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Resolving tissue complexity by multimodal spatial omics modeling with MISO.

Nat 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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Background: CblC type methylmalonic aciduria (cblC disease) is the most common inborn error of vitamin B12 metabolism and due to mutations in the MMACHC gene. The earlier the diagnosis, the better the prognosis. Therefore, convenient and inexpensive detection method is needed.

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X-linked hypophosphataemia (XLH) is a rare metabolic bone disorder caused by pathogenic variants in the PHEX gene, which is predominantly expressed in osteoblasts, osteocytes and odontoblasts. XLH is characterized by increased synthesis of the bone-derived phosphaturic hormone fibroblast growth factor 23 (FGF23), which results in renal phosphate wasting with consecutive hypophosphataemia, rickets, osteomalacia, disproportionate short stature, oral manifestations, pseudofractures, craniosynostosis, enthesopathies and osteoarthritis. Patients with XLH should be provided with multidisciplinary care organized by a metabolic bone expert.

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The fishmeal is boon for aquaculture production in this recent pollution and climate change era. However, the demand of fishmeal is enhancing in many folds which needs to find alternative to fishmeal in cheap price. The present investigation addresses these issues with quinoa husk (QH).

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Causality-driven candidate identification for reliable DNA methylation biomarker discovery.

Nat Commun

January 2025

The Medical Image and Health Informatics Lab, the School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Despite vast data support in DNA methylation (DNAm) biomarker discovery to facilitate health-care research, this field faces huge resource barriers due to preliminary unreliable candidates and the consequent compensations using expensive experiments. The underlying challenges lie in the confounding factors, especially measurement noise and individual characteristics. To achieve reliable identification of a candidate pool for DNAm biomarker discovery, we propose a Causality-driven Deep Regularization framework to reinforce correlations that are suggestive of causality with disease.

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