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Bottom-up proteomics is a mass spectrometry-based method to analyze the contents of complex protein samples. Pioneered in the 1990s, it consists of converting protein samples into peptide samples by enzymatic digestion, the separation of peptides by (typically) reverse phase liquid chromatography (LC), and the analysis of the eluting peptides by tandemmass spectrometry. This general approach, while tremendously successful and widely used, has faced from the beginning the fundamental challenge that the number of peptides generated by the digestion of a complex protein sample, like cell extracts or body fluids, is significantly larger than the number of peptides expected by the application of the tryptic digestion rule [1]. In fact, the number of peptides expected from a proteome is presently unknown. Ironically, while the genes and transcripts could be comprehensively sequenced and characterized, the exact number of protein types or their cellular copy number in any biomedical sample remains unknown. The challenge to address this fundamental issue has spawned a large number of strategies for mass spectrometric data acquisition and analysis. Two major bottom-up proteomics approaches have been developed. Data-dependent acquisition (DDA) essentially prioritizes peptide precursors based on their signal intensity in a precursor ion scan in themass spectrometer, and then subsequentially selects a number of precursors for fragmentation, generating MS2 spectra. This is a well-established MS method, which gains sample throughput when coupled with stable isotope-labeling of the peptides using, for example, TMTpro. However, since the number of peptide precursors is substantially larger than the number of fragment ion spectra a mass spectrometer can acquire, only a limited number of peptide precursors could be analyzed, leaving out a varying and unknown portion of the proteome uncharacterized in each DDA data acquisition. This undersampling issue becomesmore pronounced when the LC gradient is minimized to maximize sample throughput. Therefore, it is unlikely that DDA data acquisition, even with extensive sample fractionation and extremely long LC gradient, will overcome this fundamental undersampling issue. Another emerging and widely adopted approach for bottom-up proteomics data acquisition is data-independent acquisition (DIA). DIA bins the peptide precursors into predefined groups based on their m/z values, performs fragmentation for each group (also called “window”) of peptide precursors sequentially, and records the highly convoluted MS2 spectrum for the fragments and unfragmented precursors in each window [2]. This method essentially generates a comprehensive digital map of all the flyable and fragmentable peptide precursors of a proteome. Therefore, compared with DDA which is inherently limited by the undersampling issue, it is theoretically possible to identify every protein in a proteome from a digital proteome map generated by DIA. Various computational methods have been developed to analyze data acquired by DIA. They can be grouped conceptually into peptidecentric and spectrum-centric approaches, the terminology ofMacCoss and colleagues [3]. With the spectrum-centric approach, each tandem mass spectrum is interpreted by searching against a theoretical or experimental protein sequence database and a matched decoy database. This approach is usually used for DDA data. Principally, it can also be applied to the highly convoluted DIA data, but DIA data is most effectively interpreted with the peptide-centric approach, which basically asks the question: is a peptide of interest present in the data? Briefly, the peptide-centric approach first compiles the characteristics (including them/z of peptide precursors and fragments, retention time and the elution profiles, among others) of a peptide precursor of interest into a data table (eg. reference spectral library), and tries to find this pattern in theDIA data using statistical andmachine learning algorithms [4]. In principle, the combination of DIA data acquisition and peptide-centric data analysis strategy allows analysis of every protein which is analyzable in a proteome within the limits of the analytical techniques used. Since 2010, over 1000 publications have been published using DIA. This special issue features some of the latest advances in the field. Penny et al. reported a gas phase fractionation acquisition scheme called (ion mobility) IM (gas phase fractionation) GPF, for rapid diaPASEF library generation [5]. Most DIA analyses are performed in single injections even for complex samples. The elimination of extensive sample fractionation not only minimizes technical variability and required sample amount, but also substantially increases the sample throughput. In this issue, Bons et al. appliedDIA to study small amounts of extracellular matrix of lung cancer tissue specimens [6], whileWang et al. analyzed enriched glycoproteins in urine samples from prostate cancer patients [7]. Kverneland et al. developed a simple ultracentrifugation protocol for the enrichment of extracellular vesicles from plasma samples, enabling characterization of over 2500 plasma proteins with DIA runs of less than 1 h [8]. These three applications exemplify the superb sensitivity and comprehensiveness ofDIA-MS for analyzing a specific subproteome. Oliinyk et al. reported that only 1 hMS timeusing dia-PASEF characterizedover13,000phosphopeptides fromabout20ugproteindigests, while shortening the gradient by a factor of 4 led to similar coverage of the phosphoproteome [9]. The type of application which requires both high sensitivity and high throughput is currently only practical