Project description:Histologic diagnosis of sellar masses can be challenging, particularly in rare neoplasms and tumors without definitive biomarkers. DNA methylation has recently emerged as a useful diagnostic tool. To illustrate the clinical utility of machine-learning-based DNA methylation classifiers, we report a rare case of primary sellar esthesioneuroblastoma diagnosed by DNA methylation classificiation but histologically mimicking a nonfunctioning pituitary adenoma.
Project description:The Australian Acute Care Genomics program provides ultra-rapid diagnostic testing to critically ill infants and children with suspected genetic conditions. Over two years, we performed whole genome sequencing (WGS) in 290 families, with average time to result of 2.9 days, and diagnostic yield of 47%. We performed additional bioinformatic analyses and transcriptome sequencing in all patients who remained undiagnosed. Long-read sequencing and functional assays, ranging from clinically accredited enzyme analysis to bespoke quantitative proteomics, were deployed in selected cases. This resulted in an additional 19 diagnoses, and an overall diagnostic yield of 54%. Diagnostic variants ranged from structural chromosomal abnormalities through to an intronic retrotransposon, disrupting splicing. Critical care management changed in 120 diagnosed patients (77%). Results informed precision treatments; surgical and transplant decisions; and palliation in 94 (60%). We propose that integration of multi-omic approaches into mainstream diagnostic practice is necessary to realise the full potential of genomic testing.
Project description:The demo datasets available for MSCohort analysis. You can download to inspect their formats and practice using the software tool. This dataset contains raw files of 7 urine QC samples, spectronaut analysis results and MSCohort report results
Project description:Microarrays with 1,205 human microRNAs and 142 viral microRNAs were used for screening candidate diagnostic markers in the 3 categories of subjects from 24 plasma samples including acute aortic dissection, healthy and aortic aneurysm subjects. There were two microRNAs overlapping in the 3 group comparisons. Finally, 16 candidate microRNAs discovered via microarrays were selected for the further validation.
Project description:Isobaric labeling is a powerful strategy for quantitative mass spectrometry-based proteomic investigations. A complication of such analyses has been the co-isolation of multiple analytes of similar mass-to-charge resulting in the distortion of relative protein abundance measurements across samples. When properly implemented, triple-stage mass spectrometry and synchronous precursor selection (SPS-MS3) can reduce the occurrence of this phenomena, referred to as ion interference. However, no diagnostic tool is available currently to rapidly and accurately assess ion interference. To address this need, we developed a multiplexed TMT-based standard, termed the triple knockout (TKO). This standard is comprised of three yeast proteomes in triplicate, each from a strain deficient in a highly abundant protein (Met6, Pfk2, or Ura2). The relative abundance patterns of these proteins, which can be inferred from dozens of peptide measurements, are representative of ion interference in peptide quantification. We expect no signal in channels where the protein is knocked out, permitting maximum sensitivity for measurements of ion interference against a null background. Here, we emphasize the need to investigate further ion interference-generated ratio distortion and promote the TKO standard as a tool to investigate such issues.
