Mendelspod
Mendelspod Podcast
Jennifer Dionne Wants to Read the Whole Proteome Using Nanophotonics, i.e. Raman Spectroscopy
Preview
0:00
-5:50

Jennifer Dionne Wants to Read the Whole Proteome Using Nanophotonics, i.e. Raman Spectroscopy

Jennifer Dionne, Stanford physicist and co-founder of Pumpkinseed, joins us to talk about a radically different way of reading proteins. Pumpkinseed’s deSIPHR technology combines nanophotonics with Raman spectroscopy to detect the molecular vibrations of individual amino acids. The goal is de novo protein sequencing that can read not only the 20 canonical amino acids, but potentially the enormous alphabet created by post-translational modifications and other forms of protein variation.

Raman spectroscopy is super cool even if nearly a century old. Shine a laser on a molecule and a tiny fraction of the photons change color as they interact with the molecule’s vibrations, producing a characteristic molecular signature. The problem has always been sensitivity. As Dionne explains, only about one photon in a million undergoes this Raman scattering. Her work uses nanophotonics for specially patterned materials to amplify that faint signal by orders of magnitude.

Why does that matter? Mass spectrometry has been the workhorse of proteomics, but it loses much of the sample during ionization and generally depends on existing catalogs for identification. Pumpkinseed wants to read what is actually there, including proteins and modifications we may never have seen before.

One early application is particularly timely with the recent news of cancer vaccines. Working with Genentech, Pumpkinseed is studying immunopeptides, the protein fragments displayed on the surface of cells that allow the immune system to distinguish healthy from diseased tissue. Direct sequencing of these peptides is a way to improve personalized cancer vaccines by identifying the mutations actually present in an individual patient’s tumor.

Ultimately, the ambition is much larger. “We want to be able to sequence all of the proteins that are in individual cells,” Dionne says. For biology and for AI models trying to learn biology, she argues, we first need to learn how to read much more of its language.

This post is for paid subscribers