Omics Methods Research Articles

Evaluation of antibody-based single cell type imaging techniques coupled to multiplexed imaging of N-glycans and collagen peptides by matrix-assisted laser desorption/ionization mass spectrometry imaging

The integration of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) with single cell spatial omics methods allows for a comprehensive investigation of single cell spatial information and matrisomal N-glycan and extracellular matrix protein imaging. Here, the performance of the antibody-directed single cell workflows coupled with MALDI-MSI are evaluated. Miralys™ photocleavable mass-tagged antibody probes (MALDI-IHC, AmberGen, Inc.), GeoMx DSP® (NanoString, Inc.), and Imaging Mass Cytometry (IMC, Standard BioTools Inc.) were used in series with MALDI-MSI of N-glycans and extracellular matrix peptides on formalin-fixed paraffin-embedded tissues. Single cell omics protocols were performed before and after MALDI-MSI. The data suggests that for each modality combination, there is an optimal order for performing both techniques on the same tissue section. An overall conclusion is that MALDI-MSI studies may be completed on the same tissue section as used for antibody-directed single cell modalities. This work increases access to combined cellular and extracellular information within the tissue microenvironment to enhance research on the pathological origins of disease.Graphical

A primer on correlation-based dimension reduction methods for multi-omics analysis.

The continuing advances of omic technologies mean that it is now more tangible to measure the numerous features collectively reflecting the molecular properties of a sample. When multiple omic methods are used, statistical and computational approaches can exploit these large, connected profiles. Multi-omics is the integration of different omic data sources from the same biological sample. In this review, we focus on correlation-based dimension reduction approaches for single omic datasets, followed by methods for pairs of omics datasets, before detailing further techniques for three or more omic datasets. We also briefly detail network methods when three or more omic datasets are available and which complement correlation-oriented tools. To aid readers new to this area, these are all linked to relevant R packages that can implement these procedures. Finally, we discuss scenarios of experimental design and present road maps that simplify the selection of appropriate analysis methods. This review will help researchers navigate emerging methods for multi-omics and integrating diverse omic datasets appropriately. This raises the opportunity of implementing population multi-omics with large sample sizes as omics technologies and our understanding improve.

Predicting Blomia tropicalis allergens using a multiomics approach.

The domestic mite Blomia tropicalis is a major source of allergens in tropical and subtropical regions. Despite its great medical importance, the allergome of this mite has not been sufficiently studied. Only 14 allergen groups have been identified in B. tropicalis thus far, even though early radioimmunoelectrophoresis techniques (27 uncharacterized allergen complexes) and comparative data based on 40 allergen groups officially recognized by the World Health Organization (WHO)/IUIS in domestic astigmatid mites suggest the presence of a large set of additional allergens. Here, we employ a multiomics approach to assess the allergome of B. tropicalis using genomic and transcriptomic sequence data and perform highly sensitive protein abundance quantification. Among the 14 known allergen groups, we confirmed 13 (one WHO/IUIS allergen, Blo t 19, was not found) and identified 16 potentially novel allergens based on sequence similarity. These data indicate that B. tropicalis shares 27 known/deduced allergen groups with pyroglyphid house dust mites (genus Dermatophagoides). Among these groups, five allergen-encoding genes are highly expressed at the transcript level: Blo t 1, Blo t 5, Blo t 21 (known), Blo t 15, and Blo t 18 (predicted). However, at the protein level, a different set of most abundant allergens was found: Blo t 2, 10, 11, 20 and 21 (mite bodies) or Blo t 3, 4, 6 and predicted Blo t 13, 14 and 36 (mite feces). We report the use of an integrated omics method to identify and predict an array of mite allergens and advanced, label-free proteomics to determine allergen protein abundance. Our research identifies a large set of novel putative allergens and shows that the expression levels of allergen-encoding genes may not be strictly correlated with the actual allergenic protein abundance in mite bodies.

Emerging trends in computational approaches for drug discovery in molecular biology

Purpose of Research: This review paper delves into the transformative impact of computational approaches on drug discovery within molecular biology. It explores how these methods offer efficient and cost-effective solutions to identify and optimize potential drug candidates, addressing the shortcomings of traditional drug discovery methods. Scope of the Experiments: Algorithms, machine learning, artificial intelligence, and quantum mechanics are studied. These approaches analyse large datasets, anticipate drug- target interactions, and improve drug design. Supervised and unsupervised learning techniques enable chemical space exploration, target identification, and compound classification. Bioinformatics and data mining enable target identification, drug discovery, and personalised treatment by analysing large biological databases. Quantum mechanics- based techniques reveal molecular structures, interactions, and reactions, improving drug design and optimisation. Results and Findings: The review demonstrates that computational approaches have the potential to expedite drug discovery by leveraging machine learning, artificial intelligence, quantum mechanics, Big Data, and omics methods. These techniques enable accurate prediction of drug-target interactions and efficient exploration of chemical and biological spaces. The integration of diverse datasets enhances target identification and personalized medicine, while quantum mechanics-based insights improve drug design. Conclusions: Despite their benefits, computational approaches face challenges such as model accuracy, efficiency, and validation. Nonetheless, this review underscores the significance of these approaches and their applications in drug discovery. By addressing challenges and embracing emerging technologies, the field can propel advancements in computational drug discovery. This progress will not only benefit patients but also advance the overall landscape of healthcare.

Pharmacological and Nutritional Modulation of Metabolome and Metagenome in Cardiometabolic Disorders.

Cardiometabolic disorders are major causes of morbidity and mortality worldwide. A growing body of research indicates that the gut microbiota, whether it interacts favorably or not, plays an important role in host metabolism. Elucidating metabolic pathways may be crucial in preventing and treating cardiometabolic diseases, and omics methods are key to studying the interaction between the fecal microbiota and host metabolism. This review summarizes available studies that combine metabolomic and metagenomic approaches to describe the effects of drugs, diet, nutrients, and specific foods on cardiometabolic health and to identify potential targets for future research.

Heat Stress in French Bean and Garden Pea : A Review

This review paper addresses the effect of high temperature on pea and similar legumes. The areas covered are field pea and its production, temperature stress effects on New Zealand agriculture, seed vigour which discusses hollow heart and conductivity, high temperature stress on seed germination, threshold temperature, high temperature effects on vegetative growth and reproductive development. This is followed by adaptation mechanisms for high temperature stress, and breeding strategies which cover conventional plant breeding. The quality traits refer to protein, sugars, and the effects of high temperature on the grain composition and quality. Because of various factors related to global climate change, ambient temperatures are expected to rise in the future. Heat stress, which is a serious danger to crop output in most nations, can emerge from these temperature rises. Legumes are well-known for their nutritional and health benefits, as well as their impact on agricultural sustainability. Heat stress is a problem for legume crops because it affects the morphology, physiology, and reproductive growth of the plants. As grain legume agriculture grows to warmer areas and temperature fluctuation increases owing to climate change, high-temperature stress during the reproductive stage is becoming a serious constraint for output. The reproductive stage is critical in the life cycle of all plants, and it is vulnerable to high-temperature stress because many metabolic processes are harmed at this time, resulting in lower crop yields. Flower abortion, pollen and ovule sterility, decreased fertilization, and reduced seed filling are all observed in food legumes exposed to high-temperature stress during reproduction, resulting in smaller seeds and poorer yields. Heat tolerance in major legumes can be improved through several breeding approaches to improve field performance. Understanding the processes of molecular responses to high-temperature stress necessitates the use of omics methods to uncover the pathways behind thermo-tolerance.

Genotyping by Sequencing Reveals Genetic Relatedness and Duplicates amongst Local Cassava (Manihot esculenta Crantz) Landraces and Improved Genotypes in Kenya

Future demand for cassava is expected to increase in order to mitigate climatic changes, sustain food security and provide raw materials for industry. To meet these demands, adoption of modern omics methods ensures reliability, precision and timely delivery of more productive and resilient varieties. A total of 112 mix of duplicate clones, diverse local cassava landraces (LARs) and improved genotypes (IMGs) were genotyped using single nucleotide polymorphisms (SNPs) generated through genotyping by sequencing (GBS) approach. About 17% (5808) of the 33672 SNPs were used for hierarchical clustering and ADMIXTURE analysis for ancestries. Approximately 48 and 52% of the germplasms respectively formed 17 independent clusters (identical clones or duplicates) and admixtures (unique or non-duplicated clones). Of the duplicates, 10 clusters were formed from LARs, four from IMGs and three from a mix of both LARs and IMGs, revealing their genetic relatedness. Approximately 71 and 29% of clusters comprised cassava accessions from the same and different geographical regions, respectively, with the geographical restriction of clusters attributed to the limited movement of planting materials across the country, possibly due to a weak seed distribution system or disease-driven quarantine measures. The historical sharing or exchange of stakes or stem cuttings by farmers was linked to the duplication of LARs, whereas IMGs duplication may be associated with convergent evolution, selection, or sharing of common parentage. The high number of admixtures or unique clones implied minimal loss of genetic diversity. These findings can aid designing efficient and effective cassava improvement programs through development of a core set of diagnostic markers.

An omics data analysis method based on feature linear relationship and graph convolutional network

Biological networks are known to be highly modular, and the dysfunction of network modules may cause diseases. Defining the key modules from the omics data and establishing the classification model is helpful in promoting the research of disease diagnosis and prognosis. However, for applying modules in downstream analysis such as disease states discrimination, most methods only utilize the node information, and ignore the node interactions or topological information, which may lead to false positives and limit the model performance. In this study, we propose an omics data analysis method based on feature linear relationship and graph convolutional network (LCNet). In LCNet, we adopt a way of applying the difference of feature linear relationships during disease development to characterize physiological and pathological changes and construct the differential linear relation network, which is simple and interpretable from the perspective of feature linear relationship. A greedy strategy is developed for searching the highly interactive modules with a strong discrimination ability. To fully utilize the information of the detected modules, the personalized sub-graphs for each sample based on the modules are defined, and the graph convolutional network (GCN) classifiers are trained to predict the sample labels. The experimental results on public datasets show the superiority of LCNet in classification performance. For Breast Cancer metabolic data, the identified metabolites by LCNet involve important pathways. Thus, LCNet can identify the module biomarkers by feature linear relationship and a greedy strategy, and label samples by personalized sub-graphs and GCN. It provides a new manner of utilizing node (molecule) information and topological information in the defined modules for better disease classification.

Simultaneous profiling of chromatin architecture and transcription in single cells.

The three-dimensional structure of chromatin plays a crucial role in development and disease, both of which are associated with transcriptional changes. However, given the heterogeneity in single-cell chromatin architecture and transcription, the regulatory relationship between the three-dimensional chromatin structure and gene expression is difficult to explain based on bulk cell populations. Here we develop a single-cell, multimodal, omics method allowing the simultaneous detection of chromatin architecture and messenger RNA expression by sequencing (single-cell transcriptome sequencing (scCARE-seq)). Applying scCARE-seq to examine chromatin architecture and transcription from 2i to serum single mouse embryonic stem cells, we observe improved separation of cell clusters compared with single-cell chromatin conformation capture. In addition, after defining the cell-cycle phase of each cell through chromatin architecture extracted by scCARE-seq, we find that periodic changes in chromatin architecture occur in parallel with transcription during the cell cycle. These findings highlight the potential of scCARE-seq to facilitate comprehensive analyses that may boost our understanding of chromatin architecture and transcription in the same single cell.

Consistency across multi-omics layers in a drug-perturbed gut microbial community.

Multi-omics analyses are used in microbiome studies to understand molecular changes in microbial communities exposed to different conditions. However, it is not always clear how much each omics data type contributes to our understanding and whether they are concordant with each other. Here, we map the molecular response of a synthetic community of 32 human gut bacteria to three non-antibiotic drugs by using five omics layers (16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics and metabolomics). We find that all the omics methods with species resolution are highly consistent in estimating relative species abundances. Furthermore, different omics methods complement each other for capturing functional changes. For example, while nearly all the omics data types captured that the antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in the community, the metatranscriptome and metaproteome suggested that the drug induces stress responses related to protein quality control. Metabolomics revealed a decrease in oligosaccharide uptake, likely caused by Bacteroidota depletion. Our study highlights how multi-omics datasets can be utilized to reveal complex molecular responses to external perturbations in microbial communities.

Untangling Alzheimer's disease with spatial multi-omics: a brief review.

Alzheimer's disease (AD) is the most common form of neurological dementia, specified by extracellular β-amyloid plaque deposition, neurofibrillary tangles, and cognitive impairment. AD-associated pathologies like cerebral amyloid angiopathy (CAA) are also affiliated with cognitive impairment and have overlapping molecular drivers, including amyloid buildup. Discerning the complexity of these neurological disorders remains a significant challenge, and the spatiomolecular relationships between pathogenic features of AD and AD-associated pathologies remain poorly understood. This review highlights recent developments in spatial omics, including profiling and molecular imaging methods, and how they are applied to AD. These emerging technologies aim to characterize the relationship between how specific cell types and tissue features are organized in combination with mapping molecular distributions to provide a systems biology view of the tissue microenvironment around these neuropathologies. As spatial omics methods achieve greater resolution and improved molecular coverage, they are enabling deeper characterization of the molecular drivers of AD, leading to new possibilities for the prediction, diagnosis, and mitigation of this debilitating disease.

Integrative meta-omics in Galaxy and beyond

Background‘Omics methods have empowered scientists to tackle the complexity of microbial communities on a scale not attainable before. Individually, omics analyses can provide great insight; while combined as “meta-omics”, they enhance the understanding of which organisms occupy specific metabolic niches, how they interact, and how they utilize environmental nutrients. Here we present three integrative meta-omics workflows, developed in Galaxy, for enhanced analysis and integration of metagenomics, metatranscriptomics, and metaproteomics, combined with our newly developed web-application, ViMO (Visualizer for Meta-Omics) to analyse metabolisms in complex microbial communities.ResultsIn this study, we applied the workflows on a highly efficient cellulose-degrading minimal consortium enriched from a biogas reactor to analyse the key roles of uncultured microorganisms in complex biomass degradation processes. Metagenomic analysis recovered metagenome-assembled genomes (MAGs) for several constituent populations including Hungateiclostridium thermocellum, Thermoclostridium stercorarium and multiple heterogenic strains affiliated to Coprothermobacter proteolyticus. The metagenomics workflow was developed as two modules, one standard, and one optimized for improving the MAG quality in complex samples by implementing a combination of single- and co-assembly, and dereplication after binning. The exploration of the active pathways within the recovered MAGs can be visualized in ViMO, which also provides an overview of the MAG taxonomy and quality (contamination and completeness), and information about carbohydrate-active enzymes (CAZymes), as well as KEGG annotations and pathways, with counts and abundances at both mRNA and protein level. To achieve this, the metatranscriptomic reads and metaproteomic mass-spectrometry spectra are mapped onto predicted genes from the metagenome to analyse the functional potential of MAGs, as well as the actual expressed proteins and functions of the microbiome, all visualized in ViMO.ConclusionOur three workflows for integrative meta-omics in combination with ViMO presents a progression in the analysis of ‘omics data, particularly within Galaxy, but also beyond. The optimized metagenomics workflow allows for detailed reconstruction of microbial community consisting of MAGs with high quality, and thus improves analyses of the metabolism of the microbiome, using the metatranscriptomics and metaproteomics workflows.

The Microbial Connection to Sustainable Agriculture.

Microorganisms are an important element in modeling sustainable agriculture. Their role in soil fertility and health is crucial in maintaining plants' growth, development, and yield. Further, microorganisms impact agriculture negatively through disease and emerging diseases. Deciphering the extensive functionality and structural diversity within the plant-soil microbiome is necessary to effectively deploy these organisms in sustainable agriculture. Although both the plant and soil microbiome have been studied over the decades, the efficiency of translating the laboratory and greenhouse findings to the field is largely dependent on the ability of the inoculants or beneficial microorganisms to colonize the soil and maintain stability in the ecosystem. Further, the plant and its environment are two variables that influence the plant and soil microbiome's diversity and structure. Thus, in recent years, researchers have looked into microbiome engineering that would enable them to modify the microbial communities in order to increase the efficiency and effectiveness of the inoculants. The engineering of environments is believed to support resistance to biotic and abiotic stressors, plant fitness, and productivity. Population characterization is crucial in microbiome manipulation, as well as in the identification of potential biofertilizers and biocontrol agents. Next-generation sequencing approaches that identify both culturable and non-culturable microbes associated with the soil and plant microbiome have expanded our knowledge in this area. Additionally, genome editing and multidisciplinary omics methods have provided scientists with a framework to engineer dependable and sustainable microbial communities that support high yield, disease resistance, nutrient cycling, and management of stressors. In this review, we present an overview of the role of beneficial microbes in sustainable agriculture, microbiome engineering, translation of this technology to the field, and the main approaches used by laboratories worldwide to study the plant-soil microbiome. These initiatives are important to the advancement of green technologies in agriculture.

Clustering single-cell multimodal omics data with jrSiCKLSNMF.

Introduction: The development of multimodal single-cell omics methods has enabled the collection of data across different omics modalities from the same set of single cells. Each omics modality provides unique information about cell type and function, so the ability to integrate data from different modalities can provide deeper insights into cellular functions. Often, single-cell omics data can prove challenging to model because of high dimensionality, sparsity, and technical noise. Methods: We propose a novel multimodal data analysis method called joint graph-regularized Single-Cell Kullback-Leibler Sparse Non-negative Matrix Factorization (jrSiCKLSNMF, pronounced "junior sickles NMF") that extracts latent factors shared across omics modalities within the same set of single cells. Results: We compare our clustering algorithm to several existing methods on four sets of data simulated from third party software. We also apply our algorithm to a real set of cell line data. Discussion: We show overwhelmingly better clustering performance than several existing methods on the simulated data. On a real multimodal omics dataset, we also find our method to produce scientifically accurate clustering results.

The technological landscape and applications of single-cell multi-omics.

Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods.

Spatial Omics Imaging of Fresh-Frozen Tissue and Routine FFPE Histopathology of a Single Cancer Needle Core Biopsy: A Freezing Device and Multimodal Workflow

The complex molecular alterations that underlie cancer pathophysiology are studied in depth with omics methods using bulk tissue extracts. For spatially resolved tissue diagnostics using needle biopsy cores, however, histopathological analysis using stained FFPE tissue and the immunohistochemistry (IHC) of a few marker proteins is currently the main clinical focus. Today, spatial omics imaging using MSI or IRI is an emerging diagnostic technology for the identification and classification of various cancer types. However, to conserve tissue-specific metabolomic states, fast, reliable, and precise methods for the preparation of fresh-frozen (FF) tissue sections are crucial. Such methods are often incompatible with clinical practice, since spatial metabolomics and the routine histopathology of needle biopsies currently require two biopsies for FF and FFPE sampling, respectively. Therefore, we developed a device and corresponding laboratory and computational workflows for the multimodal spatial omics analysis of fresh-frozen, longitudinally sectioned needle biopsies to accompany standard FFPE histopathology of the same biopsy core. As a proof-of-concept, we analyzed surgical human liver cancer specimens using IRI and MSI with precise co-registration and, following FFPE processing, by sequential clinical pathology analysis of the same biopsy core. This workflow allowed for a spatial comparison between different spectral profiles and alterations in tissue histology, as well as a direct comparison for histological diagnosis without the need for an extra biopsy.

Rapid and Accurate Identification of Cell Phenotypes of Different Drug Mechanisms by Using Single-Cell Fluorescence Images Via Deep Learning.

Identification of a drug mechanism is vital for drug development. However, it often resorts to the expensive and cumbersome omics methods along with complex data analysis. Herein, we developed a methodology to analyze organelle staining images of single cells using a deep learning algorithm (TL-ResNet50) for rapid and accurate identification of different drug mechanisms. Based on the organelle-related cell morphological changes caused by drug action, the constructed deep learning model can fast predict the drug mechanism with a high accuracy of 92%. Further analysis reveals that drug combination at different ratios can enhance a certain mechanism or generate a new mechanism. This work would highly facilitate clinical medication and drug screening.

Interpretable Machine Learning on Metabolomics Data Reveals Biomarkers for Parkinson's Disease.

The use of machine learning (ML) with metabolomics provides opportunities for the early diagnosis of disease. However, the accuracy of ML and extent of information obtained from metabolomics can be limited owing to challenges associated with interpreting disease prediction models and analyzing many chemical features with abundances that are correlated and "noisy". Here, we report an interpretable neural network (NN) framework to accurately predict disease and identify significant biomarkers using whole metabolomics data sets without a priori feature selection. The performance of the NN approach for predicting Parkinson's disease (PD) from blood plasma metabolomics data is significantly higher than other ML methods with a mean area under the curve of >0.995. PD-specific markers that predate clinical PD diagnosis and contribute significantly to early disease prediction were identified including an exogenous polyfluoroalkyl substance. It is anticipated that this accurate and interpretable NN-based approach can improve diagnostic performance for many diseases using metabolomics and other untargeted 'omics methods.

Subcellular spatially resolved gene neighborhood networks in single cells

Image-based spatial omics methods such as fluorescence in situ hybridization (FISH) generate molecular profiles of single cells at single-molecule resolution. Current spatial transcriptomics methods focus on the distribution of single genes. However, the spatial proximity of RNA transcripts can play an important role in cellular function. We demonstrate a spatially resolved gene neighborhood network (spaGNN) pipeline for the analysis of subcellular gene proximity relationships. In spaGNN, machine-learning-based clustering of subcellular spatial transcriptomics data yields subcellular density classes of multiplexed transcript features. The nearest-neighbor analysis produces heterogeneous gene proximity maps in distinct subcellular regions. We illustrate the cell-type-distinguishing capability of spaGNN using multiplexed error-robust FISH data of fibroblast and U2-OS cells and sequential FISH data of mesenchymal stem cells (MSCs), revealing tissue-source-specific MSC transcriptomics and spatial distribution characteristics. Overall, the spaGNN approach expands the spatial features that can be used for cell-type classification tasks.

Omics Approaches in Invasion Biology: Understanding Mechanisms and Impacts on Ecological Health.

Invasive species and rapid climate change are affecting the control of new plant diseases and epidemics. To effectively manage these diseases under changing environmental conditions, a better understanding of pathophysiology with holistic approach is needed. Multiomics approaches can help us to understand the relationship between plants and microbes and construct predictive models for how they respond to environmental stresses. The application of omics methods enables the simultaneous analysis of plant hosts, soil, and microbiota, providing insights into their intricate relationships and the mechanisms underlying plant-microbe interactions. This can help in the development of novel strategies for enhancing plant health and improving soil ecosystem functions. The review proposes the use of omics methods to study the relationship between plant hosts, soil, and microbiota, with the aim of developing a new technique to regulate soil health. This approach can provide a comprehensive understanding of the mechanisms underlying plant-microbe interactions and contribute to the development of effective strategies for managing plant diseases and improving soil ecosystem functions. In conclusion, omics technologies offer an innovative and holistic approach to understanding plant-microbe interactions and their response to changing environmental conditions.