High Throughput Multi-Omics Approaches for Clinical Trial Evaluation and Drug Discovery

Artificial Intelligence in Metabolomics: A Current Review

Metabolomics and artificial intelligence (AI) form a synergistic partnership. Metabolomics generates large datasets comprising hundreds to thousands of metabolites with complex relationships. AI, aiming to mimic human intelligence through computational modeling, possesses extraordinary capabilities for big data analysis. In this review, we provide a recent overview of the methodologies and applications of AI in metabolomics studies in the context of systems biology and human health. We first introduce the AI concept, history, and key algorithms for machine learning and deep learning, summarizing their strengths and weaknesses. We then discuss studies that have successfully used AI across different aspects of metabolomic analysis, including analytical detection, data preprocessing, biomarker discovery, predictive modeling, and multi-omics data integration. Lastly, we discuss the existing challenges and future perspectives in this rapidly evolving field. Despite limitations and challenges, the combination of metabolomics and AI holds great promises for revolutionary advancements in enhancing human health.

Opportunities offered by latent-based multiblock strategies to integrate biomarkers of chemical exposure and biomarkers of effect in environmental health studies

Modern environmental epidemiology benefits from a new generation of technologies that enable comprehensive profiling of biomarkers, including environmental chemical exposure and omic datasets. The integration and analysis of large and structured datasets to identify functional associations is constrained by computational challenges that cannot be overcome using conventional regression methods. Some extensions of Partial Least Squares (PLS) regression have been developed to efficently integrate multiple datasets, including Multiblock PLS (MB-PLS) and Sequential and Orthogonalized PLS; however, these approaches remain seldom applied in environmental epidemiology. To address that research gap, this study aimed to assess and compare the applicability of PLS-based multiblock models in an observational case study, where biomarkers of exposure to environmental chemicals and endogenous biomarkers of effect were simultaneously integrated to highlight biological links related to a health outcome. The methods were compared with and without sparsity coupling two metrics to support the variable selection: Variable Importance in Projection (VIP) and Selectivity Ratio (SR). The framework was applied to a case-study dataset mimicking the structure of 36 environmental exposure biomarkers (E-block), 61 inflammation biomarkers (M-block), and their relationships with the gestational age at delivery of 161 mother-infant pairs. The results showed an overall consistency in the selected variables across models, although some specific selection patterns were identified. The block-scaled concatenation-based approaches (e.g. MB-PLS) tended to select more variables from the E-block, while these methods were unable to identify certain variables in the M-block. Overall, the number of variables selected using the SR criterion was higher than using the VIP criterion, with lower predictive performances. The multiblock models coupled to VIP, appeared to be the methods of choice for identifying relevant variables with similar statistical performances. Overall, the use of multiblock PLS-based methods appears to be a good strategy to efficiently support the variable selection process in modern environmental epidemiology.

Deciphering metabolic crosstalk in context: lessons from inflammatory diseases

Metabolism plays a crucial role in regulating the function of immune cells in both health and disease, with altered metabolism contributing to the pathogenesis of cancer and many inflammatory diseases. The local microenvironment has a profound impact on the metabolism of immune cells. Therefore, immunological and metabolic heterogeneity as well as the spatial organization of cells in tissues should be taken into account when studying immunometabolism. Here, we highlight challenges of investigating metabolic communication. Additionally, we review the capabilities and limitations of current technologies for studying metabolism in inflamed microenvironments, including single-cell omics techniques, flow cytometry-based methods (Met-Flow, single-cell energetic metabolism by profiling translation inhibition (SCENITH)), cytometry by time of flight (CyTOF), cellular indexing of transcriptomes and epitopes by sequencing (CITE-Seq), and mass spectrometry imaging. Considering the importance of metabolism in regulating immune cells in diseased states, we also discuss the applications of metabolomics in clinical research, as well as some hurdles to overcome to implement these techniques in standard clinical practice. Finally, we provide a flowchart to assist scientists in designing effective strategies to unravel immunometabolism in disease-relevant contexts.

Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs

Drug discovery is a generally inefficient and capital-intensive process. For neurodegenerative diseases (NDDs), the development of novel therapeutics is particularly urgent considering the long list of late-stage drug candidate failures. Although our knowledge on the pathogenic mechanisms driving neurodegeneration is growing, additional efforts are required to achieve a better and ultimately complete understanding of the pathophysiological underpinnings of NDDs. Beyond the etiology of NDDs being heterogeneous and multifactorial, this process is further complicated by the fact that current experimental models only partially recapitulate the major phenotypes observed in humans. In such a scenario, multi-omic approaches have the potential to accelerate the identification of new or repurposed drugs against a multitude of the underlying mechanisms driving NDDs. One major advantage for the implementation of multi-omic approaches in the drug discovery process is that these overarching tools are able to disentangle disease states and model perturbations through the comprehensive characterization of distinct molecular layers (i.e., genome, transcriptome, proteome) up to a single-cell resolution. Because of recent advances increasing their affordability and scalability, the use of omics technologies to drive drug discovery is nascent, but rapidly expanding in the neuroscience field. Combined with increasingly advanced in vitro models, which particularly benefited from the introduction of human iPSCs, multi-omics are shaping a new paradigm in drug discovery for NDDs, from disease characterization to therapeutics prediction and experimental screening. In this review, we discuss examples, main advantages and open challenges in the use of multi-omic approaches for the in vitro discovery of targets and therapies against NDDs.

Machine learning and artificial intelligence within pediatric autoimmune diseases: applications, challenges, future perspective

INTRODUCTION

Autoimmune disorders affect 4.5% to 9.4% of children, significantly reducing their quality of life. The diagnosis and prognosis of autoimmune diseases are uncertain because of the variety of onset and development. Machine learning can identify clinically relevant patterns from vast amounts of data. Hence, its introduction has been beneficial in the diagnosis and management of patients.

AREAS COVERED

This narrative review was conducted through searching various electronic databases, including PubMed, Scopus, and Web of Science. This study thoroughly explores the current knowledge and identifies the remaining gaps in the applications of machine learning specifically in the context of pediatric autoimmune and related diseases.

EXPERT OPINION

Machine learning algorithms have the potential to completely change how pediatric autoimmune disorders are identified, treated, and managed. Machine learning can assist physicians in making more precise and fast judgments, identifying new biomarkers and therapeutic targets, and personalizing treatment strategies for each patient by utilizing massive datasets and powerful analytics.

Applications of Flow Cytometry in Drug Discovery and Translational Research

Flow cytometry is a mainstay technique in cell biology research, where it is used for phenotypic analysis of mixed cell populations. Quantitative approaches have unlocked a deeper value of flow cytometry in drug discovery research. As the number of drug modalities and druggable mechanisms increases, there is an increasing drive to identify meaningful biomarkers, evaluate the relationship between pharmacokinetics and pharmacodynamics (PK/PD), and translate these insights into the evaluation of patients enrolled in early clinical trials. In this review, we discuss emerging roles for flow cytometry in the translational setting that supports the transition and evaluation of novel compounds in the clinic.

Prediction of resveratrol target proteins: a bioinformatics analysis

Resveratrol is a natural compound with a wide range of biological functions that generate health benefits under normal conditions and in multiple diseases. This has attracted the attention of the scientific community, which has revealed that this compound exerts these effects through its action on different proteins. Despite the great efforts made, due to the challenges involved, not all the proteins with which resveratrol interacts have yet been identified. In this work, using protein target prediction bioinformatics systems, RNA sequencing analysis and protein-protein interaction networks, 16 proteins were identified as potential targets of resveratrol. Due to its biological relevance, the interaction of resveratrol with the predicted target CDK5 was further investigated. A docking analysis found that resveratrol can interact with CDK5 and be positioned in its ATP-binding pocket. Resveratrol forms hydrogen bonds between its three hydroxyl groups (-OH) and CDK5 residues C83, D86, K89 and D144. Molecular dynamics analysis showed that these bonds allow resveratrol to remain in the pocket and suggest inhibition of CDK5 activity. All this allows us to better understand how resveratrol acts and to consider CDK5 inhibition within its biological actions, mainly in neurodegenerative diseases where this protein has been shown to be relevant.Communicated by Ramaswamy H. Sarma.

An update on the global treatment of invasive fungal infections

Fungal infections are a serious problem affecting many people worldwide, creating critical economic and medical consequences. Fungi are ubiquitous and can cause invasive diseases in individuals mostly living in developing countries or with weakened immune systems, and antifungal drugs currently available have important limitations in tolerability and efficacy. In an effort to counteract the high morbidity and mortality rates associated with invasive fungal infections, various approaches are being utilized to discover and develop new antifungal agents. This review discusses the challenges posed by fungal infections, outlines different methods for developing antifungal drugs and reports on the status of drugs currently in clinical trials, which offer hope for combating this serious global problem.

As a potential predictor of pan-cancer, UBE2S is related to tumor-associated macrophage infiltration

Background: At the pan-cancer level, exploring the expression and prognostic significance of a gene, such as UBE2S, will help to gain insight into the role of the gene and its feasibility for cancer screening, prognosis assessment and even gene therapy. Methods: The Cancer Genome Atlas, Human Protein Atlas, Kaplan-Meier, Tumor Immunology Estimation Resource and other databases were used to analyze the expression of UBE2S at the pan-cancer level, its prognosis and the role of the immune microenvironment. Immunohistochemistry samples of tumor tissue collected in our clinic were taken as verification. Results: UBE2S is significantly overexpressed in pan-cancer and is closely associated with malignant clinical features, poor prognosis and tumor-associated macrophages. Conclusion: UBE2S may be a potential diagnostic and prognostic marker for pan-cancer and is associated with tumor-associated macrophages.

Molecular mechanisms of multi-omic regulation in breast cancer

Breast cancer is a complex disease that is influenced by the concurrent influence of multiple genetic and environmental factors. Recent advances in genomics and other high throughput biomolecular techniques (-omics) have provided numerous insights into the molecular mechanisms underlying breast cancer development and progression. A number of these mechanisms involve multiple layers of regulation. In this review, we summarize the current knowledge on the role of multiple omics in the regulation of breast cancer, including the effects of DNA methylation, non-coding RNA, and other epigenomic changes. We comment on how integrating such diverse mechanisms is envisioned as key to a more comprehensive understanding of breast carcinogenesis and cancer biology with relevance to prognostics, diagnostics and therapeutics. We also discuss the potential clinical implications of these findings and highlight areas for future research. Overall, our understanding of the molecular mechanisms of multi-omic regulation in breast cancer is rapidly increasing and has the potential to inform the development of novel therapeutic approaches for this disease.

Proteomics-based scoring of cellular response to stimuli for improved characterization of signaling pathway activity

Omics technologies focus on uncovering the complex nature of molecular mechanisms in cells and organisms, including biomarkers and drug targets discovery. Aiming at these tasks, we see that information extracted from omics data is still underused. In particular, characteristics of differentially regulated molecules can be combined in a single score to quantify the signaling pathway activity. Such a metric can be useful for comprehensive data interpretation to follow: (1) developing molecular responses in time; (2) potency of a drug on a certain cell culture; (3) ranking the signaling pathway activity in stimulated cells; and (4) integration of the omics data and assay-based measurements of cell viability, cytotoxicity, and proliferation. With recent advances in ultrafast mass spectrometry for quantitative proteomics allowing data collection in a few minutes, proteomics score for cellular response to stimuli can become a fast, accurate, and informative complement to bioassays. Here, we utilized an interquartile-based selection of differentially regulated features and a variety of schemes for quantifying cellular responses to come up with the quantitative metric for total cellular response and pathway activity. Validation was performed using antiproliferative and virus assays and label-free proteomics data collected for cancer cells subjected to drug stimulation.

Preparation of noninfectious scRNAseq samples from SARS-CoV-2-infected epithelial cells

Coronavirus disease (COVID-19) is an infectious disease caused by the SARS coronavirus 2 (SARS-CoV-2) virus. Direct assessment, detection, and quantitative analysis using high throughput methods like single-cell RNA sequencing (scRNAseq) is imperative to understanding the host response to SARS-CoV-2. One barrier to studying SARS-CoV-2 in the laboratory setting is the requirement to process virus-infected cell cultures, and potentially infectious materials derived therefrom, under Biosafety Level 3 (BSL-3) containment. However, there are only 190 BSL3 laboratory facilities registered with the U.S. Federal Select Agent Program, as of 2020, and only a subset of these are outfitted with the equipment needed to perform high-throughput molecular assays. Here, we describe a method for preparing non-hazardous RNA samples from SARS-CoV-2 infected cells, that enables scRNAseq analyses to be conducted safely in a BSL2 facility-thereby making molecular assays of SARS-CoV-2 cells accessible to a much larger community of researchers. Briefly, we infected African green monkey kidney epithelial cells (Vero-E6) with SARS-CoV-2 for 96 hours, trypsin-dissociated the cells, and inactivated them with methanol-acetone in a single-cell suspension. Fixed cells were tested for the presence of infectious SARS-CoV-2 virions using the Tissue Culture Infectious Dose Assay (TCID50), and also tested for viability using flow cytometry. We then tested the dissociation and methanol-acetone inactivation method on primary human lung epithelial cells that had been differentiated on an air-liquid interface. Finally, we performed scRNAseq quality control analysis on the resulting cell populations to evaluate the effects of our virus inactivation and sample preparation protocol on the quality of the cDNA produced. We found that methanol-acetone inactivated SARS-CoV-2, fixed the lung epithelial cells, and could be used to obtain noninfectious, high-quality cDNA libraries. This methodology makes investigating SARS-CoV-2, and related high-containment RNA viruses at a single-cell level more accessible to an expanded community of researchers.

Current challenges and future implications of exploiting the omics data into nutrigenetics and nutrigenomics for personalized diagnosis and nutrition-based care

Nutrigenetics and nutrigenomics, combined with the omics technologies, are a demanding and an increasingly important field in personalizing nutrition-based care to understand an individual's response to nutrition-guided therapy. Omics is defined as the analysis of the large data sets of the biological system featuring transcriptomics, proteomics, and metabolomics and providing new insights into cell regulation. The effect of combining nutrigenetics and nutrigenomics with omics will give insight into molecular analysis, as human nutrition requirements vary per individual. Omics measures modest intraindividual variability and is critical to exploit these data for use in the development of precision nutrition. Omics, combined with nutrigenetics and nutrigenomics, is instrumental in the creation of goals for improving the accuracy of nutrition evaluations. Although dietary-based therapies are provided for various clinical conditions such as inborn errors of metabolism, limited advancement has been done to expand the omics data for a more mechanistic understanding of cellular networks dependent on nutrition-based expression and overall regulation of genes. The greatest challenge remains in the clinical sector to integrate the current data available, overcome the well-established limits of self-reported methods in research, and provide omics data, combined with nutrigenetics and nutrigenomics research, for each individual. Hence, the future seems promising if a design for personalized, nutrition-based diagnosis and care can be implemented practically in the health care sector.

Progress on COVID-19 Chemotherapeutics Discovery and Novel Technology

COVID-19 is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel highly contagious and pathogenic coronavirus that emerged in late 2019. SARS-CoV-2 spreads primarily through virus-containing droplets and small particles of air pollution, which greatly increases the risk of inhaling these virus particles when people are in close proximity. COVID-19 is spreading across the world, and the COVID-19 pandemic poses a threat to human health and public safety. To date, there are no specific vaccines or effective drugs against SARS-CoV-2. In this review, we focus on the enzyme targets of the virus and host that may be critical for the discovery of chemical compounds and natural products as antiviral drugs, and describe the development of potential antiviral drugs in the preclinical and clinical stages. At the same time, we summarize novel emerging technologies applied to the research on new drug development and the pathological mechanisms of COVID-19.

Multi-omics research strategies in ischemic stroke: A multidimensional perspective

Ischemic stroke (IS) is a multifactorial and heterogeneous neurological disorder with high rate of death and long-term impairment. Despite years of studies, there are still no stroke biomarkers for clinical practice, and the molecular mechanisms of stroke remain largely unclear. The high-throughput omics approach provides new avenues for discovering biomarkers of IS and explaining its pathological mechanisms. However, single-omics approaches only provide a limited understanding of the biological pathways of diseases. The integration of multiple omics data means the simultaneous analysis of thousands of genes, RNAs, proteins and metabolites, revealing networks of interactions between multiple molecular levels. Integrated analysis of multi-omics approaches will provide helpful insights into stroke pathogenesis, therapeutic target identification and biomarker discovery. Here, we consider advances in genomics, transcriptomics, proteomics and metabolomics and outline their use in discovering the biomarkers and pathological mechanisms of IS. We then delineate strategies for achieving integration at the multi-omics level and discuss how integrative omics and systems biology can contribute to our understanding and management of IS.

Downregulation of MACC1 facilitates the reversal effect of verapamil on the chemoresistance to active metabolite of irinotecan in human colon cancer cells

The aim of this study is to investigate the reversal effect of verapamil (VER) on chemoresistance to irinotecan (CPT-11) in human colon cancer cells and relevant mechanisms. Cell counting kit-8 (CCK-8) test and colony-forming unit (CFU) experiment results show that VER strengthens the sensitivity of human colon cancer cell line HT29 to CPT-11 but has a small effect on SW480 cells. High-throughput transcriptome sequencing, RT-PCR, and Western blot results show that the inhibition of metastasis-associated in colon cancer-1 (MACC1) expression by VER is the key factor for reversal effect on chemoresistance to CPT-11. Transfection experiments further show that VER can reverse the resistance of human colon cancer cells to SN-38, the active metabolite of CPT-11, when MACC1 is overexpressed. The nude mouse transplantation tumor experiment provides an in vivo proof that VER can strengthen sensitivity to CPT-11 in drug-resistant human colon cancer cells, and the effect might be related to the inhibited expression of MACC1. In summary, VER might strengthen the reversal effect of VER on chemoresistance to CPT-11 in human colon cancer cells and facilitate the apoptosis of human colon cancer cells by downregulating MACC1 expression.

Matrix proteoglycans in tumor inflammation and immunity

Cancer immunoediting progresses through elimination, equilibrium, and escape. Each of these phases is characterized by breaching, remodeling, and rebuilding tissue planes and structural barriers that engage extracellular matrix (ECM) components, in particular matrix proteoglycans. Some of the signals emanating from matrix proteoglycan remodeling are readily co-opted by the growing tumor to sustain an environment of tumor-promoting and immune-suppressive inflammation. Yet other matrix-derived cues can be viewed as part of a homeostatic response by the host, aiming to eliminate the tumor and restore tissue integrity. These latter signals may be harnessed for therapeutic purposes to tip the polarity of the tumor immune milieu toward anticancer immunity. In this review, we attempt to showcase the importance and complexity of matrix proteoglycan signaling in both cancer-restraining and cancer-promoting inflammation. We propose that the era of matrix diagnostics and therapeutics for cancer is fast approaching the clinic.

ATP and Adenosine Metabolism in Cancer: Exploitation for Therapeutic Gain

Adenosine is an evolutionary ancient metabolic regulator linking energy state to physiologic processes, including immunomodulation and cell proliferation. Tumors create an adenosine-rich immunosuppressive microenvironment through the increased release of ATP from dying and stressed cells and its ectoenzymatic conversion into adenosine. Therefore, the adenosine pathway becomes an important therapeutic target to improve the effectiveness of immune therapies. Prior research has focused largely on the two major ectonucleotidases, ectonucleoside triphosphate diphosphohydrolase 1/cluster of differentiation (CD)39 and ecto-5'-nucleotidase/CD73, which catalyze the breakdown of extracellular ATP into adenosine, and on the subsequent activation of different subtypes of adenosine receptors with mixed findings of antitumor and protumor effects. New findings, needed for more effective therapeutic approaches, require consideration of redundant pathways controlling intratumoral adenosine levels, including the alternative NAD-inactivating pathway through the CD38-ectonucleotide pyrophosphatase phosphodiesterase (ENPP)1-CD73 axis, the counteracting ATP-regenerating ectoenzymatic pathway, and cellular adenosine uptake and its phosphorylation by adenosine kinase. This review provides a holistic view of extracellular and intracellular adenosine metabolism as an integrated complex network and summarizes recent data on the underlying mechanisms through which adenosine and its precursors ATP and ADP control cancer immunosurveillance, tumor angiogenesis, lymphangiogenesis, cancer-associated thrombosis, blood flow, and tumor perfusion. Special attention is given to differences and commonalities in the purinome of different cancers, heterogeneity of the tumor microenvironment, subcellular compartmentalization of the adenosine system, and novel roles of purine-converting enzymes as targets for cancer therapy. SIGNIFICANCE STATEMENT: The discovery of the role of adenosine as immune checkpoint regulator in cancer has led to the development of novel therapeutic strategies targeting extracellular adenosine metabolism and signaling in multiple clinical trials and preclinical models. Here we identify major gaps in knowledge that need to be filled to improve the therapeutic gain from agents targeting key components of the adenosine metabolic network and, on this basis, provide a holistic view of the cancer purinome as a complex and integrated network.

Harnessing the Full Potential of Multi-Omic Analyses to Advance the Study and Treatment of Chronic Kidney Disease

Chronic kidney disease (CKD) was the 12th leading cause of death globally in 2017 with the prevalence of CKD estimated at ~9%. Early detection and intervention for CKD may improve patient outcomes, but standard testing approaches even in developed countries do not facilitate identification of patients at high risk of developing CKD, nor those progressing to end-stage kidney disease (ESKD). Recent advances in CKD research are moving towards a more personalised approach for CKD. Heritability for CKD ranges from 30% to 75%, yet identified genetic risk factors account for only a small proportion of the inherited contribution to CKD. More in depth analysis of genomic sequencing data in large cohorts is revealing new genetic risk factors for common diagnoses of CKD and providing novel diagnoses for rare forms of CKD. Multi-omic approaches are now being harnessed to improve our understanding of CKD and explain some of the so-called 'missing heritability'. The most common omic analyses employed for CKD are genomics, epigenomics, transcriptomics, metabolomics, proteomics and phenomics. While each of these omics have been reviewed individually, considering integrated multi-omic analysis offers considerable scope to improve our understanding and treatment of CKD. This narrative review summarises current understanding of multi-omic research alongside recent experimental and analytical approaches, discusses current challenges and future perspectives, and offers new insights for CKD.

From Omics to Multi-Omics Approaches for In-Depth Analysis of the Molecular Mechanisms of Prostate Cancer

Cancer arises following alterations at different cellular levels, including genetic and epigenetic modifications, transcription and translation dysregulation, as well as metabolic variations. High-throughput omics technologies that allow one to identify and quantify processes involved in these changes are now available and have been instrumental in generating a wealth of steadily increasing data from patient tumors, liquid biopsies, and from tumor models. Extensive investigation and integration of these data have led to new biological insights into the origin and development of multiple cancer types and helped to unravel the molecular networks underlying this complex pathology. The comprehensive and quantitative analysis of a molecule class in a biological sample is named omics and large-scale omics studies addressing different prostate cancer stages have been performed in recent years. Prostate tumors represent the second leading cancer type and a prevalent cause of cancer death in men worldwide. It is a very heterogenous disease so that evaluating inter- and intra-tumor differences will be essential for a precise insight into disease development and plasticity, but also for the development of personalized therapies. There is ample evidence for the key role of the androgen receptor, a steroid hormone-activated transcription factor, in driving early and late stages of the disease, and this led to the development and approval of drugs addressing diverse targets along this pathway. Early genomic and transcriptomic studies have allowed one to determine the genes involved in prostate cancer and regulated by androgen signaling or other tumor-relevant signaling pathways. More recently, they have been supplemented by epigenomic, cistromic, proteomic and metabolomic analyses, thus, increasing our knowledge on the intricate mechanisms involved, the various levels of regulation and their interplay. The comprehensive investigation of these omics approaches and their integration into multi-omics analyses have led to a much deeper understanding of the molecular pathways involved in prostate cancer progression, and in response and resistance to therapies. This brings the hope that novel vulnerabilities will be identified, that existing therapies will be more beneficial by targeting the patient population likely to respond best, and that bespoke treatments with increased efficacy will be available soon.

Validation of an Accurate Automated Multiplex Immunofluorescence Method for Immuno-Profiling Melanoma

Multiplex immunofluorescence staining enables the simultaneous detection of multiple immune markers in a single tissue section, and is a useful tool for the identification of specific cell populations within the tumour microenvironment. However, this technology has rarely been validated against standard clinical immunohistology, which is a barrier for its integration into clinical practice. This study sought to validate and investigate the accuracy, precision and reproducibility of a multiplex immunofluorescence compared with immunohistochemistry (IHC), including tissue staining, imaging and analysis, in characterising the expression of immune and melanoma markers in both the tumour and its microenvironment. Traditional chromogenic IHC, single-plex immunofluorescence and multiplex immunofluorescence were each performed on serial tissue sections of a formalin-fixed paraffin-embedded (FFPE) tissue microarray containing metastatic melanoma specimens from 67 patients. The panel included the immune cell markers CD8, CD68, CD16, the immune checkpoint PD-L1, and melanoma tumour marker SOX10. Slides were stained with the Opal^™ 7 colour Kit (Akoya Biosciences) on the intelliPATH autostainer (Biocare Medical) and imaged using the Vectra 3.0.5 microscope. Marker expression was quantified using Halo v.3.2.181 (Indica Labs). Comparison of the IHC and single-plex immunofluorescence revealed highly significant positive correlations between the cell densities of CD8, CD68, CD16, PD-L1 and SOX10 marker positive cells (Spearman’s rho = 0.927 to 0.750, p < 0.0001). Highly significant correlations were also observed for all markers between single-plex immunofluorescence and multiplex immunofluorescence staining (Spearman’s rho >0.9, p < 0.0001). Finally, correlation analysis of the three multiplex replicates revealed a high degree of reproducibility between slides (Spearman’s rho >0.940, p < 0.0001). Together, these data highlight the reliability and validity of multiplex immunofluorescence in accurately profiling the tumour and its associated microenvironment using FFPE metastatic melanoma specimens. This validated multiplex panel can be utilised for research evaluating melanoma and its microenvironment, such as studies performed to predict patient response or resistance to immunotherapies.

Cutting-Edge Platforms for Analysis of Immune Cells in the Hepatic Microenvironment-Focus on Tumor-Associated Macrophages in Hepatocellular Carcinoma

The role of tumor-associated macrophages (TAMs) in the pathogenesis of hepatocellular carcinoma (HCC) is poorly understood. Most studies rely on platforms that remove intrahepatic macrophages from the microenvironment prior to evaluation. Cell isolation causes activation and phenotypic changes that may not represent their actual biology and function in situ. State-of-the-art methods provides new strategies to study TAMs without losing the context of tissue architecture and spatial relationship with neighboring cells. These technologies, such as multispectral imaging (e.g., Vectra Polaris), mass cytometry by time-of-flight (e.g., Fluidigm CyTOF), cycling of fluorochromes (e.g., Akoya Biosciences CODEX/PhenoCycler-Fusion, Bruker Canopy, Lunaphore Comet, and CyCIF) and digital spatial profiling or transcriptomics (e.g., GeoMx or Visium, Vizgen Merscope) are being utilized to accurately assess the complex cellular network within the tissue microenvironment. In cancer research, these platforms enable characterization of immune cell phenotypes and expression of potential therapeutic targets, such as PDL-1 and CTLA-4. Newer spatial profiling platforms allow for detection of numerous protein targets, in combination with whole transcriptome analysis, in a single liver biopsy tissue section. Macrophages can also be specifically targeted and analyzed, enabling quantification of both protein and gene expression within specific cell phenotypes, including TAMs. This review describes the workflow of each platform, summarizes recent research using these approaches, and explains the advantages and limitations of each.

The landscape of receptor-mediated precision cancer combination therapy via a single-cell perspective

Mining a large cohort of single-cell transcriptomics data, here we employ combinatorial optimization techniques to chart the landscape of optimal combination therapies in cancer. We assume that each individual therapy can target any one of 1269 genes encoding cell surface receptors, which may be targets of CAR-T, conjugated antibodies or coated nanoparticle therapies. We find that in most cancer types, personalized combinations composed of at most four targets are then sufficient for killing at least 80% of tumor cells while sparing at least 90% of nontumor cells in the tumor microenvironment. However, as more stringent and selective killing is required, the number of targets needed rises rapidly. Emerging individual targets include PTPRZ1 for brain and head and neck cancers and EGFR in multiple tumor types. In sum, this study provides a computational estimate of the identity and number of targets needed in combination to target cancers selectively and precisely.

Intra-tumor heterogeneity is often associated with resistance to targeted therapy, requiring the design of combinatorial therapies. Here, based on tumor single-cell transcriptomic datasets, the authors develop a computational approach to identify optimal combinatorial treatments targeting membrane receptors for cancer therapy.

Type I Interferons in Systemic Lupus Erythematosus: A Journey from Bench to Bedside

Dysregulation of type I interferons (IFNs) has been implicated in the pathogenesis of systemic lupus erythematosus (SLE) since the late 1970s. The majority of SLE patients demonstrate evidence of type I IFN pathway activation; however, studies attempting to address the relationship between type I IFN signature and SLE disease activity have yielded conflicting results. In addition to type I IFNs, type II and III IFNs may overlap and also contribute to the IFN signature. Different genetic backgrounds lead to overproduction of type I IFNs in SLE and contribute to the breakdown of peripheral tolerance by activation of antigen-presenting myeloid dendritic cells, thus triggering the expansion and differentiation of autoreactive lymphocytes. The consequence of the continuous stimulation of the immune system is manifested in different organ systems typical of SLE (e.g., mucocutaneous and cardiovascular involvement). After the discovery of the type I IFN signature, a number of different strategies have been developed to downregulate the IFN system in SLE patients, finally leading to the successful trial of anifrolumab, the second biologic to be approved for the treatment of SLE in 10 years. In this review, we will discuss the bench to bedside translation of the type I IFN pathway and put forward some issues that remain unresolved when selecting SLE patients for treatment with biologics targeting type I IFNs.

Recent advances in metabolomics analysis for early drug development

The pharmaceutical industry adapted proteomics and other 'omics technologies for drug research early following their initial introduction. Although metabolomics lacked behind in this development, it has now become an accepted and widely applied approach in early drug development. Over the past few decades, metabolomics has evolved from a pure exploratory tool to a more mature and quantitative biochemical technology. Several metabolomics-based platforms are now applied during the early phases of drug discovery. Metabolomics analysis assists in the definition of the physiological response and target engagement (TE) markers as well as elucidation of the mode of action (MoA) of drug candidates under investigation. In this review, we highlight recent examples and novel developments of metabolomics analyses applied during early drug development.

Methodological Approaches To Assess Innate Immunity and Innate Memory in Marine Invertebrates and Humans

Assessing the impact of drugs and contaminants on immune responses requires methodological approaches able to represent real-life conditions and predict long-term effects. Innate immunity/inflammation is the evolutionarily most widespread and conserved defensive mechanism in living organisms, and therefore we will focus here on immunotoxicological methods that specifically target such processes. By exploiting the conserved mechanisms of innate immunity, we have examined the most representative immunotoxicity methodological approaches across living species, to identify common features and human proxy models/assays. Three marine invertebrate organisms are examined in comparison with humans, i.e., bivalve molluscs, tunicates and sea urchins. In vivo and in vitro approaches are compared, highlighting common mechanisms and species-specific endpoints, to be applied in predictive human and environmental immunotoxicity assessment. Emphasis is given to the 3R principle of Replacement, Refinement and Reduction of Animals in Research and to the application of the ARRIVE guidelines on reporting animal research, in order to strengthen the quality and usability of immunotoxicology research data.

Capturing the third dimension in drug discovery: Spatially-resolved tools for interrogation of complex 3D cell models

Advanced three-dimensional (3D) cell models have proven to be capable of depicting architectural and microenvironmental features of several tissues. By providing data of higher physiological and pathophysiological relevance, 3D cell models have been contributing to a better understanding of human development, pathology onset and progression mechanisms, as well as for 3D cell-based assays for drug discovery. Nonetheless, the characterization and interrogation of these tissue-like structures pose major challenges on the conventional analytical methods, pushing the development of spatially-resolved technologies. Herein, we review recent advances and pioneering technologies suitable for the interrogation of multicellular 3D models, while capable of retaining biological spatial information. We focused on imaging technologies and omics tools, namely transcriptomics, proteomics and metabolomics. The advantages and shortcomings of these novel methodologies are discussed, alongside the opportunities to intertwine data from the different tools.

SCHNAPPs - Single Cell sHiNy APPlication(s)

Single-cell RNA-sequencing (scRNAseq) experiments are becoming a standard tool for bench-scientists to explore the cellular diversity present in all tissues. Data produced by scRNAseq is technically complex and requires analytical workflows that are an active field of bioinformatics research, whereas a wealth of biological background knowledge is needed to guide the investigation. Thus, there is an increasing need to develop applications geared towards bench-scientists to help them abstract the technical challenges of the analysis so that they can focus on the science at play. It is also expected that such applications should support closer collaboration between bioinformaticians and bench-scientists by providing reproducible science tools. We present SCHNAPPs, a Graphical User Interface (GUI), designed to enable bench-scientists to autonomously explore and interpret scRNAseq data and associated annotations. The R/Shiny-based application allows following different steps of scRNAseq analysis workflows from Seurat or Scran packages: performing quality control on cells and genes, normalizing the expression matrix, integrating different samples, dimension reduction, clustering, and differential gene expression analysis. Visualization tools for exploring each step of the process include violin plots, 2D projections, Box-plots, alluvial plots, and histograms. An R-markdown report can be generated that tracks modifications and selected visualizations. The modular design of the tool allows it to easily integrate new visualizations and analyses by bioinformaticians. We illustrate the main features of the tool by applying it to the characterization of T cells in a scRNAseq and Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) experiment of two healthy individuals.

A Review on Recent Progress in Machine Learning and Deep Learning Methods for Cancer Classification on Gene Expression Data

Data-driven model with predictive ability are important to be used in medical and healthcare. However, the most challenging task in predictive modeling is to construct a prediction model, which can be addressed using machine learning (ML) methods. The methods are used to learn and trained the model using a gene expression dataset without being programmed explicitly. Due to the vast amount of gene expression data, this task becomes complex and time consuming. This paper provides a recent review on recent progress in ML and deep learning (DL) for cancer classification, which has received increasing attention in bioinformatics and computational biology. The development of cancer classification methods based on ML and DL is mostly focused on this review. Although many methods have been applied to the cancer classification problem, recent progress shows that most of the successful techniques are those based on supervised and DL methods. In addition, the sources of the healthcare dataset are also described. The development of many machine learning methods for insight analysis in cancer classification has brought a lot of improvement in healthcare. Currently, it seems that there is highly demanded further development of efficient classification methods to address the expansion of healthcare applications.

Best Practices and Progress in Precision-Cut Liver Slice Cultures

Thirty-five years ago, precision-cut liver slices (PCLS) were described as a promising tool and were expected to become the standard in vitro model to study liver disease as they tick off all characteristics of a good in vitro model. In contrast to most in vitro models, PCLS retain the complex 3D liver structures found in vivo, including cell–cell and cell–matrix interactions, and therefore should constitute the most reliable tool to model and to investigate pathways underlying chronic liver disease in vitro. Nevertheless, the biggest disadvantage of the model is the initiation of a procedure-induced fibrotic response. In this review, we describe the parameters and potential of PCLS cultures and discuss whether the initially described limitations and pitfalls have been overcome. We summarize the latest advances in PCLS research and critically evaluate PCLS use and progress since its invention in 1985.

Undisclosed, unmet and neglected challenges in multi-omics studies

Multi-omics approaches have become a reality in both large genomics projects and small laboratories. However, the multi-omics research community still faces a number of issues that have either not been sufficiently discussed or for which current solutions are still limited. In this Perspective, we elaborate on these limitations and suggest points of attention for future research. We finally discuss new opportunities and challenges brought to the field by the rapid development of single-cell high-throughput molecular technologies.

Omics Data and Their Integrative Analysis to Support Stratified Medicine in Neurodegenerative Diseases

Molecular and clinical heterogeneity is increasingly recognized as a common characteristic of neurodegenerative diseases (NDs), such as Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis. This heterogeneity makes difficult the development of early diagnosis and effective treatment approaches, as well as the design and testing of new drugs. As such, the stratification of patients into meaningful disease subgroups, with clinical and biological relevance, may improve disease management and the development of effective treatments. To this end, omics technologies-such as genomics, transcriptomics, proteomics and metabolomics-are contributing to offer a more comprehensive view of molecular pathways underlying the development of NDs, helping to differentiate subtypes of patients based on their specific molecular signatures. In this article, we discuss how omics technologies and their integration have provided new insights into the molecular heterogeneity underlying the most prevalent NDs, aiding to define early diagnosis and progression markers as well as therapeutic targets that can translate into stratified treatment approaches, bringing us closer to the goal of personalized medicine in neurology.