Instruments That Changed Science

Conventional microscopes struggle with thick or translucent samples because light from out-of-focus planes muddies the image. Minsky’s design used a pinhole to block everything except light from a single focal point, then scanned that point across the sample to build a sharp image one point at a time. The idea was decades ahead of the technology needed to use it well — there was no practical light source until the laser matured — and the first commercial confocal microscope wasn’t built until 1982, more than 25 years after Minsky’s original patent. It’s now a standard tool in cell biology.

Bacteria have long used CRISPR sequences paired with Cas proteins as a defense system against viruses, cutting invading DNA at precise, programmable sites. Doudna and Charpentier showed that this natural system could be repurposed as a general-purpose gene-editing tool: supply Cas9 with a short guide RNA and it will cut essentially any DNA sequence you specify, in essentially any organism. What had taken years of trial-and-error genetic engineering became a benchtop technique any molecular biology lab could run. The work won the 2020 Nobel Prize in Chemistry and reshaped research, agriculture, and medicine — including CRISPR-based therapies now approved for treating sickle cell disease.

Before PCR, detecting or studying a specific stretch of DNA required enough starting material to work with directly — often impossible from a small or degraded sample. Mullis realized that a heat-stable DNA polymerase, combined with short primers and repeated heating-and-cooling cycles, could copy a target DNA sequence exponentially: one strand becomes two, two become four, and so on, doubling with each cycle until a single molecule yields billions of copies within hours. The technique won Mullis the 1993 Nobel Prize in Chemistry and became foundational infrastructure for the field — underlying DNA sequencing, forensic identification, genetic testing, and the PCR tests used to diagnose infectious disease, including COVID-19.

Determining a protein’s atomic structure traditionally meant coaxing it into a crystal for X-ray diffraction — a slow, failure-prone process that never worked for many important molecules. Cryo-EM takes a different approach: flash-freeze a protein sample in a thin layer of vitreous ice fast enough to prevent damaging ice crystals from forming, then image thousands of individual frozen molecules with an electron microscope and computationally combine those 2D images into a 3D structure. Early versions produced only blurry, low-resolution images, but a combination of better detectors and image-processing algorithms around 2012–2013 triggered what’s now called the resolution revolution, suddenly allowing routine atomic-level detail. The technique won the 2017 Nobel Prize in Chemistry and has become the method of choice for structures — including many drug targets and the SARS-CoV-2 spike protein — that resist crystallization.

Predicting a protein’s 3D shape from its amino acid sequence was a fifty-year-open problem in biology, because a protein’s function depends almost entirely on its folded shape, and shape had generally required years of experimental work per protein to determine. AlphaFold, a deep learning system, solved this at scale: at the CASP14 competition in 2020 it predicted structures with accuracy competitive with experimental methods, and its public database now includes predicted structures for over 200 million proteins. It’s a useful edge case for your own eligibility question: AlphaFold is pure software, no hardware at all, and it’s arguably the highest-impact scientific instrument of the last decade.