Quantitative proteomics employing mass spectrometry is an indispensable tool in life science research. Targeted proteomics has emerged as a powerful approach for reproducible quantification but is limited in the number of proteins quantified. SWATH-mass spectrometry consists of data-independent acquisition and a targeted data analysis strategy that aims to maintain the favorable quantitative characteristics (accuracy, sensitivity, and selectivity) of targeted proteomics at large scale. While previous SWATH-mass spectrometry studies have shown high intra-lab reproducibility, this has not been evaluated between labs. In this multi-laboratory evaluation study including 11 sites worldwide, we demonstrate that using SWATH-mass spectrometry data acquisition we can consistently detect and reproducibly quantify >4000 proteins from HEK293 cells. Using synthetic peptide dilution series, we show that the sensitivity, dynamic range and reproducibility established with SWATH-mass spectrometry are uniformly achieved. This study demonstrates that the acquisition of reproducible quantitative proteomics data by multiple labs is achievable, and broadly serves to increase confidence in SWATH-mass spectrometry data acquisition as a reproducible method for large-scale protein quantification.

Outer membrane proteins (OMPs) of Gram‐negative bacteria are key molecules that interface the cell with the environment. Traditional biochemical and genetic approaches have yielded a wealth of knowledge relating to the function of OMPs. Nonetheless, with the completion of the Escherichia coli genome sequencing project there is the opportunity to further expand our understanding of the organization, expression and function of the OMPs in this Gram‐negative bacterium. In this report we describe a proteomic approach which provides a platform for parallel analysis of OMPs. We propose a rapid method for isolation of bacterial OMPs using carbonate incubation, purification and protein array by two‐dimensional electrophoresis, followed by protein identification using mass spectrometry. Applying this method to examine E. coli K‐12 cells grown in minimal media we identified 21 out of 26 (80%) of the predicted integral OMPs that are annotated in SWISS‐PROT release 37 and predicted to separate within the range of pH 4–7 and molecular mass 10–80 kDa. Five outer membrane lipoproteins were also identified and only minor contamination by nonmembrane proteins was observed. Importantly, this research readily demonstrates that integral OMPs, commonly missing from 2D gel maps, are amenable to separation by two‐dimensional electrophoresis. Two of the identified OMPs (YbiL, YeaF) were previously known only from their ORFs, and their identification confirms the cognate genes are transcribed and translated. Furthermore, we show that like the E. coli iron receptors FhuE and FhuA, the expression of YbiL is markedly increased by iron limitation, suggesting a putative role for this protein in iron transport. In an additional demonstration we show the value of parallel protein analysis to document changes in E. coli OMP expression as influenced by culture temperature.

Introduction: Plasma amyloid beta (Aβ)1‐42/Aβ1‐40 ratio, phosphorylated‐tau181 (p‐tau181), glial fibrillary acidic protein (GFAP), and neurofilament light (NfL) are putative blood biomarkers for Alzheimer's disease (AD). However, head‐to‐head cross‐sectional and longitudinal comparisons of the aforementioned biomarkers across the AD continuum are lacking.Methods: Plasma Aβ1‐42, Aβ1‐40, p‐tau181, GFAP, and NfL were measured utilizing the Single Molecule Array (Simoa) platform and compared cross‐sectionally across the AD continuum, wherein Aβ‐PET (positron emission tomography)–negative cognitively unimpaired (CU Aβ−, n = 81) and mild cognitive impairment (MCI Aβ−, n = 26) participants were compared with Aβ‐PET–positive participants across the AD continuum (CU Aβ+, n = 39; MCI Aβ+, n = 33; AD Aβ+, n = 46) from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) cohort. Longitudinal plasma biomarker changes were also assessed in MCI (n = 27) and AD (n = 29) participants compared with CU (n = 120) participants. In addition, associations between baseline plasma biomarker levels and prospective cognitive decline and Aβ‐PET load were assessed over a 7 to 10‐year duration.Results: Lower plasma Aβ1‐42/Aβ1‐40 ratio and elevated p‐tau181 and GFAP were observed in CU Aβ+, MCI Aβ+, and AD Aβ+, whereas elevated plasma NfL was observed in MCI Aβ+ and AD Aβ+, compared with CU Aβ− and MCI Aβ−. Among the aforementioned plasma biomarkers, for models with and without AD risk factors (age, sex, and apolipoprotein E (APOE) ε4 carrier status), p‐tau181 performed equivalent to or better than other biomarkers in predicting a brain Aβ−/+ status across the AD continuum. However, for models with and without the AD risk factors, a biomarker panel of Aβ1‐42/Aβ1‐40, p‐tau181, and GFAP performed equivalent to or better than any of the biomarkers alone in predicting brain Aβ−/+ status across the AD continuum. Longitudinally, plasma Aβ1‐42/Aβ1‐40, p‐tau181, and GFAP were altered in MCI compared with CU, and plasma GFAP and NfL were altered in AD compared with CU. In addition, lower plasma Aβ1‐42/Aβ1‐40 and higher p‐tau181, GFAP, and NfL were associated with prospective cognitive decline and lower plasma Aβ1‐42/Aβ1‐40, and higher p‐tau181 and GFAP were associated with increased Aβ‐PET load prospectively.Discussion: These findings suggest that plasma biomarkers are altered cross‐sectionally and longitudinally, along the AD continuum, and are prospectively associated with cognitive decline and brain Aβ‐PET load. In addition, although p‐tau181 performed equivalent to or better than other biomarkers in predicting an Aβ−/+ status across the AD continuum, a panel of biomarkers may have superior Aβ−/+ status predictive capability across the AD continuum.HIGHLIGHTS: Area under the curve (AUC) of p‐tau181 ≥ AUC of Aβ42/40, GFAP, NfL in predicting PET Aβ−/+ status (Aβ−/+). AUC of Aβ42/40+p‐tau181+GFAP panel ≥ AUC of Aβ42/40/p‐tau181/GFAP/NfL for Aβ−/+. Longitudinally, Aβ42/40, p‐tau181, and GFAP were altered in MCI versus CU. Longitudinally, GFAP and NfL were altered in AD versus CU. Aβ42/40, p‐tau181, GFAP, and NfL are associated with prospective cognitive decline. Aβ42/40, p‐tau181, and GFAP are associated with increased PET Aβ load prospectively.