Browse
Submit Data
Databases
API
Help

Dataset Information

20 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Improving oligo-conjugated antibody signal in multimodal single-cell analysis.

ABSTRACT: Simultaneous measurement of surface proteins and gene expression within single cells using oligo-conjugated antibodies offers high-resolution snapshots of complex cell populations. Signal from oligo-conjugated antibodies is quantified by high-throughput sequencing and is highly scalable and sensitive. We investigated the response of oligo-conjugated antibodies towards four variables: concentration, staining volume, cell number at staining, and tissue. We find that staining with recommended antibody concentrations causes unnecessarily high background and amount of antibody used can be drastically reduced without loss of biological information. Reducing staining volume only affects antibodies targeting abundant epitopes used at low concentrations and is counteracted by reducing cell numbers. Adjusting concentrations increases signal, lowers background, and reduces costs. Background signal can account for a major fraction of total sequencing and is primarily derived from antibodies used at high concentrations. This study provides new insight into titration response and background of oligo-conjugated antibodies and offers concrete guidelines to improve such panels.

SUBMITTER: Buus TB

PROVIDER: S-EPMC8051954 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

SIGNAL-seq: Multimodal Single-cell Inter- and Intra-cellular Signalling Analysis

Project description:We present SIGNAL-seq (Split-pool Indexing siGNalling AnaLysis by sequencing): a multiplexed split-pool combinatorial barcoding method that simultaneously measures RNA and post-translational modifications (PTMs) in fixed single cells from 3D models. SIGNAL-seq PTM measurements are equivalent to mass cytometry and RNA gene detection is analogous to split-pool barcoding scRNA-seq. By measuring both mRNA ligand-receptor pairs and PTMs in single cells, SIGNAL-seq can simultaneously un- cover inter- and intra-cellular regulation of tumour microenvironment plasticity. This SuperSeries is composed of the SubSeries listed below.

2024-02-24 | GSE256405 | GEO

SIGNAL-seq: Multimodal Single-cell Inter- and Intra-cellular Signalling Analysis II

Project description:We present SIGNAL-seq (Split-pool Indexing siGNalling AnaLysis by sequencing): a multiplexed split-pool combinatorial barcoding method that simultaneously measures RNA and post-translational modifications (PTMs) in fixed single cells from 3D solid-tumour models. SIGNAL-seq PTM measurements are equivalent to mass cytometry and RNA gene detection is analogous to split-pool barcoding scRNA-seq. By measuring both mRNA ligand-receptor pairs and PTMs in single cells, SIGNAL-seq simultaneously reveals both inter- and intra-cellular signalling in tumour microenvironment organoids.

2024-02-24 | GSE256404 | GEO

SIGNAL-seq: Multimodal Single-cell Inter- and Intra-cellular Signalling Analysis I

Project description:We present SIGNAL-seq (Split-pool Indexing siGNalling AnaLysis by sequencing): a multiplexed split-pool combinatorial barcoding method that simultaneously measures RNA and post-translational modifications (PTMs) in fixed single cells from 3D solid-tumour models. SIGNAL-seq PTM measurements are equivalent to mass cytometry and RNA gene detection is analogous to split-pool barcoding scRNA-seq. By measuring both mRNA ligand-receptor pairs and PTMs in single cells, SIGNAL-seq simultaneously reveals both inter- and intra-cellular signalling in tumour microenvironment organoids.

2024-02-24 | GSE256403 | GEO

Integrated analysis of multimodal single-cell data.

Project description:The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce "weighted-nearest neighbor" analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.

| S-EPMC8238499 | biostudies-literature

Nuclear oligo hashing improves differential analysis of single-cell RNA-seq.

Project description:Single-cell RNA sequencing (scRNA-seq) offers a high-resolution molecular view into complex tissues, but suffers from high levels of technical noise which frustrates efforts to compare the gene expression programs of different cell types. "Spike-in" RNA standards help control for technical variation in scRNA-seq, but using them with recently developed, ultra-scalable scRNA-seq methods based on combinatorial indexing is not feasible. Here, we describe a simple and cost-effective method for normalizing transcript counts and subtracting technical variability that improves differential expression analysis in scRNA-seq. The method affixes a ladder of synthetic single-stranded DNA oligos to each cell that appears in its RNA-seq library. With improved normalization we explore chemical perturbations with broad or highly specific effects on gene regulation, including RNA pol II elongation, histone deacetylation, and activation of the glucocorticoid receptor. Our methods reveal that inhibiting histone deacetylation prevents cells from executing their canonical program of changes following glucocorticoid stimulation.

| S-EPMC9106741 | biostudies-literature

Benchmarking single-cell hashtag oligo demultiplexing methods.

Project description:Sample multiplexing is often used to reduce cost and limit batch effects in single-cell RNA sequencing (scRNA-seq) experiments. A commonly used multiplexing technique involves tagging cells prior to pooling with a hashtag oligo (HTO) that can be sequenced along with the cells' RNA to determine their sample of origin. Several tools have been developed to demultiplex HTO sequencing data and assign cells to samples. In this study, we critically assess the performance of seven HTO demultiplexing tools: hashedDrops, HTODemux, GMM-Demux, demuxmix, deMULTIplex, BFF (bimodal flexible fitting) and HashSolo. The comparison uses data sets where each sample has also been demultiplexed using genetic variants from the RNA, enabling comparison of HTO demultiplexing techniques against complementary data from the genetic 'ground truth'. We find that all methods perform similarly where HTO labelling is of high quality, but methods that assume a bimodal count distribution perform poorly on lower quality data. We also suggest heuristic approaches for assessing the quality of HTO counts in an scRNA-seq experiment.

| S-EPMC10566318 | biostudies-literature

SIGNAL-seq: Multimodal Single-cell Inter- and Intra-cellular Signalling Analysis

Project description:SIGNAL-seq: Multimodal Single-cell Inter- and Intra-cellular Signalling Analysis

| PRJNA1079350 | ENA

Cobolt: integrative analysis of multimodal single-cell sequencing data.

Project description:A growing number of single-cell sequencing platforms enable joint profiling of multiple omics from the same cells. We present Cobolt, a novel method that not only allows for analyzing the data from joint-modality platforms, but provides a coherent framework for the integration of multiple datasets measured on different modalities. We demonstrate its performance on multi-modality data of gene expression and chromatin accessibility and illustrate the integration abilities of Cobolt by jointly analyzing this multi-modality data with single-cell RNA-seq and ATAC-seq datasets.

| S-EPMC8715620 | biostudies-literature

Multimodal Routing: Improving Local and Global Interpretability of Multimodal Language Analysis.

Project description:The human language can be expressed through multiple sources of information known as modalities, including tones of voice, facial gestures, and spoken language. Recent multimodal learning with strong performances on human-centric tasks such as sentiment analysis and emotion recognition are often black-box, with very limited interpretability. In this paper we propose Multimodal Routing, which dynamically adjusts weights between input modalities and output representations differently for each input sample. Multimodal routing can identify relative importance of both individual modalities and cross-modality features. Moreover, the weight assignment by routing allows us to interpret modality-prediction relationships not only globally (i.e. general trends over the whole dataset), but also locally for each single input sample, mean-while keeping competitive performance compared to state-of-the-art methods.

| S-EPMC8106385 | biostudies-literature

Dictionary learning for integrative, multimodal and scalable single-cell analysis.

Project description:Mapping single-cell sequencing profiles to comprehensive reference datasets provides a powerful alternative to unsupervised analysis. However, most reference datasets are constructed from single-cell RNA-sequencing data and cannot be used to annotate datasets that do not measure gene expression. Here we introduce 'bridge integration', a method to integrate single-cell datasets across modalities using a multiomic dataset as a molecular bridge. Each cell in the multiomic dataset constitutes an element in a 'dictionary', which is used to reconstruct unimodal datasets and transform them into a shared space. Our procedure accurately integrates transcriptomic data with independent single-cell measurements of chromatin accessibility, histone modifications, DNA methylation and protein levels. Moreover, we demonstrate how dictionary learning can be combined with sketching techniques to improve computational scalability and harmonize 8.6 million human immune cell profiles from sequencing and mass cytometry experiments. Our approach, implemented in version 5 of our Seurat toolkit ( http://www.satijalab.org/seurat ), broadens the utility of single-cell reference datasets and facilitates comparisons across diverse molecular modalities.

| S-EPMC10928517 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

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