Back

Subcellular ToF-SIMS imaging of the snow algae Sanguina nivaloides by combining high mass and high lateral resolution acquisitions

Seydoux, C.; Ezzedine, J. A.; Si Larbi, G.; Ravanel, S.; Marechal, E.; Barnes, J.-P.; Jouneau, P.-H.

2024-07-17 plant biology
10.1101/2024.07.15.603549 bioRxiv
Show 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.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

1
Journal of Extracellular Vesicles
50 papers in training set
Top 0.1%
17.6%
2
Journal of Structural Biology
58 papers in training set
Top 0.2%
8.5%
3
Nature Communications
4913 papers in training set
Top 26%
6.9%
4
Scientific Reports
3102 papers in training set
Top 23%
4.9%
5
PLOS ONE
4510 papers in training set
Top 33%
4.4%
6
Frontiers in Plant Science
240 papers in training set
Top 2%
4.0%
7
Journal of Nanobiotechnology
10 papers in training set
Top 0.1%
3.6%
8
iScience
1063 papers in training set
Top 7%
2.9%
50% of probability mass above
9
Advanced Science
249 papers in training set
Top 7%
2.6%
10
The Plant Journal
197 papers in training set
Top 2%
2.6%
11
eLife
5422 papers in training set
Top 34%
2.4%
12
New Phytologist
309 papers in training set
Top 3%
2.1%
13
Communications Biology
886 papers in training set
Top 6%
1.9%
14
Molecular & Cellular Proteomics
158 papers in training set
Top 1.0%
1.8%
15
Frontiers in Molecular Biosciences
100 papers in training set
Top 2%
1.7%
16
Talanta
12 papers in training set
Top 0.4%
1.5%
17
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 36%
1.3%
18
Peer Community Journal
254 papers in training set
Top 3%
1.2%
19
The Journal of Physical Chemistry Letters
58 papers in training set
Top 1%
1.2%
20
PROTEOMICS
35 papers in training set
Top 0.5%
1.1%
21
International Journal of Molecular Sciences
453 papers in training set
Top 12%
1.0%
22
Science Advances
1098 papers in training set
Top 26%
0.9%
23
GigaScience
172 papers in training set
Top 2%
0.9%
24
Frontiers in Cell and Developmental Biology
218 papers in training set
Top 8%
0.8%
25
Biosensors and Bioelectronics
52 papers in training set
Top 1%
0.8%
26
Analytical Chemistry
205 papers in training set
Top 3%
0.7%
27
Optica
25 papers in training set
Top 0.8%
0.7%
28
Structure
175 papers in training set
Top 4%
0.6%
29
Frontiers in Microbiology
375 papers in training set
Top 10%
0.6%
30
JACS Au
35 papers in training set
Top 1%
0.6%