Fig. 2: Benchmarking packed vs µPAC columns. | Nature Communications

Fig. 2: Benchmarking packed vs µPAC columns.

From: Micropillar arrays, wide window acquisition and AI-based data analysis improve comprehensiveness in multiple proteomic applications

Fig. 2

12.5 ng of a K562 QC mix were injected each using a trap-and-elute setting. Peptides were separated over 30 min using a linear gradient over 30 min from 1–35% buffer B (80% acetonitrile, 0.1% formic acid). Data acquisition using a standard DDA method with an isolation window of m/z 1 and data analysis using CHIMERYS at 1% FDR on peptide and protein level, n = 16, 10, 3 and 8 technical replicates for the PepMap, nanoEase, Aurora and μPAC runs, respectively. Bars indicate means, while error bars indicate standard deviations. A Protein and (B) peptide identifications are visualized. Statistical significance between means of different groups was assessed by two-tailed, unpaired Student t tests for all comparisons but those involving nanoEase and Aurora protein IDs due to differences in variance, for which two-tailed, unpaired Welch’s tests were performed. ns not significant, ***p ≤ 0.001, ****p ≤ 0.0001. Exact p values for (A) µPAC Neo vs PepMap <0.0001, µPAC Neo vs nanoEase 0.0008, nanoEase vs PepMap 0.9715, µPAC Neo vs Aurora 0.0002, and (B) µPAC Neo vs PepMap <0.0001, µPAC Neo vs nanoEase <0.0001, nanoEase vs PepMap 0.3443, µPAC Neo vs Aurora <0.0001. Source data are provided as a Source Data file.

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