In the Drögemöller Lab, we use a comprehensive suite of advanced tools and methodologies to drive our research forward. Our approach combines genomic analyses, computational pipelines, and state-of-the-art single-cell sequencing technologies to uncover the genetic and molecular mechanisms underlying hearing loss and related conditions.
In the Lab
Thanks to support from the Canadian Foundation for Innovation-John R. Evans Leaders Fund, our laboratory is equipped with state-of-the-art single-cell sequencing technologies that enable us to gather high-resolution genetic and molecular data through the Precision Genomics Suite (PGS).
Single Cell and Spatial Transcriptomics Suite
This advanced platform allows us to generate single cell multiomic and spatial transcriptomic data. With single cell multiomic data, we can examine gene expression and chromatin accessibility at the individual cell level, providing detailed insights into cellular functions and responses.
Spatial transcriptomics adds another layer to these unique data by revealing the spatial organization of gene expression within tissues, helping us understand the functional organization of cellular niches. This in turn can help us to identify the cellular origins of disease and examine the dynamics of pathology over space and time.
Equipment housed in this suite include:
- Chromium iX Single-Cell Library Preparation System (10x Genomics)
- CytAssist Visium System (10x Genomics)
- Xenium In Situ Sequencing System (10x Genomics)
- TapeStation QC Platform (Agilent)
- QuantStudio Pro 6 (Applied Biosystems)
On the Server
Behind the scenes, our dedicated computational workflows process and analyze vast amounts of data generated in the lab. Our server infrastructure supports a range of sophisticated analyses that transform raw data into meaningful insights using analyses such as the ones described below:
Genome-Wide Association Studies (GWAS)
We use GWAS to identify genetic variants associated with hearing loss and cognitive decline. Tools like PLINK and Saige facilitate efficient whole-genome data analysis, while MAGMA and PolyFun help annotate and interpret these genetic markers. Mendelian randomization can be applied to these data to examine potential causality between traits such as hearing loss and cognitive impairment.
Transcriptome-Wide Association Studies (TWAS)
By combining information from our genome-wide association studies with expression quantitative trait loci (eQTL) data obtained from large-scale databases such as Genotype-Tissue Expression (GTEx), we can investigate the association between heritable gene expression profiles and specific traits of interest. Tools like SPrediXcan and scPrediXcan can help us perform these analyses at both the bulk and single cell level.
Polygenic Scores
By aggregating the effects of multiple genetic variants, we create polygenic scores to predict an individual’s risk of developing conditions like age-related hearing loss (ARHL) and dementia. Tools like SBayesRC are used to incorporate functional genomic annotations to improve the transferability of these scores across diverse populations.
Single Cell Polygenic Enrichment Analyses
By integrating single cell RNA sequencing data with genome-wide association study data, we can identify specific cell types and cellular pathways that are most relevant to the conditions we study. This can be achieved using tools such as the single cell Disease Relevance Score (scDRS).
Multiomic Data Integration
Our in-house multiome pipeline, developed using R and Nextflow and containerized with Docker, integrates various types of genomic data. You can explore our pipeline on GitHub.
The Drögemöller lab aims to stay up to date with new analytical techniques. Such techniques employed by the lab include:
- Trajectory Inference (Slingshot & Psupertime): Maps the progression of cellular states over time.
- Differential Abundance (miloR): Detects changes in cellular abundance of specific cell populations across different conditions.
- CellChat: Analyzes communication networks between different cell types.
- Neuroestimator: Predicts neural activity based on genetic and molecular data.