Advances and applications of single-cell sequencing technologies

Navigating single-cell RNA-sequencing: protocols, tools, databases, and applications

Single-cell RNA-sequencing (scRNA-seq) technology brought about a revolutionary change in the transcriptomic world, paving the way for comprehensive analysis of cellular heterogeneity in complex biological systems. It enabled researchers to see how different cells behaved at single-cell levels, providing new insights into the process. However, despite all these advancements, scRNA-seq also experiences challenges related to the complexity of data analysis, interpretation, and multi-omics data integration. In this review, these complications were discussed in detail, directly pointing at the optimization of scRNA-seq approaches and understanding the world of single-cell and its dynamics. Different protocols and currently functional single-cell databases were also covered. This review highlights different tools for the analysis of scRNA-seq and their methodologies, emphasizing innovative techniques that enhance resolution and accuracy at a single-cell level. Various applications were explored across domains including drug discovery, tumor microenvironment (TME), biomarker discovery, and microbial profiling, and case studies were discussed to explain the importance of scRNA-seq by uncovering novel and rare cell types and their identification. This review underlines a crucial aspect of scRNA-seq in the advancement of personalized medicine and highlights its potential to understand the complexity of biological systems.

Crosstalk between kidney and bones: New perspective for modulating osteoporosis

Growing evidence indicates an interesting interplay between kidney and bone. The pathophysiological condition of the skeletal system is intricately associated with the normal functioning of the kidneys. This relationship is modulated by various factors, including calcium and phosphate, 1-α-hydroxylase, erythropoietin (EPO), klotho, fibroblast growth factor 23 (FGF23), bone morphogenetic protein-7 (BMP-7), and extracellular vesicles (EVs). These interactions are notably evident in conditions such as chronic kidney disease with bone mineral density (CKD-BMD), renal osteodystrophy (ROD), and osteoporosis (OP). Furthermore, innovative methodologies such as cell co-culture, organ-on-a-chip, single-cell sequencing, and spatial transcriptomics are highlighted as instrumental in advancing the study of inter-organ interactions. This review, grounded in the pathogenesis, diagnostic and therapeutic modalities, and pharmacological treatments of OP, synthesizes evidence from molecular biology to clinical perspectives. It aims to establish a foundation for the development of more complex and physiologically relevant in vitro models and to propose potential therapeutic strategies.

Isoform-resolution single-cell RNA sequencing reveals the transcriptional panorama of adult Baoshan pig testis cells

BACKGROUND

As the primary organ of the male reproductive system, the testis facilitates spermatogenesis and androgen secretion. Due to the complexity of spermatogenesis, elucidating cellular heterogeneity and gene expression dynamics within the porcine testis is critical for advancing reproductive biology. Nevertheless, the cellular composition and regulatory mechanisms of porcine testes remain insufficiently characterized. In this study, we applied integrated long-read (Nanopore) and short-read (Illumina) scRNA-seq to Baoshan pig testes, establishing a comprehensive transcriptional profile to delineate cellular heterogeneity and molecular regulation.

RESULTS

Through systematic analysis of testicular architecture and the temporal progression of spermatogenesis, we characterized 11,520 single cells and 23,402 genes, delineating germ cell developmental stages: proliferative-phase spermatogonia (SPG), early-stage spermatocytes (Early SPC) and late-stage spermatocytes (Late SPC) during meiosis, and spermiogenic-phase round spermatids (RS) followed by elongating/elongated spermatids (ES), culminating in mature spermatozoa (Sperm). We further identified nine distinct testicular cell types, with germ cells spanning all developmental stages and somatic components comprising Sertoli cells, macrophages, and peritubular myoid cells as microenvironmental constituents, revealing the cellular heterogeneity of testicular tissue and dynamic characteristics of spermatogenesis. We obtained the dynamic expression changes of 16 vital marker genes during spermatogenesis and performed immunofluorescence validation on 7 marker genes. Gene ontology analysis revealed that germ cells at various stages were involved in specific biological processes, while cell communication networks highlighted eight pivotal signaling pathways, including MIF, NRG, WNT, VEGF, BMP, CCL, PARs, and ENHO pathways. Long-read sequencing further captured the full integrity and diversity of RNA transcripts, identifying 60% of the novel annotated isoforms and revealing that FSM isoforms exhibited longer transcript lengths, longer coding sequences, longer open reading frames, and a great number of exons, suggesting the complexity of isoforms within the testicular microenvironment.

CONCLUSIONS

Our results provide insight into the cellular heterogeneity, intercellular communication, and gene expression/transcript diversity in porcine testes, and offer a valuable resource for understanding the molecular mechanisms of porcine spermatogenesis.

Advances in Microfluidic Single-Cell RNA Sequencing and Spatial Transcriptomics

The development of micro- and nano-fabrication technologies has greatly advanced single-cell and spatial omics technologies. With the advantages of integration and compartmentalization, microfluidic chips are capable of generating high-throughput parallel reaction systems for single-cell screening and analysis. As omics technologies improve, microfluidic chips can now integrate promising transcriptomics technologies, providing new insights from molecular characterization for tissue gene expression profiles and further revealing the static and even dynamic processes of tissues in homeostasis and disease. Here, we survey the current landscape of microfluidic methods in the field of single-cell and spatial multi-omics, as well as assessing their relative advantages and limitations. We highlight how microfluidics has been adapted and improved to provide new insights into multi-omics over the past decade. Last, we emphasize the contributions of microfluidic-based omics methods in development, neuroscience, and disease mechanisms, as well as further revealing some perspectives for technological advances in translational and clinical medicine.

Omics sciences for cervical cancer precision medicine from the perspective of the tumor immune microenvironment

Immunotherapies have demonstrated notable clinical benefits in the treatment of cervical cancer (CC). However, the development of therapeutic resistance and diverse adverse effects in immunotherapy stem from complex interactions among biological processes and factors within the tumor immune microenvironment (TIME). Advanced omic technologies offer novel insights into a more expansive and thorough layer of the TIME. Furthermore, integrating multidimensional omics within the frameworks of systems biology and computational methodologies facilitates the generation of interpretable data outputs to characterize the clinical and biological trajectories of tumor behavior. In this review, we present advanced omics technologies that utilize various clinical samples to address scientific inquiries related to immunotherapies for CC, highlighting their utility in identifying metastasis dissemination, recurrence risk, and therapeutic resistance in patients treated with immunotherapeutic approaches. This review elaborates on the strategy for integrating multi-omics data through artificial intelligence algorithms. Additionally, an analysis of the obstacles encountered in the multi-omics analysis process and potential avenues for future research in this domain are presented.

Comparing the predictors of mutability among healthy human tissues inferred from mutations in single-cell genome data

Studying mutation in healthy somatic tissues is the key for understanding the genesis of cancer and other genetic diseases. Mutation rate varies from site to site in the human genome by up to 100-fold and is influenced by numerous epigenetic and genetic factors including GC content, trinucleotide sequence context, and DNAse accessibility. These factors influence mutation at both local and regional scales and are often interrelated with one another, meaning that predicting mutability or uncovering its drivers requires modelling multiple factors and scales simultaneously. Historically, most investigations have focused either on analyzing the local sequence scale through triplet signatures or on examining the impact of epigenetic processes at larger scales, but not both concurrently. Additionally, sequencing technology limitations have restricted analyses of healthy mutations to coding regions (RNA-seq) or to those that have been influenced by selection (e.g. bulk samples from cancer tissue). Here, we leverage single-cell mutations and present a comprehensive analysis of epigenetic and genetic factors at multiple scales in the germline and 3 healthy somatic tissues. We create models that predict mutability with on average 2% error and find up to 63-fold variation among sites within the same tissue. We observe varying degrees of similarity between tissues: the mutability of genomic positions was 93.4% similar between liver and germline tissues, but sites in germline and skin were only 85.9% similar. We observe both universal and tissue-specific mutagenic processes in healthy tissues, with implications for understanding the maintenance of germline vs soma and the mechanisms underlying early tumorigenesis.

SCANER: robust and sensitive identification of malignant cells from the scRNA-seq profiled tumor ecosystem

Single-cell RNA sequencing (scRNA-seq) has enabled the dissection of complex tumor ecosystems. Recognition of malignant cells as an essential step has a profound impact on downstream interpretation. However, most existing computational strategies are based on prior knowledge of canonical cell-type markers. We have developed a marker-free approach, the Seed-Cluster based Approach for NEoplastic cells Recognition (SCANER), to identify malignant cells based on significant gene expression variations caused by genomic instability. Upon analyzing different cancer types, SCANER achieved superior accuracy and robustness in identifying malignant cells, effectively addressing dropout events and tumor purity variations. Besides, SCANER can significantly detect copy number variations (CNVs) in malignant cells compared to nonmalignant cells, which is further confirmed through the paired whole exome sequencing data. In conclusion, SCANER has the potential to facilitate the biological exploration of the tumor ecosystem by accurately identifying malignant cells and it is applicable across various solid cancer types regardless of prior knowledge. SCANER is available at https://github.com/woolingxiang/SCANER.

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.

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.

Single-Cell Hi-C Technologies and Computational Data Analysis

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

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.

Revealing the core suppression effects of various Di (2-ethylhexyl) phthalate exposure on early meiosis progression in postnatal male mice via single-cell RNA sequencing

The male reproductive system has been the subject of considerable attention in recent years due to the adverse effects of Di (2-ethylhexyl) phthalate (DEHP). Although previous research has suggested that DEHP exposure hinders the early meiotic progression of male germ cells, the underlying mechanisms are still not well understood. The transcriptomic changes in testicular cells of postnatal male rodents following DEHP exposure were meticulously analyzed using 10X Genomics single-cell RNA sequencing in this study. For downstream analysis, we acquired 42,000 cells and generated 3172,754,990 reads. DEHP exposure at concentrations of 40 μg/kg/day (DEHP40) and 80 μg/kg/day (DEHP80) substantially decreased the proportion of pachytene and diplotene spermatocytes, indicating a shared inhibitory effect on early meiosis, as demonstrated by our findings. In addition, DEHP exposure disrupted the cellular communication between Sertoli cells and germ cells, which had a significant impact on the p38-MAPK signaling pathway. The expression of key ligand genes Tgfb1 and Tgfb3 in Sertoli cells was significantly reduced. DEHP exposure resulted in a substantial decrease in the expression of the Trp53 gene, which in turn down-regulated three critical downstream genes (Stmn1, Tubb5, and Ccnb1) that are implicated in spindle organization from a mechanistic perspective. This study offers the first comprehensive evidence that DEHP inhibits early meiotic progression in male germ cells through the Trp53-mediated p38-MAPK pathway, providing crucial insights into the molecular mechanisms underlying DEHP-induced male reproductive toxicity. Our results emphasize the enduring negative effects of DEHP exposure on male fertility, which have substantial ramifications for the comprehension and mitigation of the influence of environmental estrogens on reproductive health.

Unraveling the pathogenesis of Barrett's esophagus and esophageal adenocarcinoma: the "omics" era

Barrett's esophagus (BE) represents a pre-cancerous condition that is characterized by the metaplastic conversion of the squamous esophageal epithelium to a columnar intestinal-like phenotype. BE is the consequence of chronic reflux disease and has a potential progression burden to esophageal adenocarcinoma (EAC). The pathogenesis of BE and EAC has been extensively studied but not completely understood, and it is based on two main hypotheses: "transdifferentiation" and "transcommitment". Omics technologies, thanks to the potentiality of managing huge amounts of genetic and epigenetic data, sequencing the whole genome, have revolutionized the understanding of BE carcinogenesis, paving the way for biomarker development helpful in early diagnosis and risk progression assessment. Genomics and transcriptomics studies, implemented with the most advanced bioinformatics technologies, have brought to light many new risk loci and genomic alterations connected to BE and its progression to EAC, further exploring the complex pathogenesis of the disease. Early mutations of the TP53 gene, together with late aberrations of other oncosuppressor genes (SMAD4 or CKND2A), represent a genetic driving force behind BE. Genomic instability, nonetheless, is the central core of the disease. The implementation of transcriptomic and proteomic analysis, even at the single-cell level, has widened the horizons, complementing the genomic alterations with their transcriptional and translational bond. Increasing interest has been gathered around small circulating genetic traces (circulating-free DNA and micro-RNAs) with a potential role as blood biomarkers. Epigenetic alterations (such as hyper or hypo-methylation) play a meaningful role in esophageal carcinogenesis as well as the study of the tumor micro-environment, which has led to the development of novel immunological therapeutic options. Finally, the esophageal microbiome could be the protagonist to be investigated, deepening our understanding of the subtle association between the host microbiota and tumor development.

Single-Cell Sequencing Reveals PD-L1-Mediated Immune Escape Signaling in Lung Adenocarcinoma

Background: Lung cancer has the highest mortality rate among all cancers, for which immunotherapy can frequently lead to drug resistance. To understand the molecular mechanisms behind immune escape in patients with lung cancer and develop predictive and therapeutic targets, we carried out analytical experiments using single-cell sequencing. Methods: We collected eight tumor tissue samples from eight patients with lung adenocarcinoma and categorized them based on the positive reactions for programmed cell death ligand 1 (PD-L1) expression levels. Single-cell sequencing analysis was employed to create a comprehensive cellular landscape. Uniform Manifold Approximation and Projection was used to show the proportion of immune and endothelial cells, along with a map depicting the distribution of different cell types. Cells were subdivided according to molecular markers; the subpopulations were grouped based on PD-L1 levels and tumor marker-positive reactions. The correlation between the occurrence of the PD-L1 reaction and the response time of immune cells was explored; differential gene expression between the groups was elucidated. Finally, quantitative polymerase chain reaction (qPCR) was used to examine the relationship between key differentially expressed genes and PD-L1 immune escape checkpoint response. Results: A total of 58,810 single cells were analyzed, identifying seven distinct cell types. In the PD-L1-positive sample group, B cells, astrocytes, endothelial cells, outer skin cells, and tissue stem cells were present in higher proportions, whereas T and dendritic cells were the main cells in the PD-L1-negative sample group. According to the molecular markers, the seven cell types were divided into 17 cell clusters, with one cluster classified as tumor cells, showing PD-L1 positivity. Eleven molecular markers with different expression levels were simultaneously screened (NAPSA, MUC1, WFDC2, MYO6, LYZ, IGHG4, IGLL5, IGHM, IGKC, AQP3, and IGFBP7), and their association with the PD-L1/PD-1 immune escape axis response was confirmed by qPCR. Conclusion: Our study suggests that PD-L1-mediated immune escape may occur at a later stage of tumor progression, involving both PD-L1-positive and negative immune cells. Additionally, we identified 11 differentially expressed genes that could provide insights into the potential mechanisms of immune escape in patients with lung cancer. These findings offer promising molecular targets for the detection and treatment of immune escape in clinical settings.

Integrative analysis of ferroptosis in the hypoxic microenvironment of gastric cancer unveils the immune landscape and personalized therapeutic strategies

Background

Ferroptosis is a cell death mode caused by excessive accumulation of lipid peroxides caused by disturbance of intracellular metabolic pathway, which is closely related to iron and cholesterol metabolism homeostasis. Its regulation within the hypoxic metabolic tumor microenvironment (TME) has the potential to improve the effectiveness of tumor immunotherapy. The predictive role of ferroptosis in gastric cancer (GC) hypoxia TME, particularly in relation to TME immune cell infiltration, has not been fully explained.

Methods

By analyzing the mRNA expression data of ferroptosis and hypoxia-related genes, a prediction model was constructed to evaluate further the predictive value of immune cell infiltration, clinical characteristics, and immunotherapy efficacy of gastric cancer, and the essential genes were validated.

Results

Two distinct molecular states of ferroptosis-hypoxia were identified in GC. Notably, patients with high ferroptosis-hypoxia risk scores (FHRS) displayed significant levels of hypoxia and epithelial-mesenchymal transition (EMT), which were associated with unfavorable prognosis, increased chemoresistance, and heightened immunosuppression.

Conclusions

This study demonstrates that ferroptosis under hypoxic conditions significantly affects the modulation of the tumor immune microenvironment. The FHRS can independently predict prognosis in gastric cancer. Assessing the molecular status of ferroptosis-hypoxia in individual patients will help in selecting more suitable immunotherapy regimens by providing a better understanding of TME characteristics and predicting immunotherapeutic outcomes.

Quantitative Analysis of Tomato Flower Pedicel Abscission and Single-Cell Transcriptome Analysis

Plant organ abscission is a highly organized biological process in response to developmental and environmental cues. The understanding of the regulation of abscission is of great importance for the improvement of crop productivity and quality. The abscission process can be quantitatively analyzed by investigating the abscission of the tomato flower pedicel of different genotypes at a specific period. Single-cell transcriptomics is a novel approach to dissect the characteristics of pedicel abscission zone cells. In this chapter, we provide a detailed description of an abscission assay of flower pedicel explants and the method of single-cell transcriptome sequencing, including sample preparation, data determination, and analysis.

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.

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.

Transcriptional Profiling of Testis Development in Pre-Sexually-Mature Hezuo Pig

Spermatogenesis is an advanced biological process, relying on intricate interactions between somatic and germ cells in testes. Investigating various cell types is challenging because of cellular heterogeneity. Single-cell RNA sequencing (scRNA-seq) offers a method to analyze cellular heterogeneity. In this research, we performed 10× Genomics scRNA-seq to conduct an unbiased single-cell transcriptomic analysis in Hezuo pig (HZP) testis at one month of age during prepuberty. We collected 14,276 cells and identified 8 cell types (including 2 germ cells types and 6 somatic cell types). Pseudo-timing analysis demonstrated that Leydig cells (LCs) and myoid cells (MCs) originated from a shared progenitor cell lineage. Moreover, the functional enrichment analyses showed that the genes of differential expression were enriched in spermatogonia (SPG) and were enriched in the cell cycle, reproduction, and spermatogenesis. Expressed genes in spermatocytes (SPCs) were enriched in the cAMP, cell cycle, male gamete generation, reproductive system development, and sexual reproduction, while growth hormone synthesis, gamete generation, reproductive process, and spermine synthase activity were enriched in Sertoli cells (SCs). Additionally, chemokine, B cell receptor, activation of immune response, and enzyme binding were enriched in macrophages. Our study investigated transcriptional alterations across different cell types during spermatogenesis, yielding new understandings of spermatogenic processes and cell development. This research delivers an exploration of spermatogenesis and testicular cell biology in HZP, establishing the groundwork for upcoming breeding initiatives.

Bibliometric analysis of research hotspots and trends of lncRNA in angiogenesis-related diseases

OBJECTIVES

Long noncoding RNAs (lncRNA) play important roles in the pathological processes of angiogenesis-related diseases such as cancer and diabetic retinopathy. This study aims to identify global research trends and hotspots in the field of lncRNAs in angiogenesis-related diseases and to explore future research directions.

METHODS

Relevant literature published between 2012 and 2022 was retrieved from the Web of Science Core Collection (WoSCC). A total of 1 516 articles on lncRNAs and angiogenesis-related diseases were included for bibliometric analysis. CiteSpace and VOSviewer were used to analyze publication countries, institutions, journals, authors, co-cited references, and key words.

RESULTS

The number of publications in this field has shown a steadily increasing trend from 2012 to 2022, peaking in 2021. China has the highest number of publications, while the United States ranked highest in centrality. Nanjing Medical University was the most prolific institution. Liu Y was the most productive author, while Wang Y ranked first in co-citation frequency. Cell was the most frequently cited journal. The latest terms of burst key words were vascular remodeling, dysfunction, heart, target, suppress, and pulmonary arterial hypertension.

CONCLUSIONS

From 2012 to 2022, research on lncRNAs in angiogenesis-related diseases has grown significantly. China leads in publication volume, while the United States holds the most academic influence. Emerging research hotspots such as vascular remodeling and dysfunction point to key directions for future research.

Innovative Insights into Single-Cell Technologies and Multi-Omics Integration in Livestock and Poultry

In recent years, single-cell RNA sequencing (scRNA-seq) has marked significant strides in livestock and poultry research, especially when integrated with multi-omics approaches. These advancements provide a nuanced view into complex regulatory networks and cellular dynamics. This review outlines the application of scRNA-seq in key species, including poultry, swine, and ruminants, with a focus on outcomes related to cellular heterogeneity, developmental biology, and reproductive mechanisms. We emphasize the synergistic power of combining scRNA-seq with epigenomic, proteomic, and spatial transcriptomic data, enhancing molecular breeding precision, optimizing health management strategies, and refining production traits in livestock and poultry. The integration of these technologies offers a multidimensional approach that not only broadens the scope of data analysis but also provides actionable insights for improving animal health and productivity.

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.

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.

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.

Epigenetic regulation of cardiovascular diseases induced by behavioral and environmental risk factors: Mechanistic, diagnostic, and therapeutic insights

Behavioral and environmental risk factors are critical in the development and progression of cardiovascular disease (CVD). Understanding the molecular mechanisms underlying these risk factors will offer valuable insights for targeted preventive and therapeutic strategies. Epigenetic modifications, including DNA methylation, histone modifications, chromatin remodeling, noncoding RNA (ncRNA) expression, and epitranscriptomic modifications, have emerged as key mediators connecting behavioral and environmental risk factors to CVD risk and progression. These epigenetic alterations can profoundly impact on cardiovascular health and susceptibility to CVD by influencing cellular processes, development, and disease risk over an individual's lifetime and potentially across generations. This review examines how behavioral and environmental risk factors affect CVD risk and health outcomes through epigenetic regulation. We review the epigenetic effects of major behavioral risk factors (such as smoking, alcohol consumption, physical inactivity, unhealthy diet, and obesity) and environmental risk factors (including air and noise pollution) in the context of CVD pathogenesis. Additionally, we explore epigenetic biomarkers, considering their role as causal or surrogate indicators, and discuss epigenetic therapeutics targeting the mechanisms through which these risk factors contribute to CVD. We also address future research directions and challenges in leveraging epigenetic insights to reduce the burden of CVD related to behavioral and environmental factors and improve public health outcomes. This review aims to provide a comprehensive understanding of behavioral and environmental epigenetics in CVD and offer valuable strategies for therapeutic intervention.

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.

Analyzing Large-Scale Single-Cell RNA-Seq Data Using Coreset

The recent boom in single-cell sequencing technologies provides valuable insights into the transcriptomes of individual cells. Through single-cell data analyses, a number of biological discoveries, such as novel cell types, developmental cell lineage trajectories, and gene regulatory networks, have been uncovered. However, the massive and increasingly accumulated single-cell datasets have also posed a seriously computational and analytical challenge for researchers. To address this issue, one typically applies dimensionality reduction approaches to reduce the large-scale datasets. However, these approaches are generally computationally infeasible for tall matrices. In addition, the downstream data analysis tasks such as clustering still take a large time complexity even on the dimension-reduced datasets. We present single-cell Coreset (scCoreset), a data summarization framework that extracts a small weighted subset of cells from a huge sparse single-cell RNA-seq data to facilitate the downstream data analysis tasks. Single-cell data analyses run on the extracted subset yield similar results to those derived from the original uncompressed data. Tests on various single-cell datasets show that scCoreset outperforms the existing data summarization approaches for common downstream tasks such as visualization and clustering. We believe that scCoreset can serve as a useful plug-in tool to improve the efficiency of current single-cell RNA-seq data analyses.

Metabolomics-driven approaches for identifying therapeutic targets in drug discovery

Identification of therapeutic targets can directly elucidate the mechanism and effect of drug therapy, which is a central step in drug development. The disconnect between protein targets and phenotypes under complex mechanisms hampers comprehensive target understanding. Metabolomics, as a systems biology tool that captures phenotypic changes induced by exogenous compounds, has emerged as a valuable approach for target identification. A comprehensive overview was provided in this review to illustrate the principles and advantages of metabolomics, delving into the application of metabolomics in target identification. This review outlines various metabolomics-based methods, such as dose-response metabolomics, stable isotope-resolved metabolomics, and multiomics, which identify key enzymes and metabolic pathways affected by exogenous substances through dose-dependent metabolite-drug interactions. Emerging techniques, including single-cell metabolomics, artificial intelligence, and mass spectrometry imaging, are also explored for their potential to enhance target discovery. The review emphasizes metabolomics' critical role in advancing our understanding of disease mechanisms and accelerating targeted drug development, while acknowledging current challenges in the field.

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.

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.