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DNA methylation plays an important role in various biological processes,including cell differentiation,ageing,and cancer development. The most important methylation in mammals is 5-methylcytosine mostly occurring in the context of CpG dinucleotides. Sequencing methods such as whole-genome bisulfite sequencing successfully detect 5-methylcytosine DNA modifications. However,they suffer from the serious drawbacks of short read lengths and might introduce an amplification bias. Here we present Rockfish,a deep learning algorithm that significantly improves read-level 5-methylcytosine detection by using Nanopore sequencing. Rockfish is compared with other methods based on Nanopore sequencing on R9.4.1 and R10.4.1 datasets. There is an increase in the single-base accuracy and the F1 measure of up to 5 percentage points on R.9.4.1 datasets,and up to 0.82 percentage points on R10.4.1 datasets. Moreover,Rockfish shows a high correlation with whole-genome bisulfite sequencing,requires lower read depth,and achieves higher confidence in biologically important regions such as CpG-rich promoters while being computationally efficient. Its superior performance in human and mouse samples highlights its versatility for studying 5-methylcytosine methylation across varied organisms and diseases. Finally,its adaptable architecture ensures compatibility with new versions of pores and chemistry as well as modification types. Subject terms: Genome informatics,Epigenomics,Computational models,DNA sequencing,DNA methylation View Publication

During the metastatic cascade,cancer cells travel through the bloodstream as circulating tumor cells (CTCs) to a secondary site. Clustered CTCs have greater shear stress and treatment resistance,yet their biology remains poorly understood. We therefore engineered a tunable superhydrophobic array device (SHArD). The SHArD-C was applied to culture a clinically relevant model of CTC clusters. Using our device,we cultured a model of cancer cell aggregates of various sizes with immortalized cancer cell lines. These exhibited higher E-cadherin expression and are significantly more capable of surviving high fluid shear stress-related forces compared to single cells and model clusters grown using the control method,helping to explain why clustering may provide a metastatic advantage. Additionally,the SHArD-S,when compared with the AggreWell 800 method,provides a more consistent spheroid-forming device culturing reproducible sizes of spheroids for multiple cancer cell lines. Overall,we designed,fabricated,and validated an easily tunable engineered device which grows physiologically relevant three-dimensional (3D) cancer models containing tens to thousands of cells. View Publication

Alternative splicing is a major contributor of transcriptomic complexity,but the extent to which transcript isoforms are translated into stable,functional protein isoforms is unclear. Furthermore,detection of relatively scarce isoform-specific peptides is challenging,with many protein isoforms remaining uncharted due to technical limitations. Recently,a family of advanced targeted MS strategies,termed internal standard parallel reaction monitoring (IS-PRM),have demonstrated multiplexed,sensitive detection of pre-defined peptides of interest. Such approaches have not yet been used to confirm existence of novel peptides. Here,we present a targeted proteogenomic approach that leverages sample-matched long-read RNA sequencing (LR RNAseq) data to predict potential protein isoforms with prior transcript evidence. Predicted tryptic isoform-specific peptides,which are specific to individual gene product isoforms,serve as “triggers” and “targets” in the IS-PRM method,Tomahto. Using the model human stem cell line WTC11,LR RNAseq data were generated and used to inform the generation of synthetic standards for 192 isoform-specific peptides (114 isoforms from 55 genes). These synthetic “trigger” peptides were labeled with super heavy tandem mass tags (TMT) and spiked into TMT-labeled WTC11 tryptic digest,predicted to contain corresponding endogenous “target” peptides. Compared to DDA mode,Tomahto increased detectability of isoforms by 3.6-fold,resulting in the identification of five previously unannotated isoforms. Our method detected protein isoform expression for 43 out of 55 genes corresponding to 54 resolved isoforms. This LR RNA seq-informed Tomahto targeted approach,called LRP-IS-PRM,is a new modality for generating protein-level evidence of alternative isoforms – a critical first step in designing functional studies and eventually clinical assays. View Publication

The ability to analyze protein function in a native context is central to understanding cellular physiology. This study explores whether tagging endogenous proteins with a reporter is a scalable strategy for generating cell models that accurately quantitate protein dynamics. Specifically,it investigates whether CRISPR-mediated integration of the HiBiT luminescent peptide tag can easily be accomplished on a large-scale and whether integrated reporter faithfully represents target biology. For this purpose,a large set of proteins representing diverse structures and functions,some of which are known or potential drug targets,were targeted for tagging with HiBiT in multiple cell lines. Successful insertion was detected for 86{\%} of the targets,as determined by luminescence-based plate assays,blotting,and imaging. In order to determine whether endogenously tagged proteins yield more representative models,cells expressing HiBiT protein fusions either from endogenous loci or plasmids were directly compared in functional assays. In the tested cases,only the edited lines were capable of accurately reproducing the anticipated biology. This study provides evidence that cell lines expressing HiBiT fusions from endogenous loci can be rapidly generated for many different proteins and that these cellular models provide insight into protein function that may be unobtainable using overexpression-based approaches. View Publication

Recent research in breast biology has provided support for the cancer stem-cell hypothesis. Two important components of this hypothesis are that tumors originate in mammary stem or progenitor cells as a result of dysregulation of the normally tightly regulated process of self-renewal. As a result,tumors contain and are driven by a cellular subcomponent that retains key stem-cell properties including self-renewal,which drives tumorigenesis and differentiation that contributes to cellular heterogeneity. Advances in stem-cell technology have led to the identification of stem cells in normal and malignant breast tissue. The study of these stem cells has helped to elucidate the origin of the molecular complexity of human breast cancer. The cancer stem-cell hypothesis has important implications for early detection,prevention,and treatment of breast cancer. Both hereditary and sporadic breast cancers may develop through dysregulation of stem-cell self-renewal pathways. These aberrant stem cells may provide targets for the development of cancer prevention strategies. Furthermore,because breast cancer stem cells may be highly resistant to radiation and chemotherapy,the development of more effective therapies for this disease may require the effective targeting of this cell population. View Publication