Joint single-cell DNA accessibility and protein epitope profiling reveals environmental regulation of epigenomic heterogeneity

Microfluidic Biochips for Single-Cell Isolation and Single-Cell Analysis of Multiomics and Exosomes

Single-cell multiomic and exosome analyses are potent tools in various fields, such as cancer research, immunology, neuroscience, microbiology, and drug development. They facilitate the in-depth exploration of biological systems, providing insights into disease mechanisms and aiding in treatment. Single-cell isolation, which is crucial for single-cell analysis, ensures reliable cell isolation and quality control for further downstream analyses. Microfluidic chips are small lightweight systems that facilitate efficient and high-throughput single-cell isolation and real-time single-cell analysis on- or off-chip. Therefore, most current single-cell isolation and analysis technologies are based on the single-cell microfluidic technology. This review offers comprehensive guidance to researchers across different fields on the selection of appropriate microfluidic chip technologies for single-cell isolation and analysis. This review describes the design principles, separation mechanisms, chip characteristics, and cellular effects of various microfluidic chips available for single-cell isolation. Moreover, this review highlights the implications of using this technology for subsequent analyses, including single-cell multiomic and exosome analyses. Finally, the current challenges and future prospects of microfluidic chip technology are outlined for multiplex single-cell isolation and multiomic and exosome analyses.

Dominance reversals: the resolution of genetic conflict and maintenance of genetic variation

Beneficial reversals of dominance reduce the costs of genetic trade-offs and can enable selection to maintain genetic variation for fitness. Beneficial dominance reversals are characterized by the beneficial allele for a given context (e.g. habitat, developmental stage, trait or sex) being dominant in that context but recessive where deleterious. This context dependence at least partially mitigates the fitness consequence of heterozygotes carrying one non-beneficial allele for their context and can result in balancing selection that maintains alternative alleles. Dominance reversals are theoretically plausible and are supported by mounting empirical evidence. Here, we highlight the importance of beneficial dominance reversals as a mechanism for the mitigation of genetic conflict and review the theory and empirical evidence for them. We identify some areas in need of further research and development and outline three methods that could facilitate the identification of antagonistic genetic variation (dominance ordination, allele-specific expression and allele-specific ATAC-Seq (assay for transposase-accessible chromatin with sequencing)). There is ample scope for the development of new empirical methods as well as reanalysis of existing data through the lens of dominance reversals. A greater focus on this topic will expand our understanding of the mechanisms that resolve genetic conflict and whether they maintain genetic variation.

Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges

Machine learning (ML), particularly deep learning (DL), has made rapid and substantial progress in synthetic biology in recent years. Biotechnological applications of biosystems, including pathways, enzymes, and whole cells, are being probed frequently with time. The intricacy and interconnectedness of biosystems make it challenging to design them with the desired properties. ML and DL have a synergy with synthetic biology. Synthetic biology can be employed to produce large data sets for training models (for instance, by utilizing DNA synthesis), and ML/DL models can be employed to inform design (for example, by generating new parts or advising unrivaled experiments to perform). This potential has recently been brought to light by research at the intersection of engineering biology and ML/DL through achievements like the design of novel biological components, best experimental design, automated analysis of microscopy data, protein structure prediction, and biomolecular implementations of ANNs (Artificial Neural Networks). I have divided this review into three sections. In the first section, I describe predictive potential and basics of ML along with myriad applications in synthetic biology, especially in engineering cells, activity of proteins, and metabolic pathways. In the second section, I describe fundamental DL architectures and their applications in synthetic biology. Finally, I describe different challenges causing hurdles in the progress of ML/DL and synthetic biology along with their solutions.

scATAC-seq preprocessing and imputation evaluation system for visualization, clustering and digital footprinting

Single-cell ATAC-seq (scATAC-seq) is a recently developed approach that provides means to investigate open chromatin at single cell level, to assess epigenetic regulation and transcription factors binding landscapes. The sparsity of the scATAC-seq data calls for imputation. Similarly, preprocessing (filtering) may be required to reduce computational load due to the large number of open regions. However, optimal strategies for both imputation and preprocessing have not been yet evaluated together. We present SAPIEnS (scATAC-seq Preprocessing and Imputation Evaluation System), a benchmark for scATAC-seq imputation frameworks, a combination of state-of-the-art imputation methods with commonly used preprocessing techniques. We assess different types of scATAC-seq analysis, i.e. clustering, visualization and digital genomic footprinting, and attain optimal preprocessing-imputation strategies. We discuss the benefits of the imputation framework depending on the task and the number of the dataset features (peaks). We conclude that the preprocessing with the Boruta method is beneficial for the majority of tasks, while imputation is helpful mostly for small datasets. We also implement a SAPIEnS database with pre-computed transcription factor footprints based on imputed data with their activity scores in a specific cell type. SAPIEnS is published at: https://github.com/lab-medvedeva/SAPIEnS. SAPIEnS database is available at: https://sapiensdb.com.

Identification of ATF3 as a novel protective signature of quiescent colorectal tumor cells

Colorectal cancer (CRC) is the third most common cancer and the second leading cause of death in the world. In most cases, drug resistance and tumor recurrence are ultimately inevitable. One obstacle is the presence of chemotherapy-insensitive quiescent cancer cells (QCCs). Identification of unique features of QCCs may facilitate the development of new targeted therapeutic strategies to eliminate tumor cells and thereby delay tumor recurrence. Here, using single-cell RNA sequencing, we classified proliferating and quiescent cancer cell populations in the human colorectal cancer spheroid model and identified ATF3 as a novel signature of QCCs that could support cells living in a metabolically restricted microenvironment. RNA velocity further showed a shift from the QCC group to the PCC group indicating the regenerative capacity of the QCCs. Our further results of epigenetic analysis, STING analysis, and evaluation of TCGA COAD datasets build a conclusion that ATF3 can interact with DDIT4 and TRIB3 at the transcriptional level. In addition, decreasing the expression level of ATF3 could enhance the efficacy of 5-FU on CRC MCTS models. In conclusion, ATF3 was identified as a novel marker of QCCs, and combining conventional drugs targeting PCCs with an option to target QCCs by reducing ATF3 expression levels may be a promising strategy for more efficient removal of tumor cells.

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.

The technological landscape and applications of single-cell multi-omics

Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods.

System analysis based on the lysosome-related genes identifies HPS4 as a novel therapy target for liver hepatocellular carcinoma

Background

Liver cancer is a leading cause of cancer-related deaths worldwide. Lysosomal dysfunction is implicated in cancer progression; however, prognostic prediction models based on lysosome-related genes (LRGs) are lacking in liver cancer. This study aimed to establish an LRG-based model to improve prognosis prediction and explore potential therapeutic targets in liver cancer.

Methods

Expression profiles of 61 LRGs were analyzed in The Cancer Genome Atlas liver cancer cohorts. There were 14 LRGs identified, and their association with clinical outcomes was evaluated. Unsupervised clustering, Cox regression, and functional assays were performed.

Results

Patients were classified into high-risk and low-risk subgroups based on the 14 LRGs. The high-risk group had significantly worse overall survival. Aberrant immune infiltration and checkpoint expression were observed in the high-risk group. Furthermore, HPS4 was identified as an independent prognostic indicator. Knockdown of HPS4 suppressed liver cancer cell proliferation and induced apoptosis.

Conclusion

This study developed an LRG-based prognostic model to improve risk stratification in liver cancer. The potential value of HPS4 as a therapeutic target and biomarker was demonstrated. Regulation of HPS4 may offer novel strategies for precision treatment in liver cancer patients.

Systematic pan-cancer analysis identifies SLC35C1 as an immunological and prognostic biomarker

GDP-amylose transporter protein 1 (SLC35C1) plays an important role in many types of cancer. Therefore, it is clinically important to further investigate the expression profile of SLC35C1 in human tumors to provide new molecular clues for the pathogenesis of glioma. In this study, we performed a comprehensive pan-cancer analysis of SLC35C1 using a series of bioinformatics approaches and validated its differential tissue expression and biological function. The results showed that SLC35C1 was aberrantly expressed in different types of tumors and significantly correlated with overall survival (OS) and progression-free interval (PFI). More importantly, the expression level of SLC35C1 was closely correlated with Tumor Microenvironment (TME), immune infiltration and immune-related genes. In addition, we found that SLC35C1 expression was also closely related to Tumor Mutation Burden (TMB), Microsatellite Instability (MSI) and antitumor drug sensitivity in various cancer types. Functional bioinformatics analysis indicated that SLC35C1 may be involved in multiple signaling pathways and biological processes in glioma. Based on SLC35C1 expression, a risk factor model was found to predict OS of glioma. In addition, in vitro experiments showed that SLC35C1 knockdown significantly inhibited the proliferation, migration and invasive ability of glioma cells, while SLC35C1 overexpression promoted proliferation, migration, invasion and colony formation of glioma cells. Finally, quantitative real-time PCR confirmed that SLC35C1 was highly expressed in gliomas.

Dynamics and regulation of mitotic chromatin accessibility bookmarking at single-cell resolution

Although mitotic chromosomes are highly compacted and transcriptionally inert, some active chromatin features are retained during mitosis to ensure the proper postmitotic reestablishment of maternal transcriptional programs, a phenomenon termed "mitotic bookmarking." However, the dynamics and regulation of mitotic bookmarking have not been systemically surveyed. Using single-cell transposase-accessible chromatin sequencing (scATAC-seq), we examined 6538 mitotic L02 human liver cells of variable stages and found that chromatin accessibility remained changing throughout cell division, with a constant decrease until metaphase and a gradual increase as chromosomes segregated. In particular, a subset of chromatin regions were identified to remain open throughout mitosis, and genes associated with these bookmarked regions are primarily linked to rapid reactivation upon mitotic exit. We also demonstrated that nuclear transcription factor Y subunit α (NF-YA) preferentially occupied bookmarked regions and contributed to transcriptional reactivation after mitosis. Our study uncovers the dynamic and regulatory blueprint of mitotic bookmarking.

Applications of deep learning in understanding gene regulation

Gene regulation is a central topic in cell biology. Advances in omics technologies and the accumulation of omics data have provided better opportunities for gene regulation studies than ever before. For this reason deep learning, as a data-driven predictive modeling approach, has been successfully applied to this field during the past decade. In this article, we aim to give a brief yet comprehensive overview of representative deep-learning methods for gene regulation. Specifically, we discuss and compare the design principles and datasets used by each method, creating a reference for researchers who wish to replicate or improve existing methods. We also discuss the common problems of existing approaches and prospectively introduce the emerging deep-learning paradigms that will potentially alleviate them. We hope that this article will provide a rich and up-to-date resource and shed light on future research directions in this area.

Developmental cell fate choice in neural tube progenitors employs two distinct cis-regulatory strategies

In many developing tissues, the patterns of gene expression that assign cell fate are organized by graded secreted signals. Cis-regulatory elements (CREs) interpret these signals to control gene expression, but how this is accomplished remains poorly understood. In the neural tube, a gradient of the morphogen sonic hedgehog (Shh) patterns neural progenitors. We identify two distinct ways in which CREs translate graded Shh into differential gene expression in mouse neural progenitors. In most progenitors, a common set of CREs control gene activity by integrating cell-type-specific inputs. By contrast, the most ventral progenitors use a unique set of CREs, established by the pioneer factor FOXA2. This parallels the role of FOXA2 in endoderm, where FOXA2 binds some of the same sites. Together, the data identify distinct cis-regulatory strategies for the interpretation of morphogen signaling and raise the possibility of an evolutionarily conserved role for FOXA2 across tissues.

Characterizing cis-regulatory elements using single-cell epigenomics

Cell type-specific gene expression patterns and dynamics during development or in disease are controlled by cis-regulatory elements (CREs), such as promoters and enhancers. Distinct classes of CREs can be characterized by their epigenomic features, including DNA methylation, chromatin accessibility, combinations of histone modifications and conformation of local chromatin. Tremendous progress has been made in cataloguing CREs in the human genome using bulk transcriptomic and epigenomic methods. However, single-cell epigenomic and multi-omic technologies have the potential to provide deeper insight into cell type-specific gene regulatory programmes as well as into how they change during development, in response to environmental cues and through disease pathogenesis. Here, we highlight recent advances in single-cell epigenomic methods and analytical tools and discuss their readiness for human tissue profiling.

The autism risk factor CHD8 is a chromatin activator in human neurons and functionally dependent on the ERK-MAPK pathway effector ELK1

The chromodomain helicase DNA-binding protein CHD8 is the most frequently mutated gene in autism spectrum disorder. Despite its prominent disease involvement, little is known about its molecular function in the human brain. CHD8 is a chromatin regulator which binds to the promoters of actively transcribed genes through genomic targeting mechanisms which have yet to be fully defined. By generating a conditional loss-of-function and an endogenously tagged allele in human pluripotent stem cells, we investigated the molecular function and the interaction of CHD8 with chromatin in human neurons. Chromatin accessibility analysis and transcriptional profiling revealed that CHD8 functions as a transcriptional activator at its target genes in human neurons. Furthermore, we found that CHD8 chromatin targeting is cell context-dependent. In human neurons, CHD8 preferentially binds at ETS motif-enriched promoters. This enrichment is particularly prominent on the promoters of genes whose expression significantly changes upon the loss of CHD8. Indeed, among the ETS transcription factors, we identified ELK1 as being most highly correlated with CHD8 expression in primary human fetal and adult cortical neurons and most highly expressed in our stem cell-derived neurons. Remarkably, ELK1 was necessary to recruit CHD8 specifically to ETS motif-containing sites. These findings imply that ELK1 and CHD8 functionally cooperate to regulate gene expression and chromatin states at MAPK/ERK target genes in human neurons. Our results suggest that the MAPK/ERK/ELK1 axis potentially contributes to the pathogenesis caused by CHD8 mutations in human neurodevelopmental disorders.

ISSAAC-seq enables sensitive and flexible multimodal profiling of chromatin accessibility and gene expression in single cells

Joint profiling of chromatin accessibility and gene expression from the same single cell provides critical information about cell types in a tissue and cell states during a dynamic process. Here, we develop in situ sequencing hetero RNA-DNA-hybrid after assay for transposase-accessible chromatin-sequencing (ISSAAC-seq), a highly sensitive and flexible single-cell multi-omics method to interrogate chromatin accessibility and gene expression from the same single nucleus. We demonstrated that ISSAAC-seq is sensitive and provides high quality data with orders of magnitude more features than existing methods. Using the joint profiles from over 10,000 nuclei from the mouse cerebral cortex, we uncovered major and rare cell types and cell-type specific regulatory elements and identified heterogeneity at the chromatin level within established cell types defined by gene expression. Finally, we revealed distinct dynamics and relationships of gene expression and chromatin accessibility during an oligodendrocyte maturation trajectory.

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.

Advances in Single-Cell Multi-Omics and Application in Cardiovascular Research

With the development of ever more powerful and versatile high-throughput sequencing techniques and innovative ways to capture single cells, mapping the multicellular tissues at the single-cell level is becoming routine practice. However, it is still challenging to depict the epigenetic landscape of a single cell, especially the genome-wide chromatin accessibility, histone modifications, and DNA methylation. We summarize the most recent methodologies to profile these epigenetic marks at the single-cell level. We also discuss the development and advancement of several multi-omics sequencing technologies from individual cells. Advantages and limitations of various methods to compare and integrate datasets obtained from different sources are also included with specific practical notes. Understanding the heart tissue at single-cell resolution and multi-modal levels will help to elucidate the cell types and states involved in physiological and pathological events during heart development and disease. The rich information produced from single-cell multi-omics studies will also promote the research of heart regeneration and precision medicine on heart diseases.

Cell-lineage controlled epigenetic regulation in glioblastoma stem cells determines functionally distinct subgroups and predicts patient survival

There is ample support for developmental regulation of glioblastoma stem cells. To examine how cell lineage controls glioblastoma stem cell function, we present a cross-species epigenome analysis of mouse and human glioblastoma stem cells. We analyze and compare the chromatin-accessibility landscape of nine mouse glioblastoma stem cell cultures of three defined origins and 60 patient-derived glioblastoma stem cell cultures by assay for transposase-accessible chromatin using sequencing. This separates the mouse cultures according to cell of origin and identifies three human glioblastoma stem cell clusters that show overlapping characteristics with each of the mouse groups, and a distribution along an axis of proneural to mesenchymal phenotypes. The epigenetic-based human glioblastoma stem cell clusters display distinct functional properties and can separate patient survival. Cross-species analyses reveals conserved epigenetic regulation of mouse and human glioblastoma stem cells. We conclude that epigenetic control of glioblastoma stem cells primarily is dictated by developmental origin which impacts clinically relevant glioblastoma stem cell properties and patient survival.

The epigenetic regulation of glioblastoma stem cell (GSC) function remains poorly understood. Here, the authors compare the chromatin accessibility landscape of GSC cultures from mice and patients and suggest that the epigenome of GSCs is cell lineage-regulated and could predict patient survival.

Epigenomic priming of immune genes implicates oligodendroglia in multiple sclerosis susceptibility

Multiple sclerosis (MS) is characterized by a targeted attack on oligodendroglia (OLG) and myelin by immune cells, which are thought to be the main drivers of MS susceptibility. We found that immune genes exhibit a primed chromatin state in single mouse and human OLG in a non-disease context, compatible with transitions to immune-competent states in MS. We identified BACH1 and STAT1 as transcription factors involved in immune gene regulation in oligodendrocyte precursor cells (OPCs). A subset of immune genes presents bivalency of H3K4me3/H3K27me3 in OPCs, with Polycomb inhibition leading to their increased activation upon interferon gamma (IFN-γ) treatment. Some MS susceptibility single-nucleotide polymorphisms (SNPs) overlap with these regulatory regions in mouse and human OLG. Treatment of mouse OPCs with IFN-γ leads to chromatin architecture remodeling at these loci and altered expression of interacting genes. Thus, the susceptibility for MS may involve OLG, which therefore constitutes novel targets for immunological-based therapies for MS.

The Intriguing Landscape of Single-Cell Protein Analysis

Profiling protein expression at single-cell resolution is essential for fundamental biological research (such as cell differentiation and tumor microenvironmental examination) and clinical precision medicine where only a limited number of primary cells are permitted. With the recent advances in engineering, chemistry, and biology, single-cell protein analysis methods are developed rapidly, which enable high-throughput and multiplexed protein measurements in thousands of individual cells. In combination with single cell RNA sequencing and mass spectrometry, single-cell multi-omics analysis can simultaneously measure multiple modalities including mRNAs, proteins, and metabolites in single cells, and obtain a more comprehensive exploration of cellular signaling processes, such as DNA modifications, chromatin accessibility, protein abundance, and gene perturbation. Here, the recent progress and applications of single-cell protein analysis technologies in the last decade are summarized. Current limitations, challenges, and possible future directions in this field are also discussed.

Integrating transcription-factor abundance with chromatin accessibility in human erythroid lineage commitment

Master transcription factors (TFs) directly regulate present and future cell states by binding DNA regulatory elements and driving gene-expression programs. Their abundance influences epigenetic priming to different cell fates at the chromatin level, especially in the context of differentiation. In order to link TF protein abundance to changes in TF motif accessibility and open chromatin, we developed InTAC-seq, a method for simultaneous quantification of genome-wide chromatin accessibility and intracellular protein abundance in fixed cells. Our method produces high-quality data and is a cost-effective alternative to single-cell techniques. We showcase our method by purifying bone marrow (BM) progenitor cells based on GATA-1 protein levels and establish high GATA-1-expressing BM cells as both epigenetically and functionally similar to erythroid-committed progenitors.

Extensive evaluation of ATAC-seq protocols for native or formaldehyde-fixed nuclei

BACKGROUND

The "Assay for Transposase Accessible Chromatin sequencing" (ATAC-seq) is an efficient and easy to implement protocol to measure chromatin accessibility that has been widely used in multiple applications studying gene regulation. While several modifications or variants of the protocol have been published since it was first described, there has not yet been an extensive evaluation of the effects of specific protocol choices head-to-head in a consistent experimental setting. In this study, we tested multiple protocol options for major ATAC-seq components (including three reaction buffers, two reaction temperatures, two enzyme sources, and the use of either native or fixed nuclei) in a well-characterized cell line. With all possible combinations of components, we created 24 experimental conditions with four replicates for each (a total of 96 samples). In addition, we tested the 12 native conditions in a primary sample type (mouse lung tissue) with two different input amounts. Through these extensive comparisons, we were able to observe the effect of different ATAC-seq conditions on data quality and to examine the utility and potential redundancy of various quality metrics.

RESULTS

In general, native samples yielded more peaks (particularly at loci not overlapping transcription start sites) than fixed samples, and the temperature at which the enzymatic reaction was carried out had a major impact on data quality metrics for both fixed and native nuclei. However, the effect of various conditions tested was not always consistent between the native and fixed samples. For example, the Nextera and Omni buffers were largely interchangeable across all other conditions, while the THS buffer resulted in markedly different profiles in native samples. In-house and commercial enzymes performed similarly.

CONCLUSIONS

We found that the relationship between commonly used measures of library quality differed across temperature and fixation, and so evaluating multiple metrics in assessing the quality of a sample is recommended. Notably, we also found that these choices can bias the functional class of elements profiled and so we recommend evaluating several formulations in any new experiments. Finally, we hope the ATAC-seq workflow formulated in this study on crosslinked samples will help to profile archival clinical specimens.

WASF2 Serves as a Potential Biomarker and Therapeutic Target in Ovarian Cancer: A Pan-Cancer Analysis

Background

Wiskott-Aldrich syndrome protein family member 2 (WASF2) has been shown to play an important role in many types of cancer. Therefore, it is worthwhile to further study expression profile of WASF2 in human cancer, which provides new molecular clues about the pathogenesis of ovarian cancer.

Methods

We used a series of bioinformatics methods to comprehensively analyze the relationship between WASF2 and prognosis, tumor microenvironment (TME), immune infiltration, tumor mutational burden (TMB), microsatellite instability (MSI), and tried to find the potential biological processes of WASF2 in ovarian cancer. Biological behaviors of ovarian cancer cells were investigated through CCK8 assay, scratch test and transwell assay. We also compared WASF2 expression between epithelial ovarian cancer tissues and normal ovarian tissues by using immunohistochemical staining.

Results

In the present study, we found that WASF2 was abnormally expressed across the diverse cancer and significantly correlated with overall survival (OS) and progression-free interval (PFI). More importantly, the WASF2 expression level also significantly related to the TME. Our results also showed that the expression of WASF2 was closely related to immune infiltration and immune-related genes. In addition, WASF2 expression was associated with TMB, MSI, and antitumor drugs sensitivity across various cancer types. Functional bioinformatics analysis demonstrated that the WASF2 might be involved in several signaling pathways and biological processes of ovarian cancer. A risk factor model was found to be predictive for OS in ovarian cancer based on the expression of WASF2. Moreover, in vitro experiments, it was demonstrated that the proliferative, migratory and invasive capacity of ovarian cancer cells was significantly inhibited due to WASF2 knockdown. Finally, the immunohistochemistry data confirmed that WASF2 were highly expressed in ovarian cancer.

Conclusions

Our study demonstrated that WASF2 expression was associated with a poor prognosis and may be involved in the development of ovarian cancer, which might be explored as a potential prognostic marker and new targeted treatments.

Cell-Type Resolved Insights into the Cis-Regulatory Genome of NAFLD

The prevalence of non-alcoholic fatty liver disease (NAFLD) is increasing rapidly, and unmet treatment can result in the development of hepatitis, fibrosis, and liver failure. There are difficulties involved in diagnosing NAFLD early and for this reason there are challenges involved in its treatment. Furthermore, no drugs are currently approved to alleviate complications, a fact which highlights the need for further insight into disease mechanisms. NAFLD pathogenesis is associated with complex cellular changes, including hepatocyte steatosis, immune cell infiltration, endothelial dysfunction, hepatic stellate cell activation, and epithelial ductular reaction. Many of these cellular changes are controlled by dramatic changes in gene expression orchestrated by the cis-regulatory genome and associated transcription factors. Thus, to understand disease mechanisms, we need extensive insights into the gene regulatory mechanisms associated with tissue remodeling. Mapping cis-regulatory regions genome-wide is a step towards this objective and several current and emerging technologies allow detection of accessible chromatin and specific histone modifications in enriched cell populations of the liver, as well as in single cells. Here, we discuss recent insights into the cis-regulatory genome in NAFLD both at the organ-level and in specific cell populations of the liver. Moreover, we highlight emerging technologies that enable single-cell resolved analysis of the cis-regulatory genome of the liver.

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application

Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through  GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.

Variant to function mapping at single-cell resolution through network propagation.. [PMID: 35118467 PMCID: PMC8811900 DOI: 10.1101/2022.01.23.477426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

With burgeoning human disease genetic associations and single-cell genomic atlases covering a range of tissues, there are unprecedented opportunities to systematically gain insights into the mechanisms of disease-causal variation. However, sparsity and noise, particularly in the context of single-cell epigenomic data, hamper the identification of disease- or trait-relevant cell types, states, and trajectories. To overcome these challenges, we have developed the SCAVENGE method, which maps causal variants to their relevant cellular context at single-cell resolution by employing the strategy of network propagation. We demonstrate how SCAVENGE can help identify key biological mechanisms underlying human genetic variation including enrichment of blood traits at distinct stages of human hematopoiesis, defining monocyte subsets that increase the risk for severe coronavirus disease 2019 (COVID-19), and identifying intermediate lymphocyte developmental states that are critical for predisposition to acute leukemia. Our approach not only provides a framework for enabling variant-to-function insights at single-cell resolution, but also suggests a more general strategy for maximizing the inferences that can be made using single-cell genomic data.

Variant to function mapping at single-cell resolution through network propagation

Genome-wide association studies in combination with single-cell genomic atlases can provide insights into the mechanisms of disease-causal genetic variation. However, identification of disease-relevant or trait-relevant cell types, states and trajectories is often hampered by sparsity and noise, particularly in the analysis of single-cell epigenomic data. To overcome these challenges, we present SCAVENGE, a computational algorithm that uses network propagation to map causal variants to their relevant cellular context at single-cell resolution. We demonstrate how SCAVENGE can help identify key biological mechanisms underlying human genetic variation, applying the method to blood traits at distinct stages of human hematopoiesis, to monocyte subsets that increase the risk for severe Coronavirus Disease 2019 (COVID-19) and to intermediate lymphocyte developmental states that predispose to acute leukemia. Our approach not only provides a framework for enabling variant-to-function insights at single-cell resolution but also suggests a more general strategy for maximizing the inferences that can be made using single-cell genomic data.

Deep learning-based advances and applications for single-cell RNA-sequencing data analysis

The rapid development of single-cell RNA-sequencing (scRNA-seq) technology has raised significant computational and analytical challenges. The application of deep learning to scRNA-seq data analysis is rapidly evolving and can overcome the unique challenges in upstream (quality control and normalization) and downstream (cell-, gene- and pathway-level) analysis of scRNA-seq data. In the present study, recent advances and applications of deep learning-based methods, together with specific tools for scRNA-seq data analysis, were summarized. Moreover, the future perspectives and challenges of deep-learning techniques regarding the appropriate analysis and interpretation of scRNA-seq data were investigated. The present study aimed to provide evidence supporting the biomedical application of deep learning-based tools and may aid biologists and bioinformaticians in navigating this exciting and fast-moving area.

Toward a Liver Cell Atlas: Understanding Liver Biology in Health and Disease at Single-Cell Resolution

Single-cell technologies are revolutionizing our understanding of cellular heterogeneity and functional diversity in health and disease. Here, we review the current knowledge and advances in liver biology using single-cell approaches. We focus on the landscape of the composition and the function of cells in a healthy liver in the context of its spatial organization. We also highlight the alterations of the molecular landscape in chronic liver disease and liver cancer, which includes the identification of disease-related cell types, altered cellular functions, dynamic cell-cell interactions, the plasticity of malignant cells, the collective behavior of a cell community, and microenvironmental reprogramming. We anticipate that the uncovered liver cell atlas will help deciphering the molecular and cellular mechanisms driving a healthy liver into a disease state. It also offers insight into the detection of new therapeutic targets and paves the way for effective disease interventions.

A plate-based single-cell ATAC-seq workflow for fast and robust profiling of chromatin accessibility

Profiling chromatin accessibility at the single-cell level provides critical information about cell type composition and cell-to-cell variation within a complex tissue. Emerging techniques for the interrogation of chromatin accessibility in individual cells allow investigation of the fundamental mechanisms that lead to the variability of different cells. This protocol describes a fast and robust method for single-cell chromatin accessibility profiling based on the assay for transposase-accessible chromatin using sequencing (ATAC-seq). The method combines up-front bulk Tn5 tagging of chromatin with flow cytometry to isolate single nuclei or cells. Reagents required to generate sequencing libraries are added to the same well in the plate where cells are sorted. The protocol described here generates data of high complexity and excellent signal-to-noise ratio and can be combined with index sorting for in-depth characterization of cell types. The whole experimental procedure can be finished within 1 or 2 d with a throughput of hundreds to thousands of nuclei, and the data can be processed by the provided computational pipeline. The execution of the protocol only requires basic techniques and equipment in a molecular biology laboratory with flow cytometry support.

Profiling chromatin accessibility in formalin-fixed paraffin-embedded samples

Archived formalin-fixed paraffin-embedded (FFPE) samples are the global standard format for preservation of the majority of biopsies in both basic research and translational cancer studies, and profiling chromatin accessibility in the archived FFPE tissues is fundamental to understanding gene regulation. Accurate mapping of chromatin accessibility from FFPE specimens is challenging because of the high degree of DNA damage. Here, we first showed that standard ATAC-seq can be applied to purified FFPE nuclei but yields lower library complexity and a smaller proportion of long DNA fragments. We then present FFPE-ATAC, the first highly sensitive method for decoding chromatin accessibility in FFPE tissues that combines Tn5-mediated transposition and T7 in vitro transcription. The FFPE-ATAC generates high-quality chromatin accessibility profiles with 500 nuclei from a single FFPE tissue section, enables the dissection of chromatin profiles from the regions of interest with the aid of hematoxylin and eosin (H&E) staining, and reveals disease-associated chromatin regulation from the human colorectal cancer FFPE tissue archived for more than 10 years. In summary, the approach allows decoding of the chromatin states that regulate gene expression in archival FFPE tissues, thereby permitting investigators, to better understand epigenetic regulation in cancer and precision medicine.

Single-cell technologies and analyses in hematopoiesis and hematological malignancies

In recent years, single-cell technologies have emerged as breakthrough techniques that enable the characterization of hematopoietic cell populations of normal and malignant tissue samples and will be combined in the near future with bulk technologies, currently used in clinical practice, to improve diagnosis, prognosis, and the search for novel molecular targets. These single-cell methods have the advantage of not masking cell-to-cell variation features and involve the study of genetic, epigenetic, transcriptional, and proteomic landscapes from a single-cell perspective. Latest advances in this field have enabled the development of novel strategies that significantly increase both sensitivity and high throughput. In this review, we emphasize emerging techniques aimed at assessing individual or multiomic parameters at single-cell resolution and analyze how these technologies have helped us understand hematopoietic variability and identify unknown and/or rare subpopulations. We also summarize the impact of these single-cell profiling strategies on the characterization of cell diversity within the tumor and the clonal evolution of multiple hematological malignancies in samples from untreated and treated patients, which provide valuable information for diagnosis, prognosis, and future treatments and explain why current therapies may fail. However, despite these improvements, new challenges lie ahead.

Deregulation of enhancer structure, function, and dynamics in acute lymphoblastic leukemia

Enhancers control dynamic changes in gene expression and orchestrate the tightly controlled transcriptional circuitries that direct and coordinate cell growth, proliferation, survival, lineage commitment, and differentiation during lymphoid development. Enhancer hijacking and neoenhancer formation at oncogene loci, as well as aberrant activation of oncogene-associated enhancers, can induce constitutive activation of self-perpetuating oncogenic transcriptional circuitries, and contribute to the malignant transformation of immature lymphoid progenitors in acute lymphoblastic leukemia (ALL). In this review, we present recent discoveries of the role of enhancer dynamics in mouse and human lymphoid development, and discuss how genetic and epigenetic alterations of enhancer function can promote leukemogenesis, and potential strategies for targeting the enhancer machinery in the treatment of ALL.

Chromatin accessibility in neuropsychiatric disorders

Epigenetic mechanisms have recently emerged as critical regulators of brain function in health and disease. By controlling the accessibility and the expression of specific genes, these pathways can mediate transient and long-lasting changes in neuronal function in both physiological and pathological contexts. Due to the extreme complexity of the epigenetic regulatory landscape, emerging methods that directly assay chromatin accessibility are of particular interest. Here, I review recent insights gained on open and closed chromatin states in the brain, with emphasis on neuropsychiatric disorders. These advances generated an invaluable wealth of information that can help us better understand gene regulation in the brain and its impairments that contribute to the development of disease.

BABEL enables cross-modality translation between multiomic profiles at single-cell resolution

Simultaneous profiling of multiomic modalities within a single cell is a grand challenge for single-cell biology. While there have been impressive technical innovations demonstrating feasibility-for example, generating paired measurements of single-cell transcriptome (single-cell RNA sequencing [scRNA-seq]) and chromatin accessibility (single-cell assay for transposase-accessible chromatin using sequencing [scATAC-seq])-widespread application of joint profiling is challenging due to its experimental complexity, noise, and cost. Here, we introduce BABEL, a deep learning method that translates between the transcriptome and chromatin profiles of a single cell. Leveraging an interoperable neural network model, BABEL can predict single-cell expression directly from a cell's scATAC-seq and vice versa after training on relevant data. This makes it possible to computationally synthesize paired multiomic measurements when only one modality is experimentally available. Across several paired single-cell ATAC and gene expression datasets in human and mouse, we validate that BABEL accurately translates between these modalities for individual cells. BABEL also generalizes well to cell types within new biological contexts not seen during training. Starting from scATAC-seq of patient-derived basal cell carcinoma (BCC), BABEL generated single-cell expression that enabled fine-grained classification of complex cell states, despite having never seen BCC data. These predictions are comparable to analyses of experimental BCC scRNA-seq data for diverse cell types related to BABEL's training data. We further show that BABEL can incorporate additional single-cell data modalities, such as protein epitope profiling, thus enabling translation across chromatin, RNA, and protein. BABEL offers a powerful approach for data exploration and hypothesis generation.

Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq

Single-cell measurements of cellular characteristics have been instrumental in understanding the heterogeneous pathways that drive differentiation, cellular responses to signals, and human disease. Recent advances have allowed paired capture of protein abundance and transcriptomic state, but a lack of epigenetic information in these assays has left a missing link to gene regulation. Using the heterogeneous mixture of cells in human peripheral blood as a test case, we developed a novel scATAC-seq workflow that increases signal-to-noise and allows paired measurement of cell surface markers and chromatin accessibility: integrated cellular indexing of chromatin landscape and epitopes, called ICICLE-seq. We extended this approach using a droplet-based multiomics platform to develop a trimodal assay that simultaneously measures transcriptomics (scRNA-seq), epitopes, and chromatin accessibility (scATAC-seq) from thousands of single cells, which we term TEA-seq. Together, these multimodal single-cell assays provide a novel toolkit to identify type-specific gene regulation and expression grounded in phenotypically defined cell types.

Genetic and Non-Genetic Mechanisms Underlying Cancer Evolution

Simple Summary

Our manuscript summarizes the up-to-date data on the complex and dynamic nature of adaptation mechanisms and evolutionary processes taking place during cancer initiation, development and progression. Although for decades cancer has been viewed as a process governed by genetic mechanisms, it is becoming more and more clear that non-genetic mechanisms may play an equally important role in cancer evolution. In this review, we bring together these fundamental concepts and discuss how those tightly interconnected mechanisms lead to the establishment of highly adaptive quickly evolving cancers. Furthermore, we argue that in depth understanding of cancer progression from the evolutionary perspective may allow the prediction and direction of the evolutionary path of cancer populations towards drug sensitive phenotypes and thus facilitate the development of more effective anti-cancer approaches.

Abstract

Cancer development can be defined as a process of cellular and tissular microevolution ultimately leading to malignancy. Strikingly, though this concept has prevailed in the field for more than a century, the precise mechanisms underlying evolutionary processes occurring within tumours remain largely uncharacterized and rather cryptic. Nevertheless, although our current knowledge is fragmentary, data collected to date suggest that most tumours display features compatible with a diverse array of evolutionary paths, suggesting that most of the existing macro-evolutionary models find their avatar in cancer biology. Herein, we discuss an up-to-date view of the fundamental genetic and non-genetic mechanisms underlying tumour evolution with the aim of concurring into an integrated view of the evolutionary forces at play throughout the emergence and progression of the disease and into the acquisition of resistance to diverse therapeutic paradigms. Our ultimate goal is to delve into the intricacies of genetic and non-genetic networks underlying tumour evolution to build a framework where both core concepts are considered non-negligible and equally fundamental.

simATAC: a single-cell ATAC-seq simulation framework

Single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) identifies regulated chromatin accessibility modules at the single-cell resolution. Robust evaluation is critical to the development of scATAC-seq pipelines, which calls for reproducible datasets for benchmarking. We hereby present the simATAC framework, an R package that generates scATAC-seq count matrices that highly resemble real scATAC-seq datasets in library size, sparsity, and chromatin accessibility signals. simATAC deploys statistical models derived from analyzing 90 real scATAC-seq cell groups. simATAC provides a robust and systematic approach to generate in silico scATAC-seq samples with known cell labels for assessing analytical pipelines.

Profiling Chromatin Accessibility at Single-cell Resolution

How distinct transcriptional programs are enacted to generate cellular heterogeneity and plasticity, and enable complex fate decisions are important open questions. One key regulator is the cell’s epigenome state that drives distinct transcriptional programs by regulating chromatin accessibility. Genome-wide chromatin accessibility measurements can impart insights into regulatory sequences (in)accessible to DNA-binding proteins at a single-cell resolution. This review outlines molecular methods and bioinformatic tools for capturing cell-to-cell chromatin variation using single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) in a scalable fashion. It also covers joint profiling of chromatin with transcriptome/proteome measurements, computational strategies to integrate multi-omic measurements, and predictive bioinformatic tools to infer chromatin accessibility from single-cell transcriptomic datasets. Methodological refinements that increase power for cell discovery through robust chromatin coverage and integrate measurements from multiple modalities will further expand our understanding of gene regulation during homeostasis and disease.

SITC cancer immunotherapy resource document: a compass in the land of biomarker discovery

Since the publication of the Society for Immunotherapy of Cancer's (SITC) original cancer immunotherapy biomarkers resource document, there have been remarkable breakthroughs in cancer immunotherapy, in particular the development and approval of immune checkpoint inhibitors, engineered cellular therapies, and tumor vaccines to unleash antitumor immune activity. The most notable feature of these breakthroughs is the achievement of durable clinical responses in some patients, enabling long-term survival. These durable responses have been noted in tumor types that were not previously considered immunotherapy-sensitive, suggesting that all patients with cancer may have the potential to benefit from immunotherapy. However, a persistent challenge in the field is the fact that only a minority of patients respond to immunotherapy, especially those therapies that rely on endogenous immune activation such as checkpoint inhibitors and vaccination due to the complex and heterogeneous immune escape mechanisms which can develop in each patient. Therefore, the development of robust biomarkers for each immunotherapy strategy, enabling rational patient selection and the design of precise combination therapies, is key for the continued success and improvement of immunotherapy. In this document, we summarize and update established biomarkers, guidelines, and regulatory considerations for clinical immune biomarker development, discuss well-known and novel technologies for biomarker discovery and validation, and provide tools and resources that can be used by the biomarker research community to facilitate the continued development of immuno-oncology and aid in the goal of durable responses in all patients.

Applications of Single-Cell Omics to Dissect Tumor Microenvironment

The recent technical and computational advances in single-cell sequencing technologies have significantly broaden our toolkit to study tumor microenvironment (TME) directly from human specimens. The TME is the complex and dynamic ecosystem composed of multiple cell types, including tumor cells, immune cells, stromal cells, endothelial cells, and other non-cellular components such as the extracellular matrix and secreted signaling molecules. The great success on immune checkpoint blockade therapy has highlighted the importance of TME on anti-tumor immunity and has made it a prime target for further immunotherapy strategies. Applications of single-cell transcriptomics on studying TME has yielded unprecedented resolution of the cellular and molecular complexity of the TME, accelerating our understanding of the heterogeneity, plasticity, and complex cross-interaction between different cell types within the TME. In this review, we discuss the recent advances by single-cell sequencing on understanding the diversity of TME and its functional impact on tumor progression and immunotherapy response driven by single-cell sequencing. We primarily focus on the major immune cell types infiltrated in the human TME, including T cells, dendritic cells, and macrophages. We further discuss the limitations of the existing methodologies and the prospects on future studies utilizing single-cell multi-omics technologies. Since immune cells undergo continuous activation and differentiation within the TME in response to various environmental cues, we highlight the importance of integrating multimodal datasets to enable retrospective lineage tracing and epigenetic profiling of the tumor infiltrating immune cells. These novel technologies enable better characterization of the developmental lineages and differentiation states that are critical for the understanding of the underlying mechanisms driving the functional diversity of immune cells within the TME. We envision that with the continued accumulation of single-cell omics datasets, single-cell sequencing will become an indispensable aspect of the immune-oncology experimental toolkit. It will continue to drive the scientific innovations in precision immunotherapy and will be ultimately adopted by routine clinical practice in the foreseeable future.

Epigenetic crosstalk between hypoxia and tumor driven by HIF regulation

Hypoxia is the major influence factor in physiological and pathological courses which are mainly mediated by hypoxia-inducible factors (HIFs) in response to low oxygen tensions within solid tumors. Under normoxia, HIF signaling pathway is inhibited due to HIF-α subunits degradation. However, in hypoxic conditions, HIF-α is activated and stabilized, and HIF target genes are successively activated, resulting in a series of tumour-specific activities. The activation of HIFs, including HIF-1α, HIF-2α and HIF-3α, subsequently induce downstream target genes which leads to series of responses, the resulting abnormal processes or metabolites in turn affect HIFs stability. Given its functions in tumors progression, HIFs have been regarded as therapeutic targets for improved treatment efficacy. Epigenetics refers to alterations in gene expression that are stable between cell divisions, and sometimes between generations, but do not involve changes in the underlying DNA sequence of the organism. And with the development of research, epigenetic regulation has been found to play an important role in the development of tumors, which providing accumulating basic or clinical evidences for tumor treatments. Here, given how little has been reported about the overall association between hypoxic tumors and epigenetics, we made a more systematic review from epigenetic perspective in hope of helping others better understand hypoxia or HIF pathway, and providing more established and potential therapeutic strategies in tumors to facilitate epigenetic studies of tumors.

Single-cell ATAC-seq signal extraction and enhancement with SCATE

Single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) is the state-of-the-art technology for analyzing genome-wide regulatory landscapes in single cells. Single-cell ATAC-seq data are sparse and noisy, and analyzing such data is challenging. Existing computational methods cannot accurately reconstruct activities of individual cis-regulatory elements (CREs) in individual cells or rare cell subpopulations. We present a new statistical framework, SCATE, that adaptively integrates information from co-activated CREs, similar cells, and publicly available regulome data to substantially increase the accuracy for estimating activities of individual CREs. We demonstrate that SCATE can be used to better reconstruct the regulatory landscape of a heterogeneous sample.

Single-cell ATAC sequencing analysis: From data preprocessing to hypothesis generation

Most genetic variations associated with human complex traits are located in non-coding genomic regions. Therefore, understanding the genotype-to-phenotype axis requires a comprehensive catalog of functional non-coding genomic elements, most of which are involved in epigenetic regulation of gene expression. Genome-wide maps of open chromatin regions can facilitate functional analysis of cis- and trans-regulatory elements via their connections with trait-associated sequence variants. Currently, Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) is considered the most accessible and cost-effective strategy for genome-wide profiling of chromatin accessibility. Single-cell ATAC-seq (scATAC-seq) technology has also been developed to study cell type-specific chromatin accessibility in tissue samples containing a heterogeneous cellular population. However, due to the intrinsic nature of scATAC-seq data, which are highly noisy and sparse, accurate extraction of biological signals and devising effective biological hypothesis are difficult. To overcome such limitations in scATAC-seq data analysis, new methods and software tools have been developed over the past few years. Nevertheless, there is no consensus for the best practice of scATAC-seq data analysis yet. In this review, we discuss scATAC-seq technology and data analysis methods, ranging from preprocessing to downstream analysis, along with an up-to-date list of published studies that involved the application of this method. We expect this review will provide a guideline for successful data generation and analysis methods using appropriate software tools and databases for the study of chromatin accessibility at single-cell resolution.

APEC: an accesson-based method for single-cell chromatin accessibility analysis

The development of sequencing technologies has promoted the survey of genome-wide chromatin accessibility at single-cell resolution. However, comprehensive analysis of single-cell epigenomic profiles remains a challenge. Here, we introduce an accessibility pattern-based epigenomic clustering (APEC) method, which classifies each cell by groups of accessible regions with synergistic signal patterns termed “accessons”. This python-based package greatly improves the accuracy of unsupervised single-cell clustering for many public datasets. It also predicts gene expression, identifies enriched motifs, discovers super-enhancers, and projects pseudotime trajectories. APEC is available at https://github.com/QuKunLab/APEC.

Fast analysis of scATAC-seq data using a predefined set of genomic regions

Background: Analysis of scATAC-seq data has been recently scaled to thousands of cells. While processing of other types of single cell data was boosted by the implementation of alignment-free techniques, pipelines available to process scATAC-seq data still require large computational resources. We propose here an approach based on pseudoalignment, which reduces the execution times and hardware needs at little cost for precision. Methods: Public data for 10k PBMC were downloaded from 10x Genomics web site. Reads were aligned to various references derived from DNase I Hypersensitive Sites (DHS) using kallisto and quantified with bustools. We compared our results with the ones publicly available derived by cellranger-atac. We subsequently tested our approach on scATAC-seq data for K562 cell line. Results: We found that kallisto does not introduce biases in quantification of known peaks; cells groups identified are consistent with the ones identified from standard method. We also found that cell identification is robust when analysis is performed using DHS-derived reference in place of de novo identification of ATAC peaks. Lastly, we found that our approach is suitable for reliable quantification of gene activity based on scATAC-seq signal, thus allows for efficient labelling of cell groups based on marker genes. Conclusions: Analysis of scATAC-seq data by means of kallisto produces results in line with standard pipelines while being considerably faster; using a set of known DHS sites as reference does not affect the ability to characterize the cell populations.

From reads to insight: a hitchhiker's guide to ATAC-seq data analysis

Assay of Transposase Accessible Chromatin sequencing (ATAC-seq) is widely used in studying chromatin biology, but a comprehensive review of the analysis tools has not been completed yet. Here, we discuss the major steps in ATAC-seq data analysis, including pre-analysis (quality check and alignment), core analysis (peak calling), and advanced analysis (peak differential analysis and annotation, motif enrichment, footprinting, and nucleosome position analysis). We also review the reconstruction of transcriptional regulatory networks with multiomics data and highlight the current challenges of each step. Finally, we describe the potential of single-cell ATAC-seq and highlight the necessity of developing ATAC-seq specific analysis tools to obtain biologically meaningful insights.

Epigenetic heterogeneity in cancer

Phenotypic and functional heterogeneity is one of the hallmarks of human cancers. Tumor genotype variations among tumors within different patients are known as interpatient heterogeneity, and variability among multiple tumors of the same type arising in the same patient is referred to as intra-patient heterogeneity. Subpopulations of cancer cells with distinct phenotypic and molecular features within a tumor are called intratumor heterogeneity (ITH). Since Nowell proposed the clonal evolution of tumor cell populations in 1976, tumor heterogeneity, especially ITH, was actively studied. Research has focused on the genetic basis of cancer, particularly mutational activation of oncogenes or inactivation of tumor-suppressor genes (TSGs). The phenomenon of ITH is commonly explained by Darwinian-like clonal evolution of a single tumor. Despite the monoclonal origin of most cancers, new clones arise during tumor progression due to the continuous acquisition of mutations. It is clear that disruption of the "epigenetic machinery" plays an important role in cancer development. Aberrant epigenetic changes occur more frequently than gene mutations in human cancers. The epigenome is at the intersection of the environment and genome. Epigenetic dysregulation occurs in the earliest stage of cancer. The current trend of epigenetic therapy is to use epigenetic drugs to reverse and/or delay future resistance to cancer therapies. A majority of cancer therapies fail to achieve durable responses, which is often attributed to ITH. Epigenetic therapy may reverse drug resistance in heterogeneous cancer. Complete understanding of genetic and epigenetic heterogeneity may assist in designing combinations of targeted therapies based on molecular information extracted from individual tumors.

SCALE method for single-cell ATAC-seq analysis via latent feature extraction

Single-cell ATAC-seq (scATAC-seq) profiles the chromatin accessibility landscape at single cell level, thus revealing cell-to-cell variability in gene regulation. However, the high dimensionality and sparsity of scATAC-seq data often complicate the analysis. Here, we introduce a method for analyzing scATAC-seq data, called Single-Cell ATAC-seq analysis via Latent feature Extraction (SCALE). SCALE combines a deep generative framework and a probabilistic Gaussian Mixture Model to learn latent features that accurately characterize scATAC-seq data. We validate SCALE on datasets generated on different platforms with different protocols, and having different overall data qualities. SCALE substantially outperforms the other tools in all aspects of scATAC-seq data analysis, including visualization, clustering, and denoising and imputation. Importantly, SCALE also generates interpretable features that directly link to cell populations, and can potentially reveal batch effects in scATAC-seq experiments.