Biology subjects

Chou, L. Y. T.

Publications and source records attributed to Chou, L. Y. T..

3 recordsLinked to original sources

bioRxiv2023

High-throughput, label-free detection of DNA origami in single-cell suspensions using origamiFISH-Flow

Structural DNA nanotechnology enables custom fabrication of nanoscale devices and promises diverse biological applications. However, the effects of design on DNA nanostructure (DN)-cell interactions in vitro and in vivo are not yet well-characterized. origamiFISH is a recently developed technique for imaging DNs in cells and tissues. Compared to the use of fluorescent tags, origamiFISH offers label-free and structure-agnostic detection of DNs with significantly improved sensitivity. Here, we extend the origamiFISH technique to quantifying DNs in single-cell suspensions, including nonadherent cells such as subsets of immune cells, via readout by flow cytometry. This method, referred to as origamiFISH-Flow, is high-throughput (e.g., 10,000 cells per second) and compatible with immunostaining for concurrent cell-type and -state characterization. We demonstrate that origamiFISH-Flow enhances signal-to-noise ratio by up to 20-fold compared to dye labeling approaches, leading to the capture of >25-fold more DN+ cells at low, single-picomolar DN uptake concentrations. We additionally show the use of origamiFISH-Flow to profile cell-type and shape-specific DN uptake patterns across cell lines and splenocytes and quantify in vivo DN accumulation in lymphoid organs. Together, origamiFISH-Flow offers a new tool to interrogate DN interactions with cells and tissues, while providing insights for tailoring their designs in bio-applications.

bioengineering↗

bioRxiv2023

Simple and rewireable biomolecular building blocks for DNA machine-learning algorithms

Deep learning algorithms, such as neural networks, enable the processing of complex datasets with many related variables, and have applications in disease diagnosis, cell profiling, and drug discovery. Beyond its use in electronic computers, neural networks have been implemented using programmable biomolecules such as DNA. This confers unique advantages such as greater portability, ability to operate without electricity, and direct analysis of patterns of biomolecules in solution. Analogous to past bottlenecks in electronic computers, the computing power of DNA-based neural networks is limited by the ability to add more computing units, i.e. neurons. This limitation exists because current architectures require many nucleic acids to model a single neuron. Each addition of a neuron to the network compounds existing problems such as long assembly times, high background signal, and cross-talk between components. Here we test three strategies to solve this limitation and improve the scalability of DNA-based neural networks: (i) enzymatic synthesis to generate high-purity neurons, (ii) spatial patterning of neuron clusters based on their network position, and (iii) encoding neuron connectivity on a universal single-stranded DNA backbone. We show that neurons implemented via these strategies activate quickly, with high signal-to-background ratio, and respond to varying input concentrations and weights. Using this neuron design, we implemented basic neural network motifs such as cascading, fan-in, and fan-out circuits. Since this design is modular, easy to synthesize, and compatible with multiple neural network architectures, we envision it will help scale DNA-based neural networks in a variety of settings. This will enable portable computing power for applications such as portable diagnostics, compact data storage, and autonomous decision making for lab-on-a-chips.

bioengineering↗

bioRxiv2022

origamiFISH allows universal, label-free, single molecule visualization of DNA origami nanodevices across biological samples

Structural DNA nanotechnology enables user-prescribed design of DNA nanostructures (DNs) for biological applications, but how DN design determines their bio-distribution and cellular interactions remain poorly understood. One challenge is that current methods for tracking DN fates in situ, including fluorescent-dye labeling, suffer from low sensitivity and dye-induced artifacts. Here we present origamiFISH, a label-free and universal method for single-molecule fluorescence detection of DNA origami nanostructures in cells and tissues. origamiFISH targets pan-DN scaffold sequences with hybridization chain reaction (HCR) probes to achieve thousand-fold signal amplification. We identify cell-type and shape-specific spatiotemporal uptake patterns within 1 minute of uptake and at picomolar DN concentrations, 10,000x lower than field standards. We additionally optimized compatibility with immunofluorescence and tissue clearing to visualize DN distribution within tissue cryo/vibratome-sections, slice cultures, and whole-mount organoids. Together, origamiFISH enables faithful mapping of DN interactions across subcellular and tissue barriers for guiding the development of DN-based therapeutics.

synthetic biology↗