Journal: bioRxiv
Article Title: Machine learning-assisted enzyme engineering through ultra-high throughput sorting and large-scale sequence-function data generation
doi: 10.1101/2025.03.30.645636
Figure Lengend Snippet: Ultra-HTS of FNS mutant library. (A) Ultra-HTS workflow. Single cell encapsulation was performed to encapsulate the single mutant into individual droplets. After 24 hours of incubation, the inducer 200 µM IPTG and substrate 200 µM naringenin were injected into the droplets. After another 24 hours of incubation, the droplets were sorted based on their fluorescence intensity. The sorter would sort the droplets with stronger fluorescence signals into the positive channel based on the set threshold. (B) Images of the droplets with encapsulated mutants before and after sorting. (C) GFP distribution of the droplets with encapsulated mutants before and after sorting. (D) Apigenin production of the sorted mutants characterized in 96-wells plate. (E) Apigenin production of the sorted mutants characterized in falcon tube. The sorted mutants remain consistent performance at larger scale. (F) Ultra-HTS of the mutant library into three performance groups based on the PMT signals. Data information: The experimental data were represented as mean ± S.D. (n = 20 per group). Statistical significances of ****P ≤ 0.0001, ***P < 0.001, **P < 0.01, and *P < 0.05 were calculated based on two-sample unpaired t-test.
Article Snippet: The optical setup for droplet sorting included a fluorescence light source (CoolLED pE-4000) and a customized photomultiplier tube (PMT) detection system.
Techniques: Mutagenesis, Encapsulation, Incubation, Injection, Fluorescence