Efficient combinatorial targeting of RNA transcripts in single cells with Cas13 RNA Perturb-seq
Open accessPublished version of a preprint
Abstract
This work introduces CaRPool-seq that utilizes the RNA-targeting CRISPR–Cas13d system to perform combinatorial perturbations in single-cell screens. Pooled CRISPR screens coupled with single-cell RNA-sequencing have enabled systematic interrogation of gene function and regulatory networks. Here, we introduce Cas13 RNA Perturb-seq (CaRPool-seq), which leverages the RNA-targeting CRISPR–Cas13d system and enables efficient combinatorial perturbations alongside multimodal single-cell profiling. CaRPool-seq encodes multiple perturbations on a cleavable CRISPR array that is associated with a detectable barcode sequence, allowing for the simultaneous targeting of multiple genes. We compared CaRPool-seq to existing Cas9-based methods, highlighting its unique strength to efficiently profile combinatorially perturbed cells. Finally, we apply CaRPool-seq to perform multiplexed combinatorial perturbations of myeloid differentiation regulators in an acute myeloid leukemia (AML) model system and identify extensive interactions between different chromatin regulators that can enhance or suppress AML differentiation phenotypes.
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- Findingown data
In singlet cells, RNA and bcgRNA labels showed 99.2% concordance.
“Moreover, we observed extremely high concordance between RNA and bcgRNA labels in singlet cells (99.2%) (Fig. 1d,e).”
Quote in the paperSubstantial claim - Findingown data
Transcriptomic pseudobulk profiles for all 26 targeting gRNA groups showed decreased mRNA expression for each targeted transcript, including when three genes were perturbed simultaneously.
“When examining transcriptomic pseudobulk profiles for all 26 targeting gRNA groups, we observed decreased messenger RNA expression for each targeted transcript, even when perturbing transcripts of three genes simultaneously (15 examples in Extended Data Fig. 3f,g).”
Quote in the paperSubstantial claim - Findingown data
Indirect guide capture with an optimized configuration gave the strongest crRNA transcript detection among the tested methods, for both catalytically active and inactive Cas13 proteins.
“While all methods successfully induced robust knockdown (Fig. 1b), we found that indirect guide capture with an optimized configuration resulted in the strongest crRNA transcript detection ability (configuration X, Fig. 1c), for both catalytically active and inactive Cas13 proteins.”
Quote in the paperSubstantial claim - Findingown data
Knockdown strength was similar for multi-gRNA crRNA arrays compared with single-gRNA perturbations.
“Moreover, the strength of knockdown was similar for multi-gRNA crRNA arrays relative to single gRNA perturbations (Fig. 2b and Extended Data Fig. 3c–e).”
Quote in the paperSubstantial claim - Findingown data
Both synergistic and dampening effects were observed among dual perturbations.
“We also observed both synergistic and dampening effects.”
Quote in the paperSubstantial claim- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
- Findingown data
Cell cycle distributions did not differ between perturbed cells and NT control cells.
“Last, we classified cell cycles stage for each cell, and found no difference in cell cycle distributions (indicative of their proliferation state) compared to NT control cells (Extended Data Fig. 4d–f).”
Quote in the paperSubstantial claim - Findingown data
Alternative Cas9-based approaches struggled to efficiently identify and detect combinatorial perturbations compared with CaRPool-seq.
“Our benchmarking analysis found that, in contrast to CaRPool-seq, alternative Cas9-based approaches struggled to efficiently identify and detect combinatorial perturbations (Fig. 3b).”
Quote in the paperSubstantial claim - Findingown data
Differentially regulated gene modules were typically associated with genetic programs of myeloid cell differentiation and function.
“These differentially regulated gene modules were typically associated with genetic programs associated with the differentiation and function of myeloid cells (Extended Data Fig. 8c).”
Quote in the paperNot a substantial claim- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
- Findingown data
Fitting regression models to pairwise perturbations revealed a diversity of genetic interactions that clustered into four groups.
“Fitting these models to each of our pairwise perturbations revealed a diversity of genetic interactions, which we broadly clustered into four groups (Fig. 4g).”
Quote in the paperSubstantial claim- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
- Findingown data
Observed log2 fold changes for all perturbations were reproducible across technical replicate perturbations using independent gRNAs (R = 0.82).
“Observed log2 fold changes for all perturbations were also reproducible (R = 0.82, Extended Data Fig. 7f) across technical replicate perturbations when comparing effects measured for independent gRNAs.”
Quote in the paperSubstantial claim- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
- Findingown data
The CaRPool-seq experiments did not observe direct or indirect evidence for RfxCas13d promiscuous collateral activity.
“Lastly, while we did not observe direct or indirect evidence for RfxCas13d’s promiscuous collateral activity in our CaRPool-seq experiments, other RNA-targeting CRISPR effectors37,40,41 may represent an alternative for future experiments.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [37] Özcan, A. et al. Programmable RNA targeting with the single-protein CRISPR effector Cas7-11. Nature 597, 720–725 (2021). source
Not checked in the free run.
- cites [40] Tong, H. et al. High-fidelity Cas13 variants for targeted RNA degradation with minimal collateral effects. Nat. Biotechnol. https://doi.org/10.1038/s41587-022-01419-7 (2022). source
Not checked in the free run.
- cites [41] Wei, J. et al. Deep learning and CRISPR-Cas13d ortholog discovery for optimized RNA targeting. Preprint at bioRxiv https://doi.org/10.1101/2021.09.14.460134 (2022). source
Not checked in the free run.
- Findingown data
Eight genes with potential off-target binding sites for the three perturbations showed no significant expression change in the CaRPool-seq data.
“We identified eight genes whose sequence contains potential off-target binding sites for any of our three perturbations, but found that the expression of these genes was not significantly changed in our CaRPool-seq data (Extended Data Fig. 4a).”
Quote in the paperSubstantial claim - Findingown data
Differential gene expression signatures for single-gene perturbations from ECCITE-seq (5′ scRNA-seq, Cas9) were readily reproduced in CaRPool-seq (3′ scRNA-seq, Cas13d) data.
“We found that the differential gene expression signatures for single-gene perturbations obtained using ECCITE-seq (5′ scRNA-seq, Cas9 perturbation)23 can be readily reproduced in our CaRPool-seq (3′ scRNA-seq, Cas13d perturbation) data (Extended Data Fig. 8a,b).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [23] Wang, E. et al. Surface antigen-guided CRISPR screens identify regulators of myeloid leukemia differentiation. Cell Stem Cell 28, 718–731 (2021). source
Not checked in the free run.
- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
- Findingown data
Transcriptional responses varied widely when KDM1A knockdown was paired with different chromatin regulators.
“We generally observed that transcriptional responses varied widely when pairing KDM1A knockdown with different chromatin regulators.”
Quote in the paperSubstantial claim- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
- Findingown data
In KRAB-dCas9-MeCP2 THP1 cells, dual perturbation of KDM1A and HDAC3 synergistically upregulated CD11b and CD14 surface markers after 7 days.
“To further validate the synergistic relationship between KDM1A and HDAC3 we infected KRAB–dCas9–MeCP2 expressing THP1 cells with multiple independent sgRNAs and measured CD11b and CD14 cell surface protein expression 7 days postinfection using flow cytometry. Again, we observed synergistic upregulation of both surface markers (Fig. 4d) after dual perturbation.”
Quote in the paperSubstantial claim- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
- Rests on an assumption: CD14 and CD11b are immunophenotypic markers of monocyte differentiation.
- Findingown data
When EP300 and KDM1A were combinatorially perturbed, the EP300 signature appeared more strongly than the KDM1A signature.
“For example, we found that the EP300-signature appeared more strongly than the KDM1A-signature when combinatorial perturbing both genes (Extended Data Fig. 8g).”
Quote in the paperNot a substantial claim- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
- Findingown data
The increased sensitivity systematically improved the signal-to-noise ratio for distinguishing true bcgRNA UMI counts from spurious secondary bcgRNA UMI counts.
“The increased sensitivity also systematically improved the signal-to-noise ratio to distinguish true bcgRNA UMI counts from spurious secondary bcgRNA UMI counts (Extended Data Fig. 9d).”
Quote in the paperSubstantial claim - Findingown data
Many cluster 1 pairs represented perturbations of two proteins in the same complex (e.g., MED14/MED24, SUPT16H/SUPT6H), which showed similar gene expression responses for single and double perturbations.
“As a positive control, many of the pairs in this cluster represented perturbations of two proteins in the same complex (that is, MED14/MED24, SUPT16H/SUPT6H), which show similar gene expression responses for singly and doubly perturbed cells (Extended Data Fig. 8d–f).”
Quote in the paperNot a substantial claim- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
- Findingown data
Cluster 1 also included pairs of proteins in separate complexes (MED24/SMARCD1) that share similar perturbation signatures.
“This cluster also represented pairs of proteins residing in separate complexes (MED24/SMARCD1 of mediator and SWI/SNF complexes), which share similar perturbation signatures (Extended Data Fig. 8d).”
Quote in the paperNot a substantial claim- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
- Findingown data
Mitochondrial gene expression was not upregulated in cells receiving one or more targeting gRNAs compared with NT controls in the CaRPool-seq data.
“Therefore, we compared mitochondrial gene expression levels in cells that have received one or more targeting gRNAs compared to NT control cells (Extended Data Fig. 4c), and did not observe upregulation in our CaRPool-seq data.”
Quote in the paperSubstantial claim - Findingown data
When KDM1A was paired with RING1 perturbation, the KDM1A response signature dominated.
“In contrast, the KDM1A response signature dominated when paired with perturbation of the polycomb repressive complex member RING1 (Extended Data Fig. 8h).”
Quote in the paperNot a substantial claim- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
- Findingown data
Individual perturbations of selected target genes led to detectable CD11b expression changes after 3 days.
“Through individual perturbations with a flow-cytometry readout, we found that target gene perturbations led to detectable CD11b expression changes after 3 days (Extended Data Fig. 6i).”
Quote in the paperSubstantial claim- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
- Rests on an assumption: CD14 and CD11b are immunophenotypic markers of monocyte differentiation.
- Findingown data
The transcriptional response to dual KDM1A/HDAC3 perturbation placed cells at later segments of the integrated myeloid differentiation trajectory, showing enhanced differentiation compared with either single perturbation.
“This transcriptional response led dually perturbed cells to be distributed at later segments of our integrated myeloid differentiation trajectory, exhibiting enhanced differentiation compared to either single perturbation (Fig. 4i,j).”
Quote in the paperSubstantial claim- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
- Findingown data
The evopreQ1 pseudoknot element led to sixfold higher bcgRNA detection UMI counts compared with the initial design.
“In particular, the evopreQ1 pseudoknot element led to a sixfold higher bcgRNA detection UMI counts compared to our initial design (Extended Data Fig. 9c).”
Quote in the paperSubstantial claim - Findingown data
In the KRAB-dCas9-MeCP2 experiment, of 1,570 cells that received vectors targeting three genes, only 779 (49.6%) were associated with the correct three sgRNAs after sequencing.
“For example, in the KRAB–dCas9–MeCP2 experiment, we recovered 1,570 cells that received vectors targeting three genes. Among these cells, only 779 (49.6%) were associated with the correct three sgRNA after sequencing.”
Quote in the paperSubstantial claim - Findingown data
In cluster 4, genetic interactions were identified where one gene's effect appeared to dominate over the other.
“In cluster 4, we identified genetic interactions where one gene’s effect appeared to dominate over the other.”
Quote in the paperSubstantial claim- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
- Findingown data
Bulk RNA-seq after CD55 knockdown found no evidence for elevated Cas13d off-targets.
“We also performed CD55 target knockdown followed by more sensitive bulk RNA-seq and found no evidence for elevated levels of Cas13d off-targets (Extended Data Fig. 4b and Supplementary Table 3).”
Quote in the paperSubstantial claim - Findingown data
When considering cells with three successfully detected perturbations, all methods induced similarly strong depletion of all three surface proteins (Cas13d 74.5%, Cas9 75.5%, KRAB-dCas9 75.2%, KRAB-dCas9-MeCP2 77.3%).
“When considering these cells, all methods successfully induced a similarly strong depletion of all three surface proteins (Cas13d 74.5%, Cas9 75.5%, KRAB–dCas9 75.2%, KRAB–dCas9–MeCP2 77.3%) (Fig. 3c and Extended Data Fig. 5c,d).”
Quote in the paperSubstantial claim - Findingown data
The three targeting approaches (Cas13d, Cas9-nuclease, KRAB-dCas9-MeCP2) produced different numbers of differentially expressed genes in addition to CD55 reduction: 1 for Cas13d, 3 for Cas9, and 30 for KRAB-dCas9-MeCP2.
“The three approaches show a varying number of differentially expressed genes in addition to CD55 reduction (n = 1 Cas13d, n = 3 Cas9, n = 30 KRAB-dCas9-MeCP2).”
Quote in the paperSubstantial claim - Findingown data
Direct capture with pA-handle shows strongly reduced knockdown efficiency compared to regular gRNAs.
“Direct capture with pA-handle shows strongly reduced knockdown efficiency compared to regular gRNAs (standard condition).”
Quote in the paperSubstantial claim - Findingown data
Cell cycle stage assignment did not differ significantly between NT controls and populations targeting varying numbers of genes or using increasing numbers of gRNAs.
“Statistical testing for differences in cell cycle stage assignment for cell populations targeting e) varying numbers of target genes per cell, or using f) increasing numbers of gRNAs per cell did not show significant differences to NT control cell populations expressing zero targeting gRNAs”
Quote in the paperSubstantial claim - Findingown data
Indirect capture constructs show strong target gene knockdown similar to regular gRNAs for all three target genes.
“Indirect capture constructs show strong target gene knockdown similar to regular gRNAs (standard condition) for all three target genes.”
Quote in the paperSubstantial claim - Findingown data
Direct and indirect Cas13 guide RNA capture allow robust target knockdown.
“Extended Data Fig. 1 | Direct and indirect Cas13 guide RNA capture allow for robust target knockdown.”
Quote in the paperSubstantial claim - Findingown data
Across 26 differential gene expression analyses, none of the 13 mitochondrial genes showed significant expression differences from control cells (adjusted p < 0.05).
“Across all differential gene expression analyses (n = 26), none of the 13 mitochondrial genes encoded on the mitochondrial chromosome was expressed significantly different from control cells (adjusted p-value < 0.05; Wilcoxon’s rank sum test).”
Quote in the paperSubstantial claim - Findingown data
Significantly differentially regulated genes other than the target genes had a median expression change of 12.8% (s.d. 9.9%).
“Genes highlighted in black showed a median expression change of 12.8% (s.d. 9.9%).”
Quote in the paperNot a substantial claim - Findingown data
All 28 target genes showed consistent gRNA enrichment or depletion.
“Consistent gRNA enrichment/depletion of all 28 target genes.”
Quote in the paperSubstantial claim - Findingown data
CD14 was the most depleted gene, as expected.
“As expected, CD14 was the most depleted gene.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Wang et al.
Not checked in the free run.
- Findingown data
Differences in target gene knockdown between varying numbers of gRNAs per array are larger than differences between gRNA identities at the same total gRNA count, assuming comparable gRNA efficiencies.
“The Analysis suggests that target gene knockdown differences between the number of gRNAs per array are more pronounced than differences between gRNA identities with the same total gRNA count, given that gRNA efficiencies are comparable as shown in (c).”
Quote in the paperSubstantial claim - Findingown data
MALAT1 and NEAT1 (MENß) sequences required nucleotide exchanges to remove potential terminator sequences and enable full transcription by RNA polymerase III.
“Sequences found in MALAT1 and NEAT1 (MENß) required nucleotide exchanges (shown in red) to remove potential terminator sequences (≥ 4U) and allow the sequences to be fully transcribed by RNA polymerase III.”
Quote in the paperNot a substantial claim - Findingown data
Indirect capture constructs achieve strong knockdown of all three target genes, comparable to standard gRNAs.
“Indirect capture constructs show strong target gene knockdown like regular gRNAs (standard condition) for all three target genes.”
Quote in the paperSubstantial claim - Findingown data
Reverse transcription and amplification succeeded for Type-X crRNAs captured indirectly with a smallRNA PCR-handle and polyA-tail.
“Indirect capture of Type-X crRNAs with smallRNA PCR-handle and polyA-tail (arrow) allowed for reverse transcription and amplification.”
Quote in the paperSubstantial claim - Findingown data
GFI1 g2 did not lead to strong effects in the pooled screen and CaRPool-seq experiment.
“We noticed GFI1 g2 did not lead to strong effects in the pooled screen and in the CaRPool-seq experiment.”
- Also stated in: § CaRPool-seq data analysis · plain
“Comparing differential gene expression results for technical replicates embedded in the CaRPool-seq library, we noticed GFI1 g2 did not lead to upregulation of CD11b ADT or upregulation of the expected gene expression signature.”
Quote in the paperNot a substantial claim - Also stated in: § CaRPool-seq data analysis · plain
- Findingown data
Despite potential off-target binding, no transcriptomic perturbation was observed for these genes.
“In Extended Data Fig. 4a, we demonstrate that despite the potential for off-target binding, we do not observe transcriptomic perturbation for these genes.”
Quote in the paperSubstantial claim- Rests on an assumption: gRNA matches shorter than 17 bases do not cause target knockdown.
- Findingown data
In CaRPool-seq, 74.7% of cells were assigned a single bcgRNA.
“total cells n = 9,355, cells with single bcgRNA n = 6,986, (74.7%)”
Quote in the paperSubstantial claim - Findingown data
The evopreQ1 element increased bcgRNA detection sensitivity by an average of 6-fold (n = 4 per condition).
“The evopreQ1 element yielded on average 6-fold higher bcgRNA detection sensitivity (n = 4 per condition).”
Quote in the paperSubstantial claim - Findingown data
Cas13-mediated knockdown shows a unimodal response, suggesting limited cell-to-cell differences in target gene knockdown.
“Importantly, the Cas13-mediated function shows a unimodal response, suggesting limited cell-to-cell differences in target gene knockdown.”
Quote in the paperSubstantial claim - Findingown data
Knockdown via direct capture using a polyA-tail sequence is limited.
“Target knockdown with direct capture through a polyA-tail sequence is limited.”
Quote in the paperSubstantial claim - Findingown data
All but two gene pairs showed elevated CD11b levels.
“All but two gene pairs show elevated levels of CD11b.”
Quote in the paperNot a substantial claim - Interpretationreasoning
When two or more perturbations are performed simultaneously, the fraction of cells in which all perturbations are both successfully introduced and successfully detected can decrease dramatically.
“Therefore, when performing two or more simultaneous perturbations, the fraction of cells where all perturbations are both successfully introduced and successfully detected can decrease dramatically.”
Quote in the paperSubstantial claim - Interpretationreasoning
The indirect detection strategy is well suited for delivering multiple gRNAs into one cell together with a detectable bcgRNA that encodes their collective identity.
“Notably, our strategy for indirect detection is well-suited for delivering multiple gRNAs into a single cell alongside a detectable bcgRNA that encodes the collective identity of these perturbations.”
Quote in the paperSubstantial claim - Interpretationown data
Compared with existing Cas9-based methods, CaRPool-seq has a distinctive advantage in efficiently profiling cells that receive combinatorial perturbations.
“We compared CaRPool-seq to existing Cas9-based methods, highlighting its unique strength to efficiently profile combinatorially perturbed cells.”
Quote in the paperSubstantial claim - Premisecitation
Undetected or incorrectly assigned single-guide RNAs can affect up to 20% of cells.
“For example, undetected or incorrectly assigned single-guide RNAs can affect up to 20% of cells6; this is compounded when multiple independent sgRNAs are introduced and independently detected in each cell.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [6] Replogle, J. M. et al. Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing. Nat. Biotechnol. 38, 954–961 (2020). source
Not checked in the free run.
- Interpretationreasoning
Using a unique reverse transcription handle and Illumina PCR priming sequence in the modified crRNA allows perturbations to be detected alongside scRNA-seq and additional modalities such as CITE-seq.
“In addition, using a unique set of reverse transcription handle and Illumina PCR priming sequence in our modified crRNA (Extended Data Fig. 2) ensures that these perturbations can be detected not only alongside scRNA-seq, but also when profiling additional molecular modalities (for example, CITE-seq12 for simultaneous transcriptome and surface protein profiling).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [12] Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017). source
Not checked in the free run.
- Interpretationnone given
CaRPool-seq places multiple perturbations on a cleavable CRISPR array linked to a detectable barcode, which allows several genes to be targeted at once.
“CaRPool-seq encodes multiple perturbations on a cleavable CRISPR array that is associated with a detectable barcode sequence, allowing for the simultaneous targeting of multiple genes.”
Quote in the paperSubstantial claim - Interpretationown data
RfxCas13d crRNAs can be modified by adding a common reverse transcription handle either directly to the gRNA or as a separate bcgRNA in a CRISPR array, enabling reverse transcription and amplification.
“These results demonstrate that RfxCas13d crRNAs can be modified by adding a common reverse transcription handle either directly to the gRNA or as a separate bcgRNA as part of a CRISPR array, allowing for reverse transcription and amplification.”
Quote in the paperSubstantial claim- Rests on an assumption: Cas13 crRNAs lack a common priming site for reverse transcription.
- Interpretationown data
CaRPool-seq enables pooled perturbation screens to be efficiently and accurately demultiplexed into a single-cell readout.
“These numbers demonstrate that CaRPool-seq enables pooled perturbation screens that can be efficiently and accurately demultiplexed into a single-cell readout.”
Quote in the paperSubstantial claim - Premisecitation
RfxCas13d can process a CRISPR array into multiple mature CRISPR RNAs.
“Notably, RfxCas13d is also capable of processing a CRISPR array into multiple mature CRISPR RNAs (crRNAs)10, presenting an attractive option for combinatorial perturbations at the RNA level.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [10] Konermann, S. et al. Transcriptome engineering with RNA-targeting type VI-D CRISPR effectors. Cell 173, 665–676 (2018). source
Not checked in the free run.
- Interpretationreasoning
The strategy is well suited for combinatorial perturbations whose identity is encoded in a single barcode reliably detected alongside multiple modalities including scRNA-seq and CITE-seq.
“We demonstrate that this strategy is well-suited for performing combinatorial perturbations, whose identity is encoded in a single barcode that can be reliably detected alongside multiple molecule modalities including scRNA-seq and CITE-seq.”
Quote in the paperSubstantial claim - Interpretationown data
Benchmarking shows CaRPool-seq is more efficient and accurate than Cas9-based technologies when assigning multiple perturbations in single cells.
“Through benchmarking, we show that CaRPool-seq is more efficient and accurate when assigning multiple perturbations in single cells when compared to Cas9-based technologies.”
Quote in the paperSubstantial claim - Interpretationreasoning
Because CaRPool-seq associates combinatorial perturbations with a single bcgRNA, its detection efficiency does not vary between single and multiple perturbations.
“Since CaRPool-seq associates combinatorial perturbations with a single bcgRNA, the efficiency of detection does not vary between single and multiple perturbations.”
Quote in the paperSubstantial claim - Interpretationown data
The modified bcgRNA structure further improves CaRPool-seq performance.
“This modified bcgRNA structure therefore further improves the performance of CaRPool-seq, and is recommended for future experiments.”
Quote in the paperSubstantial claim - Study design and conductcitation
The selected gRNAs had no secondary target sites with 0-2 mismatches to the cognate site.
“Selected gRNAs had no secondary target sites with 0–2 mismatches to the cognate site42.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [42] Guo, X. et al. Transcriptome-wide Cas13 guide RNA design for model organisms and viral RNA pathogens. Cell Genomics 1, 100001 (2021). source
Not checked in the free run.
- Interpretationcitation
The absence of observed collateral activity likely reflects controlled delivery of Cas13d and gRNAs via single-integration lentiviral systems rather than transient overexpression.
“Our findings likely reflect CaRPool-seq’s controlled delivery of Cas13d and gRNAs using single integration lentiviral systems instead of transient overexpression, which is consistent with the suggestion that lower and more controlled Cas13 expression can mitigate collateral activity16.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [16] Kelley, C. P., Haerle, M. C. & Wang, E. T. Negative autoregulation mitigates collateral RNase activity of repeat-targeting CRISPR-Cas13d in mammalian cells. Cell Rep. 40, 111226 (2022). source
Not checked in the free run.
- Interpretationreasoning
Cas13 cleavage may produce RNA molecules that are detectable by scRNA-seq but cannot be translated into functional protein, so measured RNA knockdown may underestimate the phenotypic effect of Cas13 perturbation.
“Further, analogous to how Cas9-nuclease targeting often produces RNAs degraded by nonsense mediated decay13, it is possible that Cas13 cleavage produces RNA molecules that can be detected by scRNA-seq but cannot be translated into functional protein14, suggesting that the level of measured RNA knockdown underestimates the phenotypic effect of Cas13 perturbation.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [13] Tuladhar, R. et al. CRISPR-Cas9-based mutagenesis frequently provokes on-target mRNA misregulation. Nat. Commun. 10, 1–10 (2019). source
Not checked in the free run.
- cites [14] Burris, B. J. D., Molina Vargas, A. M., Park, B. J. & O’Connell, M. R. Optimization of specific RNA knockdown in mammalian cells with CRISPR-Cas13. Methods 206, 58–68 (2022). source
Not checked in the free run.
- Rests on an assumption: Target RNAs are continuously produced and degraded before translation.
- Study design and conductown data
The CaRPool-seq library had correct gene pair to bcgRNA linkage greater than 94%.
“Complete library representation with minimal bias (90th percentile/10th percentile crRNA read ratio 2.6/4.8), and correct gene pair to bcgRNA linkage (>94%) was verified by Illumina sequencing (MiSeq).”
Quote in the paperSubstantial claim - Interpretationnone given
The optimized strategy delivers multiple gRNAs as part of a single CRISPR array that is subsequently cleaved into individual crRNAs.
“We introduced an optimized strategy to deliver multiple gRNA as part of a single CRISPR array, which is subsequently cleaved into individual crRNAs.”
Quote in the paperSubstantial claim - Interpretationreasoning
CaRPool-seq is a flexible method for CRISPR-Cas13 RNA-targeting screens with a single-cell sequencing readout.
“Here, we present CaRPool-seq, a flexible method for performing CRISPR–Cas13 RNA-targeting screens with a single-cell sequencing-based readout.”
Quote in the paperSubstantial claim - Interpretationown data
Structured RNAs can improve bcgRNA detection in CaRPool-seq.
“Structured RNAs can improve bcgRNA detection in CaRPool-seq experiments.”
Quote in the paperSubstantial claim - Interpretationown data
Using CRISPR arrays with multiple gRNAs against the same target may further enhance target knockdown.
“CRISPR arrays encoding multiple gRNAs against the same target may be used to further enhance target knockdown.”
Quote in the paperSubstantial claim - Interpretationown data
The results indicate that indirect gRNA capture can be achieved using polyA-tail capture instead of CS1-based capture.
“These results show that indirect gRNA capture can be facilitated with polyA-tail capture as an alternative to CS1-based capture.”
Quote in the paperSubstantial claim - Interpretationown data
CaRPool-seq enables specific target RNA knockdown.
“CaRPool-seq enables efficient bcgRNA capture and specific target RNA knockdown.”
Quote in the paperSubstantial claim - Interpretationown data
The observed expression changes are independent of target RNA and gRNA amounts.
“indicating that observed changes are independent of target RNA and gRNA amounts.”
Quote in the paperSubstantial claim - Interpretationown data
CaRPool-seq enables efficient bcgRNA capture.
“CaRPool-seq enables efficient bcgRNA capture and specific target RNA knockdown.”
Quote in the paperSubstantial claim
- May combine two claims. 39
- The quote only partly states this claim. 1
- Classified as probably not a substantial claim. 25
May combine two claims.
Findingown dataIn an AML model system, CaRPool-seq multiplexed combinatorial perturbations of myeloid differentiation regulators and found extensive interactions among chromatin regulators that can enhance or suppress AML differentiation phenotypes.
“Finally, we apply CaRPool-seq to perform multiplexed combinatorial perturbations of myeloid differentiation regulators in an acute myeloid leukemia (AML) model system and identify extensive interactions between different chromatin regulators that can enhance or suppress AML differentiation phenotypes.”
Quote in the paperSubstantial claim- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
The quote only partly states this claim.
Findingown dataTargeting CD46, CD55, or CD71 with Cas13 produced an average 76.5% (±5.7%) reduction in protein levels for each targeted gene.
“We observed, on average, a 76.5% (±5.7%) mean reduction in protein levels for each targeted gene after perturbation with Cas13 demonstrating clear evidence of robust molecular perturbation (Fig. 2b–d).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataIn a species-mixing experiment of 2,387 cells, 78.5% expressed a single bcgRNA, 1.1% expressed more than one bcgRNA, and 20.4% had no detected bcgRNA.
“Of 2,387 cells, we found that 78.5% expressed a single bcgRNA (1.1% >1 bcgRNAs; 20.4% no detected bcgRNA).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataIn the CITE-seq experiment, 9,355 single-cell profiles were obtained; 74.7% expressed a single bcgRNA and 80.8% expressed at least one bcgRNA.
“We obtained 9,355 single-cell profiles and demultiplexed them into groups based on the detected bcgRNA (Extended Data Fig. 3b; 74.7% expressed a single bcgRNA, 80.8% expressed at least one bcgRNA).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataDually perturbed EP300/KDM1A cells had higher expression of progenitor genes (AZU1) and reduced expression of differentiated marker genes (S100A4) compared with KDM1A perturbation alone.
“Dually perturbed cells exhibited higher expression of progenitor genes (that is the progenitor marker AZU1), and reduced expression of differentiated marker genes (that is, myeloid marker S100A4) compared to individual KDM1A perturbation.”
Quote in the paperSubstantial claim- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
May combine two claims.
Findingown dataCaRPool-seq and Perturb-seq cells clustered together and grouped by gRNA identity based on ADT levels, indicating similar strength of phenotypic protein perturbation across methods.
“CaRPool-seq and Perturb-seq cells clustered together (Fig. 3d), and grouped by gRNA identity (Fig. 3e and Extended Data Fig. 5e), again demonstrating that the strength of phenotypic protein perturbation was similar across all methods.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataEP300 knockdown (log2FC 1.57) upregulated CD11b, but dual perturbation with KDM1A (log2FC 2.05) was weaker than KDM1A knockdown alone.
“In contrast, while individual knockdown of EP300 also leads to CD11b upregulation (log2FC 1.57), dual perturbation with KDM1A (log2FC 2.05) was weaker than the individual KDM1A knockdown.”
Quote in the paperNot a substantial claim- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
- Rests on an assumption: CD14 and CD11b are immunophenotypic markers of monocyte differentiation.
May combine two claims.
Findingown dataFor 33 gene pairs in cluster 1, each individual gene contributed equally to the dual perturbation response and the linear model fit strongly.
“For 33 gene pairs in cluster 1, we saw that each individual gene’s profile contributed equally to the dual perturbation response and the linear model exhibited a strong fit.”
Quote in the paperNot a substantial claim- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
Probably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenAlternative polymerases KAPA and Q5 can lead to stronger bias in relative array abundance during library cloning.
“During library cloning, we noticed two critical details: alternative polymerase KAPA and Q5 can lead to a stronger bias in relative array abundance.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataCas13d-mediated gene knockdown did not introduce notable off-target effects or deleteriously alter cellular fitness.
“We performed several analyses to demonstrate that Cas13d-mediated gene knockdown does not introduce notable off-target effects or deleteriously alter cellular fitness.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataTwo stabilizing RNA elements (Zika virus-derived xrRNA1 dumbbell and evopreQ1 pseudoknot) led to a robust increase in bcgRNA UMI counts, while protein knockdown was indistinguishable among six structures.
“While protein knockdown was indistinguishable for all six structures (Fig. 5b,c and Extended Data Fig. 9b), we found that two elements (Zika virus-derived xrRNA1 dumbbell33; evopreQ1 pseudoknot34) led to a robust increase in bcgRNA unique molecular identifier (UMI) counts (Fig. 5d).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [33] Akiyama, B. M. et al. Zika virus produces noncoding RNAs using a multi-pseudoknot structure that confounds a cellular exonuclease. Science 354, 1148–1152 (2016). source
Not checked in the free run.
- cites [34] Anzalone, A. V., Lin, A. J., Zairis, S., Rabadan, R. & Cornish, V. W. Reprogramming eukaryotic translation with ligand-responsive synthetic RNA switches. Nat. Methods 13, 453–458 (2016). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Findingnone givenReducing PCR cycles while increasing oligo pool input amounts can decrease bcgRNA reassortment.
“Further, reducing the number of PCR cycles with increased oligo pool input amounts can decrease bcgRNA reassortment.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataKDM1A knockdown (log2FC 2.41) and HDAC3 knockdown (log2FC 0.53) individually upregulated CD11b, and dual perturbation produced a synergistic effect (log2FC 2.85).
“For example, individual knockdown of the histone demethylase KDM1A (log2fold change (FC) 2.41) and the histone deacetylase HDAC3 (log2FC 0.53) lead to strong and weak CD11b upregulation, respectively, but dual perturbation led to a synergistic effect (log2FC 2.85).”
Quote in the paperSubstantial claim- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
- Rests on an assumption: CD14 and CD11b are immunophenotypic markers of monocyte differentiation.
May combine two claims.
Findingown dataDual perturbation of HDAC3 enhanced the KDM1A transcriptional response signature, consistent with immunophenotypic results.
“Dual perturbation of HDAC3 enhanced the KDM1A transcriptional response signature (Fig. 4h), consistent with our previously described immunophenotypic results for these cells (Fig. 4c).”
Quote in the paperSubstantial claim- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
May combine two claims.
Findingown dataEach single-gene perturbation affected CD11b expression, and observed log2 fold changes were strongly correlated with gRNA enrichment from bulk CRISPR screens (R = 0.86).
“As expected, we found that each single-gene perturbation affected CD11b expression, with observed log2 fold changes that were in strong agreement with the level of gRNA enrichment from bulk CRISPR screens (R = 0.86; Fig. 4b, Extended Data Fig. 7c–e and Supplementary Table 5).”
Quote in the paperSubstantial claim- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
- Rests on an assumption: CD14 and CD11b are immunophenotypic markers of monocyte differentiation.
May combine two claims.
FindingreasoningThe average transcriptomic knockdown was 65% (s.d. 8.7%), consistently lower than the observed protein reduction.
“The average strength of transcriptomic knockdown (mean 65%, s.d. 8.7%) was consistently reduced compared to the observed protein reduction.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAdditional synergistic combinations HDAC3/GFI and HDAC3/GSE1 enhanced immunophenotypic marker expression and transcriptional differentiation state.
“Moreover, we identified additional synergistic combinations between HDAC3/GFI and HDAC3/GSE1, both of which enhanced the expression of immunophenotypic markers (Fig. 4c), as well as transcriptional differentiation state (Fig. 4j).”
Quote in the paperSubstantial claim- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
- Rests on an assumption: CD14 and CD11b are immunophenotypic markers of monocyte differentiation.
May combine two claims.
Findingown dataFor the 158 dual-gene perturbations, effects were strongly correlated with the corresponding single perturbations; dual perturbation was typically stronger than the average of individual knockdowns but weaker than the product.
“We next compared the observed effects of the 158 dual gene perturbations to the effects resulting from the two corresponding single perturbations. We observed a strong correlation and found that the dual perturbation was typically stronger than the average of individual knockdowns, but weaker than the product (Fig. 4c).”
Quote in the paperSubstantial claim- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
May combine two claims.
Findingown dataIn a bulk Cas13d CRISPR screen of 439 genes, 26 genes were selected that consistently influenced differentiation across multiple independent gRNAs and had been identified in orthogonal pooled Cas9 screens.
“By comparing gRNA representation between low and high-expressing bins, we selected 26 genes that influenced differentiation consistently across multiple independent gRNAs and that have also been identified previously in orthogonal pooled Cas9 screens23 (Extended Data Fig. 6a–h and Supplementary Table 4).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [23] Wang, E. et al. Surface antigen-guided CRISPR screens identify regulators of myeloid leukemia differentiation. Cell Stem Cell 28, 718–731 (2021). source
Not checked in the free run.
- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
May combine two claims.
Findingown dataIn an integrated trajectory with hematopoietic reference data, NT control cells localized to early points, while single perturbations (KDM1A, GFI1, GSE1) pushed cells further down the differentiation trajectory.
“We found that NT control cells localized to early points, but that single perturbations (that is, KDM1A, GFI1, GSE1) pushed cells further down the differentiation trajectory (Fig. 4f), consistent with their role in enhancing leukemic differentiation.”
Quote in the paperSubstantial claim- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
Probably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThe THP1 CaRPool-seq experiment detected 52,496 single cells after filtering.
“For the THP1 experiment we detected 52,496 single cells (nFeature_RNA > 1,000, nFeature_RNA < 8,000, percent.mt < 20) after HTO demultiplexing using HTOdemux as described above.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThe direct capture Perturb-seq analysis detected 16,842 cells.
“We detected 16,842 cells.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataDirect capture constructs with CS1 or CS2 enable strong knockdown for CD46, but reduced knockdown for CD55 and CD71.
“Direct capture constructs with CS1 or CS2 enable strong knockdown for CD46, but reduced knockdown for CD55 and CD71.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataThe final bcgRNA amplicon is 203 bp and can be sequenced with standard Illumina sequencing primers.
“The final bcgRNA amplicon (203 bp) can be sequenced with standard Illumina sequencing primers (≥28 cycles read1 and ≥15 cycles read2) (Extended Data Figs. 2 and 3a).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataTarget gene enrichments for CD14 upregulation correlated best with CD14 target gene enrichments from Wang et al., and correlations were similarly high for the top-ranked 4-5 gRNAs.
“This analysis indicated that, as expected, target gene enrichments for CD14 upregulation correlated best with CD14 target gene enrichments in Wang et al., and that correlations were similarly high for the top-ranked 4-5 gRNA.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Wang et al.
Not checked in the free run.
May combine two claims.
Findingown dataThe heatmaps show a range of interactions between gene pairs, including equal contributions from both genes and dominance by one gene's perturbation signature.
“These heatmaps visualize a range of observed interactions between gene pairs, including cases where genes contribute equally to the dual perturbation response (d-f), and where one gene’s perturbation signature dominates over the other (g-h).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown data31,308 cells were assigned a single bcgRNA.
“We assigned 31,308 with a single bcgRNA.”
Quote in the paperNot a substantial claimMay combine two claims.
FindingreasoningModel-based bcgRNA assignments (HTODemux or MultiSeqDemux) did not yield satisfying results supported by observed phenotypic changes, likely due to model limitations from high bcgRNA feature number.
“Model-based bcgRNA assignments (HTODemux or MultiSeqDemux) did not yield satisfying results supported by the observed phenotypic changes, likely due to model limitations imposed by the high number of bcgRNA features.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
FindingcitationLog2FC enrichments were generally more pronounced in samples using the enhanced direct repeat.
“As we have previously described11, we noticed that log2FC enrichments were generally more pronounced in samples using the enhanced direct repeat.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [11] Wessels, H. H. et al. Massively parallel Cas13 screens reveal principles for guide RNA design. Nat. Biotechnol. 38, 722–727 (2020). source
Not checked in the free run.
May combine two claims.
InterpretationreasoningCaRPool-seq uses RNA-targeting Cas13d and permits efficient combinatorial perturbations together with multimodal single-cell profiling.
“Here, we introduce Cas13 RNA Perturb-seq (CaRPool-seq), which leverages the RNA-targeting CRISPR–Cas13d system and enables efficient combinatorial perturbations alongside multimodal single-cell profiling.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationThe authors previously confirmed that RfxCas13d can cause strong target-RNA knockdown and derived optimal targeting rules from thousands of guide RNAs tiling multiple transcripts.
“Recently, we confirmed that RfxCas13d can lead to striking target-RNA knockdown, and learned a set of optimal targeting rules from thousands of guide RNAs tiling multiple transcripts11.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [11] Wessels, H. H. et al. Massively parallel Cas13 screens reveal principles for guide RNA design. Nat. Biotechnol. 38, 722–727 (2020). source
Not checked in the free run.
May combine two claims.
PremisecitationCas9 perturbations are not uniformly efficient, and a considerable fraction of targeted cells may show no phenotypic effect.
“Moreover, perturbations introduced by Cas9 are not uniformly efficient, and a considerable fraction of targeted cells may exhibit no phenotypic effects of perturbation8,9.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [8] Michlits, G. et al. Multilayered VBC score predicts sgRNAs that efficiently generate loss-of-function alleles. Nat. Methods 17, 708–716 (2020). source
Not checked in the free run.
- cites [9] Papalexi, E. et al. Characterizing the molecular regulation of inhibitory immune checkpoints with multimodal single-cell screens. Nat. Genet. 53, 322–331 (2021). source
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationCombinatorial perturbations can reveal epistasis and other genetic interactions.
“In particular, the ability to perform combinatorial perturbations represents an opportunity to decode complex regulatory networks, with pioneering work demonstrating the ability to identify epistasis and other genetic interactions5–7.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [5] Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1882 (2016). source
Not checked in the free run.
- cites [6] Replogle, J. M. et al. Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing. Nat. Biotechnol. 38, 954–961 (2020). source
Not checked in the free run.
- cites [7] Norman, T. M. et al. Exploring genetic interaction manifolds constructed from rich single-cell phenotypes. Science 365, 786–793 (2019). source
Not checked in the free run.
May combine two claims.
Interpretationown dataThese findings support and provide a molecular explanation for observations that combination therapies of KDM1A antagonist and HDAC inhibitors show enhanced response.
“These findings support and provide a molecular explanation for recent observations that combination therapies of KDM1A antagonist and HDAC inhibitors exhibit an enhanced response29.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [29] Fiskus, W. et al. Highly effective combination of LSD1 (KDM1A) antagonist and pan-histone deacetylase inhibitor against human AML cells. Leukemia 28, 2155–2164 (2014). source
Not checked in the free run.
- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
May combine two claims.
InterpretationcitationDual perturbation of KDM1A and GSE1 fell in cluster 1, consistent with a cooperative interaction via colocalization at repressed promoters to inhibit myeloid differentiation.
“Dual perturbation of KDM1A and the transcriptional repressor GSE1 also fell in this cluster (Extended Data Fig. 8f), consistent with previous work that suggests a cooperative interaction via colocalization at repressed promoters to inhibit myeloid differentiation28.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [28] Nicosia, L. et al. Pharmacological inhibition of LSD1 triggers myeloid differentiation by targeting GSE1 oncogenic functions in AML. Oncogene https://doi.org/10.1038/s41388-021-02123-7 (2021). source
Not checked in the free run.
- Rests on an assumption: The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataThe single-gRNA pooled screen included four independent transductions, with CD14 sorts for all four and CD11b sorts for three.
“In total, we prepared four independent transductions (two multiplicities of infection (MOI) and two alternative direct repeats), performed CD14 sorts for all four transduction replicates, and CD11b sorts for three transduction replicates collecting 1 × 106 to 1.5 × 106 cells per bin.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenDuring pooled screens, guide representation was maintained above 1,000×.
“cells were split as needed always maintaining a guide representation of >1,000×.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductreasoningThe CaRPool-seq library contained 385 arrays with 186 single or double perturbations, each represented by two independent technical replicate gRNA combinations.
“In total, we design 385 arrays with 186 single or double perturbations, each represented by two independent technical replicate gRNA combinations.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationNelson et al. reported that structured RNA motifs on pegRNAs protect from exonuclease degradation, increase stability, and enhance efficiency.
“While our paper was in review, Nelson et al.30 reported that the inclusion of structured RNA motifs on prime editing gRNAs (pegRNAs) led to protection from exonuclease degradation, increased stability, and enhanced efficiency.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [30] Nelson, J. W. et al. Engineered pegRNAs improve prime editing efficiency. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-01039-7 (2021). source
Not checked in the free run.
May combine two claims.
Study design and conductown dataThe CaRPool-seq library achieved complete representation with minimal bias, with 90th/10th percentile crRNA read ratios of 2.6/4.8.
“Complete library representation with minimal bias (90th percentile/10th percentile crRNA read ratio 2.6/4.8), and correct gene pair to bcgRNA linkage (>94%) was verified by Illumina sequencing (MiSeq).”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationIn prior Cas9 screens, 240 genes led to CD11b or CD14 upregulation.
“We selected 240 genes that led to CD11b or CD14 upregulation in Cas9 screens23, in addition to 199 control genes in TLR4-signaling.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [23] Wang, E. et al. Surface antigen-guided CRISPR screens identify regulators of myeloid leukemia differentiation. Cell Stem Cell 28, 718–731 (2021). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataThe combinatorial perturbation pooled screen was performed in three transduction replicates at MOI 0.13-0.20.
“For the combinatorial perturbation pooled screen, we performed three transduction replicates (MOI 0.13–0.20).”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductown dataThe single-gRNA library achieved complete representation with minimal bias, with a 90th/10th percentile crRNA read ratio of 1.8 for both libraries.
“Complete library representation with minimal bias (90th percentile/10th percentile crRNA read ratio of 1.8 for both libraries) was verified by Illumina sequencing (MiSeq).”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataThe observed drop-off in combinatorial perturbation detection is consistent with the theoretical expectation for multiple independently detected gRNAs and highlights the challenge of profiling multiple perturbations with existing approaches.
“This observed drop-off is fully consistent with the theoretical expectation of recovery for multiple independently detected gRNAs and highlights the challenge of efficiently profiling multiple perturbations with existing approaches.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataCaRPool-seq and Perturb-seq can both effectively introduce combinatorial perturbations into single cells, but CaRPool-seq has clear advantages in successfully identifying and detecting these perturbations.
“We conclude that CaRPool-seq and Perturb-seq can both effectively introduce combinatorial perturbations into single cells. However, CaRPool-seq exhibits clear advantages in the ability to successfully identify and detect these perturbations and therefore represents an attractive approach for performing combinatorial single-cell CRISPR screens.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
PremisecitationcrRNAs are typically protected from degradation while complexed with Cas13, but the ends of longer bcgRNA molecules may still be accessible and susceptible to degradation.
“While we expect that crRNA are typically protected from degradation while complexed with Cas13 (refs. 31,32), the ends of longer bcgRNA molecules may still be accessible and susceptible to degradation.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [31] Méndez-Mancilla, A. et al. Chemically modified guide RNAs enhance CRISPR-Cas13 knockdown in human cells. Cell Chem. Biol. https://doi.org/10.1016/j.chembiol.2021.07.011 (2021). source
Not checked in the free run.
- cites [32] Zhang, C. et al. Structural basis for the RNA-guided ribonuclease activity of CRISPR-Cas13d. Cell 175, 212–223 (2018). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe bcgRNAs were designed as random 15mer sequences with hamming distance greater than four to one another.
“For the bcgRNAs, we designed random 15mer sequences with hamming distance greater than four to one another.”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductown dataCell survival after selection ranged between 1.7 and 5.5% at MOI <0.1, assuring high single integration probability.
“Cell survival after selection ranged between 1.7 and 5.5% (MOI < 0.1) assuring a high single integration probability.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataMore than 98% of cells were BFP-positive before the 10X experiment.
“We confirmed that >98% of cells were BFP-positive before the 10X experiment.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataCell viability was ≥96% before the direct capture Perturb-seq run.
“Before the run, cell viability was determined (≥96%).”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationThe linear regression model δab = c1δa + c2δb + ε was used to decompose double perturbation profiles, as previously introduced.
“As previously introduced7, we model the average z-scale profiles using: δab=c1δa+c2δb+ϵ with δa and δb being the pseudobulk z-scaled profile for cells assigned to single perturbations a and b, repectively, while δab is the pseudobulk z-scaled profile for cells assigned to double perturbation ab.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [7] Norman, T. M. et al. Exploring genetic interaction manifolds constructed from rich single-cell phenotypes. Science 365, 786–793 (2019). source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataCell viability was ≥95% for each CaRPool-seq experiment before the runs.
“Before the runs, cell viability was determined ≥95% for each experiment.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenThe Illumina 5′ PCR handle allows efficient sequencing of the bcgRNA amplicon with the first base of read2 being the first barcode base.
“Moreover, usage of the Illumina 5′ PCR handle allows for efficient sequencing of the bcgRNA amplicon with the first base of read2 being the first barcode base.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataFor the single-gRNA pooled screen, the average mapping rate was 82.3% and the median gRNA count was 167.
“string-match intersection with the reference to retain only perfectly matching alignments (average mapping rate 82.3%, median gRNA count 167).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataFor the combinatorial pooled screen, the average mapping rate was 97%, the median barcode read count was 635, and one barcode was not detected in input samples.
“preprocessed reads were aligned to the barcode reference using bowtie43 (v.1.1.2) with parameters -v 1 -m 1–best –strata (average mapping rate 97%; median barcode read count 635; one barcode was not detected in input samples).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningThe bcgRNA design allowed specific amplification of a bcgRNA amplicon with a unique combination of forward and reverse primers.
“This composition allowed the specific amplification of a bcgRNA amplicon with a unique combination of forward and reverse primers.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductcitationRaw counts were normalized using median of ratios as in DESeq2 and batch corrected using ComBat.
“For each dataset, raw counts were normalized using a median of ratios method as in DESeq2 (ref. 44) and batch corrected using combat implemented in SVA (v.3.34.0)45.”
Quote in the paperSubstantial claim
The paper relies on this without support.
Cas13 crRNAs lack a common priming site for reverse transcription.
“Type VI-A, C and D Cas13 crRNAs consist of a short 5′ direct repeat and a variable spacer (also called gRNA) at the 3′ end, and therefore lack a common priming site for reverse transcription (RT).”
The paper relies on this without support.
Target RNAs are continuously produced and degraded before translation.
“Target RNAs are continuously being produced and degraded before the target can be translated into protein.”
The paper relies on this without support.
The THP1 MLL-AF9 NRASG12D cell line is a valid model system for AML differentiation.
“We generated a human MLL-AF9 NRASG12D AML cell line (THP1 cells), with a stably integrated doxycycline-inducible Cas13d cassette, as a model system.”
The paper relies on this without support.
CD14 and CD11b are immunophenotypic markers of monocyte differentiation.
“cells were sorted into bins based on their surface expression of CD14 and CD11b, immunophenotypic markers of monocyte differentiation.”
The paper relies on this without support.
gRNA matches shorter than 17 bases do not cause target knockdown.
“Second, candidates were further filtered to match with at least 17 bases, as shorter matches do not lead to target knockdown and show a blastn e.value of <100.”
Claims other papers make about this one
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High multiplicity-of-infection CRISPR screens with multiple targets per cell can complicate the calculation of distances.
“For example, high multiplicity-of-infection CRISPR screens with multiple targets per cell can complicate the calculation of distances5,53,54.”
From scPerturb: harmonized single-cell perturbation data (2024) · cites this paper as [5]
Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106 · see citing works
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849 · see citing works
Integrated analysis of multimodal single-cell data
Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zagar, M., et al. (2020) · doi:10.1101/2020.10.12.335331 · Cited by 15,508 · see citing works
Self-citationHighly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110 · see citing works
Self-citationLeek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E., & Storey, J. D. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts034 · Cited by 6,231 · see citing works
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 · see citing works
Self-citationOptimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9
Doench, J. G., Fusi, N., Sullender, M., Hegde, M., Vaimberg, E. W., Donovan, K. F., Smith, I., Tothova, Z., Wilen, C., Orchard, R., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3437 · Cited by 4,637 · see citing works
CRISPR-Mediated Modular RNA-Guided Regulation of Transcription in Eukaryotes
Gilbert, L. A., Larson, M. H., Morsut, L., Liu, Z., Brar, G. A., Torres, S. E., Stern-Ginossar, N., Brandman, O., Whitehead, E. H., Doudna, J. A., et al. (2013) · Cell · doi:10.1016/j.cell.2013.06.044 · Cited by 3,696 · see citing works
Simultaneous epitope and transcriptome measurement in single cells
Stoeckius, M., Hafemeister, C., Stephenson, W., Houck-Loomis, B., Chattopadhyay, P. K., Swerdlow, H., Satija, R., & Smibert, P. (2017) · Nature Methods · doi:10.1038/nmeth.4380 · Cited by 3,098 · see citing works
Self-citationSMARCAD1 ATPase activity is required to silence endogenous retroviruses in embryonic stem cells
Sachs, P., Ding, D., Bergmaier, P., Lamp, B., Schlagheck, C., Finkernagel, F., Nist, A., Stiewe, T., & Mermoud, J. E. (2019) · Nature Communications · doi:10.1038/s41467-019-09078-0 · Cited by 2,388 · see citing works
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232 · see citing works
Self-citationNo evidence that carotenoid pigments boost either immune or antioxidant defenses in a songbird
Koch, R. E., Kavazis, A. N., Hasselquist, D., Hood, W. R., Zhang, Y., Toomey, M. B., & Hill, G. E. (2018) · Nature Communications · doi:10.1038/s41467-018-02974-x · Cited by 1,924 · see citing works
Dixit, A., Parnas, O., Li, B., Chen, J., Fulco, C. P., Jerby-Arnon, L., Marjanovic, N. D., Dionne, D., Burks, T., Raychowdhury, R., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.038 · Cited by 1,863 · see citing works
RNA virus interference via CRISPR/Cas13a system in plants
Aman, R., Ali, Z., Butt, H., Mahas, A., Aljedaani, F., Khan, M. Z., Ding, S., & Mahfouz, M. (2018) · Genome Biology · doi:10.1186/s13059-017-1381-1 · Cited by 1,497 · see citing works
Robust rank aggregation for gene list integration and meta-analysis
Kolde, R., Laur, S., Adler, P., & Vilo, J. (2012) · Bioinformatics · doi:10.1093/bioinformatics/btr709 · Cited by 1,195 · see citing works
Adamson, B., Norman, T. M., Jost, M., Cho, M. Y., Nuñez, J. K., Chen, Y., Villalta, J. E., Gilbert, L. A., Horlbeck, M. A., Hein, M. Y., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.048 · Cited by 1,193 · see citing works
Transcriptome Engineering with RNA-Targeting Type VI-D CRISPR Effectors
Konermann, S., Lotfy, P., Brideau, N. J., Oki, J., Shokhirev, M. N., & Hsu, P. D. (2018) · Cell · doi:10.1016/j.cell.2018.02.033 · Cited by 1,164 · see citing works
Pooled CRISPR screening with single-cell transcriptome readout.
Datlinger, P., Rendeiro, A. F., Schmidl, C., Krausgruber, T., Traxler, P., Klughammer, J., Schuster, L. C., Kuchler, A., Alpar, D., & Bock, C. (2017) · Nature Methods · doi:10.1038/nmeth.4177 · Cited by 1,096 · see citing works
Stoeckius, M., Zheng, S., Houck-Loomis, B., Hao, S., Yeung, B. Z., Mauck, W. M., Smibert, P., & Satija, R. (2018) · Genome Biology · doi:10.1186/s13059-018-1603-1 · Cited by 1,046 · see citing works
Self-citationOptimized libraries for CRISPR-Cas9 genetic screens with multiple modalities
Sanson, K. R., Hanna, R. E., Hegde, M., Donovan, K. F., Strand, C., Sullender, M. E., Vaimberg, E. W., Goodale, A., Root, D. E., Piccioni, F., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07901-8 · Cited by 1,043 · see citing works
Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq
Jaitin, D. A., Weiner, A., Yofe, I., Lara-Astiaso, D., Keren-Shaul, H., David, E., Salame, T. M., Tanay, A., van Oudenaarden, A., & Amit, I. (2016) · Cell · doi:10.1016/j.cell.2016.11.039 · Cited by 777 · see citing works
Engineered pegRNAs improve prime editing efficiency
Nelson, J. W., Randolph, P. B., Shen, S. P., Everette, K. A., Chen, P. J., Anzalone, A. V., An, M., Newby, G. A., Chen, J. C., Hsu, A., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01039-7 · Cited by 727 · see citing works
The human body at cellular resolution: the NIH Human Biomolecular Atlas Program
HuBMAP Consortium, Writing Group, Snyder, M. P., Lin, S., Posgai, A., Atkinson, M., Regev, A., Rood, J., Rozenblatt-Rosen, O., Gaffney, L., et al. (2019) · Nature · doi:10.1038/s41586-019-1629-x · Cited by 595 · see citing works
Self-citationMimitou, E. P., Cheng, A., Montalbano, A., Hao, S., Stoeckius, M., Legut, M., Roush, T., Herrera, A., Papalexi, E., Ouyang, Z., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0392-0 · Cited by 520 · see citing works
Self-citationSingle-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia
Granja, J. M., Klemm, S., McGinnis, L. M., Kathiria, A. S., Mezger, A., Corces, M. R., Parks, B., Gars, E., Liedtke, M., Zheng, G. X. Y., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0332-7 · Cited by 498 · see citing works
An enhanced CRISPR repressor for targeted mammalian gene regulation
Yeo, N. C., Chavez, A., Lance‐Byrne, A., Chan, Y., Menn, D. J., Milanova, D., Kuo, C., Guo, X., Sharma, S., Tung, A., et al. (2018) · Nature Methods · doi:10.1038/s41592-018-0048-5 · Cited by 472 · see citing works
Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing
Replogle, J. M., Norman, T. M., Xu, A., Hussmann, J. A., Chen, J., Cogan, J. Z., Meer, E. J., Terry, J. M., Riordan, D. P., Srinivas, N., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0470-y · Cited by 465 · see citing works
Massively parallel Cas13 screens reveal principles for guide RNA design
Wessels, H.-H., Méndez-Mancilla, A., Guo, X., Legut, M., Daniloski, Z., & Sanjana, N. E. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0456-9 · Cited by 418 · see citing works
Self-citationExploring genetic interaction manifolds constructed from rich single-cell phenotypes
Norman, T. M., Horlbeck, M. A., Replogle, J. M., Ge, A. Y., Xu, A., Jost, M., Gilbert, L. A., & Weissman, J. S. (2019) · Science · doi:10.1126/science.aax4438 · Cited by 392 · see citing works
Fiskus, W., Sharma, S., Shah, B., Portier, B. P., Devaraj, S. G. T., Liu, K., Iyer, S. P., Bearss, D., & Bhalla, K. N. (2014) · Leukemia · doi:10.1038/leu.2014.119 · Cited by 284 · see citing works
Multiplexed genome engineering by Cas12a and CRISPR arrays encoded on single transcripts
Campa, C. C., Weisbach, N. R., Santinha, A. J., Incarnato, D., & Platt, R. J. (2019) · Nature Methods · doi:10.1038/s41592-019-0508-6 · Cited by 284 · see citing works
Structural Basis for the RNA-Guided Ribonuclease Activity of CRISPR-Cas13d
Zhang, C., Konermann, S., Brideau, N. J., Lotfy, P., Wu, X., Novick, S. J., Strutzenberg, T., Griffin, P. R., Hsu, P. D., & Lyumkis, D. (2018) · Cell · doi:10.1016/j.cell.2018.09.001 · Cited by 276 · see citing works
Human bone marrow assessment by single-cell RNA sequencing, mass cytometry, and flow cytometry
Oetjen, K. A., Lindblad, K. E., Goswami, M., Gui, G., Dagur, P. K., Lai, C., Dillon, L. W., McCoy, J. P., & Hourigan, C. S. (2018) · JCI Insight · doi:10.1172/jci.insight.124928 · Cited by 272 · see citing works
Programmable RNA targeting with the single-protein CRISPR effector Cas7-11
Özcan, A., Krajeski, R., Ioannidi, E., Lee, B., Gardner, A., Makarova, K. S., Koonin, E. V., Abudayyeh, O. O., & Gootenberg, J. S. (2021) · Nature · doi:10.1038/s41586-021-03886-5 · Cited by 263 · see citing works
Akiyama, B. M., Laurence, H. M., Massey, A. R., Costantino, D. A., Xie, X., Yang, Y., Shi, P.-Y., Nix, J. C., Beckham, J. D., & Kieft, J. S. (2016) · Science · doi:10.1126/science.aah3963 · Cited by 245 · see citing works
Targeted Perturb-seq enables genome-scale genetic screens in single cells
Schraivogel, D., Gschwind, A. R., Milbank, J. H., Leonce, D. R., Jakob, P., Mathur, L., Korbel, J. O., Merten, C. A., Velten, L., & Steinmetz, L. M. (2020) · Nature Methods · doi:10.1038/s41592-020-0837-5 · Cited by 245 · see citing works
Papalexi, E., Mimitou, E. P., Butler, A. W., Foster, S., Bracken, B., Mauck, W. M., Wessels, H.-H., Hao, Y., Yeung, B. Z., Smibert, P., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00778-2 · Cited by 238 · cited 1× · see citing works
“Moreover, perturbations introduced by Cas9 are not uniformly efficient, and a considerable fraction of targeted cells may exhibit no phenotypic effects of perturbation 8,9 .”
BackgroundSelf-citationHigh-fidelity Cas13 variants for targeted RNA degradation with minimal collateral effects
Tong, H., Huang, J., Xiao, Q., He, B., Dong, X., Liu, Y., Yang, X., Han, D., Wang, Z., Wang, X., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01419-7 · Cited by 230 · see citing works
CRISPR-Cas9-based mutagenesis frequently provokes on-target mRNA misregulation
Tuladhar, R., Yeu, Y., Tyler Piazza, J., Tan, Z., Rene Clemenceau, J., Wu, X., Barrett, Q., Herbert, J., Mathews, D. H., Kim, J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12028-5 · Cited by 219 · see citing works
Discovery of target genes and pathways at GWAS loci by pooled single-cell CRISPR screens
Morris, J. A., Caragine, C., Daniloski, Z., Domingo, J., Barry, T., Lu, L., Davis, K., Ziosi, M., Glinos, D. A., Hao, S., et al. (2023) · Science · doi:10.1126/science.adh7699 · Cited by 208 · see citing works
Self-citationMultilayered VBC score predicts sgRNAs that efficiently generate loss-of-function alleles
Michlits, G., Jude, J., Hinterndorfer, M., de Almeida, M., Vainorius, G., Hubmann, M., Neumann, T., Schleiffer, A., Burkard, T. R., Fellner, M., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0850-8 · Cited by 154 · see citing works
Transcriptome-wide Cas13 guide RNA design for model organisms and viral RNA pathogens
Guo, X., Rahman, J. A., Wessels, H.-H., Méndez-Mancilla, A., Haro, D., Chen, X., & Sanjana, N. E. (2021) · Cell Genomics · doi:10.1016/j.xgen.2021.100001 · Cited by 79 · see citing works
Self-citationChemically modified guide RNAs enhance CRISPR-Cas13 knockdown in human cells
Méndez-Mancilla, A., Wessels, H.-H., Legut, M., Kadina, A., Mabuchi, M., Walker, J., Robb, G. B., Holden, K., & Sanjana, N. E. (2022) · Cell Chemical Biology · doi:10.1016/j.chembiol.2021.07.011 · Cited by 68 · see citing works
Self-citationDeep learning and CRISPR-Cas13d ortholog discovery for optimized RNA targeting
Wei, J., Lotfy, P., Faizi, K., Baungaard, S., Gibson, E., Wang, E., Slabodkin, H., Kinnaman, E., Chandrasekaran, S. S., Kitano, H. C., et al. (2023) · Cell Systems · doi:10.1016/j.cels.2023.11.006 · Cited by 68 · see citing works
Deep learning and CRISPR-Cas13d ortholog discovery for optimized RNA targeting
Wei, J., Lotfy, P., Faizi, K., Baungaard, S., Gibson, E., Wang, E., Slabodkin, H., Kinnaman, E., Chandrasekaran, S., Kitano, H., et al. (2021) · doi:10.1101/2021.09.14.460134 · Cited by 67 · see citing works
Surface antigen-guided CRISPR screens identify regulators of myeloid leukemia differentiation
Wang, E., Zhou, H., Nadorp, B., Cayanan, G., Chen, X., Yeaton, A. H., Nomikou, S., Witkowski, M. T., Narang, S., Kloetgen, A., et al. (2021) · Cell Stem Cell · doi:10.1016/j.stem.2020.12.005 · Cited by 66 · see citing works
Kelley, C. P., Haerle, M. C., & Wang, E. T. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111226 · Cited by 61 · see citing works
Otoupal, P. B., Cress, B. F., Doudna, J. A., & Schoeniger, J. S. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac680 · Cited by 53 · see citing works
Papalexi, E., Mimitou, E. P., Butler, A. W., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.28.175596 · Cited by 39 · see citing works
Self-citationLensch, S., Herschl, M. H., Ludwig, C., Sinha, J., Hinks, M. M., Mukund, A. X., Fujimori, T., & Bintu, L. (2022) · eLife · doi:10.7554/elife.75115 · Cited by 36 · see citing works
Reprogramming eukaryotic translation with ligand-responsive synthetic RNA switches
Anzalone, A. V., Lin, A. J., Zairis, S., Rabadan, R., & Cornish, V. W. (2016) · Nature Methods · doi:10.1038/nmeth.3807 · Cited by 33 · see citing works
Nicosia, L., Boffo, F. L., Ceccacci, E., Conforti, F., Pallavicini, I., Bedin, F., Ravasio, R., Massignani, E., Somervaille, T. C. P., Minucci, S., et al. (2022) · Oncogene · doi:10.1038/s41388-021-02123-7 · Cited by 31 · see citing works
RNA-guided cell targeting with CRISPR/RfxCas13d collateral activity in human cells
Shi, P., Murphy, M. R., Aparicio, A. O., Kesner, J. S., Fang, Z., Chen, Z., Trehan, A., & Wu, X. (2021) · doi:10.1101/2021.11.30.470032 · Cited by 21 · see citing works
Morris, J. A., Daniloski, Z., Domingo, J., Barry, T., Ziosi, M., Glinos, D. A., Hao, S., Mimitou, E. P., Smibert, P., Roeder, K., et al. (2021) · bioRxiv · doi:10.1101/2021.04.07.438882 · Cited by 20 · see citing works
Self-citationCRISPR/Cas9-based mutagenesis frequently provokes on-target mRNA misregulationPreprint
Tuladhar, R., Yeu, Y., Piazza, J. T., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/583138 · Cited by 19 · see citing works
Transcriptome-wide Cas13 guide RNA design for model organisms and viral RNA pathogensPreprint
Guo, X., Wessels, H., Méndez‐Mancilla, A., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.20.259762 · Cited by 17 · see citing works
Self-citationOptimization of specific RNA knockdown in mammalian cells with CRISPR-Cas13
Burris, B. J. D., Molina Vargas, A. M., Park, B. J., & O'Connell, M. R. (2022) · Methods · doi:10.1016/j.ymeth.2022.08.007 · Cited by 13 · see citing works
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Best practices for single-cell analysis across modalities
Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051 · see citing works
“3).”
Past, present, and future of CRISPR genome editing technologies
Pacesa, M., Pelea, O., & Jinek, M. (2024) · Cell · doi:10.1016/j.cell.2024.01.042 · Cited by 609 · see citing works
Cancer organoids 2.0: modelling the complexity of the tumour immune microenvironment
Polak, R., Zhang, E., & Kuo, C. J. (2024) · Nature reviews. Cancer · doi:10.1038/s41568-024-00706-6 · Cited by 322 · see citing works
CRISPR technologies for genome, epigenome and transcriptome editing
Villiger, L., Joung, J., Koblan, L. W., Weissman, J. S., Abudayyeh, O. O., & Gootenberg, J. S. (2024) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-023-00697-6 · Cited by 264 · see citing works
Chehelgerdi, M., Chehelgerdi, M., Khorramian-Ghahfarokhi, M., Shafieizadeh, M., Mahmoudi, E., Eskandari, F., Rashidi, M., Arshi, A., & Mokhtari-Farsani, A. (2024) · Molecular Cancer · doi:10.1186/s12943-023-01925-5 · Cited by 251 · see citing works
scPerturb: harmonized single-cell perturbation data
Peidli, S., Green, T. D., Shen, C., Gross, T., Min, J., Garda, S., Yuan, B., Schumacher, L. J., Taylor-King, J. P., Marks, D. S., et al. (2024) · Nature Methods · doi:10.1038/s41592-023-02144-y · Cited by 194 · see citing works
Rood, J. E., Hupalowska, A., & Regev, A. (2024) · Cell · doi:10.1016/j.cell.2024.07.035 · Cited by 139 · see citing works
Tieu, V., Sotillo, E., Bjelajac, J. R., Chen, C., Malipatlolla, M. B., Guerrero, J. A., Xu, P., Quinn, P. J., Fisher, C., Klysz, D. D., et al. (2024) · Cell · doi:10.1016/j.cell.2024.01.035 · Cited by 119 · see citing works
Le, J., Dian, Y., Zhao, D., Guo, Z., Luo, Z., Chen, X., Zeng, F., & Deng, G. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02426-3 · Cited by 109 · see citing works
The future of rapid and automated single-cell data analysis using reference mapping
Lotfollahi, M., Hao, Y., Theis, F. J., & Satija, R. (2024) · Cell · doi:10.1016/j.cell.2024.03.009 · Cited by 77 · see citing works
Self-citationTranscriptome-scale RNA-targeting CRISPR screens reveal essential lncRNAs in human cells
Liang, W.-W., Müller, S., Hart, S. K., Wessels, H., Méndez-Mancilla, A., Sookdeo, A., Choi, O., Caragine, C. M., Corman, A., Lu, L., et al. (2024) · Cell · doi:10.1016/j.cell.2024.10.021 · Cited by 62 · see citing works
Self-citationRetractedJiang, L., Dalgarno, C., Papalexi, E., Mascio, I., Wessels, H., Yun, H., Iremadze, N., Lithwick‐Yanai, G., Lipson, D., & Satija, R. (2025) · Nature Cell Biology · doi:10.1038/s41556-025-01622-z · Cited by 62 · see citing works
Self-citationMultiplexed single-cell characterization of alternative polyadenylation regulators
Kowalski, M. H., Wessels, H., Linder, J., Dalgarno, C., Mascio, I., Choudhary, S., Hartman, A., Hao, Y., Kundaje, A. B., & Satija, R. (2024) · Cell · doi:10.1016/j.cell.2024.06.005 · Cited by 55 · see citing works
Self-citationEngineered CRISPR-Cas12a for higher-order combinatorial chromatin perturbations
Hsiung, C. C. ‐., Wilson, C. M., Sambold, N. A., Dai, R., Chen, Q., Teyssier, N., Misiukiewicz, S., Arab, A., O’Loughlin, T. A., Cofsky, J. C., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02224-0 · Cited by 52 · see citing works
Programmable RNA targeting with CRISPR-Cas13
Shi, P., & Wu, X. (2024) · RNA Biology · doi:10.1080/15476286.2024.2351657 · Cited by 48 · see citing works
Massively Parallel CRISPR‐Based Genetic Perturbation Screening at Single‐Cell Resolution
Cheng, J., Lin, G., Wang, T., Wang, Y., Guo, W., Liao, J., Yang, P., Chen, J., Shao, X., Lu, X., et al. (2022) · Advanced Science · doi:10.1002/advs.202204484 · Cited by 40 · see citing works
Benchmarking algorithms for generalizable single-cell perturbation response prediction
Wei, Z., Wang, Y., Gao, Y., Wang, S., Li, P., Si, D., Gao, Y., Wu, S., Li, D., Dong, K., et al. (2026) · Nature Methods · doi:10.1038/s41592-025-02980-0 · Cited by 42 · see citing works
Precise RNA targeting with CRISPR–Cas13d
Hart, S. K., Müller, S., Wessels, H., Méndez‐Mancilla, A., Drabavicius, G., Choi, O., & Sanjana, N. E. (2025) · Nature Biotechnology · doi:10.1038/s41587-025-02558-3 · Cited by 35 · see citing works
Self-citationDecoding heterogeneous single-cell perturbation responses
Song, B., Liu, D., Dai, W., McMyn, N. F., Wang, Q., Yang, D., Krejčí, A., Vasilyev, A., Untermoser, N., Loregger, A., et al. (2025) · Nature Cell Biology · doi:10.1038/s41556-025-01626-9 · Cited by 36 · see citing works
Morris, J. A., Sun, J. S., & Sanjana, N. E. (2023) · Trends in Genetics · doi:10.1016/j.tig.2023.10.012 · Cited by 30 · see citing works
Self-citationEnhanced RNA-targeting CRISPR-Cas technology in zebrafish
Moreno-Sánchez, I., Hernández-Huertas, L., Nahón-Cano, D., Martínez‐García, P. M., Treichel, A. J., Gómez-Marín, C., Tomás‐Gallardo, L., Pescador, G. D. S., Kushawah, G., Egidy, R., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-57792-9 · Cited by 29 · see citing works
Methods and applications of in vivo CRISPR screening
Santinha, A. J., Strano, A., & Platt, R. J. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00873-8 · Cited by 29 · see citing works
Barry, T., Mason, K., Roeder, K., & Katsevich, E. (2024) · Genome Biology · doi:10.1186/s13059-024-03254-2 · Cited by 28 · see citing works
A CRISPR-dCas13 RNA-editing tool to study alternative splicing
Núñez‐Álvarez, Y., Espie-Caullet, T., Buhagiar‐Labarchède, G., Rubio-Zulaika, A., Alonso‐Marañón, J., Luna-Pérez, E., Blázquez, L., & Luco, R. F. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae682 · Cited by 26 · see citing works
Pooled Genome-Scale CRISPR Screens in Single Cells
Schraivogel, D., Steinmetz, L. M., & Parts, L. (2023) · Annual Review of Genetics · doi:10.1146/annurev-genet-072920-013842 · Cited by 26 · see citing works
Hu, W., Kumar, A., Ahmed, S. F., Qi, S., Ma, D. K. G., Chen, H., Singh, G. J., Casan, J. M. L., Haber, M., Voskoboinik, I., et al. (2024) · Nature Structural & Molecular Biology · doi:10.1038/s41594-024-01336-0 · Cited by 24 · see citing works
Applications of single-cell technologies in drug discovery for tumor treatment
Liu, B., Hu, S., & Wang, X. (2024) · iScience · doi:10.1016/j.isci.2024.110486 · Cited by 24 · see citing works
A Bayesian active learning platform for scalable combination drug screens
Tosh, C., Tec, M., White, J. B., Quinn, J. F., Sanchez, G. D. I., Calder, P., Kung, A. L., Cruz, F. S. D., & Tansey, W. (2025) · Nature Communications · doi:10.1038/s41467-024-55287-7 · Cited by 22 · see citing works
Synthetic lethality in cancer drug discovery: challenges and opportunities
Gonçalves, E., Ryan, C. J., & Adams, D. J. (2025) · Nature Reviews Drug Discovery · doi:10.1038/s41573-025-01273-7 · Cited by 24 · see citing works
CRISPR screening in hematology research: from bulk to single-cell level
Meyers, S., Demeyer, S., & Cools, J. (2023) · Journal of Hematology & Oncology · doi:10.1186/s13045-023-01495-5 · Cited by 23 · see citing works
Adapting systems biology to address the complexity of human disease in the single-cell era
Fischer, D. S., Villanueva, M. A., Winter, P. S., & Shalek, A. K. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00821-6 · Cited by 30 · see citing works
Unveiling novel insights in acute myeloid leukemia through single-cell RNA sequencing
Zhou, J., & Chng, W. J. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1365330 · Cited by 16 · see citing works
MultiCPA: Multimodal Compositional Perturbation Autoencoder
Inecik, K., Uhlmann, A., Lotfollahi, M., & Theis, F. (2022) · doi:10.1101/2022.07.08.499049 · Cited by 16 · see citing works
Manipulating and studying gene function in human pluripotent stem cell models
Balmas, E., Sozza, F., Bottini, S., Ratto, M. L., Savoré, G., Becca, S., Snijders, K. E., & Bertero, A. (2023) · FEBS Letters · doi:10.1002/1873-3468.14709 · Cited by 17 · see citing works
Optimization of specific RNA knockdown in mammalian cells with CRISPR-Cas13
Burris, B. J. D., Molina Vargas, A. M., Park, B. J., & O'Connell, M. R. (2022) · Methods · doi:10.1016/j.ymeth.2022.08.007 · Cited by 13 · see citing works
Expanding horizons of CRISPR applications beyond genome editing
Liang, Y., Tong, S., Zhang, J., Tan, G., Zhang, L., Lee, S. Y., & Tong, Y. (2025) · Trends in Genetics · doi:10.1016/j.tig.2025.06.003 · Cited by 12 · see citing works
Nadal‐Ribelles, M., Solé, C., Díez-Villanueva, A., Attolini, C. S., Matas, Y., Steinmetz, L. M., de Nadal, E., & Posas, F. (2025) · Nature Communications · doi:10.1038/s41467-025-57600-4 · Cited by 11 · see citing works
Elucidating the coordination of RNA processing using short-read and long-read RNA-sequencing methods
Alfonso-Gonzalez, C., & Hilgers, V. (2025) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-025-00895-4 · Cited by 13 · see citing works
Recent advances in drug delivery and treatment strategies for acute myeloid leukemia
Pan, Q., & Mahato, R. I. (2025) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2025.126078 · Cited by 7 · see citing works
Beyond the gene: decoding alternative isoforms
Mattioli, K., & Bulyk, M. L. (2025) · Trends in Genetics · doi:10.1016/j.tig.2025.11.003 · Cited by 9 · see citing works
Lee-Yow, Y. C., Valbuena, R., Richter, C. S., Chang, H. Y., & Engreitz, J. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1447 · Cited by 7 · see citing works
Miller, H. E., Mejia, G., Leblanc, F., Wang, B., Swain, B., & Camillo, L. P. D. L. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.20.683304 · Cited by 6 · see citing works
Mapping transcriptional responses to cellular perturbation dictionaries with RNA fingerprintingPreprint
Grabski, I. N., Lee, J., Blair, J. D., Dalgarno, C., Mascio, I., Bradu, A., Knowles, D. A., & Satija, R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.19.676866 · Cited by 5 · see citing works
Self-citationDecoding Heterogenous Single-cell Perturbation ResponsesPreprint
Song, B., Liu, D., Dai, W., McMyn, N. F., Wang, Q., Yang, D., Krejčí, A., Vasilyev, A., Untermoser, N., Loregger, A., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.30.564796 · Cited by 4 · see citing works
Next generation genetic screens in kinetoplastids
Budzak, J., & Siegel, T. N. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf515 · Cited by 3 · see citing works
Engineered RNA‐Binding Proteins: Studying and Controlling RNA Regulation
Sinnott, R. W., Cao, Y., & Dickinson, B. C. (2024) · Israel Journal of Chemistry · doi:10.1002/ijch.202300169 · Cited by 3 · see citing works
Evaluation of Cas13d as a tool for genetic interaction mapping
Kassem, G. E., Hillmer, J., & Boettcher, M. (2025) · Nature Communications · doi:10.1038/s41467-025-56747-4 · Cited by 3 · see citing works
Decoding cell fate: integrated experimental and computational analysis at the single-cell level
Zhou, Y., Hou, S., Miao, X., Zhang, G., Li, Z., Zhang, D., Lin, Y., & Lin, Y. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf603 · Cited by 4 · see citing works
Adding a Chemical Biology Twist to CRISPR Screening
Huang, S., & Baskin, J. M. (2022) · Israel Journal of Chemistry · doi:10.1002/ijch.202200056 · Cited by 3 · see citing works
Jackson, C. A., Beheler-Amass, M., Tjärnberg, A., Suresh, I., Hickey, A. S.-M., Bonneau, R., & Gresham, D. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.21.558277 · Cited by 3 · see citing works
Tian, Z., Octaviani, S., & Huang, J. Y. (2023) · Expert Opinion on Therapeutic Targets · doi:10.1080/14728222.2023.2293751 · Cited by 3 · see citing works
Kösters, D., & Marienhagen, J. (2025) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2025.103381 · Cited by 2 · see citing works
Dissecting cellular ecosystem with single-cell CRISPR screens
Liu, Z., Lan, Z., Kang, X., Yao, Y., & Rao, S. (2025) · Blood Science · doi:10.1097/bs9.0000000000000266 · Cited by 2 · see citing works
Next-generation CRISPR screens enable causal systems immunology
Shi, H., & Chi, H. (2026) · The Journal of Experimental Medicine · doi:10.1084/jem.20241266 · Cited by 2 · see citing works
Zhao, M., Tang, X., Li, J., Liang, C., Tang, J., & Guo, F. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014054 · Cited by 2 · see citing works
Essential lncRNAs in the human transcriptome
Liang, W.-W., Mueller, S. J., Hart, S. K., Wessels, H., Méndez‐Mancilla, A., Sookdeo, A., Choi, O., Caragine, C. M., Corman, A., Lu, L., et al. (2026) · Cell Genomics · doi:10.1016/j.xgen.2026.101253 · Cited by 3 · see citing works
Self-citationGhamsari, M. R. E., Rosenbluh, J., Menon, A. V., Lovell, N. H., & Alinejad-Rokny, H. (2023) · Cancers · doi:10.3390/cancers15143566 · Cited by 2 · see citing works
A CRISPR-Cas13b System Degrades SARS-CoV and SARS-CoV-2 RNA In Vitro
Andersson, K., Azatyan, A., Ekenberg, M., Akpinar, G. G., Puig, L. S., Puumalainen, M.-R., Pramer, T., Monteil, V., & Mirazimi, A. (2024) · Viruses · doi:10.3390/v16101539 · Cited by 2 · see citing works
Li, C., Wei, L., & Zhang, X. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.13.688367 · Cited by 3 · see citing works
Choi, H., Byeon, G., Park, H., Park, J., Lim, S., & An, J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.01.748703 · Cited by 2 · see citing works
Hypoxia-inducible factor 1 protects neurons from Sarm1-mediated neurodegeneration
Meraner, P., Avetisyan, A. A., Swift, K. A., Cheng, Y.-C., Barría, R., & Freeman, M. R. (2026) · Cell Reports · doi:10.1016/j.celrep.2025.116873 · Cited by 1 · see citing works
Kumari, B., Damodaran, A. P., Guiblet, W. M., Xiao, M., Behera, A. K., On, T. A., MCINTOSH, C. E., Teszler, M., Holloway, C., Le, S. M., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-68774-w · Cited by 1 · see citing works
Deep learning perturbation models can outperform baselines on calibrated metrics
Miller, H. E., Mejia, G. M., Leblanc, F. J. A., Swain, B., Wang, B., & de Lima Camillo, L. P. (2026) · Nature Biotechnology · doi:10.1038/s41587-026-03307-w · Cited by 5 · see citing works
A Manual for Genome and Transcriptome Engineering
Doctor, Y., Sanghvi, M., & Mali, P. (2024) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2024.3494715 · Cited by 1 · see citing works
A system perspective on drug response
Elgart, V., & Loscalzo, J. (2025) · British Journal of Pharmacology · doi:10.1111/bph.70128 · Cited by 1 · see citing works
Scalable single-cell pooled CRISPR screens with conventional knockout vector librariesPreprint
Islam, M., Yang, Y., Simmons, A. J., Xu, Y., Fisher, E. L., Deng, W., Grieb, B. C., Molina, P., de Caestecker, C. R., Ramirez-Solano, M. A., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.01.578192 · Cited by 1 · see citing works
Chen, H., Hu, W., Impicciche, V., Singh, G. J., King, J., Shembrey, C., Rawat, P., Casan, J. M. L., Boskovic, S., Paterson, S., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.07.716849 · Cited by 1 · see citing works
Identification of multi-cancer risk genes
Rivera, S. (2024) · Queensland University of Technology · doi:10.5204/thesis.eprints.248736
A review on single-cell perturbation data analysis
Fu, Z., Yang, M., & Hou, L. (2025) · Scientia Sinica Mathematica · doi:10.1360/ssm-2024-0315
Identification of Target Genes Using Innovative Screening Systems
Sugita, K., & Kurata, M. (2025) · Pathology International · doi:10.1111/pin.70019
Advances in single-cell perturbation modeling
Qi, X., Zhao, L., Chen, R., & Zhao, Y. C. (2025) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/csb-2025-0682
Genetic Convergence Analysis of CRISPR Perturbations Deciphers Gene Functional SimilarityPreprint
Zhang, T., Shang, E., & Roeder, K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.13.688060
Transcriptome-wide profiling of alternative splicing regulators with CRISPore-seqPreprint
Müller, S., Andrews, N., Yan, R., Sookdeo, A., Burrell, W. H., Dai, X., Rughani, P., Daniloski, Z., Juul, S., & Sanjana, N. E. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.25.690515
Self-citationVaikunthan, M., Schoonen, A. C. M., Lia, I.-T., Guo, J., & McFaline‐Figueroa, J. L. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.06.703342
A Multi-modal LLM-Knowledge Fusion Framework for Predicting Single-cell Genetic Perturbation EffectsPreprint
Lu, M., You, N., Zhang, H., Zheng, L., Li, B., Jiang, W., Zhang, Y., Sun, H., & Zhou, Y. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.24.720560 · Cited by 1 · see citing works
Fareh, M., Chen, H., Hu, W., Impicciche, V., Singh, G., King, J., Shembrey, C., Rawat, P., Casan, J. M. L., Boskovic, S., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-10351181/v1
The emerging roles of noncoding RNAs in cancer
Samuel, A., & Călin, G. A. (2026) · Carcinogenesis · doi:10.1093/carcin/bgag053
High‐Content CRISPR Screening: Methods and Applications
Zhang, Y., Zhang, Y., Tang, X., Deng, X., Zhou, Y., Wu, Y., & Luo, J. (2026) · MedComm · doi:10.1002/mco2.70964
Mapping transcriptional responses to cellular perturbations with RNA fingerprinting
Grabski, I. N., Lee, J., Blair, J. D., Dalgarno, C., Mascio, I., Bradu, A., Knowles, D. A., & Satija, R. (2026) · Cell · doi:10.1016/j.cell.2026.09.017 · Cited by 5 · see citing works
Self-citation
- Authors
- Wessels, H.-H., Méndez-Mancilla, A., Hao, Y., Papalexi, E., Mauck, W. M., Lu, L., Morris, J. A., Mimitou, E. P., Smibert, P., Sanjana, N. E., & Satija, R.
- Affiliations
- New York Genome Center (US)
- Corresponding author
- Sanjana, Neville E.
- Satija, Rahul
- ORCID
- Venue
- Nature Methods
- Published
- 2022-12-22
- Volume
- 20
- Issue
- 1
- Pages
- 86-94
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC10030154
- OpenAlex
- W4312114042
- Semantic Scholar
- 7466a7cb2eb4aa0081b3fc94fe5c5f232730e4bc
- Cited by
- 79
- References
- 52
- Citations by year
- 2026: 14 · 2025: 30 · 2024: 22 · 2023: 9 · 2022: 4
- FWCI
- 5.31
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Cancer Research Institute
- Chan Zuckerberg Initiative (HCA-A1704-01895, EOSS-0000000082)
- York University
- National Institutes of Health (1dp2hg009623-01)
- Defense Advanced Research Projects Agency (D18AP00053)
- National Human Genome Research Institute (DP2-HG-009623, DP2HG010099, RM1HG011014)
- National Cancer Institute (DP2HG009623-01, R01CA218668)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Keywords
- CRISPR-Cas13d, single-cell RNA sequencing, RNA-targeting CRISPR, barcode encoding, acute myeloid leukemia, myeloid differentiation, chromatin regulators, gene regulatory networks
- MeSH terms
- Chromatin, RNA, CRISPR-Cas Systems
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