09b3feee-b6b4-42b2-b9ef-9a2054632547 - samples
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ABSTRACT:
PROVIDER: EGAD00001009755 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:scWGS-seq of flow sorted blast and normal cells from SJBALL030072with 66 high quality cells sequenced (61 blast and 5 normal)
| EGAD00001010288 | EGA
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: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
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.
2011-12-24 | GSE34714 | 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. Training set.
2011-12-21 | E-GEOD-34577 | biostudies-arrayexpress
Project description:Ten volunteers underwent 13 research IVM cycles. Immature oocytes were retrieved after mild ovarian stimulation with gonadotropins but no hCG ovulation trigger, in vitro matured and fertilized using ICSI. Oocyte maturation rate in vitro was 47% (94/199), the mean fertilization rate was 71% (67/94) and 37% (25/67) of mature oocytes developed into a morphologically good quality embryo on day 3 after ICSI, after which the blastomeres were analysed by single-cell aCGH. Sixty-seven of the 111 blastomeres analysed showed at least one chromosomal abnormality. Two out of seventeen embryos had only normal cells while one embryo carried a meiotic abnormality, 11 were mosaic and three were chaotic. Although the number of embryos tested as well as the data available in the literature are too small to allow any statistical analysis, these data are comparable to the few data available on the total chromosome complement of day 3 ICSI embryos. This would indicate that other mechanisms than chromosomal abnormalities explain the low efficiency and high miscarriage rate in IVM cycles.
2014-11-12 | GSE56799 | GEO
Project description:This study has two components: (1) Human colon adenoma organoids (n=4 patients) were dissociated into single cells. Cells were incubated with a magnetic bead bound to an LGR5 antibody and run through a magnetic column. Magnet bound cells and flow through negative (FTN) cells were obtained. Magnet bound and FTN cells were incubated with an APC-check reagent (which binds to the magnetic bead on the LGR5 antibody) and DAPI, before being sorted by flow cytometry. 3 populations of live (DAPI-) cells were collected: FTN: Flow through negative. LGR5 negative by magnet and by flow cytometry SortedNeg: Magnet bound cells that were negative for LGR5 by flow cytometry SortedPos: Magnet bound cells that were positive for LGR5 by flow cytometry (2) Human colon organoids, as well as the tissue the organoid was derived from and adjcacent normal tissue (from n=19) were also profiled for known colorectal cancer associated mutations using the Qiagen Qiaseq Colorectal Cancer Panel, which provides targeted sequencing information for 71 genes.
2020-04-30 | E-MTAB-4698 | biostudies-arrayexpress
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