proliferation: in MGUS, proliferative activity associates primarily with carbohydrate and UDP-sugar metabolism, whereas in MM it aligns with bioactive lipids (sphingosine-1-phosphate and arachidonic acid) and increased purine and pyrimidine biosynthesis (Fig. 4)^20,25,26^. Together, these changes position immune-modulatory tryptophan catabolism within a broader anabolic program that scales with disease stage and proliferative demand.

Beyond individual pathways, our study addresses a key limitation of prior metabolomic analyses in plasma cell disorders, which have typically examined either bone marrow microenvironmental compartments or peripheral circulation in isolation^11,13,32^. By profiling metabolites in intact bone marrow tissue alongside matched bone marrow plasma from the same individuals (Fig. 5), we demonstrate that some metabolic alterations are conserved across compartments, whereas others are compartment restricted. For example, arachidonic acid and unmetabolised substrates such as tryptophan accumulated locally within MM marrow, whereas long-chain acyl-carnitines increased predominantly in bone marrow plasma, consistent with systemic lipid mobilization^33^ and cancer-associated catabolism^34^. Metabolites displaying opposite directional changes between tissue and plasma (e.g., 2′-deoxyuridine) suggest localized nucleotide salvage coupled to systemic redistribution of one-carbon and amino-acid flux. These findings extend prior observations in bone marrow plasma by directly linking systemic metabolic alterations to tumor-intrinsic metabolic programs^11^. By integrating spatial and plasma metabolomics, our study provides a more comprehensive view of MGUS and MM metabolism and informs which tumor-associated metabolites may be most amenable to evaluation as circulating biomarkers or therapeutic targets.

A key biological insight from our spatial analysis is the pronounced metabolic heterogeneity within MGUS and its clinical relevance. Unsupervised clustering of bone-marrow metabolite images (Fig. 6) demonstrates that MGUS is not metabolically uniform: some cases harbor plasma-cell tissue metabolomes that already resemble MM, whereas others retain indolent-like profiles. Importantly, this tissue-resolved stratification identified MGUS-like bone marrows from SBPmm patients who later progressed to MM, distinguishing them from true MGUS cases that remained progression-free, whereas matched bone marrow plasma metabolomes were less discriminative (Fig. 6). Given the variability in MGUS progression risk and the limitations of current clinical and molecular risk models^35^, these findings suggest that spatial metabolomics provides an orthogonal layer of risk assessment grounded in tumor metabolism. Similar principles of tissue-based metabolic subtyping have proven prognostically informative in solid tumors^36^, and our data extend this concept to a pre-malignant hematologic condition. Consistent with evolutionary models derived from single-cell studies^37^ and recent reviews^38^, our results indicate that MGUS progression can be captured at the metabolic level through spatially resolved tissue profiling.

These observations prompted us to examine how heterogeneity is organized across spatial scales. Using a scale-aware diversity framework based on Hill numbers (q = 2) (Fig. 6G), we identify two complementary features of progression. First, local metabolic dominance: evenness within plasma-cell–rich regions was reduced in MM relative to MGUS and declined with increasing marrow plasma-cell burden, linking tumor expansion to a narrowing of the active metabolic repertoire. Second, regional decoupling: paired β diversity between plasma-cell cores and surrounding microenvironment increased in MM, indicating progressive metabolic divergence between tumor and niche compartments. These findings parallel spatial-immunology studies showing compartmentalization of immune architecture during MGUS-to-MM transition^39^.

Together, these scale-dependent metrics suggest that disease progression reflects ecological reorganization—local clonal dominance combined with regional niche separation—rather than simple accumulation of intratumoral complexity. Metrics capturing between-compartment divergence may therefore complement existing staging approaches and highlight tumor–microenvironment interactions as potential intervention points, consistent with emerging ecological and systems-level models of cancer progression^38^.

A major strength of our study lies in the first systematic application of MALDI–FT-ICR spatial metabolomics to diagnostic FFPE bone marrow biopsies. While prior studies have largely focused on fresh or frozen tissues, we demonstrate the feasibility and robustness of this approach on routinely archived material. High-resolution spatial mapping not only enables characterization of individual plasma cell niches but also quantifies metabolic interactions with the surrounding microenvironment. This methodological advance expands the potential for retrospective analyses across large clinical cohorts and provides a directly translatable strategy for integrating spatial metabolomics into diagnostic and prognostic workflows in plasma cell disorders.

In summary, our study demonstrates the power of combining spatial and systemic metabolomics to unravel disease-associated metabolism and clarifies how local tumor programs and systemic remodeling jointly contribute to myeloma pathogenesis. These insights lay a foundation for metabolite-based biomarkers and metabolic interventions aimed at intercepting MGUS-to-MM progression. Nevertheless, interpretation is tempered by a modest cohort, the largely cross-sectional design, and the absence of genomic and immune-composition integration. Validation in larger, prospectively accrued cohorts—including SMM—and longitudinal sampling will be required to determine whether MM-like tissue profiles in MGUS anticipate progression. Future integration of spatial metabolomics with cytogenetics and single-cell phenotyping should further determine whether the observed regional decoupling reflects tumor-intrinsic programs or microenvironmental remodeling.

Supplementary Material

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