Research · Methods

The toolbox

The methods behind my projects, each explained in a sentence or two. Science is mostly careful measurement plus a good question.

Seeing

Confocal microscopy: A laser microscope that takes sharp optical “slices” through cells and tissues, so you can see one layer at a time and build 3D views.
Single-molecule FISH (smFISH): Glowing DNA probes stick to one RNA type, so every single RNA molecule appears as its own bright dot.
Immunostaining: Antibodies, nature’s precise “grabbers”, are tagged with dyes to show where a protein or chemical mark sits.
Live imaging of embryos: Watching development happen in real time, gently, without fixing the sample.
Genetically encoded sensors: Proteins built into an animal that glow more or less depending on a molecule, so chemistry becomes visible.

GAL4/UAS: A two-part genetic switch from yeast. Turn a gene on only in the tissue you choose.
RNA interference (RNAi): Turn a gene down by destroying its messenger RNAs.
Mutants and genetic crosses: Classic fly genetics: combine traits through careful breeding, tracked with visible markers such as curly wings.
Cloning and site-directed mutagenesis: Cut, paste and edit DNA to make a protein with exactly the change you want.

Protein purification: Make a protein in bacteria, then fish it out using a molecular “handle” (affinity) and charge (ion exchange).
Fluorescent labelling: Attach a dye at one precise spot, so the protein reports what it is doing.
Spectrophotometry: Measure how a sample absorbs or emits light to count molecules or watch droplets form.
Enzyme kinetics: Measure how fast an enzyme works at different fuel levels to learn how it behaves (Michaelis–Menten).
Western blotting: Separate proteins by size and detect specific ones with antibodies.

Drosophila Activity Monitor (DAM): Each fly lives in a small tube and crosses an infrared beam as it walks; counts per minute give activity, sleep (five or more minutes still) and daily rhythms.
Fertility assays: Count eggs laid and offspring that hatch, to see whether a change matters for the animal.

Image analysis (Fiji/ImageJ): Turn pictures into numbers: count, measure intensities, and compare fairly with the same settings.
Python and R: Reproducible analysis pipelines and statistics, from raw fluorescence to fitted curves.
Good practice: Calibrations, blanks, replicates and honest error bars, plus knowing what a result does not show.