A DNA nanoscope that identifies and precisely localizes over a hundred unique molecular features with nanometer accuracy

Abstract

Techniques that can both spatially map out molecular features and discriminate many targets would be highly valued for their utility in studying fundamental nanoscale processes. In spite of decades of development, no current technique can achieve both nanoscale resolution and discriminate hundreds of targets. Here, we report the development of a novel bottom-up technology that: (a) labels a sample with DNA barcodes, (b) measures pairwise-distances between labeled sites and writes them into DNA molecules, (c) reads the pairwise-distances by sequencing and (d) robustly integrates this noisy information to reveal the geometry of the underlying sample. We demonstrate our technology on DNA origami, which are complex synthetic nanostructures. We both spatially localized and uniquely identified over a hundred densely packed unique elements, some spaced just 6 nm apart, with an average spatial localization accuracy (RMS deviation) of ~2 nm. The bottom-up, sequencing-enabled mechanism of the DNA nanoscope is fundamentally different from top-down imaging, and hence offers unique advantages in precision, throughput and accessibility.

The emerging landscape of single-molecule protein sequencing technologies

“…The distribution of the lengths of these molecular records is then analyzed to decode the pairwise distance between two DNA tags. It is possible to use unique molecular identifier (UMI) barcoding and repetitive enzymatic recording, such that each lysine and cysteine residue can be studied and used to construct a pairwise distance map, allowing for single-molecule protein identification 48,49 . DNA proximity recording takes advantage of high-throughput next-generation DNA sequencing methods for efficient protein fingerprinting analysis and will be useful for the analysis of both purified proteins and complex protein mixtures.…”

DNA-based customized functional modules for signal transformation

“…This strategy involves the aid of a corresponding extension hairpin ( Figure 10A ). Specifically, the target is labeled with DNA barcodes, and users can measure pairwise distances between labeled sites by measuring the DNA sequence that is written between the barcodes ( Gopalkrishnan et al, 2020 ).…”

Image recovery from unknown network mechanisms for DNA sequencing-based microscopy

“…Imaging-by-sequencing has seen a variety of network generating rules giving rise to different systematic patterns of sub-graphs or network structural motifs. For example: networks where nodes are connected to most other nodes (Weinstein et al, 2019), locally connected Voronoi meshes (Hoffecker et al, 2019), locally connected neighbor networks (Boulgakov et al, 2018; Schaus et al, 2017; Gopalkrishnan et al, 2020; Boulgakov et al, 2020), “GPS” networks (Greenstreet et al, 2022), or boundary-sharing cell networks (Zador et al, 2012; Glaser et al, 2015). Strategies moreover fall into distance-weighted(Weinstein et al, 2019; Gopalkrishnan et al, 2020; Greenstreet et al, 2022) or binary unweighted categories(Schaus et al, 2017; Hoffecker et al, 2019; Boulgakov et al, 2018).…”

“…Imaging-by-sequencing methods (Zador et al, 2012; Glaser et al, 2015; Schaus et al, 2017; Boulgakov et al, 2018; Weinstein et al, 2019; Hoffecker et al, 2019; Gopalkrishnan et al, 2020; Boulgakov et al, 2020; Greenstreet et al, 2022) arose recently as a potential alternative molecular imaging strategy based entirely on DNA sequencing information in contrast to optical or optics-coupled techniques like spatial omics (Ke et al, 2013; Lee et al, 2015; Ståhl et al, 2016; Wang et al, 2018; Karaiskos et al, 2017; Satija et al, 2015; Achim et al, 2015; Halpern et al, 2017), single molecule localization microscopy (Söderberg et al, 2006; Jungmann et al, 2010; Rust et al, 2006; Betzig et al, 2006), or fluorescence imaging more broadly. Individual nanoscale molecular associations in imaging-by-sequencing lead to unique sequence-based records that denote local proximity between molecules.…”

“…For example: networks where nodes are connected to most other nodes (Weinstein et al, 2019), locally connected Voronoi meshes (Hoffecker et al, 2019), locally connected neighbor networks (Boulgakov et al, 2018; Schaus et al, 2017; Gopalkrishnan et al, 2020; Boulgakov et al, 2020), “GPS” networks (Greenstreet et al, 2022), or boundary-sharing cell networks (Zador et al, 2012; Glaser et al, 2015). Strategies moreover fall into distance-weighted(Weinstein et al, 2019; Gopalkrishnan et al, 2020; Greenstreet et al, 2022) or binary unweighted categories(Schaus et al, 2017; Hoffecker et al, 2019; Boulgakov et al, 2018). These design differences at the fundamental structure level seem to indicate that there cannot be a one-size-fits-all reconstruction strategy.…”