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Homo sapiens
71 Downloadable Samples
NextSeq 500
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. Overall design: PBMCs were isolated from 8 individuals bearing or not TLR10 polymorphism and were infected ex vivo with Salmonella enterica serovar Typhimurium. RNA was extracted before infection, 4 hours post infection and 8 hours post infection.
Publication Title
Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells.
Sample Metadata Fields
Specimen part, Subject
View Samples- Homo sapiens
- 62 Downloadable Samples
- NextSeq 500
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. Overall design: Whole-blood (WB) cells and PBMCs were isolated from 4 healthy individuals and were infected ex vivo with Salmonella enterica serovar Typhimurium or with PBS as control. RNA was extracted 4 hours later.
Publication Title
Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells.
Sample Metadata Fields
Specimen part, Disease stage, Subject
View Samples- Homo sapiens
- 13 Downloadable Samples
- NextSeq 500
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. Overall design: PBMCs were isolated from a healthy individual and were infected ex vivo with Salmonella enterica serovar Typhimurium or with PBS as control. Monocytes and NKT cells were sorted from naïve and infected PBMCs. RNA was extracted 4 hours post infection.
Publication Title
Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells.
Sample Metadata Fields
Subject
View Samples- Homo sapiens
- 12 Downloadable Samples
- NextSeq 500
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. Overall design: Frozen PBMCs from healthy individual were defrosted and infectd ex vivo with Salmonella enterica serovar Typhimurium.
Publication Title
Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells.
Sample Metadata Fields
Specimen part, Subject
View Samples- Homo sapiens
- 5 Downloadable Samples
Affymetrix Human Gene 1.0 ST Array (hugene10st)
Description
Commonalities and dissimilarities between the IGF1R and INSR pathways
Publication Title
Systems Analysis of Insulin and IGF1 Receptors Networks in Breast Cancer Cells Identifies Commonalities and Divergences in Expression Patterns.
Sample Metadata Fields
Cell line
View Samples- Rattus norvegicus
- 46 Downloadable Samples
- Affymetrix Rat Genome 230 2.0 Array (rat2302)
Description
Gene expression along the crypt-villus (C-V) axis was analyzed using cryostat sectioning to isolate fractions representing the crypts (bottom) and villus tops (top). These fractions were used for analyzing gene expression in iron replete Wistar rats (++), iron deficient Wistar rats (low iron), and in iron deficient Wistar rats fed iron for 3 and 6 days (iron-fed). Differences were observed between the crypts and villus tops in the expression of genes associated with Wnt and BNP signaling, cell proliferation and apoptosis, lipid and iron transport and metabolism. Gene expression in villus crypts and tops was also compared between Wistar and Belgrade rats (bb) and Belgrade rats fed iron (iron-fed) particularly as related to iron absorption and metabolism to define the affects of the mutation in DMT1 in the Belgrade rat on the expression of genes related to iron absorption and metabolism and the response to iron feeding.
Publication Title
Hypoxia-inducible factor-2α and iron absorptive gene expression in Belgrade rat intestine.
Sample Metadata Fields
No sample metadata fields
View Samples- Mus musculus
- 20 Downloadable Samples
- Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
Dusp5 regulates ERK phosporylation following IL-33 receptor ligation in cultured eosinophils. Dusp5 deficient eosinophils show increased ERK phosphorylation, and as a result are less apoptotic. Since ERK stimulation results in downstream activation of transcription factors, we are utilizing a microarray approach to find alterations in gene expression to uncover potential mechanisms for increased cell survival.
Publication Title
Dusp5 negatively regulates IL-33-mediated eosinophil survival and function.
Sample Metadata Fields
Specimen part, Treatment
View Samples- Homo sapiens
- 80 Downloadable Samples
- Affymetrix Human Gene 2.0 ST Array (hugene20st)
Description
211 FFPE NSLC surgical samples were used to generate recurrence prediction models
Publication Title
Generating a robust prediction model for stage I lung adenocarcinoma recurrence after surgical resection.
Sample Metadata Fields
No sample metadata fields
View Samples
GSE60304- Rattus norvegicus
- 59 Downloadable Samples
- Illumina ratRef-12 v1.0 expression beadchip
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
Expression profiling associates blood and brain glucocorticoid receptor signaling with trauma-related individual differences in both sexes.
Sample Metadata Fields
Sex, Specimen part
View Samples- Rattus norvegicus
- 29 Downloadable Samples
- Illumina ratRef-12 v1.0 expression beadchip
Description
Delineating the molecular basis of individual differences in the stress response is critical to understanding the pathophysiology and treatment of posttraumatic stress disorder (PTSD). In this study, 7 d after predator-scent-stress (PSS) exposure, male and female rats were classified into vulnerable (i.e., PTSD-like) and resilient (i.e.,minimally affected) phenotypes on the basis of their performance on a variety of behavioral measures. Genome-wide expression profiling in blood and two limbic brain regions (amygdala and hippocampus), followed by quantitative PCR validation, was performed in these two groups of animals, as well as in an unexposed control group. Differentially expressed genes were identified in blood and brain associated with PSS-exposure and with distinct behavioral profiles postexposure. There was a small but significant between-tissue overlap (421%) for the genes associated with exposure-related individual differences, indicating convergent gene expression in both sexes. To uncover convergent signaling pathways across tissue and sex, upstream activated/deactivated transcription factorswere first predicted for each tissue and then the respective pathways were identified. Glucocorticoid receptor (GR) signaling was the only convergent pathway associatedwith individual differences when using the most stringent statistical threshold.
Publication Title
Expression profiling associates blood and brain glucocorticoid receptor signaling with trauma-related individual differences in both sexes.
Sample Metadata Fields
Sex, Specimen part
View Samples