bims-mascan 2023-11-05 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2023‒11‒05
seventeen papers selected by
Giovanny Rodriguez Blanco, University of Edinburgh

Metabolic pathway analysis using stable isotopes in patients with cancer.
Quantitative analysis of the glutathione pathway cellular metabolites by targeted liquid chromatography - tandem mass spectrometry.
Differential lipid analysis of oxaliplatin-sensitive and resistant HCT116 cells reveals different levels of drug-induced lipid droplet formation.
Data-Driven Tool for Cross-Run Ion Selection and Peak-Picking in Quantitative Proteomics with Data-Independent Acquisition LC-MS/MS.
Sexual dimorphism in the gonad lipidome of blue mussels (Mytilus sp.): New insights from a global lipidomics approach.
Characteristics of amino acid metabolism in colorectal cancer.
Medium Depth Influences O2 Availability and Metabolism in Human RPE Cultures.
A framework for ultra-low-input spatial tissue proteomics.
Phospholipid isotope tracing reveals β-catenin-driven suppression of phosphatidylcholine metabolism in hepatocellular carcinoma.
Farnesoid X receptor activation by bile acids suppresses lipid peroxidation and ferroptosis.
Workflow for Evaluating Normalization Tools for Omics Data Using Supervised and Unsupervised Machine Learning.
Increasing taxonomic and functional characterization of host-microbiome interactions by DIA-PASEF metaproteomics.
Achieving quantitative reproducibility in label-free multisite DIA experiments through multirun alignment.
Current perspectives on mass spectrometry-based immunopeptidomics: the computational angle to tumor antigen discovery.
Merging Full-Spectrum and Fragment Ion Intensity Predictions from Deep Learning for High-Quality Spectral Libraries.
Mass Spectrometry Analysis for Clinical Applications: A Review.
Loss of Grem1-lineage chondrogenic progenitor cells causes osteoarthritis.

Nat Rev Cancer. 2023 Oct 31.

Metabolic pathway analysis using stable isotopes in patients with cancer.

Caroline R Bartman, Brandon Faubert, Joshua D Rabinowitz, Ralph J DeBerardinis.

Metabolic reprogramming is central to malignant transformation and cancer cell growth. How tumours use nutrients and the relative rates of reprogrammed pathways are areas of intense investigation. Tumour metabolism is determined by a complex and incompletely defined combination of factors intrinsic and extrinsic to cancer cells. This complexity increases the value of assessing cancer metabolism in disease-relevant microenvironments, including in patients with cancer. Stable-isotope tracing is an informative, versatile method for probing tumour metabolism in vivo. It has been used extensively in preclinical models of cancer and, with increasing frequency, in patients with cancer. In this Review, we describe approaches for using in vivo isotope tracing to define fuel preferences and pathway engagement in tumours, along with some of the principles that have emerged from this work. Stable-isotope infusions reported so far have revealed that in humans, tumours use a diverse set of nutrients to supply central metabolic pathways, including the tricarboxylic acid cycle and amino acid synthesis. Emerging data suggest that some activities detected by stable-isotope tracing correlate with poor clinical outcomes and may drive cancer progression. We also discuss current challenges in isotope tracing, including comparisons of in vivo and in vitro models, and opportunities for future discovery in tumour metabolism.

DOI: https://doi.org/10.1038/s41568-023-00632-z
J Sep Sci. 2023 Oct 29. e2300780

Quantitative analysis of the glutathione pathway cellular metabolites by targeted liquid chromatography - tandem mass spectrometry.

Kristian Serafimov, Yüsra Aydin, Michael Lämmerhofer.

  Glutathione, its biosynthesis intermediates and other thiol metabolites are of central relevance for the redox homeostasis of cells. Their analysis is critical due to the facile interconversion of redox pairs during sampling, sample preparation, and data acquisition, in particular in the electrospray ionization interface. In this work we propose a fast targeted LC-MS/MS method to accurately analyze 14 metabolites from the glutathione pathway. N-Ethylmaleimide reagent is added with the extraction solvent and instantly stabilizes the thiol-redox state by derivatization. Liquid chromatographic separation of the analytes was performed on a sub-2μm superficially porous HILIC column with sulfobetaine chemistry. Tandem MS with triple-quadrupole mass spectrometry in multiple-reaction monitoring acquisition mode allowed sensitive detection of the targeted metabolites with LOQs in the range of 5-25 nM. Run times of 3 min enable a high throughput analysis of cellular samples. For calibration a 13 C-labelled cell extract was used as internal standard. The method was validated and the concentrations of glutathione and its biosynthesis intermediates determined in HeLa cells. This article is protected by copyright. All rights reserved.

Keywords:  Targeted metabolomics; bioanalytical; derivatization; glutathione; hydrophilic interaction chromatography; quantitative; redox; redox homeostasis; tandem mass spectrometry; thiol

DOI:  https://doi.org/10.1002/jssc.202300780
Anal Bioanal Chem. 2023 Nov 02.

Differential lipid analysis of oxaliplatin-sensitive and resistant HCT116 cells reveals different levels of drug-induced lipid droplet formation.

Tyler S Larson, Thomas J DiProspero, Gary L Glish, Matthew R Lockett.

  Lipid droplets (LDs) are intracellular storage vesicles composed of a neutral lipid core surrounded by a glycerophospholipid membrane. LD accumulation is associated with different stages of cancer progression and stress responses resulting from chemotherapy. In previous work, a novel dual nano-electrospray ionization source and data-dependent acquisition method for measuring the relative abundances of lipid species between two extracts were described and validated. Here, this same source and method were used to determine if oxaliplatin-sensitive and resistant cells undergo similar lipid profile changes, with the goal of identifying potential signatures that could predict the effectiveness of an oxaliplatin-containing treatment. Oxaliplatin is commonly used in the treatment of colorectal cancer. When compared to a no-drug control, oxaliplatin dosing caused significant increases in triglyceride (TG) and cholesterol ester (CE) species. These increases were more pronounced in the oxaliplatin-sensitive cells than in oxaliplatin-resistant cells. The increased neutral lipid abundance correlated with LD formation, as confirmed by confocal micrographs of Nile Red-stained cells. Untargeted proteomic analyses also support LD formation after oxaliplatin treatment, with an increased abundance of LD-associated proteins in both the sensitive and resistant cells.

Keywords:  Colorectal cancer; Differential ion mobility spectrometry; Mass spectrometry; Shotgun lipidomics

DOI:  https://doi.org/10.1007/s00216-023-05010-0
Anal Chem. 2023 Oct 31.

Data-Driven Tool for Cross-Run Ion Selection and Peak-Picking in Quantitative Proteomics with Data-Independent Acquisition LC-MS/MS.

Binjun Yan, Mengtian Shi, Siyu Cai, Yuan Su, Renhui Chen, Chiyuan Huang, David Da Yong Chen.

Proteomics provides molecular bases of biology and disease, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a platform widely used for bottom-up proteomics. Data-independent acquisition (DIA) improves the run-to-run reproducibility of LC-MS/MS in proteomics research. However, the existing DIA data processing tools sometimes produce large deviations from true values for the peptides and proteins in quantification. Peak-picking error and incorrect ion selection are the two main causes of the deviations. We present a cross-run ion selection and peak-picking (CRISP) tool that utilizes the important advantage of run-to-run consistency of DIA and simultaneously examines the DIA data from the whole set of runs to filter out the interfering signals, instead of only looking at a single run at a time. Eight datasets acquired by mass spectrometers from different vendors with different types of mass analyzers were used to benchmark our CRISP-DIA against other currently available DIA tools. In the benchmark datasets, for analytes with large content variation among samples, CRISP-DIA generally resulted in 20 to 50% relative decrease in error rates compared to other DIA tools, at both the peptide precursor level and the protein level. CRISP-DIA detected differentially expressed proteins more efficiently, with 3.3 to 90.3% increases in the numbers of true positives and 12.3 to 35.3% decreases in the false positive rates, in some cases. In the real biological datasets, CRISP-DIA showed better consistencies of the quantification results. The advantages of assimilating DIA data in multiple runs for quantitative proteomics were demonstrated, which can significantly improve the quantification accuracy.

DOI: https://doi.org/10.1021/acs.analchem.3c02689
Comp Biochem Physiol Part D Genomics Proteomics. 2023 Oct 20. pii: S1744-117X(23)00095-3. [Epub ahead of print]48 101150

Sexual dimorphism in the gonad lipidome of blue mussels (Mytilus sp.): New insights from a global lipidomics approach.

Vincenzo Alessandro Laudicella, Stefano Carboni, Phillip D Whitfield, Mary K Doherty, Adam D Hughes.

  Blue mussels (Mytilus sp.) are an economically important species for European aquaculture. Their importance as a food source is expected to increase in the coming net-zero society due to their low environmental footprint; however, their production is affected by anthropogenic stressors and climate change. During reproduction, lipids are key molecules for mussels as they are the main source of energy on which newly hatched embryos depend in the first days of their development. In this work, blue mussels of different origins are analysed, focusing on the differences in lipid composition between the ovary (BMO) and the testis (BMT). The lipidome of blue mussel gonads (BMG) is studied here by combining traditional lipid profiling methods, such as fatty acid and lipid class analysis, with untargeted liquid chromatography-mass spectrometry (LC-MS) lipidomics. The approach used here enabled the identification of 770 lipid molecules from 23 different lipid classes in BMG. BMT, which consists of billions of spermatocytes, had greater amounts of cell membrane and membrane lipid components such as FA18:0, C20 polyunsaturated fatty acids (PUFA), free sterols (ST), ceramide phosphoethanolamines (CerPE), ceramide aminoethylphosphonates (CAEP), cardiolipins (CL), glycerophosphocholines (PC), glycerophosphoethanolamines (PE) and glycerophosphoserines (PS). In BMO, saturated fatty acids (FA14:0 and FA16:0), monounsaturated fatty acids (MUFA) and other storage components such as C18-PUFA accumulated in triradylglycerolipids (TG) and alkyldiacylglycerols (neutral plasmalogens, TG O-), which, together with terpenes, wax esters and cholesterol esters, make up most of oocytes yolk reserves. BMO also had higher levels of ceramides (Cer) and generally alkyl/alkenyl glycerophospholipids (mainly plasmanyl/plasmenyl PC), suggesting a role for these lipids in vitellogenesis. Non-methylene interrupted dienoic fatty acids (NMID FA), typically found in plasmalogens, were the only membrane-forming PUFA predominantly detected in BMO. The results of this study are of great importance for clarifying the lipid composition of BMG and provide an important basis for future studies on the reproductive physiology of these organisms.

Keywords:  Fatty acid; Gonads; HPLC; LC-MS; Lipid classes; Lipidomics; Lipids; Mussel; Ovary; Testis

DOI:  https://doi.org/10.1016/j.cbd.2023.101150
World J Clin Cases. 2023 Sep 26. 11(27): 6318-6326

Characteristics of amino acid metabolism in colorectal cancer.

Fen Xu, Hong-Liang Jiang, Wei-Wei Feng, Chen Fu, Jiang-Chang Zhou.

  In recent years, metabolomics research has become a hot spot in the screening and treatment of cancer. It is a popular technique for the quantitative characterization of small molecular compounds in biological cells, tissues, organs or organisms. Further study of the tumor revealed that amino acid changes may occur early in the tumor. The rapid growth and metabolism required for survival result in tumors exhibiting an increased demand for amino acids. An abundant supply of amino acids is important for cancer to maintain its proliferative driving force. Changes in amino acid metabolism can be used to screen malignant tumors and improve therapeutic outcomes. Therefore, it is particularly important to study the characteristics of amino acid metabolism in colorectal cancer. This article reviews several specific amino acid metabolism characteristics in colorectal cancer.

Keywords:  Amino acid metabolism; Colorectal cancer; Glutamine; Metabolomics

DOI:  https://doi.org/10.12998/wjcc.v11.i27.6318
Invest Ophthalmol Vis Sci. 2023 Nov 01. 64(14): 4

Medium Depth Influences O2 Availability and Metabolism in Human RPE Cultures.

Daniel T Hass, Qitao Zhang, Gillian A Autterson, Richard A Bryan, James B Hurley, Jason M L Miller.

Purpose: Retinal pigment epithelium (RPE) oxidative metabolism is critical for normal retinal function and is often studied in cell culture systems. Here, we show that conventional culture media volumes dramatically impact O2 availability, limiting oxidative metabolism. We suggest optimal conditions to ensure cultured RPE is in a normoxic environment permissive to oxidative metabolism.Methods: We altered the availability of O2 to human primary and induced pluripotent stem cell-derived RPE cultures directly via a hypoxia chamber or indirectly via the amount of medium over cells. We measured oxygen consumption rates (OCRs), glucose consumption, lactate production, 13C6-glucose and 13C5-glutamine flux, hypoxia inducible factor 1α (HIF-1α) stability, intracellular lipid droplets after a lipid challenge, transepithelial electrical resistance, cell morphology, and pigmentation.
Results: Medium volumes commonly employed during RPE culture limit diffusion of O2 to cells, triggering hypoxia, activating HIF-1α, limiting OCR, and dramatically altering cell metabolism, with only minor effects on typical markers of RPE health. Media volume effects on O2 availability decrease acetyl-CoA utilization, increase glycolysis and reductive carboxylation, and alter the size and number of intracellular lipid droplets under lipid-rich conditions.
Conclusions: Despite having little impact on visible and typical markers of RPE culture health, media volume dramatically affects RPE physiology "under the hood." As RPE-centric diseases like age-related macular degeneration involve oxidative metabolism, RPE cultures need to be optimized to study such diseases. We provide guidelines for optimal RPE culture volumes that balance ample nutrient availability from larger media volumes with adequate O2 availability seen with smaller media volumes.

DOI: https://doi.org/10.1167/iovs.64.14.4
Cell Syst. 2023 Oct 26. pii: S2405-4712(23)00287-9. [Epub ahead of print]

A framework for ultra-low-input spatial tissue proteomics.

Anuar Makhmut, Di Qin, Sonja Fritzsche, Jose Nimo, Janett König, Fabian Coscia.

  Spatial proteomics combining microscopy-based cell phenotyping with ultrasensitive mass-spectrometry-based proteomics is an emerging and powerful concept to study cell function and heterogeneity in (patho)physiology. However, optimized workflows that preserve morphological information for phenotype discovery and maximize proteome coverage of few or even single cells from laser microdissected tissue are currently lacking. Here, we report a robust and scalable workflow for the proteomic analysis of ultra-low-input archival material. Benchmarking in murine liver resulted in up to 2,000 quantified proteins from single hepatocyte contours and nearly 5,000 proteins from 50-cell regions. Applied to human tonsil, we profiled 146 microregions including T and B lymphocyte niches and quantified cell-type-specific markers, cytokines, and transcription factors. These data also highlighted proteome dynamics within activated germinal centers, illuminating sites undergoing B cell proliferation and somatic hypermutation. This approach has broad implications in biomedicine, including early disease profiling and drug target and biomarker discovery. A record of this paper's transparent peer review process is included in the supplemental information.

Keywords:  FFPE; deep visual proteomics; histopathology; mass spectrometry; proteomics; single-cell proteomics; spatial proteomics

DOI:  https://doi.org/10.1016/j.cels.2023.10.003
bioRxiv. 2023 Oct 16. pii: 2023.10.12.562134. [Epub ahead of print]

Phospholipid isotope tracing reveals β-catenin-driven suppression of phosphatidylcholine metabolism in hepatocellular carcinoma.

Chad VanSant-Webb, Hayden K Low, Junko Kuramoto, Claire E Stanley, Hantao Qiang, Audrey Su, Alexis N Ross, Chad G Cooper, James E Cox, Scott A Summers, Kimberley J Evason, Gregory S Ducker.

Background and Aims: Activating mutations in the CTNNB1 gene encoding β-catenin are among the most frequently observed oncogenic alterations in hepatocellular carcinoma (HCC). HCC with CTNNB1 mutations show profound alterations in lipid metabolism including increases in fatty acid oxidation and transformation of the phospholipidome, but it is unclear how these changes arise and whether they contribute to the oncogenic program in HCC.Methods: We employed untargeted lipidomics and targeted isotope tracing to quantify phospholipid production fluxes in an inducible human liver cell line expressing mutant β-catenin, as well as in transgenic zebrafish with activated β-catenin-driven HCC.
Results: In both models, activated β-catenin expression was associated with large changes in the lipidome including conserved increases in acylcarnitines and ceramides and decreases in triglycerides. Lipid flux analysis in human cells revealed a large reduction in phosphatidylcholine (PC) production rates as assayed by choline tracer incorporation. We developed isotope tracing lipid flux analysis for zebrafish and observed similar reductions in phosphatidylcholine synthesis flux accomplished by sex-specific mechanisms.
Conclusions: The integration of isotope tracing with lipid abundances highlights specific lipid class transformations downstream of β-catenin signaling in HCC and suggests future HCC-specific lipid metabolic targets.
Synopsis: In this work, we show by lipid specific isotope tracing that mutations in the oncogene CTNNB1 leads to conserved changes in lipid metabolism in hepatocellular carcinoma. These include the stimulation of fatty acid oxidation and a suppression of phosphorylcholine synthesis.

DOI: https://doi.org/10.1101/2023.10.12.562134
Nat Commun. 2023 Oct 30. 14(1): 6908

Farnesoid X receptor activation by bile acids suppresses lipid peroxidation and ferroptosis.

Juliane Tschuck, Lea Theilacker, Ina Rothenaigner, Stefanie A I Weiß, Banu Akdogan, Van Thanh Lam, Constanze Müller, Roman Graf, Stefanie Brandner, Christian Pütz, Tamara Rieder, Philippe Schmitt-Kopplin, Michelle Vincendeau, Hans Zischka, Kenji Schorpp, Kamyar Hadian.

Ferroptosis is a regulated cell death modality that occurs upon iron-dependent lipid peroxidation. Recent research has identified many regulators that induce or inhibit ferroptosis; yet, many regulatory processes and networks remain to be elucidated. In this study, we performed a chemical genetics screen using small molecules with known mode of action and identified two agonists of the nuclear receptor Farnesoid X Receptor (FXR) that suppress ferroptosis, but not apoptosis or necroptosis. We demonstrate that in liver cells with high FXR levels, knockout or inhibition of FXR sensitized cells to ferroptotic cell death, whereas activation of FXR by bile acids inhibited ferroptosis. Furthermore, FXR inhibited ferroptosis in ex vivo mouse hepatocytes and human hepatocytes differentiated from induced pluripotent stem cells. Activation of FXR significantly reduced lipid peroxidation by upregulating the ferroptosis gatekeepers GPX4, FSP1, PPARα, SCD1, and ACSL3. Together, we report that FXR coordinates the expression of ferroptosis-inhibitory regulators to reduce lipid peroxidation, thereby acting as a guardian of ferroptosis.

DOI: https://doi.org/10.1038/s41467-023-42702-8
J Am Soc Mass Spectrom. 2023 Oct 28.

Workflow for Evaluating Normalization Tools for Omics Data Using Supervised and Unsupervised Machine Learning.

Aleesa E Chua, Leah D Pfeifer, Emily R Sekera, Amanda B Hummon, Heather Desaire.

To achieve high quality omics results, systematic variability in mass spectrometry (MS) data must be adequately addressed. Effective data normalization is essential for minimizing this variability. The abundance of approaches and the data-dependent nature of normalization have led some researchers to develop open-source academic software for choosing the best approach. While these tools are certainly beneficial to the community, none of them meet all of the needs of all users, particularly users who want to test new strategies that are not available in these products. Herein, we present a simple and straightforward workflow that facilitates the identification of optimal normalization strategies using straightforward evaluation metrics, employing both supervised and unsupervised machine learning. The workflow offers a "DIY" aspect, where the performance of any normalization strategy can be evaluated for any type of MS data. As a demonstration of its utility, we apply this workflow on two distinct datasets, an ESI-MS dataset of extracted lipids from latent fingerprints and a cancer spheroid dataset of metabolites ionized by MALDI-MSI, for which we identified the best-performing normalization strategies.

DOI: https://doi.org/10.1021/jasms.3c00295
Front Microbiol. 2023 ;14 1258703

Increasing taxonomic and functional characterization of host-microbiome interactions by DIA-PASEF metaproteomics.

David Gómez-Varela, Feng Xian, Sabrina Grundtner, Julia Regina Sondermann, Giacomo Carta, Manuela Schmidt.

  Introduction: Metaproteomics is a rapidly advancing field that offers unique insights into the taxonomic composition and the functional activity of microbial communities, and their effects on host physiology. Classically, data-dependent acquisition (DDA) mass spectrometry (MS) has been applied for peptide identification and quantification in metaproteomics. However, DDA-MS exhibits well-known limitations in terms of depth, sensitivity, and reproducibility. Consequently, methodological improvements are required to better characterize the protein landscape of microbiomes and their interactions with the host.Methods: We present an optimized proteomic workflow that utilizes the information captured by Parallel Accumulation-Serial Fragmentation (PASEF) MS for comprehensive metaproteomic studies in complex fecal samples of mice.
Results and discussion: We show that implementing PASEF using a DDA acquisition scheme (DDA-PASEF) increased peptide quantification up to 5 times and reached higher accuracy and reproducibility compared to previously published classical DDA and data-independent acquisition (DIA) methods. Furthermore, we demonstrate that the combination of DIA, PASEF, and neuronal-network-based data analysis, was superior to DDA-PASEF in all mentioned parameters. Importantly, DIA-PASEF expanded the dynamic range towards low-abundant proteins and it doubled the quantification of proteins with unknown or uncharacterized functions. Compared to previous classical DDA metaproteomic studies, DIA-PASEF resulted in the quantification of up to 4 times more taxonomic units using 16 times less injected peptides and 4 times shorter chromatography gradients. Moreover, 131 additional functional pathways distributed across more and even uniquely identified taxa were profiled as revealed by a peptide-centric taxonomic-functional analysis. We tested our workflow on a validated preclinical mouse model of neuropathic pain to assess longitudinal changes in host-gut microbiome interactions associated with pain - an unexplored topic for metaproteomics. We uncovered the significant enrichment of two bacterial classes upon pain, and, in addition, the upregulation of metabolic activities previously linked to chronic pain as well as various hitherto unknown ones. Furthermore, our data revealed pain-associated dynamics of proteome complexes implicated in the crosstalk between the host immune system and the gut microbiome. In conclusion, the DIA-PASEF metaproteomic workflow presented here provides a stepping stone towards a deeper understanding of microbial ecosystems across the breadth of biomedical and biotechnological fields.

Keywords:  chronic neuropathic pain; data-independent acquisition; host-microbiome interactions; metaproteomics; mouse gut microbiome; parallel accumulation-serial fragmentation

DOI:  https://doi.org/10.3389/fmicb.2023.1258703
Commun Biol. 2023 10 30. 6(1): 1101

Achieving quantitative reproducibility in label-free multisite DIA experiments through multirun alignment.

Shubham Gupta, Justin C Sing, Hannes L Röst.

DIA is a mainstream method for quantitative proteomics, but consistent quantification across multiple LC-MS/MS instruments remains a bottleneck in parallelizing data acquisition. One reason for this inconsistency and missing quantification is the retention time shift which current software does not adequately address for runs from multiple sites. We present multirun chromatogram alignment strategies to map peaks across columns, including the traditional reference-based Star method, and two novel approaches: MST and Progressive alignment. These reference-free strategies produce a quantitatively accurate data-matrix, even from heterogeneous multi-column studies. Progressive alignment also generates merged chromatograms from all runs which has not been previously achieved for LC-MS/MS data. First, we demonstrate the effectiveness of multirun alignment strategies on a gold-standard annotated dataset, resulting in a threefold reduction in quantitation error-rate compared to non-aligned DIA results. Subsequently, on a multi-species dataset that DIAlignR effectively controls the quantitative error rate, improves precision in protein measurements, and exhibits conservative peak alignment. We next show that the MST alignment reduces cross-site CV by 50% for highly abundant proteins when applied to a dataset from 11 different LC-MS/MS setups. Finally, the reanalysis of 949 plasma runs with multirun alignment revealed a more than 50% increase in insulin resistance (IR) and respiratory viral infection (RVI) proteins, identifying 11 and 13 proteins respectively, compared to prior analysis without it. The three strategies are implemented in our DIAlignR workflow (>2.3) and can be combined with linear, non-linear, or hybrid pairwise alignment.

DOI: https://doi.org/10.1038/s42003-023-05437-2
J Immunother Cancer. 2023 10;pii: e007073. [Epub ahead of print]11(10):

Current perspectives on mass spectrometry-based immunopeptidomics: the computational angle to tumor antigen discovery.

Bing Zhang, Michal Bassani-Sternberg.

  Identification of tumor antigens presented by the human leucocyte antigen (HLA) molecules is essential for the design of effective and safe cancer immunotherapies that rely on T cell recognition and killing of tumor cells. Mass spectrometry (MS)-based immunopeptidomics enables high-throughput, direct identification of HLA-bound peptides from a variety of cell lines, tumor tissues, and healthy tissues. It involves immunoaffinity purification of HLA complexes followed by MS profiling of the extracted peptides using data-dependent acquisition, data-independent acquisition, or targeted approaches. By incorporating DNA, RNA, and ribosome sequencing data into immunopeptidomics data analysis, the proteogenomic approach provides a powerful means for identifying tumor antigens encoded within the canonical open reading frames of annotated coding genes and non-canonical tumor antigens derived from presumably non-coding regions of our genome. We discuss emerging computational challenges in immunopeptidomics data analysis and tumor antigen identification, highlighting key considerations in the proteogenomics-based approach, including accurate DNA, RNA and ribosomal sequencing data analysis, careful incorporation of predicted novel protein sequences into reference protein database, special quality control in MS data analysis due to the expanded and heterogeneous search space, cancer-specificity determination, and immunogenicity prediction. The advancements in technology and computation is continually enabling us to identify tumor antigens with higher sensitivity and accuracy, paving the way toward the development of more effective cancer immunotherapies.

Keywords:  Antigens, Neoplasm; Computational Biology; Immunity

DOI:  https://doi.org/10.1136/jitc-2023-007073
J Proteome Res. 2023 Nov 01.

Merging Full-Spectrum and Fragment Ion Intensity Predictions from Deep Learning for High-Quality Spectral Libraries.

Chak Ming Jerry Chan, Henry Lam.

  Spectral libraries are useful resources in proteomic data analysis. Recent advances in deep learning allow tandem mass spectra of peptides to be predicted from their amino acid sequences. This enables predicted spectral libraries to be compiled, and searching against such libraries has been shown to improve the sensitivity in peptide identification over conventional sequence database searching. However, current prediction models lack support for longer peptides, and thus far, predicted library searching has only been demonstrated for backbone ion-only spectrum prediction methods. Here, we propose a deep learning-based full-spectrum prediction method to generate predicted spectral libraries for peptide identification. We demonstrated the superiority of using full-spectrum libraries over backbone ion-only prediction approaches in spectral library searching. Furthermore, merging spectra from different prediction models, as a form of ensemble learning, can produce improved spectral libraries, in terms of identification sensitivity. We also show that a hybrid library combining predicted and experimental spectra can lead to 20% more confident identifications over experimental library searching or sequence database searching.

Keywords:  deep learning; peptide identification; spectral library

DOI:  https://doi.org/10.1021/acs.jproteome.3c00180
Crit Rev Anal Chem. 2023 Nov 01. 1-20

Mass Spectrometry Analysis for Clinical Applications: A Review.

Hong Zhang, Yali Yang, Yanxiao Jiang, Meng Zhang, Zhilong Xu, Xiaofei Wang, Jie Jiang.

  Mass spectrometry (MS) has become an attractive analytical method in clinical analysis due to its comprehensive advantages of high sensitivity, high specificity and high throughput. Separation techniques coupled MS detection (e.g., LC-MS/MS) have shown unique advantages over immunoassay and have developed as golden criterion for many clinical applications. This review summarizes the characteristics and applications of MS, and emphasizes the high efficiency of MS in clinical research. In addition, this review also put forward further prospects for the future of mass spectrometry technology, including the introduction of miniature MS instruments, point-of-care detection and high-throughput analysis, to achieve better development of MS technology in various fields of clinical application. Moreover, as ambient ionization mass spectrometry (AIMS) requires little or no sample pretreatment and improves the flux of MS, this review also summarizes its potential applications in clinic.

Keywords:  Mass spectrometry; ambient ionization mass spectrometry; application; clinical research

DOI:  https://doi.org/10.1080/10408347.2023.2274039
Nat Commun. 2023 Oct 31. 14(1): 6909

Loss of Grem1-lineage chondrogenic progenitor cells causes osteoarthritis.

Jia Q Ng, Toghrul H Jafarov, Christopher B Little, Tongtong Wang, Abdullah M Ali, Yan Ma, Georgette A Radford, Laura Vrbanac, Mari Ichinose, Samuel Whittle, David J Hunter, Tamsin R M Lannagan, Nobumi Suzuki, Jarrad M Goyne, Hiroki Kobayashi, Timothy C Wang, David R Haynes, Danijela Menicanin, Stan Gronthos, Daniel L Worthley, Susan L Woods, Siddhartha Mukherjee.

Osteoarthritis (OA) is characterised by an irreversible degeneration of articular cartilage. Here we show that the BMP-antagonist Gremlin 1 (Grem1) marks a bipotent chondrogenic and osteogenic progenitor cell population within the articular surface. Notably, these progenitors are depleted by injury-induced OA and increasing age. OA is also caused by ablation of Grem1 cells in mice. Transcriptomic and functional analysis in mice found that articular surface Grem1-lineage cells are dependent on Foxo1 and ablation of Foxo1 in Grem1-lineage cells caused OA. FGFR3 signalling was confirmed as a promising therapeutic pathway by administration of pathway activator, FGF18, resulting in Grem1-lineage chondrocyte progenitor cell proliferation, increased cartilage thickness and reduced OA. These findings suggest that OA, in part, is caused by mechanical, developmental or age-related attrition of Grem1 expressing articular cartilage progenitor cells. These cells, and the FGFR3 signalling pathway that sustains them, may be effective future targets for biological management of OA.

DOI: https://doi.org/10.1038/s41467-023-42199-1