Back

QuantiTrack: A unified software to study protein dynamics in living cells

Ball, D. A.; Wagh, K.; Stavreva, D. A.; Hoang, L.; Schiltz, R. L.; Chari, R.; Raziuddin, R.; Mazza, D.; Upadhyaya, A.; Hager, G. L.; Karpova, T. S.

2026-02-27 biophysics
10.64898/2026.02.19.706877 bioRxiv
Show abstract

Linking the spatiotemporal dynamics of proteins in live cells to physiological functions is a fundamental challenge in biology and robust quantification of protein dynamics is a major step towards this endeavor. Single molecule tracking (SMT) has emerged as a powerful technique to investigate protein dynamics at the single molecule level in living cells. Most SMT analyses require familiarity with biophysical models and programming and the results from different analyses cannot be easily integrated. To mitigate these shortcomings, we developed QuantiTrack - a MATLAB-based SMT analysis software that can be operated from a simple graphical user interface. This provides a much-needed end-to-end solution where a user can load a movie, track single molecules, and perform a range of analyses. In addition to a detailed user guide with step-by-step instructions, QuantiTrack includes quality control metrics that can be used to systematically determine tracking parameters. As a practical example, we address by QuantiTrack a question relevant to hormonal therapy: How does the glucocorticoid receptor (GR), a hormone-regulated transcription factor (TF), respond to treatment and washout of its cognate hormone. Hormone washout results in rapid (in minutes) downregulation of GR target genes to basal levels. We observe dynamics of the Halo tagged GR (Halo-GR) and by integrating several analyses, show that hormone washout results in a substantially lower bound fraction of GR, reduced occupancy in the mobility state associated with GR activation, and shorter GR dwell times. These analyses showcase QuantiTrack as a convenient tool for comprehensive SMT analysis for a wide range of biologists.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

1
Nucleic Acids Research
1128 papers in training set
Top 2%
9.0%
2
PLOS ONE
4510 papers in training set
Top 23%
8.0%
3
PLOS Computational Biology
1633 papers in training set
Top 5%
6.7%
4
Physical Biology
43 papers in training set
Top 0.2%
6.2%
5
Molecular Systems Biology
142 papers in training set
Top 0.1%
6.2%
6
Journal of Molecular Biology
217 papers in training set
Top 0.3%
6.2%
7
Frontiers in Molecular Biosciences
100 papers in training set
Top 0.2%
4.5%
8
BMC Bioinformatics
383 papers in training set
Top 2%
4.2%
50% of probability mass above
9
Molecular Biology of the Cell
272 papers in training set
Top 0.7%
3.5%
10
Scientific Reports
3102 papers in training set
Top 42%
3.0%
11
Bioinformatics
1061 papers in training set
Top 6%
2.7%
12
Biophysical Journal
545 papers in training set
Top 2%
2.7%
13
eLife
5422 papers in training set
Top 33%
2.5%
14
Bioinformatics Advances
184 papers in training set
Top 2%
2.4%
15
Protein Science
221 papers in training set
Top 0.6%
2.3%
16
iScience
1063 papers in training set
Top 9%
2.3%
17
Methods
29 papers in training set
Top 0.2%
1.7%
18
Nature Communications
4913 papers in training set
Top 52%
1.7%
19
European Biophysics Journal
11 papers in training set
Top 0.1%
1.6%
20
SoftwareX
15 papers in training set
Top 0.2%
1.3%
21
Computational and Structural Biotechnology Journal
216 papers in training set
Top 6%
1.2%
22
NAR Genomics and Bioinformatics
214 papers in training set
Top 3%
0.9%
23
Frontiers in Physiology
93 papers in training set
Top 5%
0.9%
24
Cell Reports Methods
141 papers in training set
Top 5%
0.8%
25
Biological Imaging
15 papers in training set
Top 0.3%
0.7%
26
Wellcome Open Research
57 papers in training set
Top 2%
0.7%
27
Entropy
20 papers in training set
Top 0.5%
0.6%
28
PLOS Biology
408 papers in training set
Top 24%
0.6%
29
Biophysical Reports
36 papers in training set
Top 0.6%
0.6%