Profiling the Metabolism of Human Cells by Deep C Labeling.

Cell Chem Biol

Cardiovascular Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm 171 76, Sweden; Karolinska University Hospital, Stockholm 171 76, Sweden; Center for Molecular Medicine, Karolinska Institutet, Stockholm 171 76, Sweden. Electronic address:

Published: November 2018


Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6239935PMC
http://dx.doi.org/10.1016/j.chembiol.2018.09.004DOI Listing

Publication Analysis

Top Keywords

deep labeling
8
metabolic activities
8
deep-labeling method
8
profiling metabolism
4
metabolism human
4
human cells
4
cells deep
4
labeling studying
4
metabolic
4
studying metabolic
4

Similar Publications

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 PDF

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 PDF

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 PDF

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 PDF

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.

View Article and Find Full Text PDF