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Label-Free Optical Transmission Tomography for Direct Mycological Examination and Monitoring of Intracellular Dynamics

Summary

Scientists have developed a new imaging technology called optical transmission tomography that can directly observe living fungi without staining or special dyes. This technique reveals not only the structure of fungal cells but also shows their internal activity and metabolism in real-time. When combined with artificial intelligence, this technology could help doctors quickly identify dangerous fungal infections and choose the best treatments, potentially saving lives by speeding up diagnosis.

Background

Invasive fungal infections remain underdiagnosed due to lack of appropriate diagnostic tools and slow fungal growth. Current visualization methods rely on fluorescence microscopy with staining, which is time-consuming and impractical for clinical settings. Label-free optical techniques could provide faster alternatives for fungal identification and metabolic monitoring.

Objective

To demonstrate the first application of dynamic full-field optical transmission tomography (D-FF-OTT) for direct examination and live-cell imaging of fungi responsible for invasive fungal infections without requiring fluorescent labels or staining.

Results

D-FF-OTT successfully visualized fungal structures including membranes, organelles, septa, and metabolic dynamics in all tested species without staining. Dynamic signals showed distinctive colors for different cellular components: orange for plasma membranes, yellow-green for nuclear membranes, and pink-white for organelles. Automated segmentation revealed statistically significant differences in fluctuation properties between cellular structures (p<0.0001).

Conclusion

D-FF-OTT enables label-free, real-time imaging of living fungi with sufficient resolution to observe intracellular dynamics and metabolic activity. Combined with artificial intelligence-based recognition algorithms, this technology has potential to significantly accelerate invasive fungal infection diagnosis and improve patient outcomes through faster identification and characterization of pathogenic fungi.
  • Published in:Journal of Fungi,
  • Study Type:Methodological/Technical Study,
  • Source: PMID: 39590661, DOI: 10.3390/jof10110741
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