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Neue organische und metallorganische Verbindungen machen Smartphones, Virtual-Reality-Brillen oder künstliche Intelligenz immer leistungsfähiger und nachhaltiger. Wie ist es, als Chemiker:in in der industriellen Halbleiterforschung zu arbeiten? Ein Blick ins Unternehmen Merck in Darmstadt.Die Abt...

Label-free, noninvasive, rapid and cost-efficient: combining Raman spectroscopy with high-throughput flow cytometry reveals the metabolic performance of individual cells of a population in their natural context.Cellular metabolism supports pivotal processes: from microbial carbon and nitrogen cycling, to crop production for food and energy homeostasis, to cancer progression. Although powerful, traditional bulk metabolomic approaches obscure the heterogeneity inherent in cell populations, masking rare but functionally significant subpopulations. Single-cell sorting technologies have emerged to analyze this complexity and isolate cells with specific metabolic functions from a mixed population.Fluorescence-activated cell sorting (FACS) has been a powerful workhorse for metabolic profiling and cell sorting for over half a century. However, FACS depends on predefined markers and labels, perturbing native cellular states and failing to capture the full spectrum of metabolic activity. In contrast, Raman-activated cell sorting (RACS) is a transformative label-free technology, combining the high-throughput capabilities of flow cytometry with the rich, vibrationally derived metabolic fingerprints identifiable by Raman spectroscopy.1) By probing intrinsic molecular vibrations in real-time, Raman flow cytometry unlocks new dimensions in cellular analysis: from the dynamic profiling of single cells to the sorting of rare, metabolically distinct populations. Raman analysis is non-destructive. Thus, sorted cells remain viable for downstream functional assays, multi-omics integration, or transplantation – a key advantage of RACS over mass spectrometry-based methods.<h2 type="main">Metabolomic group photo</h2>Raman spectroscopy detects inelastic light scattering. Shifts in photon energy correspond to the vibrational modes of molecular bonds. Unlike fluorescence, Raman signals arise without exogenous labels, capturing a holistic view of cellular composition (Figure above), such as lipids, proteins, nucleic acids, polysaccharides, pigments and other metabolites.1) Moreover, Raman shifts provide information on the extent and rate of substrate intake: If cells are fed with stable isotope-labeled substrates, they incorporate into molecular bonds in the cell, which causes shifts in the specific Raman bands. A single-cell Raman spectrum (SCRS) can encode the cell’s phylogenetic type, substrate intake rate, response to physical, chemical or biological perturbations, inter-cellular exchange of metabolites, and conversions of intracellular metabolites. The SCRS collection for a cell population or consortium is called a ramanome or meta-ramanome., It represents a <link href="#nadc20254152251-fig-0001">metabolic group photo at a given state.1)<figure xml:id="nadc20254152251-fig-0001"><figure><img alt="image" src="https://eu-central-1.graphassets.com/Aype6X9u2QGewIgZKbFflz/cmg61ksewmwjo08usjbwi3wd5"><figcaption><label>Abb.1: </label>Left: Raman spectrum of a single cell showing the Raman shifts of typical biological molecule groups. Right: A ramanome (or metaramanome) data point that consists of single-cell Raman spectra from a mixture of N cells, with each spectrum carrying M metabolic phenotypes.</figcaption></figure></figure>SCRS are<list><listitem>live-cell based,</listitem><listitem>information-rich,</listitem><listitem>label-free, and</listitem><listitem>recorded quickly at low-cost.</listitem></list>They are broadly applicable to microbial, plant and human cells and can be validated by downstream -omics sequencing (RACS-Seq) or cultivation (RACS-Culture): ramanomes and meta-ramanomes are a new type of biological big data.<h2 type="main">Fast and low-cost</h2>To mine cells with a particular metabolic profile directly from a microbiota, a human tissue sample, or an animal, plant or microbial mutant library, researchers have integrated Raman microspectroscopy into high-throughput flow cytometry to establish high-throughput RACS technologies.2) At the company eCyte, we have developed the commercial platform FlowRACS for high-throughput RACS. The system is based on positive dielectrophoresis-induced deterministic lateral displacement (pDEP-DLD)-based RACS (pDEP-DLD-RACS). A modulated pDEP-DLD force is applied to focus, trap, and functionally sort fast-moving single cells in a wide channel. For pigment- and oil-producing yeasts, pDEP-DLD-RACS shows a sorting accuracy of over 90%, a throughput of about 600 events per minute and a yield of over 85%. It runs continuously without decrease in signal-to-noise ratio for over 20 hours and can sort rare cells while preserving full cellular vitality.An exemplary use case: Aurantiochytrium sp. microorganisms produce polyunsaturated fatty acids. Using FlowRACS, we sorted cells from a genome-wide random mutagenesis library containing more than 105 Aurantiochytrium sp. mutants according to their intracellular docosahexaenoic acid (DHA) concentration. After two one-day runs, we cultivated the cells sorted with FlowRACS and obtained superior mutant cells that produced 58% more DHA. This screen-first, culture-second approach with FlowRACS is 100 times faster and cheaper than conventional approaches, which generally follow a culture-first, screen-second paradigm, involving tedious, time-consuming agar plating, colony picking, liquid cultures of monoclones, extraction of total lipids, and analyzing DHA concentrations by mass spectrometry.<h2 type="main">From sorting to ramanome</h2>Our <link href="#nadc20254152251-fig-0002">meta-Ramanomics platform supports the complete workflow of converting a human or environmental sample into a vault of functional cells (Figure left). It includes:<figure xml:id="nadc20254152251-fig-0002"><figure><img alt="image" src="https://eu-central-1.graphassets.com/Aype6X9u2QGewIgZKbFflz/cmg61kvhlmwlh08us25pnq1m1"><figcaption><label>Abb.2: </label>The meta-Ramanomics platform for metabolic profile directed single-cell multiomics that also include cell pure-culturing with the target in-situ metabolic function.</figcaption></figure></figure><list><listitem>FlowRACS for rapidly generating a single-cell metabolic profile from a natural or synthetic population or consortium of cells, and then sorting cells with target metabolic profiles;</listitem><listitem>Digital Colony Picker (DCP) for parallel single-cell culturing, phenotyping, and exporting of FlowRACS-sorted cells to create a culturebank of functional cells;3)</listitem><listitem>scRACS-Seq/Culture (single-cell Raman-activated Cell Sorting coupled to Sequencing or Cultivation) for multimodal RACS that profiles and sorts cells via bright-field, fluorescence, and Raman spectroscopy;4)</listitem><listitem>Raman artificial intelligence (RamanAI) for data analysis and modeling of ramanomes and meta-ramanomes through a cloud-based informatics platform.</listitem></list>Currently, <link href="#nadc20254152251-tbl-0001">hundreds of projects employ the Ramanomics or Meta-Ramanomics platform for label-free, metabolic profiling and sorting of mammalian, higher-plant, microalgal, fungal and bacterial cells, some of which have already been published (Table).<tabular xml:id="nadc20254152251-tbl-0001"><figure><img alt="image" src="https://eu-central-1.graphassets.com/Aype6X9u2QGewIgZKbFflz/cmg61kwx6a4e506urbp6kyzlu"><figcaption><label>Tab.1: </label>Selected applications of the meta-Ramanomics platform.</figcaption></figure></tabular>Since the platform does not require a specific biomarker or labeling at all, it is suited for almost any cellular sample. Furthermore, the sample pre-treatment process, which involves extracting cells from the sample, is typically completed within an hour. Thus, a rapid, fully automated workflow can be established – sample in, probiotics out.However, there are still several challenges to be addressed: The high-dimensional spectral data generated by Raman flow cytometry demand sophisticated computational tools to extract, organize and interpret features. Machine learning approaches, including principal component analysis (PCA) and artificial neural networks, help to decode spectral libraries into biological insights. To ensure reproducibility and cross-study comparisons, we need to standardize protocols and develop reference spectral databases. Finally, while Raman flow cytometry excels in metabolic phenotyping, its integration with other modalities – such as fluorescence for targeted marker detection or mass spectrometry for deep metabolomic validation – will further expand its utility.<h2 type="main">International network</h2>With meta-Ramanomics it is now possible to systematically explore and utilize the vast natural microbiome, of which only a small fraction has been studied to date. Motivated by this opportunity, we have launched the in-situ Metabolic Atlas Project @ Single-cell (iMAPS) – a global consortium aiming at performing metabolic health assessments by rapidly identifying and sorting microbes directly from a microbiota or a mutant library. We can then cultivate or sequence sorted individual microbial cells that exhibit specific metabolic traits. The iMAPS network consists of nearly 60 members in four countries, including China, the UK, France and the US. We invite the German and European scientific community to join the network to redefine the concepts, strategies, and applications of microbiome science together through the meta-ramanomics platform. The network‘s discoveries will serve agriculture, health, medicine, biomanufacturing, and environmental restoration.<h2 type="main">The authors</h2>The authors are Jian Xu (photo below), Bo Ma, Ethan Yin and Rolf Schmid (top photo). Xu and Ma are both professors at the Single-Cell Center at the Qingdao Institute of Bioenergy and Bioprocess Technology (Qibebt) of the Chinese Academy of Sciences. Yin is the general manager of the biotechnology company eCyte. Schmid is an emeritus professor at the University of Stuttgart, and the founder and CEO of the news platform Bio4business.<img alt="image" src="https://eu-central-1.graphassets.com/Aype6X9u2QGewIgZKbFflz/cmg61l01va4hx06urbiucqeq9"><img alt="image" src="https://eu-central-1.graphassets.com/Aype6X9u2QGewIgZKbFflz/cmg61l1dbmwpd08us8o3uhxoi"><a href="https://english.qibebt.cas.cn" target="_blank">english.qibebt.cas.cn</a><a href="https://ecytebio.com" target="_blank">ecytebio.com</a><a href="https://bio4business.eu/home" target="_blank">bio4business.eu/home</a><ul class="references" cited="no" style="numbered" xml:id="nadc20254152251-bibl-0001"><li xml:id="nadc20254152251-bib-0001">1 Y. He, X. Wang, B. Ma, J. Xu, Biotechnol. Adv. 2019, 37, 107388</li><li xml:id="nadc20254152251-bib-0002">2 X. Wang, L. Ren, Z. Diao, Y. He, J. Zhang et al., Adv. Sci. 2023, 10, e2207497</li><li xml:id="nadc20254152251-bib-0003">3 Z. Diao, Q. Peng, S. Luo, L. Kan, A. Ge et al., Nat. Commun. 2025, in press</li><li xml:id="nadc20254152251-bib-0004">4 T. Xu, Y. Li, X. Han, L. Kan, J. Ren et al., Lab Chip 2023, 23, 125</li></ul>

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