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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%.

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. The spatial context of epitranscriptomic features in the tumour microenvironment remains poorly understood. Here, a method for transcriptomic and epitranscriptomic analysis of immunofluorescence-stained tissue, Select-seq, is applied to stem cell-like microniches in triple negative breast cancer.

Complex oligonucleotide (oligo) libraries are essential materials for diverse applications in synthetic biology, pharmaceutical production, nanotechnology and DNA-based data storage. However, the error rates in synthesizing complex oligo libraries can be substantial, leading to increment in cost and labor for the applications. As most synthesis errors arise from faulty insertions and deletions, we developed a length-based method with single-base resolution for purification of complex libraries containing oligos of identical or different lengths. Our method—purification of multiplex oligonucleotide libraries by synthesis and selection—can be performed either step-by-step manually or using a next-generation sequencer. When applied to a digital data-encoded library containing oligos of identical length, the method increased the purity of full-length oligos from 83% to 97%. We also show that libraries encoding the complementarity-determining region H3 with three different lengths (with an empirically achieved diversity >10 6 ) can be simultaneously purified in one pot, increasing the in-frame oligo fraction from 49.6% to 83.5%. Accurate oligonucleotide libraries are produced by synthesis and selection.

Effective subset selection from complex oligonucleotide libraries is crucial for genomics, synthetic biology, and DNA data storage. The polymerase chain reaction, foundational for amplifying target subsets is limited by primer design and length for specificity, which constrains the scalability of oligo libraries and increases the synthesis burden for primers. We introduce an oligo subset selection methodology that utilizes sequence-specific cyclic nucleotide synthesis and blocking of the template oligos. This approach eliminates the need for primers for selective hybridization and enables the encoding and selection of hundreds of subsets with barcode lengths of fewer than five nucleotides. Moreover, cyclic selection enables a hierarchical data structure in the oligo library, enhancing the programmability. This advancement offers a scalable and cost-effective solution for handling complex oligo libraries. Selecting subsets of specific sequences from complex oligonucleotide libraries is crucial for genomics and data storage but faces scalability limits due to primer design constraints. Here, the authors present a barcode-based selection method that enables efficient, hierarchical, and programmable oligo subset retrieval.

The advent of next-generation sequencing (NGS) has accelerated biomedical research by enabling the high-throughput analysis of DNA sequences at a very low cost. However, NGS has limitations in detecting rare-frequency variants (< 1%) because of high sequencing errors (> 0.1~1%). NGS errors could be filtered out using molecular barcodes, by comparing read replicates among those with the same barcodes. Accordingly, these barcoding methods require redundant reads of non-target sequences, resulting in high sequencing cost. Here, we present a cost-effective NGS error validation method in a barcode-free manner. By physically extracting and individually amplifying the DNA clones of erroneous reads, we distinguish true variants of frequency > 0.003% from the systematic NGS error and selectively validate NGS error after NGS. We achieve a PCR-induced error rate of 2.5×10 −6 per base per doubling event, using 10 times less sequencing reads compared to those from previous studies. Next generation sequencing has difficulty in detecting rare-frequency variants due to high sequencing errors. Here the authors present a barcode-free error validation method that physically extracts erroneous reads to identify true variants.

Determining mutational landscapes in a spatial context is essential for understanding genetically heterogeneous cell microniches. Current approaches, such as Multiple Displacement Amplification (MDA), offer high genome coverage but limited multiplexing, which hinders large-scale spatial genomic studies. Here, we introduce barcoded MDA (bMDA), a technique that achieves high-coverage genomic analysis of low-input DNA while enhancing the multiplexing capabilities. By incorporating cell barcodes during MDA, bMDA streamlines library preparation in one pot, thereby overcoming a key bottleneck in spatial genomics. We apply bMDA to the integrative spatial analysis of triple-negative breast cancer tissues by examining copy number alterations, single nucleotide variations, structural variations, and kataegis signatures for each spatial microniche. This enables the assessment of subclonal evolutionary relationships within a spatial context. Therefore, bMDA has emerged as a scalable technology with the potential to advance the field of spatial genomics significantly. Spatial genomics offers insights into cellular interactions within tissues. Here, the authors develop barcoded multiple displacement amplification, achieving high-coverage sequencing to map complex genomic variations within cellular landscapes.

The United States Food and Drug Administration (FDA) has approved several oligo-based therapeutics, and more than 155 active clinical trials are currently ongoing, advancing the clinical use of oligos as a paradigm-shifting methodology.1,2 When utilizing oligos in clinical applications, fidelity is one of the most critical criteria affecting their performance.3 Oligos are synthesized by coupling oligo monomers individually, following the desired sequence. Errors in oligo sequences, including deletions, insertions, and substitutions, inevitably occur during synthesis, since the monomer coupling is not 100% efficient.4 For therapeutical uses, oligos are either hybridized to the target gene, genome, or RNA to regulate or edit gene expression (e.g., ASO, siRNA, sgRNA) or are translated in vivo to be utilized as a drug or immune stimulatory molecule (e.g., mRNA vaccine). [...]MOPSS does not require any additional (dummy) sequences for purification and can be applied to oligos of various purposes, because the nucleotide-recognizing region is located at a pre-existing site (e.g., primer sequence). [...]we applied MOPSS to digital data encoded oligos, synthetic antibody encoding oligos, and human genome capture oligos from a previously reported article.

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.

Immunoglobulin G-based monoclonal antibodies (mAbs) have been effective in treating various diseases, but their large molecular size can limit their penetration of tissue and efficacy in multifactorial diseases, necessitating the exploration of alternative forms. In this study, we constructed a phage display library comprising single-domain antibodies (sdAbs; or “VHHs”), known for their small size and remarkable stability, using a total of 1.6 × 109 lymphocytes collected from 20 different alpacas, resulting in approximately 7.16 × 1010 colonies. To assess the quality of the constructed library, next-generation sequencing-based high-throughput profiling was performed, analyzing approximately 5.65 × 106 full-length VHH sequences, revealing 92% uniqueness and confirming the library’s diverse composition. Systematic characterization of the library revealed multiple sdAbs with high affinity for three therapeutically relevant antigens. In conclusion, our alpaca sdAb phage display library provides a versatile resource for diagnostics and therapeutics. Furthermore, the library’s vast natural VHH antibody repertoire offers insights for generating humanized synthetic sdAb libraries, further advancing sdAb-based therapeutics.

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. One of the biggest bottlenecks in large-scale DNA synthesis is the retrieval of target clonal DNA from high-density sequencing platforms. Here, the authors present a method called ‘Sniper Cloning’ that allows for precise mapping of target clone features and rapid retrieval of targets for full utilization of DNA clones.