The paper introduces MaxLFQ, a novel method for label-free protein quantification that addresses the challenges of accurate and robust proteome-wide quantification. MaxLFQ includes two key components: "delayed normalization" and "maximal peptide ratio extraction." Delayed normalization allows for the comparison of sample fractions that have been handled differently, while maximal peptide ratio extraction maximizes the information from peptide signals across samples. The method is fully compatible with any peptide or protein separation prior to LC-MS analysis and can be applied to a wide range of biological questions. The authors demonstrate the effectiveness of MaxLFQ through benchmark datasets, showing accurate detection of mixing ratios, high dynamic range quantification, and compatibility with standard statistical analysis workflows. MaxLFQ is implemented in the MaxQuant software platform and has been validated in various biological projects, including large-scale experiments with over 500 samples. The method is particularly useful for analyzing complex proteomes and has been shown to outperform other label-free quantification methods in terms of accuracy and precision.The paper introduces MaxLFQ, a novel method for label-free protein quantification that addresses the challenges of accurate and robust proteome-wide quantification. MaxLFQ includes two key components: "delayed normalization" and "maximal peptide ratio extraction." Delayed normalization allows for the comparison of sample fractions that have been handled differently, while maximal peptide ratio extraction maximizes the information from peptide signals across samples. The method is fully compatible with any peptide or protein separation prior to LC-MS analysis and can be applied to a wide range of biological questions. The authors demonstrate the effectiveness of MaxLFQ through benchmark datasets, showing accurate detection of mixing ratios, high dynamic range quantification, and compatibility with standard statistical analysis workflows. MaxLFQ is implemented in the MaxQuant software platform and has been validated in various biological projects, including large-scale experiments with over 500 samples. The method is particularly useful for analyzing complex proteomes and has been shown to outperform other label-free quantification methods in terms of accuracy and precision.