Experimental scheme of IFN-γ treatment and bulk preparation of U-2 OS cells in the DIA-ME workflow, followed by protein identification metrics. From Krull et al., 2024. “Enhanced feature matching in single-cell proteomics characterizes IFN-γ response and co-existence of cell states.” Nat Commun 15, 8262 (2024). Licensed under an Attribution 4.0 international license.

This study introduces DIA-ME (Data Independent Acquisition-Matching Enhancer), a novel approach for improving proteome coverage in single-cell proteomics.

The method combines low-input samples with higher-input reference samples during analysis, leveraging DIA analysis tools like DIA-NN and Spectronaut to enhance protein identification. The researchers demonstrated that using reference samples containing 10x higher input significantly increases proteome coverage of low-input samples while maintaining high quantitative accuracy. Unlike traditional library-based DIA applications, DIA-ME eliminates the need for time-intensive spectral library generation and maximizes the use of scarce biological material.

The team extensively validated the method using a two-proteome model system, showing that DIA-NN maintained high specificity with false transfer rates below 0.2%, even when analyzing samples with 100-fold differences in input amounts. They applied DIA-ME to study interferon-gamma responses in U-2 OS cells at both bulk and single-cell levels, successfully detecting subtle proteome changes and revealing previously unknown patterns of protein co-regulation within cell populations.

The study employed a rapid 15-minute gradient on an IonOpticks Aurora Ultimate CSI 25×75 C18 UHPLC column operated at 300 nL/min and 50°C, coupled to a timsTOF Pro mass spectrometer. This research represents a significant advancement in single-cell proteomics, offering a scalable solution for analyzing limited biological samples while providing deeper insights into cellular heterogeneity and protein regulation at the individual cell level.

Publication
Nature Communications

Authors

Karl K. Krull; Syed Azmal Ali; & Jeroen Krijgsveld

Title

Enhanced feature matching in single-cell proteomics characterizes IFN-γ response and co-existence of cell states