Dataset Information

High-throughput, Microscope-based Sorting to Dissect Cellular Heterogeneity

ABSTRACT: Microscopy is a powerful tool for characterizing complex cellular phenotypes, but linking these phenotypes to genotype or RNA expression at scale remains challenging. Here, we present Visual Cell Sorting, a method that physically separates hundreds of thousands of live cells based on their visual phenotype. Automated imaging and phenotypic analysis directs selective illumination of Dendra2, a photoconvertible fluorescent protein expressed in live cells; these photoactivated cells are then isolated using fluorescence-activated cell sorting. First, we use Visual Cell Sorting to assess hundreds of nuclear localization sequence variants in a pooled format, identifying variants that improve nuclear localization and enabling annotation of nuclear localization sequences in thousands of human proteins. Second, we recover cells that retain normal nuclear morphologies after paclitaxel treatment, then derive their single cell transcriptomes to identify pathways associated with paclitaxel resistance in cancers. Unlike alternative methods, Visual Cell Sorting depends on inexpensive reagents and commercially available hardware. As such, it can be readily deployed to uncover the relationships between visual cellular phenotypes and internal states, including genotypes and gene expression programs.

ORGANISM(S): Homo sapiens

PROVIDER: GSE141030 | GEO | 2020/04/01

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Pervasive transcription in the mammalian genome produces thousands of long noncoding RNAs (lncRNAs) and promoter- or enhancer-associated unstable transcripts. They preferentially locate to chromatin, at which some regulate chromatin structure, transcription and RNA processing. While several RNA sequences responsible for nuclear localization have been identified, such as repeats in the lncRNA Xist and Alu-like elements for long RNAs, how lncRNAs as a class are enriched on chromatin remains elusive. To screen for cis-elements that contribute to RNA-chromatin localization, we developed a high-throughput method named RNA elements for subcellular localization by sequencing (REL-seq), and discovered a U1 small nuclear ribonucleoprotein (snRNP)-recognition motif being critical for chromatin localization of reporter RNAs. Across the genome, chromatin-bound lncRNAs, which are enriched with 5’ splice sites and depleted of 3’ splice sites, exhibit high levels of U1 snRNA binding compared to cytoplasm-localized protein-coding mRNAs. Acute depletion of U1 snRNA, or U1 snRNP protein component SNRNP70, drastically reduces the chromatin association of hundreds of lncRNAs and unstable transcripts without altering the overall transcription rate in cells. In addition, rapid degradation of SNRNP70 reduces the localization of both nascent and polyadenylated lncRNA transcripts to chromatin, and disrupts the nuclear-speckles and genome-wide localization of Malat1, a highly conserved and abundant lncRNA. Moreover, chromatin-bound U1 snRNP interacts with transcriptionally engaged RNA polymerase (Pol) II. Together, these results demonstrate that U1 snRNP acts widely to tether and mobilize lncRNAs to chromatin in a Pol II transcription-dependent manner. Our findings uncover a novel role of U1 snRNP beyond pre-mRNA processing and provide molecular insights into how lncRNAs are recruited to Pol II-transcribed genes and have a propensity for chromatin-associated functions.

Dataset Information

High-throughput, Microscope-based Sorting to Dissect Cellular Heterogeneity

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets