Reading mixtures of uniform sequence-defined macromolecules to increase data storage capacity

Area of Science:

• Macromolecular Chemistry
• Synthetic Chemistry
• Data Storage

Background:

• Molecular data storage is a rapidly growing research area.
• Storing and retrieving information using synthetic macromolecules offers high potential density.
• Current methods for reading molecular data can be complex and time-consuming.

Purpose of the Study:

• To develop a simultaneous and automated method for reading data stored in mixtures of sequence-defined oligomers.
• To demonstrate the feasibility of using mass markers for unambiguous sequence identification.
• To achieve high data storage capacity using short oligomers in mixtures.

Main Methods:

• Synthesis of sequence-defined tetramers and hexamers using iterative Passerini three-component reactions.
• Incorporation of distinct mass markers and side chains into oligomers.
• Development of a Python script for automated electrospray ionization tandem mass spectrometry (ESI-MS/MS) analysis.
• Simultaneous analysis of mixed oligomer samples.

Main Results:

• Successful synthesis of twelve tetramers and three hexamers.
• Automated sequencing and data readout from oligomer mixtures in under one second.
• Demonstration that mass markers simplify MS/MS data interpretation.
• Unambiguous reading of sequences from mixtures of sequence-defined oligomers.
• Achieved high data storage capacity (up to 64.5 bits) using mixtures of shorter oligomers.

Conclusions:

• Simultaneous and automated readout of data stored in oligomer mixtures is feasible.
• Mass markers significantly enhance the ease and accuracy of data retrieval.
• This approach enables high data storage density without requiring the synthesis of long sequences.
• The method represents a significant advancement in molecular data storage technology.