Human peripheral monocytes have been categorized into three subsets based on differential expression levels of CD14 and CD16. However, the factors that influence the distribution of monocyte subsets and the roles which each subset plays in autoimmunity are not well studied. To compare the gene expression profiling 1) on intermediate monocytes CD14++CD16+ monocytes between healthy donors and autoimmune uveitis patients and 2) among 3 monocyte subsets in health donors, here we purified circulating intermediate CD14++CD16+ monocytes from 5 patients with autoimmune uveitis (labeled as P1-5) and 4 healthy donors (labeled as HD1-4) by flow cytometry and isolated total RNA to proceed microarray assay. In addition, we also purified CD14+CD16++ (non-classical monocytes) and CD14++CD16- (classical monocytes) from 4 healthy donors to do microarray. We demonstrate that CD14++CD16+ monocytes from patients and healthy control donors share a similar gene expression profile. The CD14+CD16++ cells (non-classical monocytes) display the most distinctive gene expression profiling when compared to intermediate CD14++CD16+ monocytes and classical CD14++CD16- monocytes.
CD14++CD16+ Monocytes Are Enriched by Glucocorticoid Treatment and Are Functionally Attenuated in Driving Effector T Cell Responses.
Specimen part, Disease stage, Subject
View SamplesThe objective is to generate a robust and validated predictor profile for chemotherapy response in patients with mCRC using microarray gene expression profiles of primary colorectal cancer tissue.
Gene expression profile predictive of response to chemotherapy in metastatic colorectal cancer.
Disease, Disease stage
View SamplesThis SuperSeries is composed of the SubSeries listed below.
A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia.
Age, Specimen part, Disease, Disease stage
View SamplesWe demonstrate a promising approach to identify robust molecular markers for targeted treatment of acute myeloid leukemia. We show that our method outperforms several state-of-the-art approaches in identifying molecular markers replicated in validation data and predicting drug sensitivity accurately.
A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia.
Age, Specimen part, Disease, Disease stage
View SamplesWe demonstrate a promising approach to identify robust molecular markers for targeted treatment of acute myeloid leukemia. We show that our method outperforms several state-of-the-art approaches in identifying molecular markers replicated in validation data and predicting drug sensitivity accurately. Finally, we identify SMARCA4 as a marker and driver of sensitivity to topoisomerase II inhibitors, mitoxantrone and etoposide, in AML by showing that cell lines transduced to have high SMARCA4 expression reveal dramatically increased sensitivity to these agents. Overall design: We measured the gene expression of samples from 30 different AML patients with acute myeloid leukemia in order to identify reliable gene expression markers for drug sensitivity. We used this dataset for validation. This Series represents 12 patient samples.
A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia.
Age, Specimen part, Disease, Disease stage, Subject
View SamplesDouble Hit Lymphoma (DHL) were treated with the BRD4 inhibitor 100 nM CPI203 for 6h
The BET bromodomain inhibitor CPI203 overcomes resistance to ABT-199 (venetoclax) by downregulation of BFL-1/A1 in in vitro and in vivo models of MYC+/BCL2+ double hit lymphoma.
Specimen part, Cell line, Subject
View SamplesC33-A is a Homo sapiens cervix carcinoma cell line. In this experiment we determine the level of gene expression under exponentially growing conditions.
The chromatin remodeller CHD8 is required for E2F-dependent transcription activation of S-phase genes.
Cell line
View SamplesWhsc1 gene codes for a SET domain-containing H3K36 dimethylase, whose activity has been suggested, in ex vivo cell culture experiments, to control many aspects of DNA and RNA processing (replication, repair, transcription, etc). Its precise function in vivo is still unclear. Here, we use RNA-seq transcriptome analysis to study the changes in gene expression in the absence of Whsc1. Our results show that, in the experimental system used, loss of Whsc1 caused massive changes in genes affecting many fundamental cellular processes, from cell cycle to ribosome synthesis, DNA repair, replication, etc. Overall design: Whsc1-KO mice are embryonic lethal. We therefore took hematopoietic cells from fetal liver of WT and Whsc1-KO embryo littermates and injected them in to lethally irradiated RAG1-KO recipients and allowed the generation of a full Whsc1-KO hematopoietic system. Then, WT and Whsc1-KO B cells were obtained from the spleen and stimulated with LPS to induce proliferation and class switch recombination. Flow cytometry and cell cycle analyses (among others) showed the existence of serious proliferative alterations in Whsc1-KO cells. Then, we performed paired-end RNAseq analyses of 7 independent WT and 6 independent Whsc1-KO biological replicates and we used these data to identify differentially expressed genes and pathways regulated by Whsc1 in B cells.
Wolf-Hirschhorn Syndrome Candidate 1 Is Necessary for Correct Hematopoietic and B Cell Development.
Cell line, Subject
View SamplesCrohn's Disease (CD) is a chronic inflammatory disease of the intestinal tract.
Commensal-Specific CD4(+) Cells From Patients With Crohn's Disease Have a T-Helper 17 Inflammatory Profile.
Sex, Age, Disease, Subject
View SamplesIdentification of genes involved in trophoblast differentiation is of great interest in understanding cellular and molecular mechanisms involved in placental development and is relevant clinically to fetal development, fertility, and maternal health. To understand, on a global scale, changes in the transcriptome during the differentiation of hESCs down the trophoblast lineage, a large-scale microarray analysis was performed. This work provides an in vitro functional genomic model with which to identify genes involved in trophoblast development.
Transcriptomic signature of trophoblast differentiation in a human embryonic stem cell model.
Specimen part, Cell line
View Samples