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Establishing connections between morphoelectric characteristics and transcriptomic-defined cell types
Goals and Approach
The Integrated Cell Physiology team at the Allen Institute for Brain Science conducts Patch-seq experiments using a whole-cell patch clamp technique to establish connections between morphoelectric characteristics and transcriptomic-defined cell types. This research helps to bridge the gap between genotype and phenotype.
The efficient and automated pipeline developed by this team is generating high-quality large-scale data at rates previously deemed impractical for electrophysiology experiments. Investigators on the Integrated Cell Physiology team utilize animal models and human tissue specimens acquired through neurosurgical procedures in combination with state-of-the-art genetic tools to identify rare cell types.
The “In-Vitro Single Cell Characterization Project (IVSCC)” employs a method called patch-seq to establish a direct link between the anatomical and electrical properties of individual cells and their genetic characteristics. This research is conducted using both animal models and human subjects.
The Integrated Cell Physiology department at the Allen Institute plays a crucial role in supporting gene therapy research. They provide important information about the effects of gene therapy interventions by conducting physiological assays that directly relate to gene function. These assays allow researchers to understand how the introduced genes are influencing the cellular behavior and function. By analyzing these physiological readouts, scientists gain valuable insights into the success and impact of the gene therapy interventions, which helps in advancing the field of gene therapy and its potential applications for various medical conditions.
The PatchLink project, supported by the National Institutes of Health (NIH), aims to create valuable tools that connect types of brain cells, defined by their genetics, to comprehensive brain circuit diagrams. The goal is to gain a deeper understanding of how different cell types are wired together. To achieve this, the project will develop cutting-edge automation technologies that allow us to measure shared features between genetic and circuit data sets at scale.
Data and Tools