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

Wenxin Jiang; Huanmin Du; Xingjie Huang; Luke P. Lee; Chia-Hung Chen

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 (Co-first Author)
, Huanmin Du (Co-first Author)
, Xingjie Huang
, Luke P. Lee^*
, Chia-Hung Chen^*

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

2 Citations (Scopus)

6 Downloads (CityUHK Scholars)

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. © The Author(s) 2025.

Original language	English
Article number	8908
Number of pages	14
Journal	Nature Communications
Volume	16
Online published	7 Oct 2025
DOIs	https://doi.org/10.1038/s41467-025-63964-4
Publication status	Published - 2025

Funding

We gratefully acknowledge funding support from the Health and Medical Research Fund (HMRF09203596 to C.H.C.), and the Research Grants Council of the Hong Kong Special Administrative Region, China (GRF CityU11212822, GRF CityU11204923, GRF CityU11201624, RIF R4020-22 and RIF R1007-24F to C.H.C.). We also thank the Innovation and Technology Fund (PRP/037/22FX and PRP/011/24Ti to C.H.C.) and the City University of Hong Kong (9610467, 7005639, 7020030, 9667239, and 9229502 to C.H.C.) for their financial support.

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This full text is made available under CC-BY-NC-ND 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/

RGC Funding Information

RGC-funded

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

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. {\textcopyright} The Author(s) 2025.",

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journal = "Nature Communications",

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AU - Jiang, Wenxin

AU - Du, Huanmin

AU - Huang, Xingjie

AU - Lee, Luke P.

AU - Chen, Chia-Hung

PY - 2025

Y1 - 2025

N2 - 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. © The Author(s) 2025.

AB - 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. © The Author(s) 2025.

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U2 - 10.1038/s41467-025-63964-4

DO - 10.1038/s41467-025-63964-4

M3 - RGC 21 - Publication in refereed journal

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SN - 2041-1723

VL - 16

JO - Nature Communications

JF - Nature Communications

M1 - 8908

ER -

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

Abstract

Funding

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RIF: Uncovering Dynamically Functional Cells Using a Time-Resolved Intelligent Cell Sorter (TRIC) for Cancer Treatments

GRF: Uncovering Rare Inherent Resistant Single Cells Through an Automatic Microfluidic Cell Shower Device to Investigate Intratumor Heterogeneity

DON_RMG: IDM Cluster 1: Biomarkers, Diagnostics and Therapeutics - RMGS

Cite this

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

Abstract

Funding

Publisher's Copyright Statement

RGC Funding Information

Access Output

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Projects

RIF: Uncovering Dynamically Functional Cells Using a Time-Resolved Intelligent Cell Sorter (TRIC) for Cancer Treatments

GRF: Uncovering Rare Inherent Resistant Single Cells Through an Automatic Microfluidic Cell Shower Device to Investigate Intratumor Heterogeneity

DON_RMG: IDM Cluster 1: Biomarkers, Diagnostics and Therapeutics - RMGS

Cite this