Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Detecting eukaryotic microbiota with single-cell sensitivity in human tissue.

ABSTRACT: BACKGROUND:Fetal growth restriction, pre-eclampsia, and pre-term birth are major adverse pregnancy outcomes. These complications are considerable contributors to fetal/maternal morbidity and mortality worldwide. A significant proportion of these cases are thought to be due to dysfunction of the placenta. However, the underlying mechanisms of placental dysfunction are unclear. The aim of the present study was to investigate whether adverse pregnancy outcomes are associated with evidence of placental eukaryotic infection. RESULTS:We modified the 18S Illumina Amplicon Protocol of the Earth Microbiome Project and made it capable of detecting just a single spiked-in genome copy of Plasmodium falciparum, Saccharomyces cerevisiae, or Toxoplasma gondii among more than 70,000 human cells. Using this method, we were unable to detect eukaryotic pathogens in placental biopsies in instances of adverse pregnancy outcome (n?=?199) or in healthy controls (n?=?99). CONCLUSIONS:Eukaryotic infection of the placenta is not an underlying cause of the aforementioned pregnancy complications. Possible clinical applications for this non-targeted, yet extremely sensitive, eukaryotic screening method are manifest.

SUBMITTER: Lager S

PROVIDER: S-EPMC6119588 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Ischaemic sensitivity of human tissue by single cell RNA seq

Project description: Not available

| S-SUBS3 | biostudies-other

The sensitivity of massively parallel sequencing for detecting candidate infectious agents associated with human tissue.

Project description:Massively parallel sequencing technology now provides the opportunity to sample the transcriptome of a given tissue comprehensively. Transcripts at only a few copies per cell are readily detectable, allowing the discovery of low abundance viral and bacterial transcripts in human tissue samples. Here we describe an approach for mining large sequence data sets for the presence of microbial sequences. Further, we demonstrate the sensitivity of this approach by sequencing human RNA-seq libraries spiked with decreasing amounts of an RNA-virus. At a modest depth of sequencing, viral transcripts can be detected at frequencies less than 1 in 1,000,000. With current sequencing platforms approaching outputs of one billion reads per run, this is a highly sensitive method for detecting putative infectious agents associated with human tissues.

| S-EPMC3094400 | biostudies-literature

Microbiota of human breast tissue.

Project description:In recent years, a greater appreciation for the microbes inhabiting human body sites has emerged. In the female mammary gland, milk has been shown to contain bacterial species, ostensibly reaching the ducts from the skin. We decided to investigate whether there is a microbiome within the mammary tissue. Using 16S rRNA sequencing and culture, we analyzed breast tissue from 81 women with and without cancer in Canada and Ireland. A diverse population of bacteria was detected within tissue collected from sites all around the breast in women aged 18 to 90, not all of whom had a history of lactation. The principal phylum was Proteobacteria. The most abundant taxa in the Canadian samples were Bacillus (11.4%), Acinetobacter (10.0%), Enterobacteriaceae (8.3%), Pseudomonas (6.5%), Staphylococcus (6.5%), Propionibacterium (5.8%), Comamonadaceae (5.7%), Gammaproteobacteria (5.0%), and Prevotella (5.0%). In the Irish samples the most abundant taxa were Enterobacteriaceae (30.8%), Staphylococcus (12.7%), Listeria welshimeri (12.1%), Propionibacterium (10.1%), and Pseudomonas (5.3%). None of the subjects had signs or symptoms of infection, but the presence of viable bacteria was confirmed in some samples by culture. The extent to which these organisms play a role in health or disease remains to be determined.

| S-EPMC4018903 | biostudies-other

Microbiota-based model improves the sensitivity of fecal immunochemical test for detecting colonic lesions.

Project description:BackgroundColorectal cancer (CRC) is the second leading cause of death among cancers in the United States. Although individuals diagnosed early have a greater than 90% chance of survival, more than one-third of individuals do not adhere to screening recommendations partly because the standard diagnostics, colonoscopy and sigmoidoscopy, are expensive and invasive. Thus, there is a great need to improve the sensitivity of non-invasive tests to detect early stage cancers and adenomas. Numerous studies have identified shifts in the composition of the gut microbiota associated with the progression of CRC, suggesting that the gut microbiota may represent a reservoir of biomarkers that would complement existing non-invasive methods such as the widely used fecal immunochemical test (FIT).MethodsWe sequenced the 16S rRNA genes from the stool samples of 490 patients. We used the relative abundances of the bacterial populations within each sample to develop a random forest classification model that detects colonic lesions using the relative abundance of gut microbiota and the concentration of hemoglobin in stool.ResultsThe microbiota-based random forest model detected 91.7% of cancers and 45.5% of adenomas while FIT alone detected 75.0% and 15.7%, respectively. Of the colonic lesions missed by FIT, the model detected 70.0% of cancers and 37.7% of adenomas. We confirmed known associations of Porphyromonas assaccharolytica, Peptostreptococcus stomatis, Parvimonas micra, and Fusobacterium nucleatum with CRC. Yet, we found that the loss of potentially beneficial organisms, such as members of the Lachnospiraceae, was more predictive for identifying patients with adenomas when used in combination with FIT.ConclusionsThese findings demonstrate the potential for microbiota analysis to complement existing screening methods to improve detection of colonic lesions.

| S-EPMC4823848 | biostudies-literature

Processing Human Thymic Tissue for Single Cell RNA-Seq.

Project description:Single cell RNA sequencing of human thymic cells is dependent on isolation of highly pure and viable cell populations. This protocol describes the isolation of CD34+ progenitor and more differentiated CD34- fractions from post-natal thymic tissue to study thymopoiesis. CD34+ cells represent <1% of thymic cells, so this protocol uses magnetic- followed by fluorescence-activated cell separation to isolate highly enriched CD34+ cells. For complete details on the use and execution of this protocol, please refer to Le et al. (2020).

| S-EPMC7580228 | biostudies-literature

Detecting intramolecular conformational dynamics of single molecules in short distance range with subnanometer sensitivity.

Project description:Single molecule detection is useful for characterizing nanoscale objects such as biological macromolecules, nanoparticles and nanodevices with nanometer spatial resolution. Fluorescence resonance energy transfer (FRET) is widely used as a single-molecule assay to monitor intramolecular dynamics in the distance range of 3-8 nm. Here we demonstrate that self-quenching of two rhodamine derivatives can be used to detect small conformational dynamics corresponding to subnanometer distance changes in a FRET-insensitive short-range at the single molecule level. A ParM protein mutant labeled with two rhodamines works as a single molecule adenosine 5'-diphosphate (ADP) sensor that has 20 times brighter fluorescence signal in the ADP bound state than the unbound state. Single molecule time trajectories show discrete transitions between fluorescence on and off states that can be directly ascribed to ADP binding and dissociation events. The conformational changes observed with 20:1 contrast are only 0.5 nm in magnitude and are between crystallographic distances of 1.6 and 2.1 nm, demonstrating exquisite sensitivity to short distance scale changes. The systems also allowed us to gain information on the photophysics of self-quenching induced by rhodamine stacking: (1) photobleaching of either of the two rhodamines eliminates quenching of the other rhodamine fluorophore and (2) photobleaching from the highly quenched, stacked state is only 2-fold slower than from the unstacked state.

| S-EPMC3237907 | biostudies-literature

Single cell and tissue-transcriptomic analysis of murine bladders reveals age- and TNFα-dependent but microbiota-independent tertiary lymphoid tissue formation.

Project description:Aging has multifaceted effects on the immune system, but how aging affects tissue-specific immunity is not well-defined. Bladder diseases characterized by chronic inflammation are highly prevalent in older women, but mechanisms by which aging promotes these pathologies remain unknown. Tissue transcriptomics of unperturbed, young and aged bladders identified a highly altered immune landscape as a fundamental feature of the aging female bladder. Detailed mapping of immune cells using single cell RNA-sequencing revealed novel subsets of macrophages and dendritic cells and unique changes to the immune repertoire in the aged bladder. B and T cells are highly enriched in aged bladders and spontaneously form organized bladder tertiary lymphoid tissues (bTLTs). Naïve, activated, and germinal center B cells and IgA+ plasma cells are found within bTLT and associated with increased urinary IgA concentrations. bTLTs form with increasing age and their formation is dependent on TNFα. Microbiota are not required to form bTLT, as aged germfree mice also harbor them. Thus, bTLTs require age-dependent TNFα but are independent of the microbiota. Our results indicate that chronic, age-associated inflammation underlies a fundamental alteration to the bladder and establishes a resource for further investigation of the bladder immune system in homeostasis, aging, and disease.

| S-EPMC7572484 | biostudies-literature

Single-cell analysis of human adipose tissue identifies depot and disease specific cell types.

Project description:The complex relationship between metabolic disease risk and body fat distribution in humans involves cellular characteristics which are specific to body fat compartments. Here we show depot-specific differences in the stromal vascual fraction of visceral and subcutaneous adipose tissue by performing single-cell RNA sequencing of tissue specimen from obese individuals. We characterize multiple immune cells, endothelial cells, fibroblasts, adipose and hematopoietic stem cell progenitors. Subpopulations of adipose-resident immune cells are metabolically active and associated with metabolic disease status and those include a population of potential dysfunctional CD8+ T cells expressing metallothioneins. We identify multiple types of adipocyte progenitors that are common across depots, including a subtype enriched in individuals with type 2 diabetes. Depot-specific analysis reveals a class of adipocyte progenitors unique to visceral adipose tissue, which shares common features with beige preadipocytes. Our human single-cell transcriptome atlas across fat depots provides a resource to dissect functional genomics of metabolic disease.

| S-EPMC7025882 | biostudies-literature

doubletD: detecting doublets in single-cell DNA sequencing data.

Project description:MotivationWhile single-cell DNA sequencing (scDNA-seq) has enabled the study of intratumor heterogeneity at an unprecedented resolution, current technologies are error-prone and often result in doublets where two or more cells are mistaken for a single cell. Not only do doublets confound downstream analyses, but the increase in doublet rate is also a major bottleneck preventing higher throughput with current single-cell technologies. Although doublet detection and removal are standard practice in scRNA-seq data analysis, options for scDNA-seq data are limited. Current methods attempt to detect doublets while also performing complex downstream analyses tasks, leading to decreased efficiency and/or performance.ResultsWe present doubletD, the first standalone method for detecting doublets in scDNA-seq data. Underlying our method is a simple maximum likelihood approach with a closed-form solution. We demonstrate the performance of doubletD on simulated data as well as real datasets, outperforming current methods for downstream analysis of scDNA-seq data that jointly infer doublets as well as standalone approaches for doublet detection in scRNA-seq data. Incorporating doubletD in scDNA-seq analysis pipelines will reduce complexity and lead to more accurate results.Availability and implementationhttps://github.com/elkebir-group/doubletD.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC8275324 | biostudies-literature

Single-Cell Transcriptome Analysis Profile of Meniscal Tissue Macrophages in Human Osteoarthritis.

Project description:Osteoarthritis (OA) has long been considered as a degenerative disease, but growing evidence suggests that inflammation plays a vital role in its pathogenesis. Unlike rheumatoid arthritis and other autoimmune diseases, inflammation in OA is chronic and, in relatively low grade, mainly mediated by the innate immune system, especially macrophages. However, due to its low abundance, there is a lack of systematic studies on macrophages in the OA condition. Here, we have used single-cell RNA sequencing analysis to gain insight into the heterogeneity and functional specialization of human knee macrophages. We also compared the gene expression profiles of macrophages in healthy people and OA patients and found the characteristic changes of special macrophages in the OA knee. We believe that this in-depth understanding of the basis of OA inflammation will bring hope for the development of new therapies.

| S-EPMC7407017 | 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