Combined personal saliva proteome and microbioproteome
Ontology highlight
ABSTRACT:
INSTRUMENT(S): instrument model, LTQ Velos
ORGANISM(S): Homo Sapiens (human)
TISSUE(S): Saliva
DISEASE(S): Not Available
SUBMITTER: 
Attila Csordas
PROVIDER: PXD000002 | Pride | 2012-03-15
REPOSITORIES: Pride
Similar Datasets
Project description:When using cell lines to study cancer, phenotypic similarity to the original tumor is paramount. Yet, little has been done to characterize how closely Merkel cell carcinoma (MCC) cell lines model native tumors. To determine their similarity to MCC tumor samples, we characterized MCC cell lines via gene expression microarrays. Using whole transcriptome gene expression signatures and a computational bioinformatic approach, we identified significant differences between variant cell lines (UISO, MCC13, and MCC26) and fresh frozen MCC tumors. Conversely, the classic WaGa and Mkl-1 cell lines more closely represented the global transcriptome of MCC tumors. When compared to publicly available cancer lines, WaGa and Mkl-1 cells were similar to other neuroendocrine tumors, but the variant cell lines were not. WaGa and Mkl-1 cells grown as xenografts in mice had histological and immunophenotypical features consistent with MCC, while UISO xenograft tumors were atypical for MCC. Spectral karyotyping and short tandem repeat analysis of the UISO cells matched the original cell line's description, ruling out contamination. Our results validate the use of transcriptome analysis to assess the cancer cell line representativeness and indicate that UISO, MCC13, and MCC26 cell lines are not representative of MCC tumors, whereas WaGa and Mkl-1 more closely model MCC. RNA was extracted from MCC cell lines and MCC and SCLC tumor samples and hybridized to Affymetrix microarrays for transcriptome profiling.
2014-12-23 | E-GEOD-50451 | biostudies-arrayexpress
Project description:When using cell lines to study cancer, phenotypic similarity to the original tumor is paramount. Yet, little has been done to characterize how closely Merkel cell carcinoma (MCC) cell lines model native tumors. To determine their similarity to MCC tumor samples, we characterized MCC cell lines via gene expression microarrays. Using whole transcriptome gene expression signatures and a computational bioinformatic approach, we identified significant differences between variant cell lines (UISO, MCC13, and MCC26) and fresh frozen MCC tumors. Conversely, the classic WaGa and Mkl-1 cell lines more closely represented the global transcriptome of MCC tumors. When compared to publicly available cancer lines, WaGa and Mkl-1 cells were similar to other neuroendocrine tumors, but the variant cell lines were not. WaGa and Mkl-1 cells grown as xenografts in mice had histological and immunophenotypical features consistent with MCC, while UISO xenograft tumors were atypical for MCC. Spectral karyotyping and short tandem repeat analysis of the UISO cells matched the original cell line's description, ruling out contamination. Our results validate the use of transcriptome analysis to assess the cancer cell line representativeness and indicate that UISO, MCC13, and MCC26 cell lines are not representative of MCC tumors, whereas WaGa and Mkl-1 more closely model MCC.
2014-12-23 | GSE50451 | GEO
Project description:Screening cDNA clones from SSH library by hybridization with cDNA used to construct the library. Treated samples were drought-stressed cowpea plants, and control samples were cowpea plants subjected to a standard watering regime. cDNA clones from forward and reverse libraries are spotted on the same array, but data from each library were analysed separately after normalization using SSHscreen software (http://microarray.up.ac.za/SSHscreen ) to calculate Enrichment Ratio 3 (ER3) and Enrichment Ratio 2 (ER2) values for each clone. ER3 is a measure of differential expression, and was determined using a set of hybridizations with unsubtracted treated (UT) and unsubtracted control (UC) cDNA. ER2 is a measure of the relative abundance of a clone's transcript in the original tester sample, relative to other transcripts in the sample prior to the SSH process. ER2 for the forward library was determined using a set of hybridizations with subtracted treated (ST) and unsubtracted treated (UT) cDNA. ER2 for the reverse library is determined using a set of hybridizations with subtracted control (SC) and unsubtracted control (UC) cDNA.
2010-03-22 | GSE20273 | GEO
Project description:Screening cDNA clones from SSH library by hybridization with cDNA used to construct the library. Treated samples were drought-stressed cowpea plants, and control samples were cowpea plants subjected to a standard watering regime. cDNA clones from forward and reverse libraries are spotted on the same array, but data from each library were analysed separately after normalization using SSHscreen software (http://microarray.up.ac.za/SSHscreen ) to calculate Enrichment Ratio 3 (ER3) and Enrichment Ratio 2 (ER2) values for each clone. ER3 is a measure of differential expression, and was determined using a set of hybridizations with unsubtracted treated (UT) and unsubtracted control (UC) cDNA. ER2 is a measure of the relative abundance of a clone's transcript in the original tester sample, relative to other transcripts in the sample prior to the SSH process. ER2 for the forward library was determined using a set of hybridizations with subtracted treated (ST) and unsubtracted treated (UT) cDNA. ER2 for the reverse library is determined using a set of hybridizations with subtracted control (SC) and unsubtracted control (UC) cDNA. Direct comparison of UT and UC cDNAs to calculate ER3 values for F and R library, including dye swaps and replicate arrays. Direct comparison of ST and UT cDNAs to calculate ER2 values for F library, including dye swaps and replicate arrays. Direct comparison of SC and UC cDNAs to calculate ER2 values for R library, including dye swaps and replicate arrays.
2010-05-05 | E-GEOD-20273 | biostudies-arrayexpress
Project description:Recent studies have reported both mRNA and microRNA (miRNA) in saliva, but little information has been documented on the quality and yield of RNA collected. Therefore, the aim of the present study was to develop an improved RNA isolation method from saliva and to identify major miRNA species in human whole saliva. RNA samples were isolated from normal human saliva using a combined protocol based on the Oragene RNA collection kit and the mirVana miRNA isolation kit in tandem. RNA samples were analyzed for quality and subjected to miRNA array analysis. Twelve representing 6 males and 6 females, were selected for miRNA profiling using the TaqManM-BM-. Low Density Array Card (TLDA) Human miRNA Panel v2.0 (Applied Biosystems). The analysis of expression of the >700 miRNAs was performed by the DNA Core at the Interdisciplinary Center for Biotechnology Research Center at the University of Florida, according to the manufacturerM-bM-^@M-^Ys protocol except the pre-amplification step was omitted. The NormFinder algorithm was used to identify the optimal normalization of miRNA among the 25 most abundantly expressed miRNAs detected.
2014-04-12 | E-GEOD-28659 | biostudies-arrayexpress
Project description:A spectral library of mouse proteins suitable for SWATH analysis. Library comprises spectra from publicly available dataset available on PRIDE obtained from mouse tissue with a new data set acquired from a time course of mouse epithelial cells transitioning to pluripotent stem cells, including markers of transition.
2021-02-26 | PXD023010 | Pride
Project description:The samples were collected from the participants of the Finnish Diabetes Prediction and Prevention (DIPP) Study, born between 1995 and 2006. DIPP is a prospective follow-up cohort of children with a moderate or high risk of type 1 diabetes, based on the HLA-DR-DQ genotype. Islet cell autoantibodies (ICA, GADA, IAA, IA2A and ZnT8A) were measured 1 - 4 times per year until age 15 or year 2018. The aim was to study associations between perinatal DNA methylation marks and later progression to type 1 diabetes. Case individuals who became persistently positive for at least two biochemical autoantibodies (GADA, IAA, IA2A or ZnT8A) and/or were diagnosed with type 1 diabetes during the follow-up were compared to the control individuals who remained autoantibody-negative throughout the follow-up. These data were also used in the development of data analysis methodology in bisulfite sequencing studies. To protect the privacy of the study participants, the sequence read data are not publicly available. However, the processed data can be downloaded here. These include two count matrices: \\"methylated_reads\\" and \\"total_reads\\". The matrix \\"methylated_reads\\" contains methylated read counts at each high-coverage CpG site (altogether approximately 2.5 million rows) at each of the 173 samples (173 columns), and the matrix \\"total_reads\\" contains the corresponding total read counts (coverage). Please notice that the methylated read counts are read counts, not percentages. Methylation proportions can be calculated as methylated_reads/total_reads. The row names are the genomic locations of these CpG sites in hg19 (GRCh37) coordinates (1,2). For privacy reasons, all potential SNPs were excluded from these publicly available count matrices. Specifically, we removed all common (minor allele frequency > 1 %) human SNPs, as listed in dbSNP (3). We also removed all SNPs that were detected in one or more samples even with \\"low\\" evidence by BS-SNPer, which is a software for detecting SNPs from bisulfite sequencing data (4). Altogether 204443 out of 2752981 rows were removed from the original coverage-filtered count matrices that were analyzed in the present study. Description of the sample attributes: Individual: The individual-specific identifiers, such as “Subject1”. Since each sample is from a different individual, these correspond to the sample identifiers (Subject1 == Sample 1 etc.) Experimental Group: The variable of interest (called \\"class\\" in the associated publications) with three possible values: 1) case, 2) control and 3) NA (neither case nor control). 1) Case: became persistently positive for at least two biochemical autoantibodies (GADA, IAA, IA2A or ZnT8A) and/or diagnosed with type 1 diabetes during the follow-up. 2) Control: remained autoantibody-negative throughout the follow-up. 3) NA: The remaining 51 individuals with a missing value (“NA”) did not qualify as cases or controls, since they were either persistently positive for only 1 biochemical autoantibody or transiently positive for one or more autoantibodies. We excluded these 51 individuals from the case-control-comparison but included them in the comparison between the sexes. Library preparation batch: The sequencing libraries were prepared in 7 batches. The names of the batches do not have any special meaning. That is, \\"1A\\" is not necessarily more similar to \\"1B\\" than it is to \\"3B\\". We treated this as a categorical technical variable with 7 categories. PC1 and PC2: Projections of the sample-specific methylation proportion vectors on the first two orthonormal principal components. The principal component analysis (PCA) was performed on the original coverage-filtered methylation proportion matrix (methylated/total reads), where missing values at each CpG site were imputed by the median over samples with non-missing values. The original methylation proportion matrix included 2752981 rows, whereas these publicly available matrices include 2548538 rows (all potential SNPs excluded). Hence, PCA on the publicly available data would result in slightly different values for PC1 and PC2. We included these as covariates in the differential methylation analysis to represent technical variation (in addition to the library preparation batches). References 1. Church DM, Schneider VA, Graves T, Auger K, Cunningham F, Bouk N, et al. Modernizing reference genome assemblies. PLoS Biol. 2011 Jul;9(7):e1001091. 2. Genome Reference Consortium. NCBI downloads: https://hgdownload.soe.ucsc.edu/goldenPath/hg19/bigZips/hg19.fa.gz, accessed Feb 10th, 2019 3. NCBI. dbSNP: https://ftp.ncbi.nih.gov/snp/organisms/human_9606_b151_GRCh37p13/VCF/common_all_20180423.vcf.gz, accessed April 29th, 2021 4. Gao S, Zou D, Mao L, Liu H, Song P, Chen Y, et al. BS-SNPer: SNP calling in bisulfite-seq data. Bioinformatics. 2015 Dec 15;31(24):4006–8.
2022-06-06 | E-MTAB-10530 | biostudies-arrayexpress
Project description:original SWATH files (zipped), spectral library used and sample annotation table
2015-08-04 | MSV000079208 | MassIVE
Project description:Food safety evaluation of new, genetically modified (GM) plant varieties has led to basic questions regarding the safety assessment of new plant varieties and whole foods derived thereof. An important part of the hazard identification in the European approach is a targeted compositional analysis of new GM plant varieties compared to one or more conventional reference varieties. Comparative analysis will become much more informative with unbiased analytical approaches, such as omics profiling. Analysis tools that estimate the similarity of new varieties to the reference would in turn greatly facilitate hazard identification. Further in-depth biological, functional and eventually toxicological analysis of the data would then only be necessary for varieties that fall outside the scale of those with a history of safe human consumption. For this purpose, the use of a one-class classifier tool was explored in this study to assess and classify transcriptome profiles of potato varieties. Five potato varieties were grown in the Netherlands during the same year (NL samples) and included four biological replicates for four varieties or two biological replicates for the fifth one. They were all analysed in 2011. A sixth variety was grown in the UK in a previous study and a previous year, for which the data are submitted in E-MTAB-605. The two UK samples were analysed in the original study in 2008 and again together with the NL samples in the present study, resulting in four profiles for two samples.
2014-03-04 | E-MTAB-1707 | biostudies-arrayexpress