The Genexpress IMAGE Knowledge Base of the Human Brain Transcriptome: A Prototype Integrated Resource for Functional and Computational Genomics

Experimental reporting of fish transcriptomic responses in environmental toxicology and ecotoxicology

Due to its increasing affordability and efforts to understand transcriptional responses of organisms to biotic and abiotic stimuli, transcriptomics has become an important tool with significant impact on toxicological investigations and hazard and risk assessments, especially during development and application of new approach methodologies (NAMs). Data generated using transcriptomic methodologies have directly informed adverse outcome pathway frameworks, chemical and biological read across, and aided in the identification of points of departure. Using data reporting frameworks for transcriptomics data offers improved transparency and reproducibility of research and an opportunity to identify barriers to adoption of these NAMs, especially in environmental toxicology and ecotoxicology with aquatic models. Improved reporting also allows for reexamination of existing data, limiting needs for experiment replication and further reducing animal experimentation. Here, we use a standardized form of data reporting for omics-based studies, the Organisation for Economic Co-operation and Development omics reporting framework, which specifically reports on a list of parameters that should be included in transcriptomics studies used in a regulatory context. We focused specifically on fish studies using RNA- Sequencing (Seq)/microarray technologies within a toxicology context. Inconsistencies in reporting and methodologies among the experimental designs (toxicology vs. molecular characterization) were observed in addition to foundational differences in reporting of sample concentration or preparation or quality assessments, which can affect reproducibility and read across, confidence in results, and contribute substantially to understanding molecular mechanisms of toxicants and toxins. Our findings present an opportunity for improved research reporting. We also provide several recommendations as logical steps to reduce barriers to adoption of transcriptomics within environmental toxicology and ecotoxicology.

To image or not to image: Use of imaging mass spectrometry in biomedical lipidomics

Lipid imaging mass spectrometry (LIMS) allows for establishing the bidimensional distribution of lipid species within a tissue section. One of the main advantages is the generation of spatial information on lipid species distribution at a spatial (lateral) resolution bordering on single-cell resolution with no need to isolate cells. Thus, LIMS images demonstrate, with a level of detail never described before, that lipid profiles are highly sensitive to cell type and pathophysiological state. The wealth and relevance of the information conveyed by LIMS makes up for the lack of a separation stage before sample injection into the mass analyzer, which can somehow be circumvented by other means. Hence, the possibility of describing the lipidome at the cellular level while preserving the microenvironment offers an incomparable opportunity to investigate physiological and pathological contexts. However, to fully grasp the biological implications of the lipid profiles, it is essential to contextualize LIMS data within the broader multiscale 'omic' landscape, entailing genomics, epigenomics, and proteomics, each offering a unique window into the regulatory layers of the cell. In this line, the number of techniques that can be combined with LIMS to delve into the molecular mechanisms underlying differential lipid profiles is continuously increasing. Herein, we aim to describe the key features of LIMS analyses, from sample preparation to data interpretation, as well as the current methodologies to enrich and complete the final outcome. While the field is rapidly advancing, we consider there is solid evidence to foresee the incorporation of LIMS into clinical environments.

Advances in Integrated Multi-omics Analysis for Drug-Target Identification

As an essential component of modern drug discovery, the role of drug-target identification is growing increasingly prominent. Additionally, single-omics technologies have been widely utilized in the process of discovering drug targets. However, it is difficult for any single-omics level to clearly expound the causal connection between drugs and how they give rise to the emergence of complex phenotypes. With the progress of large-scale sequencing and the development of high-throughput technologies, the tendency in drug-target identification has shifted towards integrated multi-omics techniques, gradually replacing traditional single-omics techniques. Herein, this review centers on the recent advancements in the domain of integrated multi-omics techniques for target identification, highlights the common multi-omics analysis strategies, briefly summarizes the selection of multi-omics analysis tools, and explores the challenges of existing multi-omics analyses, as well as the applications of multi-omics technology in drug-target identification.

Single-cell transcriptomics: background, technologies, applications, and challenges

Single-cell sequencing was developed as a high-throughput tool to elucidate unusual and transient cell states that are barely visible in the bulk. This technology reveals the evolutionary status of cells and differences between populations, helps to identify unique cell subtypes and states, reveals regulatory relationships between genes, targets and molecular mechanisms in disease processes, tumor heterogeneity, the state of the immune environment, etc. However, the high cost and technical limitations of single-cell sequencing initially prevented its widespread application, but with advances in research, numerous new single-cell sequencing techniques have been discovered, lowering the cost barrier. Many single-cell sequencing platforms and bioinformatics methods have recently become commercially available, allowing researchers to make fascinating observations. They are now increasingly being used in various industries. Several protocols have been discovered in this context and each technique has unique characteristics, capabilities and challenges. This review presents the latest advancements in single-cell transcriptomics technologies. This includes single-cell transcriptomics approaches, workflows and statistical approaches to data processing, as well as the potential advances, applications, opportunities and challenges of single-cell transcriptomics technology. You will also get an overview of the entry points for spatial transcriptomics and multi-omics.

Unexplored power of CRISPR-Cas9 in neuroscience, a multi-OMICs review

The neuroscience and neurobiology of gene editing to enhance learning and memory is of paramount interest to the scientific community. The advancements of CRISPR system have created avenues to treat neurological disorders by means of versatile modalities varying from expression to suppression of genes and proteins. Neurodegenerative disorders have also been attributed to non-canonical DNA secondary structures by affecting neuron activity through controlling gene expression, nucleosome shape, transcription, translation, replication, and recombination. Changing DNA regulatory elements which could contribute to the fate and function of neurons are thoroughly discussed in this review. This study presents the ability of CRISPR system to boost learning power and memory, treat or cure genetically-based neurological disorders, and alleviate psychiatric diseases by altering the activity and the irritability of the neurons at the synaptic cleft through DNA manipulation, and also, epigenetic modifications using Cas9. We explore and examine how each different OMIC techniques can come useful when altering DNA sequences. Such insight into the underlying relationship between OMICs and cellular behaviors leads us to better neurological and psychiatric therapeutics by intelligently designing and utilizing the CRISPR/Cas9 technology.

Dissecting the Immune System through Gene Regulation

The immune system plays a dual role in human health, functioning both as a protector against pathogens and, at times, as a contributor to disease. This feature emphasizes the importance to uncover the underlying causes of its malfunctions, necessitating an in-depth analysis in both pathological and physiological conditions to better understand the immune system and immune disorders. Recent advances in scientific technology have enabled extensive investigations into gene regulation, a crucial mechanism governing cellular functionality. Studying gene regulatory mechanisms within the immune system is a promising avenue for enhancing our understanding of immune cells and the immune system as a whole. The gene regulatory mechanisms, revealed through various methodologies, and their implications in the field of immunology are discussed in this chapter.

Genome-Wide Integrative Transcriptional Profiling Identifies Age-Associated Signatures in Dogs

Mammals experience similar stages of embryonic development, birth, infancy, youth, adolescence, maturity, and senescence. While embryonic developmental processes have been extensively researched, many molecular mechanisms regulating the different life stages after birth, such as aging, remain unresolved. We investigated the conserved and global molecular transitions in transcriptional remodeling with age in dogs of 15 breeds, which revealed that genes underlying hormone level regulation and developmental programs were differentially regulated during aging. Subsequently, we show that the candidate genes associated with tumorigenesis also exhibit age-dependent DNA methylation patterns, which might have contributed to the tumor state through inhibiting the plasticity of cell differentiation processes during aging, and ultimately suggesting the molecular events that link the processes of aging and cancer. These results highlight that the rate of age-related transcriptional remodeling is influenced not only by the lifespan, but also by the timing of critical physiological milestones.

Improved biomarker discovery through a plot twist in transcriptomic data analysis

Background

Transcriptomic analysis is crucial for understanding the functional elements of the genome, with the classic method consisting of screening transcriptomics datasets for differentially expressed genes (DEGs). Additionally, since 2005, weighted gene co-expression network analysis (WGCNA) has emerged as a powerful method to explore relationships between genes. However, an approach combining both methods, i.e., filtering the transcriptome dataset by DEGs or other criteria, followed by WGCNA (DEGs + WGCNA), has become common. This is of concern because such approach can affect the resulting underlying architecture of the network under analysis and lead to wrong conclusions. Here, we explore a plot twist to transcriptome data analysis: applying WGCNA to exploit entire datasets without affecting the topology of the network, followed with the strength and relative simplicity of DEG analysis (WGCNA + DEGs). We tested WGCNA + DEGs against DEGs + WGCNA to publicly available transcriptomics data in one of the most transcriptomically complex tissues and delicate processes: vertebrate gonads undergoing sex differentiation. We further validate the general applicability of our approach through analysis of datasets from three distinct model systems: European sea bass, mouse, and human.

Results

In all cases, WGCNA + DEGs clearly outperformed DEGs + WGCNA. First, the network model fit and node connectivity measures and other network statistics improved. The gene lists filtered by each method were different, the number of modules associated with the trait of interest and key genes retained increased, and GO terms of biological processes provided a more nuanced representation of the biological question under consideration. Lastly, WGCNA + DEGs facilitated biomarker discovery.

Conclusions

We propose that building a co-expression network from an entire dataset, and only thereafter filtering by DEGs, should be the method to use in transcriptomic studies, regardless of biological system, species, or question being considered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01398-w.

Precision medicine journey through omics approach

It has been well established that understanding the underlying heterogeneity of numerous complex disease process needs new strategies that present in precision medicine for prediction, prevention and personalized treatment strategies. This approach must be tailored for each individual's unique omics that lead to personalized management of disease. The correlation between different omics data should be considered in precision medicine approach. The interaction provides a hypothesis which is called domino effect in the present minireview. Here we review the various potentials of omics data including genomics, transcriptomics, proteomics, metabolomics, pharmacogenomics. We comprehensively summarize the impact of omics data and its major role in precision medicine and provide a description about the domino effect on the pathophysiology of diseases. Each constituent of the omics data typically provides different information in associated with disease. Current research, although inadequate, clearly indicate that the information of omics data can be applicable in the concept of precision medicine. Integration of different omics data type in domino effect hypothesis can explain the causative changes of disease as it is discussed in the system biology too. While most existing studies investigate the omics data separately, data integration is needed on the horizon of precision medicine by using machine learning.

Guidelines for Setting Up a mRNA Sequencing Experiment and Best Practices for Bioinformatic Data Analysis

RNA-sequencing, commonly referred to as RNA-seq, is the most recently developed method for the analysis of transcriptomes. It uses high-throughput next-generation sequencing technologies and has revolutionized our understanding of the complexity and dynamics of whole transcriptomes.In this chapter, we recall the key developments in transcriptome analysis and dissect the different steps of the general workflow that can be run by users to design and perform a mRNA-seq experiment as well as to process mRNA-seq data obtained by the Illumina technology. The chapter proposes guidelines for completing a mRNA-seq study properly and makes available recommendations for best practices based on recent literature and on the latest developments in technology and algorithms. We also remark the large number of choices available (especially for bioinformatic data analysis) in front of which the scientist may be in trouble.In the last part of the chapter we discuss the new frontiers of single-cell RNA-seq and isoform sequencing by long read technology.

Advances in transcriptome analysis of human brain aging

Aging is associated with gradual deterioration of physiological and biochemical functions, including cognitive decline. Transcriptome profiling of brain samples from individuals of varying ages has identified the whole-transcriptome changes that underlie age-associated cognitive declines. In this review, we discuss transcriptome-based research on human brain aging performed by using microarray and RNA sequencing analyses. Overall, decreased synaptic function and increased immune function are prevalent in most regions of the aged brain. Age-associated gene expression changes are also cell dependent and region dependent and are affected by genotype. In addition, the transcriptome changes that occur during brain aging include different splicing events, intersample heterogeneity, and altered levels of various types of noncoding RNAs. Establishing transcriptome-based hallmarks of human brain aging will improve the understanding of cognitive aging and neurodegenerative diseases and eventually lead to interventions that delay or prevent brain aging.

A computational framework for complex disease stratification from multiple large-scale datasets

BACKGROUND

Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states.

METHODS

The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification.

RESULTS

We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes.

CONCLUSIONS

This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.

Cloning and functional analysis of cDNAs with open reading frames for 300 previously undefined genes expressed in CD34+ hematopoietic stem/progenitor cells

Three hundred cDNAs containing putatively entire open reading frames (ORFs) for previously undefined genes were obtained from CD34+ hematopoietic stem/progenitor cells (HSPCs), based on EST cataloging, clone sequencing, in silico cloning, and rapid amplification of cDNA ends (RACE). The cDNA sizes ranged from 360 to 3496 bp and their ORFs coded for peptides of 58-752 amino acids. Public database search indicated that 225 cDNAs exhibited sequence similarities to genes identified across a variety of species. Homology analysis led to the recognition of 50 basic structural motifs/domains among these cDNAs. Genomic exon-intron organization could be established in 243 genes by integration of cDNA data with genome sequence information. Interestingly, a new gene named as HSPC070 on 3p was found to share a sequence of 105bp in 3' UTR with RAF gene in reversed transcription orientation. Chromosomal localizations were obtained using electronic mapping for 192 genes and with radiation hybrid (RH) for 38 genes. Macroarray technique was applied to screen the gene expression patterns in five hematopoietic cell lines (NB4, HL60, U937, K562, and Jurkat) and a number of genes with differential expression were found. The resource work has provided a wide range of information useful not only for expression genomics and annotation of genomic DNA sequence, but also for further research on the function of genes involved in hematopoietic development and differentiation.

High-resolution quantification of specific mRNA levels in human brain autopsies and biopsies

Quantification of mRNA levels in human cortical brain biopsies and autopsies was performed using a fluorogenic 5' nuclease assay. The reproducibility of the assay using replica plates was 97%-99%. Relative quantities of mRNA from 16 different genes were evaluated using a statistical approach based on ANCOVA analysis. Comparison of the relative mRNA levels between two groups of samples with different time postmortem revealed unchanged relative expression levels for most genes. Only CYP26A1 mRNA levels showed a significant decrease with prolonged time postmortem (p = 0.00004). Also, there was a general decrease in measured mRNA levels for all genes in autopsies compared to biopsies; however, on comparing mRNA levels after adjusting with reference genes, no significant differences were found between mRNA levels in autopsies and biopsies. This observation indicates that studies of postmortem material can be performed to reveal the relative in vivo mRNA levels of genes. Power calculations were done to determine the number of individuals necessary to detect differences in mRNA levels of 1.5-fold to tenfold using the strategy described here. This analysis showed that samples from at least 50 individuals per group, patients and controls, are required for high-resolution ( approximately twofold changes) differential expression screenings in the human brain. Experiments done on ten individuals per group will result in a resolution of approximately fivefold changes in expression levels. In general, the sensitivity and resolution of any differential expression study will depend on the sample size used and the between-individual variability of the genes analyzed.

Identification of novel human genes evolutionarily conserved in Caenorhabditis elegans by comparative proteomics

Modern biomedical research greatly benefits from large-scale genome-sequencing projects ranging from studies of viruses, bacteria, and yeast to multicellular organisms, like Caenorhabditis elegans. Comparative genomic studies offer a vast array of prospects for identification and functional annotation of human ortholog genes. We presented a novel comparative proteomic approach for assembling human gene contigs and assisting gene discovery. The C. elegans proteome was used as an alignment template to assist in novel human gene identification from human EST nucleotide databases. Among the available 18,452 C. elegans protein sequences, our results indicate that at least 83% (15,344 sequences) of C. elegans proteome has human homologous genes, with 7,954 records of C. elegans proteins matching known human gene transcripts. Only 11% or less of C. elegans proteome contains nematode-specific genes. We found that the remaining 7,390 sequences might lead to discoveries of novel human genes, and over 150 putative full-length human gene transcripts were assembled upon further database analyses. [The sequence data described in this paper have been submitted to the

d2_cluster: a validated method for clustering EST and full-length cDNAsequences

Several efforts are under way to condense single-read expressed sequence tags (ESTs) and full-length transcript data on a large scale by means of clustering or assembly. One goal of these projects is the construction of gene indices where transcripts are partitioned into index classes (or clusters) such that they are put into the same index class if and only if they represent the same gene. Accurate gene indexing facilitates gene expression studies and inexpensive and early partial gene sequence discovery through the assembly of ESTs that are derived from genes that have yet to be positionally cloned or obtained directly through genomic sequencing. We describe d2_cluster, an agglomerative algorithm for rapidly and accurately partitioning transcript databases into index classes by clustering sequences according to minimal linkage or "transitive closure" rules. We then evaluate the relative efficiency of d2_cluster with respect to other clustering tools. UniGene is chosen for comparison because of its high quality and wide acceptance. It is shown that although d2_cluster and UniGene produce results that are between 83% and 90% identical, the joining rate of d2_cluster is between 8% and 20% greater than UniGene. Finally, we present the first published rigorous evaluation of under and over clustering (in other words, of type I and type II errors) of a sequence clustering algorithm, although the existence of highly identical gene paralogs means that care must be taken in the interpretation of the type II error. Upper bounds for these d2_cluster error rates are estimated at 0.4% and 0.8%, respectively. In other words, the sensitivity and selectivity of d2_cluster are estimated to be >99.6% and 99.2%.