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bioRxiv2025

A coupled fluorescence assay for high-throughput screening of polyurethane-degrading enzymes

The global accumulation of plastic waste, particularly persistent polymers like polyurethane (PU), demands urgent solutions. Enzymatic depolymerization offers a viable strategy for PU waste valorization. However, progress has been hindered by the lack of reliable high-throughput screening (HTS) assays capable of precise and quantitative evaluation of enzymatic activity. Current enzyme screening assays face significant limitations due to poor substrate relevance and inadequate quantification methods. Here, we present a novel HTS assay featuring a chemically defined, synthetic poly (ethylene adipate)-based PU substrate that enables unambiguous structure-activity analysis and direct quantification of degradation products (adipic acid and ethylene glycol). Coupled with this substrate is a highly sensitive fluorescence-based detection cascade, in which released ethylene glycol is stoichiometrically converted to resorufin via a two-enzyme system (glycerol dehydrogenase and diaphorase). This assay overcomes key limitations of commercial substrates (e.g., Impranil DLN) by providing quantitative, real-time monitoring of PU hydrolysis with molecular precision. Validation with known PU-degrading enzymes demonstrated that the assay can sensitively distinguish differences in enzymatic activity with high reproducibility and quantitative accuracy. Our platform enables rapid, cost-effective screening of enzyme libraries and engineered variants, significantly advancing enzyme discovery and optimization efforts. By bridging the gap between laboratory research and industrial application, this HTS assay accelerates the development of sustainable PU recycling solutions, offering a critical tool against plastic pollution. Graphical Abstract Legend O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=137 SRC="FIGDIR/small/663332v4_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@17ba5c0org.highwire.dtl.DTLVardef@bef89aorg.highwire.dtl.DTLVardef@1b3770eorg.highwire.dtl.DTLVardef@14d5ef5_HPS_FORMAT_FIGEXP M_FIG (A) Workflow of the enzymatic degradation assay. Enzymes are incubated with chemically defined PEA-PU films. The resulting hydrolysate is collected and analysed using a fluorescence-based assay in microplate format (Ex/Em = 535/588 nm). (B) Fluorescence-based detection mechanism. Assay principle based on ethylene glycol (EG), one of the major degradation products. EG is converted through a two-step enzymatic cascade involving glycerol dehydrogenase (GldA) and diaphorase, leading to the generation of fluorescent resorufin from resazurin. C_FIG

synthetic biology↗

bioRxiv2023

TriTan: An efficient triple non-negative matrix factorisation method for integrative analysis of single-cell multiomics data

MotivationSingle-cell multi-omics have opened up tremendous opportunities for understanding gene regulatory networks underlying cell states by simultaneously profiling transcriptomes, epigenomes and proteomes of the same cell. However, existing computational methods for integrative analysis of these high-dimensional multi-modal data are either computationally expensive or limited in interpretation ans scope. These limitations pose challenges in the implementation of these methods in large-scale studies and hinder a more in-depth understanding of the underlying regulatory mechanisms. ResultsHere, we propose TriTan (Triple inTegrative fast non-negative matrix factorisation), an efficient joint factorisation method for single-cell multiomics data. TriTan implements a highly efficient triple non-negative matrix factorisation algorithm which greatly enhances its computational speed, and facilitates interpretation by clustering both the cells and features simultaneously as well as identifying signature feature sets for each cell cluster. Additionally, three matrix factorisation produced by TriTan helps in finding associations of features across modalities, facilitating the prediction of cell type specific regulatory networks. We applied TriTan to single-cell multi-modal data obtained from different technologies and benchmarked it against the state-of-the-art methods where it shows highly competitive performance. Furthermore, we showed a range of downstream analyses that can be conducted utilising the outputs from TriTan. Availabilityhttps://github.com/maxxxxxxxin/TriTan online.

bioinformatics↗