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Studying metabolic activities in living cells is crucial for understanding human metabolism, but facile methods for profiling metabolic activities in an unbiased, hypothesis-free manner are still lacking. To address this need, we here introduce the deep-labeling method, which combines a custom C medium with high-resolution mass spectrometry. A proof-of-principle study on human cancer cells demonstrates that deep labeling can identify hundreds of endogenous metabolites as well as active and inactive pathways. For example, protein and nucleic acids were almost exclusively de novo synthesized, while lipids were partly derived from serum; synthesis of cysteine, carnitine, and creatine was absent, suggesting metabolic dependencies; and branched-chain keto acids (BCKAs) were formed and metabolized to short-chain acylcarnitines, but did not enter the tricarboxylic acid cycle. Remarkably, BCKAs could substitute for essential amino acids to support growth. The deep-labeling method may prove useful to map metabolic phenotypes across a range of cell types and conditions.
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http://dx.doi.org/10.1016/j.chembiol.2018.09.004 | DOI Listing |
J Food Sci
September 2025
College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou, China.
Primary agricultural products are closely related to our daily lives, as they serve not only as raw materials for food processing but also as products directly purchased by consumers. These products face the issue of freshness decline and spoilage during both production and consumption. Freshness degradation induces sensory deterioration and nutritional loss and promotes harmful substance accumulation, causing gastrointestinal issues or even endangering life.
View Article and Find Full Text PDFComput Biol Med
September 2025
Department of Neurosurgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Intracranial aneurysms (IAs) are common vascular pathologies with a risk of fatal rupture. Human assessment of rupture risk is error prone, and treatment decision for unruptured IAs often rely on expert opinion and institutional policy. Therefore, we aimed to develop a computer-assisted aneurysm rupture prediction framework to help guide the decision-making process and create future decision criteria.
View Article and Find Full Text PDFArtif Intell Med
September 2025
Department of Mathematics, Hangzhou Dianzi University, Hangzhou 310018, China. Electronic address:
The hippocampus is an important brain structure involved in various psychiatric disorders, and its automatic and accurate segmentation is vital for studying these diseases. Recently, deep learning-based methods have made significant progress in hippocampus segmentation. However, training deep neural network models requires substantial computational resources, time, and a large amount of labeled training data, which is frequently scarce in medical image segmentation.
View Article and Find Full Text PDFIEEE J Biomed Health Inform
September 2025
Interictal Epileptiform Discharge is essential for identifying epilepsy. However, the unpredictable and non-stationary nature of electroencephalogram (EEG) patterns poses considerable challenges for reliable identification. Manual interpretation of EEG is subjective and time-consuming.
View Article and Find Full Text PDFJ Acoust Soc Am
September 2025
Centre de Vision Numérique, CentraleSupélec, Université Paris-Saclay, Inria, Gif-Sur-Yvette, France.
Conventional techniques for underwater source localization have traditionally relied on optimization methods, matched-field processing, beamforming, and, more recently, deep learning. However, these methods often fall short to fully exploit the data correlation crucial for accurate source localization. This correlation can be effectively captured using graphs, which consider the spatial relationship among data points through edges.
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