Abstract
Soil microbes play a crucial role in soil organic carbon storage by processing plant-derived carbon and producing microbial carbon, which can accumulate over time and form a substantial fraction of the soil organic carbon stock. Whereas the contribution of microbial cellular residues, the remains of dead microbial cells, to soil carbon storage is well established, the role of extracellular residues remains poorly quantified. Here we develop an 18O-H2O isotope-tracing method to quantify the production of extracellular polymeric substances, mucus-like mixtures of sugars, proteins and other biopolymers released by microbes and apply it across a national-scale survey of cropland soils in China. We find that extracellular polymeric substances constitute a major microbial carbon input to soils, with production rates comparable to those of microbial cellular residues and they exhibit statistical associations with mineral-associated organic carbon across soils. In 13C-labelling microcosm experiments, carbon derived from extracellular polymeric substances is incorporated into the mineral-associated organic carbon pool at rates comparable to or exceeding those of microbial cellular residues. These results identify the extracellular pathway as an important and previously underappreciated mechanism contributing to the mineral-associated carbon pool and provide mechanistic insights into how microbiomes shape the fate of soil carbon under global change.
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Data availability
Climatic variables used in this study, including MAT and MAP, were obtained from the National Earth System Science Data Center67,68. The MAT and MAP datasets are available at https://doi.org/10.12041/geodata.164304785536614.ver1.db and https://doi.org/10.12041/geodata.192891852410344.ver1.db, respectively. The amplicon sequencing data are accessible in the National Center for Biotechnology Information repository under BioProject ID 1007936. Data supporting the findings of this study are available via figshare at https://doi.org/10.6084/m9.figshare.27637089 (ref. 69). Source data are provided with this paper.
Code availability
All the code used in the analyses is available via figshare at https://doi.org/10.6084/m9.figshare.27637089 (ref. 69).
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Acknowledgements
We thank T. Zhang, H. He and L. Zhang (Huazhong Agricultural University) for their assistance with isotope ratio mass spectrometry analysis and the National Key Laboratory of Agricultural Microbiology Core Facility for providing GC analysis and lyophilization support. We are grateful to M. Geng and Z. Yuan (Institute of Subtropical Agriculture, Chinese Academy of Sciences) for NMR analyses, J. Zhang (Institute of Applied Ecology, CAS) for amino sugar analyses, and G. Yu and B. Yin (Tianjin University) for NanoSIMS and SEM analyses. We also thank K. Ouyang (Hunan Agricultural University) for field sampling assistance and M. Kleber for his insightful comments on the paper. We further acknowledge M. Chen, Xue Zhang, Xuewen Zhang, Y. Chen, M. Yun, H. Ouyang, C. Wang, Z. Chen, C. Fu and J. Tian for their experimental and laboratory support.
Funding
This work was financially supported by the National Natural Science Foundation of China (42225706, 42377297, 42177283, 42177281, 32241037), Fundamental Research Funds for the Central Universities (2662025ZHPY007, 2662026ZHPY001), Independent Research Project of National Key Laboratory of Agricultural Microbiology (AML2026D05, AML2026A01), Innovative Research Group Project of Hubei Provincial Natural Science Foundation (2023AFA009, 2024CSA060), the Natural Science Foundation of Hubei Province of China (460324005), Major Program (Jian Dao) of Hubei Province (2025BEA005), Major Special Program on Agricultural Microbial Industry Development in Hubei Province (NYWSWZX2025-2-27-09) and the Science and Technology Talents and Platform Program (Workstation for Academicians and Experts) (202405AF140079).
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Y.W. and P.Q. conceived of and designed the project. P.Q. conducted the field and laboratory experiments and performed data analysis. J.R., X.C. and Z.L. assisted with laboratory analyses. The paper was drafted by P.Q. and Y.W. P.C. and Y.W. provided supervision throughout the project. C.L.P., C.L., X.Z., X.F., M.A.R.-G., X.H., K.-Q.X., Y.-G.Z., M.Z., K.D., C.G., C.Q., Y.-R.L., W.T. and Q.H. contributed to the review and editing process. All authors reviewed and approved the final paper.
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Extended data
Extended Data Fig. 1 Quantification of EPS carbon content and production.
a, Comparison of predicted and observed EPS carbon content. The black line represents a perfect match between predictions and observations. Data points represent the training and test subsets used for model development and evaluation, respectively. R2 indicates the coefficient of determination from the weighted least squares regression analysis. b, Experimental setup for measuring microbial growth and EPS production using 18O tracing. Soil microbial growth and EPS production were quantified by measuring the incorporation of 18O from H218O into specific biomarkers. DNA serves as a proxy for cellular growth, whereas EPS-Poly and EPS-Prot are the EPS biomarkers. Soil samples were amended with H218O and incubated for 24 h. After incubation, EPS was extracted using the cation exchange resin (CER) method. To purify the EPS biomarkers, EPS-Prot and humic substances were first co-precipitated with trichloroacetic acid (TCA). The EPS-Prot fraction was subjected to tryptic digestion. The resulting peptides were separated from humic substances and trypsin by filtration. EPS-Poly in the extract was purified by dialysis to remove TCA and other low-molecular-weight compounds. Finally, 18O enrichment in DNA, EPS-Poly and EPS-Prot were analyzed to calculate microbial growth and EPS production. c, d, Correlations between total and H2O-derived EPS-Poly and EPS-Prot using ordinary least squares regression. The gray shading represents the 95% confidence interval. e, Alignment between observed and predicted carbon content of newly produced EPS. Correlations were analyzed using ordinary least squares regression. The gray solid line indicates a perfect match.
Source data
Extended Data Fig. 2 Relationships between MAOC and extracellular and cellular residue traits across soils.
Partial correlation analysis (Pearson’s r) between MAOC and the traits of extracellular residues (ProductionEPS and unassociated EPS-C) and cellular residues (ProductionCellular, MBC and cellular residue carbon). The color intensity and numerical values indicate the strength of the correlation. Significant levels are indicated as follows: *: P < 0.05, **: P < 0.01 and ***: P < 0.001. Exact P values are provided in the Source Data Extended Data Fig. 2. N = 55 independent soil samples.
Source data
Extended Data Fig. 3 Random forest analysis of MAOC variation.
Relative importance of explanatory variables in explaining MAOC variation, quantified by the percentage increase in mean square error (MSE) through permutation. Model performance was assessed using the coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE). Asterisks indicate the statistical significance of variable importance (*: P < 0.05, **: P < 0.01 and ***: P < 0.001). Exact P values are provided in the Source Data Extended Data Fig. 3. N = 55 independent soil samples.
Source data
Extended Data Fig. 4 TOC-normalized amino sugar concentrations in microbial EPS and MCR.
TOC-normalized concentrations of amino sugars in EPS and MCR produced by microbial communities from three soil types. Each bar represents the mean ± standard deviation (n = 3 independent replicates). Different letters indicate significant differences between EPS and MCR within each amino sugar (two-sided paired t-tests, P < 0.05). Detailed statistical results are provided in Source Data Extended Data Fig. 4.
Source data
Extended Data Fig. 5 Mineral-associated organic carbon content after 60 days of incubation across three soil types.
The content of mineral-associated organic carbon after 60 days of incubation in three soils under non-sterile conditions. Control indicates unamended soil. Values represent the mean ± standard deviation (n = 3 independent microcosms). Different letters indicate significant differences within each soil type based on a one-way ANOVA followed by Tukey’s HSD post-hoc test (P < 0.05). Detailed statistical results are provided in Source Data Extended Data Fig. 5.
Source data
Extended Data Fig. 6 NanoSIMS visualization of 13C enrichment in mineral particles following EPS and MCR amendments.
SEM and NanoSIMS imaging of 13C enriched areas in mineral particles from non-sterile microcosms amended with EPS (a) and MCR (b). Boxplots of 13 C−:(12 C− + 13 C−) isotope ratios in individual 13C-enriched regions, obtained from NanoSIMS measurements (c). Each data point represents the mean isotopic enrichment of a single region of interest (ROI), with 12-18 independent ROIs analyzed per treatment. The black horizontal dashed line indicates the natural abundance of the isotopic ratio (0.011). Statistical significance was assessed using Welch’s one-way ANOVA followed by Games–Howell post hoc tests (for unequal variances). Different letters indicate significant differences (P < 0.05). Detailed statistical results are provided in Source Data Extended Data Fig. 6. Scale bars represent 5 μm.
Source data
Extended Data Fig. 7 Solid-state 13C CP-MAS NMR characterization of mineral-associated organic carbon.
Solid-state 13C CP-MAS NMR spectra of MAOC formed under sterile and non-sterile conditions across three soils. Treatments include amendments of glycerol, EPS, MCR, straw and an unamended control. Panels are organized by soil type and sterility: Alfisol (a, b), Inceptisol (c, d) and Ultisol (e, f), with sterile conditions shown in the left column (a, c, e) and non-sterile conditions in the right column (b, d, f).
Source data
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Qian, P., Wu, Y., Ren, J. et al. Microbial extracellular residues are a major source of mineral-associated soil carbon.
Nat. Geosci. (2026). https://doi.org/10.1038/s41561-026-02103-0
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DOI: https://doi.org/10.1038/s41561-026-02103-0
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