Project description:we describe the utility of this novel platform in the field of genome medicine using a full-length approach to evaluate the relationship between transcriptional and mutational heterogeneity on single cells in dermal fibroblasts derived from patients with Hutchinson-Gilford progeria syndrome (HGPS)
Project description:we describe the utility of this novel platform in the field of genome medicine using a full-length approach to evaluate the relationship between transcriptional and mutational heterogeneity on single cells in dermal fibroblasts derived from patients with Hutchinson-Gilford progeria syndrome (HGPS)
Project description:we describe the utility of this novel platform in the field of genome medicine using a full-length approach to evaluate the relationship between transcriptional and mutational heterogeneity on single cells in dermal fibroblasts derived from patients with Hutchinson-Gilford progeria syndrome (HGPS)
Project description:demonstrate the clinical utility of our in-depth serum proteomics platform to identify specific diagnostic and predictive biomarkers for psoriasis, suggesting a great potential for application in translation studies and precision medicine in psoriasis and other immune-mediated diseases
Project description:Changes in alternative splicing are associated with several pathological conditions, including cancer. Microarrays strategies, which allow for the characterization of thousands of alternative splice forms in a single test, can be applied to identify differential alternative splicing events. In this study, a novel splice array platform was developed, including the design of a high-density oligonucleotide array, a labeling procedure, and an algorithm to identify splice events. The array consists of exon probes and thermodynamically balanced junction probes. Suboptimal probes are tagged and considered in the final analysis. An unbiased labeling protocol was developed using random primers. The algorithm used to distinguish changes in expression from changes in splicing was calibrated using internal non-spliced control sequences. The performance of this splice array was first validated with artificial constructs for CDC6, VEGF, and PCBP4 isoforms. The platform was then applied to the analysis of differential splice forms for 8000 genes in lung cancer samples compared to matched normal lung tissue. The expression of lung cancer-associated splice isoforms was validated by RT-PCR. Overexpression of splice isoforms was identified for genes encoding CEACAM1, FHL-1, MLPH, and SUSD2. None of these splicing isoforms had been previously associated with lung cancer. In conclusion, this highly accurate methodology enables the detection of alternative splicing events in complex biological samples, providing a powerful tool to identify novel diagnostic and prognostic biomarkers for cancer and other pathologies. 20 normal/tumor paired specimens. Tumor samples are from non-small cell lung cancer (NSCLC) whereas normals are from adjacent normal lung tissue.
Project description:Changes in alternative splicing are associated with several pathological conditions, including cancer. Microarrays strategies, which allow for the characterization of thousands of alternative splice forms in a single test, can be applied to identify differential alternative splicing events. In this study, a novel splice array platform was developed, including the design of a high-density oligonucleotide array, a labeling procedure, and an algorithm to identify splice events. The array consists of exon probes and thermodynamically balanced junction probes. Suboptimal probes are tagged and considered in the final analysis. An unbiased labeling protocol was developed using random primers. The algorithm used to distinguish changes in expression from changes in splicing was calibrated using internal non-spliced control sequences. The performance of this splice array was first validated with artificial constructs for CDC6, VEGF, and PCBP4 isoforms. The platform was then applied to the analysis of differential splice forms for 8000 genes in lung cancer samples compared to matched normal lung tissue. The expression of lung cancer-associated splice isoforms was validated by RT-PCR. Overexpression of splice isoforms was identified for genes encoding CEACAM1, FHL-1, MLPH, and SUSD2. None of these splicing isoforms had been previously associated with lung cancer. In conclusion, this highly accurate methodology enables the detection of alternative splicing events in complex biological samples, providing a powerful tool to identify novel diagnostic and prognostic biomarkers for cancer and other pathologies.