Project description:We sequenced 8 colorectal cancer patients' PBMC samples, and 6 healthy donors' PBMC samples. These individuals' plasma RNA have been profiled.
Project description:Aim: To discovery biomarkers in JIA base on gene expression from RNA sequencing on PBMC Method: Paired-end Ilumina sequencing to capture gene expression of PBMC from JIA individuals and healthy controls Results:sample heterogeneity makes RNA sequencing on PBMC unsuitable as a first-step method for screening biomarker candidates in JIA
Project description:Transcription profiling of human colon biopsy samples from healthy individuals and patients with colon adenomas, colorectal cancer or inflammatory bowel disease
Project description:Background: Clinical transcriptomics of peripheral blood mononuclear cells (PBMC) are coming into focus as a surrogate approach for prognosis, diagnosis, biomarker discovery and examination disease mechanisms. However, bioassays paired with transcriptomic analytic tools are yet to be developed and made available at point of care. Harnessing personal dynamic genomic responses to tailor patient asthma treatment or prevent disease exacerbations remain unmet medical needs. Method: We developed a rhinovirus-stimulated peripheral blood based-assay (virogram assay) coupled with single-subject analytics (N-of-1-patwhays) to capture dynamic genome-wide expression and dysregulated pathways to retrospectively predict childhood asthma exacerbation. We hypothesized that some genomic factors might predispose any given individual, healthy or asthmatic, to a set of similar transcriptional responses to rhinovirus stimulation. We first generated a classifier from paired sample microarrays, control and stimulated PBMC from healthy subjects and applied this classifier on the transcriptomic analysis of control and HRV-stimulated PBMC samples (virogram assay) from children with asthma. Results: The analysis of the different genomic responses of single-subject paired PBMC samples (HRV-stimulated and control) derived from healthy individuals (external dataset) enabled the discovery of dysregulated pathways related to acquired immunity, epigenetics and morphogenesis. The classifier built on these results and applied on the transcriptional analysis derived from the virogram assay predicted that the risk of asthma exacerbation among asthmatic subjects with an accuracy of 70%. Conclusion: We provide evidence that clinical prognosis can be predicted with a PBMC based-bioassay aligned with adequate single-subject analytics to assess dynamic transcriptomic response to specific disease-associated stimuli.