Using genetically faithful models and breakthrough technologies to study disease
Human cellular models, derived from real patients, provide the gold standard for studying human disease. These models capture the full complexity of the patient genome in an experimentally accessible format, and CRISPR manipulations further test the causal role of specific genes in disease pathophysiology. Physiological rescue in human disease models provides a stringent measure of efficacy for genetically targeted therapies.
Working with human stem cells and derived neurons isn’t easy, but our team has spent years honing techniques to generate and phenotype mass quantities of these cells for research studies. We have established strong, collaborative relationships with leading medical centers to procure patient samples in a responsible and ethical manner to enable this work. Our commitment to precision also means that our cellular models display tremendous quality and consistency for the study of human disease.
The core function of neurons is to fire electrical impulses, but these impulses are not directly visible. Q-State scientists have developed a proprietary suite of OptopatchTM technologies to convert the electrical activity of cells into visible flashes of light. Our custom optical instrumentation and advanced software enable recordings from neurons at ~10,000-fold higher throughput than is possible with most conventional techniques.
Each cell is evaluated across 200 parameters of neuronal excitability and synaptic signaling. The richness of these high-content datasets provides unprecedented sensitivity in assessing cellular patterns of disease and therapeutic response. Datasets comprising tens of thousands of cells lend statistical power in subsequent analyses that provides greater confidence in interpreting results.
Our robust technologies generate terabytes of intricate biological data every day. We deploy internally-developed software for customized, comprehensive analysis, and we are constantly refining this software to enhance its capabilities. We further maintain computational efficiencies through automated scripts, cloud computing, and machine learning to quickly parse complex, multi-dimensional datasets. From a vast collection of datapoints, we confidently identify distinct parameters that are most meaningfully associated with disease and therapeutic response.