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1,977 results for “CRISPR”
CRISPR-PAINT nanorulers
<p>The GATTA-PAINT 40 RG nanoruler (GATTAquant, DNA nanotechnologies) consists of three fluorophores (ATTO 655/ATTO 542) separated 40 nm from each other. Imaging of the sample was performed on an Olympus IX-81 inverted microscope equipped with an Olympus UApo N 100X/1.49 NA, oil-immersion objective in TIRF illumination mode (Olympus, cellTIRF Illuminator) with ~200 nm evanescence field depth. Sample excitation was provided by 488 nm and 561 nm lasers at 23.1 mW laser power. 300 total images were collected with an EM-CCD sensor (Andor iXon 897), exposure time set to 50 ms per frame and a pixel size of 160 nm.</p>
Research data supporting "Nanozyme-catalysed CRISPR assay for preamplification-free detection of non-coding RNAs"
<p>Raw research data supporting: Broto, M., Kaminski, M.M., Adrianus, C. <em>et al.</em> Nanozyme-catalysed CRISPR assay for preamplification-free detection of non-coding RNAs. <em>Nat. Nanotechnol.</em> (2022). https://doi.org/10.1038/s41565-022-01179-0</p>
PAM-altering SNP-based allele-specific CRISPR-Cas9 therapeutic strategies for Huntington's disease
<p>Huntington's disease (HD) is caused by an expanded CAG repeat in huntingtin (<em>HTT</em>). Since HD is dominant, and loss of <em>HTT </em>leads to neurological abnormalities, safe therapeutic strategies require selective inactivation of mutant <em>HTT</em>. Previously, we proposed a concept of CRISPR-Cas9 using mutant-specific PAM sites generated by SNPs to selectively inactivate mutant <em>HTT</em>. Aiming at revealing suitable targets for clinical development, we analyzed the largest HD genotype dataset to reveal target <strong>P</strong>AM-<strong>a</strong>ltering <strong>S</strong>NPs (PAS) and subsequently evaluated their allele specificities. The gRNAs based on the PAM sites generated by rs2857935, rs16843804, and rs16843836 showed high levels of allele specificity in patient-derived cells. Simultaneous use of two gRNAs based on rs2857935-rs16843804 or rs2857935-rs16843836 produced selective genomic deletions in mutant <em>HTT </em>and prevented the transcription of mutant <em>HTT </em>mRNA without impacting the expression of normal counterpart or re-integration of the excised fragment elsewhere in the genome. RNAseq and off-target analysis confirmed high levels of allele specificity and the lack of recurrent off-targeting. Approximately 60% of HD subjects are eligible for mutant-specific CRISPR-Cas9 strategies of targeting one of these 3 PAS in conjunction with one non-allele-specific site, supporting high applicability of PAS-based allele-specific CRISPR approaches in the HD patient population.</p>
Type IV-A3 CRISPR-Cas systems drive inter-plasmid conflicts by acquiring spacers in trans
<p>Plasmid-encoded type IV-A CRISPR-Cas systems lack an acquisition module, feature a DinG helicase instead of a nuclease, and form ribonucleoprotein complexes. Type IV-A3 systems are carried by conjugative plasmids that often harbor antibiotic resistance genes. Their CRISPR array contents suggest a role in inter-plasmid conflicts, but this function remains unexplored. Here, we demonstrate that a plasmid-encoded type IV-A3 system co-opts the type I-E adaptation machinery from its host, Klebsiella pneumoniae, to update its CRISPR array. Furthermore, we reveal that robust interference of conjugative plasmids and phages is elicited through CRISPR RNA-dependent transcriptional repression. By silencing plasmid core functions, type IV-A3 impacts the horizontal transfer and stability of targeted plasmids, supporting its role in plasmid competition. Our findings shed light on the mechanisms and ecological function of type IV-A3 systems and demonstrate their practical efficacy for countering antibiotic resistance in clinically relevant strains.</p>
CRISPR-Cas9 screen upon Olaparib+CldU treatment
<p>DNA single-strand breaks (SSBs) disrupt DNA replication and induce chromosome breakage. However, whether SSBs induce chromosome breakage when present in nascent strands behind replication forks or template strands ahead of replication forks is unclear. To address this question, we exploited an exquisite sensitivity of SSB repair-defective human cells lacking PARP activity or XRCC1 to the thymidine analog 5-chloro-2'-deoxyuridine (CldU). We show that incubation with CldU in these cells results in chromosome breakage, sister chromatid exchange, and cytotoxicity by a mechanism that depends on the S phase activity of uracil DNA glycosylase (UNG). Importantly, we show that CldU incorporation in one cell cycle is cytotoxic only during the following cell cycle when it is present in template DNA. In agreement with this, while UNG induces SSBs both in nascent strands behind replication forks and in template strands ahead of replication forks, only the latter triggers fork collapse and chromosome breakage. Finally, we show that BRCA-defective cells are hypersensitive to CldU, either alone and/or in combination with PARP inhibitor, suggesting that CldU may have clinical utility.</p>
StArt Protocol for systematic review: Use of CRISPR technology in gene editing for tolerance to biotic factors in plants: A systematic review.
<p>StArt Protocol for systematic review: Use of CRISPR technology in gene editing for tolerance to biotic factors in plants: A systematic review.</p>
An efficient CRISPR-mediated genome editing system in diploid and polyploid Tragopogon (Asteraceae) enables functional studies of complex phenotypes and polyploid genome evolution
<p>Polyploidy or whole-genome duplication (WGD) is a significant evolutionary force, especially in angiosperms. However, the underlying mechanisms governing polyploid genome evolution remain unclear, limited largely by a lack of functional analysis tools in organisms that best exemplify the earliest stages of WGD. <em>Tragopogon</em> (Asteraceae) includes an evolutionary model system for studying the immediate consequences of polyploidy. In this study, we significantly improved the genetic transformation of <em>Tragopogon</em> and obtained genome-edited <em>T. porrifolius</em> (2<em>x</em>) and <em>T. mirus</em> (4<em>x</em>) primary generation (T<sub>0</sub>) individuals. Using CRISPR/Cas9, we knocked out the dihydroflavonol 4-reductase (<em>DFR</em>) gene, which controls anthocyanin synthesis, in both <em>T. porrifolius</em> and <em>T. mirus</em>. All transgenic allotetraploid <em>T. mirus</em> individuals had at least one mutant <em>DFR</em> allele and 71.4% of the plants had all four <em>DFR</em> alleles (from both homeologs) edited, indicating a high efficiency of the CRISPR system in polyploid <em>Tragopogon</em>. The anticipated absence of the anthocyanin was observed in both leaf and floral tissues from <em>T. porrifolius</em> and <em>T. mirus</em> mutants. In addition, the mutations were inherited in the T<sub>1</sub> generation. This study demonstrates a highly efficient CRISPR platform producing genome-edited <em>Tragopogon</em> individuals that have successfully completed their life cycle. The approaches used and challenges faced in building the CRISPR system in <em>Tragopogon</em> provide a framework for building similar systems in other nongenetic models. Genome editing in <em>Tragopogon</em> paves the way for novel functional biology studies of polyploid genome evolution and the consequences of WGD on complex traits, which holds enormous potential for both basic and applied research.</p>
Repurposing Type I-A CRISPR-Cas3 for a robust diagnosis of human papillomavirus (HPV)
<p>R-loop-triggered collateral single-stranded DNA (ssDNA) nuclease activity within Class 1 Type I CRISPR-Cas systems holds immense potential for nucleic acid detection. However, the hyperactive ssDNase activity of Cas3 introduces unwanted noise and false-positive results. In this study, we identified a novel Type I-A Cas3 variant derived from <em>Thermococcus siculi</em>, which remains in an auto-inhibited state until it is triggered by Cascade complex and R-loop formation. This Type I-A CRISPR-Cas3 system not only exhibits an expanded protospacer adjacent motif (PAM) recognition capability but also demonstrates remarkable intolerance towards mismatched sequences. Furthermore, it exhibits dual activation modes—responding to both DNA and RNA targets. The culmination of our research efforts has led to the development of the Hyper-Active-Verification Establishment (HAVE, 惠父). This innovation enables swift and precise <em>human papillomavirus</em> (HPV) diagnosis in clinical samples, providing a robust molecular diagnostic tool based on the Type I-A CRISPR-Cas3 system. Our findings contribute to understanding type I-A CRISPR-Cas3 system regulation and facilitate the creation of advanced diagnostic solutions with broad clinical applicability.</p>
Reporter CRISPR screens decipher cis- and trans-regulatory principles at the Xist locus [Microscope images - Oct4 KDs]
<p>Microscope images related to Figure 2 in Schwämmle et al. 2025. </p> <p>The cells are undifferentiated or day 2 differentiated TX1072 XX SP427 mESCs with (or without) Oct4 knockdown in the indicated media.</p> <p>Exonic Xist is stained using Cy5 Stellaris probes. The nuclei are stained using DAPI.</p> <p>These files were used to manually count Xist clouds (see fish_sgOct4_manual.txt for counts). For code to visualize the results see https://github.com/EddaSchulz/TFiScreen_Paper. RA samples relate only to the biorXiv release of the manuscript (https://doi.org/10.1101/2024.10.08.617282).</p>
Reporter CRISPR screens decipher cis- and trans-regulatory principles at the Xist locus [FACS + BAMs + tables]
<div>FACS data related to Figures 1 and 4 in Schwämmle et al. 2025. </div> <div>Also include BAM files of published data and other files needed to replicate analyses included in https://github.com/EddaSchulz/TFiScreen_Paper. </div>
CRISPR-Cas9 off-targeting assessment with nucleic acid duplex energy parameters
<p>CRISPR-Cas9 off-targeting assessment with nucleic acid duplex energy parameters</p> <p>Collected and generated data for the paper</p> <p>## Data Tables</p> <p>Off-target score data for the ROC analysis using Haeussler dataset [2].</p> <p>Data from the table below is used to generate the Figure-2, Table-1 and Supplementary Figure-1 in the corresponding paper [1]. Don't forget to cite the corresponding studies as well if you use this table.</p> <ul> <li><strong>Haeussler_mm6_scores.csv.gz</strong>: This table includes the off-targeting scores of 1167036 off-target sequences, computed with CRISPRoff[1], CCTop[3], CFD[4], Cropit[5], Elevation (Elevation-score)[6], MIT[2,7] and VfoldCAS[8] methods. Off-target data has been taken from the Haeussler dataset [2].</li> </ul> <p>Analysis with CIRCLE-seq dataset [9]</p> <p>Data in all the three tables below has been generated to analyze the CIRCLE-seq dataset [9]. This data is further used to generate the Figure-3, Figure-4, and Supplementary Figure-4 in the corresponding paper. Don't forget to cite the corresponding studies as well if you use these tables.</p> <ul> <li> <p><strong>CIRCLEseq_known_off_scores.csv.gz</strong>: This table is used when generating the Figure-3 in the paper. It includes the 7 different off-targeting scores of CIRCLE-seq reported off-target sequences and the read counts from CIRCLE-seq experiments.</p> </li> <li> <p><strong>CIRCLEseq_mm6_off_scores.csv.gz</strong>: This table is used when generating the Figure-4 in the paper. It includes the 7 different off-targeting scores of RIsearch2(v2.1)[10] based off-target predictions for CIRCLE-seq gRNAs.</p> </li> <li> <p><strong>CIRCLEseq_specificities.csv.gz</strong>: This table is used when generating the Supplementary Figure-4 in the supplementary document of the paper. It includes the specificty scores of CIRCLE-seq gRNAs, computed with CRISPRspec[1], MIT[2,7], MIT*[1,2,7] and Elevation (Elevation-aggregate)[6] methods.</p> </li> </ul> <p>Analysis with SITE-seq dataset [11]</p> <p>Data in all the three tables below has been generated to analyze the SITE-seq dataset [11]. This data is further used to generate the Figure-5, Supplementary Figure-2 and Supplementary Figure-3 in the corresponding paper. Don't forget to cite the corresponding studies as well if you use these tables.</p> <ul> <li> <p><strong>SITEseq_known_off_scores.csv.gz</strong>: This table is used when generating the Supplementary Figure-2 in the supplementary document of the paper. It includes the 7 different off-targeting scores of SITE-seq reported off-target sequences and the read counts from SITE-seq experiments.</p> </li> <li> <p><strong>SITEseq_mm6_off_scores.csv.gz</strong>: This table is used when generating the Supplementary Figure-3 in the supplementary document of the paper. It includes the 7 different off-targeting scores of RIsearch2(v2.1) based off-target predictions for SITE-seq gRNAs.</p> </li> <li> <p><strong>SITEseq_specificities.csv.gz</strong>: This table is used when generating the Figure-5 in the paper. It includes the 4 different specificty scores of SITE-seq gRNAs.</p> </li> </ul> <p>Specificity-Efficiency Analysis</p> <p>This data is used to generate the Figure-6 and Supplementary Figure-5 in the corresponding paper. Don't forget to cite the corresponding studies as well if you use these tables.</p> <ul> <li><strong>Doench_Wang_specificity_grps.csv.gz</strong>: This table includes the specificity group of 3802 gRNA/on-target sequences, computed with CRISPRspec and MIT methods. gRNA sequence and modulation frequency data have been taken from the Haeussler dataset [2].</li> </ul> <p>## Citation</p> <p>If you find this data useful for your research, please cite the following works where appropriate:</p> <ol> <li>[Our citation comes here]</li> <li>Haeussler, M., Schonig, K., Eckert, H., Eschstruth, A., Mianne, J., Renaud, J.B., Schneider-Maunoury, S., Shkumatava, A., Teboul, L., Kent, J., Joly, J.S., Concordet, J.P.: Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biol. 17(1), 148 (2016). <a href="https://www.ncbi.nlm.nih.gov/pubmed/27380939">PMID 27380939</a></li> <li>Stemmer, M., Thumberger, T., Del Sol Keyer, M., Wittbrodt, J., Mateo, J.L.: CCTop: An Intuitive, Flexible and Reliable CRISPR/Cas9 Target Prediction Tool. PLoS ONE 10(4), 0124633 (2015). <a href="https://www.ncbi.nlm.nih.gov/pubmed/25909470">PMID 25909470</a></li> <li>Doench, J.G., Fusi, N., Sullender, M., Hegde, M., Vaimberg, E.W., Donovan, K.F., Smith, I., Tothova, Z., Wilen, C., Orchard, R., Virgin, H.W., Listgarten, J., Root, D.E.: Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 34(2), 184–191 (2016). <a href="https://www.ncbi.nlm.nih.gov/pubmed/26780180">PMID 26780180</a></li> <li>Singh, R., Kuscu, C., Quinlan, A., Qi, Y., Adli, M.: Cas9-chromatin binding information enables more accurate CRISPR off-target prediction. Nucleic Acids Res. 43(18), 118 (2015). <a href="https://www.ncbi.nlm.nih.gov/pubmed/26032770">PMID 26032770</a></li> <li>Listgarten, J., Weinstein, M., Kleinstiver, B.P., Sousa, A.A., Joung, J.K., Crawford, J., Gao, K., Hoang, L., Elibol, M., Doench, J.G., Fusi, N.: Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs. Nature Biomedical Engineering 2, 38–47 (2018). <a href="https://www.ncbi.nlm.nih.gov/pubmed/29998038">PMID 29998038</a></li> <li>Hsu, P.D., Scott, D.A., Weinstein, J.A., Ran, F.A., Konermann, S., Agarwala, V., Li, Y., Fine, E.J., Wu, X., Shalem, O., Cradick, T.J., Marraffini, L.A., Bao, G., Zhang, F.: DNA targeting specificity of RNA-guided Cas9 nucleases. Nat. Biotechnol. 31(9), 827–832 (2013). <a href="https://www.ncbi.nlm.nih.gov/pubmed/23873081">PMID 23873081</a></li> <li>Xu, X., Duan, D., Chen, S.J.: CRISPR-Cas9 cleavage efficiency correlates strongly with target-sgRNA folding stability: from physical mechanism to off-target assessment. Sci Rep 7(1), 143 (2017). <a href="https://www.ncbi.nlm.nih.gov/pubmed/28273945">PMID 28273945</a></li> <li>Tsai, S.Q., Nguyen, N.T., Malagon-Lopez, J., Topkar, V.V., Aryee, M.J., Joung, J.K.: CIRCLE-seq: a highly sensitive in vitro screen for genome-wide CRISPR-Cas9 nuclease off-targets. Nat. Methods 14(6), 607–614 (2017). <a href="https://www.ncbi.nlm.nih.gov/pubmed/28459458">PMID 28459458</a></li> <li>Alkan, F., Wenzel, A., Palasca, O., Kerpedjiev, P., Rudebeck, A.F., Stadler, P.F., Hofacker, I.L., Gorodkin, J.: RIsearch2: suffix array-based large-scale prediction of RNA-RNA interactions and siRNA off-targets. Nucleic Acids Res. (2017). <a href="https://www.ncbi.nlm.nih.gov/pubmed/28108657">PMID 28108657</a></li> <li>Cameron, P., Fuller, C.K., Donohoue, P.D., Jones, B.N., Thompson, M.S., Carter, M.M., Gradia, S., Vidal, B., Garner, E., Slorach, E.M., Lau, E., Banh, L.M., Lied, A.M., Edwards, L.S., Settle, A.H., Capurso, D., Llaca, V., Deschamps, S., Cigan, M., Young, J.K., May, A.P.: Mapping the genomic landscape of CRISPR-Cas9 cleavage. Nat. Methods 14(6), 600–606 (2017). <a href="https://www.ncbi.nlm.nih.gov/pubmed/28459459">PMID 28459459</a></li> </ol> <p>## Contact</p> <p>ferro@rth.dk gorodkin@rth.dk</p>
Reporter CRISPR screens decipher cis- and trans-regulatory principles at the Xist locus [Extra tables + files]
<p>This zenodo contains extra files and tables necessary to run code specified in https://github.com/EddaSchulz/TFiScreen_Paper/.</p> <p>These files are associated with analysis in Schwämmle et al., 2025.</p>
Analysis of single-cell CRISPR perturbations indicates that enhancers predominantly act multiplicatively
<p>This repository contains simulated data generated with GLiMMIRS-sim that was analyzed and presented in Zhou & Guyuvayurappan et al, as well as data pertaining to the <em>NMU</em> RT-qPCR experiment described in the manuscript. </p> <p> </p> <ul> <li><span>sim_base_data.tar.gz: directory containing simulated data for baseline model </span></li> <li> <p><span>sim_data_interactions_pos.tar.gz: directory containing simulated data for interactions model with positive interaction effects </span></p> </li> <li> <p><span>sim_data_interactions_neg.tar.gz: directory containing simulated data for interactions model with negative interaction effects </span></p> </li> <li><span>NMU.xlsx: spreadsheet containing data from the <em>NMU</em> RT-qPCR experiment </span></li> </ul>
The raw data of the genome-wide CRISPR screens for VHL-WT and VHL-L188V Kelly cells
<p>The raw data including the conditions, reference, and PoolQ output files of the genome-wide CRISPR screens for VHL-WT and VHL-L188V Kelly cells were published in the paper "Total loss of VHL gene function impairs neuroendocrine cancer cell fitness due to excessive HIF2α activity".</p>
Similarity of CRISPR genes grouped by chromosome location with chromosome arm correction
Open the record for dataset details and reuse information.
Similarity of CRISPR genes grouped by chromosome location without chromosome arm correction
Open the record for dataset details and reuse information.
Diversity in CRISPR-based immunity protects susceptible genotypes by restricting phage spread and evolution
Diversity in host resistance often associates with reduced pathogen spread. This may result from ecological and evolutionary processes, likely with feedback between them. Theory and experiments on bacteria-phage interactions have shown that genetic diversity of the bacterial adaptive immune system can limit phage evolution to overcome resistance. Using the CRISPR-Cas bacterial immune system and lytic phage, we engineered a host-pathogen system where each bacterial host genotype could be infected by only one phage genotype. With this model system, we explored how CRISPR diversity impacts the spread of phage when they can overcome a resistance allele, how immune diversity affects the evolution of the phage to increase its host range, and if there was feedback between these processes. We show that increasing CRISPR diversity benefits susceptible bacteria via a dilution effect, which limits the spread of the phage. We suggest that this ecological effect impacts the evolution of novel phage genotypes, which then feeds back into phage population dynamics.
Data from: Genome-wide CRISPR synthetic lethality screen identifies a role for the ADP-ribosyltransferase PARP14 in replication fork stability controlled by ATR
<p>The DNA damage response is essential to maintain genomic stability, suppress replication stress, and protect against carcinogenesis. The ATR-CHK1 pathway is an essential component of this response, which regulates cell cycle progression in the face of replication stress. PARP14 is an ADP-ribosyltransferase with multiple roles in transcription, signaling, and DNA repair. To understand the biological functions of PARP14, we catalogued the genetic components that impact cellular viability upon loss of PARP14 by performing an unbiased, comprehensive, genome-wide CRISPR knockout genetic screen in PARP14-deficient cells. We uncovered the ATR-CHK1 pathway as essential for viability of PARP14-deficient cells, and identified regulation of DNA replication dynamics as an important mechanistic contributor to the synthetic lethality observed. Our work shows that PARP14 is an important modulator of the response to ATR-CHK1 pathway inhibitors.</p>
CRISPR knockout of EZH1 in AML cell line
<p>We wanted to ensure the specificity of the EZH1 antibody that I was working with in previous posts, as well as creating a useful reagent to use in future experiments by generating a CRISPR knockout line of EZH1.</p>
NGS data from: Detection of unintended on-target effects in CRISPR genome editing by DNA donors carrying diagnostic mutations
<p>We developed a method to detect copy number variation and loss of heterozygosity in genome-edited cell lines by only sequencing the edited site. We introduced different substitutions on both chromosomes of diploid H9 human embryonic stem cells using CRISPR/Cas9 and a mixture of homology-directed repair donor DNAs carrying different substitutions. Here, we deposit the associated sequencing data (BAM-files) generated by extracting genomic DNA from genome-edited H9 cells, PCR amplifying the region of interest and sequencing on a MiSeq platform. We sequenced the edited site of cell bulks to investigate the editing efficiencies of individual donors from the donor mixtures. For proof of concept, we isolated over 850 cellular clones from the edited cell bulks and sequenced the edited site and heterozygous positions flanking the edited site to determine the number of alleles at the edited site and detect copy-neutral loss of heterozygosity. Additionally, we sequenced the DNA donors to assess, the distribution of individual donors in the mixtures.</p>
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