Automated modeling of protein accumulation at DNA damage sites using qFADD.py

Abstract

Eukaryotic cells are constantly subject to DNA damage, often with detrimental consequences for the health of the organism. Cells mitigate this DNA damage through a variety of repair pathways involving a diverse and large number of different proteins. To better understand the cellular response to DNA damage, one needs accurate measurements of the accumulation, retention, and dissipation timescales of these repair proteins. Here, we describe an automated implementation of the "quantitation of fluorescence accumulation after DNA damage" method that greatly enhances the analysis and quantitation of the widely used technique known as laser microirradiation, which is used to study the recruitment of DNA repair proteins to sites of DNA damage. This open-source implementation ("qFADD.py") is available as a stand-alone software package that can be run on laptops or computer clusters. Our implementation includes corrections for nuclear drift, an automated grid search for the model of a best fit, and the ability to model both horizontal striping and speckle experiments. To improve statistical rigor, the grid-search algorithm also includes automated simulation of replicates. As a practical example, we present and discuss the recruitment dynamics of the early responder PARP1 to DNA damage sites. Impact StatementLive-cell microscopy and data analysis have been particularly impactful in the study of complex DNA repair processes. Over recent years, innovations in microscopy have allowed for imaging of processes occurring inside cells or even inside nuclei. These improvements have been particularly impactful in the study of DNA repair processes. For example, the method known as laser microirradiation has become a popular in vivo technique to study how fluorescently tagged DNA repair proteins accumulate, reside, and then dissipate from sites of DNA damage induced by laser damage of the DNA in live cells. Unfortunately, analysis of laser microirradiation data is often performed incorrectly or incompletely, thus precluding comparisons between different studies or with other methods. Because the correct and quantitative analysis of biological processes is essential for a more complete understanding of life processes, we previously developed the "quantitation of fluorescence accumulation after DNA damage" method, which allows for the determination of true diffusion coefficients and thus enables comparison with other methods. Here, we describe the automated and improved implementation known as qFADD.py, which allows any biologist to accurately quantitate their data from laser microirradiation.

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 ensemble live-cell laser microirradiation 3 , 51 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). 14 , 52 , 53 To visualize the fluorescently tagged proteins, we used a high nanomolar concentration of the HaloTag ligand, JF646. We found that endogenous Halo-PARP1 accumulates significantly faster than endogenous Halo-PARP2 at laser-induced DNA lesions, as measured by a higher effective diffusion coefficient ( D eff ) ( Figure S1 F, Table S1 ).…”

“…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. 52 The source code for qFADD.py is available at https://github.com/Luger-Lab/Q-FADD . Ensemble dissipation kinetics from the DNA damage region were determined from the ensemble of individual dissipation trajectories, each fit using a single-exponential model.…”