Abstract
Biomolecular information systems offer exciting potential advantages and opportunities to complement conventional semiconductor technologies. Much attention has been paid to information-encoding polymers, but small molecules also play important roles in biochemical information systems. Downstream from DNA, the metabolome is an information-rich molecular system with diverse chemical dimensions which could be harnessed for information storage and processing. As a proof of principle of small-molecule postgenomic data storage, here we demonstrate a workflow for representing abstract data in synthetic mixtures of metabolites. Our approach leverages robotic liquid handling for writing digital information into chemical mixtures, and mass spectrometry for extracting the data. We present several kilobyte-scale image datasets stored in synthetic metabolomes, which can be decoded with accuracy exceeding 99% using multi-mass logistic regression. Cumulatively, >100,000 bits of digital image data was written into metabolomes. These early demonstrations provide insight into some of the benefits and limitations of small-molecule chemical information systems.
Highlights
The metabolome is the complete set of small molecules found in a biological system [1]
Our synthetic metabolome is a diverse set of 36 compounds (Table A in S1 File) including vitamins, nucleosides, nucleotides, amino acids, sugars, and metabolic pathway intermediates
A 2.25 mm pitch grid was chosen for compatibility with standard wellplate protocols
Summary
The metabolome is the complete set of small molecules found in a biological system [1] The properties of this set of compounds are an amplified and dynamic measure of an organism’s genome, transcriptome, proteome, and environment [2]. Much remains to be understood, improvements in protocols and efficient mass spectrometry (MS) have enabled metabolomic disease screening and drug discovery [6,7,8,9,10,11,12]. These technologies are supported by continually improving statistical tools and databases [13, 14]. They may suggest exciting alternative applications for metabolomics
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