Single-cell metabolomics is a powerful tool that can reveal cellular heterogeneity and can elucidate the mechanisms of biological phenomena in detail. It is a promising approach in studying plants, especially when cellular heterogeneity has an impact on different biological processes. In addition, metabolomics, which can be regarded as a detailed phenotypic analysis, is expected to answer previously unrequited questions which will lead to expansion of crop production, increased understanding of resistance to diseases, and in other applications as well. In this review, we will introduce the flow of sample acquisition and single-cell techniques to facilitate the adoption of single-cell metabolomics. Furthermore, the applications of single-cell metabolomics will be summarized and reviewed.
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http://dx.doi.org/10.1093/plphys/kiad357 | DOI Listing |
Nat Commun
January 2025
Zhengzhou Research Base, State Key Laboratory of Cotton Bio-breeding and Integrated Utilization, School of Agricultural Sciences, Zhengzhou University, Zhengzhou, 450000, China.
Somatic embryogenesis (SE) is a developmental process related to the regeneration of tissue-cultured plants, which serves as a useful technique for crop breeding and improvement. However, SE in cotton is difficult and elusive due to the lack of precise cellular level information on the reprogramming of gene expression patterns involved in somatic embryogenesis. Here, we investigate the spatial and single-cell expression profiles of key genes and the metabolic patterns of key metabolites by integrated single-cell RNA-sequencing (scRNA-seq), spatial transcriptomics (ST), and spatial metabolomics (SM).
View Article and Find Full Text PDFNat Commun
January 2025
State Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research of the Chinese Academy of Agricultural Sciences, Anyang, China.
Cotton fibers are single cells that develop from the epidermal cells in the outer integument of developing seeds. The processes regulating fiber cell development have been extensively studied; however, the spatiotemporal transcriptome and metabolome profiles during the early stages of fiber development remain largely unknown. In this study, we profile the dynamics of transcriptome and metabolome during the early stages of cotton fiber cell development using a combination of spatial transcriptomic, single-cell transcriptomic, and spatial metabolomic analyses.
View Article and Find Full Text PDFEnviron Int
January 2025
School of Environmental Science and Engineering, Tianjin University, Tianjin 300354, China. Electronic address:
Micro-and-nano plastics (MNPs) are pervasive in terrestrial ecosystems and represent an increasing threat to plant health; however, the mechanisms underlying their phytotoxicity remain inadequately understood. MNPs can infiltrate plants through roots or leaves, causing a range of toxic effects, including inhibiting water and nutrient uptake, reducing seed germination rates, and impeding photosynthesis, resulting in oxidative damage within the plant system. The effects of MNPs are complex and influenced by various factors including size, shape, functional groups, and concentration.
View Article and Find Full Text PDFFASEB J
January 2025
Department of Internal Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China.
Serum uric acid is an end-product of purine metabolism. Uric acid concentrations in excess of the physiological range may lead to diseases such as gout, cardiovascular disease, and kidney injury. The kidney includes a variety of cell types with specialized functions such as fluid and electrolyte homeostasis, detoxification, and endocrine functions.
View Article and Find Full Text PDFNat Methods
January 2025
Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Spatial molecular profiling has provided biomedical researchers valuable opportunities to better understand the relationship between cellular localization and tissue function. Effectively modeling multimodal spatial omics data is crucial for understanding tissue complexity and underlying biology. Furthermore, improvements in spatial resolution have led to the advent of technologies that can generate spatial molecular data with subcellular resolution, requiring the development of computationally efficient methods that can handle the resulting large-scale datasets.
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