Advances and applications of single-cell sequencing technologies

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.

A role of Lhx2 in the migration and axonal projection of cortical postmitotic neurons in the cortical upper layer of the mouse neocortex

The transcription factor LHX2 contains a LIM domain and plays an important role in the development of the vertebrate nervous system. Although much research has been conducted on the function of Lhx2 during cerebral development, its role in postmitotic neuron differentiation in the cerebral cortex remains unknown. Therefore, this study was conducted to determine the function of Lhx2 in dynamic and elaborate developmental processes, including neurogenesis. We first created and confirmed an Lhx2-BAC Gfp transgenic model to three-dimensionally confirm the spatiotemporal expression pattern of Lhx2 during brain development. On this basis, we used the bilateral in utero electroporation technique to express the dominant-negative form of LHX2. LHX2 was confirmed to be important for the migration and callosal projection of postmitotic neurons that form the upper layer of the cerebral cortex during neurogenesis. Additionally, transcriptome analysis confirmed that LHX2 affected the genes involved in neuronal migration and axonal projection. We demonstrated that Lhx2 is important for postmitotic neurons in the cerebral cortex, which migrate to normal positions and extend nerve axons. Taken together, our findings can provide important clues to understanding the relationship between human Lhx2 gene mutations and brain developmental diseases.

Prognostic prediction signature and molecular subtype for liver cancer: A CTC/CTM‑related gene prediction model and independent external validation

Liver cancer is the second leading cause of tumor-related death worldwide, and a serious threat to lives and health. Circulating tumor cells (CTCs) facilitate the progression of various cancers, including liver cancer. The relationship between CTC/circulating tumor microemboli-related genes (CRGs) and the prognosis of liver cancer is unclear. The aim of the present study was to identify CTC/circulating tumour microemboli-related genes (CRGs) in hepatocellular carcinoma and to investigate their clinical significance. Transcriptomic data from The Cancer Genome Atlas (International Cancer Genome Consortium (ICGC) and GSE117623 databases were combined, and differentially expressed CRGs were identified. These were subsequently analyzed via least absolute shrinkage and selection operator and multivariate Cox analyses, and a five-gene risk signature was constructed. The signature was validated in the ICGC and GSE14520 dataset with survival analysis and receiver operating characteristic curve analysis. Immunocyte infiltration, tumor mutation burden (TMB), tumor immune dysfunction and exclusion (TIDE), and the somatic mutation rate were also compared between high- and low-risk groups, based on the median predictive index, to further evaluate the immunotherapeutic value of the model. Molecular subtypes of liver cancer were characterized by the non-negative matrix factorization method and potential therapeutic compounds were evaluated for different subtypes. Nomograms were utilized to predict the prognosis of patients, and the signature was compared with previous literature models. Additionally, the biological function of one of the CRGs, tumor protein p53 inducible protein 3 (TP53I3), in liver cancer was further explored through in vitro experiments. Analysis of the prognostic characteristics of the five CRGs led to the identification of two liver cancer subtypes. Patients in the low-risk group had a longer survival compared with those in the high-risk group, and patients in the latter group were associated with a higher TMB, immunocyte infiltration and somatic mutation rate, and lower TIDE scores. The prognostic profile was validated in the ICGC and GSE14520 datasets and exhibited a good predictive performance. In vitro analysis showed that the knockdown of TP53I3 suppressed liver cancer cell proliferation. In summary, CRGs were used to develop a new prognostic signature to predict the prognosis of patients with liver cancer. This signature may be used to assess the prognosis of patients and may provide new insights for clinical management strategies. In addition, TP53I3 is potentially a therapeutic target for liver cancer.

Machine Learning Classification of Integrin-Expression-Based Magnetic Sorted SW 620 Cells by Simultaneous O-PTIR and SERS

Immortalized cell lines are commonly used for in vitro studies such as drug efficacy, toxicology, and life cycle due to their cost effectiveness and accessibility; however, subpopulations within a cell line can arise from random mutations or asynchronous cell cycles which may lead to results that make interpretation difficult. A method that could classify these differences and separate unique subpopulations would increase our understanding of heterogeneous cellular responses. In the present work, we explore spectroscopic signals associated with subpopulations of cells magnetically sorted on the basis of α5β1 integrin binding to cyclic-RGDfC which mimics fibronectin in the extracellular matrix. SW620 colon cancer cells were incubated with cyclic-RGDfC functionalized gold-coated, iron core nanoparticles and magnetically sorted. The subpopulations from the sort were imaged (N = 10 positive and N = 10 negative, number of cells) via simultaneous surface-enhanced Raman scattering (SERS) and optical-photothermal infrared spectroscopy (O-PTIR). Pearson correlations of the standard peptide-protein interaction in the SERS channel allowed for visualization of the cyclic RGDfC-integrin α5β1 interaction. Partial least-squares discriminant analysis of the O-PTIR spectra collected from cell maps successfully classified the positively or negatively sorted cells. These results demonstrate that biochemical changes within a single cell line can be sorted via an integrin-activity-based assay using simultaneous SERS and O-PTIR.

Recover then aggregate: unified cross-modal deep clustering with global structural information for single-cell data

Single-cell cross-modal joint clustering has been extensively utilized to investigate the tumor microenvironment. Although numerous approaches have been suggested, accurate clustering remains the main challenge. First, the gene expression matrix frequently contains numerous missing values due to measurement limitations. The majority of existing clustering methods treat it as a typical multi-modal dataset without further processing. Few methods conduct recovery before clustering and do not sufficiently engage with the underlying research, leading to suboptimal outcomes. Additionally, the existing cross-modal information fusion strategy does not ensure consistency of representations across different modes, potentially leading to the integration of conflicting information, which could degrade performance. To address these challenges, we propose the 'Recover then Aggregate' strategy and introduce the Unified Cross-Modal Deep Clustering model. Specifically, we have developed a data augmentation technique based on neighborhood similarity, iteratively imposing rank constraints on the Laplacian matrix, thus updating the similarity matrix and recovering dropout events. Concurrently, we integrate cross-modal features and employ contrastive learning to align modality-specific representations with consistent ones, enhancing the effective integration of diverse modal information. Comprehensive experiments on five real-world multi-modal datasets have demonstrated this method's superior effectiveness in single-cell clustering tasks.

Dynamics of alternative polyadenylation in single root cells of Arabidopsis thaliana

Introduction

Single-cell RNA-seq (scRNA-seq) technologies have been widely used to reveal the diversity and complexity of cells, and pioneering studies on scRNA-seq in plants began to emerge since 2019. However, existing studies on plants utilized scRNA-seq focused only on the gene expression regulation. As an essential post-transcriptional mechanism for regulating gene expression, alternative polyadenylation (APA) generates diverse mRNA isoforms with distinct 3' ends through the selective use of different polyadenylation sites in a gene. APA plays important roles in regulating multiple developmental processes in plants, such as flowering time and stress response.

Methods

In this study, we developed a pipeline to identify and integrate APA sites from different scRNA-seq data and analyze APA dynamics in single cells. First, high-confidence poly(A) sites in single root cells were identified and quantified. Second, three kinds of APA markers were identified for exploring APA dynamics in single cells, including differentially expressed poly(A) sites based on APA site expression, APA markers based on APA usages, and APA switching genes based on 3' UTR (untranslated region) length change. Moreover, cell type annotations of single root cells were refined by integrating both the APA information and the gene expression profile.

Results

We comprehensively compiled a single-cell APA atlas from five scRNA-seq studies, covering over 150,000 cells spanning four major tissue branches, twelve cell types, and three developmental stages. Moreover, we quantified the dynamic APA usages in single cells and identified APA markers across tissues and cell types. Further, we integrated complementary information of gene expression and APA profiles to annotate cell types and reveal subtle differences between cell types.

Discussion

This study reveals that APA provides an additional layer of information for determining cell identity and provides a landscape of APA dynamics during Arabidopsis root development.

Batch-effect correction in single-cell RNA sequencing data using JIVE

Motivation

In single-cell RNA sequencing analysis, addressing batch effects-technical artifacts stemming from factors such as varying sequencing technologies, equipment, and capture times-is crucial. These factors can cause unwanted variation and obfuscate the underlying biological signal of interest. The joint and individual variation explained (JIVE) method can be used to extract shared biological patterns from multi-source sequencing data while adjusting for individual non-biological variations (i.e. batch effect). However, its current implementation is originally designed for bulk sequencing data, making it computationally infeasible for large-scale single-cell sequencing datasets.

Results

In this study, we enhance JIVE for large-scale single-cell data by boosting its computational efficiency. Additionally, we introduce a novel application of JIVE for batch-effect correction on multiple single-cell sequencing datasets. Our enhanced method aims to decompose single-cell sequencing datasets into a joint structure capturing the true biological variability and individual structures, which capture technical variability within each batch. This joint structure is then suitable for use in downstream analyses. We benchmarked the results against four popular tools, Seurat v5, Harmony, LIGER, and Combat-seq, which were developed for this purpose. JIVE performed best in terms of preserving cell-type effects and in scenarios in which the batch sizes are balanced.

Availability and implementation

The JIVE implementation used for this analysis can be found at https://github.com/oconnell-statistics-lab/scJIVE.

Single-Cell RNA Sequencing Reveals an Atlas of Hezuo Pig Testis Cells

Spermatogenesis is a complex biological process crucial for male reproduction and is characterized by intricate interactions between testicular somatic cells and germ cells. Due to the cellular heterogeneity of the testes, investigating different cell types across developmental stages has been challenging. Single-cell RNA sequencing (scRNA-seq) has emerged as a valuable approach for addressing this limitation. Here, we conducted an unbiased transcriptomic study of spermatogenesis in sexually mature 4-month-old Hezuo pigs using 10× Genomics-based scRNA-seq. A total of 16,082 cells were collected from Hezuo pig testes, including germ cells (spermatogonia (SPG), spermatocytes (SPCs), spermatids (SPTs), and sperm (SP)) and somatic cells (Sertoli cells (SCs), Leydig cells (LCs), myoid cells (MCs), endothelial cells (ECs), and natural killer (NK) cells/macrophages). Pseudo-time analysis revealed that LCs and MCs originated from common progenitors in the Hezuo pig. Functional enrichment analysis indicated that the differentially expressed genes (DEGs) in the different types of testicular germ cells were enriched in the PI3K-AKT, Wnt, HIF-1, and adherens junction signaling pathways, while the DEGs in testicular somatic cells were enriched in ECM-receptor interaction and antigen processing and presentation. Moreover, genes related to spermatogenesis, male gamete generation, sperm part, sperm flagellum, and peptide biosynthesis were expressed throughout spermatogenesis. Using immunohistochemistry, we verified several stage-specific marker genes (such as UCHL1, WT1, SOX9, and ACTA2) for SPG, SCs, and MCs. By exploring the changes in the transcription patterns of various cell types during spermatogenesis, our study provided novel insights into spermatogenesis and testicular cells in the Hezuo pig, thereby laying the foundation for the breeding and preservation of this breed.

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.

Characteristics of the Dynamic Evolutionary Pathway of ADSCs Induced Differentiation into Astrocytes Based on scRNA-Seq Analysis

We employed single-cell transcriptome sequencing to reveal the dynamic gene expression changes during the differentiation of adipose-derived stromal cells (ADSCs) into astrocytes. Single-cell RNA sequencing was conducted on cells from the ADSCs group and the induced groups at 2, 7, 14, and 21 days using the 10 × Chromium platform. Data underwent quality control and dimensionality reduction. Cell differentiation trajectories were constructed using Monocle2, and differentially expressed genes (DEGs) in each cell cluster were identified using differential selection algorithms. DEGs at each time point were annotated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), and regulatory intensities of transcription factors were analyzed using SCENIC. Integrating all groups, a total of five samples were divided into 13 cell clusters (0-12 clusters). DEGs between clusters and those compared with ADSCs at various induced time points showed distinct specificities. Monocle2 constructed cell differentiation trajectories; ADSCs can differentiate into mature astrocytes not only through the direct pathway from the 1 branch to the 3 branch but also through an indirect pathway, involving the 1 branch to the 2 branch before progressing to the 3 branch. SCENIC analysis highlighted the critical regulatory roles of STAT1, MYEF2, and SOX6 transcription factors during the differentiation of ADSCs into astrocytes. ADSCs can differentiate into mature astrocytes through two distinct pathways: direct and indirect. By the 14th day of induction, mature astrocytes have formed, characterized by a cell cycle arrest in mitosis. Further induction leads to degenerative senescence changes in differentiated cells.

Assessing a single-cell multi-omic analytic platform to characterize ex vivo-engineered T-cell therapy products

Genetically engineered CD8+ T cells are being explored for the treatment of various cancers. Analytical characterization represents a major challenge in the development of genetically engineered cell therapies, especially assessing the potential off-target editing and product heterogeneity. As conventional sequencing techniques only provide information at the bulk level, they are unable to detect off-target CRISPR translocation or editing events occurring in minor cell subpopulations. In this study, we report the analytical development of a single-cell multi-omics DNA and protein assay to characterize genetically engineered cell products for safety and genotoxicity assessment. We were able to quantify on-target edits, off-target events, and potential translocations at the targeting loci with per-cell granularity, providing important characterization data of the final cell product. Conclusion: A single-cell multi-omics approach provides the resolution required to understand the composition of cellular products and identify critical quality attributes (CQAs).

CellCover Captures Neural Stem Cell Progression in Mammalian Neocortical Development

Definition of cell classes across the tissues of living organisms is central in the analysis of growing atlases of single-cell RNA sequencing (scRNA-seq) data across biomedicine. Marker genes for cell classes are most often defined by differential expression (DE) methods that serially assess individual genes across landscapes of diverse cells. This serial approach has been extremely useful, but is limited because it ignores possible redundancy or complementarity across genes that can only be captured by analyzing multiple genes simultaneously. We aim to identify discriminating panels of genes. To efficiently explore the vast space of possible marker panels, leverage the large number of cells often sequenced, and overcome zero-inflation in scRNA-seq data, we propose viewing gene panel selection as a variation of the "minimal set-covering problem" in combinatorial optimization. We show that this new method, CellCover, captures cell-class-specific signals in the developing mouse neocortex that are distinct from those defined by DE methods. Transfer learning experiments across mouse, primate, and human data demonstrate that CellCover identifies markers of conserved cell classes in neurogenesis, as well as temporal progression in both progenitors and neurons. Exploring markers of human outer radial glia (oRG, or basal RG) across mammals, we show that transcriptomic elements of this key cell type in the expansion of the human cortex appeared in gliogenic precursors of the rodent before the full program emerged in the primate lineage. We have assembled the public datasets we use in this report at NeMO analytics where the expression of individual genes {NeMO Individual Genes} and marker gene panels can be freely explored {NeMO: Telley 3 Sets Covering Panels}, {NeMO: Telley 12 Sets Covering Panels}, and {NeMO: Sorted Brain Cell Covering Panels}. CellCover is available in {CellCover R} and {CellCover Python}.

Single-cell technologies in psoriasis

Psoriasis is a chronic and recurrent inflammatory skin disorder. The primary manifestation of psoriasis arises from disturbances in the cutaneous immune microenvironment, but the specific functions of the cellular components within this microenvironment remain unknown. Recent advancements in single-cell technologies have enabled the detection of multi-omics at the level of individual cells, including single-cell transcriptome, proteome, and metabolome, which have been successfully applied in studying autoimmune diseases, and other pathologies. These techniques allow the identification of heterogeneous cell clusters and their varying contributions to disease development. Considering the immunological traits of psoriasis, an in-depth exploration of immune cells and their interactions with cutaneous parenchymal cells can markedly advance our comprehension of the mechanisms underlying the onset and recurrence of psoriasis. In this comprehensive review, we present an overview of recent applications of single-cell technologies in psoriasis, aiming to improve our understanding of the underlying mechanisms of this disorder.

Single-Cell RNA Sequencing: Technological Progress and Biomedical Application in Cancer Research

Single-cell RNA-seq (scRNA-seq) is a revolutionary technology that allows for the genomic investigation of individual cells in a population, allowing for the discovery of unusual cells associated with cancer and metastasis. ScRNA-seq has been used to discover different types of cancers with poor prognosis and medication resistance such as lung cancer, breast cancer, ovarian cancer, and gastric cancer. Besides, scRNA-seq is a promising method that helps us comprehend the biological features and dynamics of cell development, as well as other disorders. This review gives a concise summary of current scRNA-seq technology. We also explain the main technological steps involved in implementing the technology. We highlight the present applications of scRNA-seq in cancer research, including tumor heterogeneity analysis in lung cancer, breast cancer, and ovarian cancer. In addition, this review elucidates potential applications of scRNA-seq in lineage tracing, personalized medicine, illness prediction, and disease diagnosis, which reveals that scRNA-seq facilitates these events by producing genetic variations on the single-cell level.

Spatially resolved multiomics on the neuronal effects induced by spaceflight in mice

Impairment of the central nervous system (CNS) poses a significant health risk for astronauts during long-duration space missions. In this study, we employed an innovative approach by integrating single-cell multiomics (transcriptomics and chromatin accessibility) with spatial transcriptomics to elucidate the impact of spaceflight on the mouse brain in female mice. Our comparative analysis between ground control and spaceflight-exposed animals revealed significant alterations in essential brain processes including neurogenesis, synaptogenesis and synaptic transmission, particularly affecting the cortex, hippocampus, striatum and neuroendocrine structures. Additionally, we observed astrocyte activation and signs of immune dysfunction. At the pathway level, some spaceflight-induced changes in the brain exhibit similarities with neurodegenerative disorders, marked by oxidative stress and protein misfolding. Our integrated spatial multiomics approach serves as a stepping stone towards understanding spaceflight-induced CNS impairments at the level of individual brain regions and cell types, and provides a basis for comparison in future spaceflight studies. For broader scientific impact, all datasets from this study are available through an interactive data portal, as well as the National Aeronautics and Space Administration (NASA) Open Science Data Repository (OSDR).

Single-cell transcriptome atlas of testes from mice with high-fat diets

Obesity is accompanied by multiple known health risks and increased morbidity, and obese men display reduced reproductive health. However, the impact of obesity on the testes at the molecular levels remain inadequately explored. This is partially attributed to the lack of monitoring tools for tracking alterations within cell clusters in testes associated with obesity. Here, we utilized single-cell RNA sequencing to analyze over 70,000 cells from testes of obese and lean mice, and to study changes related to obesity in non-spermatogenic cells and spermatogenesis. The Testicular Library encompasses all non-spermatogenic cells and spermatogenic cells spanning from spermatogonia to spermatozoa, which will significantly aid in characterizing alterations in cellular niches and the testicular microenvironment during high-fat diet (HFD)-induced obesity. This comprehensive dataset is indispensable for studying how HFD disrupts cell-cell communication networks within the testis and impacts alterations in the testicular microenvironment that regulate spermatogenesis. Being the inaugural dataset of single-cell RNA-seq in the testes of diet-induced obese (DIO) mice, this holds the potential to offer innovative insights and directions in the realm of single-cell transcriptomics concerning male reproductive injury associated with HFD.

Targeting IL-13 with tralokinumab normalizes type 2 inflammation in atopic dermatitis both early and at 2 years

BACKGROUND

Tralokinumab is a monoclonal antibody that specifically neutralizes interleukin (IL)-13, a key driver of skin inflammation and barrier abnormalities in atopic dermatitis (AD). This study evaluated early and 2-year impacts of IL-13 neutralization on skin and serum biomarkers following tralokinumab treatment in adults with moderate-to-severe AD.

METHODS

Skin biopsies and blood samples were evaluated from a subset of patients enrolled in the Phase 3 ECZTRA 1 (NCT03131648) and the long-term extension ECZTEND (NCT03587805) trials. Gene expression was assessed by RNA sequencing; protein expression was assessed by immunohistochemistry and immunoassay.

RESULTS

Tralokinumab improved the transcriptomic profile of lesional skin by Week 4. Mean improvements in the expression of genes dysregulated in AD were 39% at Week 16 and 85% at 2 years with tralokinumab, with 15% worsening at Week 16 with placebo. At Week 16, tralokinumab significantly decreased type 2 serum biomarkers (CCL17/TARC, periostin, and IgE), reduced epidermal thickness versus placebo, and increased loricrin coverage versus baseline. Two years of tralokinumab treatment significantly reduced expression of genes in the Th2 (IL4R, IL31, CCL17, and CCL26), Th1 (IFNG), and Th17/Th22 (IL22, S100A7, S100A8, and S100A9) pathways as well as increased expression of epidermal differentiation and barrier genes (CLDN1 and LOR). Tralokinumab also shifted atherosclerosis signaling pathway genes (SELE, IL-37, and S100A8) toward non-lesional expression.

CONCLUSION

Tralokinumab treatment improved epidermal pathology, reduced systemic markers of type 2 inflammation, and shifted expression of key AD biomarkers in skin towards non-lesional levels, further highlighting the key role of IL-13 in the pathogenesis of AD.

CLINICAL TRIAL REGISTRATION

NCT03131648, NCT03587805.

A novel approach to generate enzyme-free single cell suspensions from archived tissues for miRNA sequencing

Obtaining high-quality omics data at the single-cell level from archived human tissue samples is crucial for gaining insights into cellular heterogeneity and pushing the field of personalized medicine forward. In this technical brief we present a comprehensive methodological framework for the efficient enzyme-free preparation of tissue-derived single cell suspensions and their conversion into single-cell miRNA sequencing libraries. The resulting data from this study have the potential to deepen our understanding of miRNA expression at the single-cell level and its relevance in the context of the examined tissues. The workflow encompasses tissue collection, RNALater immersion, storage, thawing, TissueGrinder-mediated dissociation, miRNA lysis, library preparation, sequencing, and data analysis. Quality control measures ensure reliable miRNA data, with specific attention to sample quality. The UMAP analysis reveals tissue-specific cell clustering, while miRNA diversity reflects tissue variations. The presented workflow effectively processes preserved tissues, extending opportunities for retrospective analysis and biobank utilization.

Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine

Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.

Next-Generation Sequencing-Based Spatial Transcriptomics: A Perspective from Barcoding Chemistry

Gene expression profiling of tissue cells with spatial context is in high demand to reveal cell types, locations, and intercellular or molecular interactions for physiological and pathological studies. With rapid advances in barcoding chemistry and sequencing chemistry, spatially resolved transcriptome (SRT) techniques have emerged to quantify spatial gene expression in tissue samples by correlating transcripts with their spatial locations using diverse strategies. These techniques provide both physical tissue structure and molecular characteristics and are poised to revolutionize many fields, such as developmental biology, neuroscience, oncology, and histopathology. In this context, this Perspective focuses on next-generation sequencing-based SRT methods, particularly highlighting spatial barcoding chemistry. It delves into optically manipulated spatial indexing methods and DNA array-barcoded spatial indexing methods by exploring current advances, challenges, and future development directions in this nascent field.

Advanced sequencing-based high-throughput and long-read single-cell transcriptome analysis

Cells are the fundamental building blocks of living systems, exhibiting significant heterogeneity. The transcriptome connects the cellular genotype and phenotype, and profiling single-cell transcriptomes is critical for uncovering distinct cell types, states, and the interplay between cells in development, health, and disease. Nevertheless, single-cell transcriptome analysis faces daunting challenges due to the low abundance and diverse nature of RNAs in individual cells, as well as their heterogeneous expression. The advent and continuous advancements of next-generation sequencing (NGS) and third-generation sequencing (TGS) technologies have solved these problems and facilitated the high-throughput, sensitive, full-length, and rapid profiling of single-cell RNAs. In this review, we provide a broad introduction to current methodologies for single-cell transcriptome sequencing. First, state-of-the-art advancements in high-throughput and full-length single-cell RNA sequencing (scRNA-seq) platforms using NGS are reviewed. Next, TGS-based long-read scRNA-seq methods are summarized. Finally, a brief conclusion and perspectives for comprehensive single-cell transcriptome analysis are discussed.

DEPArray™ single-cell technology: A validation study for forensic applications

In forensics investigations, it is common to encounter biological mixtures consisting of homogeneous or heterogeneous components from multiple individuals and with different genetic contributions. One promising mixture deconvolution strategy is the DEPArray™ technology, which enables the separation of cell populations before genetic analysis. While technological advances are fundamental, their reliable validation is crucial for successful implementation and use for casework. Thus, this study aimed to 1) systematically validate the DEPArray™ system concerning specificity, sensitivity, repeatability, and contamination occurrences for blood, epithelial, and sperm cells, and 2) evaluate its potential for single-cell analysis in the field of forensic science. Our findings confirmed the effective identification of different cell types and the correct assignment of successfully genotyped single cells to their respective donor(s). Using the NGM Detect™ Amplification Kit, the average profile completeness for diploid cells was approximately 80%, with ∼ 290 RFUs. In contrast, haploid sperm analysis yielded an average completeness of 51% referring to the haploid reference profile, accompanied by mean peak heights of ∼ 176 RFUs. Although certain alleles of heterozygous loci in diploid cells showed strong imbalances, the overall peak balances yielded acceptable values above ≥ 60% with a mean value of 72% ± 0.21, a median of 77%, but with a maximum imbalance of 9% between heterozygous peaks. Locus dropouts were considered stochastic events, exhibiting variations among donors and cell types, with a notable failure incidence observed for TH01. Within the wet-lab experimentation with >500 single cells for the validation, profiling was performed using the consensus approach, where profiles were selected randomly from all data to better mirror real casework results. Nevertheless, complete profiles could be achieved with as few as three diploid cells, while the average success rate increased to 100% when using profiles of 6-10 cells. For sperms, however, a consensus profile with completeness >90% of the autosomal diploid genotype could be attained using ≥15 cells. In addition, the robustness of the consensus approach was evaluated in the absence of the respective reference profile without severe deterioration. Here, increased stutter peaks (≥ 15%) were found as the main artifact in single-cell profiles, while contamination and drop-ins were ascertained as rare events. Lastly, the technique's potential and limitations are discussed, and practical guidance is provided, particularly valuable for cold cases, multiple perpetrator rapes, and analyses of homogeneous mixed evidence.

Single-nucleotide variant calling in single-cell sequencing data with Monopogen

Single-cell omics technologies enable molecular characterization of diverse cell types and states, but how the resulting transcriptional and epigenetic profiles depend on the cell's genetic background remains understudied. We describe Monopogen, a computational tool to detect single-nucleotide variants (SNVs) from single-cell sequencing data. Monopogen leverages linkage disequilibrium from external reference panels to identify germline SNVs and detects putative somatic SNVs using allele cosegregating patterns at the cell population level. It can identify 100 K to 3 M germline SNVs achieving a genotyping accuracy of 95%, together with hundreds of putative somatic SNVs. Monopogen-derived genotypes enable global and local ancestry inference and identification of admixed samples. It identifies variants associated with cardiomyocyte metabolic levels and epigenomic programs. It also improves putative somatic SNV detection that enables clonal lineage tracing in primary human clonal hematopoiesis. Monopogen brings together population genetics, cell lineage tracing and single-cell omics to uncover genetic determinants of cellular processes.

p53 accelerates endothelial cell senescence in diabetic retinopathy by enhancing FoxO3a ubiquitylation and degradation via UBE2L6

Diabetic retinopathy (DR) is the most common ocular fundus disease in diabetic patients. Chronic hyperglycemia not only promotes the development of diabetes and its complications, but also aggravates the occurrence of senescence. Previous studies have shown that DR is associated with senescence, but the specific mechanism has not been fully elucidated. Here, we first detected the differentially expressed genes (DEGs) and cellular senescence level of db/db mouse retinas by bulk RNA sequencing. Then, we used single-cell sequencing (scRNA-seq) to identify the main cell types in the retina and analyzed the DEGs in each cluster. We demonstrated that p53 expression was significantly increased in retinal endothelial cell cluster of db/db mice. Inhibition of p53 can reduce the expression of SA-β-Gal and the senescence-associated secretory phenotype (SASP) in HRMECs. Finally, we found that p53 can promote FoxO3a ubiquitination and degradation by increasing the expression of the ubiquitin-conjugating enzyme UBE2L6. Overall, our results demonstrate that p53 can accelerate the senescence process of endothelial cells and aggravate the development of DR. These data reveal new targets and insights that may be used to treat DR.

Unlocking the potential of T-cell metabolism reprogramming: Advancing single-cell approaches for precision immunotherapy in tumour immunity

As single-cell RNA sequencing enables the detailed clustering of T-cell subpopulations and facilitates the analysis of T-cell metabolic states and metabolite dynamics, it has gained prominence as the preferred tool for understanding heterogeneous cellular metabolism. Furthermore, the synergistic or inhibitory effects of various metabolic pathways within T cells in the tumour microenvironment are coordinated, and increased activity of specific metabolic pathways generally corresponds to increased functional activity, leading to diverse T-cell behaviours related to the effects of tumour immune cells, which shows the potential of tumour-specific T cells to induce persistent immune responses. A holistic understanding of how metabolic heterogeneity governs the immune function of specific T-cell subsets is key to obtaining field-level insights into immunometabolism. Therefore, exploring the mechanisms underlying the interplay between T-cell metabolism and immune functions will pave the way for precise immunotherapy approaches in the future, which will empower us to explore new methods for combating tumours with enhanced efficacy.

Single cell RNA-seq: a novel tool to unravel virus-host interplay

Over the last decade, single cell RNA sequencing (scRNA-seq) technology has caught the momentum of being a vital revolutionary tool to unfold cellular heterogeneity by high resolution assessment. It evades the inadequacies of conventional sequencing technology which was able to detect only average expression level among cell populations. In the era of twenty-first century, several epidemic and pandemic viruses have emerged. Being an intracellular entity, viruses totally rely on host. Complex virus-host dynamics result when the virus tend to obtain factors from host cell required for its replication and establishment of infection. As a prevailing tool, scRNA-seq is able to understand virus-host interplay by comprehensive transcriptome profiling. Because of technological and methodological advancement, this technology is capable to recognize viral genome and host cell response heterogeneity. Further development in analytical methods with multiomics approach and increased availability of accessible scRNA-seq datasets will improve the understanding of viral pathogenesis that can be helpful for development of novel antiviral therapeutic strategies.

Emerging tools for uncovering genetic and transcriptomic heterogeneities in bacteria

Bacterial communities display an astonishing degree of heterogeneities among their constituent cells across both the genomic and transcriptomic levels, giving rise to diverse social interactions and stress-adaptation strategies indispensable for proliferating in the natural environment (Ackermann in Nat Rev Microbiol 13:497-508, 2015). Our knowledge about bacterial heterogeneities and their physiological ramifications critically depends on our ability to unambiguously resolve the genetic and phenotypic states of the individual cells that make up the population. In this short review, I highlight several recently developed methods for studying bacterial heterogeneities, primarily focusing on single-cell techniques based on advanced sequencing and microscopy technologies. I will discuss the working principle of each technique as well as the types of problems each technique is best positioned to address. With significant improvements in resolution and throughput, these emerging tools together offer unprecedented and complementary views of various types of heterogeneities found within bacterial populations, paving the way for mechanistic dissections and systematic interventions in laboratory and clinical settings.

Introducing single cell stereo-sequencing technology to transform the plant transcriptome landscape

Single cell RNA-sequencing (scRNA-seq) advancements have helped detect transcriptional heterogeneities in biological samples. However, scRNA-seq cannot currently provide high-resolution spatial transcriptome information or identify subcellular organs in biological samples. These limitations have led to the development of spatially enhanced-resolution omics-sequencing (Stereo-seq), which combines spatial information with single cell transcriptomics to address the challenges of scRNA-seq alone. In this review, we discuss the advantages of Stereo-seq technology. We anticipate that the application of such an integrated approach in plant research will advance our understanding of biological process in the plant transcriptomics era. We conclude with an outlook of how such integration will enhance crop improvement.

Uncovering transcriptomic biomarkers for enhanced diagnosis of methamphetamine use disorder: a comprehensive review

Introduction

Methamphetamine use disorder (MUD) is a chronic relapsing disorder characterized by compulsive Methamphetamine (MA) use despite its detrimental effects on physical, psychological, and social well-being. The development of MUD is a complex process that involves the interplay of genetic, epigenetic, and environmental factors. The treatment of MUD remains a significant challenge, with no FDA-approved pharmacotherapies currently available. Current diagnostic criteria for MUD rely primarily on self-reporting and behavioral assessments, which have inherent limitations owing to their subjective nature. This lack of objective biomarkers and unidimensional approaches may not fully capture the unique features and consequences of MA addiction.

Methods

We performed a literature search for this review using the Boolean search in the PubMed database.

Results

This review explores existing technologies for identifying transcriptomic biomarkers for MUD diagnosis. We examined non-invasive tissues and scrutinized transcriptomic biomarkers relevant to MUD. Additionally, we investigated transcriptomic biomarkers identified for diagnosing, predicting, and monitoring MUD in non-invasive tissues.

Discussion

Developing and validating non-invasive MUD biomarkers could address these limitations, foster more precise and reliable diagnostic approaches, and ultimately enhance the quality of care for individuals with MA addiction.

Robust Surfactant-Assisted One-Pot Sample Preparation for Label-Free Single-Cell and Nanoscale Proteomics

With advanced mass spectrometry (MS)-based proteomics, genome-scale proteome coverage can be achieved from bulk cells. However, such bulk measurement obscures cell-to-cell heterogeneity, precluding proteome profiling of single cells and small numbers of cells of interest. To address this issue, in the recent 5 years, there has been a surge of small sample preparation methods developed for robust and effective collection and processing of single cells and small numbers of cells for in-depth MS-based proteome profiling. Based on their broad accessibility, they can be categorized into two types: methods based on specific devices and those based on standard PCR tubes or multi-well plates. In this chapter, we describe the detailed protocol of our recently developed, easily adoptable, Surfactant-assisted One-Pot (SOP) sample preparation coupled with MS method termed SOP-MS for label-free single-cell and nanoscale proteomics. SOP-MS capitalizes on the combination of an MS-compatible surfactant, n-dodecyl-β-D-maltoside (DDM), and standard low-bind PCR tube or multi-well plate for "all-in-one" one-pot sample preparation without sample transfer. With its robust and convenient features, SOP-MS can be readily implemented in any MS laboratory for single-cell and nanoscale proteomics. With further improvements in MS detection sensitivity and sample throughput, we believe that SOP-MS could open an avenue for single-cell proteomics with broad applicability in biological and biomedical research.

Enzymatic Digestion and Single Cell Isolation of Peri-implantation Stage Human Trophoblast Cells

Recent developments in the in vitro culture of peri-implantation stage human embryos have expanded opportunities to investigate implantation stage human development and trophoblast differentiation in the absence of maternal tissues. Emerging single cell omics analyses have offered researchers new tools to explore unanswered biological questions to new depths. In order to investigate the dynamics of human trophoblast cell differentiation during implantation at the single-cell resolution, efficient cell dissociation approaches of trophoblasts from embryos are necessary. Here, we describe the protocol for extended culture of peri-implantation stage human embryos with enzymatic digestion and manual collection of individual cells for downstream assays.

Single-Cell DNA Sequencing and Immunophenotypic Profiling to Track Clonal Evolution in an Acute Myeloid Leukemia Patient

Single-cell DNA sequencing can address the sequence of somatic genetic events during myeloid transformation in relapsed acute myeloid leukemia (AML). We present an NPM1-mutated AML patient with an initial low ratio of FLT3-ITD (low-risk ELN-2017), treated with midostaurin combined with standard chemotherapy as front-line treatment, and with salvage therapy plus gilteritinib following allogenic stem cell transplantation after relapse. Simultaneous single-cell DNA sequencing and cell-surface immunophenotyping was used in diagnostic and relapse samples to understand the clinical scenario of this patient and to reconstruct the clonal composition of both tumors. Four independent clones were present before treatment: DNMT3A/DNMT3A/NPM1 (63.9%), DNMT3A/DNMT3A (13.9%), DNMT3A/DNMT3A/NPM1/FLT3 (13.8%), as well as a wild-type clone (8.3%), but only the minor clone with FLT3-ITD survived and expanded after therapy, being the most represented one (58.6%) at relapse. FLT3-ITD was subclonal and was found only in the myeloid blast population (CD38/CD117/CD123). Our study shows the usefulness of this approach to reveal the clonal architecture of the leukemia and the identification of small subclones at diagnosis and relapse that may explain how the neoplastic cells can escape from the activity of different treatments in a stepwise process that impedes the disease cure despite different stages of complete remission.

Accurate integration of single-cell DNA and RNA for analyzing intratumor heterogeneity using MaCroDNA

Cancers develop and progress as mutations accumulate, and with the advent of single-cell DNA and RNA sequencing, researchers can observe these mutations and their transcriptomic effects and predict proteomic changes with remarkable temporal and spatial precision. However, to connect genomic mutations with their transcriptomic and proteomic consequences, cells with either only DNA data or only RNA data must be mapped to a common domain. For this purpose, we present MaCroDNA, a method that uses maximum weighted bipartite matching of per-gene read counts from single-cell DNA and RNA-seq data. Using ground truth information from colorectal cancer data, we demonstrate the advantage of MaCroDNA over existing methods in accuracy and speed. Exemplifying the utility of single-cell data integration in cancer research, we suggest, based on results derived using MaCroDNA, that genomic mutations of large effect size increasingly contribute to differential expression between cells as Barrett's esophagus progresses to esophageal cancer, reaffirming the findings of the previous studies.

Plants, pollinators and their interactions under global ecological change: The role of pollen DNA metabarcoding

Anthropogenic activities are triggering global changes in the environment, causing entire communities of plants, pollinators and their interactions to restructure, and ultimately leading to species declines. To understand the mechanisms behind community shifts and declines, as well as monitoring and managing impacts, a global effort must be made to characterize plant-pollinator communities in detail, across different habitat types, latitudes, elevations, and levels and types of disturbances. Generating data of this scale will only be feasible with rapid, high-throughput methods. Pollen DNA metabarcoding provides advantages in throughput, efficiency and taxonomic resolution over traditional methods, such as microscopic pollen identification and visual observation of plant-pollinator interactions. This makes it ideal for understanding complex ecological networks and their responses to change. Pollen DNA metabarcoding is currently being applied to assess plant-pollinator interactions, survey ecosystem change and model the spatiotemporal distribution of allergenic pollen. Where samples are available from past collections, pollen DNA metabarcoding has been used to compare contemporary and past ecosystems. New avenues of research are possible with the expansion of pollen DNA metabarcoding to intraspecific identification, analysis of DNA in ancient pollen samples, and increased use of museum and herbarium specimens. Ongoing developments in sequencing technologies can accelerate progress towards these goals. Global ecological change is happening rapidly, and we anticipate that high-throughput methods such as pollen DNA metabarcoding are critical for understanding the evolutionary and ecological processes that support biodiversity, and predicting and responding to the impacts of change.

Advances in the discovery of genetic elements underlying longissimus dorsi muscle growth and development in the pig

As a major source of protein in human diets, pig meat plays a crucial role in ensuring global food security. Key determinants of meat production refer to the chemical and physical compositions or characteristics of muscle fibers, such as the number, hypertrophy potential, fiber-type conversion and intramuscular fat deposition. However, the growth and formation of muscle fibers comprises a complex process under spatio-temporal regulation, that is, the intermingled and concomitant proliferation, differentiation, migration and fusion of myoblasts. Recently, with the fast and continuous development of next-generation sequencing technology, the integration of quantitative trait loci mapping with genome-wide association studies (GWAS) has greatly helped animal geneticists to discover and explore thousands of functional or causal genetic elements underlying muscle growth and development. However, owing to the underlying complex molecular mechanisms, challenges to in-depth understanding and utilization remain, and the cost of large-scale sequencing, which requires integrated analyses of high-throughput omics data, is high. In this review, we mainly elaborate on research advances in integrative analyses (e.g. GWAS, omics) for identifying functional genes or genomic elements for longissimus dorsi muscle growth and development for different pig breeds, describing several successful transcriptome analyses and functional genomics cases, in an attempt to provide some perspective on the future functional annotation of genetic elements for muscle growth and development in pigs.

Single-cell trapping and retrieval in open microfluidics

Among various single-cell analysis platforms, hydrodynamic cell trapping systems remain relevant because of their versatility. Among those, deterministic hydrodynamic cell-trapping systems have received significant interest; however, their applications are limited because trapped cells are kept within the closed microchannel, thus prohibiting access to external cell-picking devices. In this study, we develop a hydrodynamic cell-trapping system in an open microfluidics architecture to allow external access to trapped cells. A technique to render only the inside of a polydimethylsiloxane (PDMS) microchannel hydrophilic is developed, which allows the precise confinement of spontaneous capillary flow in the open-type microchannel with a width on the order of several tens of micrometers. Efficient trapping of single beads and single cells is achieved, in which trapped cells can be retrieved via automated robotic pipetting. The present system can facilitate the development of new single-cell analytical systems by bridging between microfluidic devices and macro-scale apparatus used in conventional biology.

Advanced Omics Techniques for Understanding Cochlear Genome, Epigenome, and Transcriptome in Health and Disease

Advanced genomics, transcriptomics, and epigenomics techniques are providing unprecedented insights into the understanding of the molecular underpinnings of the central nervous system, including the neuro-sensory cochlea of the inner ear. Here, we report for the first time a comprehensive and updated overview of the most advanced omics techniques for the study of nucleic acids and their applications in cochlear research. We describe the available in vitro and in vivo models for hearing research and the principles of genomics, transcriptomics, and epigenomics, alongside their most advanced technologies (like single-cell omics and spatial omics), which allow for the investigation of the molecular events that occur at a single-cell resolution while retaining the spatial information.

Assessing the performance of methods for cell clustering from single-cell DNA sequencing data

BACKGROUND

Many cancer genomes have been known to contain more than one subclone inside one tumor, the phenomenon of which is called intra-tumor heterogeneity (ITH). Characterizing ITH is essential in designing treatment plans, prognosis as well as the study of cancer progression. Single-cell DNA sequencing (scDNAseq) has been proven effective in deciphering ITH. Cells corresponding to each subclone are supposed to carry a unique set of mutations such as single nucleotide variations (SNV). While there have been many studies on the cancer evolutionary tree reconstruction, not many have been proposed that simply characterize the subclonality without tree reconstruction. While tree reconstruction is important in the study of cancer evolutionary history, typically they are computationally expensive in terms of running time and memory consumption due to the huge search space of the tree structure. On the other hand, subclonality characterization of single cells can be converted into a cell clustering problem, the dimension of which is much smaller, and the turnaround time is much shorter. Despite the existence of a few state-of-the-art cell clustering computational tools for scDNAseq, there lacks a comprehensive and objective comparison under different settings.

RESULTS

In this paper, we evaluated six state-of-the-art cell clustering tools-SCG, BnpC, SCClone, RobustClone, SCITE and SBMClone-on simulated data sets given a variety of parameter settings and a real data set. We designed a simulator specifically for cell clustering, and compared these methods' performances in terms of their clustering accuracy, specificity and sensitivity and running time. For SBMClone, we specifically designed an ultra-low coverage large data set to evaluate its performance in the face of an extremely high missing rate.

CONCLUSION

From the benchmark study, we conclude that BnpC and SCG's clustering accuracy are the highest and comparable to each other. However, BnpC is more advantageous in terms of running time when cell number is high (> 1500). It also has a higher clustering accuracy than SCG when cluster number is high (> 16). SCClone's accuracy in estimating the number of clusters is the highest. RobustClone and SCITE's clustering accuracy are the lowest for all experiments. SCITE tends to over-estimate the cluster number and has a low specificity, whereas RobustClone tends to under-estimate the cluster number and has a much lower sensitivity than other methods. SBMClone produced reasonably good clustering (V-measure > 0.9) when coverage is > = 0.03 and thus is highly recommended for ultra-low coverage large scDNAseq data sets.

Prospects of single-cell nuclear magnetic resonance spectroscopy with quantum sensors

Single-cell analysis can unravel functional heterogeneity within cell populations otherwise obscured by ensemble measurements. However, noninvasive techniques that probe chemical entities and their dynamics are still lacking. This challenge could be overcome by novel sensors based on nitrogen-vacancy (NV) centers in diamond, which enable nuclear magnetic resonance (NMR) spectroscopy on unprecedented sample volumes. In this perspective, we briefly introduce NV-based quantum sensing and review the progress made in microscale NV-NMR spectroscopy. Last, we discuss approaches to enhance the sensitivity of NV ensemble magnetometers to detect biologically relevant concentrations and provide a roadmap toward their application in single-cell analysis.

Single-cell sequencing: New insights for intervertebral disc degeneration

Over the past decade, single-cell RNA sequencing (scRNA-seq) has revolutionized research on biological mechanisms of diseases. Moreover, this technique has been utilized to identify and characterize unique cell types and subpopulations, thereby illuminating cellular heterogeneity. The true value of scRNA-seq lies in its ability to detect transcriptional alterations or perturbed pathways within specific cell types under pathological conditions. In the context of intervertebral disc degeneration (IVDD), the pathophysiological foundation is largely rooted in inflammation. The primary target cells of IVDD are nucleus pulposus cells, annulus fibrosus cells, cartilage endplate cells, and macrophages. The advancements in scRNA-seq technology have triggered remarkable progress in IVDD treatment, leading to breakthroughs in the identification of cell subsets, functional analysis, novel therapeutic targets, and the differentiation and development of various cell types. This review is the first of its kind to introduce the application of scRNA-seq techniques in IVDD, with a focus on the most recent scRNA-seq studies that have defined the populations of various cell types and specific cell-cell interactions in IVDD. Furthermore, we highlight several promising future research directions for scRNA-seq in IVDD.

Clonal evolution and hierarchy in myeloid malignancies

Myeloid malignancies, a group of hematopoietic disorders that includes acute myeloid leukemia (AML), myelodysplastic syndromes (MDS), and myeloproliferative neoplasms (MPNs), are caused by the accumulation of genetic and epigenetic changes in hematopoietic stem and progenitor cells (HSPCs) over time. Despite the relatively low number of genomic drivers compared with other forms of cancer, the process by which these changes shape the genomic architecture of myeloid malignancies remains elusive. Recent advancements in clonal hematopoiesis research and the use of cutting-edge single cell technologies have shed new light on the developmental process of myeloid malignancies. In this review, we delve into the intricacies of clonal evolution in myeloid malignancies and its implications for the development of new diagnostic and therapeutic approaches.

DeCOOC Deconvoluted Hi-C Map Characterizes the Chromatin Architecture of Cells in Physiologically Distinctive Tissues

Deciphering variations in chromosome conformations based on bulk three-dimensional (3D) genomic data from heterogenous tissues is a key to understanding cell-type specific genome architecture and dynamics. Surprisingly, computational deconvolution methods for high-throughput chromosome conformation capture (Hi-C) data remain very rare in the literature. Here, a deep convolutional neural network (CNN), deconvolve bulk Hi-C data (deCOOC) that remarkably outperformed all the state-of-the-art tools in the deconvolution task is developed. Interestingly, it is noticed that the chromatin accessibility or the Hi-C contact frequency alone is insufficient to explain the power of deCOOC, suggesting the existence of a latent embedded layer of information pertaining to the cell type specific 3D genome architecture. By applying deCOOC to in-house-generated bulk Hi-C data from visceral and subcutaneous adipose tissues, it is found that the characteristic chromatin features of M2 cells in the two anatomical loci are distinctively bound to different physiological functionalities. Taken together, deCOOC is both a reliable Hi-C data deconvolution method and a powerful tool for functional extraction of 3D genome architecture.

Archival single-cell genomics reveals persistent subclones during DCIS progression

Ductal carcinoma in situ (DCIS) is a common precursor of invasive breast cancer. Our understanding of its genomic progression to recurrent disease remains poor, partly due to challenges associated with the genomic profiling of formalin-fixed paraffin-embedded (FFPE) materials. Here, we developed Arc-well, a high-throughput single-cell DNA-sequencing method that is compatible with FFPE materials. We validated our method by profiling 40,330 single cells from cell lines, a frozen tissue, and 27 FFPE samples from breast, lung, and prostate tumors stored for 3-31 years. Analysis of 10 patients with matched DCIS and cancers that recurred 2-16 years later show that many primary DCIS had already undergone whole-genome doubling and clonal diversification and that they shared genomic lineages with persistent subclones in the recurrences. Evolutionary analysis suggests that most DCIS cases in our cohort underwent an evolutionary bottleneck, and further identified chromosome aberrations in the persistent subclones that were associated with recurrence.

Mapping the Potential of Microfluidics in Early Diagnosis and Personalized Treatment of Head and Neck Cancers

Head and neck cancers (HNCs) account for ~4% of all cancers in North America and encompass cancers affecting the oral cavity, pharynx, larynx, sinuses, nasal cavity, and salivary glands. The anatomical complexity of the head and neck region, characterized by highly perfused and innervated structures, presents challenges in the early diagnosis and treatment of these cancers. The utilization of sub-microliter volumes and the unique phenomenon associated with microscale fluid dynamics have facilitated the development of microfluidic platforms for studying complex biological systems. The advent of on-chip microfluidics has significantly impacted the diagnosis and treatment strategies of HNC. Sensor-based microfluidics and point-of-care devices have improved the detection and monitoring of cancer biomarkers using biological specimens like saliva, urine, blood, and serum. Additionally, tumor-on-a-chip platforms have allowed the creation of patient-specific cancer models on a chip, enabling the development of personalized treatments through high-throughput screening of drugs. In this review, we first focus on how microfluidics enable the development of an enhanced, functional drug screening process for targeted treatment in HNCs. We then discuss current advances in microfluidic platforms for biomarker sensing and early detection, followed by on-chip modeling of HNC to evaluate treatment response. Finally, we address the practical challenges that hinder the clinical translation of these microfluidic advances.

Dissociation protocols used for sarcoma tissues bias the transcriptome observed in single-cell and single-nucleus RNA sequencing

BACKGROUND

Single-cell RNA-seq has emerged as an innovative technology used to study complex tissues and characterize cell types, states, and lineages at a single-cell level. Classification of bulk tumors by their individual cellular constituents has also created new opportunities to generate single-cell atlases for many organs, cancers, and developmental models. Despite the tremendous promise of this technology, recent evidence studying epithelial tissues and diverse carcinomas suggests the methods used for tissue processing, cell disaggregation, and preservation can significantly bias gene expression and alter the observed cell types. To determine whether sarcomas - tumors of mesenchymal origin - are subject to the same technical artifacts, we profiled patient-derived tumor explants (PDXs) propagated from three aggressive subtypes: osteosarcoma (OS), Ewing sarcoma (ES), desmoplastic small round cell tumor (DSRCT). Given the rarity of these sarcoma subtypes, we explored whether single-nuclei RNA-seq from more widely available archival frozen specimens could accurately be identified by gene expression signatures linked to tissue phenotype or pathognomonic fusion proteins.

RESULTS

We systematically assessed dissociation methods across different sarcoma subtypes. We compared gene expression from single-cell and single-nucleus RNA-sequencing of 125,831 whole-cells and nuclei from ES, DSRCT, and OS PDXs. We detected warm dissociation artifacts in single-cell samples and gene length bias in single-nucleus samples. Classic sarcoma gene signatures were observed regardless of the dissociation method. In addition, we showed that dissociation method biases could be computationally corrected.

CONCLUSIONS

We highlighted transcriptional biases, including warm dissociation and gene-length biases, introduced by the dissociation method for various sarcoma subtypes. This work is the first to characterize how the dissociation methods used for sc/snRNA-seq may affect the interpretation of the molecular features in sarcoma PDXs.

Single-cell technologies in multiple myeloma: new insights into disease pathogenesis and translational implications

Multiple myeloma (MM) is a hematological malignancy characterized by clonal proliferation of plasma cells. Although therapeutic advances have been made to improve clinical outcomes and to prolong patients' survival in the past two decades, MM remains largely incurable. Single-cell sequencing (SCS) is a powerful method to dissect the cellular and molecular landscape at single-cell resolution, instead of providing averaged results. The application of single-cell technologies promises to address outstanding questions in myeloma biology and has revolutionized our understanding of the inter- and intra-tumor heterogeneity, tumor microenvironment, and mechanisms of therapeutic resistance in MM. In this review, we summarize the recently developed SCS methodologies and latest MM research progress achieved by single-cell profiling, including information regarding the cancer and immune cell landscapes, tumor heterogeneities, underlying mechanisms and biomarkers associated with therapeutic response and resistance. We also discuss future directions of applying transformative SCS approaches with contribution to clinical translation.

A Novel Defined PANoptosis-Related miRNA Signature for Predicting the Prognosis and Immune Characteristics in Clear Cell Renal Cell Carcinoma: A miRNA Signature for the Prognosis of ccRCC

Clear cell renal cell carcinoma (ccRCC) is one of the most prevalent cancers, and PANoptosis is a distinct, inflammatory-programmed cell death regulated by the PANoptosome. The essential regulators of cancer occurrence and progression are microRNAs (miRNAs). However, the potential function of PANoptosis-related microRNAs (PRMs) in ccRCC remains obscure. This study retrieved ccRCC samples from The Cancer Genome Atlas database and three Gene Expression Omnibus datasets. PRMs were recognized based on previous reports in the scientific literature. Regression analyses were used to identify the prognosis PRMs and construct a PANoptosis-related miRNA prognostic signature based on the risk score. We discovered that high-risk patients had poorer survival prognoses and were significantly linked to high-grade and advanced-stage tumors, using a variety of R software packages and web analysis tools. Furthermore, we demonstrated that the low-risk group had significant changes in their metabolic pathways. In contrast, the high-risk group was characterized by high immune cell infiltration, immune checkpoint expression, and low half-maximum inhibition concentration (IC50) values of chemotherapeutic agents. This suggests that high-risk patients may benefit more from immunotherapy and chemotherapy. In conclusion, we constructed a PANoptosis-related microRNA signature and revealed its potential significance in clinicopathological features and tumor immunity, thereby providing new precise treatment strategies.

Evaluating the transcriptional landscape and cell-cell communication networks in chronically irradiated parotid glands

Understanding the transcriptional landscape that results in chronic salivary hypofunction after irradiation will help identify injury mechanisms and develop regenerative therapies. We present scRNA-seq analysis from control and irradiated murine parotid glands collected 10 months after irradiation. We identify a population of secretory cells defined by specific expression of Etv1, which may be an acinar cell precursor. Acinar and Etv1+ secretory express Ntrk2 and Erbb3, respectively while the ligands for these receptors are expressed in myoepithelial and stromal cells. Furthermore, our data suggests that secretory cells and CD4+CD8+T-cells are the most transcriptionally affected during chronic injury with radiation, suggesting active immune involvement. Lastly, evaluation of cell-cell communication networks predicts that neurotrophin, neuregulin, ECM, and immune signaling are dysregulated after irradiation, and thus may play a role in the lack of repair. This resource will be helpful to understand cell-specific pathways that may be targeted to repair chronic damage in irradiated glands.

Single-cell transcriptomics and data analyses for prokaryotes-Past, present and future concepts

Transcriptomics, or more specifically mRNA sequencing, is a powerful tool to study gene expression at the single-cell level (scRNA-seq) which enables new insights into a plethora of biological processes. While methods for single-cell RNA-seq in eukaryotes are well established, application to prokaryotes is still challenging. Reasons for that are rigid and diverse cell wall structures hampering lysis, the lack of polyadenylated transcripts impeding mRNA enrichment, and minute amounts of RNA requiring amplification steps before sequencing. Despite those obstacles, several promising scRNA-seq approaches for bacteria have been published recently, albeit difficulties in the experimental workflow and data processing and analysis remain. In particular, bias is often introduced by amplification which makes it difficult to distinguish between technical noise and biological variation. Future optimization of experimental procedures and data analysis algorithms are needed for the improvement of scRNA-seq but also to aid in the emergence of prokaryotic single-cell multi-omics. to help address 21st century challenges in the biotechnology and health sector.