Project description:Multiplexed mosaic tumor models reveal natural phenotypic variations in drug response within and between populations [8 cancer cell lines]
Project description:Heterogeneity between patients poses a significant challenge in cancer drug development. Although success of a drug candidate in the clinic depends on efficacy across large numbers of diverse patients, only a few in vivo preclinical models are used for assaying a preclinical drug candidate due to the lack of scalability of traditional xenograft models. To address this limitation, we developed GENEVA, a scalable platform that enables the measurement of molecular and phenotypic responses to drug perturbations at single-cell resolution, both in 3D and in vivo. GENEVA models the genetic diversity of cancer by combining multiple patient-derived cell lines and cancer cell lines into pooled 3D cultures and xenograft models, allowing us to study drug responses across a wide range of genetic backgrounds within a single experiment. This platform integrates high-resolution transcriptomics with multiplexed single-cell profiling, providing a unique ability to uncover the interplay between cancer genotypes and drug responses. In this study, we apply GENEVA to investigate KRAS G12C inhibitors and demonstrate that mitochondrial activation is a key driver of cell death following KRAS inhibition. Additionally, we identify epithelial to mesenchymal transition (EMT) as a prominent resistance mechanism to KRAS G12C inhibition using in vivo GENEVA mice. These findings highlight the utility of GENEVA in identifying novel therapeutic targets and optimizing combination therapies, while underscoring its potential to bridge the gap between preclinical cancer models and patient outcomes by modeling the complexity of tumor heterogeneity in a scalable and robust assay.
Project description:Heterogeneity between patients poses a significant challenge in cancer drug development. Although success of a drug candidate in the clinic depends on efficacy across large numbers of diverse patients, only a few in vivo preclinical models are used for assaying a preclinical drug candidate due to the lack of scalability of traditional xenograft models. To address this limitation, we developed GENEVA, a scalable platform that enables the measurement of molecular and phenotypic responses to drug perturbations at single-cell resolution, both in 3D and in vivo. GENEVA models the genetic diversity of cancer by combining multiple patient-derived cell lines and cancer cell lines into pooled 3D cultures and xenograft models, allowing us to study drug responses across a wide range of genetic backgrounds within a single experiment. This platform integrates high-resolution transcriptomics with multiplexed single-cell profiling, providing a unique ability to uncover the interplay between cancer genotypes and drug responses. In this study, we apply GENEVA to investigate KRAS G12C inhibitors and demonstrate that mitochondrial activation is a key driver of cell death following KRAS inhibition. Additionally, we identify epithelial to mesenchymal transition (EMT) as a prominent resistance mechanism to KRAS G12C inhibition using in vivo GENEVA mice. These findings highlight the utility of GENEVA in identifying novel therapeutic targets and optimizing combination therapies, while underscoring its potential to bridge the gap between preclinical cancer models and patient outcomes by modeling the complexity of tumor heterogeneity in a scalable and robust assay.
Project description:Transcriptional enhancers orchestrate cell-type specific gene expression programs critical to eukaryotic development, physiology, and disease. However, despite the large number of enhancers now identified, only a small number have been functionally assessed. Here, we develop MOsaic Single-cell Analysis by Indexed CRISPR Sequencing (Mosaic-seq), a method that measures one direct phenotype of enhancer repression: change of the transcriptome, at the single cell level. Using dCas9-KRAB to suppress enhancer function, we first implement a multiplexed system to allow the simultaneous measurement of the transcriptome and detection of sgRNAs by single cell RNA sequencing. We validate this approach by targeting the HS2 enhancer in the well-studied beta-globin locus. Next, through computational simulation, we demonstrate strategies to robustly detect changes in gene expression in these single cell measurements. Finally, we use Mosaic-seq to target 71 hypersensitive regions belonging to 15 super-enhancers in K562 cells by utilizing a lentiviral library containing 241 unique-barcoded sgRNAs. Our results demonstrate that Mosaic-seq is a reliable approach to study enhancer function in single cells in a high-throughput manner.
Project description:Transcriptional enhancers orchestrate cell-type specific gene expression programs critical to eukaryotic development, physiology, and disease. However, despite the large number of enhancers now identified, only a small number have been functionally assessed. Here, we develop MOsaic Single-cell Analysis by Indexed CRISPR Sequencing (Mosaic-seq), a method that measures one direct phenotype of enhancer repression: change of the transcriptome, at the single cell level. Using dCas9-KRAB to suppress enhancer function, we first implement a multiplexed system to allow the simultaneous measurement of the transcriptome and detection of sgRNAs by single cell RNA sequencing. We validate this approach by targeting the HS2 enhancer in the well-studied beta-globin locus. Next, through computational simulation, we demonstrate strategies to robustly detect changes in gene expression in these single cell measurements. Finally, we use Mosaic-seq to target 71 hypersensitive regions belonging to 15 super-enhancers in K562 cells by utilizing a lentiviral library containing 241 unique-barcoded sgRNAs. Our results demonstrate that Mosaic-seq is a reliable approach to study enhancer function in single cells in a high-throughput manner.
Project description:Transcriptional enhancers orchestrate cell-type specific gene expression programs critical to eukaryotic development, physiology, and disease. However, despite the large number of enhancers now identified, only a small number have been functionally assessed. Here, we develop MOsaic Single-cell Analysis by Indexed CRISPR Sequencing (Mosaic-seq), a method that measures one direct phenotype of enhancer repression: change of the transcriptome, at the single cell level. Using dCas9-KRAB to suppress enhancer function, we first implement a multiplexed system to allow the simultaneous measurement of the transcriptome and detection of sgRNAs by single cell RNA sequencing. We validate this approach by targeting the HS2 enhancer in the well-studied beta-globin locus. Next, through computational simulation, we demonstrate strategies to robustly detect changes in gene expression in these single cell measurements. Finally, we use Mosaic-seq to target 71 hypersensitive regions belonging to 15 super-enhancers in K562 cells by utilizing a lentiviral library containing 241 unique-barcoded sgRNAs. Our results demonstrate that Mosaic-seq is a reliable approach to study enhancer function in single cells in a high-throughput manner.
Project description:Transcriptional enhancers orchestrate cell-type specific gene expression programs critical to eukaryotic development, physiology, and disease. However, despite the large number of enhancers now identified, only a small number have been functionally assessed. Here, we develop MOsaic Single-cell Analysis by Indexed CRISPR Sequencing (Mosaic-seq), a method that measures one direct phenotype of enhancer repression: change of the transcriptome, at the single cell level. Using dCas9-KRAB to suppress enhancer function, we first implement a multiplexed system to allow the simultaneous measurement of the transcriptome and detection of sgRNAs by single cell RNA sequencing. We validate this approach by targeting the HS2 enhancer in the well-studied beta-globin locus. Next, through computational simulation, we demonstrate strategies to robustly detect changes in gene expression in these single cell measurements. Finally, we use Mosaic-seq to target 71 hypersensitive regions belonging to 15 super-enhancers in K562 cells by utilizing a lentiviral library containing 241 unique-barcoded sgRNAs. Our results demonstrate that Mosaic-seq is a reliable approach to study enhancer function in single cells in a high-throughput manner.