Abstract

Using a DNA polymerase to record intracellular calcium levels has been proposed as a novel neural recording technique, promising massive-scale, single-cell resolution monitoring of large portions of the brain. This technique relies on local storage of neural activity in strands of DNA, followed by offline analysis of that DNA. In simple implementations of this scheme, the time when each nucleotide was written cannot be determined directly by post-hoc DNA sequencing; the timing data must be estimated instead. Here, we use a Dynamic Time Warping-based algorithm to perform this estimation, exploiting correlations between neural activity and observed experimental variables to translate DNA-based signals to an estimate of neural activity over time. This algorithm improves the parallelizability of traditional Dynamic Time Warping, allowing several-fold increases in computation speed. The algorithm also provides a solution to several critical problems with the molecular recording paradigm: determining recording start times and coping with DNA polymerase pausing. The algorithm can generally locate DNA-based records to within <10% of a recording window, allowing for the estimation of unobserved incorporation times and latent neural tunings. We apply our technique to an in silico motor control neuroscience experiment, using the algorithm to estimate both timings of DNA-based data and the directional tuning of motor cortical cells during a center-out reaching task. We also use this algorithm to explore the impact of polymerase characteristics on system performance, determining the precision of a molecular recorder as a function of its kinetic and error-generating properties. We find useful ranges of properties for DNA polymerase-based recorders, providing guidance for future protein engineering attempts. This work demonstrates a useful general extension to dynamic alignment algorithms, as well as direct applications of that extension toward the development of molecular recorders, providing a necessary stepping stone for future biological work.

Highlights

  • As we seek to understand complex questions in neuroscience, we are increasingly interested in the feasibility of massive-scale methods for neural recording [1,2,3,4,5]

  • Molecular recorders use proteins to encode levels of a substance we want to measure as detectable changes in a linear cellular structure, e.g. misincorporations in a strand of DNA, or fluorescent proteins traveling down a microtubule

  • We demonstrate the application of this method in a simulated neuroscience paradigm, investigate the effect of experimental design on this method, and determine protein properties that would be desirable in molecular recorders

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Summary

Introduction

As we seek to understand complex questions in neuroscience, we are increasingly interested in the feasibility of massive-scale methods for neural recording [1,2,3,4,5]. One such proposed method is molecular recording, which uses engineered DNA polymerases (DNAPs) to encode information about neural activity onto a newly synthesized DNA strand, such that the position in the DNA sequence corresponds to the order and approximate timing of recorded events [6,7,8].

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