Lastly, it is important to note that QC samples can be used regardless of experimental design, but certain approaches require specific materials to provide desired quality metrics (discussed in detail in the QC Metrics section below). Historically, most QC metrics in proteomics, such as the numbers of peptide-spectrum matches or identified proteins, have been based on bottom-up, data-dependent acquisition (DDA) and label-free analysis. However, in data-independent acquisition (DIA) and isotopically labeled experiments, modified or additional quality metrics are useful. In the future, DIA performance metrics may borrow more from DDA-centric metrics.