Reproducible research is the bedrock of experimental science. To enable the deployment of large-scale proteomics, we assess the reproducibility of mass spectrometry (MS) over time and across instruments and develop computational methods for improving quantitative accuracy. We perform 1560 data independent acquisition (DIA)-MS runs of eight samples containing known proportions of ovarian and prostate cancer tissue and yeast, or control HEK293T cells. Replicates are run on six mass spectrometers operating continuously with varying maintenance schedules over four months, interspersed with ~5000 other runs. We utilise negative controls and replicates to remove unwanted variation and enhance biological signal, outperforming existing methods. We also design a method for reducing missing values. Integrating these computational modules into a pipeline (ProNorM), we mitigate variation among instruments over time and accurately predict tissue proportions. We demonstrate how to improve the quantitative analysis of large-scale DIA-MS data, providing a pathway toward clinical proteomics.

Two‐dimensional gel electrophoresis (2‐DE) is currently the method of choice for separating complex mixtures of proteins for visual comparison in proteome analysis. This technology, however, is biased against certain classes of proteins including low abundance and hydrophobic proteins. Proteins with extremely alkaline isoelectric points (pI) are often very poorly represented using 2‐DE technology, even when complex mixtures are separated using commercially available pH 6–11 or pH 7–10 immobilized pH gradients. The genome of the human gut pathogen, Helicobacter pylori, is dominated by genes encoding basic proteins, and is therefore a useful model for examining methodology suitable for separating such proteins. H. pylori proteins were separated on pH 6–11 and novel pH 9–12 immobilized pH gradients and 65 protein spots were subjected to matrix‐assisted laser desorption/ionization‐time of flight mass spectrometry, leading to the identification of 49 unique proteins. No proteins were characterized with a theoretical pI of greater than 10.23. A second approach to examine extremely alkaline proteins (pI > 9.0) utilized a prefractionation isoelectric focusing. Proteins were separated into two fractions using Gradiflow technology, and the extremely basic fraction subjected to both sodium dodecyl sulphate‐polyacrylamide gel electrophoresis and liquid chromatography (LC) – tandem mass spectrometry post‐tryptic digest, allowing the identification of 17 and 13 proteins, respectively. Gradiflow separations were highly specific for proteins with pI > 9.0, however, a single LC separation only allowed the identification of peptides from highly abundant proteins. These methods and those encompassing multiple LC ‘dimensions’ may be a useful complement to 2‐DE for ‘near‐to‐total’ proteome coverage in the alkaline pH range.