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Vo, T. Q. A.

Publications and source records attributed to Vo, T. Q. A..

3 recordsLinked to original sources

A systemic clock brake: Period1 stabilizes the circadian network under environmental stress

Precise alignment between internal circadian clocks and environmental light cycles is essential for physiological homeostasis and survival. However, the molecular mechanisms that preserve this synchrony across central and peripheral tissues remain poorly defined. Here, we uncover an unexpected role for the core clock gene Period1 (Per1) as a systemic modulator of circadian stability, regulating light-induced re-entrainment across the brain and body. In Per1-deficient mice, we show that loss of Per1 accelerates clock realignment, influencing transcriptomic, metabolic, hormonal, and behavioral indicators of circadian realignment across multiple organ systems, including the suprachiasmatic nucleus (SCN) and peripheral tissues such as the liver, adipose tissue, and adrenal glands. Notably, this accelerated adaptation confers protection against jetlag-induced sleep disturbances, weight gain, and metabolic imbalance, underscoring a systemic role for Per1 in maintaining circadian network stability. Mechanistically, unbiased spatial transcriptomics identified reduced expression of the arginine vasopressin (AVP), a key neuropeptide mediating SCN intercellular coupling, as the driver of circadian network instability. Weakened SCN synchrony permits enhanced flexibility of peripheral oscillator responses, expediting whole-body adaptation to shifted light-dark schedules. These findings position Per1 as a critical regulator of circadian robustness, a buffer against light over-responsiveness, identifying a potential molecular target for mitigating circadian misalignment in contexts such as jetlag, shift work, and metabolic disease. TeaserWhat if beating jetlag was as simple as switching off a gene? Researchers show that disabling Per1, a core circadian regulator, accelerates body clock realignment and protects against sleep and metabolic disruption--highlighting new therapeutic possibilities for jetlag and shift work.

neuroscience↗

SpaMTP: Integrative Statistical Analysis and Visualisation of Spatial Metabolomics and Transcriptomics data.

The ability to spatially measure multi-modal data provides an unprecedented opportunity to comprehensively explore molecular regulation at transcriptional, translational and metabolic levels to acquire insights on cellular activities underpinning health and disease. However, there is currently a lack of analytical tools to integrate complementary information across different spatial-omics data modalities, particularly with respect to spatial metabolomics data, which is becoming increasingly invaluable. We introduce SpaMTP, a versatile software that implements an end-to-end integrative analysis of spatial metabolomics and transcriptomics data. Based in R, SpaMTP bridges processing functionalities for metabolomics data from Cardinal with user-friendly cell-centric analyses implemented in Seurat. Furthermore, SpaMTPs comprehensive analysis pipeline covers (1) automated mass-to-charge ratio (m/z) metabolite annotation; (2) a wide range of metabolite-gene based downstream statistical analyses including differential expression, pathway analysis, and correlation analysis; (3) integrative spatial-omics analysis; and (4) a suite of visualisation functions. For flexibility and interoperability, SpaMTP includes various functions for data import/export and object conversion, enabling seamless integration with other R and Python packages. We demonstrated the utility of SpaMTP to draw new biological understandings through analysing two biological system. We believe this software and implemented methods will be broadly utilised in spatial multi-omics and spatial metabolomics analyses.

bioinformatics↗

A robust Platform for Integrative Spatial Multi-omics Analysis to Map Immune Responses to SARS-CoV-2 infection in Lung Tissues

The SARS-CoV-2 (COVID-19) virus has caused a devastating global pandemic of respiratory illness. To understand viral pathogenesis, methods are available for studying dissociated cells in blood, nasal samples, bronchoalveolar lavage fluid, and similar, but a robust platform for deep tissue characterisation of molecular and cellular responses to virus infection in the lungs is still lacking. We developed an innovative spatial multi-omics platform to investigate COVID-19-infected lung tissues. Five tissue-profiling technologies were combined by a novel computational mapping methodology to comprehensively characterise and compare the transcriptome and targeted proteome of virus infected and uninfected tissues. By integrating spatial transcriptomics data (Visium, GeoMx and RNAScope) and proteomics data (CODEX and PhenoImager HT) at different cellular resolutions across lung tissues, we found strong evidence for macrophage infiltration and defined the broader microenvironment surrounding these cells. By comparing infected and uninfected samples, we found an increase in cytokine signalling and interferon responses at different sites in the lung and showed spatial heterogeneity in the expression level of these pathways. These data demonstrate that integrative spatial multi-omics platforms can be broadly applied to gain a deeper understanding of viral effects on cellular environments at the site of infection and to increase our understanding of the impact of SARS-CoV-2 on the lungs.

bioinformatics↗