Data · dataset · 2026
Multimodal Hyperspectral Imaging Unveils Pigment Rearrangement in the Diatom Cyclotella meneghiniana under Changing Physicochemical Conditions
Listed in ZivaHub
Revealing the spatial organization and stress-induced rearrangements of the photosynthetic machinery in thylakoids of diatoms is crucial for understanding their metabolic responses and/or adaptation.
Description
Using a multimodal framework that combines coherent anti-Stokes Raman scattering (CARS), two-photon excited fluorescence, and confocal fluorescence microscopy with machine learning, we investigated the spatial pigment distribution in thylakoids of <i>Cyclotella meneghiniana</i>.
Under physiological conditions, we observed dense central packing of fucoxanthins (Fx’s). In contrast, the peripheral girdle lamella had a lower packing of mostly red-absorbing Fx accompanied by enhanced 710–750 nm chlorophyll (Chl) emission. Glycerol treatment induced thylakoid disintegration, which was reflected in decreased resonance-to-nonresonance CARS ratios (Fx disordering), emergence of a 690–700 nm fluorescence component, and 680 nm band narrowing (possibly fucoxanthin-chlorophyll protein (FCP) aggregation), as well as 640 nm emission (Chl <i>c</i> release).
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Our findings show that this multimodal framework provides a powerful, label-free tool for mapping the photosynthetic machinery and subcellular metabolic responses.
Links
Where it is published
- DOI doi.org/10.1021/acs.jpclett.6c02711.s001 ↗
DOI / persistent id · from zivahub uct ac za
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from zivahub uct ac za
Topics
- From keywords
- Biochemistry and cell biology · Computer Science & AI · Earth & Environmental Science · Ecology · Life Sciences · Machine learning
- Inferred from text
- Imaging 75% · Microscopy 75%
Provenance · 1 source records, 13 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ZivaHub | oai:figshare.com:article/34028597 | 13 d ago | JSON v1 |
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|---|---|---|---|
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| concepts[field].anzsrc:group:3103 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['Ecology'] |
| concepts[field].anzsrc:group:4611 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['machine learning'] |
| concepts[field].local:field:computer-science-ai | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['machine learning'] |
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| concepts[modality].local:modality:imaging | enrichment · zivahub uct ac za | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[modality].local:modality:microscopy | enrichment · zivahub uct ac za | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/description |
| license | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/rights |
| publication_date | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| title | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/title |