Peer review of: A scalable CRISPR/Cas9-based fluorescent reporter assay to study DNA double-strand break (2020)

Reviewers' comments: 
 
Reviewer #1 (Remarks to the Author): 
 
The manuscript from Dr. Mardin’s group describes a colorimetric assay system (color assay tracing 
repair (CAT-R))  to simultaneously monitor outcomes of DSB repair via end-protection and end-
resection pathways. This method relies on CRISPR/Cas9 to introduce DSBs in a tandem fluorescent 
reporter, which can distinguish small insertions/deletions from large deletions. Further, they 
validated the utility of the CAT-R system using 1) clinically relevant small molecule 
pharmacological inhibitors and 2) in a custom-designed genetic screen targeting gene involved in 
DDR to evaluate the contribution of these genes in DNA DSB repair choice. Notably, they found 
through the assay system that impairing nucleotide excision repair (NER) favor error-free repair, 
providing a possible way to increase the rate of CRISPR-based knock-ins by homology-directed 
repair. 
The concept of measuring DSB repair choices in cells has been widely studied in DNA damage 
repair field, and many similar assay system has been developed Certo, M. T. et al. Nat. Methods 8, 
671–676 (2011), Gunn, A. & Stark, J. M. Methods Mol. Biol. 920, 379–91 (2012), Kuhar, R. et al., 
Nucleic Acids Res. 42, e4 (2016), Moynahan et. Al. Molecular Cell, 263-272 (2001). The unique 
feature of the assay system described in this manuscript is the usage of a CRISPR/ Cas9 to 
introduce specific lesions in the eGFP gene. They generated a HEK293CAT-R cell-line and tested 
the system by targeting individual DSB repair genes or chemical inhibitors. Most experiments are 
well designed, and data are organized in a way to support the main conclusion. However, there are 
some concerns regarding the novelty of the study and result interpretations. The most interesting 
observation that the Authors just touched upon was the importance of modulating NER to increase 
CRISPR-based knock-in efficiency. Developing a story based on that observation would have been 
more exciting and will be of general interest. 
Comments: 
1) The Authors need to clearly mention superiority of the proposed assay system from other DSB 
repair assays already present. Additionally, multiple cell lines should be tested as clearly HEK293 
and RPE1 showed differences in basal level. 
2) The double-positive (mCherry and eGFP) population shows both un-transfected and error-free 
repair active cells. This might affect the interpretation of the overall repair capacity and repair 
choice of the cells. The author needs to state how they have overcome this issue clearly. 
3) Between the large deletions (double negative population) Vs. Indels (eGFP negative) there will 
be a range of other events, is the assay sensitive enough to measure the array of fluorescence 
change? This might be helpful in understanding the extent of DDR protein's influence on pathway 
choice. 
4) Using a CRISPR library to asses the role of different repair proteins on DSB repair choices is a 
robust way to test the system and identify new dependencies on pathway choice. However, the 
authors need to address the transfection efficiency of both gRNA to eGFP and CRISPR library to 
cells. From that screen, authors found that the NER pathway plays an active role in CRISPR-knock 
in ability (although clear data is missing), this data has a potential and should be expanded and 
studied with more positive and negative controls to make a claim. 
 
 
 
 
Reviewer #2 (Remarks to the Author): 
 
The manuscript by Roidos et al describes a scalable system to analyze events that can happen at 
the site of a double-strand break. The system is based on generating a cell line stably expressing 
(1) two proteins with different fluorescent properties from the same promoter and (2) inducible 
Cas9 endonuclease. The authors then claim that transfection of a gRNA targeting one of the two 
fluorescent proteins (mCherry and GFP) would lead to one of three events after induction of Cas9: 
(1) error-free repair and expression of both proteins, (2) “small” deletions or insertions leading to 

loss of that protein or (3) “large” deletions leading to loss of both proteins. The three populations 
of cells are then detected by flow cytometry. The authors provide evidence that the balance of the 
three outcomes in this two-color assay could distinguish the choice of the DNA double-strand 
repair system used to repair the lesion. They go on to show potential applicability of this assay in 
drug and genetic screen to search for chemical or genetic entities that could affect certain DNA 
repair pathways by measuring shifts in the three populations of cells. 
 
The method is interesting and potentially applicable for high-throughput screens, however, the 
manuscript has some major issues related to interpretation, validation and reproducibility of the 
method that need to be addresses before it can be suitable for publication as described below. 
 
1. The approach has one fundamental flaw – the DNA molecules digested by Cas9 and repaired 
error-free can also be cleaved again until they are repaired with errors such that they can longer 
be substrates for the Cas9 enzyme. Therefore, this is not a very good system to estimate the 
fraction of error-free repair. 
 
2. Related to the point above, it is impossible to distinguish error-free repair and uncleaved DNA, 
further arguing that error-free repair can not be estimated reliably based on this system. 
 
3. The authors should validate their system by sorting the different populations of cells 
(mCherry+/GFP-, mCherry-/GFP-, mCherry+/GFP+) and performing NGS on each one of them to 
make sure that the profiles of indels and deletions actually follow their flow analysis. Furthermore, 
the authors should actually measure the sizes of their large deletions using some long read NGS 
techniques on the sorted populations of cells. These are very important issues not addressed in the 
manuscript. 
 
4. The authors designed the targeting gRNA such that deletions >250bp would inactivate both 
proteins. Such deletions are called “large”. The ratio between “small” indels and “large” deletions 
serves as the basis for distinguishing the contributions of different repair pathways. However, the 
reason for choosing the 250 bp threshold to distinguish different repair pathways is not described 
in the paper. 
 
5. I was very surprised to not find a single p-value estimate of significance of the many effects 
described in the manuscript. For example, there are no estimates of significance of the observed 
effects in the knockouts of specific genes (Figure 2), in the genetic screen (Figure 4) or in the 
treatments with specific drugs (Figure 3). The authors must provide these estimates, and they 
have to be based on analyses of these various knockouts and treatments in different biological 
replicas, ie different batches of cells. 
 
6. Related to the above, the authors do not provide any information on the stability of their 
method in different biological replicas. They only mention 3 technical replicas in Methods. The 
information on biological replication is critical to understanding the robustness of their method and 
validity of the conclusions. 
 
7. I am somewhat surprised that the authors did not develop cell lines stably expressing the gRNA. 
Transfection of gRNA complexes can be quite variable and also lead to non-physiologically high 
amounts of the synthetic RNAs in cells that in turn can lead to all sorts of artifacts (PMID 
26697058). For example, there is a big difference between the two WT samples in Figure 2A. This 
is another reason to show how reproducible the system is in different biological replicas. Also, a 
stable cell line would be an improvement since it is more likely to stabilize performance of this 
method across multiple laboratories if it is to be adopted. 
 
8. Related to the above, the authors chose immortalized non-cancerous cell line HEK293. Would it 
not be more appropriate to test this system in cancerous cell lines since they are more likely to be 
used as drug targets? I would imagine that the reason HEK293 cells are used here is because they 

are easy to transfect, but this significantly limits applicability of this method. This is another 
reason to generate cell lines stably expressing the gRNA(s). 
 
9. The attempt to use this method to build a predictor based on machine-learning approach 
random forest to classify different drugs is interesting. However, I see a number of issues with it: 
 
9.1. I can not follow the “Random forest model – bioinformatics pipeline” section in the Methods. It 
definitely needs to be improved for clarity and logic. Among others, the authors need to explain 
why they remove data points similar to DSB controls and what are those DSB controls. What is “N” 
in the Table 1? Number of wells? And so on… 
 
9.2. The performance of this model in the text needs to be described better. Did the authors 
validate it only on drugs they tested? What if they used drugs not used to build the model? If this 
method is to be applied broadly, this question has to be addressed. 
 
Some additional comments: 
 
1. In Material and Methods section, the authors wrote: "Single-cell suspensions were analyzed, 
and cells with strong eGFP (488-440 530/30) and mCherry (561-610/20) signals were sorted using 
FACSAria I cell sorter (BD 441 Biosciences) to enrich cells harboring the reporter.” Is the reporter 
cell line represented by clonal or mixed population? Do all the cells have the same single genomic 
locus insertion? This point is not very clear. 
 
2. Inhibitor studies in Figure 3. Do the authors have evidence that these drugs work as expected 
at these concentrations in this cell line? 
 
3. In the Discussion, the authors state that “Our results are consistent with a recent study 
describing large deletions ranging from 250 bps to 6 kbs to occur more than 20% of the cases 
upon Cas9 induced DSBs in mouse embryonic stem cells and in RPE-1 cells (ref 32).” The 
reference for this sentence seems to be incorrect. 
 
 
 
 
 
Reviewer #3 (Remarks to the Author): 
 
In this manuscript the authors assembled a fluorescence-based reporter that allows simultaneous 
monitoring of end-protection and end-resection DSB repair processes, and showed that it enabled 
classification of a panel of DDR-targeting small molecules / DDR genes in choice of DSB repair 
pathways. The authors went on to demonstrate that inhibiting NER led to increased efficiency in 
SSTR.  Overall the manuscript is clearly written and conclusions were supported by experimental 
findings. 
 
I have a few comments that the authors may consider when revising the manuscript - 
 
1) While it appears that the eGFP-targeting gRNAs are efficient in inducing DSBs at the integrated 
cassette (Figure 1b; From the scatter plot one would estimate that DSBs were induced in at least 
92% of cells) the genomic cleavage assay suggests otherwise (Supp Figure 1b). The authors 
should explain this apparent discrepancy. 
 
2) It remains unclear how "error-free" repair is effected upon DSB induction in CAT-R. The authors 
described HR as one possible means but is there a homologous template to allow HR to take place? 
What is the contribution of small InDels that took place in the multiplicity of 3 nucleotides that 
would presumably preserve eGFP? 

 
3) The statement that "These results agree well with the idea that in the presence of PARP, the 
DSBs are repaired by alternative NHEJ pathway, which may contribute to the formation of large 
deletions due to extensive end-resection" is confusing given that "PARP inhibitors led to a 
reduction of small InDels and an increase of large deletions on average by 4% (+/-  1.6) at 50nM 
(Figure 3f). 
 
4) CAT-R does not appear to be very sensitive in detecting change in "error-free" repair when 
established HR factors are inactivated. Perhaps the authors can discuss the limitation of the DSB 
repair reporter. 
 
5) The authors showed that pre-treatment with the DDR gene-targeting small molecules affect cell 
cycle distribution. (How) Does change in cell cycle distribution affect DSB induction per se? 
 
6) I see that there are multiple "WT" plots in Figure 2a, 2c and 2e. Are they "WT" representative 
plots?