Back

Celldetective: an AI-enhanced image analysis toolfor unraveling dynamic cell interactions

Torro, R.; Diaz Bello, B.; El Arawi, D.; Ammer, L.; Chames, P.; Sengupta, K.; Limozin, L.

2024-03-17 bioinformatics
10.1101/2024.03.15.585250 bioRxiv
Show abstract

A current challenge in bioimaging for immunology and immunotherapy research lies in analyzing multimodal and multidimensional data that capture dynamic interactions between diverse cell populations. Here, we introduce Celldetective, an open-source Python-based software designed for high-performance, end-to-end analysis of image-based in vitro immune and immunotherapy assays. Purpose-built for multicondition, 2D multichannel time-lapse microscopy of mixed cell populations, Celldetective is optimized for the needs of immunology assays. The software seamlessly integrates AI-based segmentation, Bayesian tracking, and automated single-cell event detection, all within an intuitive graphical interface that supports interactive visualization, annotation, and training capabilities. We demonstrate its utility with original data on immune effector cell interactions with an activating surface, mediated by bispecific antibodies, and further showcase its potential for analyzing extensive sets of pairwise interactions in antibody-dependent cell cytotoxicity events.

Published in eLife (predicted rank #6) · training set

Matching journals

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

1
Nature Methods
○ 385 papers in training set
Top 0.4%
18.8%
Similar papers in this journal
2
Nature Communications
○ 5641 papers in training set
Top 13%
13.1%
Similar papers in this journal
3
PLOS Computational Biology
● 1863 papers in training set
Top 4%
8.0%
Similar papers in this journal
4
Cell Reports Methods
○ 165 papers in training set
Top 0.1%
6.8%
Similar papers in this journal
5
Communications Biology
○ 993 papers in training set
Top 2%
4.9%
Similar papers in this journal
50% of probability mass above
eLife · published here
● 5828 papers in training set
Top 30%
4.1%
7
BMC Biology
○ 265 papers in training set
Top 0.6%
3.5%
8
Advanced Science
○ 286 papers in training set
Top 2%
3.3%
9
Nature Biotechnology
○ 172 papers in training set
Top 1%
3.3%
10
Molecular Systems Biology
○ 162 papers in training set
Top 0.9%
2.4%
11
Science Advances
● 1243 papers in training set
Top 14%
2.4%
12
Scientific Reports
○ 3612 papers in training set
Top 51%
1.8%
13
iScience
○ 1154 papers in training set
Top 17%
1.8%
14
Molecular Biology of the Cell
● 311 papers in training set
Top 2%
1.8%
15
Small Methods
○ 29 papers in training set
Top 0.3%
1.5%
16
Bioinformatics
◐ 1204 papers in training set
Top 8%
1.1%
17
Cytometry Part A
○ 33 papers in training set
Top 0.2%
1.1%
18
Patterns
○ 78 papers in training set
Top 2%
1.1%
19
Briefings in Bioinformatics
◐ 354 papers in training set
Top 5%
1.1%
20
Biological Imaging
◐ 15 papers in training set
Top 0.2%
1.1%
21
Life Science Alliance
● 285 papers in training set
Top 6%
1.0%
22
Genome Biology
○ 637 papers in training set
Top 8%
1.0%
23
Journal of Cell Science
● 393 papers in training set
Top 4%
0.9%
24
Nucleic Acids Research
◐ 1281 papers in training set
Top 13%
0.9%
25
PLOS ONE
● 5266 papers in training set
Top 61%
0.6%
26
Cell Systems
○ 201 papers in training set
Top 5%
0.6%
27
Small
○ 78 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.