The design of next-generation nanobiomaterials requires precise engineering of both physical properties of the core material and chemical properties of the material’s surface to meet a biological function. A bio-inspired modular and versatile technology was developed to allow biodegradable polymeric nanoparticles to circulate through the blood for extended periods of time while also acting as a detoxification device. To mimic red blood cells, physical and chemical biomimicry are combined to enhance the biological function of nanomaterials in vitro and in vivo. The anisotropic shape and membrane coating synergize to resist cellular uptake and reduce clearance from the blood. This approach enhances the detoxification properties of nanoparticles, markedly improving survival in a mouse model of sepsis. The anisotropic membrane-coated nanoparticles have enhanced biodistribution and therapeutic efficacy. These biomimetic biodegradable nanodevices and their derivatives have promise for applications ranging from detoxification agents, to drug delivery vehicles, and to biological sensors.

The RecQ DNA helicase WRN is a synthetic lethal target for cancers with microsatellite instability (MSI), a form of genetic hypermutability arising from impaired mismatch repair 1-4 . WRN depletion induces widespread DNA double strand breaks (DSBs) in MSI cells, leading to cell cycle arrest and/or apoptosis. However, the mechanism by which WRN protects MSI cancers from DSBs remains unclear. Here, we demonstrate that TAdinucleotide repeats are highly unstable in MSI cells and exhibit surprisingly large-scale expansions, distinct from previously described insertion/deletion mutations of a few nucleotides 5 . We show that expanded TA repeats form non-B DNA secondary structures that stall replication forks, activate the ATR checkpoint kinase, and necessitate unwinding by the WRN helicase. In the absence of WRN, the expanded TA-dinucleotide repeats are susceptible to MUS81 nuclease cleavage, leading to massive chromosome shattering. Thus, our study uncovers a distinct biomarker within MSI tumors that underlies the synthetic lethal dependence on WRN, thereby supporting the development of WRN-based therapeutics.

Alternative splicing is a core biological process that enables profound and essential diversification of gene function. Short-read RNA sequencing approaches fail to resolve RNA isoforms and therefore primarily enable gene expression measurements -an isoform unaware representation of the transcriptome. Conversely, full-length RNA sequencing using long-read technologies are able to capture complete transcript isoforms, but their utility is deeply constrained due to throughput limitations. Here, we introduce MAS-ISO-seq, a technique for programmably concatenating cDNAs into single molecules optimal for long-read sequencing, boosting the throughput >15 fold to nearly 40 million cDNA reads per run on the Sequel IIe sequencer. We validated unambiguous isoform assignment with MAS-ISO-seq using a synthetic RNA isoform library and applied this approach to single-cell RNA sequencing of tumor-infiltrating T cells. Results demonstrated a >30 fold boosted discovery of differentially spliced genes and robust cell clustering, as well as canonical PTPRC splicing patterns across T cell subpopulations and the concerted expression of the associated hnRNPLL splicing factor. Methods such as MAS-ISO-seq will drive discovery of novel isoforms and the transition from gene expression to transcript isoform expression analyses. MainWhile RNA sequencing has dramatically accelerated our understanding of biology, accurate quantification and discovery of RNA isoforms, especially at single-cell resolution, remains a steep challenge 1 . Alternative splicing is a core regulatory process that modulates the structure, expression, and localization of proteins through differential exon and/or UTR splicing during transcript maturation in eukaryotes. Beyond being an integral component of cellular/organismal development and homeostatic maintenance, alternative splicing is implicated in a wide range of pathologies with hallmark isoforms being linked to cardiovascular, neurological, and immunological diseases 2,3 . Additionally, mutated and/or dysregulated splicing factors make up a major class of phenotypic alterations associated with tumor progression and therapeutic resistance 4 .

Alternative splicing is a core biological process that enables profound and essential diversification of gene function. Short-read RNA sequencing approaches fail to resolve RNA isoforms and therefore primarily enable gene expression measurements - an isoform unaware representation of the transcriptome. Conversely, full-length RNA sequencing using long-read technologies are able to capture complete transcript isoforms, but their utility is deeply constrained due to throughput limitations. Here, we introduce MAS-ISO-seq, a technique for programmably concatenating cDNAs into single molecules optimal for long-read sequencing, boosting the throughput >15 fold to nearly 40 million cDNA reads per run on the Sequel IIe sequencer. We validated unambiguous isoform assignment with MAS-ISO-seq using a synthetic RNA isoform library and applied this approach to single-cell RNA sequencing of tumor-infiltrating T cells. Results demonstrated a >30 fold boosted discovery of differentially spliced genes and robust cell clustering, as well as canonical PTPRC splicing patterns across T cell subpopulations and the concerted expression of the associated hnRNPLL splicing factor. Methods such as MAS-ISO-seq will drive discovery of novel isoforms and the transition from gene expression to transcript isoform expression analyses.

Next Generation Sequencing (NGS) workloads largely consist of pipelines of tasks with heterogeneous compute, memory, and storage requirements. Identifying the optimal system configuration has historically required expertise in both system architecture and bioinformatics. This paper outlines infrastructure recommendations for one commonly used genomics workload based on extensive benchmarking and profiling, along with recommendations on how to tune genomics workflows for high performance computing (HPC) infrastructure. The demonstrated methodology and learnings can be extended for other genomics workloads and for other infrastructures such as the cloud.