Synthetic heparan sulfate standards and machine learning facilitate the development of solid-state nanopore analysis
Synthetic heparan sulfate standards and machine learning facilitate the development of solid-state nanopore analysis
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DOI：
10.1073/pnas.2022806118
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发表时间：
2021-03-16
影响因子：
11.1
通讯作者：
Linhardt, Robert J.
中科院分区：
文献类型：
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作者：
Xia, Ke;Hagan, James T.;Linhardt, Robert J.
将固态（SS）纳米孔装置应用于单分子核酸测序具有挑战性。因此，在将SS纳米孔装置应用于更困难的一类生物聚合物（糖胺聚糖（GAG））中的早期成功是令人惊讶的，促使我们研究SS纳米孔的潜在用途，以分析通过有希望的最近开发的化学酶途径制备的受控组成和序列的合成硫酸乙酰肝素GAG链。仅使用信号幅度和持续时间的纳米孔数据的最小表示通过眼睛和图像识别算法揭示了由四种合成GAG产生的信号之间的明显差异。通过随后的机器学习，可以使用少至500个事件（对应于zeptomole的样品）来确定这四种合成GAG的二糖甚至单糖组成。这些数据表明，使用SS纳米孔检测和良好表征的分子训练集，超灵敏的GAG分析是可能的。
The application of solid-state (SS) nanopore devices to single-molecule nucleic acid sequencing has been challenging. Thus, the early successes in applying SS nanopore devices to the more difficult class of biopolymer, glycosaminoglycans (GAGs), have been surprising, motivating us to examine the potential use of an SS nanopore to analyze synthetic heparan sulfate GAG chains of controlled composition and sequence prepared through a promising, recently developed chemoenzymatic route. A minimal representation of the nanopore data, using only signal magnitude and duration, revealed, by eye and image recognition algorithms, clear differences between the signals generated by four synthetic GAGs. By subsequent machine learning, it was possible to determine disaccharide and even monosaccharide composition of these four synthetic GAGs using as few as 500 events, corresponding to a zeptomole of sample. These data suggest that ultrasensitive GAG analysis may be possible using SS nanopore detection and well-characterized molecular training sets.