Perturb-tracing enables high-content screening of multiscale 3D genome regulators
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Abstract
Three-dimensional (3D) genome organization becomes altered during development, aging, and disease 1–23 , but the factors regulating chromatin topology are incompletely understood and currently no technology can efficiently screen for new regulators of multiscale chromatin organization. Here, we developed an image-based high-content screening platform (Perturb-tracing) that combines pooled CRISPR screen, a new cellular barcode readout method (BARC-FISH), and chromatin tracing. We performed a loss-of-function screen in human cells, and visualized alterations to their genome organization from 13,000 imaging target-perturbation combinations, alongside perturbation-paired barcode readout in the same single cells. Using 1.4 million 3D positions along chromosome traces, we discovered tens of new regulators of chromatin folding at different length scales, ranging from chromatin domains and compartments to chromosome territory. A subset of the regulators exhibited 3D genome effects associated with loop-extrusion and A-B compartmentalization mechanisms, while others were largely unrelated to these known 3D genome mechanisms. We found that the ATP-dependent helicase CHD7, the loss of which causes the congenital neural crest syndrome CHARGE 24 and a chromatin remodeler previously shown to promote local chromatin openness 25–27 , counter-intuitively compacts chromatin over long range in different genomic contexts and cell backgrounds including neural crest cells, and globally represses gene expression. The DNA compaction effect of CHD7 is independent of its chromatin remodeling activity and does not require other protein partners. Finally, we identified new regulators of nuclear architectures and found a functional link between chromatin compaction and nuclear shape. Altogether, our method enables scalable, high-content identification of chromatin and nuclear topology regulators that will stimulate new insights into the 3D genome functions, such as global gene and nuclear regulation, in health and disease.
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Here, we developed an image-based high-content CRISPR screening platform thatcombines a new FISH-based barcode readout method (BARC-FISH) with chromatin tracing.
Summary This work is a powerful extension of pooled optical screening protocols, combining a new barcoding scheme and a high-content phenotyping approach via chromatin tracing. Overall, the experiment yields high-resolution phenotypic information resulting from a complex genetic perturbation. These experiments combine pooled genome-wide perturbations with single-cell imaging, and they are very promising since they provide matched genetic and phenotypic analysis at very large scale. The manuscript is clear, well-written, and provides experimental and technical details as well as a discussion of the many results obtained from this proof-of-concept experiment.
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Altogether, our method enables scalable, high-throughput identification of30chromatin topology regulators that will stimulate new insights into the 3D genome.
This paper builds on previous pooled optical screens by adding another mode of high-content phenotyping: 3D chromosome imaging. Scaled perturbations along with complex phenotypes are a unique capability of this experimental platform.
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For each of the ten digits, we hybridized dye-labeled secondary probes to the RCA product and observed a strong signal over background(Fig. 1, B and C).
This method appears sounds and robust. It's great to have another way to read out genetic perturbations in situ. Crucially, the authors present evidence of sgRNA recovery and barcode fidelity from their new barcoding technique. Guide RNA recovery is fairly high, meaning most of the designed genetic perturbations are recovered in the experiment, but barcode assignment is somewhat low, at around 50% even after error correction. On top of this, cell cycle stage and variable CRISPR knockout efficiency reduce both the number of cells that can be included in downstream analysis and dilute the analysis with cells that have not received a knockout. Moreover, the effect of off-target gene …
For each of the ten digits, we hybridized dye-labeled secondary probes to the RCA product and observed a strong signal over background(Fig. 1, B and C).
This method appears sounds and robust. It's great to have another way to read out genetic perturbations in situ. Crucially, the authors present evidence of sgRNA recovery and barcode fidelity from their new barcoding technique. Guide RNA recovery is fairly high, meaning most of the designed genetic perturbations are recovered in the experiment, but barcode assignment is somewhat low, at around 50% even after error correction. On top of this, cell cycle stage and variable CRISPR knockout efficiency reduce both the number of cells that can be included in downstream analysis and dilute the analysis with cells that have not received a knockout. Moreover, the effect of off-target gene editing is difficult to determine given the experimental design. Overall, these inefficiencies degrade data quality in the experiment by lowering the number of cells passing filter and by mixing on-target edits and off-target and unedited cells. There is a significant challenge in matching experimental scale (number of targeted genes) to the quality and scale of imaging data that can be obtained.
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For our screen, we generated a plasmid library of 420 sgRNAs composed of 10 non-targeting15control sgRNAs and 410 sgRNAs targeting 137 selected genes
It would be great to see a smaller or simpler experiment to build confidence in the method. In single-cell genomics, species-mixing experiments are common to demonstrate single-cell isolation and to estimate data quality. In this case, an experiment with two cell lines or with a positive control sgRNA and a control could help to demonstrate the reliability of the method and to estimate how much data is required to ensure statistical confidence. Some type of simplified experiment would greatly improve the manuscript and add confidence for readers.
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Overall, our10current work included ~13,000 imaging target-perturbation combinations (420 gRNAs multiply30 phenotypic imaging targets including 27 TADs, DAPI, total protein, and Geminin stains).
Do you have a sense for the scale of this experiment in relation to the phenotype? How would sampling more or fewer cells change the results? The observed differences are not extremely large (Figure 2), so can you comment on how many cells per guide are needed to make determinations? Is chromatin structure a particularly challenging phenotype to observe in a perturbation experiment?
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