Automated Modeling of Protein Accumulation at DNA Damage Sites using qFADD.py

Abstract

Cells are exposed to a plethora of influences that can cause damage to DNA and alter the genome, often with detrimental consequences for health. Cells mitigate this damage through a variety of repair protein pathways, and accurate measurement of the accumulation, action, and dissipation timescales of these repair proteins is required to fully understand the DNA damage response. Recently, we described the Q-FADD (Quantitation of Fluorescence Accumulation after DNA Damage) method, which enhances the analytical power of the widely used laser microirradiation technique. In that study, Q-FADD and its preprocessing operations required licensed software and a significant amount of user overhead to find the model of best fit. Here, we present "qFADD.py", an open-source implementation of the Q-FADD algorithm that is available as both a stand-alone software package and on a publicly accessible webserver (https://qfadd.colorado.edu/). Furthermore, we describe significant improvements to the fitting and preprocessing methods that include corrections for nuclear drift and an automated grid-search for the model of best fit. To improve statistical rigor, the grid-search algorithm also includes automated simulation of replicates. As an example, we discuss the recruitment dynamics of the signaling protein PARP1 to DNA damage sites, and we show how to compare different populations of qFADD.py models.

Dynamics of endogenous PARP1 and PARP2 during DNA damage revealed by live-cell single-molecule imaging

“…We first validated our genome edited cell lines by performing bulk live-cell laser microirradiation (Aleksandrov et al, 2018; Mortusewicz et al, 2007) on Halo-PARP1 and Halo-PARP2 expressing U2OS cells and analyzed our data using the method of Quantitation of Fluorescence Accumulation after DNA damage (Q-FADD) (Bowerman et al, 2021; Mahadevan et al, 2019a; Mahadevan et al, 2019b). To visualize the fluorescently tagged proteins, we used a high nanomolar concentration of the HaloTag ligand, JF646.…”

“…Analysis of bulk microirradiation data was carried out using qFADD.py. qFADD.py is a Python implementation of the Q-FADD algorithm and its preprocessing steps, that includes the improvements of correction for nuclear drift and automated grid-search for identifying the best-fit model (Bowerman et al, 2021). The source code for qFADD.py is available at https://github.com/Luger-Lab/Q-FADD.…”

Dynamics of endogenous PARP1 and PARP2 during DNA damage revealed by live-cell single-molecule imaging

“… 85 Addgene: 11923 Software and algorithms GraphPad Prism 9.0 N/A https://www.graphpad.com qFADD.py Bowerman et al. 86 https://github.com/Luger-Lab/Q-FADD Spot-On Hansen et al. 57 https://SpotOn.berkeley.edu/ MatLab R2021a Mathworks Inc. USA http://mathworks.com Spot-On (MatLab script) Hansen et al.…”

New Methodologies to Study DNA Repair Processes in Space and Time Within Living Cells

“…Therefore, more elaborated approaches involving the fitting of the data with numerically solved reaction-diffusion models need to be implemented. A clear demonstration of the interest to develop such strategies has been recently highlighted by work from the Luger lab ( Mahadevan et al, 2019 ; Bowerman et al, 2021 ). Using a Monte-Carlo based model that assumes the repair factors can either diffuse by pure Brownian motion or bind to the DNA lesions, Mahadevan et al were able to simulate their accumulation kinetics at DNA lesions and adjust these simulations to the experimental data.…”