Raman spectroscopy: A tool for host response and virus characterisation
The rapid emergence of zoonotic and human viruses, such as SARS-CoV-2 and IAV H1N1, underscores the urgent need for advanced technologies capable of swiftly identifying molecular markers and phenotypic response patterns of virus-cell interactions. Current diagnostic and analytical approaches often rely on limited molecular data, hindering our understanding of the complex interplay between viruses and their hosts, which is crucial for initial management of virus outbreaks. A critical gap in virus infection diagnostics is the ability to rapidly determine virus tropism and host response. We hypothesise that analysing the biochemical changes and activation levels in host cells provides the fastest route to characterising a virus infection and developing effective host-based classifiers. This project will address this need by using Raman microscopy, which offers a universal platform for cellular analysis due to its broad compatibility with diverse cell types.
Integrated Raman spectroscopy. Raman spectroscopy, positioned within the omics landscape as phenomics, captures the biochemical fingerprints of cells. The Raman spectrum1 of a cell can be simulated almost perfectly by fitting it with a combination of characteristic Raman spectra of its components – water, proteins, nucleic acids, lipids, and polysaccharides. With this technique, biopolymer classes that make up a cell can be quantified. Modern, machine-learning guided Raman analyses integrate these spectral patterns with multi-omics data to map molecular dynamics and host responses to infection.
Its rapid, label-free measurements provide a versatile tool for investigating the changes in a cell’s morphochemical pattern, which controls many key processes during infection. Modern Raman-based analyses leverage machine learning to integrate spectral information within the biological context, revealing biochemical changes that drive phenotypic adaptations during infection – opening new dimensions for rapidly understanding underlying virus tropism. Building on established Raman spectroscopic fingerprinting of bacterial infections, we aim to extend this powerful technique to virus systems, offering rapid, scalable, and reproducible insights essential for pandemic preparedness, early detection of emerging pathogens, and zoonotic disease surveillance. By leveraging advanced Raman spectroscopic workflows combined with machine learning algorithms, the project aims to translate complex spectral data into actionable phenotypic insights. To ensure reproducibility and comparability, we will establish standardised and rigorously validated sample preparation protocols optimised specifically for Raman analysis, providing a robust foundation for elucidating virus infection cycles. Crucially, the tool we develop will systematically integrate Raman spectroscopy data with multiomics datasets (genomic, transcriptomic, proteomic, and metabolomic) for the analysis of virus interactions across different host systems by investigating infections with the eukaryotic virus SARS-CoV-2 and the vibriophage N4.
Our goal is to create a holistic analytical framework that comprehensively maps the molecular and functional dynamics of virus-host interactions, Fig. C04.1. A pivotal component of our approach will be the collaborative integration with Z02 (Barth/Cassman/Gerlach/König-Ries) and NFDI4Microbiota to enrich and expand the virus reference database VirJenDB with high-dimensional, interoperable Raman spectral datasets. The core research question driving this project is: How can Raman spectroscopic data be systematically and effectively combined with genomic, transcriptomic, and metabolomic datasets – either sequentially or simultaneously – to accurately map the molecular dynamics and host response patterns of virus-host interactions, thereby enabling precise predictions of virus pathogenicity and virulence? By addressing this critical question, our project will develop a robust and unified analytical framework leveraging Raman spectroscopy’s powerful non-invasive and high-throughput capabilities to decode the molecular mechanisms underlying virus infections. Ultimately, this will support the development of rapid and targeted diagnostic strategies as well as effective therapeutic interventions.
- WP 1: Raman spectroscopy platform optimised for virus research in the eukaryotic domain (Beer/Popp)
- WP 2: Raman spectroscopy platform optimised for virus research in the prokaryotic domain (Popp)
- WP 3: Technological benchmark for the label-free characterisation of virus particles (Popp/Beer)
Team Members
N. N.
Doctoral Researcher
N. N.
Doctoral Researcher
Doreen Schulz
Associated Technician
Sophie Girnius
Associated Lab Assistant
Project-Specific Publications
2025
Raman Spectroscopy Can Identify Acute and Persistent Biochemical Changes in Leukocytes From Patients With COVID-19 and Non-COVID-19-Associated Sepsis Journal Article
In: Biotechnology Journal, vol. 20, no. 9, pp. e70105, 2025.
2021
High genetic variability of Schmallenberg virus M-segment leads to efficient immune escape from neutralizing antibodies Journal Article
In: PLoS pathogens, vol. 17, no. 1, pp. e1009247, 2021.
Chemometric analysis in Raman spectroscopy from experimental design to machine learning–based modeling Journal Article
In: Nature protocols, vol. 16, no. 12, pp. 5426–5459, 2021.
2020
Autonomously replicating RNAs of Bungowannah pestivirus: ERNS is not essential for the generation of infectious particles Journal Article
In: Journal of Virology, vol. 94, no. 14, pp. 10–1128, 2020.
A genome-wide CRISPR-Cas9 screen reveals the requirement of host cell sulfation for Schmallenberg virus infection Journal Article
In: Journal of Virology, vol. 94, no. 17, pp. 10–1128, 2020.
Detection and differentiation of bacterial and fungal infection of neutrophils from peripheral blood using Raman spectroscopy Journal Article
In: Analytical chemistry, vol. 92, no. 15, pp. 10560–10568, 2020.
Laser spectroscopic technique for direct identification of a single virus I: FASTER CARS Journal Article
In: Proceedings of the National Academy of Sciences, vol. 117, no. 45, pp. 27820–27824, 2020.
2018
Simple ciprofloxacin resistance test and determination of minimal inhibitory concentration within 2 h using Raman spectroscopy Journal Article
In: Analytical chemistry, vol. 90, no. 3, pp. 1811–1818, 2018.
2015
A variegated squirrel bornavirus associated with fatal human encephalitis Journal Article
In: New England Journal of Medicine, vol. 373, no. 2, pp. 154–162, 2015.
2012
Novel orthobunyavirus in cattle, Europe, 2011 Journal Article
In: Emerging infectious diseases, vol. 18, no. 3, pp. 469, 2012.