Robust Spontaneous Raman Flow Cytometry for Single‐Cell Metabolic Phenome Profiling via pDEP‐DLD‐RFC

• Published in: Advanced science Vol. 10; no. 16; pp. e2207497 - n/a
• Main Authors: Wang, Xixian, Ren, Lihui, Diao, Zhidian, He, Yuehui, Zhang, Jiaping, Liu, Min, Li, Yuandong, Sun, Lijun, Chen, Rongze, Ji, Yuetong, Xu, Jian, Ma, Bo
• Format: Journal Article
• Language: English
• Published: Germany John Wiley & Sons, Inc 01.06.2023; John Wiley and Sons Inc; Wiley
• Subjects: Bacteria; Cells; Design; Efficiency; Electrodes; Flow cytometry; Flow Cytometry - methods; Flow velocity; high generality; high throughput; Humans; Lasers; metabolic phenome profiling; Metabolism; Metabolites; Metabolomics; Raman flow cytometry; single cell; Spectrum Analysis, Raman - methods; Systems stability; Raman flow cytometry; single cell; metabolic phenome profiling; high generality; high throughput
• ISSN: 2198-3844; 2198-3844
• DOI: 10.1002/advs.202207497

A full‐spectrum spontaneous single‐cell Raman spectrum (fs‐SCRS) captures the metabolic phenome for a given cellular state of the cell in a label‐free, landscape‐like manner. Herein a positive dielectrophoresis induced deterministic lateral displacement‐based Raman flow cytometry (pDEP‐DLD‐RFC) is established. This robust flow cytometry platform utilizes a periodical positive dielectrophoresis induced deterministic lateral displacement (pDEP‐DLD) force that is exerted to focus and trap fast‐moving single cells in a wide channel, which enables efficient fs‐SCRS acquisition and extended stable running time. It automatically produces deeply sampled, heterogeneity‐resolved, and highly reproducible ramanomes for isogenic cell populations of yeast, microalgae, bacteria, and human cancers, which support biosynthetic process dissection, antimicrobial susceptibility profiling, and cell‐type classification. Moreover, when coupled with intra‐ramanome correlation analysis, it reveals state‐ and cell‐type‐specific metabolic heterogeneity and metabolite‐conversion networks. The throughput of ≈30–2700 events min−1 for profiling both nonresonance and resonance marker bands in a fs‐SCRS, plus the >5 h stable running time, represent the highest performance among reported spontaneous Raman flow cytometry (RFC) systems. Therefore, pDEP‐DLD‐RFC is a valuable new tool for label‐free, noninvasive, and high‐throughput profiling of single‐cell metabolic phenomes. Here, the development of a robust Raman flow cytometry capable of acquiring single‐cell Raman spectra (SCRS) of a variety of phenotypes rapidly is presented. By coupling with Intra‐Ramanome Correlation Analysis and deep learning, the SCRS‐based high‐throughput and high‐generality biosynthetic process dissection, antimicrobial susceptibility profiling, and cell‐type classification for isogenic cell populations of yeast, microalgae, bacteria, and human cancers are demonstrated.