Skill
🧬 Wet Lab: Molecular Neuroscience
Genetic Engineering: Designed and cloned a novel pAAV vector enabling simultaneous labeling of two dopamine neuron subtypes using intersectional genetics. Quantified gene expression by FACS flow cytometry.
Genotyping: Performed DNA extraction, PCR, and gel electrophoresis to determine mouse genotypes.
Immunohistochemistry (IHC): Experienced in sectioning and mounting brain slices.
Neuroanatomy: Performed 3D reconstruction of dopaminergic neurons at the single-cell level to map circuit morphology.
Surgical/Handling: Proficient in mouse restraint, subcutaneous (SC), and intraperitoneal (IP) injections.
Behavioral Assays: Analyzing open-field assays to study dopaminergic neural circuits.
Cell Culture: Maintained HEK293 cell cultures, including passaging and transfection.
💻 Dry Lab: Computation & Data Science
Programming Language: Python | Tools: NumPy, Matplotlib
Statistical Analysis: Utilize Prism GraphPad for hypothesis testing and visual representation of behavioral data.
Genetic mapping: Visualize and annotate DNA sequences with SnapGene.
🔗 Resources
Some resources I utilized and found very helpful during self-study:
Computer Science
Mathematics
- Statistics: Statistical Thinking for the 21st Century
- Linear Algebra: Essence of linear algebra, Chapter 2 of Deep Learning, NumPy Linear Algebra Exercises
- Multivariable Calculus: Khan Academy Course
- Differential Equation: Differential Equations for Engineers by Jiřà Lebl