Quantification Of Hundreds Research Articles

DiaPASEF Enables High-Throughput Proteomic Analysis of Host Cell Proteins for Biopharmaceutical Process Development.

Monitoring and quantifying host cell proteins (HCPs) in biotherapeutic production processes is crucial to ensure product quality, stability, and safety. Liquid chromatography-mass spectrometry (LC-MS) analysis has emerged as an important tool for identifying and quantifying individual HCPs. However, LC-MS-based approaches face challenges due to the wide dynamic range between HCPs and the therapeutic protein as well as laborious sample preparation and long instrument time. To address these limitations, we evaluated the application of parallel accumulation-serial fragmentation combined with data-independent acquisition (diaPASEF) to HCP analysis for biopharmaceutical process development applications. We evaluated different library generation strategies and LC methods, demonstrating the suitability of these workflows for various HCP analysis needs, such as in-depth characterization and high-throughput analysis of process intermediates. Remarkably, the diaPASEF approach enabled the quantification of hundreds of HCPs that were undetectable by a standard data-dependent acquisition mode while considerably improving sample requirement, throughput, coverage, quantitative precision, and data completeness.

A basic phosphoproteomic-DIA workflow integrating precise quantification of phosphosites in systems biology.

Phosphorylation is one of the most important post-translational modifications (PTMs) of proteins, governing critical protein functions. Most human proteins have been shown to undergo phosphorylation, and phosphoproteomic studies have been widely applied due to recent advancements in high-resolution mass spectrometry technology. Although the experimental workflow for phosphoproteomics has been well-established, it would be useful to optimize and summarize a detailed, feasible protocol that combines phosphoproteomics and data-independent acquisition (DIA), along with follow-up data analysis procedures due to the recent instrumental and bioinformatic advances in measuring and understanding tens of thousands of site-specific phosphorylation events in a single experiment. Here, we describe an optimized Phos-DIA protocol, from sample preparation to bioinformatic analysis, along with practical considerations and experimental configurations for each step. The protocol is designed to be robust and applicable for both small-scale phosphoproteomic analysis and large-scale quantification of hundreds of samples for studies in systems biology and systems medicine.

Profiling the Mammalian Lipidome by Quantitative Shotgun Lipidomics.

The emerging field of lipidomics presents the systems biology approach to identify and quantify the full lipid repertoire of cells, tissues, and organisms. The importance of the lipidome is demonstrated by a number of biological studies on dysregulation of lipid metabolism in human diseases such as cancer, diabetes, and neurodegenerative diseases. Exploring changes and regulations in the huge networks of lipids and their metabolic pathways requires a lipidomics methodology: advanced mass spectrometry that resolves the complexity of the lipidome. Here, we report a comprehensive protocol of quantitative shotgun lipidomics that enables identification and quantification of hundreds of molecular lipid species, covering a wide range of lipid classes, extracted from cultured mammalian cells.

FAST-IT: Find A Simple Test — In TIA (transient ischaemic attack): a prospective cohort study to develop a multivariable prediction model for diagnosis of TIA through proteomic discovery and candidate lipid mass spectrometry, neuroimaging and machine learning—study protocol

IntroductionTransient ischaemic attack (TIA) may be a warning sign of stroke and difficult to differentiate from minor stroke and TIA-mimics. Urgent evaluation and diagnosis is important as treating TIA early...

New Non-Invasive Method for the Authentication of Apple Cultivars.

Food authentication is very important to protect consumers, sellers, and producers from fraud. Although several methods have been developed using a wide range of analytical techniques, most of them require sample destruction and do not allow in situ sampling or analysis, nor reliable quantification of hundreds of molecules at the same time. To overcome these limitations, we have developed and validated a new noninvasive analytical workflow for food authentication. The method uses a functionalized strip to adsorb small molecules from the surface of the food product, followed by gas chromatography–mass spectrometry analysis of the desorbed analytes. We validated the method and applied it to the classification of five different apple varieties. Molecular concentrations obtained from the analysis of 44 apples were used to identify markers for apple cultivars or, in combination with machine learning techniques, to perform cultivar classification. The overall reproducibility of the method was very good, showing a good coefficient of variation for both targeted and untargeted analysis. The approach was able to correctly classify all samples. In addition, the method was also used to detect pesticides and the following molecules were found in almost all samples: chlorpyrifos-methyl, deltamethrin, and malathion. The proposed approach not only showed very good analytical performance, but also proved to be suitable for noninvasive food authentication and pesticide residue analysis.

Individualized Proteogenomics Reveals the Mutational Landscape of Melanoma Patients in Response to Immunotherapy.

Simple SummaryMelanoma is the most aggressive form of skin cancer, with a rapidly increasing incidence rate. Due to ineffective treatment options in the late stage melanoma, patients have an overall poor prognosis. Over the last decades, the role of the immune system in the control of tumor progression has been established and immune checkpoint inhibitors (ICi) have shown remarkable clinical activity. While current trials suggest durable responses in patient under ICi therapy, there is increasing evidence pointing towards existence of innate and acquired resistance to ICi therapy; and it is now clear that personalized medicine will be critical for effective patient therapy. Proteogenomics is a powerful tool to study the mode of action of disease-associated mutations at the genome, transcriptome, proteome and PTM level. Here, we applied a proteogenomic workflow to study melanoma samples from human tumors. Such workflow may be applicable to other patient-derived samples and different cancer types.Immune checkpoint inhibitors are used to restore or augment antitumor immune responses and show great promise in the treatment of melanoma and other types of cancers. However, only a small percentage of patients are fully responsive to immune checkpoint inhibition, mostly due to tumor heterogeneity and primary resistance to therapy. Both of these features are largely driven by the accumulation of patient-specific mutations, pointing to the need for personalized approaches in diagnostics and immunotherapy. Proteogenomics integrates patient-specific genomic and proteomic data to study cancer development, tumor heterogeneity and resistance mechanisms. Using this approach, we characterized the mutational landscape of four clinical melanoma patients. This enabled the quantification of hundreds of sample-specific amino acid variants, among them many that were previously not reported in melanoma. Changes in abundance at the protein and phosphorylation site levels revealed patient-specific over-represented pathways, notably linked to melanoma development (MAPK1 activation) or immunotherapy (NLRP1 inflammasome). Personalized data integration resulted in the prediction of protein drug targets, such as the drugs vandetanib and bosutinib, which were experimentally validated and led to a reduction in the viability of tumor cells. Our study emphasizes the potential of proteogenomic approaches to study personalized mutational landscapes, signaling networks and therapy options.

High-throughput single-cell quantification of hundreds of proteins using conventional flow cytometry and machine learning.

Modern immunologic research increasingly requires high-dimensional analyses to understand the complex milieu of cell types that comprise the tissue microenvironments of disease. To achieve this, we developed Infinity Flow combining hundreds of overlapping flow cytometry panels using machine learning to enable the simultaneous analysis of the coexpression patterns of hundreds of surface-expressed proteins across millions of individual cells. In this study, we demonstrate that this approach allows the comprehensive analysis of the cellular constituency of the steady-state murine lung and the identification of previously unknown cellular heterogeneity in the lungs of melanoma metastasis–bearing mice. We show that by using supervised machine learning, Infinity Flow enhances the accuracy and depth of clustering or dimensionality reduction algorithms. Infinity Flow is a highly scalable, low-cost, and accessible solution to single-cell proteomics in complex tissues.

Quantitative Top-Down Proteomics in Complex Samples Using Protein-Level Tandem Mass Tag Labeling.

Labeling approaches using isobaric chemical tags (e.g., isobaric tagging for relative and absolute quantification, iTRAQ and tandem mass tag, TMT) have been widely applied for the quantification of peptides and proteins in bottom-up MS. However, until recently, successful applications of these approaches to top-down proteomics have been limited because proteins tend to precipitate and "crash" out of solution during TMT labeling of complex samples making the quantification of such samples difficult. In this study, we report a top-down TMT MS platform for confidently identifying and quantifying low molecular weight intact proteoforms in complex biological samples. To reduce the sample complexity and remove large proteins from complex samples, we developed a filter-SEC technique that combines a molecular weight cutoff filtration step with high-performance size exclusion chromatography (SEC) separation. No protein precipitation was observed in filtered samples under the intact protein-level TMT labeling conditions. The proposed top-down TMT MS platform enables high-throughput analysis of intact proteoforms, allowing for the identification and quantification of hundreds of intact proteoforms from Escherichia coli cell lysates. To our knowledge, this represents the first high-throughput TMT labeling-based, quantitative, top-down MS analysis suitable for complex biological samples.

Spray-Capillary-Based Capillary Electrophoresis Mass Spectrometry for Metabolite Analysis in Single Cells.

Single-cell capillary electrophoresis mass spectrometry (CE-MS) is a promising platform to analyze cellular contents and probe cell heterogeneity. However, current single-cell CE-MS methods often rely on offline microsampling processes and may demonstrate low sampling precision and accuracy. We have recently developed an electrospray-assisted device, spray-capillary, for low-volume sample extraction. With the spray-capillary, low-volume samples (pL-nL) are drawn into the sampling end of the device, which can be used directly for CE separation and online MS detection. Here, we redesigned the spray-capillary by utilizing a capillary with a <15 μm tapered tip so that it can be directly inserted into single cells for sample collection and on-capillary CE-MS analysis. We evaluated the performance of the modified spray-capillary by performing single-cell microsampling on single onion cells with varying sample injection times and direct MS analysis or online CE-MS analysis. We have demonstrated, for the first time, online sample collection and CE-MS for the analysis of single cells. This application of the modified spray-capillary device facilitates the characterization and relative quantification of hundreds of metabolites in single cells.

Surfactant-assisted one-pot sample preparation for label-free single-cell proteomics

Large numbers of cells are generally required for quantitative global proteome profiling due to surface adsorption losses associated with sample processing. Such bulk measurement obscures important cell-to-cell variability (cell heterogeneity) and makes proteomic profiling impossible for rare cell populations (e.g., circulating tumor cells (CTCs)). Here we report a surfactant-assisted one-pot sample preparation coupled with mass spectrometry (MS) method termed SOP-MS for label-free global single-cell proteomics. SOP-MS capitalizes on the combination of a MS-compatible nonionic surfactant, n-Dodecyl-β-D-maltoside, and hydrophobic surface-based low-bind tubes or multi-well plates for ‘all-in-one’ one-pot sample preparation. This ‘all-in-one’ method including elimination of all sample transfer steps maximally reduces surface adsorption losses for effective processing of single cells, thus improving detection sensitivity for single-cell proteomics. This method allows convenient label-free quantification of hundreds of proteins from single human cells and ~1200 proteins from small tissue sections (close to ~20 cells). When applied to a patient CTC-derived xenograft (PCDX) model at the single-cell resolution, SOP-MS can reveal distinct protein signatures between primary tumor cells and early metastatic lung cells, which are related to the selection pressure of anti-tumor immunity during breast cancer metastasis. The approach paves the way for routine, precise, quantitative single-cell proteomics.

Lipidomics in Biomarker Research.

Lipids are natural substances found in all living organisms and involved in many biological functions. Imbalances in the lipid metabolism are linked to various diseases such as obesity, diabetes, or cardiovascular disease. Lipids comprise thousands of chemically distinct species making them a challenge to analyze because of their great structural diversity.Thanks to the technological improvements in the fields of chromatography, high-resolution mass spectrometry, and bioinformatics over the last years, it is now possible to perform global lipidomics analyses, allowing the concomitant detection, identification, and relative quantification of hundreds of lipid species. This review shall provide an insight into a general lipidomics workflow and its application in metabolic biomarker research.

Dilemmas With Absolute Quantification of Pharmacologically Relevant Proteins Using Mass Spectrometry

Determination of abundances of proteins involved in uptake, distribution, metabolism and excretion of xenobiotics is a prerequisite to understand and predict elimination mechanisms in tissue. Mass spectrometry promises simple and accurate measurements of individual proteins in complex mixtures using isotopically labeled peptide standards. However, comparisons of measurements performed in different laboratories have shown considerable discrepancies in the data generated. Even when very similar approaches are compared, the results differ significantly. An alternative method of measuring protein titers is global proteomics. Depending on sample type, this allows quantification of hundreds to thousands of proteins in a single analysis. It enables system-wide insights by providing protein copy numbers and cell sizes. Regardless of differences, the workflows of both the labeled standard-based and the proteomic approach share several steps. Each can be critical. Selection of optimal techniques is the prerequisite for accurate and reproducible protein quantification.

Fishing for newly synthesized proteins with phosphonate-handles

Bioorthogonal chemistry introduces affinity-labels into biomolecules with minimal disruption to the original system and is widely applicable in a range of contexts. In proteomics, immobilized metal affinity chromatography (IMAC) enables enrichment of phosphopeptides with extreme sensitivity and selectivity. Here, we adapt and combine these superb assets in a new enrichment strategy using phosphonate-handles, which we term PhosID. In this approach, click-able phosphonate-handles are introduced into proteins via 1,3-dipolar Huisgen-cycloaddition to azido-homo-alanine (AHA) and IMAC is then used to enrich exclusively for phosphonate-labeled peptides. In interferon-gamma (IFNγ) stimulated cells, PhosID enabled the identification of a large number of IFN responsive newly synthesized proteins (NSPs) whereby we monitored the differential synthesis of these proteins over time. Collectively, these data validate the excellent performance of PhosID with efficient analysis and quantification of hundreds of NSPs by single LC-MS/MS runs. We envision PhosID as an attractive and alternative tool for studying stimuli-sensitive proteome subsets.

Infinity Flow: comprehensive single-cell protein profiling via massively parallel flow cytometry and machine learning

Abstract Modern flow cytometers can quantify around 20 proteins at the single-cell level. Higher-dimensional alternatives exist, such as mass cytometry, or oligonucleotides-tagged antibodies - but they feature lower cellular throughput, higher cost and are not as widely available. To overcome these limitations, we propose a pipeline which allows the quantification of hundreds of proteins across millions of single cells. Experimentally, cells are stained with a customizable 10–20 “backbone” antibody panel which defines the cellular population structure of the sample. This is followed by aliquoting into hundreds (200–300) of individual wells, each containing a unique “exploratory” antibody. Data acquisition is performed using conventional flow cytometry. Computationally, multivariate non-linear regression models are trained, one for each well. These models learn how to impute an exploratory antibody signal from backbone measurements. The models are each applied to the backbone data to obtain hundreds of imputed measurements across millions of single cells. We applied this workflow cell suspensions from the lungs of C57/B6 mice, resulting in 2,660,000 events with imputed expression levels for 266 proteins. Imputed expression and co-expression patterns are accurate and consensual across regression models, allowing near-exhaustive annotation of both immune and non-immune cells, and finer granularity than dimensionality reduction or clustering. By enabling broad characterization of cellular networks at the protein level, this pipeline is well-suited for systems immunology approaches. Given the wealth of data generated by this pipeline and its accessibility, we anticipate it will be widely adopted.

Streamlined particle quantification (SParQ) plug-in is an automated fluorescent vesicle quantification plug-in for particle quantification in Fiji/ImageJ

ABSTRACT The endolysosomal system is critical for protein homeostasis in cells. A common way of studying protein transport and degradation (e.g. via autophagy) is by labeling vesicular structures such as endosomes, autophagosomes, lysosomes, or model substrates with fluorescent tags or by fluorescent antibody staining. Detailed analyses require quantification of hundreds of structures under various conditions. Typically, the images are analyzed individually with software such as the widely available Fiji/ImageJ (https://imagej.net/Fiji/Downloads), adjusting and thresholding each image and channel independently, which is a very labor intensive and fastidious task. To streamline the process, we developed a plug-in that, integrated into Fiji, enables the automated quantification of vesicular (i.e. punctate) structures. Importantly, the process still allows the operator to evaluate and have control over all the phases of quantification process. Abbreviations CMA: chaperone-mediated autophagy; CSV: comma separated values; eMI: endosomal microautophagy; Fiji: Fiji is just ImageJ; MA: macroautophagy; SParQ: Streamlined Particle Quantification

Parahydrogen induced hyperpolarization provides a tool for NMR metabolomics at nanomolar concentrations

An NMR approach based on parahydrogen hyperpolarization is presented to detect and resolve specific classes of metabolites in complex biomixtures at down to nanomolar concentrations. We demonstrate our method on solid phase extracts of urine, by simultaneously observing hundreds of metabolites well below the limits of detection of thermal NMR.

Absolute quantitative lipidomics reveals lipidome-wide alterations in aging brain.

The absolute quantitation of lipids at the lipidome-wide scale is a challenge but plays an important role in the comprehensive study of lipid metabolism. We aim to develop a high-throughput quantitative lipidomics approach to enable the simultaneous identification and absolute quantification of hundreds of lipids in a single experiment. Then, we will systematically characterize lipidome-wide changes in the aging mouse brain and provide a link between aging and disordered lipid homeostasis. We created an in-house lipid spectral library, containing 76,361 lipids and 181,300 MS/MS spectra in total, to support accurate lipid identification. Then, we developed a response factor-based approach for the large-scale absolute quantifications of lipids. Using the lipidomics approach, we absolutely quantified 1212 and 864 lipids in human cells and mouse brains, respectively. The quantification accuracy was validated using the traditional approach with a median relative error of 12.6%. We further characterized the lipidome-wide changes in aging mouse brains, and dramatic changes were observed in both glycerophospholipids and sphingolipids. Sphingolipids with longer acyl chains tend to accumulate in aging brains. Membrane-esterified fatty acids demonstrated diverse changes with aging, while most polyunsaturated fatty acids consistently decreased. We developed a high-throughput quantitative lipidomics approach and systematically characterized the lipidome-wide changes in aging mouse brains. The results proved a link between aging and disordered lipid homeostasis.

Assessment of Quantification Precision of Histone Post-Translational Modifications by Using an Ion Trap and down To 50 000 Cells as Starting Material.

Histone post-translational modifications (PTMs) are fundamental players of chromatin regulation, as they contribute to editing histone chemical properties and recruiting proteins for gene transcription and DNA repair. Mass spectrometry (MS)-based proteomics is currently the most widely adopted strategy for high-throughput quantification of hundreds of histone PTMs. Samples such as primary tissues, complex model systems, and biofluids are hard to retrieve in large quantities. Because of this, it is critical to know whether the amount of sample available would lead to an exhaustive analysis if subjected to MS. In this work, we assessed the reproducibility in quantification of histone PTMs using a wide range of starting material, that is, from 5 000 000 to 50 000 cells. We performed the experiment using four different cell lines, that is, HeLa, 293T, human embryonic stem cells (hESCs), and myoblasts, and we quantified a list of 205 histone peptides using ion trap MS and our in-house software. Results highlighted that the relative abundance of some histone PTMs deviated as little as just 4% when comparing high starting material with histone samples extracted from 50 000 cells, for example, H3K9me2 (40% average abundance). Low abundance PTMs such as H3K4me2 (<3% average abundance) showed higher variability, but still ∼34%. This indicates that most PTMs, and especially abundant ones, are quantified with high precision starting from low cell counts. This study will help scientists to decide whether specific experiments are feasible and to plan how much sample should be reserved for histone analysis using MS.

Abstract 223: Targeting critical signaling nodes using multiplexed antibody based phosphopeptide enrichment with iMRM validation

Abstract Introduction: A challenge for biomedical researchers is to develop assays to analyze complex systems that interrogate whole cellular signaling networks. Here we show the results of employing antibody based immunoaffinity enrichment of phosphopeptides derived from critical signaling nodes to quantitate changes in phosphorylation upon kinase inhibitor treatment. Human cancer cell lines, including gastric and breast adenocarcinomas were treated with kinase inhibitors and cellular extracts were digested with trypsin and peptides were purified. A pool of phosphorylation dependent site-specific antibodies was employed to enrich for known sites of phosphorylation. Following antibody based enrichment these peptides were identified and quantified using liquid chromatography/tandem mass spectrometry (LC-MS/MS). Phosphopeptides were identified using SEQUEST, and differences in abundance were determined by employing label-free MS1 based quantification using Progenesis from Non-Linear Dynamics. We also show validation data for a number of the antibodies employed using immuno-multiple reaction monitoring, or iMRM. Employing a QExactive mass Spectrometer (Thermo Scientific) we developed calibration curves for a number of the targeted tryptic phosphopeptides using combinations of heavy and light peptides with endogenous cancer cell line peptides as a background matrix. Experimental Results: Cell lines were treated with DMSO or inhibitors to which the cell lines should be sensitive/insensitive. Following peptide purification, a pool of antibodies were multiplexed and used to enrich for phosphopeptides derived from signaling proteins across a number of signaling pathways. The results show significant changes in abundance for many proteins/sites, including activation loop tyrosine residues of tyrosine kinases, changes in sites on proteins in the AKT/PI3K pathway, sites in the MAPK pathway, and other critical regulators of cellular signaling such as sites on PKC family members. Targeted LC-MS/MS methods using iMRM were used to confirm these results and show the promise of using such targeted strategies for quantifying changes in phosphorylation consistently across many samples. Conclusion:The strategy of multiplexing antibodies for immuno-enrichment allows for simultaneous detection and quantification of hundreds to thousands of known phosphorylation sites that have been shown to be important nodes of regulation. Targeted LC-MS/MS methods such as iMRM will allow researchers to confidently profile changes in both total levels as well as phosphorylation site occupancy for critical signaling proteins. Citation Format: Charles L. Farnsworth, Yiying Zhu, Matthew Stokes. Targeting critical signaling nodes using multiplexed antibody based phosphopeptide enrichment with iMRM validation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 223. doi:10.1158/1538-7445.AM2017-223

Estimation of the influence of sequencing errors and distribution of random-sequence tags on quantitative sequencing

To simultaneously sequence and quantify target DNA, quantitative sequencing (qSeq) employs stochastic labeling of target DNA molecules with random-sequence tags (RSTs). This recently developed approach allows parallel quantification of hundreds of microorganisms in natural habitats in a single sequencing run. Yet, no study has addressed to what extent sequencing errors affect quantification and how many sequence reads are needed for quantification. Here, we addressed those issues by using numerical simulations and experimental data from second-generation sequencing of various RSTs. We found that heterogeneous distribution of observed RSTs affected the number of sequence reads required to quantitate target genes, whereas the effect of sequencing errors is smaller than of the RSTs distribution. Because of the heterogeneous RSTs distribution, 15-fold more sequence reads than the number of observed RSTs should be obtained to retrieve almost all RSTs needed for quantification; in that case, quantification error is estimated to be within 5%.