Journal: bioRxiv

Article Title: KLF5 controls subtype-independent highly interactive enhancers in pancreatic cancer to regulate cell survival

doi: 10.1101/2025.06.30.662301

Figure Lengend Snippet: KLF5 regulates pancreatic cancer cell viability in a subtype-independent manner. ( A ) CRISPR-dependency data from DepMap for transcription factors averaged over classical (AsPC1, HPAFII, PaTu8988s, SUIT2, TCCPAN2) and basal-like A (BxPC3, DanG, Hs766t, L3.3, PK1, T3M4) cell lines. ( B ) Schematic depicting workflow of identifying subtype-independent highly interactive enhancers. The motifs from these regions were intersected with the dependent genes from (A). ( C ) Heatmap of KLF5 read density in AsPC1, HPAFII, BxPC3 and L3.6pl cells at subtype-independent highly interactive enhancers. Reads normalized to reads per genome coverage (RPGC), n = 2 biological replicates. ( D ) Aggregate peak analysis of highly interactive (contact counts ≥ 5) and lowly interactive (contact counts < 5) enhancers in L3.6pl cells. Normalized to the number of loops (number of rows present in the bedpe file). ( E ) Cumulative percentage plots of the number of KLF5 peaks that interact with H3K4me3 marked KLF5-dependent genes (Log2FC < −0.25, FDR < 0.05) or KLF5-nonregulated (−0.15 < Log2FC < 0.15, FDR > 0.25) genes across all four (AsPC1, HPAFII, BxPC3, L3.6pl) cell lines in AsPC1 and L3.6pl cells. 355 down genes and 396 nonregulated were included in the analysis. Mann-Whitney test (Wilcoxon rank-sum test), **** p < 0.0001. ( F ) Schematic depicting the number of KLF5 peaks that connect to 1) KLF5-nonregulated and KLF5-dependent or 2) early and late KLF5-depdnent genes.

Article Snippet: To discern subtype-independent mechanisms of cell survival in pancreatic cancer, we probed publicly available genome-wide CRISPR knockout screen data from the Broad Institute DepMap project.

Techniques: CRISPR, MANN-WHITNEY

Journal: bioRxiv

Article Title: KLF5 controls subtype-independent highly interactive enhancers in pancreatic cancer to regulate cell survival

doi: 10.1101/2025.06.30.662301

Figure Lengend Snippet: KLF5 and Bcl-xL are co-expressed in patients independent of subtype-identity. ( A ) Pearson r correlation (left) in pancreatic cancer patients from The Cancer Genome Atlas (TCGA) database of KLF5 gene expression against KLF5-dependent genes in all four cell lines (AsPC1, HPAFII, BxPC3, L3.6pl). KLF5 and BCL2L1 gene expression are correlated in patients (middle) and Kaplan-Meier survival analysis of KLF5 -hi, BLC2L1 -hi vs KLF5 -lo, BLC2L1 -lo patients (right). FPKM cut off of 46.46 and 91.25 were used for KLF5 and BCL2L1 , respectively. ( B ) Pearson r correlation (left) of CRISPR-dependency data from DepMap data of KLF5 against KLF5-dependent genes in all four cell lines as described in (A). Correlation of KLF5 and BCL2L1 gene effect (right). ( C ) Pearson r correlation of KLF5 gene expression against KLF5-dependent genes in all four cell lines as described in (A) in patient single-cell RNA-seq (scRNA-seq). ( D ) Cell state diagram on the tumorigenic cells showing KLF5 and BCL2L1 expression in scRNA-seq. scBasal-scClassical commitment score (x-axis) and intermediate co-expressor score (y-axis). ( E ) Representative multiplex immunofluorescence image validating KLF5 and Bcl-xL co-expression in patient samples independent of subtype-identity. Scale bar represents 50 µm. ( F ) Quantification of patients 1 to 4 of KLF5 and Bcl-xL co-expression in total tumorigenic cells (pan-CK+), classical cells (pan-CK+, HNF4A+, KRT5/6-) or basal-like cells (pan-CK+, HNF4A-, KRT5/6+).

Article Snippet: To discern subtype-independent mechanisms of cell survival in pancreatic cancer, we probed publicly available genome-wide CRISPR knockout screen data from the Broad Institute DepMap project.

Techniques: Gene Expression, CRISPR, RNA Sequencing, Expressing, Multiplex Assay, Immunofluorescence

Journal: Scientific Data

Article Title: Comprehensive genome-scale CRISPR knockout screening of CHO cells

doi: 10.1038/s41597-025-04438-6

Figure Lengend Snippet: Schematic illustration of a non-viral, genome-scale CRISPR screening platform in Chinese hamster ovary (CHO) cells. ( a ) Plasmid library construction. CHO genome-wide CRISPR guide RNA (gRNA) oligonucleotides were designed and synthesized. Recombinase mediated cassette exchange (RMCE) donor plasmid library was then constructed by cloning gRNA oligos into a backbone plasmid that contained a promoter-less puromycin resistance (PuroR) gene and gRNA scaffold, flanked by attB and mutant attB recombination sites. The plasmid library was verified using next-generation sequencing (NGS) analysis. ( b ) Workflow of cell library and knockout (KO) library development. The CHO-K1 host and recombinant cell-based master cell lines (MCLs) were established through CRISPR/Cas9-mediated site-specific integration (SSI) of the landing pad (LP) plasmid donor, which contained mCherry and hygromycin resistance (HygR) gene flanked by attP and mutant attP recombination sites. The host and recombinant cell libraries were generated through the Bxb1-att recombination system and puromycin selection. Promoter-trapping ensures the expression of a single gRNA per cell. The host and recombinant KO libraries were then generated through transient Cas9 expression and blasticidin semi-selection. The distribution of gRNAs in the pooled cell libraries and KO libraries was confirmed using NGS analysis. The KO libraries were also analyzed after long-term cultivation. ( c ) Functional genomic screens. Phenotypes of interest in the KO library can be enriched through reporter-based fluorescence-activated cell sorting (FACS) or pool selection under selective pressure. Highly productive recombinant cell populations were sorted in this study. The distribution of gRNAs was confirmed using NGS analysis. The screening hits are candidate targets for KO cell engineering.

Article Snippet: CRISPR screening data analysis is available at https://sourceforge.net/p/mageck/wiki/Home/#usage and http://pinapl-py.ucsd.edu .

Techniques: CRISPR, Plasmid Preparation, Genome Wide, Synthesized, Construct, Cloning, Mutagenesis, Next-Generation Sequencing, Knock-Out, Recombinant, Generated, Selection, Expressing, Functional Assay, Fluorescence, FACS

Journal: Scientific Data

Article Title: Comprehensive genome-scale CRISPR knockout screening of CHO cells

doi: 10.1038/s41597-025-04438-6

Figure Lengend Snippet: The development process of the cell and KO libraries using a non-viral genome-scale CRISPR screening platform. Entire profiles of ( a ) viability and ( b ) cell growth (VCD; viable cell density) during the generation of the cell (red solid lines) and KO (blue solid lines) libraries in both CHO-K1 host and recombinant cells. Arrows indicate the transfection of RMCE (red) and Cas9 (blue). Shaded areas denote periods of chemical treatment for cell library enrichment (red, puromycin) and KO library enrichment (blue, blasticidin). The pEGFP-c1 expression vector was used as the transfection control (green lines), while chemically untreated cells served as non-enriched controls (dotted lines). ( c ) The percentage of mCherry-negative populations in the CHO-K1 host (blue line) and recombinant cells (red line) during cell library development with and without puromycin selection. Dotted line indicates the threshold required to maintain 500 × gRNA coverage. The mCherry negativity in enriched populations indicated successful implementation of the genome-scale plasmid library into cells. ( d ) Bar plot showing the transfection efficiency of Cas9 in CHO-K1 host cells and recombinant cells as measured through 2 A peptide-linked reporter expression.

Article Snippet: CRISPR screening data analysis is available at https://sourceforge.net/p/mageck/wiki/Home/#usage and http://pinapl-py.ucsd.edu .

Techniques: CRISPR, Recombinant, Transfection, Expressing, Plasmid Preparation, Control, Selection

Journal: Nature Communications

Article Title: Epigenetic regulation of CD38/CD48 by KDM6A mediates NK cell response in multiple myeloma

doi: 10.1038/s41467-024-45561-z

Figure Lengend Snippet: a Schematic of genome-wide CRISPR screen in a MM cell line treated with Dara and human primary NK cells. b Top genes for enriched (red) and depleted (blue) sgRNAs from the screen. Candidate genes were plotted based on the beta score, computed by MaGeCK (Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout) of sgRNAs normalized to control. c Schematic of the CRISPR assay to identify essential genes for Dara-NK-mediated ADCC. d TCGA RNA-seq data from 36 human cancer types were analyzed to obtain genes positively correlated with cytolytic (CYT) activity. The number of overlapped genes between our top candidates and CYT activity-related genes was quantified in each cancer type. e Heatmap showing the partitioning of the clusters of genes based on Pearson’s correlation coefficient values of CRISPR screen hits with CYT activity using pan-cancer TCGA data. Figure 1c was created with BioRender.com.

Article Snippet: CRISPR screen sequencing data, RNA-seq, ChIP-seq and ATAC-seq data generated in this study are deposited in the National Institute of Health Gene Expression Omnibus (GEO) database ( GSE228771 ). are provided with this paper.

Techniques: Genome Wide, CRISPR, Knock-Out, Control, RNA Sequencing, Activity Assay

Journal: Nature Communications

Article Title: Epigenetic regulation of CD38/CD48 by KDM6A mediates NK cell response in multiple myeloma

doi: 10.1038/s41467-024-45561-z

Figure Lengend Snippet: a Schematic of the second genome-wide CRISPR screen in a MM cell line. Five percent of cells with the lowest expression of CD38 were collected for next-generation sequencing. b Top genes for enriched (red) and depleted (blue) gRNAs from the screen. c Venn diagram showing the overlapped enriched genes between the two CRISPR screens. d Western blot of CD38 protein levels in MM cell lines with CRISPR-mediated knock (KO) of KDM6A . e q-RT-PCR for CD38 mRNA in MM cell lines transfected with indicated sgRNAs. Data were normalized against GAPDH (mean ± SEM, n = 3 biologically independent experiments). *** p < 0.001; **** p < 0.0001 (two-sided student’s t test). f Representa t ive flow cytometry analysis of CD38 expression in H929 single clones transfected with indicated sgRNAs. g Western blotting of CD38 protein levels after ectopic overexpression of KDM6A in KDM6A -KO H929 cells. OE, overexpression. Three independent experiments were performed and similar results were obtained. h Representative flow cytometry analysis of CD38 expression level after ectopic overexpression of KDM6A in KDM6A -KO H929 cells. Source data are provided as a Source Data file.

Article Snippet: CRISPR screen sequencing data, RNA-seq, ChIP-seq and ATAC-seq data generated in this study are deposited in the National Institute of Health Gene Expression Omnibus (GEO) database ( GSE228771 ). are provided with this paper.

Techniques: Genome Wide, CRISPR, Expressing, Next-Generation Sequencing, Western Blot, Reverse Transcription Polymerase Chain Reaction, Transfection, Flow Cytometry, Clone Assay, Over Expression

Journal: Nature Communications

Article Title: Epigenetic regulation of CD38/CD48 by KDM6A mediates NK cell response in multiple myeloma

doi: 10.1038/s41467-024-45561-z

Figure Lengend Snippet: a Normalized lysis percentage of KDM6A WT or KO H929 cells after co-culture with primary NK cells at different E:T ratios with IL-2 for 6 hours (mean ± SEM, n = 3 biologically independent experiments). * p < 0.05; ** p < 0.01; *** p < 0.001 (two-sided student’s t test). b Intracellular IFN-γ s t aining of primary NK cells co-cultured with KDM6A WT or KO H929 cells with IL-2 for 6 hours (mean ± SEM, n = 3 biologically independent experiments). ** p < 0.01 (two-sided student’s t test). c Volcano plot of differentially expressed genes in KDM6A KO H929 cells compared with WT cells assessed by RNA-seq. d Venn diagram showing the overlapped enriched genes between the CRISPR screen and RNA-seq. e , Representative flow cytometry analysis of CD48 expression in H929 transfected with indicated sgRNAs. f q-RT-PCR for CD48 mRNA in H929 cells transfected with indicated sgRNAs. Data were normalized against GAPDH (mean ± SEM, n = 3 biologically independent experiments). **** p < 0.0001 (two-sided student’s t test). g , ChIP-seq density profiles for H3K27me3 on t he CD48 gene in KDM6A WT and KO H929 cells. h , H3K27me3 ChIP-qPCR analysis at the CD48 gene in KDM6A WT and KO H929 cells (mean ± SEM, n = 3 biologically independent experiments). *** p < 0.001 (two-sided student’s t test). i , ATAC-seq density profiles at the CD48 promoter region in KDM6A WT and KO H929 cells. j q-RT-PCR for CD48 mRNA in newly diagnosed (N/D) ( n = 7) and Dara-resistant (R/R) ( n = 12) patient samples. Data were normalized against GAPDH (mean ± SEM). * p < 0.05 (two-sided student’s t test). Source data are provided as a Source Data file.

Article Snippet: CRISPR screen sequencing data, RNA-seq, ChIP-seq and ATAC-seq data generated in this study are deposited in the National Institute of Health Gene Expression Omnibus (GEO) database ( GSE228771 ). are provided with this paper.

Techniques: Lysis, Co-Culture Assay, Cell Culture, RNA Sequencing, CRISPR, Flow Cytometry, Expressing, Transfection, Reverse Transcription Polymerase Chain Reaction, ChIP-sequencing, ChIP-qPCR

Journal: Nature reviews. Methods primers

Article Title: High-content CRISPR screening

doi: 10.1038/s43586-022-00098-7

Figure Lengend Snippet: Software tools for analysing CRISPR screens

Article Snippet: This was confirmed by a comparative analysis of two large CRISPR screening data sets for cancer cell lines generated independently by the Broad Institute and the Sanger Institute, which identified essential genes with high reproducibility 20 , 235 .

Techniques: Software, CRISPR, Control, Genome Wide, Sequencing, Expressing

Journal: Nature reviews. Methods primers

Article Title: High-content CRISPR screening

doi: 10.1038/s43586-022-00098-7

Figure Lengend Snippet: Sequencing-based counting of guide RNAs (gRNAs) is a straight-forward and widely used read-out of pooled CRISPR screens, especially for phenotypes that affect cell proliferation and survival. To broaden CRISPR screening to additional cellular phenotypes, several high-content read-outs have been introduced. Cell sorting prior to gRNA sequencing makes it possible to identify gRNAs and genes that affect predefined, sortable cellular phenotypes — such as expression of a fluorescent reporter. Single-cell sequencing of transcriptomes and matched gRNAs can identify regulators of gene expression and transcriptome-linked cell states. Spatial imaging combined with methods to distinguish gRNAs in individual cells makes it possible to identify genes that affect imaging-based cellular phenotypes, for example inducing changes in cell shape.

Article Snippet: This was confirmed by a comparative analysis of two large CRISPR screening data sets for cancer cell lines generated independently by the Broad Institute and the Sanger Institute, which identified essential genes with high reproducibility 20 , 235 .

Techniques: Sequencing, CRISPR, FACS, Expressing, Gene Expression, Imaging

Journal: Nature reviews. Methods primers

Article Title: High-content CRISPR screening

doi: 10.1038/s43586-022-00098-7

Figure Lengend Snippet: a | Gene repression by CRISPR interference (CRISPRi) facilitated genome-wide screening for essential genes in the model organism Escherichia coli283. In the depicted system, the guide RNA (gRNA) sequence is constitutively expressed from a replicating plasmid, while the Cas9 endonuclease dead (dCas9) gene is integrated in the bacterial genome under an inducible Ptet promoter. Gene repression is induced by addition of anhydrotetracycline (aTc), an antibiotic derivative of tetracycline. b | A similar inducible CRISPRi approach was used in the bacterial pathogen Streptococcus pneumoniae to study population bottlenecks during in vivo infection of a murine host290. c | CRISPR knockout (CRISPRko) enabled the large-scale construction of double mutants to map genetic interactions in the yeast pathogen Candida albicans280. d | CRISPRko screening and barcode tagging of clones in the parasite Leishmania mexicana uncovered genes involved in stress adaptation during sandfly infection336.

Article Snippet: This was confirmed by a comparative analysis of two large CRISPR screening data sets for cancer cell lines generated independently by the Broad Institute and the Sanger Institute, which identified essential genes with high reproducibility 20 , 235 .

Techniques: CRISPR, Genome Wide, Sequencing, Plasmid Preparation, In Vivo, Infection, Knock-Out, Clone Assay