High-throughput, Microscope-based Sorting to Dissect Cellular Heterogeneity
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA591809 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| Other | ||||
| SRR10533612.fastq.gz | Fastqsanger.gz | |||
| SRR10533613.fastq.gz | Fastqsanger.gz | |||
| SRR10533614.fastq.gz | Fastqsanger.gz | |||
| SRR10533615.fastq.gz | Fastqsanger.gz |
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Project description: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.
2020-04-01 | GSE141030 | GEO
Project description: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, and 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.
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Project description:Cellular heterogeneity may bias cell type specific epigenetic patterns leading to a dramatic increase in false positive or negative findings in psychiatric epigenetic studies. To address this issue, we performed fluorescence activated cell sorting (FACS) of neuronal nuclei in post mortem frontal cortex 58 samples followed by Illumina HM450 microarray based DNA methylation profiling. We characterized the extent of neuron and glia specific DNA methylation variation independent of disease status and identified significant cell type specific epigenetic variation at 51% of loci. DNA methylation was profiled for sorted neuronal nuclei from post mortem frontal cortex of 29 major depression and 29 matched control samples using Illumina HM450 microarrays
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Project description:KDM5B links cellular transcriptome heterogeneity to therapy resistance
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Project description:Background: Rare cell subtypes can profoundly impact the course of human health and disease, yet their presence within a sample is often missed with bulk molecular analysis. Single-cell analysis tools such as FACS, FISH-FC and single-cell barcode-based sequencing can investigate cellular heterogeneity; however, they have significant limitations that impede their ability to identify and transcriptionally characterize many rare cell subpopulations. Results: PCR-activated cell sorting (PACS) is a novel cytometry method that uses single-cell TaqMan PCR reactions performed in microfluidic droplets to identify and isolate cell subtypes with high-throughput. Here, we extend this method and demonstrate that PACS enables high-dimensional molecular profiling on TaqMan-targeted cells. Using a random priming RNA-Seq strategy, we obtained high-fidelity transcriptome measurements following PACS-sorting of prostate cancer cells from a heterogeneous population. The sequencing data revealed prostate cancer gene expression profiles that were obscured in the unsorted populations. Single-cell expression analysis with PACS was subsequently used to confirm a number of the differentially expressed genes identified with RNA sequencing. Conclusions: PACS requires minimal sample processing, uses readily available TaqMan assays and can isolate cell subtypes with high sensitivity. We have now validated a method for performing next-generation sequencing on mRNA obtained from PACS isolated cells. This capability makes PACS well suited for transcriptional profiling of rare cells from complex populations to obtain maximal biological insight into cell states and behaviors.
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Project description:Variable tumor cellularity can limit sensitivity and precision in comparative genomics, because differences in tumor content can result in misclassifying truncal mutations as region-specific private mutations in stroma-rich regions, especially when studying tissue specimens of mediocre tumor cellularity such as LUADs. To address this issue, we refined a nuclei flow-sorting approach by sorting nuclei based on ploidy and the LUAD lineage marker thyroid transcription factor 1 (TTF-1) and applied this method to investigate genome-wide somatic copy number aberrations (SCNA) and mutations of 409 cancer genes in 39 tumor populations obtained from 16 primary tumors and 21 matched metastases. This approach increased the mean tumor purity from 54% (range: 7-89%) of unsorted material to 92% (range: 79-99%) after sorting. Despite this rise in tumor purity, we detected limited genetic heterogeneity between primary tumors and their metastases. In fact, 88% of SCNAs and 80% of mutations were propagated from primary tumors to metastases and low allele frequency mutations accounted for much of the mutational heterogeneity. Even though the presence of SCNAs indicated a history of chromosomal instability (CIN) in all tumors, metastases did not have more SCNAs than primary tumors. Moreover, tumors with biallelic TP53 or ATM mutations had high numbers of SCNAs, yet they were associated with a low interlesional genetic heterogeneity. The results of our study thus provide evidence that most macroevolutionary events occur in primary tumors before metastatic dissemination and advocate for a limited degree of CIN over time and space in this cohort of LUADs. Sampling of primary tumors thus may suffice to detect most mutations and SCNAs. In addition, metastases but not primary tumors had seeded additional metastases in three of four patients; this provides a genomic rational for surgical treatment of such oligometastatic LUADs.
2018-09-11 | GSE119763 | GEO
Project description:Glioblastoma is the most malignant primary brain tumor for which the prognosis is still dismal despite aggressive surgical, medical, and radiation therapies. Glioblastoma stem cells (GSCs) promote therapeutic resistance and cellular heterogeneity due to their self-renewal properties and capacity for plasticity. To understand the molecular processes essential for maintaining GSCs, we performed an integrative analysis comparing active enhancer landscapes, transcriptional profiles, and functional genomics profiles of GSCs and non-neoplastic neural stem cells (NSCs). We identified sorting nexin 10 (SNX10), an endosomal protein sorting factor, as selectively expressed in GSCs compared to NSCs and essential for GSC survival. Targeting SNX10 impaired GSC viability and proliferation, induced apoptosis, and reduced self-renewal capacity. Mechanistically, GSCs utilized endosomal protein sorting to promote platelet-derived growth factor receptor β (PDGFRβ) proliferative and stem cell signaling pathways via post-transcriptional regulation of the PDGF receptor tyrosine kinase. Targeting SNX10 expression extended survival of orthotopic xenograft-bearing mice, and high SNX10 expression correlated with poor glioblastoma patient prognosis, suggesting its potential clinical importance. Thus, our study reveals an essential connection between endosomal protein sorting and oncogenic receptor tyrosine kinase signaling and suggests that targeting endosomal sorting may represent a promising therapeutic approach for glioblastoma treatment.
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