Pushing Boundaries in Proteomics: Emerging Trends and Challenges in Data Independent Acquisition

Proteomics has seen a considerable shift in methodology with the introduction and advancement of Data Independent Acquisition (DIA). Unlike Data Dependent Acquisition (DDA), which selects a limited number of peptides for analysis throughout the chromatographic separation based on peak intensity, DIA analyzes all peptides within a specified mass range, significantly enhancing reproducibility and depth of analysis. This change addresses the substantial limitations of missing data often encountered in DDA, promoting a more comprehensive analysis environment. The enthusiasm around DIA has spurred investments into DIA-specific technologies and workflows, aiming to deepen proteome coverage and improve quantitative accuracy.

This Research Topic aims to scrutinize and elevate the effectiveness of DIA in proteomics. It seeks to address the core challenges of creating reliable data analysis tools capable of managing the complex datasets DIA generates. These challenges include the different outcomes reported by existing analytical software, thus emphasizing the need for standardized methodologies in protein identification and quantification. The aim is set to critically examine the varying capabilities of DIA software tools and establish agreed-upon standards within the proteomics community to ensure the integrity and reproducibility of results.

To gather further insights on the advancements and standardizations in Data Independent Acquisition, we welcome articles addressing, but not limited to, the following themes:

- New technological advancements facilitating the transition from DDA to DIA.
- Data analysis strategies specific to DIA.
- Comparative analyses of different DIA software tools.
- Innovative mass spectrometry acquisition techniques in DIA.
- Reviews on the progression and impact of DIA methodologies.
- Current challenges in applying DIA to complex proteomics studies.
- Case studies demonstrating DIA’s efficacy over DDA.
- DIA application in diverse biological studies, such as microbial community research, and methods to enhance outcomes.