Jun 17, 2026
CRISPRa screen analysis to identify CCRs associated with DE genes
- Julian Pulecio1,
- Michael Beer2,
- Danwei Huangfu1
- 1Sloan Kettering Institute;
- 2Johns Hopkins University
Protocol Citation: Julian Pulecio, Michael Beer, Danwei Huangfu 2026. CRISPRa screen analysis to identify CCRs associated with DE genes. protocols.io https://dx.doi.org/10.17504/protocols.io.3byl4mex2lo5/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: May 31, 2026
Last Modified: June 17, 2026
Protocol Integer ID: 318241
Keywords: CRISPRa, GATA6, SOX17, MIXL1, EOMES, Definitive Endoderm, Human Embryonic Stem Cell, Competent Chromatin Regions, FACS Screen, crispr activation, crispra screening, competent chromatin region, human embryonic stem cell, embryonic stem cell, crispra screen analysis, mixl1 gene, de gene, screening approach in human, crispra
Funders Acknowledgements:
NHGRI
Grant ID: U01 HG012051
Abstract
To identify and validate competent chromatin regions (CCRs) of the GATA6, EOMES, SOX17, and MIXL1 gene using a CRISPR activation (CRISPRa) screening approach in human embryonic stem cells (hESCs).
CRISPRa screen analysis to identify CCRs associated with DE genes
To identify the competent chromatin regions (CCRs) associated with GATA6, EOMES, SOX17, and MIXL1, the gRNA sequencing reads for the positive (i.e., cells expressing any of the 4 genes after CRISPRa interrogation) and NGS obtained the negative FACS-sorted fraction, and the following analysis was performed for each gene, respectively.
First, off-target gRNAs were filtered out using CRISPOR, as previously reported.
For each replicate, after adding pseudocount=1000 to the read counts, log fold change values between the positive and the negative sorted fractions were calculated as described below ("Read count normalization and computing fold changes").
See the Method section of Pulecio 2026 Functional chromatin signatures premark future lineage-specific enhancers.
For this analysis, the log fold change values of individual gRNAs were used instead of the moving average; in other words, window i is the ith gRNA, and the notation in "Calculating the moving average" holds for N=1.
See the Method section of Pulecio 2026 Functional chromatin signatures premark future lineage-specific enhancers.
Then, the significance and reproducibility of each gRNA were quantified as described in "Quantifying significance and reproducibility".
See the Method section of Pulecio 2026 Functional chromatin signatures premark future lineage-specific enhancers.
Significant gRNAs were selected based on their significance in each replicate and reproducibility between the replicates: gRNA i is discarded if Z_i^Ι < 1 or Z_i^ΙΙ < 1 or IDR_i > 0.05.
We defined a genomic region for each group of nearby significant gRNAs, extended until there were none closer than 100bp.
Finally, we identified CCRs by extending the resulting regions by 150bp around the center and merging the overlapping regions.
We identified non-CCRs by subtracting the CCRs from the differential ATAC peaks between the ESC and DE stages that were targeted by the screen: ATAC peaks and parts of the ATAC peaks that did not overlap CCRs and were longer than 150bp were considered non-CCRs.
gRNA enrichment for each interrogated region and the final list of CCRs and non-CCRs are provided in Tables S2 and S3.
See the supplemental material section of Pulecio 2026 Functional chromatin signatures premark future lineage-specific enhancers.
Protocol references
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