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Discovery of a DNA-based Optical Nanotube Sensor for Glucose Using Clustering and Deep Learning Algorithms

Rabbani, Y.; Bregy, J.; Rousseau, B.; Sajjadi, S. H.; De Benedittis, L.; Behjati, S.; Mouhib, M.; Dehury, S.; Boghossian, A.

2025-05-11 bioengineering
10.1101/2025.05.06.652529 bioRxiv
Show abstract

Glucose sensing is vital for managing diabetes. However, current sensors are invasive and secrete enzymatic byproducts that are inflammatory and toxic. Though DNA-wrapped single-walled carbon nanotubes (DNA-SWCNTs) emit fluorescence that is ideal for enzyme-free optical sensing, existing approaches have yet to identify a DNA sequence that can elicit a fluorescence response to glucose. We develop an approach based on clustering to design a diverse library of 90 DNA sequences to screen for a glucose response. The most responsive sequence was further improved based on favorable mutations predicted by deep learning and pattern recognition. This combination of experimental screening and supervised and unsupervised machine learning represents a generalizable approach to developing DNA-SWCNT sensors for even the most elusive analytes. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=186 HEIGHT=200 SRC="FIGDIR/small/652529v1_ufig1.gif" ALT="Figure 1"> View larger version (78K): org.highwire.dtl.DTLVardef@e01org.highwire.dtl.DTLVardef@1977c45org.highwire.dtl.DTLVardef@5fc0e2org.highwire.dtl.DTLVardef@70763f_HPS_FORMAT_FIGEXP M_FIG C_FIG

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.