Mapping the complex and dense arrangement of cells and their connectivity in brain tissue demands nanoscale spatial resolution imaging. Super-resolution optical microscopy excels at visualizing specific molecules and individual cells but fails to provide tissue context. Here we developed Comprehensive Analysis of Tissues across Scales (CATS), a technology to densely map brain tissue architecture from millimeter regional to nanometer synaptic scales in diverse chemically fixed brain preparations, including rodent and human. CATS uses fixation-compatible extracellular labeling and optical imaging, including stimulated emission depletion or expansion microscopy, to comprehensively delineate cellular structures. It enables three-dimensional reconstruction of single synapses and mapping of synaptic connectivity by identification and analysis of putative synaptic cleft regions. Applying CATS to the mouse hippocampal mossy fiber circuitry, we reconstructed and quantified the synaptic input and output structure of identified neurons. We furthermore demonstrate applicability to clinically derived human tissue samples, including formalin-fixed paraffin-embedded routine diagnostic specimens, for visualizing the cellular architecture of brain tissue in health and disease.

Single-cell assay for transposase-accessible chromatin by sequencing (scATAC-seq) has emerged as a powerful tool for dissecting regulatory landscapes and cellular heterogeneity. However, an exploration of systemic biases among scATAC-seq technologies has remained absent. In this study, we benchmark the performance of eight scATAC-seq methods across 47 experiments using human peripheral blood mononuclear cells (PBMCs) as a reference sample and develop PUMATAC, a universal preprocessing pipeline, to handle the various sequencing data formats. Our analyses reveal significant differences in sequencing library complexity and tagmentation specificity, which impact cell-type annotation, genotype demultiplexing, peak calling, differential region accessibility and transcription factor motif enrichment. Our findings underscore the importance of sample extraction, method selection, data processing and total cost of experiments, offering valuable guidance for future research. Finally, our data and analysis pipeline encompasses 169,000 PBMC scATAC-seq profiles and a best practices code repository for scATAC-seq data analysis, which are freely available to extend this benchmarking effort to future protocols.

Here we present a method to reduce the photobleaching of fluorescent proteins and the associated phototoxicity. It exploits a photophysical process known as reverse intersystem crossing, which we induce by near-infrared co-illumination during fluorophore excitation. This dual illumination method reduces photobleaching effects 1.5-9.2-fold, can be easily implemented on commercial microscopes and is effective in eukaryotic and prokaryotic cells with a wide range of fluorescent proteins.

Targeted protein degradation is an emerging strategy for the elimination of classically undruggable proteins. Here, to expand the landscape of targetable substrates, we designed degraders that achieve substrate selectivity via recognition of a discrete peptide and glycan motif and achieve cell-type selectivity via antigen-driven cell-surface binding. We applied this approach to mucins, O-glycosylated proteins that drive cancer progression through biophysical and immunological mechanisms. Engineering of a bacterial mucin-selective protease yielded a variant for fusion to a cancer antigen-binding nanobody. The resulting conjugate selectively degraded mucins on cancer cells, promoted cell death in culture models of mucin-driven growth and survival, and reduced tumor growth in mouse models of breast cancer progression. This work establishes a blueprint for the development of biologics that degrade specific protein glycoforms on target cells.

Therapeutic applications of nuclease-based genome editing would benefit from improved methods for transgene integration via homology-directed repair (HDR). To improve HDR efficiency, we screened six small-molecule inhibitors of DNA-dependent protein kinase catalytic subunit (DNA-PKcs), a key protein in the alternative repair pathway of non-homologous end joining (NHEJ), which generates genomic insertions/deletions (INDELs). From this screen, we identified AZD7648 as the most potent compound. The use of AZD7648 significantly increased HDR (up to 50-fold) and concomitantly decreased INDELs across different genomic loci in various therapeutically relevant primary human cell types. In all cases, the ratio of HDR to INDELs markedly increased, and, in certain situations, INDEL-free high-frequency (>50%) targeted integration was achieved. This approach has the potential to improve the therapeutic efficacy of cell-based therapies and broaden the use of targeted integration as a research tool.

Transcription-activator-like effector (TALE)-based tools for base editing of nuclear and organellar DNA rely on double-stranded DNA deaminases, which edit substrate bases on both strands of DNA, reducing editing precision. Here, we present CyDENT base editing, a CRISPR-free, strand-selective, modular base editor. CyDENT comprises a pair of TALEs fused with a FokI nickase, a single-strand-specific cytidine deaminase and an exonuclease to generate a single-stranded DNA substrate for deamination. We demonstrate effective base editing in nuclear, mitochondrial and chloroplast genomes. At certain mitochondrial sites, we show editing efficiencies of 14% and strand specificity of 95%. Furthermore, by exchanging the CyDENT deaminase with one that prefers editing GC motifs, we demonstrate up to 20% mitochondrial base editing at sites that are otherwise inaccessible to editing by other methods. The modular nature of CyDENT enables a suite of bespoke base editors for various applications.

Oxidative modification of 5-methylcytosine (5mC) by ten-eleven translocation (TET) DNA dioxygenases generates 5-hydroxymethylcytosine (5hmC), the most abundant form of oxidized 5mC. Existing single-cell bisulfite sequencing methods cannot resolve 5mC and 5hmC, leaving the cell-type-specific regulatory mechanisms of TET and 5hmC largely unknown. Here, we present joint single-nucleus (hydroxy)methylcytosine sequencing (Joint-snhmC-seq), a scalable and quantitative approach that simultaneously profiles 5hmC and true 5mC in single cells by harnessing differential deaminase activity of APOBEC3A toward 5mC and chemically protected 5hmC. Joint-snhmC-seq profiling of single nuclei from mouse brains reveals an unprecedented level of epigenetic heterogeneity of both 5hmC and true 5mC at single-cell resolution. We show that cell-type-specific profiles of 5hmC or true 5mC improve multimodal single-cell data integration, enable accurate identification of neuronal subtypes and uncover context-specific regulatory effects on cell-type-specific genes by TET enzymes.

Understanding cellular responses to genetic perturbation is central to numerous biomedical applications, from identifying genetic interactions involved in cancer to developing methods for regenerative medicine. However, the combinatorial explosion in the number of possible multigene perturbations severely limits experimental interrogation. Here, we present graph-enhanced gene activation and repression simulator (GEARS), a method that integrates deep learning with a knowledge graph of gene-gene relationships to predict transcriptional responses to both single and multigene perturbations using single-cell RNA-sequencing data from perturbational screens. GEARS is able to predict outcomes of perturbing combinations consisting of genes that were never experimentally perturbed. GEARS exhibited 40% higher precision than existing approaches in predicting four distinct genetic interaction subtypes in a combinatorial perturbation screen and identified the strongest interactions twice as well as prior approaches. Overall, GEARS can predict phenotypically distinct effects of multigene perturbations and thus guide the design of perturbational experiments.

Single-cell omics technologies enable molecular characterization of diverse cell types and states, but how the resulting transcriptional and epigenetic profiles depend on the cell's genetic background remains understudied. We describe Monopogen, a computational tool to detect single-nucleotide variants (SNVs) from single-cell sequencing data. Monopogen leverages linkage disequilibrium from external reference panels to identify germline SNVs and detects putative somatic SNVs using allele cosegregating patterns at the cell population level. It can identify 100 K to 3 M germline SNVs achieving a genotyping accuracy of 95%, together with hundreds of putative somatic SNVs. Monopogen-derived genotypes enable global and local ancestry inference and identification of admixed samples. It identifies variants associated with cardiomyocyte metabolic levels and epigenomic programs. It also improves putative somatic SNV detection that enables clonal lineage tracing in primary human clonal hematopoiesis. Monopogen brings together population genetics, cell lineage tracing and single-cell omics to uncover genetic determinants of cellular processes.

Although single-nucleotide variants (SNVs) make up the majority of cancer-associated genetic changes and have been comprehensively catalogued, little is known about their impact on tumor initiation and progression. To enable the functional interrogation of cancer-associated SNVs, we developed a mouse system for temporal and regulatable in vivo base editing. The inducible base editing (iBE) mouse carries a single expression-optimized cytosine base editor transgene under the control of a tetracycline response element and enables robust, doxycycline-dependent expression across a broad range of tissues in vivo. Combined with plasmid-based or synthetic guide RNAs, iBE drives efficient engineering of individual or multiple SNVs in intestinal, lung and pancreatic organoids. Temporal regulation of base editor activity allows controlled sequential genome editing ex vivo and in vivo, and delivery of sgRNAs directly to target tissues facilitates generation of in situ preclinical cancer models.

Germ-free (GF) mice, which are depleted of their resident microbiota, are the gold standard for exploring the role of the microbiome in health and disease; however, they are of limited value in the study of human-specific pathogens because they do not support their replication. Here, we develop GF mice systemically reconstituted with human immune cells and use them to evaluate the role of the resident microbiome in the acquisition, replication and pathogenesis of two human-specific pathogens, Epstein-Barr virus (EBV) and human immunodeficiency virus (HIV). Comparison with conventional (CV) humanized mice showed that resident microbiota enhance the establishment of EBV infection and EBV-induced tumorigenesis and increase mucosal HIV acquisition and replication. HIV RNA levels were higher in plasma and tissues of CV humanized mice compared with GF humanized mice. The frequency of CCR5+ CD4+ T cells throughout the intestine was also higher in CV humanized mice, indicating that resident microbiota govern levels of HIV target cells. Thus, resident microbiota promote the acquisition and pathogenesis of two clinically relevant human-specific pathogens.