DIA vs DDA: Choosing Your MS Acquisition Strategy

Data-Dependent Acquisition (DDA)

DDA selects the most abundant precursor ions from a survey scan (MS1) for fragmentation (MS2). This "top-N" approach is the traditional workhorse of discovery proteomics. It delivers deep coverage and is ideal for identifying post-translational modifications, but can suffer from stochastic precursor selection across runs, leading to missing values in large cohorts.

Best for: Deep proteome coverage, PTM characterisation, studies where completeness per run matters more than cross-sample consistency.

Data-Independent Acquisition (DIA)

DIA fragments all precursor ions within pre-defined isolation windows (e.g., 24 × 24 m/z windows covering 400 to 1000 m/z) in a systematic, unbiased manner. The result is a complete digital map of every detectable peptide in the sample. Computational demultiplexing extracts peptide identities and intensities from the complex multiplexed spectra.

Best for: Large clinical cohorts, biomarker discovery, longitudinal studies - where consistent quantification across all samples is critical.

Which one should you choose?

The answer depends on your biological question. For deep discovery in a small number of samples - DDA. For consistent quantification in large cohorts - DIA. Many projects benefit from both: DDA for spectral library generation and DIA for quantification.

We routinely run both acquisition modes and can help you decide the best strategy at the experimental design stage at no extra cost.