D28ad5f1-7f12-4505-b073-fc68f1c97e2f - samples
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PROVIDER: EGAD00001010288 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:scWGS-seq of flow sorted blast and normal cells from SJBALL021901 with 71 high quality cells sequenced (67 blast and 4 normal)
| EGAD00001009755 | EGA
Project description:Purpose: Investigate cellular heterogeneity in a fresh human ovarian cancer tissue sample Methods: Enzymatic digestion of fresh tissue sample collected from the operating room to produce single cell suspension. Cells were labelled with fluorescent antibodies to CD3, CD14, CD19, CD20, CD56 and FACS sorted to remove immune cells. The negative population was used for sequencing. Single cells were processed using the Fluidigm C1 Chip to generate barcoded cDNA for each cell. Amplifed cDNA was sequenced using an Illumina HiSeq 2500 machine. Results: Single cell RNA sequence data was obtained for 92 cells and a "bulk" sample of 1000 cells. 26 cells were removed from analysis due to quality control standards. The remaining 66 cells and the bulk sample were analyzed. Conclusion: Single cell RNA sequence analysis reveals heterogeneity in gene expression in cells harvested from a high grade ovarian serous cancer
2018-12-07 | GSE123476 | GEO
Project description:Single cell studies have transformed our understanding of cellular heterogeneity in disease but the need for fresh starting material can be an obstacle, especially in the context of international multicenter studies and archived tissue. We developed a protocol to obtain high-quality cells and nuclei from dissected human skeletal muscle archived in the preservative Allprotect® Tissue Reagent. After fluorescent imaging microscopy confirmed intact nuclei, we performed four protocol variations that compared sequencing metrics between cells and nuclei enriched by either filtering or flow cytometry sorting. Cells and nuclei (either sorted or filtered) produced statistically identical transcriptional profiles and recapitulated 8 cell types present in skeletal muscle. Flow cytometry sorting successfully enriched for higher-quality cells and nuclei but resulted in an overall decrease in input material. Our protocol provides an important resource for obtaining high-quality single cell genomic material from archived tissue and to streamline global collaborative efforts.
2024-11-21 | GSE282362 | GEO
Project description:We report the use of single-cell RNA sequencing (scRNA-seq) technique to compare the transcriptomic profiles of human breast cancer cells with high and low expression of miR-125b. Methods: MDA-MB-231(miR-125b-promo-EGFP) cells were flow sorted at maximum and minimum 5% of miR-125b expression level, and deep sequenced using Illumina HiSeq 2000. The sequencing reads that passed quality filters were mapped to the reference genome using TopHat, followed by counting of reads numbers using HTSeq Results: Using an optimized data analysis workflow, we mapped about 30 million sequence reads per sample to the human DNA reference genome (build hg19) and identified 48162 transcripts in MDA-MB-231(miR-125b-promo-EGFP) cells with low and high miR-125b expression. A total of 303 transcripts showed differential expression between low and high miR-125b expression cells, with |log2 (Fold change)| ≥ 2 and adjusted p value ≤ 0.05
2021-12-03 | GSE123358 | GEO
Project description:RNA-seq analysis of FVD–CD4+CD45.1+Ly6C–PD1+ TFH and FVD–CD4+CD45.1+Ly6C+PD1– TH1 cells sorted by flow cytometry at 7 days post-LCMV infection, sequenced via Illumina NovaSeq 6000.
2026-04-01 | GSE295937 | GEO
Project description:Gene expression profiling of normal hematopoietic cell subpopulations RNA extracted from flow sorted normal hematopietic cells were hybridized onto Affymetrix U133 Plus 2.0 arrays.
2010-02-21 | GSE19599 | GEO
Project description:Flow sorted mouse LNEP cells isolated from normal lungs and influenza-infected lungs (day 17 PR8)
2017-06-20 | GSE83467 | GEO
Project description:Background: Gene expression profiling has shown its ability to identify with high accuracy low cytogenetic risk acute myeloid leukemia such as acute promyelocytic leukemia and leukemias with t(8;21) or inv(16). The aim of this gene expression profiling study was to evaluate to what extent suboptimal samples with low leukemic blast load (range, 2-59%) and/or poor quality control criteria could also be correctly identified. Methods: Specific signatures were first defined so that all 71 acute promyelocytic leukemia, leukemia with t(8;21) or inv(16)-AML as well as cytogenetically normal acute myeloid leukemia samples with at least 60% blasts and good quality control criteria were correctly classified (training set). The classifiers were then evaluated for their ability to assign to the expected class 111 samples considered as suboptimal because of a low leukemic blast load (n=101) and/or poor quality control criteria (n=10) (test set). Results: With 10-marker classifiers, all training set samples as well as 97 of the 101 test samples with a low blast load, and all 10 samples with poor quality control criteria were correctly classified. Regarding test set samples, the overall error rate of the class prediction was below 4 percent, even though the leukemic blast load was as low as 2%. Sensitivity, specificity, negative and positive predictive values of the class assignments ranged from 91% to 100%. Of note, for acute promyelocytic leukemia and leukemias with t(8;21) or inv(16), the confidence level of the class assignment was influenced by the leukemic blast load. Conclusion: Gene expression profiling and a supervised method requiring 10-marker classifiers enable the identification of favorable cytogenetic risk acute myeloid leukemia even when samples contain low leukemic blast loads or display poor quality control criterion. Specific signatures were first defined so that all 71 acute promyelocytic leukemia, leukemia with t(8;21) or inv(16)-AML as well as cytogenetically normal acute myeloid leukemia samples with at least 60% blasts and good quality control criteria were correctly classified (training set). The classifiers were then evaluated for their ability to assign to the expected class 111 samples considered as suboptimal because of a low leukemic blast load (n=101) and/or poor quality control criteria (n=10) (test set).
2011-12-24 | E-GEOD-34714 | biostudies-arrayexpress
Project description:Rice blast is one of the most serious diseases and is caused by Magnaporthe grisea. SHZ-2, an indica cultivar with broad spectrum resistance to multiple races of the blast pathogen, was crossed to TXZ-13, a blast susceptible but high-quality variety, to produce one BC3 line, BC10 line, which showed strong to moderate blast resistance over eight cropping seasons in the field. In this study, we compared the transcription between blast-resistant and -susceptive lines by custom microarray. Keywords: time course, blast infection, disease response
2011-05-31 | GSE16470 | GEO
Project description:Background: Gene expression profiling has shown its ability to identify with high accuracy low cytogenetic risk acute myeloid leukemia such as acute promyelocytic leukemia and leukemias with t(8;21) or inv(16). The aim of this gene expression profiling study was to evaluate to what extent suboptimal samples with low leukemic blast load (range, 2-59%) and/or poor quality control criteria could also be correctly identified. Methods: Specific signatures were first defined so that all 71 acute promyelocytic leukemia, leukemia with t(8;21) or inv(16)-AML as well as cytogenetically normal acute myeloid leukemia samples with at least 60% blasts and good quality control criteria were correctly classified (training set). The classifiers were then evaluated for their ability to assign to the expected class 111 samples considered as suboptimal because of a low leukemic blast load (n=101) and/or poor quality control criteria (n=10) (test set). Results: With 10-marker classifiers, all training set samples as well as 97 of the 101 test samples with a low blast load, and all 10 samples with poor quality control criteria were correctly classified. Regarding test set samples, the overall error rate of the class prediction was below 4 percent, even though the leukemic blast load was as low as 2%. Sensitivity, specificity, negative and positive predictive values of the class assignments ranged from 91% to 100%. Of note, for acute promyelocytic leukemia and leukemias with t(8;21) or inv(16), the confidence level of the class assignment was influenced by the leukemic blast load. Conclusion: Gene expression profiling and a supervised method requiring 10-marker classifiers enable the identification of favorable cytogenetic risk acute myeloid leukemia even when samples contain low leukemic blast loads or display poor quality control criterion.
2011-12-21 | GSE34577 | GEO