Abstract: Antifungal susceptibility test (AFST) is essential for guiding effective therapy, but it remains challenged by long turnaround times and the limited generalizability of single-parameter phenotypic readouts across diverse fungal species and antifungal classes. Here, we present a multifeature AFST (MAFST) platform that integrates single-cell Raman spectroscopy and brightfield imaging to enable rapid and pleiotropic assessment of antifungal responses. By unifying complementary metabolic and morphological features into a composite inhibition index (CII), MAFST captures coordinated cellular responses to antifungal exposure that cannot be reliably resolved by individual metrics alone. Benchmarking against the broth microdilution demonstrates that CII-based MAFST achieves high concordance across multiple Candida species and antifungal classes while substantially reducing the time required for susceptibility determination. Thus, by capturing single-cell morphological, developmental, and metabolic drug responses in a fast, accurate, and widely applicable fashion, MAFST can contribute to tackling fungal infections.

Abstract: The global decline of coral reefs underscores the urgency of understanding how corals enhance resilience in stressful environmental conditions. As metaorganisms, or holobionts, corals rely on dynamic interactions with their associated microbial communities, with bacterial restructuring proposed as a potential mechanism of holobiont adaptation. Here, we reconstructed coral symbiosis in the bleached tissues of Acropora hyacinthus by introducing beneficial bacteria and thermally domesticated Symbiodiniaceae to assess their roles in bleaching recovery. Raman spectroscopy metabolomics (RS metabolomics) enables in situ detection, providing temporal evidence of metabolic exchange within the tripartite relationship among corals, Symbiodiniaceae, and associated bacteria.

Abstract: High altitudes pose extreme survival challenges for organisms, yet the origins and molecular strategies underlying their resilience remain poorly understood. Here, we report the molecular and evolutionary mechanisms underlying stress resilience in Apourosomoida sp. LHA081A01, a ciliate isolated from a high-altitude Tibetan salt lake that endures high salinity, low temperature, and hypoxia. We identified TreT glycosyltransferases, acquired through horizontal gene transfer from an anaerobic and halophilic Desulfobacteraceae bacterium, to be involved in the synthesis of α,α-trehalose—a universal protein stabilizer absent in most other ciliates but essential for counteracting multiple environmental stressors. Additional strategies include β-carotene accumulation to mitigate oxidative stress from hypoxia, along with numerous others common to many eukaryotes. Extensive gene family expansions and rapid divergence of stress‑responsive genes underscore their evolutionary significance and critical role in surviving harsh habitats. Intolerance to low salinity may render this ciliate, and other protists, vulnerable to climate‑driven salinity declines in Tibetan salt lakes. Together, these extraordinary features—shaped by horizontal gene transfer, natural selection, and regulatory plasticity—position high-altitude microbial eukaryotes as powerful extremophile models for uncovering the molecular mechanisms of stress resilience and adaptive evolution across life.

Abstract: Microorganisms play pivotal roles in ecosystems, where precise taxonomic identification is fundamental to understanding and harnessing their functions. Conventional microbial classification, however, relies on pure-culture techniques that suffer from prolonged cultivation cycles, low throughput, high costs, and limited resolution. To address these constraints, we developed an integrated platform combining positive dielectrophoresis-activated Raman-activated cell sorting (pDEP-RACS) with deep ResNet. Using the ecologically versatile and taxonomically challenging genus Pantoea as a model, we constructed a reference Ramanome database by pDEP-RACS that consists of 180 000 single-cell Raman spectra (SCRS) from 12 Pantoea species (22 strains) and two phylogenetically related species and established a classification model by ResNet-18. The model achieved optimal performance 96.9% mean accuracy and 97.3% recall for 24 SCRS colony isolates. Cross-batch validation shows the highest reproducibility in nutrient-starved samples, with 87.9% accuracy postpreprocessing with reduced batch effects. For optimal accuracy (97.6% ± 2.0%), classification accuracy plateaus at >1,500 SCRS of Raman detection depth. In synthetic communities, the model shows ≤3.21% absolute abundance error for species identification. For the rice seed microbiome, a good consistency was observed between Raman-derived Pantoea abundance (34.8%) and 16S rRNA sequencing results (45%). This platform enables species- and strain-level classification of Pantoea spp. cultures, acquiring >7,200 SCRS per hour to facilitate rapid identification in both synthetic microbial communities and field-derived samples.

Abstract: Background: Microbial single-cell Raman spectroscopy (SCRS) has emerged as a powerful tool for label-free phenotyping, enabling rapid characterization of microbial diversity, metabolic states, and functional interactions within complex communities. However, high-throughput SCRS datasets often contain spectral anomalies from noise and fluorescence interference, which obscure microbial signatures and hinder accurate classification. Robust algorithms for outlier detection and microbial ramanome analysis remain underdeveloped. Results: Here, we introduce RamEx, an R package specifically designed for high-throughput microbial ramanome analyses with robust quality control and phenotypic classification. At the core of RamEx is the Iterative Convolutional Outlier Detection (ICOD) algorithm, which dynamically detects spectral anomalies without requiring predefined thresholds. Benchmarking on both simulated and real microbial datasets-including pathogenic bacteria, probiotic strains, and yeast fermentation populations-demonstrated that ICOD achieves an F1 score of 0.97 on simulated datasets and 0.74 on real datasets, outperforming existing approaches by at least 19.8%. Beyond anomaly detection, RamEx provides a modular and scalable workflow for microbial phenotype differentiation, taxonomic marker identification, metabolic-associated fingerprinting, and intra-population heterogeneity analysis. It integrates Raman-based species-specific biomarkers, enabling precise classification of microbial communities and facilitating functional trait mapping at the single-cell level. To support large-scale studies, RamEx incorporates C++ acceleration, GPU parallelization, and optimized memory management, enabling the rapid processing of over one million microbial spectra within an hour. Conclusions: By bridging the gap between high-throughput Raman-based microbial phenotyping and computational analysis, RamEx provides a comprehensive toolkit for exploring microbial ecology, metabolic interactions, and antibiotic susceptibility at the single-cell resolution. RamEx is freely available under the MIT license at https://github.com/qibebt-bioinfo/RamEx. Video Abstract.

Abstract: Heterogeneity within clonal cell populations remains a critical bottleneck within bioprocess engineering, notably by undermining bioproduction yields. Efforts to mitigate its impact have, however, been hampered by technological difficulties quantifying metabolism at the single-cell level. Here, we propose a framework based on single-cell biosensor analysis that enables robust characterisation of cell’s metabolic states, leveraging it to detect and isolate isogeneic heterogeneity in response to environmental perturbations and within microbial cell factories. We identify acute and gradual glucose depletion to induce differentiation of metabolically distinct subpopulations and reveal these subpopulations to exhibit differential production capabilities, with lower intracellular pH subpopulations exhibiting enhanced product accumulation within violacein-producing strains but reduced yields within lycopene-producing strains. Lastly, we highlight galactose cultivation as a method to modulate subpopulation dynamics towards higher-producing lycopene phenotypes. Altogether, our research provides insights into subpopulation differentiation and establishes promising avenues for the engineering of more robust and higher-producing strains.