Channing Transcriptomic Technologies

Transcriptomic Methods to Measure the Expression of RNA Transcripts throughout the Genome and the Relation to Clinical Outcomes

Transcriptomic methods measure the expression of RNA transcripts throughout the genome. RNA is transcribed from DNA, the genetic code. Some forms of RNA are translated into proteins, which form the machinery of the cell. Unlike DNA sequence, which is the same in all normal cells in the body throughout the lifetime, RNA expression depends on tissue or cell type, disease status, and environmental factors. Therefore, RNA expression may be more closely related to clinical outcomes.

Current Research

Previously, transcriptomic analyses have used microarrays. Now such analyses largely rely on RNA-sequencing (RNA-seq), which can quantify not only messenger RNAs, which code for proteins, but also other types of RNA, such as microRNAs and long non-coding RNAs, which regulate gene expression.

Achievements

• CDNM investigator Dr. Carey was awarded a grant from the Chan Zuckerberg Initiative's Human Cell Atlas Project
• CDNM investigators played a leading role in drafting "High-Throughput Sequencing in Respiratory, Critical Care, and Sleep Medicine Research. An Official American Thoracic Society Workshop Report"

Microarray expression profiling and RNA-sequencing

Channing Division of Network Medicine (CDNM) investigators have generated gene expression data in samples from subjects with respiratory disease and cancer, such as:

• RNA-sequencing in thousands of subjects from the COPDGene Study, which has shown blood gene expression changes related to cigarette smoking
• Lung-specific samples, including adult and fetal lung tissue, as well as airway brushings and alveolar macrophages from COPDGene subjects
• Whole blood samples and isolated immune cells from asthmatic subjects
• Gene expression profiling in breast tumors and normal adjacent breast tissue
• Gene expression profiling in prostate tumors and normal adjacent prostate tissue

Integrative and network approaches to gene expression

Since gene expression is dynamic and highly regulated, it lends itself to methods development and applications in genomic data integration and methods development, including:

• Defining networks of co-expressed genes in health and disease
• Describing expression quantitative trait loci (eQTLs), which are genetic variants associated with gene expression levels
• Delineating transcription factors influencing gene expression
• Identifying microRNAs regulating gene expression

Bioinformatics

• Vincent Carey, Professor of Medicine, is a co-founder of the Bioconductor Project, an open source suite of tools for genomic data analysis in the R statistical programming language
• The large RNA-sequencing datasets with corresponding clinical and whole genome sequencing data are a rich resource for development of novel methods for data analysis and integration
• RNA-sequencing data are being used to identify RNA splicing events and differential exon use related to disease