Back

PinkyCaMP a mScarlet-based calcium sensor with exceptional brightness, photostability, and multiplexing capabilities

Fink, R.; Imai, S.; Gockel, N.; Lauer, G.; Renken, K.; Wietek, J.; Lamothe-Molina, P. J.; Furhmann, F.; Mittag, M.; Ziebarth, T.; Canziani, A.; Kubitschke, M.; Kistmacher, V.; Kretschmer, A.; Sebastian, E.; Schmitz, D.; Terai, T.; Gruendemann, J.; Hassan, S.; Patriarchi, T.; Reiner, A.; Fuhrmann, M.; Campbell, R. E.; Masseck, O. A.

2024-12-17 neuroscience
10.1101/2024.12.16.628673 bioRxiv
Show abstract

Genetically encoded calcium (Ca2+) indicators (GECIs) are widely used for imaging neuronal activity, yet current limitations of existing red fluorescent GECIs have constrained their applicability. The inherently dim fluorescence and low signal-to-noise ratio of red-shifted GECIs have posed significant challenges. More critically, several red-fluorescent GECIs exhibit photoswitching when exposed to blue light, thereby limiting their applicability in all-optical experimental approaches. Here, we present the development of PinkyCaMP, the first mScarlet-based Ca2+ sensor that outperforms current red fluorescent sensors in brightness, photostability, signal-to-noise ratio, and compatibility with optogenetics and neurotransmitter imaging. PinkyCaMP is well-tolerated by neurons, showing no toxicity or aggregation, both in vitro and in vivo. All imaging approaches, including single-photon excitation methods such as fiber photometry, widefield imaging, miniscope imaging, as well as two-photon imaging in awake mice, are fully compatible with PinkyCaMP.

Published in Nature Methods (predicted rank #4) · training set

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 0.4%
44.7%
Similar papers in this journal
2
Science Advances
● 1243 papers in training set
Top 2%
7.7%
Similar papers in this journal
50% of probability mass above
3
eLife
● 5828 papers in training set
Top 25%
5.0%
Similar papers in this journal
Nature Methods · published here
○ 385 papers in training set
Top 2%
4.7%
Similar papers in this journal
5
Communications Biology
○ 993 papers in training set
Top 4%
3.9%
Similar papers in this journal
6
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 17%
3.2%
7
Cell Reports Methods
○ 165 papers in training set
Top 0.7%
3.0%
8
Neuron
○ 337 papers in training set
Top 3%
2.6%
9
Advanced Science
○ 286 papers in training set
Top 3%
2.4%
10
Cell Reports
○ 1498 papers in training set
Top 17%
2.1%
11
iScience
○ 1154 papers in training set
Top 15%
2.0%
12
Scientific Reports
○ 3612 papers in training set
Top 50%
1.9%
13
Nature Neuroscience
○ 252 papers in training set
Top 4%
1.6%
14
Nature
○ 645 papers in training set
Top 8%
1.4%
15
Cell
○ 431 papers in training set
Top 8%
1.1%
16
Current Biology
○ 665 papers in training set
Top 8%
1.1%
17
EMBO Reports
○ 263 papers in training set
Top 6%
1.0%
18
PLOS Biology
● 486 papers in training set
Top 10%
1.0%
19
Science
● 477 papers in training set
Top 9%
0.8%
20
Nature Biotechnology
○ 172 papers in training set
Top 4%
0.8%
21
Neurophotonics
● 42 papers in training set
Top 0.6%
0.8%
22
The Journal of Neuroscience
● 1025 papers in training set
Top 11%
0.6%
23
Nature Biomedical Engineering
○ 47 papers in training set
Top 2%
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.