Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing

Biophysical model for joint analysis of chromatin and RNA sequencing data

The assay for transposase-accessible chromatin using sequencing (ATAC-seq) can be used to identify open chromatin regions, providing complementary information to RNA-seq which measures gene expression by sequencing. Single-cell multiome methods offer the possibility of measuring both modalities simultaneously in cells, raising the question of how to analyze them jointly, and also the extent to which the information they provide is better than unregistered data, where single-cell ATAC-seq and single-cell RNA-seq are performed on the same sample, but on different cells. We propose and motivate a biophysical model for chromatin dynamics and subsequent transcription that can be used to parametrize multiome data, and use it to assess the benefits of multiome data over unregistered single-cell RNA-seq and single-cell ATAC-seq. We also show that our model provides a biophysically grounded approach to the integration of chromatin accessibility data with other modalitie, and apply the model to single-cell ATAC-seq data.

Long-range transcription factor binding sites clustered regions may mediate transcriptional regulation through phase-separation interactions in early human embryo

In mammals, during the post-fertilization pre-implantation phase, the expression of cell type-specific genes is crucial for normal embryonic development, which is regulated by cis-regulatory elements (CREs). TFs control gene expression by interacting with CREs. Research shows that transcription factor binding sites (TFBSs) reflect the general characteristics of the regulatory genome. Here, we identified TFBSs from chromatin accessibility data in five stages of early human embryonic development, and quantified transcription factor binding sites-clustered regions (TFCRs) and their complexity (TC). Assigning TC values to TFCRs has made it possible to assess the functionality of these regulatory elements in a more quantitative way. Our findings reveal a robust correlation between TFCR complexity and gene expression starting from the 8Cell stage, which is when the zygotic genome is activated in humans. Furthermore, we have defined long-range TFCRs (LR-TFCRs) and conjecture that LR-TFCRs may regulate gene expression through phase-separation mechanisms during the early stages of human embryonic development.

scBlood: A comprehensive single-cell accessible chromatin database of blood cells

The advent of single cell transposase-accessible chromatin sequencing (scATAC-seq) technology enables us to explore the genomic characteristics and chromatin accessibility of blood cells at the single-cell level. To fully make sense of the roles and regulatory complexities of blood cells, it is critical to collect and analyze these rapidly accumulating scATAC-seq datasets at a system level. Here, we present scBlood (https://bio.liclab.net/scBlood/), a comprehensive single-cell accessible chromatin database of blood cells. The current version of scBlood catalogs 770,907 blood cells and 452,247 non-blood cells from ∼400 high-quality scATAC-seq samples covering 30 tissues and 21 disease types. All data hosted on scBlood have undergone preprocessing from raw fastq files and multiple standards of quality control. Furthermore, we conducted comprehensive downstream analyses, including multi-sample integration analysis, cell clustering and annotation, differential chromatin accessibility analysis, functional enrichment analysis, co-accessibility analysis, gene activity score calculation, and transcription factor (TF) enrichment analysis. In summary, scBlood provides a user-friendly interface for searching, browsing, analyzing, visualizing, and downloading scATAC-seq data of interest. This platform facilitates insights into the functions and regulatory mechanisms of blood cells, as well as their involvement in blood-related diseases.

CosGeneGate selects multi-functional and credible biomarkers for single-cell analysis

MOTIVATION

Selecting representative genes or marker genes to distinguish cell types is an important task in single-cell sequencing analysis. Although many methods have been proposed to select marker genes, the genes selected may have redundancy and/or do not show cell-type-specific expression patterns to distinguish cell types.

RESULTS

Here, we present a novel model, named CosGeneGate, to select marker genes for more effective marker selections. CosGeneGate is inspired by combining the advantages of selecting marker genes based on both cell-type classification accuracy and marker gene specific expression patterns. We demonstrate the better performance of the marker genes selected by CosGeneGate for various downstream analyses than the existing methods with both public datasets and newly sequenced datasets. The non-redundant marker genes identified by CosGeneGate for major cell types and tissues in human can be found at the website as follows: https://github.com/VivLon/CosGeneGate/blob/main/marker gene list.xlsx.

Spatial Transcriptomics and Single-Nucleus Multi-Omics Analysis Revealing the Impact of High Maternal Folic Acid Supplementation on Offspring Brain Development

Background: Folate, an essential vitamin B9, is crucial for diverse biological processes, including neurogenesis. Folic acid (FA) supplementation during pregnancy is a standard practice for preventing neural tube defects (NTDs). However, concerns are growing over the potential risks of excessive maternal FA intake. Objectives/Methods: Here, we employed a mouse model and spatial transcriptomic and single-nucleus multi-omics approaches to investigate the impact of high maternal FA supplementation during the periconceptional period on offspring brain development. Results: Maternal high FA supplementation affected gene pathways linked to neurogenesis and neuronal axon myelination across multiple brain regions, as well as gene expression alterations related to learning and memory in thalamic and ventricular regions. Single-nucleus multi-omics analysis revealed that maturing excitatory neurons in the dentate gyrus (DG) are particularly vulnerable to high maternal FA intake, leading to aberrant gene expressions and chromatin accessibility in pathways governing ribosomal biogenesis critical for synaptic formation. Conclusions: Our findings provide new insights into specific brain regions, cell types, gene expressions and pathways that can be affected by maternal high FA supplementation.

CRISPR Screening of Transcribed Super-Enhancers Identifies Drivers of Triple-Negative Breast Cancer Progression

Triple-negative breast cancer (TNBC) is the most therapeutically recalcitrant form of breast cancer, which is due in part to the paucity of targeted therapies. A systematic analysis of regulatory elements that extend beyond protein-coding genes could uncover avenues for therapeutic intervention. To this end, we analyzed the regulatory mechanisms of TNBC-specific transcriptional enhancers together with their noncoding enhancer RNA (eRNA) transcripts. The functions of the top 30 eRNA-producing super-enhancers were systematically probed using high-throughput CRISPR-interference assays coupled to RNA sequencing that enabled unbiased detection of target genes genome-wide. Generation of high-resolution Hi-C chromatin interaction maps enabled annotation of the direct target genes for each super-enhancer, which highlighted their proclivity for genes that portend worse clinical outcomes in patients with TNBC. Illustrating the utility of this dataset, deletion of an identified super-enhancer controlling the nearby PODXL gene or specific degradation of its eRNAs led to profound inhibitory effects on target gene expression, cell proliferation, and migration. Furthermore, loss of this super-enhancer suppressed tumor growth and metastasis in TNBC mouse xenograft models. Single-cell RNA sequencing and assay for transposase-accessible chromatin with high-throughput sequencing analyses demonstrated the enhanced activity of this super-enhancer within the malignant cells of TNBC tumor specimens compared with nonmalignant cell types. Collectively, this work examines several fundamental questions about how regulatory information encoded into eRNA-producing super-enhancers drives gene expression networks that underlie the biology of TNBC. Significance: Integrative analysis of eRNA-producing super-enhancers defines molecular mechanisms controlling global patterns of gene expression that regulate clinical outcomes in breast cancer, highlighting the potential of enhancers as biomarkers and therapeutic targets.

Overloading And unpacKing (OAK) - droplet-based combinatorial indexing for ultra-high throughput single-cell multiomic profiling

Multiomic profiling of single cells by sequencing is a powerful technique for investigating cellular diversity. Existing droplet-based microfluidic methods produce many cell-free droplets, underutilizing bead barcodes and reagents. Combinatorial indexing on microplates is more efficient for barcoding but labor-intensive. Here we present Overloading And unpacKing (OAK), which uses a droplet-based barcoding system for initial compartmentalization followed by a second aliquoting round to achieve combinatorial indexing. We demonstrate OAK's versatility with single-cell RNA sequencing as well as paired single-nucleus RNA sequencing and accessible chromatin profiling. We further showcase OAK's performance on complex samples, including differentiated bronchial epithelial cells and primary retinal tissue. Finally, we examine transcriptomic responses of over 400,000 melanoma cells to a RAF inhibitor, belvarafenib, discovering a rare resistant cell population (0.12%). OAK's ultra-high throughput, broad compatibility, high sensitivity, and simplified procedures make it a powerful tool for large-scale molecular analysis, even for rare cells.

Best practices for differential accessibility analysis in single-cell epigenomics

Differential accessibility (DA) analysis of single-cell epigenomics data enables the discovery of regulatory programs that establish cell type identity and steer responses to physiological and pathophysiological perturbations. While many statistical methods to identify DA regions have been developed, the principles that determine the performance of these methods remain unclear. As a result, there is no consensus on the most appropriate statistical methods for DA analysis of single-cell epigenomics data. Here, we present a systematic evaluation of statistical methods that have been applied to identify DA regions in single-cell ATAC-seq (scATAC-seq) data. We leverage a compendium of scATAC-seq experiments with matching bulk ATAC-seq or scRNA-seq in order to assess the accuracy, bias, robustness, and scalability of each statistical method. The structure of our experiments also provides the opportunity to define best practices for the analysis of scATAC-seq data beyond DA itself. We leverage this understanding to develop an R package implementing these best practices.

Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data

Assay for Transposase-Accessible Chromatin sequencing (ATAC-Seq) is a widely used technique to explore gene regulatory mechanisms. For most ATAC-Seq data from healthy and diseased tissues such as tumors, chromatin accessibility measurement represents a mixed signal from multiple cell types. In this work, we derive reliable chromatin accessibility marker peaks and reference profiles for most non-malignant cell types frequently observed in the microenvironment of human tumors. We then integrate these data into the EPIC deconvolution framework (Racle et al., 2017) to quantify cell-type heterogeneity in bulk ATAC-Seq data. Our EPIC-ATAC tool accurately predicts non-malignant and malignant cell fractions in tumor samples. When applied to a human breast cancer cohort, EPIC-ATAC accurately infers the immune contexture of the main breast cancer subtypes.

scACT: Accurate Cross-modality Translation via Cycle-consistent Training from Unpaired Single-cell Data

Single-cell sequencing technologies have revolutionized genomics by enabling the simultaneous profiling of various molecular modalities within individual cells. Their integration, especially cross-modality translation, offers deep insights into cellular regulatory mechanisms. Many methods have been developed for cross-modality translation, but their reliance on scarce high-quality co-assay data limits their applicability. Addressing this, we introduce scACT, a deep generative model designed to extract cross-modality biological insights from unpaired single-cell data. scACT tackles three major challenges: aligning unpaired multi-modal data via adversarial training, facilitating cross-modality translation without prior knowledge via cycle-consistent training, and enabling interpretable regulatory interconnections explorations via in-silico perturbations. To test its performance, we applied scACT on diverse single-cell datasets and found it outperformed existing methods in all three tasks. Finally, we have developed scACT as an individual open-source software package to advance single-cell omics data processing and analysis within the research community.

Gene expression and chromatin conformation of microglia in virally suppressed people with HIV

The presence of HIV in sequestered reservoirs is a central impediment to a functional cure, allowing HIV to persist despite life-long antiretroviral therapy (ART), and driving a variety of comorbid conditions. Our understanding of the latent HIV reservoir in the central nervous system is incomplete, because of difficulties in accessing human central nervous system tissues. Microglia contribute to HIV reservoirs, but the molecular phenotype of HIV-infected microglia is poorly understood. We leveraged the unique "Last Gift" rapid autopsy program, in which people with HIV are closely followed until days or even hours before death. Microglial populations were heterogeneous regarding their gene expression profiles but showed similar chromatin accessibility landscapes. Despite ART, we detected occasional microglia containing cell-associated HIV RNA and HIV DNA integrated into open regions of the host's genome (∼0.005%). Microglia with detectable HIV RNA showed an inflammatory phenotype. These results demonstrate a distinct myeloid cell reservoir in the brains of people with HIV despite suppressive ART. Strategies for curing HIV and neurocognitive impairment will need to consider the myeloid compartment to be successful.

Single-cell sequencing to multi-omics: technologies and applications

Cells, as the fundamental units of life, contain multidimensional spatiotemporal information. Single-cell RNA sequencing (scRNA-seq) is revolutionizing biomedical science by analyzing cellular state and intercellular heterogeneity. Undoubtedly, single-cell transcriptomics has emerged as one of the most vibrant research fields today. With the optimization and innovation of single-cell sequencing technologies, the intricate multidimensional details concealed within cells are gradually unveiled. The combination of scRNA-seq and other multi-omics is at the forefront of the single-cell field. This involves simultaneously measuring various omics data within individual cells, expanding our understanding across a broader spectrum of dimensions. Single-cell multi-omics precisely captures the multidimensional aspects of single-cell transcriptomes, immune repertoire, spatial information, temporal information, epitopes, and other omics in diverse spatiotemporal contexts. In addition to depicting the cell atlas of normal or diseased tissues, it also provides a cornerstone for studying cell differentiation and development patterns, disease heterogeneity, drug resistance mechanisms, and treatment strategies. Herein, we review traditional single-cell sequencing technologies and outline the latest advancements in single-cell multi-omics. We summarize the current status and challenges of applying single-cell multi-omics technologies to biological research and clinical applications. Finally, we discuss the limitations and challenges of single-cell multi-omics and potential strategies to address them.

MultiSC: a deep learning pipeline for analyzing multiomics single-cell data

Single-cell technologies enable researchers to investigate cell functions at an individual cell level and study cellular processes with higher resolution. Several multi-omics single-cell sequencing techniques have been developed to explore various aspects of cellular behavior. Using NEAT-seq as an example, this method simultaneously obtains three kinds of omics data for each cell: gene expression, chromatin accessibility, and protein expression of transcription factors (TFs). Consequently, NEAT-seq offers a more comprehensive understanding of cellular activities in multiple modalities. However, there is a lack of tools available for effectively integrating the three types of omics data. To address this gap, we propose a novel pipeline called MultiSC for the analysis of MULTIomic Single-Cell data. Our pipeline leverages a multimodal constraint autoencoder (single-cell hierarchical constraint autoencoder) to integrate the multi-omics data during the clustering process and a matrix factorization-based model (scMF) to predict target genes regulated by a TF. Moreover, we utilize multivariate linear regression models to predict gene regulatory networks from the multi-omics data. Additional functionalities, including differential expression, mediation analysis, and causal inference, are also incorporated into the MultiSC pipeline. Extensive experiments were conducted to evaluate the performance of MultiSC. The results demonstrate that our pipeline enables researchers to gain a comprehensive view of cell activities and gene regulatory networks by fully leveraging the potential of multiomics single-cell data. By employing MultiSC, researchers can effectively integrate and analyze diverse omics data types, enhancing their understanding of cellular processes.

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.

Single cell genome and epigenome co-profiling reveals hardwiring and plasticity in breast cancer

Understanding the impact of genetic alterations on epigenomic phenotypes during breast cancer progression is challenging with unimodal measurements. Here, we report wellDA-seq, the first high-genomic resolution, high-throughput method that can simultaneously measure the whole genome and chromatin accessibility profiles of thousands of single cells. Using wellDA-seq, we profiled 22,123 single cells from 2 normal and 9 tumors breast tissues. By directly mapping the epigenomic phenotypes to genetic lineages across cancer subclones, we found evidence of both genetic hardwiring and epigenetic plasticity. In 6 estrogen-receptor positive breast cancers, we directly identified the ancestral cancer cells, and found that their epithelial cell-of-origin was Luminal Hormone Responsive cells. We also identified cell types with copy number aberrations (CNA) in normal breast tissues and discovered non-epithelial cell types in the microenvironment with CNAs in breast cancers. These data provide insights into the complex relationship between genetic alterations and epigenomic phenotypes during breast tumor evolution.

Distinct epigenomic landscapes underlie tissue-specific memory T cell differentiation

The memory CD8+ T cell pool contains phenotypically and transcriptionally heterogeneous subsets with specialized functions and recirculation patterns. Here, we examined the epigenetic landscape of CD8+ T cells isolated from seven non-lymphoid organs across four distinct infection models, alongside their circulating T cell counterparts. Using single-cell transposase-accessible chromatin sequencing (scATAC-seq), we found that tissue-resident memory T (TRM) cells and circulating memory T (TCIRC) cells develop along distinct epigenetic trajectories. We identified organ-specific transcriptional regulators of TRM cell development, including FOSB, FOS, FOSL1, and BACH2, and defined an epigenetic signature common to TRM cells across organs. Finally, we found that although terminal TEX cells share accessible regulatory elements with TRM cells, they are defined by TEX-specific epigenetic features absent from TRM cells. Together, this comprehensive data resource shows that TRM cell development is accompanied by dynamic transcriptome alterations and chromatin accessibility changes that direct tissue-adapted and functionally distinct T cell states.

DOGMA-seq and multimodal, single-cell analysis in acute myeloid leukemia

Acute myeloid leukemia (AML) is a complex cancer, yet advances in recent years from integrated genomics methods have helped improve diagnosis, treatment, and means of patient stratification. A recent example of a powerful, multimodal method is DOGMA-seq, which can measure chromatin accessibility, gene expression, and cell-surface protein levels from the same individual cell simultaneously. Previous bimodal single-cell techniques, such as CITE-seq (Cellular indexing of transcriptomes and epitopes), have only permitted the transcriptome and cell-surface protein expression measurement. DOGMA-seq, however, builds on this foundation and has implications for examining epigenomic, transcriptomic, and proteomic interactions between various cell types. This technique has the potential to be particularly useful in the study of cancers such as AML. This is because the cellular mechanisms that drive AML are rather heterogeneous and require a more complete understanding of the interplay between the genetic mutations, disruptions in RNA transcription and translation, and surface protein expression that cause these cancers to develop and evolve. This technique will hopefully contribute to a more clear and complete understanding of the growth and progression of complex cancers.

scConfluence: single-cell diagonal integration with regularized Inverse Optimal Transport on weakly connected features

The abundance of unpaired multimodal single-cell data has motivated a growing body of research into the development of diagonal integration methods. However, the state-of-the-art suffers from the loss of biological information due to feature conversion and struggles with modality-specific populations. To overcome these crucial limitations, we here introduce scConfluence, a method for single-cell diagonal integration. scConfluence combines uncoupled autoencoders on the complete set of features with regularized Inverse Optimal Transport on weakly connected features. We extensively benchmark scConfluence in several single-cell integration scenarios proving that it outperforms the state-of-the-art. We then demonstrate the biological relevance of scConfluence in three applications. We predict spatial patterns for Scgn, Synpr and Olah in scRNA-smFISH integration. We improve the classification of B cells and Monocytes in highly heterogeneous scRNA-scATAC-CyTOF integration. Finally, we reveal the joint contribution of Fezf2 and apical dendrite morphology in Intra Telencephalic neurons, based on morphological images and scRNA.

Revealing gene function with statistical inference at single-cell resolution

Single-cell and spatial molecular profiling assays have shown large gains in sensitivity, resolution and throughput. Applying these technologies to specimens from human and model organisms promises to comprehensively catalogue cell types, reveal their lineage origins in development and discern their contributions to disease pathogenesis. Moreover, rapidly dropping costs have made well-controlled perturbation experiments and cohort studies widely accessible, illuminating mechanisms that give rise to phenotypes at the scale of the cell, the tissue and the whole organism. Interpreting the coming flood of single-cell data, much of which will be spatially resolved, will place a tremendous burden on existing computational pipelines. However, statistical concepts, models, tools and algorithms can be repurposed to solve problems now arising in genetic and molecular biology studies of development and disease. Here, I review how the questions that recent technological innovations promise to answer can be addressed by the major classes of statistical tools.

Single-cell EpiChem jointly measures drug-chromatin binding and multimodal epigenome

Studies of molecular and cellular functions of small-molecule inhibitors in cancer treatment, eliciting effects by targeting genome and epigenome associated proteins, requires measurement of drug-target engagement in single-cell resolution. Here we present EpiChem for in situ single-cell joint mapping of small molecules and multimodal epigenomic landscape. We demonstrate single-cell co-assays of three small molecules together with histone modifications, chromatin accessibility or target proteins in human colorectal cancer (CRC) organoids. Integrated multimodal analysis reveals diverse drug interactions in the context of chromatin states within heterogeneous CRC organoids. We further reveal drug genomic binding dynamics and adaptive epigenome across cell types after small-molecule drug treatment in CRC organoids. This method provides a unique tool to exploit the mechanisms of cell type-specific drug actions.

Progress in multifactorial single-cell chromatin profiling methods

Chromatin states play a key role in shaping overall cellular states and fates. Building a complete picture of the functional state of chromatin in cells requires the co-detection of several distinct biochemical aspects. These span DNA methylation, chromatin accessibility, chromosomal conformation, histone posttranslational modifications, and more. While this certainly presents a challenging task, over the past few years many new and creative methods have been developed that now enable co-assay of these different aspects of chromatin at single cell resolution. This field is entering an exciting phase, where a confluence of technological improvements, decreased sequencing costs, and computational innovation are presenting new opportunities to dissect the diversity of chromatin states present in tissues, and how these states may influence gene regulation. In this review, I discuss the spectrum of current experimental approaches for multifactorial chromatin profiling, highlight some of the experimental and analytical challenges, as well as some areas for further innovation.

Benchmarking computational methods for single-cell chromatin data analysis

BACKGROUND

Single-cell chromatin accessibility assays, such as scATAC-seq, are increasingly employed in individual and joint multi-omic profiling of single cells. As the accumulation of scATAC-seq and multi-omics datasets continue, challenges in analyzing such sparse, noisy, and high-dimensional data become pressing. Specifically, one challenge relates to optimizing the processing of chromatin-level measurements and efficiently extracting information to discern cellular heterogeneity. This is of critical importance, since the identification of cell types is a fundamental step in current single-cell data analysis practices.

RESULTS

We benchmark 8 feature engineering pipelines derived from 5 recent methods to assess their ability to discover and discriminate cell types. By using 10 metrics calculated at the cell embedding, shared nearest neighbor graph, or partition levels, we evaluate the performance of each method at different data processing stages. This comprehensive approach allows us to thoroughly understand the strengths and weaknesses of each method and the influence of parameter selection.

CONCLUSIONS

Our analysis provides guidelines for choosing analysis methods for different datasets. Overall, feature aggregation, SnapATAC, and SnapATAC2 outperform latent semantic indexing-based methods. For datasets with complex cell-type structures, SnapATAC and SnapATAC2 are preferred. With large datasets, SnapATAC2 and ArchR are most scalable.

MOCHA's advanced statistical modeling of scATAC-seq data enables functional genomic inference in large human cohorts

Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) is being increasingly used to study gene regulation. However, major analytical gaps limit its utility in studying gene regulatory programs in complex diseases. In response, MOCHA (Model-based single cell Open CHromatin Analysis) presents major advances over existing analysis tools, including: 1) improving identification of sample-specific open chromatin, 2) statistical modeling of technical drop-out with zero-inflated methods, 3) mitigation of false positives in single cell analysis, 4) identification of alternative transcription-starting-site regulation, and 5) modules for inferring temporal gene regulatory networks from longitudinal data. These advances, in addition to open chromatin analyses, provide a robust framework after quality control and cell labeling to study gene regulatory programs in human disease. We benchmark MOCHA with four state-of-the-art tools to demonstrate its advances. We also construct cross-sectional and longitudinal gene regulatory networks, identifying potential mechanisms of COVID-19 response. MOCHA provides researchers with a robust analytical tool for functional genomic inference from scATAC-seq data.

Optics-free reconstruction of 2D images via DNA barcode proximity graphs

Spatial genomic technologies include imaging- and sequencing-based methods (1-3). An emerging subcategory of sequencing-based methods relies on a surface coated with coordinate-associated DNA barcodes, which are leveraged to tag endogenous nucleic acids or cells in an overlaid tissue section (4-7). However, the physical registration of DNA barcodes to spatial coordinates is challenging, necessitating either high density printing of coordinate-specific oligonucleotides or in situ sequencing/probing of randomly deposited, oligonucleotide-bearing beads. As a consequence, the surface areas available to sequencing-based spatial genomic methods are constrained by the time, labor, cost, and instrumentation required to either print, synthesize or decode a coordinate-tagged surface. To address this challenge, we developed SCOPE (Spatial reConstruction via Oligonucleotide Proximity Encoding), an optics-free, DNA microscopy (8) inspired method. With SCOPE, the relative positions of randomly deposited beads on a 2D surface are inferred from the ex situ sequencing of chimeric molecules formed from diffusing "sender" and tethered "receiver" oligonucleotides. As a first proof-of-concept, we apply SCOPE to reconstruct an asymmetric "swoosh" shape resembling the Nike logo (16.75 × 9.25 mm). Next, we use a microarray printer to encode a "color" version of the Snellen eye chart for visual acuity (17.18 × 40.97 mm), and apply SCOPE to achieve optics-free reconstruction of individual letters. Although these are early demonstrations of the concept and much work remains to be done, we envision that the optics-free, sequencing-based quantitation of the molecular proximities of DNA barcodes will enable spatial genomics in constant experimental time, across fields of view and at resolutions that are determined by sequencing depth, bead size, and diffusion kinetics, rather than the limitations of optical instruments or microarray printers.

Unlocking biological mechanisms with integrative functional genomics approaches

Reverse genetics offers precise functional insights into genes through the targeted manipulation of gene expression followed by phenotypic assessment. While these approaches have proven effective in model organisms such as Saccharomyces cerevisiae, large-scale genetic manipulations in human cells were historically unfeasible due to methodological limitations. However, recent advancements in functional genomics, particularly clustered regularly interspaced short palindromic repeats (CRISPR)-based screening technologies and next-generation sequencing platforms, have enabled pooled screening technologies that allow massively parallel, unbiased assessments of biological phenomena in human cells. This review provides a comprehensive overview of cutting-edge functional genomic screening technologies applicable to human cells, ranging from short hairpin RNA screens to modern CRISPR screens. Additionally, we explore the integration of CRISPR platforms with single-cell approaches to monitor gene expression, chromatin accessibility, epigenetic regulation, and chromatin architecture following genetic perturbations at the omics level. By offering an in-depth understanding of these genomic screening methods, this review aims to provide insights into more targeted and effective strategies for genomic research and personalized medicine.

GAGE-seq concurrently profiles multiscale 3D genome organization and gene expression in single cells

The organization of mammalian genomes features a complex, multiscale three-dimensional (3D) architecture, whose functional significance remains elusive because of limited single-cell technologies that can concurrently profile genome organization and transcriptional activities. Here, we introduce genome architecture and gene expression by sequencing (GAGE-seq), a scalable, robust single-cell co-assay measuring 3D genome structure and transcriptome simultaneously within the same cell. Applied to mouse brain cortex and human bone marrow CD34+ cells, GAGE-seq characterized the intricate relationships between 3D genome and gene expression, showing that multiscale 3D genome features inform cell-type-specific gene expression and link regulatory elements to target genes. Integration with spatial transcriptomic data revealed in situ 3D genome variations in mouse cortex. Observations in human hematopoiesis unveiled discordant changes between 3D genome organization and gene expression, underscoring a complex, temporal interplay at the single-cell level. GAGE-seq provides a powerful, cost-effective approach for exploring genome structure and gene expression relationships at the single-cell level across diverse biological contexts.

scCross: a deep generative model for unifying single-cell multi-omics with seamless integration, cross-modal generation, and in silico exploration

Single-cell multi-omics data reveal complex cellular states, providing significant insights into cellular dynamics and disease. Yet, integration of multi-omics data presents challenges. Some modalities have not reached the robustness or clarity of established transcriptomics. Coupled with data scarcity for less established modalities and integration intricacies, these challenges limit our ability to maximize single-cell omics benefits. We introduce scCross, a tool leveraging variational autoencoders, generative adversarial networks, and the mutual nearest neighbors (MNN) technique for modality alignment. By enabling single-cell cross-modal data generation, multi-omics data simulation, and in silico cellular perturbations, scCross enhances the utility of single-cell multi-omics studies.

A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data

Eukaryotic gene regulation is a combinatorial, dynamic, and quantitative process that plays a vital role in development and disease and can be modeled at a systems level in gene regulatory networks (GRNs). The wealth of multi-omics data measured on the same samples and even on the same cells has lifted the field of GRN inference to the next stage. Combinations of (single-cell) transcriptomics and chromatin accessibility allow the prediction of fine-grained regulatory programs that go beyond mere correlation of transcription factor and target gene expression, with enhancer GRNs (eGRNs) modeling molecular interactions between transcription factors, regulatory elements, and target genes. In this review, we highlight the key components for successful (e)GRN inference from (sc)RNA-seq and (sc)ATAC-seq data exemplified by state-of-the-art methods as well as open challenges and future developments. Moreover, we address preprocessing strategies, metacell generation and computational omics pairing, transcription factor binding site detection, and linear and three-dimensional approaches to identify chromatin interactions as well as dynamic and causal eGRN inference. We believe that the integration of transcriptomics together with epigenomics data at a single-cell level is the new standard for mechanistic network inference, and that it can be further advanced with integrating additional omics layers and spatiotemporal data, as well as with shifting the focus towards more quantitative and causal modeling strategies.

CloudATAC: a cloud-based framework for ATAC-Seq data analysis

Assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) generates genome-wide chromatin accessibility profiles, providing valuable insights into epigenetic gene regulation at both pooled-cell and single-cell population levels. Comprehensive analysis of ATAC-seq data involves the use of various interdependent programs. Learning the correct sequence of steps needed to process the data can represent a major hurdle. Selecting appropriate parameters at each stage, including pre-analysis, core analysis, and advanced downstream analysis, is important to ensure accurate analysis and interpretation of ATAC-seq data. Additionally, obtaining and working within a limited computational environment presents a significant challenge to non-bioinformatic researchers. Therefore, we present Cloud ATAC, an open-source, cloud-based interactive framework with a scalable, flexible, and streamlined analysis framework based on the best practices approach for pooled-cell and single-cell ATAC-seq data. These frameworks use on-demand computational power and memory, scalability, and a secure and compliant environment provided by the Google Cloud. Additionally, we leverage Jupyter Notebook's interactive computing platform that combines live code, tutorials, narrative text, flashcards, quizzes, and custom visualizations to enhance learning and analysis. Further, leveraging GPU instances has significantly improved the run-time of the single-cell framework. The source codes and data are publicly available through NIH Cloud lab https://github.com/NIGMS/ATAC-Seq-and-Single-Cell-ATAC-Seq-Analysis. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Spatial Transcriptomics and Single-Nucleus Multi-omics Analysis Revealing the Impact of High Maternal Folic Acid Supplementation on Offspring Brain Development

Folate, an essential vitamin B9, is crucial for diverse biological processes including neurogenesis. Folic acid (FA) supplementation during pregnancy is a standard practice for preventing neural tube defects (NTDs). However, concerns are growing over the potential risks of excessive maternal FA intake. Here, we employed mouse model and spatial transcriptomics and single-nucleus multi-omics approaches to investigate the impact of high maternal FA supplementation during the periconceptional period on offspring brain development. Maternal high FA supplementation affected gene pathways linked to neurogenesis and neuronal axon myelination across multiple brain regions, as well as gene expression alterations related to learning and memory in thalamic and ventricular regions. Single-nucleus multi-omics analysis revealed that maturing excitatory neurons in the dentate gyrus (DG) are particularly vulnerable to high maternal FA intake, leading to aberrant gene expressions and chromatin accessibility in pathways governing ribosomal biogenesis critical for synaptic formation. Our findings provide new insights into specific brain regions, cell types, gene expressions and pathways that can be affected by maternal high FA supplementation.

OpenTn5: Open-Source Resource for Robust and Scalable Tn5 Transposase Purification and Characterization

Tagmentation combines DNA fragmentation and sequencing adapter addition by leveraging the transposition activity of the bacterial cut-and-paste Tn5 transposase, to enable efficient sequencing library preparation. Here we present an open-source protocol for the generation of multi-purpose hyperactive Tn5 transposase, including its benchmarking in CUT&Tag, bulk and single-cell ATAC-seq. The OpenTn5 protocol yields multi-milligram quantities of pG-Tn5E54K, L372P protein per liter of E. coli culture, sufficient for thousands of tagmentation reactions and the enzyme retains activity in storage for more than a year.

A Retinoic Acid:YAP1 signaling axis controls atrial lineage commitment

Vitamin A/Retinoic Acid (Vit A/RA) signaling is essential for heart development. In cardiac progenitor cells (CPCs), RA signaling induces the expression of atrial lineage genes while repressing ventricular genes, thereby promoting the acquisition of an atrial cardiomyocyte cell fate. To achieve this, RA coordinates a complex regulatory network of downstream effectors that is not fully identified. To address this gap, we applied a functional genomics approach (i.e scRNAseq and snATACseq) to untreated and RA-treated human embryonic stem cells (hESCs)-derived CPCs. Unbiased analysis revealed that the Hippo effectors YAP1 and TEAD4 are integrated with the atrial transcription factor enhancer network, and that YAP1 is necessary for activation of RA-enhancers in CPCs. Furthermore, in vivo analysis of control and conditionally YAP1 KO mouse embryos (Sox2-cre) revealed that the expression of atrial lineage genes, such as NR2F2, is compromised by YAP1 deletion in the CPCs of the second heart field. Accordingly, we found that YAP1 is required for the formation of an atrial chamber but is dispensable for the formation of a ventricle, in hESC-derived patterned cardiac organoids. Overall, our findings revealed that YAP1 is a non-canonical effector of RA signaling essential for the acquisition of atrial lineages during cardiogenesis.

Data enhancement in the age of spatial biology

Unveiling the intricate interplay of cells in their native environment lies at the heart of understanding fundamental biological processes and unraveling disease mechanisms, particularly in complex diseases like cancer. Spatial transcriptomics (ST) offers a revolutionary lens into the spatial organization of gene expression within tissues, empowering researchers to study both cell heterogeneity and microenvironments in health and disease. However, current ST technologies often face limitations in either resolution or the number of genes profiled simultaneously. Integrating ST data with complementary sources, such as single-cell transcriptomics and detailed tissue staining images, presents a powerful solution to overcome these limitations. This review delves into the computational approaches driving the integration of spatial transcriptomics with other data types. By illuminating the key challenges and outlining the current algorithmic solutions, we aim to highlight the immense potential of these methods to revolutionize our understanding of cancer biology.

Panpipes: a pipeline for multiomic single-cell and spatial transcriptomic data analysis

Single-cell multiomic analysis of the epigenome, transcriptome, and proteome allows for comprehensive characterization of the molecular circuitry that underpins cell identity and state. However, the holistic interpretation of such datasets presents a challenge given a paucity of approaches for systematic, joint evaluation of different modalities. Here, we present Panpipes, a set of computational workflows designed to automate multimodal single-cell and spatial transcriptomic analyses by incorporating widely-used Python-based tools to perform quality control, preprocessing, integration, clustering, and reference mapping at scale. Panpipes allows reliable and customizable analysis and evaluation of individual and integrated modalities, thereby empowering decision-making before downstream investigations.

Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data

Gene set scoring (GSS) has been routinely conducted for gene expression analysis of bulk or single-cell RNA sequencing (RNA-seq) data, which helps to decipher single-cell heterogeneity and cell type-specific variability by incorporating prior knowledge from functional gene sets. Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a powerful technique for interrogating single-cell chromatin-based gene regulation, and genes or gene sets with dynamic regulatory potentials can be regarded as cell type-specific markers as if in single-cell RNA-seq (scRNA-seq). However, there are few GSS tools specifically designed for scATAC-seq, and the applicability and performance of RNA-seq GSS tools on scATAC-seq data remain to be investigated. Here, we systematically benchmarked ten GSS tools, including four bulk RNA-seq tools, five scRNA-seq tools, and one scATAC-seq method. First, using matched scATAC-seq and scRNA-seq datasets, we found that the performance of GSS tools on scATAC-seq data was comparable to that on scRNA-seq, suggesting their applicability to scATAC-seq. Then, the performance of different GSS tools was extensively evaluated using up to ten scATAC-seq datasets. Moreover, we evaluated the impact of gene activity conversion, dropout imputation, and gene set collections on the results of GSS. Results show that dropout imputation can significantly promote the performance of almost all GSS tools, while the impact of gene activity conversion methods or gene set collections on GSS performance is more dependent on GSS tools or datasets. Finally, we provided practical guidelines for choosing appropriate preprocessing methods and GSS tools in different application scenarios.

Opportunities and Challenges in Advancing Plant Research with Single-cell Omics

Plants possess diverse cell types and intricate regulatory mechanisms to adapt to the ever-changing environment of nature. Various strategies have been employed to study cell types and their developmental progressions, including single-cell sequencing methods which provide high-dimensional catalogs to address biological concerns. In recent years, single-cell sequencing technologies in transcriptomics, epigenomics, proteomics, metabolomics, and spatial transcriptomics have been increasingly used in plant science to reveal intricate biological relationships at the single-cell level. However, the application of single-cell technologies to plants is more limited due to the challenges posed by cell structure. This review outlines the advancements in single-cell omics technologies, their implications in plant systems, future research applications, and the challenges of single-cell omics in plant systems.

Mapping the chromatin accessibility landscape of zebrafish embryogenesis at single-cell resolution by SPATAC-seq

Currently, the dynamic accessible elements that determine regulatory programs responsible for the unique identity and function of each cell type during zebrafish embryogenesis lack detailed study. Here we present SPATAC-seq: a split-pool ligation-based assay for transposase-accessible chromatin using sequencing. Using SPATAC-seq, we profiled chromatin accessibility in more than 800,000 individual nuclei across 20 developmental stages spanning the sphere stage to the early larval protruding mouth stage. Using this chromatin accessibility map, we identified 604 cell states and inferred their developmental relationships. We also identified 959,040 candidate cis-regulatory elements (cCREs) and delineated development-specific cCREs, as well as transcription factors defining diverse cell identities. Importantly, enhancer reporter assays confirmed that the majority of tested cCREs exhibited robust enhanced green fluorescent protein expression in restricted cell types or tissues. Finally, we explored gene regulatory programs that drive pigment and notochord cell differentiation. Our work provides a valuable open resource for exploring driver regulators of cell fate decisions in zebrafish embryogenesis.

Single-Cell RNA Sequencing: Technological Progress and Biomedical Application in Cancer Research

Single-cell RNA-seq (scRNA-seq) is a revolutionary technology that allows for the genomic investigation of individual cells in a population, allowing for the discovery of unusual cells associated with cancer and metastasis. ScRNA-seq has been used to discover different types of cancers with poor prognosis and medication resistance such as lung cancer, breast cancer, ovarian cancer, and gastric cancer. Besides, scRNA-seq is a promising method that helps us comprehend the biological features and dynamics of cell development, as well as other disorders. This review gives a concise summary of current scRNA-seq technology. We also explain the main technological steps involved in implementing the technology. We highlight the present applications of scRNA-seq in cancer research, including tumor heterogeneity analysis in lung cancer, breast cancer, and ovarian cancer. In addition, this review elucidates potential applications of scRNA-seq in lineage tracing, personalized medicine, illness prediction, and disease diagnosis, which reveals that scRNA-seq facilitates these events by producing genetic variations on the single-cell level.

Discovering mechanisms of human genetic variation and controlling cell states at scale

Population-scale sequencing efforts have catalogued substantial genetic variation in humans such that variant discovery dramatically outpaces interpretation. We discuss how single-cell sequencing is poised to reveal genetic mechanisms at a rate that may soon approach that of variant discovery. The functional genomics toolkit is sufficiently modular to systematically profile almost any type of variation within increasingly diverse contexts and with molecularly comprehensive and unbiased readouts. As a result, we can construct deep phenotypic atlases of variant effects that span the entire regulatory cascade. The same conceptual approach to interpreting genetic variation should be applied to engineering therapeutic cell states. In this way, variant mechanism discovery and cell state engineering will become reciprocating and iterative processes towards genomic medicine.

Defining the Regulatory Logic of Breast Cancer Using Single-Cell Epigenetic and Transcriptome Profiling

Annotation of the cis-regulatory elements that drive transcriptional dysregulation in cancer cells is critical to improving our understanding of tumor biology. Herein, we present a compendium of matched chromatin accessibility (scATAC-seq) and transcriptome (scRNA-seq) profiles at single-cell resolution from human breast tumors and healthy mammary tissues processed immediately following surgical resection. We identify the most likely cell-of-origin for luminal breast tumors and basal breast tumors and then introduce a novel methodology that implements linear mixed-effects models to systematically quantify associations between regions of chromatin accessibility (i.e. regulatory elements) and gene expression in malignant cells versus normal mammary epithelial cells. These data unveil regulatory elements with that switch from silencers of gene expression in normal cells to enhancers of gene expression in cancer cells, leading to the upregulation of clinically relevant oncogenes. To translate the utility of this dataset into tractable models, we generated matched scATAC-seq and scRNA-seq profiles for breast cancer cell lines, revealing, for each subtype, a conserved oncogenic gene expression program between in vitro and in vivo cells. Together, this work highlights the importance of non-coding regulatory mechanisms that underlie oncogenic processes and the ability of single-cell multi-omics to define the regulatory logic of BC cells at single-cell resolution.

Single-nucleus chromatin landscape dataset of mouse brain development and aging

The development and aging of the brain constitute a lifelong dynamic process, marked by structural and functional changes that entail highly coordinated cellular differentiation and epigenetic regulatory mechanisms. Chromatin accessibility serves as the foundational basis for genetic activity. However, the holistic and dynamic chromatin landscape that spans various brain regions throughout development and ageing remains predominantly unexplored. In this study, we employed single-nucleus ATAC-seq to generate comprehensive chromatin accessibility maps, incorporating data from 69,178 cells obtained from four distinct brain regions - namely, the olfactory bulb (OB), cerebellum (CB), prefrontal cortex (PFC), and hippocampus (HP) - across key developmental time points at 7 P, 3 M, 12 M, and 18 M. We delineated the distribution of cell types across different age stages and brain regions, providing insight into chromatin accessible regions and key transcription factors specific to different cell types. Our data contribute to understanding the epigenetic basis of the formation of different brain regions, providing a dynamic landscape and comprehensive resource for revealing gene regulatory programs during brain development and aging.

Multiplex profiling of developmental cis-regulatory elements with quantitative single-cell expression reporters

The inability to scalably and precisely measure the activity of developmental cis-regulatory elements (CREs) in multicellular systems is a bottleneck in genomics. Here we develop a dual RNA cassette that decouples the detection and quantification tasks inherent to multiplex single-cell reporter assays. The resulting measurement of reporter expression is accurate over multiple orders of magnitude, with a precision approaching the limit set by Poisson counting noise. Together with RNA barcode stabilization via circularization, these scalable single-cell quantitative expression reporters provide high-contrast readouts, analogous to classic in situ assays but entirely from sequencing. Screening >200 regions of accessible chromatin in a multicellular in vitro model of early mammalian development, we identify 13 (8 previously uncharacterized) autonomous and cell-type-specific developmental CREs. We further demonstrate that chimeric CRE pairs generate cognate two-cell-type activity profiles and assess gain- and loss-of-function multicellular expression phenotypes from CRE variants with perturbed transcription factor binding sites. Single-cell quantitative expression reporters can be applied in developmental and multicellular systems to quantitatively characterize native, perturbed and synthetic CREs at scale, with high sensitivity and at single-cell resolution.

Direct transposition of native DNA for sensitive multimodal single-molecule sequencing

Concurrent readout of sequence and base modifications from long unamplified DNA templates by Pacific Biosciences of California (PacBio) single-molecule sequencing requires large amounts of input material. Here we adapt Tn5 transposition to introduce hairpin oligonucleotides and fragment (tagment) limiting quantities of DNA for generating PacBio-compatible circular molecules. We developed two methods that implement tagmentation and use 90-99% less input than current protocols: (1) single-molecule real-time sequencing by tagmentation (SMRT-Tag), which allows detection of genetic variation and CpG methylation; and (2) single-molecule adenine-methylated oligonucleosome sequencing assay by tagmentation (SAMOSA-Tag), which uses exogenous adenine methylation to add a third channel for probing chromatin accessibility. SMRT-Tag of 40 ng or more human DNA (approximately 7,000 cell equivalents) yielded data comparable to gold standard whole-genome and bisulfite sequencing. SAMOSA-Tag of 30,000-50,000 nuclei resolved single-fiber chromatin structure, CTCF binding and DNA methylation in patient-derived prostate cancer xenografts and uncovered metastasis-associated global epigenome disorganization. Tagmentation thus promises to enable sensitive, scalable and multimodal single-molecule genomics for diverse basic and clinical applications.

Advances in single-cell long-read sequencing technologies

With an increase in accuracy and throughput of long-read sequencing technologies, they are rapidly being assimilated into the single-cell sequencing pipelines. For transcriptome sequencing, these techniques provide RNA isoform-level information in addition to the gene expression profiles. Long-read sequencing technologies not only help in uncovering complex patterns of cell-type specific splicing, but also offer unprecedented insights into the origin of cellular complexity and thus potentially new avenues for drug development. Additionally, single-cell long-read DNA sequencing enables high-quality assemblies, structural variant detection, haplotype phasing, resolving high-complexity regions, and characterization of epigenetic modifications. Given that significant progress has primarily occurred in single-cell RNA isoform sequencing (scRiso-seq), this review will delve into these advancements in depth and highlight the practical considerations and operational challenges, particularly pertaining to downstream analysis. We also aim to offer a concise introduction to complementary technologies for single-cell sequencing of the genome, epigenome and epitranscriptome. We conclude by identifying certain key areas of innovation that may drive these technologies further and foster more widespread application in biomedical science.

Clonal tracking in cancer and metastasis

The eradication of many cancers has proven challenging due to the presence of functionally and genetically heterogeneous clones maintained by rare cancer stem cells (CSCs), which contribute to disease progression, treatment refractoriness, and late relapse. The characterization of functional CSC activity has necessitated the development of modern clonal tracking strategies. This review describes viral-based and CRISPR-Cas9-based cellular barcoding, lineage tracing, and imaging-based approaches. DNA-based cellular barcoding technology is emerging as a powerful and robust strategy that has been widely applied to in vitro and in vivo model systems, including patient-derived xenograft models. This review also highlights the potential of these methods for use in the clinical and drug discovery contexts and discusses the important insights gained from such approaches.

Next generation sequencing technologies to address aberrant mRNA translation in cancer

In this review, we explore the transformative impact of next generation sequencing technologies in the realm of translatomics (the study of how translational machinery acts on a genome-wide scale). Despite the expectation of a direct correlation between mRNA and protein content, the complex regulatory mechanisms that affect this relationship remark the limitations of standard RNA-seq approaches. Then, the review characterizes crucial techniques such as polysome profiling, ribo-seq, trap-seq, proximity-specific ribosome profiling, rnc-seq, tcp-seq, qti-seq and scRibo-seq. All these methods are summarized within the context of cancer research, shedding light on their applications in deciphering aberrant translation in cancer cells. In addition, we encompass databases and bioinformatic tools essential for researchers that want to address translatome analysis in the context of cancer biology.

Single-Cell Sequencing Technology in Ruminant Livestock: Challenges and Opportunities

Advancements in single-cell sequencing have transformed the genomics field by allowing researchers to delve into the intricate cellular heterogeneity within tissues at greater resolution. While single-cell omics are more widely applied in model organisms and humans, their use in livestock species is just beginning. Studies in cattle, sheep, and goats have already leveraged single-cell and single-nuclei RNA-seq as well as single-cell and single-nuclei ATAC-seq to delineate cellular diversity in tissues, track changes in cell populations and gene expression over developmental stages, and characterize immune cell populations important for disease resistance and resilience. Although challenges exist for the use of this technology in ruminant livestock, such as the precise annotation of unique cell populations and spatial resolution of cells within a tissue, there is vast potential to enhance our understanding of the cellular and molecular mechanisms underpinning traits essential for healthy and productive livestock. This review intends to highlight the insights gained from published single-cell omics studies in cattle, sheep, and goats, particularly those with publicly accessible data. Further, this manuscript will discuss the challenges and opportunities of this technology in ruminant livestock and how it may contribute to enhanced profitability and sustainability of animal agriculture in the future.

A hybrid demultiplexing strategy that improves performance and robustness of cell hashing

Cell hashing, a nucleotide barcode-based method that allows users to pool multiple samples and demultiplex in downstream analysis, has gained widespread popularity in single-cell sequencing due to its compatibility, simplicity, and cost-effectiveness. Despite these advantages, the performance of this method remains unsatisfactory under certain circumstances, especially in experiments that have imbalanced sample sizes or use many hashtag antibodies. Here, we introduce a hybrid demultiplexing strategy that increases accuracy and cell recovery in multi-sample single-cell experiments. This approach correlates the results of cell hashing and genetic variant clustering, enabling precise and efficient cell identity determination without additional experimental costs or efforts. In addition, we developed HTOreader, a demultiplexing tool for cell hashing that improves the accuracy of cut-off calling by avoiding the dominance of negative signals in experiments with many hashtags or imbalanced sample sizes. When compared to existing methods using real-world datasets, this hybrid approach and HTOreader consistently generate reliable results with increased accuracy and cell recovery.

Accelerating Single-Cell Sequencing Data Analysis with SciDAP: A User-Friendly Approach

Single-cell (sc) RNA, ATAC and Multiome sequencing became powerful tools for uncovering biological and disease mechanisms. Unfortunately, manual analysis of sc data presents multiple challenges due to large data volumes and complexity of configuration parameters. This complexity, as well as not being able to reproduce a computational environment, affects the reproducibility of analysis results. The Scientific Data Analysis Platform (https://SciDAP.com) allows biologists without computational expertise to analyze sequencing-based data using portable and reproducible pipelines written in Common Workflow Language (CWL). Our suite of computational pipelines addresses the most common needs in scRNA-Seq, scATAC-Seq and scMultiome data analysis. When executed on SciDAP, it offers a user-friendly alternative to manual data processing, eliminating the need for coding expertise. In this protocol, we describe the use of SciDAP to analyze scMultiome data. Similar approaches can be used for analysis of scRNA-Seq, scATAC-Seq and scVDJ-Seq datasets.

scAI-SNP: a method for inferring ancestry from single-cell data

Collaborative efforts, such as the Human Cell Atlas, are rapidly accumulating large amounts of single-cell data. To ensure that single-cell atlases are representative of human genetic diversity, we need to determine the ancestry of the donors from whom single-cell data are generated. Self-reporting of race and ethnicity, although important, can be biased and is not always available for the datasets already collected. Here, we introduce scAI-SNP, a tool to infer ancestry directly from single-cell genomics data. To train scAI-SNP, we identified 4.5 million ancestry-informative single-nucleotide polymorphisms (SNPs) in the 1000 Genomes Project dataset across 3201 individuals from 26 population groups. For a query single-cell data set, scAI-SNP uses these ancestry-informative SNPs to compute the contribution of each of the 26 population groups to the ancestry of the donor from whom the cells were obtained. Using diverse single-cell data sets with matched whole-genome sequencing data, we show that scAI-SNP is robust to the sparsity of single-cell data, can accurately and consistently infer ancestry from samples derived from diverse types of tissues and cancer cells, and can be applied to different modalities of single-cell profiling assays, such as single-cell RNA-seq and single-cell ATAC-seq. Finally, we argue that ensuring that single-cell atlases represent diverse ancestry, ideally alongside race and ethnicity, is ultimately important for improved and equitable health outcomes by accounting for human diversity.