Presentation Information
[3GteX-04-KL]AI-Integrated Microfluidics for Rapid Protein Screening and Discovery
○Huabing Yin1 (1. University of Glasgow (UK))
Keywords:
Microfluidics,Raman spectroscopy,high throughput,label-free,single cell,protein engineering,artificial intelligence
Protein engineering is an important foundation of the modern economy, enabling the creation of enzymes and proteins with tailored functions for diverse applications in life science, medicine, and industry. The field is being revolutionised by advances in artificial intelligence and engineering biology. Tools such as AlphaFold have dramatically improved our ability to predict protein structures and design diverse variant libraries, while modern biofoundry technologies now allow rapid expression of de novo protein designs in cells. As a result, creating large variant libraries is no longer the principal bottleneck; instead, efficiently screening and selecting functional variants from these vast libraries has become the central challenge.
Microfluidic technologies offer a powerful solution by enabling high throughput functional screening at single cell resolution. Over recent years, we have developed microfluidic platforms that integrate Raman spectroscopy to identify and sort cells directly based on their intrinsic biochemical phenotypes (1,2,3). Unlike traditional screening methods that rely on proxy signals such as fluorescence, our label free, Raman based approach measures true functional outputs, greatly expanding the range of activities and phenotypes that can be discovered.
In this talk, I will present our recent Raman flow cytometry and AI assisted Raman activated cell sorting platform. This integrated system enables automated, high throughput isolation of individual cells with desirable traits from complex microbial communities or engineered cell populations, allowing direct linkage between phenotype and genotype. I will demonstrate how this technology accelerates the engineering of synthetic cells and opens new opportunities for exploring natural microbial diversity.
Together, these developments provide a transformative framework for rapid protein discovery and functional screening, with broad applications across microbiology, synthetic biology, life sciences, and diagnostics.
References
1. Li, Y. et al. Rapid culture free diagnosis of clinical pathogens via integrated microfluidic Raman microspectroscopy. Nature Communications, 17, 283 (2025).
2. Y. K. Lyu et al., Automated Raman based cell sorting with 3D microfluidics. Lab Chip 20, 4235 (2020).
3. D. McIlvenna et al., Continuous cell sorting in a flow based on single cell resonance Raman spectra. Lab Chip 16, 1420 (2016).
Microfluidic technologies offer a powerful solution by enabling high throughput functional screening at single cell resolution. Over recent years, we have developed microfluidic platforms that integrate Raman spectroscopy to identify and sort cells directly based on their intrinsic biochemical phenotypes (1,2,3). Unlike traditional screening methods that rely on proxy signals such as fluorescence, our label free, Raman based approach measures true functional outputs, greatly expanding the range of activities and phenotypes that can be discovered.
In this talk, I will present our recent Raman flow cytometry and AI assisted Raman activated cell sorting platform. This integrated system enables automated, high throughput isolation of individual cells with desirable traits from complex microbial communities or engineered cell populations, allowing direct linkage between phenotype and genotype. I will demonstrate how this technology accelerates the engineering of synthetic cells and opens new opportunities for exploring natural microbial diversity.
Together, these developments provide a transformative framework for rapid protein discovery and functional screening, with broad applications across microbiology, synthetic biology, life sciences, and diagnostics.
References
1. Li, Y. et al. Rapid culture free diagnosis of clinical pathogens via integrated microfluidic Raman microspectroscopy. Nature Communications, 17, 283 (2025).
2. Y. K. Lyu et al., Automated Raman based cell sorting with 3D microfluidics. Lab Chip 20, 4235 (2020).
3. D. McIlvenna et al., Continuous cell sorting in a flow based on single cell resonance Raman spectra. Lab Chip 16, 1420 (2016).
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