Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing

Barcodes based on nucleic acid sequences: Applications and challenges (Review)

Cells are the fundamental structural and functional units of living organisms and the study of these entities has remained a central focus throughout the history of biological sciences. Traditional cell research techniques, including fluorescent protein tagging and microscopy, have provided preliminary insights into the lineage history and clonal relationships between progenitor and descendant cells. However, these techniques exhibit inherent limitations in tracking the full developmental trajectory of cells and elucidating their heterogeneity, including sensitivity, stability and barcode drift. In developmental biology, nucleic acid barcode technology has introduced an innovative approach to cell lineage tracing. By assigning unique barcodes to individual cells, researchers can accurately identify and trace the origin and differentiation pathways of cells at various developmental stages, thereby illuminating the dynamic processes underlying tissue development and organogenesis. In cancer research, nucleic acid barcoding has played a pivotal role in analyzing the clonal architecture of tumor cells, exploring their heterogeneity and resistance mechanisms and enhancing our understanding of cancer evolution and inter‑clonal interactions. Furthermore, nucleic acid barcodes play a crucial role in stem cell research, enabling the tracking of stem cells from diverse origins and their derived progeny. This has offered novel perspectives on the mechanisms of stem cell self‑renewal and differentiation. The present review presented a comprehensive examination of the principles, applications and challenges associated with nucleic acid barcode technology.

Profiling regulatory elements in vivo by genome-wide methods

The biology of gene regulation in eukaryotic genomes is a mature field. The biochemical principles of factor binding to DNA are well-known from in vitro studies, as are the structural interactions in which specific domains of these proteins interface across a short stretch of DNA to confer sequence-specific recognition. Whereas the basic principles of binding and dissociation defined in vitro apply in vivo, the living nucleus is a dynamic compartment crowded with molecules, including motors that drive chromatin movements critical for the regulation of gene expression. Understanding these dynamics in vivo has spurred the development of cutting-edge technologies to observe factor-DNA interactions. The biological significance of chromatin dynamics is now revealed by a wide variety of high-resolution chromatin profiling methods.

PerturbSeq.db: An integrated repository for comprehensive analysis of single-cell perturbation data

Single-cell perturbation studies have emerged as a transformative approach in biological research, offering unprecedented insights into cellular responses to genetic and chemical interventions. However, the field faces challenges related to data accessibility and integration. To address this, we present PerturbSeq.db (http://bioailab.com/PerturbSeq.db/), a comprehensive database that consolidates and harmonizes single-cell perturbation datasets from a diverse array of sources. PerturbSeq.db comprises 189 datasets from 77 studies, including 165 scRNA-seq and 24 scATAC-seq datasets, spanning approximately 50 distinct cell lines or tissues. To ensure data consistency and comparability, PerturbSeq.db employs a uniform processing pipeline across all datasets. The database is complemented by an interactive, user-friendly interface that facilitates efficient data exploration and analysis, empowering researchers to navigate the complexities of single-cell perturbation data. Overall, PerturbSeq.db serves as a critical resource for the scientific community, providing a comprehensive, well-annotated collection of datasets for analyzing and interpreting the effects of perturbation at the single-cell level.

A retinoic acid:YAP1 signaling axis controls atrial lineage commitment

In cardiac progenitor cells (CPCs), retinoic acid (RA) signaling induces atrial lineage gene expression and acquisition of an atrial 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., scRNA-seq and snATAC-seq) to untreated and RA-treated human embryonic stem cell (hESC)-derived CPCs. Unbiased analysis revealed that the Hippo effectors YAP1 and TEAD4 are integrated with the atrial transcription factor enhancer network and that YAP1 activates RA enhancers in CPCs. Furthermore, Yap1 deletion in mouse embryos compromises the expression of RA-induced genes, such as Nr2f2, in the CPCs of the second heart field. Accordingly, in hESC-derived patterned heart organoids, YAP1 regulates the formation of an atrial chamber but is dispensable for the formation of a ventricle. Overall, our findings revealed that YAP1 cooperates with RA signaling to induce atrial lineages during cardiogenesis.

Molecular circuits for genomic recording of cellular events

Advances in precise genome editing are enabling genomic recordings of cellular events. Since the initial demonstration of CRISPR-based genome editing, the field of genomic recording has witnessed key strides in lineage recording, where clonal lineage relationships among cells are indirectly recorded as synthetic mutations. However, methods for directly recording and reconstructing past cellular events are still limited, and their potential for revealing new insights into cell fate decisions has yet to be realized. The field needs new sensing modules and genetic circuit architectures that faithfully encode past cellular states into genomic DNA recordings to achieve such goals. Here we review recently developed strategies to construct diverse sensors and explore how emerging synthetic biology tools may help to build molecular circuits for genomic recording of diverse cellular events.

Single-cell epigenetics and multiomics analysis in kidney research

The rapid evolution of single-cell sequencing technologies has significantly advanced our knowledge of cellular heterogeneity and the underlying molecular basis in healthy and diseased kidneys. While single-cell transcriptomic analysis excels in characterizing cell states in the heterogeneous population, the complex regulatory mechanisms governing the gene expressions are difficult to decipher using transcriptomic data alone. Single-cell sequencing technology has recently extended to include epigenome and other modalities, allowing single-cell multiomics analysis. Especially, the integrative analysis of epigenome and transcriptome dissects the cell-specific, gene-regulatory mechanisms driving cellular heterogeneity. An increasing number of single-cell multimodal atlases are being generated in nephrology research, offering novel insights into cellular diversity and the underpinning epigenetic regulation. This ongoing paradigm shift in kidney research accelerates the identification of new biomarkers and potential therapeutic targets, promoting clinical translation. In this era of transformative nephrology research, the basic knowledge of single-cell sequencing analysis and multiomics approach is valuable not only for basic science researchers but for all nephrologists. This review overview single-cell analysis, with a focus on emerging epigenomic and multiomics approaches and their application to kidney research.

Unraveling the future of genomics: CRISPR, single-cell omics, and the applications in cancer and immunology

The CRISPR system has transformed many research areas, including cancer and immunology, by providing a simple yet effective genome editing system. Its simplicity has facilitated large-scale experiments to assess gene functionality across diverse biological contexts, generating extensive datasets that boosted the development of computational methods and machine learning/artificial intelligence applications. Integrating CRISPR with single-cell technologies has further advanced our understanding of genome function and its role in many biological processes, providing unprecedented insights into human biology and disease mechanisms. This powerful combination has accelerated AI-driven analyses, enhancing disease diagnostics, risk prediction, and therapeutic innovations. This review provides a comprehensive overview of CRISPR-based genome editing systems, highlighting their advancements, current progress, challenges, and future opportunities, especially in cancer and immunology.

High-resolution reconstruction of cell-type specific transcriptional regulatory processes from bulk sequencing samples

Biological systems exhibit remarkable heterogeneity, characterized by intricate interplay among diverse cell types. Resolving the regulatory processes of specific cell types is crucial for delineating developmental mechanisms and disease etiologies. While single-cell sequencing methods such as scRNA-seq and scATAC-seq have revolutionized our understanding of individual cellular functions, adapting bulk genome-wide assays to achieve single-cell resolution of other genomic features remains a significant technical challenge. Here, we introduce Deep-learning-based DEconvolution of Tissue profiles with Accurate Interpretation of Locus-specific Signals (DeepDETAILS), a novel quasi-supervised framework to reconstruct cell-type-specific genomic signals with base-pair precision. DeepDETAILS' core innovation lies in its ability to perform cross-modality deconvolution using scATAC-seq reference libraries for other bulk datasets, benefiting from the affordability and availability of scATAC-seq data. DeepDETAILS enables high-resolution mapping of genomic signals across diverse cell types, with great versatility for various omics datasets, including nascent transcript sequencing (such as PRO-cap and PRO-seq) and ChIP-seq for chromatin modifications. Our results demonstrate that DeepDETAILS significantly outperformed traditional statistical deconvolution methods. Using DeepDETAILS, we developed a comprehensive compendium of high-resolution nascent transcription and histone modification signals across 39 diverse human tissues and 86 distinct cell types. Furthermore, we applied our compendium to fine-map risk variants associated with Primary Sclerosing Cholangitis (PSC), a progressive cholestatic liver disorder, and revealed a potential etiology of the disease. Our tool and compendium provide invaluable insights into cellular complexity, opening new avenues for studying biological processes in various contexts.

Are We There Yet? Assessing the Readiness of Single-Cell Proteomics to Answer Biological Hypotheses

Single-cell analysis is an active area of research in many fields of biology. Measurements at single-cell resolution allow researchers to study diverse populations without losing biologically meaningful information to sample averages. Many technologies have been used to study single cells, including mass spectrometry-based single-cell proteomics (SCP). SCP has seen a lot of growth over the past couple of years through improvements in data acquisition and analysis, leading to greater proteomic depth. Because method development has been the main focus in SCP, biological applications have been sprinkled in only as proof-of-concept. However, SCP methods now provide significant coverage of the proteome and have been implemented in many laboratories. Thus, a primary question to address in our community is whether the current state of technology is ready for widespread adoption for biological inquiry. In this Perspective, we examine the potential for SCP in three thematic areas of biological investigation: cell annotation, developmental trajectories, and spatial mapping. We identify that the primary limitation of SCP is sample throughput. As proteome depth has been the primary target for method development to date, we advocate for a change in focus to facilitate measuring tens of thousands of single-cell proteomes to enable biological applications beyond proof-of-concept.

Shining a light on cell biology of the nucleus with single-cell sequencing

From the preservation of genomic integrity to the regulation of RNA translation, nearly all cellular processes are regulated in a cell context-dependent manner. To fully understand the context-specific function of involved nuclear processes, a vast number of single-cell sequencing technologies were developed over the last decade. This instrumental work demonstrated the heterogeneity between cell types and individual cells, bringing about new understanding of nuclear mechanisms and their crosstalk to cell states. In this review, we will cover new technological advances and their exciting applications as well as future opportunities to discover new nuclear processes and the crosstalk between them.

Chromatin accessibility landscape of mouse early embryos revealed by single-cell NanoATAC-seq2

In mammals, fertilized eggs undergo genome-wide epigenetic reprogramming to generate the organism. However, our understanding of epigenetic dynamics during preimplantation development at single-cell resolution remains incomplete. Here, we developed scNanoATAC-seq2, a single-cell assay for transposase-accessible chromatin using long-read sequencing for scarce samples. We present a detailed chromatin accessibility landscape of mouse preimplantation development, revealing distinct chromatin signatures in the epiblast, primitive endoderm, and trophectoderm during lineage segregation. Differences between zygotes and two-cell embryos highlight reprogramming in chromatin accessibility during the maternal-to-zygotic transition. Single-cell long-read sequencing enables in-depth analysis of chromatin accessibility in noncanonical imprinting, imprinted X chromosome inactivation, and low-mappability genomic regions, such as repetitive elements and paralogs. Our data provide insights into chromatin dynamics during mammalian preimplantation development and lineage differentiation.

Single-cell analysis of bidirectional reprogramming between early embryonic states identify mechanisms of differential lineage plasticities in mice

Two distinct lineages, pluripotent epiblast (EPI) and primitive (extra-embryonic) endoderm (PrE), arise from common inner cell mass (ICM) progenitors in mammalian embryos. To study how these sister identities are forged, we leveraged mouse embryonic stem (ES) cells and extra-embryonic endoderm (XEN) stem cells-in vitro counterparts of the EPI and PrE. Bidirectional reprogramming between ES and XEN coupled with single-cell RNA and ATAC-seq analyses showed distinct rates, efficiencies, and trajectories of state conversions, identifying drivers and roadblocks of reciprocal conversions. While GATA4-mediated ES-to-iXEN conversion was rapid and nearly deterministic, OCT4-, KLF4-, and SOX2-induced XEN-to-induced pluripotent stem (iPS) reprogramming progressed with diminished efficiency and kinetics. A dominant PrE transcriptional program, safeguarded by GATA4, alongside elevated chromatin accessibility and reduced DNA methylation of the EPI underscored the differential plasticities of the two states. Mapping in vitro to embryo trajectories tracked reprogramming cells in either direction along EPI and PrE in vivo states, without transitioning through the ICM.

Human Body Single-Cell Atlas of 3D Genome Organization and DNA Methylation

Higher-order chromatin structure and DNA methylation are critical for gene regulation, but how these vary across the human body remains unclear. We performed multi-omic profiling of 3D genome structure and DNA methylation for 86,689 single nuclei across 16 human tissues, identifying 35 major and 206 cell subtypes. We revealed extensive changes in CG and non-CG methylation across almost all cell types and characterized 3D chromatin structure at an unprecedented cellular resolution. Intriguingly, extensive discrepancies exist between cell types delineated by DNA methylation and genome structure, indicating that the role of distinct epigenomic features in maintaining cell identity may vary by lineage. This study expands our understanding of the diversity of DNA methylation and chromatin structure and offers an extensive reference for exploring gene regulation in human health and disease.

Semi-automated IT-scATAC-seq profiles cell-specific chromatin accessibility in differentiation and peripheral blood populations

Single-cell ATAC-seq (scATAC-seq) enables high-resolution mapping of chromatin accessibility but is often limited by throughput, cost, and equipment requirements. Here, we present indexed Tn5 tagmentation-based scATAC-seq (IT-scATAC-seq), a semi-automated, cost-effective, and scalable approach that leverages indexed Tn5 transposomes and a three-round barcoding strategy. This workflow prepares libraries for up to 10,000 cells in a single day, reduces the per-cell cost to approximately $0.01, and maintains high data quality. Comprehensive benchmarking demonstrates that IT-scATAC-seq achieves robust library complexity, high signal specificity, and improved cost-efficiency compared to existing methods. We apply IT-scATAC-seq to mouse embryonic stem cells, capturing chromatin remodelling during early differentiation, and to human peripheral blood mononuclear cells, resolving cell-type-specific regulatory programs. Here, we show that IT-scATAC-seq provides a robust and efficient approach for high-resolution single-cell epigenomic investigations, balancing scalability, data quality, and accessibility.

Alevin-fry-atac enables rapid and memory frugal mapping of single-cell ATAC-seq data using virtual colors for accurate genomic pseudoalignment

Ultrafast mapping of short reads via lightweight mapping techniques such as pseudoalignment has significantly accelerated transcriptomic and metagenomic analyses, often with minimal accuracy loss compared to alignment-based methods. However, applying pseudoalignment to large genomic references, like chromosomes, is challenging due to their size and repetitive sequences. We introduce a new and modified pseudoalignment scheme that partitions each reference into "virtual colors…. These are essentially overlapping bins of fixed maximal extent on the reference sequences that are treated as distinct "colors" from the perspective of the pseudoalignment algorithm. We apply this modified pseudoalignment procedure to process and map single-cell ATAC-seq data in our new tool alevin-fry-atac . We compare alevin-fry-atac to both Chromap and Cell Ranger ATAC . Alevin-fry-atac is highly scalable and, when using 32 threads, is approximately 2.8 times faster than Chromap (the second fastest approach) while using approximately one third of the memory and mapping slightly more reads. The resulting peaks and clusters generated from alevin-fry-atac show high concordance with those obtained from both Chromap and the Cell Ranger ATAC pipeline, demonstrating that virtual colorenhanced pseudoalignment directly to the genome provides a fast, memory-frugal, and accurate alternative to existing approaches for single-cell ATAC-seq processing. The development of alevin-fry-atac brings single-cell ATAC-seq processing into a unified ecosystem with single-cell RNA-seq processing (via alevin-fry ) to work toward providing a truly open alternative to many of the varied capabilities of CellRanger . Furthermore, our modified pseudoalignment approach should be easily applicable and extendable to other genome-centric mapping-based tasks and modalities such as standard DNA-seq, DNase-seq, Chip-seq and Hi-C.

Identifying cross-lineage dependencies of cell-type-specific regulators in mouse gastruloids

Correct gene expression levels are crucial for normal development. Advances in genomics enable the inference of gene regulatory programs active during development but cannot capture the complex multicellular interactions occurring during mammalian embryogenesis in utero. In vitro models of mammalian development, like gastruloids, can overcome this limitation. Using time-resolved single-cell chromatin accessibility analysis, we delineated the regulatory profile during mouse gastruloid development, identifying critical drivers of developmental transitions. Gastruloids develop from bipotent progenitor cells driven by the transcription factors (TFs) OCT4, SOX2, and TBXT, differentiating into the mesoderm (characterized by the mesogenin 1 [MSGN1]) and spinal cord (characterized by CDX2). ΔCDX gastruloids fail to form spinal cord, while Msgn1 ablation inhibits paraxial mesoderm and spinal cord development. Chimeric gastruloids with ΔMSGN1 and wild-type cells formed both tissues, indicating that inter-tissue communication is necessary for spinal cord formation. Our work has important implications for studying inter-tissue communication and gene regulatory programs in development.

Decoding the blueprints of embryo development with single-cell and spatial omics

Embryonic development is a complex and intricately regulated process that encompasses precise control over cell differentiation, morphogenesis, and the underlying gene expression changes. Recent years have witnessed a remarkable acceleration in the development of single-cell and spatial omic technologies, enabling high-throughput profiling of transcriptomic and other multi-omic information at the individual cell level. These innovations offer fresh and multifaceted perspectives for investigating the intricate cellular and molecular mechanisms that govern embryonic development. In this review, we provide an in-depth exploration of the latest technical advancements in single-cell and spatial multi-omic methodologies and compile a systematic catalog of their applications in the field of embryonic development. We deconstruct the research strategies employed by recent studies that leverage single-cell sequencing techniques and underscore the unique advantages of spatial transcriptomics. Furthermore, we delve into both the current applications, data analysis algorithms and the untapped potential of these technologies in advancing our understanding of embryonic development. With the continuous evolution of multi-omic technologies, we anticipate their widespread adoption and profound contributions to unraveling the intricate molecular foundations underpinning embryo development in the foreseeable future.

Genetically encoded protein oscillators for FM streaming of single-cell data

Radios and cellphones use frequency modulation (FM) of an oscillating carrier signal to reliably transmit multiplexed data while rejecting noise. Here, we establish a biochemical analogue of this paradigm using genetically encoded protein oscillators (GEOs) as carrier signals in circuits that enable continuous, real-time FM streaming of single-cell data. GEOs are constructed from evolutionarily diverse MinDE-family ATPase and activator modules that generate fast synthetic protein oscillations when co-expressed in human cells. These oscillations serve as a single-cell carrier signal, with frequency and amplitude controlled by GEO component levels and activity. We systematically characterize 169 ATPase/activator GEO pairs and engineer composite GEOs with multiple competing activators to develop a comprehensive platform for waveform programming. Using these principles, we design circuits that modulate GEO frequency in response to cellular activity and decode their responses using a calibrated machine-learning model to demonstrate sensitive, real-time FM streaming of transcription and proteasomal degradation dynamics in single cells. GEOs establish a dynamically controllable biochemical carrier signal, unlocking noise-resistant FM data-encoding paradigms that open new avenues for dynamic single-cell analysis.

Single-cell assay for transposase-accessible chromatin sequencing of human clear cell renal cell carcinoma

Regulating the occurrence and progression of tumor cells at the epigenetic level is a new insight of clear cell renal cell carcinoma (ccRCC). Chromatin accessibility is an important pathway of epigenetic regulation, which may explain the mystery of tumor occurrence. Assay for transposase-accessible chromatin sequencing (ATAC-seq) provides insight into the epigenetic regulatory features of ccRCC, especially at the single-cell level. In this study, we performed scATAC-seq of 3 ccRCC samples and captured a total of 18,703 high-quality cell nuclei and 104,818 unique peaks. Our protocol for nuclear extraction was reliable and stable, which can be used to deal with fresh and frozen single-cell suspensions. We presented basic methods for scATAC-seq data analysis, such as cell clustering, gene activity scoring, cell subtype specific peaks, transcription factors, motif and motif footprinting analysis. Taken together, our data indicated the valuable epigenetic features of ccRCC, which will provide more references for the study of ccRCC.

Topological identification and interpretation for single-cell epigenetic regulation elucidation in multi-tasks using scAGDE

Single-cell ATAC-seq technology advances our understanding of single-cell heterogeneity in gene regulation by enabling exploration of epigenetic landscapes and regulatory elements. However, low sequencing depth per cell leads to data sparsity and high dimensionality, limiting the characterization of gene regulatory elements. Here, we develop scAGDE, a single-cell chromatin accessibility model-based deep graph representation learning method that simultaneously learns representation and clustering through explicit modeling of data generation. Our evaluations demonstrated that scAGDE outperforms existing methods in cell segregation, key marker identification, and visualization across diverse datasets while mitigating dropout events and unveiling hidden chromatin-accessible regions. We find that scAGDE preferentially identifies enhancer-like regions and elucidates complex regulatory landscapes, pinpointing putative enhancers regulating the constitutive expression of CTLA4 and the transcriptional dynamics of CD8A in immune cells. When applied to human brain tissue, scAGDE successfully annotated cis-regulatory element-specified cell types and revealed functional diversity and regulatory mechanisms of glutamatergic neurons.

Defining the regulatory logic of breast cancer using single-cell epigenetic and transcriptome profiling

Annotation of cis-regulatory elements that drive transcriptional dysregulation in cancer cells is critical to understanding tumor biology. Herein, we present matched chromatin accessibility (single-cell assay for transposase-accessible chromatin by sequencing [scATAC-seq]) and transcriptome (single-cell RNA sequencing [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 subtype-specific breast tumors and implement linear mixed-effects modeling to quantify associations between regulatory elements and gene expression in malignant versus normal cells. These data unveil cancer-specific regulatory elements and putative silencer-to-enhancer switching events in cells that lead to the upregulation of clinically relevant oncogenes. In addition, we generate matched scATAC-seq and scRNA-seq profiles for breast cancer cell lines, revealing a conserved oncogenic gene expression program between in vitro and in vivo cells. 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 cancer cells.

A multi-omic single-cell landscape of the aging mouse ovary

The ovary is one of the first organs in humans to exhibit age-related functional impairments. As an organ composed of diverse heterogeneous cell types, the ovary exhibits cell-type-specific changes during the aging process, ultimately leading to a decline in female fertility. Investigating the molecular mechanisms of ovarian aging is crucial for understanding age-related fertility dysfunction in females. In this study, we combine scRNA-seq and scATAC-seq from mouse young/aged ovaries to characterize molecular features during ovarian aging. Using the single-cell multi-omic data, we revealed the cell-type-specific transcriptional changes during the aging process in seven major ovarian cell types and identified the cis/trans-regulatory elements governing these transcriptional changes. Specifically, we uncovered the transcriptional alterations of TGF-beta signaling in mesenchymal cells and endoplasmic reticulum stress in granulosa cells of aged mouse ovaries and further identified the potential corresponding cis/trans-regulatory elements. These molecular alterations may contribute to aging-induced functional impairments in mouse ovaries. In summary, this work provides transcriptome and chromatin accessibility landscape of ovarian aging in mice, which serve as a resource for identifying the cell-type-specific molecular mechanisms underlying ovarian aging, aiding in the identification of potential diagnostic biomarkers and treatment strategies.

Integrative Multi-Omics Approaches for Identifying and Characterizing Biological Elements in Crop Traits: Current Progress and Future Prospects

The advancement of multi-omics tools has revolutionized the study of complex biological systems, providing comprehensive insights into the molecular mechanisms underlying critical traits across various organisms. By integrating data from genomics, transcriptomics, metabolomics, and other omics platforms, researchers can systematically identify and characterize biological elements that contribute to phenotypic traits. This review delves into recent progress in applying multi-omics approaches to elucidate the genetic, epigenetic, and metabolic networks associated with key traits in plants. We emphasize the potential of these integrative strategies to enhance crop improvement, optimize agricultural practices, and promote sustainable environmental management. Furthermore, we explore future prospects in the field, underscoring the importance of cutting-edge technological advancements and the need for interdisciplinary collaboration to address ongoing challenges. By bridging various omics platforms, this review aims to provide a holistic framework for advancing research in plant biology and agriculture.

Multiscale footprints reveal the organization of cis-regulatory elements

Cis-regulatory elements (CREs) control gene expression and are dynamic in their structure and function, reflecting changes in the composition of diverse effector proteins over time1. However, methods for measuring the organization of effector proteins at CREs across the genome are limited, hampering efforts to connect CRE structure to their function in cell fate and disease. Here we developed PRINT, a computational method that identifies footprints of DNA-protein interactions from bulk and single-cell chromatin accessibility data across multiple scales of protein size. Using these multiscale footprints, we created the seq2PRINT framework, which uses deep learning to allow precise inference of transcription factor and nucleosome binding and interprets regulatory logic at CREs. Applying seq2PRINT to single-cell chromatin accessibility data from human bone marrow, we observe sequential establishment and widening of CREs centred on pioneer factors across haematopoiesis. We further discover age-associated alterations in the structure of CREs in murine haematopoietic stem cells, including widespread reduction of nucleosome footprints and gain of de novo identified Ets composite motifs. Collectively, we establish a method for obtaining rich insights into DNA-binding protein dynamics from chromatin accessibility data, and reveal the architecture of regulatory elements across differentiation and ageing.

Harnessing Epigenetics: Innovative Approaches in Diagnosing and Combating Viral Acute Respiratory Infections

Acute respiratory infections (ARIs) caused by viruses such as SARS-CoV-2, influenza viruses, and respiratory syncytial virus (RSV), pose significant global health challenges, particularly for the elderly and immunocompromised individuals. Substantial evidence indicates that acute viral infections can manipulate the host's epigenome through mechanisms like DNA methylation and histone modifications as part of the immune response. These epigenetic alterations can persist beyond the acute phase, influencing long-term immunity and susceptibility to subsequent infections. Post-infection modulation of the host epigenome may help distinguish infected from uninfected individuals and predict disease severity. Understanding these interactions is crucial for developing effective treatments and preventive strategies for viral ARIs. This review highlights the critical role of epigenetic modifications following viral ARIs in regulating the host's innate immune defense mechanisms. We discuss the implications of these modifications for diagnosing, preventing, and treating viral infections, contributing to the advancement of precision medicine. Recent studies have identified specific epigenetic changes, such as hypermethylation of interferon-stimulated genes in severe COVID-19 cases, which could serve as biomarkers for early detection and disease progression. Additionally, epigenetic therapies, including inhibitors of DNA methyltransferases and histone deacetylases, show promise in modulating the immune response and improving patient outcomes. Overall, this review provides valuable insights into the epigenetic landscape of viral ARIs, extending beyond traditional genetic perspectives. These insights are essential for advancing diagnostic techniques and developing innovative treatments to address the growing threat of emerging viruses causing ARIs globally.

Just a SNP away: The future of in vivo massively parallel reporter assay

The human genome is largely noncoding, yet the field is still grasping to understand how noncoding variants impact transcription and contribute to disease etiology. The massively parallel reporter assay (MPRA) has been employed to characterize the function of noncoding variants at unprecedented scales, but its application has been largely limited by the in vitro context. The field will benefit from establishing a systemic platform to study noncoding variant function across multiple tissue types under physiologically relevant conditions. However, to date, MPRA has been applied to only a handful of in vivo conditions. Given the complexity of the central nervous system and its widespread interactions with all other organ systems, our understanding of neuropsychiatric disorder-associated noncoding variants would be greatly advanced by studying their functional impact in the intact brain. In this review, we discuss the importance, technical considerations, and future applications of implementing MPRA in the in vivo space with the focus on neuropsychiatric disorders.

A rare PRIMER cell state in plant immunity

Plants lack specialized and mobile immune cells. Consequently, any cell type that encounters pathogens must mount immune responses and communicate with surrounding cells for successful defence. However, the diversity, spatial organization and function of cellular immune states in pathogen-infected plants are poorly understood1. Here we infect Arabidopsis thaliana leaves with bacterial pathogens that trigger or supress immune responses and integrate time-resolved single-cell transcriptomic, epigenomic and spatial transcriptomic data to identify cell states. We describe cell-state-specific gene-regulatory logic that involves transcription factors, putative cis-regulatory elements and target genes associated with disease and immunity. We show that a rare cell population emerges at the nexus of immune-active hotspots, which we designate as primary immune responder (PRIMER) cells. PRIMER cells have non-canonical immune signatures, exemplified by the expression and genome accessibility of a previously uncharacterized transcription factor, GT-3A, which contributes to plant immunity against bacterial pathogens. PRIMER cells are surrounded by another cell state (bystander) that activates genes for long-distance cell-to-cell immune signalling. Together, our findings suggest that interactions between these cell states propagate immune responses across the leaf. Our molecularly defined single-cell spatiotemporal atlas provides functional and regulatory insights into immune cell states in plants.

INSTINCT: Multi-sample integration of spatial chromatin accessibility sequencing data via stochastic domain translation

Recent advances in spatial epigenomic techniques have given rise to spatial assay for transposase-accessible chromatin using sequencing (spATAC-seq) data, enabling the characterization of epigenomic heterogeneity and spatial information simultaneously. Integrative analysis of multiple spATAC-seq samples, for which no method has been developed, allows for effective identification and elimination of unwanted non-biological factors within the data, enabling comprehensive exploration of tissue structures and providing a holistic epigenomic landscape, thereby facilitating the discovery of biological implications and the study of regulatory processes. In this article, we present INSTINCT, a method for multi-sample INtegration of Spatial chromaTIN accessibility sequencing data via stochastiC domain Translation. INSTINCT can efficiently handle the high dimensionality of spATAC-seq data and eliminate the complex noise and batch effects of samples through a stochastic domain translation procedure. We demonstrate the superiority and robustness of INSTINCT in integrating spATAC-seq data across multiple simulated scenarios and real datasets. Additionally, we highlight the advantages of INSTINCT in spatial domain identification, visualization, spot-type annotation, and various downstream analyses, including motif enrichment analysis, expression enrichment analysis, and partitioned heritability analysis.

Single-cell multiomics to advance cell therapy

Single-cell RNA-sequencing (scRNAseq) was first introduced in 2009 and has evolved with many technological advancements over the last decade. Not only are there several scRNAseq platforms differing in many aspects, but there are also a large number of computational pipelines available for downstream analyses which are being developed at an exponential rate. Such computational data appear in many scientific publications in virtually every field of study; thus, investigators should be able to understand and interpret data in this rapidly evolving field. Here, we discuss key differences in scRNAseq platforms, crucial steps in scRNAseq experiments, standard downstream analyses and introduce newly developed multimodal approaches. We then discuss how single-cell omics has been applied to advance the field of cell therapy.

A panoramic view of cell population dynamics in mammalian aging

To elucidate aging-associated cellular population dynamics, we present PanSci, a single-cell transcriptome atlas profiling >20 million cells from 623 mouse tissues across different life stages, sexes, and genotypes. This comprehensive dataset reveals >3000 different cellular states and >200 aging-associated cell populations. Our panoramic analysis uncovered organ-, lineage-, and sex-specific shifts in cellular dynamics during life-span progression. Moreover, we identify both systematic and organ-specific alterations in immune cell populations associated with aging. We further explored the regulatory roles of the immune system on aging and pinpointed specific age-related cell population expansions that are lymphocyte dependent. Our "cell-omics" strategy enhances comprehension of cellular aging and lays the groundwork for exploring the complex cellular regulatory networks in aging and aging-associated diseases.

Application of computational algorithms for single-cell RNA-seq and ATAC-seq in neurodegenerative diseases

Recent advancements in single-cell technologies, including single-cell RNA sequencing (scRNA-seq) and Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), have greatly improved our insight into the epigenomic landscapes across various biological contexts and diseases. This paper reviews key computational tools and machine learning approaches that integrate scRNA-seq and scATAC-seq data to facilitate the alignment of transcriptomic data with chromatin accessibility profiles. Applying these integrated single-cell technologies in neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease, reveals how changes in chromatin accessibility and gene expression can illuminate pathogenic mechanisms and identify potential therapeutic targets. Despite facing challenges like data sparsity and computational demands, ongoing enhancements in scATAC-seq and scRNA-seq technologies, along with better analytical methods, continue to expand their applications. These advancements promise to revolutionize our approach to medical research and clinical diagnostics, offering a comprehensive view of cellular function and disease pathology.

Co-profiling of single-cell gene expression and chromatin landscapes in stickleback pituitary

The pituitary gland is a key endocrine gland with various physiological functions including metabolism, growth, and reproduction. It comprises several distinct cell populations that release multiple polypeptide hormones. Although the major endocrine cell types are conserved across taxa, the regulatory mechanisms of gene expression and chromatin organization in specific cell types remain poorly understood. Here, we performed simultaneous profiling of the transcriptome and chromatin landscapes in the pituitary cells of the three-spined stickleback (Gasterosteus aculeatus), which represents a good model for investigating the genetic mechanisms underlying adaptive evolution. We obtained pairwise gene expression and chromatin profiles for 5184 cells under short- and long-day conditions. Using three independent clustering analyses, we identified 16 distinct cell clusters and validated their consistency. These results advance our understanding of the regulatory dynamics in the pituitary gland and provide a reference for future research on comparative physiology and evolutionary biology.

ChromBPNet: bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variants

Despite extensive mapping of cis-regulatory elements (cREs) across cellular contexts with chromatin accessibility assays, the sequence syntax and genetic variants that regulate transcription factor (TF) binding and chromatin accessibility at context-specific cREs remain elusive. We introduce ChromBPNet, a deep learning DNA sequence model of base-resolution accessibility profiles that detects, learns and deconvolves assay-specific enzyme biases from regulatory sequence determinants of accessibility, enabling robust discovery of compact TF motif lexicons, cooperative motif syntax and precision footprints across assays and sequencing depths. Extensive benchmarks show that ChromBPNet, despite its lightweight design, is competitive with much larger contemporary models at predicting variant effects on chromatin accessibility, pioneer TF binding and reporter activity across assays, cell contexts and ancestry, while providing interpretation of disrupted regulatory syntax. ChromBPNet also helps prioritize and interpret regulatory variants that influence complex traits and rare diseases, thereby providing a powerful lens to decode regulatory DNA and genetic variation.

Simultaneous single-cell analysis of 5mC and 5hmC with SIMPLE-seq

Dynamic 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) modifications to DNA regulate gene expression in a cell-type-specific manner and are associated with various biological processes, but the two modalities have not yet been measured simultaneously from the same genome at the single-cell level. Here we present SIMPLE-seq, a scalable, base resolution method for joint analysis of 5mC and 5hmC from thousands of single cells. Based on orthogonal labeling and recording of 'C-to-T' mutational signals from 5mC and 5hmC sites, SIMPLE-seq detects these two modifications from the same molecules in single cells and enables unbiased DNA methylation dynamics analysis of heterogeneous biological samples. We applied this method to mouse embryonic stem cells, human peripheral blood mononuclear cells and mouse brain to give joint epigenome maps at single-cell and single-molecule resolution. Integrated analysis of these two cytosine modifications reveals distinct epigenetic patterns associated with divergent regulatory programs in different cell types as well as cell states.

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.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Integration of unpaired single cell omics data by deep transfer graph convolutional network

The rapid advance of large-scale atlas-level single cell RNA sequences and single-cell chromatin accessibility data provide extraordinary avenues to broad and deep insight into complex biological mechanism. Leveraging the datasets and transfering labels from scRNA-seq to scATAC-seq will empower the exploration of single-cell omics data. However, the current label transfer methods have limited performance, largely due to the lower capable of preserving fine-grained cell populations and intrinsic or extrinsic heterogeneity between datasets. Here, we present a robust deep transfer model based graph convolutional network, scTGCN, which achieves versatile performance in preserving biological variation, while achieving integration hundreds of thousands cells in minutes with low memory consumption. We show that scTGCN is powerful to the integration of mouse atlas data and multimodal data generated from APSA-seq and CITE-seq. Thus, scTGCN shows high label transfer accuracy and effectively knowledge transfer across different modalities.

Single-Cell Analysis in the Mouse and Human Mammary Gland

The mammary gland is a complex organ, host to a rich array of different cell types. As the only organ to complete its development in adulthood, it delicately balances both cell intrinsic and external signalling from hormones, growth factors and other stimulants. The gland can undergo vast proliferation, restructuring and functional maturation during pregnancy and undo these gross changes to a nearly identical resting state during involution. The adaptive nature of the mammary gland underpins its function but also increases its susceptibility to cancer. While already characterised at a macro scale, understanding the complexities of mammary gland morphogenesis, development and tumorigenesis requires interrogation of cellular and molecular mechanisms. As outlined below, single-cell analysis is a key approach for this, allowing us to unbiasedly explore and characterise the functions and properties of individual cells from the genome to the proteome. Here, we introduce key single-cell analysis methods and give brief introductions to their respective workflows. We then discuss the structure, cell types and development of the mammary gland from birth, puberty and through pregnancy, as well as cancer formation. Additionally, we highlight the benefits and caveats of implementing single-cell methodologies and mouse models for studying critical time points of human development and disease. Finally, we highlight some limitations and future directions of single-cell techniques. This chapter provides a starting point for users hoping to further their understanding of mammary gland development and its link to cancer as explained by single-cell analysis studies.

Epigenomic heterogeneity as a source of tumour evolution

In the past decade, remarkable progress in cancer medicine has been achieved by the development of treatments that target DNA sequence variants. However, a purely genetic approach to treatment selection is hampered by the fact that diverse cell states can emerge from the same genotype. In multicellular organisms, cell-state heterogeneity is driven by epigenetic processes that regulate DNA-based functions such as transcription; disruption of these processes is a hallmark of cancer that enables the emergence of defective cell states. Advances in single-cell technologies have unlocked our ability to quantify the epigenomic heterogeneity of tumours and understand its mechanisms, thereby transforming our appreciation of how epigenomic changes drive cancer evolution. This Review explores the idea that epigenomic heterogeneity and plasticity act as a reservoir of cell states and therefore as a source of tumour evolution. Best practices to quantify epigenomic heterogeneity and explore its various causes and consequences are discussed, including epigenomic reprogramming, stochastic changes and lasting memory. The design of new therapeutic approaches to restrict epigenomic heterogeneity, with the long-term objective of limiting cancer development and progression, is also addressed.

HIPSD&R-seq enables scalable genomic copy number and transcriptome profiling

Single-cell DNA sequencing (scDNA-seq) enables decoding somatic cancer variation. Existing methods are hampered by low throughput or cannot be combined with transcriptome sequencing in the same cell. We propose HIPSD&R-seq (HIgh-throughPut Single-cell Dna and Rna-seq), a scalable yet simple and accessible assay to profile low-coverage DNA and RNA in thousands of cells in parallel. Our approach builds on a modification of the 10X Genomics platform for scATAC and multiome profiling. In applications to human cell models and primary tissue, we demonstrate the feasibility to detect rare clones and we combine the assay with combinatorial indexing to profile over 17,000 cells.

Single-cell multi-omic analysis of fibrolamellar carcinoma reveals rewired cell-to-cell communication patterns and unique vulnerabilities

Fibrolamellar carcinoma (FLC) is a rare malignancy disproportionately affecting adolescents and young adults with no standard of care. FLC is characterized by thick stroma, which has long suggested an important role of the tumor microenvironment. Over the past decade, several studies have revealed aberrant markers and pathways in FLC. However, a significant drawback of these efforts is that they were conducted on bulk tumor samples. Consequently, identities and roles of distinct cell types within the tumor milieu, and the patterns of intercellular communication, have yet to be explored. In this study we unveil cell-type specific gene signatures, transcription factor networks, and super-enhancers in FLC using a multi-omics strategy that leverages both single-nucleus ATAC-seq and single-nucleus RNA-seq. We also infer completely rewired cell-to-cell communication patterns in FLC including signaling mediated by SPP1-CD44, MIF-ACKR3, GDF15-TGFBR2, and FGF7-FGFR. Finally, we validate findings with loss-of-function studies in several models including patient tissue slices, identifying vulnerabilities that merit further investigation as candidate therapeutic targets in FLC.

scTrends: A living review of commercial single-cell and spatial 'omic technologies

Understanding the rapidly evolving landscape of single-cell and spatial omic technologies is crucial for advancing biomedical research and drug development. We provide a living review of both mature and emerging commercial platforms, highlighting key methodologies and trends shaping the field. This review spans from foundational single-cell technologies such as microfluidics and plate-based methods to newer approaches like combinatorial indexing; on the spatial side, we consider next-generation sequencing and imaging-based spatial transcriptomics. Finally, we highlight emerging methodologies that may fundamentally expand the scope for data generation within pharmaceutical research, creating opportunities to discover and validate novel drug mechanisms. Overall, this review serves as a critical resource for navigating the commercialization and application of single-cell and spatial omic technologies in pharmaceutical and academic research.

Evaluating the Utilities of Foundation Models in Single-cell Data Analysis

Foundation Models (FMs) have made significant strides in both industrial and scientific domains. In this paper, we evaluate the performance of FMs for single-cell sequencing data analysis through comprehensive experiments across eight downstream tasks pertinent to single-cell data. Overall, the top FMs include scGPT, Geneformer, and CellPLM by considering model performances and user accessibility among ten single-cell FMs. However, by comparing these FMs with task-specific methods, we found that single-cell FMs may not consistently excel than task-specific methods in all tasks, which challenges the necessity of developing foundation models for single-cell analysis. In addition, we evaluated the effects of hyper-parameters, initial settings, and stability for training single-cell FMs based on a proposed scEval framework, and provide guidelines for pre-training and fine-tuning, to enhance the performances of single-cell FMs. Our work summarizes the current state of single-cell FMs, points to their constraints and avenues for future development, and offers a freely available evaluation pipeline to benchmark new models and improve method development.

Single-cell RNA sequencing in stroke and traumatic brain injury: Current achievements, challenges, and future perspectives on transcriptomic profiling

Single-cell RNA sequencing (scRNA-seq) is a high-throughput transcriptomic approach with the power to identify rare cells, discover new cellular subclusters, and describe novel genes. scRNA-seq can simultaneously reveal dynamic shifts in cellular phenotypes and heterogeneities in cellular subtypes. Since the publication of the first protocol on scRNA-seq in 2009, this evolving technology has continued to improve, through the use of cell-specific barcodes, adoption of droplet-based systems, and development of advanced computational methods. Despite induction of the cellular stress response during the tissue dissociation process, scRNA-seq remains a popular technology, and commercially available scRNA-seq methods have been applied to the brain. Recent advances in spatial transcriptomics now allow the researcher to capture the positional context of transcriptional activity, strengthening our knowledge of cellular organization and cell-cell interactions in spatially intact tissues. A combination of spatial transcriptomic data with proteomic, metabolomic, or chromatin accessibility data is a promising direction for future research. Herein, we provide an overview of the workflow, data analyses methods, and pros and cons of scRNA-seq technology. We also summarize the latest achievements of scRNA-seq in stroke and acute traumatic brain injury, and describe future applications of scRNA-seq and spatial transcriptomics.

SCRMshaw: Supervised cis-regulatory module prediction for insect genomes

As the number of sequenced insect genomes continues to grow, there is a pressing need for rapid and accurate annotation of their regulatory component. SCRMshaw is a computational tool designed to predict cis-regulatory modules ("enhancers") in the genomes of various insect species. A key advantage of SCRMshaw is its accessibility. It requires minimal resources-just a genome sequence and training data from known Drosophila regulatory sequences, which are readily available for download. Even users with modest computational skills can run SCRMshaw on a desktop computer for basic applications, although a high-performance computing cluster is recommended for optimal results. SCRMshaw can be tailored to specific needs: users can employ a single set of training data to predict enhancers associated with a particular gene expression pattern, or utilize multiple sets to provide a first-pass regulatory annotation for a newly-sequenced genome. This protocol provides an extensive update to the previously published SCRMshaw protocol and aligns with the methods used in a recent annotation of over 30 insect regulatory genomes. It includes the most recent modifications to the SCRMshaw protocol and details an end-to-end pipeline that begins with a sequenced genome and ends with a fully-annotated regulatory genome. Relevant scripts are available via GitHub, and a living protocol that will be updated as necessary is linked to this article at protocols.io.

Cut&tag: a powerful epigenetic tool for chromatin profiling

Analysis of transcription factors and chromatin modifications at the genome-wide level provides insights into gene regulatory processes, such as transcription, cell differentiation and cellular response. Chromatin immunoprecipitation is the most popular and powerful approach for mapping chromatin, and other enzyme-tethering techniques have recently become available for living cells. Among these, Cleavage Under Targets and Tagmentation (CUT&Tag) is a relatively novel chromatin profiling method that has rapidly gained popularity in the field of epigenetics since 2019. It has also been widely adapted to map chromatin modifications and TFs in different species, illustrating the association of these chromatin epitopes with various physiological and pathological processes. Scalable single-cell CUT&Tag can be combined with distinct platforms to distinguish cellular identity, epigenetic features and even spatial chromatin profiling. In addition, CUT&Tag has been developed as a strategy for joint profiling of the epigenome, transcriptome or proteome on the same sample. In this review, we will mainly consolidate the applications of CUT&Tag and its derivatives on different platforms, give a detailed explanation of the pros and cons of this technique as well as the potential development trends and applications in the future.

Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery

Over the past decade, single-cell genomics technologies have allowed scalable profiling of cell-type-specific features, which has substantially increased our ability to study cellular diversity and transcriptional programs in heterogeneous tissues. Yet our understanding of mechanisms of gene regulation or the rules that govern interactions between cell types is still limited. The advent of new computational pipelines and technologies, such as single-cell epigenomics and spatially resolved transcriptomics, has created opportunities to explore two new axes of biological variation: cell-intrinsic regulation of cell states and expression programs and interactions between cells. Here, we summarize the most promising and robust technologies in these areas, discuss their strengths and limitations and discuss key computational approaches for analysis of these complex datasets. We highlight how data sharing and integration, documentation, visualization and benchmarking of results contribute to transparency, reproducibility, collaboration and democratization in neuroscience, and discuss needs and opportunities for future technology development and analysis.

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.