Subcellular ToF-SIMS imaging of the snow algaSanguina nivaloidesby combining high mass and high lateral resolution acquisitions

preprint OA: closed
📄 Open PDF Full text JSON View at publisher
Full text 1,844 characters · extracted from oa-doi-fallback · click to expand
ABSTRACT Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging has demonstrated great potential for metabolic imaging, yet achieving sufficiently high lateral and mass resolution to reach the organelle scale remains challenging. We have developed an approach by combining ToF-SIMS imaging acquisitions at high lateral resolution (> 150 nm) and high mass resolution (9,000). The data were then merged and processed using multivariate analysis (MVA), allowing for the precise identification and annotation of 85% of the main contributors to the multivariate analysis components at high lateral resolution. Insights into the electron microscopy sample preparation are provided, especially as we reveal that at least three different osmium-containing complexes can be found depending on the specific chemical environment of organelles. In cells of the snow alga Sanguina nivaloides, living in a natural environment limited in nutrients such as phosphorus (P), we were able to map elements and molecules within their subcellular context, allowing for the molecular fingerprinting of organelles at a resolution of 100 nm, as confirmed by correlative electron microscopy. It was thus possible to highlight that S. nivaloides likely absorbed selectively some inorganic P forms provided by P-rich dust deposited on the snow surface. S. nivaloides cells could maintain phosphorylations in the stroma of the chloroplast, consistently with the preservation of photosynthetic activity. The presented method can thus overcome the current limitations of ToF-SIMS for subcellular imaging and contribute to the understanding of key questions such as P homeostasis and other cell physiological processes. Competing Interest Statement The authors have declared no competing interest. Footnotes Typos in Title, Discussion and Results have been revised.

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2024) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00