Simultaneous single-cell three-dimensional genome and gene expression profiling uncovers dynamic enhancer connectivity underlying olfactory receptor choice

Genomic snowflakes: how the uniqueness of DNA folding allows us to smell the chemical universe

Olfactory receptor (OR) gene choice, the stable expression of one out of >2000 OR alleles by olfactory sensory neurons, constitutes a gene regulatory process that is driven by three-dimensional nuclear architecture. Moreover, the differentiation-dependent process that culminates in monogenic and monoallelic OR transcription represents a powerful demonstration of the rich mechanistic insight that single-cell genomics and multiomics can provide toward the understanding of a biological process. At this review, we describe the latest advances in the understanding of OR gene regulation and highlight important standing questions regarding the emerging specificity of ultra-long-range genomic interaction and the contribution of transcription and noncoding RNAs.

Exploring the untapped potential of single-cell and spatial omics in plant biology

Advances in single-cell and spatial omics technologies have revolutionised biology by revealing the diverse molecular states of individual cells and their spatial organization within tissues. The field of plant biology has widely adopted single-cell transcriptome and chromatin accessibility profiling and spatial transcriptomics, which extend traditional cell biology and genomics analyses and provide unique opportunities to reveal molecular and cellular dynamics of tissues. Using these technologies, comprehensive cell atlases have been generated in several model plant species, providing valuable platforms for discovery and tool development. Other emerging technologies related to single-cell and spatial omics, such as multiomics, lineage tracing, molecular recording, and high-content genetic and chemical perturbation phenotyping, offer immense potential for deepening our understanding of plant biology yet remain underutilised due to unique technical challenges and resource availability. Overcoming plant-specific barriers, such as cell wall complexity and limited antibody resources, alongside community-driven efforts in developing more complete reference atlases and computational tools, will accelerate progress. The synergy between technological innovation and targeted biological questions is poised to drive significant discoveries, advancing plant science. This review highlights the current applications of single-cell and spatial omics technologies in plant research and introduces emerging approaches with the potential to transform the field.

Spatiotemporal 3D chromatin organization across multiple brain regions during human fetal development

Elucidating the regulatory mechanisms underlying the development of different brain regions in humans is essential for understanding advanced cognition and neuropsychiatric disorders. However, the spatiotemporal organization of three-dimensional (3D) chromatin structure and its regulatory functions across different brain regions remain poorly understood. Here, we generated an atlas of high-resolution 3D chromatin structure across six developing human brain regions, including the prefrontal cortex (PFC), primary visual cortex (V1), cerebellum (CB), subcortical corpus striatum (CS), thalamus (TL), and hippocampus (HP), spanning gestational weeks 11-26. We found that the spatial and temporal dynamics of 3D chromatin organization play a key role in regulating brain region development. We also identified H3K27ac-marked super-enhancers as key contributors to shaping brain region-specific 3D chromatin structures and gene expression patterns. Finally, we uncovered hundreds of neuropsychiatric GWAS SNP-linked genes, shedding light on critical molecules in various neuropsychiatric disorders. In summary, our findings provide important insights into the 3D chromatin regulatory mechanisms governing brain region-specific development and can serve as a valuable resource for advancing our understanding of neuropsychiatric disorders.

Solid phase transitions as a solution to the genome folding paradox

Ultra-long-range genomic contacts, which are key components of neuronal genome architecture1-3, constitute a biochemical enigma. This is because regulatory DNA elements make selective and stable contacts with DNA sequences located hundreds of kilobases away, instead of interacting with proximal sequences occupied by the exact same transcription factors1,4. This is exemplified in olfactory sensory neurons (OSNs), in which only a fraction of LHX2-, EBF1- and LDB1-bound sites interact with each other, converging into highly selective multi-chromosomal enhancer hubs5. To obtain biochemical insight into this process, here we assembled olfactory receptor (OR) enhancer hubs in vitro with recombinant proteins and enhancer DNA. Cell-free reconstitution of enhancer hubs revealed that OR enhancers form nucleoprotein condensates with unusual, solid-like characteristics. Assembly of these solid condensates is orchestrated by specific DNA motifs enriched in OR enhancers, which are likely to confer distinct homotypic properties on their resident LHX2-EBF1-LDB1 complexes. Single-molecule tracking and pulse-chase experiments in vivo confirmed that LHX2 and EBF1 assemble OR-transcription-competent condensates with solid properties in OSN nuclei, under physiological concentrations of protein. Thus, homophilic nucleoprotein interactions that are influenced by DNA sequence generate new types of biomolecular condensate, which might provide a generalizable explanation for the stability and specificity of long-range genomic contacts across cell types.

Tri-omic single-cell mapping of the 3D epigenome and transcriptome in whole mouse brains throughout the lifespan

Exploring the genomic basis of transcriptional programs has been a long-standing research focus. Here we report a single-cell method, ChAIR, to map chromatin accessibility, chromatin interactions and RNA expression simultaneously. After validating in cultured cells, we applied ChAIR to whole mouse brains and delineated the concerted dynamics of epigenome, three-dimensional (3D) genome and transcriptome during maturation and aging. In particular, gene-centric chromatin interactions and open chromatin states provided 3D epigenomic mechanism underlying cell-type-specific transcription and revealed spatially resolved specificity. Importantly, the composition of short-range and ultralong chromatin contacts in individual cells is remarkably correlated with transcriptional activity, open chromatin state and genome folding density. This genomic property, along with associated cellular properties, differs in neurons and non-neuronal cells across different anatomic regions throughout the lifespan, implying divergent nuclear mechano-genomic mechanisms at play in brain cells. Our results demonstrate ChAIR's robustness in revealing single-cell 3D epigenomic states of cell-type-specific transcription in complex tissues.

Advances and applications in single-cell and spatial genomics

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

Unveiling Multi-Scale Architectural Features in Single-Cell Hi-C Data Using scCAFE

Single-cell Hi-C (scHi-C) has provided unprecedented insights into the heterogeneity of 3D genome organization. However, its sparse and noisy nature poses challenges for computational analyses, such as chromatin architectural feature identification. Here, scCAFE is introduced, which is a deep learning model for the multi-scale detection of architectural features at the single-cell level. scCAFE provides a unified framework for annotating chromatin loops, TAD-like domains (TLDs), and compartments across individual cells. This model outperforms previous scHi-C loop calling methods and delivers accurate predictions of TLDs and compartments that are biologically consistent with previous studies. The resulting single-cell annotations also offer a measure to characterize the heterogeneity of different levels of architectural features across cell types. This heterogeneity is then leveraged to identify a series of marker loop anchors, demontrating the potential of the 3D genome data to annotate cell identities without the aid of simultaneously sequenced omics data. Overall, scCAFE not only serves as a useful tool for analyzing single-cell genomic architecture, but also paves the way for precise cell-type annotations solely based on 3D genome features.

Dual Desalting Electrospray Strategy for In-Cell Mass Spectrometry to Reveal Novel Sphingolipid Metabolism in an Epithelial-Mesenchymal Transition

The metabolome offers a direct snapshot of cell function and can respond to external changes within a very brief time scale of seconds or minutes. In situ in-cell mass spectrometry, with minimal pretreatment, enables direct analysis in a nonvolatile salt environment. However, it is challenging to obtain abundant metabolomes due to the inherent incompatibility of nonvolatile salts with mass spectrometry. Here, we developed a dual desalting electrospray ionization mass spectrometry (dd-ESI MS) technology for in-cell MS measurement to obtain a comprehensive and native cellular metabolome in nonvolatile salt buffers. The salt ions and metabolites were initially separated through the mild electrophoretic effect of induced nanoelectrospray ionization (InESI). In the following electrospray process, the complex interactions between aqueous droplets and methanol droplets further enhanced the desalting effect. Compared with nanoESI, dd-ESI MS exhibited stronger salt tolerance and higher sensitivity for cell metabolome analysis in PBS buffer. Interestingly, we observed a significant enrichment of the sphingolipid metabolism pathway during the epithelial-mesenchymal transition, a metabolic pathway not previously confirmed by metabolomics techniques. In addition, the transcriptome analysis also revealed consistent gene changes, further confirming the validity of our findings. dd-ESI MS enabled the acquisition of a more comprehensive and native metabolome, providing novel insights into complex physiological processes.

Single-Cell Hi-C Technologies and Computational Data Analysis

Single-cell chromatin conformation capture (scHi-C) techniques have evolved to provide significant insights into the structural organization and regulatory mechanisms in individual cells. Although many scHi-C protocols have been developed, they often involve intricate procedures and the resulting data are sparse, leading to computational challenges for systematic data analysis and limited applicability. This review provides a comprehensive overview, quantitative evaluation of thirteen protocols and practical guidance on computational topics. It is first assessed the efficiency of these protocols based on the total number of contacts recovered per cell and the cis/trans ratio. It is then provided systematic considerations for scHi-C quality control and data imputation. Additionally, the capabilities and implementations of various analysis methods, covering cell clustering, A/B compartment calling, topologically associating domain (TAD) calling, loop calling, 3D reconstruction, scHi-C data simulation and differential interaction analysis is summarized. It is further highlighted key computational challenges associated with the specific complexities of scHi-C data and propose potential solutions.

Uncovering topologically associating domains from three-dimensional genome maps with TADGATE

Topologically associating domains (TADs) are essential components of three-dimensional (3D) genome organization and significantly influence gene transcription regulation. However, accurately identifying TADs from sparse chromatin contact maps and exploring the structural and functional elements within TADs remain challenging. To this end, we develop TADGATE, a graph attention auto-encoder that can generate imputed maps from sparse Hi-C contact maps while adaptively preserving or enhancing the underlying topological structures, thereby facilitating TAD identification. TADGATE captures specific attention patterns with two types of units within TADs and demonstrates TAD organization relates to chromatin compartmentalization with diverse biological properties. We identify many structural and functional elements within TADs, with their abundance reflecting the overall properties of these domains. We applied TADGATE to sparse and noisy Hi-C contact maps from 21 human tissues or cell lines. That improved the clarity of TAD structures, allowing us to investigate conserved and cell-type-specific boundaries and uncover cell-type-specific transcriptional regulatory mechanisms associated with topological domains. We also demonstrated TADGATE's capability to fill in sparse single-cell Hi-C contact maps and identify TAD-like domains within them, revealing the specific domain boundaries with distinct heterogeneity and the shared backbone boundaries characterized by strong CTCF enrichment and high gene expression levels.

Nuclear remodeling during cell fate transitions

Totipotent stem cells, the earliest cells in embryonic development, can differentiate into complete embryos and extra-embryonic tissues, making them essential for understanding both development and regenerative medicine. This review examines recent advances in the dynamic remodeling of nuclear structures during the transition between totipotency and pluripotency, as well as other cell fate transition processes. Additionally, we highlight innovative experimental and computational methods that elucidate the relationship between nuclear architecture and cell fate decisions. By integrating these insights, we aim to enhance our understanding of how nuclear remodeling influences totipotency and other cell fate transitions, paving the way for future research in this critical field.

SEE: A Method for Predicting the Dynamics of Chromatin Conformation Based on Single-Cell Gene Expression

The dynamics of chromatin conformation involve continuous and reversible changes within the nucleus of a cell, which participate in regulating processes such as gene expression, DNA replication, and damage repair. Here, SEE is introduced, an artificial intelligence (AI) method that utilizes autoencoder and transformer techniques to analyze chromatin dynamics using single-cell RNA sequencing data and a limited number of single-cell Hi-C maps. SEE is employed to investigate chromatin dynamics across different scales, enabling the detection of (i) rearrangements in topologically associating domains (TADs), and (ii) oscillations in chromatin interactions at gene loci. Additionally, SEE facilitates the interpretation of disease-associated single-nucleotide polymorphisms (SNPs) by leveraging the dynamic features of chromatin conformation. Overall, SEE offers a single-cell, high-resolution approach to analyzing chromatin dynamics in both developmental and disease contexts.

Droplet-based high-throughput 3D genome structure mapping of single cells with simultaneous transcriptomics

Single-cell three-dimensional (3D) genome techniques have advanced our understanding of cell-type-specific chromatin structures in complex tissues, yet current methodologies are limited in cell throughput. Here we introduce a high-throughput single-cell Hi-C (dscHi-C) approach and its transcriptome co-assay (dscHi-C-multiome) using droplet microfluidics. Using dscHi-C, we investigate chromatin structural changes during mouse brain aging by profiling 32,777 single cells across three developmental stages (3 months, 12 months, and 23 months), yielding a median of 78,220 unique contacts. Our results show that genes with significant structural changes are enriched in pathways related to metabolic process and morphology change in neurons, and innate immune response in glial cells, highlighting the role of 3D genome organization in physiological brain aging. Furthermore, our multi-omics joint assay, dscHi-C-multiome, enables precise cell type identification in the adult mouse brain and uncovers the intricate relationship between genome architecture and gene expression. Collectively, we developed the sensitive, high-throughput dscHi-C and its multi-omics derivative, dscHi-C-multiome, demonstrating their potential for large-scale cell atlas studies in development and disease.

Mapping the 3D genome architecture

The spatial organization of the genome plays a critical role in regulating gene expression, cellular differentiation, and genome stability. This review provides an in-depth examination of the methodologies, computational tools, and frameworks developed to map the three-dimensional (3D) architecture of the genome, focusing on both ligation-based and ligation-free techniques. We also explore the limitations of these methods, including biases introduced by restriction enzyme digestion and ligation inefficiencies, and compare them to more recent ligation-free approaches such as Genome Architecture Mapping (GAM) and Split-Pool Recognition of Interactions by Tag Extension (SPRITE). These techniques offer unique insights into higher-order chromatin structures by bypassing ligation steps, thus enabling the capture of complex multi-way interactions that are often challenging to resolve with traditional methods. Furthermore, we discuss the integration of chromatin interaction data with other genomic layers through multimodal approaches, including recent advances in single-cell technologies like sci-HiC and scSPRITE, which help unravel the heterogeneity of chromatin architecture in development and disease.

A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles

Advances in three-dimensional (3D) genomics have revealed the spatial characteristics of chromatin interactions in gene expression regulation, which is crucial for understanding molecular mechanisms in biological processes. High-throughput technologies like ChIA-PET, Hi-C, and their derivatives methods have greatly enhanced our knowledge of 3D chromatin architecture. However, the chromatin interaction mechanisms remain largely unexplored. Deep learning, with its powerful feature extraction and pattern recognition capabilities, offers a promising approach for integrating multi-omics data, to build accurate predictive models of chromatin interaction matrices. This review systematically summarizes recent advances in chromatin interaction matrix prediction models. By integrating DNA sequences and epigenetic signals, we investigate the latest developments in these methods. This article details various models, focusing on how one-dimensional (1D) information transforms into the 3D structure chromatin interactions, and how the integration of different deep learning modules specifically affects model accuracy. Additionally, we discuss the critical role of DNA sequence information and epigenetic markers in shaping 3D genome interaction patterns. Finally, this review addresses the challenges in predicting chromatin interaction matrices, in order to improve the precise mapping of chromatin interaction matrices and DNA sequence, and supporting the transformation and theoretical development of 3D genomics across biological systems.

Single-cell sequencing: Current applications in various tuberculosis specimen types

Tuberculosis (TB) is a chronic disease caused by Mycobacterium tuberculosis (M.tb) and responsible for millions of deaths worldwide each year. It has a complex pathogenesis that primarily affects the lungs but can also impact systemic organs. In recent years, single-cell sequencing technology has been utilized to characterize the composition and proportion of immune cell subpopulations associated with the pathogenesis of TB disease since it has a high resolution that surpasses conventional techniques. This paper reviews the current use of single-cell sequencing technologies in TB research and their application in analysing specimens from various sources of TB, primarily peripheral blood and lung specimens. The focus is on how these technologies can reveal dynamic changes in immune cell subpopulations, genes and proteins during disease progression after M.tb infection. Based on the current findings, single-cell sequencing has significant potential clinical value in the field of TB research. Next, we will focus on the real-world applications of the potential targets identified through single-cell sequencing for diagnostics, therapeutics and the development of effective vaccines.

ImputeHiFI: An Imputation Method for Multiplexed DNA FISH Data by Utilizing Single-Cell Hi-C and RNA FISH Data

Although multiplexed DNA fluorescence in situ hybridization (FISH) enables tracking the spatial localization of thousands of genomic loci using probes within individual cells, the high rates of undetected probes impede the depiction of 3D chromosome structures. Current data imputation methods neither utilize single-cell Hi-C data, which elucidate 3D genome architectures using sequencing nor leverage multimodal RNA FISH data that reflect cell-type information, limiting the effectiveness of these methods in complex tissues such as the mouse brain. To this end, a novel multiplexed DNA FISH imputation method named ImputeHiFI is proposed, which fully utilizes the complementary structural information from single-cell Hi-C data and the cell type signature from RNA FISH data to obtain a high-fidelity and complete spatial location of chromatin loci. ImputeHiFI enhances cell clustering, compartment identification, and cell subtype detection at the single-cell level in the mouse brain. ImputeHiFI improves the recognition of cell-type-specific loops in three high-resolution datasets. In short, ImputeHiFI is a powerful tool capable of imputing multiplexed DNA FISH data from various resolutions and imaging protocols, facilitating studies of 3D genome structures and functions.

The 3D Genome in Brain Development: An Exploration of Molecular Mechanisms and Experimental Methods

The human brain contains multiple cell types that are spatially organized into functionally distinct regions. The proper development of the brain requires complex gene regulation mechanisms in both neurons and the non-neuronal cell types that support neuronal function. Studies across the last decade have discovered that the 3D nuclear organization of the genome is instrumental in the regulation of gene expression in the diverse cell types of the brain. In this review, we describe the fundamental biochemical mechanisms that regulate the 3D genome, and comprehensively describe in vitro and ex vivo studies on mouse and human brain development that have characterized the roles of the 3D genome in gene regulation. We highlight the significance of the 3D genome in linking distal enhancers to their target promoters, which provides insights on the etiology of psychiatric and neurological disorders, as the genetic variants associated with these disorders are primarily located in noncoding regulatory regions. We also describe the molecular mechanisms that regulate chromatin folding and gene expression in neurons. Furthermore, we describe studies with an evolutionary perspective, which have investigated features that are conserved from mice to human, as well as human gained 3D chromatin features. Although most of the insights on disease and molecular mechanisms have been obtained from bulk 3C based experiments, we also highlight other approaches that have been developed recently, such as single cell 3C approaches, as well as non-3C based approaches. In our future perspectives, we highlight the gaps in our current knowledge and emphasize the need for 3D genome engineering and live cell imaging approaches to elucidate mechanisms and temporal dynamics of chromatin interactions, respectively.

Emerging methods and applications in 3D genomics

Since the advent of Hi-C in 2009, a plethora of high-throughput sequencing methods have emerged to profile the three-dimensional (3D) organization of eukaryotic genomes, igniting the era of 3D genomics. In recent years, the genomic resolution achievable by these approaches has dramatically increased and several single-cell versions of Hi-C have been developed. Moreover, a new repertoire of tools not based on proximity ligation of digested chromatin has emerged, enabling the investigation of the higher-order organization of chromatin in the nucleus. In this review, we summarize the expanding portfolio of 3D genomic technologies, highlighting recent developments and applications from the past three years. Lastly, we present an outlook of where this technology-driven field might be headed.

Establishment and maintenance of random monoallelic expression

This Review elucidates the regulatory principles of random monoallelic expression by focusing on two well-studied examples: the X-chromosome inactivation regulator Xist and the olfactory receptor gene family. Although the choice of a single X chromosome or olfactory receptor occurs in different developmental contexts, common gene regulatory principles guide monoallelic expression in both systems. In both cases, an event breaks the symmetry between genetically and epigenetically identical copies of the gene, leading to the expression of one single random allele, stabilized through negative feedback control. Although many regulatory steps that govern the establishment and maintenance of monoallelic expression have been identified, key pieces of the puzzle are still missing. We provide an overview of the current knowledge and models for the monoallelic expression of Xist and olfactory receptors. We discuss their similarities and differences, and highlight open questions and approaches that could guide the study of other monoallelically expressed genes.