Carbon nanodots (CDs) were initially synthesized by dehydrating carbohydrates using a commercial household microwave (700 W). To prepare BN-CD, 960 mg of citric acid (5.0 mmol, Aldrich) and 310 mg of boric acid (5.0 mmol) were dissolved in 10 mL of water. To this transparent solution, 347 µL of EDA (5.0 mmol) was added under vigorous stirring for 2 min. The solution was placed into a microwave oven and heated for 2 min, and a yellow solid was obtained after cooling to room temperature. The solid was diluted in 5.0 mL of water. The yellow suspension was dialyzed (SpectraPore MWCO 500 -1,000) for 2 days to remove salts and unreacted chemicals. To synthesize N-CD, microwave pyrolysis was performed in the absence of boric acid. BN-CD0.5 and BN-CD2 were prepared with 2.5 mmol (0.5 equiv. of citric acid and ethylene diamine) and 10 mmol (2 equiv. of citric acid and ethylene diamine) of boric acid, with the same concentrations of other precursors as described above. Non-doped plain CD was synthesized with 5 mmol of citric acid through hydrothermal method at 180 o C for 6 hr. B-CD was synthesized with 5 mmol of boric acid and citric acid.

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SUMMARY Omics experiments are ubiquitous in biological studies, leading to a deluge of data. However, it is still challenging to connect changes in these data to changes in cell functions because of complex interdependencies between genes, proteins, and metabolites. Here, we present a framework allowing researchers to infer how metabolic functions change on the basis of omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. Genome-scale metabolic networks were used to define gene sets associated with each metabolic task. We further developed a framework to overlay omics data on these sets and predict pathway usage for each metabolic task. We demonstrated how this approach can be used to quantify metabolic functions of diverse biological samples from the single cell to whole tissues and organs by using multiple transcriptomic datasets. To facilitate its adoption, we integrated the approach into GenePattern ( www.genepattern.org —CellFie).