Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Prox-seq: Simultaneous quantification of proteins, protein complexes and mRNA in single-cells via proximity-sequencing

ABSTRACT: A novel assay based on proximity ligation assay for high-throughput quantification of proteins, protein complexes and mRNA in single cells Here we present proximity-sequencing (Prox-seq), a method for simultaneous measurement of an individual cell’s proteins, protein complexes and mRNA. Prox-seq utilizes deep sequencing and barcoded proximity assays to measure proteins and their complexes from all pairwise combinations of targeted proteins, without any prior knowledge of which proteins can form complexes. The number of measured protein complexes scales quadratically with the number of targeted proteins, allowing for unparalleled multiplexing capacity. We developed a high-throughput experimental and computational pipeline and demonstrated the potential of Prox-Seq for multi-omic analysis with a panel of 13 proximity probes, enabling the measurement of 91 protein complexes, along with thousands of mRNA molecules in single T cells and B cells. Prox-seq is compatible with current single-cell RNA sequencing assays, such as Drop-seq and Smart-seq2. Prox-seq provides access to an untapped yet powerful measurement modality for single-cell phenotyping and has the potential to discover new protein interactions in single-cells.

ORGANISM(S): Homo sapiens

PROVIDER: GSE149574 | GEO | 2020/05/16

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Computational frameworks for predicting protein interactions via single-cell proximity sequencing

Project description:Proximity sequencing (Prox-seq) is a method that can simultaneously measure gene expression, protein abundance and protein complex information in single cells. By making use of proximity information from proteins, Prox-seq can infer surface protein complexes at the single-cell level. Previously, we proposed a statistical method for predicting protein complex abundance in single cells from Prox-seq data. However, it is difficult to experimentally validate the prediction results. Here, we first introduces a physical model which enables simulation of Prox-seq data. The simulated data is then used to validate the statistical method that we proposed previously, and to understand how protein abundance can influence protein complex prediction. Second, we propose a modification to Prox-seq, and use the new measurements from the modification to develop a second, independent method for predicting protein complexes. We demonstrate that on experimental data, the two methods generally agree with each other, with the second method generally producing more accurate protein complex abundance results. Thus, we present a simple way to investigate the behavior of Prox-seq data under various biological conditions, and independent methods for predicting and quantifying protein complexes.

2022-02-07 | GSE196130 | GEO

Prox-seq: Simultaneous quantification of proteins, protein complexes and mRNA in single-cells via proximity-sequencing

Project description:Prox-seq: Simultaneous quantification of proteins, protein complexes and mRNA in single-cells via proximity-sequencing

| PRJNA629294 | ENA

Spatially resolved mapping of proteome turnover dynamics with subcellular precision

Project description:We report the development of prox-SILAC method that combines proximity-dependent protein labeling (APEX/HRP) with metabolic incorporation of stable isotopes (pulse-SILAC) to map newly synthesized proteins with subcellular spatial resolution. We apply prox-SILAC to investigate proteome dynamics in the mitochondrial matrix and the ER lumen. Our analysis reveals a highly heterogeneous distribution in protein turnover dynamics within macromolecular machineries such as the mitochondrial ribosome and respiratory complexes I-V, thus shedding light on their mechanism of hierarchical assembly. Furthermore, we investigate the dynamic changes of ER proteome when cells are challenged with stress or when cells are undergoing stimulated differentiation, identifying subsets of proteins with unique patterns of turnover dynamics, which may play key regulatory roles in alleviating stress or promoting differentiation.

2024-01-26 | PXD037569 | Pride

Proximity RNA labeling by APEX-Seq Reveals the Organization of Translation Initiation Complexes and Repressive RNA Granules

Project description:Stress granules are dynamic non-membrane bound organelles made up of untranslating messenger ribonucleoproteins (mRNPs) that form when cells integrate stressful environmental cues resulting in stalled translation initiation complexes. Although stress granules dramatically alter mRNA and protein localization, understanding these complexes has proven to be challenging through conventional imaging, purification, and crosslinking approaches. We therefore developed an RNA proximity labeling technique, APEX-Seq, which uses the ascorbate peroxidase APEX2 to probe the spatial organization of the transcriptome. We show that APEX-Seq can resolve the localization of RNAs within the cell and determine their enrichment or depletion near key RNA-binding proteins. Matching both the spatial transcriptome using APEX-seq, and the spatial proteome using APEX-mass spectrometry (APEX-MS) provide new insights into the organization of translation initiation complexes on active mRNAs, as well as revealing unanticipated complexity in stress granule contents, and provides a powerful approach to explore the spatial environment of macromolecules.

2018-10-26 | GSE121575 | GEO

Time-resolved assessment of single-cell protein secretion by sequencing

Project description:Secreted proteins play critical roles in cellular communication. Methods enabling concurrent measurement of cellular protein secretion, phenotypes and transcriptomes are still unavailable. Here, we describe Time-Resolved Assessment of Protein Secretion from single cells by sequencing (TRAPS-seq). Released proteins are trapped onto cell surface and probed by oligonucleotide-barcoded antibodies before simultaneously sequenced with transcriptomes in single cells. TRAPS-seq unravels the phenotypic and transcriptional determinants of the secretion of pleiotropic Th1 cytokines (IFN-γ, IL-2 and TNF-α) in activated T cells. We further demonstrate the use of TRAPS-seq to track dynamic secretion of multiple cytokines over time, uncovering unique molecular signatures that govern the preservation or transition of single-cell cytokine secretions. Our results revealed that early central memory T cells with CD45RA expression (TCMRA) are important in both the production and maintenance of polyfunctional cytokines. TRAPS-seq presents a unique tool for seamless integration of secretomics measurement with multi-omics profiling in single cells.

2023-01-11 | GSE200690 | GEO

Project description:Simultaneous Measurement of Transcripts and Proteins from Single Cells

| PRJNA472815 | ENA

S100A4 mRNA-protein relationship uncovered by measurement noise reduction

Project description:S100A4 mRNA-protein relationship uncovered by measurement noise reduction

| PRJNA615229 | ENA

A targeted multi-omic analysis approach measures protein expression and low abundance transcripts on the single cell level

Project description:High throughput single-cell RNA sequencing (sc-RNAseq) has become a frequently used tool to assess immune cell function and heterogeneity. Recently, the combined measurement of RNA and protein expression by sequencing was developed, which is commonly known as CITE-Seq. Acquisition of protein expression data along with transcriptome data resolves some of the limitations inherent to only assessing transcript, but also nearly doubles the sequencing read depth required per single cell. Furthermore, there is still a paucity of analysis tools to visualize combined transcript-protein datasets. Here, we describe a novel targeted transcriptomics approach that combines analysis of over 400 genes with simultaneous measurement of over 40 proteins on more than 25,000 cells. This targeted approach requires only about 1/10 of the read depth compared to a whole transcriptome approach while retaining high sensitivity for low abundance transcripts. To analyze these multi-omic transcript-protein datasets, we adapted One-SENSE for intuitive visualization of the relationship of proteins and transcripts on a single-cell level.

2020-03-30 | GSE135325 | GEO

RNA-seq and ribosome profiling of heat shocked wild type and Mettl14 -/- mouse embryonic stem cells

Project description:N6-methyladenosine (m6A) is a dynamic and reversible nucleotide modification in mRNA. m6A alters mRNA fate, but it is unclear why the effects of m6A can vary in different cellular contexts. Here we show that methylated mRNAs are catalysts for liquid-liquid phase separation of the YTHDF family of m6A-binding proteins. RNAs that contain multiple, but not single, m6A residues recruit multiple YTHDF proteins, causing them to undergo a proximity-induced phase separation. YTHDF proteins show liquid-like properties upon binding polymethylated mRNAs in cells, and partition into endogenous phase-separated compartments, such as P-bodies, neuronal RNA granules, and stress granules. The complexes of YTHDF proteins and polymethylated mRNAs are targeted to different compartments depending on the cell context, leading to different effects on m6A mRNAs. These studies reveal a role for nucleotide modifications in regulating phase separation and indicate that the cellular properties of m6A-modified mRNAs can be explained by liquid-liquid phase separation principles.

2019-07-15 | GSE125725 | GEO

Mapping of Long-Range Chromatin Interactions using Proximity Ligation Assisted ChIP-seq

Project description:We report Proximity Ligation Assisted ChIP-sequencing (PLAC-seq), a method for comprehensive detection of long-range interactions associated with proteins of interest. PLAC-seq requires up to 500-fold less starting material compared to ChIA-PET and using experimentally determined input as control precisely reveals protein associated interaction upto single-element resolution. Application of PLAC-seq to mouse embryonic stem cells revealed a comprehensive map of regulatory interactions.

2016-11-15 | GSE86150 | GEO

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