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. 2025 Oct 7;16(1):8908.
doi: 10.1038/s41467-025-63964-4.

Sensitive, high-throughput, metabolic analysis by molecular sensors on the membrane surface of mother yeast cells

Wenxin Jiang #  1 , Huanmin Du #  1 , Xingjie Huang  1 , Luke P Lee  2   3   4   5 , Chia-Hung Chen  6   7
Affiliations

Sensitive, high-throughput, metabolic analysis by molecular sensors on the membrane surface of mother yeast cells

Wenxin Jiang et al. Nat Commun. .

Abstract

Due to its genetic similarity to humans, yeast serves as a vital model organism in life sciences and medicine, allowing for the study of crucial biological processes such as cell division and metabolism for drug development. However, current tools for measuring yeast extracellular secretion lack the sensitivity, throughput, and speed required for large-scale metabolic analysis. Here, we present an ultrasensitive, large-scale analysis of yeast extracellular secretion using molecular sensors on the membrane surface of mother yeast cells. These sensors remain selectively confined to mother yeast cells during cell division, enabling high-sensitivity detection, high-throughput screening and rapid single-yeast assays. Their detection limit is 100 nM, and they can screen over 107 single cells per run. We achieve a > 30-fold speed boost compared to conventional droplet-based screening, allowing us to identify the top 0.05% of secretory strains from 2.2 × 106 variants within just 12 minutes. The platform offers potential for large-scale single-yeast metabolic analysis and bio-fabrication.

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Conflict of interest statement

Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. MOMS for single-yeast secretion assays.
a An artwork illustrating that molecular sensors on the membrane surface of mother yeast cells (MOMS) selectively anchor to mother yeast cells without being transmitted to daughter cells during the budding process (scale bar: 0.5 μm). b With advancements in selective sensor coating on mother yeast cells, MOMS provides high-sensitivity, high-throughput screening, and ultra-high-speed sorting capabilities, enabling the practical analysis of over 107 cells within 40 minutes. The schematic was created using BioRender.com and was released under a CC BY 4.0 license. c In contrast to many droplet screening methods, MOMS offers a > 10-fold enhancement in extracellular secretion assay sensitivity and a > 30-fold improvement in sorting speed of secreted strains (figure with full references can be seen in Supplementary Fig. 1)–,–,–,–. d A comparative analysis of various technologies showed that MOMS is the optimal molecular sensor for single microbe extracellular secretion assay to measure varied metabolic molecules, combining high-sensitivity, high screening throughput, and ultra-high sorting speed. (Figure with full references can be seen in Supplementary Fig. 2),,,,,,,. Source data are provided as a Source Data file.
Fig. 2
Fig. 2. Sensitive multiplexed assay via dense MOMS coating.
a Yeast surface was functionalized using a biotin–streptavidin binding strategy to attach biotin-labeled aptamers, forming molecular sensors on the membrane surface of mother yeast cells (MOMS). b Cy5-labeled MOMS on Alexa Fluor 488-ConA-stained yeast cells were visualized during budding, demonstrating exclusive localization of MOMS on mother cells (scale bar: 1.5 μm). c Incubation of yeast cells with 0–3 μM MOMS resulted in varying coating densities, reaching saturation at 1.5 μM (Scale bar: 2.0 μm). Data are representative of three biological replicates. NC: Negative control. d After over 30 generations (3 days), ~77% of MOMS remained on mother cells, maintaining strong Cy5 fluorescence, while daughter cells exhibited MOMS-free coatings. eHigh-density MOMS (~1.0 × 107 per cell) persisted on mother cells for 72 hours, despite declining proportions of mother cells during proliferation (n = 3 biological replicates, mean ± s.d.). (f) Densely grafted MOMS enabled highly sensitive multiplexed assays on mother cells. (g) MOMS outperformed current droplet-based aptamer sensors by leveraging ~1.4 × 107 immobilized aptamers per cell, enhancing assay sensitivity by >100-fold and reducing the detection limit from 50 μM to 100 nM,, (n = 3 biological replicates, mean ± s.d.). h MOMS’ high sensitivity allowed direct detection of ATP secreted by single yeast cells. ATP levels were distinguishable across 0, 1, 5, 10 and 15 minutes (n = 20 individual cells, mean ± s.d.). i A multiplexed assay was achieved by coating yeast cells with different MOMS targeting vanillin, ATP, and glucose. j Fluorescence intensity increased over time following incubation of MOMS-coated yeast cells with vanillin (1.0 mM), ATP (1.0 mM), glucose (1.5 M), and Zn2+ (2.0 mM) (n = 3 biological replicates, mean ± s.d.). k Confocal microscopy confirmed simultaneous triplex aptamer coating on yeast cells for multiplexed detection (scale bar: 1.5 μm). l Heatmap validation confirmed 100% assay specificity for multiplexed metabolite detection, data were averaged from three biological replicates. VAN: Vanillin, GLU: Glucose. Schematics in (a, f, i) were created in BioRender.com, released under a CC-BY 4.0 license. Source data are provided as a Source Data file.
Fig. 3
Fig. 3. High-throughput screening.
a MOMS-enabled high-throughput screening involved capturing single yeast cells and screening them via flow cytometry to identify secretory strains. The schematic was created in BioRender.com and released under a CC BY 4.0 license. b MOMS removed droplets before screening, allowing conventional flow cytometry (103–104 Hz) despite low droplet encapsulation rates,,,. c Vanillin secretion (VAN-3 > VAN-2 > VAN-1»WT) was monitored over time, showing signal amplification during incubation (n = 3 biological replicates, mean ± s.d.). WT: the wild-type strain, NC: negative control. d Simultaneous vanillin and ATP secretion measurements revealed two clusters: VAN-3 and the negative control. e All yeast strains (VAN-1, VAN-2, VAN-3, and WT) showed similar ATP secretion profiles. In contrast, VAN-3 exhibited the highest vanillin secretion, followed by VAN-2 and VAN-1, outperforming the wild-type strain. f Coupled assays identified three clusters (VAN-3, WT, and daughter cells) during vanillin secretion in mixed yeast samples (VAN-3: WT = 1: 1). g Mixed WT and VAN-3 samples, measured at different ratios, showed distinct fluorescence peaks for sorting VAN-3 strains. Source data are provided as a Source Data file.
Fig. 4
Fig. 4. High-speed sorting for rapid directed evolution.
a MOMS efficiently screened a mixed population containing 0.5% VAN-3 and 99.5% wild-type (WT) yeast strains at a throughput of ~3.0 × 103 cells/s, processing 1.2 × 106 cells in under 7 minutes. Secretory yeast strains were enriched to >90% purity of VAN-3 cells. Vanillin production from individual clones was randomly sampled from pre- and post-sorting populations (n = 3 biological replicates, mean ± s.d., individual replicates plotted as dots). b For ECH enzyme evolution, MOMS was used to screen a mutant library of ~2.2 × 106 variants at ~3.0 × 103 cells/s, isolating the top 0.05% of strains with superior vanillin secretion within approximately 12 minutes. The enriched pool exhibited a ~1.93-fold increase in vanillin production relative to the parental population (n = 3 biological replicates, mean ± s.d., individual replicates plotted as dots). c Time-course high-performance liquid chromatography (HPLC) analysis of fermentation supernatants from VAN-3 and selected mutant strains showed a ~2.7-fold increase in vanillin titer after 48 hours (n = 3 biological replicates, mean ± s.d., individual replicates plotted as dots). d The schematic illustrates the sorted ECH mutant from the S-10 strain, with amino acid substitutions highlighted in red: I90N, Y169C, N212Q, and P213L. e For directed evolution of the vanillin transporter PP_0179, a library of 2 × 107 yeast variants was screened using MOMS to isolate top-performing strains with enhanced extracellular vanillin secretion. f Four high-performing transporter variants were validated through batch fermentation, each exhibiting increased vanillin titers (n = 3 biological replicates, mean ± s.d., individual replicates plotted as dots). g Two key mutations, K40R and V79D, identified in the top-performing strain, were found to enhance transporter efficiency and vanillin export. Source data are provided as a Source Data file.
Fig. 5
Fig. 5. High-flexibility of MOMS for protein analysis.
a The dynamic detection range of IFN-γ was evaluated using aptamer-functionalized MOMS (aptamer-MOMS) over concentrations ranging from 1 to 1000 nM. b Specificity of glycosylated human serum albumin (gHSA)-MOMS was assessed by comparing responses to gHSA, bovine serum albumin (BSA), and non-glycosylated human serum albumin (HSA). c Antibody-functionalized MOMS (antibody-MOMS) were fabricated by coating biotinylated yeast cells with streptavidin-modified IFN-γ capture antibodies. Coating efficiency was evaluated using Alexa Fluor 488-labeled secondary antibodies. d Antibody-MOMS were incubated with varying concentrations of IFN-γ, and the captured proteins were labeled with APC-tagged detection antibodies to generate quantifiable fluorescence signals. The dynamic detection range for IFN-γ was determined to be 1–1000 nM, with a limit of detection (LOD) of approximately 1 nM. All experiments in this figure were independently repeated twice with similar results, representative data are shown. Source data are provided as a Source Data file.
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References

    1. Siddhardha, B., Dyavaiah, M. & Syed, A. Model Organisms for Microbial Pathogenesis, Biofilm Formation and Antimicrobial Drug Discovery Ch. 28 (Springer, Singapore, 2020).
    1. Lian, J., Mishrab, S. & Zhao, H. Recent advances in metabolic engineering of Saccharomyces cerevisiae: new tools and their applications. Metab. Eng.50, 85–108 (2018). - PubMed
    1. Wang, B. L. et al. Microfluidic high-throughput culturing of single cells for selection based on extracellular metabolite production or consumption. Nat. Biotechnol.32, 473–478 (2014). - PMC - PubMed
    1. Hasunuma, T. & Kondo, A. Development of yeast cell factories for consolidated bioprocessing of lignocellulose to bioethanol through cell surface engineering. Biotechnol. Adv.30, 1207–1218 (2012). - PubMed
    1. Billerbeck, S., Walker, R. S. K. & Pretorius, I. S. Killer yeasts: expanding frontiers in the age of synthetic biology. Trends Biotechnol.42, 1081–1096 (2024). - PubMed

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