* This page is currently being developed and will be updated with new links as they become available.
Cell Handling and Maintenance: Culturing, passaging, and maintaining mammalian cells. (Audio, PDF)
Aseptic Techniques in Cell Culture: Avoid contamination, including proper use of laminar flow hoods, sterilization of equipment, and maintaining a sterile environment. (Audio, PDF)
Cryopreservation and Thawing: Freeze and store cells for long-term use and properly thaw them for experiments. (Audio, PDF)
DNA/RNA Extraction: Isolateg DNA/RNA from cells. (Audio, PDF)
Gene Expression Analysis Techniques: Use techniques like quantitative PCR (qPCR), Western blotting, or ELISA to measure gene and protein expression. (Audio, PDF)
Transfection and Transduction: Introduce foreign DNA or RNA into cells using methods such as lipofection, electroporation, or viral vectors. (Audio, PDF)
Fluorescent Probes and Staining: Use fluorescent dyes and antibodies for cellular imaging (e.g., for cell surface markers, intracellular components, or apoptosis detection). (Audio, PDF)
Live-Cell Imaging: Use time-lapse microscopy and other imaging techniques to study dynamic cellular behaviors. (Audio, PDF)
Fluorescence Microscopy: Use various fluorescent probes and techniques (e.g., confocal or multi-photon microscopy) to study subcellular structures and protein localization. (Audio, PDF)
Image Analysis: Use image processing software (e.g., ImageJ, FIJI, or CellProfiler) to analyze cellular dynamics and quantify cellular events (e.g., migration, division, apoptosis). (Audio, PDF)
Proliferation and Cytotoxicity Assays: Understand and perform assays like MTT, WST, or CellTiter-Glo to measure cell viability, proliferation, and cytotoxicity. (Audio, PDF)
Migration and Invasion Assays: Use techniques such as wound healing, microfluidic channels, transwell migration, or 3D invasion assays to study cellular dynamics like motility and invasive potential. (Audio, PDF)
Apoptosis Assays: Identify apoptotic cells through flow cytometry or assays like Annexin V/PI staining. (Audio, PDF)
Cell Sorting and Analysis: Use flow cytometry to analyze cell populations, characterize cell surface markers, and perform cell cycle analysis or apoptosis detection. (Audio, PDF)
Fluorescence-Activated Cell Sorting (FACS): Sort and isolate specific cell populations based on markers or other properties. (Audio, PDF)
Photolithography: Creating microstructures using light and masks to define patterns on substrates.
Soft Lithography: Use elastomeric materials (e.g., PDMS) to mold microfluidic channels and devices.
Etching and Deposition: Knowledge of techniques like wet and dry etching, sputtering, and chemical vapor deposition to create or modify device structures.
Bonding Techniques: Understanding of methods like plasma bonding, thermal bonding, or adhesive bonding for sealing and assembling microfluidic layers.
Microfluidic Materials: Familiarity with common materials used in microfluidic fabrication, such as PDMS, glass, silicon, hydrogels, and polymers.
Material Characterization: Use tools like atomic force microscopy (AFM), scanning electron microscopy (SEM), or profilometry for characterizing material surfaces and microstructures.
Microfluidic Flow Principles: Understanding the behavior of fluids at the microscale, including laminar flow, capillary action, and surface tension.
Flow Simulation: Use computational fluid dynamics (CFD) modeling tools like COMSOL or OpenFOAM to simulate and optimize flow within microchannels.
Pressure and Flow Control: Use micro-pumps, pressure regulators, and valves to control fluid flow in devices.
Hypothesis Testing: Design experiments, including controls, replicates, and proper use of positive/negative controls.
Data Interpretation: Critical thinking to interpret results in the context of cell dynamics and cellular behavior.
Troubleshooting: Identify and resolve issues related to cell culture, assays, or equipment malfunctions.
Statistical Software: Familiarity with statistical tools such as GraphPad Prism, R, or MATLAB to analyze experimental data, determine statistical significance, and visualize results.
Quantitative Analysis: Extract quantitative data from images or experimental assays, and apply appropriate statistical tests.
Cell Cycle and Signal Transduction: A deep understanding of cellular processes such as the cell cycle, apoptosis, autophagy, and cellular responses to external stimuli.
Mammalian Cell Dynamics: Knowledge of how cells interact with their environment, including extracellular matrix, cell signaling, and cell-cell communication in the context of disease models.
Linear Algebra: Crucial for solving systems of linear equations and for understanding matrix operations, especially in areas like systems biology and neural networks.
Differential Equations: Use ordinary differential equations (ODEs) and partial differential equations (PDEs) to model biophysical systems.
Probability and Statistics: For analyzing data, uncertainty, and noise in models, and for modeling stochastic processes that occur in biological systems (e.g., population dynamics or molecular interactions).
Optimization: Techniques for model calibration and parameter fitting, such as linear programming, convex optimization, and nonlinear optimization.
Numerical Methods: Understanding of numerical techniques like finite difference methods, Monte Carlo simulations, and other algorithms to solve complex mathematical models computationally.
Programming: Proficiency in languages like MATLAB or Python, which are commonly used in mathematical modeling and data analysis in biology.
Biosafety Practices: Understanding of biosafety protocols when working with potentially hazardous materials, including handling viral vectors or genetically modified organisms (GMOs).
Ethical Considerations: Knowledge of ethical issues and regulations related to mammalian cell research, including working with human-derived cells or animal models.
Documentation and Record-Keeping: Attention to detail for proper lab notebook documentation, experimental protocols, and ensuring compliance with regulations.
Teamwork: Work collaboratively in interdisciplinary teams (e.g., with biologists, biochemists, or bioinformaticians).
Scientific Writing and Communication: Write research papers, present data at conferences, and discuss results with colleagues or supervisors.