Genetic engineering of allogeneic cell therapeutics that fully prevents rejection by a recipient's immune system would abolish the requirement for immunosuppressive drugs or encapsulation and support large-scale manufacturing of off-the-shelf cell products. Previously, we generated mouse and human hypoimmune pluripotent (HIP) stem cells by depleting HLA class I and II molecules and overexpressing CD47 (B2M-/-CIITA-/-CD47+). To determine whether this strategy is successful in non-human primates, we engineered rhesus macaque HIP cells and transplanted them intramuscularly into four allogeneic rhesus macaques. The HIP cells survived unrestricted for 16 weeks in fully immunocompetent allogeneic recipients and differentiated into several lineages, whereas allogeneic wild-type cells were vigorously rejected. We also differentiated human HIP cells into endocrinologically active pancreatic islet cells and showed that they survived in immunocompetent, allogeneic diabetic humanized mice for 4 weeks and ameliorated diabetes. HIP-edited primary rhesus macaque islets survived for 40 weeks in an allogeneic rhesus macaque recipient without immunosuppression, whereas unedited islets were quickly rejected.

As structure prediction methods are generating millions of publicly available protein structures, searching these databases is becoming a bottleneck. Foldseek aligns the structure of a query protein against a database by describing tertiary amino acid interactions within proteins as sequences over a structural alphabet. Foldseek decreases computation times by four to five orders of magnitude with 86%, 88% and 133% of the sensitivities of Dali, TM-align and CE, respectively.

Organoids generated from human pluripotent stem cells provide experimental systems to study development and disease, but quantitative measurements across different spatial scales and molecular modalities are lacking. In this study, we generated multiplexed protein maps over a retinal organoid time course and primary adult human retinal tissue. We developed a toolkit to visualize progenitor and neuron location, the spatial arrangements of extracellular and subcellular components and global patterning in each organoid and primary tissue. In addition, we generated a single-cell transcriptome and chromatin accessibility timecourse dataset and inferred a gene regulatory network underlying organoid development. We integrated genomic data with spatially segmented nuclei into a multimodal atlas to explore organoid patterning and retinal ganglion cell (RGC) spatial neighborhoods, highlighting pathways involved in RGC cell death and showing that mosaic genetic perturbations in retinal organoids provide insight into cell fate regulation.

Realizing the promise of prime editing for the study and treatment of genetic disorders requires efficient methods for delivering prime editors (PEs) in vivo. Here we describe the identification of bottlenecks limiting adeno-associated virus (AAV)-mediated prime editing in vivo and the development of AAV-PE vectors with increased PE expression, prime editing guide RNA stability and modulation of DNA repair. The resulting dual-AAV systems, v1em and v3em PE-AAV, enable therapeutically relevant prime editing in mouse brain (up to 42% efficiency in cortex), liver (up to 46%) and heart (up to 11%). We apply these systems to install putative protective mutations in vivo for Alzheimer's disease in astrocytes and for coronary artery disease in hepatocytes. In vivo prime editing with v3em PE-AAV caused no detectable off-target effects or significant changes in liver enzymes or histology. Optimized PE-AAV systems support the highest unenriched levels of in vivo prime editing reported to date, facilitating the study and potential treatment of diseases with a genetic component.

Currently available single-cell omics technologies capture many unique features with different biological information content. Data integration aims to place cells, captured with different technologies, onto a common embedding to facilitate downstream analytical tasks. Current horizontal data integration techniques use a set of common features, thereby ignoring non-overlapping features and losing information. Here we introduce StabMap, a mosaic data integration technique that stabilizes mapping of single-cell data by exploiting the non-overlapping features. StabMap first infers a mosaic data topology based on shared features, then projects all cells onto supervised or unsupervised reference coordinates by traversing shortest paths along the topology. We show that StabMap performs well in various simulation contexts, facilitates 'multi-hop' mosaic data integration where some datasets do not share any features and enables the use of spatial gene expression features for mapping dissociated single-cell data onto a spatial transcriptomic reference.

Due to technical limitations, most gut microbiome studies have focused on prokaryotes, overlooking viruses. Phanta, a virome-inclusive gut microbiome profiling tool, overcomes the limitations of assembly-based viral profiling methods by using customized k-mer-based classification tools and incorporating recently published catalogs of gut viral genomes. Phanta's optimizations consider the small genome size of viruses, sequence homology with prokaryotes and interactions with other gut microbes. Extensive testing of Phanta on simulated data demonstrates that it quickly and accurately quantifies prokaryotes and viruses. When applied to 245 fecal metagenomes from healthy adults, Phanta identifies ~200 viral species per sample, ~5× more than standard assembly-based methods. We observe a ~2:1 ratio between DNA viruses and bacteria, with higher interindividual variability of the gut virome compared to the gut bacteriome. In another cohort, we observe that Phanta performs equally well on bulk versus virus-enriched metagenomes, making it possible to study prokaryotes and viruses in a single experiment, with a single analysis.

We present avidity sequencing, a sequencing chemistry that separately optimizes the processes of stepping along a DNA template and that of identifying each nucleotide within the template. Nucleotide identification uses multivalent nucleotide ligands on dye-labeled cores to form polymerase-polymer-nucleotide complexes bound to clonal copies of DNA targets. These polymer-nucleotide substrates, termed avidites, decrease the required concentration of reporting nucleotides from micromolar to nanomolar and yield negligible dissociation rates. Avidity sequencing achieves high accuracy, with 96.2% and 85.4% of base calls having an average of one error per 1,000 and 10,000 base pairs, respectively. We show that the average error rate of avidity sequencing remained stable following a long homopolymer.

Little is known about the biological roles of glycosylated RNAs (glycoRNAs), a recently discovered class of glycosylated molecules, because of a lack of visualization methods. We report sialic acid aptamer and RNA in situ hybridization-mediated proximity ligation assay (ARPLA) to visualize glycoRNAs in single cells with high sensitivity and selectivity. The signal output of ARPLA occurs only when dual recognition of a glycan and an RNA triggers in situ ligation, followed by rolling circle amplification of a complementary DNA, which generates a fluorescent signal by binding fluorophore-labeled oligonucleotides. Using ARPLA, we detect spatial distributions of glycoRNAs on the cell surface and their colocalization with lipid rafts as well as the intracellular trafficking of glycoRNAs through SNARE protein-mediated secretory exocytosis. Studies in breast cell lines suggest that surface glycoRNA is inversely associated with tumor malignancy and metastasis. Investigation of the relationship between glycoRNAs and monocyte-endothelial cell interactions suggests that glycoRNAs may mediate cell-cell interactions during the immune response.

A number of mitochondrial diseases in humans are caused by point mutations that could be corrected by base editors, but delivery of CRISPR guide RNAs into the mitochondria is difficult. In this study, we present mitochondrial DNA base editors (mitoBEs), which combine a transcription activator-like effector (TALE)-fused nickase and a deaminase for precise base editing in mitochondrial DNA. Combining mitochondria-localized, programmable TALE binding proteins with the nickase MutH or Nt.BspD6I(C) and either the single-stranded DNA-specific adenine deaminase TadA8e or the cytosine deaminase ABOBEC1 and UGI, we achieve A-to-G or C-to-T base editing with up to 77% efficiency and high specificity. We find that mitoBEs are DNA strand-selective mitochondrial base editors, with editing results more likely to be retained on the nonnicked DNA strand. Furthermore, we correct pathogenic mitochondrial DNA mutations in patient-derived cells by delivering mitoBEs encoded in circular RNAs. mitoBEs offer a precise, efficient DNA editing tool with broad applicability for therapy in mitochondrial genetic diseases.

Recent advances in wearable ultrasound technologies have demonstrated the potential for hands-free data acquisition, but technical barriers remain as these probes require wire connections, can lose track of moving targets and create data-interpretation challenges. Here we report a fully integrated autonomous wearable ultrasonic-system-on-patch (USoP). A miniaturized flexible control circuit is designed to interface with an ultrasound transducer array for signal pre-conditioning and wireless data communication. Machine learning is used to track moving tissue targets and assist the data interpretation. We demonstrate that the USoP allows continuous tracking of physiological signals from tissues as deep as 164 mm. On mobile subjects, the USoP can continuously monitor physiological signals, including central blood pressure, heart rate and cardiac output, for as long as 12 h. This result enables continuous autonomous surveillance of deep tissue signals toward the internet-of-medical-things.

Applications of base editing are frequently restricted by the requirement for a protospacer adjacent motif (PAM), and selecting the optimal base editor (BE) and single-guide RNA pair (sgRNA) for a given target can be difficult. To select for BEs and sgRNAs without extensive experimental work, we systematically compared the editing windows, outcomes and preferred motifs for seven BEs, including two cytosine BEs, two adenine BEs and three C•G to G•C BEs at thousands of target sequences. We also evaluated nine Cas9 variants that recognize different PAM sequences and developed a deep learning model, DeepCas9variants, for predicting which variants function most efficiently at sites with a given target sequence. We then develop a computational model, DeepBE, that predicts editing efficiencies and outcomes of 63 BEs that were generated by incorporating nine Cas9 variants as nickase domains into the seven BE variants. The predicted median efficiencies of BEs with DeepBE-based design were 2.9- to 20-fold higher than those of rationally designed SpCas9-containing BEs.

Genetically engineered mouse models only capture a small fraction of the genetic lesions that drive human cancer. Current CRISPR-Cas9 models can expand this fraction but are limited by their reliance on error-prone DNA repair. Here we develop a system for in vivo prime editing by encoding a Cre-inducible prime editor in the mouse germline. This model allows rapid, precise engineering of a wide range of mutations in cell lines and organoids derived from primary tissues, including a clinically relevant Kras mutation associated with drug resistance and Trp53 hotspot mutations commonly observed in pancreatic cancer. With this system, we demonstrate somatic prime editing in vivo using lipid nanoparticles, and we model lung and pancreatic cancer through viral delivery of prime editing guide RNAs or orthotopic transplantation of prime-edited organoids. We believe that this approach will accelerate functional studies of cancer-associated mutations and complex genetic combinations that are challenging to construct with traditional models.

We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories and spatial locations; and generates in silico negative and positive controls for benchmarking computational tools.

Inefficient knock-in of transgene cargos limits the potential of cell-based medicines. In this study, we used a CRISPR nuclease that targets a site within an exon of an essential gene and designed a cargo template so that correct knock-in would retain essential gene function while also integrating the transgene(s) of interest. Cells with non-productive insertions and deletions would undergo negative selection. This technology, called SLEEK (SeLection by Essential-gene Exon Knock-in), achieved knock-in efficiencies of more than 90% in clinically relevant cell types without impacting long-term viability or expansion. SLEEK knock-in rates in T cells are more efficient than state-of-the-art TRAC knock-in with AAV6 and surpass more than 90% efficiency even with non-viral DNA cargos. As a clinical application, natural killer cells generated from induced pluripotent stem cells containing SLEEK knock-in of CD16 and mbIL-15 show substantially improved tumor killing and persistence in vivo.