# TANGO: Analysis and curation of particles in cryo-electron tomography

## TL;DR

TANGO is a new tool for analyzing the spatial organization of particles in cryo-electron tomography data, enabling better understanding of cellular structures.

## Contribution

TANGO introduces a modular framework using twist vectors for rotationally invariant analysis of particle arrangements in cryo-ET.

## Key findings

- TANGO encodes particle positions and orientations as twist vectors for spatial pattern analysis.
- The framework supports features like neighborhood occupancy and angular deviations in cryo-ET datasets.
- TANGO's open-source design allows customization for diverse biological samples.

## Abstract

Cryo-electron tomography (cryo-ET) enables the visualization of cellular structures in near-native environments, but its potential for spatial analysis has been underutilized due to a lack of versatile tools accommodating biological sample diversity. Available solutions often rely on case-specific or hypothesis-driven approaches, while holistic analyses remain challenging. In this work, we introduce TANGO (Twist-Aware Neighborhoods for Geometric Organization), a framework leveraging point cloud descriptors to analyze spatial arrangements of particles, such as macromolecular complexes, in cryo-ET. By encoding relative positions and orientations of particles as twist vectors, TANGO enables rotationally invariant feature extraction, including structured neighborhood occupancy, lattice topology, or angular deviations. Its modular design and user-friendly interface allow for customization of features, facilitating exploratory analyses of spatial patterns in diverse experimental datasets. With its open-source Python implementation, TANGO advances the ability to decode complex cellular architectures and their functional relationships, offering a particle data analysis tool for the cryo-ET community.

Cryo-electron tomography visualizes molecules inside cells, but it lacks flexible tools to study their spatial organization. The authors present TANGO, a framework that utilizes neighborhoods of particles to detect patterns in their organization.