Evidence map›Paper›PMID 41315904›Full record

ArticleBMC bioinformatics2025

ProTrack3D: a comprehensive tool for segmentation and tracking of proteins with split and fusion.

Ramu Gautam, Yang Jiao, Yasong Pang, Mo Weng, Mei Yang

Abstract read
In one paragraph

Article in BMC bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ramu GautamDepartment of Electrical and Computer Engineering, University of Nevada, Las Vegas, 4505 S Maryland Pkwy, Las Vegas, NV, 89154, USA.
Yang JiaoDepartment of Electrical and Computer Engineering, University of Nevada, Las Vegas, 4505 S Maryland Pkwy, Las Vegas, NV, 89154, USA.
Yasong PangSchool of Life Sciences, University of Nevada, Las Vegas, 4505 S Maryland Pkwy, Las Vegas, NV, 89154, USA.
Mo WengSchool of Life Sciences, University of Nevada, Las Vegas, 4505 S Maryland Pkwy, Las Vegas, NV, 89154, USA.
Mei YangDepartment of Electrical and Computer Engineering, University of Nevada, Las Vegas, 4505 S Maryland Pkwy, Las Vegas, NV, 89154, USA. mei.yang@unlv.edu.

Funding

Mechanosensitive remodeling of adherens junctions during snail-driven EMTR00HD088764 · NICHD · UNIVERSITY OF NEVADA LAS VEGAS · PI WENG, MO · 2018 to 2020
$742k
National Science of Foundation,United States 2341010NICHD NIH HHS R00 HD088764NIH HHS R00HD088764UNLV Research Foundation FOA
6 · The paper itself

Abstract

backgroundQuantitatively tracking subcellular protein puncta over time is critical for understanding protein functions and related cellular processes, as many proteins assemble into discrete protein puncta when performing functions. Monitoring these protein puncta over an extended period enables the quantitation of their dynamic behaviors, such as repositioning, remodeling, and inter-object interactions such as fusion and split. These characteristics are essential for deciphering the underlying regulatory mechanisms of protein function. However, tracking protein puncta is challenging due to these clusters undergoing rapid and complex temporal changes.

resultsTo address these challenges, a comprehensive tool ‘ProTrack3D’ is developed. It implements a multi-stage multi-object tracking workflow specifically designed to handle the complexity of dynamic protein puncta. The segmentation stage can adopt any advanced deep neural network to detect protein puncta. The Tracking stage follows individual puncta over time and detects birth, death, split and fusion events. The Life Path Reconstruction stage visualizes the life history of protein puncta using tree structures. Applying ProTrack3D tool to the 4D fluorescence microscopy images of developing Drosophila embryos confirms the effectiveness of the method implemented at each stage. Compared with existing tracking tools, our tracking algorithm achieves significant improvement in tracking performance by utilizing deep feature maps which encode rich spatial and intensity information.

conclusionsBy integrating all stages of tracking and offering quantitative analysis functions with a user-friendly graphical interface, ProTrack3D enables researchers with basic computer skills to perform segmentation, tracking, and analysis of protein puncta in fluorescence microscopy images, thus facilitating the broader accessibility and usability of advanced protein tracking techniques. ProTrack3D is available for distribution via GitHub at https://github.com/ramugautam1/ProTrack3D .

Indexed as

Image Processing, Computer-AssistedProteinsSoftwareAlgorithmsAnimalsDrosophilaMicroscopy, FluorescenceProteinsFluorescence microscopyFusionMergeMulti-object trackingPortion trackingProtein trackingSegmentationSplit

Identifiers

PMID41315904
PMCPMC12777108

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.