Updated project metadata. A comprehensive proteome map is essential to elucidate molecular pathways and protein functions. Although great improvements in sample preparation, instrumentation and data analysis already yield-ed impressive results, current studies suffer from a limited proteomic depth and dynamic range there-fore lacking low abundant or highly hydrophobic proteins. Here, we combine and benchmark advanced micro pillar array columns (µPAC) operated at nanoflow with Wide Window Acquisition (WWA) and the AI-based CHIMERYS search engine for data analysis to maximize chromatographic separation power, sensitivity and proteome coverage. Our data shows that µPAC columns clearly outperform classical packed bed columns boosting peptide IDs by up to 50% and protein IDs by up to 24%. Using the above-mentioned analysis platform, more than 10,000 proteins could be identified from a single 2 h gradient shotgun analysis for a triple proteome mix of human, yeast and E. coli digests. At high sample loads of 400 ng all three uPAC types yielded comparable number of protein identifications, whereas the 50cm neo column performed best when lower inputs of less than 200 ng were injected. Due to its unique architecture, the 5.5 cm brick column facilitates a highly meandering flow along the brick-shaped micropillars leading to an effective flow path length similar to the 50 cm neo column, while at the same time allowing high flow rates up to 2.5 µL/min. This enables to reduce overhead time by applying high flow rates during sample loading and column equilibration improving sample throughput to ~100 samples per day, while maintaining high protein ID numbers. Particularly for the single cell field, for which throughput is currently one of the most limiting factors, this column could present a valuable asset.