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DNA is considered as a prospective candidate for the next-generation data storage medium, due to its high coding density, long cold-storage lifespan, and low energy consumption. Despite these advantages, challenges remain in achieving high-fidelity, fully integrated, and cost-efficient DNA storage system. In this study, a homemade digital microfluidic (DMF)-based compact DNA data storing pipeline is orchestrated to complete the entire process from the synthesis to the sequencing. The synthetic half employs phosphoramidite chemistry on 200 nm magnetic beads (MBs), where the dimethyltrityl protecting group is removed by droplet manipulation of trichloroacetic acid. The sequencing counterpart relies on pyrophosphate releasing originated from polymerase-catalyzed primer extension, which leads to photon-countable chemiluminescence (CL) signal in 2.5-μL drops of trienzyme cascading reactions. Further by DNA denaturation, repeated pyrosequencing plus plurality voting can improve the nucleobase accuracy beyond 95 %. As a proof-of-concept trial, semantic information is saved in DNA via the Huffman coding algorithm plus the Reed-Solomon error-correction, and then robustly retrieved from this streamlined platform. As a result, it took a net total of approximately 6.5 h to writing and reading 8 bytes of data, that equal to a storaging speed of 49 min/byte, much quicker than the previously reported 2.8-4.2 h/byte. This bead-based miniaturized device promises an unattended protocol for achieving high-throughput, full-packaged, and above all, neatly precision DNA storage., (© 2024 Wiley-VCH GmbH.)

The cooperative diagnosis of non‐coding RNAs (ncRNAs) can accurately reflect the state of cell differentiation and classification, laying the foundation of precision medicine. However, there are still challenges in simultaneous analyses of multiple ncRNAs and the integration of biomarker data for cell typing. In this study, DNA framework‐based programmable atom‐like nanoparticles (PANs) are designed to develop molecular classifiers for intra‐cellular imaging of multiple ncRNAs associated with cell differentiation. The PANs‐based molecular classifier facilitates signal amplification through the catalytic hairpin assembly. The interaction between PAN reporters and ncRNAs enables high‐fidelity conversion of ncRNAs expression level into binding events, and the assessment of in situ ncRNAs levels via measurement of the fluorescent signal changes of PAN reporters. Compared to non‐amplified methods, the detection limits of PANs are reduced by four orders of magnitude. Using human gastric cancer cell lines as a model system, the PANs‐based molecular classifier demonstrates its capacity to measure multiple ncRNAs in living cells and assesses the degree of cell differentiation. This approach can serve as a universal strategy for the classification of cancer cells during malignant transformation and tumor progression. [ABSTRACT FROM AUTHOR]