Smart-RRBS for single-cell methylome and transcriptome analysis

Advances in single-cell long-read sequencing technologies

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

Extraembryonic gut endoderm cells undergo programmed cell death during development

Despite a distinct developmental origin, extraembryonic cells in mice contribute to gut endoderm and converge to transcriptionally resemble their embryonic counterparts. Notably, all extraembryonic progenitors share a non-canonical epigenome, raising several pertinent questions, including whether this landscape is reset to match the embryonic regulation and if extraembryonic cells persist into later development. Here we developed a two-colour lineage-tracing strategy to track and isolate extraembryonic cells over time. We find that extraembryonic gut cells display substantial memory of their developmental origin including retention of the original DNA methylation landscape and resulting transcriptional signatures. Furthermore, we show that extraembryonic gut cells undergo programmed cell death and neighbouring embryonic cells clear their remnants via non-professional phagocytosis. By midgestation, we no longer detect extraembryonic cells in the wild-type gut, whereas they persist and differentiate further in p53-mutant embryos. Our study provides key insights into the molecular and developmental fate of extraembryonic cells inside the embryo.

Mutant IDH inhibitors induce lineage differentiation in IDH-mutant oligodendroglioma

A subset of patients with IDH-mutant glioma respond to inhibitors of mutant IDH (IDHi), yet the molecular underpinnings of such responses are not understood. Here, we profiled by single-cell or single-nucleus RNA-sequencing three IDH-mutant oligodendrogliomas from patients who derived clinical benefit from IDHi. Importantly, the tissues were sampled on-drug, four weeks from treatment initiation. We further integrate our findings with analysis of single-cell and bulk transcriptomes from independent cohorts and experimental models. We find that IDHi treatment induces a robust differentiation toward the astrocytic lineage, accompanied by a depletion of stem-like cells and a reduction of cell proliferation. Furthermore, mutations in NOTCH1 are associated with decreased astrocytic differentiation and may limit the response to IDHi. Our study highlights the differentiating potential of IDHi on the cellular hierarchies that drive oligodendrogliomas and suggests a genetic modifier that may improve patient stratification.

Understanding the role of DNA methylation in colorectal cancer: Mechanisms, detection, and clinical significance

Colorectal cancer (CRC) is one of the deadliest malignancies worldwide, ranking third in incidence and second in mortality. Remarkably, early stage localized CRC has a 5-year survival rate of over 90%; in stark contrast, the corresponding 5-year survival rate for metastatic CRC (mCRC) is only 14%. Compounding this problem is the staggering lack of effective therapeutic strategies. Beyond genetic mutations, which have been identified as critical instigators of CRC initiation and progression, the importance of epigenetic modifications, particularly DNA methylation (DNAm), cannot be underestimated, given that DNAm can be used for diagnosis, treatment monitoring and prognostic evaluation. This review addresses the intricate mechanisms governing aberrant DNAm in CRC and its profound impact on critical oncogenic pathways. In addition, a comprehensive review of the various techniques used to detect DNAm alterations in CRC is provided, along with an exploration of the clinical utility of cancer-specific DNAm alterations.

Population epigenetics: DNA methylation in the plant omics era

DNA methylation plays an important role in many biological processes. The mechanisms underlying the establishment and maintenance of DNA methylation are well understood thanks to decades of research using DNA methylation mutants, primarily in Arabidopsis (Arabidopsis thaliana) accession Col-0. Recent genome-wide association studies (GWASs) using the methylomes of natural accessions have uncovered a complex and distinct genetic basis of variation in DNA methylation at the population level. Sequencing following bisulfite treatment has served as an excellent method for quantifying DNA methylation. Unlike studies focusing on specific accessions with reference genomes, population-scale methylome research often requires an additional round of sequencing beyond obtaining genome assemblies or genetic variations from whole-genome sequencing data, which can be cost prohibitive. Here, we provide an overview of recently developed bisulfite-free methods for quantifying methylation and cost-effective approaches for the simultaneous detection of genetic and epigenetic information. We also discuss the plasticity of DNA methylation in a specific Arabidopsis accession, the contribution of DNA methylation to plant adaptation, and the genetic determinants of variation in DNA methylation in natural populations. The recently developed technology and knowledge will greatly benefit future studies in population epigenomes.

Mapping crossover events of mouse meiotic recombination by restriction fragment ligation-based Refresh-seq

Single-cell whole-genome sequencing methods have undergone great improvements over the past decade. However, allele dropout, which means the inability to detect both alleles simultaneously in an individual diploid cell, largely restricts the application of these methods particularly for medical applications. Here, we develop a new single-cell whole-genome sequencing method based on third-generation sequencing (TGS) platform named Refresh-seq (restriction fragment ligation-based genome amplification and TGS). It is based on restriction endonuclease cutting and ligation strategy in which two alleles in an individual cell can be cut into equal fragments and tend to be amplified simultaneously. As a new single-cell long-read genome sequencing method, Refresh-seq features much lower allele dropout rate compared with SMOOTH-seq. Furthermore, we apply Refresh-seq to 688 sperm cells and 272 female haploid cells (secondary polar bodies and parthenogenetic oocytes) from F1 hybrid mice. We acquire high-resolution genetic map of mouse meiosis recombination at low sequencing depth and reveal the sexual dimorphism in meiotic crossovers. We also phase the structure variations (deletions and insertions) in sperm cells and female haploid cells with high precision. Refresh-seq shows great performance in screening aneuploid sperm cells and oocytes due to the low allele dropout rate and has great potential for medical applications such as preimplantation genetic diagnosis.

Advances in single-cell omics and multiomics for high-resolution molecular profiling

Single-cell omics technologies have revolutionized molecular profiling by providing high-resolution insights into cellular heterogeneity and complexity. Traditional bulk omics approaches average signals from heterogeneous cell populations, thereby obscuring important cellular nuances. Single-cell omics studies enable the analysis of individual cells and reveal diverse cell types, dynamic cellular states, and rare cell populations. These techniques offer unprecedented resolution and sensitivity, enabling researchers to unravel the molecular landscape of individual cells. Furthermore, the integration of multimodal omics data within a single cell provides a comprehensive and holistic view of cellular processes. By combining multiple omics dimensions, multimodal omics approaches can facilitate the elucidation of complex cellular interactions, regulatory networks, and molecular mechanisms. This integrative approach enhances our understanding of cellular systems, from development to disease. This review provides an overview of the recent advances in single-cell and multimodal omics for high-resolution molecular profiling. We discuss the principles and methodologies for representatives of each omics method, highlighting the strengths and limitations of the different techniques. In addition, we present case studies demonstrating the applications of single-cell and multimodal omics in various fields, including developmental biology, neurobiology, cancer research, immunology, and precision medicine.

Circulating cell-free DNA-based methylation pattern in plasma for early diagnosis of esophagus cancer

With the increased awareness of early tumor detection, the importance of detecting and diagnosing esophageal cancer in its early stages has been underscored. Studies have consistently demonstrated the crucial role of methylation levels in circulating cell-free DNA (cfDNA) in identifying and diagnosing early-stage cancer. cfDNA methylation pertains to the methylation state within the genomic scope of cfDNA and is strongly associated with cancer development and progression. Several research teams have delved into the potential application of cfDNA methylation in identifying early-stage esophageal cancer and have achieved promising outcomes. Recent research supports the high sensitivity and specificity of cfDNA methylation in early esophageal cancer diagnosis, providing a more accurate and efficient approach for early detection and improved clinical management. Accordingly, this review aims to present an overview of methylation-based cfDNA research with a focus on the latest developments in the early detection of esophageal cancer. Additionally, this review summarizes advanced analytical technologies for cfDNA methylation that have significantly benefited from recent advancements in separation and detection techniques, such as methylated DNA immunoprecipitation sequencing (MeDIP-seq). Recent findings suggest that biomarkers based on cfDNA methylation may soon find successful applications in the early detection of esophageal cancer. However, large-scale prospective clinical trials are required to identify the potential of these biomarkers.

Exploring Potential Epigenetic Biomarkers for Colorectal Cancer Metastasis

Metastatic progression is a complex, multistep process and the leading cause of cancer mortality. There is growing evidence that emphasises the significance of epigenetic modification, specifically DNA methylation and histone modifications, in influencing colorectal (CRC) metastasis. Epigenetic modifications influence the expression of genes involved in various cellular processes, including the pathways associated with metastasis. These modifications could contribute to metastatic progression by enhancing oncogenes and silencing tumour suppressor genes. Moreover, specific epigenetic alterations enable cancer cells to acquire invasive and metastatic characteristics by altering cell adhesion, migration, and invasion-related pathways. Exploring the involvement of DNA methylation and histone modification is crucial for identifying biomarkers that impact cancer prediction for metastasis in CRC. This review provides a summary of the potential epigenetic biomarkers associated with metastasis in CRC, particularly DNA methylation and histone modifications, and examines the pathways associated with these biomarkers.

Shortcut barcoding and early pooling for scalable multiplex single-cell reduced-representation CpG methylation sequencing at single nucleotide resolution

DNA methylation is essential for a wide variety of biological processes, yet the development of a highly efficient and robust technology remains a challenge for routine single-cell analysis. We developed a multiplex scalable single-cell reduced representation bisulfite sequencing (msRRBS) technology. It allows cell-specific barcoded DNA fragments of individual cells to be pooled before bisulfite conversion, free of enzymatic modification or physical capture of the DNA ends, and achieves read mapping rates of 62.5 ± 3.9%, covering 60.0 ± 1.4% of CpG islands and 71.6 ± 1.6% of promoters in K562 cells. Its reproducibility is shown in duplicates of bulk cells with close to perfect correlation (R = 0.97-0.99). At a low 1 Mb of clean reads, msRRBS provides highly consistent coverage of CpG islands and promoters, outperforming the conventional methods with orders of magnitude reduction in cost. Here, we use this method to characterize the distinct methylation patterns and cellular heterogeneity of six cell lines, plus leukemia and hepatocellular carcinoma models. Taking 4 h of hands-on time, msRRBS offers a unique, highly efficient approach for dissecting methylation heterogeneity in a variety of multicellular systems.

An in-depth comparison of linear and non-linear joint embedding methods for bulk and single-cell multi-omics

Multi-omic analyses are necessary to understand the complex biological processes taking place at the tissue and cell level, but also to make reliable predictions about, for example, disease outcome. Several linear methods exist that create a joint embedding using paired information per sample, but recently there has been a rise in the popularity of neural architectures that embed paired -omics into the same non-linear manifold. This work describes a head-to-head comparison of linear and non-linear joint embedding methods using both bulk and single-cell multi-modal datasets. We found that non-linear methods have a clear advantage with respect to linear ones for missing modality imputation. Performance comparisons in the downstream tasks of survival analysis for bulk tumor data and cell type classification for single-cell data lead to the following insights: First, concatenating the principal components of each modality is a competitive baseline and hard to beat if all modalities are available at test time. However, if we only have one modality available at test time, training a predictive model on the joint space of that modality can lead to performance improvements with respect to just using the unimodal principal components. Second, -omic profiles imputed by neural joint embedding methods are realistic enough to be used by a classifier trained on real data with limited performance drops. Taken together, our comparisons give hints to which joint embedding to use for which downstream task. Overall, product-of-experts performed well in most tasks and was reasonably fast, while early integration (concatenation) of modalities did quite poorly.

Single-cell sequencing technology applied to epigenetics for the study of tumor heterogeneity

BACKGROUND

Previous studies have traditionally attributed the initiation of cancer cells to genetic mutations, considering them as the fundamental drivers of carcinogenesis. However, recent research has shed light on the crucial role of epigenomic alterations in various cell types present within the tumor microenvironment, suggesting their potential contribution to tumor formation and progression. Despite these significant findings, the progress in understanding the epigenetic mechanisms regulating tumor heterogeneity has been impeded over the past few years due to the lack of appropriate technical tools and methodologies.

RESULTS

The emergence of single-cell sequencing has enhanced our understanding of the epigenetic mechanisms governing tumor heterogeneity by revealing the distinct epigenetic layers of individual cells (chromatin accessibility, DNA/RNA methylation, histone modifications, nucleosome localization) and the diverse omics (transcriptomics, genomics, multi-omics) at the single-cell level. These technologies provide us with new insights into the molecular basis of intratumoral heterogeneity and help uncover key molecular events and driving mechanisms in tumor development.

CONCLUSION

This paper provides a comprehensive review of the emerging analytical and experimental approaches of single-cell sequencing in various omics, focusing specifically on epigenomics. These approaches have the potential to capture and integrate multiple dimensions of individual cancer cells, thereby revealing tumor heterogeneity and epigenetic features. Additionally, this paper outlines the future trends of these technologies and their current technical limitations.

Decoding Human Biology and Disease Using Single-cell Omics Technologies

Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.

Multimodal deep learning approaches for single-cell multi-omics data integration

Integrating single-cell multi-omics data is a challenging task that has led to new insights into complex cellular systems. Various computational methods have been proposed to effectively integrate these rapidly accumulating datasets, including deep learning. However, despite the proven success of deep learning in integrating multi-omics data and its better performance over classical computational methods, there has been no systematic study of its application to single-cell multi-omics data integration. To fill this gap, we conducted a literature review to explore the use of multimodal deep learning techniques in single-cell multi-omics data integration, taking into account recent studies from multiple perspectives. Specifically, we first summarized different modalities found in single-cell multi-omics data. We then reviewed current deep learning techniques for processing multimodal data and categorized deep learning-based integration methods for single-cell multi-omics data according to data modality, deep learning architecture, fusion strategy, key tasks and downstream analysis. Finally, we provided insights into using these deep learning models to integrate multi-omics data and better understand single-cell biological mechanisms.

DNA methylation profiling to determine the primary sites of metastatic cancers using formalin-fixed paraffin-embedded tissues

Identifying the primary site of metastatic cancer is critical to guiding the subsequent treatment. Approximately 3-9% of metastatic patients are diagnosed with cancer of unknown primary sites (CUP) even after a comprehensive diagnostic workup. However, a widely accepted molecular test is still not available. Here, we report a method that applies formalin-fixed, paraffin-embedded tissues to construct reduced representation bisulfite sequencing libraries (FFPE-RRBS). We then generate and systematically evaluate 28 molecular classifiers, built on four DNA methylation scoring methods and seven machine learning approaches, using the RRBS library dataset of 498 fresh-frozen tumor tissues from primary cancer patients. Among these classifiers, the beta value-based linear support vector (BELIVE) performs the best, achieving overall accuracies of 81-93% for identifying the primary sites in 215 metastatic patients using top-k predictions (k = 1, 2, 3). Coincidentally, BELIVE also successfully predicts the tissue of origin in 81-93% of CUP patients (n = 68).

Methods and applications for single-cell and spatial multi-omics

The joint analysis of the genome, epigenome, transcriptome, proteome and/or metabolome from single cells is transforming our understanding of cell biology in health and disease. In less than a decade, the field has seen tremendous technological revolutions that enable crucial new insights into the interplay between intracellular and intercellular molecular mechanisms that govern development, physiology and pathogenesis. In this Review, we highlight advances in the fast-developing field of single-cell and spatial multi-omics technologies (also known as multimodal omics approaches), and the computational strategies needed to integrate information across these molecular layers. We demonstrate their impact on fundamental cell biology and translational research, discuss current challenges and provide an outlook to the future.

Technologies and applications of single-cell DNA methylation sequencing

DNA methylation is the most stable epigenetic modification. In mammals, it usually occurs at the cytosine of CpG dinucleotides. DNA methylation is essential for many physiological and pathological processes. Aberrant DNA methylation has been observed in human diseases, particularly cancer. Notably, conventional DNA methylation profiling technologies require a large amount of DNA, often from a heterogeneous cell population, and provide an average methylation level of many cells. It is often not realistic to collect sufficient numbers of cells, such as rare cells and circulating tumor cells in peripheral blood, for bulk sequencing assays. It is therefore essential to develop sequencing technologies that can accurately profile DNA methylation using small numbers of cells or even single cells. Excitingly, many single-cell DNA methylation sequencing and single-cell omics sequencing technologies have been developed, and applications of these methods have greatly expanded our understanding of the molecular mechanism of DNA methylation. Here, we summaries single-cell DNA methylation and multi-omics sequencing methods, delineate their applications in biomedical sciences, discuss technical challenges, and present our perspective on future research directions.

A Systemic Approach to Isolate, Retrieve, and Characterize Trophoblasts from the Maternal Circulation Using a Centrifugal Microfluidic Disc and a Multiple Single-Cell Retrieval Strategy

Rare cells in the blood often have rich clinical significance. Although their isolation is highly desirable, this goal remains elusive due to their rarity. This paper presents a systemic approach to isolate and characterize trophoblasts from the maternal circulation. A microfluidic rare cell disc assay (RaCDA) was designed to process an extremely large volume of up to 15 mL of blood in 30 min, depleting red blood cells (RBCs) and RBC-bound white blood cells (WBC) while isolating trophoblasts in the collection chip. To minimize cell loss, on-disc labeling of cells with fluorescent immuno-staining identified the trophoblasts. Retrieval of trophoblasts utilized an optimized strategy in which multiple single cells were retrieved within the same micropipette column, with each cell encapsulated in a fluid volume (50 nL) separated by an air pocket (10 nL). Further, whole-genome amplification (WGA) amplified contents from a few retrieved cells, followed by quality control (QC) on the success of WGA via housekeeping genes. For definitive confirmation of trophoblasts, short-tandem repeat (STR) of the WGA-amplified content was compared against STR from maternal WBC and amniocytes from amniocentesis. Results showed a mean recovery rate (capture efficiency) of 91.0% for spiked cells with a WBC depletion rate of 99.91%. The retrieval efficiency of single target cells of 100% was achieved for up to four single cells retrieved per micropipette column. Comparison of STR signatures revealed that the RaCDA can retrieve trophoblasts from the maternal circulation.

Computational Methods for Single-cell DNA Methylome Analysis

Dissecting intercellular epigenetic differences is key to understanding tissue heterogeneity. Recent advances in single-cell DNA methylome profiling have presented opportunities to resolve this heterogeneity at the maximum resolution. While these advances enable us to explore frontiers of chromatin biology and better understand cell lineage relationships, they pose new challenges in data processing and interpretation. This review surveys the current state of computational tools developed for single-cell DNA methylome data analysis. We discuss critical components of single-cell DNA methylome data analysis, including data preprocessing, quality control, imputation, dimensionality reduction, cell clustering, supervised cell annotation, cell lineage reconstruction, gene activity scoring, and integration with transcriptome data. We also highlight unique aspects of single-cell DNA methylome data analysis and discuss how techniques common to other single-cell omics data analyses can be adapted to analyze DNA methylomes. Finally, we discuss existing challenges and opportunities for future development.

Single-cell analysis of acute lymphoblastic and lineage-ambiguous leukemia: approaches and molecular insights

Despite recent progress in identifying the genetic drivers of acute lymphoblastic leukemia (ALL), prognosis remains poor for those individuals who experience disease recurrence. Moreover, acute leukemias of ambiguous lineage lack a biologically informed framework to guide classification and therapy. These needs have driven the adoption of multiple complementary single-cell sequencing approaches to explore key issues in the biology of these leukemias, including cell of origin, developmental hierarchy and ontogeny, and the molecular heterogeneity driving pathogenesis, progression, and therapeutic responsiveness. There are multiple single-cell techniques for profiling a specific modality, including RNA, DNA, chromatin accessibility and methylation; and an expanding range of approaches for simultaneous analysis of multiple modalities. Single-cell sequencing approaches have also enabled characterization of cell-intrinsic and -extrinsic features of ALL biology. In this review we describe these approaches and highlight the extensive heterogeneity that underpins ALL gene expression, cellular differentiation, and clonal architecture throughout disease pathogenesis and treatment resistance. In addition, we discuss the importance of the dynamic interactions that occur between leukemia cells and the nonleukemia microenvironment. We discuss potential opportunities and limitations of single-cell sequencing for the study of ALL biology and treatment responsiveness.

Non-Association of Driver Alterations in PTEN with Differential Gene Expression and Gene Methylation in IDH1 Wildtype Glioblastomas

During oncogenesis, alterations in driver genes called driver alterations (DAs) modulate the transcriptome, methylome and proteome through oncogenic signaling pathways. These modulatory effects of any DA may be analyzed by examining differentially expressed mRNAs (DEMs), differentially methylated genes (DMGs) and differentially expressed proteins (DEPs) between tumor samples with and without that DA. We aimed to analyze these modulations with 12 common driver genes in Isocitrate Dehydrogenase 1 wildtype glioblastomas (IDH1-W-GBs). Using Cbioportal, groups of tumor samples with and without DAs in these 12 genes were generated from the IDH1-W-GBs available from "The Cancer Genomics Atlas Firehose Legacy Study Group" (TCGA-FL-SG) on Glioblastomas (GBs). For all 12 genes, samples with and without DAs were compared for DEMs, DMGs and DEPs. We found that DAs in PTEN were unassociated with any DEM or DMG in contrast to DAs in all other drivers, which were associated with several DEMs and DMGs. This contrasting PTEN-related property of being unassociated with differential gene expression or methylation in IDH1-W-GBs was unaffected by concurrent DAs in other common drivers or by the types of DAs affecting PTEN. From the lists of DEMs and DMGs associated with some common drivers other than PTEN, enriched gene ontology terms and insights into the co-regulatory effects of these drivers on the transcriptome were obtained. The findings from this study can improve our understanding of the molecular mechanisms underlying gliomagenesis with potential therapeutic benefits.

Reduced Representation Bisulfite Sequencing (RRBS)

Reduced representation bisulfite sequencing (RRBS) enriches CpG-rich genomic regions using the MspI restriction enzyme-which cuts DNA at all CCGG sites, regardless of their DNA methylation status at the CG site-and enables the measurement of DNA methylation levels at 5% ~ 10% of all CpG sites in the mammalian genome. RRBS has been utilized in a large number of studies as a cost-effective method to investigate DNA methylation patterns, mainly at gene promoters and CpG islands. Here, we describe protocols for gel-free preparation of RRBS libraries, quality control, sequencing, and data analysis. Our protocols typically require nine cycles of polymerase chain reaction (PCR) amplification to obtain a sufficient amount of library for sequencing when 100 ng of genomic DNA is used as a starting material; moreover, it takes 3 days to complete library preparation and quality control procedures for up to eight samples.

Single-Cell DNA Methylation Analysis in Cancer

Morphological, transcriptomic, and genomic defects are well-explored parameters of cancer biology. In more recent years, the impact of epigenetic influences, such as DNA methylation, is becoming more appreciated. Aberrant DNA methylation has been implicated in many types of cancers, influencing cell type, state, transcriptional regulation, and genomic stability to name a few. Traditionally, large populations of cells from the tissue of interest are coalesced for analysis, producing averaged methylome data. Considering the inherent heterogeneity of cancer, analysing populations of cells as a whole denies the ability to discover novel aberrant methylation patterns, identify subpopulations, and trace cell lineages. Due to recent advancements in technology, it is now possible to obtain methylome data from single cells. This has both research and clinical implications, ranging from the identification of biomarkers to improved diagnostic tools. As with all emerging technologies, distinct experimental, bioinformatic, and practical challenges present themselves. This review begins with exploring the potential impact of single-cell sequencing on understanding cancer biology and how it could eventually benefit a clinical setting. Following this, the techniques and experimental approaches which made this technology possible are explored. Finally, the present challenges currently associated with single-cell DNA methylation sequencing are described.

Into the multiverse: advances in single-cell multiomic profiling

Single-cell transcriptomic approaches have revolutionised the study of complex biological systems, with the routine measurement of gene expression in thousands of cells enabling construction of whole-organism cell atlases. However, the transcriptome is just one layer amongst many that coordinate to define cell type and state and, ultimately, function. In parallel with the widespread uptake of single-cell RNA-seq (scRNA-seq), there has been a rapid emergence of methods that enable multiomic profiling of individual cells, enabling parallel measurement of intercellular heterogeneity in the genome, epigenome, transcriptome, and proteomes. Linking measurements from each of these layers has the potential to reveal regulatory and functional mechanisms underlying cell behaviour in healthy development and disease.

Single-cell technologies: a new lens into epigenetic regulation in development

The totipotent zygote gives rise to diverse cell types through a series of well-orchestrated regulatory mechanisms. Epigenetic modifiers play an essential, though still poorly understood, role in the transition from pluripotency towards organogenesis. However, recent advances in single-cell technologies have enabled an unprecedented, high-resolution dissection of this crucial developmental window, highlighting more cell-type-specific functions of these ubiquitous regulators. In this review, we discuss and contextualize several recent studies that explore epigenetic regulation during mouse embryogenesis, emphasizing the opportunities presented by single-cell technologies, in vivo perturbation approaches as well as advanced in vitro models to characterize dynamic developmental transitions.

Single cell cancer epigenetics

Bulk sequencing methodologies have allowed us to make great progress in cancer research. Unfortunately, these techniques lack the resolution to fully unravel the epigenetic mechanisms that govern tumor heterogeneity. Consequently, many novel single cell-sequencing methodologies have been developed over the past decade, allowing us to explore the epigenetic components that regulate different aspects of cancer heterogeneity, namely: clonal heterogeneity, tumor microenvironment (TME), spatial organization, intratumoral differentiation programs, metastasis, and resistance mechanisms. In this review, we explore the different sequencing techniques that enable researchers to study different aspects of epigenetics (DNA methylation, chromatin accessibility, histone modifications, DNA-protein interactions, and chromatin 3D architecture) at the single cell level, their potential applications in cancer, and their current technical limitations.

Machine learning: its challenges and opportunities in plant system biology

Sequencing technologies are evolving at a rapid pace, enabling the generation of massive amounts of data in multiple dimensions (e.g., genomics, epigenomics, transcriptomic, metabolomics, proteomics, and single-cell omics) in plants. To provide comprehensive insights into the complexity of plant biological systems, it is important to integrate different omics datasets. Although recent advances in computational analytical pipelines have enabled efficient and high-quality exploration and exploitation of single omics data, the integration of multidimensional, heterogenous, and large datasets (i.e., multi-omics) remains a challenge. In this regard, machine learning (ML) offers promising approaches to integrate large datasets and to recognize fine-grained patterns and relationships. Nevertheless, they require rigorous optimizations to process multi-omics-derived datasets. In this review, we discuss the main concepts of machine learning as well as the key challenges and solutions related to the big data derived from plant system biology. We also provide in-depth insight into the principles of data integration using ML, as well as challenges and opportunities in different contexts including multi-omics, single-cell omics, protein function, and protein-protein interaction. KEY POINTS: • The key challenges and solutions related to the big data derived from plant system biology have been highlighted. • Different methods of data integration have been discussed. • Challenges and opportunities of the application of machine learning in plant system biology have been highlighted and discussed.

Deciphering the Immune-Tumor Interplay During Early-Stage Lung Cancer Development via Single-Cell Technology

Lung cancer is the leading cause of cancer-related death worldwide. Cancer immunotherapy has shown great success in treating advanced-stage lung cancer but has yet been used to treat early-stage lung cancer, mostly due to lack of understanding of the tumor immune microenvironment in early-stage lung cancer. The immune system could both constrain and promote tumorigenesis in a process termed immune editing that can be divided into three phases, namely, elimination, equilibrium, and escape. Current understanding of the immune response toward tumor is mainly on the "escape" phase when the tumor is clinically detectable. The detailed mechanism by which tumor progenitor lesions was modulated by the immune system during early stage of lung cancer development remains elusive. The advent of single-cell sequencing technology enables tumor immunologists to address those fundamental questions. In this perspective, we will summarize our current understanding and big gaps about the immune response during early lung tumorigenesis. We will then present the state of the art of single-cell technology and then envision how single-cell technology could be used to address those questions. Advances in the understanding of the immune response and its dynamics during malignant transformation of pre-malignant lesion will shed light on how malignant cells interact with the immune system and evolve under immune selection. Such knowledge could then contribute to the development of precision and early intervention strategies toward lung malignancy.

Epigenetics and tissue immunity-Translating environmental cues into functional adaptations

There is an increasing appreciation that many innate and adaptive immune cell subsets permanently reside within non-lymphoid organs, playing a critical role in tissue homeostasis and defense. The best characterized are macrophages and tissue-resident T lymphocytes that work in concert with organ structural cells to generate appropriate immune responses and are functionally shaped by organ-specific environmental cues. The interaction of tissue epithelial, endothelial and stromal cells is also required to attract, differentiate, polarize and maintain organ immune cells in their tissue niche. All of these processes require dynamic regulation of cellular transcriptional programmes, with epigenetic mechanisms playing a critical role, including DNA methylation and post-translational histone modifications. A failure to appropriately regulate immune cell transcription inevitably results in inadequate or inappropriate immune responses and organ pathology. Here, with a focus on the mammalian kidney, an organ which generates differing regional environmental cues (including hypersalinity and hypoxia) due to its physiological functions, we will review the basic concepts of tissue immunity, discuss the technologies available to profile epigenetic modifications in tissue immune cells, including those that enable single-cell profiling, and consider how these mechanisms influence the development, phenotype, activation and function of different tissue immune cell subsets, as well as the immunological function of structural cells.

Single-cell omics: Overview, analysis, and application in biomedical science

Single-cell sequencing methods provide the highest resolution insight into cellular heterogeneity. Owing to their rapid growth and decreasing cost, they are now widely accessible to scientists worldwide. Single-cell technologies enable analysis of a large number of cells, making them powerful tools to characterise rare cell types and refine our understanding of diverse cell states. Moreover, single-cell application in biomedical sciences helps to unravel mechanisms related to disease pathogenesis and outcome. In this Viewpoint, we briefly describe existing single-cell methods (genomics, transcriptomics, epigenomics, proteomics, and mulitomics), comment on available analysis tools, and give examples of method applications in the biomedical field.