DNA-based data storage has emerged as a promising method to satisfy the exponentially increasing demand for information storage. However, practical implementation of DNA-based data storage remains a challenge because of the high cost of data writing through DNA synthesis. Here, we propose the use of degenerate bases as encoding characters in addition to A, C, G, and T, which augments the amount of data that can be stored per length of DNA sequence designed (information capacity) and lowering the amount of DNA synthesis per storing unit data. Using the proposed method, we experimentally achieved an information capacity of 3.37 bits/character. The demonstrated information capacity is more than twice when compared to the highest information capacity previously achieved. The proposed method can be integrated with synthetic technologies in the future to reduce the cost of DNA-based data storage by 50%.

DNA‐based data storage has attracted attention because of its higher physical density of the data and longer retention time than those of conventional digital data storage. However, previous DNA‐based data storage lacked index features and the data quality of storage after a single access was not preserved, obstructing its industrial use. Here, DNA micro‐disks, QR‐coded micro‐sized disks that harbor data‐encoded DNA molecules for the efficient management of DNA‐based data storage, are proposed. The two major features that previous DNA‐based data‐storage studies could not achieve are demonstrated. One feature is accessing data items efficiently by indexing the data‐encoded DNA library. Another is achieving write‐once–read‐many (WORM) memory through the immobilization of DNA molecules on the disk and their enrichment through in situ DNA production. Through these features, the reliability of DNA‐based data storage is increased by allowing selective and multiple accession of data‐encoded DNA with lower data loss than previous DNA‐based data storage methods.

Writing DNA plays a significant role in the fields of synthetic biology, functional genomics and bioengineering. DNA clones on next-generation sequencing (NGS) platforms have the potential to be a rich and cost-effective source of sequence-verified DNAs as a precursor for DNA writing. However, it is still very challenging to retrieve target clonal DNA from high-density NGS platforms. Here we propose an enabling technology called ‘Sniper Cloning’ that enables the precise mapping of target clone features on NGS platforms and non-contact rapid retrieval of targets for the full utilization of DNA clones. By merging the three cutting-edge technologies of NGS, DNA microarray and our pulse laser retrieval system, Sniper Cloning is a week-long process that produces 5,188 error-free synthetic DNAs in a single run of NGS with a single microarray DNA pool. We believe that this technology has potential as a universal tool for DNA writing in biological sciences.

Red peppers are a remarkable source of nutrients in the human diet. However, comprehensive studies have not reported on the effects of genotype, cultivation region, and year on pepper fruit characteristics. To address this, 12 commercial pepper varieties were grown at two locations in South Korea, during 2016 and 2017, representing four environments, and concentrations of proximate, minerals, amino acids, fatty acids, capsaicinoids, and free sugars in pepper pericarps were determined. Variation in most nutrients was observed among the 12 varieties grown within each location in each year, indicating a significant genotype effect. Statistical analysis of combined data showed significant differences among varieties, locations, and years for the measured components. The % variability analysis demonstrated that environment (location and year) and genotype-environment interaction contributed more to the nutritional contents than genotype alone. Particularly, variation in many amino acids, capsaicinoids, free sugars, and myristic acid was attributed to location. Year effect was significant for palmitoleic acid, ash, tryptophan, copper, linolenic acid, crude fiber, and tyrosine. Insoluble dietary fiber, soluble dietary fiber, sodium, sulfate, linoleic acid, and alanine were primarily varied by genotype–environment interaction. Palmitic acid was the trait the most highly affected by genotype. Cultivation and the genotype–environment interaction have a major role in determining the composition of 12 pepper varieties across four environments. The data from this study could explain the natural variation in the compositional data of peppers by genotypes and environments.

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Epitranscriptomic features, such as single-base RNA editing, are sources of transcript diversity in cancer, but little is understood in terms of their spatial context in the tumour microenvironment. Here, we introduce spatial-histopathological examination-linked epitranscriptomics converged to transcriptomics with sequencing (Select-seq), which isolates regions of interest from immunofluorescence-stained tissue and obtains transcriptomic and epitranscriptomic data. With Select-seq, we analyse the cancer stem cell-like microniches in relation to the tumour microenvironment of triple-negative breast cancer patients. We identify alternative splice variants, perform complementarity-determining region analysis of infiltrating T cells and B cells, and assess adenosine-to-inosine base editing in tumour tissue sections. Especially, in triple-negative breast cancer microniches, adenosine-to-inosine editome specific to different microniche groups is identified.

In antibody discovery, in-depth analysis of an antibody library and high-throughput retrieval of clones in the library are crucial to identifying and exploiting rare clones with different properties. However, existing methods have technical limitations, such as low process throughput from the laborious cloning process and waste of the phenotypic screening capacity from unnecessary repetitive tests on the dominant clones. To overcome the limitations, we developed a new high-throughput platform for the identification and retrieval of clones in the library, TrueRepertoire™. This new platform provides highly accurate sequences of the clones with linkage information between heavy and light chains of the antibody fragment. Additionally, the physical DNA of clones can be retrieved in high throughput based on the sequence information. We validated the high accuracy of the sequences and demonstrated that there is no platform-specific bias. Moreover, the applicability of TrueRepertoire™ was demonstrated by a phage-displayed single-chain variable fragment library targeting human hepatocyte growth factor protein.

c-Met is a promising target in cancer therapy for its intrinsic oncogenic properties. However, there are currently no c-Met-specific inhibitors available in the clinic. Antibodies blocking the interaction with its only known ligand, hepatocyte growth factor, and/or inducing receptor internalization have been clinically tested. To explore other therapeutic antibody mechanisms like Fc-mediated effector function, bispecific T cell engagement, and chimeric antigen T cell receptors, a diverse panel of antibodies is essential. We prepared a chicken immune scFv library, performed four rounds of bio-panning, obtained 641 clones using a high-throughput clonal retrieval system (TrueRepertoireTM, TR), and found 149 antigen-reactive scFv clones. We also prepared phagemid DNA before the start of bio-panning (round 0) and, after each round of bio-panning (round 1–4), performed next-generation sequencing of these five sets of phagemid DNA, and identified 860,207 HCDR3 clonotypes and 443,292 LCDR3 clonotypes along with their clonal abundance data. We then established a TR data set consisting of antigen reactivity for scFv clones found in TR analysis and the clonal abundance of their HCDR3 and LCDR3 clonotypes in five sets of phagemid DNA. Using the TR data set, a random forest machine learning algorithm was trained to predict the binding properties of in silico HCDR3 and LCDR3 clonotypes. Subsequently, we synthesized 40 HCDR3 and 40 LCDR3 clonotypes predicted to be antigen reactive (AR) and constructed a phage-displayed scFv library called the AR library. In parallel, we also prepared an antigen non-reactive (NR) library using 10 HCDR3 and 10 LCDR3 clonotypes predicted to be NR. After a single round of bio-panning, we screened 96 randomly-selected phage clones from the AR library and found out 14 AR scFv clones consisting of 5 HCDR3 and 11 LCDR3 AR clonotypes. We also screened 96 randomly-selected phage clones from the NR library, but did not identify any AR clones. In summary, machine learning algorithms can provide a method for identifying AR antibodies, which allows for the characterization of diverse antibody libraries inaccessible by traditional methods.