FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2898075889> ?p ?o ?g. }

• W2898075889 endingPage "1155.e16" @default.
• W2898075889 startingPage "1141" @default.
• W2898075889 abstract "•Generated 100s of genetic barcodes detectable as proteins (Pro-Codes)•Pro-Codes provide a single-cell resolution means for vector and cell tracking•Pro-Codes enable high-dimensional phenotyping for CRISPR screens•This approach identified roles for Rtp4, Psmb8, and Socs1 in cancer immune editing CRISPR pools are being widely employed to identify gene functions. However, current technology, which utilizes DNA as barcodes, permits limited phenotyping and bulk-cell resolution. To enable novel screening capabilities, we developed a barcoding system operating at the protein level. We synthesized modules encoding triplet combinations of linear epitopes to generate >100 unique protein barcodes (Pro-Codes). Pro-Code-expressing vectors were introduced into cells and analyzed by CyTOF mass cytometry. Using just 14 antibodies, we detected 364 Pro-Code populations; establishing the largest set of protein-based reporters. By pairing each Pro-Code with a different CRISPR, we simultaneously analyzed multiple phenotypic markers, including phospho-signaling, on dozens of knockouts. Pro-Code/CRISPR screens found two interferon-stimulated genes, the immunoproteasome component Psmb8 and a chaperone Rtp4, are important for antigen-dependent immune editing of cancer cells and identified Socs1 as a negative regulator of Pd-l1. The Pro-Code technology enables simultaneous high-dimensional protein-level phenotyping of 100s of genes with single-cell resolution. CRISPR pools are being widely employed to identify gene functions. However, current technology, which utilizes DNA as barcodes, permits limited phenotyping and bulk-cell resolution. To enable novel screening capabilities, we developed a barcoding system operating at the protein level. We synthesized modules encoding triplet combinations of linear epitopes to generate >100 unique protein barcodes (Pro-Codes). Pro-Code-expressing vectors were introduced into cells and analyzed by CyTOF mass cytometry. Using just 14 antibodies, we detected 364 Pro-Code populations; establishing the largest set of protein-based reporters. By pairing each Pro-Code with a different CRISPR, we simultaneously analyzed multiple phenotypic markers, including phospho-signaling, on dozens of knockouts. Pro-Code/CRISPR screens found two interferon-stimulated genes, the immunoproteasome component Psmb8 and a chaperone Rtp4, are important for antigen-dependent immune editing of cancer cells and identified Socs1 as a negative regulator of Pd-l1. The Pro-Code technology enables simultaneous high-dimensional protein-level phenotyping of 100s of genes with single-cell resolution. There are more than 20,000 protein-coding genes in the human genome, as well as 100s of non-coding RNA genes, including microRNAs. Although there has been progress in assigning functions to many genes, we still do not know all the functions of each gene or the role of many genes in driving or affecting disease. Determining the functions of every gene, in different normal and disease processes, is one of the major goals of the post-genome era. The technology exists to knockout (KO), knockdown (KD), or overexpress (OE) any gene using vectors encoding a CRISPR guide RNA (gRNA) or shRNA. However, KO, KD, or OE of every gene in a genome in distinct experimental systems is cumbersome, costly, and very time consuming. For in vivo studies, it is even more challenging and not practically feasible. This has led to the increasing use of pooled genetic screens aimed at determining the functions of 100s of genes simultaneously in a single experimental system. Pooled screens have been made possible by using DNA to barcode vectors. Unique nucleotide sequences can be incorporated in to a vector, or alternatively, when the vector encodes an shRNA or CRISPR gRNA, the shRNA or gRNA sequence becomes the barcode (Bassik et al., 2009Bassik M.C. Lebbink R.J. Churchman L.S. Ingolia N.T. Patena W. LeProust E.M. Schuldiner M. Weissman J.S. McManus M.T. Rapid creation and quantitative monitoring of high coverage shRNA libraries.Nat. Methods. 2009; 6: 443-445Crossref PubMed Scopus (84) Google Scholar, Shalem et al., 2015Shalem O. Sanjana N.E. Zhang F. High-throughput functional genomics using CRISPR-Cas9.Nat. Rev. Genet. 2015; 16: 299-311Crossref PubMed Scopus (749) Google Scholar). Cells can be transduced with 100s of vectors simultaneously, and the frequency of cells carrying each vector can be determined by deep-sequencing (Mullokandov et al., 2012Mullokandov G. Baccarini A. Ruzo A. Jayaprakash A.D. Tung N. Israelow B. Evans M.J. Sachidanandam R. Brown B.D. High-throughput assessment of microRNA activity and function using microRNA sensor and decoy libraries.Nat. Methods. 2012; 9: 840-846Crossref PubMed Scopus (294) Google Scholar). The function of a particular gene is inferred by applying a selective pressure, such as time or a drug, and measuring changes in the frequency of each barcode associated with a particular shRNA or gRNA. DNA barcoding has major limitations. One of the most significant is that the readout is performed on bulk cells, which means single cells cannot be readily analyzed. This is a problem for many reasons, but one is that KO, KD, and OE does not occur in 100% of cells, and thus analyzing in bulk includes a mix of cells with and without the genetic perturbation. Another limitation is that DNA barcoding does not enable cells to be directly phenotyped. Instead, the phenotype associated with each gene perturbation is inferred from changes in barcode frequency. This has limited pooled screens largely to vetting genes for their potential impact on cell fitness and inferring a change in shRNA/gRNA frequency is due to KD/KO influencing proliferation or survival (Shalem et al., 2015Shalem O. Sanjana N.E. Zhang F. High-throughput functional genomics using CRISPR-Cas9.Nat. Rev. Genet. 2015; 16: 299-311Crossref PubMed Scopus (749) Google Scholar). More informative phenotypes, such as upregulation or downregulation of specific proteins, cannot be easily assessed in screens using DNA barcodes. Recently, CRISPR screens have been coupled with single-cell RNA sequencing (scRNA-seq) and vector-encoded RNA used as a barcode (Adamson et al., 2016Adamson 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.A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response.Cell. 2016; 167: 1867-1882Abstract Full Text Full Text PDF PubMed Scopus (488) Google Scholar, Datlinger et al., 2017Datlinger P. Rendeiro A.F. Schmidl C. Krausgruber T. Traxler P. Klughammer J. Schuster L.C. Kuchler A. Alpar D. Bock C. Pooled CRISPR screening with single-cell transcriptome readout.Nat. Methods. 2017; 14: 297-301Crossref PubMed Scopus (411) Google Scholar, Dixit et al., 2016Dixit A. Parnas O. Li B. Chen J. Fulco C.P. Jerby-Arnon L. Marjanovic N.D. Dionne D. Burks T. Raychowdhury R. et al.Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens.Cell. 2016; 167: 1853-1866Abstract Full Text Full Text PDF PubMed Scopus (659) Google Scholar, Jaitin et al., 2016Jaitin D.A. Weiner A. Yofe I. Lara-Astiaso D. Keren-Shaul H. David E. Salame T.M. Tanay A. van Oudenaarden A. Amit I. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq.Cell. 2016; 167: 1883-1896Abstract Full Text Full Text PDF PubMed Scopus (397) Google Scholar). This enabled more high-content and high-resolution screens. However, the cell throughput is relatively restrained, and important protein-level phenotypic information, such as signaling alterations, cannot be measured. Here, we show that combinatorial arrangements of linear epitopes can be used to generate a protein barcoding system (Pro-Codes), which is capable of overcoming many limitations of current pooled screening technology. We synthesized sequences encoding 3 combinations of 14 different linear epitopes to create 364 Pro-Codes. Pro-Code-expressing vectors were introduced into cells, and we could simultaneously detect all 364 Pro-Code-expressing cell populations. By pairing each Pro-Code with a different CRISPR gRNA, we were able to analyze multiple proteins on dozens of knockouts with single-cell resolution. We used Pro-Code/CRISPR vectors to screen for genes that influence breast cancer sensitivity and resistance to antigen-specific T cell killing and found evidence that two interferon-gamma (IFNγ) stimulated genes, the immunoproteasome component Psmb8 and a poorly characterized chaperone Rtp4, are important for antigen-dependent immune editing. Within the same screen, we also found that Socs1 is a negative regulator of the immune checkpoint Pd-l1. This work establishes a new barcoding system that enables simultaneous high-dimensional phenotypic analysis of 100s of genes, at single-cell resolution, with broad applications for helping to advance gene annotation. We sought to generate a vector barcoding system that operates at the protein level, as this would allow us to multiplex many gene delivery vectors together and detect them in cells using high-throughput, single-cell resolution technologies, such as flow and mass cytometry, and enable complex phenotyping. Proteins such as GFP and RFP can be used as vector reporters, but each fluorescent protein requires its own detection channel, which limits the number of unique fluorescent reporters that can be used together, generally to 3 or 4, because fluorescent proteins have broad emission spectrums that can overlap. To solve this problem, we hypothesized that combinations of a limited number of antibody-detectable epitopes (n) could be arranged together in specific multiples (r) to form a higher order set of barcodes (C) (Figure 1A). Using this strategy, as few as 10 epitopes can be arranged in sets of 3 to create 120 unique combinations, and with just 20 epitopes and 7 positions, 77,520 combinations can be generated. We reasoned linear epitopes would be needed to assemble the barcodes because they can be encoded by a short sequence (18–42 nucleotides). We selected 10 linear epitopes for which there are antibodies for detection. Among these were epitopes commonly used as protein tags, such as HA and FLAG (Table S1). We synthesized the DNA sequence encoding each epitope and assembled them in every possible combination of 3, for a total of 120 different 3-epitope combinations. We fused each epitope combination to dNGFR, a truncated receptor without an intracellular domain, which is commonly used as a reporter protein (Mullokandov et al., 2012Mullokandov G. Baccarini A. Ruzo A. Jayaprakash A.D. Tung N. Israelow B. Evans M.J. Sachidanandam R. Brown B.D. High-throughput assessment of microRNA activity and function using microRNA sensor and decoy libraries.Nat. Methods. 2012; 9: 840-846Crossref PubMed Scopus (294) Google Scholar). This served as a scaffold to facilitate epitope transport to the cell surface (Figure 1A). Each of the 120 3-epitope/dNGFR combinations (herein referred to as Pro-Codes) were cloned into lentiviral vectors (LV) downstream of the EF1a promoter. Vector plasmids were pooled in equimolar ratio and used to make a pool of LV encoding the Pro-Codes. We transduced 293T kidney cells with a pool of 18 LVs each encoding a different 3-epitope Pro-Code. The cells were transduced at a low multiplicity of infection (MOI) so each cell was only transduced with a single Pro-Code vector. The cells were then harvested and stained with antibodies for NGFR and all 10 of the linear epitopes. Each of the antibodies were conjugated with a different metal, and samples were analyzed on a time-of-flight mass cytometer (CyTOF) (Figure 1B). We used mass cytometry because CyTOF permits detection of over 45 different metal-conjugated antibodies (Bendall et al., 2011Bendall S.C. Simonds E.F. Qiu P. Amir E. -a. D. Krutzik P.O. Finck R. Bruggner R.V. Melamed R. Trejo A. Ornatsky O.I. et al.Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum.Science. 2011; 332: 687-696Crossref PubMed Scopus (1672) Google Scholar) and would thus enable detection of the Pro-Code epitopes along with more than 35 phenotypic markers. All 10 epitope tags were detected with a clear signal over background, and all of the epitope-positive cells were positive for NGFR (Figure S1A). To determine if we could resolve cells expressing specific Pro-Codes, we analyzed NGFR+ cells using a debarcoder algorithm (Fread et al., 2017Fread K.I. Strickland W.D. Nolan G.P. Zunder E.R. An unpdated debarcoding tool for mass cytometry with cell type-specific and cell sample-specific stringency adjustment.Pac. Symp. Biocomput. 2017; 22: 588-598PubMed Google Scholar). Eighteen distinct cell populations were detected (Figures 1C and S1B), with each population corresponding to a unique Pro-Code (i.e., positive for precisely 3 of the 10 epitopes). For example, one population of cells was positive for the E3, E4, and E5 epitopes and negative for all other epitopes, indicating the cells expressed the E3-E4-E5 Pro-Code (Figure S1C). We clustered NGFR+ cells based on their epitope tag expression. Once again 18 distinct populations were identified with each cluster positive for only 3 epitopes and thus corresponding precisely to a specific Pro-Code (Figures 1D and 1E). To determine if we could increase the number of epitopes per Pro-Code, we generated 14 Pro-Codes with 4 epitopes per Pro-Code. We cloned each one in to an LV, transduced 293T with the pool, and analyzed by CyTOF. We detected all 10 epitopes, and cells were positive for 4 epitopes. This enabled us to identify all 14 4-epitope Pro-Code populations (Figure S1D). Next, we pooled 120 different 3-epitope Pro-Code plasmids together in a roughly equimolar ratio and made a library of LV. We transduced 293T, as well as monocytic cells (THP1), leukemic T cells (Jurkat), and mammary carcinoma cells (4T1) with the 120 vector library. After 1 week, cells were stained with the 10 metal-conjugated antibodies and analyzed by CyTOF. Unsupervised clustering resolved 120 distinct populations (Figures 1F–1I), with each population corresponding to one Pro-Code (Figures 1J and S2A–S2C). The frequency of each population ranged from 0.1% to 3%, with the majority of Pro-Code populations (65%) being between 0.4%–1.5% (Figure S2D), which is close to the expected frequency of 0.83% if each of the 120 Pro-Codes was in equimolar concentration. Using an expanded set of 14 epitopes, we generated 364 3-epitope Pro-Code vectors and introduced them in to 293T. The cells were stained for NGFR and all 14 epitopes, analyzed by CyTOF, and all 364 Pro-Code-expressing populations were readily identified and clustered (Figure S3). Thus, with only 14 antibodies (i.e., 14 detection channels), we could detect 364 different vector-expressing cell populations. These results demonstrate that combinations of linear epitopes can be used to generate protein barcodes detectable at the protein level and at single-cell resolution. One important application of vector barcoding is in cell clone and lineage tracing (Lu et al., 2011Lu R. Neff N.F. Quake S.R. Weissman I.L. Tracking single hematopoietic stem cells in vivo using high-throughput sequencing in conjunction with viral genetic barcoding.Nat. Biotechnol. 2011; 29: 928-933Crossref PubMed Scopus (298) Google Scholar). Fluorescent proteins provide a powerful way to do this, but the number of populations that can be tracked is quite limited. DNA barcodes can tag an almost infinite number of cells, but only provide bulk resolution. The Pro-Codes could potentially be used for clone tracking, but an important requirement is in vivo use. To address this, we transduced 4T1 mammary carcinoma cells with a pool of 120 Pro-Code vectors at low MOI. Cells were sorted based on NGFR, as dNGFR serves not only as a Pro-Code scaffold, but also as a selectable marker of transduced cells. The transduced cells were injected in to the right and left mammary gland of wild-type (WT) mice (n = 5 mice, 2 tumors per mouse) (Figure 2A). Because cells expressing non-self-proteins can be subject to immune clearance in immunocompetent animals, we also injected Rag1−/− immunodeficient mice for comparison (n = 6 mice, 2 tumors per mouse). Mice were sacrificed 14 days after cell injection, and 18 different tumors were removed and cultured for 3 days to enrich for the cancer cells. The cells were stained for NGFR and each of the 10 Pro-Code epitopes. We were able to identify 118–120 Pro-Code populations in each tumor (Figure 2B). While the proportion of each population varied for different Pro-Codes, this reflected a bias in the original population, as indicated by the comparison of each Pro-Code’s frequency in the pre-inoculation cells versus the tumors. Importantly, there was no significant difference in the proportion of the vast majority of Pro-Code populations in WT or Rag1−/− mice. This demonstrates the Pro-Codes are not differentially rejected and can be used in vivo. Although each mouse was injected with the same pool of cells, the specific Pro-Code composition of each tumor was different (Figure 2C). While most individual Pro-Codes were present in <1% of tumor cells, there was variability in the percent of each Pro-Code between tumors and mice. For each tumor, we plotted the proportion of the 10 most abundant Pro-Codes (Figure 2D). The same initial mix of 120 Pro-Code subpopulations developed into heterogenic tumors, in which 10 populations accounted for up to 50% of the total cell number. Some Pro-Code populations were abundant in every tumor (e.g., Pro-Codes 108 and 21), but their proportion within each tumor varied greatly, whereas other Pro-Code populations were only abundant in a single tumor, such as Pro-Code 6 (Figure 2D). These results support a model in which clonal growth was largely stochastic and not impacted by the Pro-Codes and demonstrate Pro-Codes can be used for cell tracking studies. One of the advantages of the Pro-Codes is that it could permit addition of protein-level phenotyping in genetic screens. To test this possibility, we generated 96 CRISPR gRNAs targeting 54 different genes (1–3 gRNA/gene) and paired each gRNA with a different Pro-Code. As it has recently been reported that packaging vector pools together can lead to varying degrees of barcode swapping (Hill et al., 2018Hill A.J. McFaline-Figueroa J.L. Starita L.M. Gasperini M.J. Matreyek K.A. Packer J. Jackson D. Shendure J. Trapnell C. On the design of CRISPR-based single-cell molecular screens.Nat. Methods. 2018; 15: 271-274Crossref PubMed Scopus (79) Google Scholar, Sack et al., 2016Sack L.M. Davoli T. Xu Q. Li M.Z. Elledge S.J. Sources of error in mammalian genetic screens.G3 (Bethesda). 2016; 6: 2781-2790Crossref PubMed Scopus (32) Google Scholar), we made each vector individually and subsequently pooled them in equimolar ratio, as this eliminates the possibility of swapping (Adamson et al., 2016Adamson 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.A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response.Cell. 2016; 167: 1867-1882Abstract Full Text Full Text PDF PubMed Scopus (488) Google Scholar). THP1 human monocytes were engineered to express Cas9 (THP1-Cas9) and transduced with all 96 Pro-Code/CRISPR vectors together as a pool. The cells were cultured for 10 days, then stained for NGFR, the Pro-Codes, and CD4, CD40, CD44, CD45, CD116, CD164, CD220, HLA-A, HLA-DR, and IFNGR1, which were all targeted by CRISPR gRNAs in the vector library (Figure 3A). We then analyzed 500,000 cells by CyTOF. All 96 Pro-Code populations were resolved and clustered. This enabled us to examine expression of the surface proteins on each of the 96 Pro-Code/CRISPR populations with single-cell resolution. In each Pro-Code population in which one of the membrane-bound proteins was targeted, there was an increase in the percent of cells negative for the cognate protein (Figures 3B and 3C). For example, in cells expressing Pro-Code 3, which was linked to a gRNA targeting the CD4 gene, 85% of the cells were CD4 negative, whereas cells expressing Pro-Codes linked to gRNAs targeting unrelated genes were almost all CD4 positive (Figures 3B, 3C, and S4A). High efficiency protein loss was also observed for CD44, CD45, CD116, CD164, CD220, and IFNGR1. HLA-A (MHC class I) was expressed by >90% of cells in each Pro-Code/CRISPR cluster, except those expressing Pro-Codes 23 and 24, which were linked to gRNAs targeting B2m; in these populations 45% and 80% of THP1 were HLA-A negative, respectively. As B2m is required for HLA stability, the loss of HLA in these clusters represents a downstream phenotype of B2m KO. There was little evidence of KO for some gRNAs, consistent with the known variability in CRISPR efficiency between gRNAs. These results demonstrate Pro-Codes can mark cells encoding a specific CRISPR gRNA. They also demonstrate how Pro-Codes enable protein-level phenotyping in pooled CRISPR screens. The library used above was made with vectors packaged individually and pooled subsequently. This prevents the possibility of barcode swapping. Recently, it was reported in pre-print studies that swapping can be reduced by co-packaging libraries with a low homology transfer vector (Adamson et al., 2018Adamson B. Norman T.M. Jost M. Weissman J.S. Approaches to maximize sgRNA-barcode coupling in Perturb-seq screens.bioRxiv. 2018; https://doi.org/10.1101/298349Crossref Google Scholar, Feldman et al., 2018Feldman D. Singh A. Garrity A.J. Blainey P.C. Lentiviral co-packaging mitigates the effects of intermolecular recombination and multiple integrations in pooled genetic screens.bioRxiv. 2018; https://doi.org/10.1101/262121Crossref Scopus (0) Google Scholar). To determine if this would be compatible with the Pro-Codes, we produced the 96 Pro-Code/CRISPR library as a pool and during vector packaging we spiked in a plasmid encoding an LV-expressing GFP and no CRISPR or Pro-Code. THP1-Cas9 cells were transduced with the 96 Pro-Code/CRISPR library at low MOI. The cells were stained for NGFR, the Pro-Code epitopes, and all 10 membrane-bound molecules, as above. We also stained for GFP to distinguish cells transduced with the GFP encoding LV in the pool and analyzed cells by CyTOF. Similar to the library made with individually packaged vectors, we resolved all 96 Pro-Code populations and consistently observed loss of a specific protein on a high percent of cells expressing a Pro-Code linked to a gRNA targeting the cognate gene (Figures S4B and S4C). The frequency of cells negative for the targeted protein was ∼90% similar between the libraries generated with vectors produced individually or as a pool with the low homology spike in vector. These results indicate Pro-Code/CRISPR libraries can be produced as a pool and function at high efficiency and further support the ability of Pro-Codes to facilitate high-dimensional phenotypic screens. Intracellular signaling plays an essential role in numerous cellular processes. The activation and de-activation of specific proteins in signaling pathways is a post-translational event and is thus optimally studied at the protein level. This makes it challenging to directly assess signaling alterations with current screening approaches. We decided to test if the Pro-Codes would facilitate a genetic screen of STAT signaling. STAT proteins function downstream of cytokine receptors. When different cytokines engage their cognate receptors, specific STAT proteins are phosphorylated and transmit the cytokine signal. IFNγ engagement of the IFNγ receptor (comprised of IFNGR1 and IFNGR2 subunits) triggers phosphorylation of STAT1 (pSTAT1), whereas interleukin (IL)-6 induces pSTAT1 and pSTAT3, and GM-CSF induces pSTAT5 (Figures 4A and 4B ). We constructed a library of 24 LVs, each encoding a different Pro-Code and gRNA (Figure 4C). The gRNAs targeted the IFNGR1, IFNGR2, IL6R (IL-6 receptor), and CD116 (GM-CSF receptor) genes. We generated 5–6 gRNAs/gene, as well as one control gRNA targeting an irrelevant gene and cloned each one with a different Pro-Code. THP1-Cas9 cells were transduced with the pool of Pro-Code/CRISPR vectors. After 1 week the cells were stimulated with IFNγ, GM-CSF, IL-6, or PBS. After 15 min, cells were fixed, stained with metal-conjugated antibodies specific for the Pro-Code epitopes, as well as pSTAT1, pSTAT3, and pSTAT5, and analyzed by CyTOF. All 24 Pro-Code populations were resolved and uniquely clustered (Figures 4D and S5A). We examined the expression of pSTAT1, pSTAT3, and pSTAT5 in each Pro-Code population. In all cases, we observed decreased phospho-signaling in cells expressing a Pro-Code linked to a gRNA targeting the cognate receptor (Figures 4E–4H and S5B). Looking at the mean change in signaling, there was a 15-fold decrease in pSTAT1 levels in cells expressing Pro-Codes linked to gRNAs targeting IFNGR1 and IFNGR2 (Figures 4E and 4F). In cells expressing the same Pro-Code/CRISPRs, pSTAT5, pSTAT1, and pSTAT3 levels were normal in response to GM-CSF and IL-6. This indicated the IFNGR1 and IFNGR2 gRNAs only impaired pSTAT1 signaling in response to IFNγ. Similarly, in cells encoding the Pro-Codes linked to gRNAs targeting GM-CSF there was a 3-fold reduction in pSTAT5 levels in response to GM-CSF, and in cells carrying gRNAs targeting IL6R there was a 2-fold reduction in both pSTAT1 and pSTAT3 levels in response to IL-6 (Figures 4G and 4H). The ability to analyze the cells at single-cell resolution enabled us to look at the heterogeneity in each Pro-Code/CRISPR population of cells. When we treated cells with IFNγ, 70% of the cells in the Pro-Code clusters linked to gRNAs targeting CD116 and IL6R had increased pSTAT1, whereas in the Pro-Code clusters linked to gRNAs targeting IFNGR1 and IFNGR2, only ∼25% of the cells had increased pSTAT1 (Figures 4I and 4J). When the cells were treated with GM-CSF, 60%–70% of the cells in the clusters encoding gRNAs targeting IL6R, IFNGR1, and IFNGR2 upregulated pSTAT5, but only 30%–40% of the cells in the Pro-Code clusters encoding CD116 gRNAs upregulated pSTAT5 (Figures 4I and 4J). Looking at the viSNE clusters, in which each dot is representative of a single cell, there were cells positive and negative for pSTAT (Figures 4J and S5). Thus, while the bulk analysis indicated a major reduction in pSTAT signaling downstream of the receptor targeted by a specific CRISPR, single-cell analysis indicated there was significant heterogeneity between cells even within the same Pro-Code cluster. This heterogeneity reflects biological differences between cells in their response to cytokine stimulation, but also reveals cell-to-cell heterogeneity in CRISPR KO, as observed in our studies above measuring the protein levels of the gene targeted by specific CRISPRs. This is not unexpected, as the editing efficiency of CRISPR is variable, but highlights the important utility of single-cell analysis in CRISPR screens. Together, these results demonstrate Pro-Codes enable direct single-cell phenotypic analysis of signaling pathways in CRISPR screens, which is not feasible with DNA or RNA level analysis. Cancer cells acquire mutations, which generate neo-antigens that are loaded on to MHC class I (MHC-I) and make the cancer cells targets for CD8+ T cell killing (Schumacher and Hacohen, 2016Schumacher T.N. Hacohen N. Neoantigens encoded in the cancer genome.Curr. Opin. Immunol. 2016; 41: 98-103Crossref PubMed Scopus (56) Google Scholar). However, cancer cells can alter gene expression to resist being killed. Although some of the genes important for cancer immune editing have been identified, the potential contributions of many genes still need to be interrogated. Recently, several studies have performed pooled CRISPR screens, using DNA barcodes for deconvolution, to identify novel sensitivity and resistance genes (Manguso et al., 2017Manguso 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.In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target.Nature. 2017; 547: 413-418Crossref PubMed Scopus (568) Google Scholar, Pan et al., 2018Pan D. Kobayashi A. Jiang P. Ferrari de Andrade L. Tay R.E. Luoma A. Tsoucas D. Qiu X. Lim K. Rao P. et al.A major chromatin regulator determines resistance of tumor cells to T cell-mediated killing.Science. 2018; 359: 770-775Crossref PubMed Scopus (452) Google Scholar, Patel et al., 2017Patel 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.Identification of essential genes for cancer immunotherapy.Nature. 2017; 548: 537-542Crossref PubMed Scopus (485) Google Scholar). We set out to determine if we could use the Pro-Codes to aid in the identification of genes conferring cancer cell sensitivity or resistance to T cell immunity. We generated a library of 56 CRISPR gRNAs targeting 14 different genes (3–4 gRNAs/gene) and paired each CRISPR with a unique Pro-Code, to form a pool of 56 Pro-Code/CRISPR vectors (including 4 scrambled gRNAs) (Figure 5A). We selected the 14 genes to contain known regulators of immunity, such as B2m, and several genes with no known role, such as Cldn4. As a model of breast cancer, we utilized the 4T1 mammary carcinoma line. In previous screens, antigen-specific T cells targeting model tumor-associated antigen (TAA), such as OVA, gp100, and NY-ESO-1, were used (Manguso et al., 2017Manguso 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.In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target.Nature. 2017; 547: 413-418Crossref PubMed Scopus (568) Google Scholar, Pan et al., 2018" @default.
• W2898075889 created "2018-10-26" @default.
• W2898075889 creator A5011054247 @default.
• W2898075889 creator A5011248615 @default.
• W2898075889 creator A5016370530 @default.
• W2898075889 creator A5017642120 @default.
• W2898075889 creator A5020454256 @default.
• W2898075889 creator A5023121415 @default.
• W2898075889 creator A5039833294 @default.
• W2898075889 creator A5062171528 @default.
• W2898075889 creator A5068360947 @default.
• W2898075889 creator A5070354041 @default.
• W2898075889 creator A5080001526 @default.
• W2898075889 date "2018-11-01" @default.
• W2898075889 modified "2023-10-12" @default.
• W2898075889 title "Protein Barcodes Enable High-Dimensional Single-Cell CRISPR Screens" @default.
• W2898075889 cites W1963130477 @default.
• W2898075889 cites W1967350125 @default.
• W2898075889 cites W1970170720 @default.
• W2898075889 cites W1973401140 @default.
• W2898075889 cites W1982729887 @default.
• W2898075889 cites W2009283961 @default.
• W2898075889 cites W2024626836 @default.
• W2898075889 cites W2024827262 @default.
• W2898075889 cites W2026592619 @default.
• W2898075889 cites W2031211461 @default.
• W2898075889 cites W2043565262 @default.
• W2898075889 cites W2047977722 @default.
• W2898075889 cites W2053129129 @default.
• W2898075889 cites W2058955985 @default.
• W2898075889 cites W2064815984 @default.
• W2898075889 cites W2086196752 @default.
• W2898075889 cites W2113432847 @default.
• W2898075889 cites W2158333823 @default.
• W2898075889 cites W2252568502 @default.
• W2898075889 cites W2314851760 @default.
• W2898075889 cites W2465860350 @default.
• W2898075889 cites W2485511343 @default.
• W2898075889 cites W2512065543 @default.
• W2898075889 cites W2522313999 @default.
• W2898075889 cites W2550962735 @default.
• W2898075889 cites W2561508962 @default.
• W2898075889 cites W2561754210 @default.
• W2898075889 cites W2562947047 @default.
• W2898075889 cites W2577994754 @default.
• W2898075889 cites W2728365508 @default.
• W2898075889 cites W2736476343 @default.
• W2898075889 cites W2739492614 @default.
• W2898075889 cites W2744743363 @default.
• W2898075889 cites W2752426001 @default.
• W2898075889 cites W2782124401 @default.
• W2898075889 cites W2788425782 @default.
• W2898075889 cites W2788483724 @default.
• W2898075889 cites W2950261377 @default.
• W2898075889 doi "https://doi.org/10.1016/j.cell.2018.09.022" @default.
• W2898075889 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6319269" @default.
• W2898075889 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30343902" @default.
• W2898075889 hasPublicationYear "2018" @default.
• W2898075889 type Work @default.
• W2898075889 sameAs 2898075889 @default.
• W2898075889 citedByCount "94" @default.
• W2898075889 countsByYear W28980758892018 @default.
• W2898075889 countsByYear W28980758892019 @default.
• W2898075889 countsByYear W28980758892020 @default.
• W2898075889 countsByYear W28980758892021 @default.
• W2898075889 countsByYear W28980758892022 @default.
• W2898075889 countsByYear W28980758892023 @default.
• W2898075889 crossrefType "journal-article" @default.
• W2898075889 hasAuthorship W2898075889A5011054247 @default.
• W2898075889 hasAuthorship W2898075889A5011248615 @default.
• W2898075889 hasAuthorship W2898075889A5016370530 @default.
• W2898075889 hasAuthorship W2898075889A5017642120 @default.
• W2898075889 hasAuthorship W2898075889A5020454256 @default.
• W2898075889 hasAuthorship W2898075889A5023121415 @default.
• W2898075889 hasAuthorship W2898075889A5039833294 @default.
• W2898075889 hasAuthorship W2898075889A5062171528 @default.
• W2898075889 hasAuthorship W2898075889A5068360947 @default.
• W2898075889 hasAuthorship W2898075889A5070354041 @default.
• W2898075889 hasAuthorship W2898075889A5080001526 @default.
• W2898075889 hasBestOaLocation W28980758891 @default.
• W2898075889 hasConcept C104317684 @default.
• W2898075889 hasConcept C54355233 @default.
• W2898075889 hasConcept C70721500 @default.
• W2898075889 hasConcept C86803240 @default.
• W2898075889 hasConcept C98108389 @default.
• W2898075889 hasConceptScore W2898075889C104317684 @default.
• W2898075889 hasConceptScore W2898075889C54355233 @default.
• W2898075889 hasConceptScore W2898075889C70721500 @default.
• W2898075889 hasConceptScore W2898075889C86803240 @default.
• W2898075889 hasConceptScore W2898075889C98108389 @default.
• W2898075889 hasFunder F4320306621 @default.
• W2898075889 hasFunder F4320314233 @default.
• W2898075889 hasFunder F4320332161 @default.
• W2898075889 hasIssue "4" @default.
• W2898075889 hasLocation W28980758891 @default.
• W2898075889 hasLocation W28980758892 @default.
• W2898075889 hasLocation W28980758893 @default.
• W2898075889 hasLocation W28980758894 @default.

• next