Scientists from the Qingdao Institute of Bioenergy and Bioprocess Technology of the Chinese Academy of Sciences (CAS) have developed a novel computational tool, RamEx, designed to resolve the computational bottleneck in high-throughput microbial Ramanomics. The findings were recently published in Microbiome.

The study is expected to accelerate the analysis of massive spectral datasets produced by Raman flow cytometry, opening new avenues for high-resolution microbial profiling.

Ramanomics has emerged as a powerful big-data approach for single-cell metabolic phenotyping, enabling label-free, non-invasive characterization of cell types and physiological states. However, the rise of high-throughput Raman flow cytometry has led to the generation of massive Ramanomic datasets, creating a critical computational bottleneck. Conventional analysis tools are often restricted to basic preprocessing and poorly adapted to the high-dimensional, noisy nature of Ramanomic data, making it difficult to convert the data deluge into meaningful biological insights.

RamEx addresses these limitations by streamlining the full Ramanomic analysis pipeline, from data preprocessing and automated quality control to advanced data mining. As highlighted by the research team, the tool's key innovation is its design tailored to the unique properties of microbial spectral data. The researchers developed the Iterative Convolutional Outlier Detection (ICOD) algorithm to tackle spectral noise. Unlike traditional methods that depend on manual, experience-driven thresholds, ICOD operates in an unsupervised manner to dynamically identify and eliminate spectral artifacts, ensuring high-quality input for downstream analysis.

The platform's performance was rigorously validated using diverse datasets, including pathogenic bacteria, probiotics, and yeast fermentation systems. Notably, RamEx successfully captured phenotypic heterogeneity in isogenic yeast populations—genetically identical cells—by detecting subtle metabolic fluctuations and tracking the dynamic accumulation of intracellular macromolecules, including lipids, proteins, and nucleic acids.

"RamEx bridges the gap between high-throughput Ramanomic data acquisition and computational analysis," said Prof. SUN Luyang, corresponding author of the study. "It delivers a robust toolkit that empowers researchers to explore microbial ecology, metabolic interactions, and environmental stress responses at an unprecedented single-cell resolution."

RamEX: a modularized pipeline for ramanome analysis. (Image by LIU Yang)