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TransiScope: An Interactive Open-Source Platform for Automated Detection and Analysis of Transient Events in Time-Lapse Microscopy

Dasgupta, R.; Das, K.

2025-12-17 bioinformatics
10.1101/2025.06.24.661279 bioRxiv
Show abstract

The quantitative analysis of dynamic cellular events from time-lapse microscopy is critical for understanding biological processes but is often hindered by low signal-to-signal ratios and subjective manual parameter 1,2.To address these limitations, TransiScope was developed as an open-source tool built on Python and the napari viewer, offering a seamless workflow within a single graphical user interface2-4. Its key innovation is a data driven, interactive approach to parameter optimization, where the software analyzes user defined regions of interest (ROIs) to propose optimal settings for algorithms like the Difference of Gaussians (DoG) filter5,6, ensuring consistency by averaging signals from multiple ROIs. The platforms performance, validated using open-resource .avi files from published studies, demonstrates high specificity and a low false-positive rate, accurately quantifying events in signal-positive regions while correctly identifying zero events in background areas7-11. By replacing manual trial-and-error with a guided workflow, TransiScope enhances the objectivity, speed, and reproducibility of transient event analysis, providing an accessible solution for robust quantitative imaging12.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

1
Scientific Reports
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Top 2%
12.7%
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2
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Top 0.1%
7.9%
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3
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Top 0.1%
7.9%
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4
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● 218 papers in training set
Top 0.7%
5.5%
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5
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Top 29%
4.9%
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6
PLOS ONE
● 5266 papers in training set
Top 32%
4.1%
7
BMC Methods
○ 15 papers in training set
Top 0.1%
3.2%
8
Communications Biology
○ 993 papers in training set
Top 6%
3.1%
9
Journal of Neuroscience Methods
○ 122 papers in training set
Top 0.7%
2.7%
50% of probability mass above
10
Cell Reports Methods
○ 165 papers in training set
Top 0.9%
2.5%
11
Bioinformatics
◐ 1204 papers in training set
Top 6%
2.4%
12
Methods
○ 34 papers in training set
Top 0.2%
2.1%
13
Lab on a Chip
● 96 papers in training set
Top 0.6%
1.9%
14
eLife
● 5828 papers in training set
Top 50%
1.7%
15
BMC Neuroscience
○ 11 papers in training set
Top 0.1%
1.7%
16
BMC Biology
○ 265 papers in training set
Top 3%
1.3%
17
Nature Methods
○ 385 papers in training set
Top 5%
1.3%
18
Molecular Biology of the Cell
● 311 papers in training set
Top 2%
1.1%
19
Briefings in Bioinformatics
◐ 354 papers in training set
Top 6%
1.1%
20
PLOS Computational Biology
● 1863 papers in training set
Top 17%
1.1%
21
STAR Protocols
○ 18 papers in training set
Top 0.2%
1.1%
22
Frontiers in Cell and Developmental Biology
○ 233 papers in training set
Top 3%
1.1%
23
iScience
○ 1154 papers in training set
Top 26%
1.1%
24
Biophysical Journal
○ 631 papers in training set
Top 3%
1.1%
25
Nature Protocols
○ 33 papers in training set
Top 0.4%
1.1%
26
Protein Science
○ 246 papers in training set
Top 3%
1.0%
27
SLAS Discovery
○ 25 papers in training set
Top 0.2%
1.0%
28
ACS Pharmacology & Translational Science
● 40 papers in training set
Top 0.7%
1.0%
29
Journal of Proteome Research
● 234 papers in training set
Top 2%
0.8%
30
Journal of Cell Science
● 393 papers in training set
Top 4%
0.8%

"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.