| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 3.27 MB | Adobe PDF |
Orientador(es)
Resumo(s)
The viability of kelp microscopic gametophytes is currently analyzed using subjective visual methods based on bright-field (BF) microscope images. Fluorescence microscopy (FM) can be employed, but the dyes used can be toxic and blue/UV light intensity may induce gametogenesis. This study aimed to develop a non-invasive and accurate methodology for assessing gametophyte viability over time, using FM observation of chlorophyll autofluorescence emitted by live gametophytes. Six isolated gametophyte strains of Laminaria ochroleuca (Italy), maintained at CCMAR Biobank, were cultured in triplicate (female, male, and both sexes combined) in Petri dishes containing 10 mL of half-strength Provasoli’s enriched seawater medium (PES), under red light for 1 month. Survival and growth were assessed on days 1, 7 and 14 by photographing 20 fields-of-view, both in BF and FM. Then, cultures were transferred to white light to induce gametogenesis and test whether the autofluorescence analysis (AFA) method affected gametophyte reproduction. Image analysis was performed using FIJI software either by manually counting live gametophytes (survival) and measuring their area (growth), or by implementing a machine-learning (ML) model using FIJI’s WEKA segmentation plugin. Results revealed that both methods were correlated, validating our ML model. However, the AFA method was faster and more accurate than BF image analysis, especially for male gametophytes, and did not compromise reproductive capacity. After 9 days under white light, sporophytes were present in mixed cultures. The AFA method provides a reliable technique for assessing gametophyte viability without compromising gametogenesis.
Descrição
Palavras-chave
Phaeophyceae Chlorophyll Pigments Fluorescence Viability Machine-learning
Contexto Educativo
Citação
Editora
Springer Science and Business Media LLC