Impact of preanalytical factors on liquid biopsy in the canine cancer model.

While liquid biopsy has potential to transform cancer diagnostics through minimally-invasive detection and monitoring of tumors, the impact of preanalytical factors such as the timing and anatomical location of blood draw is not well understood. To address this gap, we leveraged pet dogs with spontaneous cancer as a model system, as their compressed disease timeline facilitates rapid diagnostic benchmarking. Key liquid biopsy metrics from dogs were consistent with existing reports from human patients. The tumor content of samples was higher from venipuncture sites closer to the tumor and from a central vein. Metrics also differed between lymphoma and non-hematopoietic cancers, urging cancer-type-specific interpretation. Liquid biopsy was highly sensitive to disease status, with changes identified soon after post chemotherapy administration, and trends of increased tumor fraction and other metrics observed prior to clinical relapse in dogs with lymphoma or osteosarcoma. These data support the utility of pet dogs with cancer as a relevant system for advancing liquid biopsy platforms.

Flnc: Machine Learning Improves the Identification of Novel Long Noncoding RNAs from Stand-Alone RNA-Seq Data.

Long noncoding RNAs (lncRNAs) play critical regulatory roles in human development and disease. Although there are over 100,000 samples with available RNA sequencing (RNA-seq) data, many lncRNAs have yet to be annotated. The conventional approach to identifying novel lncRNAs from RNA-seq data is to find transcripts without coding potential but this approach has a false discovery rate of 30-75%. Other existing methods either identify only multi-exon lncRNAs, missing single-exon lncRNAs, or require transcriptional initiation profiling data (such as H3K4me3 ChIP-seq data), which is unavailable for many samples with RNA-seq data. Because of these limitations, current methods cannot accurately identify novel lncRNAs from existing RNA-seq data. To address this problem, we have developed software, Flnc, to accurately identify both novel and annotated full-length lncRNAs, including single-exon lncRNAs, directly from RNA-seq data without requiring transcriptional initiation profiles. Flnc integrates machine learning models built by incorporating four types of features: transcript length, promoter signature, multiple exons, and genomic location. Flnc achieves state-of-the-art prediction power with an AUROC score over 0.92. Flnc significantly improves the prediction accuracy from less than 50% using the conventional approach to over 85%. Flnc is available via GitHub platform.

High-throughput retrieval of physical DNA for NGS-identifiable clones in phage display library.

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.

High-throughput construction of multiple cas9 gene variants via assembly of high-depth tiled and sequence-verified oligonucleotides.

Selective retrieval of sequence-verified oligonucleotides (oligos) from next-generation sequencing (NGS) flow cells, termed megacloning, promises accurate and reliable gene synthesis. However, gene assembly requires a complete collection of overlapping sense and nonsense oligos, and megacloning does not typically guarantee the complete production of sequence-verified oligos. Therefore, missing oligos must be provided via repetitive rounds of megacloning, which introduces a bottleneck for scaled-up efforts at gene assembly. Here, we introduce the concept of high-depth tiled oligo design to successfully utilize megacloned oligos for gene synthesis. Using acquired oligos from a single round of the megacloning process, we assembled 72 of 81 target Cas9-coding gene variants. We further validated 62 of these cas9 constructs, and deposited the plasmids to Addgene for subsequent functional characterization by the scientific community. This study demonstrates the utility of using sequence-verified oligos for DNA assembly and provides a practical and reliable optimized method for high-throughput gene synthesis.

Repetitive genomic insertion of gene-sized dsDNAs by targeting the promoter region of a counter-selectable marker.

Genome engineering can be used to produce bacterial strains with a wide range of desired phenotypes. However, the incorporation of gene-sized DNA fragments is often challenging due to the intricacy of the procedure, off-target effects, and low insertion efficiency. Here we report a genome engineering method enabling the continuous incorporation of gene-sized double-stranded DNAs (dsDNAs) into the Escherichia coli genome. DNA substrates are inserted without introducing additional marker genes, by synchronously turning an endogenous counter-selectable marker gene ON and OFF. To accomplish this, we utilized λ Red protein-mediated recombination to insert dsDNAs within the promoter region of a counter-selectable marker gene, tolC. By repeatedly switching the marker gene ON and OFF, a number of desired gene-sized dsDNAs can be inserted consecutively. With this method, we successfully inserted approximately 13 kb gene clusters to generate engineered E. coli strains producing 1,4-butanediol (1,4-BDO).