Back

Photochromic reversion enables long-term tracking of single molecules in living plants.

von Arx, M.; Xhelilaj, K.; Schulz, P.; zur Oven-Krockhaus, S.; Gronnier, J.

2026-03-08 cell biology
10.1101/2024.04.10.585335 bioRxiv
Show abstract

Single-molecule imaging enables the observation of individual molecules in living cells (DEste et al., 2024; Kusumi et al., 2014; Lelek et al., 2021; Nguyen et al., 2023). In plants, however, the tracking of single molecules is typically limited to a few hundred milliseconds (Bayle et al., 2021; Gronnier et al., 2017; Hosy et al., 2015), precluding the observation of dynamic cellular processes at molecular resolution. Here, we describe photochromic reversion, an imaging modality that enables long-term single-molecule tracking of genetically encoded translational fusions. Using this approach, we achieve minute-long tracking of individual cell-surface receptors and reveal previously inaccessible dynamic spatial arrest events of single plasma membrane proteins. We further developed and benchmarked computational analysis of spatial arrests (CASTA), a machine learning-based tool that automatically detects and analyses spatial, temporal, and diffusional properties of these events, thereby enabling precise nanoscale kinetic measurements. Together, these advances provide a powerful framework for deciphering the principles governing membrane dynamics and function.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 5%
25.8%
Similar papers in this journal
2
Nature Methods
○ 385 papers in training set
Top 1.0%
10.3%
Similar papers in this journal
3
Nature Cell Biology
○ 118 papers in training set
Top 0.5%
5.3%
Similar papers in this journal
4
Communications Biology
○ 993 papers in training set
Top 2%
5.0%
Similar papers in this journal
5
eLife
● 5828 papers in training set
Top 32%
3.9%
Similar papers in this journal
50% of probability mass above
6
Scientific Reports
○ 3612 papers in training set
Top 30%
3.3%
7
Journal of Cell Biology
● 392 papers in training set
Top 1%
3.3%
8
iScience
○ 1154 papers in training set
Top 8%
3.1%
9
Nano Letters
● 71 papers in training set
Top 0.4%
3.1%
10
Nature Biotechnology
○ 172 papers in training set
Top 2%
2.7%
11
Nature Plants
○ 94 papers in training set
Top 0.8%
2.7%
12
Current Biology
○ 665 papers in training set
Top 5%
2.7%
13
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 19%
2.7%
14
Angewandte Chemie International Edition
○ 93 papers in training set
Top 0.7%
2.3%
15
The EMBO Journal
○ 309 papers in training set
Top 2%
2.3%
16
Cell Reports Methods
○ 165 papers in training set
Top 1%
1.8%
17
Science Advances
● 1243 papers in training set
Top 21%
1.7%
18
Nature Chemical Biology
○ 119 papers in training set
Top 2%
1.6%
19
Nature
○ 645 papers in training set
Top 9%
1.1%
20
Journal of the American Chemical Society
● 217 papers in training set
Top 2%
1.1%
21
Developmental Cell
○ 196 papers in training set
Top 3%
1.1%
22
Light: Science & Applications
○ 16 papers in training set
Top 0.3%
0.8%
23
ACS Sensors
● 49 papers in training set
Top 0.9%
0.6%
24
Cell
○ 431 papers in training set
Top 12%
0.6%
25
Cell Reports
○ 1498 papers in training set
Top 30%
0.6%
26
Molecular Systems Biology
○ 162 papers in training set
Top 4%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.