# A semi‐automated ASC speck assay to evaluate pyrin inflammasome activation

## TL;DR

A new semi-automated test using machine learning helps identify harmful genetic variants linked to inflammasome overactivity in a disease called familial Mediterranean fever.

## Contribution

A semi-automated ASC speck assay using machine learning is introduced to evaluate pyrin inflammasome activation.

## Key findings

- The assay can distinguish healthy controls from FMF patients with high sensitivity.
- It also differentiates FMF from other autoinflammatory diseases with high specificity.

## Abstract

To develop a rapid functional assay to validate variants of uncertain significance (VUS) in the MEFV gene.

Overactivity of the pyrin inflammasome pathway and ASC speck oligomerisation in response to stimulation with low concentrations of Clostridium difficile toxin A was directly visualised by immunofluorescence microscopy. A semi‐automated algorithm was developed to count cells and ASC specks.

The semi‐automated ASC speck assay is able to discriminate between healthy controls and patients with familial Mediterranean fever (FMF) and pyrin inflammasome overactivity with high sensitivity. It is also able to discriminate pyrin inflammasome overactivity from other autoinflammatory disease controls with high specificity.

The semi‐automated ASC speck assay may be a useful test to functionally validate VUS in the MEFV gene and screen for pyrin inflammasome overactivity.

A semi‐automated ASC speck assay using machine learning is able to discriminate between healthy controls and patients with familial Mediterranean fever (FMF) with high sensitivity. It is also able to discriminate FMF from other autoinflammatory diseases with high specificity.