Analyzing Human, Plant, Fungal, and Bacterial Metabolism Using A New Raman-based Flow Cytometry Tool

A new platform established by researchers at the Single-Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology of the Chinese Academy of Sciences (QIBEBT/CAS) improves accuracy, throughput, and stability in profiling dynamic metabolic features of cells.

Their study, which was published in Advanced Science on March 4, 2023, describes how this new technology takes metabolism-based snapshots of cell populations, including plants (microalgae), yeast, bacteria like E. coli, and human cancers (1).

Characteristics that arise from factors such as diet, lifestyle, gut microbiome, and genetics, all of which are metabolic phenotypes, involves deep analysis of cellular populations through analytical techniques like mass spectrometry (MS) and fluorescence-based flow cytometry.

However, to use these techniques effectively, these methods require labelling the cells with fluorescent dyes or destroying them altogether. These requirements have thwarted wider deployment. To tackle these challenges, the QIBEBT/CAS research team initiated a new robust flow cytometry platform that does not need to label or destroy the cell to conduct these metabolism-based snapshots.

"The platform's universally applicable, high-throughput nature suggests Raman-based flow cytometry can start to serve a diverse array of novel applications that involve metabolic phenome profiling," said Wang Xixian, who is the study author and an associate professor in the Single-Cell Center of QIBEBT.

In their previous work, the QIBEBT/CAS research team proposed the "ramanome" concept, which was a fast, low-cost, high-throughput method for profiling dynamic metabolic features from just one population of genetically identical cells. This approach relied heavily on a collection of single-cell Raman spectra (SCRS), which are structural fingerprints by which molecules can be identified. Each full-spectrum spontaneous SCRS (fs-SCRS) harbored thousands of peaks, which corresponded to a specific molecular bond vibration and potentially represented a metabolic phenotype.

Acquisition of a ramanome via Raman microscopy was generally a low-throughput method when cells are static, according to the study. For example, collecting a microalgal ramanome can take four hours and only achieve a shallow sampling depth. In contrast, other methods, such as Raman-based flow cytometry (RFC) and spontaneous Raman flow cytometry, are associated with much higher ramanome acquisition throughput. These methods are limited by elevated levels of background noise, poor information content because of narrow spectral range, and low sensitivity because of low spectral resolution.

To combat these limitations, the QIBEBT/CAS research team advanced the ramanome technology by developing and launching a robust Raman-based flow cytometry system for fs-SCRS with high accuracy, high throughput, and stable operation. Referred to as positive dielectrophoresis-based Raman-activated droplet sorting-induced deterministic lateral displacement-based Raman flow cytometry (pDEP-DLD-RFC), the new system achieves an operating stability with sustained running time for deep sampling of a metabolically heterogeneous cell populations, precise entrapment of fast-moving cells and laser target alignment for efficient spectra acquisition.

Early tests in FlowRACS demonstrated chemical specificity and discrimination accuracy of 99.9% as well as high profiling speeds and throughput.

"For isogenic populations of yeast, microalgae, bacteria like E. coli, and cancer cells, pDEP-DLD-RFC produces deep, highly producible ramanomes that reveal rich, single-cell-resolution phenomes with high throughput and without the need to label the cell with fluorescence probes, thus FlowRACS is a universally applicable tool to profile and sort cells in nature based on their metabolic function," said co-corresponding author Xu Jian, from Single-Cell Center of QIBEBT.

The improvements achieved by pDEP-DLD-RFC promises broad utility in metabolic profiling of cellular systems with applications in medical research and life sciences among other fields.

In future studies, the research team will explore methods to try and accomplish four objectives: to more efficiently align smaller cells; integrate pDEP-DLD with surface-enhanced Raman scattering to reduce the acquisition time; incorporate a line-focusing strategy to detect multiple cells for SCRS in parallel; and streamline single-cell sequencing or cultivation.

In summary, Chinese researchers at the Single-Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology of the Chinese Academy of Sciences, have developed a new Raman-based flow cytometry system that offers high accuracy, throughput and stability for deep sampling of a metabolically heterogeneous cell population without labeling or destroying the cell. The new technology offers a significant improvement in metabolic profiling of cellular systems and promises broad utility in life sciences and medical research, providing information-rich metabolic phenome for a given state of a cellular population at a single-cell resolution, a concept the team calls "ramanome".

Reference

(1) Wang, X.; Ren, L.; Diao, Z.; He, Y.; Zhang, J.; Liu, M.; Li, Y.; Sun, L.; Chen, R.; Ji, Y.; Xu, J.; Ma, B. Robust Spontaneous Raman Flow Cytometry for Single-Cell Metabolic Phenome Profiling via pDEP-DLD-RFC. Adv. Sci. DOI: https://onlinelibrary.wiley.com/doi/10.1002/advs.202207497