Browse
Submit Data
Databases
API
Help

Dataset Information

22 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Linked optical and gene expression profiling of single cells at high-throughput.

ABSTRACT: Single-cell RNA sequencing has emerged as a powerful tool for characterizing cells, but not all phenotypes of interest can be observed through changes in gene expression. Linking sequencing with optical analysis has provided insight into the molecular basis of cellular function, but current approaches have limited throughput. Here, we present a high-throughput platform for linked optical and gene expression profiling of single cells. We demonstrate accurate fluorescence and gene expression measurements on thousands of cells in a single experiment. We use the platform to characterize DNA and RNA changes through the cell cycle and correlate antibody fluorescence with gene expression. The platform's ability to isolate rare cell subsets and perform multiple measurements, including fluorescence and sequencing-based analysis, holds potential for scalable multi-modal single-cell analysis.

SUBMITTER: Zhang JQ

PROVIDER: S-EPMC7041248 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Linked optical and gene expression profiling of single cells at high-throughput.

Zhang Jesse Q JQ Siltanen Christian A CA Liu Leqian L Chang Kai-Chun KC Gartner Zev J ZJ Abate Adam R AR

Genome biology 20200224 1

Single-cell RNA sequencing has emerged as a powerful tool for characterizing cells, but not all phenotypes of interest can be observed through changes in gene expression. Linking sequencing with optical analysis has provided insight into the molecular basis of cellular function, but current approaches have limited throughput. Here, we present a high-throughput platform for linked optical and gene expression profiling of single cells. We demonstrate accurate fluorescence and gene expression measu ...[more]

PMID: 32093753

Similar Datasets

Linked optical and gene expression profiling of single cells at high throughput

Project description:Single cell RNA sequencing has emerged as a powerful tool for characterizing cells, but not all phenotypes of interest can be observed through gene expression alone. Linking sequencing with optical analysis has provided insight into the molecular basis behind cellular function, but current approaches have limited throughput. Here, we present a high throughput platform for linked optical and gene expression profiling of single cells. We demonstrate accurate fluorescence and gene expression measurements from thousands of cells in a single experiment and use the platform to characterize DNA and RNA changes in Jurkat cells through the cell-cycle. In addition to its scalability, our integration of microfluidics and array-based molecular biology holds promise for comprehensive multi-omics profiling of single cells.

2019-09-05 | GSE136871 | GEO

Linked optical and gene expression profiling of single cells at high throughput

Project description:Linked optical and gene expression profiling of single cells at high throughput

| PRJNA563900 | ENA

High-throughput gene expression profiling of memory differentiation in primary human T cells.

Project description:BackgroundThe differentiation of naive T and B cells into memory lymphocytes is essential for immunity to pathogens. Therapeutic manipulation of this cellular differentiation program could improve vaccine efficacy and the in vitro expansion of memory cells. However, chemical screens to identify compounds that induce memory differentiation have been limited by 1) the lack of reporter-gene or functional assays that can distinguish naive and memory-phenotype T cells at high throughput and 2) a suitable cell-line representative of naive T cells.ResultsHere, we describe a method for gene-expression based screening that allows primary naive and memory-phenotype lymphocytes to be discriminated based on complex genes signatures corresponding to these differentiation states. We used ligation-mediated amplification and a fluorescent, bead-based detection system to quantify simultaneously 55 transcripts representing naive and memory-phenotype signatures in purified populations of human T cells. The use of a multi-gene panel allowed better resolution than any constituent single gene. The method was precise, correlated well with Affymetrix microarray data, and could be easily scaled up for high-throughput.ConclusionThis method provides a generic solution for high-throughput differentiation screens in primary human T cells where no single-gene or functional assay is available. This screening platform will allow the identification of small molecules, genes or soluble factors that direct memory differentiation in naive human lymphocytes.

| S-EPMC2529265 | biostudies-literature

High-Throughput Isolation of Cell Protrusions with Single-Cell Precision for Profiling Subcellular Gene Expression.

Project description:Invading cancer cells extend cell protrusions, which guide cancer-cell migration and invasion, eventually leading to metastasis. The formation and activity of cell protrusions involve the localization of molecules and organelles at the cell front; however, it is challenging to precisely isolate these subcellular structures at the single-cell level for molecular analysis. Here, we describe a newly developed microfluidic platform capable of high-throughput isolation of cell protrusions at single-cell precision for profiling subcellular gene expression. Using this microfluidic platform, we demonstrate the efficient generation of uniform cell-protrusion arrays (more than 5000 cells with protrusions) for a series of cell types. We show precise isolation of cell protrusions with high purity at single-cell precision for subsequent RNA-Seq analysis, which was further validated by RT-qPCR and RNA FISH. Our highly controlled protrusion isolation method opens a new avenue for the study of subcellular functional mechanisms and signaling pathways in metastasis.

| S-EPMC7239316 | biostudies-literature

High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization.

Project description:Image-based approaches to single-cell transcriptomics, in which RNA species are identified and counted in situ via imaging, have emerged as a powerful complement to single-cell methods based on RNA sequencing of dissociated cells. These image-based approaches naturally preserve the native spatial context of RNAs within a cell and the organization of cells within tissue, which are important for addressing many biological questions. However, the throughput of these image-based approaches is relatively low. Here we report advances that lead to a drastic increase in the measurement throughput of multiplexed error-robust fluorescence in situ hybridization (MERFISH), an image-based approach to single-cell transcriptomics. In MERFISH, RNAs are identified via a combinatorial labeling approach that encodes RNA species with error-robust barcodes followed by sequential rounds of single-molecule fluorescence in situ hybridization (smFISH) to read out these barcodes. Here we increase the throughput of MERFISH by two orders of magnitude through a combination of improvements, including using chemical cleavage instead of photobleaching to remove fluorescent signals between consecutive rounds of smFISH imaging, increasing the imaging field of view, and using multicolor imaging. With these improvements, we performed RNA profiling in more than 100,000 human cells, with as many as 40,000 cells measured in a single 18-h measurement. This throughput should substantially extend the range of biological questions that can be addressed by MERFISH.

| S-EPMC5047202 | biostudies-literature

High-throughput protein profiling for detecting colorectal cancer protein expression and prognosis

Project description:Interventions: Gold Standard:1. physical examination - digital rectal examination; 2. laboratory examination - blood routine, fecal occult blood test; 3. imaging examination - X-ray angiography, ultrasound; 4. endoscopy - rectal sigmoidoscopy; 5. clinical outcomes The gold standard is histopathology.;Index test:Disease or healthy tissue specimen proteomics, peripheral blood, exosome protein and free DNA, urine and fecal metabolites. Primary outcome(s): Proteomics;SPF;Survival time Study Design: Sequential

| 2684555 | ecrin-mdr-crc

High-throughput profiling of influenza A virus hemagglutinin gene at single-nucleotide resolution.

Project description:Genetic research on influenza virus biology has been informed in large part by nucleotide variants present in seasonal or pandemic samples, or individual mutants generated in the laboratory, leaving a substantial part of the genome uncharacterized. Here, we have developed a single-nucleotide resolution genetic approach to interrogate the fitness effect of point mutations in 98% of the amino acid positions in the influenza A virus hemagglutinin (HA) gene. Our HA fitness map provides a reference to identify indispensable regions to aid in drug and vaccine design as targeting these regions will increase the genetic barrier for the emergence of escape mutations. This study offers a new platform for studying genome dynamics, structure-function relationships, virus-host interactions, and can further rational drug and vaccine design. Our approach can also be applied to any virus that can be genetically manipulated.

| S-EPMC4018626 | biostudies-literature

Gene expression profiling in single cells within tissue.

Project description:We developed a robust multiplex fluorescent in situ hybridization (FISH) technique in archival formalin-fixed, paraffin-embedded (FFPE) human tissue sections while preserving the microanatomical context. This identifies single-cell gene expression patterns by probing multiple, unique nascent RNA transcripts and yields predictive quantitative gene expression signatures.

| S-EPMC1976602 | biostudies-literature

High-throughput chromatin accessibility profiling at single-cell resolution.

Project description:Here we develop a high-throughput single-cell ATAC-seq (assay for transposition of accessible chromatin) method to measure physical access to DNA in whole cells. Our approach integrates fluorescence imaging and addressable reagent deposition across a massively parallel (5184) nano-well array, yielding a nearly 20-fold improvement in throughput (up to ~1800 cells/chip, 4-5?h on-chip processing time) and library preparation cost (~81¢ per cell) compared to prior microfluidic implementations. We apply this method to measure regulatory variation in peripheral blood mononuclear cells (PBMCs) and show robust, de novo clustering of single cells by hematopoietic cell type.

| S-EPMC6128862 | biostudies-literature

High throughput gene expression profiling of yeast colonies with microgel-culture Drop-seq.

Project description:Yeast can be engineered into "living foundries" for non-natural chemical production by reprogramming them via a "design-build-test" cycle. While methods for "design" and "build" are relatively scalable and efficient, "test" remains a bottleneck, limiting the effectiveness of the procedure. Here we describe isogenic colony sequencing (ICO-seq), a massively-parallel strategy to assess the gene expression, and thus engineered pathway efficacy, of large numbers of genetically distinct yeast colonies. We use the approach to characterize opaque-white switching in 658 C. albicans colonies. By profiling the transcriptomes of 1642 engineered S. cerevisiae strains, we assess gene expression heterogeneity in a protein mutagenesis library. Our approach will accelerate synthetic biology by allowing facile and cost-effective transcriptional profiling of large numbers of genetically distinct yeast strains.

| S-EPMC7909483 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data