Integration of Single-Cell Genomics Datasets.

Welch et al. and Stuart et al. present novel techniques for the integration of single-cell RNA-seq datasets across multiple platforms, individuals, and species. They both extend these strategies to map cell types between RNA-seq datasets with epigenetic properties and in situ transcript profiling. The ability to transfer information between datasets and spatial methods will enable more comprehensive profiling and comparisons of cell populations in complex biological systems.

High-Throughput Single-Cell Sequencing with Linear Amplification.

Conventional methods for single-cell genome sequencing are limited with respect to uniformity and throughput. Here, we describe sci-L3, a single-cell sequencing method that combines combinatorial indexing (sci-) and linear (L) amplification. The sci-L3 method adopts a 3-level (3) indexing scheme that minimizes amplification biases while enabling exponential gains in throughput. We demonstrate the generalizability of sci-L3 with proof-of-concept demonstrations of single-cell whole-genome sequencing (sci-L3-WGS), targeted sequencing (sci-L3-target-seq), and a co-assay of the genome and transcriptome (sci-L3-RNA/DNA). We apply sci-L3-WGS to profile the genomes of >10,000 sperm and sperm precursors from F1 hybrid mice, mapping 86,786 crossovers and characterizing rare chromosome mis-segregation events in meiosis, including instances of whole-genome equational chromosome segregation. We anticipate that sci-L3 assays can be applied to fully characterize recombination landscapes, to couple CRISPR perturbations and measurements of genome stability, and to other goals requiring high-throughput, high-coverage single-cell sequencing.

Atlas-scale single-cell chromatin accessibility using nanowell-based combinatorial indexing.

Here we present advancements in single-cell combinatorial indexed Assay for Transposase Accessible Chromatin (sciATAC) to measure chromatin accessibility that leverage nanowell chips to achieve atlas-scale cell throughput (>105 cells) at low cost. The platform leverages the core of the sciATAC workflow where multiple indexed tagmentation reactions are performed, followed by pooling and distribution to a second set of reaction wells for polymerase chain reaction (PCR)-based indexing. In this work, we instead leverage a chip containing 5184 nanowells at the PCR stage of indexing, enabling a 52-fold improvement in scale and reduction in per-cell preparation costs. We detail three variants that balance cell throughput and depth of coverage, and apply these methods to banked mouse brain tissue, producing maps of cell types as well as neuronal subtypes that include integration with existing single-cell Assay for Transposase Accessible Chromatin (scATAC) and scRNA-seq data sets. Our optimized workflow achieves a high fraction of reads that fall within called peaks (>80%) and low cell doublet rates. The high cell coverage technique produces high unique reads per cell, while retaining high enrichment for open chromatin regions, enabling the assessment of >70,000 unique accessible loci on average for each cell profiled. When compared to current methods in the field, our technique provides similar or superior per-cell information with very low levels of cell-to-cell cross talk, and achieves this at a cost point much lower than existing assays.

Cicero Predicts cis-Regulatory DNA Interactions from Single-Cell Chromatin Accessibility Data.

Linking regulatory DNA elements to their target genes, which may be located hundreds of kilobases away, remains challenging. Here, we introduce Cicero, an algorithm that identifies co-accessible pairs of DNA elements using single-cell chromatin accessibility data and so connects regulatory elements to their putative target genes. We apply Cicero to investigate how dynamically accessible elements orchestrate gene regulation in differentiating myoblasts. Groups of Cicero-linked regulatory elements meet criteria of "chromatin hubs"-they are enriched for physical proximity, interact with a common set of transcription factors, and undergo coordinated changes in histone marks that are predictive of changes in gene expression. Pseudotemporal analysis revealed that most DNA elements remain in chromatin hubs throughout differentiation. A subset of elements bound by MYOD1 in myoblasts exhibit early opening in a PBX1- and MEIS1-dependent manner. Our strategy can be applied to dissect the architecture, sequence determinants, and mechanisms of cis-regulation on a genome-wide scale.

Tagmentation-based single-cell genomics.

It has been just over 10 years since the initial description of transposase-based methods to prepare high-throughput sequencing libraries, or "tagmentation," in which a hyperactive transposase is used to simultaneously fragment target DNA and append universal adapter sequences. Tagmentation effectively replaced a series of processing steps in traditional workflows with one single reaction. It is the simplicity, coupled with the high efficiency of tagmentation, that has made it a favored means of sequencing library construction and fueled a diverse range of adaptations to assay a variety of molecular properties. In recent years, this has been centered in the single-cell space with a catalog of tagmentation-based assays that have been developed, covering a substantial swath of the regulatory landscape. To date, there have been a number of excellent reviews on single-cell technologies structured around the molecular properties that can be profiled. This review is instead framed around the central components and properties of tagmentation and how they have enabled the development of innovative molecular tools to probe the regulatory landscape of single cells. Furthermore, the granular specifics on cell throughput or richness of data provided by the extensive list of individual technologies are not discussed. Such metrics are rapidly changing and highly sample specific and are better left to studies that directly compare technologies for assays against one another in a rigorously controlled framework. The hope for this review is that, in laying out the diversity of molecular techniques at each stage of these assay platforms, new ideas may arise for others to pursue that will further advance the field of single-cell genomics.

Shared and Distinct Functional Effects of Patient-Specific Tbr1 Mutations on Cortical Development.

T-Box Brain Transcription Factor 1 (TBR1) plays essential roles in brain development, mediating neuronal migration, fate specification, and axon tract formation. While heterozygous loss-of-function and missense TBR1 mutations are associated with neurodevelopmental conditions, the effects of these heterogeneous mutations on brain development have yet to be fully explored. We characterized multiple mouse lines carrying Tbr1 mutations differing by type and exonic location, including the previously generated Tbr1 exon 2-3 knock-out (KO) line, and we analyzed male and female mice at neonatal and adult stages. The frameshift patient mutation A136PfsX80 (A136fs) caused reduced TBR1 protein in cortex similar to Tbr1 KO, while the missense patient mutation K228E caused significant TBR1 upregulation. Analysis of cortical layer formation found similar defects between KO and A136fs homozygotes in their CUX1+ and CTIP2+ layer positions, while K228E homozygosity produced layering defects distinct from these mutants. Meanwhile, the examination of cortical apoptosis found extensive cell death in KO homozygotes but limited cell death in A136fs or K228E homozygotes. Despite their discordant cortical phenotypes, these Tbr1 mutations produced several congruent phenotypes, including anterior commissure reduction in heterozygotes, which was previously observed in humans with TBR1 mutations. These results indicate that patient-specific Tbr1 mutant mice will be valuable translational models for pinpointing shared and distinct etiologies among patients with TBR1-related developmental conditions.SIGNIFICANCE STATEMENT Mutations of the TBR1 gene increase the likelihood of neurodevelopmental conditions such as intellectual disability and autism. Therefore, the study of TBR1 can offer insights into the biological mechanisms underlying these conditions, which affect millions worldwide. To improve the modeling of TBR1-related conditions over current Tbr1 knock-out mice, we created mouse lines carrying Tbr1 mutations identical to those found in human patients. Mice with one mutant Tbr1 copy show reduced amygdalar connections regardless of mutation type, suggesting a core biomarker for TBR1-related disorders. In mice with two mutant Tbr1 copies, brain phenotypes diverge by mutation type, suggesting differences in Tbr1 gene functionality in different patients. These mouse models will serve as valuable tools for understanding genotype-phenotype relationships among patients with neurodevelopmental conditions.

The accessible chromatin landscape of the murine hippocampus at single-cell resolution.

Here we present a comprehensive map of the accessible chromatin landscape of the mouse hippocampus at single-cell resolution. Substantial advances of this work include the optimization of a single-cell combinatorial indexing assay for transposase accessible chromatin (sci-ATAC-seq); a software suite, scitools, for the rapid processing and visualization of single-cell combinatorial indexing data sets; and a valuable resource of hippocampal regulatory networks at single-cell resolution. We used sci-ATAC-seq to produce 2346 high-quality single-cell chromatin accessibility maps with a mean unique read count per cell of 29,201 from both fresh and frozen hippocampi, observing little difference in accessibility patterns between the preparations. By using this data set, we identified eight distinct major clusters of cells representing both neuronal and nonneuronal cell types and characterized the driving regulatory factors and differentially accessible loci that define each cluster. Within pyramidal neurons, we identified four major clusters, including CA1 and CA3 neurons, and three additional subclusters. We then applied a recently described coaccessibility framework, Cicero, which identified 146,818 links between promoters and putative distal regulatory DNA. Identified coaccessibility networks showed cell-type specificity, shedding light on key dynamic loci that reconfigure to specify hippocampal cell lineages. Lastly, we performed an additional sci-ATAC-seq preparation from cultured hippocampal neurons (899 high-quality cells, 43,532 mean unique reads) that revealed substantial alterations in their epigenetic landscape compared with nuclei from hippocampal tissue. This data set and accompanying analysis tools provide a new resource that can guide subsequent studies of the hippocampus.

Age-dependent transcriptional alterations in cardiac endothelial cells.

Aging is a significant risk factor for cardiovascular disease. Despite the fact that endothelial cells play critical roles in cardiovascular function and disease, the molecular impact of aging on this cell population in many organ systems remains unknown. In this study, we sought to determine age-associated transcriptional alterations in cardiac endothelial cells. Highly enriched populations of endothelial cells (ECs) isolated from the heart, brain, and kidney of young (3 mo) and aged (24 mo) C57/BL6 mice were profiled for RNA expression via bulk RNA sequencing. Approximately 700 cardiac endothelial transcripts significantly differ by age. Gene set enrichment analysis indicated similar patterns for cellular pathway perturbations. Receptor-ligand comparisons indicated parallel alterations in age-affected circulating factors and cardiac endothelial-expressed receptors. Gene and pathway enrichment analyses show that age-related transcriptional response of cardiac endothelial cells is distinct from that of endothelial cells derived from the brain or kidney vascular bed. Furthermore, single-cell analysis identified nine distinct EC subtypes and shows that the Apelin Receptor-enriched subtype is reduced with age in mouse heart. Finally, we identify age-dysregulated genes in specific aged cardiac endothelial subtypes.

Single-cell DNA methylation analysis tool Amethyst reveals distinct noncanonical methylation patterns in human glial cells.

Single-cell sequencing technologies have revolutionized biomedical research by enabling deconvolution of cell type-specific properties in highly heterogeneous tissue. While robust tools have been developed to handle bioinformatic challenges posed by single-cell RNA and ATAC data, options for emergent modalities such as methylation are much more limited, impeding the utility of results. Here we present Amethyst, a comprehensive R package for atlas-scale single-cell methylation sequencing data analysis. Amethyst begins with base-level methylation calls and expedites batch integration, doublet detection, dimensionality reduction, clustering, cell type annotation, differentially methylated region calling, and interpretation of results, facilitating rapid data interaction in a local environment. We introduce the workflow using published single-cell methylation human peripheral blood mononuclear cell (PBMC) and human cortex data. We further leverage Amethyst on an atlas-scale brain dataset to describe a noncanonical methylation pattern in human astrocytes and oligodendrocytes, challenging the notion that this form of methylation is principally relevant to neurons in the brain. Tools such as Amethyst will increase accessibility to single-cell methylation data analysis, catalyzing research progress across diverse contexts.

txci-ATAC-seq: a massive-scale single-cell technique to profile chromatin accessibility.

We develop a large-scale single-cell ATAC-seq method by combining Tn5-based pre-indexing with 10× Genomics barcoding, enabling the indexing of up to 200,000 nuclei across multiple samples in a single reaction. We profile 449,953 nuclei across diverse tissues, including the human cortex, mouse brain, human lung, mouse lung, mouse liver, and lung tissue from a club cell secretory protein knockout (CC16-/-) model. Our study of CC16-/- nuclei uncovers previously underappreciated technical artifacts derived from remnant 129 mouse strain genetic material, which cause profound cell-type-specific changes in regulatory elements near many genes, thereby confounding the interpretation of this commonly referenced mouse model.

sciMET-cap: high-throughput single-cell methylation analysis with a reduced sequencing burden.

DNA methylation is a key component of the mammalian epigenome, playing a regulatory role in development, disease, and other processes. Robust, high-throughput single-cell DNA methylation assays are now possible (sciMET); however, the genome-wide nature of DNA methylation results in a high sequencing burden per cell. Here, we leverage target enrichment with sciMET to capture sufficient information per cell for cell type assignment using substantially fewer sequence reads (sciMET-cap). Accumulated off-target coverage enables genome-wide differentially methylated region (DMR) calling for clusters with as few as 115 cells. We characterize sciMET-cap on human PBMCs and brain (middle frontal gyrus).

Multiplex imaging of localized prostate tumors reveals altered spatial organization of AR-positive cells in the microenvironment.

Mapping the spatial interactions of cancer, immune, and stromal cell states presents novel opportunities for patient stratification and for advancing immunotherapy. While single-cell studies revealed significant molecular heterogeneity in prostate cancer cells, the impact of spatial stromal cell heterogeneity remains poorly understood. Here, we used cyclic immunofluorescent imaging on whole-tissue sections to uncover novel spatial associations between cancer and stromal cells in low- and high-grade prostate tumors and tumor-adjacent normal tissues. Our results provide a spatial map of single cells and recurrent cellular neighborhoods in the prostate tumor microenvironment of treatment-naive patients. We report unique populations of mast cells that show distinct spatial associations with M2 macrophages and regulatory T cells. Our results show disease-specific neighborhoods that are primarily driven by androgen receptor-positive (AR+) stromal cells and identify inflammatory gene networks active in AR+ prostate stroma.

Meet the author: Andrew Adey.

In this Q&A, Scientific Editor Emily Marcinkevicius talks with author Andrew Adey about developing a more broadly accessible method for paired whole-genome and chromatin accessibility sequencing from single cells, as well as the current and future landscape of genome-scale molecular profiling.

sciMET-cap: High-throughput single-cell methylation analysis with a reduced sequencing burden.

DNA methylation is a key component of the mammalian epigenome, playing a regulatory role in development, disease, and other processes. Robust, high-throughput single-cell DNA methylation assays are now possible (sciMET); however, the genome-wide nature of DNA methylation results in a high sequencing burden per cell. Here, we leverage target enrichment with sciMET to capture sufficient information per cell for cell type assignment using substantially fewer sequence reads (sciMET-cap). Sufficient off-target coverage further enables the production of near-complete methylomes for individual cell types. We characterize sciMET-cap on human PBMCs and brain (middle frontal gyrus).

Accessible high-throughput single-cell whole-genome sequencing with paired chromatin accessibility.

Single-cell whole-genome sequencing (scWGS) enables the assessment of genome-level molecular differences between individual cells with particular relevance to genetically diverse systems like solid tumors. The application of scWGS was limited due to a dearth of accessible platforms capable of producing high-throughput profiles. We present a technique that leverages nucleosome disruption methodologies with the widely adopted 10× Genomics ATAC-seq workflow to produce scWGS profiles for high-throughput copy-number analysis without new equipment or custom reagents. We further demonstrate the use of commercially available indexed transposase complexes from ScaleBio for sample multiplexing, reducing the per-sample preparation costs. Finally, we demonstrate that sequential indexed tagmentation with an intervening nucleosome disruption step allows for the generation of both ATAC and WGS data from the same cell, producing comparable data to the unimodal assays. By exclusively utilizing accessible commercial reagents, we anticipate that these scWGS and scWGS+ATAC methods can be broadly adopted by the research community.

BET inhibitors rescue anti-PD1 resistance by enhancing TCF7 accessibility in leukemia-derived terminally exhausted CD8+ T cells.

Many acute myeloid leukemia (AML) patients exhibit hallmarks of immune exhaustion, such as increased myeloid-derived suppressor cells, suppressive regulatory T cells and dysfunctional T cells. Similarly, we have identified the same immune-related features, including exhausted CD8+ T cells (TEx) in a mouse model of AML. Here we show that inhibitors that target bromodomain and extra-terminal domain (BET) proteins affect tumor-intrinsic factors but also rescue T cell exhaustion and ICB resistance. Ex vivo treatment of cells from AML mice and AML patients with BET inhibitors (BETi) reversed CD8+ T cell exhaustion by restoring proliferative capacity and expansion of the more functional precursor-exhausted T cells. This reversal was enhanced by combined BETi and anti-PD1 treatment. BETi synergized with anti-PD1 in vivo, resulting in the reduction of circulating leukemia cells, enrichment of CD8+ T cells in the bone marrow, and increase in expression of Tcf7, Slamf6, and Cxcr5 in CD8+ T cells. Finally, we profiled the epigenomes of in vivo JQ1-treated AML-derived CD8+ T cells by single-cell ATAC-seq and found that JQ1 increases Tcf7 accessibility specifically in Tex cells, suggesting that BETi likely acts mechanistically by relieving repression of progenitor programs in Tex CD8+ T cells and maintaining a pool of anti-PD1 responsive CD8+ T cells.

Supervised learning of high-confidence phenotypic subpopulations from single-cell data.

Accurately identifying phenotype-relevant cell subsets from heterogeneous cell populations is crucial for delineating the underlying mechanisms driving biological or clinical phenotypes. Here, by deploying a learning with rejection strategy, we developed a novel supervised learning framework called PENCIL to identify subpopulations associated with categorical or continuous phenotypes from single-cell data. By embedding a feature selection function into this flexible framework, for the first time, we were able to select informative features and identify cell subpopulations simultaneously, which enables the accurate identification of phenotypic subpopulations otherwise missed by methods incapable of concurrent gene selection. Furthermore, the regression mode of PENCIL presents a novel ability for supervised phenotypic trajectory learning of subpopulations from single-cell data. We conducted comprehensive simulations to evaluate PENCILs versatility in simultaneous gene selection, subpopulation identification and phenotypic trajectory prediction. PENCIL is fast and scalable to analyze 1 million cells within 1 hour. Using the classification mode, PENCIL detected T-cell subpopulations associated with melanoma immunotherapy outcomes. Moreover, when applied to scRNA-seq of a mantle cell lymphoma patient with drug treatment across multiple time points, the regression mode of PENCIL revealed a transcriptional treatment response trajectory. Collectively, our work introduces a scalable and flexible infrastructure to accurately identify phenotype-associated subpopulations from single-cell data.

Systematic benchmarking of single-cell ATAC-sequencing protocols.

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.