Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Prediction of bacterial infection outcome using single cell RNA-seq analysis of human immune cells [scRNA-seq ind.1]

ABSTRACT: During host-pathogen encounters, the complex interactions between different immune cell-types can determine the outcome of infection. Advances in single cell RNA-seq (scRNA-seq) allow to probe this complexity of immunity, and afforded the basis for deconvolution algorithms that infer cell-type compositions from bulk RNA-seq measurements. However, immune activation, an important aspect of immune surveillance, is not represented in current algorithms. Here, using scRNA-seq of human peripheral blood cells infected with Salmonella, we developed a novel deconvolution algorithm to infer dynamic immune states from bulk measurements. We applied our dynamic deconvolution algorithm both to cohorts of healthy individuals challenged ex vivo with Salmonella and to cohorts of tuberculosis patients during different stages of disease. We revealed cell-type specific immune responses associated not only with ex vivo infection phenotype but also with clinical disease stage. We propose that our approach provides a predictive power to identify risk for disease, and can be applied to comprehensively study human infection outcome.

ORGANISM(S): Homo sapiens

PROVIDER: GSE122083 | GEO | 2019/06/07

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Prediction of bacterial infection outcome using single cell RNA-seq analysis of human immune cells [WB/PBMCs bulk RNA-seq]

Project description:During host-pathogen encounters, the complex interactions between different immune cell-types can determine the outcome of infection. Advances in single cell RNA-seq (scRNA-seq) allow to probe this complexity of immunity, and afforded the basis for deconvolution algorithms that infer cell-type compositions from bulk RNA-seq measurements. However, immune activation, an important aspect of immune surveillance, is not represented in current algorithms. Here, using scRNA-seq of human peripheral blood cells infected with Salmonella, we developed a novel deconvolution algorithm to infer dynamic immune states from bulk measurements. We applied our dynamic deconvolution algorithm both to cohorts of healthy individuals challenged ex vivo with Salmonella and to cohorts of tuberculosis patients during different stages of disease. We revealed cell-type specific immune responses associated not only with ex vivo infection phenotype but also with clinical disease stage. We propose that our approach provides a predictive power to identify risk for disease, and can be applied to comprehensively study human infection outcome.

2019-06-07 | GSE128627 | GEO

Prediction of bacterial infection outcome using single cell RNA-seq analysis of human immune cells [sorted population Bulk RNA-seq]

2019-06-07 | GSE128626 | GEO

Prediction of bacterial infection outcome using single cell RNA-seq analysis of human immune cells [scRNA-seq ind. 2]

2019-06-07 | GSE132300 | GEO

Prediction of bacterial infection outcome using single cell RNA-seq analysis of human immune cells [WT/TLR10 bulk RNA-seq]

2019-06-07 | GSE122058 | GEO

c6a5c546-60b8-4a79-894b-46adcd4bb912 - samples

Project description:Results of comprehensive immune deconvolution analysis through the TIMER2 web portal with algorithms specified in the publication.

| EGAD00001008789 | EGA

Enhancing Top-Down Proteomics Data Analysis by Combining Deconvolution Results through a Machine Learning Strategy

Project description:Top-down mass spectrometry (MS) is a powerful tool for identification and comprehensive characterization of proteoforms arising from alternative splicing, sequence variation, and post-translational modifications. While the technique is powerful, it suffered from the complex dataset generated from top-down MS experiments, which requires sequential data processing steps for data interpretation. Deconvolution of the complex isotopic distribution that arises from naturally occurring isotopes is a critical step in the data processing process. Multiple algorithms are currently available to deconvolute top-down mass spectra; however, each algorithm generates different deconvoluted peak lists with varied accuracy comparing to true positive annotations. In this study, we have designed a machine learning strategy that can process and combine the peak lists from different deconvolution results. By optimizing clustering results, deconvolution results from THRASH, TopFD, MS-Deconv, and SNAP algorithms were combined into consensus peak lists at various thresholds using either a simple voting ensemble method or a random forest machine learning algorithm. The random forest model outperformed the single best algorithm. This machine learning strategy could enhance the accuracy and confidence in protein identification during database search by accelerating detection of true positive peaks while filtering out false positive peaks. Thus, this method showed promises in enhancing proteoform identification and characterization for high-throughput data analysis in top-down proteomics.

2020-05-06 | PXD018043 | Pride

quanTIseq: quantifying immune contexture of human tumors

Project description:We introduce quanTIseq, a method to quantify the tumor immune contexture, determined by the type and density of tumor-infiltrating immune cells. quanTIseq is based on a novel deconvolution algorithm for RNA sequencing data that was validated with independent data sets. Complementing the deconvolution output with image data from tissue slides enables in silico multiplexed immunodetection and provides an efficient method for the immunophenotyping of a large number of tumor samples.

2019-02-06 | GSE107572 | GEO

Breast Cancer - immune clusters - RNA-seq

Project description:We use expression data from breast cancer tumors to define immune clusters in breast cancer. Immune clusters have gradual levels of immune infiltration. In the intermediate immune infiltration cluster, we found a worse prognosis which is independent of known clinicopathological features. We also found the immune clusters associated with treatment response. Further we use gene expression data and deconvolution algorithms to dissect the immune contexture of the clusters.

2019-12-10 | GSE135298 | GEO

Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces

Project description:Recent advances in spatial transcriptomics (ST) enable gene expression measurements from a tissue sample while retaining its spatial context. This technology enables unprecedented in situ resolution of the regulatory pathways that underlie the heterogeneity in the tumor and its microenvironment (TME). The direct characterization of cellular co-localization with spatial technologies facilities quantification of the molecular changes resulting from direct cell-cell interaction, as occurs in tumor-immune interactions. We present SpaceMarkers, a novel bioinformatics algorithm to infer molecular changes from cell-cell interaction from latent space analysis of ST data. We apply this approach to infer molecular changes from tumor-immune interactions in Visium spatial transcriptomics data of metastasis, invasive and precursor lesions, and immunotherapy treatment. Further transfer learning in matched scRNA-seq data enabled further quantification of the specific cell types in which SpaceMarkers are enriched. Altogether, SpaceMarkers can identify the location and context-specific molecular interactions within the TME from ST data.

2023-04-19 | GSE224411 | GEO

Comprehensive Evaluation of Human Brain Gene Expression Deconvolution Methods

Project description:We benchmarked deconvolution algorithms on human brain gene expression data. The data deposited here include A) mixtures on which algorithms were benchmarked, as well as B) signatures of pure brain cell-type expression

2021-08-28 | GSE175772 | GEO

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