Diagnosis and treatment approaches for central nervous system germ cell tumors (GCTs) have been evolving over decades ; however, they remain globally heterogeneous. There are major differences in how ...tumor markers and histopathological findings are weighed in Japan and Europe/North America at the time of diagnosis. It remains debatable whether non-germinomatous GCTs (NGGCTs) could be diagnosed solely based on elevated tumor markers without a biopsy, partly because of the suboptimal specificity of tumor markers of tumor tissues for future research. The historical three-class system in Japan (germinoma, intermediate prognosis group, and poor prognosis group) contrasts with the two-class system in Europe/North America (germinoma and NGGCTs). The difference stems from the debate over whether NGGCTs mixed with teratoma should be treated as intensively as other malignant NGGCTs. Hopefully, thorough analyses of previous clinical trials conducted across continents would result in clinical questions that would best guide the design of clinical trials. Additionally, the Intracranial GCT Genome Analysis Consortium of Japan (2012) has yielded multi-omics analyses on the pathogenesis, investigating the unique biological characteristics of GCTs. It is essential to conduct translational research based on biological investigation to develop novel treatments by unveiling crucial elements in the pathogenesis.
Abiotic stresses are the foremost limiting factors for agricultural productivity. Crop plants need to cope up adverse external pressure created by environmental and edaphic conditions with their ...intrinsic biological mechanisms, failing which their growth, development, and productivity suffer. Microorganisms, the most natural inhabitants of diverse environments exhibit enormous metabolic capabilities to mitigate abiotic stresses. Since microbial interactions with plants are an integral part of the living ecosystem, they are believed to be the natural partners that modulate local and systemic mechanisms in plants to offer defense under adverse external conditions. Plant-microbe interactions comprise complex mechanisms within the plant cellular system. Biochemical, molecular and physiological studies are paving the way in understanding the complex but integrated cellular processes. Under the continuous pressure of increasing climatic alterations, it now becomes more imperative to define and interpret plant-microbe relationships in terms of protection against abiotic stresses. At the same time, it also becomes essential to generate deeper insights into the stress-mitigating mechanisms in crop plants for their translation in higher productivity. Multi-omics approaches comprising genomics, transcriptomics, proteomics, metabolomics and phenomics integrate studies on the interaction of plants with microbes and their external environment and generate multi-layered information that can answer what is happening in real-time within the cells. Integration, analysis and decipherization of the big-data can lead to a massive outcome that has significant chance for implementation in the fields. This review summarizes abiotic stresses responses in plants in-terms of biochemical and molecular mechanisms followed by the microbe-mediated stress mitigation phenomenon. We describe the role of multi-omics approaches in generating multi-pronged information to provide a better understanding of plant-microbe interactions that modulate cellular mechanisms in plants under extreme external conditions and help to optimize abiotic stresses. Vigilant amalgamation of these high-throughput approaches supports a higher level of knowledge generation about root-level mechanisms involved in the alleviation of abiotic stresses in organisms.
Summary
Kiwifruit (Actinidia chinensis) is one of the popular fruits world‐wide, and its quality is mainly determined by key metabolites (sugars, flavonoids, and vitamins). Previous works on ...kiwifruit are mostly done via a single omics approach or involve only limited metabolites. Consequently, the dynamic metabolomes during kiwifruit development and ripening and the underlying regulatory mechanisms are poorly understood.
In this study, using high‐resolution metabolomic and transcriptomic analyses, we investigated kiwifruit metabolic landscapes at 11 different developmental and ripening stages and revealed a parallel classification of 515 metabolites and their co‐expressed genes into 10 distinct metabolic vs gene modules (MM vs GM). Through integrative bioinformatics coupled with functional genomic assays, we constructed a global map and uncovered essential transcriptomic and transcriptional regulatory networks for all major metabolic changes that occurred throughout the kiwifruit growth cycle.
Apart from known MM vs GM for metabolites such as soluble sugars, we identified novel transcription factors that regulate the accumulation of procyanidins, vitamin C, and other important metabolites.
Our findings thus shed light on the kiwifruit metabolic regulatory network and provide a valuable resource for the designed improvement of kiwifruit quality.
Understanding and monitoring virus-mediated infections has gained importance since the global outbreak of the coronavirus disease 2019 (COVID-19) pandemic. Studies of high-throughput omics-based ...immune profiling of COVID-19 patients can help manage the current pandemic and future virus-mediated pandemics. Although COVID-19 is being studied since past 2 years, detailed mechanisms of the initial induction of dynamic immune responses or the molecular mechanisms that characterize disease progression remains unclear. This study involved comprehensively collected biospecimens and longitudinal multi-omics data of 300 COVID-19 patients and 120 healthy controls, including whole genome sequencing (WGS), single-cell RNA sequencing combined with T cell receptor (TCR) and B cell receptor (BCR) sequencing (scRNA(+scTCR/BCR)-seq), bulk BCR and TCR sequencing (bulk TCR/BCR-seq), and cytokine profiling. Clinical data were also collected from hospitalized COVID-19 patients, and HLA typing, laboratory characteristics, and COVID-19 viral genome sequencing were performed during the initial diagnosis. The entire set of biospecimens and multi-omics data generated in this project can be accessed by researchers from the National Biobank of Korea with prior approval. This distribution of largescale multi-omics data of COVID-19 patients can facilitate the understanding of biological crosstalk involved in COVID-19 infection and contribute to the development of potential methodologies for its diagnosis and treatment. BMB Reports 2022; 55(9): 465-472
We present an integrated analysis of the clinical measurements, immune cells, and plasma multi-omics of 139 COVID-19 patients representing all levels of disease severity, from serial blood draws ...collected during the first week of infection following diagnosis. We identify a major shift between mild and moderate disease, at which point elevated inflammatory signaling is accompanied by the loss of specific classes of metabolites and metabolic processes. Within this stressed plasma environment at moderate disease, multiple unusual immune cell phenotypes emerge and amplify with increasing disease severity. We condensed over 120,000 immune features into a single axis to capture how different immune cell classes coordinate in response to SARS-CoV-2. This immune-response axis independently aligns with the major plasma composition changes, with clinical metrics of blood clotting, and with the sharp transition between mild and moderate disease. This study suggests that moderate disease may provide the most effective setting for therapeutic intervention.
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•Analysis of serial blood from 139 COVID-19 patients reveals immune coordination•A major immunological shift is seen between mild and moderate infection•Moderate and severe cases exhibit inflammation and a sharp drop in blood nutrients•Novel immune cell subsets emerge in moderate cases and increase with severity
Using serial blood draws from COVID-19 patients, Su et al. present an extensive multi-omics dataset of plasma and single PBMCs covering the first week of infection following clinical diagnosis, which includes information on plasma proteins, metabolites, and on PBMC transcriptomic and surface-protein data, immune receptor sequences, secreted proteins, and electronic health record data. Their integrated analysis identifies a major immunological shift between mild and moderate infection, which includes an increase in inflammation, drop in blood nutrients, and the emergence of novel immune cell subpopulations that intensify with disease severity.
Given the rapidly decreasing cost and increasing speed and accessibility of massively parallel technologies, the integration of comprehensive genomic, transcriptomic, and proteomic data into a ...“multi‐omics” diagnostic pipeline is within reach. Even though genomic analysis has the capability to reveal all possible perturbations in our genetic code, analysis typically reaches a diagnosis in just 35% of cases, with a diagnostic gap arising due to limitations in prioritization and interpretation of detected variants. Here we review the utility of complementing genetic data with transcriptomic data and give a perspective for the introduction of proteomics into the diagnostic pipeline. Together these methodologies enable comprehensive capture of the functional consequence of variants, unobtainable by the analysis of each methodology in isolation. This facilitates functional annotation and reprioritization of candidate genes and variants—a promising approach to shed light on the underlying molecular cause of a patient's disease, increasing diagnostic rate, and allowing actionability in clinical practice.
This study aimed to establish the Japanese Cancer Genome Atlas (JCGA) using data from fresh frozen tumor tissues obtained from 5143 Japanese cancer patients, including those with colorectal cancer ...(31.6%), lung cancer (16.5%), gastric cancer (10.8%) and other cancers (41.1%). The results are part of a single‐center study called “High‐tech Omics‐based Patient Evaluation” or “Project HOPE” conducted at the Shizuoka Cancer Center, Japan. All DNA samples and most RNA samples were analyzed using whole‐exome sequencing, cancer gene panel sequencing, fusion gene panel sequencing and microarray gene expression profiling, and the results were annotated using an analysis pipeline termed “Shizuoka Multi‐omics Analysis Protocol” developed in‐house. Somatic driver alterations were identified in 72.2% of samples in 362 genes (average, 2.3 driver events per sample). Actionable information on drugs that is applicable in the current clinical setting was associated with 11.3% of samples. When including those drugs that are used for investigative purposes, actionable information was assigned to 55.0% of samples. Germline analysis revealed pathogenic mutations in hereditary cancer genes in 9.2% of samples, among which 12.2% were confirmed as pathogenic mutations by confirmatory test. Pathogenic mutations associated with non–cancerous hereditary diseases were detected in 0.4% of samples. Tumor mutation burden (TMB) analysis revealed 5.4% of samples as having the hypermutator phenotype (TMB ≥ 20). Clonal hematopoiesis was observed in 8.4% of samples. Thus, the JCGA dataset and the analytical procedures constitute a fundamental resource for genomic medicine for Japanese cancer patients.
The present study aims to establish the Japanese Cancer Genome Atlas (JCGA) by analyzing fresh frozen tumor tissues obtained from 5143 Japanese cancer patients. Somatic driver and druggable alterations were detected in 72.2% and 11.3% of samples, respectively, and germline pathogenic mutations in hereditary cancer genes were identified in 9.2% of samples. The JCGA dataset and analytical procedures constitute a fundamental resource for genomic medicine for Japanese cancer patients.
Transformer‐based models have revolutionized single cell RNA‐seq (scRNA‐seq) data analysis. However, their applicability is challenged by the complexity and scale of single‐cell multi‐omics data. ...Here a novel single‐cell multi‐modal/multi‐task transformer (scmFormer) is proposed to fill up the existing blank of integrating single‐cell proteomics with other omics data. Through systematic benchmarking, it is demonstrated that scmFormer excels in integrating large‐scale single‐cell multimodal data and heterogeneous multi‐batch paired multi‐omics data, while preserving shared information across batchs and distinct biological information. scmFormer achieves 54.5% higher average F1 score compared to the second method in transferring cell‐type labels from single‐cell transcriptomics to proteomics data. Using COVID‐19 datasets, it is presented that scmFormer successfully integrates over 1.48 million cells on a personal computer. Moreover, it is also proved that scmFormer performs better than existing methods on generating the unmeasured modality and is well‐suited for spatial multi‐omic data. Thus, scmFormer is a powerful and comprehensive tool for analyzing single‐cell multi‐omics data.
scmFormer, a Transformer‐based model, employs multi‐task learning for single‐cell multi‐omics integration and unmeasured data generation. It excels in preserving shared information across diverse datasets, achieving a 54.5% higher average F1 score in cell‐type label transfer. Impressively scalable, scmFormer seamlessly integrates millions of cells on personal computers, outperforming existing methods in generating unmeasured modalities and excelling in spatial multi‐omic data analysis.
Single‐cell technologies capture cellular heterogeneity to focus on previously poorly described subpopulations of cells. Work by our laboratory and many others has metagenomically characterised a low ...biomass intrauterine microbial community, alongside microbial transcripts, antigens and metabolites, but the functional importance of low biomass microbial communities in placental immuno‐microenvironments is still being elucidated. Given their hypothesised role in modulating inflammation and immune ontogeny to enable tolerance of beneficial microbes while warding off pathogens, there is a need for single‐cell resolution. Herein, we summarise the potential for mechanistic understanding of these and other key fundamental early developmental processes by applying single‐cell approaches.
Single‐cell approaches will be vital to understanding how placenta cells differentiate maternal and microbial antigens #BJOG #scRNA‐seq @norsketexsci @barrozophd.
SUMMARY
The plant community lags far behind the animal and human fields concerning the application of single‐cell methodologies. This is primarily due to the challenges associated with plant tissue ...dissection and the limitations of the available technologies. However, recent advances in spatial transcriptomics enable the study of single‐cells derived from plant tissues from a spatial perspective. This technology is already successfully used to identify cell types, reconstruct cell‐fate lineages, and reveal cell‐to‐cell interactions. Future technological advancements will overcome the challenges in sample processing, data analysis, and the integration of multiple‐omics technologies. Thanks to spatial transcriptomics, we anticipate several plant research projects to significantly advance our understanding of critical aspects of plant biology.
Significance Statement
Detecting spatial gene expression patterns is quite important to uncover the developmental mechanism of plant tissues and organs. The development of spatial transcriptome technology opens a new era for plant science.
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