Simple Tool for Rapidly Assessing the Quality of Multiplexed Single Cell Proteomics Data

Area of Science:

• Proteomics
• Mass Spectrometry
• Bioinformatics

Background:

• Single-cell proteomics is rapidly advancing due to improved mass spectrometry and sample preparation.
• Tandem mass tags enable high-throughput multiplexing for analyzing thousands of single cells.
• Large datasets from single-cell proteomics can contain poor-quality data due to experimental losses.

Purpose of the Study:

• To develop a tool for assessing data quality in single-cell proteomics prior to extensive data processing.
• To identify and filter out low-quality single-cell samples early in the workflow.
• To accelerate data processing by reducing the number of spectra with excessive zero values.

Main Methods:

• Development of a lightweight Python script and graphical user interface (GUI) named DIDAR.
• Quantification of reporter ion peaks within MS/MS spectra to assess data quality.
• Creation of reduced MGF files containing only spectra with a user-specified number of reporter ions.

Main Results:

• DIDAR rapidly quantifies reporter ion peaks, enabling identification of samples below quality thresholds.
• Summary reports help reduce wasted analysis time on poor-quality data.
• Reduced MGF files speed up downstream processing with minimal loss of data completeness.

Conclusions:

• DIDAR provides a crucial quality control step for single-cell proteomics workflows.
• The tool enhances efficiency by filtering low-quality data and reducing file sizes.
• DIDAR is compatible with all modern operating systems and available via GitHub.