Strategies to enable large-scale proteomics for reproducible research

Abstract

Reproducible research is the bedrock of experimental science. To enable the deployment of large-scale proteomics, we assess the reproducibility of mass spectrometry (MS) over time and across instruments and develop computational methods for improving quantitative accuracy. We perform 1560 data independent acquisition (DIA)-MS runs of eight samples containing known proportions of ovarian and prostate cancer tissue and yeast, or control HEK293T cells. Replicates are run on six mass spectrometers operating continuously with varying maintenance schedules over four months, interspersed with~5000 other runs. We utilise negative controls and replicates to remove unwanted variation and enhance biological signal, outperforming existing methods. We also design a method for reducing missing values. Integrating these computational modules into a pipeline (ProNorM), we mitigate variation among instruments over time and accurately predict tissue proportions. We demonstrate how to improve the quantitative analysis of large-scale DIA-MS data, providing a pathway toward clinical proteomics.

Multi-Omic Profiling of the Liver Across Diets and Age in a Diverse Mouse Population

“…Consequently, we must design studies keeping in mind that 99% of gene variants will vary by less than 2-fold across age. Fortunately, it is now possible to quantify the transcriptome and proteome across hundreds of samples with sufficient precision to detect small fold changes [58]. This improves our ability to detect smaller effect sizes, but more importantly opens new avenues in biostatistics.…”

The separation sciences, the front end to proteomics: An historical perspective

“…showing how data can be processed to enable substantial improvements in reproducibility using computational compensation (Poulos et al, 2020).…”

“…While HPLC has been a well‐proven mainstay of proteomics research, for routine clinical application it may also be the “bête noire.” There is little intra‐laboratory consistency in the choice of instrumentation or column type/manufacturer and things such as column “plumbing” and column maintenance can affect data. However, longitudinal studies over several months have also been carried out, showing how data can be processed to enable substantial improvements in reproducibility using computational compensation (Poulos et al, 2020).…”

DIAproteomics: A multi-functional data analysis pipeline for data-independent-acquisition proteomics and peptidomics

“…The analysis of six DIA-SWATH MS runs applying a library and pseudo iRTs generated from three DDA MS runs was carried out in approximately 2h and 10min using AWS. ( Figure 5) dia_raw_ le_conversion (4) dia_raw_ le_conversion (5) dia_raw_ le_conversion (6) dda_id_format_conversion (1) dda_id_format_conversion 3dda_id_format_conversion (2) dda_library_generation 1dda_library_generation (2) dda_library_generation 3assay_generation 1assay_generation (2) assay_generation (3) library_merging_and_alignment (1) decoy_generation (1) pseudo_irt_generation (1) dia_spectral_library_search (2) dia_spectral_library_search (1) dia_spectral_library_search (3) dia_spectral_library_search (4) dia_spectral_library_search (5) dia_spectral_library_search (6) chromatogram_indexing (1) chromatogram_indexing (2) chromatogram_indexing (3) chromatogram_indexing (4) chromatogram_indexing (5) chromatogram_indexing (6) dia_search_output_merging (1) global_false_discovery_rate_estimation (1) export_of_scoring_results (1) chromatogram_alignment (1) output_visualization (1) reformatting (1) statistical_post_processing (1) Created with Next ow --http://next ow.io and H.R. were involved in the study design.…”

“…Recently, data-independent acquisition (DIA) using sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) 1 has attracted much attention in the field of proteomics due to its ability to overcome shortcomings of the classical data-dependent (DDA) strategy. [2][3][4][5][6] Because of its outstanding performance in reproducibility and quantification, DIA is likely to become the state-of-the-art technology in clinical mass spectrometry (MS). 7 In addition, recent tailored applications of DIA have enabled new approaches for the chemoproteomic screening of drug targets.…”