A Multipathway Phosphopeptide Standard for Rapid Phosphoproteomics Assay Development.

Recent advances in methodology have made phosphopeptide analysis a tractable problem for many proteomics researchers. There are now a wide variety of robust and accessible enrichment strategies to generate phosphoproteomes while free or inexpensive software tools for quantitation and site localization have simplified phosphoproteome analysis workflow tremendously. As a research group under the Association for Biomolecular Resource Facilities umbrella, the Proteomics Standards Research Group has worked to develop a multipathway phosphopeptide standard based on a mixture of heavy-labeled phosphopeptides designed to enable researchers to rapidly develop assays. This mixture contains 131 mass spectrometry vetted phosphopeptides specifically chosen to cover as many known biologically interesting phosphosites as possible from seven different signaling networks: AMPK signaling, death and apoptosis signaling, ErbB signaling, insulin/insulin-like growth factor-1 signaling, mTOR signaling, PI3K/AKT signaling, and stress (p38/SAPK/JNK) signaling. Here, we describe a characterization of this mixture spiked into a HeLa tryptic digest stimulated with both epidermal growth factor and insulin-like growth factor-1 to activate the MAPK and PI3K/AKT/mTOR pathways. We further demonstrate a comparison of phosphoproteomic profiling of HeLa performed independently in five labs using this phosphopeptide mixture with data-independent acquisition. Despite different experimental and instrumentation processes, we found that labs could produce reproducible, harmonized datasets by reporting measurements as ratios to the standard, while intensity measurements showed lower consistency between labs even after normalization. Our results suggest that widely available, biologically relevant phosphopeptide standards can act as a quantitative "yardstick" across laboratories and sample preparations enabling experimental designs larger than a single laboratory can perform. Raw data files are publicly available in the MassIVE dataset MSV000090564.

Computational approaches to identify sites of phosphorylation.

Due to their oftentimes ambiguous nature, phosphopeptide positional isomers can present challenges in bottom-up mass spectrometry-based workflows as search engine scores alone are often not enough to confidently distinguish them. Additional scoring algorithms can remedy this by providing confidence metrics in addition to these search results, reducing ambiguity. Here we describe challenges to interpreting phosphoproteomics data and review several different approaches to determine sites of phosphorylation for both data-dependent and data-independent acquisition-based workflows. Finally, we discuss open questions regarding neutral losses, gas-phase rearrangement, and false localization rate estimation experienced by both types of acquisition workflows and best practices for managing ambiguity in phosphosite determination.

Data acquisition approaches for single cell proteomics.

Single-cell proteomics (SCP) aims to characterize the proteome of individual cells, providing insights into complex biological systems. It reveals subtle differences in distinct cellular populations that bulk proteome analysis may overlook, which is essential for understanding disease mechanisms and developing targeted therapies. Mass spectrometry (MS) methods in SCP allow the identification and quantification of thousands of proteins from individual cells. Two major challenges in SCP are the limited material in single-cell samples necessitating highly sensitive analytical techniques and the efficient processing of samples, as each biological sample requires thousands of single cell measurements. This review discusses MS advancements to mitigate these challenges using data-dependent acquisition (DDA) and data-independent acquisition (DIA). Additionally, we examine the use of short liquid chromatography gradients and sample multiplexing methods that increase the sample throughput and scalability of SCP experiments. We believe these methods will pave the way for improving our understanding of cellular heterogeneity and its implications for systems biology.

Gas-Phase Fractionation Data-Independent Acquisition Analysis of 3D Cocultured Spheroid Tumor Model Reveals Altered Translational Processes and Signaling Using Proteomics.

Colorectal cancer (CRC) contains considerable heterogeneity; therefore, models of the disease must also reflect the multifarious components. Compared to traditional 2D models, 3D cellular models, such as tumor spheroids, have the utility to determine the drug efficacy of potential therapeutics. Monoculture spheroids are well-known to recapitulate gene expression, cell signaling, and pathophysiological gradients of avascularized tumors. However, they fail to mimic the stromal cell influence present in CRC, which is known to perturb drug efficacy and is associated with metastatic, late-stage colorectal cancer. This study seeks to develop a cocultured spheroid model using carcinoma and noncancerous fibroblast cells. We characterized the proteomic profile of cocultured spheroids in comparison to monocultured spheroids using data-independent acquisition with gas-phase fractionation. Specifically, we determined that proteomic differences related to translation and mTOR signaling are significantly increased in cocultured spheroids compared to monocultured spheroids. Proteins related to fibroblast function, such as exocytosis of coated vesicles and secretion of growth factors, were significantly differentially expressed in the cocultured spheroids. Finally, we compared the proteomic profiles of both the monocultured and cocultured spheroids against a publicly available data set derived from solid CRC tumors. We found that the proteome of the cocultured spheroids more closely resembles that of the patient samples, indicating their potential as tumor mimics.

A differential proteomics study of cerebrospinal fluid from individuals with Niemann-Pick disease, Type C1.

Niemann-Pick, type C1 (NPC1) is a fatal, neurodegenerative disease, which belongs to the family of lysosomal diseases. In NPC1, endo/lysosomal accumulation of unesterified cholesterol and sphingolipids arise from improper intracellular trafficking resulting in multi-organ dysfunction. With the proximity between the brain and cerebrospinal fluid (CSF), performing differential proteomics provides a means to shed light to changes occurring in the brain. In this study, CSF samples obtained from NPC1 individuals and unaffected controls were used for protein biomarker identification. A subset of these individuals with NPC1 are being treated with miglustat, a glycosphingolipid synthesis inhibitor. Of the 300 identified proteins, 71 proteins were altered in individuals with NPC1 compared to controls including cathepsin D, and members of the complement family. Included are a report of 10 potential markers for monitoring therapeutic treatment. We observed that pro-neuropeptide Y (NPY) was significantly increased in NPC1 individuals relative to healthy controls; however, individuals treated with miglustat displayed levels comparable to healthy controls. In further investigation, NPY levels in a NPC1 mouse model corroborated our findings. We posit that NPY could be a potential therapeutic target for NPC1 due to its multiple roles in the central nervous system such as attenuating neuroinflammation and reducing excitotoxicity.

Scribe: Next Generation Library Searching for DDA Experiments.

Spectrum library searching is a powerful alternative to database searching for data dependent acquisition experiments, but has been historically limited to identifying previously observed peptides in libraries. Here we present Scribe, a new library search engine designed to leverage deep learning fragmentation prediction software such as Prosit. Rather than relying on highly curated DDA libraries, this approach predicts fragmentation and retention times for every peptide in a FASTA database. Scribe embeds Percolator for false discovery rate correction and an interference tolerant, label-free quantification integrator for an end-to-end proteomics workflow. By leveraging expected relative fragmentation and retention time values, we find that library searching with Scribe can outperform traditional database searching tools both in terms of sensitivity and quantitative precision. Scribe and its graphical interface are easy to use, freely accessible, and fully open source.

nf-encyclopedia: A Cloud-Ready Pipeline for Chromatogram Library Data-Independent Acquisition Proteomics Workflows.

Data-independent acquisition (DIA) mass spectrometry methods provide systematic and comprehensive quantification of the proteome; yet, relatively few open-source tools are available to analyze DIA proteomics experiments. Fewer still are tools that can leverage gas phase fractionated (GPF) chromatogram libraries to enhance the detection and quantification of peptides in these experiments. Here, we present nf-encyclopedia, an open-source NextFlow pipeline that connects three open-source tools, MSConvert, EncyclopeDIA, and MSstats, to analyze DIA proteomics experiments with or without chromatogram libraries. We demonstrate that nf-encyclopedia is reproducible when run on either a cloud platform or a local workstation and provides robust peptide and protein quantification. Additionally, we found that MSstats enhances protein-level quantitative performance over EncyclopeDIA alone. Finally, we benchmarked the ability of nf-encyclopedia to scale to large experiments in the cloud by leveraging the parallelization of compute resources. The nf-encyclopedia pipeline is available under a permissive Apache 2.0 license; run it on your desktop, cluster, or in the cloud: https://github.com/TalusBio/nf-encyclopedia.

Evaluating the Performance of the Astral Mass Analyzer for Quantitative Proteomics Using Data-Independent Acquisition.

We evaluate the quantitative performance of the newly released Asymmetric Track Lossless (Astral) analyzer. Using data-independent acquisition, the Thermo Scientific Orbitrap Astral mass spectrometer quantifies 5 times more peptides per unit time than state-of-the-art Thermo Scientific Orbitrap mass spectrometers, which have long been the gold standard for high-resolution quantitative proteomics. Our results demonstrate that the Orbitrap Astral mass spectrometer can produce high-quality quantitative measurements across a wide dynamic range. We also use a newly developed extracellular vesicle enrichment protocol to reach new depths of coverage in the plasma proteome, quantifying over 5000 plasma proteins in a 60 min gradient with the Orbitrap Astral mass spectrometer.

Optimizing Linear Ion-Trap Data-Independent Acquisition toward Single-Cell Proteomics.

A linear ion trap (LIT) is an affordable, robust mass spectrometer that provides fast scanning speed and high sensitivity, where its primary disadvantage is inferior mass accuracy compared to more commonly used time-of-flight or orbitrap (OT) mass analyzers. Previous efforts to utilize the LIT for low-input proteomics analysis still rely on either built-in OTs for collecting precursor data or OT-based library generation. Here, we demonstrate the potential versatility of the LIT for low-input proteomics as a stand-alone mass analyzer for all mass spectrometry (MS) measurements, including library generation. To test this approach, we first optimized LIT data acquisition methods and performed library-free searches with and without entrapment peptides to evaluate both the detection and quantification accuracy. We then generated matrix-matched calibration curves to estimate the lower limit of quantification using only 10 ng of starting material. While LIT-MS1 measurements provided poor quantitative accuracy, LIT-MS2 measurements were quantitatively accurate down to 0.5 ng on the column. Finally, we optimized a suitable strategy for spectral library generation from low-input material, which we used to analyze single-cell samples by LIT-DIA using LIT-based libraries generated from as few as 40 cells.

High Sensitivity Limited Material Proteomics Empowered by Data-Independent Acquisition on Linear Ion Traps.

In recent years, the concept of cell heterogeneity in biology has gained increasing attention, concomitant with a push toward technologies capable of resolving such biological complexity at the molecular level. For single-cell proteomics using Mass Spectrometry (scMS) and low-input proteomics experiments, the sensitivity of an orbitrap mass analyzer can sometimes be limiting. Therefore, low-input proteomics and scMS could benefit from linear ion traps, which provide faster scanning speeds and higher sensitivity than an orbitrap mass analyzer, however at the cost of resolution. We optimized an acquisition method that combines the orbitrap and linear ion trap, as implemented on a tribrid instrument, while taking advantage of the high-field asymmetric waveform ion mobility spectrometry (FAIMS) pro interface, with a prime focus on low-input applications. First, we compared the performance of orbitrap- versus linear ion trap mass analyzers. Subsequently, we optimized critical method parameters for low-input measurement by data-independent acquisition on the linear ion trap mass analyzer. We conclude that linear ion traps mass analyzers combined with FAIMS and Whisper flow chromatography are well-tailored for low-input proteomics experiments, and can simultaneously increase the throughput and sensitivity of large-scale proteomics experiments where limited material is available, such as clinical samples and cellular subpopulations.

Data-Independent Acquisition Protease-Multiplexing Enables Increased Proteome Sequence Coverage Across Multiple Fragmentation Modes.

The use of multiple proteases has been shown to increase protein sequence coverage in proteomics experiments; however, due to the additional analysis time required, it has not been widely adopted in routine data-dependent acquisition (DDA) proteomic workflows. Alternatively, data-independent acquisition (DIA) has the potential to analyze multiplexed samples from different protease digests, but has been primarily optimized for fragmenting tryptic peptides. Here we evaluate a DIA multiplexing approach that combines three proteolytic digests (Trypsin, AspN, and GluC) into a single sample. We first optimize data acquisition conditions for each protease individually with both the canonical DIA fragmentation mode (beam type CID), as well as resonance excitation CID, to determine optimal consensus conditions across proteases. Next, we demonstrate that application of these conditions to a protease-multiplexed sample of human peptides results in similar protein identifications and quantitative performance as compared to trypsin alone, but enables up to a 63% increase in peptide detections, and a 45% increase in nonredundant amino acid detections. Nontryptic peptides enabled noncanonical protein isoform determination and resulted in 100% sequence coverage for numerous proteins, suggesting the utility of this approach in applications where sequence coverage is critical, such as protein isoform analysis.

Thesaurus: quantifying phosphopeptide positional isomers.

Proteins can be phosphorylated at neighboring sites resulting in different functional states, and studying the regulation of these sites has been challenging. Here we present Thesaurus, a search engine that detects and quantifies phosphopeptide positional isomers from parallel reaction monitoring and data-independent acquisition mass spectrometry experiments. We apply Thesaurus to analyze phosphorylation events in the PI3K/AKT signaling pathway and show neighboring sites with distinct regulation.

Acquiring and Analyzing Data Independent Acquisition Proteomics Experiments without Spectrum Libraries.

Data independent acquisition (DIA) is an attractive alternative to standard shotgun proteomics methods for quantitative experiments. However, most DIA methods require collecting exhaustive, sample-specific spectrum libraries with data dependent acquisition (DDA) to detect and quantify peptides. In addition to working with non-human samples, studies of splice junctions, sequence variants, or simply working with small sample yields can make developing DDA-based spectrum libraries impractical. Here we illustrate how to acquire, queue, and validate DIA data without spectrum libraries, and provide a workflow to efficiently generate DIA-only chromatogram libraries using gas-phase fractionation (GPF). We present best-practice methods for collecting DIA data using Orbitrap-based instruments and develop an understanding for why DIA using an Orbitrap mass spectrometer should be approached differently than when using time-of-flight instruments. Finally, we discuss several methods for analyzing DIA data without libraries.

Assessing Protein Sequence Database Suitability Using De Novo Sequencing.

The analysis of samples from unsequenced and/or understudied species as well as samples where the proteome is derived from multiple organisms poses two key questions. The first is whether the proteomic data obtained from an unusual sample type even contains peptide tandem mass spectra. The second question is whether an appropriate protein sequence database is available for proteomic searches. We describe the use of automated de novo sequencing for evaluating both the quality of a collection of tandem mass spectra and the suitability of a given protein sequence database for searching that data. Applications of this method include the proteome analysis of closely related species, metaproteomics, and proteomics of extinct organisms.

CIDer: A Statistical Framework for Interpreting Differences in CID and HCD Fragmentation.

Library searching is a powerful technique for detecting peptides using either data independent or data dependent acquisition. While both large-scale spectrum library curators and deep learning prediction approaches have focused on beam-type CID fragmentation (HCD), resonance CID fragmentation remains a popular technique. Here we demonstrate an approach to model the differences between HCD and CID spectra, and present a software tool, CIDer, for converting libraries between the two fragmentation methods. We demonstrate that just using a combination of simple linear models and basic principles of peptide fragmentation, we can explain up to 43% of the variation between ions fragmented by HCD and CID across an array of collision energy settings. We further show that in some circumstances, searching converted CID libraries can detect more peptides than searching existing CID libraries or libraries of machine learning predictions from FASTA databases. These results suggest that leveraging information in existing libraries by converting between HCD and CID libraries may be an effective interim solution while large-scale CID libraries are being developed.

The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics.

Skyline is a freely available, open-source Windows client application for accelerating targeted proteomics experimentation, with an emphasis on the proteomics and mass spectrometry community as users and as contributors. This review covers the informatics encompassed by the Skyline ecosystem, from computationally assisted targeted mass spectrometry method development, to raw acquisition file data processing, and quantitative analysis and results sharing.

Matrix-Matched Calibration Curves for Assessing Analytical Figures of Merit in Quantitative Proteomics.

Mass spectrometry is a powerful tool for quantifying protein abundance in complex samples. Advances in sample preparation and the development of data-independent acquisition (DIA) mass spectrometry approaches have increased the number of peptides and proteins measured per sample. Here, we present a series of experiments demonstrating how to assess whether a peptide measurement is quantitative by mass spectrometry. Our results demonstrate that increasing the number of detected peptides in a proteomics experiment does not necessarily result in increased numbers of peptides that can be measured quantitatively.

Lysine and Arginine Protein Post-translational Modifications by Enhanced DIA Libraries: Quantification in Murine Liver Disease.

Proteoforms containing post-translational modifications (PTMs) represent a degree of functional diversity only harnessed through analytically precise simultaneous quantification of multiple PTMs. Here we present a method to accurately differentiate an unmodified peptide from its PTM-containing counterpart through data-independent acquisition-mass spectrometry, leveraging small precursor mass windows to physically separate modified peptidoforms from each other during MS2 acquisition. We utilize a lysine and arginine PTM-enriched peptide assay library and site localization algorithm to simultaneously localize and quantify seven PTMs including mono-, di-, and trimethylation, acetylation, and succinylation in addition to total protein quantification in a single MS run without the need to enrich experimental samples. To evaluate biological relevance, this method was applied to liver lysate from differentially methylated nonalcoholic steatohepatitis (NASH) mouse models. We report that altered methylation and acetylation together with total protein changes drive the novel hypothesis of a regulatory function of PTMs in protein synthesis and mRNA stability in NASH.

PECAN: library-free peptide detection for data-independent acquisition tandem mass spectrometry data.

Data-independent acquisition (DIA) is an emerging mass spectrometry (MS)-based technique for unbiased and reproducible measurement of protein mixtures. DIA tandem mass spectrometry spectra are often highly multiplexed, containing product ions from multiple cofragmenting precursors. Detecting peptides directly from DIA data is therefore challenging; most DIA data analyses require spectral libraries. Here we present PECAN (http://pecan.maccosslab.org), a library-free, peptide-centric tool that robustly and accurately detects peptides directly from DIA data. PECAN reports evidence of detection based on product ion scoring, which enables detection of low-abundance analytes with poor precursor ion signal. We demonstrate the chromatographic peak picking accuracy and peptide detection capability of PECAN, and we further validate its detection with data-dependent acquisition and targeted analyses. Lastly, we used PECAN to build a plasma proteome library from DIA data and to query known sequence variants.