Ontology highlight
ABSTRACT:
PROVIDER: PRJNA578549 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR10315000_1.fastq.gz | Fastqsanger.gz | |||
| SRR10315000_2.fastq.gz | Fastqsanger.gz | |||
| SRR10315001_1.fastq.gz | Fastqsanger.gz | |||
| SRR10315001_2.fastq.gz | Fastqsanger.gz | |||
| SRR10315002_1.fastq.gz | Fastqsanger.gz |
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Project description:Maintenance of proper phenotypic state of mammary epithelial cells is crucial for normal function, however the molecular networks underlying control of various cellular phenotypes are not well understood. To date most studies have assessed the impact of extracellular signals on a single phenotypic response, such as proliferation, or have focused on elucidation of molecular changes associated with single perturbations. However, these approaches ignore that ligands frequently induce multiple phenotypic changes and that similar phenotypic responses can be induced by multiple ligands. As a consequence, little is known about the core molecular mechanisms that drive cells to different phenotypic states. Here we deeply profiled the phenotypic and molecular responses of MCF10A mammary epithelial cells to a diverse panel of ligands known to have a role in the normal mammary gland. These ligands elicited multiple phenotypic responses, including changes in proliferation, migration, and differentiation status. Analysis of companion RNAseq, ATACseq, and proteomic data identified distinct molecular features associated with ligand treatments and phenotypic changes. These data provide a robust resource to address questions about how environmental signals are encoded into molecular and phenotypic responses, the associations between molecular modalities, and temporal encoding of molecular and phenotypic responses.
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Project description:Maintenance of proper phenotypic state of mammary epithelial cells is crucial for normal function, however the molecular networks underlying control of various cellular phenotypes are not well understood. To date most studies have assessed the impact of extracellular signals on a single phenotypic response, such as proliferation, or have focused on elucidation of molecular changes associated with single perturbations. However, these approaches ignore that ligands frequently induce multiple phenotypic changes and that similar phenotypic responses can be induced by multiple ligands. As a consequence, little is known about the core molecular mechanisms that drive cells to different phenotypic states. Here we deeply profiled the phenotypic and molecular responses of MCF10A mammary epithelial cells to a diverse panel of ligands known to have a role in the normal mammary gland. These ligands elicited multiple phenotypic responses, including changes in proliferation, migration, and differentiation status. Analysis of companion RNAseq, ATACseq, and proteomic data identified distinct molecular features associated with ligand treatments and phenotypic changes. These data provide a robust resource to address questions about how environmental signals are encoded into molecular and phenotypic responses, the associations between molecular modalities, and temporal encoding of molecular and phenotypic responses.
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Project description:Genome sequencing studies have identified millions of somatic variants in cancer, but it remains challenging to predict the phenotypic impact of most. Experimental approaches to distinguish impactful variants often use phenotypic assays that report on predefined gene-specific functional effects in bulk cell populations. Here, we develop an approach to functionally assess variant impact in single cells by pooled Perturb-seq. We measured the impact of 200 TP53 and KRAS variants on RNA profiles in over 300,000 single lung cancer cells, and used the profiles to categorize variants into phenotypic subsets to distinguish gain-of-function, loss-of-function and dominant negative variants, which we validated by comparison with orthogonal assays. We discovered that KRAS variants did not merely fit into discrete functional categories, but spanned a continuum of gain-of-function phenotypes, and that their functional impact could not have been predicted solely by their frequency in patient cohorts. Our work provides a scalable, gene-agnostic method for coding variant impact phenotyping, with potential applications in multiple disease settings.
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