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.

The TLK-ASF1 histone chaperone pathway plays a critical role in IL-1b-mediated AML progression.

Identifying and targeting microenvironment-driven pathways that are active across acute myeloid leukemia (AML) genetic subtypes should allow the development of more broadly effective therapies. The pro-inflammatory cytokine IL-1 is abundant in the AML microenvironment and promotes leukemic growth. Through RNA-sequencing analysis, we identify that IL-1 upregulated ASF1B (anti-silencing function-1B), a histone chaperone, in AML progenitors compared to healthy progenitors. ASF1B, along with its paralogous protein ASF1A recruits H3-H4 histones onto the replication fork during S-phase, a process regulated by tousled-like kinase 1 and 2 (TLKs). While ASF1s and TLKs are known to be overexpressed in multiple solid tumors and associated with poor prognosis, their functional roles in hematopoiesis and inflammation-driven leukemia remain unexplored. In this study, we identify that ASF1s and TLKs are over-expressed in multiple genetic subtypes of AML. We demonstrate that depletion of ASF1s significantly reduces leukemic cell growth in both in vitro and in vivo models using human cells. Using a murine model we show that overexpression of ASF1B accelerates leukemia progression. Moreover, Asf1b or Tlk2 deletion delayed leukemia progression while these proteins are dispensable for normal hematopoiesis. Through proteomics and phosphoproteomics analyses, we uncover that the TLK-ASF1 pathway promotes leukemogenesis by impacting the cell cycle and DNA damage pathways. Collectively, our findings identify the TLK1-ASF1 pathway as a novel mediator of inflammatory signaling and a promising therapeutic target for AML treatment across diverse genetic subtypes. Selective inhibition of this pathway offers potential opportunities to intervene effectively, address intratumoral heterogeneity, and ultimately improve clinical outcomes in AML.

Novel recruitment approaches and operational results for a statewide population Cohort for cancer research: The Healthy Oregon Project.

Background: Cancer health research relies on large-scale cohorts to derive generalizable results for different populations. While traditional epidemiological cohorts often use costly random sampling or self-motivated, preselected groups, a shift toward health system-based cohorts has emerged. However, such cohorts depend on participants remaining within a single system. Recent consumer engagement models using smartphone-based communication, driving projects, and social media have begun to upend these paradigms. Methods: We initiated the Healthy Oregon Project (HOP) to support basic and clinical cancer research. HOP study employs a novel, cost-effective remote recruitment approach to effectively establish a large-scale cohort for population-based studies. The recruitment leverages the unique email account, the HOP website, and social media platforms to direct smartphone users to the study app, which facilitates saliva sample collection and survey administration. Monthly newsletters further facilitate engagement and outreach to broader communities. Results: By the end of 2022, the HOP has enrolled approximately 35,000 participants aged 18-100 years (median = 44.2 years), comprising more than 1% of the Oregon adult population. Among those who have app access, ∼87% provided consent to genetic screening. The HOP monthly email newsletters have an average open rate of 38%. Efforts continue to be made to improve survey response rates. Conclusion: This study underscores the efficacy of remote recruitment approaches in establishing large-scale cohorts for population-based cancer studies. The implementation of the study facilitates the collection of extensive survey and biological data into a repository that can be broadly shared and supports collaborative clinical and translational research.

Identification of Cellular Interactions in the Tumor Immune Microenvironment Underlying CD8 T Cell Exhaustion.

While immune checkpoint inhibitors show success in treating a subset of patients with certain late-stage cancers, these treatments fail in many other patients as a result of mechanisms that have yet to be fully characterized. The process of CD8 T cell exhaustion, by which T cells become dysfunctional in response to prolonged antigen exposure, has been implicated in immunotherapy resistance. Single-cell RNA sequencing (scRNA-seq) produces an abundance of data to analyze this process; however, due to the complexity of the process, contributions of other cell types to a process within a single cell type cannot be simply inferred. We constructed an analysis framework to first rank human skin tumor samples by degree of exhaustion in tumor-infiltrating CD8 T cells and then identify immune cell type-specific gene-regulatory network patterns significantly associated with T cell exhaustion. Using this framework, we further analyzed scRNA-seq data from human tumor and chronic viral infection samples to compare the T cell exhaustion process between these two contexts. In doing so, we identified transcription factor activity in the macrophages of both tissue types associated with this process. Our framework can be applied beyond the tumor immune microenvironment to any system involving cell-cell communication, facilitating insights into key biological processes that underpin the effective treatment of cancer and other complicated diseases.

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.

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.

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.

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).

Population screening shows risk of inherited cancer and familial hypercholesterolemia in Oregon.

The Healthy Oregon Project (HOP) is a statewide effort that aims to build a large research repository and influence the health of Oregonians through providing no-cost genetic screening to participants for a next-generation sequencing 32-gene panel comprising genes related to inherited cancers and familial hypercholesterolemia. This type of unbiased population screening can detect at-risk individuals who may otherwise be missed by conventional medical approaches. However, challenges exist for this type of high-throughput testing in an academic setting, including developing a low-cost high-efficiency test and scaling up the clinical laboratory for processing large numbers of samples. Modifications to our academic clinical laboratory including efficient test design, robotics, and a streamlined analysis approach increased our ability to test more than 1,000 samples per month for HOP using only one dedicated HOP laboratory technologist. Additionally, enrollment using a HIPAA-compliant smartphone app and sample collection using mouthwash increased efficiency and reduced cost. Here, we present our experience three years into HOP and discuss the lessons learned, including our successes, challenges, opportunities, and future directions, as well as the genetic screening results for the first 13,670 participants tested. Overall, we have identified 730 pathogenic/likely pathogenic variants in 710 participants in 24 of the 32 genes on the panel. The carrier rate for pathogenic/likely pathogenic variants in the inherited cancer genes on the panel for an unselected population was 5.0% and for familial hypercholesterolemia was 0.3%. Our laboratory experience described here may provide a useful model for population screening projects in other states.

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.

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.

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.

High-throughput robust single-cell DNA methylation profiling with sciMETv2.

DNA methylation is a key epigenetic property that drives gene regulatory programs in development and disease. Current single-cell methods that produce high quality methylomes are expensive and low throughput without the aid of extensive automation. We previously described a proof-of-principle technique that enabled high cell throughput; however, it produced only low-coverage profiles and was a difficult protocol that required custom sequencing primers and recipes and frequently produced libraries with excessive adapter contamination. Here, we describe a greatly improved version that generates high-coverage profiles (~15-fold increase) using a robust protocol that does not require custom sequencing capabilities, includes multiple stopping points, and exhibits minimal adapter contamination. We demonstrate two versions of sciMETv2 on primary human cortex, a high coverage and rapid version, identifying distinct cell types using CH methylation patterns. These datasets are able to be directly integrated with one another as well as with existing snmC-seq2 datasets with little discernible bias. Finally, we demonstrate the ability to determine cell types using CG methylation alone, which is the dominant context for DNA methylation in most cell types other than neurons and the most applicable analysis outside of brain tissue.

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.

Cas12a-Capture: A Novel, Low-Cost, and Scalable Method for Targeted Sequencing.

Targeted sequencing remains a valuable technique for clinical and research applications. However, many existing technologies suffer from pervasive guanine-cytosine (GC) sequence content bias, high input DNA requirements, and high cost for custom panels. We have developed Cas12a-Capture, a low-cost and highly scalable method for targeted sequencing. The method utilizes preprogrammed guide RNAs to direct CRISPR-Cas12a cleavage of double-stranded DNA in vitro and then takes advantage of the resulting four to five nucleotide overhangs for selective ligation with a custom sequencing adapter. Addition of a second sequencing adapter and enrichment for ligation products generates a targeted sequence library. We first performed a pilot experiment with 7176 guides targeting 3.5 Mb of DNA. Using these data, we modeled the sequence determinants of Cas12a-Capture efficiency, then designed an optimized set of 11,438 guides targeting 3.0 Mb. The optimized guide set achieves an average 64-fold enrichment of targeted regions with minimal GC bias. Cas12a-Capture variant calls had strong concordance with Illumina Platinum Genome calls, especially for single nucleotide variants, which could be improved by applying basic variant quality heuristics. We believe Cas12a-Capture has a wide variety of potential clinical and research applications and is amendable for selective enrichment for any double-stranded DNA template or genome.

Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data.

Single-cell RNA sequencing (scRNA-seq) distinguishes cell types, states and lineages within the context of heterogeneous tissues. However, current single-cell data cannot directly link cell clusters with specific phenotypes. Here we present Scissor, a method that identifies cell subpopulations from single-cell data that are associated with a given phenotype. Scissor integrates phenotype-associated bulk expression data and single-cell data by first quantifying the similarity between each single cell and each bulk sample. It then optimizes a regression model on the correlation matrix with the sample phenotype to identify relevant subpopulations. Applied to a lung cancer scRNA-seq dataset, Scissor identified subsets of cells associated with worse survival and with TP53 mutations. In melanoma, Scissor discerned a T cell subpopulation with low PDCD1/CTLA4 and high TCF7 expression associated with an immunotherapy response. Beyond cancer, Scissor was effective in interpreting facioscapulohumeral muscular dystrophy and Alzheimer's disease datasets. Scissor identifies biologically and clinically relevant cell subpopulations from single-cell assays by leveraging phenotype and bulk-omics datasets.

Epigenetic loss of heterogeneity from low to high grade localized prostate tumours.

Identifying precise molecular subtypes attributable to specific stages of localized prostate cancer has proven difficult due to high levels of heterogeneity. Bulk assays represent a population-average, which mask the heterogeneity that exists at the single-cell level. In this work, we sequence the accessible chromatin regions of 14,424 single-cells from 18 flash-frozen prostate tumours. We observe shared chromatin features among low-grade prostate cancer cells are lost in high-grade tumours. Despite this loss, high-grade tumours exhibit an enrichment for FOXA1, HOXB13 and CDX2 transcription factor binding sites, indicating a shared trans-regulatory programme. We identify two unique genes encoding neuronal adhesion molecules that are highly accessible in high-grade prostate tumours. We show NRXN1 and NLGN1 expression in epithelial, endothelial, immune and neuronal cells in prostate cancer using cyclic immunofluorescence. Our results provide a deeper understanding of the active gene regulatory networks in primary prostate tumours, critical for molecular stratification of the disease.

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.

Neutralization of SARS-CoV-2 variants by convalescent and BNT162b2 vaccinated serum.

SARS-CoV-2 and its variants continue to infect hundreds of thousands every day despite the rollout of effective vaccines. Therefore, it is essential to understand the levels of protection that these vaccines provide in the face of emerging variants. Here, we report two demographically balanced cohorts of BNT162b2 vaccine recipients and COVID-19 patients, from which we evaluate neutralizing antibody titers against SARS-CoV-2 as well as the B.1.1.7 (alpha) and B.1.351 (beta) variants. We show that both B.1.1.7 and B.1.351 are less well neutralized by serum from vaccinated individuals, and that B.1.351, but not B.1.1.7, is less well neutralized by convalescent serum. We also find that the levels of variant-specific anti-spike antibodies are proportional to neutralizing activities. Together, our results demonstrate the escape of the emerging SARS-CoV-2 variants from neutralization by serum antibodies, which may lead to reduced protection from re-infection or increased risk of vaccine breakthrough.

High-content single-cell combinatorial indexing.

Single-cell combinatorial indexing (sci) with transposase-based library construction increases the throughput of single-cell genomics assays but produces sparse coverage in terms of usable reads per cell. We develop symmetrical strand sci ('s3'), a uracil-based adapter switching approach that improves the rate of conversion of source DNA into viable sequencing library fragments following tagmentation. We apply this chemistry to assay chromatin accessibility (s3-assay for transposase-accessible chromatin, s3-ATAC) in human cortical and mouse whole-brain tissues, with mouse datasets demonstrating a six- to 13-fold improvement in usable reads per cell compared with other available methods. Application of s3 to single-cell whole-genome sequencing (s3-WGS) and to whole-genome plus chromatin conformation (s3-GCC) yields 148- and 14.8-fold improvements, respectively, in usable reads per cell compared with sci-DNA-sequencing and sci-HiC. We show that s3-WGS and s3-GCC resolve subclonal genomic alterations in patient-derived pancreatic cancer cell lines. We expect that the s3 platform will be compatible with other transposase-based techniques, including sci-MET or CUT&Tag.