# Lipidomic profiling of influenza A virus production in MDCK cells towards targeted clone selection

## TL;DR

This study uses lipid profiling to compare two MDCK cell clones for influenza virus production, identifying lipid signatures linked to high-yield clones.

## Contribution

The study introduces lipidomic profiling as a novel method to identify and select high-yield MDCK cell clones for influenza vaccine production.

## Key findings

- High-yield clone C113 showed elevated lipid levels across most classes compared to C59.
- Triacylglycerols showed key differences between clones during IAV infection.
- Progeny virions from both clones had similar lipid raft domains with a strong positive correlation (R2 = 0.77).

## Abstract

High-yield influenza virus production is essential for efficient vaccine manufacturing to support global demands. Using Madin-Darby canine kidney (MDCK) cells to produce influenza viruses is an attractive alternative to the conventional method of manufacturing vaccines using embryonated eggs. MDCK cells exhibit heterogeneity which can impact viral yields. However, the factors driving the variation between MDCK cells are not fully understood. Utilizing an untargeted liquid chromatography-mass spectrometry lipidomic approach, we investigated two proprietary MDCK clones (C59 and C113) provided by Sartorius (Germany) that differ in biochemical and viral production properties and examined their lipid profiles and dynamics upon influenza A virus (IAV) infection between 24 and 72 h. C113, a high-yield clone, displayed elevated levels across all lipid classes, aside from ether lipids compared to C59, a clone with superior growth properties. IAV infection in clone C59 and C113 displayed key differences, specifically triacylglycerols. Analysis of progeny virions from C59 and C113 clones revealed subtle differences with a positive correlation in lipid profile (R2 = 0.77), suggesting similar lipid raft domains between clones. Overall, these findings highlight specific cellular lipid signatures associated with high-yield production and demonstrate the value of integrating lipidomics methods into biomanufacturing pipelines, providing complimentary quality assurance markers.

The online version contains supplementary material available at 10.1038/s41598-025-33499-1.