Automated workflow for deep proteomic profiling of cervical fluid

Introduction

Endometrial cancer is the most common gynecological cancer in developed countries, with increasing incidence. Its diagnosis relies on the invasive sampling of endometrial tissue after abnormal vaginal bleeding (AVB). However, only 5 to 10% of sampling leads to a diagnosis of endometrial cancer, rendering the majority of procedures unnecessary. An alternative diagnostic test for endometrial cancer could be based on the analysis of cervical fluid, which is non-invasive. To overcome this challenge, we present a deep proteomic profiling of cervical fluid, combined with automated sample preparation, using Automated ProteinPrep 96 (APP96^TM) and LC-MS-based measurement pipeline, enabling high-throughput analysis for large-cohort studies.

Methods

A cohort comprising 130 cervical fluid samples collected from patients with abnormal vaginal bleeding or cervical pathology. The samples were categorized into three groups: patients presenting AVB diagnosed with endometrial cancer confirmed by histopathological examination, patients with AVB diagnosis of benign conditions, and patients with other cervical pathologies. For each sample, an equivalent of 10 µg of protein was automatically processed using the APP96 platform, following the PreOmics iST protocol, including lysis, digestion, and cleanup steps.

Chromatography was performed using a 44-minute gradient, separating the equivalent of 200 ng peptides on the Evosep One system with a 15 cm column and a 30 samples per day method. Mass spectrometry data were acquired in dia-PASEF mode on a timsTOF Pro mass spectrometer and processed using DIA-NN library-free search.

Additionally, a subset of samples was selected to compare reproducibility and performance between the APP96 and our standard sample preparation workflow.

Results

The precision tests showed that the APP96 platform resulted in high reproducibility and precise chromatography between replicates, with an average peak length of 10.2 seconds. This approach enabled the identification of 5,600 protein groups across all samples, with an average of 3,000 protein groups identified per sample. Notably, some proteins were differentially expressed, suggesting potential targets for further analysis to identify pathological states.

Conclusions

Precise sample preparation with the APP96 automation system resulted in high-quality chromatographical profiles, which translates to a high number of identifications in the analysis of cervical fluid. The high-throughput setup makes the system particularly suitable for analyzing large cohorts.