Explore the vast range of titles available.

Animal production and health (APH) is an important sector in the world economy, representing a large proportion of the budget of all member states in the European Union and in other continents. APH is a highly competitive sector with a strong emphasis on innovation and, albeit with country to country variations, on scientific research. Proteomics (the study of all proteins present in a given tissue or fluid – i.e. the proteome) has an enormous potential when applied to APH. Nevertheless, for a variety of reasons and in contrast to disciplines such as plant sciences or human biomedicine, such potential is only now being tapped. To counter such limited usage, 6 years ago we created a consortium dedicated to the applications of Proteomics to APH, specifically in the form of a Cooperation in Science and Technology (COST) Action, termed FA1002 – Proteomics in Farm Animals: www.cost-faproteomics.org. In 4 years, the consortium quickly enlarged to a total of 31 countries in Europe, as well as Israel, Argentina, Australia and New Zealand. This article has a triple purpose. First, we aim to provide clear examples on the applications and benefits of the use of proteomics in all aspects related to APH. Second, we provide insights and possibilities on the new trends and objectives for APH proteomics applications and technologies for the years to come. Finally, we provide an overview and balance of the major activities and accomplishments of the COST Action on Farm Animal Proteomics. These include activities such as the organization of seminars, workshops and major scientific conferences, organization of summer schools, financing Short-Term Scientific Missions (STSMs) and the generation of scientific literature. Overall, the Action has attained all of the proposed objectives and has made considerable difference by putting proteomics on the global map for animal and veterinary researchers in general and by contributing significantly to reduce the East–West and North–South gaps existing in the European farm animal research. Future activities of significance in the field of scientific research, involving members of the action, as well as others, will likely be established in the future.

Many research questions in fields such as personalized medicine, drug screens or systems biology depend on obtaining consistent and quantitatively accurate proteomics data from many samples. SWATH‐MS is a specific variant of data‐independent acquisition (DIA) methods and is emerging as a technology that combines deep proteome coverage capabilities with quantitative consistency and accuracy. In a SWATH‐MS measurement, all ionized peptides of a given sample that fall within a specified mass range are fragmented in a systematic and unbiased fashion using rather large precursor isolation windows. To analyse SWATH‐MS data, a strategy based on peptide‐centric scoring has been established, which typically requires prior knowledge about the chromatographic and mass spectrometric behaviour of peptides of interest in the form of spectral libraries and peptide query parameters. This tutorial provides guidelines on how to set up and plan a SWATH‐MS experiment, how to perform the mass spectrometric measurement and how to analyse SWATH‐MS data using peptide‐centric scoring. Furthermore, concepts on how to improve SWATH‐MS data acquisition, potential trade‐offs of parameter settings and alternative data analysis strategies are discussed. Graphical Abstract SWATH‐MS combines deep proteome coverage with quantitative consistency and accuracy and is often the method of choice for personalized medicine, drug screens or systems biology. This tutorial provides guidelines on how to set up SWATH‐MS experiments, perform the mass spectrometric measurements and analyse the data.

Advances in molecular biology, microfluidics and bioinformatics have empowered the study of thousands or even millions of individual cells from malignant tumours at the single-cell level of resolution. This high-dimensional, multi-faceted characterization of the genomic, transcriptomic, epigenomic and proteomic features of the tumour and/or the associated immune and stromal cells enables the dissection of tumour heterogeneity, the complex interactions between tumour cells and their microenvironment, and the details of the evolutionary trajectory of each tumour. Single-cell transcriptomics, the ability to track individual T cell clones through paired sequencing of the T cell receptor genes and high-dimensional single-cell spatial analysis are all areas of particular relevance to immuno-oncology. Multidimensional biomarker signatures will increasingly be crucial to guiding clinical decision-making in each patient with cancer. High-dimensional single-cell technologies are likely to provide the resolution and richness of data required to generate such clinically relevant signatures in immuno-oncology. In this Perspective, we describe advances made using transformative single-cell analysis technologies, especially in relation to clinical response and resistance to immunotherapy, and discuss the growing utility of single-cell approaches for answering important research questions.The availability of ever more sensitive cell sorting and sequencing technologies has enabled the interrogation of tumour cell biology at the highest possible level of resolution — analysis of a single cell. In this Perspective, the authors describe the application of such approaches to the analysis of single tumour-associated immune cells and their potential for improving the outcomes in patients receiving anti-cancer immunotherapies.

In their Perspective, Schubert et al . discuss developments and challenges in mass-spectrometry-based proteomics technology in the past decade and explore its role in molecular systems biology, clinical research and personalized medicine. In this Perspective, we discuss developments in mass-spectrometry-based proteomic technology over the past decade from the viewpoint of our laboratory. We also reflect on existing challenges and limitations, and explore the current and future roles of quantitative proteomics in molecular systems biology, clinical research and personalized medicine.

Key Points The recent significant advances in the development of single-cell analytical tools have enabled increasingly deep cellular analyses at the genomic, transcriptomic and proteomic levels. Genomic and transcriptomic methods utilize next-generation sequencing technologies, which complement protocols for improved quantification and cost-effective analyses of statistically significant numbers of single cells. Single-cell proteomics methods range from cytometry tools to microchip platforms. All these methods rely on antibodies, but different platforms yield different levels of quantification. Single-cell analyses reveal biology that is masked when cell populations or tissues are analysed. Illustrative examples include tracing the lineage of diseased cells back to the healthy tissue of origin, or a deep analysis of how targeted inhibitors can alter the structure of signalling pathways. Single-cell analysis tools are already playing important parts in drug discovery, particularly in the rapidly emerging field of cancer immunotherapy. Single-cell analysis tools are rapidly evolving, enabling cellular analyses at the genomic, transcriptomic and proteomic levels. Here, Heath and colleagues provide an overview of recently developed single-cell analysis technologies, discuss their biological applications and highlight their potential uses in drug discovery and development, focusing on the field of oncology. The genetic, functional or compositional heterogeneity of healthy and diseased tissues presents major challenges in drug discovery and development. Such heterogeneity hinders the design of accurate disease models and can confound the interpretation of biomarker levels and of patient responses to specific therapies. The complex nature of virtually all tissues has motivated the development of tools for single-cell genomic, transcriptomic and multiplex proteomic analyses. Here, we review these tools and assess their advantages and limitations. Emerging applications of single cell analysis tools in drug discovery and development, particularly in the field of oncology, are discussed.

Metabolomics activity screening can identify small-molecule metabolites that are readily available and can be used to alter phenotypes, including stem cell differentiation, T-cell survival, oligodendrocyte maturation, and insulin signaling. Metabolomics, in which small-molecule metabolites (the metabolome) are identified and quantified, is broadly acknowledged to be the omics discipline that is closest to the phenotype 1 , 2 , 3 . Although appreciated for its role in biomarker discovery programs, metabolomics can also be used to identify metabolites that could alter a cell's or an organism's phenotype. Metabolomics activity screening (MAS) as described here integrates metabolomics data with metabolic pathways and systems biology information, including proteomics and transcriptomics data, to produce a set of endogenous metabolites that can be tested for functionality in altering phenotypes. A growing literature reports the use of metabolites to modulate diverse processes, such as stem cell differentiation, oligodendrocyte maturation, insulin signaling, T-cell survival and macrophage immune responses. This opens up the possibility of identifying and applying metabolites to affect phenotypes. Unlike genes or proteins, metabolites are often readily available, which means that MAS is broadly amenable to high-throughput screening of virtually any biological system.

In this Review, the authors provide a guide through to the different steps involved in selected reaction monitoring as well as discuss its applications. Selected reaction monitoring (SRM) is a targeted mass spectrometry technique that is emerging in the field of proteomics as a complement to untargeted shotgun methods. SRM is particularly useful when predetermined sets of proteins, such as those constituting cellular networks or sets of candidate biomarkers, need to be measured across multiple samples in a consistent, reproducible and quantitatively precise manner. Here we describe how SRM is applied in proteomics, review recent advances, present selected applications and provide a perspective on the future of this powerful technology.

Key Points Our understanding of cellular function depends on exquisite knowledge of all of the molecular components acting in a system. Mass spectrometry (MS)-based proteomics has matured immensely in the last decade, allowing quantitative system-wide analysis of the proteome, including post-translational modifications (PTMs), protein–protein interactions and cellular localization. Quantification of the entire set of proteins expressed in a complex biological system (for example, mammalian cells) is now possible with a high sensitivity and in a reasonable amount of time. With the availability of genomic information, the massive capacity for peptide identification by MS is being used to annotate gene sequences and to find new protein-coding genes and splicing variants. In combination with new approaches to isolate specific PTMs, MS-based studies are revealing a much higher order of proteome complexity in which most proteins are modified by several PTMs that crosstalk in intricate mechanisms to regulate protein function. Protein affinity strategies allow purification of candidate proteins and their interacting partners, which are subsequently identified by MS. These studies describe, with a high degree of detail, dynamic and context-specific protein–protein interaction networks and protein complexes. The improvements in sensitivity, robustness and high-throughput of MS-based proteomics now permits applications in the clinical field, including the possibility of discovering disease-related biomarkers and screening molecular targets of candidate drugs. Despite their complexity and dynamics, proteomes are starting to be comprehensively characterized; this has been made possible particularly by various technical advances in mass spectrometry. This Review highlights how proteomic studies are contributing to our understanding of various cellular processes in health and disease. Next-generation sequencing allows the analysis of genomes, including those representing disease states. However, the causes of most disorders are multifactorial, and systems-level approaches, including the analysis of proteomes, are required for a more comprehensive understanding. The proteome is extremely multifaceted owing to splicing and protein modifications, and this is further amplified by the interconnectivity of proteins into complexes and signalling networks that are highly divergent in time and space. Proteome analysis heavily relies on mass spectrometry (MS). MS-based proteomics is starting to mature and to deliver through a combination of developments in instrumentation, sample preparation and computational analysis. Here we describe this emerging next generation of proteomics and highlight recent applications.

Fast-developing single-cell multimodal omics (scMulti-omics) technologies enable the measurement of multiple modalities, such as DNA methylation, chromatin accessibility, RNA expression, protein abundance, gene perturbation, and spatial information, from the same cell. scMulti-omics can comprehensively explore and identify cell characteristics, while also presenting challenges to the development of computational methods and tools for integrative analyses. Here, we review these integrative methods and summarize the existing tools for studying a variety of scMulti-omics data. The various functionalities and practical challenges in using the available tools in the public domain are explored through several case studies. Finally, we identify remaining challenges and future trends in scMulti-omics modeling and analyses. Applying integrative methods to scMulti-omics data opens a new window into the understanding of heterogeneous mechanism landscapes and cell–cell interactions.Integration of cross-experiment data poses a special challenge.A comprehensive understanding of the underlying methods is necessary to determine which pipeline is appropriate for a given scMulti-omics data set.We designed and implemented two case studies to demonstrate the application of available scMulti-omics tools, where new insights and practical challenges are generated.Among the numerous remaining challenges in scMulti-omics, establishing a robust benchmarking pipeline is paramount.Trends observed in traditional multi-omics, including machine learning, artificial intelligence, and evolving technologies, are paralleled in scMulti-omics methods.

Proteins are the key players in many cellular processes. Their composition, trafficking, and interactions underlie the dynamic processes of life. Furthermore, diseases are frequently accompanied by malfunction of proteins at multiple levels. Understanding how biological processes are regulated at the protein level is critically important to understanding the molecular basis for diseases and often shed light on disease prevention, diagnosis, and treatment. With rapid advances in mass spectrometry（MS）instruments and experimental methodologies, MS-based proteomics has become a reliable and essential tool for elucidating biological processes at the protein level. Over the past decade, we have witnessed great expansion of knowledge of human diseases with the application of MS-based proteomic technologies, which has led to many exciting discoveries. Herein we review the recent progress in MS-based proteomics in biomedical research, including that in establishing disease-related proteomes and interactomes. We also discuss how this progress will benefit biomedical research and clinical diagnosis and treatment of disease.