Mass Spectrometry-based Omics Research Articles

Development of a Global Metabo-Lipid-Prote-omics Workflow to Compare Healthy Proximal and Distal Colonic Epithelium in Mice.

A multimetabo-lipid-prote-omics workflow was developed to characterize the molecular interplay within proximal (PC) and distal (DC) colonic epithelium of healthy mice. This multiomics data set lays the foundation to better understand the two tissue types and can be used to study, for example, colon-related diseases like colorectal cancer or inflammatory bowel disease. First, the methyl tert-butyl ether extraction method was optimized, so that from a single tissue biopsy >350 reference-matched metabolites, >1850 reference-matched lipids, and >4500 proteins were detected by using targeted and untargeted metabolomics, untargeted lipidomics, and proteomics. Next, each omics-data set was analyzed individually and then merged with the additional omics disciplines to generate a deep understanding of the underlying complex regulatory network within the colon. Our data demonstrates, for example, differences in mucin formation, detected on substrate level as well as on enzyme level, and altered lipid metabolism by the detection of phospholipases hydrolyzing sphingomyelins to ceramides. In conclusion, the combination of the three mass spectrometry-based omics techniques can better entangle the functional and regional differences between PC and DC tissue compared to each single omics technique.

Memory effects of prior subculture may impact the quality of multiomic perturbation profiles

Mass spectrometry-based omics technologies are increasingly used in perturbation studies to map drug effects to biological pathways by identifying significant molecular events. Significance is influenced by fold change and variation of each molecular parameter, but also by multiple testing corrections. While the fold change is largely determined by the biological system, the variation is determined by experimental workflows. Here, it is shown that memory effects of prior subculture can influence the variation of perturbation profiles using the two colon carcinoma cell lines SW480 and HCT116. These memory effects are largely driven by differences in growth states that persist into the perturbation experiment. In SW480 cells, memory effects combined with moderate treatment effects amplify the variation in multiple omics levels, including eicosadomics, proteomics, and phosphoproteomics. With stronger treatment effects, the memory effect was less pronounced, as demonstrated in HCT116 cells. Subculture homogeneity was controlled by real-time monitoring of cell growth. Controlled homogeneous subculture resulted in a perturbation network of 321 causal conjectures based on combined proteomic and phosphoproteomic data, compared to only 58 causal conjectures without controlling subculture homogeneity in SW480 cells. Some cellular responses and regulatory events were identified that extend the mode of action of arsenic trioxide (ATO) only when accounting for these memory effects. Controlled prior subculture led to the finding of a synergistic combination treatment of ATO with the thioredoxin reductase 1 inhibitor auranofin, which may prove useful in the management of NRF2-mediated resistance mechanisms.

OmicsMIC: a comprehensive benchmarking platform for robust comparison of imputation methods in mass spectrometry-based omics data.

Mass spectrometry is a powerful and widely used tool for generating proteomics, lipidomics and metabolomics profiles, which is pivotal for elucidating biological processes and identifying biomarkers. However, missing values in mass spectrometry-based omics data may pose a critical challenge for the comprehensive identification of biomarkers and elucidation of the biological processes underlying human complex disorders. To alleviate this issue, various imputation methods for mass spectrometry-based omics data have been developed. However, a comprehensive comparison of these imputation methods is still lacking, and researchers are frequently confronted with a multitude of options without a clear rationale for method selection. To address this pressing need, we developed omicsMIC (mass spectrometry-based omics with Missing values Imputation methods Comparison platform), an interactive platform that provides researchers with a versatile framework to evaluate the performance of 28 diverse imputation methods. omicsMIC offers a nuanced perspective, acknowledging the inherent heterogeneity in biological data and the unique attributes of each dataset. Our platform empowers researchers to make data-driven decisions in imputation method selection based on real-time visualizations of the outcomes associated with different imputation strategies. The comprehensive benchmarking and versatility of omicsMIC make it a valuable tool for the scientific community engaged in mass spectrometry-based omics research. omicsMIC is freely available at https://github.com/WQLin8/omicsMIC.

Making MS Omics Data ML-Ready: SpeCollate Protocols.

The increasing complexity and volume of mass spectrometry (MS) data have presented new challenges and opportunities for proteomics data analysis and interpretation. In this chapter, we provide a comprehensive guide to transforming MS data for machine learning (ML) training, inference, and applications. The chapter is organized into three parts. The first part describes the data analysis needed for MS-based experiments and a general introduction to our deep learning model SpeCollate-which we will use throughout the chapter for illustration. The second part of the chapter explores the transformation of MS data for inference, providing a step-by-step guide for users to deduce peptides from their MS data. This section aims to bridge the gap between data acquisition and practical applications by detailing the necessary steps for data preparation and interpretation. In the final part, we present a demonstrative example of SpeCollate, a deep learning-based peptide database search engine that overcomes the problems of simplistic simulation of theoretical spectra and heuristic scoring functions for peptide-spectrum matches by generating joint embeddings for spectra and peptides. SpeCollate is a user-friendly tool with an intuitive command-line interface to perform the search, showcasing the effectiveness of the techniques and methodologies discussed in the earlier sections and highlighting the potential of machine learning in the context of mass spectrometry data analysis. By offering a comprehensive overview of data transformation, inference, and ML model applications for mass spectrometry, this chapter aims to empower researchers and practitioners in leveraging the power of machine learning to unlock novel insights and drive innovation in the field of mass spectrometry-based omics.

Implementation of multiomic mass spectrometry approaches for the evaluation of human health following environmental exposure.

Omics analyses collectively refer to the possibility of profiling genetic variants, RNA, epigenetic markers, proteins, lipids, and metabolites. The most common analytical approaches used for detecting molecules present within biofluids related to metabolism are vibrational spectroscopy techniques, represented by infrared, Raman, and nuclear magnetic resonance (NMR) spectroscopies and mass spectrometry (MS). Omics-based assessments utilizing MS are rapidly expanding and being applied to various scientific disciplines and clinical settings. Most of the omics instruments are operated by specialists in dedicated laboratories; however, the development of miniature portable omics has made the technology more available to users for field applications. Variations in molecular information gained from omics approaches are useful for evaluating human health following environmental exposure and the development and progression of numerous diseases. As MS technology develops so do statistical and machine learning methods for the detection of molecular deviations from personalized metabolism, which are correlated to altered health conditions, and they are intended to provide a multi-disciplinary overview for researchers interested in adding multiomic analysis to their current efforts. This includes an introduction to mass spectrometry-based omics technologies, current state-of-the-art capabilities and their respective strengths and limitations for surveying molecular information. Furthermore, we describe how knowledge gained from these assessments can be applied to personalized medicine and diagnostic strategies.

Multi-Omics Strategies to Investigate the Biodegradation of Hexahydro-1,3,5-trinitro-1,3,5-triazine in Rhodococcus sp. Strain DN22.

Hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) is an energetic and persistent explosive with long-lasting properties. Rhodococcus sp. strain DN22 has been discovered to be a microbe capable of degrading RDX. Herein, the complete genome of Rhodococcus sp. strain DN22 was sequenced and analyzed. The entire sequences of genes that encoded the two proteins participating in RDX degradation in Rhodococcus sp. strain DN22 were obtained, and were validated through proteomic data. In addition, few studies have investigated the physiological changes and metabolic pathways occurring within Rhodococcus sp. cells when treated with RDX, particularly through mass spectrometry-based omics. Hence, proteomic and metabolomic analyses were carried out on Rhodococcus sp. strain DN22 with the existence or lack of RDX in the medium. A total of 3186 proteins were identified between the two groups, with 115 proteins being significantly differentially expressed proteins. There were 1056 metabolites identified in total, among which 130 metabolites were significantly different. Through the combined analysis of differential proteomics and metabolomics, KEGG pathways including two-component system, ABC transporters, alanine, aspartate and glutamate metabolism, arginine biosynthesis, purine metabolism, nitrogen metabolism, and phosphotransferase system (PTS), were observed to be significantly enriched. These findings provided ponderable perspectives on the physiological alterations and metabolic pathways in Rhodococcus sp. strain DN22, responding to the existence or lack of RDX. This study is anticipated to expand the knowledge of Rhodococcus sp. strain DN22, as well as advancing understanding of microbial degradation.

Recent advances in mass spectrometry-based proteomics and metabolomics in chronic rhinosinusitis with nasal polyps.

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a complex and heterogeneous disease, typically diagnosed through endoscopy and computed tomography and treated with glucocorticoid or surgery. There is an urgent need to develop molecular-level diagnostic or prognostic tools to better understand the pathophysiology of CRSwNP. Proteomics and metabolomics, emerging fields, offer significant potential in elucidating the mechanisms underlying CRSwNP. Mass spectrometry, a powerful and sensitive tool for trace substance detection, is broadly applied for proteomics and metabolomics analysis in CRSwNP research. While previous literature has summarized the advancement of mass spectrometry-based CRSwNP proteomics from 2004 to 2018, recent years have seen new advances in this field, particularly about non-invasive samples and exosomes. Furthermore, mass spectrometry-based CRSwNP metabolomics research has opened new avenues for inquiry. Therefore, we present a comprehensive review of mass spectrometry-based proteomics and metabolomics studies on CRSwNP conducted between 2019 and 2022. Specifically, we highlight protein and metabolic biomarkers that have been utilized as diagnostic or prognostic markers for CRSwNP. Lastly, we conclude with potential directions for future mass spectrometry-based omics studies of CRSwNP.

Investigating the effects of a FAAH inhibitor in the laterodorsal tegmental nucleus using a new ex vivo mouse preparation

As part of the brainstem reticular activating system, the laterodorsal tegmentum (LDT) is a central player in regulating sleep, arousal, and motivated behaviors including addiction. The neuronal lipids within the LDT that could regulate neuronal activity and LDT signaling are not fully characterized due, in part, to the complication of the presence of fatty acid amide hydrolases (FAAH). To determine the lipids in the LDT, mouse brain slices at different stages of brain development (16, 26 and 66 days) were exposed ex vivo to the FAAH inhibitor oleyl trifluoromethyl ketone (OTMK). Metabolomics and proteomic analyses were conducted on matched samples. Metabolomics analysis revealed differences between OTMK treated and untreated LDT. Furthermore, a distinct phenotype (proteomic profile) as a function of OTMK treatment was observed in LDT from adolescent (66 days) mice indicating an effect of treatment with OTMK at later stages of brain development. Our data indicate that this ex vivo preparation could facilitate screening of different FAAH inhibitors in mammalian tissues, and more importantly, this preparation should allow a deeper characterization of global mass spectrometry-based omics profiles within the LDT.

Omics markers of platelet transfusion in trauma patients.

Even in the era of the COVID-19 pandemic, trauma remains the global leading cause of mortality under the age of 49. Trauma-induced coagulopathy is a leading driver of early mortality in critically ill patients, and transfusion of platelet products is a life-saving intervention to restore hemostasis in the bleeding patient. However, despite extensive functional studies based on viscoelastic assays, limited information is available about the impact of platelet transfusion on the circulating molecular signatures in trauma patients receiving platelet transfusion. To bridge this gap, we leveraged metabolomics and proteomics approaches to characterize longitudinal plasma samples (n = 118; up to 11 time points; total samples: 759) from trauma patients enrolled in the Control Of Major Bleeding After Trauma (COMBAT) study. Samples were collected in the field, in the emergency department (ED), and at intervals up to 168 h (7 days) post-hospitalization. Transfusion of platelet (PLT) products was performed (n = 30; total samples: 250) in the ED through 24 h post-hospitalization. Longitudinal plasma samples were subjected to mass spectrometry-based metabolomics and proteomics workflows. Multivariate analyses were performed to determine omics markers of transfusion of one, two, three, or more PLT transfusions. Higher levels of tranexamic acid (TXA), inflammatory proteins, carnitines, and polyamines were detected in patients requiring PLT transfusion. Correlation of PLT units with omics data suggested sicker patients required more units and partially overlap with the population requiring transfusion of packed red blood cell products. Furthermore, platelet activation was likely increased in the most severely injured patients. Fatty acid levels were significantly lower in PLT transfusion recipients (at time of maximal transfusion: Hour 4) compared with non-recipients, while carnitine levels were significantly higher. Fatty acid levels restore later in the time course (e.g., post-PLT transfusion). The present study provides the first multi-omics characterization of platelet transfusion efficacy in a clinically relevant cohort of trauma patients. Physiological alterations following transfusion were detected, highlighting the efficacy of mass spectrometry-based omics techniques to improve personalized transfusion medicine. More specialized clinical research studies focused on PLT transfusion, including organized pre and post transfusion sample collection and limitation to PLT products only, are required to fully understand subsequent metabolomic and proteomic alterations.

Mass Spectrometry-Based Omics and Imaging Technique: A Novel Tool for Molecular Toxicology and Health Impacts

Mass spectrometry (MS)-based omics and MS imaging (MSI) technique have been adopted as a powerful analytical strategy to investigate the toxicological effect on human health and molecular mechanism of environmental pollutants. In contrast to conventional toxicological methodologies, MS-based omics and MSI technique provides the possibility to analysis a plenty of molecules in qualitative, quantitative and spatial distribution variation including parent compounds and their metabolites as well as related endogenous metabolome, lipidome and proteome. In this perspective, we showcase the progress in MS-based omics analysis and MSI, and highlight sample preparation, high-throughput screening, data mining and more which are critical to the success of acquiring the exogenous exposure-induced molecule profiling variation and effective risk assessment method at the cellular and organ scale. The aim of this review is to contribute to a better understanding of potential molecule toxicity, pathological findings and action mechanisms caused by pollutant exposure, avoiding the environmental damage on human health if possible.

Comprehensive Profiling of Rapamycin Interacting Proteins with Multiple Mass Spectrometry-Based Omics Techniques.

Profiling drug-protein interactions is critical for understanding a drug's mechanism of action and predicting the possible adverse side effects. However, to comprehensively profile drug-protein interactions remains a challenge. To address this issue, we proposed a strategy that integrates multiple mass spectrometry-based omics analysis to provided global drug-protein interactions, including physical interactions and functional interactions, with rapamycin (Rap) as a model. Chemoproteomics profiling reveals 47 Rap binding proteins including the known target protein FKBP12 with high confidence. Gen Ontology enrichment analysis suggested that the Rap binding proteins are implicated in several important cellular processes, such as DNA replication, immunity, autophagy, programmed cell death, aging, transcription modulation, vesicle-mediated transport, membrane organization, and carbohydrate and nucleobase metabolic processes. The phosphoproteomics profiling revealed 255 down-regulated and 150 up-regulated phosphoproteins responding to Rap stimulation; they mainly involve the PI3K-Akt-mTORC1 signaling axis. Untargeted metabolomic profiling revealed 22 down-regulated metabolites and 75 up-regulated metabolites responding to Rap stimulation; they are mainly associated with the synthesis processes of pyrimidine and purine. The integrative multiomics data analysis provides deep insight into the drug-protein interactions and reveals Rap's complicated mechanism of action.

Mass spectrometry and machine learning in the identification of COVID-19 biomarkers

Identifying specific diagnostic and prognostic biological markers of COVID-19 can improve disease surveillance and therapeutic opportunities. Mass spectrometry combined with machine and deep learning techniques has been used to identify pathways that could be targeted therapeutically. Moreover, circulating biomarkers have been identified to detect individuals infected with SARS-CoV-2 and at high risk of hospitalization. In this review, we have surveyed studies that have combined mass spectrometry-based omics techniques (proteomics, lipdomics, and metabolomics) and machine learning/deep learning to understand COVID-19 pathogenesis. After a literature search, we show 42 studies that applied reproducible, accurate, and sensitive mass spectrometry-based analytical techniques and machine/deep learning methods for COVID-19 biomarker discovery and validation. We also demonstrate that multiomics data results in classification models with higher performance. Furthermore, we focus on the combination of MALDI-TOF Mass Spectrometry and machine learning as a diagnostic and prognostic tool already present in the clinics. Finally, we reiterate that despite advances in this field, more optimization in the analytical and computational parts, such as sample preparation, data acquisition, and data analysis, will improve biomarkers that can be used to obtain more accurate diagnostic and prognostic tools.

Defining atherosclerotic plaque biology by mass spectrometry-based omics approaches.

Atherosclerosis is the principal cause of vascular diseases and one of the leading causes of worldwide death. Even though several insights into its natural course, risk factors and interventions have been identified, it is still an ongoing global pandemic. Since the structure and biochemical composition of the plaques show high heterogeneity, a comprehensive understanding of the intraplaque composition, its microenvironment, and the mechanisms of the progression and instability across different vascular beds at their progression stages is crucial for better risk stratification and treatment modalities. Even though several cell-based studies, animal studies, and extensive multicentric population studies have been conducted concerning cardiovascular diseases for assessing the risk factors and plaque biology, the studies on human clinical samples are very limited. New novel approaches utilize samples from percutaneous coronary interventions, which could possibly gain more access to clinical samples at different stages of the diseases without complex invasive resections. As an emerging technological platform in disease discovery research, mass spectrometry-based omics technologies offer capabilities for a comprehensive understanding of the mechanisms linked to several vascular diseases. Here, we discuss the cellular and molecular processes of atherosclerosis, different mass spectrometry-based omics approaches, and the studies mostly done on clinical samples of atheroma plaque using mass spectrometry-based proteomics, metabolomics and lipidomics approaches.

An optimized approach and inflation media for obtaining complimentary mass spectrometry-based omics data from human lung tissue.

Human disease states are biomolecularly multifaceted and can span across phenotypic states, therefore it is important to understand diseases on all levels, across cell types, and within and across microanatomical tissue compartments. To obtain an accurate and representative view of the molecular landscape within human lungs, this fragile tissue must be inflated and embedded to maintain spatial fidelity of the location of molecules and minimize molecular degradation for molecular imaging experiments. Here, we evaluated agarose inflation and carboxymethyl cellulose embedding media and determined effective tissue preparation protocols for performing bulk and spatial mass spectrometry-based omics measurements. Mass spectrometry imaging methods were optimized to boost the number of annotatable molecules in agarose inflated lung samples. This optimized protocol permitted the observation of unique lipid distributions within several airway regions in the lung tissue block. Laser capture microdissection of these airway regions followed by high-resolution proteomic analysis allowed us to begin linking the lipidome with the proteome in a spatially resolved manner, where we observed proteins with high abundance specifically localized to the airway regions. We also compared our mass spectrometry results to lung tissue samples preserved using two other inflation/embedding media, but we identified several pitfalls with the sample preparation steps using this preservation method. Overall, we demonstrated the versatility of the inflation method, and we can start to reveal how the metabolome, lipidome, and proteome are connected spatially in human lungs and across disease states through a variety of different experiments.

Advances in ionisation techniques for mass spectrometry-based omics research.

Omics analysis by mass spectrometry (MS) is a vast field, with proteomics, metabolomics and lipidomics dominating recent research by exploiting biological MS ionisation techniques. Traditional MS ionisation techniques such as electrospray ionisation have limitations in analyte-specific sensitivity, modes of sampling and throughput, leading to many researchers investigating new ionisation methods for omics research. In this review, we examine the current landscape of these new ionisation techniques, divided into the three groups of (electro)spray-based, laser-based and other miscellaneous ionisation techniques. Due to the wide range of new developments, this review can only provide a starting point for further reading on each ionisation technique, as each have unique benefits, often for specialised applications, which promise beneficial results for different areas in the omics world.

A Preprocessing Tool for Enhanced Ion Mobility-Mass Spectrometry-Based Omics Workflows.

The ability to improve the data quality of ion mobility-mass spectrometry (IM-MS) measurements is of great importance for enabling modular and efficient computational workflows and gaining better qualitative and quantitative insights from complex biological and environmental samples. We developed the PNNL PreProcessor, a standalone and user-friendly software housing various algorithmic implementations to generate new MS-files with enhanced signal quality and in the same instrument format. Different experimental approaches are supported for IM-MS based on Drift-Tube (DT) and Structures for Lossless Ion Manipulations (SLIM), including liquid chromatography (LC) and infusion analyses. The algorithms extend the dynamic range of the detection system, while reducing file sizes for faster and memory-efficient downstream processing. Specifically, multidimensional smoothing improves peak shapes of poorly defined low-abundance signals, and saturation repair reconstructs the intensity profile of high-abundance peaks from various analyte types. Other functionalities are data compression and interpolation, IM demultiplexing, noise filtering by low intensity threshold and spike removal, and exporting of acquisition metadata. Several advantages of the tool are illustrated, including an increase of 19.4% in lipid annotations and a two-times faster processing of LC-DT IM-MS data-independent acquisition spectra from a complex lipid extract of a standard human plasma sample. The software is freely available at https://omics.pnl.gov/software/pnnl-preprocessor.

First person – Marte Molenaars and Bauke Schomakers

ABSTRACTFirst Person is a series of interviews with the first authors of a selection of papers published in Disease Models & Mechanisms, helping early-career researchers promote themselves alongside their papers. Marte Molenaars and Bauke Schomakers are co-first authors on ‘Metabolomics and lipidomics in Caenorhabditis elegans using a single-sample preparation’, published in DMM. Marte conducted the research described in the article while a PhD student in the lab of Riekelt Houtkooper at Amsterdam UMC, Amsterdam, The Netherlands, and is now a postdoc in the lab of Richard Possemato at New York University School of Medicine, New York, NY, USA. During her PhD, she was investigating cellular metabolic pathways and how to use them to target ageing and age-related diseases, and currently she focuses on metabolic pathways in cancer cells as a postdoc. Bauke is a Research Analyst in the lab of Riekelt Houtkooper at Amsterdam UMC, Amsterdam, The Netherlands, investigating liquid chromatography–mass spectrometry-based omics methods for the comprehensive analysis of biological samples.

Mass Spectrometry-Based Omics for the Characterization of Triple-Negative Breast Cancer Bio-Signature.

Triple-negative breast cancer (TNBC) represents an unmet medical need due to a high rate of metastatic occurrence and poor overall survival, pathology aggressiveness, heterogeneous clinical behavior and limited cytotoxic chemotherapy options available because of the absence of targetable receptors. The current standard of care in TNBC is represented by chemotherapy and surgery associated with low overall survival and high relapse rates. Hopes of overcoming current limited and unspecific approaches of TNBC therapy lie in studying the metabolic rewiring of these types of breast cancer, thus understanding the mechanisms involved in the occurrence and progression of the disease. Due to its heterogeneity, a clinically relevant sub-classification of this type of breast cancer based on biomarker panels is greatly needed in order to guide treatment decisions. Mass spectrometry-based omics may provide very useful tools to address the current needs of targetable biomarker discovery and validation. The present review aims to provide a comprehensive view of the current clinical diagnosis and therapy of TNBC highlighting the need for a new approach. Therefore, this paper offers a detailed mass spectrometry-based snapshot of TNBC metabolic adjustment, emphasizing a complex network of variables governing the diverse and aggressive clinical behavior of TNBC.

StatTarget: A streamlined tool for signal drift correction and interpretations of quantitative mass spectrometry-based omics data

Large-scale quantitative mass spectrometry-based metabolomics and proteomics study requires the long-term analysis of multiple batches of biological samples, which often accompanied with significant signal drift and various inter- and intra-batch variations. The unwanted variations can lead to poor inter- and intra-day reproducibility, which is a hindrance to discover real significance. The use of quality control samples and data treatment strategies in the quality assurance procedure provides a mechanism to evaluate the quality and remove the analytical variance of the data. The statTarget we developed is a streamlined tool with an easy-to-use graphical user interface and an integrated suite of algorithms specifically developed for the evaluation of data quality and removal of unwanted variations for quantitative mass spectrometry-based omics data. A novel quality control-based random forest signal correction algorithm, which can remove inter- and intra-batch unwanted variations at feature-level was implanted in the statTarget. Our evaluation based on real samples showed the developed algorithm could improve the data precision and statistical accuracy for mass spectrometry-based metabolomics and proteomics data. Additionally, the statTarget offers the streamlined procedures for data imputation, data normalization, univariate analysis, multivariate analysis, and feature selection. To conclude, the statTarget allows user-friendly the improvement of the data precision for uncovering the biologically differences, which largely facilitates quantitative mass spectrometry-based omics data processing and statistical analysis.

Metabotypes with elevated protein and lipid catabolism and inflammation precede clinical mastitis in prepartal transition dairy cows

Clinical mastitis (CM), the most prevalent and costly disease in dairy cows, is diagnosed most commonly shortly after calving. Current indicators do not satisfactorily predict CM. This study aimed to develop a robust and comprehensive mass spectrometry-based metabolomic and lipidomic workflow using untargeted ultra-performance liquid chromatography high-resolution mass spectrometry for predictive biomarker detection. Using a nested case-control design, we measured weekly during the prepartal transition period differences in serum metabolites, lipids, inflammation markers, and minerals between clinically healthy Holstein dairy cows diagnosed with mastitis postcalving (CMP; n = 8; CM diagnosis d 1 = 3 cows, d 2 = 2 cows, d 4 = 1 cow; d 25 = 1 cow, and d 43 = 1 cow that had subclinical mastitis since d 3) or not (control; n = 9). The largest fold differences between CMP and control cows during the prepartal transition period were observed for 3'-sialyllactose in serum. Seven metabolites (N-methylethanolamine phosphate, choline, phosphorylcholine, free carnitine, trimethyl lysine, tyrosine, and proline) and 3 metabolite groups (carnitines, AA metabolites, and water-soluble phospholipid metabolites) could correctly classify cows for their future CM status at both 21 and 14 d before calving. Biochemical analysis using lipid and metabolite-specific commercial diagnostic kits supported our mass spectrometry-based omics results and additionally showed elevated inflammatory markers (serum amyloid A and visfatin) in CMP cows. In conclusion, metabolic phenotypes (i.e., metabotype) with elevated protein and lipid metabolism and inflammation may precede CM in prepartal transition dairy cows. The discovered serum metabolites and lipids may assist in predictive diagnostics, prevention strategies, and early treatment intervention against CM, and thereby improve cow health and welfare.