Genome-wide CRISPR Screens in Primary Human T Cells Reveal Key Regulators of Immune Function
Open accessPublished version of a preprint
Abstract
Human T cells are central effectors of immunity and cancer immunotherapy. CRISPR-based functional studies in T cells could prioritize novel targets for drug development and improve the design of genetically reprogrammed cell-based therapies. However, large-scale CRISPR screens have been challenging in primary human cells. We developed a new method, single guide RNA (sgRNA) lentiviral infection with Cas9 protein electroporation (SLICE), to identify regulators of stimulation responses in primary human T cells. Genome-wide loss-of-function screens identified essential T cell receptor signaling components and genes that negatively tune proliferation following stimulation. Targeted ablation of individual candidate genes characterized hits and identified perturbations that enhanced cancer cell killing. SLICE coupled with single-cell RNA sequencing (RNA-seq) revealed signature stimulation-response gene programs altered by key genetic perturbations. SLICE genome-wide screening was also adaptable to identify mediators of immunosuppression, revealing genes controlling responses to adenosine signaling. The SLICE platform enables unbiased discovery and characterization of functional gene targets in primary cells.
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- Findingown data
At day 4 after electroporation, more than 80% of transduced (mCherry+) cells were CD8 negative following CD8A targeting.
“At day 4 post-electroporation, the transduced cells (mCherry+) were largely (>80%) CD8 negative (Figure 1B and Figure S1E), indicative of successful targeting by the Cas9-sgRNA combination.”
Quote in the paperSubstantial claim - Findingown data
Sorting dividing and non-dividing primary cells by CFSE gave stronger sgRNA enrichment than growth-based screens with otherwise identical timelines.
“Importantly, sorting dividing and non-dividing primary cells based on CFSE provided stronger enrichment of sgRNA sequences than simple growth-based screens with otherwise identical experimental timelines (Figure S1J).”
Quote in the paperSubstantial claim - Findingown data
SLICE pooled CRISPR screens can discover both positive and negative regulators of proliferation in primary human T cells.
“Taken together, these data demonstrate that SLICE pooled CRISPR screens can be used to discover positive and negative regulators of proliferation in primary human T cells.”
Quote in the paperSubstantial claim- Rests on an assumption: Critical biology of human immune cells, including key signaling pathways and effector functions, may not be recapitulated in immortalized cell lines.
- Rests on an assumption: Primary human T cells can only be cultured ex vivo for limited time spans.
- Rests on an assumption: Current pooled CRISPR screening methods rely on establishing cell lines with stably integrated Cas9 expression cassettes.
- Findingcitation
Targeting CD5 or CBLB enhanced proliferation in human T cells.
“We also found that proliferation could be enhanced in human T cells by targeting CD5 or CBLB, which have reported roles in the negative regulation of T cell stimulation responses (Voisinne et al., 2016).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Voisinne et al., 2016 source
Not checked in the free run.
- Findingown data
The primary and secondary genome-wide screens were well correlated.
“The results were well correlated between the primary and secondary screens (Figure 2C).”
Quote in the paperSubstantial claim
- Findingcitation
sgRNAs targeting essential TCR signaling components such as CD3D and LCP2 inhibited T cell proliferation.
“We found that sgRNAs targeting essential components of TCR signaling such as CD3D and LCP2, inhibited cell proliferation (de Saint Basile et al., 2004; Shen et al., 2009).”
Quote in the paperNot a substantial claim0 of 2 sources checked - Findingown data
Top positive and negative regulators from the pilot screen were confirmed in both biological replicates of the genome-wide screen.
“Top positive and negative regulators from the pilot screen were confirmed in both biological replicates of the GW screen along with numerous other hits (Figure 2A, B).”
Quote in the paperSubstantial claim- Rests on an assumption: Critical biology of human immune cells, including key signaling pathways and effector functions, may not be recapitulated in immortalized cell lines.
- Rests on an assumption: Primary human T cells can only be cultured ex vivo for limited time spans.
- Findingown data
Attempts to stably express S. pyogenes Cas9 by lentivirus in primary human T cells yielded extremely poor transduction efficiencies.
“Our attempts to stably express Streptococcus pyogenes Cas9 by lentivirus in primary human T cells resulted in extremely poor transduction efficiencies.”
Quote in the paperSubstantial claim - Findingown data
sgRNAs targeting CD5 or CBLB ranked in the top 1% by rank difference in both biological replicates.
“sgRNAs targeting these genes were in the top 1% by rank difference in both biological replicates (Figure 1C).”
Quote in the paperSubstantial claim - Findingown data
Several targets from the genome-wide screen for stimulation-induced proliferation also enhanced in vitro tumor killing.
“In summary, several gene targets identified in the genome-wide screen for proliferative response to stimulation also potentiated in vitro tumor killing activity.”
Quote in the paperSubstantial claim - Findingown data
SLICE coupled with single-cell RNA-Seq revealed signature stimulation-response gene programs altered by key genetic perturbations.
“SLICE coupled with single-cell RNA-Seq revealed signature stimulation-response gene programs altered by key genetic perturbations.”
Quote in the paperSubstantial claim - Findingown data
Arrayed RNP gene targeting validated that many tested gene targets increase T cell proliferation after stimulation, consistent with their pooled screen effects.
“This validated the ability of many of the tested gene targets to increase T cell proliferation post stimulation, consistent with their robust effects in the pooled screens (Figure S3A).”
Quote in the paperSubstantial claim - Findingown data
Multiple guides targeting FAM105A were specifically enriched nearly as much as ADORA2A.
“Strikingly, we found that multiple guides targeting a previously uncharacterized gene, FAM105A (rank 15 in CGS-21680 vs. rank 13390 in vehicle control), were specifically enriched nearly to the same extent as ADORA2A (Figure 6B).”
Quote in the paperSubstantial claim - Findingown data
Targeting multiple negative regulators identified in the genome-wide screens such as CD5, RASA2, SOCS1, and CBLB promoted the cluster 10-12 programs.
“Targeting multiple negative regulators identified in the GW screens such as CD5, RASA2, SOCS1, and CBLB promoted the cluster 10–12 programs.”
Quote in the paperSubstantial claim - Findingown data
Several guanine nucleotide binding proteins (GCGR, GNG3, GNAS) ranked higher in the adenosine agonist GW screen.
“We noted that several guanine nucleotide binding proteins with potential roles in adenosine-responsive signaling had higher positive rank scores in the adenosine agonist GW screen, including GCGR (rank 35 vs. 1149), GNG3 (rank 199 vs. 12976), and GNAS (rank 836 vs. 2803).”
Quote in the paperNot a substantial claim - Findingown data
The screens identified dozens of genetic perturbations that positively and negatively modulate T cell proliferation.
“Taken together these screens identified dozens of genetic perturbations that positively and negatively modulate T cell proliferation.”
Quote in the paperSubstantial claim- Rests on an assumption: Critical biology of human immune cells, including key signaling pathways and effector functions, may not be recapitulated in immortalized cell lines.
- Rests on an assumption: Primary human T cells can only be cultured ex vivo for limited time spans.
- Findingown data
Several sgRNAs were enriched only in dividing cells when CGS-21680 was present.
“While many gene modifications promoted TCR proliferative responses to stimulation in the presence or absence of the adenosine receptor agonist, we identified several sgRNAs that were only enriched in the dividing cells in the presence of CGS-21680 (Figure 6A).”
Quote in the paperSubstantial claim- Rests on an assumption: For solid tumors, effective adoptive cell therapies need T cells that can respond strongly to tumor antigens despite immunosuppressive microenvironments.
- Rests on an assumption: Proliferation in response to TCR stimulation is a broad phenotype governed by complex genetics.
- Findingown data
Some sgRNA targets yielded more efficient clearance of cancer cells at 36 hours relative to non-targeting controls.
“NY-ESO specific T cells started to cluster around RFP+ cancer cells at 12 hours, with more efficient cancer cell clearance at 36 hours for certain sgRNA targets compared to non-targeting controls (Figure 5B).”
Quote in the paperSubstantial claim - Findingown data
ADORA2B did not show a preferential proliferative advantage with CGS-21680.
“In contrast, ADORA2B, which is not targeted by CGS-21680, did not show a preferential proliferative advantage in the presence of this selective A2A agonist (Figure 6A).”
Quote in the paperNot a substantial claim- Rests on an assumption: ADORA2B is not targeted by CGS-21680.
- Findingown data
TCEB2 ablation also improved tumor clearance.
“Ablation of TCEB2, a binding partner of SOCS1, also improved tumor clearance, suggesting that the SOCS1/TCEB2 complex restrains T cell responses and is a potential target for immunotherapy (Ilangumaran et al., 2017; Kamizono et al., 2001; Liau et al., 2018).”
Quote in the paperSubstantial claim - Findingown data
T cells engineered to lack negative regulators of proliferation such as SOCS1, CBLB, CD5, and others showed increased surface expression of both CD69 and CD154 compared to non-targeting control cells.
“We found that T cells engineered to lack negative regulators of proliferation, such as SOCS1, CBLB, CD5, and others, also showed increased surface expression levels of both CD69 and CD154 compared to non-targeting control cells (Figure 3D and Figure S3C–D).”
Quote in the paperSubstantial claim - Findingown data
The magnitude of gene perturbation effects was blunted at higher levels of TCR stimulation.
“While similar gene targets appeared as positive and negative regulators across the conditions, the magnitude of the effects were blunted at higher levels of TCR stimulation, suggesting that stronger TCR stimulation can override the effects of these genetic perturbations (Figure 2D and Figure S2E).”
Quote in the paperSubstantial claim - Findingown data
Gene set enrichment analysis revealed significant overrepresentation of gene targets depleted from proliferating cells in the TCR signaling pathway (FDR < 0.01).
“Gene set enrichment analysis (GSEA) revealed significant overrepresentation of gene targets depleted from proliferating cells in the TCR signaling pathway (FDR < 0.01, Figure 2E and Figure S2F).”
Quote in the paperSubstantial claim - Findingown data
Targeted ablation of individual candidate genes identified perturbations that enhanced cancer cell killing.
“Targeted ablation of individual candidate genes characterized hits and identified perturbations that enhanced cancer cell killing.”
Quote in the paperSubstantial claim - Findingown data
A different shared activation program was observed as a result of targeting CD5, TCEB2, RASA2 or CDKN1B.
“A different shared activation program was observed as a result of targeting CD5, TCEB2, RASA2 or CDKN1B.”
Quote in the paperSubstantial claim - Findingown data
These programs tended to be characterized by induction of activation markers (e.g., IL2RA, TNFRSF18/GITR), cell cycle genes (e.g., MKI67, UBE2S, CENPF, TOP2A), and effector molecules (e.g., GZMB).
“These programs tended to be characterized by the induction of markers of activation states (e.g. IL2RA, TNFRSF18/GITR), cell cycle genes (e.g. MKI67, UBE2S, CENPF and TOP2A), and effector molecules (e.g GZMB) (Figure 4F and Figure S4E).”
Quote in the paperSubstantial claim - Findingown data
Guides against CD3D and LCP2 reduced the proliferation index compared to non-targeting controls.
“In contrast, guides against gene targets that were depleted in the proliferating cells in the pooled screens (CD3D and LCP2), showed a reduction in the proliferation index compared to the non-targeting controls.”
Quote in the paperSubstantial claim - Findingown data
LCP2 knockout strongly impaired the ability of T cells to kill A375 cells.
“As expected, knockout of LCP2 – dentified in our screens as essential for a strong TCR stimulation response – robustly disabled T cell killing of A375 cells.”
Quote in the paperSubstantial claim - Findingcitation
Targeting SOCS1 improved T cell clearance to a degree similar to CBLB.
“Targeting SOCS1, a negative regulator of JAK/STAT signaling in T cells, showed enhanced T cell clearance comparable to CBLB (Liau et al., 2018).”
Quote in the paperNot a substantial claim - Findingown data
Arrayed editing and phenotyping revealed targets that boost stimulation-dependent proliferation and activation programs.
“In summary, arrayed editing and phenotyping characterized the effects of genetic perturbations and revealed targets that boost stimulation-dependent proliferation and activation programs.”
Quote in the paperSubstantial claim - Findingcitation
DGKA (rank 17) and DGKZ (rank 1), negative regulators of DAG-mediated signals, restrain human T cell proliferation following stimulation.
“Diacylglycerol (DAG) kinases, DGKA (rank 17) and DGKZ (rank 1), which are negative regulators of DAG-mediated signals, were both found to restrain human T cell proliferation following stimulation (Chen et al., 2016).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Chen et al., 2016 source
Not checked in the free run.
- Findingown data
Many negative regulators in the screen were enriched among hits from a published shRNA screen (Zhou et al., 2014) designed to find targets that boost T cell proliferation in tumor tissue in vivo (FDR < 0.01).
“Many of the negative regulators in our screen were enriched in the list of hits from a published shRNA screen (Zhou et al., 2014) that was designed to discover gene targets that boost T cell proliferation in tumor tissue in vivo (FDR < 0.01).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Zhou et al., 2014 source
Not checked in the free run.
- Findingown data
Knockout of CBLB tended to induce a cell state signature similar to targeting known checkpoint genes BTLA or LAG3.
“Knockout of CBLB tended to induce a cell state signature similar to targeting known checkpoint genes BTLA or LAG3, as evidenced by similarity in cluster representation (Figure S4F).”
Quote in the paperNot a substantial claim - Findingown data
Targets depleted from proliferating cells in the genome-wide screen encode key protein complexes critical for TCR signaling.
“Targets depleted from proliferating cells in this GW screen encode key protein complexes critical for TCR signaling (Figure 2F).”
Quote in the paperSubstantial claim - Findingown data
Stimulated cells generally induced expression of cell cycle genes (e.g., MKI67) and cytolytic granzymes (e.g., GZMB).
“For example, stimulated cells generally induced expression of cell cycle genes (e.g.: MKI67) and cytolytic granzymes (e.g.: GZMB) (Figure 4B).”
Quote in the paperNot a substantial claim - Findingown data
Unstimulated cells largely expressed markers of a resting state, such as IL7R and CCR7.
“In contrast, unstimulated cells largely expressed markers of a resting state, such as IL7R and CCR7.”
Quote in the paperNot a substantial claim - Findingown data
Stimulated cells up-regulated many cell cycle genes, indicating response to TCR stimulus.
“Synthetic bulk gene expression profiles showed that stimulated cells up-regulated many cell cycle genes, indicating response to TCR stimulus (Figure S4B).”
Quote in the paperNot a substantial claim - Findingown data
Targeting LCP2 reduced expression of CD69 and CD154 in stimulated cells.
“Conversely, targeting a positive regulator of TCR signaling, LCP2, reduced expression of CD69 and CD154 in stimulated cells.”
Quote in the paperSubstantial claim - Findingown data
ADORA2A had a large rank difference between CGS-21680 and vehicle conditions (rank 19 vs rank 7399).
“Importantly, ADORA2A – encoding the receptor specifically targeted by CGS-21680 – showed a high rank difference between the two treatment conditions (rank 19 in CGS-21680 vs rank 7399 in vehicle control), indicating that its knockout provided a specific escape from CGS-21680 (Figure 6A and Figure S6C).”
Quote in the paperSubstantial claim- Rests on an assumption: CGS-21680 specifically targets ADORA2A.
- Findingown data
Targeting ADORA2A and FAM105A with two different sgRNAs each led to resistance to suppression by CGS-21680.
“We found that targeting each of these genes with two different sgRNAs each led to resistance to suppression by CGS-21680, as predicted by our screen (Figure 6C).”
Quote in the paperSubstantial claim- Rests on an assumption: For solid tumors, effective adoptive cell therapies need T cells that can respond strongly to tumor antigens despite immunosuppressive microenvironments.
- Findingown data
Stimulated cells were enriched in clusters 9-12, characterized by preferential expression of mitotic cell cycle and T cell activation programs.
“Stimulated cells were enriched in clusters 9–12, which were characterized by preferential expression of mitotic cell cycle and T cell activation cellular programs (Figure S4C).”
Quote in the paperNot a substantial claim - Findingown data
Targeting these genes did not increase proliferation in unstimulated cells.
“Notably, targeting these genes did not increase proliferation in the unstimulated cells, indicating that they are not general regulators of proliferation but instead appear to modulate proliferation induced by TCR signaling.”
Quote in the paperSubstantial claim - Findingown data
These edits did not increase T cell proliferation without TCR stimulation.
“Importantly, these edits did not lead to increased T cell proliferation in the absence of TCR stimulation, suggesting that they selectively overcome CGS-21680 suppression of TCR stimulation.”
Quote in the paperSubstantial claim - Findingown data
Adding a suppressive pressure to the screen can identify perturbations that confer resistance.
“We demonstrated that a suppressive pressure can be added to the screen, in our case an adenosine agonist, to identify gene perturbations that confer resistance.”
Quote in the paperSubstantial claim - Findingown data
ADORA2A and FAM105A targeted T cells resisted CGS-21680 suppression in the in vitro cancer cell killing assay.
“Lastly, we showed that ADORA2A and FAM105A targeted T cells were resistant to suppression by CGS-21680 in the in vitro cancer cell killing assay (Figure S6E).”
Quote in the paperSubstantial claim - Findingown data
SLICE can be employed in CD4+ T cells.
“We focused on CD8+ T cells, but showed that SLICE also can be employed in CD4+ T cells, and eventually may be generalizable to many other primary human cell types.”
Quote in the paperNot a substantial claim - Findingown data
Knocking out at least four targets (SOCS1, TCEB2, RASA2, and CBLB) in human T cells improved both proliferation and in vitro anti-cancer activity.
“We found that ablation of at least four targets (SOCS1, TCEB2, RASA2, and CBLB) in human T cells enhanced both proliferation and in vitro anti-cancer function.”
Quote in the paperSubstantial claim - Premisecitation
Electroporation of Cas9 protein pre-loaded with sgRNAs efficiently edits primary human T cells.
“We previously showed efficient gene editing of primary human T cells by electroporation of Cas9 protein pre-loaded in vitro with sgRNAs (Hultquist et al., 2016; Schumann et al., 2015).”
Quote in the paperSubstantial claim0 of 2 sources checked - Study design and conductown data
Integrated analysis of two independent screens on four human blood donors improved power for target discovery, especially for negative regulators of T cell proliferation.
“Integrated analysis of the two independent screens performed on a total of four human blood donors provided improved power for target discovery, particularly for negative regulators of T cell proliferation (Table S4 and Figure S2D).”
Quote in the paperSubstantial claim- Rests on an assumption: Critical biology of human immune cells, including key signaling pathways and effector functions, may not be recapitulated in immortalized cell lines.
- Rests on an assumption: Primary human T cells can only be cultured ex vivo for limited time spans.
- Interpretationown data
The low lentiviral Cas9 transduction efficiency prevents large-scale pooled screens in primary cells.
“This low efficiency was prohibitive of large-scale pooled screens in primary cells, which are not immortalized and can only be expanded in culture for a limited amount of time.”
Quote in the paperSubstantial claim- Rests on an assumption: Primary human T cells can only be cultured ex vivo for limited time spans.
- Premisecitation
SOCS1 negatively regulates JAK/STAT signaling in T cells.
“Targeting SOCS1, a negative regulator of JAK/STAT signaling in T cells, showed enhanced T cell clearance comparable to CBLB (Liau et al., 2018).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Liau et al., 2018 source
Not checked in the free run.
- Study design and conductown data
Efficient editing for the majority of gene targets was validated by reduced expression of sgRNA target transcripts compared to non-targeting control sgRNAs.
“Efficient editing for the majority of gene targets was validated by reduced expression of sgRNA target transcripts compared to levels in cells with non-targeting control sgRNAs (Figure S4D).”
Quote in the paperSubstantial claim - Interpretationown data
The majority of top genes identified by the screens robustly modulate stimulation-dependent proliferation in human CD8+ T cells.
“This arrayed RNP gene targeting system demonstrated that the majority of top genes identified by our screens robustly modulate stimulation-dependent proliferation in human CD8+ T cells.”
Quote in the paperSubstantial claim- Rests on an assumption: Critical biology of human immune cells, including key signaling pathways and effector functions, may not be recapitulated in immortalized cell lines.
- Rests on an assumption: Primary human T cells can only be cultured ex vivo for limited time spans.
- Interpretationown data
Targeting negative regulators of proliferation can induce specialized stimulation-dependent effector gene programs that could enhance the potency of T cells.
“These data also demonstrate that targeting negative regulators of proliferation can induce specialized stimulation-dependent effector gene programs that could enhance the potency of T cells.”
Quote in the paperSubstantial claim - Interpretationown data
FAM105A is important for transmitting adenosine-mediated suppression in T cells.
“These results suggest an important role for FAM105A in mediating adenosine immunosuppressive signals in T cells.”
Quote in the paperSubstantial claim- Rests on an assumption: For solid tumors, effective adoptive cell therapies need T cells that can respond strongly to tumor antigens despite immunosuppressive microenvironments.
- Premisecitation
CBLB (rank 4) and CD5 (rank 12) work together to inhibit TCR activation via ubiquitination leading to TCR degradation.
“The E3 ubiquitin-protein ligase, CBLB (rank 4) and its interacting partner, CD5 (rank 12), work together to inhibit TCR activation via ubiquitination leading to degradation of the TCR (Voisinne et al., 2016).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Voisinne et al., 2016 source
Not checked in the free run.
- Interpretationown data
The integration of SLICE pooled CRISPR screens and single cell RNA-Seq provides a powerful approach to discover and characterize critical gene pathways in primary human cells.
“The integration of SLICE pooled CRISPR screens and single cell RNA-Seq provides a powerful approach to both discover and characterize critical gene pathways in primary human cells.”
Quote in the paperSubstantial claim- Rests on an assumption: Critical biology of human immune cells, including key signaling pathways and effector functions, may not be recapitulated in immortalized cell lines.
- Rests on an assumption: Primary human T cells can only be cultured ex vivo for limited time spans.
- Interpretationown data
ADORA2A knockout provided specific escape from CGS-21680.
“Importantly, ADORA2A – encoding the receptor specifically targeted by CGS-21680 – showed a high rank difference between the two treatment conditions (rank 19 in CGS-21680 vs rank 7399 in vehicle control), indicating that its knockout provided a specific escape from CGS-21680 (Figure 6A and Figure S6C).”
Quote in the paperSubstantial claim- Rests on an assumption: CGS-21680 specifically targets ADORA2A.
- Interpretationreasoning
Because CRISPR-based target perturbations require pre-stimulation, the results are generally expected to reflect modulators of the TCR response in antigen-experienced cells.
“Because these CRISPR-based target perturbations require pre-stimulation, our results are generally expected to reflect modulators of the TCR-response in antigen-experienced cells.”
Quote in the paperSubstantial claim - Premisecitation
UBASH3A (rank 10), TNFAIP3, and TNIP1 (ranks 13 and 24) inhibit TCR-induced NFkB signaling, a critical survival and growth signal for activated CD8+ T cells.
“UBASH3A (rank 10), TNFAIP3, and its partner TNIP1 (rank 13 and 24, respectively) inhibit TCR-induced NFkB signaling, a critical survival and growth signal for activated CD8+ T cells (Düwel et al., 2009; Ge et al., 2017).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Düwel et al., 2009 source
Not checked in the free run.
- cites Ge et al., 2017
Not checked in the free run.
- Interpretationown data
The SLICE platform enables unbiased discovery and characterization of functional gene targets in primary cells.
“The SLICE platform enables unbiased discovery and characterization of functional gene targets in primary cells.”
Quote in the paperSubstantial claim- Rests on an assumption: Critical biology of human immune cells, including key signaling pathways and effector functions, may not be recapitulated in immortalized cell lines.
- Rests on an assumption: Primary human T cells can only be cultured ex vivo for limited time spans.
- Rests on an assumption: Current pooled CRISPR screening methods rely on establishing cell lines with stably integrated Cas9 expression cassettes.
- Interpretationown data
These targets appear to selectively affect adenosine receptor-mediated T cell suppression.
“These gene targets appear to play a selective role in adenosine receptor-mediated T cell suppression.”
Quote in the paperSubstantial claim- Rests on an assumption: For solid tumors, effective adoptive cell therapies need T cells that can respond strongly to tumor antigens despite immunosuppressive microenvironments.
- Interpretationown data
Stronger TCR stimulation can override the effects of these genetic perturbations.
“While similar gene targets appeared as positive and negative regulators across the conditions, the magnitude of the effects were blunted at higher levels of TCR stimulation, suggesting that stronger TCR stimulation can override the effects of these genetic perturbations (Figure 2D and Figure S2E).”
Quote in the paperSubstantial claim - Interpretationown data
The edits selectively overcome CGS-21680 suppression of TCR stimulation.
“Importantly, these edits did not lead to increased T cell proliferation in the absence of TCR stimulation, suggesting that they selectively overcome CGS-21680 suppression of TCR stimulation.”
Quote in the paperSubstantial claim - Interpretationown data
The study found extracellular and intracellular targets whose modification could make T cells resistant to adenosine suppression.
“Thus, we identified both extracellular and intracellular targets that could be modified to generate T cells resistant to adenosine suppression.”
Quote in the paperSubstantial claim - Interpretationown data
Consistent hits are probably not caused by heterogeneity in the starting populations.
“However, the reproducible trends of top targets across multiple donors and screens, as well as in the RNP validation experiments, suggest that consistent hits are most likely not determined by heterogeneity of the starting populations.”
Quote in the paperSubstantial claim - Interpretationown data
Combining these CRISPR-based functional genetic studies quickly identified human T cell genes whose targeting boosts stimulation-dependent proliferation, activation, effector programs, and in vitro cancer cell killing.
“Integration of these CRISPR-based functional genetic studies rapidly pinpointed genes in human T cells that can be targeted to enhance stimulation-dependent proliferation, activation responses, effector programs, and in vitro cancer cell killing.”
Quote in the paperSubstantial claim - Premisecitation
A recent case of CAR T cell therapy for CLL illustrated the potential of human primary T cell loss-of-function screens.
“The potential power of human primary T cell loss-of-function screens was recently demonstrated in a patient who received CAR T cell therapy for chronic lymphocytic leukemia (CLL) (Fraietta et al., 2018).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Fraietta et al., 2018 source
Not checked in the free run.
- Interpretationnone given
SLICE screens can be run routinely at large scale using primary cells from multiple donors.
“SLICE screens can be performed routinely at large-scale in primary cells from multiple human donors, ensuring biologically reproducible discoveries.”
Quote in the paperSubstantial claim - Interpretationreasoning
SLICE pooled screening is flexible and versatile, adaptable to probing diverse genetic programs in primary T cell biology.
“The SLICE pooled screening approach is flexible and versatile as it can be adapted to probe diverse genetic programs that regulate primary T cell biology.”
Quote in the paperSubstantial claim - Interpretationreasoning
The platform could be used to find targets that improve specialized T cell functions.
“This exemplifies the potential for this platform to be used to discover gene targets that can enhance specialized T cell functions.”
Quote in the paperSubstantial claim - Interpretationreasoning
SLICE offers a new method for genome-wide CRISPR loss-of-function screening in primary human T cells.
“SLICE provides a new platform for genome-wide CRISPR loss-of-function screens in primary human T cells, a cell type that has been central to a revolution in cancer immunotherapies.”
Quote in the paperSubstantial claim - Interpretationown data
SLICE can identify known and novel components of a pathway that primary cells need to respond to a specific extracellular cue.
“Overall, these findings demonstrate that SLICE is able to identify both known and novel components of a pathway required for primary cell-response to a specific extracellular cue.”
Quote in the paperSubstantial claim- Rests on an assumption: For solid tumors, effective adoptive cell therapies need T cells that can respond strongly to tumor antigens despite immunosuppressive microenvironments.
- Interpretationown data
RASA2 and SOCS1/TCEB2 complex members might also be modifiable targets for immunotherapy.
“Our data suggest that RASA2 and the SOCS1/TCEB2 complex members may also be targets for modulation in immunotherapies.”
Quote in the paperSubstantial claim
- May combine two claims. 29
- The quote only partly states this claim. 1
- Classified as probably not a substantial claim. 15
May combine two claims.
Findingown dataTargeting PTPRC (CD45) confirmed successful knockout at a second target and showed efficacy in both CD8+ and CD4+ T cells.
“By targeting PTPRC (CD45) with the same delivery strategy, we confirmed successful knockout at a second target and demonstrated efficacy of the system in both CD8+ and CD4+ T cells (Figure S1F).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataLoss of CD8 protein was specifically caused by the targeting sgRNA, since cells with a non-targeting control sgRNA retained high CD8 expression.
“Loss of CD8 protein was specifically programmed by the targeting sgRNA, as cells transduced with a non-targeting control sgRNA retained high levels of CD8 expression.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataGenome-wide loss-of-function screens in primary human T cells identified essential TCR signaling components and genes that negatively tune proliferation after stimulation.
“Genome-wide loss-of-function screens identified essential T cell receptor signaling components and genes that negatively tune proliferation following stimulation.”
Quote in the paperSubstantial claim- Rests on an assumption: Critical biology of human immune cells, including key signaling pathways and effector functions, may not be recapitulated in immortalized cell lines.
- Rests on an assumption: Primary human T cells can only be cultured ex vivo for limited time spans.
- Rests on an assumption: Current pooled CRISPR screening methods rely on establishing cell lines with stably integrated Cas9 expression cassettes.
May combine two claims.
Findingown dataSLICE (sgRNA lentiviral infection with Cas9 protein electroporation) achieves effective and specific disruption of target genes in primary human T cells.
“We conclude that sgRNA lentiviral infection with Cas9 protein electroporation (SLICE) results in effective and specific disruption of target genes.”
Quote in the paperSubstantial claim- Rests on an assumption: Critical biology of human immune cells, including key signaling pathways and effector functions, may not be recapitulated in immortalized cell lines.
- Rests on an assumption: Primary human T cells can only be cultured ex vivo for limited time spans.
- Rests on an assumption: Current pooled CRISPR screening methods rely on establishing cell lines with stably integrated Cas9 expression cassettes.
May combine two claims.
Findingown dataUnstimulated T cells had donor-dependent basal gene expression patterns, while stimulated cells from the two donors tended to share a transcriptional signature and cluster together.
“While the unstimulated T cells had donor-dependent basal gene expression patterns, stimulated cells from the two donors tended to have a shared transcriptional signature and clustered together.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown datasgRNAs targeting CD3D or LCP2 inhibited the cluster 10 activation program and promoted the cluster 1-2 programs, characterized by expression of IL7R and CCR7.
“In contrast, sgRNAs targeting CD3D or LCP2 inhibited the cluster 10 activation program and promoted the cluster 1–2 programs, characterized by expression of IL7R and CCR7.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataGene targets that impaired TCR-dependent proliferation included those encoding the delta and zeta chains of the TCR complex (negative ranks 18 and 6), LCK (negative rank 20), and ZAP70 (negative rank 299).
“gene targets that impaired TCR dependent proliferation included those encoding delta and zeta chains of the TCR complex itself (negative rank 18 and 6, respectively), and LCK (negative rank 20) which directly phosphorylates and activates the TCR ITAMs and the central signaling mediator ZAP70 (negative rank 299) (Dave et al., 1998; Tsuchihashi et al., 2000; Wang et al., 2010).”
Quote in the paperSubstantial claim0 of 3 sources checkedMay combine two claims.
Findingown dataCells with non-targeting control sgRNAs were relatively evenly distributed among clusters, while cells with CBLB and CD5 sgRNAs were enriched in clusters associated with proliferation and activation, and cells with LCP2 sgRNAs were mostly in clusters characterized by resting profiles.
“Cells with non-targeting control sgRNAs were relatively evenly distributed among clusters. In contrast, cells with CBLB and CD5 sgRNAs were enriched in clusters associated with proliferation and activation, and those with LCP2 sgRNAs were found mostly in clusters characterized by resting profiles (Figure 4D).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataCBLB and CD5 knockout cells showed a marked increase in divisions after stimulation compared to controls, persistent across different guide RNAs and blood donors.
“For example, CBLB and CD5 knockout cells showed a marked increase in number of divisions post stimulation compared to controls, persistent across different guide RNAs and blood donors (Figure 3B).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe percent of cells expressing activation markers was higher for positive hits than for non-targeting control guides, consistent across two sgRNAs per gene for four donors.
“Overall, the percent of cells expressing these activation markers in each condition was higher for positive hits compared to non-targeting control guides, consistent across two sgRNAs per gene, for four donors (Figure 3E).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataTCR stimulation had a strong effect in inducing an activated cell state across biological replicates, although more cells appeared strongly stimulated in Donor 1 than in Donor 2.
“TCR stimulation thus had a strong effect in inducing an activated cell state across biological replicates, although more cells appeared to have been stimulated strongly in Donor 1 than in Donor 2.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataNY-ESO TCR-transduced T cells triggered caspase-dependent death of A375 targets, evidenced by a caspase increase and a time-dependent drop in RFP-tagged A375 nuclei.
“These transduced T cells were able to induce caspase-mediated cell death in the target A375 cells, which was exhibited by a rise in the level of caspase and a decline in the level of RFP-tagged A375 nuclei over time (Figure S5B).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataUBASH3A, CBLB, CD5, and RASA2 knockout T cells all showed greater than 2-fold increase in proliferation index compared to non-targeting control cells.
“UBASH3A, CBLB, CD5, and RASA2 knockout T cells all showed greater than 2-fold increase in the proliferation index compared to non-targeting control cells (Figure 3C).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataSLICE genome-wide screening was adaptable to identify mediators of immunosuppression, revealing genes controlling responses to adenosine signaling.
“SLICE genome-wide screening was also adaptable to identify mediators of immunosuppression, revealing genes controlling responses to adenosine signaling.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAblating TCEB2, SOCS1, CBLB, or RASA2 each led to significantly greater tumor cell clearance than non-targeting control T cells.
“In contrast, CRISPR-ablation of negative regulators TCEB2, SOCS1, CBLB and RASA2 each significantly increased tumor cell clearance compared to control T cells electroporated with a non-targeting guide RNA (Figure 5C).”
- Also stated in: § Screen Hits Boost Cancer Cell Killing in vitro by Engineered Human T Cells · plain
“Targeted deletion of these four genes led to improved kinetics of cancer cell clearance in our assay compared to the non-targeting control conditions (Figure 5D and Figure S5C–D).”
Quote in the paperSubstantial claim- Also stated in: § Screen Hits Boost Cancer Cell Killing in vitro by Engineered Human T Cells · plain
May combine two claims. The quote only partly states this claim.
Study design and conductown dataMultiple sgRNAs targeting CD5 or CBLB had concordant effects, indicating the phenotype was not due to off-target effects.
“Furthermore, multiple sgRNAs targeting these genes had concordant effects, increasing our confidence that the phenotype was not due to off-target effects (Figure S1I, Table S2).”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationTCEB2 (rank 5) complexes with RNF7 (rank 34), CUL5 (rank 162), and SOCS1 (rank 3), a key suppressor of JAK/STAT signaling in activated T cells.
“TCEB2 (rank 5) complexes with RNF7 (rank 34), CUL5 (rank 162), and SOCS1 (rank 3), a key suppressor of JAK/STAT signaling in activated T cells (Kamura et al., 1998; Liau et al., 2018).”
Quote in the paperNot a substantial claim0 of 2 sources checkedMay combine two claims.
Interpretationown dataRASA2 may modulate T cell proliferation, activation, and anti-tumor immunity.
“RASA2, a GTPase-activating protein that stimulates the GTPase activity of wild-type RAS (Arafeh et al., 2015), has not been studied in the context of primary T cells and the immune system, but our findings suggest it may be a modulator of T cell proliferation, activation, and anti-tumor immunity.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationA GWAS of allergic diseases points to a credible missense risk variant in FAM105A.
“Although little is known about FAM105A function, GWAS of allergic diseases implicates a credible missense risk variant in this gene (Ferreira et al., 2017).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Ferreira et al., 2017 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationCBLB is a known intracellular checkpoint; mouse studies show that targeting it in T cells improves tumor control.
“Among these, CBLB has been best studied as an intracellular immune checkpoint that can be targeted in T cells to improve tumor control in mouse models (Chiang et al., 2007; Hinterleitner et al., 2012).”
Quote in the paperNot a substantial claim0 of 2 sources checkedMay combine two claims.
Interpretationown dataThese genes are not general regulators of proliferation but modulate proliferation induced by TCR signaling.
“Notably, targeting these genes did not increase proliferation in the unstimulated cells, indicating that they are not general regulators of proliferation but instead appear to modulate proliferation induced by TCR signaling.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataThe observed dose-response confirmed that most screen hits depend on the TCR stimulus and tune proliferative responses.
“The observed dose-response confirmed that the majority of the screen hits are dependent on the TCR stimulus and serve to tune resulting proliferative responses.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationLCK directly phosphorylates and activates the TCR ITAMs and ZAP70.
“gene targets that impaired TCR dependent proliferation included those encoding delta and zeta chains of the TCR complex itself (negative rank 18 and 6, respectively), and LCK (negative rank 20) which directly phosphorylates and activates the TCR ITAMs and the central signaling mediator ZAP70 (negative rank 299) (Dave et al., 1998; Tsuchihashi et al., 2000; Wang et al., 2010).”
Quote in the paperNot a substantial claim0 of 3 sources checkedMay combine two claims.
Interpretationown dataThe SOCS1/TCEB2 complex restrains T cell responses and is a potential immunotherapy target.
“Ablation of TCEB2, a binding partner of SOCS1, also improved tumor clearance, suggesting that the SOCS1/TCEB2 complex restrains T cell responses and is a potential target for immunotherapy (Ilangumaran et al., 2017; Kamizono et al., 2001; Liau et al., 2018).”
Quote in the paperSubstantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationOtulin (FAM105B) is a deubiquitinase essential for immune regulation.
“A neighboring paralogue gene, Otulin (FAM105B), encodes a deubiquitinase with an essential role in immune regulation (Damgaard et al., 2016; Fiil and Gyrd-Hansen, 2016) (Figure S6D).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Damgaard et al., 2016 source
Not checked in the free run.
- cites Fiil and Gyrd-Hansen, 2016
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationRASA2 is a GTPase-activating protein that boosts GTP hydrolysis by wild-type RAS.
“RASA2, a GTPase-activating protein that stimulates the GTPase activity of wild-type RAS (Arafeh et al., 2015), has not been studied in the context of primary T cells and the immune system, but our findings suggest it may be a modulator of T cell proliferation, activation, and anti-tumor immunity.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Arafeh et al., 2015 source
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
Study design and conductown dataNormalized enrichment scores and p-values in the gene set enrichment analysis were determined by a permutation test with 10,000 iterations using same-size randomized gene sets and adjusted by the FDR method.
“Normalized enrichment scores and p-values were determined by a permutation test with 10,000 iterations with same size randomized gene sets and adjusted with the FDR method.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductown dataGene-level p-values in the screen analysis were obtained by a permutation test that randomizes guide assignments and were adjusted for false discovery using the Benjamini-Hochberg method.
“The p-value for each gene is determined by a permutation test, randomizing guide assignments and adjusted for false discovery rates by the Benjamini–Hochberg method.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe proliferation index calculated from the fitted CFSE models is robust to variation in starting CFSE staining intensities.
“This parameter is robust to variation in the starting CFSE staining intensities.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Premisenone givenMouse studies have examined CBLB as an intracellular checkpoint whose targeting enhances CD8+ T cell tumor control.
“Of these, CBLB has been studied in mouse models as an intracellular checkpoint that can be targeted to enhance tumor control by CD8+ T cells.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
InterpretationreasoningSLICE allows more systematic searching for genetic perturbations that improve expansion and effector function in adoptive T cell therapies.
“SLICE now provides an opportunity to search more systematically for genetic perturbations that enhance cell expansion and effector function for adoptive T cell therapies.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenAll donor replicates within each screen were grouped in the analysis to account for biological noise.
“All donor replicates in each screen were grouped for analysis to account for biological noise.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationIn that patient, T cells carrying a lentiviral insertion in TET2 expanded preferentially at peak response and probably contributed to the complete response.
“T cells with a lentiviral integration disrupting TET2 showed strong preferential expansion at the peak of a patient’s response and likely contributed to this complete response to CAR T cell therapy.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Fraietta et al., 2018 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenPrimary T cell screens can use various extracellular selective pressures or FACS-based phenotypic selections.
“Primary T cell screens can be performed with various extracellular selective pressures and/or FACS-based phenotypic selections.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe fitted CFSE models were visually inspected and fitting parameters adjusted to minimize deviance from the original CFSE signal.
“The fitted models were inspected visually, adjusting fitting parameters to minimize deviance from the original CFSE signal.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationThe gene set used as external reference for the in vivo immunotherapy hits in Figure 2E comprises 43 genes identified by Zhou et al. as having at least three shRNA guides enriched more than fourfold in tumor-tissue T cells relative to spleen.
“For the external gene set for in vivo immunotherapy hits shown in Figure 2E, we used the 43 genes as determined by Zhou et al. (Zhou et al., 2014), having 3 or more shRNA guides with over 4-fold enrichment in T cells from tumor tissue compared to spleen.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Zhou et al., 2014 source
Not checked in the free run.
May combine two claims.
InterpretationreasoningThe authors developed a new pooled CRISPR screening technology that could explore unmapped genetic circuits in primary human cells and inform engineered cell therapy design.
“In summary, we have developed a novel pooled CRISPR screening technology with the potential to explore unmapped genetic circuits in primary human cells and to guide the design of engineered cell therapies.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductown dataIn the pooled CRISPR screen analysis, non-targeting control guides were used to estimate the normalization size factor and to construct the mean-variance model for the null distribution used to call significant guide enrichment.
“We used the non-targeting control guides to estimate the size factor for normalization, as well as to build the mean-variance model for null distribution, which is used to find significant guide enrichment.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductcitationAll single-cell samples were processed simultaneously in the same experiment, and sample origin was not regressed out in the Seurat analysis.
“Of note, all samples were processed simultaneously in the same experiment and thus sample origin was not regressed out (Butler et al., 2018).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Butler et al., 2018 source
Not checked in the free run.
The paper relies on this without support.
Critical biology of human immune cells, including key signaling pathways and effector functions, may not be recapitulated in immortalized cell lines.
“Critical biology of human immune cells, including key signaling pathways and effector functions, may not be recapitulated in immortalized cell lines.”
The paper relies on this without support.
Primary human T cells can only be cultured ex vivo for limited time spans.
“primary human T cells – which can only be cultured ex vivo for limited time spans”
“which are not immortalized and can only be expanded in culture for a limited amount of time”
The paper relies on this without support.
Current pooled CRISPR screening methods rely on establishing cell lines with stably integrated Cas9 expression cassettes.
“Current pooled CRISPR screening methods rely on establishing cell lines with stably integrated Cas9 expression cassettes.”
The paper relies on this without support.
Systematic strategies to comprehensively analyze the function of genes that regulate human T cell responses are still lacking.
“systematic strategies to comprehensively analyze the function of genes that regulate human T cell responses are still lacking”
The paper relies on this without support.
For solid tumors, effective adoptive cell therapies need T cells that can respond strongly to tumor antigens despite immunosuppressive microenvironments.
“Adoptive cell therapies that are effective for the treatment of solid tumors will require cells that can respond robustly to tumor antigens even in immunosuppressive tumor microenvironments.”
The paper relies on this without support.
Proliferation in response to TCR stimulation is a broad phenotype governed by complex genetics.
“We elected to use proliferation in response to TCR stimulation as our screening output, as it is a broad phenotype governed by complex genetics.”
The paper relies on this without support.
CGS-21680 specifically targets ADORA2A.
“ADORA2A – encoding the receptor specifically targeted by CGS-21680 – showed a high rank difference between the two treatment conditions (rank 19 in CGS-21680 vs rank 7399 in vehicle control), indicating that its knockout provided a specific escape from CGS-21680 (Figure 6A and Figure S6C).”
The paper relies on this without support.
ADORA2B is not targeted by CGS-21680.
“In contrast, ADORA2B, which is not targeted by CGS-21680, did not show a preferential proliferative advantage in the presence of this selective A2A agonist (Figure 6A).”
The paper relies on this without support.
The parental CFSE signal peak can shift slightly from well to well in arrayed experiments.
“As CFSE staining for arrays was done for individual populations of edited cells, the signal peak for the parental population might shift slightly from well to well.”
The paper relies on this without support.
An unstimulated well is expected to have a single CFSE peak at the end of the assay.
“for each well the stimulated well was compared to an identical unstimulated well, expected to have a single peak at the end of the assay.”
The authors state this as an assumption.
CFSE profiles can be modeled as Gaussian distributions with log2-distanced peaks from successive cell divisions and CFSE dilution.
“These CFSE profiles are modeled as Gaussian distributions, with log2 distanced peaks resulting from cell divisions and CFSE dilution.”
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?
Earlier studies limited differential expression testing to predefined candidate genes or grouped genes into signatures.
“differential gene expression testing was restricted to pre-defined candidate genes7,9,10 or genes were grouped into gene signatures3,4,6,8.”
From Targeted Perturb-seq enables genome-scale genetic screens in single cells (2020) · cites this paper as [3]
PROTACs have the potential to be first-in-class medicines in immuno-oncology as small-molecule drugs that target immune cell activation to phenocopy PD1/PDL1-directed agents.
“PROTACs have the potential to be first-in-class medicines in immuno-oncology as small-molecule drugs that target immune cell activation to phenocopy PD1/PDL1-directed agents178,179.”
From PROTAC targeted protein degraders: the past is prologue (2022) · cites this paper as [179]
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.
Genome-wide indexing PCR lists conflicting template volumes
MinorThe preparation method says 1 μL of purified product was taken for the second PCR, but its recipe specifies 5 μL of that product per reaction.
“Next 1 μL was taken from each purified PCR product to go into a second PCR for indexing.”
“Each reaction included 5μL of PCR product, 25μL of NEBNext 2x Master Mix (NEB, cat #M0541L), 1.25μL each of the 10μM p5-i5-read1 and read2-i7-p7 indexing primers, and water to 50μL total per reaction.”
Pilot-screen CFSE staining has two different dates
MinorThe results place CFSE labeling 10 days after electroporation, whereas the screen methods place electroporation on Day 2 and CFSE staining on Day 14-12 days later.
“At day 10 post-electroporation, cells were labeled with CFSE to track cell divisions and then TCR stimulated.”
“24 hours after transduction (Day 2), cells were electroporated with Cas9 protein. Cells were then cultured in media with IL-2 at 50U/mL and split every two days, keeping a density of 1e6 cells/mL. On day 14, cells were CFSE stained and then restimulated”
Cites
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174 · see citing works
Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-Small-Cell Lung Cancer
Reck, M., Rodríguez-Abreu, D., Robinson, A. G., Hui, R., Csőszi, T., Fülöp, A., Gottfried, M., Peled, N., Tafreshi, A., Cuffe, S., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1606774 · Cited by 10,089 · see citing works
UMAP: Uniform Manifold Approximation and Projection
McInnes, L., Healy, J., Saul, N., & Großberger, L. (2018) · Journal of Open Source Software · doi:10.21105/joss.00861 · Cited by 9,826 · see citing works
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
McInnes, L., Healy, J., & Melville, J. (2018) · arXiv · doi:10.48550/arxiv.1802.03426 · Cited by 7,101 · see citing works
Primary, Adaptive, and Acquired Resistance to Cancer Immunotherapy
Sharma, P., Hu-Lieskovan, S., Wargo, J. A., & Ribas, A. (2017) · Cell · doi:10.1016/j.cell.2017.01.017 · Cited by 5,588 · see citing works
The Reactome pathway Knowledgebase
Fabregat, A., Sidiropoulos, K., Garapati, P. V., Gillespie, M. E., Hausmann, K., Haw, R., Jassal, B., Jupe, S., Korninger, F., McKay, S., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1351 · Cited by 5,568 · see citing works
Overall Survival with Combined Nivolumab and Ipilimumab in Advanced Melanoma
Wolchok, J. D., Chiarion-Sileni, V., Gonzalez, R., Rutkowski, P., Grob, J.-J., Cowey, C. L., Lao, C. D., Wagstaff, J., Schadendorf, D., Ferrucci, P. F., et al. (2017) · New England Journal of Medicine · doi:10.1056/nejmoa1709684 · Cited by 5,357 · 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
Genome-scale CRISPR-Cas9 knockout screening in human cells
Shalem, O., Sanjana, N. E., Hartenian, E., Shi, X., Scott, D. A., Mikkelsen, T. S., Heckl, D., Ebert, B. L., Root, D. E., Doench, J. G., et al. (2014) · Science · doi:10.1126/science.1247005 · Cited by 5,234 · see citing works
Repurposing CRISPR as an RNA-Guided Platform for Sequence-Specific Control of Gene Expression
Qi, L. S., Larson, M. H., Gilbert, L. A., Doudna, J. A., Weissman, J. S., Arkin, A. P., & Lim, W. A. (2013) · Cell · doi:10.1016/j.cell.2013.02.022 · Cited by 5,016 · 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
Fast gene set enrichment analysisPreprint
Korotkevich, G., Sukhov, V., Budin, N., Shpak, B., Artyomov, M. N., & Sergushichev, A. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/060012 · Cited by 3,742 · see citing works
Car t cell immunotherapy for human cancer
June, C. H., O’Connor, R. S., Kawalekar, O. U., Ghassemi, S., & Milone, M. C. (2018) · Science · doi:10.1126/science.aar6711 · Cited by 3,207 · see citing works
Genetic screens in human cells using the CRISPR-Cas9 system
Wang, T., Wei, J. J., Sabatini, D. M., & Lander, E. S. (2014) · Science · doi:10.1126/science.1246981 · Cited by 2,856 · see citing works
The Reactome Pathway Knowledgebase
Fabregat, A., Jupe, S., Matthews, L. R., Sidiropoulos, K., Gillespie, M. E., Garapati, P. V., Haw, R., Jassal, B., Korninger, F., May, B., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1132 · Cited by 2,798 · see citing works
Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation
Gilbert, L. A., Horlbeck, M. A., Adamson, B., Villalta, J. E., Chen, Y., Whitehead, E. H., Guimaraes, C., Panning, B., Ploegh, H. L., Bassik, M. C., et al. (2014) · Cell · doi:10.1016/j.cell.2014.09.029 · Cited by 2,728 · see citing works
Li, W., Xu, H., Xiao, T., Cong, L., Love, M. I., Zhang, F., Irizarry, R. A., Liu, J. S., Brown, M., & Liu, X. S. (2014) · Genome Biology · doi:10.1186/s13059-014-0554-4 · Cited by 2,684 · 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
Adenosine Receptors as Therapeutic Targets
Jacobson, K. A., & Gao, Z.-G. (2006) · Nature Reviews Drug Discovery · doi:10.1038/nrd1983 · Cited by 1,431 · see citing works
Epigenetic stability of exhausted T cells limits durability of reinvigoration by PD-1 blockade
Pauken, K. E., Sammons, M. A., Odorizzi, P. M., Manne, S., Godec, J., Khan, O., Drake, A. M., Chen, Z., Sen, D. R., Kurachi, M., et al. (2016) · Science · doi:10.1126/science.aaf2807 · Cited by 1,348 · see citing works
Genome-scale CRISPR-Cas9 Knockout and Transcriptional Activation Screening
Joung, J., Konermann, S., Gootenberg, J. S., Abudayyeh, O. O., Platt, R. J., Brigham, M. D., Sanjana, N. E., & Zhang, F. (2017) · Nature Protocols · doi:10.1038/nprot.2017.016 · Cited by 1,243 · 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 1,215 · 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
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
The epigenetic landscape of T cell exhaustion
Sen, D. R., Kaminski, J., Barnitz, R. A., Kurachi, M., Gerdemann, U., Yates, K. B., Tsao, H.-W., Godec, J., LaFleur, M. W., Brown, F. D., et al. (2016) · Science · doi:10.1126/science.aae0491 · Cited by 986 · see citing works
Multiplex genome editing to generate universal CAR T cells resistant to PD1 inhibition
Ren, J., Liu, X., Fang, C., Jiang, S., June, C. H., & Zhao, Y. (2017) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-16-1300 · Cited by 942 · see citing works
Chromatin states define tumour-specific T cell dysfunction and reprogramming
Philip, M., Fairchild, L., Sun, L., Horste, E. L., Camara, S., Shakiba, M., Scott, A. C., Viale, A., Lauer, P., Merghoub, T., et al. (2017) · Nature · doi:10.1038/nature22367 · Cited by 933 · 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
Disruption of TET2 promotes the therapeutic efficacy of CD19-targeted T cells
Fraietta, J. A., Nobles, C. L., Sammons, M. A., Lundh, S., Carty, S. A., Reich, T. J., Cogdill, A. P., Morrissette, J. J. D., DeNizio, J. E., Reddy, S., et al. (2018) · Nature · doi:10.1038/s41586-018-0178-z · Cited by 862 · 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
Reprogramming human T cell function and specificity with non-viral genome targeting
Roth, T. L., Puig-Saus, C., Yu, R., Shifrut, E., Carnevale, J., Li, P. J., Hiatt, J., Saco, J., Krystofinski, P., Li, H., et al. (2018) · Nature · doi:10.1038/s41586-018-0326-5 · Cited by 839 · see citing works
Self-citationDissecting 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
Rupp, L. J., Schumann, K., Roybal, K. T., Gate, R. E., Ye, C. J., Lim, W. A., & Marson, A. (2017) · Scientific Reports · doi:10.1038/s41598-017-00462-8 · Cited by 757 · see citing works
Self-citationGeneration of knock-in primary human T cells using Cas9 ribonucleoproteins
Schumann, K., Lin, S., Boyer, E., Simeonov, D. R., Subramaniam, M., Gate, R. E., Haliburton, G. E., Ye, C. J., Bluestone, J. A., Doudna, J. A., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1512503112 · Cited by 702 · see citing works
Self-citationKamura, T., Sato, S., Haque, D., Liu, L., Kaelin, W. G., Conaway, R. C., & Conaway, J. W. (1998) · Genes & Development · doi:10.1101/gad.12.24.3872 · Cited by 595 · see citing works
Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology
23andMe Research Team, Ferreira, M. A., collaborators, A., BIOS consortium, Study, L. C., Vonk, J. M., Baurecht, H., Marenholz, I., Tian, C., Hoffman, J. D., et al. (2017) · Nature Genetics · doi:10.1038/ng.3985 · Cited by 550 · see citing works
Morgan, M. T., Anders, S., Lawrence, M. F., Aboyoun, P., Pagès, H., & Gentleman, R. C. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp450 · Cited by 545 · see citing works
NetPath: a public resource of curated signal transduction pathways
Kandasamy, K., Mohan, S. S., Raju, R., Keerthikumar, S., Kumar, G. S. S., Venugopal, A. K., Telikicherla, D., Navarro, J. D., Mathivanan, S., Pecquet, C., et al. (2010) · Genome biology · doi:10.1186/gb-2010-11-1-r3 · Cited by 506 · see citing works
The molecular basis of JAK/STAT inhibition by SOCS1
Liau, N. P. D., Laktyushin, A., Lucet, I. S., Murphy, J. M., Yao, S., Whitlock, E., Callaghan, K., Nicola, N. A., Kershaw, N. J., & Babon, J. J. (2018) · Nature Communications · doi:10.1038/s41467-018-04013-1 · Cited by 456 · see citing works
Am I ready for CRISPR? A user's guide to genetic screens
Doench, J. G. (2018) · Nature Reviews Genetics · doi:10.1038/nrg.2017.97 · Cited by 452 · see citing works
A Genome-wide CRISPR Screen in Primary Immune Cells to Dissect Regulatory Networks
Parnas, O., Jovanovic, M., Eisenhaure, T. M., Herbst, R. H., Dixit, A., Ye, C. J., Przybylski, D., Platt, R. J., Tirosh, I., Sanjana, N. E., et al. (2015) · Cell · doi:10.1016/j.cell.2015.06.059 · Cited by 440 · see citing works
c-Cbl and Cbl-b regulate T cell responsiveness by promoting ligand-induced TCR down-modulation
Naramura, M., Jang, I. K., Kole, H. K., Huang, F., Haines, D. C., & Gu, H. (2002) · Nature Immunology · doi:10.1038/ni855 · Cited by 396 · see citing works
ZAP-70: an essential kinase in T-cell signaling
Wang, H., Kadlecek, T. A., Au-Yeung, B. B., Goodfellow, H. E. S., Hsu, L. Y., Freedman, T. S., & Weiss, A. (2010) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a002279 · Cited by 395 · see citing works
A versatile system for rapid multiplex genome-edited CAR T cell generation
Ren, J., Zhang, X., Liu, X., Fang, C., Jiang, S., June, C. H., & Zhao, Y. (2017) · Oncotarget · doi:10.18632/oncotarget.15218 · Cited by 395 · see citing works
Single and Dual Amino Acid Substitutions in TCR CDRs Can Enhance Antigen-Specific T Cell Functions
Robbins, P. F., Li, Y. F., El‐Gamil, M., Zhao, Y., Wargo, J. A., Zheng, Z., Xu, H., Morgan, R. A., Feldman, S. A., Johnson, L. A., et al. (2008) · The Journal of Immunology · doi:10.4049/jimmunol.180.9.6116 · Cited by 385 · see citing works
Seki, A., & Rutz, S. (2018) · The Journal of Experimental Medicine · doi:10.1084/jem.20171626 · Cited by 369 · see citing works
The deubiquitinase OTULIN is an essential negative regulator of inflammation and autoimmunity
Damgaard, R. B., Walker, J. A., Marco-Casanova, P., Morgan, N. V., Titheradge, H. L., Elliott, P. R., McHale, D., Maher, E. R., McKenzie, A. N. J., & Komander, D. (2016) · Cell · doi:10.1016/j.cell.2016.07.019 · Cited by 348 · see citing works
Vav-family proteins in T-cell signalling
Tybulewicz, V. L. J. (2005) · Current Opinion in Immunology · doi:10.1016/j.coi.2005.04.003 · Cited by 339 · see citing works
Targeting the adenosine 2A receptor enhances chimeric antigen receptor T cell efficacy
Beavis, P. A., Henderson, M. A., Giuffrida, L., Mills, J. K., Sek, K., Cross, R. S., Davenport, A. J., John, L. B., Mardiana, S., Slaney, C. Y., et al. (2017) · Journal of Clinical Investigation · doi:10.1172/jci89455 · Cited by 326 · see citing works
The SOCS box of SOCS-1 accelerates ubiquitin-dependent proteolysis of TEL-JAK2
Kamizono, S., Hanada, T., Yasukawa, H., Minoguchi, S., Kato, R., Minoguchi, M., Hattori, K., Hatakeyama, S., Yada, M., Morita, S., et al. (2001) · Journal of Biological Chemistry · doi:10.1074/jbc.m010074200 · Cited by 311 · see citing works
López‐Cabrera, M., Santis, A. G., Fernández‐Ruiz, E., Blacher, R. W., Esch, F. S., Sánchez‐Mateos, P., & Sánchez‐Madrid, F. (1993) · The Journal of Experimental Medicine · doi:10.1084/jem.178.2.537 · Cited by 279 · see citing works
Fine Tuning of TCR Signaling by CD5
Azzam, H. S., Dejarnette, J. B., Huang, K. L., Emmons, R. P., Park, C., Sommers, C. L., El‐Khoury, D., Shores, E. W., & Love, P. E. (2001) · The Journal of Immunology · doi:10.4049/jimmunol.166.9.5464 · Cited by 279 · see citing works
In vivo discovery of immunotherapy targets in the tumour microenvironment
Zhou, P., Shaffer, D. R., Alvarez Arias, D. A., Nakazaki, Y., Pos, W., Torres, A. J., Cremasco, V., Dougan, S. K., Cowley, G. S., Elpek, K., et al. (2014) · Nature · doi:10.1038/nature12988 · Cited by 237 · see citing works
A20 Negatively Regulates T Cell Receptor Signaling to NF-κB by Cleaving Malt1 Ubiquitin Chains
Düwel, M., Welteke, V., Oeckinghaus, A., Baens, M., Kloo, B., Ferch, U., Darnay, B. G., Ruland, J., Marynen, P., & Krappmann, D. (2009) · The Journal of Immunology · doi:10.4049/jimmunol.0803313 · Cited by 233 · see citing works
Regulation of Vav–SLP-76 Binding by ZAP-70 and Its Relevance to TCRζ/CD3 Induction of Interleukin-2
Raab, M., da Silva, A. J., Findell, P. R., & Rudd, C. E. (1997) · Immunity · doi:10.1016/s1074-7613(00)80422-7 · Cited by 222 · see citing works
Surface markers of lymphocyte activation and markers of cell proliferation
Shipkova, M., & Wieland, E. (2011) · Clinica Chimica Acta · doi:10.1016/j.cca.2011.11.006 · Cited by 210 · see citing works
Severe combined immunodeficiency caused by deficiency in either the δ or the ε subunit of CD3
Basile, G. D. S., Geissmann, F., Flori, E., Uring-Lambert, B., Soudais, C., Cavazzana-Calvo, M., Durandy, A., Jabado, N., Fischer, A., & Deist, F. L. (2004) · Journal of Clinical Investigation · doi:10.1172/jci200422588 · Cited by 171 · see citing works
Hultquist, J. F., Schumann, K., Woo, J. M., Manganaro, L., McGregor, M. J., Doudna, J., Simon, V., Krogan, N. J., & Marson, A. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.09.080 · Cited by 170 · see citing works
Self-citationAn expanding role for RAS GTPase activating proteins (RAS GAPs) in cancer
Maertens, O., & Cichowski, K. M. (2014) · Advances in Biological Regulation · doi:10.1016/j.jbior.2014.04.002 · Cited by 170 · see citing works
Adenosine and adenosine receptors in the immunopathogenesis and treatment of cancer
Kazemi, M., Mohseni, S. R., Hojjat-Farsangi, M., Anvari, E., Ghalamfarsa, G., Mohammadi, H., & Jadidi-Niaragh, F. (2018) · Journal of Cellular Physiology · doi:10.1002/jcp.25873 · Cited by 139 · see citing works
Severe combined immunodeficiency caused by deficiency in either the δ or the ε subunit of CD3
Basile, G. D. S., Geissmann, F., Flori, E., et al. (2004) · Journal of Clinical Investigation · doi:10.1172/jci22588 · Cited by 132 · see citing works
Ablation of Cbl-b provides protection against transplanted and spontaneous tumors
Chiang, J. Y., Jang, I. K., Hodes, R., & Gu, H. (2007) · Journal of Clinical Investigation · doi:10.1172/jci29472 · Cited by 124 · see citing works
Targeting A2 adenosine receptors in cancer
Allard, D., Turcotte, M., & Stagg, J. (2017) · Immunology and Cell Biology · doi:10.1038/icb.2017.8 · Cited by 117 · see citing works
Shang, W., Jiang, Y., Boettcher, M., Ding, K., Mollenauer, M. N., Liu, Z., Wen, X., Liu, C., Hao, P., Zhao, S., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1801340115 · Cited by 115 · see citing works
Recurrent inactivating RASA2 mutations in melanoma
Arafeh, R., Qutob, N., Emmanuel, R., Keren-Paz, A., Madore, J., Elkahloun, A., Wilmott, J. S., Gartner, J. J., Di Pizio, A., Winograd-Katz, S., et al. (2015) · Nature Genetics · doi:10.1038/ng.3427 · Cited by 107 · see citing works
Wargo, J. A., Robbins, P. F., Li, Y., Zhao, Y., El‐Gamil, M., Caragacianu, D. L., Zheng, Z., Hong, J. A., Downey, S. G., Schrump, D. S., et al. (2008) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-008-0562-x · Cited by 87 · see citing works
Ge, Y., Paisie, T. K., Newman, J. R. B., McIntyre, L. M., & CONCANNON, P. J. (2017) · Diabetes · doi:10.2337/db16-1023 · Cited by 75 · see citing works
Aarts, M., Georgilis, A., Beniazza, M., Beolchi, P., Banito, A., Carroll, T., Kulisic, M., Kaemena, D. F., Dharmalingam, G., Martin, N., et al. (2017) · Genes & Development · doi:10.1101/gad.297796.117 · Cited by 64 · see citing works
Hinterleitner, R., Gruber, T., Pfeifhofer-Obermair, C., Lutz-Nicoladoni, C., Tzankov, A., Schuster, M., Penninger, J. M., Loibner, H., Lametschwandtner, G., Wolf, D., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0044295 · Cited by 59 · see citing works
Voisinne, G., García‐Blesa, A., Chaoui, K., Fiore, F., Bergot, E., Girard, L., Malissen, M., Burlet‐Schiltz, O., Gonzalez de Peredo, A., Malissen, B., et al. (2016) · Molecular Systems Biology · doi:10.15252/msb.20166837 · Cited by 50 · see citing works
Diacylglycerol kinase ζ controls diacylglycerol metabolism at the immunological synapse
Gharbi, S., Rincón, E., Ávila‐Flores, A., Torres‐Ayuso, P., Almena, M., Cobos-Fernández, M., Albar, J. P., & Mérida, I. (2011) · Molecular Biology of the Cell · doi:10.1091/mbc.e11-03-0247 · Cited by 50 · see citing works
Dennehy, K. M., Elias, F., Na, S.-Y., Fischer, K.-D., Hünig, T., & Lühder, F. (2007) · The Journal of Immunology · doi:10.4049/jimmunol.178.3.1363 · Cited by 45 · see citing works
Diacylglycerol kinases in T cell tolerance and effector function
Chen, S. S., Hu, Z., & Zhong, X.-P. (2016) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2016.00130 · Cited by 44 · see citing works
SOCS1: Regulator of T Cells in Autoimmunity and Cancer
Ilangumaran, S., Bobbala, D., & Ramanathan, S. (2017) · Current topics in microbiology and immunology · doi:10.1007/82_2017_63 · Cited by 37 · see citing works
RhoH Regulates Subcellular Localization of ZAP-70 and Lck in T Cell Receptor Signaling
Chae, H.-D., Siefring, J. E., Hildeman, D. A., Gu, Y., & Williams, D. A. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013970 · Cited by 35 · see citing works
Bartelt, R. R., & Houtman, J. C. D. (2012) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1194 · Cited by 34 · see citing works
Arranz‐Nicolás, J., Ogando, J., Soutar, D., Arcos, R., Meraviglia-Crivelli, D., Mañes, S., Mérida, I., & Ávila‐Flores, A. (2018) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-018-2154-8 · Cited by 33 · see citing works
An improved technique for calculating relative response in cellular proliferation experiments
Munson, M. E. (2010) · Cytometry Part A · doi:10.1002/cyto.a.20935 · Cited by 26 · see citing works
OTULIN deficiency causes auto-inflammatory syndrome
Fiil, B. K., & Gyrd‐Hansen, M. (2016) · Cell Research · doi:10.1038/cr.2016.113 · Cited by 15 · see citing works
Shen, S., Lau, J., Zhu, M., Zou, J., Fuller, D., Li, Q.-J., & Zhang, W. (2009) · Blood · doi:10.1182/blood-2008-09-177832 · Cited by 13 · see citing works
Dave, V. (1998) · International Immunology · doi:10.1093/intimm/10.10.1481 · Cited by 8 · see citing works
flowFit: a Bioconductor package to estimate proliferation in cell-tracking dye studies
Rambaldi, D., Pece, S., & Fiore, P. P. D. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu127 · Cited by 8 · see citing works
Tsuchihashi (2000) · doi:10.1002/1521-4141(200006)30:6
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PROTAC targeted protein degraders: the past is prologue
Békés, M., Langley, D. R., & Crews, C. M. (2022) · Nature Reviews Drug Discovery · doi:10.1038/s41573-021-00371-6 · Cited by 3,395 · see citing works
Recent advances and discoveries in the mechanisms and functions of CAR T cells
Larson, R. C., & Maus, M. V. (2021) · Nature Reviews Cancer · doi:10.1038/s41568-020-00323-z · Cited by 852 · see citing works
Natural killer cells in antitumour adoptive cell immunotherapy
Laskowski, T. J., Biederstadt, A., & Rezvani, K. (2022) · Nature Reviews. Cancer · doi:10.1038/s41568-022-00491-0 · Cited by 680 · see citing works
Treg cell-based therapies: challenges and perspectives
Raffin, C., Vo, L. T., & Bluestone, J. A. (2019) · Nature reviews. Immunology · doi:10.1038/s41577-019-0232-6 · Cited by 728 · see citing works
CAR immune cells: design principles, resistance and the next generation
Labanieh, L., & Mackall, C. (2023) · Nature · doi:10.1038/s41586-023-05707-3 · Cited by 606 · 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
Cano-Gamez, E., & Trynka, G. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00424 · Cited by 581 · see citing works
APOE4/4 is linked to damaging lipid droplets in Alzheimer’s disease microglia
Haney, M. S., Pálovics, R., Munson, C. N., Long, C., Johansson, P. K., Yip, O., Dong, W., Rawat, E., West, E., Schlachetzki, J. C. M., et al. (2024) · Nature · doi:10.1038/s41586-024-07185-7 · Cited by 536 · see citing works
Current applications and future perspective of CRISPR/Cas9 gene editing in cancer
Wang, S.-W., Gao, C., Zheng, Y.-M., Yi, L., Lu, J.-C., Huang, X.-Y., Cai, J.-B., Zhang, P.-F., Cui, Y.-H., & Ke, A.-W. (2022) · Molecular Cancer · doi:10.1186/s12943-022-01518-8 · Cited by 478 · see citing works
Targeting REGNASE-1 programs long-lived effector T cells for cancer therapy
Wei, J., Long, L., Zheng, W., Dhungana, Y., Lim, S. A., Guy, C. S., Wang, Y., Wang, Y., Qian, C., Xu, B., et al. (2019) · Nature · doi:10.1038/s41586-019-1821-z · Cited by 442 · see citing works
Applications of single-cell RNA sequencing in drug discovery and development
de Sande, B. V., Lee, J. S., Mutasa-Gottgens, E., Naughton, B. J., Bacon, W., Manning, J. R., Wang, Y., Pollard, J. R., Mendez, M., Hill, J., et al. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-023-00688-4 · Cited by 441 · 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
An NK-like CAR T cell transition in CAR T cell dysfunction
Good, C. R., Aznar, M. A., Kuramitsu, S., Samareh, P., Agarwal, S., Donahue, G., Ishiyama, K., Wellhausen, N., Rennels, A. K., Ma, Y., et al. (2021) · Cell · doi:10.1016/j.cell.2021.11.016 · Cited by 396 · see citing works
CAR T Cell Therapy for Solid Tumors: Bright Future or Dark Reality?
Wagner, J., Wickman, E., DeRenzo, C. C., & Gottschalk, S. M. (2020) · Molecular Therapy · doi:10.1016/j.ymthe.2020.09.015 · Cited by 383 · see citing works
Belk, J. A., Yao, W., Ly, N., Freitas, K. A., Chen, Y.-T., Shi, Q., Valencia, A. M., Shifrut, E., Kale, N., Yost, K. E., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.06.001 · Cited by 382 · see citing works
Self-citationAn Activity-Guided Map of Electrophile-Cysteine Interactions in Primary Human T Cells
Vinogradova, E. V., Zhang, X., Remillard, D., Lazar, D. C., Suciu, R. M., Wang, Y., Bianco, G., Yamashita, Y., Crowley, V. M., Schafroth, M. A., et al. (2020) · Cell · doi:10.1016/j.cell.2020.07.001 · Cited by 351 · see citing works
Epigenetic regulation of T cell exhaustion
Belk, J. A., Dániel, B., & Satpathy, A. T. (2022) · Nature Immunology · doi:10.1038/s41590-022-01224-z · Cited by 334 · see citing works
Systematic Immunotherapy Target Discovery Using Genome-Scale In Vivo CRISPR Screens in CD8 T Cells
Dong, M. B., Wang, G., Chow, R. D., Ye, L., Zhu, L., Dai, X., Park, J. J., Kim, H. R., Errami, Y., Guzman, C. D., et al. (2019) · Cell · doi:10.1016/j.cell.2019.07.044 · Cited by 330 · see citing works
Lozano, A. X., Chaudhuri, A. A., Nene, A., Bacchiocchi, A., Earland, N. J., Vesely, M. D., Usmani, A., Turner, B. E., Steen, C. B., Luca, B. A., et al. (2022) · Nature Medicine · doi:10.1038/s41591-021-01623-z · Cited by 314 · see citing works
CRISPR activation and interference screens decode stimulation responses in primary human T cells
Schmidt, R., Steinhart, Z., Layeghi, M., Freimer, J. W., Bueno, R., Nguyen, V., Blaeschke, F., Ye, C. J., & Marson, A. (2022) · Science · doi:10.1126/science.abj4008 · Cited by 285 · see citing works
Self-citationTargeting the DNA damage response in immuno-oncology: developments and opportunities
Chabanon, R. M., Rouanne, M., Lord, C. J., Soria, J., Pasero, P., & Postel-Vinay, S. C. (2021) · Nature reviews. Cancer · doi:10.1038/s41568-021-00386-6 · Cited by 300 · 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
Promises and challenges of adoptive T-cell therapies for solid tumours
Morotti, M., Albukhari, A., Alsaadi, A., Artibani, M., Brenton, J. D., Curbishley, S. M., Dong, T., Dustin, M. L., Hu, Z., McGranahan, N., et al. (2021) · British Journal of Cancer · doi:10.1038/s41416-021-01353-6 · Cited by 267 · 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/s43586-022-00098-7 · Cited by 254 · see citing works
Function and Role of Regulatory T Cells in Rheumatoid Arthritis
Jiang, Q., Yang, G., Liu, Q., Wang, S., & Cui, D. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.626193 · Cited by 248 · see citing works
Targeted Perturb-seq enables genome-scale genetic screens in single cells
Schraivogel, D., Gschwind, A. R., Milbank, J. H., Leonce, D. R., Jakob, P., Mathur, L., Korbel, J. O., Merten, C. A., Velten, L., & Steinmetz, L. M. (2020) · Nature Methods · doi:10.1038/s41592-020-0837-5 · Cited by 245 · see citing works
Mechanisms of immune activation and regulation: lessons from melanoma
Kalaora, S., Nagler, A., Wargo, J. A., & Samuels, Y. (2022) · Nature reviews. Cancer · doi:10.1038/s41568-022-00442-9 · Cited by 243 · see citing works
Dimensionality reduction by UMAP to visualize physical and genetic interactions
Dorrity, M. W., Saunders, L. M., Queitsch, C., Fields, S., & Trapnell, C. (2020) · Nature Communications · doi:10.1038/s41467-020-15351-4 · Cited by 242 · see citing works
CRISPR screen in regulatory T cells reveals modulators of Foxp3
Cortez, J. T., Montauti, E. I., Shifrut, E., Gatchalian, J., Zhang, Y., Shaked, O., Xu, Y., Roth, T. L., Simeonov, D. R., Zhang, Y., et al. (2020) · Nature · doi:10.1038/s41586-020-2246-4 · Cited by 237 · see citing works
Self-citationRASA2 ablation in T cells boosts antigen sensitivity and long-term function
Carnevale, J. C., Shifrut, E., Kale, N., Nyberg, W. A., Blaeschke, F., Chen, Y. Y., Li, Z., Bapat, S. P., Diolaiti, M. E., O’Leary, P. C., et al. (2022) · Nature · doi:10.1038/s41586-022-05126-w · Cited by 236 · see citing works
Self-citationFrequent aneuploidy in primary human T cells after CRISPR–Cas9 cleavage
Nahmad, A. D., Reuveni, E., Goldschmidt, E., Tenne, T., Liberman, M., Horovitz‐Fried, M., Khosravi, R., Kobo, H., Reinstein, E., Madi, A., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01377-0 · Cited by 235 · see citing works
Harnessing IL-15 signaling to potentiate NK cell-mediated cancer immunotherapy
Ma, S., Caligiuri, M. A., & Yu, J. (2022) · Trends in Immunology · doi:10.1016/j.it.2022.08.004 · Cited by 226 · see citing works
Perspectives of tumor-infiltrating lymphocyte treatment in solid tumors
Wang, S., Sun, J., Chen, K., Ma, P., Lei, Q., Xing, S., Cao, Z., Sun, S., Yu, Z., Liu, Y., et al. (2021) · BMC Medicine · doi:10.1186/s12916-021-02006-4 · Cited by 222 · see citing works
An expanded universe of cancer targets
Hahn, W. C., Bader, J. S., Braun, T. P., Califano, A., Clemons, P. A., Druker, B. J., Ewald, A. J., Fu, H., Jagu, S., Kemp, C. J., et al. (2021) · Cell · doi:10.1016/j.cell.2021.02.020 · Cited by 213 · see citing works
Mitigation of chromosome loss in clinical CRISPR-Cas9-engineered T cells
Tsuchida, C. A., Brandes, N., Bueno, R., Trinidad, M. I., Mazumder, T., Yu, B., Hwang, B., Chang, C. R., Liu, J., Sun, Y., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.041 · Cited by 208 · see citing works
A genome-wide atlas of co-essential modules assigns function to uncharacterized genes
Wainberg, M., Kamber, R. A., Balsubramani, A., Meyers, R. M., Sinnott-Armstrong, N., Hornburg, D., Jiang, L., Chan, J., Jian, R., Gu, M., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00840-z · Cited by 204 · see citing works
Ye, L., Park, J. J.-Y., Peng, L., Yang, Q., Chow, R. D.-W., Dong, M. B., Lam, S. Z., Guo, J., Tang, E., Zhang, Y., et al. (2022) · Cell Metabolism · doi:10.1016/j.cmet.2022.02.009 · Cited by 204 · see citing works
In vivo CD8+ T cell CRISPR screening reveals control by Fli1 in infection and cancer
Chen, Z., Arai, E., Khan, O., Zhang, Z., Ngiow, S. F., He, Y. M., Huang, H., Manne, S., Cao, Z., Baxter, A. E., et al. (2021) · Cell · doi:10.1016/j.cell.2021.02.019 · Cited by 201 · see citing works
Ye, L., Park, J. J.-Y., Dong, M. B., Yang, Q., Chow, R. D.-W., Peng, L., Du, Y., Guo, J., Dai, X., Wang, G., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0246-4 · Cited by 199 · see citing works
From concept to cure: The evolution of CAR-T cell therapy
Patel, K. K., Tariveranmoshabad, M., Kadu, S., Shobaki, N., & June, C. H. (2025) · Molecular Therapy · doi:10.1016/j.ymthe.2025.03.005 · Cited by 198 · see citing works
scPerturb: harmonized single-cell perturbation data
Peidli, S., Green, T. D., Shen, C., Gross, T., Min, J., Garda, S., Yuan, B., Schumacher, L. J., Taylor-King, J. P., Marks, D. S., et al. (2024) · Nature Methods · doi:10.1038/s41592-023-02144-y · Cited by 194 · see citing works
Pooled Knockin Targeting for Genome Engineering of Cellular Immunotherapies
Roth, T. L., Li, P. J., Blaeschke, F., Nies, J. F., Apathy, R., Mowery, C., Yu, R., Nguyen, M. L. T., Lee, Y., Truong, A., et al. (2020) · Cell · doi:10.1016/j.cell.2020.03.039 · Cited by 186 · see citing works
Self-citationWang, D., Prager, B. C., Gimple, R. C., Aguilar, B., Alizadeh, D., Tang, H., Lv, D., Starr, R., Brito, A., Wu, Q., et al. (2020) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-1243 · Cited by 185 · see citing works
Next-Generation CAR T-cell Therapies
Young, R. M., Engel, N. W., Uslu, U., Wellhausen, N., & June, C. H. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-1683 · Cited by 182 · see citing works
Mechanisms of resistance to chimeric antigen receptor-T cells in haematological malignancies
Ruella, M., Korell, F., Porazzi, P., & Maus, M. V. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-023-00807-1 · Cited by 182 · see citing works
A genome-scale screen for synthetic drivers of T cell proliferation
Legut, M., Gajic, Z. Z., Guarino, M., Daniloski, Z., Rahman, J. A., Xue, X., Lu, C., Lu, L., Mimitou, E. P., Hao, S., et al. (2022) · Nature · doi:10.1038/s41586-022-04494-7 · Cited by 177 · see citing works
Zhang, X., Zhang, C., Qiao, M., Cheng, C., Tang, N., Lu, S., Sun, W. C., Xu, B., Cao, Y., Wei, X., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.09.013 · Cited by 173 · see citing works
Genetic engineering of T cells for immunotherapy
Ellis, G. I., Sheppard, N. C., & Riley, J. L. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00329-9 · Cited by 166 · see citing works
T cell receptor therapeutics: immunological targeting of the intracellular cancer proteome
Klebanoff, C. A., Chandran, S. S., Baker, B. M., Quezada, S. A., & Ribas, A. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-023-00809-z · Cited by 158 · see citing works
Constrained Nanoparticles Deliver siRNA and sgRNA to T Cells In Vivo without Targeting Ligands
Lokugamage, M. P., Sago, C. D., Gan, Z., Krupczak, B. R., & Dahlman, J. E. (2019) · Advanced Materials · doi:10.1002/adma.201902251 · Cited by 155 · see citing works
Enhanced T cell effector activity by targeting the Mediator kinase module
Freitas, K. A., Belk, J. A., Sotillo, E., Quinn, P. J., Ramello, M. C., Malipatlolla, M. B., Dániel, B., Sandor, K. D., Klysz, D. D., Bjelajac, J. R., et al. (2022) · Science · doi:10.1126/science.abn5647 · Cited by 155 · see citing works
CRISPR-Cas-mediated transcriptional modulation: The therapeutic promises of CRISPRa and CRISPRi
Bendixen, L., Jensen, T. I., & Bak, R. O. (2023) · Molecular Therapy · doi:10.1016/j.ymthe.2023.03.024 · Cited by 144 · see citing works
Gonatopoulos-Pournatzis, T., Aregger, M., Brown, K. R., Farhangmehr, S., Braunschweig, U., Ward, H. N., Ha, K. C. H., Weiss, A., Billmann, M., Durbic, T., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0437-z · Cited by 142 · see citing works
Nobles, C. L., Sherrill-Mix, S. A., Everett, J. K., Reddy, S. G., Fraietta, J. A., Porter, D. L., Frey, N. V., Gill, S. I., Grupp, S. A., Maude, S. L., et al. (2019) · Journal of Clinical Investigation · doi:10.1172/jci130144 · Cited by 132 · see citing works
Advancing CAR T cell therapy through the use of multidimensional omics data
Yang, J., Chen, Y., Jing, Y., Green, M. R., & Han, L. (2023) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-023-00729-2 · Cited by 128 · see citing works
A practical view of fine-mapping and gene prioritization in the post-genome-wide association era
Broekema, R., Bakker, O. B., & Jonkers, I. H. (2020) · Open Biology · doi:10.1098/rsob.190221 · Cited by 127 · see citing works
Single-Cell Heterogeneity Analysis and CRISPR Screen Identify Key β-Cell-Specific Disease Genes
Fang, Z., Weng, C., Li, H., Tao, R., Mai, W., Liu, X., Lu, L., Lai, S., Duan, Q., Álvarez‐González, C. A., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.02.043 · Cited by 124 · see citing works
Wang, Y., Wang, F., Wang, L., Qiu, S., Yao, Y., Yan, C., Xiong, X., Chen, X., Ji, Q., Cao, J., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109516 · Cited by 123 · see citing works
Massively parallel base editing to map variant effects in human hematopoiesis
Martin-Rufino, J. D., Castano, N., Pang, M., Grody, E. I., Joubran, S. R., Caulier, A., Wahlster, L., Li, T., Qiu, X., Riera-Escandell, A. M., et al. (2023) · Cell · doi:10.1016/j.cell.2023.03.035 · Cited by 123 · see citing works
Epigenome editing technologies for discovery and medicine
McCutcheon, S. R., Rohm, D., Iglesias, N., & Gersbach, C. A. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02320-1 · Cited by 122 · see citing works
Loo, C.-S., Gatchalian, J., Liang, Y., Leblanc, M., Xie, M., Ho, J., Venkatraghavan, B., Hargreaves, D. C., & Zheng, Y. (2020) · Immunity · doi:10.1016/j.immuni.2020.06.011 · Cited by 121 · see citing works
Standing on the shoulders of mice
Medetgul-Ernar, K., & Davis, M. M. (2022) · Immunity · doi:10.1016/j.immuni.2022.07.008 · Cited by 119 · see citing works
Tieu, V., Sotillo, E., Bjelajac, J. R., Chen, C., Malipatlolla, M. B., Guerrero, J. A., Xu, P., Quinn, P. J., Fisher, C., Klysz, D. D., et al. (2024) · Cell · doi:10.1016/j.cell.2024.01.035 · Cited by 119 · see citing works
Recent Advancements in Electroporation Technologies: From Bench to Clinic
Campelo, S. N., Huang, P., Buie, C., & Davalos, R. V. (2023) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-110220-023800 · Cited by 115 · see citing works
Park, J., Daniels, J., Wartewig, T., Ringbloom, K. G., Martínez‐Escala, M. E., Choi, S., Thomas, J. J., Doukas, P. G., Yang, J., Snowden, C. K., et al. (2021) · Blood · doi:10.1182/blood.2020009655 · Cited by 108 · see citing works
The ZFP36 family of RNA binding proteins regulates homeostatic and autoreactive T cell responses
Cook, M. E., Bradstreet, T. R., Webber, A. M., Kim, J., Santeford, A. C., Harris, K. M., Murphy, M. K., Tran, J., Abdalla, N. M., Schwarzkopf, E. A., et al. (2022) · Science Immunology · doi:10.1126/sciimmunol.abo0981 · Cited by 106 · see citing works
Liposomal delivery of CRISPR/Cas9
Zhen, S., & Li, X. (2019) · Cancer Gene Therapy · doi:10.1038/s41417-019-0141-7 · Cited by 104 · see citing works
CAR-T cells in solid tumors: Challenges and breakthroughs
Escobar, G., Berger, T. R., & Maus, M. V. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102353 · Cited by 104 · see citing works
Delivery technologies for T cell gene editing: Applications in cancer immunotherapy
Atsavapranee, E., Billingsley, M. M., & Mitchell, M. J. (2021) · EBioMedicine · doi:10.1016/j.ebiom.2021.103354 · Cited by 102 · see citing works
CRISPR screens decode cancer cell pathways that trigger γδ T cell detection
Mamedov, M. R., Vedova, S., Freimer, J. W., Sahu, A. D., Ramesh, A., Arce, M. M., Meringa, A. D., Ota, M., Chen, P. A., Hanspers, K., et al. (2023) · Nature · doi:10.1038/s41586-023-06482-x · Cited by 100 · see citing works
Self-citationEngineering Strategies to Enhance TCR-Based Adoptive T Cell Therapy
Rath, J. A., & Arber, C. (2020) · Cells · doi:10.3390/cells9061485 · Cited by 99 · see citing works
Goodman, D. B., Azimi, C. S., Kearns, K., Talbot, A., Garakani, K., Garcia, J. M., Patel, N. A., Hwang, B., Lee, D. S., Park, M., et al. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abm1463 · Cited by 98 · see citing works
Self-citationBase-editing mutagenesis maps alleles to tune human T cell functions
Schmidt, R. L. J., Ward, C. C., Dajani, R., Armour-Garb, Z., Ota, M., Allain, V., Hernandez, R., Layeghi, M., Xing, G. K., Goudy, L., et al. (2023) · Nature · doi:10.1038/s41586-023-06835-6 · 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
scMAGeCK links genotypes with multiple phenotypes in single-cell CRISPR screens
Yang, L., Zhu, Y., Yu, H., Cheng, X., Chen, S., Chu, Y., Huang, H., Zhang, J., & Li, W. (2020) · Genome Biology · doi:10.1186/s13059-020-1928-4 · Cited by 94 · see citing works
Advancing cell-based cancer immunotherapy through stem cell engineering
Li, Y.-R., Dunn, Z. S., Yu, Y., Li, M., Wang, P., & Yang, L. (2023) · Cell stem cell · doi:10.1016/j.stem.2023.02.009 · Cited by 94 · see citing works
Freimer, J. W., Shaked, O., Naqvi, S., Sinnott-Armstrong, N., Kathiria, A. S., Garrido, C. M., Chen, A. F., Cortez, J. T., Greenleaf, W. J., Pritchard, J. K., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01106-y · Cited by 93 · see citing works
Self-citationBase editing screens map mutations affecting interferon-γ signaling in cancer
Coelho, M. A., Cooper, S., Strauß, M. E., Karakoç, E., Bhosle, S. G., Gonçalves, E., Picco, G., Burgold, T., Cattaneo, C. M., Veninga, V., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2022.12.009 · Cited by 93 · see citing works
Deletion of SNX9 alleviates CD8 T cell exhaustion for effective cellular cancer immunotherapy
Trefny, M. P., Kirchhammer, N., der Maur, P. A., Natoli, M., Schmid, D., Germann, M., Fernandez-Rodriguez, L., Herzig, P., Lötscher, J., Akrami, M., et al. (2023) · Nature Communications · doi:10.1038/s41467-022-35583-w · Cited by 93 · see citing works
Functional CRISPR dissection of gene networks controlling human regulatory T cell identity
Schumann, K., Raju, S. S., Lauber, M., Kolb, S., Shifrut, E., Cortez, J. T., Skartsis, N., Nguyen, V. Q., Woo, J. M., Roth, T. L., et al. (2020) · Nature Immunology · doi:10.1038/s41590-020-0784-4 · Cited by 87 · see citing works
Self-citationDubrot, J., Lane-Reticker, S. K., Kessler, E., Ayer, A., Mishra, G., Wolfe, C. H., Zimmer, M. D., Du, P. P., Mahapatra, A., Ockerman, K. M., et al. (2021) · Immunity · doi:10.1016/j.immuni.2021.01.001 · Cited by 87 · see citing works
McCutcheon, S. R., Swartz, A. M., Brown, M. C., Barrera, A., Amador, C. M., Siklenka, K., Humayun, L. L., ter Weele, M. A., Isaacs, J. M., Reddy, T. E., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01554-0 · Cited by 86 · see citing works
NF-κB in Cancer Immunity: Friend or Foe?
Lalle, G., Twardowski, J., & Grinberg‐Bleyer, Y. (2021) · Cells · doi:10.3390/cells10020355 · Cited by 82 · see citing works
Feng, X., Li, Z. Y., Liu, Y., Chen, D., & Zhou, Z. (2024) · Experimental Hematology and Oncology · doi:10.1186/s40164-024-00570-y · Cited by 82 · see citing works
Microglia transcriptional states and their functional significance: Context drives diversity
Depp, C., Doman, J. L., Hingerl, M., Xia, J., & Stevens, B. (2025) · Immunity · doi:10.1016/j.immuni.2025.04.009 · Cited by 81 · see citing works
Multiscale engineering of immune cells and lymphoid organs
Kim, S., Shah, S. B., Graney, P. L., & Singh, A. (2019) · Nature Reviews Materials · doi:10.1038/s41578-019-0100-9 · Cited by 79 · see citing works
Galy, A. S. D., Menegatti, S., Fuentealba, J. R., Perrin, L., Lucibello, F., Helft, J., Darbois, A., Saitakis, M., Tosello, J., Rookhuizen, D. C., et al. (2021) · Science Immunology · doi:10.1126/sciimmunol.abe8219 · Cited by 79 · see citing works
Integrative multi-omics approaches to explore immune cell functions: Challenges and opportunities
Wang, X., Dian, F., Yang, Y., Gimple, R. C., & Zhou, S. (2023) · iScience · doi:10.1016/j.isci.2023.106359 · Cited by 79 · see citing works
Rewired signaling network in T cells expressing the chimeric antigen receptor (CAR)
Dong, R., Libby, K. A., Blaeschke, F., Fuchs, W., Marson, A., Vale, R. D., & Su, X. (2020) · The EMBO Journal · doi:10.15252/embj.2020104730 · Cited by 77 · see citing works
Self-citationEmerging Trends in Immunotherapy for Cancer
Mishra, A., Ali, A., Dutta, S., Banday, S., & Malonia, S. K. (2022) · Diseases · doi:10.3390/diseases10030060 · Cited by 75 · see citing works
Zhao, L., Liu, P., Mao, M., Zhang, S., Bigenwald, C., Dutertre, C., Lehmann, C. H. K., Pan, H., Paulhan, N., Amon, L., et al. (2023) · Cancer Discovery · doi:10.1158/2159-8290.cd-22-1338 · Cited by 75 · see citing works
Functional Genomics for Cancer Drug Target Discovery
Haley, B., & Roudnicky, F. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.04.006 · Cited by 73 · see citing works
Ubiquitin ligases in cancer: Functions and clinical potentials
Duan, S., & Pagano, M. (2021) · Cell chemical biology · doi:10.1016/j.chembiol.2021.04.008 · Cited by 73 · see citing works
CRISPR–Cas9 screens reveal regulators of ageing in neural stem cells
Ruetz, T. J., Pogson, A. N., Kashiwagi, C. M., Gagnon, S. D., Morton, B., Sun, E., Na, J., Yeo, R. W., Leeman, D. S., Morgens, D. W., et al. (2024) · Nature · doi:10.1038/s41586-024-07972-2 · Cited by 71 · see citing works
Preclinical Evaluation of CAR T Cell Function: In Vitro and In Vivo Models
Si, X., Xiao, L., Brown, C. E., & Wang, D. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23063154 · Cited by 69 · see citing works
Modular pooled discovery of synthetic knockin sequences to program durable cell therapies
Blaeschke, F., Chen, Y. Y., Apathy, R., Dániel, B., Chen, A. Y., Chen, P. A., Sandor, K. D., Zhang, W., Li, Z., Mowery, C. T., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.013 · 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
DCision-making in tumors governs T cell anti-tumor immunity
Alfei, F., Ho, P., & Lo, W. (2021) · Oncogene · doi:10.1038/s41388-021-01946-8 · Cited by 67 · see citing works
Expression of inducible factors reprograms CAR-T cells for enhanced function and safety
Smole, A., Benton, A., Poussin, M., Eiva, M. A., Mezzanotte, C., Camisa, B., Greco, B. M., Sharma, P., Minutolo, N. G., Gray, F., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.11.006 · Cited by 67 · see citing works
Paralog dependency indirectly affects the robustness of human cells
Dandage, R., & Landry, C. R. (2019) · Molecular Systems Biology · doi:10.15252/msb.20198871 · Cited by 65 · see citing works
- Authors
- Shifrut, E., Carnevale, J., Tobin, V., Roth, T. L., Woo, J. M., Bui, C. T., Li, P. J., Diolaiti, M. E., Ashworth, A., & Marson, A.
- Affiliations
- University of California, San Francisco (US)
- Corresponding author
- Marson, Alexander
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2018-12
- Volume
- 175
- Issue
- 7
- Pages
- 1958-1971.e15
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC6689405
- OpenAlex
- W2885096157
- Cited by
- 573
- References
- 84
- Citations by year
- 2026: 79 · 2025: 74 · 2024: 76 · 2023: 83 · 2022: 80 · 2021: 80 · 2020: 65 · 2019: 32 · 2018: 2
- FWCI
- 16.37
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Parker Institute for Cancer Immunotherapy
- Burroughs Wellcome Fund
- Innovative Genomics Institute
- NIH (P30 DK063720)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
- Health Sciences › Medicine › Oncology › CAR-T cell therapy research
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Keywords
- primary human T cells, CRISPR screen, slices, T cell receptor signaling, T cell proliferation, cancer immunotherapy, adenosine signaling, immunosuppression, gene ablation, single-cell RNA sequencing
- MeSH terms
- CRISPR-Associated Protein 9, Humans, T-Lymphocytes, Genome, Human, Genome-Wide Association Study, Gene Knockout Techniques, Clustered Regularly Interspaced Short Palindromic Repeats, CRISPR-Cas Systems
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