De novo visual proteomics in single cells through pattern mining

TL;DR

This paper introduces 'Multi Pattern Pursuit', a novel template-free framework for de novo visual proteomics in single cells using cryo-electron tomography, enabling discovery of macromolecular complexes without prior structural templates.

Contribution

The paper presents a new pattern mining framework for proteome-scale de novo discovery of cellular macromolecular complexes from cryo-electron tomograms, eliminating the need for known structural templates.

Findings

Effective on simulated tomograms

Promising results on experimental data

Supports proteome-scale analysis

Abstract

Cryo-electron tomography enables 3D visualization of cells in a near native state at molecular resolution. The produced cellular tomograms contain detailed information about all macromolecular complexes, their structures, their abundances and their specific spatial locations in the cell. However, extracting this information is very challenging and current methods usually rely on templates of known structure. Here, we formulate a template-free visual proteomics analysis as a de novo pattern mining problem and propose a new framework called "Multi Pattern Pursuit" for supporting proteome-scale de novo discovery of macromolecular complexes in cellular tomograms without using templates of known structures. Our tests on simulated and experimental tomograms show that our method is a promising tool for template-free visual proteomics analysis.