Research and medical genomics require comprehensive, scalable methods for the discovery of novel disease targets, evolutionary drivers and genetic markers with clinical significance. This necessitates a framework to identify all types of variants independent of their size or location. Here we present DRAGEN, which uses multigenome mapping with pangenome references, hardware acceleration and machine learning-based variant detection to provide insights into individual genomes, with ~30 min of computation time from raw reads to variant detection. DRAGEN outperforms current state-of-the-art methods in speed and accuracy across all variant types (single-nucleotide variations, insertions or deletions, short tandem repeats, structural variations and copy number variations) and incorporates specialized methods for analysis of medically relevant genes. We demonstrate the performance of DRAGEN across 3,202 whole-genome sequencing datasets by generating fully genotyped multisample variant call format files and demonstrate its scalability, accuracy and innovation to further advance the integration of comprehensive genomics. Overall, DRAGEN marks a major milestone in sequencing data analysis and will provide insights across various diseases, including Mendelian and rare diseases, with a highly comprehensive and scalable platform.

To assist the translation of genetic findings to disease pathobiology and therapeutics discovery, we present an ensemble deep learning framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that predicts protein-binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms to generate comprehensive structurally informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods and experimentally validate its predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces and explore their impact on disease prognosis and drug responses. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from analysis of approximately 11,000 whole exomes across 33 cancer types and show significant associations of oncoPPIs with patient survival and drug responses. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.

Strategies to enhance the anti-tumor immune response of stereotactic ablative radiotherapy (SABR) at primary tumors and abscopal sites are under intensive investigation. Here we report a metabolizable binary supracluster (BSCgal) that combines gold nanoclusters as radiosensitizing adjuvants with small interfering RNA (siRNA) targeting the immunosuppressive mediator galectin-1 (Gal-1). BSCgal comprises reversibly crosslinked cationic gold nanoclusters and siRNA complexes in a polymer matrix that biodegrades over weeks, facilitating clearance (90.3% in vivo clearance at 4 weeks) to reduce toxicity. The particle size well above the renal filtration threshold facilitates passive delivery to tumors. Using mouse models of head and neck cancer, we show that BSCgal augments the radiodynamic and immunotherapeutic effects of SABR at the primary and metastatic tumors by promoting tumor-inhibitory leukocytes, upregulating cytotoxic granzyme B and reducing immunosuppressive cell populations. It outperforms SABR plus Gal-1 antagonists, chemoradiation drug cisplatin or PD-1 inhibitor. This work presents a translatable strategy to converge focal radiosensitization with targeted immune checkpoint silencing for personalized radioimmunotherapy.

Current methods for analyzing chromatin architecture are not readily scalable to heterogeneous tissues. Here we introduce Droplet Hi-C, which uses a commercial microfluidic device for high-throughput, single-cell chromatin conformation profiling in droplets. Using Droplet Hi-C, we mapped the chromatin architecture of the mouse cortex and analyzed gene regulatory programs in major cortical cell types. In addition, we used this technique to detect copy number variations, structural variations and extrachromosomal DNA in human glioblastoma, colorectal and blood cancer cells, revealing clonal dynamics and other oncogenic events during treatment. We refined the technique to allow joint profiling of chromatin architecture and transcriptome in single cells, facilitating exploration of the links between chromatin architecture and gene expression in both normal tissues and tumors. Thus, Droplet Hi-C both addresses critical gaps in chromatin analysis of heterogeneous tissues and enhances understanding of gene regulation.

Methods to systematically monitor protein complex dynamics are needed. We introduce serial ultrafiltration combined with limited proteolysis-coupled mass spectrometry (FLiP-MS), a structural proteomics workflow that generates a library of peptide markers specific to changes in PPIs by probing differences in protease susceptibility between complex-bound and monomeric forms of proteins. The library includes markers mapping to protein-binding interfaces and markers reporting on structural changes that accompany PPI changes. Integrating the marker library with LiP-MS data allows for global profiling of protein-protein interactions (PPIs) from unfractionated lysates. We apply FLiP-MS to Saccharomyces cerevisiae and probe changes in protein complex dynamics after DNA replication stress, identifying links between Spt-Ada-Gcn5 acetyltransferase activity and the assembly state of several complexes. FLiP-MS enables protein complex dynamics to be probed on any perturbation, proteome-wide, at high throughput, with peptide-level structural resolution and informing on occupancy of binding interfaces, thus providing both global and molecular views of a system under study.

Lipid nanoparticle (LNP) delivery of clustered regularly interspaced short palindromic repeat (CRISPR) ribonucleoproteins (RNPs) could enable high-efficiency, low-toxicity and scalable in vivo genome editing if efficacious RNP-LNP complexes can be reliably produced. Here we engineer a thermostable Cas9 from Geobacillus stearothermophilus (GeoCas9) to generate iGeoCas9 variants capable of >100× more genome editing of cells and organs compared with the native GeoCas9 enzyme. Furthermore, iGeoCas9 RNP-LNP complexes edit a variety of cell types and induce homology-directed repair in cells receiving codelivered single-stranded DNA templates. Using tissue-selective LNP formulations, we observe genome-editing levels of 16‒37% in the liver and lungs of reporter mice that receive single intravenous injections of iGeoCas9 RNP-LNPs. In addition, iGeoCas9 RNPs complexed to biodegradable LNPs edit the disease-causing SFTPC gene in lung tissue with 19% average efficiency, representing a major improvement over genome-editing levels observed previously using viral or nonviral delivery strategies. These results show that thermostable Cas9 RNP-LNP complexes can expand the therapeutic potential of genome editing.

CRISPR guide RNA sequences deriving exactly from natural sequences may not perform optimally in every application. Here we implement and evaluate algorithms for designing maximally fit, artificial CRISPR-Cas13a guides with multiple mismatches to natural sequences that are tailored for diagnostic applications. These guides offer more sensitive detection of diverse pathogens and discrimination of pathogen variants compared with guides derived directly from natural sequences and illuminate design principles that broaden Cas13a targeting.

The accurate identification and prioritization of antigenic peptides is crucial for the development of personalized cancer immunotherapies. Publicly available pipelines to predict clinical neoantigens do not allow direct integration of mass spectrometry immunopeptidomics data, which can uncover antigenic peptides derived from various canonical and noncanonical sources. To address this, we present an end-to-end clinical proteogenomic pipeline, called NeoDisc, that combines state-of-the-art publicly available and in-house software for immunopeptidomics, genomics and transcriptomics with in silico tools for the identification, prediction and prioritization of tumor-specific and immunogenic antigens from multiple sources, including neoantigens, viral antigens, high-confidence tumor-specific antigens and tumor-specific noncanonical antigens. We demonstrate the superiority of NeoDisc in accurately prioritizing immunogenic neoantigens over recent prioritization pipelines. We showcase the various features offered by NeoDisc that enable both rule-based and machine-learning approaches for personalized antigen discovery and neoantigen cancer vaccine design. Additionally, we demonstrate how NeoDisc's multiomics integration identifies defects in the cellular antigen presentation machinery, which influence the heterogeneous tumor antigenic landscape.