Statistical Analysis of Molecular Signal Recording

A molecular device that records time-varying signals would enable new approaches in neuroscience. We have recently proposed such a device, termed a “molecular ticker tape”, in which an engineered DNA polymerase (DNAP) writes time-varying signals into DNA in the form of nucleotide misincorporation patterns. Here, we define a theoretical framework quantifying the expected capabilities of molecular ticker tapes as a function of experimental parameters. We present a decoding algorithm for estimating time-dependent input signals, and DNAP kinetic parameters, directly from misincorporation rates as determined by sequencing. We explore the requirements for accurate signal decoding, particularly the constraints on (1) the polymerase biochemical parameters, and (2) the amplitude, temporal resolution, and duration of the time-varying input signals. Our results suggest that molecular recording devices with kinetic properties similar to natural polymerases could be used to perform experiments in which neural activity is compared across several experimental conditions, and that devices engineered by combining favorable biochemical properties from multiple known polymerases could potentially measure faster phenomena such as slow synchronization of neuronal oscillations. Sophisticated engineering of DNAPs is likely required to achieve molecular recording of neuronal activity with single-spike temporal resolution over experimentally relevant timescales.

可记录时变信号的分子器件，将为神经科学研究带来全新的研究范式。我们近期提出了一款此类器件，命名为「分子纸带（molecular ticker tape）」：通过工程化改造的DNA聚合酶（DNA polymerase, DNAP），以核苷酸错掺模式为载体，将时变信号写入DNA分子中。
本研究构建了一套理论框架，可量化分子纸带的预期性能与实验参数之间的依赖关系。我们提出了一种解码算法，可直接通过测序测得的核苷酸错掺速率，估算随时间变化的输入信号以及DNA聚合酶的动力学参数。
本研究探讨了实现精准信号解码的必要条件，重点关注两类约束：其一为聚合酶的生化参数约束，其二为时变输入信号的振幅、时间分辨率与时长约束。
我们的研究结果显示，动力学特性与天然聚合酶相仿的分子记录器件，可用于开展跨多实验条件的神经活动对比实验；而通过整合多种已知聚合酶的优良生化特性所构建的器件，则有望检测诸如神经元振荡缓慢同步这类快速生理现象。
若要在实验相关的时间尺度上实现具备单尖峰时间分辨率的神经元活动分子记录，尚需对DNA聚合酶开展精密的工程化改造。