Multi-omics analysis of intra-tumoural and inter-tumoural heterogeneity in pancreatic ductal adenocarcinoma

The poor prognosis of pancreatic ductal adenocarcinoma (PDAC) is associated with the tumour heterogeneity. To explore intra- and inter-tumoural heterogeneity in PDAC, we analysed the multi-omics profiles of 61 PDAC lesion samples, along with the matched pancreatic normal tissue samples, from 19 PDAC patients. Haematoxylin and Eosin (H&E) staining revealed that diversely differentiated lesions coexisted both within and across individual tumours. Whole exome sequencing (WES) of samples from multi-region revealed diverse types of mutations in diverse genes between cancer cells within a tumour and between tumours from different individuals. The copy number variation (CNV) analysis also showed that PDAC exhibited intra- and inter-tumoural heterogeneity in CNV and that high average CNV burden was associated poor prognosis of the patients. Phylogenetic tree analysis and clonality/timing analysis of mutations displayed diverse evolutionary pathways and spatiotemporal characteristics of genomic alterations between different lesions from the same or different tumours. Hierarchical clustering analysis illustrated higher inter-tumoural heterogeneity than intra-tumoural heterogeneity of PDAC at the transcriptional levels as lesions from the same patients are grouped into a single cluster. Immune marker genes are differentially expressed in different regions and tumour samples as shown by tumour microenvironment (TME) analysis. TME appeared to be more heterogeneous than tumour cells in the same patient. Lesion-specific differentially methylated regions (DMRs) were identified by methylated DNA immunoprecipitation sequencing (MeDIP-seq). Furthermore, the integration analysis of multi-omics data showed that the mRNA levels of some genes, such as PLCB4, were significantly correlated with the gene copy numbers. The mRNA expressions of potential PDAC biomarkers ZNF521 and KDM6A were correlated with copy number alteration and methylation, respectively. Taken together, our results provide a comprehensive view of molecular heterogeneity and evolutionary trajectories of PDAC and may guide personalised treatment strategies in PDAC therapy.

Recent Advances in Developmental Hematopoiesis: Diving Deeper With New Technologies

The journey of a hematopoietic stem cell (HSC) involves the passage through successive anatomical sites where HSCs are in direct contact with their surrounding microenvironment, also known as niche. These spatial and temporal cellular interactions throughout development are required for the acquisition of stem cell properties, and for maintaining the HSC pool through balancing self-renewal, quiescence and lineage commitment. Understanding the context and consequences of these interactions will be imperative for our understanding of HSC biology and will lead to the improvement of in vitro production of HSCs for clinical purposes. The aorta-gonad-mesonephros (AGM) region is in this light of particular interest since this is the cradle of HSC emergence during the embryonic development of all vertebrate species. In this review, we will focus on the developmental origin of HSCs and will discuss the novel technological approaches and recent progress made to identify the cellular composition of the HSC supportive niche and the underlying molecular events occurring in the AGM region.

Current and prospective computational approaches and challenges for developing COVID-19 vaccines

SARS-CoV-2, which causes COVID-19, was first identified in humans in late 2019 and is a coronavirus which is zoonotic in origin. As it spread around the world there has been an unprecedented effort in developing effective vaccines. Computational methods can be used to speed up the long and costly process of vaccine development. Antigen selection, epitope prediction, and toxicity and allergenicity prediction are areas in which computational tools have already been applied as part of reverse vaccinology for SARS-CoV-2 vaccine development. However, there is potential for computational methods to assist further. We review approaches which have been used and highlight additional bioinformatic approaches and PK modelling as in silico methods which may be useful for SARS-CoV-2 vaccine design but remain currently unexplored. As more novel viruses with pandemic potential are expected to arise in future, these techniques are not limited to application to SARS-CoV-2 but also useful to rapidly respond to novel emerging viruses.

Nutrition and Rheumatoid Arthritis in the 'Omics' Era

Modern high-throughput 'omics' science tools (including genomics, transcriptomics, proteomics, metabolomics and microbiomics) are currently being applied to nutritional sciences to unravel the fundamental processes of health effects ascribed to particular nutrients in humans and to contribute to more precise nutritional advice. Diet and food components are key environmental factors that interact with the genome, transcriptome, proteome, metabolome and the microbiota, and this life-long interplay defines health and diseases state of the individual. Rheumatoid arthritis (RA) is a chronic autoimmune disease featured by a systemic immune-inflammatory response, in genetically susceptible individuals exposed to environmental triggers, including diet. In recent years increasing evidences suggested that nutritional factors and gut microbiome have a central role in RA risk and progression. The aim of this review is to summarize the main and most recent applications of 'omics' technologies in human nutrition and in RA research, examining the possible influences of some nutrients and nutritional patterns on RA pathogenesis, following a nutrigenomics approach. The opportunities and challenges of novel 'omics technologies' in the exploration of new avenues in RA and nutritional research to prevent and manage RA will be also discussed.

Spatially resolved transcriptomics and its applications in cancer

Spatially resolved transcriptomics (SRT) offers the promise of understanding cells and their modes of dysfunction in the context of intact tissues. Technologies for SRT have advanced rapidly with a large number being published in recent years. Diverse methods for SRT produce data at widely varying depth, throughput, accessibility and cost. Many published SRT methods have been demonstrated only in their labs of origin, while others have matured to the point of commercialization and widespread availability. Here we review technologies for SRT, and their application in studies of tumor heterogeneity.

A core transcriptional signature of human microglia: Derivation and utility in describing region-dependent alterations associated with Alzheimer's disease

Growing recognition of the pivotal role microglia play in neurodegenerative and neuroinflammatory disorders has accentuated the need to characterize their function in health and disease. Studies in mouse have applied transcriptome-wide profiling of microglia to reveal key features of microglial ontogeny, functional profile, and phenotypic diversity. While similar, human microglia exhibit clear differences to their mouse counterparts, underlining the need to develop a better understanding of the human microglial profile. On examining published microglia gene signatures, limited consistency was observed between studies. Hence, we sought to derive a core microglia signature of the human central nervous system (CNS), through a comprehensive analysis of existing transcriptomic datasets. Nine datasets derived from cells and tissues, isolated from various regions of the CNS across numerous donors, were subjected independently to an unbiased correlation network analysis. From each dataset, a list of coexpressing genes corresponding to microglia was identified, with 249 genes highly conserved between them. This core signature included known microglial markers, and compared with other signatures provides a gene set specific to microglia in the context of the CNS. The utility of this signature was demonstrated by its use in detecting qualitative and quantitative region-specific alterations in aging and Alzheimer's disease. These analyses highlighted the reactive response of microglia in vulnerable brain regions such as the entorhinal cortex and hippocampus, additionally implicating pathways associated with disease progression. We believe this resource and the analyses described here, will support further investigations to the contribution of human microglia in CNS health and disease.

Co-expression in Single-Cell Analysis: Saving Grace or Original Sin?

As a fundamental unit of life, the cell has rightfully been the subject of intense investigation throughout the history of biology. Technical innovations now make it possible to assay cellular features at genomic scale, yielding breakthroughs in our understanding of the molecular organization of tissues, and even whole organisms. As these data accumulate we will soon be faced with a new challenge: making sense of the plethora of results. Early investigations into the replicability of cell type profiles inferred from single-cell RNA sequencing data have indicated that this is likely to be surprisingly straightforward due to consistent gene co-expression. In this opinion article we discuss the evidence for this claim and its implications for interpreting cell type-specific gene expression.

Identification of key genes of papillary thyroid cancer using integrated bioinformatics analysis

OBJECTIVE

To identify novel clinically relevant genes in papillary thyroid carcinoma from public databases.

METHODS

Four original microarray datasets, GSE3678, GSE3467, GSE33630 and GSE58545, were downloaded. Differentially expressed genes (DEGs) were filtered from integrated data. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, followed by protein-protein interaction (PPI) network construction. The CentiScape pug-in was performed to scale degree. The genes at the top of the degree distribution (≥ 95% percentile) in the significantly perturbed networks were defined as central genes. UALCAN and The Cancer Genome Atlas Clinical Explorer were used to verify clinically relevant genes and perform survival analysis.

RESULT

225 commonly changed DEGs (111 up-regulated and 114 down-regulated) were identified. The DEGs were classified into three groups by GO terms. KEGG pathway enrichment analysis showed DEGs mainly enriched in the PI3K-Akt signaling pathway, pathways in cancer, focal adhesion and proteoglycans in cancer. DEGs' protein-protein interaction (PPI) network complex was developed; six central genes (BCL2, CCND1, FN1, IRS1, COL1A1, CXCL12) were identified. Among them, BCL2, CCND1 and COL1A1 were identified as clinically relevant genes.

CONCLUSION

BCL2, CCND1 and COL1A1 may be key genes for papillary thyroid carcinoma. Further molecular biological experiments are required to confirm the function of the identified genes.

Cell type discovery using single-cell transcriptomics: implications for ontological representation

Cells are fundamental function units of multicellular organisms, with different cell types playing distinct physiological roles in the body. The recent advent of single-cell transcriptional profiling using RNA sequencing is producing 'big data', enabling the identification of novel human cell types at an unprecedented rate. In this review, we summarize recent work characterizing cell types in the human central nervous and immune systems using single-cell and single-nuclei RNA sequencing, and discuss the implications that these discoveries are having on the representation of cell types in the reference Cell Ontology (CL). We propose a method, based on random forest machine learning, for identifying sets of necessary and sufficient marker genes, which can be used to assemble consistent and reproducible cell type definitions for incorporation into the CL. The representation of defined cell type classes and their relationships in the CL using this strategy will make the cell type classes being identified by high-throughput/high-content technologies findable, accessible, interoperable and reusable (FAIR), allowing the CL to serve as a reference knowledgebase of information about the role that distinct cellular phenotypes play in human health and disease.

Impact of Gene Expression Profiling on Decision-Making in Clinically Node Negative Melanoma Patients after Surgical Staging

INTRODUCTION

The surgeon's role in the follow-up of pathologic stage I and II melanoma patients has traditionally been minimal. Melanoma genetic expression profile (GEP) testing provides binary risk assessment (Class 1-low risk, Class 2-high risk), which can assist in predicting metastasis and formulating appropriate follow up. We sought to determine the impact of GEP results on the management of clinically node negative cutaneous melanoma patients staged with sentinel lymph node biopsy (SLNB).

METHODS

A retrospective review of prospectively gathered data consisting of patients seen from September 2015 - August 2016 was performed to determine whether GEP class influenced follow-up recommendations. Patients were stratified into four groups based on recommended follow-up plan: Dermatology alone, Surgical Oncology, Surgical Oncology with recommendation for adjuvant clinical trial, or Medical and Surgical Oncology.

RESULTS

Of ninety-one patients, 38 were pathologically stage I, 42 stage II, 10 stage III, and 1 stage IV. Combining all stages, GEP Class 1 patients were more likely to be followed by Dermatology alone and less like to be followed by Surgical Oncology with recommendation for adjuvant trial compared to Class 2 patients (P less than 0.001). Among stage 1 patients, Class 1 were more likely to follow up with Dermatology alone compared to Class 2 patients (82 vs. 0%; P less than 0.001). Among stage II patients, GEP Class 1 were more likely to follow up with Dermatology alone (21 vs 0%) and more Class 2 patients followed up with surgery and recommendations for adjuvant trial (36 vs 64%; P less than 0.05). There was no difference in follow up for stage III patients based on the GEP results (P=0.76).

CONCLUSION

GEP results were significantly associated with the management of stage I-II melanoma patients after staging with SLNB. For node negative patients, Class 2 results led to more aggressive follow up and management. J Drugs Dermatol. 2018;17(2):196-199.

Integration of metabolomics and transcriptomics in nanotoxicity studies

Biomedical research involving nanoparticles has produced useful products with medical applications. However, the potential toxicity of nanoparticles in biofluids, cells, tissues, and organisms is a major challenge. The '-omics' analyses provide molecular profiles of multifactorial biological systems instead of focusing on a single molecule. The 'omics' approaches are necessary to evaluate nanotoxicity because classical methods for the detection of nanotoxicity have limited ability in detecting miniscule variations within a cell and do not accurately reflect the actual levels of nanotoxicity. In addition, the 'omics' approaches allow analyses of in-depth changes and compensate for the differences associated with high-throughput technologies between actual nanotoxicity and results from traditional cytotoxic evaluations. However, compared with a single omics approach, integrated omics provides precise and sensitive information by integrating complex biological conditions. Thus, these technologies contribute to extended safety evaluations of nanotoxicity and allow the accurate diagnoses of diseases far earlier than was once possible in the nanotechnology era. Here, we review a novel approach for evaluating nanotoxicity by integrating metabolomics with metabolomic profiling and transcriptomics, which is termed "metabotranscriptomics". [BMB Reports 2018; 51(1): 14-20].

β-Cells are not uniform after all-Novel insights into molecular heterogeneity of insulin-secreting cells

While the β-cells of the endocrine pancreas are defined as cells with high levels of insulin production and tight stimulus-secretion coupling, the existence of functional heterogeneity among them has been known for decades. Recent advances in molecular technologies, in particular single-cell profiling on both the protein and messenger RNA level, have uncovered that β-cells exist in several antigenically and molecularly definable states. Using antibodies to cell surface markers or multidimensional clustering of β-cells using more than 20 protein markers by mass cytometry, 4 distinct groups of β-cells could be differentiated. However, whether these states represent permanent cell lineages or are readily interconvertible from one group to another remains to be determined. Nevertheless, future analysis of the pathogenesis of type 1 and type 2 diabetes will certainly benefit from a growing appreciation of β-cell heterogeneity. Here, we aim to summarize concisely the recent advances in the field and their possible impact on our understanding of β-cell physiology and pathophysiology.

Accelerating Live Single-Cell Signalling Studies

The dynamics of signalling networks that couple environmental conditions with cellular behaviour can now be characterised in exquisite detail using live single-cell imaging experiments. Recent improvements in our abilities to introduce fluorescent sensors into cells, coupled with advances in pipelines for quantifying and extracting single-cell data, mean that high-throughput systematic analyses of signalling dynamics are becoming possible. In this review, we consider current technologies that are driving progress in the scale and range of such studies. Moreover, we discuss novel approaches that are allowing us to explore how pathways respond to changes in inputs and even predict the fate of a cell based upon its signalling history and state.

What's New in Genetic Testing for Cancer Susceptibility?

The advent of next-generation sequencing, and its transition further into the clinic with the US Food and Drug Administration approval of a cystic fibrosis assay in 2013, have increased the speed and reduced the cost of DNA sequencing. Coupled with a historic ruling by the Supreme Court of the United States that human genes are not patentable, these events have caused a seismic shift in genetic testing in clinical medicine. More labs are offering genetic testing services; more multigene panels are available for gene testing; more genes and gene mutations are being identified; and more variants of uncertain significance, which may or may not be clinically actionable, have been found. All these factors, taken together, are increasing the complexity of clinical management. While these developments have led to a greater interest in genetic testing, risk assessment, and large-scale population screening, they also present unique challenges. The dilemma for clinicians is how best to understand and manage this rapidly growing body of information to improve patient care. With millions of genetic variants of potential clinical significance and thousands of genes associated with rare but well-established genetic conditions, the complexities of genetic data management clearly will require improved computerized clinical decision support tools, as opposed to continued reliance on traditional rote, memory-based medicine.

Multi-Omics of Single Cells: Strategies and Applications

Most genome-wide assays provide averages across large numbers of cells, but recent technological advances promise to overcome this limitation. Pioneering single-cell assays are now available for genome, epigenome, transcriptome, proteome, and metabolome profiling. Here, we describe how these different dimensions can be combined into multi-omics assays that provide comprehensive profiles of the same cell.

The first decade of estrogen receptor cistromics in breast cancer

The advent of genome-wide transcription factor profiling has revolutionized the field of breast cancer research. Estrogen receptor α (ERα), the major drug target in hormone receptor-positive breast cancer, has been known as a key transcriptional regulator in tumor progression for over 30 years. Even though this function of ERα is heavily exploited and widely accepted as an Achilles heel for hormonal breast cancer, only since the last decade we have been able to understand how this transcription factor is functioning on a genome-wide scale. Initial ChIP-on-chip (chromatin immunoprecipitation coupled with tiling array) analyses have taught us that ERα is an enhancer-associated factor binding to many thousands of sites throughout the human genome and revealed the identity of a number of directly interacting transcription factors that are essential for ERα action. More recently, with the development of massive parallel sequencing technologies and refinements thereof in sample processing, a genome-wide interrogation of ERα has become feasible and affordable with unprecedented data quality and richness. These studies have revealed numerous additional biological insights into ERα behavior in cell lines and especially in clinical specimens. Therefore, what have we actually learned during this first decade of cistromics in breast cancer and where may future developments in the field take us?

Advances in genomics, transcriptomics and proteomics of toxin-producing cyanobacteria

A common misconception persists that the genomes of toxic and non-toxic cyanobacterial strains are largely conserved with the exception of the presence or absence of the genes responsible for toxin production. Implementation of -omics era technologies has challenged this paradigm, with comparative analyses providing increased insight into the differences between strains of the same species. The implementation of genomic, transcriptomic and proteomic approaches has revealed distinct profiles between toxin-producing and non-toxic strains. Further, metagenomics and metaproteomics highlight the genomic potential and functional state of toxic bloom events over time. In this review, we highlight how these technologies have shaped our understanding of the complex relationship between these molecules, their producers and the environment at large within which they persist.

Precision medicine in breast cancer: genes, genomes, and the future of genomically driven treatments

Remarkable progress in sequencing technology over the past 20 years has made it possible to comprehensively profile tumors and identify clinically relevant genomic alterations. In breast cancer, the most common malignancy affecting women, we are now increasingly able to use this technology to help specify the use of therapies that target key molecular and genetic dependencies. Large sequencing studies have confirmed the role of well-known cancer-related genes and have also revealed numerous other genes that are recurrently mutated in breast cancer. This growing understanding of patient-to-patient variability at the genomic level in breast cancer is advancing our ability to direct the appropriate treatment to the appropriate patient at the appropriate time--a hallmark of "precision cancer medicine." This review focuses on the technological advances that have catalyzed these developments, the landscape of mutations in breast cancer, the clinical impact of genomic profiling, and the incorporation of genomic information into clinical care and clinical trials.

The past, present, and future of Leishmania genomics and transcriptomics

It has been nearly 10 years since the completion of the first entire genome sequence of a Leishmania parasite. Genomic and transcriptomic analyses have advanced our understanding of the biology of Leishmania, and shed new light on the complex interactions occurring within the parasite-host-vector triangle. Here, we review these advances and examine potential avenues for translation of these discoveries into treatment and control programs. In addition, we argue for a strong need to explore how disease in dogs relates to that in humans, and how an improved understanding in line with the 'One Health' concept may open new avenues for the control of these devastating diseases.

LinkRbrain: multi-scale data integrator of the brain

BACKGROUND

LinkRbrain is an open-access web platform for multi-scale data integration and visualization of human brain data. This platform integrates anatomical, functional, and genetic knowledge produced by the scientific community.

NEW METHOD

The linkRbrain platform has two major components: (1) a data aggregation component that integrates multiple open databases into a single platform with a unified representation; and (2) a website that provides fast multi-scale integration and visualization of these data and makes the results immediately available.

RESULTS

LinkRbrain allows users to visualize functional networks or/and genetic expression over a standard brain template (MNI152). Interrelationships between these components based on topographical overlap are displayed using relational graphs. Moreover, linkRbrain enables comparison of new experimental results with previous published works.

COMPARISON WITH EXISTING METHODS

Previous tools and studies illustrate the opportunities of data mining across multiple tiers of neuroscience and genetic information. However, a global systematic approach is still missing to gather cognitive, topographical, and genetic knowledge in a common framework in order to facilitate their visualization, comparison, and integration.

CONCLUSIONS

LinkRbrain is an efficient open-access tool that affords an integrative understanding of human brain function.

The application of 3D cell models to support drug safety assessment: opportunities & challenges

The selection of drug candidates early in development has become increasingly important to minimize the use of animals and to avoid costly failures of drugs later in development. In vitro systems to predict and assess organ toxicity have so far been of limited value due to difficulties in demonstrating in vivo-relevant toxicity at a cell culture level. To overcome the limitations of single-cell type monolayer cultures and short-lived primary cell preparations, researchers have created novel 3-dimensional culture systems which appear to more closely resemble in vivo biology. These could become a key for the pharmaceutical industry in the evaluation of drug candidates. However, the value and acceptance of those new models in standard drug safety applications have yet to be demonstrated. This review aims to provide an overview of the different approaches undertaken in the field of pre-clinical safety assessment, organ toxicity, in particular, with an emphasis on examples and technical challenges.