Project description:In this study, mutations present in a series of human melanomas (stage IV disease) will be determined, using autologous blood cells to obtain a reference genome. From each of the samples that are analyzed, tumour-infiltrating T lymphocytes have also been isolated. This offers a unique opportunity to determine which (fraction of) mutations in human cancer leads to epitopes that are recognized by T cells. The resulting information is likely to be of value to understand how T cell activating drugs exert their action.
Project description:In order to study molecular changes in the stroma from tissue samples it is recommended to separate tumor tissue from stromal tissue. This is particularly relevant to mouse tumor xenograft models where tumor, particularly metastatic tumors, can be small and difficult to separate from the host tissue. In our research we compared qualitatively the ability of high-throughput mRNA sequencing, RNA-Seq, and microarrays to detect tumor (human) and stromal (mouse) expression from mixed tumor-stromal samples in terms of the genes and pathways that are involved in cross-alignment (RNA-Seq) and cross-hybridization (microarrays). Human samples consisted of total RNA obtained from MDA-MB-231 human breast carcinoma cell line and isolated from three independent cultures of sub-confluent MDA-MB-231 cell lines in exponential phase of growth. Mouse samples were obtained from NOD scid gamma mice, and normal lung tissue was harvested from three independent age-matched mice.
Project description:miR-Blood is a high-quality, small RNA expression atlas for the major components of human peripheral blood (plasma, erythrocytes, thrombocytes, monocytes, neutrophils, eosinophils, basophils, natural killer cells, CD4+ T cells, CD8+ T cells, and B cells). *** The data provided in this GEO dataset is licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ***
Project description:Whole-transcriptome sequencing ('RNA-Seq') has been drastically changing the scale and scope of genomic research. In order to fully understand the power and limitations of this technology, the US Food and Drug Administration (FDA) launched the third phase of the MicroArray Quality Control (MAQC-III) project, also known as the SEquencing Quality Control (SEQC) project. Using two well-established human reference RNA samples from the first phase of the MAQC project, three sequencing platforms were tested across more than ten sites with built-in truths including spike-in of external RNA controls (ERCC), titration data and qPCR verification. The SEQC project generated over 30 billion sequence reads representing the largest RNA-Seq data ever generated by a single project on individual RNA samples. This extraordinarily ultradeep transcriptomic data set and the known truths built into the study design provide many opportunities for further research and development to advance the improvement and application of RNA-Seq.
Project description:There is much interest in analysing RNA, particularly with RNA Sequencing, across both research and diagnostic domains. However, its inherent instability renders it susceptible to degradation. Given the imperative for RNA integrity in such applications, proper storage and biobanking of blood samples and successful subsequent RNA isolation is essential to guarantee optimal integrity for downstream analyses. Especially for larger collections, it would be particularly beneficial if these methods would additionally offer affordability, minimal blood volume requirements and also long-term storage. In this study, RNA of high quality, suitable for transcriptomics, has been successfully isolated from 400 µL of EDTA and citrated whole blood samples in Boom's lysis buffer stored at -85 °C for 10 years. Isolation was carried out using a modified Zymo Research Quick-RNA kit protocol. This isolation method showed significant improvement in RNA integrity when compared to RNA extracted using the original Boom method. RNA Sequencing provided high-quality data comparable to that of other studies using recently frozen blood in RNA stabilisation tubes. Additionally, sequencing data from blood collected in citrate and EDTA anticoagulants also showed excellent correlation.
Project description:OBJECTIVE:Many tools have been developed to profile microRNA (miRNA) expression from small RNA-seq data. These tools must contend with several issues: the small size of miRNAs, the small number of unique miRNAs, the fact that similar miRNAs can be transcribed from multiple loci, and the presence of miRNA isoforms known as isomiRs. Methods failing to address these issues can return misleading information. We propose a novel quantification method designed to address these concerns. RESULTS:We present miR-MaGiC, a novel miRNA quantification method, implemented as a cross-platform tool in Java. miR-MaGiC performs stringent mapping to a core region of each miRNA and defines a meaningful set of target miRNA sequences by collapsing the miRNA space to "functional groups". We hypothesize that these two features, mapping stringency and collapsing, provide more optimal quantification to a more meaningful unit (i.e., miRNA family). We test miR-MaGiC and several published methods on 210 small RNA-seq libraries, evaluating each method's ability to accurately reflect global miRNA expression profiles. We define accuracy as total counts close to the total number of input reads originating from miRNAs. We find that miR-MaGiC, which incorporates both stringency and collapsing, provides the most accurate counts.
Project description:Alzheimer's disease (AD), the leading form of dementia, is associated with abnormal tau and β-amyloid accumulation in the brain. We conducted a miRNA-seq study to identify miRNAs associated with AD in the post-mortem brain from the inferior frontal gyrus (IFG, n = 69) and superior temporal gyrus (STG, n = 81). Four and 64 miRNAs were differentially expressed (adjusted p-value < 0.05) in AD compared to cognitively normal controls in the IFG and STG, respectively. We observed down-regulation of several miRNAs that have previously been implicated in AD, including hsa-miR-212-5p and hsa-miR-132-5p, in AD samples across both brain regions, and up-regulation of hsa-miR-146a-5p, hsa-miR-501-3p, hsa-miR-34a-5p, and hsa-miR-454-3p in the STG. The differentially expressed miRNAs were previously implicated in the formation of amyloid-β plaques, the dysregulation of tau, and inflammation. We have also observed differential expressions for dozens of other miRNAs in the STG, including hsa-miR-4446-3p, that have not been described previously. Putative targets of these miRNAs (adjusted p-value < 0.1) were found to be involved in Wnt signaling pathway, MAPK family signaling cascades, sphingosine 1-phosphate (S1P) pathway, adaptive immune system, innate immune system, and neurogenesis. Our results support the finding of dysregulated miRNAs previously implicated in AD and propose additional miRNAs that appear to be dysregulated in AD for experimental follow-up.
Project description:SummarySingle cell RNA sequencing is a revolutionary technique to characterize inter-cellular transcriptomics heterogeneity. However, the data are noise-prone because gene expression is often driven by both technical artifacts and genuine biological variations. Proper disentanglement of these two effects is critical to prevent spurious results. While several tools exist to detect and remove low-quality cells in one single cell RNA-seq dataset, there is lack of approach to examining consistency between sample sets and detecting systematic biases, batch effects and outliers. We present scRNABatchQC, an R package to compare multiple sample sets simultaneously over numerous technical and biological features, which gives valuable hints to distinguish technical artifact from biological variations. scRNABatchQC helps identify and systematically characterize sources of variability in single cell transcriptome data. The examination of consistency across datasets allows visual detection of biases and outliers.Availability and implementationscRNABatchQC is freely available at https://github.com/liuqivandy/scRNABatchQC as an R package.Supplementary informationSupplementary data are available at Bioinformatics online.