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The livestock industry faces significant challenges, with disease outbreaks being a particularly devastating issue. These diseases can disrupt the food supply chain and the livelihoods of those involved in the sector. To address this, there is a growing need to enhance the health and well‐being of livestock animals, ultimately improving their performance while minimizing their environmental impact. To tackle the considerable challenge posed by disease epidemics, multiomics approaches offer an excellent opportunity for scientists, breeders, and policymakers to gain a comprehensive understanding of animal biology, pathogens, and their genetic makeup. This understanding is crucial for enhancing the health of livestock animals. Multiomic approaches, including phenomics, genomics, epigenomics, metabolomics, proteomics, transcriptomics, microbiomics, and metaproteomics, are widely employed to assess and enhance animal health. High‐throughput phenotypic data collection allows for the measurement of various fitness traits, both discrete and continuous, which, when mathematically combined, define the overall health and resilience of animals, including their ability to withstand diseases. Omics methods are routinely used to identify genes involved in host‐pathogen interactions, assess fitness traits, and pinpoint animals with disease resistance. Genome‐wide association studies (GWAS) help identify the genetic factors associated with health status, heat stress tolerance, disease resistance, and other health‐related characteristics, including the estimation of breeding value. Furthermore, the interaction between hosts and pathogens, as observed through the assessment of host gut microbiota, plays a crucial role in shaping animal health and, consequently, their performance. Integrating and analyzing various heterogeneous datasets to gain deeper insights into biological systems is a challenging task that necessitates the use of innovative tools. Initiatives like MiBiOmics, which facilitate the visualization, analysis, integration, and exploration of multiomics data, are expected to improve prediction accuracy and identify robust biomarkers linked to animal health. In this review, we discuss the details of multiomics concerning the health and well‐being of livestock animals.

The recent acceleration of commercial, private and multi-national spaceflight has created an unprecedented level of activity in low Earth orbit, concomitant with the largest-ever number of crewed missions entering space and preparations for exploration-class (lasting longer than one year) missions. Such rapid advancement into space from many new companies, countries and space-related entities has enabled a ‘second space age’. This era is also poised to leverage, for the first time, modern tools and methods of molecular biology and precision medicine, thus enabling precision aerospace medicine for the crews. The applications of these biomedical technologies and algorithms are diverse, and encompass multi-omic, single-cell and spatial biology tools to investigate human and microbial responses to spaceflight. Additionally, they extend to the development of new imaging techniques, real-time cognitive assessments, physiological monitoring and personalized risk profiles tailored for astronauts. Furthermore, these technologies enable advancements in pharmacogenomics, as well as the identification of novel spaceflight biomarkers and the development of corresponding countermeasures. In this Perspective, we highlight some of the recent biomedical research from the National Aeronautics and Space Administration, Japan Aerospace Exploration Agency, European Space Agency and other space agencies, and detail the entrance of the commercial spaceflight sector (including SpaceX, Blue Origin, Axiom and Sierra Space) into aerospace medicine and space biology, the first aerospace medicine biobank, and various upcoming missions that will utilize these tools to ensure a permanent human presence beyond low Earth orbit, venturing out to other planets and moons. The current ‘second space age’ has enabled multiple studies on the effects of spaceflight on human physiology and health, which are contributing to the development of measures that will be needed to maintain astronaut health in future space missions.

Aging is a global issue that affects human health and increases disease risk. The traditional concept of the \"age gap (AG),\" defined as the difference between estimated biological age and an individual's chronological age, has been used for self-monitoring the risk of age-related diseases. However, the current AG does not account for the stratified aging patterns across different stages of chronological age, which may lead to biased or paradoxical interpretations of aging acceleration. To address these limitations, we propose Personalized-context-Aware Age Gap (PAAG), a robust metric to estimate aging acceleration, based on our new pre-training model AOE-Net (Age Order Enhanced Network). AOE-Net employs age-order enhanced contrastive learning on multi-omics data from healthy populations to learn latent representations that accurately reconstruct aging trajectories by capturing biological deviation rather than technical deviation in omics data. We demonstrate that PAAG, generated via fine-tuning AOE-Net, significantly outperforms AG of conventional first- and second-generation aging clocks in predicting clinical outcomes. This superior predictive power was validated across diverse age-related diseases and phenotypes: pan-cancer (overall survival), subclinical atherosclerosis (PESA score), and osteoporosis (bone mineral density). Crucially, PAAG serves as a context-aware metric that may improve the clinical outcome prediction of existing aging clocks. Furthermore, interpretive analysis of PAAG's molecular drivers revealed a strong functional enrichment for immune-response pathways, providing a shared mechanistic link between accelerated aging and disease. Collectively, PAAG could serve as a stable indicator of aging acceleration for clinically assessing age-related diseases, and AOE-Net provides an effective pre-training model for aging study and PAAG evaluation.

With the advent of multiomics, software capable of multidimensional enrichment analysis has become increasingly crucial for uncovering gene set variations in biological processes and disease pathways. This is essential for elucidating disease mechanisms and identifying potential therapeutic targets. clusterProfiler stands out for its comprehensive utilization of databases and advanced visualization features. Importantly, clusterProfiler supports various biological knowledge, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, through performing over-representation and gene set enrichment analyses. A key feature is that clusterProfiler allows users to choose from various graphical outputs to visualize results, enhancing interpretability. This protocol describes innovative ways in which clusterProfiler has been used for integrating metabolomics and metagenomics analyses, identifying and characterizing transcription factors under stress conditions, and annotating cells in single-cell studies. In all cases, the computational steps can be completed within ~2 min. clusterProfiler is released through the Bioconductor project and can be accessed via https://bioconductor.org/packages/clusterProfiler/ . Key points clusterProfiler is a software package for characterizing and interpreting omics data. Functional enrichment can be achieved using either over-representation or gene set enrichment analyses; it supports the use of a variety of databases, e.g., Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Three procedures show specific R commands for example applications asking different research questions and having different graphical outputs. Advice is provided on how to modify the procedures for other applications. clusterProfiler is a tool for characterizing and visualizing omics data. The example procedures show integration of metabolomics and metagenomics analyses, characterization of transcription factors and annotation of cells in single-cell studies.

Summary Cadmium (Cd) is one of the most toxic heavy metals faced by plants and, additionally, via the food chain, threatens human health. It is principally dispersed through agro‐ecosystems via anthropogenic activities and geogenic sources. Given its high mobility and persistence, Cd, although not required, can be readily assimilated by plants thereby posing a threat to plant growth and productivity as well as animal and human health. Thus, breeding crop plants in which the edible parts contain low to zero Cd as safe food stuffs and harvesting shoots of high Cd‐containing plants as a route for decontaminating soils are vital strategies to cope with this problem. Recently, multiomics approaches have been employed to considerably enhance our understanding of the mechanisms underlying (i) Cd toxicity, (ii) Cd accumulation, (iii) Cd detoxification and (iv) Cd acquisition tolerance in plants. This information can be deployed in the development of the biotechnological tools for developing plants with modulated Cd tolerance and detoxification to safeguard cellular and genetic integrity as well as to minimize food chain contamination. The aim of this review is to provide a current update about the mechanisms involved in Cd uptake by plants and the recent developments in the area of multiomics approach in terms of Cd stress responses, as well as in the development of Cd tolerant and low Cd accumulating crops.

The joint analysis of the genome, epigenome, transcriptome, proteome and/or metabolome from single cells is transforming our understanding of cell biology in health and disease. In less than a decade, the field has seen tremendous technological revolutions that enable crucial new insights into the interplay between intracellular and intercellular molecular mechanisms that govern development, physiology and pathogenesis. In this Review, we highlight advances in the fast-developing field of single-cell and spatial multi-omics technologies (also known as multimodal omics approaches), and the computational strategies needed to integrate information across these molecular layers. We demonstrate their impact on fundamental cell biology and translational research, discuss current challenges and provide an outlook to the future.In this Review, the authors discuss the latest advances in profiling multiple molecular modalities from single cells, including genomic, transcriptomic, epigenomic and proteomic information. They describe the diverse strategies for separately analysing different modalities, how the data can be computationally integrated, and approaches for obtaining spatially resolved data.

Diet can influence the composition of the human microbiome, and yet relatively few dietary ingredients have been systematically investigated with respect to their impact on the functional potential of the microbiome. Dietary resistant starch (RS) has been shown to have health benefits, but we lack a mechanistic understanding of the metabolic processes that occur in the gut during digestion of RS. Here, we collected samples during a dietary crossover study with diets containing large or small amounts of RS. We determined the impact of RS on the gut microbiome and metabolic pathways in the gut, using a combination of “omics” approaches, including 16S rRNA gene sequencing, metaproteomics, and metabolomics. This multiomics approach captured changes in the abundance of specific bacterial species, proteins, and metabolites after a diet high in resistant starch (HRS), providing key insights into the influence of dietary interventions on the gut microbiome. The combined data showed that a high-RS diet caused an increase in the ratio of Firmicutes to Bacteroidetes , including increases in relative abundances of some specific members of the Firmicutes and concurrent increases in enzymatic pathways and metabolites involved in lipid metabolism in the gut. IMPORTANCE This work was undertaken to obtain a mechanistic understanding of the complex interplay between diet and the microorganisms residing in the intestine. Although it is known that gut microbes play a key role in digestion of the food that we consume, the specific contributions of different microorganisms are not well understood. In addition, the metabolic pathways and resultant products of metabolism during digestion are highly complex. To address these knowledge gaps, we used a combination of molecular approaches to determine the identities of the microorganisms in the gut during digestion of dietary starch as well as the metabolic pathways that they carry out. Together, these data provide a more complete picture of the function of the gut microbiome in digestion, including links between an RS diet and lipid metabolism and novel linkages between specific gut microbes and their metabolites and proteins produced in the gut. This work was undertaken to obtain a mechanistic understanding of the complex interplay between diet and the microorganisms residing in the intestine. Although it is known that gut microbes play a key role in digestion of the food that we consume, the specific contributions of different microorganisms are not well understood. In addition, the metabolic pathways and resultant products of metabolism during digestion are highly complex. To address these knowledge gaps, we used a combination of molecular approaches to determine the identities of the microorganisms in the gut during digestion of dietary starch as well as the metabolic pathways that they carry out. Together, these data provide a more complete picture of the function of the gut microbiome in digestion, including links between an RS diet and lipid metabolism and novel linkages between specific gut microbes and their metabolites and proteins produced in the gut.

Generative pretrained models have achieved remarkable success in various domains such as language and computer vision. Specifically, the combination of large-scale diverse datasets and pretrained transformers has emerged as a promising approach for developing foundation models. Drawing parallels between language and cellular biology (in which texts comprise words; similarly, cells are defined by genes), our study probes the applicability of foundation models to advance cellular biology and genetic research. Using burgeoning single-cell sequencing data, we have constructed a foundation model for single-cell biology, scGPT, based on a generative pretrained transformer across a repository of over 33 million cells. Our findings illustrate that scGPT effectively distills critical biological insights concerning genes and cells. Through further adaptation of transfer learning, scGPT can be optimized to achieve superior performance across diverse downstream applications. This includes tasks such as cell type annotation, multi-batch integration, multi-omic integration, perturbation response prediction and gene network inference. Pretrained using over 33 million single-cell RNA-sequencing profiles, scGPT is a foundation model facilitating a broad spectrum of downstream single-cell analysis tasks by transfer learning.

Single‐cell multiomic and exosome analyses are potent tools in various fields, such as cancer research, immunology, neuroscience, microbiology, and drug development. They facilitate the in‐depth exploration of biological systems, providing insights into disease mechanisms and aiding in treatment. Single‐cell isolation, which is crucial for single‐cell analysis, ensures reliable cell isolation and quality control for further downstream analyses. Microfluidic chips are small lightweight systems that facilitate efficient and high‐throughput single‐cell isolation and real‐time single‐cell analysis on‐ or off‐chip. Therefore, most current single‐cell isolation and analysis technologies are based on the single‐cell microfluidic technology. This review offers comprehensive guidance to researchers across different fields on the selection of appropriate microfluidic chip technologies for single‐cell isolation and analysis. This review describes the design principles, separation mechanisms, chip characteristics, and cellular effects of various microfluidic chips available for single‐cell isolation. Moreover, this review highlights the implications of using this technology for subsequent analyses, including single‐cell multiomic and exosome analyses. Finally, the current challenges and future prospects of microfluidic chip technology are outlined for multiplex single‐cell isolation and multiomic and exosome analyses. This review offers detailed guidance on microfluidic chip technologies for single‐cell isolation and analysis. It details the isolation mechanisms, features, and cellular effects of various microfluidic chips. Additionally, the review highlights the critical contributions of these technologies to advancing single‐cell multiomics and exosome analysis across diverse scientific disciplines. Lastly, it discusses present challenges and future prospects for enhancing these technologies.

Technological advances have enabled the profiling of multiple molecular layers at single-cell resolution, assaying cells from multiple samples or conditions. Consequently, there is a growing need for computational strategies to analyze data from complex experimental designs that include multiple data modalities and multiple groups of samples. We present Multi-Omics Factor Analysis v2 (MOFA+), a statistical framework for the comprehensive and scalable integration of single-cell multi-modal data. MOFA+ reconstructs a low-dimensional representation of the data using computationally efficient variational inference and supports flexible sparsity constraints, allowing to jointly model variation across multiple sample groups and data modalities.