Advances in Higher Order Multiplexing Techniques in Proteomics

Single-cell proteomics by mass spectrometry: Advances and implications in cancer research

Cancer harbours extensive proteomic heterogeneity. Inspired by the prior success of single-cell RNA sequencing (scRNA-seq) in characterizing minute transcriptomics heterogeneity in cancer, researchers are now actively searching for information regarding the proteomics counterpart. Therefore recently, single-cell proteomics by mass spectrometry (SCP) has rapidly developed into state-of-the-art technology to cater the need. This review aims to summarize application of SCP in cancer research, while revealing current development progress of SCP technology. The review also aims to contribute ideas into research gaps and future directions, ultimately promoting the application of SCP in cancer research.

Determining DNA-Protein Binding Affinities and Specificities from Crude Lysates Using a Combined SILAC/TMT Labeling Strategy

In recent years, quantitative mass spectrometry-based interaction proteomics technology has proven very useful in identifying specific DNA-protein interactions using single pull-downs from crude lysates. Here, we applied a SILAC/TMT-based higher-order multiplexing approach to develop an interaction proteomics workflow called Protein-nucleic acid Affinity and Specificity quantification by MAss spectrometry in Nuclear extracts or PASMAN. In PASMAN, DNA pull-downs using a concentration range of specific and control DNA baits are performed in SILAC-labeled nuclear extracts. MS1-based quantification to determine specific DNA-protein interactions is then combined with sequential TMT-based quantification of fragmented SILAC peptides, allowing the generation of Hill-like curves and determination of apparent binding affinities. We benchmarked PASMAN using the SP/KLF motif and further applied it to gain insights into two CGCG-containing consensus DNA motifs. These motifs are recognized by two BEN domain-containing proteins, BANP and BEND3, which we find to interact with these motifs with distinct affinities. Finally, we profiled the BEND3 proximal proteome, revealing the NuRD complex as the major BEND3 proximal protein complex in vivo. In summary, PASMAN represents, to our knowledge, the first higher-order multiplexing-based interaction proteomics method that can be used to decipher specific DNA-protein interactions and their apparent affinities in various biological and pathological contexts.

Enhanced Multiplexing Technology for Proteomics

The identification of thousands of proteins and their relative levels of expression has furthered understanding of biological processes and disease and stimulated new systems biology hypotheses. Quantitative proteomics workflows that rely on analytical assays such as mass spectrometry have facilitated high-throughput measurements of proteins partially due to multiplexing. Multiplexing allows proteome differences across multiple samples to be measured simultaneously, resulting in more accurate quantitation, increased statistical robustness, reduced analysis times, and lower experimental costs. The number of samples that can be multiplexed has evolved from as few as two to more than 50, with studies involving more than 10 samples being denoted as enhanced multiplexing or hyperplexing. In this review, we give an update on emerging multiplexing proteomics techniques and highlight advantages and limitations for enhanced multiplexing strategies.

HyperSCP: Combining Isotopic and Isobaric Labeling for Higher Throughput Single-Cell Proteomics

Recent developments in mass spectrometry-based single-cell proteomics (SCP) have resulted in dramatically improved sensitivity, yet the relatively low measurement throughput remains a limitation. Isobaric and isotopic labeling methods have been separately applied to SCP to increase throughput through multiplexing. Here we combined both forms of labeling to achieve multiplicative scaling for higher throughput. Two-plex stable isotope labeling of amino acids in cell culture (SILAC) and isobaric tandem mass tag (TMT) labeling enabled up to 28 single cells to be analyzed in a single liquid chromatography-mass spectrometry (LC-MS) analysis, in addition to carrier, reference, and negative control channels. A custom nested nanowell chip was used for nanoliter sample processing to minimize sample losses. Using a 145-min total LC-MS cycle time, ∼280 single cells were analyzed per day. This measurement throughput could be increased to ∼700 samples per day with a high-duty-cycle multicolumn LC system producing the same active gradient. The labeling efficiency and achievable proteome coverage were characterized for multiple analysis conditions.

Tandem mass tag-based thermal proteome profiling for the discovery of drug-protein interactions in cancer cells

Identification of effector targets is imperative to the characterization of the mechanisms of action of novel small molecules. Here, we describe steps to identify effector drug-protein interactions in lysates derived from cancer cell lines using a thermal proteome profiling (TPP) protocol. Building on existing TTP approaches, we detail the use of an in-solution trypsin digestion technique to streamline sample preparation, a nonparametric analysis to rank proteins for prioritization, and a follow-up strategy for identifying effector interactors. For complete details on the use and execution of this protocol, please refer to Johnson et al. (2022).1.

Chemical isotope labeling for quantitative proteomics

Advancements in liquid chromatography and mass spectrometry over the last decades have led to a significant development in mass spectrometry-based proteome quantification approaches. An increasingly attractive strategy is multiplex isotope labeling, which significantly improves the accuracy, precision and throughput of quantitative proteomics in the data-dependent acquisition mode. Isotope labeling-based approaches can be classified into MS1-based and MS2-based quantification. In this review, we give an overview of approaches based on chemical isotope labeling and discuss their principles, benefits, and limitations with the goal to give insights into fundamental questions and provide a useful reference for choosing a method for quantitative proteomics. As a perspective, we discuss the current possibilities and limitations of multiplex, isotope labeling approaches for the data-independent acquisition mode, which is increasing in popularity.

Recent advances in isobaric labeling and applications in quantitative proteomics

Mass spectrometry (MS) has emerged at the forefront of quantitative proteomic techniques. Liquid chromatography-mass spectrometry (LC-MS) can be used to determine abundances of proteins and peptides in complex biological samples. Several methods have been developed and adapted for accurate quantification based on chemical isotopic labeling. Among various chemical isotopic labeling techniques, isobaric tagging approaches rely on the analysis of peptides from MS2-based quantification rather than MS1-based quantification. In this review, we will provide an overview of several isobaric tags along with some recent developments including complementary ion tags, improvements in sensitive quantitation of analytes with lower abundance, strategies to increase multiplexing capabilities, and targeted analysis strategies. We will also discuss limitations of isobaric tags and approaches to alleviate these restrictions through bioinformatic tools and data acquisition methods. This review will highlight several applications of isobaric tags, including biomarker discovery and validation, thermal proteome profiling, cross-linking for structural investigations, single-cell analysis, top-down proteomics, along with applications to different molecules including neuropeptides, glycans, metabolites, and lipids, while providing considerations and evaluations to each application.

Advances in stable isotope labeling: dynamic labeling for spatial and temporal proteomic analysis

The field of proteomics is continually improving, requiring the development of new quantitative methods. Stable isotope labeling in cell culture (SILAC) is a metabolic labeling technique originating in the early 2000s. By incorporating isotopically labeled amino acids into the media used for cell culture, unlabeled versus labeled cells can be differentiated by the mass spectrometer. Traditional SILAC labeling has been expanded to pulsed applications allowing for a new quantitative dimension of proteomics - temporal analysis. The complete introduction of Heavy SILAC labeling chased with surplus unlabeled medium mimics traditional pulse-chase experiments and allows for the loss of heavy signal to track proteomic changes over time. In a similar fashion, pulsed SILAC (pSILAC) monitors the initial incorporation of a heavy label across a period of time, which allows for the rate of protein label integration to be assessed. These innovative techniques have aided in inspiring numerous SILAC-based temporal and spatial labeling applications, including super SILAC, spike-in SILAC, spatial SILAC, and a revival in label multiplexing. This review reflects upon the evolution of SILAC and the pulsed SILAC application, introduces advances in SILAC labeling, and proposes future perspectives for this novel and exciting field.

Studying Autophagy Using a TMT-Based Quantitative Proteomics Approach

Maintenance of cellular homeostasis through regulated degradation of proteins and organelles is a defining feature of autophagy. This process itself is tightly regulated in a series of well-defined biochemical reactions governed largely by the highly conserved ATG protein family. Given its crucial role in regulating protein levels under both basal and stress conditions such as starvation and infection, genetic or pharmacological perturbation of autophagy results in massive changes in the cellular proteome and impacts nearly every biological process. Therefore, studying autophagy perturbations at a global scale assumes prime importance. In recent years, quantitative mass spectrometry (MS)-based proteomics has emerged as a powerful approach to explore biological processes through global proteome quantification analysis. Tandem mass tag (TMT)-based MS proteomics is one such robust quantitative technique that can examine relative protein abundances in multiple samples (parallel multiplexing). Investigating autophagy through TMT-based MS approach can give great insights into autophagy-regulated biological processes, protein-protein interaction networks, spatiotemporal protein dynamics, and identification of new autophagy substrates. This chapter provides a detailed protocol for studying the impact of a dysfunctional autophagy pathway on the cellular proteome and pathways in a healthy vs. disease (virus infection) condition using a 16-plex TMT-based quantitative proteomics approach. We also provide a pipeline on data processing and analysis using available web-based tools.

Advances in functional proteomics to study plant-pathogen interactions

Pathogen infection triggers complex signaling networks in plant cells that ultimately result in either susceptibility or resistance. We have made substantial progress in dissecting many of these signaling events, and it is becoming clear that changes in proteome composition and protein activity are major drivers of plant-microbe interactions. Here, we highlight different approaches to analyze the functional proteomes of hosts and pathogens and discuss how they have been used to further our understanding of plant disease. Global proteome profiling can quantify the dynamics of proteins, posttranslational modifications, and biological pathways that contribute to immune-related outcomes. In addition, emerging techniques such as enzyme activity-based profiling, proximity labeling, and kinase-substrate profiling are being used to dissect biochemical events that operate during infection. Finally, we discuss how these functional approaches can be integrated with other profiling data to gain a mechanistic, systems-level view of plant and pathogen signaling.

NHS-Ester Tandem Labeling in One Pot Enables 48-Plex Quantitative Proteomics

Stable-isotope labeling strategies are extensively used for multiplex quantitative proteomics. Hybrid-isotope labeling strategies that combine the use of isotopic mass difference labeling and isobaric tags can greatly increase sample multiplexity. In this work, we present a novel hybrid-isotope labeling approach that we termed NHS-ester tandem labeling in one pot (NETLOP). We first optimized 16-plex isobaric TMTpro labeling of lysine residues followed by 2-plex or 3-plex isotopic mTRAQ labeling of peptide N-termini, both of which with commercially available NHS-ester reactive reagents. We then demonstrated the utility of the NETLOP approach by labeling HeLa cell samples and performing proof-of-principle quantitative 32-plex and 48-plex proteomic analyses, each in a single LC-MS/MS experiment. Compared to current hybrid-isotope labeling methods, our NETLOP approach requires no sample cleanup between different labeling steps to minimize sample loss, induces no retention time shifts that compromise quantification accuracy, can be adapted to other NHS-ester isotopic labeling reagents to further increase multiplexity, and is compatible with samples from any origin in a wide array of biological and clinical proteomics applications.

Posttranslational modifications in systems biology

The biological complexity cannot be captured by genes or proteins alone. The protein posttranslational modifications (PTMs) impart functional diversity to the proteome and regulate protein structure, activity, localization and interactions. Their dynamics drive cellular signaling, growth and development while their dysregulation causes many diseases. Mass spectrometry based quantitative profiling of PTMs and bioinformatics analysis tools allow systems level insights into their network architecture. High-resolution profiling of PTM networks will advance disease understanding and precision medicine. It can accelerate the discovery of biomarkers and drug targets. This requires better tools for unbiased, high-throughput and accurate PTM identification, site localization and automated annotation on a systems level.

Proteomics and plant biology: contributions to date and a look towards the next decade

INTRODUCTION

This review presents the view of the author, that is opinionable and even speculative, on the field of proteomics, its application to plant biology knowledge, and translation to biotechnology. Written in a more academic than scientific style, it is based on past original and review articles by the author´s group, and those published by leading scientists in the last two years.

AREAS COVERED

Starting with a general definition and references to historical milestones, it covers sections devoted to the different platforms employed, the plant biology discourse in the protein language, challenges and future prospects, ending with the author opinion.

EXPERT OPINION

In 25 years, five proteomics platform generations have appeared. We are now moving from proteomics to Systems Biology. While feasible with model organisms, proteomics of orphan species remains challenging. Proteomics, even in its simplest approach, sheds light on plant biological processes, central dogma, and molecular bases of phenotypes of interest, and it can be translated to areas such as food traceability and allergen detection. Proteomics should be validated and optimized to each experimental system, objectives, and hypothesis. It has limitations, artifacts, and biases. We should not blindly accept proteomics data and just create a list of proteins, networks, and avoid speculative biological interpretations. From the hundred to thousand proteins identified and quantified, it is important to obtain a focus and validate some of them, otherwise it is merely. We are starting to have the protein pieces, so let, from now, build the proteomics and biological puzzle.

Aggregation and disaggregation features of the human proteome

Protein aggregates have negative implications in disease. While reductionist experiments have increased our understanding of aggregation processes, the systemic view in biological context is still limited. To extend this understanding, we used mass spectrometry-based proteomics to characterize aggregation and disaggregation in human cells after non-lethal heat shock. Aggregation-prone proteins were enriched in nuclear proteins, high proportion of intrinsically disordered regions, high molecular mass, high isoelectric point, and hydrophilic amino acids. During recovery, most aggregating proteins disaggregated with a rate proportional to the aggregation propensity: larger loss in solubility was counteracted by faster disaggregation. High amount of intrinsically disordered regions were associated with faster disaggregation. However, other characteristics enriched in aggregating proteins did not correlate with the disaggregation rates. In addition, we analyzed changes in protein thermal stability after heat shock. Soluble remnants of aggregated proteins were more thermally stable compared with control condition. Therefore, our results provide a rich resource of heat stress-related protein solubility data and can foster further studies related to protein aggregation diseases.

Mass Spectrometry Techniques: Principles and Practices for Quantitative Proteomics

In the current omics-age of research, major developments have been made in technologies that attempt to survey the entire repertoire of genes, transcripts, proteins, and metabolites present within a cell. While genomics has led to a dramatic increase in our understanding of such things as disease morphology and how organisms respond to medications, it is critical to obtain information at the proteome level since proteins carry out most of the functions within the cell. The primary tool for obtaining proteome-wide information on proteins within the cell is mass spectrometry (MS). While it has historically been associated with the protein identification, developments over the past couple of decades have made MS a robust technology for protein quantitation as well. Identifying quantitative changes in proteomes is complicated by its dynamic nature and the inability of any technique to guarantee complete coverage of every protein within a proteome sample. Fortunately, the combined development of sample preparation and MS methods have made it capable of quantitatively comparing many thousands of proteins obtained from cells and organisms.

The ataxin-1 interactome reveals direct connection with multiple disrupted nuclear transport pathways

The expanded polyglutamine (polyQ) tract form of ataxin-1 drives disease progression in spinocerebellar ataxia type 1 (SCA1). Although known to form distinctive intranuclear bodies, the cellular pathways and processes that polyQ-ataxin-1 influences remain poorly understood. Here we identify the direct and proximal partners constituting the interactome of ataxin-1[85Q] in Neuro-2a cells, pathways analyses indicating a significant enrichment of essential nuclear transporters, pointing to disruptions in nuclear transport processes in the presence of elevated levels of ataxin-1. Our direct assessments of nuclear transporters and their cargoes confirm these observations, revealing disrupted trafficking often with relocalisation of transporters and/or cargoes to ataxin-1[85Q] nuclear bodies. Analogous changes in importin-β1, nucleoporin 98 and nucleoporin 62 nuclear rim staining are observed in Purkinje cells of ATXN1[82Q] mice. The results highlight a disruption of multiple essential nuclear protein trafficking pathways by polyQ-ataxin-1, a key contribution to furthering understanding of pathogenic mechanisms initiated by polyQ tract proteins.

HyperQuant-A Computational Pipeline for Higher Order Multiplexed Quantitative Proteomics

Quantitative proteomics has evolved considerably over the last decade with the advent of higher order multiplexing (HOM) techniques. With the development of methods such as-multitagging, cPILOT, hyperplexing, BONPlex, and MITNCAT, the HOM technique is rapidly taking the center stage in multiplexed quantitative proteomics. These studies combined MS¹ and MS² labels in a single experiment enabling higher sample throughput. While HOM is highly promising, the computational analysis is still a big challenge, as the available tools cannot harness its power completely. We have developed a new quantitative pipeline, HyperQuant to aid in accurately quantitating complex HOM data. The pipeline uses identification results from either MaxQuant or any other search engine and quantitation results from QuantWiz^IQ. The Mapper and Combiner modules of HyperQuant allow facile integration of the labeled data, along with peptide spectrum match (PSM) intensity/ratio integration for proteins, respectively, for each PSM label combination. This also includes appropriate combination of replicates/fractions before summarizing the protein intensity/ratio, leading to robust quantitation. To the best of our knowledge, this is the first tool for the quantitation of HOM data with flexibility for any combination of MS¹ and MS² labels. We demonstrate its utility in analyzing two 18-plex data sets from the hyperplexing and the BONplex studies. The tool is open source and freely available for noncommercial use. HyperQuant is a highly valuable tool that will help in advancing the field of multiplexed quantitative proteomics.

Proteomic Investigations of In Vitro and In Vivo Models of Periodontal Disease

Proteomics has currently been a developing field in periodontal diseases to obtain protein information of certain samples. Periodontal disease is an inflammatory disorder that attacks the teeth, connective tissues, and alveolar bone within the oral cavity. Proteomics information can provide proteins that are differentially expressed in diseased or healthy samples. This review provides insight into approaches researching single species, multi species, bacteria, non-human, and human models of periodontal disease for proteomics information. The approaches that have been taken include gel electrophoresis and qualitative and quantitative mass spectrometry. This review is carried out by extracting information about in vitro and in vivo studies of proteomics in models of periodontal diseases that have been carried out in the past two decades. The research has concentrated on a relatively small but well-known group of microorganisms. A wide range of models has been reviewed and conclusions across the breadth of these studies are presented in this review.