Linear regression analysis between 7 different concentrations of 1,500 proteins from kidney tissue lysates. All fold differences fall in the ±28% range, and the minimal R² is 0.9376. The largest fold difference (64-fold) was accurately determined as 6.30 (log2 transformed), with an error of only 5%.

Linear regression analysis between 8 different concentrations of 6 standard proteins (0.3125–1,000 fmol, 0.000017–0.016239 µg) spiked in 10 µg of kidney tissue lysates. All 6 proteins have an excellent correlation in the range, and the minimal R² is 0.9763.

Percentage of each distribution of peptides from (A) kidney tissue lysates, (B) HT29-MTX cell lysates, (C) depleted human serum, (D) human serum albumin-bound proteins, and (E) 10 µg of kidney tissue lysates spiked with 6 standard proteins (0.3125–1,000 fmol, 0.000017–0.016239 µg). The percentage of each distribution is sample-dependent: 77.4–92.1%, 4.2–12.8%, and 2.2–9.8% of peptides have distributions A and B; distributions C, D, and E; and distribution F, respectively.

Typical MS/MS spectrum distributions of six selected peptides across multiple injections. The x-axis is indicated according to run order. There is no difference or trend when the x-axis is indicated according to different samples (Supplementary Figure 1). (A) Multiple spectra are limited to a 3 min retention time range. (B) Most spectra are limited to a 3 min range; a few are scattered in a broad range. (C) Some spectra are concentrated in a narrow window of longer retention time, while other spectra are scattered in a wide range with a shorter retention time. (D) Some spectra are concentrated in a narrow window of shorter retention time, while other spectra are scattered in a wide range with a longer retention time. (E) Some spectra are concentrated in a narrow window of retention time, while other spectra are scattered in a wide range with a shorter and longer retention times. (F) All spectra are scattered in a wide range of retention time.

Multiple filters used in the approach for protein quantification. (A, B) Distribution of peptide identification in 35 injections of rat kidney sample lysates. In total, 7,361 peptides were identified. However, 1,883 (25.6%) of the peptides were identified in only 1 injection, and only 61 (0.8%) were identified in all 35 injections. Peptide Frequency is used as the first filter in this approach. Only peptides with identification frequencies higher than the cutoff are considered for the next step in the filtration process. (C–E) Peak areas of three standard peptides from 10 replicates (Angiotensin III, 449.49; Fibrinopeptide B, 786.24; and Angiotensin I,433.23). Data clearly show that some peptides cannot be used to calculate protein quantity. (F, G) Correlation of peptides from two proteins. The arrow indicates peptides significantly different from other peptides, their correlation coefficients are much lower than others, and they are excluded from subsequent protein quantification. Correlation represents the fourth and final filter in this approach.

Typical workflow for label-free quantification using a novel alignment method and multiple filters for exclusion of unqualified peptides, illustrating a hypothetical experiment consisting of 2 groups, 2 samples in each group, and 2 injections in each sample. In the 8 injections, 8,000 peptides are identified. Some peptides exist in only one or two injections and thus are excluded (Filter 1: Peptide Frequency). Filtered by peptide frequency, 7,000 peptides are analyzed individually across all injections to obtain their peak retention time using a novel retention time determination method. Some peptides are excluded, because they lack a stable peak (Filter 2: Peptide Retention Time). Using the computed retention time, the peak area of 6,500 peptides is extracted. The coefficient of variation (CV) of each peptide between 2 injections in each sample is calculated, and peptides with a high CV are removed (Filter 3: Peptide CV). After Filter 3, 6,000 peptides remain. When multiple peptides are used to calculate a protein’s abundance, the correlation among them is calculated. A few peptides with poor correlation coefficients are excluded (Filter 4: Peptide Correlation). Using Filter 4, 5,500 peptidesare finally used to calculate protein abundance.

Performance of the approach in protein quantification. (A) The calibration curve of two standard peptides from 7 concentrations in 5 replicates each (Angiotensin III, 449.49; Fibrinopeptide B, 786.24). Extraordinary quantitative linearity was observed with R² of 0.9964 and 0.9980 across 7 concentrations with 2 orders of magnitude. (B) The repeatability analysis of 1,011 proteins in the current kidney tissue lysates indicates 96.2% of proteins had CVs ≤30%. (C) The linearity analysis of 1,500 proteins in the kidney tissue lysates indicates 97.2% of proteins had an R² of ≥ 0.9500. (D–F) Label-free quantification of identical concentrations of lysozyme with CV = 8.6%, 8.2%, and 3.2% for eight injections (D), four samples (E), and two groups (F), respectively. (G–I) The quantification of prolactin using this approach versus Western blot. Prolactin was 17.1-fold lower in dwarfed mice (KI/KI) compared to wild-type (+/+) using the label-free quantification strategy (G). The quantification of GAPDH reported as a control is 1.14-fold lower (H). Western blot of prolactin with GAPDH as a loading control (I) is reprinted with permission of the authors and shows very low or undetectable prolactin in the pituitaries of adult dwarfed mice compared with wild type controls³⁴. Western blot analysis of prolactin is consistent with the fold change detected by the label-free quantification strategy.