Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Comprehensive integration of single-cell data

ABSTRACT: Single-cell transcriptomics (scRNA-seq) has transformed our ability to discover and annotate cell types and states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, including high-dimensional immunophenotypes, chromatin accessibility, and spatial positioning, a key analytical challenge is to integrate these datasets into a harmonized atlas that can be used to better understand cellular identity and function. Here, we developed a computational strategy to "anchor" diverse datasets together, enabling us to integrate and compare single-cell measurements not only across scRNA-seq technologies, but different modalities as well. As one demonstration of the method, we anchor single-cell protein measurements with a human bone marrow atlas to annotate and characterize lymphocyte populations. The multimodal data, generated using CITE-seq, is provided here alongside a corresponding bulk validation experiment.

ORGANISM(S): Homo sapiens

PROVIDER: GSE128639 | GEO | 2019/06/06

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

Integrated analysis of multimodal single-cell data

Project description:The simultaneous measurement of multiple modalities, known as multimodal analysis, represents an exciting frontier for single-cell genomics and necessitates new computational methods that can define cellular states based on multiple data types. 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 hundreds of thousands of human white blood cells alongside a panel of 228 antibodies to construct a multimodal reference atlas of the circulating immune system. We demonstrate that integrative analysis substantially improves our ability to resolve cell states and validate the presence of 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 COVID-19. Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets, including paired measurements of RNA and chromatin state, and to look beyond the transcriptome towards a unified and multimodal definition of cellular identity.

2021-01-15 | GSE164378 | GEO

A unified single cell atlas of the mouse retina

Project description:Single cell RNA sequencing (scRNA-seq) has advanced the assessment of cellular heterogeneity at the single-cell resolution by identifying transcriptional similarities and differences. Data resources of scRNA-seq have been largely produced and extensively studied for the mouse retina. They serve as a powerful tool to study cellular components, transcriptome relationships, and regulatory mechanisms underlying various retinal diseases and biological processes. The large volume of mouse retinal scRNA-seq data has been released in separate repositories, limiting their widespread use in mouse retina communities. In this work, we are presenting a unified single-cell atlas for adult wild-type mouse retina using our in-house generated single-cell RNA-seq data complementing public datasets. The collected data account for over 323,000 single cells. After data integration, cell clustering, and cell type annotation, we have annotated 11 major classes and over 120 retinal cell types to form a unified single-cell reference for the mouse retina. To facilitate the public use of the reference, we have deposited it on CELLxGENE, UCSC Cell Browser, and Single Cell Portal for visualization and gene expression exploration. The unified atlas is also released to annotate new mouse retinal cells using scArches utilities. This unified reference serves an easy-to-use data resource of mouse retina communities.

2024-02-15 | GSE243413 | GEO

Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro

Project description:New technologies that profile chromatin modifications at single-cell resolution offer enormous promise for functional genomic characterization. However, the sparsity of these measurements and the challenge of integrating multiple binding maps represent significant challenges. Here we introduce scCUT&Tag-pro, a multimodal assay for profiling protein-DNA interactions coupled with the abundance of surface proteins in single cells. In addition, we introduce scChromHMM, which integrates data from multiple experiments to infer and annotate chromatin states based on combinatorial histone modification patterns. We apply these tools to perform an integrated analysis across nine different molecular modalities in circulating human immune cells. We demonstrate how these two approaches can characterize dynamic changes in the function of individual genomic elements across both discrete cell states and continuous developmental trajectories, nominate associated motifs and regulators that establish chromatin states, and identify extensive and cell type-specific regulatory priming. Finally, we demonstrate how our integrated reference can serve as a scaffold to map and improve the interpretation of additional scCUT&Tag datasets.

2022-02-24 | GSE195725 | GEO

Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets

Project description:Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that dynamically reconfigure to drive diverse cellular states. Single-cell transcriptomic technologies, such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine the transcriptional state at individual cell resolution, allowing the study of cellular heterogeneity and cell-type specific gene regulation in dynamic processes such as cell fate specification. However, current approaches to infer cell type-specific gene regulatory networks from these datasets are limited in their ability to integrate scRNA-seq and scATACseq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the gene regulatory network for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated, published, and newly collected single cell omic datasets, we show that scMTNI is able to accurately infer gene regulatory networks and captures meaningful network dynamics that identify GRN components associated with cell type transitions. Application of our method to mouse cellular reprogramming identified key regulators associated with cell populations that reprogram versus those that are stalled. Taken together, scMTNI is a powerful framework to infer cell type-specific gene regulatory networks and their dynamics from scRNA-seq and scATAC-seq datasets.

2023-04-14 | GSE208620 | GEO

Characterizing Local Extension of Retinoblastoma by Single-cell Transcriptomics

Project description:Retinoblastoma (RB) is the most prevalent ocular tumor of childhood whose extraocular invasion largely increase metastasis risk. Nevertheless, a single-cell characterization of RB local extension has been lacking. Here, we perform single-cell RNA sequencing (scRNA-seq) 4 RB samples (2 from intraocular and 2 from extraocular RB patients), and integrate public scRNA-seq datasets to characterize the local extension of RB at single-cell level.

2023-12-19 | GSE249995 | GEO

Integrating multimodal data sets into a mathematical framework to describe and predict therapeutic resistance in cancer

Project description:A significant challenge in the field of biomedicine is the development of methods to integrate the multitude of dispersed data sets into comprehensive frameworks to be used to generate optimal clinical decisions. Recent technological advances in single cell analysis allow for high-dimensional molecular characterization of cells and populations, but to date, few mathematical models have attempted to integrate measurements from the single cell scale with other data types. Here, we present a framework that actionizes static outputs from a machine learning model and leverages these as measurements of state variables in a dynamic mechanistic model of treatment response. We apply this framework to breast cancer cells to integrate single cell transcriptomic data with longitudinal population-size data. We demonstrate that the explicit inclusion of the transcriptomic information in the parameter estimation is critical for identification of the model parameters and enables accurate prediction of new treatment regimens. Inclusion of the transcriptomic data improves predictive accuracy in new treatment response dynamics with a concordance correlation coefficient (CCC) of 0.89 compared to a prediction accuracy of CCC = 0.79 without integration of the single cell RNA sequencing (scRNA-seq) data directly into the model calibration. To the best our knowledge, this is the first work that explicitly integrates single cell clonally-resolved transcriptome datasets with longitudinal treatment response data into a mechanistic mathematical model of drug resistance dynamics. We anticipate this approach to be a first step that demonstrates the feasibility of incorporating multimodal data sets into identifiable mathematical models to develop optimized treatment regimens from data.

2021-02-08 | GSE154932 | GEO

Mouse pancreatic islet scRNA-seq atlas across sexes, ages, and stress conditions including diabetes

Project description:Mouse pancreatic islet scRNA-seq integrated atlas encompassing different ages, sexes, chemical stress leading to dedifferentiation, and diabetes models with corresponding treatments. Two datasets (sub-series) were newly generated for the atlas.

2022-12-23 | GSE211799 | GEO

Single-cell RNA-sequencing of CD14+ monocyte differentiation with M-CSF stimulus

Project description:We obtained single-cell RNA-sequencing (scRNA-seq) profiles of CD14+ monocytes isolated from human peripheral blood at 0, 3 and 6 days after M-CSF stimulation (to differentiate the cells into macrophages) across multiple donors. Integration of single-cell RNA sequencing (scRNA-seq) data from multiple experiments, laboratories, and technologies can uncover biological insights, but current methods for scRNA-seq data integration are limited by a requirement for datasets to derive from functionally similar cells. We use a novel algorithm, Scanorama, to identify and merge the shared cell types among all pairs of datasets and to accurately integrate heterogeneous scRNA-seq datasets. Scanorama is sensitive to subtle temporal changes within the same cell lineage, successfully integrating functionally similar cells across time series data of CD14+ monocytes at different stages of differentiation into macrophages. Scanorama is not only able to differentiate between completely disparate cell types but is also sensitive to subtler transcriptional changes within a cell type due to processes like stimulation.

2019-02-06 | GSE126085 | GEO

Direct comparison of mass cytometry and single-cell RNA sequencing of human peripheral blood mononuclear cells

Project description:Single-cell methods offer a high-resolution approach for characterizing cell populations. Many studies rely on single-cell transcriptomics to draw conclusions regarding cell state and behavior, with the underlying assumption that transcriptomic readouts largely parallel their protein counterparts and subsequent activity. However, the relationship between transcriptomic and proteomic measurements is imprecise, and thus datasets that probe the extent of their concordance will be useful to refine such conclusions. Additionally, novel single-cell analysis tools often lack appropriate gold standard datasets for the purposes of assessment. Integrative (combining the two data modalities) and predictive (using one modality to improve results from the other) approaches in particular, would benefit from transcriptomic and proteomic data from the same sample of cells. For these reasons, we performed single-cell RNA sequencing, mass cytometry, and flow cytometry on a split-sample of human peripheral blood mononuclear cells. We directly compare the proportions of specific cell types resolved by each technique, and further describe the extent to which protein and mRNA measurements correlate within distinct cell types.

2024-02-16 | GSE225431 | GEO

Mapping the Cellular Biogeography of Human Bone Marrow Niches Using Single-Cell Transcriptomics

Project description:Non-hematopoietic cells contribute essentially to hematopoiesis. However, heterogeneity and spatial organization of these cells in human bone marrow remain largely uncharacterized. We used single-cell RNA sequencing (scRNA-Seq) to profile 29,325 non-hematopoietic cells and discovered nine transcriptionally distinct subtypes. We simultaneously profiled 53,417 hematopoietic cells and predicted their interactions with non-hematopoietic subsets. We employed Co-Detection by Indexing (CODEX) to spatially profile over one million single cells with a novel 53-antibody panel. We integrated scRNA-Seq and CODEX data to link cellular signaling and spatial proximity. Spatial analysis also revealed a hyperoxygenated arterio-endosteal niche for early myelopoiesis, and an adipocytic, but not endosteal or perivascular, localization for early hematopoietic stem and progenitor cells. We used our CODEX atlas to automatically annotate cell types in new bone marrow images and uncovered MSC expansion and leukemic blast/MSC-enriched spatial neighborhoods in AML patient samples. This comprehensive, spatially-resolved multiomic atlas of human bone marrow serve as a reference for future investigation of cellular interactions that drive hematopoiesis.

2024-04-24 | GSE253355 | 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