Video Notes: Experimental Design and Neuroscience Techniques

Course Goals and Objectives

  • Understand the neurobiology of cognition, behavior, development, and disease.
  • Build upon foundational neuroscience knowledge (brain regions, action potentials).
  • Apply fundamentals to processes that make us who we are.
  • Highlight the vast room for discovery in neuroscience.
  • Encourage student involvement in active research.

Boot Camp Focus

  • Experimental design skills: Asking questions, designing experiments, understanding tools.
  • Emphasis on enjoying the process of learning neuroscience.

Lecture Structure

  • Learning objectives presented at the beginning of each lecture.
  • Outline of important points, key topics, and deliverables.

Course Logistics

  • Zoom username should include first name, last initial, or full name for attendance tracking.
  • Google Calendar is crucial for staying updated on the schedule and avoiding time zone confusion.
  • Keep an eye on announcements and FAQs in the community for essential information.
  • Cameras are optional but encouraged for a more engaging experience.

Experimental Design Principles

  • Good Experiment: A well-defined question and problem-solving method that is generalizable and replicable.
  • Strive for generalizable and replicable research questions.
  • Replicating experiments addresses issues in research.
  • Techniques should have a defined, well-defined question.

Scientific Method Steps

  1. Literature Review: Search databases (e.g., PubMed) to identify existing research and gaps in knowledge. Goal is to find gaps in knowledge.
  2. Hypothesis Formation: Develop a testable statement to fill the gap, explaining your plan to address it.
  3. Experiment Design: Create suitable experiments with appropriate technology and resources for data resolution.
  4. Data Collection: Execute the experiment, keeping detailed records of the data obtained.
  5. Data Analysis: Employ statistical methods to interpret the data and identify significant patterns or trends.
  6. Conclusion: Draw conclusions based on the data analysis, determining whether the null hypothesis can be rejected.

Hypothesis

  • Must be clearly and concisely stated.
  • Should be falsifiable (capable of being proven or disproven).
Types of Hypotheses
  • Null Hypothesis: States there is no effect or relationship (e.g., a drug does not work on cancer cells).
  • Rejection makes your alternative hypothesis potentially the only or one of the alternative explanations.
  • Alternative Hypothesis: States that there is an effect or relationship.

Control Variables Importance

  • Baseline for comparison and drawing definitive conclusions is important.
  • Control group needed to reduce confounding variables.

Changing Hypothesis

  • Iterative process based on experimental results.
  • Experiments might not turn out as you like which lead you back to the drawing board.
  • Results should be useful in a useful, evolving cycle.

Data Analysis

  • Initial step: Simply look at the data for insights and potential issues.
  • Data described by its accuracy (correctness) and precision (consistency).
  • Experiment's resolution is also an important factor.
  • Reproducibility: Consistency of repeated measurements under the same conditions.
Dice Experiment
  • Importance of experiments that are ellipse reducible or sample sizes where the conclusions the tell are actually ellipse reducible.
  • Fair Dice Example:
    • If a dice is perfectly fair, we expect to see 100 in each condition when we build the six sided dice.
    • Rolling a One Dice Block:
      • Rolling it maybe six ties will not get on. Your initial conclusion is that the dice is not fair, but it is not replicable.
      • Have a large enough sample size to see what the power of the experiment is.

Statistics and Conclusions

  • Use mathematical procedures to compare predictions with data.
  • Statistical significance: p-value of 0.05 or less (likelihood that data exceeds what would be observed if the null hypothesis were true).

Reproducibility in Research

  • Ability of other scientists to obtain the same results.
  • Factors Contributing to Reproducibility Crisis:
    • Detailed conditions and settings need to be accurately described in literature.

Research Misconduct Examples

  • Detected papers with falsified data (e.g., Pierre Ann Bresum).
  • Fabricated results in mice studies.

Publication Pressure

  • Academics are often pushed to publish novel work over replication studies.
  • Negative results may not be published due to lack of perceived significance.

Research Techniques

  • Necessity for improved techniques with better resolution for nervous system investigation.
  • Historical approaches were crude and invasive.

Progress

  • Modern technologies are more precise and noninvasive now.
  • Ways of genetic analysis and manipulation for RNA proteins, etc.
  • Ways to manipulate the with genetic interventions for research.
  • Advancements with visualization of the brain using better forms of microscopy.
  • Lot more control over neural activity (ex: optogenetics).

Central Dogma of Biology

  • DNA transcribed into RNA, then translated to form proteins.
  • Analysis of DNA, RNA, or proteins can provide information on pathology, therapeutic targets, and genetic predispositions.

Gel Gel Electrophoresis: Separate protein or DNA based on size.

Techniques for Genetic Code Investigation

  • PCR (Polymerase Chain Reaction):
    • Technique used to study fragments of DNA, copying DNA segments.
    • Useful for genome sequencing, medical diagnostics, and COVID testing.
  • RNA Sequencing:
    • Technique used to quantify all the RNA transcripts that are expressed in the given sample.
    • Provide info on activation of subtypes.

Protein Quantification and Localization

  • Used to understand where a protein goes inside a cell.
  • Quantifies cell localization of different proteins within cells for cell membrane.
  • Classic Technique: Immunohistochemistry (IHC) - Use of antibodies to identify proteins.
  • Techniques:
    • Western blotting
    • Immunohistochemistry
    • Fusion Tagging

Western blotting

  • Gel Electrophoresis: Protein charge and size
    Transfered inot themembrane
  • Primary Antibody: Bind the protein
  • Sends membrane with secondary antibody to attach to the protein.

Immunohistochem

  • Use of fluently tagged antibodies
  • Antibodies used with microscopy

Fusion Tiding

  • Use genetic code of a protein in a lipid environment.

Electrophysiology

  • Study of electrical properties of biological cells and tissues.
  • Important in the function of neurons (action potentials, synaptic signaling).
  • Patch Clamping: Technique used to study the ion currents in individual isolated cells.

Logical Techniques

  • EEGs useful tool for diagnosis but also electro properties of individual cell, etc.

Microscopy in Neuroscience

  • Essential tool for understanding cell biology and cellular neuroscience.
  • Allows us to gain the struct and localization of different cell types.

Types of Microscopy

  • Fluorescence microscopy
  • Confocal microscopy
  • Electron microscopy

Fluorescence microscopy

  • Light to simulate wavelength and extract the information

Confocal microscopy

  • Use of the different images at different plants to have a whole three d image of the specimen.

Electron microscopy

  • High resolution and use of dead samples.
    High Magnification can be used to see individual proteins, organelles, etc using vacuum and electrons.

Manipulation of Neural Circuits

  • Neural Modulation: DBS (deep brain stimulation), TMS (transcranial magnetic stimulation), optogenetics.
  • DBS and TMS are used for treatment purposes in Parkinson patients.
  • DBS more invasive compared to TMS.
  • Optogenetics - Insertion of optogenetic material by the use of a virus.

Experimental Models

  • Cell Cultures: Grow cells on dishes. Lab research.
    • Can tell you about the for example, if you're testing a drug, the individual effect of a drug on that particular cell type.
  • Animal Models: Effects on the overall physiology of an organism.
    • Understand interactions and cell models that also exists in this culture.
  • In Silico Research: Used of computer to mimic
    • Atomistic simulations of Pro folding etc.
    • No ethical isuses
      • Simulations may not be fully representative, thus results may be accurate.

Cell Cultures Description

  • Controlled environment. Very cheap.
  • Less ethics
  • But cannot test effects on behavior.

Animal Model Description

  • Preclinical evaluation of drugs safety
  • Ethical issues

Insilio Research Description

  • No ethical consideration
  • Limitations with actual simulations

Gene Manipulation

  • Used to create models for interrogation of a particular question of disease.
  • Model different diseases to test interventions.
  • Can be introduced using several different ways such in virus viral vectors, lipid vectors, etc.
  • Viral vector is one of the more very popular ones because this genome is integrated into the host. These genes are integrated into the host genome, and you get a very consistent expression.
  • There is also a lot of other lipid based methods. For example, you create these liposomes that have genetic material within them.
  • Useful for screening tool and disease
  • CRISPR (clustered regularly interspaced short palindromic repeats):
    • It Prevent bacteria, DNA by disabling DNA cutting.

Developing New Types of Inhibitors for Voltage Gated Sodium Channels

  • So epilepsy affects a lot of people worldwide and because voltage gated epilepsy is basically abnormal electrical activity within the brain and neurons, and voltage gated sodium channels is the center of that. So, inevitably, they are extremely important targets in the treatment of epilepsy.
  • Gaps: Are not selective to the subtypes, so we need to develop subtypes selective inhibitors because this will reduce a lot of the off target effects and give a much better self understanding sorry, a much better clinical outcome or clinical profile for these drugs.
  • Techniques used:
    • electrophysiology batch time
    • cryo electron microscopy
      computational simulations