DPHL: A pan-human protein mass spectrometry library for robust biomarker discovery

Abstract

To answer the increasing need for detecting and validating protein biomarkers in clinical specimens, proteomic techniques are required that support the fast, reproducible and quantitative analysis of large clinical sample cohorts. Targeted mass spectrometry techniques, specifically SRM, PRM and the massively parallel SWATH/DIA technique have emerged as a powerful method for biomarker research. For optimal performance, they require prior knowledge about the fragment ion spectra of targeted peptides. In this report, we describe a mass spectrometric (MS) pipeline and spectral resource to support data-independent acquisition (DIA) and parallel reaction monitoring (PRM) based biomarker studies. To build the spectral resource we integrated common open-source MS computational tools to assemble an open source computational workflow based on Docker. It was then applied to generate a comprehensive DIA pan-human library (DPHL) from 1,096 data dependent acquisition (DDA) MS raw files, and it comprises 242,476 unique peptide sequences from 14,782 protein groups and 10,943 SwissProt-annotated proteins expressed in 16 types of cancer samples. In particular, tissue specimens from patients with prostate cancer, cervical cancer, colorectal cancer, hepatocellular carcinoma, gastric cancer, lung adenocarcinoma, squamous cell lung carcinoma, diseased thyroid, glioblastoma multiforme, sarcoma and diffuse large B-cell lymphoma (DLBCL), as well as plasma samples from a range of hematologic malignancies were collected from multiple clinics in China, the Netherlands and Singapore and included in the resource. This extensive spectral resource was then applied to a prostate cancer cohort of 17 patients, consisting of 8 patients with prostate cancer (PCa) and 9 with benign prostate hyperplasia (BPH), respectively. Data analysis of DIA data from these samples identified differential expressions of FASN, TPP1 and SPON2 in prostate tumors. Thereafter, PRM validation was applied to a larger PCa cohort of 57 patients and the differential expressions of FASN, TPP1 and SPON2 in prostate tumors were validated. As a second application, the DPHL spectral resource was applied to a patient cohort consisting of samples from 19 DLBCL patients and 18 healthy individuals. Differential expressions of CRP, CD44 and SAA1 between DLBCL cases and healthy controls were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supported that DIA-PRM MS pipeline enables robust protein biomarker discoveries.

Accelerated Lysis and Proteolytic Digestion of Biopsy-Level Fresh-Frozen and FFPE Tissue Samples Using Pressure Cycling Technology

“…12 The DIA Pan-Human Library (DPHL) was used as the spectral library for PulseDIA target extraction analysis, with 14 786 protein groups and 396 245 transitions. 15 Briefly, the peptide length range was 7−30, the precursor m/z range was 400−1200, and the product ion m/z range was 100−1500. Peptide precursors and protein FDRs were controlled below 1%.…”

“…The retention time was then calibrated using Common Internal Retention Time Standards iRT (CiRT) peptides as described previously. 15 Peptide precursors were identified by OpenSWATH and pyProphet, with peptide and protein FDR lower than 1%. For each protein, the median MS2 intensity value of Top3 peptide precursor fragments, which were detected to belong to the protein, was used to represent the protein abundance.…”

A circulating extracellular vesicles‐based novel screening tool for colorectal cancer revealed by shotgun and data‐independent acquisition mass spectrometry

“…The cycle time was 1,870 ms at least and 2,260 ms at most (read from raw data of 20, 45, 60 and 90 min gradients). OpenSWATH (version 2.0.0) was performed using a human plasma and plasma EV protein spectral library containing 629 proteins, which is the plasma subset of our previous work of a pan‐human spectral library for DIA‐MS [15]. Pyprophet 0.24.1 was used to limit the threshold to 1% FDR, and we used the top three precursors for DIA quantification.…”

“…The expression of selected four proteins in crEVs from cohort 4 was verified by PRM, which is a target proteomic strategy. Common internal Retention Time standards (CiRT) peptides were used for retention time calibration [15]. Peptides were separated at 300 nL/min along with a 10 min 10–30% buffer B linear LC gradient (buffer A: 2% ACN, 0.1% formic acid; buffer B: 98% ACN, 0.1% formic acid, time between runs was 25 min) using an analytical column (75 µm × 150 mm, 1.9 µm 120 Å C18 particles).…”

Protein Classifier for Thyroid Nodules Learned from Rapidly Acquired Proteotypes

“…We constructed a spectral library containing 33,802 peptides from 5,190 protein groups using our previously established computational pipeline (Zhu, 2020). The number of proteins in this library is relatively smaller than that of other tissue types analyzed using a similar workflow (Guo et al, 2015;Shao et al, 2019;Zhu, 2020;Zhu et al, 2019) or other proteomic protocols (Jiang et al, 2019;Sinha et al, 2019;Zhang et al, 2016) as thyroid tissue contains a high abundance protein, thyroglobulin, which weakens the signal for the rest of the proteome (M et al, 2018). Using OpenSWATH (v2.0) and our thyroid library, we analyzed 1,781 DIA maps (1,725 FFPE cores and 56 technical replicates).…”