Lipidomics: Prospects from a technological perspective

Over the last two decades, lipidomics has evolved into an ‘omics’ technology pari passu with benchmarking ‘omics’ technologies, such as genomics or proteomics. The driving force behind this development was a constant advance in mass spectrometry and related technologies. The aim of this opinion article is to give the interested reader a concise but still comprehensive overview about the technological state of the art in lipidomics, current challenges and perspectives for future development. As such, this article guides through the whole workflow of lipidomics, from sampling to data analysis. This article is part of a Special Issue entitled: BBALIP_Lipidomics Opinion Articles edited by Sepp Kohlwein.     

Is a gene‐centric human proteome project the best way for proteomics to serve biology?

With the recent developments in proteomic technologies, a complete human proteome project (HPP) appears feasible for the first time. However, there is still debate as to how it should be designed and what it should encompass. In “proteomics speak”, the debate revolves around the central question as to whether a gene‐centric or a protein‐centric proteomics approach is the most appropriate way forward. In this paper, we try to shed light on what these definitions mean, how large‐scale proteomics such as a HPP can insert into the larger omics chorus, and what we can reasonably expect from a HPP in the way it has been proposed so far.     

Plant–Microbe Symbiosis: What Has Proteomics Taught Us?

Beneficial microbes have a positive impact on the productivity and fitness of the host plant. A better understanding of the biological impacts and underlying mechanisms by which the host derives these benefits will help to address concerns around global food production and security. The recent development of omics‐based technologies has broadened our understanding of the molecular aspects of beneficial plant–microbe symbiosis. Specifically, proteomics has led to the identification and characterization of several novel symbiosis‐specific and symbiosis‐related proteins and post‐translational modifications that play a critical role in mediating symbiotic plant–microbe interactions and have helped assess the underlying molecular aspects of the symbiotic relationship. Integration of proteomic data with other “omics” data can provide valuable information to assess hypotheses regarding the underlying mechanism of symbiosis and help define the factors affecting the outcome of symbiosis. Herein, an update is provided on the current and potential applications of symbiosis‐based “omic” approaches to dissect different aspects of symbiotic plant interactions. The application of proteomics, metaproteomics, and secretomics as enabling approaches for the functional analysis of plant‐associated microbial communities is also discussed.     

Proteomics advances for precision therapy in ovarian cancer

ABSTRACT

Introduction: Due to the relatively low mutation rate and high frequency of copy number variation, finding actionable genetic drivers of high-grade serous carcinoma (HGSC) is a challenging task. Furthermore, emerging studies show that genetic alterations are frequently poorly represented at the protein level adding a layer of complexity. With improvements in large-scale proteomic technologies, proteomics studies have the potential to provide robust analysis of the pathways driving high HGSC behavior.

Areas covered: This review summarizes recent large-scale proteomics findings across adequately sized ovarian cancer sample sets. Key words combined with ‘ovarian cancer’ including ‘proteomics’, ‘proteogenomic’, ‘reverse-phase protein array’, ‘mass spectrometry’, and ‘adaptive response’, were used to search PubMed.

Expert opinion: Proteomics analysis of HGSC as well as their adaptive responses to therapy can uncover new therapeutic liabilities, which can reduce the emergence of drug resistance and potentially improve patient outcomes. There is a pressing need to better understand how the genomic and epigenomic heterogeneity intrinsic to ovarian cancer is reflected at the protein level and how this information could be used to improve patient outcomes.     

Mass spectrometry-based proteomics and peptidomics for systems biology and biomarker discovery

The scientific community has shown great interest in the field of mass spectrometry-based proteomics and peptidomics for its applications in biology.Proteomics technologies have evolved to produce larg...     

Proteomics and systems biology to tackle biological complexity: Yeast as a case study

In this note we discuss how, by using budding yeast as model organism (as has been done in the past for biochemical, genetics and genomic studies), the integration of "omics" sciences and more specifically of proteomics with systems biology offers a very profitable approach to elucidating regulatory circuits of complex biological functions.     

Targeted and Untargeted Proteomics Approaches in Biomarker Development

An enormous amount of research effort has been devoted to biomarker discovery and validation. With the completion of the human genome, proteomics is now playing an increasing role in this search for new and better biomarkers. Here, what leads to successful biomarker development is reviewed and how these features may be applied in the context of proteomic biomarker research is considered. The “fit‐for‐purpose” approach to biomarker development suggests that untargeted proteomic approaches may be better suited for early stages of biomarker discovery, while targeted approaches are preferred for validation and implementation. A systematic screening of published biomarker articles using MS‐based proteomics reveals that while both targeted and untargeted technologies are used in proteomic biomarker development, most researchers do not combine these approaches. i) The reasons for this discrepancy, (ii) how proteomic technologies can overcome technical challenges that seem to limit their translation into the clinic, and (iii) how MS can improve, complement, or replace existing clinically important assays in the future are discussed.     

From cancer big data to treatment: Artificial intelligence in cancer research

In recent years, developing the idea of “cancer big data” has emerged as a result of the significant expansion of various fields such as clinical research, genomics, proteomics and public health records. Advances in omics technologies are making a significant contribution to cancer big data in biomedicine and disease diagnosis. The increasingly availability of extensive cancer big data has set the stage for the development of multimodal artificial intelligence (AI) frameworks. These frameworks aim to analyze high-dimensional multi-omics data, extracting meaningful information that is challenging to obtain manually. Although interpretability and data quality remain critical challenges, these methods hold great promise for advancing our understanding of cancer biology and improving patient care and clinical outcomes. Here, we provide an overview of cancer big data and explore the applications of both traditional machine learning and deep learning approaches in cancer genomic and proteomic studies. We briefly discuss the challenges and potential of AI techniques in the integrated analysis of omics data, as well as the future direction of personalized treatment options in cancer.     

Proteomic analysis of cellular signaling

Protein phosphorylation events are key regulators of cellular signaling processes. In the era of functional genomics, rational drug design programs demand large-scale high-throughput analysis of signal transduction cascades. Significant improvements in the area of mass spectrometry-based proteomics have provided exciting opportunities for rapid progress toward global protein phosphorylation analysis. This review summarizes several recent advances made in the field of phosphoproteomics with an emphasis placed on mass spectrometry instrumentation, enrichment methods and quantification strategies. In the near future, these technologies will provide a tool that can be used for quantitative investigation of signal transduction pathways to generate new insights into biologic systems.     

关于组学大数据背景下《微生物学》教材框架的思考

微生物学是生物学的重要内容,是全国高等院校生物学专业或相关专业的本科生必修的一门核心基础课,其主要任务是给学生提供基础的、系统的、前沿的微生物学知识和理论。随着高通量测序、质谱、芯片等高通量技术的快速发展,生命科学领域快速进入了以海量多元组学(基因组学、转录组学、蛋白质组学、免疫组学、代谢组学等)数据为特征的大数据时代,而这势必会对微生物学教材已有的内容产生冲击和补充。本文对如何在组学大数据背景下对国内经典的微生物学教材进行改革,将目前最具突破性的组学成果整合到已有的教材框架中或革新现有教材框架进行了初步探讨。     

Proteomic analysis of cellular signaling

Protein phosphorylation events are key regulators of cellular signaling processes. In the era of functional genomics, rational drug design programs demand large-scale high-throughput analysis of signal transduction cascades. Significant improvements in the area of mass spectrometry-based proteomics have provided exciting opportunities for rapid progress toward global protein phosphorylation analysis. This review summarizes several recent advances made in the field of phosphoproteomics with an emphasis placed on mass spectrometry instrumentation, enrichment methods and quantification strategies. In the near future, these technologies will provide a tool that can be used for quantitative investigation of signal transduction pathways to generate new insights into biologic systems.     

Proteomics in cancer vaccine development

Proteomics is a new scientific field aimed at the large-scale characterization of the protein constituents of biologic systems. It facilitates comparisons between different protein preparations by searching for minute differences in their protein expression repertoires and the patterns of their post-translational modifications. These attributes make proteomics perfectly suited for searching for proteins and peptides expressed exclusively or preferentially in cancer cells as candidates for cancer vaccines. The main proteomics technologies include 2D polyacrylamide gel electrophoresis, multidimensional high-performance liquid chromatography, mass spectrometry and protein arrays. Proteomics technologies used to analyze cancer culture cells, fresh tumor specimens, human leukocyte antigen peptides, serum and serum antibodies (serologic proteomics) have successfully identified tumor markers. Turning the potential vaccine candidates identified by proteomics technologies into clinical treatments awaits demonstration.     

Oncoproteomics: current trends and future perspectives

Oncoproteomics is the application of proteomics technologies in oncology. Functional proteomics is a promising technique for the rational identification of biomarkers and novel therapeutic targets for cancers. Recent progress in proteomics has opened new avenues for tumor-associated biomarker discovery. With the advent of new and improved proteomics technologies, such as the development of quantitative proteomic methods, high-resolution, -speed and -sensitivity mass spectrometry and protein arrays, as well as advanced bioinformatics for data handling and interpretation, it is now possible to discover biomarkers that can reliably and accurately predict outcomes during cancer management and treatment. However, there are several difficulties in the study of proteins/peptides that are not inherent in the study of nucleic acids. New challenges arise in large-scale proteomic profiling when dealing with complex biological mixtures. Nevertheless, oncoproteomics offers great promise for unveiling the complex molecular events of tumorigenesis, as well as those that control clinically important tumor behaviors, such as metastasis, invasion and resistance to therapy. In this review, the development and advancement of oncoproteomics technologies for cancer research in recent years are expounded.     

Oncoproteomics: current trends and future perspectives

Oncoproteomics is the application of proteomics technologies in oncology. Functional proteomics is a promising technique for the rational identification of biomarkers and novel therapeutic targets for cancers. Recent progress in proteomics has opened new avenues for tumor-associated biomarker discovery. With the advent of new and improved proteomics technologies, such as the development of quantitative proteomic methods, high-resolution, -speed and -sensitivity mass spectrometry and protein arrays, as well as advanced bioinformatics for data handling and interpretation, it is now possible to discover biomarkers that can reliably and accurately predict outcomes during cancer management and treatment. However, there are several difficulties in the study of proteins/peptides that are not inherent in the study of nucleic acids. New challenges arise in large-scale proteomic profiling when dealing with complex biological mixtures. Nevertheless, oncoproteomics offers great promise for unveiling the complex molecular events of tumorigenesis, as well as those that control clinically important tumor behaviors, such as metastasis, invasion and resistance to therapy. In this review, the development and advancement of oncoproteomics technologies for cancer research in recent years are expounded.     

Proteomic analysis of red blood cells and the potential for the clinic: what have we learned so far?

Introduction: Red blood cells (RBC) are the most abundant host cells in the human body. Mature erythrocytes are devoid of nuclei and organelles and have always been regarded as circulating ‘bags of hemoglobin’. The advent of proteomics has challenged this assumption, revealing unanticipated complexity and novel roles for RBCs not just in gas transport, but also in systemic metabolic homeostasis in health and disease.

Areas covered: In this review we will summarize the main advancements in the field of discovery mode and redox/quantitative proteomics with respect to RBC biology. We thus focus on translational/clinical applications, such as transfusion medicine, hematology (e.g. hemoglobinopathies) and personalized medicine. Synergy of omics technologies – especially proteomics and metabolomics – are highlighted as a hallmark of clinical metabolomics applications for the foreseeable future.

Expert commentary: The introduction of advanced proteomics technologies, especially quantitative and redox proteomics, and the integration of proteomics data with omics information gathered through orthogonal technologies (especially metabolomics) promise to revolutionize many biomedical areas, from hematology and transfusion medicine to personalized medicine and clinical biochemistry.     

Drugging the undruggable proteins in cancer: A systems biology approach

In recent years, the research community has, with comprehensive systems biology approaches and related technologies, gained insight into the vast complexity of numerous cancers. These approaches allow an in-depth exploration that cannot be achieved solely using conventional low-throughput methods, which do not closely mimic the natural cellular environment. In this review, we discuss recent integrative multiple omics approaches for understanding and modulating previously identified ‘undruggable’ targets such as members of the RAS family, MYC, TP53, and various E3 ligases and deubiquitinases. We describe how these technologies have revolutionized drug discovery by overcoming an array of biological and technological challenges and how, in the future, they will be pivotal in assessing cancer states in individual patients, allowing for the prediction and application of personalized disease treatments.