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Coupled Single-Cell CRISPR Screening and Epigenomic Profiling Reveals Causal Gene Regulatory Networks
Open accessPublished version of a preprint
Abstract
Here, we present Perturb-ATAC, a method that combines multiplexed CRISPR interference or knockout with genome-wide chromatin accessibility profiling in single cells based on the simultaneous detection of CRISPR guide RNAs and open chromatin sites by assay of transposase-accessible chromatin with sequencing (ATAC-seq). We applied Perturb-ATAC to transcription factors (TFs), chromatin-modifying factors, and noncoding RNAs (ncRNAs) in ∼4,300 single cells, encompassing more than 63 genotype-phenotype relationships. Perturb-ATAC in human B lymphocytes uncovered regulators of chromatin accessibility, TF occupancy, and nucleosome positioning and identified a hierarchy of TFs that govern B cell state, variation, and disease-associated cis-regulatory elements. Perturb-ATAC in primary human epidermal cells revealed three sequential modules of cis-elements that specify keratinocyte fate. Combinatorial deletion of all pairs of these TFs uncovered their epistatic relationships and highlighted genomic co-localization as a basis for synergistic interactions. Thus, Perturb-ATAC is a powerful strategy to dissect gene regulatory networks in development and disease.
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Written from the paper’s full text.
TL;DR
The paper introduces Perturb-ATAC, a single-cell method that pairs CRISPR interference or knockout with ATAC-seq to link genetic perturbations to chromatin accessibility phenotypes. It applies the method in human B lymphoblasts and primary keratinocytes to map regulatory networks, differentiation modules, and epistatic interactions among transcription factors.
Key findings
- Perturb-ATAC detected guide RNAs or guide barcodes together with chromatin accessibility in thousands of single cells: 2,936 B lymphoblasts and 1,356 primary keratinocytes, covering 63 genotype-phenotype relationships.
- In GM12878 B cells, a 40-genotype screen across 2,627 cells identified accessibility changes at 10,103 open chromatin sites and 833 features; EZH2 depletion increased accessibility at H3K27me3-marked regions (chromVAR gain 0.78, FDR < 0.0001), while SPI1 depletion reduced SPI1-motif accessibility (loss 2.17, FDR < 0.0001).
- In keratinocytes, single-cell ATAC-seq defined three sequential regulatory modules during differentiation, and a CRISPR knockout screen of JUNB, KLF4, ZNF750, CEBPA, and EHF in 279 single-sgRNA and 235 double-sgRNA cells identified 399 altered features and showed that CEBPA loss biased cells toward early differentiation while KLF4 loss shifted cells later.
- Epistasis analysis classified 344 additive, 102 synergistic, and 101 buffering interactions; synergistic TF pairs showed greater target-region overlap (Spearman correlation = 0.4) and correlated expression across tissues (Spearman correlation = 0.358).
Methods
- Perturb-ATAC was performed on microfluidic integrated fluidic circuits, combining cell lysis, Tn5 transposition, reverse transcription of sgRNAs or guide barcodes, and PCR amplification before sequencing of ATAC and guide libraries.
- The B lymphoblast experiments used dCas9-KRAB CRISPRi targeting transcription factors, chromatin modifiers, and noncoding RNAs, with single- and dual-guide genotypes; the keratinocyte experiments used Cas9-mediated knockout of selected transcription factors, followed by differentiation and harvest at Day 3.
- Analyses included chromVAR for transcription-factor motif accessibility, permutation-based false discovery rates for differential features, nucleosome-position inference from ATAC fragment sizes, pseudotime trajectory analysis, and epistasis scoring against an additive non-interaction model.
Limitations
- The paper states that future screens should target fewer cells per genotype (about 20) while expanding the number of genotypes, and it anticipates technological improvements to increase throughput and design complexity.
- The study is limited to two cell systems, GM12878 B lymphoblasts and primary human keratinocytes, so the generality of the inferred regulatory relationships to other cell types or tissues is not established in the text.
- Evident from the text: Perturb-ATAC measures chromatin accessibility and guide identity, not transcript or protein levels directly, so inferred transcription-factor activity and network relationships rely on accessibility readouts rather than direct expression measurements.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Perturb-ATAC in primary human epidermal cells revealed three sequential modules of c/s-elements that specify keratinocyte fate.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Abstract
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In the paper
“We found that SPI1 sites exhibited a significant loss of accessibility in SP/1-targeted cells compared to NT cells (FDR < 1e-3, permutation test; Methods), similar to bulk SP/1-targeting experiments (Figure 1G).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC: simultaneous CRISPR guide detection and epigenome profiling in single cells.
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In the paper
“Cluster 2 included the ncRNA 7SK, which represses enhancer transcription (Flynn et al., 2016), and the chromatin remodeler EZH2 (Figure 2F).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC identifies epigenomic functions of chromatin regulators, transcription factors, and noncoding RNAs in B cells
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In the paper
“Module 4 contained homeobox domain TFs, and Module 5 features included specific regulators of B cell development, such as IKZF1 (also known as Ikaros), BCL11A, EBF1, NFKB, MEF2C and AP-1 factors.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Discovery of gene regulatory networks controlled by trans-factors
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In the paper
“Other altered TF motifs in SPI1-targeted cells included BCL11A, SPIB, and MEF2C factors, consistent with the function of SPI1 in regulating factors required for B cell lineage commitment (Figure 2E and Table S3) (Su et al., 1996; Liu et al., 2003; Stehling-Sun et al., 2009).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC identifies epigenomic functions of chromatin regulators, transcription factors, and noncoding RNAs in B cells
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In the paper
“Finally, targeting of the ncRNA EBER2 identified 90 significantly altered features, including regions containing the PAX5 motif, a factor that physically interacts with EBER2 to control gene expression (Lee et al., 2015).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC identifies epigenomic functions of chromatin regulators, transcription factors, and noncoding RNAs in B cells
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In the paper
“For example, depletion of IRF8 re-wired the developmental features of Module 5, such that AP-1 factor activity was no longer coordinated with the activity of IKZF1, RUNX, and MEF2C (Figure 3D and S4C).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Discovery of gene regulatory networks controlled by trans-factors
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In the paper
“Similarly, the most significant change in TF motif accessibility in SP/1-targeted cells was at SPI1-motif-containing regions (chromVAR accessibility loss 2.17, FDR < .0001; Figure 2E).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC identifies epigenomic functions of chromatin regulators, transcription factors, and noncoding RNAs in B cells
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In the paper
“Consistent with reports of its repressor activity, IRF8 depletion increased accessibility at IRF sites (Tamura et al., 2008).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC identifies epigenomic functions of chromatin regulators, transcription factors, and noncoding RNAs in B cells
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In the paper
“Single-cell ATAC-seq reads recapitulated characteristics of bulk ATAC-seq data, including insert-size periodicity and fragment enrichment at transcription start sites (Figure 1D).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC: simultaneous CRISPR guide detection and epigenome profiling in single cells.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We applied Perturb-ATAC to transcription factors (TFs), chromatin-modifying factors, and noncoding RNAs (ncRNAs) in ~4,300 single cells, encompassing more than 63 genotype-phenotype relationships.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Abstract
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In the paper
“Perturb-ATAC in human B lymphocytes uncovered regulators of chromatin accessibility, TF occupancy, and nucleosome positioning, and identified a hierarchy of TFs that govern B cell state, variation, and disease-associated c/s-regulatory elements.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Abstract
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In the paper
“Interestingly, two NFκB subunits, NFKB1 and RELA, showed overlapping and distinct effects, in line with prior studies demonstrating differences in the binding patterns of NFκB subunits through the formation of distinct protein complexes (Figures 2F and S3B; Zhao et al., 2014).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC identifies epigenomic functions of chromatin regulators, transcription factors, and noncoding RNAs in B cells
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In the paper
“and loss of either factor altered accessibility at EBF1 sites, consistent with these factors acting in the same pathway (Figure S3B).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC identifies epigenomic functions of chromatin regulators, transcription factors, and noncoding RNAs in B cells
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, depletion of EBF1 resulted in altered correlation of Module 2, as well as its own Module 5.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Discovery of gene regulatory networks controlled by trans-factors
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In the paper
“Conversely, depletion of either EBER1 or TET2 alone did not affect accessibility at IKZF1 binding sites, while dual depletion resulted in an unexpected increase in accessibility (Figure 4B).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Mapping epistatic relationships reveals cooperative functions of trans-factors in development and disease
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In the paper
“As expected, reads were almost exclusively assigned to one GBC (NT1, NT2, or SPI1); multiple GBCs were only detected in 9/309 cells.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC: simultaneous CRISPR guide detection and epigenome profiling in single cells.
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In the paper
“Indeed, depletion of EZH2 alone primarily de-repressed regions commonly marked by H3K27me3 across cell types which may not require additional targeting specificity, while dual depletion of EZH2 and other factors de-repressed regions specifically active in alternate hematopoietic lineages (Figures 4G and 4H).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Mapping epistatic relationships reveals cooperative functions of trans-factors in development and disease
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In the paper
“Cells passing filter yielded an average of 11.33 × 103 fragments mapping to the nuclear genome, and approximately 43.05% of reads were within bulk ATAC-seq peaks, similar to previously published scATAC-seq as well as high-quality bulk ATAC-seq datasets (Buenrostro et al., 2015; Corces et al., 2017) (Figure 1C).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC: simultaneous CRISPR guide detection and epigenome profiling in single cells.
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In the paper
“The two NFκB subunits, NFKB1 and RELA, exhibited distinct effects on Module 5, which includes NFKB1 and RELA, while altering Module 4 to a similar extent (Figures 3H, S4E).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Discovery of gene regulatory networks controlled by trans-factors
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In the paper
“scATAC-seq in primary human epidermal cells identified three regulatory modules in keratinocyte differentiation, and a Perturb-ATAC CRISPR-deletion screen revealed key TF regulators of each module.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Introduction
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In the paper
“For example, SPI1 motif sites were unchanged in EBF1 depleted cells, while depletion of SPI1 strongly decreased the accessibility of these sites (Figure 4B).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Mapping epistatic relationships reveals cooperative functions of trans-factors in development and disease
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In the paper
“The 12 targeted trans-factors included transcription factors (EBF1, IRF8, NFKB1, RELA, and SPI1), chromatin modifiers (BRG1, DNMT3A, EZH2, and TET2), and noncoding RNAs (7SK, EBER1, and EBER2) that have previously been shown to impact normal and neoplastic B cell development and function (Nutt and Kee, 2007; Lunning and Green, 2015).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC identifies epigenomic functions of chromatin regulators, transcription factors, and noncoding RNAs in B cells
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In the paper
“Cells passing filter yielded an average of 10.68 × 103 fragments mapping to the nuclear genome, and approximately 43.85% of reads were within peaks present in bulk profiles (Figure 2C).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC identifies epigenomic functions of chromatin regulators, transcription factors, and noncoding RNAs in B cells
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In the paper
“As expected, sub-nucleosome fragments were enriched at the CTCF motif, indicating a central nucleosome-free region. In contrast, nucleosome-sized fragments were enriched upstream and downstream relative to the CTCF motif, representing the +1 and −1 nucleosomes (Vierstra et al., 2014).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC identifies epigenomic functions of chromatin regulators, transcription factors, and noncoding RNAs in B cells
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In the paper
“Finally, Module 3 (25 features) included target regions of ETS and IRF family factors, and gained accessibility in late differentiation.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > The regulatory landscape of human epidermal differentiation
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In the paper
“Notably, ZNF750 target regions were not uniformly more or less accessible in ZNF750-targeted cells, consistent with the role of this factor as a positive and negative gene regulator (Boxer et al., 2014).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC screen for TF control of cellular differentiation trajectories
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In the paper
“While cells largely separated by their differentiation time point, a few “precocious” differentiating cells were observed in the Day 0 population, and some Day 6 cells remained in a mid-differentiation state (Figure 5B).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > The regulatory landscape of human epidermal differentiation
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In the paper
“Although perturbation of NFKB1 did not alter many other variant-enhancers in isolation, dual perturbation of NFKB1 and other factors modulated accessibility associated with several diseases, demonstrating that Perturb-ATAC uncovers epistatic interactions relevant to development and disease (Figure 4J).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Mapping epistatic relationships reveals cooperative functions of trans-factors in development and disease
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In the paper
“We identified 67 TFs with dynamic accessibility in differentiation, which clustered into three modules (Figures 5C and 5D).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > The regulatory landscape of human epidermal differentiation
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In the paper
“NFκB binding sites were shown to be enriched near causal variants associated with multiple sclerosis and system lupus erythematosus (Farh et al., 2015), and these sites demonstrated altered accessibility in cells where NFKB1 or RELA were depleted (Figure 4J).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Mapping epistatic relationships reveals cooperative functions of trans-factors in development and disease
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In the paper
“This workflow identified 344 additive relationships, 102 synergistic interactions, and 101 buffering interactions.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Comprehensive epistatic mapping reveals the logic of regulatory synergy
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In the paper
“Altogether, weidentified 279 single sgRNA-expressing cells and 235 double sgRNA-expressing cells, encompassing 23 distinct genotypes of all expected individual and doublé CRISPR perturbations (Figure 6B, Table S5).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC screen for TF control of cellular differentiation trajectories
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In the paper
“Analysis of Perturb-ATAC data enabled the study of several layers of chromatin regulation: 1) individual c/s-regulatory elements, 2) inferred TF activity from cis-regulatory modules, and 3) nucleosome positioning and occupancy.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Discussion
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In the paper
“For example, the ZNF750-bound enhancer of the epidermal cornified envelope gene SPRR2E lost accessibility upon ZNF750 disruption, while other ZNF750 binding sites gained accessibility (Figure 6D and S7A).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC screen for TF control of cellular differentiation trajectories
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In the paper
“Only features that were significantly altered in at least one genotype were considered, and redundant annotations were removed, resulting in 390 motif/ChIP feature annotations for analysis.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > STAR Methods > QUANTIFICATION AND STATISTICAL ANALYSIS
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In the paper
“This method informs the identification of enhancers, the positioning of nucleosomes, and the inference of transcription factor binding (Buenrostro et al., 2013; Schep et al., 2015, 2017).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Introduction
What the sources say
- Not checked yetcites Buenrostro et al., 2013 source
- Not matchedcites Schep et al., 2015, 2017
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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In the paper
“Combinatorial deletion of all pairs of these TFs uncovered their epistatic relationships and highlighted genomic co-localization as a basis for synergistic interactions.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Abstract
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In the paper
“We recently developed the assay for transposase-accessible chromatin with sequencing (ATAC-seq), which utilizes a hyperactive transposase (Tn5) to measure the activity of regulatory DNA elements (Buenrostro et al., 2013).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Introduction
What the sources say
- Not checked yetcites Buenrostro et al., 2013 source
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In the paper
“Importantly, ATAC-seq can be performed in single cells (scATAC-seq), uncovering cell-to-cell variability and rare epigenomic phenotypes (Cusanovich et al., 2018; Satpathy et al., 2018; Buenrostro et al., 2018).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Introduction
What the sources say
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In the paper
“These results demonstrate that Perturb-ATAC consistently detects GBCs with high confidence and a low false positive rate (Figures 1B, S1B and S1C).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC: simultaneous CRISPR guide detection and epigenome profiling in single cells.
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In the paper
“Of note, the loss of co-variation is not dependent on altered accessibility of each factor, decoupling two mechanisms of TF activity (Figures 3E and S4D).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Discovery of gene regulatory networks controlled by trans-factors
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In the paper
“Additionally, scATAC-seq profiles can be paired with orthogonal measurements of RNA or protein expression in the same cell (Satpathy et al., 2018; Chen et al., 2018a).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Introduction
What the sources say
- Not checked yetcites Satpathy et al., 2018 source
- Not checked yetcites Chen et al., 2018a
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Reflecting another distinct mechanism, regions losing accessibility upon depletion of 7SK exhibited stronger central nucleosome structure in 7SK-depleted cells, possibly indicating that 7SK interacts with nucleosome remodeling factors, as has been shown for the BAF complex (Figure S3F; Flynn et al.,”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC identifies epigenomic functions of chromatin regulators, transcription factors, and noncoding RNAs in B cells
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We mapped epistatic relationships between TFs using multiplexed perturbations and suggest that genomic co-localization and co-expression of TFs may predict genetic interaction.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Introduction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast, SPI depletion increased accessibility of IRF motif regions, demonstrating that SPI1 exhibits activating and repressive functions, as has been reported (van Riel and Rosenbauer, 2014).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC identifies epigenomic functions of chromatin regulators, transcription factors, and noncoding RNAs in B cells
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, these results demonstrate that trans-factors regulate nucleosome positioning in distinct ways and that Perturb-ATAC can read out nucleosome structure changes associated with perturbations.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC identifies epigenomic functions of chromatin regulators, transcription factors, and noncoding RNAs in B cells
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Rare ATAC-seq reads mapping to the viral construct did not appear to influence ATAC-seq profiles, and observed accessibility at potential sgRNA mismatch loci indicated no evidence of off-target repression (Methods, Figure S2D).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC identifies epigenomic functions of chromatin regulators, transcription factors, and noncoding RNAs in B cells
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To eliminate the likelihood of recombination of sgRNAs and GBCs between vectors, as has been recently described (Adamson et al., 2018; Feldman et al., 2018; Hill et al., 2018; Xie et al., 2018), we performed packaging steps in separate cultures.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC: simultaneous CRISPR guide detection and epigenome profiling in single cells.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In particular, the correlation between IKZF1 and FOS:JUN (AP-1) in NT cells (r = 0.420, p = 5.60e-6) was lost in IRF8 depleted cells (r = - 0.051, p=0.627), suggesting that IRF8 coordinates these two factors (Figures 3F and 3G).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Discovery of gene regulatory networks controlled by trans-factors
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For example, co-varying accessibility between sets of regions bound by two trans-factors may reflect common regulation. In contrast, inverse correlation may indicate that one factor controls the activity or expression of a negative regulator of the other set of regions.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Discovery of gene regulatory networks controlled by trans-factors
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
- Rests on an assumption: Co-varying accessibility between sets of regions bound by two trans-factors reflects common regulation, and inverse correlation indicates one factor controls the activity or expression of a negative regulator of the other set.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“CRISPRi guide RNAs were designed to maximize knockdown efficiency and minimize off-target effects (Figure S2A-C, Table S1).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC identifies epigenomic functions of chromatin regulators, transcription factors, and noncoding RNAs in B cells
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, regions that gained accessibility in DNMT3A-depleted cells exhibited a stronger central nucleosome, consistent with a model in which DMNT3A recruits negative regulatory factors to an open chromatin region.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC identifies epigenomic functions of chromatin regulators, transcription factors, and noncoding RNAs in B cells
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Similarly, regions marked by the repressive histone modification H3K27me3 exhibited a high degree of interaction, particularly involving EZH2, suggesting that other factors may guide EZH2 recruitment or activity (Figure 4F).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Mapping epistatic relationships reveals cooperative functions of trans-factors in development and disease
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We used stringent cutoffs to assign the presence or absence of a GBC (Figure S1B).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC: simultaneous CRISPR guide detection and epigenome profiling in single cells.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“We chose three Module 2 regulators: 1) KLF4, which induces epidermal differentiation genes, 2) ZNF750, a factor engaged in positive and negative gene regulation in epidermal differentiation, and 3) CEBPA, which has known role in murine epidermis (Lopez et al., 2009; Sen et al., 2012).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > The regulatory landscape of human epidermal differentiation
What the sources say
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“We chose three Module 2 regulators: 1) KLF4, which induces epidermal differentiation genes, 2) ZNF750, a factor engaged in positive and negative gene regulation in epidermal differentiation, and 3) CEBPA, which has known role in murine epidermis (Lopez et al., 2009; Sen et al., 2012).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > The regulatory landscape of human epidermal differentiation
What the sources say
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For a candidate Module 1 regulator, we selected JUNB, an AP-1 family member which controls epidermal homeostasis and tumorigenesis (Eckert et al., 2013).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > The regulatory landscape of human epidermal differentiation
What the sources say
- Not checked yetcites Eckert et al., 2013
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Guides were designed using the CRISPOR online tool (http://crispor.tefor.net/) and chosen to minimize the number of genomic loci with 0–4 mismatches, with the intention of minimizing the chance for any off-target CRISPRi activity.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > STAR Methods > METHOD DETAILS
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The human epidermis is a constantly renewing stratified epithelial tissue, and thousands of genes change in expression during terminal epidermal differentiation as progenitor cells migrate from the basal layer, begin keratinization, and undergo cornification to form the outer layer of the skin. (Lopez-Pajares et al., 2015).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > The regulatory landscape of human epidermal differentiation
What the sources say
- Not checked yetcites Lopez-Pajares et al., 2015 source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Together, we identified both the regulatory factors responsible for distinct epidermal states as well as rerouted differentiation trajectories in response to TF perturbation.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC screen for TF control of cellular differentiation trajectories
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Cells with high background reads (greater than 1%) were excluded, and the remaining cells clearly separated into single or double sgRNA-expressing populations (Figure S6D-F).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC screen for TF control of cellular differentiation trajectories
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Correspondingly, CEBPA knockout cells lost accessibility of Module 2 features but gained Module 1 accessibility, indicating that CEBPA knockout cells were unable to fully engage the mid-differentiation program.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC screen for TF control of cellular differentiation trajectories
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“As a Module 3 regulator, we chose EHF, an ETS factor implicated in controlling epidermal differentiation (Rubin et al., 2017).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > The regulatory landscape of human epidermal differentiation
What the sources say
- Not checked yetcites Rubin et al., 2017 source
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We developed a second Perturb-ATAC protocol to achieve two goals: 1) to directly detect the sgRNA rather than a GBC, and 2) to assess CRISPR gene knockouts rather than CRISPR interference.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Perturb-ATAC screen for TF control of cellular differentiation trajectories
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
In the paper
“TP63, a master regulator of epidermal homeostasis and differentiation”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Comprehensive epistatic mapping reveals the logic of regulatory synergy
What the sources say
- Not checked yetcites Bao et al., 2015 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Consent for all samples were obtained in accordance with the NIH genomic data sharing policy.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > STAR Methods > EXPERIMENTAL MODEL AND SUBJECT DETAILS
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. The quote only partly states this claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“reflecting a negative interaction between EHF and JUNB”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > Comprehensive epistatic mapping reveals the logic of regulatory synergy
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe quote only partly states this claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“gene expression knockdown was confirmed using the Agilent Brilliant II qRT-PCR 1-Step kit.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > STAR Methods > METHOD DETAILS
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Perturb-ATAC provides a high-throughput platform to link genotypes with epigenetic phenotypes iand ultimately reveal molecular mechanisms that govern cell fate and function through modulation of the chromatin state.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Discussion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The sgRNA sequences and GBC sequences of all plasmids were confirmed by Sanger sequencing.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > STAR Methods > METHOD DETAILS
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“We chose three Module 2 regulators: 1) KLF4, which induces epidermal differentiation genes, 2) ZNF750, a factor engaged in positive and negative gene regulation in epidermal differentiation, and 3) CEBPA, which has known role in murine epidermis (Lopez et al., 2009; Sen et al., 2012).”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > Results > The regulatory landscape of human epidermal differentiation
What the sources say
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“qRT-PCR was performed in duplicate, and expression values for each sample were normalized against 18S.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > STAR Methods > METHOD DETAILS
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“This procedure was performed separately for each feature to account for differences in scale and variability across features.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > STAR Methods > QUANTIFICATION AND STATISTICAL ANALYSIS
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Cas9 nuclease activity was assessed by PCR amplifying ~800bp fragments of cDNA surrounding sgRNA binding sites and analyzing the resulting fragments by Sanger sequencing”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > STAR Methods > METHOD DETAILS
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Cells with greater than 1% of reads associated with background were excluded from analysis.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > STAR Methods > QUANTIFICATION AND STATISTICAL ANALYSIS
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“The feature deviation matrix including unperturbed and CRISPR knockout cells was first processed using Seruat 2.0.1 (Butler et al., 2018) to regress out plate andexperiment batch effects.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > STAR Methods > QUANTIFICATION AND STATISTICAL ANALYSIS
What the sources say
- Not checked yetcites Butler et al., 2018 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We observed an extremely low rate of ATAC reads matching the CRISPR viral construct (median 0.0049%) and found no evidence of the abundance of CRISPR construct matching reads influencing epigenomic profiles.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > STAR Methods > QUANTIFICATION AND STATISTICAL ANALYSIS
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“indeed 38/40 genotypes for GM12878 cells and 23/23 genotypes for keratinocytes consist of greater than five cells.”
§ Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks > STAR Methods > QUANTIFICATION AND STATISTICAL ANALYSIS
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper relies on this without support.
Epigenetic assays require large amounts of cellular material and are difficult to couple with genetic perturbations at scale.
“However, it is challenging to characterize each trans-factor or c/s-element, since epigenetic assays require large amounts of cellular material and are difficult to couple with genetic perturbations at scale.”
The paper relies on this without support.
Most studies are limited to measuring how single genetic perturbations affect chromatin state in bulk cell populations.
“Therefore, most studies are limited to measuring how single genetic perturbations affect chromatin state in bulk cell populations.”
The paper relies on this without support.
Co-varying accessibility between sets of regions bound by two trans-factors reflects common regulation, and inverse correlation indicates one factor controls the activity or expression of a negative regulator of the other set.
“For example, co-varying accessibility between sets of regions bound by two trans-factors may reflect common regulation. In contrast, inverse correlation may indicate that one factor controls the activity or expression of a negative regulator of the other set of regions.”
The paper relies on this without support.
Previous high-throughput genetic screens have mostly introduced genetic variants into cell populations and then selected for a narrow set of phenotypes.
“Previous high-throughput genetic screens have largely relied on the introduction of genetic variants in a population of cells (for example, using CRISPR/Cas9), followed by the selection of cells containing variants that confer a limited set of cellular phenotypes, such as increased cell growth or viability.”
The paper relies on this without support.
Finding genetic variants that control genome-wide chromatin states is difficult with standard selection and needs focused experiments that perturb candidate factors one at a time.
“identifying genetic variants that regulate genome-wide chromatin states, which are challenging to isolate with standard selection protocols, requires focused experiments, where candidate factors are perturbed one-by-one.”
The authors state this as an assumption.
The expected accessibility change for a double perturbation under non-interaction is the product of the single perturbation effects, equivalent to adding their log2 fold changes.
“The additive expectation was based on a multiplicative model of non-interaction, (i.e., CRISPR AB = CRISPR A x CRISPR B), which we calculated by adding the single perturbation fold changes in log2-space.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Experimental manipulations of TF levels24,25,26 or mutagenesis of natural enhancers27 demonstrate that single TFs cause non-trivial increases and decreases of the activity of TF-bound enhancers”
From Design principles of cell-state-specific enhancers in hematopoiesis (2025) · cites this paper as [25]
Experimental — accuracy not yet measuredIn the citing paper
“However, methods to capture changes in the epigenome following CRISPR perturbations have limited throughput6.”
From Profiling the genetic determinants of chromatin accessibility with scalable single-cell CRISPR screens (2021) · cites this paper as [6]
In the citing paper
“In addition, some biological processes are better assayed using alternative modalities, including mRNA splicing, chromatin state, protein level, and imaging, all of which are now being studied at scale with single cell CRISPR screens39-44.”
From Transcriptome-wide analysis of differential expression in perturbation atlases (2025) · cites this paper as [39]
In the citing paper
“Additionally, Perturb-ATAC [19] combines genome-wide chromatin accessibility profiling in single cells based on the simultaneous detection of CRISPR guide RNAs and open chromatin sites by assaying transposase-accessible chromatin with sequencing (ATAC-seq) and multiplexed CRISPR interference or knockout (KO).”
From A mini-review on perturbation modelling across single-cell omic modalities (2024) · cites this paper as [19]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Synergy example switches transcription-factor pairs
MinorThe text identifies EHF-ZNF750 as the high-synergy pair, then presents a dual EHF-JUNB knockout as an example of "this synergy." It is therefore unclear which pair the TP63 example is intended to support.
“EHF and JUNB had relatively low synergy, in contrast to the high synergy observed for EHF and ZNF750.”
“This synergy was particularly apparent at motif sites for TP63, a master regulator of epidermal homeostasis and differentiation, with dual EHF and JUNB knockout cells increasing TP63 accessibility far more than would be predicted from the single knockouts”
RT primer mix has incompatible concentrations
MinorThe same RT primer mix is described as 6 pM and as containing each sequencing oligo at 6 μM. A mix containing an oligo at 6 μM cannot have a total primer concentration of 6 pM.
“3.7μL 6 pM RT primer mix”
“6uM each of V1 GBC sequencing oligos or 6uM each of V1 sgRNA sequencing oligos”
Citations
Cites
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
BEDTools: a flexible suite of utilities for comparing genomic features
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Model-based Analysis of ChIP-Seq (MACS)
Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-9-r137 · Cited by 18,386
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Spatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055
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Self-citationIntegrative analysis of 111 reference human epigenomes
Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368
Improved vectors and genome-wide libraries for CRISPR screening
Sanjana, N. E., Shalem, O., & Zhang, F. (2014) · Nature Methods · doi:10.1038/nmeth.3047 · Cited by 5,239
Reversed graph embedding resolves complex single-cell trajectories
Qiu, X., Mao, Q., Tang, Y., Wang, L., Chawla, R., Pliner, H. A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4402 · Cited by 5,070
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1 of the 78 references couldn’t be matched to a known paper.
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Items 101–200 of 316
Functional characterization of human genomic variation linked to polygenic diseases
Fabo, T., & Khavari, P. A. (2023) · Trends in Genetics · doi:10.1016/j.tig.2023.02.014 · Cited by 32
Self-citationKim, H. S., Grimes, S. M., Chen, T., Sathe, A., Lau, B. T., Hwang, G., Bae, S., & Ji, H. P. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01949-8 · Cited by 31
Design principles of cell-state-specific enhancers in hematopoiesis
Frömel, R., Rühle, J., Bernal Martinez, A., Szu-Tu, C., Pacheco Pastor, F., Martinez-Corral, R., & Velten, L. (2025) · Cell · doi:10.1016/j.cell.2025.04.017 · Cited by 29
Morris, J. A., Sun, J. S., & Sanjana, N. E. (2023) · Trends in Genetics · doi:10.1016/j.tig.2023.10.012 · Cited by 31
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Metadata
- Authors
- Rubin, A. J., Parker, K. R., Satpathy, A. T., Qi, Y., Wu, B., Ong, A. J., Mumbach, M. R., Ji, A. L., Kim, D. S., Cho, S. W., Zarnegar, B. J., Greenleaf, W. J., Chang, H. Y., & Khavari, P. A.
- Affiliations
- Stanford Medicine (US)
- Chan Zuckerberg Biohub San Francisco (US)
- Howard Hughes Medical Institute (US)
- VA Palo Alto Health Care System (US)
- Corresponding author
- Chang, Howard Y.
- Khavari, Paul A.
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 176
- Issue
- 1-2
- Pages
- 361-376.e17
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC6329648
- OpenAlex
- W2950195808
- Cited by
- 314
- References
- 78
- Citations by year
- 2026: 31 · 2025: 50 · 2024: 40 · 2023: 37 · 2022: 52 · 2021: 44 · 2020: 42 · 2019: 17
- FWCI
- 9.16
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Scholarworks@UNIST (Ulsan National Institute of Science and Technology) (submittedVersion)
- Funders
- Veterans Affairs Office of Research and Development
- NIH (AR45192, AR43799, P50-HG007735, R35-CA209919)
- Parker Institute for Cancer Immunotherapy
- Scleroderma Research Foundation
- Stanford Bio-X
- Stanford
- Burroughs Wellcome Fund
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Keywords
- CRISPR interference, CRISPR knockout, chromatin accessibility, gene regulatory networks, transcription factor occupancy, nucleosome positioning, cis-regulatory elements, B cells, epidermal cells, non-coding RNA, genotype-phenotype correlation
- MeSH terms
- Chromatin, Humans, Transcription Factors, Sequence Analysis, DNA, Chromatin Assembly and Disassembly, Gene Regulatory Networks, Epigenomics, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, Clustered Regularly Interspaced Short Palindromic Repeats
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-04, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-04 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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