Project description:To gain a global view of translational inhibition by microRNA (miRNA), we isolated polysomes from wild type and Dicer1 knockout HCT116 human cells using sucrose gradient fractionation technique. The polysome fraction was separated into light (9mer or less) and heavy (10mer or more) subfractions. RNA samples were extracted from both subfractions and subjected to RNA-seq analysis. A general shift from light to heavy subfractions was detected for miRNA targted mRNAs.
Project description:Comparison of gene expression patterns between phenotypic mouse HSCs (Lin- KIT+ SCA-1+ FLK2- CD150+ CD34-) separated into CD11A- and CD11A+ fractions. A link to our analysis of these populations can be found here (https://gexc.stanford.edu/model/1007). Mouse HSCs were subdivided based on expression of CD11A into positive and negative subfractions, and gene expression patterns analyzed by the Gene Expression Commons (gexc.stanford.edu)
Project description:We used SOMAscan to measure >1300 analytes in sera from healthy controls and patients with sJIA, MAS, sJIA-LD and other related diseases.
Project description:HDL proteome dynamics may determine HDL cardioprotective functions. We aimed to characterize proteome and lipid profiles in small, medium and large (S/M/L) HDL fractions and analyze their functions. We characterized mouse HDL fractions by their high phospholipid (PL) content and collected S/M/L-HDLsubfractions by using fast protein liquid chromatography. The pooled S/L/M-HDL elution was subjected to mass spectrometry (MS) analysis using a Qstar XL-MS system, performing HDL subpopulation proteomic analysis. Fifty-one HDL proteins (39 in S-HDL, 27 in M-HDL and 29 in L-HDL) were identified and grouped into 4 functional categories (5 in lipid metabolism, 24 in immune response, 7 in coagulation, and 14 others). There were 16, 3 and 7 proteins present in only the S-HDL, M-HDL and L-HDL subfractions, respectively, and 11 proteins overlapped in all S/M/L-HDL subfractions. The dynamic changes in relative HDL protein levels were then characterized based on their functional groups by biological and bioinformatical methods.
Project description:Mononuclear cells from AML patients (n=46) were sorted into CD34+ and CD34- subfractions and genome-wide expression analysis was performed using Illumina BeadChip Arrays (HT12 v3). Of 2 AML samples only the CD34+ fraction could be analyzed. AML CD34+ and CD34- gene expression was compared to a large group of normal CD34+ bone marrow cells (n=31). Mononuclear cells from AML patients (n=46) were sorted into CD34+ (46) and CD34- (44) subfractions and genome-wide expression analysis was performed using Illumina BeadChip Arrays (HT12 v3). AML CD34+ and CD34- gene expression was compared to a large group of normal CD34+ bone marrow cells (n=31).
Project description:Liver samples were lysed (15 mM Tris-HCl (pH 8.0), 300 mM NaCl and 15 mM MgCl2, plus inhibitors (1 mg/ml heparin 100 µg/ml cycloheximide and 80 U RNAsin)), added to a 10-50% sucrose gradient and ultracentrifuged at 182,000x g for 2 hours. The gradient was aliquotted into 16 1ml fractions and added to Tri reagent (Sigma). RNA was extracted as per the manufacturer's instructions and then sub-pooled into fractions corresponding to monosomes, light polysomes, medium polysomes and heavy polysomes, according to ribosomal density (more ribosomal occupancy = higher translational activity = heavier, and therefore, located in the more dense fractions). Microarray analysis was performed by hybridising control (vehicle treated) monosomes against test (high-dose treated) monosomes on one microarray, control light polysomes against test light polysomes on a second microarray, and so forth for each sub-pool of fractions. Following microarray analysis there were a maximum of four values for each mRNA, corresponding to the proportional representation of that mRNA within each sub-pool of fractions. By calculating the change in values, i.e. degree of slope, across the monosomal region, the light polysomal region, the medium polysomal region and the dense polysomal region it was possible to determine any translational change in activity. In addition, the overall transcriptional change could be analysed for each mRNA.
Project description:Mononuclear cells from AML patients (n=46) were sorted into CD34+ and CD34- subfractions and genome-wide expression analysis was performed using Illumina BeadChip Arrays (HT12 v3). Of 2 AML samples only the CD34+ fraction could be analyzed. AML CD34+ and CD34- gene expression was compared to a large group of normal CD34+ bone marrow cells (n=31).
Project description:A non-invasive diagnostic test does not exist for acute graft versus host disease (aGVHD). We therefore sought to identify biomarkers for aGVHD using antibody microarrays (Schleicher and Schuell Serum Biomarker Chips, Whatman) that simultaneously assayed 120 plasma proteins. We measured these proteins in a set of 42 patient plasma samples following an allogeneic bone marrow transplant (BMT): 21 patients with a diagnosis of aGVHD grade II-IV (+GVHD) and 21 patients without aGVHD (–GVHD) at similar times after transplant. We excluded data from 2 hybridizations that had very bright dots and appeared as outliers in preliminary principal components analysis, so that we finally compared 20 +GVHD to 20 -GVHD samples. Keywords: disease state analysis, antibody microarray