Protein Barcodes Enable High-Dimensional Single-Cell CRISPR Screens
Open accessAbstract
CRISPR pools are being widely employed to identify gene functions. However, current technology, which utilizes DNA as barcodes, permits limited phenotyping and bulk-cell resolution. To enable novel screening capabilities, we developed a barcoding system operating at the protein level. We synthesized modules encoding triplet combinations of linear epitopes to generate >100 unique protein barcodes (Pro-Codes). Pro-Code-expressing vectors were introduced into cells and analyzed by CyTOF mass cytometry. Using just 14 antibodies, we detected 364 Pro-Code populations; establishing the largest set of protein-based reporters. By pairing each Pro-Code with a different CRISPR, we simultaneously analyzed multiple phenotypic markers, including phospho-signaling, on dozens of knockouts. Pro-Code/CRISPR screens found two interferon-stimulated genes, the immunoproteasome component Psmb8 and a chaperone Rtp4, are important for antigen-dependent immune editing of cancer cells and identified Socs1 as a negative regulator of Pd-l1. The Pro-Code technology enables simultaneous high-dimensional protein-level phenotyping of 100s of genes with single-cell resolution.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 38 cited sources checked · 0 supported · 0 flagged
- Findingown data
The authors developed a barcoding system that operates at the protein level.
“we developed a barcoding system operating at the protein level.”
Quote in the paperSubstantial claim - Findingown data
Combinations of linear epitopes can be used to generate protein barcodes detectable at the protein level and at single-cell resolution.
“These results demonstrate that combinations of linear epitopes can be used to generate protein barcodes detectable at the protein level and at single-cell resolution.”
Quote in the paperSubstantial claim - Findingown data
Eighteen distinct cell populations were detected, each corresponding to a unique 3-epitope Pro-Code positive for precisely 3 of the 10 epitopes.
“Eighteen distinct cell populations were detected (Figure 1C and S1B), with each population corresponding to a unique Pro-Code (i.e. positive for precisely 3 of the 10 epitopes).”
- Also stated in: § Pro-Codes enable highly multiplexed cell barcoding at the protein level · plain
“Once again 18 distinct populations were identified with each cluster positive for only 3 epitopes, and thus corresponding precisely to a specific Pro-Code”
Quote in the paperSubstantial claim - Also stated in: § Pro-Codes enable highly multiplexed cell barcoding at the protein level · plain
- Findingown data
Triplet combinations of linear epitopes can generate more than 100 unique protein barcodes called Pro-Codes.
“We synthesized modules encoding triplet combinations of linear epitopes to generate >100 unique protein barcodes (Pro-Codes).”
Quote in the paperSubstantial claim - Findingown data
The proportion of each Pro-Code varied between tumors, reflecting bias in the original population rather than differential rejection.
“While the proportion of each population varied for different Pro-Codes, this reflected a bias in the original population, as indicated by the comparison of each Pro-Code’s frequency in the pre-inoculation cells versus the tumors.”
Quote in the paperSubstantial claim
- Findingown data
Pairing each Pro-Code with a different CRISPR allowed simultaneous analysis of multiple phenotypic markers, including phospho-signaling, on dozens of knockouts.
“By pairing each Pro-Code with a different CRISPR, we simultaneously analyzed multiple phenotypic markers, including phospho-signaling, on dozens of knockouts.”
Quote in the paperSubstantial claim - Findingown data
In cells encoding Pro-Codes linked to GM-CSF gRNAs, there was a 3-fold reduction in pSTAT5 levels in response to GM-CSF.
“in cells encoding the Pro-Codes linked to gRNAs targeting GM-CSF there was a 3-fold reduction in pSTAT5 levels in response to GM-CSF”
Quote in the paperSubstantial claim - Findingown data
The specific Pro-Code composition of each tumor was different despite injection of the same pool.
“Although each mouse was injected with the same pool of cells, the specific Pro-Code composition of each tumor was different”
Quote in the paperSubstantial claim - Findingown data
In each Pro-Code population in which a membrane-bound protein was targeted, there was an increase in the percent of cells negative for the cognate protein.
“In each Pro-Code population in which one of the membrane-bound proteins was targeted, there was an increase in the percent of cells negative for the cognate protein”
Quote in the paperSubstantial claim - Findingown data
In each tumor, 118-120 Pro-Code populations were identified.
“We were able to identify 118 – 120 Pro-Code populations in each tumor”
Quote in the paperNot a substantial claim - Findingown data
The frequency of cells negative for the targeted protein was ~90% similar between individually produced and pooled libraries.
“The frequency of cells negative for the targeted protein was ~90% similar between the libraries generated with vectors produced individually or as a pool with the low homology spike in vector.”
Quote in the paperSubstantial claim - Findingown data
There was no significant difference in the proportion of the vast majority of Pro-Code populations between WT and Rag1−/− mice.
“Importantly, there was no significant difference in the proportion of the vast majority of Pro-Code populations in WT or Rag1−/− mice.”
Quote in the paperSubstantial claim - Findingown data
In all cases, decreased phospho-signaling was observed in cells expressing a Pro-Code linked to a gRNA targeting the cognate receptor.
“In all cases, we observed decreased phospho-signaling in cells expressing a Pro-Code linked to a gRNA targeting the cognate receptor”
Quote in the paperSubstantial claim - Findingown data
In cells carrying gRNAs targeting IL6R, there was a 2-fold reduction in both pSTAT1 and pSTAT3 levels in response to IL-6.
“in cells carrying gRNAs targeting IL6R there was a 2-fold reduction in both pSTAT 1 and pSTAT3 levels in response to IL-6”
Quote in the paperSubstantial claim - Findingown data
High-efficiency protein loss was observed for CD44, CD45, CD116, CD164, CD220, and IFNGR1.
“High efficiency protein loss was also observed for CD44, CD45, CD116, CD164, CD220, and IFNGR1.”
Quote in the paperSubstantial claim - Findingown data
With only 14 antibodies, 364 different vector-expressing cell populations could be detected.
“Thus, with only 14 antibodies (i.e. 14 detection channels), we could detect 364 different vector expressing cell populations.”
Quote in the paperSubstantial claim - Findingown data
All 24 Pro-Code populations were resolved and uniquely clustered.
“All 24 Pro-Code populations were resolved and uniquely clustered”
Quote in the paperSubstantial claim - Findingown data
In cells expressing the same IFNGR1/IFNGR2 Pro-Code/CRISPRs, pSTAT5 and pSTAT1/pSTAT3 levels were normal in response to GM-CSF and IL-6.
“Whereas in cells expressing the same Pro-Code/CRISPRs, pSTAT5, and pSTAT1 and pSTAT3 levels were normal in response to GM-CSF and IL-6.”
Quote in the paperSubstantial claim - Findingown data
There was a 15-fold decrease in pSTAT1 levels in cells expressing Pro-Codes linked to gRNAs targeting IFNGR1 and IFNGR2.
“there was a 15-fold decrease in pSTAT1 levels in cells expressing Pro-Codes linked to gRNAs targeting IFNGR1 and IFNGR2”
Quote in the paperSubstantial claim - Findingown data
The authors successfully generated and resolved 364 unique Pro-Codes using 14 epitope and antibody pairs.
“We successfully generated and resolved 364 unique Pro-Codes using 14 epitope and antibody pairs for construction and detection.”
Quote in the paperSubstantial claim - Findingown data
Bulk comparison indicated GFP+ cells were almost completely eliminated in Cas9 null cultures with activated Jedi T cells.
“Bulk comparison indicated GFP+ cells were almost completely eliminated in Cas9 null cultures with activated Jedi T cells”
Quote in the paperSubstantial claim - Findingown data
Residual cells remained in each Pro-Code/CRISPR population after Jedi co-culture.
“In addition to the B2m and Ifngr2 CRISPR populations, there were residual cells remaining in each Pro-Code/CRISPR population after Jedi co-culture”
Quote in the paperSubstantial claim - Findingown data
Psmb8 and Rtp4 knockout cells were almost completely resistant to T cell killing after expansion and re-coculture.
“Strikingly, the Psmb8 and Rtp4 KO cells were almost completely resistant to T cell killing”
Quote in the paperSubstantial claim - Findingown data
CRISPR knockout efficiency varies widely across cells, with some cells fully knocked out, some partially, and some remaining wildtype.
“the efficiency of CRISPR KO is highly variable; some cells may be complete KO, while other cells have only a partial KO or remain wildtype.”
Quote in the paperSubstantial claim - Findingown data
Similar enrichment was seen with individual CRISPRs targeting only B2m or Ifngr2.
“We saw a similar result when we performed the experiments with individual CRISPRs targeting only B2m or Ifngr2”
Quote in the paperSubstantial claim - Findingown data
Lower MHC-I was detected on cells encoding B2m gRNAs.
“As expected, we could detect lower MHC-I on cells encoding the B2m gRNAs”
Quote in the paperNot a substantial claim - Findingown data
Examination of the surviving 4T1-Cas9-GFP population revealed enrichment of cells expressing Pro-Codes linked to gRNAs targeting Ifngr2 and B2m.
“Examination of the Pro-Code markers in the surviving 4T1-Cas9-GFP population revealed enrichment of cells expressing Pro-Codes linked to gRNAs targeting Ifngr2 and B2m”
Quote in the paperSubstantial claim - Findingown data
All three populations expressing a Pro-Code linked to a Socs1 gRNA had increased upregulation of PD-L1.
“all 3 populations expressing a Pro-Code linked to a gRNA targeting Socs1 had increased upregulation of PD-L1”
Quote in the paperSubstantial claim - Findingown data
A large fraction of 4T1-Cas9-GFP cells survived (8-12% of the culture) despite expressing the antigenic target of the T cells.
“In contrast, a large fraction of 4T1-Cas9-GFP cells survived (8–12% of the culture), despite their expression of the antigenic target of the T cells”
Quote in the paperSubstantial claim - Findingreasoning
With Pro-Codes, each cell expressing a CRISPR is analyzed separately.
“With Pro-Codes, every cell expressing a CRISPR is analyzed individually.”
Quote in the paperSubstantial claim - Findingown data
Knockout of two IFNγ-inducible genes, Psmb8 and Rtp4, caused resistance to antigen-specific T cell killing.
“Our studies found KO of two IFNγ inducible genes, Psmb8 and Rtp4, resulted in resistance to antigen-specific T cell killing.”
Quote in the paperSubstantial claim - Findingown data
Each of the 56 Pro-Code populations was detected.
“Each of the 56 Pro-Code populations were detected”
Quote in the paperNot a substantial claim - Findingown data
There were no changes in the relative frequency of specific Pro-Code populations in 4T1-RFP cells, with or without Cas9, in the presence or absence of Jedi T cells.
“There were no changes in the relative frequency of specific Pro-Code populations in 4T1-RFP cells, with or without Cas9, in the presence or absence of Jedi T cells”
Quote in the paperSubstantial claim - Findingreasoning
Swapping can be avoided by packaging each vector separately and then pooling them, as done for the surface-protein library.
“Fortunately, swapping can be prevented by packaging each vector individually, and pooling them subsequently, as we did in our library targeting surface proteins.”
Quote in the paperNot a substantial claim - Findingown data
Bulk comparison found that a fraction of GFP+ cells survived, indicating resistant cancer cells had emerged.
“Bulk comparison of GFP+ and mCherry+ cells found that a fraction of GFP+ cells survived, indicating resistant cancer cells had emerged”
Quote in the paperSubstantial claim - Findingown data
The PD-L1 effect was specific because the same population had similar MHC-I levels to other Pro-Code/CRISPR populations.
“This was specific to PD-L1 because the same population of cells had similar levels of MHC-I to other Pro-Code/CRISPR populations”
Quote in the paperSubstantial claim - Findingown data
Only a subset of cells carrying a given Pro-Code/CRISPR lacked the matching protein.
“only a fraction of cells expressing a particular Pro-Code/CRISPR were negative for the cognate protein.”
Quote in the paperSubstantial claim - Findingown data
Western blot confirmed Psmb8 protein was absent in expanded Psmb8 CRISPR 4T1 cells.
“Western blot confirmed Psmb8 protein was absent in the expanded Psmb8 CRISPR 4T1 cells”
Quote in the paperSubstantial claim - Findingown data
Approximately 39% of the surviving cancer cells carried an Ifngr2 CRISPR.
“Approximately 39% of the surviving cancer cells carried an Ifngr2 CRISPR”
Quote in the paperSubstantial claim - Findingown data
Differences in phenotype can be detected among individual cells that received the same CRISPR, as seen for phospho-STAT signaling and PD-L1.
“the phenotypic differences can be seen between individual cells receiving the same CRISPR, as we observed in the Pro-Code/CRISPR analysis of phospho-STAT signaling, as well as PD-L1.”
Quote in the paperSubstantial claim - Findingown data
In validation, cells encoding Psmb8 and Rtp4 CRISPR showed increased resistance compared to scramble control.
“In support of the screen results, we observed increased resistance of cells encoding the Psmb8 and Rtp4 CRISPR compared to the scramble control”
Quote in the paperSubstantial claim - Findingown data
Cells expressing Pro-Codes linked to Psmb8 and Rtp4 gRNAs were enriched in the surviving 4T1-Cas9-GFP populations.
“Examination of the Pro-Code markers revealed that cells expressing Pro-Codes linked to gRNAs targeting Psmb8 and Rtp4 were enriched in the surviving 4T1-Cas9-GFP populations.”
Quote in the paperSubstantial claim - Findingown data
Reduction, not only complete loss, of MHC-I or target antigen gave a survival advantage.
“our studies found that downregulation, and not just loss, of either factor also provided a survival advantage.”
Quote in the paperSubstantial claim - Premisecitation
Jedi T cells enable GFP to be used as a model antigen that can be easily detected.
“Jedi T cells enable GFP to be used as a model antigen that can be easily detected (Agudo et al., 2018).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Agudo et al., 2018 source
Not checked in the free run.
- Interpretationreasoning
IFNGR1 and IFNGR2 gRNAs only impaired pSTAT1 signaling in response to IFNγ.
“This indicated the IFNGR1 and IFNGR2 gRNAs only impaired pSTAT1 signaling in response to IFNγ.”
Quote in the paperSubstantial claim- Rests on an assumption: IFNγ triggers pSTAT1, IL-6 induces pSTAT1 and pSTAT3, and GM-CSF induces pSTAT5.
- Interpretationreasoning
Pro-Codes facilitate high-dimensional phenotypic screens.
“further support the ability of Pro-Codes to facilitate high-dimensional phenotypic screens.”
Quote in the paperSubstantial claim - Study design and conductcitation
Making each vector individually and pooling them in equimolar ratio eliminates the possibility of barcode swapping.
“we made each vector individually, and subsequently pooled them in equimolar ratio, as this eliminates the possibility of swapping (Adamson et al., 2016).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Adamson et al., 2016 source
Not checked in the free run.
- Premisecitation
Cancer cells acquire mutations that generate neo-antigens loaded on MHC class I, making them targets for CD8+ T cell killing.
“Cancer cells acquire mutations, which generate neo-antigens that are loaded on to MHC class I (MHC-I), and make the cancer cells targets for CD8+ T cell killing (Schumacher and Hacohen, 2016).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Schumacher and Hacohen, 2016
Not checked in the free run.
- Premisecitation
CRISPR screens coupled with single-cell RNA sequencing using vector-encoded RNA barcodes enabled higher-content, higher-resolution screens.
“Recently, CRISPR screens have been coupled with single cell RNA sequencing (scRNA-seq), and vector-encoded RNA used as a barcode”
Quote in the paperSubstantial claim0 of 3 sources checked - Interpretationown data
Pro-Code/CRISPR screens found that Psmb8 and Rtp4 are important for antigen-dependent immune editing of cancer cells.
“Pro-Code/CRISPR screens found two interferon-stimulated genes, the immunoproteasome component Psmb8 and a chaperone Rtp4, are important for antigen-dependent immune editing of cancer cells”
Quote in the paperSubstantial claim - Interpretationown data
Pro-Code/CRISPR screens identified Socs1 as a negative regulator of Pd-l1.
“identified Socs1 as a negative regulator of Pd-l1.”
Quote in the paperSubstantial claim - Premisenone given
A caveat of these antigens is that they are not readily detected in cells.
“A caveat of these antigens is they are not readily detected in cells.”
Quote in the paperNot a substantial claim - Interpretationown data
Pro-Codes can be used in vivo to track cancer cell clonality.
“Pro-Codes can be used in vivo to track cancer cell clonality”
Quote in the paperSubstantial claim - Interpretationown data
The Pro-Code technology enables simultaneous high-dimensional protein-level phenotyping of hundreds of genes with single-cell resolution.
“The Pro-Code technology enables simultaneous high-dimensional protein-level phenotyping of 100s of genes with single cell resolution.”
Quote in the paperSubstantial claim - Interpretationown data
Pro-Codes can be used for cell tracking studies.
“demonstrate Pro-Codes can be used for cell tracking studies.”
Quote in the paperSubstantial claim - Interpretationown data
Pro-Codes enable protein-level phenotyping in pooled CRISPR screens.
“They also demonstrate how Pro-Codes enable protein-level phenotyping in pooled CRISPR screens.”
Quote in the paperSubstantial claim - Premisecitation
Jedi T cells express a T cell receptor that recognizes the immunodominant epitope of GFP loaded in the H-2Kd allele of MHC-I.
“we took advantage of just eGFP death inducing (Jedi) T cells, which express a T cell receptor that recognizes the immunodominant epitope of GFP loaded in the H-2Kd allele of MHC-I (Agudo et al., 2015).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Agudo et al., 2015 source
Not checked in the free run.
- Premisecitation
DNA barcoding has largely limited pooled screens to assessing effects on cell fitness.
“This has limited pooled screens largely to vetting genes for their potential impact on cell fitness”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Shalem et al., 2015 source
Not checked in the free run.
- Interpretationown data
Pro-Codes allow high-dimensional phenotyping of CRISPR screens with single-cell resolution.
“Pro-Codes allow for high-dimensional phenotyping of CRISPR screens with single cell resolution”
Quote in the paperSubstantial claim - Interpretationown data
Pro-Codes can mark cells encoding a specific CRISPR gRNA.
“These results demonstrate Pro-Codes can mark cells encoding a specific CRISPR gRNA.”
Quote in the paperSubstantial claim - Interpretationreasoning
Thousands of new Pro-Codes can be created by adding additional epitopes and epitope positions.
“Moreover, 1000s of new Pro-Codes can be created by applying the principle we used here and introducing additional epitopes and epitope positions.”
Quote in the paperSubstantial claim - Premisecitation
At least two platforms now allow high-dimensional tissue imaging with antibodies, detecting over 40 parameters in a single section.
“There are now at least two platforms that enable high-dimensional tissue imaging with antibodies, allowing over 40 parameters to be simultaneously detected in a single section (Angelo et al., 2014; Giesen et al., 2014).”
Quote in the paperNot a substantial claim0 of 2 sources checked - Interpretationown data
One mechanism by which Ifngr2 CRISPR cells resisted T cell killing may be diminished upregulation of MHC-I.
“This suggests one of the mechanisms by which the Ifngr2 CRISPR cells resisted T cell killing may be due to diminished upregulation of MHC-I.”
Quote in the paperSubstantial claim - Interpretationcitation
Although CyTOF can measure fewer genes than scRNA-seq, expanding the phenotyping space with oligo-labeled antibodies to detect Pro-Codes and other proteins and deconvoluting with single-cell sequencing should be feasible.
“Though the number of genes that can be analyzed by CyTOF is fewer than scRNA-seq, it should be feasible to expand the phenotyping space using oligo-labeled antibodies to detect the Pro-Codes and other proteins, and to deconvolute with single cell sequencing, as has been described (Peterson et al., 2017; Stoeckius et al., 2017).”
Quote in the paperSubstantial claim0 of 2 sources checked - Interpretationreasoning
Pro-Codes allow CRISPR screens to be run at single-cell resolution.
“A key feature of the Pro-Codes is that they enable screens to be performed with single cell resolution.”
Quote in the paperSubstantial claim - Interpretationown data
This approach allows CRISPR libraries to be produced as a pool, making larger libraries more feasible.
“The advantage is that it permits the CRISPR libraries to be made as a pool, which makes it more feasible to produce larger libraries.”
Quote in the paperSubstantial claim - Premisereasoning
This high-dimensional protein phenotyping was not feasible with DNA barcodes because the readout was limited to measuring changes in barcode frequency and inferring phenotype from selective pressure.
“This was not feasible with DNA barcodes, as the screen readout was limited to measuring changes in barcode frequency, and inferring phenotype based on the selective pressure applied.”
Quote in the paperSubstantial claim - Interpretationown data
These results implicate Socs1 as a negative regulator of PD-L1.
“These results implicate Socs1 as a negative regulator of PD-L1.”
Quote in the paperSubstantial claim - Interpretationreasoning
An important advance of Pro-Codes is the ability to perform high-dimensional phenotyping of multiple proteins in pooled screens.
“An important advance provided by the Pro-Codes is the ability to perform high-dimensional phenotyping of multiple proteins in pooled screens.”
Quote in the paperSubstantial claim - Interpretationown data
This supports the notion that this escape mechanism can arise spontaneously.
“supporting the notion that this mechanism can arise spontaneously.”
Quote in the paperSubstantial claim - Interpretationreasoning
Even when complete knockout has an effect, the presence of wildtype cells reduces the observed effect size.
“Even if there is an effect of complete KO, the magnitude is diluted by the wildtype cells.”
Quote in the paperSubstantial claim- Rests on an assumption: DNA barcode deconvolution is usually done on bulk cells, so cells with complete, partial, or no knockout are combined in the analysis.
- Interpretationown data
These results suggested gene editing was resulting in resistant cancer cells.
“These results suggested gene editing was resulting in resistant cancer cells.”
Quote in the paperSubstantial claim - Interpretationown data
Psmb8 and Rtp4 have a non-redundant role in mediating sensitivity of tumor cells to antigen-dependent T cell killing.
“Together, these results indicate Psmb8 and Rtp4 have a non-redundant role in mediating sensitivity of tumor cells to antigen-dependent T cell killing.”
Quote in the paperSubstantial claim - Interpretationreasoning
The low-homology vector works by co-packaging a genome that cannot serve as a suitable swapping partner for the library genome.
“This works by co-packaging a genome in the vector particle that does not serve as a suitable partner for swapping with the library genome, and was also reported to be effective in pre-print studies (Adamson et al., 2018; Feldman et al., 2018).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Adamson et al., 2018
Not checked in the free run.
- cites Feldman et al., 2018
Not checked in the free run.
- May combine two claims. 44
- The quote only partly states this claim. 4
- Classified as probably not a substantial claim. 20
May combine two claims.
Findingown dataIn cells expressing Pro-Code 3 linked to a CD4 gRNA, 85% were CD4 negative, whereas cells with unrelated gRNAs were almost all CD4 positive.
“in cells expressing Pro-Code 3, which was linked to a gRNA targeting the CD4 gene, 85% of the cells were CD4 negative, whereas cells expressing Pro-Codes linked to gRNAs targeting unrelated genes were almost all CD4 positive”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataWhen treated with GM-CSF, 60-70% of cells in clusters encoding IL6R, IFNGR1, and IFNGR2 gRNAs upregulated pSTAT5, but only 30-40% of cells in CD116 gRNA clusters did.
“When the cells were treated with GM-CSF, 60–70% of the cells in the clusters encoding gRNAs targeting IL6R, IFNGR1, and IFNGR2 upregulated pSTAT5, but only 30–40% of the cells in the Pro-Code clusters encoding CD116 gRNAs upregulated pSTAT5”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAll 10 epitope tags were detected with clear signal over background, and all epitope-positive cells were positive for NGFR.
“All 10 epitope tags were detected with a clear signal over background, and all of the epitope-positive cells were positive for NGFR”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe same initial mix of 120 Pro-Code subpopulations developed into heterogenic tumors, with 10 populations accounting for up to 50% of total cells.
“The same initial mix of 120 Pro-Code subpopulations developed into heterogenic tumors, in which 10 populations accounted for up to 50% of the total cell number.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataFourteen 4-epitope Pro-Codes were generated, all 10 epitopes were detected, cells were positive for 4 epitopes, and all 14 populations were identified.
“We detected all 10 epitopes, and cells were positive for 4 epitopes. This enabled us to identify all 14 4-epitope Pro-Code populations”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataHLA-A was expressed by >90% of cells in each Pro-Code/CRISPR cluster except those with Pro-Codes 23 and 24 linked to B2m gRNAs, where 45% and 80% were HLA-A negative.
“HLA-A (MHC class I) was expressed by >90% of cells in each Pro-Code/CRISPR cluster, except those expressing Pro-Codes 23 and 24, which were linked to gRNAs targeting B2m; in these populations 45% and 80% of THP1 were HLA-A negative, respectively.”
Quote in the paperNot a substantial claimMay combine two claims. The quote only partly states this claim.
Findingown dataWhen the 96 Pro-Code/CRISPR library was produced as a pool with a low-homology GFP spike, all 96 Pro-Code populations were resolved and specific protein loss was consistently observed on a high percent of cells expressing the cognate gRNA.
“Similar to the library made with individually packaged vectors, we resolved all 96 Pro-Code populations, and consistently observed loss of a specific protein on a high percent of cells expressing a Pro-Code linked to a gRNA targeting the cognate gene”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataSingle-cell analysis indicated significant heterogeneity between cells even within the same Pro-Code cluster, despite bulk analysis showing major reduction in pSTAT signaling downstream of the targeted receptor.
“Thus, while the bulk analysis indicated a major reduction in pSTAT signaling downstream of the receptor targeted by a specific CRISPR, single cell analysis indicated there was significant heterogeneity between cells even within the same Pro-Code cluster.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThere was little evidence of knockout for some gRNAs, consistent with known variability in CRISPR efficiency between gRNAs.
“There was little evidence of KO for some gRNAs, consistent with the known variability in CRISPR efficiency between gRNAs.”
Quote in the paperNot a substantial claim- Rests on an assumption: CRISPR editing efficiency varies between gRNAs.
The quote only partly states this claim.
Findingown dataAll 96 Pro-Code populations were resolved and clustered after transduction of THP1-Cas9 with 96 Pro-Code/CRISPR vectors.
“All 96 Pro-Code populations were resolved and clustered.”
Quote in the paperSubstantial claimThe quote only partly states this claim.
Findingown dataUnsupervised clustering resolved 120 distinct Pro-Code populations across 293T, THP1, Jurkat, and 4T1 cells, each corresponding to one Pro-Code.
“Unsupervised clustering resolved 120 distinct populations (Figure 1F-I), with each population corresponding to one Pro-Code”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataUsing 14 antibodies, the authors detected 364 Pro-Code populations, establishing the largest set of protein-based reporters.
“Using just 14 antibodies, we detected 364 Pro-Code populations; establishing the largest set of protein-based reporters.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe frequency of each Pro-Code population ranged from 0.1% to 3%, with 65% between 0.4-1.5%, close to the expected 0.83% if equimolar.
“The frequency of each population ranged from 0.1% to 3%, with the majority of Pro-Code populations (65%) being between 0.4–1.5% (Figure S2D), which is close to the expected frequency of 0.83% if each of the 120 Pro-Codes was in equimolar concentration.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataWhen treated with IFNγ, 70% of cells in Pro-Code clusters linked to CD116 and IL6R gRNAs had increased pSTAT1, whereas only ~25% of cells in IFNGR1/IFNGR2 clusters did.
“When we treated cells with IFNγ, 70% of the cells in the Pro-Code clusters linked to gRNAs targeting CD116 and IL6R had increased pSTAT1, whereas in the Pro-Code clusters linked to gRNAs targeting IFNGR1 and IFNGR2, only ~25% of the cells had increased pSTAT1”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataSome Pro-Code populations were abundant in every tumor but varied in proportion, while others were abundant only in a single tumor.
“Some Pro-Code populations were abundant in every tumor (e.g. Pro-Codes 108 and 21), but their proportion within each tumor varied greatly, whereas other Pro-Code populations were only abundant in a single tumor, such as Pro-Code 6”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataUsing 14 epitopes, 364 3-epitope Pro-Code vectors were generated and all 364 expressing populations were identified and clustered in 293T cells.
“Using an expanded set of 14 epitopes, we generated 364 3-epitope Pro-Code vectors, and introduced them in to 293T. The cells were stained for NGFR and all 14 epitopes, analyzed by CyTOF, and all 364 Pro-Code expressing populations were readily identified and clustered”
Quote in the paperSubstantial claimThe quote only partly states this claim.
Findingown dataIn cultures containing activated Jedi T cells, populations of 4T1-GFP emerged that had downregulated H-2Kd or GFP and escaped T cell killing, even with individual scrambled gRNA vectors.
“As we observed with the pool of vectors, in cultures containing activated Jedi T cells there emerged populations of 4T1-GFP that had downregulated H-2Kd or GFP and escaped T cell killing”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataCells exposed to activated Jedi T cells upregulated both MHC-I and PD-L1.
“As expected, cells exposed to activated Jedi T cells upregulated both MHC-I and PD-L1”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataAdding a low-homology lentiviral plasmid during vector production reduces barcode swapping, since knockout efficiency was similar for libraries made with individually packaged vectors and those made as a pool with the low-homology vector.
“We also found that introducing a plasmid encoding an LV with low-homology to the library during vector production reduces barcode swapping, as the efficiency of KO was similar between Pro-Code/CRISPR libraries made with individually packaged vectors or vectors produced as a pool with the low-homology vector.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataWhereas <0.1% of control 4T1-GFP cells remained in Jedi co-cultures, ~4% of Rtp4 CRISPR and 10% of Psmb8 CRISPR 4T1-GFP cells remained.
“Whereas <0.1% of control 4T1-GFP cells remained in the Jedi co-cultures, ~4% of the Rtp4 CRISPR and 10% of the Psmb8 CRISPR 4T1-GFP cells remained.”
Quote in the paperSubstantial claimMay combine two claims.
FindingreasoningPro-Codes directly measure the frequency of each CRISPR-carrying cell in a population, whereas DNA barcodes infer vector percentage from sequence frequency.
“as opposed to DNA barcodes in which the percent of each vector is presumed from sequence frequency, with the Pro-Codes, the frequency of each CRISPR-carrying cell within a population is directly determined.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataCo-culture of 4T1 cells with activated CD8+ Jedi T cells caused selective killing of GFP+ cells, quantifiable by flow cytometry.
“When the cells were co-cultured with activated CD8+ Jedi T cells there was selective killing of the GFP+ cells, which could be quantified by flow cytometry”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataCells carrying Ifngr2 CRISPR did not upregulate MHC-I in response to IFNγ but still expressed high levels of MHC-I comparable to activated Jedi T cells.
“Though the cells carrying the Ifngr2 CRISPR did not upregulate MHC-I in response to IFNγ, the cells still expressed high levels of MHC-I (Figure S6B). Indeed, the levels of MHC-I were comparable to the activated Jedi T cells.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe frequency of 4T1-Cas9-mCherry cells expressing Psmb8 and Rtp4 gRNAs did not significantly change, indicating enrichment was dependent on antigen-specific T cell killing.
“The frequency of 4T1-Cas9-mCherry cells expressing Psmb8 and Rtp4 gRNAs did not significantly change, indicating enrichment was dependent on antigen-specific T cell killing.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataA common feature of these residual cells was decreased GFP and/or MHC-I.
“Interestingly, a common feature of these cells was decreased gFp and/or MHC-I”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataSanger sequencing and qPCR confirmed the Rtp4 gene had been mutated and was no longer expressed.
“we used Sanger sequencing and qPCR and confirmed the Rtp4 gene had been mutated and was no longer expressed”
Quote in the paperSubstantial claimMay combine two claims.
FindingreasoningPro-Codes enable analysis of millions of single cells with accurate protein-level quantification.
“The Pro-Codes makes it possible to analyze millions of single cells with precise quantification of protein levels.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataIn cells encoding Ifngr2 CRISPRs, MHC-I levels were normal at steady state, but MHC-I did not increase in Jedi co-cultures as it did in cells carrying unrelated CRISPRs.
“In cells encoding Ifngr2 CRISPRs, there were normal levels of MHC-I expression in steady-state, but the expression of MHC-I on these cells did not increase in the Jedi co-cultures, as it did in cells carrying unrelated CRISPRs.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataMany resistant cells had reduced MHC-I or reduced target antigen GFP.
“many resistant cells had lower levels of MHC-I or the target antigen, GFP.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataThe results support a model in which clonal growth was largely stochastic and not impacted by the Pro-Codes.
“These results support a model in which clonal growth was largely stochastic and not impacted by the Pro-Codes”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationPrevious screens used antigen-specific T cells targeting model tumor-associated antigens such as OVA, gp100, and NY-ESO-1.
“In previous screens, antigen-specific T cells targeting model tumor associated antigen (TAA), such as OVA, gp100, and NY-ESO-1, were used (Manguso et al., 2017; Pan et al., 2018; Patel et al., 2017).”
Quote in the paperSubstantial claim0 of 3 sources checkedMay combine two claims.
PremisereasoningSingle-cell RNA sequencing CRISPR screens have restrained cell throughput and cannot measure important protein-level phenotypic information such as signaling alterations.
“However, the cell throughput is relatively restrained, and important protein-level phenotypic information, such as signaling alterations, cannot be measured.”
Quote in the paperSubstantial claimMay combine two claims.
PremisereasoningDNA barcoding has major limitations, including a bulk-cell readout that prevents ready single-cell analysis.
“DNA barcoding has major limitations. One of the most significant is that the read-out is performed on bulk cells, which means single cells cannot be readily analyzed.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningAs few as 10 epitopes arranged in sets of 3 create 120 unique combinations, and 20 epitopes in 7 positions create 77,520 combinations.
“Using this strategy, as few as 10 epitopes can be arranged in sets of 3 to create 120 unique combinations, and with just 20 epitopes and 7 positions, 77,520 combinations can be generated.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationBarcode swapping can be reduced by co-packaging libraries with a low homology transfer vector.
“Recently, it was reported in pre-print studies that swapping can be reduced by co-packaging libraries with a low homology transfer vector (Adamson et al., 2018; Feldman et al., 2018).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Adamson et al., 2018
Not checked in the free run.
- cites Feldman et al., 2018
Not checked in the free run.
May combine two claims.
PremisereasoningDNA barcoding does not enable direct cell phenotyping; instead, phenotype is inferred from changes in barcode frequency.
“Another limitation is that DNA barcoding does not enable cells to be directly phenotyped. Instead, the phenotype associated with each gene perturbation is inferred from changes in barcode frequency.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
InterpretationreasoningLinear epitopes are needed to assemble the barcodes because they can be encoded by short sequences.
“We reasoned linear epitopes would be needed to assemble the barcodes because they can be encoded by a short sequence (18–42 nucleotides).”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationPackaging vector pools together can lead to varying degrees of barcode swapping.
“As it has recently been reported that packaging vector pools together can lead to varying degrees of barcode swapping (Hill et al., 2018; Sack et al., 2016)”
Quote in the paperSubstantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S4 (based).
Premisenone givenCancer cells can alter gene expression to resist being killed.
“However, cancer cells can alter gene expression to resist being killed.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataThis heterogeneity reflects biological differences between cells in response to cytokine stimulation and cell-to-cell heterogeneity in CRISPR knockout.
“This heterogeneity reflects biological differences between cells in their response to cytokine stimulation, but also reveals cell-to-cell heterogeneity in CRISPR KO, as observed in our studies above measuring the protein levels of the gene targeted by specific CRISPRs.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataPro-Code/CRISPR libraries can be produced as a pool and function at high efficiency.
“These results indicate Pro-Code/CRISPR libraries can be produced as a pool and function at high efficiency”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningLoss of HLA in B2m-targeted clusters represents a downstream phenotype of B2m knockout because B2m is required for HLA stability.
“As B2m is required for HLA stability, the loss of HLA in these clusters represents a downstream phenotype of B2m KO.”
Quote in the paperSubstantial claim- Rests on an assumption: B2m is required for HLA stability.
May combine two claims.
Interpretationown dataPro-Codes are not differentially rejected and can be used in vivo.
“This demonstrates the Pro-Codes are not differentially rejected, and can be used in vivo.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataPro-Codes enable direct single-cell phenotypic analysis of signaling pathways in CRISPR screens, which is not feasible with DNA or RNA level analysis.
“Together, these results demonstrate Pro-Codes enable direct single cell phenotypic analysis of signaling pathways in CRISPR screens, which is not feasible with DNA or RNA level analysis.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe IFNγ pathway has been linked to clinical response to checkpoint inhibitors.
“The IFNγ pathway has been implicated as a key component in the clinical response to checkpoint inhibitors (Minn and Wherry, 2016).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Minn and Wherry, 2016
Not checked in the free run.
May combine two claims.
Interpretationown dataWhile far fewer barcodes than DNA, Pro-Codes provide an order of magnitude more barcodes than current protein reporters.
“While this is far fewer barcodes than achieved with DNa, it is an order of magnitude greater than what currently exists with protein reporters.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationMultiple groups have used scRNA-seq in pooled screens to gain more comprehensive phenotyping and single-cell resolution.
“Several groups have incorporated scRNA-seq into pooled screens to obtain more comprehensive phenotyping than previously possible, and to achieve single cell resolution (Adamson et al., 2016; Datlinger et al., 2017; Dixit et al., 2016; Jaitin et al., 2016).”
Quote in the paperNot a substantial claim0 of 3 sources checkedClassified as probably not a substantial claim. May combine two claims.
PremisecitationSwapping arises when two different vector genomes are packaged in the same virion and template switching occurs during reverse transcription.
“Swapping occurs when two different vector genomes are packaged in the same virion, and there is template switching during reverse transcription (King et al., 2008).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites King et al., 2008 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationMutations in IFNGR1 and JAK, part of IFNγ signaling, have been found in patients who developed resistance to checkpoint inhibitors.
“Mutations in IFNGR1 and JAK, a component of the IFNγ signaling pathway, have been found in patients presenting resistance to checkpoint inhibitors (Gao et al., 2016; Zaretsky et al., 2016).”
Quote in the paperNot a substantial claim0 of 2 sources checkedMay combine two claims.
Study design and conductreasoningThe RFP+ cells serve as an internal control of non-TAA-expressing cells and enable distinction between effects of a specific knockout on cell fitness versus T cell sensitivity.
“The RFP+ cells serve as an internal control of non-TAA expressing cells, and enables distinction between the effects of a specific KO on cell fitness versus T cell sensitivity.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningBy marking hundreds of CRISPR-expressing populations and measuring many protein markers, Pro-Codes expand the types of pooled genetic screens and help facilitate gene function annotation.
“By being able to mark 100s of different CRISPR-expressing populations and measure many protein markers, Pro-Codes expand the types of pooled genetic screens that can be performed, and will help facilitate the annotation of gene functions.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
PremisecitationPsmb8 (Lmp7) is part of the immunoproteasome, which generates peptides for MHC-I.
“Psmb8 (also known as Lmp7) is a component of the immunoproteasome, which functions in generating peptides for MHC-I (Basler et al., 2013)”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Basler et al., 2013 source
Not checked in the free run.
May combine two claims.
InterpretationcitationThese findings are consistent with clinical data correlating resistance to checkpoint inhibitors with mutations in B2m and IFNγ pathways and with recent genome-wide CRISPR screening data.
“These findings are consistent with emerging clinical data correlating resistance to checkpoint inhibitors with mutations in the B2m and IFNγ pathways (Gao et al., 2016; Zaretsky et al., 2016), and with recent genome-wide CRISPR screening data (Patel et al., 2017).”
Quote in the paperNot a substantial claim0 of 3 sources checkedMay combine two claims.
PremisecitationBarcode swapping can happen in pooled retroviral vector libraries, with rates from 6% to 50% depending on the distance between the barcode and the effector molecule.
“barcode swapping can occur in retroviral vector libraries packaged as pools, and the degree of swapping can range from 6% to 50%, depending on the distance between the barcode and effector molecule (i.e. gRNA, shRNA, or cDNA) (Hill et al., 2018; Sack et al., 2016).”
Quote in the paperSubstantial claim0 of 2 sources checkedMay combine two claims.
InterpretationreasoningThis direct frequency measurement allows precise accounting of the number of cells sampled per population and improves analysis.
“This enables precise consideration of the number of cells sampled in each population and informs analysis.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationPsmb8 expression has been reported to correlate positively with tumor-infiltrating lymphocyte abundance in breast cancer.
“its expression has been found to positively correlate with tumor-infiltrating lymphocyte abundance in breast cancer (Lee et al., 2018).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Lee et al., 2018
Not checked in the free run.
May combine two claims.
InterpretationreasoningGenerating genome-wide Pro-Code/CRISPR libraries cannot be done with the relative ease of DNA-barcoded libraries, so Pro-Codes will primarily be used for focused screens targeting 100-500 genes.
“Though generating genome-wide Pro-Code/CRISPR libraries cannot be done at the relative ease of DNA barcoded libraries, the Pro-Code’s will primarily be for more focused screens; concentrating on specific pathways or gene classes, and targeting 100–500 genes.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenAfter acquisition, CyTOF data were normalized with bead-based normalization using the CyTOF software.
“After acquisition, the data were normalized using bead-based normalization using the CyTOF software.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataCas9 expression was verified by western blot with an anti-Cas9 antibody.
“Cas9 expression was confirmed by western blot using anti-Cas9 antibody (Millipore, clone 7A9).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe only known targets of Rtp4 are opioid receptors.
“The only defined targets of Rtp4 are opioid receptors (Decaillot et al., 2008)”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Decaillot et al., 2008 source
Not checked in the free run.
Classified as probably not a substantial claim.
Study design and conductreasoningTo ensure most transduced cells received only one vector, fewer than 10% of cells were transduced in all experiments.
“To ensure that a majority of transduced cells received only one vector, fewer than 10% of cells were transduced in all experiments.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe Icahn School of Medicine at Mount Sinai Institutional Animal Care and Use Committee reviewed and approved all animal protocols in the study.
“The Institutional Animal Care and Use Committee of the Icahn School of Medicine at Mount Sinai reviewed and approved all protocols for animal usage described in the present study.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationRtp4 is a chaperone protein that helps fold G protein coupled receptors.
“Rtp4 (Receptor transporter protein 4) is a chaperone protein involved in folding G protein coupled receptors (Decaillot et al., 2008).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Decaillot et al., 2008 source
Not checked in the free run.
Classified as probably not a substantial claim.
Study design and conductnone givenData were gated to remove normalization beads, debris, dead cells, and doublets, leaving NGFR+ events for clustering and high-dimensional analysis.
“The data were gated to exclude residual normalization beads, debris, dead cells and doublets, leaving NGFR+ events for clustering and high dimensional analyses.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductcitationCyTOF data were first debarcoded with Single Cell Debarcoder using post-assignment stringency filtering and outlier trimming.
“CyTOF data was first debarcoded using Single Cell Debarcoder (Zunder et al., 2015) using post-assignment debarcode stringency filter and outlier trimming.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Zunder et al., 2015 source
Not checked in the free run.
The paper relies on this without support.
Performing individual knockout, knockdown, or overexpression for every gene across experimental systems is impractical, costly, and time-consuming, especially in vivo.
“However, KO, KD, or OE of every gene in a genome in distinct experimental systems is cumbersome, costly, and very time consuming. For in vivo studies, it is even more challenging, and not practically feasible.”
The paper relies on this without support.
DNA barcoding readout is performed on bulk cells and cannot readily analyze single cells.
“One of the most significant is that the read-out is performed on bulk cells, which means single cells cannot be readily analyzed.”
The paper relies on this without support.
B2m is required for HLA stability.
“As B2m is required for HLA stability”
The paper relies on this without support.
CRISPR editing efficiency varies between gRNAs.
“consistent with the known variability in CRISPR efficiency between gRNAs”
The paper relies on this without support.
IFNγ triggers pSTAT1, IL-6 induces pSTAT1 and pSTAT3, and GM-CSF induces pSTAT5.
“IFNγ engagement of the IFNγ receptor (comprised of IFNGR1 and IFNGR2 subunits) triggers phosphorylation of STAT1 (pSTAT1), whereas IL-6 induces pSTAT1 and pSTAT3, and GM-CSF induces pSTAT5”
The paper relies on this without support.
Not all transduced cells will be complete knockouts for Rtp4 or Psmb8 because CRISPR efficiency varies.
“not all cells will be a complete KO for Rtp4 or Psmb8, due to variability in CRISPR efficiency”
The paper relies on this without support.
DNA barcode deconvolution is usually done on bulk cells, so cells with complete, partial, or no knockout are combined in the analysis.
“As DNA barcode de-convolution is generally performed on bulk cells, this means cells with complete, partial or no KO are lumped together in the analysis.”
The paper relies on this without support.
For DNA barcodes, the percentage of each vector is inferred from sequence frequency.
“as opposed to DNA barcodes in which the percent of each vector is presumed from sequence frequency”
The paper relies on this without support.
The cell throughput of scRNA-seq is much lower than that of CyTOF, on the order of thousands versus millions.
“the cell throughput of scRNA-seq is still relatively limited compared to what can be achieved with CyTOF (thousands versus millions)”
The paper relies on this without support.
Transcript capture efficiency in scRNA-seq makes quantitative per-cell gene expression comparison difficult without imputing gene levels.
“the efficiency of transcript capture makes it challenging to quantitatively compare gene expression on a per cell basis without imputing gene levels.”
The paper relies on this without support.
Gene editing does not necessarily change the level of the target transcript.
“As gene editing does not necessarily affect the level of a target transcript”
The paper relies on this without support.
The mechanisms by which IFNγ signaling is essential to immune editing are not well established.
“However, the mechanisms that make IFNγ signaling essential to immune editing are not well established.”
The paper relies on this without support.
Almost nothing is known about how Rtp4 functions in immunity.
“despite being an IFN stimulated gene, almost nothing is known about the role of Rtp4 in immunity.”
The paper relies on this without support.
Subtle reductions in neo-antigen expression on individual cancer cells have not been widely studied in tumors because such measurements are challenging.
“as subtle reductions in the expression of neo-antigens on individual cancer cells has not been widely examined in tumors owing to the challenge of making these measurements.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Protein barcodes based on epitopes offer a promising way to decode pooled elements without enzymes, but their current scale is only around 100 barcodes.
“Epitope-based protein barcodes are a promising method for enzyme-free decoding of pooled elements, but are currently limited in scale to ~100 barcodes (Wroblewska et al., 2018).”
From Optical Pooled Screens in Human Cells (2019) · cites this paper as Wroblewska et al., 2018
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Signaling-screen target switches from receptor to ligand
MinorThe library description identifies CD116, the GM-CSF receptor, as the targeted gene. The subsequent result instead says the guides target GM-CSF.
“Similarly, in cells encoding the Pro-Codes linked to gRNAs targeting GM-CSF there was a 3-fold reduction in pSTAT5 levels in response to GM-CSF, and in cells carrying gRNAs targeting IL6R there was a 2-fold reduction in both pSTAT 1 and pSTAT3 levels in response to IL-6 (Figure 4G, H).”
“The gRNAs targeted the IFNGR1, IFNGR2, IL6R (IL-6 receptor), and CD116 (GM-CSF receptor) genes.”
Figure 5 target-gene count does not match the methods list
MinorThe results state that the Figure 5 library targets 14 genes, but the methods' purported list of those targets contains only 13 names. Either one target is missing from the list or the reported count is wrong.
“We generated a library of 56 CRISPR gRNAs targeting 14 different genes (3 to 4 gRNAs/gene), and paired each CRISPR with a unique Pro-Code, to form a pool of 56 Pro-Code/CRISPR vectors (including 4 scrambled gRNAs) (Figure 5A).”
“The following genes were targeted in the Pro-Code CRISPR library used in Figure 5: B2m, Tap1, H2-D1, Pd-l1,Fak, Ccr4, Nlrc5, Cxcr7, Cd40, Ifngr2, Cldn4, Ephb2 and H2-Ke6.”
Cites
Multiplex Genome Engineering Using CRISPR/Cas Systems
Cong, L., Ran, F. A., Cox, D., Lin, S., Barretto, R., Habib, N., Hsu, P. D., Wu, X., Jiang, W., Marraffini, L. A., et al. (2013) · Science · doi:10.1126/science.1231143 · Cited by 15,339 · 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
Optimized 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
Mutations Associated with Acquired Resistance to PD-1 Blockade in Melanoma
Zaretsky, J. M., Garcia-Diaz, A., Shin, D. S., Escuin-Ordinas, H., Hugo, W., Hu-Lieskovan, S., Torrejon, D. Y., Abril-Rodriguez, G., Sandoval, S., Barthly, L., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1604958 · Cited by 3,237 · 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
An immunogenic personal neoantigen vaccine for patients with melanoma
Ott, P. A., Hu, Z., Keskin, D. B., Shukla, S. A., Sun, J., Bozym, D. J., Zhang, W., Luoma, A., Giobbie-Hurder, A., Peter, L., et al. (2017) · Nature · doi:10.1038/nature22991 · Cited by 2,936 · see citing works
Bendall, S. C., Simonds, E. F., Qiu, P., Amir, E.-A. D., Krutzik, P. O., Finck, R., Bruggner, R. V., Melamed, R., Trejo, A., Ornatsky, O. I., et al. (2011) · Science · doi:10.1126/science.1198704 · Cited by 2,279 · see citing works
Self-citationHighly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry
Giesen, C., Wang, H. A. O., Schapiro, D., Zivanovic, N., Jacobs, A., Hattendorf, B., Schüffler, P. J., Grolimund, D., Buhmann, J. M., Brandt, S., et al. (2014) · Nature Methods · doi:10.1038/nmeth.2869 · Cited by 1,951 · 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
Amir, E.-A. D., Davis, K. L., Tadmor, M. D., Simonds, E. F., Levine, J. H., Bendall, S. C., Shenfeld, D. K., Krishnaswamy, S., Nolan, G. P., & Pe'er, D. (2013) · Nature Biotechnology · doi:10.1038/nbt.2594 · Cited by 1,550 · see citing works
Self-citationLoss of IFN-γ pathway genes in tumor cells as a mechanism of resistance to anti-CTLA-4 therapy
Gao, J., Shi, L. Z., Zhao, H., Chen, J., Xiong, L., He, Q., Chen, T., Roszik, J., Bernatchez, C., Woodman, S. E., et al. (2016) · Cell · doi:10.1016/j.cell.2016.08.069 · Cited by 1,300 · see citing works
High-throughput functional genomics using CRISPR-Cas9
Shalem, O., Sanjana, N. E., & Zhang, F. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3899 · Cited by 1,233 · 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
In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target
Manguso, R. T., Pope, H. W., Zimmer, M. D., Brown, F. D., Yates, K. B., Miller, B. C., Collins, N. B., Bi, K., LaFleur, M. W., Juneja, V. R., et al. (2017) · Nature · doi:10.1038/nature23270 · Cited by 1,147 · see citing works
Multiplexed ion beam imaging of human breast tumors
Angelo, M., Bendall, S. C., Finck, R., Hale, M. B., Hitzman, C., Borowsky, A. D., Levenson, R. M., Lowe, J. B., Liu, S. D., Zhao, S., et al. (2014) · Nature Medicine · doi:10.1038/nm.3488 · Cited by 1,108 · 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
Identification of essential genes for cancer immunotherapy
Patel, S. J., Sanjana, N. E., Kishton, R. J., Eidizadeh, A., Vodnala, S. K., Cam, M., Gartner, J. J., Jia, L., Steinberg, S. M., Yamamoto, T. N., et al. (2017) · Nature · doi:10.1038/nature23477 · Cited by 870 · see citing works
A major chromatin regulator determines resistance of tumor cells to T cell–mediated killing
Pan, D., Kobayashi, A., Jiang, P., Ferrari de Andrade, L., Tay, R. E., Luoma, A. M., Tsoucas, D., Qiu, X., Lim, K., Rao, P., et al. (2018) · Science · doi:10.1126/science.aao1710 · Cited by 852 · see citing works
Multiplexed quantification of proteins and transcripts in single cells
Peterson, V. M., Zhang, K. X., Kumar, N., Wong, J., Li, L., Wilson, D. C., Moore, R., McClanahan, T. K., Sadekova, S., & Klappenbach, J. A. (2017) · Nature Biotechnology · doi:10.1038/nbt.3973 · Cited by 832 · 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
Zunder, E. R., Finck, R., Behbehani, G. K., Amir, E.-A. D., Krishnaswamy, S., Gonzalez, V. D., Lorang, C. G., Bjornson, Z., Spitzer, M. H., Bodenmiller, B., et al. (2015) · Nature Protocols · doi:10.1038/nprot.2015.020 · Cited by 596 · see citing works
Self-citationNear-infrared fluorescent proteins for multicolor in vivo imaging
Shcherbakova, D. M., & Verkhusha, V. V. (2013) · Nature Methods · doi:10.1038/nmeth.2521 · Cited by 541 · see citing works
Web‐Based Analysis and Publication of Flow Cytometry Experiments
Kotecha, N., Krutzik, P. O., & Irish, J. M. (2010) · Current Protocols in Cytometry · doi:10.1002/0471142956.cy1017s53 · Cited by 513 · see citing works
Lu, R., Neff, N. F., Quake, S. R., & Weissman, I. L. (2011) · Nature Biotechnology · doi:10.1038/nbt.1977 · Cited by 438 · see citing works
Mullokandov, G., Baccarini, A., Ruzo, A., Jayaprakash, A. D., Tung, N., Israelow, B., Evans, M. J., Sachidanandam, R., & Brown, B. D. (2012) · Nature Methods · doi:10.1038/nmeth.2078 · Cited by 394 · see citing works
Self-citationCombination Cancer Therapies with Immune Checkpoint Blockade: Convergence on Interferon Signaling
Minn, A. J., & Wherry, E. J. (2016) · Cell · doi:10.1016/j.cell.2016.03.031 · Cited by 272 · see citing works
On the design of CRISPR-based single-cell molecular screens
Hill, A. J., McFaline-Figueroa, J. L., Starita, L. M., Gasperini, M. J., Matreyek, K. A., Packer, J., Jackson, D., Shendure, J., & Trapnell, C. (2018) · Nature Methods · doi:10.1038/nmeth.4604 · Cited by 262 · see citing works
The immunoproteasome in antigen processing and other immunological functions
Basler, M., Kirk, C. J., & Groettrup, M. (2013) · Current Opinion in Immunology · doi:10.1016/j.coi.2012.11.004 · Cited by 249 · see citing works
Quiescent tissue stem cells evade immune surveillance
Agudo, J., Park, E. S., Rose, S. A., Alibo, E., Sweeney, R., Dhainaut, M., Kobayashi, K. S., Sachidanandam, R., Baccarini, A., Merad, M., et al. (2018) · Immunity · doi:10.1016/j.immuni.2018.02.001 · Cited by 244 · see citing works
Self-citationKinetic Analysis Reveals the Fate of a MicroRNA following Target Regulation in Mammalian Cells
Baccarini, A., Chauhan, H., Gardner, T. J., Jayaprakash, A. D., Sachidanandam, R., & Brown, B. D. (2011) · Current Biology · doi:10.1016/j.cub.2011.01.067 · Cited by 235 · see citing works
Self-citationCell surface targeting of μ-δ opioid receptor heterodimers by RTP4
Décaillot, F. M., Rozenfeld, R., Gupta, A., & Devi, L. A. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0804106105 · Cited by 152 · see citing works
Rapid creation and quantitative monitoring of high coverage shRNA libraries
Bassik, M. C., Lebbink, R. J., Churchman, L. S., Ingolia, N. T., Patena, W., LeProust, E. M., Schuldiner, M., Weissman, J. S., & McManus, M. T. (2009) · Nature Methods · doi:10.1038/nmeth.1330 · Cited by 99 · see citing works
Sources of Error in Mammalian Genetic Screens
Sack, L. M., Davoli, T., Xu, Q., Li, M. Z., & Elledge, S. J. (2016) · G3 Genes|Genomes|Genetics · doi:10.1534/g3.116.030973 · Cited by 76 · see citing works
Neoantigens encoded in the cancer genome
Schumacher, T. N., & Hacohen, N. (2016) · Current Opinion in Immunology · doi:10.1016/j.coi.2016.07.005 · Cited by 71 · see citing works
GFP-specific CD8 T cells enable targeted cell depletion and visualization of T-cell interactions
Agudo, J., Ruzo, A., Park, E. S., Sweeney, R., Kana, V., Wu, M., Zhao, Y., Egli, D., Merad, M., & Brown, B. D. (2015) · Nature Biotechnology · doi:10.1038/nbt.3386 · Cited by 64 · see citing works
Self-citationFeldman, D., Singh, A., Garrity, A. J., & Blainey, P. C. (2018) · doi:10.1101/262121 · Cited by 49 · see citing works
Lee, M., Song, I. H., Heo, S., Kim, Y., Park, I. A., Bang, W. S., Park, H. S., Gong, G., & Lee, H. J. (2018) · Cancer Research and Treatment · doi:10.4143/crt.2017.500 · Cited by 35 · see citing works
Approaches to maximize sgRNA-barcode coupling in Perturb-seq screens
Adamson, B., Norman, T. M., Jost, M., & Weissman, J. S. (2018) · doi:10.1101/298349 · Cited by 34 · see citing works
Fread, K., STRICKLAND, W. D., Nolan, G. P., & Zunder, E. R. (2016) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813207813_0054 · Cited by 32 · see citing works
Pseudodiploid genome organization AIDS full-length human immunodeficiency virus type 1 DNA synthesis
King, S. R., Duggal, N. K., Ndongmo, C. B., Pacut, C., & Telesnitsky, A. (2008) · Journal of Virology · doi:10.1128/jvi.02100-07 · Cited by 19 · see citing works
Flagged first, then by citations
1 of the 41 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
CRISPR technology: A decade of genome editing is only the beginning
Wang, J. Y., & Doudna, J. A. (2023) · Science · doi:10.1126/science.add8643 · Cited by 1,058 · see citing works
MDSC: Markers, development, states, and unaddressed complexity
Hegde, S., Leader, A. M., & Mérad, M. (2021) · Immunity · doi:10.1016/j.immuni.2021.04.004 · Cited by 672 · see citing works
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
Bock, C., Datlinger, P., Chardon, F. M., Coelho, M. A., Dong, M. B., Lawson, K. A., Lu, T., Maroc, L., Norman, T. M., Song, B., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00093-4 · Cited by 591 · see citing works
Mimitou, 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
Mimitou, E. P., Lareau, C. A., Chen, K. Y., Zorzetto-Fernandes, A. L., Hao, Y., Takeshima, Y., Luo, W., Huang, T.-S., Yeung, B. Z., Papalexi, E., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00927-2 · Cited by 529 · see citing works
The present and future of the Cancer Dependency Map
Arafeh, R., Shibue, T., Dempster, J. M., Hahn, W. C., & Vazquez, F. (2025) · Nature Reviews Cancer · doi:10.1038/s41568-024-00763-x · Cited by 454 · see citing works
CRISPR in cancer biology and therapy
Katti, A., Diaz, B. J., Caragine, C. M., Sanjana, N. E., & Dow, L. E. (2022) · Nature reviews. Cancer · doi:10.1038/s41568-022-00441-w · Cited by 440 · see citing works
Optical Pooled Screens in Human Cells
Feldman, D., Singh, A., Schmid-Burgk, J. L., Carlson, R. J., Mezger, A., Garrity, A. J., Zhang, F., & Blainey, P. C. (2019) · Cell · doi:10.1016/j.cell.2019.09.016 · Cited by 321 · see citing works
A new era in functional genomics screens
Przybyla, L., & Gilbert, L. A. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-021-00409-w · Cited by 281 · see citing works
Spatial CRISPR genomics identifies regulators of the tumor microenvironment
Dhainaut, M., Rose, S. A., Aktürk, G., Wroblewska, A., Nielsen, S. R., Park, E. S., Buckup, M., Roudko, V., Pia, L., Sweeney, R. E., et al. (2022) · Cell · doi:10.1016/j.cell.2022.02.015 · Cited by 280 · see citing works
Self-citationBock, C., Datlinger, P., Chardon, F. M., Coelho, M. A., Dong, M. B., Lawson, K. A., Lu, T., Maroc, L., Norman, T. M., Song, B., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00098-7 · Cited by 254 · see citing works
Single-cell analysis targeting the proteome
Labib, M., & Kelley, S. O. (2020) · Nature Reviews Chemistry · doi:10.1038/s41570-020-0162-7 · Cited by 233 · see citing works
A Critical Role for Fas-Mediated Off-Target Tumor Killing in T-cell Immunotherapy
Upadhyay, R., Boiarsky, J., Pantsulaia, G., Svensson‐Arvelund, J., Lin, M. J., Wroblewska, A., Bhalla, S., Scholler, N., Bot, A. I., Rossi, J. M., et al. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-0756 · Cited by 210 · see citing works
Self-citationMulti-pass, single-molecule nanopore reading of long protein strands
Motone, K., Kontogiorgos-Heintz, D., Wee, J., Kurihara, K., Yang, S., Roote, G., Fox, O., Fang, Y., Queen, M., Tolhurst, M., et al. (2024) · Nature · doi:10.1038/s41586-024-07935-7 · Cited by 155 · see citing works
A guide to interrogating immunometabolism
Voss, K., Hong, H. S., Bader, J. E., Sugiura, A., Lyssiotis, C. A., & Rathmell, J. C. (2021) · Nature reviews. Immunology · doi:10.1038/s41577-021-00529-8 · Cited by 161 · see citing works
Hematopoietic aging promotes cancer by fueling IL-1⍺–driven emergency myelopoiesis
Park, M. D., Bérichel, J. L., Hamon, P., Wilk, C. M., Belabed, M., Yatim, N., Saffon, A., Boumelha, J., Falcomatà, C., Tepper, A., et al. (2024) · Science · doi:10.1126/science.adn0327 · Cited by 145 · see citing works
Self-citationRood, J. E., Hupalowska, A., & Regev, A. (2024) · Cell · doi:10.1016/j.cell.2024.07.035 · Cited by 139 · see citing works
Burton, O. T., Bricard, O., Tareen, S. H. K., Gergelits, V., Andrews, S. R., Biggins, L., Roca, C. P., Whyte, C., Junius, S., Brajic, A., et al. (2024) · Immunity · doi:10.1016/j.immuni.2024.05.023 · Cited by 84 · see citing works
Self-citationYuan, B., Shen, C., Luna, A., Korkut, A., Marks, D. S., Ingraham, J., & Sander, C. (2020) · Cell Systems · doi:10.1016/j.cels.2020.11.013 · Cited by 128 · see citing works
Immune cell profiling in atherosclerosis: role in research and precision medicine
Fernandez, D. M., & Giannarelli, C. (2021) · Nature Reviews Cardiology · doi:10.1038/s41569-021-00589-2 · Cited by 124 · see citing works
Imaging-based pooled CRISPR screening reveals regulators of lncRNA localization
Wang, C., Lu, T., Emanuel, G., Babcock, H. P., & Zhuang, X. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1903808116 · Cited by 111 · see citing works
Ovarian cancer-derived IL-4 promotes immunotherapy resistance
Mollaoglu, G., Tepper, A., Falcomatà, C., Potak, H. T., Pia, L., Amabile, A., Mateus-Tique, J., Rabinovich, N., Park, M. D., LaMarche, N. M., et al. (2024) · Cell · doi:10.1016/j.cell.2024.10.006 · Cited by 120 · see citing works
Self-citationSánchez-Paulete, A. R., Mateus-Tique, J., Mollaoglu, G., Nielsen, S. R., Marks, A., Lakshmi, A., Khan, J. A., Wilk, C. M., Pia, L., Baccarini, A., et al. (2022) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-21-1075 · Cited by 98 · see citing works
Self-citationCRISPR screens for functional interrogation of immunity
Shi, H., Doench, J. G., & Chi, H. (2022) · Nature reviews. Immunology · doi:10.1038/s41577-022-00802-4 · Cited by 96 · see citing works
High-content imaging-based pooled CRISPR screens in mammalian cells
Yan, X., Stuurman, N., Ribeiro, S. A., Tanenbaum, M. E., Horlbeck, M. A., Liem, C. R., Jost, M., Weissman, J. S., & Vale, R. D. (2020) · The Journal of Cell Biology · doi:10.1083/jcb.202008158 · Cited by 83 · see citing works
High-throughput genome-wide phenotypic screening via immunomagnetic cell sorting
Mair, B., Aldridge, P. M., Atwal, R. S., Philpott, D. N., Zhang, M., Masud, S. N., Labib, M., Tong, A. H. Y., Sargent, E. H., Angers, S., et al. (2019) · Nature Biomedical Engineering · doi:10.1038/s41551-019-0454-8 · Cited by 84 · see citing works
Single-cell omics: experimental workflow, data analyses and applications
Sun, F., Li, H., Sun, D., Fu, S., Gu, L., Shao, X., Wang, Q., Dong, X., Duan, B., Xing, F., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-023-2561-0 · Cited by 81 · see citing works
Dissecting metastasis using preclinical models and methods
Hebert, J. D., Neal, J. W., & Winslow, M. M. (2023) · Nature reviews. Cancer · doi:10.1038/s41568-023-00568-4 · Cited by 75 · see citing works
Adamopoulos, C., Ahmed, T. A., Tucker, M. R., Ung, P. M. U., Xiao, M., Karoulia, Z., Amabile, A., Wu, X., Aaronson, S. A., Ang, C., et al. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-1351 · Cited by 69 · see citing works
Self-citationGuidelines for Optimized Gene Knockout Using CRISPR/Cas9
Campenhout, C. V., Cabochette, P., Veillard, A.-C., Laczik, M., Zelisko-Schmidt, A., Sabatel, C., Dhainaut, M., Vanhollebeke, B., Gueydan, C., & Kruys, V. (2019) · BioTechniques · doi:10.2144/btn-2018-0187 · Cited by 67 · see citing works
Self-citationEffect of CRISPR/Cas9-Edited PD-1/PD-L1 on Tumor Immunity and Immunotherapy
Xu, Y., Chen, C., Guo, Y., Hu, S., & Sun, Z. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.848327 · Cited by 61 · see citing works
Pooled genetic perturbation screens with image-based phenotypes
Feldman, D., Funk, L., Le, A., Carlson, R. J., Leiken, M. D., Tsai, F., Soong, B., Singh, A., & Blainey, P. C. (2022) · Nature Protocols · doi:10.1038/s41596-021-00653-8 · Cited by 59 · see citing works
Multiplexed direct detection of barcoded protein reporters on a nanopore array
Cardozo, N., Zhang, K. H., Doroschak, K. J., Nguyen, A., Siddiqui, Z., Bogard, N., Strauß, K., Ceze, L., & Nivala, J. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-01002-6 · Cited by 55 · see citing works
All-optical visualization of specific molecules in the ultrastructural context of brain tissue
M’Saad, O., Cairns, A., Gulcicek, J., Kasula, R., Liao, J., Kondratiuk, I., Bewersdorf, E. H., Kidd, P., Falahati, H., Gentile, J. E., et al. (2025) · Nature Biotechnology · doi:10.1038/s41587-025-02905-4 · Cited by 50 · see citing works
High-throughput screening for high-efficiency small-molecule biosynthesis
Rienzo, M., Jackson, S. J., Chao, L. K., Leaf, T. A., Schmidt, T. J., Navidi, A. H., Nadler, D. C., Ohler, M., & Leavell, M. D. (2020) · Metabolic Engineering · doi:10.1016/j.ymben.2020.09.004 · Cited by 50 · see citing works
Hulton, C. H., Costa, E. A., Shah, N., Quintanal-Villalonga, À., Heller, G., de Stanchina, E., Rudin, C. M., & Poirier, J. T. (2020) · Nature Cancer · doi:10.1038/s43018-020-0040-8 · Cited by 47 · see citing works
Chemical-Modified Nucleotide-Based Elemental Tags for High-Sensitive Immunoassay
Hu, Z., Sun, G., Jiang, W., Xu, F., Zhang, Y., Xia, M.-C., Pan, X., Xing, Z., Zhang, S., & Zhang, X. (2019) · Analytical Chemistry · doi:10.1021/acs.analchem.9b00405 · Cited by 48 · see citing works
Tompkins, K. J., Houtti, M., Litzau, L. A., Aird, E. J., Everett, B. A., Nelson, A. T., Pornschloegl, L., Swanson, L. K., Evans, R. L., Evans, K., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa1248 · Cited by 48 · see citing works
Single-cell genomics to understand disease pathogenesis
Nomura, S. (2020) · Journal of Human Genetics · doi:10.1038/s10038-020-00844-3 · Cited by 44 · see citing works
David, F., Davis, A. M., Gossing, M., Hayes, M. A., Romero, E., Scott, L. H., & Wigglesworth, M. J. (2021) · SLAS DISCOVERY · doi:10.1177/24725552211000669 · Cited by 45 · see citing works
CRISPR Screens in Synthetic Lethality and Combinatorial Therapies for Cancer
Castells‐Roca, L., Tejero, E., Rodríguez‐Santiago, B., & Surrallés, J. (2021) · Cancers · doi:10.3390/cancers13071591 · Cited by 42 · see citing works
Comprehensive transcription factor perturbations recapitulate fibroblast transcriptional states
Southard, K. M., Ardy, R. C., Tang, A., O'Sullivan, D., Metzner, E., Guruvayurappan, K., & Norman, T. M. (2025) · Nature Genetics · doi:10.1038/s41588-025-02284-1 · Cited by 42 · see citing works
Macrophages: Targets for next-generation cancer immunotherapy
Sun, X., Park, M. D., Mérad, M., & Brown, B. D. (2026) · Cancer Cell · doi:10.1016/j.ccell.2026.01.020 · Cited by 46 · see citing works
Self-citationMassively 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
Receptor–mediated transcytosis of macromolecules across the blood–brain barrier
Baghirov, H. (2023) · Expert Opinion on Drug Delivery · doi:10.1080/17425247.2023.2255138 · Cited by 43 · see citing works
CRISPR screen in mechanism and target discovery for cancer immunotherapy
Liu, D., Zhao, X., Tang, A., Xu, X., Liu, S., Zha, L., Ma, W., Zheng, J., & Shi, M. (2020) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2020.188378 · Cited by 43 · see citing works
Linghu, C., An, B., Shpokayte, M., Celiker, O. T., Shmoel, N., Zhang, R., Zhang, C., Park, D., Park, W. M., Ramirez, S., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01586-7 · Cited by 40 · see citing works
A microRNA expression and regulatory element activity atlas of the mouse immune system
Rose, S. A., Wroblewska, A., Dhainaut, M., Yoshida, H., Shaffer, J. M., Bektesevic, A., Ben-Zvi, B., Rhoads, A., Kim, E. Y., Yu, B., et al. (2021) · Nature Immunology · doi:10.1038/s41590-021-00944-y · Cited by 37 · see citing works
Self-citationAhmadi, S., Sukprasert, P., Vegesna, R., Sinha, S., Schischlik, F., Artzi, N., Khuller, S., Schäffer, A. A., & Ruppin, E. (2022) · Nature Communications · doi:10.1038/s41467-022-29154-2 · Cited by 38 · see citing works
Spatial epitope barcoding reveals clonal tumor patch behaviors
Clavé, X. R., Drainas, A. P., Jiang, S., Bai, Y., Baron, M., Zhu, B., Dallas, A. E., Lee, M. C., Chu, T. P., Holzem, A. M. E., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.09.014 · Cited by 40 · see citing works
Recent advances in microfluidics for single-cell functional proteomics
Gebreyesus, S. T., Muneer, G., Huang, C., Siyal, A. A., Anand, M., Chen, Y., & Tu, H. (2023) · Lab on a Chip · doi:10.1039/d2lc01096h · Cited by 35 · see citing works
Genetic interaction networks in cancer cells
Mair, B., Moffat, J., Boone, C. M., & Andrews, B. (2019) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2019.03.002 · Cited by 32 · see citing works
A Nanoparticle Platform for Accelerated In Vivo Oral Delivery Screening of Nucleic Acids
El-Mayta, R. D., Zhang, R., Shepherd, S. J., Wang, F., Billingsley, M. M., Dudkin, V., Klein, D., Lu, H. D., & Mitchell, M. J. (2020) · Advanced Therapeutics · doi:10.1002/adtp.202000111 · Cited by 31 · see citing works
Tumor immunology CRISPR screening: present, past, and future
Dong, M. B., Tang, K., Zhou, X., Zhou, J. J., & Chen, S. (2021) · Trends in cancer · doi:10.1016/j.trecan.2021.11.009 · Cited by 31 · see citing works
Pooled genetic screens with image‐based profiling
Walton, R. T., Singh, A. K., & Blainey, P. C. (2022) · Molecular Systems Biology · doi:10.15252/msb.202110768 · Cited by 29 · 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
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
Pan-cancer analysis of the prognostic and immunological role of PSMB8
Chen, D., Jin, C. G., Dong, X., Wen, J., Xia, E., Wang, Q., & Wang, O. (2021) · Scientific Reports · doi:10.1038/s41598-021-99724-9 · Cited by 27 · 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
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
Human Organoids as an Emerging Tool for Genome Screenings
Andreatta, F., Hendriks, D. F. G., & Artegiani, B. (2025) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-103023-122327 · Cited by 24 · see citing works
High-content CRISPR screening in tumor immunology
Holcomb, E. A., Pearson, A. N., Jungles, K. M., Tate, A., James, J. G., Jiang, L., Huber, A. K., & Green, M. D. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1041451 · Cited by 24 · see citing works
Interrogating immune cells and cancer with CRISPR-Cas9
Buquicchio, F. A., & Satpathy, A. T. (2021) · Trends in Immunology · doi:10.1016/j.it.2021.03.003 · Cited by 21 · see citing works
New Tools for Lineage Tracing in Cancer In Vivo
Jones, M. G., Yang, D., & Weissman, J. S. (2023) · Annual Review of Cancer Biology · doi:10.1146/annurev-cancerbio-061421-123301 · Cited by 23 · 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
Aregger, M., Xing, K., & Gonatopoulos-Pournatzis, T. (2021) · Nature Protocols · doi:10.1038/s41596-021-00595-1 · Cited by 22 · see citing works
Development of an HIV reporter virus that identifies latently infected CD4+ T cells
Kim, E. H., Manganaro, L., Schotsaert, M., Brown, B. D., Mulder, L. C. F., & Simon, V. R. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100238 · Cited by 21 · see citing works
Self-citationGenome-wide CRISPR/Cas9 screening for drug resistance in tumors
Zhang, Z., Wang, H., Yan, Q., Cui, J., Chen, Y., Ruan, S., Yang, J., Wu, Z., Han, M., Huang, S., et al. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1284610 · Cited by 21 · see citing works
Tari, H., Kessler, K., Trahearn, N., Werner, B., Vinci, M., Jones, C., & Sottoriva, A. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111283 · Cited by 19 · see citing works
Nguyen, K. B., Roerden, M., Copeland, C., Backlund, C. M., Klop-Packel, N. G., Remba, T. K., Kim, B., Singh, N. K., Birnbaum, M. E., Irvine, D. J., et al. (2023) · eLife · doi:10.7554/elife.85263 · Cited by 20 · see citing works
NIS-Seq enables cell-type-agnostic optical perturbation screening
Fandrey, C. I., Jentzsch, M., Konopka, P., Hoch, A., Blumenstock, K., Zackria, A. A., Maasewerd, S., Lovotti, M., Lapp, D. J., Gohr, F. N., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02516-5 · Cited by 25 · see citing works
Zhai, G., Yong-jia, P. A. N. G., Zou, Y., Wang, X., Liu, J., Zhang, Q., Cao, Z., Wang, N., Li, H., & Wang, Y. (2022) · Animals · doi:10.3390/ani13010092 · Cited by 19 · see citing works
Spatial perturb-seq: single-cell functional genomics within intact tissue architecture
Shen, K., Seow, W. Y., Keng, C. T., Lim, M. G. L., Lim, D. S., Guo, K., Meliani, A., Hajis, M. I. B., Wang, B., Prabhakar, S., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-69677-6 · Cited by 23 · see citing works
Marshall, N., Hutchinson, K., Marron, T. U., Aleynick, M., Hammerich, L., Upadhyay, R., Svensson‐Arvelund, J., Brown, B. D., Mérad, M., & Brody, J. (2019) · Cancer Discovery · doi:10.1158/2159-8290.cd-19-0391 · Cited by 19 · see citing works
Self-citationOptimization of CRISPR–Cas system for clinical cancer therapy
Meng, X. L., Wu, T., Lou, Q., Niu, K., Jiang, L., Xiao, Q., Xu, T., & Zhang, L. (2022) · Bioengineering & Translational Medicine · doi:10.1002/btm2.10474 · Cited by 18 · see citing works
Som, A., Pahwa, M., Bawari, S., Saha, N. D., Sasmal, R., Bosco, M. S., Mondal, J., & Agasti, S. S. (2021) · Chemical Science · doi:10.1039/d0sc06860h · Cited by 17 · see citing works
Scalable, multimodal profiling of chromatin accessibility and protein levels in single cellsPreprint
Mimitou, E. P., Lareau, C. A., Chen, K. Y., Zorzetto-Fernandes, A. L., Takeshima, Y., Luo, W., Huang, T.-S., Yeung, B. Z., Thakore, P. I., Wing, J. B., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.08.286914 · Cited by 18 · see citing works
Bao, W., Lyu, J., Feng, G., Guo, L., Zhao, D., You, K., Liu, Y., Li, H., Du, P., Chen, D., et al. (2024) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2024.05.027 · Cited by 17 · see citing works
RTP4 is a novel prognosis-related hub gene in cutaneous melanoma
Li, Y., Qi, J., & Yang, J. (2021) · Hereditas · doi:10.1186/s41065-021-00183-z · Cited by 17 · see citing works
NOTCH1 Drives Sexually Dimorphic Immune Responses in Hepatocellular Carcinoma
Lindblad, K. E., Donné, R., Liebling, I., Bárcena‐Varela, M., Lozano, A., de Galarreta, M. R., Dhainaut, M., Param, N. J., Giotti, B., Cappuyns, S., et al. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-24-1215 · Cited by 16 · see citing works
Self-citationLong noncoding RNA study: Genome-wide approaches
Tao, S., Hou, Y., Diao, L., Hu, Y., Xu, W., Xie, S., & Xiao, Z. (2022) · Genes & Diseases · doi:10.1016/j.gendis.2022.10.024 · Cited by 16 · see citing works
CRISPR/Cas9 screening: unraveling cancer immunotherapy’s ‘Rosetta Stone’
Chen, B., Deng, Y., Ren, X., Zhao, J., & Jiang, C. (2024) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2024.04.014 · Cited by 17 · see citing works
A multiplexed epitope barcoding strategy that enables dynamic cellular phenotypic screens
Kudo, T., Lane, K. M., & Covert, M. W. (2022) · Cell Systems · doi:10.1016/j.cels.2022.02.006 · Cited by 16 · see citing works
Motone, K., Kontogiorgos-Heintz, D., Wee, J., Kurihara, K., Yang, S., Roote, G., Fang, Y., Cardozo, N., & Nivala, J. (2023) · bioRxiv · doi:10.1101/2023.10.19.563182 · Cited by 6 · see citing works
Integration of CRISPR-engineering and hiPSC-based models of psychiatric genomics
Matos, M. R., Ho, S., Schrode, N., & Brennand, K. (2020) · Molecular and Cellular Neuroscience · doi:10.1016/j.mcn.2020.103532 · Cited by 15 · see citing works
Applications of Functional Genomics for Drug Discovery
Kabadi, A. M., McDonnell, E., Frank, C. L., & Drowley, L. (2020) · SLAS DISCOVERY · doi:10.1177/2472555220902092 · Cited by 14 · see citing works
SPARCS, a platform for genome-scale CRISPR screening for spatial cellular phenotypesPreprint
Schmacke, N. A., Mädler, S. C., Wallmann, G., Metousis, A., Bérouti, M., Harz, H., Leonhardt, H., Mann, M., & Hornung, V. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.01.542416 · Cited by 14 · see citing works
Human brain organoids record the passage of time over multiple years in culturePreprint
Faravelli, I., Antón-Bolaños, N., Wei, A., Faits, T., Kumar, A. S., Andreadis, S., Kästli, R., Crespo, M. M., Steiger, M., Leible, D., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.01.679721 · Cited by 14 · see citing works
Leiwe, M. N., Fujimoto, S., Baba, T., Moriyasu, D., Saha, B., Sakaguchi, R., Inagaki, S., & Imai, T. (2024) · Nature Communications · doi:10.1038/s41467-024-49455-y · Cited by 13 · see citing works
Muhtaseb, A. W., & Duan, J. (2022) · Schizophrenia Research · doi:10.1016/j.schres.2022.04.003 · Cited by 11 · see citing works
Charting single-cell lineages with synthetic and natural barcodes
Rodriguez-Fraticelli, A., & Parreno, V. (2026) · Nature Reviews Genetics · doi:10.1038/s41576-026-00943-5 · Cited by 10 · see citing works
Applying CRISPR-Cas9 screens to dissect hematological malignancies
Iyer, D. N., Schimmer, A. D., & Chang, H. (2022) · Blood Advances · doi:10.1182/bloodadvances.2022008966 · Cited by 10 · see citing works
Genome-wide CRISPR screens for the identification of therapeutic targets for cancer treatment
Xue, V. W., Wong, S. C. C., & Cho, W. C. S. (2020) · Expert Opinion on Therapeutic Targets · doi:10.1080/14728222.2020.1820986 · Cited by 11 · see citing works
In vivo Pooled Screening: A Scalable Tool to Study the Complexity of Aging and Age-Related Disease
Jensen, M. B., & Marblestone, A. H. (2021) · Frontiers in Aging · doi:10.3389/fragi.2021.714926 · Cited by 9 · see citing works
Loy, B. V., Pujol, E., Kamata, K., Lee, X. Y., Bakirtzoglou, N., Berwaer, R. V., Vandeput, J., Mestdagh, C., Persoons, L., Wijngaert, B. D., et al. (2025) · PLoS Pathogens · doi:10.1371/journal.ppat.1012571 · Cited by 8 · see citing works
CRISPR-based genetic screens advance cancer immunology
Cao, Y., Li, X., Pan, Y., Wang, H., Yang, S., Hong, L., & Ye, L. (2024) · Science China Life Sciences · doi:10.1007/s11427-023-2571-0 · Cited by 8 · see citing works
Yamamoto, Y., Shimada, S., Akiyama, Y., Tsukihara, S., Sugimoto, R., Kabashima, A., Tokunaga, M., Kinugasa, Y., Kawakami, Y., & Tanaka, S. (2023) · Journal of Gastroenterology · doi:10.1007/s00535-023-01969-w · Cited by 8 · see citing works
Spatial Epitope Barcoding Reveals Subclonal Tumor Patch BehaviorsPreprint
Clavé, X. R., Drainas, A. P., Jiang, S., Bai, Y., Baron, M., Zhu, B., Markovic, M., Coles, G. L., Bassik, M. C., Sage, J., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.29.449991 · Cited by 8 · see citing works
Elzek, M., Christopher, J. A., Breckels, L. M., & Lilley, K. S. (2021) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2021.06.003 · Cited by 7 · see citing works
- Authors
- Wroblewska, A., Dhainaut, M., Ben-Zvi, B., Rose, S. A., Park, E. S., Amir, E.-A. D., Bektesevic, A., Baccarini, A., Merad, M., Rahman, A. H., & Brown, B. D.
- Affiliations
- Icahn School of Medicine at Mount Sinai (US)
- Tisch Cancer Institute (US)
- Corresponding author
- Brown, Brian D.
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 175
- Issue
- 4
- Pages
- 1141-1155.e16
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC6319269
- OpenAlex
- W2898075889
- Cited by
- 168
- References
- 41
- Citations by year
- 2026: 19 · 2025: 25 · 2024: 26 · 2023: 19 · 2022: 26 · 2021: 26 · 2020: 16 · 2019: 9 · 2018: 2
- FWCI
- 4.71
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIH (R01AI113221, R33CA182377, U19AI128949, S10OD023547)
- Belgian American Educational Foundation
- OCRFA
- 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
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Keywords
- CRISPR screen, single-cell phenotyping, mass cytometry, PSMB8, SOCS1, PD-L1, interferon-stimulated genes, cancer immunotherapy
- MeSH terms
- THP-1 Cells, Animals, Epitopes, Flow Cytometry, Humans, Mice, Inbred BALB C, Mass Spectrometry, Immunophenotyping, Jurkat Cells, Proteome, Genomics, HEK293 Cells, Single-Cell Analysis, CRISPR-Cas Systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-05.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-05.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review