Single-cell genome sequencing: current state of the science

Developing a Cell Quenching Method to Facilitate Single Cell Mass Spectrometry Metabolomics Studies

Single-cell mass spectrometry (SCMS) has emerged as a powerful tool for analyzing metabolites in individual cells, including live cells. However, cell metabolites have a rapid turnover rate, whereas maintaining metabolites' profiles of live cells during sample transport, storage, or extended measurements can be challenging. In this study, a cell preparation method, which integrates cell washing by volatile salt solution, rapid liquid nitrogen (LN2) quenching, freeze-drying in vacuum, and freezer storage at -80 °C, to preserve cell metabolites for SCMS measurement is discussed. Experimental results revealed that LN2 quenching preserved the overall cell metabolome, whereas storage at -80 °C for 48 h slightly changed the metabolite profiles in quenched cells. However, metabolites in unquenched cells were changed regardless of low-temperature storage. The influence of omission of quenching and low-temperature storage on cell metabolites and relevant pathways were investigated. Results from this work indicate that cell quenching is necessary, but low-temperature storage time should be minimized to preserve cell metabolites. The method developed in the current work can be readily adopted by SCMS techniques with storage remaining largely unaltered, allowing for extended SCMS studies.

Effects of ER-phagy regulatory genes on the microenvironment of hepatocellular carcinoma: a comprehensive analysis

The relationships between gene regulatory functions and hepatocellular carcinoma (HCC) occurrence and progression are constantly being clarified. However, tumour microenvironment complexity has hindered the classification of the role of genes. A comprehensive analysis to further clarify gene functions could provide additional benefits to HCC patients. In the present study, we combined single-cell sequencing data, Mendelian randomization, and bioinformatics analysis for comprehensive analysis. After the study was completed we found that T cell, dendritic cell (DC), macrophage and monocyte contents and the interaction between immune cells in the HCC microenvironment differed between the microvascular invasion-positive (MVI +) and microvascular invasion-negative (MVI-) groups. Mendelian randomization analysis indicated that causal relationships between several endoplasmic reticulum autophagy (ER-phagy) genes and T cell, DC, macrophage and monocyte contents. Single-cell sequencing data were used to validate the association of these genes with immune cells in the microenvironment. Based on the above results, we preliminarily elucidated the potential role of ER autophagy in the HCC microenvironment. Furthermore, a prognostic model was constructed using these causal association genes, which could accurately predict the prognosis and survival of HCC patients.

Single-cell omics: moving towards a new era in ischemic stroke research

Ischemic stroke (IS) is a highly complex and heterogeneous disease involving multiple pathophysiological events. A better understanding of the pathophysiology of IS will enhance preventive, diagnostic and therapeutic strategies. Despite significant advances in modern medicine, the molecular mechanisms of IS are still largely unknown. The high-throughput omics approach opens new avenues for identifying IS biomarkers and elucidating disease pathogenesis mechanisms. Single-cell omics enables a more thorough and in-depth analysis of the cellular interactions and properties in IS. This will lead to a better understanding of the onset, treatment and prognosis of IS. In this paper, we first reviewed the disease signatures and mechanisms research of IS. Subsequently, the use of single-cell omics to comprehend the mechanisms of IS was discussed, along with some recent developments in the field. To further delineate the upstream pathogenic alterations and downstream molecular impacts of IS, we also discussed the current use of machine learning approaches to single-cell omics data analysis. Particularly, single-cell omics is being used to inform risk assessment, early patient diagnosis and treatment strategies, and their potential impact on precision medicine. Thus, we summarized the role of single-cell omics in precision medicine. Despite the relative youth of the field, the development of single-cell omics promises to provide a powerful tool for elucidating the pathogenesis of IS.

Mapping the temporal transcriptomic signature of a viral pathogen through CAGE and nanopore sequencing

INTRODUCTION

Equid alphaherpesvirus 1 (EHV-1), a veterinary pathogen belonging to the Varicellovirus genus, is responsible for significant economic losses in the global equine sector. This research involved timescale gene expression profiling and transcriptional reannotation of this herpesvirus.

METHODS

We employed CAGE sequencing on the Illumina platform to determine transcript start sites, alongside long-read direct cDNA sequencing on Oxford Nanopore Technology platform to detect full-length viral transcripts. Samples were collected in triplicate at nine distinct stages of the viral lifecycle. We also applied protein synthesis inhibition to determine the immediate-early gene expression of the virus. Earlier data on native RNA sequencing was also utilized to validate the results. The sequencing data were processed using the LoRTIA and NAGATA software tools.

RESULTS

The time-course analysis of viral transcript expression using long-read dcDNA-Seq enabled the characterization of these transcripts based on their kinetic behavior throughout the replication cycle. Furthermore, the study involved a comprehensive reannotation of the EHV-1 transcriptome. CAGE analysis helped identify the transcription start sites and promoter regions, while direct cDNA sequencing provided a more accurate approach to capturing full-length transcripts and isoform diversity. Through an integrated approach, we identified and validated numerous novel transcripts, thereby refining the EHV-1 transcriptome annotation. These methods allowed for a more detailed and accurate mapping of the EHV-1 transcriptome, uncovering previously unknown transcripts and refining the existing annotations.

CONCLUSIONS

The shifting patterns in transcript isoforms and overlaps suggest a sophisticated regulatory network that enables EHV-1 to precisely modulate gene expression throughout its replication cycle. The presence of multiple isoforms per gene indicates that the virus can adapt to different stages of infection by producing a variety of transcripts. This likely enhances its genomic efficiency and allows it to respond more effectively to the host's environment.

Single-cell genomics of single soil aggregates: methodological assessment and potential implications with a focus on nitrogen metabolism

Soil particles in plant rooting zones are largely clustered to form porous structural units called aggregates where highly diverse microorganisms inhabit and drive biogeochemical cycling. The complete extraction of microbial cells and DNA from soil is a substantial task as certain microorganisms exhibit strong adhesion to soil surfaces and/or inhabit deep within aggregates. However, the degree of aggregate dispersion and the efficacy of extraction have rarely been examined, and thus, adequate cell extraction methods from soil remain unclear. We aimed to develop an optimal method of cell extraction for single-cell genomics (SCG) analysis of single soil aggregates by focusing on water-stable macroaggregates (diameter: 5.6-8.2 mm) from the topsoil of cultivated Acrisol. We postulated that the extraction of microorganisms with distinct taxonomy and functions could be achieved depending on the degree of soil aggregate dispersion. To test this idea, we used six individual aggregates and performed both SCG sequencing and amplicon analysis. While both bead-vortexing and sonication dispersion techniques improved the extractability of bacterial cells compared to previous ones, the sonication technique led to more efficient dispersion and yielded a higher number and more diverse microorganisms than the bead technique. Furthermore, the analyses of nitrogen cycling and exopolysaccharides-related genes suggested that the sonication-assisted extraction led to the greater recovery of microorganisms strongly attached to soil particles and/or inhabited the aggregate subunits that were more physically stable (e.g., aggregate core). Further SCG analysis revealed that all six aggregates held intact microorganisms holding the genes (potentials) to convert nitrate into all possible nitrogen forms while some low-abundance genes showed inter-aggregate heterogeneity. Overall, all six aggregates studied showed similarities in pore characteristics, phylum-level composition, and microbial functional redundancy. Together, these results suggest that water-stable macroaggregates may act as a functional unit in soil and show potential as a useful experimental unit in soil microbial ecology. Our study also suggests that conventional methods employed for the extraction of cells and DNA may not be optimal. The findings of this study emphasize the necessity of advancing extraction methodologies to facilitate a more comprehensive understanding of microbial diversity and function in soil environments.

Indoleamine 2,3-dioxygenase 1 drives epithelial cells ferroptosis in influenza-induced acute lung injury

Acute lung injury (ALI) is a life-threatening complication of influenza A virus (IAV) infection, characterized by high morbidity and mortality. Recent studies have implicated ferroptosis, a distinct form of regulated cell death characterized by iron-dependent lipid peroxidation, in the pathogenesis of IAV-induced ALI. However, the underlying mechanisms and key regulators of IAV-induced ferroptosis remain largely unknown. In this study, we found that IAV infection induces predominant ferroptosis in alveolar and bronchial epithelial cells, contributing to tissue damage and the development of acute lung injury. Treatment with the ferroptosis inhibitor ferrostatin-1 improved survival, mitigated weight loss, and alleviated lung injury in IAV-infected mice. Mechanistically, IAV-induced ferroptosis was associated with excess lipid peroxidation, nitrative stress, and disrupted iron metabolism. Targeted lipidomic analysis revealed that phospholipid peroxidation is a crucial mechanism in IAV-induced ferroptosis. Importantly, we identified indoleamine 2,3-dioxygenase 1 (IDO1) as a key regulator of IAV-induced ferroptosis. IDO1 knockdown inhibited IAV-induced cell death, and reduced intracellular reactive oxygen species, peroxynitrite, and inducible nitric oxide synthase expression. Furthermore, pharmacological inhibition of IDO1 with 1-methyl-tryptophan improved ALI phenotype in IAV-infected mice. These findings highlight the critical role of ferroptosis in IAV-induced ALI pathogenesis and identify IDO1 as a potential therapeutic target for the treatment of this life-threatening condition.

Reproductive Choices in Haemoglobinopathies: The Role of Preimplantation Genetic Testing

Haemoglobinopathies are among the most prevalent genetic disorders globally. In the context of these conditions, preimplantation genetic testing (PGT) plays a pivotal role in preventing genetic diseases in the offspring of carrier parents, reducing the need for pregnancy termination and enabling the selection of compatible sibling donors for potential stem cell transplantation in cases of thalassemia or sickle cell disease. This review explores the evolving role of PGT as a reproductive option for haemoglobinopathy carriers, tracing the development of PGT protocols from patient-specific to comprehensive testing enabled by advanced technologies like next-generation sequencing (NGS). We discuss key technical, biological, and practical limitations of PGT, as well as the ethical considerations specific to haemoglobinopathies, such as the complexity of interpreting genotypes. Emerging technologies, such as whole-genome sequencing, non-invasive PGT, and gene editing, hold significant promise for expanding applications but also raise new challenges that must be addressed. It will be interesting to explore how advancements in technology, along with the changing management of haemoglobinopathies, will impact reproductive choices. It is anticipated that continued research will improve genetic counseling for PGT for haemoglobinopathies, while a careful evaluation of ethical and societal implications is also required. Responsible and equitable implementation of PGT is essential for ensuring that all families at risk can make informed reproductive choices.

Sc-TUSV-Ext: Single-Cell Clonal Lineage Inference from Single Nucleotide Variants, Copy Number Alterations, and Structural Variants

Clonal lineage inference ("tumor phylogenetics") has become a crucial tool for making sense of somatic evolution processes that underlie cancer development and are increasingly recognized as part of normal tissue growth and aging. The inference of clonal lineage trees from single-cell sequence data offers particular promise for revealing processes of somatic evolution in unprecedented detail. However, most such tools are based on fairly restrictive models of the types of mutation events observed in somatic evolution and of the processes by which they develop. The present work seeks to enhance the power and versatility of tools for single-cell lineage reconstruction by making more comprehensive use of the range of molecular variant types by which tumors evolve. We introduce Sc-TUSV-ext, an integer linear programming-based tumor phylogeny reconstruction method that, for the first time, integrates single nucleotide variants, copy number alterations, and structural variations into clonal lineage reconstruction from single-cell DNA sequencing data. We show on synthetic data that accounting for these variant types collectively leads to improved accuracy in clonal lineage reconstruction relative to prior methods that consider only subsets of the variant types. We further demonstrate the effectiveness of real data in resolving clonal evolution in the presence of multiple variant types, providing a path toward more comprehensive insight into how various forms of somatic mutability collectively shape tissue development.

scMUG: deep clustering analysis of single-cell RNA-seq data on multiple gene functional modules

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity by providing gene expression data at the single-cell level. Unlike bulk RNA-seq, scRNA-seq allows identification of different cell types within a given tissue, leading to a more nuanced comprehension of cell functions. However, the analysis of scRNA-seq data presents challenges due to its sparsity and high dimensionality. Since bioinformatics plays an important role in the analysis of big data and its utility for the welfare of living beings, it has been widely applied in analyzing scRNA-seq data. To address these challenges, we introduce the scMUG computational pipeline, which incorporates gene functional module information to enhance scRNA-seq clustering analysis. The pipeline includes data preprocessing, cell representation generation, cell-cell similarity matrix construction, and clustering analysis. The scMUG pipeline also introduces a novel similarity measure that combines local density and global distribution in the latent cell representation space. As far as we can tell, this is the first attempt to integrate gene functional associations into scRNA-seq clustering analysis. We curated nine human scRNA-seq datasets to evaluate our scMUG pipeline. With the help of gene functional information and the novel similarity measure, the clustering results from scMUG pipeline present deep insights into functional relationships between gene expression patterns and cellular heterogeneity. In addition, our scMUG pipeline also presents comparable or better clustering performances than other state-of-the-art methods. All source codes of scMUG have been deposited in a GitHub repository with instructions for reproducing all results (https://github.com/degiminnal/scMUG).

Single-cell copy number calling and event history reconstruction

MOTIVATION

Copy number alterations are driving forces of tumour development and the emergence of intra-tumour heterogeneity. A comprehensive picture of these genomic aberrations is therefore essential for the development of personalised and precise cancer diagnostics and therapies. Single-cell sequencing offers the highest resolution for copy number profiling down to the level of individual cells. Recent high-throughput protocols allow for the processing of hundreds of cells through shallow whole-genome DNA sequencing. The resulting low read-depth data poses substantial statistical and computational challenges to the identification of copy number alterations.

RESULTS

We developed SCICoNE, a statistical model and MCMC algorithm tailored to single-cell copy number profiling from shallow whole-genome DNA sequencing data. SCICoNE reconstructs the history of copy number events in the tumour and uses these evolutionary relationships to identify the copy number profiles of the individual cells. We show the accuracy of this approach in evaluations on simulated data and demonstrate its practicability in applications to two breast cancer samples from different sequencing protocols.

AVAILABILITY AND IMPLEMENTATION

SCICoNE is available at https://github.com/cbg-ethz/SCICoNE.

From morphology to single-cell molecules: high-resolution 3D histology in biomedicine

High-resolution three-dimensional (3D) tissue analysis has emerged as a transformative innovation in the life sciences, providing detailed insights into the spatial organization and molecular composition of biological tissues. This review begins by tracing the historical milestones that have shaped the development of high-resolution 3D histology, highlighting key breakthroughs that have facilitated the advancement of current technologies. We then systematically categorize the various families of high-resolution 3D histology techniques, discussing their core principles, capabilities, and inherent limitations. These 3D histology techniques include microscopy imaging, tomographic approaches, single-cell and spatial omics, computational methods and 3D tissue reconstruction (e.g. 3D cultures and spheroids). Additionally, we explore a wide range of applications for single-cell 3D histology, demonstrating how single-cell and spatial technologies are being utilized in the fields such as oncology, cardiology, neuroscience, immunology, developmental biology and regenerative medicine. Despite the remarkable progress made in recent years, the field still faces significant challenges, including high barriers to entry, issues with data robustness, ambiguous best practices for experimental design, and a lack of standardization across methodologies. This review offers a thorough analysis of these challenges and presents recommendations to surmount them, with the overarching goal of nurturing ongoing innovation and broader integration of cellular 3D tissue analysis in both biology research and clinical practice.

scCrab: A Reference-Guided Cancer Cell Identification Method based on Bayesian Neural Networks

Cancer is a significant global public health concern, where early detection can greatly enhance curative outcomes. Therefore, the identification of cancer cells holds significant importance as the primary method for cancer diagnosis. The advancement of single-cell RNA sequencing (scRNA-seq) technology has made it possible to address the problem of cancer cell identification at the single-cell level more efficiently with computational methods, as opposed to the time-consuming and less reproducible manual identification methods. However, existing computational methods have shown suboptimal identification performance and a lack of capability to incorporate external reference data as prior information. Here, we propose scCrab, a reference-guided automatic cancer cell identification method, which performs ensemble learning based on a Bayesian neural network (BNN) with multi-head self-attention mechanisms and a linear regression model. Through a series of experiments on various datasets, we systematically validated the superior performance of scCrab in both intra- and inter-dataset predictions. Besides, we demonstrated the robustness of scCrab to dropout rate and sample size, and conducted ablation experiments to investigate the contributions of each component in scCrab. Furthermore, as a dedicated model for cancer cell identification, scCrab effectively captures cancer-related biological significance during the identification process.

Recent Innovations and Technical Advances in High-Throughput Parallel Single-Cell Whole-Genome Sequencing Methods

Single-cell whole-genome sequencing (scWGS) detects cell heterogeneity at the aspect of genomic variations, which are inheritable and play an important role in life processes such as aging and cancer progression. The recent explosive development of high-throughput single-cell sequencing methods has enabled high-performance heterogeneity detection through a vast number of novel strategies. Despite the limitation on total cost, technical advances in high-throughput single-cell whole-genome sequencing methods are made for higher genome coverage, parallel throughput, and level of integration. This review highlights the technical advancements in high-throughput scWGS in the aspects of strategies design, data efficiency, parallel handling platforms, and their applications on human genome. The experimental innovations, remaining challenges, and perspectives are summarized and discussed.

Advancements in omics technologies: Molecular mechanisms of acute lung injury and acute respiratory distress syndrome (Review)

Acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) is an inflammatory response arising from lung and systemic injury with diverse causes and associated with high rates of morbidity and mortality. To date, no fully effective pharmacological therapies have been established and the relevant underlying mechanisms warrant elucidation, which may be facilitated by multi‑omics technology. The present review summarizes the application of multi‑omics technology in identifying novel diagnostic markers and therapeutic strategies of ALI/ARDS as well as its pathogenesis.

Inferring active mutational processes in cancer using single cell sequencing and evolutionary constraints

Ongoing mutagenesis in cancer drives genetic diversity throughout the natural history of cancers. As the activities of mutational processes are dynamic throughout evolution, distinguishing the mutational signatures of 'active' and 'historical' processes has important implications for studying how tumors evolve. This can aid in understanding mutagenic states at the time of presentation, and in associating active mutational process with therapeutic resistance. As bulk sequencing primarily captures historical mutational processes, we studied whether ultra-low-coverage single-cell whole-genome sequencing (scWGS), which measures the distribution of mutations across hundreds or thousands of individual cells, could enable the distinction between historical and active mutational processes. While technical challenges and data sparsity have limited mutation analysis in scWGS, we show that these data contain valuable information about dynamic mutational processes. To robustly interpret single nucleotide variants (SNVs) in scWGS, we introduce ArtiCull, a method to identify and remove SNV artifacts by leveraging evolutionary constraints, enabling reliable detection of mutations for signature analysis. Applying this approach to scWGS data from pancreatic ductal adenocarcinoma (PDAC), triple-negative breast cancer (TNBC), and high-grade serous ovarian cancer (HGSOC), we uncover temporal and spatial patterns in mutational processes. In PDAC, we observe a temporal increase in mismatch repair deficiency (MMRd). In cisplatin-treated TNBC patient-derived xenografts, we identify therapy-induced mutagenesis and inactivation of APOBEC3 activity. In HGSOC, we show distinct patterns of APOBEC3 mutagenesis, including late tumor-wide activation in one case and clade-specific enrichment in another. Additionally, we detect a clone-specific increase in SBS17 activity, in a clone previously linked to recurrence. Our findings establish ultra-low-coverage scWGS as a powerful approach for studying active mutational processes that may influence ongoing clonal evolution and therapeutic resistance.

Predicting cell properties with AI from 3D imaging flow cytometer data

Predicting the properties of tissues or organisms from the genomics data is widely accepted by the medical community. Here we ask a question: can we predict the properties of each individual cell? Single-cell genomics does not work because the RNA sequencing process destroys the cell, not allowing us to verify our predictions. To test the hypothesis, we investigate the approach of using AI to analyze single-cell images obtained from a 3D imaging flow cytometer. We analyze the cell image at day zero and make the AI-assisted cell property prediction. The prediction is then examined later when the cells continue to live and develop. Our preliminary results are promising, showing 88% accuracy in predicting cells that will have a high protein expression level. The technique can have strong ramifications and impact on preventive medicine, drug development, cell therapy, and fundamental biomedical research.

Dynamic Spread of Antibiotic Resistance Determinants by Conjugation to a Human-Derived Gut Microbiota in a Transplanted Mouse Model

BACKGROUND

Antibiotic-resistant (AR) bacteria pose an increasing threat to public health, but the dynamics of antibiotic resistance gene (ARG) spread in complex microbial communities are poorly understood. Conjugation is a predominant direct cell-to-cell mechanism for the horizontal gene transfer (HGT) of ARGs. We hypothesized that commensal Escherichia coli donor strains would mediate the conjugative transfer of ARGs to phylogenetically distinct bacteria without antibiotic selection pressure in gastrointestinal tracts of mice carrying a human-derived microbiota with undetectable levels of E. coli. Our objective was to identify a mouse model to study the factors regulating AR transfer by conjugation in the gut.

METHODS

Two donor E. coli strains were engineered to carry chromosomally encoded red fluorescent protein, and an ARG- and green fluorescent protein (GFP)-encoding broad host range RP4 conjugative plasmid. Mice were orally gavaged with two donor strains (1) E. coli MG1655 or (2) human-derived mouse-adapted E. coli LM715-1 and their colonization assessed by culture over time. Fluorescence-activated cell sorting (FACS) and 16S rDNA sequencing were performed to trace plasmid spread to the microbiota.

RESULTS

E. coli LM715-1 colonized mice for ten days, while E. coli MG1655 was not recovered after 72 h. Bacterial cells from fecal samples on days 1 and 3 post inoculation were sorted by FACS. Samples from mice given donor E. coli LM715-1 showed an increase in cells expressing green but not red fluorescence compared to pre-inoculation samples. 16S rRNA gene sequencing analysis of FACS GFP positive cells showed that bacterial families Lachnospiraceae, Clostridiaceae, Pseudomonadaceae, Rhodanobacteraceae, Erysipelotrichaceae, Oscillospiraceae, and Butyricicoccaceae were the primary recipients of the RP4 plasmid.

CONCLUSIONS

Results show this ARG-bearing conjugative RP4 plasmid spread to diverse human gut bacterial taxa within a live animal where they persisted. These fluorescent marker strategies and human-derived microbiota transplanted mice provided a tractable model for investigating the dynamic spread of ARGs within gut microbiota and could be applied rigorously to varied microbiotas to understand conditions facilitating their spread.

The loss of dNK1/2 and EVT1 cells at the maternal-fetal interface is associated with recurrent miscarriage†

Recurrent miscarriage is a chronic and heterogeneous pregnancy disorder lacking effective treatment. Alterations at the maternal-fetal interface are commonly observed in recurrent miscarriage, with the loss of certain cell subpopulations believed to be a key cause. Through single-cell sequencing of recurrent miscarriage patients and healthy donors, we aim to identify aberrancy of cellular features in recurrent miscarriage tissues, providing new insights into the research. Natural killer cells, the most abundant immune cells in the decidua, are traditionally classified into dNK1, dNK2, and dNK3. In this study, we identified a new subset, dNK1/2, absent in recurrent miscarriage tissues. This subset was named because it expresses biomarkers of both dNK1 and dNK2. With further analysis, we discovered that dNK1/2 cells play roles in immunoregulation and cytokine secretion. On the villous side of the interface, a notable decrease of extravillous trophoblast cells was identified in recurrent miscarriage tissues. We clustered extravillous trophoblasts into EVT1 (absent in recurrent miscarriage) and EVT2 (retained in recurrent miscarriage). Pseudotime analysis revealed distinct differentiation paths, identifying CCNB1, HMGB1, and NPM1 as EVT1 biomarkers. Additionally, we found that EVT1 is involved in the regulation of cell death, while EVT2 exhibited more angiogenic activity. Cell communication analysis revealed that interaction between EVT1 and dNK1/2 mediates chemotaxis and endothelial cell regulation, crucial for spiral artery remodeling. The loss of this interaction may impair decidualization, which is associated with recurrent miscarriage. In summary, we propose that the loss of dNK1/2 and EVT1 cells is a significant pathological feature of recurrent miscarriage.

SUMMARY SENTENCE

The communication between EVT1 and dNK1/2 mediated the chemotaxis of EVT1 and facilitated regulation of endothelial cell death, initiating spiral artery remodeling. The loss of this specific cellular interaction may result in impaired decidualization, leading to recurrent miscarriage.

HSCGD: a comprehensive database of single-cell whole-genome data and metadata

Single-cell whole-genome sequencing is a powerful tool for uncovering mutations in individual cells. In recent times, the generation of vast amounts of data has significantly advanced our understanding of key biological processes, including cell development and tumor progression. This rapid accumulation of data underscores the urgent need for a comprehensive resource platform to manage and utilize this information effectively. To address this need, we introduce HSCGD, the first open-access and comprehensive database dedicated to the collection, integration, analysis, and visualization of single-cell whole-genome data and metadata. The current release of HSCGD includes processed single-cell whole-genome sequencing data and curated metadata of 74 154 human cells derived from 63 public single-cell datasets, involving 23 cell types and 17 major single-cell whole-genome amplification methods. HSCGD is designed to help researchers interested in cellular heterogeneity explore and utilize whole genome data at the single-cell level by providing browsing, searching, visualization, downloading and online tools. The database can be accessed from the following URL: http://www.hscgd.com.

A joint analysis of single cell transcriptomics and proteomics using transformer

CITE-seq provides a powerful method for simultaneously measuring RNA and protein expression at the single-cell level. The integrated analysis of RNA and protein expression in identical cells is crucial for revealing cellular heterogeneity. However, the high experimental costs associated with CITE-seq limit its widespread application. In this paper, we propose scTEL, a deep learning framework based on Transformer encoder layers, to establish a mapping from sequenced RNA expression to unobserved protein expression in the same cells. This computation-based approach significantly reduces the experimental costs of protein expression sequencing. We are now able to predict protein expression using single-cell RNA sequencing (scRNA-seq) data, which is well-established and available at a lower cost. Moreover, our scTEL model offers a unified framework for integrating multiple CITE-seq datasets, addressing the challenge posed by the partial overlap of protein panels across different datasets. Empirical validation on public CITE-seq datasets demonstrates scTEL significantly outperforms existing methods.

Research progress and the prospect of using single-cell sequencing technology to explore the characteristics of the tumor microenvironment

In precision cancer therapy, addressing intra-tumor heterogeneity poses a significant obstacle. Due to the heterogeneity of each cell subtype and between cells within the tumor, the sensitivity and resistance of different patients to targeted drugs, chemotherapy, etc., are inconsistent. Concerning a specific tumor type, many feasible treatments or combinations can be used by specifically targeting the tumor microenvironment. To solve this problem, it is necessary to further study the tumor microenvironment. Single-cell sequencing techniques can dissect distinct tumor cell populations by isolating cells and using statistical computational methods. This technology may assist in the selection of targeted combination therapy, and the obtained cell subset information is crucial for the rational application of targeted therapy. In this review, we summarized the research and application advances of single-cell sequencing technology in the tumor microenvironment, including the most commonly used single-cell genomic and transcriptomic sequencing, and their future development direction was proposed. The application of single-cell sequencing technology has been expanded to include epigenomics, proteomics, metabolomics, and microbiome analysis. The integration of these different omics approaches has significantly advanced the development of single-cell multiomics sequencing technology. This innovative approach holds immense potential for various fields, such as biological research and medical investigations. Finally, we discussed the advantages and disadvantages of using single-cell sequencing to explore the tumor microenvironment.

Unveiling Long Non-coding RNA Networks from Single-Cell Omics Data Through Artificial Intelligence

Single-cell omics technologies have revolutionized the study of long non-coding RNAs (lncRNAs), offering unprecedented resolution in elucidating their expression dynamics, cell-type specificity, and associated gene regulatory networks (GRNs). Concurrently, the integration of artificial intelligence (AI) methodologies has significantly advanced our understanding of lncRNA functions and its implications in disease pathogenesis. This chapter discusses the progress in single-cell omics data analysis, emphasizing its pivotal role in unraveling the molecular mechanisms underlying cellular heterogeneity and the associated regulatory networks involving lncRNAs. Additionally, we provide a summary of single-cell omics resources and AI models for constructing single-cell gene regulatory networks (scGRNs). Finally, we explore the challenges and prospects of exploring scGRNs in the context of lncRNA biology.

Soft tissue sarcomas at the single-cell and spatial resolution: new markers and targets

Soft tissue sarcomas (STS) are heterogeneous and aggressive tumors, originating in connective tissues embryologically derived from the mesenchyme. Due to their rarity, crucial information about their biology is still lacking. In recent years, single-cell and spatial analyses have opened up new horizons in oncology, leading to the possibility of characterizing the internal architecture of the tumor at the single-cell and spatial levels. This review summarizes the first results acquired through these revolutionary methods for different types of STS. We discuss tumor cell populations and their evolution, interactions between tumor cells and the microenvironment, new prognostic markers, and clinically important targets. Finally, we examine the challenges presented by the single-cell and spatial omics of STS and the future perspectives in this field.

Cancer phylogenetic inference using copy number alterations detected from DNA sequencing data

Cancer is an evolutionary process involving the accumulation of diverse somatic mutations and clonal evolution over time. Phylogenetic inference from samples obtained from an individual patient offers a powerful approach to unraveling the intricate evolutionary history of cancer and provides insights that can inform cancer treatment. Somatic copy number alterations (CNAs) are important in cancer evolution and are often used as markers, alone or with other somatic mutations, for phylogenetic inferences, particularly in low-coverage DNA sequencing data. Many phylogenetic inference methods using CNAs detected from bulk or single-cell DNA sequencing data have been developed over the years. However, there have been no systematic reviews on these methods. To summarize the state-of-the-art of the field and inform future development, this review presents a comprehensive survey on the major challenges in inference, different types of methods, and applications of these methods. The challenges are discussed from the aspects of input data, models of evolution, and inference algorithms. The different methods are grouped according to the markers used for inference and the types of the reconstructed trees. The applications include using phylogenetic inference to understand intra-tumor heterogeneity, metastasis, treatment resistance, and early cancer development. This review also sheds light on future directions of cancer phylogenetic inference using CNAs, including the improvement of scalability, the utilization of new types of data, and the development of more realistic models of evolution.

High-throughput single-cell sequencing of activated sludge microbiome

Wastewater treatment plants (WWTPs) represent one of biotechnology's largest and most critical applications, playing a pivotal role in environmental protection and public health. In WWTPs, activated sludge (AS) plays a major role in removing contaminants and pathogens from wastewater. While metagenomics has advanced our understanding of microbial communities, it still faces challenges in revealing the genomic heterogeneity of cells, uncovering the microbial dark matter, and establishing precise links between genetic elements and their host cells as a bulk method. These issues could be largely resolved by single-cell sequencing, which can offer unprecedented resolution to show the unique genetic information. Here we show the high-throughput single-cell sequencing to the AS microbiome. The single-amplified genomes (SAGs) of 15,110 individual cells were clustered into 2,454 SAG bins. We find that 27.5% of the genomes in the AS microbial community represent potential novel species, highlighting the presence of microbial dark matter. Furthermore, we identified 1,137 antibiotic resistance genes (ARGs), 10,450 plasmid fragments, and 1,343 phage contigs, with shared plasmid and phage groups broadly distributed among hosts, indicating a high frequency of horizontal gene transfer (HGT) within the AS microbiome. Complementary analysis using 1,529 metagenome-assembled genomes from the AS samples allowed for the taxonomic classification of 98 SAG bins, which were previously unclassified. Our study establishes the feasibility of single-cell sequencing in characterizing the AS microbiome, providing novel insights into its ecological dynamics, and deepening our understanding of HGT processes, particularly those involving ARGs. Additionally, this valuable tool could monitor the distribution, spread, and pathogenic hosts of ARGs both within AS environments and between AS and other environments, which will ultimately contribute to developing a health risk evaluation system for diverse environments within a One Health framework.

Replication stress increases de novo CNVs across the malaria parasite genome

Changes in the copy number of large genomic regions, termed copy number variations (CNVs), contribute to important phenotypes in many organisms. CNVs are readily identified using conventional approaches when present in a large fraction of the cell population. However, CNVs that are present in only a few genomes across a population are often overlooked but important; if beneficial under specific conditions, a de novo CNV that arises in a single genome can expand during selection to create a larger population of cells with novel characteristics. While the reach of single cell methods to study de novo CNVs is increasing, we continue to lack information about CNV dynamics in rapidly evolving microbial populations. Here, we investigated de novo CNVs in the genome of the Plasmodium parasite that causes human malaria. The highly AT-rich P. falciparum genome readily accumulates CNVs that facilitate rapid adaptation to new drugs and host environments. We employed a low-input genomics approach optimized for this unique genome as well as specialized computational tools to evaluate the de novo CNV rate both before and after the application of stress. We observed a significant increase in genomewide de novo CNVs following treatment with a replication inhibitor. These stress-induced de novo CNVs encompassed genes that contribute to various cellular pathways and tended to be altered in clinical parasite genomes. This snapshot of CNV dynamics emphasizes the connection between replication stress, DNA repair, and CNV generation in this important microbial pathogen.

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

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

Should Artificial Intelligence Play a Durable Role in Biomedical Research and Practice?

During the last decade, artificial intelligence (AI) was applied to nearly all domains of human activity, including scientific research. It is thus warranted to ask whether AI thinking should be durably involved in biomedical research. This problem was addressed by examining three complementary questions (i) What are the major barriers currently met by biomedical investigators? It is suggested that during the last 2 decades there was a shift towards a growing need to elucidate complex systems, and that this was not sufficiently fulfilled by previously successful methods such as theoretical modeling or computer simulation (ii) What is the potential of AI to meet the aforementioned need? it is suggested that recent AI methods are well-suited to perform classification and prediction tasks on multivariate systems, and possibly help in data interpretation, provided their efficiency is properly validated. (iii) Recent representative results obtained with machine learning suggest that AI efficiency may be comparable to that displayed by human operators. It is concluded that AI should durably play an important role in biomedical practice. Also, as already suggested in other scientific domains such as physics, combining AI with conventional methods might generate further progress and new applications, involving heuristic and data interpretation.

Cell Type Differentiation Using Network Clustering Algorithms

Single cell RNA-seq (scRNA-seq) technologies provide unprecedented resolution representing transcriptomics at the level of single cell. One of the biggest challenges in scRNA-seq data analysis is the cell type annotation, which is usually inferred by cell separation approaches. In-silico algorithms that accurately identify individual cell types in ongoing single-cell sequencing studies are crucial for unlocking cellular heterogeneity and understanding the biological basis of diseases. In this study, we focus on robustly identifying cell types in single-cell RNA sequencing data; we conduct a comparative analysis using methods established in biology, like Seurat, Leiden, and WGCNA, as well as Infomap, statistical inference via Stochastic Block Models (SBM), and single-cell Graph Neural Networks (scGNN). We also analyze preprocessing pipelines to identify and optimize key components in the process. Leveraging two independent datasets, PBMC and ROSMAP, we employ clustering algorithms on cell-cell networks derived from gene expression data. Our findings reveal that while clusters detected by WGCNA exhibit limited correspondence with cell types, those identified by multiresolution Infomap and Leiden, and SBM show a closer alignment, with Infomap standing out as a particularly effective approach. Infomap notably offers valuable insights for the precise characterization of cellular landscapes related to neurodegenration and immunology in scRNA-seq.

Single-cell multi-omics in the study of digestive system cancers

Digestive system cancers are prevalent diseases with a high mortality rate, posing a significant threat to public health and economic burden. The diagnosis and treatment of digestive system cancer confront conventional cancer problems, such as tumor heterogeneity and drug resistance. Single-cell sequencing (SCS) emerged at times required and has developed from single-cell RNA-seq (scRNA-seq) to the single-cell multi-omics era represented by single-cell spatial transcriptomics (ST). This article comprehensively reviews the advances of single-cell omics technology in the study of digestive system tumors. While analyzing and summarizing the research cases, vital details on the sequencing platform, sample information, sampling method, and key findings are provided. Meanwhile, we summarize the commonly used SCS platforms and their features, as well as the advantages of multi-omics technologies in combination. Finally, the development trends and prospects of the application of single-cell multi-omics technology in digestive system cancer research are prospected.

Droplet-based single-cell sequencing: Strategies and applications

Notable advancements in single-cell omics technologies have not only addressed longstanding challenges but also enabled unprecedented studies of cellular heterogeneity with unprecedented resolution and scale. These strides have led to groundbreaking insights into complex biological systems, paving the way for a more profound comprehension of human biology and diseases. The droplet microfluidic technology has become a crucial component in many single-cell sequencing workflows in terms of throughput, cost-effectiveness, and automation. Utilizing a microfluidic chip to encapsulate and profile individual cells within droplets has significantly improved single-cell research. Therefore, this review aims to comprehensively elaborate the droplet microfluidics-assisted omics methods from a single-cell perspective. The strategies for using droplet microfluidics in the realms of genomics, epigenomics, transcriptomics, and proteomics analyses are first introduced. On this basis, the focus then turns to the latest applications of this technology in different sequencing patterns, including mono- and multi-omics. Finally, the challenges and further perspectives of droplet-based single-cell sequencing in both foundational research and commercial applications are discussed.

CopyMix: Mixture model based single-cell clustering and copy number profiling using variational inference

Investigating tumor heterogeneity using single-cell sequencing technologies is imperative to understand how tumors evolve since each cell subpopulation harbors a unique set of genomic features that yields a unique phenotype, which is bound to have clinical relevance. Clustering of cells based on copy number data obtained from single-cell DNA sequencing provides an opportunity to identify different tumor cell subpopulations. Accordingly, computational methods have emerged for single-cell copy number profiling and clustering; however, these two tasks have been handled sequentially by applying various ad-hoc pre- and post-processing steps; hence, a procedure vulnerable to introducing clustering artifacts. We avoid the clustering artifact issues in our method, CopyMix, a Variational Inference for a novel mixture model, by jointly inferring cell clusters and their underlying copy number profile. Our probabilistic graphical model is an improved version of the mixture of hidden Markov models, which is designed uniquely to infer single-cell copy number profiling and clustering. For the evaluation, we used likelihood-ratio test, CH index, Silhouette, V-measure, total variation scores. CopyMix performs well on both biological and simulated data. Our favorable results indicate a considerable potential to obtain clinical impact by using CopyMix in studies of cancer tumor heterogeneity.

Normal breast tissues harbour rare populations of aneuploid epithelial cells

Aneuploid epithelial cells are common in breast cancer1,2; however, their presence in normal breast tissues is not well understood. To address this question, we applied single-cell DNA sequencing to profile copy number alterations in 83,206 epithelial cells from the breast tissues of 49 healthy women, and we applied single-cell DNA and assay for transposase-accessible chromatin sequencing co-assays to the samples of 19 women. Our data show that all women harboured rare aneuploid epithelial cells (median 3.19%) that increased with age. Many aneuploid epithelial cells (median 82.22%) in normal breast tissues underwent clonal expansions and harboured copy number alterations reminiscent of invasive breast cancers (gains of 1q; losses of 10q, 16q and 22q). Co-assay profiling showed that the aneuploid cells were mainly associated with the two luminal epithelial lineages, and spatial mapping showed that they localized in ductal and lobular structures with normal histopathology. Collectively, these data show that even healthy women have clonal expansions of rare aneuploid epithelial cells in their breast tissues.

Single-cell multiomics analysis reveals dynamic clonal evolution and targetable phenotypes in acute myeloid leukemia with complex karyotype

Chromosomal instability is a major driver of intratumoral heterogeneity (ITH), promoting tumor progression. In the present study, we combined structural variant discovery and nucleosome occupancy profiling with transcriptomic and immunophenotypic changes in single cells to study ITH in complex karyotype acute myeloid leukemia (CK-AML). We observed complex structural variant landscapes within individual cells of patients with CK-AML characterized by linear and circular breakage-fusion-bridge cycles and chromothripsis. We identified three clonal evolution patterns in diagnosis or salvage CK-AML (monoclonal, linear and branched polyclonal), with 75% harboring multiple subclones that frequently displayed ongoing karyotype remodeling. Using patient-derived xenografts, we demonstrated varied clonal evolution of leukemic stem cells (LSCs) and further dissected subclone-specific drug-response profiles to identify LSC-targeting therapies, including BCL-xL inhibition. In paired longitudinal patient samples, we further revealed genetic evolution and cell-type plasticity as mechanisms of disease progression. By dissecting dynamic genomic, phenotypic and functional complexity of CK-AML, our findings offer clinically relevant avenues for characterizing and targeting disease-driving LSCs.

Highly Multiplexing, Throughput and Efficient Single-Cell Protein Analysis with Digital Microfluidics

Proteins as crucial components of cells are responsible for the majority of cellular processes. Sensitive and efficient protein detection enables a more accurate and comprehensive investigation of cellular phenotypes and life activities. Here, a protein sequencing method with high multiplexing, high throughput, high cell utilization, and integration based on digital microfluidics (DMF-Protein-seq) is proposed, which transforms protein information into DNA sequencing readout via DNA-tagged antibodies and labels single cells with unique cell barcodes. In a 184-electrode DMF-Protein-seq system, ≈1800 cells are simultaneously detected per experimental run. The digital microfluidics device harnessing low-adsorbed hydrophobic surface and contaminants-isolated reaction space supports high cell utilization (>90%) and high mapping reads (>90%) with the input cells ranging from 140 to 2000. This system leverages split&pool strategy on the DMF chip for the first time to overcome DMF platform restriction in cell analysis throughput and replace the traditionally tedious bench-top combinatorial barcoding. With the benefits of high efficiency and sensitivity in protein analysis, the system offers great potential for cell classification and drug monitoring based on protein expression at the single-cell level.

Current biological implications and clinical relevance of metastatic circulating tumor cells

Metastatic disease and cancer recurrence are the primary causes of cancer-related deaths. Circulating tumor cells (CTCs) and disseminated tumor cells (DTCs) are the driving forces behind the spread of cancer cells. The emergence and development of liquid biopsy using rare CTCs as a minimally invasive strategy for early-stage tumor detection and improved tumor management is a promising advancement in recent years. However, before blood sample analysis and clinical translation, precise isolation of CTCs from patients' blood based on their biophysical properties, followed by molecular identification of CTCs using single-cell multi-omics technologies is necessary to understand tumor heterogeneity and provide effective diagnosis and monitoring of cancer progression. Additionally, understanding the origin, morphological variation, and interaction between CTCs and the primary and metastatic tumor niche, as well as and regulatory immune cells, will offer new insights into the development of CTC-based advanced tumor targeting in the future clinical trials.

Small and long non-coding RNAs: Past, present, and future

Since the introduction of the central dogma of molecular biology in 1958, various RNA species have been discovered. Messenger RNAs transmit genetic instructions from DNA to make proteins, a process facilitated by housekeeping non-coding RNAs (ncRNAs) such as small nuclear RNAs (snRNAs), ribosomal RNAs (rRNAs), and transfer RNAs (tRNAs). Over the past four decades, a wide array of regulatory ncRNAs have emerged as crucial players in gene regulation. In celebration of Cell's 50th anniversary, this Review explores our current understanding of the most extensively studied regulatory ncRNAs-small RNAs and long non-coding RNAs (lncRNAs)-which have profoundly shaped the field of RNA biology and beyond. While small RNA pathways have been well documented with clearly defined mechanisms, lncRNAs exhibit a greater diversity of mechanisms, many of which remain unknown. This Review covers pivotal events in their discovery, biogenesis pathways, evolutionary traits, action mechanisms, functions, and crosstalks among ncRNAs. We also highlight their roles in pathophysiological contexts and propose future research directions to decipher the unknowns of lncRNAs by leveraging lessons from small RNAs.

Beyond the Heartbeat: Single-Cell Omics Redefining Cardiovascular Research

PURPOSE OF REVIEW

This review aims to explore recent advances in single-cell omics techniques as applied to various regions of the human heart, illuminating cellular diversity, regulatory networks, and disease mechanisms. We examine the contributions of single-cell transcriptomics, genomics, proteomics, epigenomics, and spatial transcriptomics in unraveling the complexity of cardiac tissues.

RECENT FINDINGS

Recent strides in single-cell omics technologies have revolutionized our understanding of the heart's cellular composition, cell type heterogeneity, and molecular dynamics. These advancements have elucidated pathological conditions as well as the cellular landscape in heart development. We highlight emerging applications of integrated single-cell omics, particularly for cardiac regeneration, disease modeling, and precision medicine, and emphasize the transformative potential of these technologies to advance cardiovascular research and clinical practice.

Endoluminal Biopsy for Vein of Galen Malformation

BACKGROUND AND OBJECTIVES

Vein of Galen malformation (VOGM), the result of arteriovenous shunting between choroidal and/or subependymal arteries and the embryologic prosencephalic vein, is among the most severe cerebrovascular disorders of childhood. We hypothesized that in situ analysis of the VOGM lesion using endoluminal tissue sampling (ETS) is feasible and may advance our understanding of VOGM genetics, pathogenesis, and maintenance.

METHODS

We collected germline DNA (cheek swab) from patients and their families for genetic analysis. In situ VOGM "endothelial" cells (ECs), defined as CD31 + and CD45 - , were obtained from coils through ETS during routine endovascular treatment. Autologous peripheral femoral ECs were also collected from the access sheath. Single-cell RNA sequencing of both VOGM and peripheral ECs was performed to demonstrate feasibility to define the transcriptional architecture. Comparison was also made with a published normative cerebrovascular transcriptome atlas. A subset of VOGM ECs was reserved for future DNA sequencing to assess for somatic and second-hit mutations.

RESULTS

Our cohort contains 6 patients who underwent 10 ETS procedures from arterial and/or venous access during routine VOGM treatment (aged 12 days to ∼6 years). No periprocedural complications attributable to ETS occurred. Six unique coil types were used. ETS captured 98 ± 88 (mean ± SD; range 17-256) experimental ECs (CD31 + and CD45 - ). There was no discernible correlation between cell yield and coil type or route of access. Single-cell RNA sequencing demonstrated hierarchical clustering and unique cell populations within the VOGM EC compartment compared with peripheral EC controls when annotated using a publicly available cerebrovascular cell atlas.

CONCLUSION

ETS may supplement investigations aimed at development of a molecular-genetic taxonomic classification scheme for VOGM. Moreover, results may eventually inform the selection of personalized pharmacologic or genetic therapies for VOGM and cerebrovascular disorders more broadly.

Mimicking and analyzing the tumor microenvironment

The tumor microenvironment (TME) is increasingly appreciated to play a decisive role in cancer development and response to therapy in all solid tumors. Hypoxia, acidosis, high interstitial pressure, nutrient-poor conditions, and high cellular heterogeneity of the TME arise from interactions between cancer cells and their environment. These properties, in turn, play key roles in the aggressiveness and therapy resistance of the disease, through complex reciprocal interactions between the cancer cell genotype and phenotype, and the physicochemical and cellular environment. Understanding this complexity requires the combination of sophisticated cancer models and high-resolution analysis tools. Models must allow both control and analysis of cellular and acellular TME properties, and analyses must be able to capture the complexity at high depth and spatial resolution. Here, we review the advantages and limitations of key models and methods in order to guide further TME research and outline future challenges.

Transcriptional landscape of sweetpotato root tip development at the single-cell level

Single-cell transcriptome sequencing (scRNA-seq) is a powerful tool for describing the transcriptome dynamics of plant development but has not yet been utilized to analyze the tissue ontology of sweetpotato. This study established a stable method for isolating single protoplast cells for scRNA-seq to reveal the cell heterogeneity of sweetpotato root tip meristems at the single-cell level. The study analyzed 12,172 single cells and 27,355 genes in the root tips of the sweetpotato variety Guangshu 87, which were distributed into 15 cell clusters. Pseudo-time analysis showed that there were transitional cells in the apical development trajectory of mature cell types from stem cell niches. Furthermore, we identified novel development regulators of sweetpotato tubers via trajectory analysis. The transcription factor IbGATA4 was highly expressed in the adventitious roots during the development of sweetpotato root tips, where it may regulate the development of sweetpotato root tips. In addition, significant differences were observed in the transcriptional profiles of cell types between sweetpotato, Arabidopsis thaliana, and maize. This study mapped the single-cell transcriptome of sweetpotato root tips, laying a foundation for studying the types, functions, differentiation, and development of sweetpotato root tip cells.

Single-cell somatic copy number variants in brain using different amplification methods and reference genomes

The presence of somatic mutations, including copy number variants (CNVs), in the brain is well recognized. Comprehensive study requires single-cell whole genome amplification, with several methods available, prior to sequencing. Here we compare PicoPLEX with two recent adaptations of multiple displacement amplification (MDA): primary template-directed amplification (PTA) and droplet MDA, across 93 human brain cortical nuclei. We demonstrate different properties for each, with PTA providing the broadest amplification, PicoPLEX the most even, and distinct chimeric profiles. Furthermore, we perform CNV calling on two brains with multiple system atrophy and one control brain using different reference genomes. We find that 20.6% of brain cells have at least one Mb-scale CNV, with some supported by bulk sequencing or single-cells from other brain regions. Our study highlights the importance of selecting whole genome amplification method and reference genome for CNV calling, while supporting the existence of somatic CNVs in healthy and diseased human brain.

Pathology and pathobiology of pulmonary hypertension: current insights and future directions

In recent years, major advances have been made in the understanding of the cellular and molecular mechanisms driving pulmonary vascular remodelling in various forms of pulmonary hypertension, including pulmonary arterial hypertension, pulmonary hypertension associated with left heart disease, pulmonary hypertension associated with chronic lung disease and hypoxia, and chronic thromboembolic pulmonary hypertension. However, the survival rates for these different forms of pulmonary hypertension remain unsatisfactory, underscoring the crucial need to more effectively translate innovative scientific knowledge into healthcare interventions. In these proceedings of the 7th World Symposium on Pulmonary Hypertension, we delve into recent developments in the field of pathology and pathophysiology, prioritising them while questioning their relevance to different subsets of pulmonary hypertension. In addition, we explore how the latest omics and other technological advances can help us better and more rapidly understand the myriad basic mechanisms contributing to the initiation and progression of pulmonary vascular remodelling. Finally, we discuss strategies aimed at improving patient care, optimising drug development, and providing essential support to advance research in this field.

Advancements in Omics and Breakthrough Gene Therapies: A Glimpse into the Future of Peripheral Artery Disease

Peripheral artery disease (PAD), a highly prevalent global disease, associates with significant morbidity and mortality in affected patients. Despite progress in endovascular and open revascularization techniques for advanced PAD, these interventions grapple with elevated rates of arterial restenosis and vein graft failure attributed to intimal hyperplasia (IH). Novel multiomics technologies, coupled with sophisticated analyses tools recently powered by advances in artificial intelligence, have enabled the study of atherosclerosis and IH with unprecedented single-cell and spatial precision. Numerous studies have pinpointed gene hubs regulating pivotal atherogenic and atheroprotective signaling pathways as potential therapeutic candidates. Leveraging advancements in viral and nonviral gene therapy (GT) platforms, gene editing technologies, and cutting-edge biomaterial reservoirs for delivery uniquely positions us to develop safe, efficient, and targeted GTs for PAD-related diseases. Gene therapies appear particularly fitting for ex vivo genetic engineering of IH-resistant vein grafts. This manuscript highlights currently available state-of-the-art multiomics approaches, explores promising GT-based candidates, and details GT delivery modalities employed by our laboratory and others to thwart mid-term vein graft failure caused by IH, as well as other PAD-related conditions. The potential clinical translation of these targeted GTs holds the promise to revolutionize PAD treatment, thereby enhancing patients' quality of life and life expectancy.

From Cell to Gene: Deciphering the Mechanism of Heart Failure With Single-Cell Sequencing

Heart failure (HF) is a prevalent cardiovascular disease with significant morbidity and mortality rates worldwide. Due to the intricate structure of the heart, diverse cell types, and the complex pathogenesis of HF, further in-depth investigation into the underlying mechanisms  is required. The elucidation of the heterogeneity of cardiomyocytes and the intercellular communication network is particularly important. Traditional high-throughput sequencing methods provide an average measure of gene expression, failing to capture the "heterogeneity" between cells and impacting the accuracy of gene function knowledge. In contrast, single-cell sequencing techniques allow for the amplification of the entire genome or transcriptome at the individual cell level, facilitating the examination of gene structure and expression with unparalleled precision. This approach offers valuable insights into disease mechanisms, enabling the identification of changes in cellular components and gene expressions during hypertrophy associated with HF. Moreover, it reveals distinct cell populations and their unique roles in the HF microenvironment, providing a comprehensive understanding of the cellular landscape that underpins HF pathogenesis. This review focuses on the insights provided by single-cell sequencing techniques into the mechanisms underlying HF and discusses the challenges encountered in current cardiovascular research.

KaryoTap Enables Aneuploidy Detection in Thousands of Single Human Cells

Investigating chromosomal instability and aneuploidy within tumors is essential for understanding tumorigenesis and developing diagnostic and therapeutic strategies. Single-cell DNA sequencing technologies have enabled such analyses, revealing aneuploidies specific to individual cells within the same tumor. However, it has been difficult to scale the throughput of these methods to detect rare aneuploidies while maintaining high sensitivity. To overcome this deficit, we developed KaryoTap, a method combining custom targeted DNA sequencing panels for the Tapestri platform with a computational framework to enable detection of chromosome- and chromosome arm-scale aneuploidy (gains or losses) and copy number neutral loss of heterozygosity in all human chromosomes across thousands of single cells simultaneously. KaryoTap allows detecting gains and losses with an average accuracy of 83% for arm events and 91% for chromosome events. Importantly, together with chromosomal copy number, our system allows us to detect barcodes and gRNAs integrated into the cells' genome, thus enabling pooled CRISPR- or ORF-based functional screens in single cells. As a proof of principle, we performed a small screen to expand the chromosomes that can be targeted by our recently described CRISPR-based KaryoCreate system for engineering aneuploidy in human cells. KaryoTap will prove a powerful and flexible approach for the study of aneuploidy and chromosomal instability in both tumors and normal tissues.

Artificial intelligence-enabled multipurpose smart detection in active-matrix electrowetting-on-dielectric digital microfluidics

An active-matrix electrowetting-on-dielectric (AM-EWOD) system integrates hundreds of thousands of active electrodes for sample droplet manipulation, which can enable simultaneous, automatic, and parallel on-chip biochemical reactions. A smart detection system is essential for ensuring a fully automatic workflow and online programming for the subsequent experimental steps. In this work, we demonstrated an artificial intelligence (AI)-enabled multipurpose smart detection method in an AM-EWOD system for different tasks. We employed the U-Net model to quantitatively evaluate the uniformity of the applied droplet-splitting methods. We used the YOLOv8 model to monitor the droplet-splitting process online. A 97.76% splitting success rate was observed with 18 different AM-EWOD chips. A 99.982% model precision rate and a 99.980% model recall rate were manually verified. We employed an improved YOLOv8 model to detect single-cell samples in nanolitre droplets. Compared with manual verification, the model achieved 99.260% and 99.193% precision and recall rates, respectively. In addition, single-cell droplet sorting and routing experiments were demonstrated. With an AI-based smart detection system, AM-EWOD has shown great potential for use as a ubiquitous platform for implementing true lab-on-a-chip applications.

Deciphering breast cancer dynamics: insights from single-cell and spatial profiling in the multi-omics era

As one of the most common tumors in women, the pathogenesis and tumor heterogeneity of breast cancer have long been the focal point of research, with the emergence of tumor metastasis and drug resistance posing persistent clinical challenges. The emergence of single-cell sequencing (SCS) technology has introduced novel approaches for gaining comprehensive insights into the biological behavior of malignant tumors. SCS is a high-throughput technology that has rapidly developed in the past decade, providing high-throughput molecular insights at the individual cell level. Furthermore, the advent of multitemporal point sampling and spatial omics also greatly enhances our understanding of cellular dynamics at both temporal and spatial levels. The paper provides a comprehensive overview of the historical development of SCS, and highlights the most recent advancements in utilizing SCS and spatial omics for breast cancer research. The findings from these studies will serve as valuable references for future advancements in basic research, clinical diagnosis, and treatment of breast cancer.

Single-Cell WGCNA Combined with Transcriptome Sequencing to Study the Molecular Mechanisms of Inflammation-Related Ferroptosis in Myocardial Ischemia-Reperfusion Injury

Purpose

Myocardial ischemia-reperfusion injury (MIRI) is characterized by inflammation and ferroptosis, but the precise mechanisms remain unknown. This study used single-cell transcriptomics technology to investigate the changes in various cell subtypes during MIRI and the regulatory network of ferroptosis-related genes and immune infiltration.

Methods

Datasets GSE146285, GSE83472, GSE61592, and GSE160516 were obtained from Gene Expression Omnibus. Each cell subtype in the tissue samples was documented. The Seurat package was used for data preprocessing, standardization, and clustering. Cellphonedb was used to investigate the ligand-receptor interactions between cells. The hdWGCNA analysis was used to create a gene co-expression network. GSVA and GSEA were combined to perform functional enrichment and pathway analysis on the gene set. Furthermore, characteristic genes of the disease were identified using Lasso regression and SVM algorithms. Immune cell infiltration analysis was also performed. MIRI rat models were created, and samples were taken for RT-qPCR and Western blot validation.

Results

The proportion of MIRI samples in the C2, C6, and C11 subtypes was significantly higher than that of control samples. Three genes associated with ferroptosis (CD44, Cfl1, and Zfp36) were identified as MIRI core genes. The expression of these core genes was significantly correlated with mast cells and monocyte immune infiltrating cells. The experimental validation confirmed the upregulation of Cd44 and Zfp36 expression levels in MIRI, consistent with current study trends.

Conclusion

This study used single-cell transcriptomics technology to investigate the molecular mechanisms underpinning MIRI. Numerous important cell subtypes, gene regulatory networks, and disease-associated immune infiltration were also discovered. These findings provide new information and potential therapeutic targets for MIRI diagnosis and treatment.

Circulating tumor cells: from new biological insights to clinical practice

The primary reason for high mortality rates among cancer patients is metastasis, where tumor cells migrate through the bloodstream from the original site to other parts of the body. Recent advancements in technology have significantly enhanced our comprehension of the mechanisms behind the bloodborne spread of circulating tumor cells (CTCs). One critical process, DNA methylation, regulates gene expression and chromosome stability, thus maintaining dynamic equilibrium in the body. Global hypomethylation and locus-specific hypermethylation are examples of changes in DNA methylation patterns that are pivotal to carcinogenesis. This comprehensive review first provides an overview of the various processes that contribute to the formation of CTCs, including epithelial-mesenchymal transition (EMT), immune surveillance, and colonization. We then conduct an in-depth analysis of how modifications in DNA methylation within CTCs impact each of these critical stages during CTC dissemination. Furthermore, we explored potential clinical implications of changes in DNA methylation in CTCs for patients with cancer. By understanding these epigenetic modifications, we can gain insights into the metastatic process and identify new biomarkers for early detection, prognosis, and targeted therapies. This review aims to bridge the gap between basic research and clinical application, highlighting the significance of DNA methylation in the context of cancer metastasis and offering new avenues for improving patient outcomes.