Comparative Analysis of Single-Cell RNA Sequencing Methods

Benchmarking of methods that identify alternative polyadenylation events in single-/multiple-polyadenylation site genes

Alternative polyadenylation (APA) is a widespread post-transcriptional mechanism that diversifies gene expression by generating messenger RNA isoforms with varying 3' untranslated regions. Accurate identification and quantification of transcriptome-wide polyadenylation site (PAS) usage are essential for understanding APA-mediated gene regulation and its biological implications. In this review, we first review the landscape of computational tools developed to identify APA events from RNA sequencing (RNA-seq) data. We then benchmarked five PAS prediction tools and seven APA detection algorithms using five RNA-seq datasets derived from clear cell renal cell carcinoma (ccRCC) and adjacent normal tissues. By evaluating tool performance across genes with either single or multiple PASs, we revealed substantial variation in accuracy, sensitivity, and consistency among the tools. Based on this comparative analysis, we offer practical guidelines for tool selection and propose considerations for improving APA detection accuracy. Additionally, our analysis identified CCNL2 as a candidate gene exhibiting significant APA regulation in ccRCC, highlighting its potential as a disease-associated biomarker.

scEVE: a single-cell RNA-seq ensemble clustering algorithm capitalizing on the differences of predictions between multiple clustering methods

Single-cell RNA sequencing measures individual cell transcriptomes in a sample. In the past decade, this technology has motivated the development of hundreds of clustering methods. These methods attempt to group cells into populations by leveraging the similarity of their transcriptomes. Because each method relies on specific hypotheses, their predictions can vary drastically. To address this issue, ensemble algorithms detect cell populations by integrating multiple clustering methods, and minimizing the differences of their predictions. While this approach is sensible, it has yet to address some conceptual challenges in single-cell data science; namely, ensemble algorithms have yet to generate clustering results with uncertainty values and multiple resolutions. In this work, we present an original approach to ensemble clustering that addresses these challenges, by describing the differences between clustering results, rather than minimizing them. We present the scEVE algorithm, and we evaluate it on 15 experimental datasets, and up to 1200 synthetic datasets. Our results reveal that scEVE outperforms the state of the art, and addresses both conceptual challenges. We also highlight how biological downstream analyses will benefit from addressing these challenges. We expect that this work will provide an alternative direction for developing single-cell ensemble clustering algorithms.

PacBio-based de novo transcriptomics of the coconut rhinoceros beetle Oryctes rhinoceros identifies physiologically important full-length genes and sheds insights into the molecular relationship (chitin synthase) between Scarabaeidae (Coleoptera) and Hymenoptera

The sparser molecular data in non-model insects such as Oryctes rhinoceros prompted us to investigate and identify its physiologically important genes using the novel PacBio Iso-Seq Sequel II platform with single-molecule real-time (SMRT) technology. SMRT library was prepared from various tissues and sequenced. In total, 16,916,297 subreads clustered into 17,547 contigs which collapsed to form 8708 full-length sequences out of which 4352 functionally annotated transcripts were identified. Genes involved in innate immunity, growth and development, hormonal regulation, cellular process, peritrophic membrane, melanogenesis, integument, circulation, cuticle formation, glycan metabolism, etc., were identified. The transcripts' orthologues were identified predominantly in Coleoptera and Hymenoptera in which chitin synthase (CHS), toll, haemocytin, serine protease/limulus clotting factor c, vitellogenin and trehalose transporter exhibited significant molecular relationships between these two insect orders. Chitin synthase 8 (CHS-8) found in ant has been identified for the first time in the order Coleoptera. (O. rhinoceros) at the translational level and projected a potential to explore evolution (horizontal gene transfer) of CHS in insects. The findings will bridge the molecular data between the genome and transcriptome of O. rhinoceros, thus helping develop molecular targets for its control and management.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-025-04348-9.

Interpretable Differential Abundance Signature (iDAS)

Single-cell technologies have revolutionized the understanding of cellular dynamics by allowing researchers to investigate individual cell responses under various conditions, such as comparing diseased versus healthy states. Many differential abundance methods have been developed in this field, however, the understanding of the gene signatures obtained from those methods is often incomplete, requiring the integration of cell type information and other biological factors to yield interpretable and meaningful results. To better interpret the gene signatures generated in the differential abundance analysis, iDAS is developed to classify the gene signatures into multiple categories. When applied to melanoma single-cell data with multiple cell states and treatment phenotypes, iDAS identified cell state- and treatment phenotype-specific gene signatures, as well as interaction effect-related gene signatures with meaningful biological interpretations. The iDAS model is further applied to a longitudinal study and spatially resolved omics data to demonstrate its versatility in different analytical contexts. These results demonstrate that the iDAS framework can effectively identify robust, cell-state specific gene signatures and is versatile enough to accommodate various study designs, including multi-factor longitudinal and spatially resolved data.

A survey of the methodological process of modeling, inference, and evaluation of gene regulatory networks using scRNA-Seq data

The advent of scRNA-Seq sequencing technology has provided unprecedented resolutions in the analysis of gene regulatory networks (GRNs) at the single-cell level. However, new technical and methodological challenges also emerged. Factors such as the large number of zeros reported in expression levels, the biological variation due to the stochastic nature of gene expression, environmental niche, and effects created by the cell cycle make it difficult to correctly interpret the data obtained in the sequencing stage. On the other hand, the development of methods for the inference of GRNs, specifically using scRNA-Seq technology, proved to be of similar quality to random predictors. The lack of adequate pre-processing of gene expression data, including selection steps for subsets of genes of interest, smoothing, and discretization of gene expression, in addition to the different ways of modeling networks and network motifs, are factors that affect the performance of inference approaches. Finally, the lack of knowledge about the ground-truth network and the non-standardization of appropriate metrics to measure the quality of inferred networks make the process of comparing performance between algorithms a major problem, given the unbalanced nature of the data and the interpretation bias caused by the chosen metric. This article brings these issues to light, aiming to show how these factors influence both the inference process and the performance evaluation of inferred networks, through comparative computational experiments and provides suggestions for a more robust methodological process for researchers dealing with inference of GRNs.

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.

Advances in spatial transcriptomics and its application in the musculoskeletal system

While bulk RNA sequencing and single-cell RNA sequencing have shed light on cellular heterogeneity and potential molecular mechanisms in the musculoskeletal system in both physiological and various pathological states, the spatial localization of cells and molecules and intercellular interactions within the tissue context require further elucidation. Spatial transcriptomics has revolutionized biological research by simultaneously capturing gene expression profiles and in situ spatial information of tissues, gradually finding applications in musculoskeletal research. This review provides a summary of recent advances in spatial transcriptomics and its application to the musculoskeletal system. The classification and characteristics of data acquisition techniques in spatial transcriptomics are briefly outlined, with an emphasis on widely-adopted representative technologies and the latest technological breakthroughs, accompanied by a concise workflow for incorporating spatial transcriptomics into musculoskeletal system research. The role of spatial transcriptomics in revealing physiological mechanisms of the musculoskeletal system, particularly during developmental processes, is thoroughly summarized. Furthermore, recent discoveries and achievements of this emerging omics tool in addressing inflammatory, traumatic, degenerative, and tumorous diseases of the musculoskeletal system are compiled. Finally, challenges and potential future directions for spatial transcriptomics, both as a field and in its applications in the musculoskeletal system, are discussed.

Intercellular communication between FAP+ fibroblasts and SPP1+ macrophages in prostate cancer via multi-omics

Background

Prostate cancer (PCa) presents substantial heterogeneity and unpredictability in its progression. Despite therapeutic advancements, mortality from advanced PCa remains a significant challenge. Understanding the intercellular communication within the tumor microenvironment (TME) is critical for uncovering mechanisms driving tumorigenesis and identifying novel therapeutic targets.

Methods

We employed an integrative approach combining bulk RNA sequencing, single-cell RNA sequencing (scRNA-seq), and spatial transcriptomics to investigate interactions between FAP+ fibroblasts and tumor-associated macrophages in PCa. Key findings were validated using immunohistochemical and immunofluorescence staining techniques.

Results

Analysis of 23,519 scRNA-seq data from 23 prostate samples revealed a pronounced accumulation of FAP+ fibroblasts in tumor tissues. Spatial transcriptomics and bulk RNA sequencing demonstrated strong associations between FAP+ fibroblasts and SPP1+ macrophages. Notably, tumor-specific intercellular signaling pathways, such as CSF1/CSF1R and CXCL/ACKR1, were identified, highlighting their potential role in fostering an immunosuppressive TME.

Conclusion

Our findings unveil a distinct pattern of crosstalk between FAP+ fibroblasts and SPP1+ macrophages in PCa, shedding light on potential therapeutic targets for advanced PCa.

From valence encoding to motivated behavior: A focus on the nucleus accumbens circuitry

How do our brains determine whether something is good or bad? The brain's ability to evaluate stimuli as positive or negative - by attributing valence - is fundamental to survival and decision-making. Different brain regions have been associated with valence encoding, including the nucleus accumbens (NAc). The NAc is predominantly composed of GABAergic medium spiny neurons (MSNs), which segregate into two distinct populations based on their dopamine receptor expression: D1-receptor-expressing (D1-MSNs) and D2-receptor-expressing neurons (D2-MSNs). Classical models propose a binary functional role, where D1-MSNs exclusively mediated reward and positive valence, while D2-MSNs processed aversion and negative valence. However, we now recognize that NAc MSN subpopulations operate in a more complex manner than previously thought, often working cooperatively rather than antagonistically in valence-related behaviors. This review synthesizes our current knowledge of valence-encoding neurocircuitry, with emphasis on the NAc. We examine electrophysiological, calcium imaging, optogenetic, chemogenetic and pharmacological studies detailing the contribution of NAc medium spiny neurons for rewarding and aversive responses. Finally, we explore emerging technical innovations that promise to advance our understanding of how the mammalian brain encodes valence and translates it into behavior.

Evaluation of out-of-distribution detection methods for data shifts in single-cell transcriptomics

Automatic cell-type annotation methods assign cell-type labels to new, unlabeled datasets by leveraging relationships from a reference RNA-seq atlas. However, new datasets may include labels absent from the reference dataset or exhibit feature distributions that diverge from it. These scenarios can significantly affect the reliability of cell type predictions, a factor often overlooked in current automatic annotation methods. The field of out-of-distribution detection (OOD), primarily focused on computer vision, addresses the identification of instances that differ from the training distribution. Therefore, the implementation of OOD methods in the context of novel cell type annotation and data shift detection for single-cell transcriptomics may enhance annotation accuracy and trustworthiness. We evaluate six OOD detection methods: LogitNorm, MC dropout, Deep Ensembles, Energy-based OOD, Deep NN, and Posterior networks, for their annotation and OOD detection performance in both synthetical and real-life application settings. We show that OOD detection methods can accurately identify novel cell types and demonstrate potential to detect significant data shifts in non-integrated datasets. Moreover, we find that integration of the OOD datasets does not interfere with OOD detection of novel cell types.

Heterogeneity of the tumor immune cell microenvironment revealed by single-cell sequencing in head and neck cancer

Head and neck cancer (HNC) is the sixth most common disease in the world. The recurrence rate of patients is relatively high, and the heterogeneity of tumor immune microenvironment (TIME) cells may be an important reason for this. Single-cell sequencing (SCS) is currently the most promising and mature application in cancer research. It can identify unique genes expressed in cells and study tumor heterogeneity. According to current research, the heterogeneity of immune cells has become an important factor affecting the occurrence and development of HNC. SCSs can provide effective therapeutic targets and prognostic factors for HNC patients through analyses of gene expression levels and cell heterogeneity. Therefore, this study analyzes the basic theory of HNC and the development of SCS technology, elaborating on the application of SCS technology in HNC and its potential value in identifying HNC therapeutic targets and biomarkers.

Decoupled GNNs based on multi-view contrastive learning for scRNA-seq data clustering

Clustering is pivotal in deciphering cellular heterogeneity in single-cell RNA sequencing (scRNA-seq) data. However, it suffers from several challenges in handling the high dimensionality and complexity of scRNA-seq data. Especially when employing graph neural networks (GNNs) for cell clustering, the dependencies between cells expand exponentially with the number of layers. This results in high computational complexity, negatively impacting the model's training efficiency. To address these challenges, we propose a novel approach, called decoupled GNNs, based on multi-view contrastive learning (scDeGNN), for scRNA-seq data clustering. Firstly, this method constructs two adjacency matrices to generate distinct views, and trains them using decoupled GNNs to derive the initial cell feature representations. These representations are then refined through a multilayer perceptron and a contrastive learning layer, ensuring the consistency and discriminability of the learned features. Finally, the learned representations are fused and applied to the cell clustering task. Extensive experimental results on nine real scRNA-seq datasets from various organisms and tissues show that the proposed scDeGNN method significantly outperforms other state-of-the-art scRNA-seq data clustering algorithms across multiple evaluation metrics.

Resveratrol targeting MDM2/P53/PUMA axis to inhibit colonocyte apoptosis in DSS-induced ulcerative colitis mice

Background

Resveratrol, a naturally occurring polyphenolic compound found in grapes, berries, and traditional medicinal plants like Polygonum cuspidatum, has been used for centuries in traditional medicine systems for its anti-inflammatory, antioxidant, and cardioprotective properties. Ulcerative colitis (UC), a chronic inflammatory bowel disease, is characterized by intestinal barrier disruption due to excessive colonocyte apoptosis, leading to increased permeability and inflammation. Targeting apoptosis is a critical therapeutic strategy for UC.

Aim of the study

This study aims to investigate the therapeutic potential of Resveratrol in ulcerative colitis (UC) by targeting excessive colonocyte apoptosis and intestinal barrier dysfunction. Specifically, we seek to elucidate the mechanisms through which Resveratrol modulates apoptosis-related pathways and evaluate its efficacy in restoring intestinal homeostasis and mitigating UC progression in both in vivo and in vitro models.

Materials and Methods

We used dextran sulfate sodium (DSS) to induce UC in a mouse model. Colonic damage was assessed through colonic length measurement, histological examination, and immunofluorescence staining. Single-cell sequencing was employed to explore changes in the colonic immune microenvironment and cellular signaling pathways after Resveratrol treatment. In vitro, colonocytes isolated from healthy mouse colonic tissue were exposed to TGF-β to induce apoptosis. DNA fragmentation, mitochondrial membrane potential, and annexin V/propidium iodide staining were used to assess apoptosis. Additionally, we employed an Adeno-Associated Virus system to overexpress MDM2 in the colon and evaluate its protective role in DSS-induced UC.

Results

Resveratrol treatment effectively repaired colonic damage in the UC mouse model by significantly increasing colon length, reducing inflammatory cell infiltration, and mitigating mucosal injury. Single-cell sequencing revealed that Resveratrol primarily targeted colonocytes, decreasing genes related to apoptosis and the P53 pathway. In vitro, Resveratrol reduced DNA fragmentation, apoptotic cell populations, and increased mitochondrial membrane potential in a dose-dependent manner. Furthermore, Resveratrol increased MDM2 expression, inhibiting P53 and downstream pro-apoptotic signaling. Nutlin-3a, an MDM2 inhibitor, reversed the anti-apoptotic effects of Resveratrol. Overexpression of MDM2 in the colon protected against DSS-induced damage.

Conclusion

Resveratrol is an effective treatment for DSS-induced UC, primarily by inhibiting excessive apoptosis in colonocytes through the MDM2/P53/PUMA axis. MDM2 presents a promising therapeutic target for UC treatment.

Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-Seq Data Analysis

Understanding the dynamic nature of biological systems is fundamental to deciphering cellular behavior, developmental processes, and disease progression. Single-cell RNA sequencing (scRNA-seq) has provided static snapshots of gene expression, offering valuable insights into cellular states at a single time point. Recent advancements in temporally resolved scRNA-seq, spatial transcriptomics (ST), and time-series spatial transcriptomics (temporal-ST) have further revolutionized our ability to study the spatiotemporal dynamics of individual cells. These technologies, when combined with computational frameworks such as Markov chains, stochastic differential equations (SDEs), and generative models like optimal transport and Schrödinger bridges, enable the reconstruction of dynamic cellular trajectories and cell fate decisions. This review discusses how these dynamical system approaches offer new opportunities to model and infer cellular dynamics from a systematic perspective.

Single-Cell RNA Sequencing Outperforms Single-Nucleus RNA Sequencing in Analyzing Pancreatic Cell Diversity and Gene Expression in Goats

The objective of this study was to determine whether single-cell RNA sequencing (scRNA-seq) or single-nucleus RNA sequencing (snRNA-seq) was more effective for studying the goat pancreas. Pancreas tissues from three healthy 10-day-old female Xiangdong black goats were processed into single-cell and single-nucleus suspensions. These suspensions were then used to compare cellular composition and gene expression levels following library construction and sequencing. Both scRNA-seq and snRNA-seq were eligible for primary analysis but produced different cell identification profiles in pancreatic tissue. Both methods successfully annotated pancreatic acinar cells, ductal cells, alpha cells, beta cells, and endothelial cells. However, pancreatic stellate cells, immune cells, and delta cells were uniquely annotated by scRNA-seq, while pancreatic stem cells were uniquely identified by snRNA-seq. Furthermore, the genes related to digestive enzymes showed a higher expression in scRNA-seq than in snRNA-seq. In the present study, scRNA-seq detected a great diversity of pancreatic cell types and was more effective in profiling key genes than snRNA-seq, demonstrating that scRNA-seq was better suited for studying the goat pancreas. However, the choice between scRNA-seq and snRNA-seq should consider the sample compatibility, technical differences, and experimental objectives.

Long-read single-cell RNA sequencing enables the study of cancer subclone-specific genotypes and phenotypes in chronic lymphocytic leukemia

Bruton tyrosine kinase (BTK) inhibitors are effective for the treatment of chronic lymphocytic leukemia (CLL) due to BTK's role in B cell survival and proliferation. Treatment resistance is most commonly caused by the emergence of the hallmark BTK C481S mutation that inhibits drug binding. In this study, we aimed to investigate cancer subclones harboring a BTK C481S mutation and identify cells with co-occurring CLL driver mutations. In addition, we sought to determine whether BTK-mutated subclones exhibit distinct transcriptomic behavior when compared to other cancer subclones. To achieve these goals, we use scBayes, which integrates bulk DNA sequencing and single-cell RNA sequencing (scRNA-seq) data to genotype individual cells for subclone-defining mutations. Although the most common approach for scRNA-seq includes short-read sequencing, transcript coverage is limited due to the vast majority of the reads being concentrated at the priming end of the transcript. Here, we utilized MAS-seq, a long-read scRNA-seq technology, to substantially increase transcript coverage and expand the set of informative mutations to link cells to cancer subclones in six CLL patients who acquired BTK C481S mutations during BTK inhibitor treatment. In two patients who developed two independent BTK-mutated subclones, we find that most BTK-mutated cells have an additional CLL driver gene mutation. When examining subclone-specific gene expression, we find that in one patient, BTK-mutated subclones are transcriptionally distinct from the rest of the malignant B cell population with an overexpression of CLL-relevant genes.

Cell-cycle dependence of bursty gene expression: insights from fitting mechanistic models to single-cell RNA-seq data

Bursty gene expression is characterized by two intuitive parameters, burst frequency and burst size, the cell-cycle dependence of which has not been extensively profiled at the transcriptome level. In this study, we estimate the burst parameters per allele in the G1 and G2/M cell-cycle phases for thousands of mouse genes by fitting mechanistic models of gene expression to messenger RNA count data, obtained by sequencing of single cells whose cell-cycle position has been inferred using a deep-learning method. We find that upon DNA replication, the median burst frequency approximately halves, while the burst size remains mostly unchanged. Genome-wide distributions of the burst parameter ratios between the G2/M and G1 phases are broad, indicating substantial heterogeneity in transcriptional regulation. We also observe a significant negative correlation between the burst frequency and size ratios, suggesting that regulatory processes do not independently control the burst parameters. We show that to accurately estimate the burst parameter ratios, mechanistic models must explicitly account for gene copy number variation and extrinsic noise due to the coupling of transcription to cell age across the cell cycle, but corrections for technical noise due to imperfect capture of RNA molecules in sequencing experiments are less critical.

Comprehensive analysis of scRNA-seq and bulk RNA-seq data via machine learning and bioinformatics reveals the role of lysine metabolism-related genes in gastric carcinogenesis

BACKGROUND

Gastric cancer (GC) is a highly aggressive and heterogeneous cancer with extremely complex biological characteristics. Lysine and its metabolism are closely related to human cancer, but little is known about how lysine metabolism-related genes contribute to gastric carcinogenesis.

METHODS

The roles of lysine metabolism-related genes in GC were investigated by in-depth analysis of single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) data via machine learning and multiple bioinformatics methods and confirmed by multiple cell and molecular biology methods.

RESULTS

By systematically analyzing the heterogeneity of GC cells and interactions among cell subtypes, two key genes, solute carrier family 7 member 7 (SLC7A7) and vimentin (VIM), were innovatively identified as lysine metabolism-related genes involved in gastric carcinogenesis. The potential functional mechanisms involved immune infiltration, signaling pathway regulation, drug sensitivity, molecular regulatory networks, tumor regulatory genes, and metabolic pathways. A reliable prognostic risk nomogram was established for GC prognosis prediction. Moreover, the expression of the lysine metabolism-related genes SLC7A7 and VIM and their effect on cellular phenotypes in gastric carcinogenesis were verified in clinical samples and in vitro experiments, including cell proliferation, migration, invasion and cell cycle assays.

CONCLUSIONS

We explored the role of lysine metabolism-related genes and prognostic models in GC with multiple datasets, providing novel metabolic targets.

Novel insights into kidney disease: the scRNA-seq and spatial transcriptomics approaches: a literature review

Over the past decade, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have revolutionized biomedical research, particularly in understanding cellular heterogeneity in kidney diseases. This review summarizes the application and development of scRNA-seq combined with ST in the context of kidney disease. By dissecting cellular heterogeneity at an unprecedented resolution, these advanced techniques have identified novel cell subpopulations and their dynamic interactions within the renal microenvironment. The integration of scRNA-seq with ST has been instrumental in elucidating the cellular and molecular mechanisms underlying kidney development, homeostasis, and disease progression. This approach has not only identified key cellular players in renal pathophysiology but also revealed the spatial organization of cells within the kidney, which is crucial for understanding their functional specialization. This paper highlights the transformative impact of these techniques on renal research that have paved the way for targeted therapeutic interventions and personalized medicine in the management of kidney disease.

Epigenetics factors in schizophrenia: future directions for etiologic and therapeutic study approaches

Schizophrenia is a complex, heterogeneous, and highly disabling severe mental disorder whose pathogenesis has not yet been fully elucidated. Epigenetics, as a bridge between genetic and environmental factors, plays an important role in the pathophysiology of schizophrenia. Over the past decade, epigenetic-wide association studies have rapidly become an important branch of psychiatric research, especially in deciphering the molecular mechanisms of schizophrenia. This review systematically analyzes recent advances in epigenome-wide association studies (EWAS) of schizophrenia, focusing on technological developments. We synthesize findings from large-scale EWAS alongside emerging evidence on DNA methylation patterns, histone modifications, and regulatory networks, emphasizing their roles in disease mechanisms and treatment responses. In addition, this review provides a prospective outlook, evaluating the impact that technological developments may have on future studies of schizophrenia. With the continuous advancement of high-throughput sequencing technology and the increasing maturity of big data analysis methods, epigenetics is expected to have a significant impact on the early diagnosis, prognosis assessment and even personalized treatment of schizophrenia.

Deciphering IGH rearrangement complexity and detection strategies in acute lymphoblastic leukaemia

Acute lymphoblastic leukaemia is a highly heterogeneous malignancy characterised by various genomic alterations that influence disease progression and therapeutic outcomes. Gene fusions involving the immunoglobulin heavy chain gene represent a complex and diverse category. These fusions often result in enhancer hijacking, upregulation of partner proto-oncogenes and contribute to leukemogenesis. This review highlights the mechanisms underlying IGH gene fusions, the critical role they play in ALL pathogenesis, and current detection technologies.

Mapping Cell Identity from scRNA-seq: A primer on computational methods

Single cell (sc) technologies mark a conceptual and methodological breakthrough in our way to study cells, the base units of life. Thanks to these technological developments, large-scale initiatives are currently ongoing aimed at mapping of all the cell types in the human body, with the ambitious aim to gain a cell-level resolution of physiological development and disease. Since its broad applicability and ease of interpretation scRNA-seq is probably the most common sc-based application. This assay uses high throughput RNA sequencing to capture gene expression profiles at the sc-level. Subsequently, under the assumption that differences in transcriptional programs correspond to distinct cellular identities, ad-hoc computational methods are used to infer cell types from gene expression patterns. A wide array of computational methods were developed for this task. However, depending on the underlying algorithmic approach and associated computational requirements, each method might have a specific range of application, with implications that are not always clear to the end user. Here we will provide a concise overview on state-of-the-art computational methods for cell identity annotation in scRNA-seq, tailored for new users and non-computational scientists. To this end, we classify existing tools in five main categories, and discuss their key strengths, limitations and range of application.

Single-cell RNA Sequencing Contributes to the Treatment of Acute Myeloid Leukaemia With Hematopoietic Stem Cell Transplantation, Chemotherapy, and Immunotherapy

Acute myeloid leukemia (AML) is caused by altered maturation and differentiation of myeloid blasts, as well as transcriptional/epigenetic alterations and impaired apoptosis, all of which lead to excessive proliferation of malignant blood cells in the bone marrow. It is these mutations that cause tumor heterogeneity, which is linked to a higher risk of relapse and death and makes anti-AML treatments like HSCT, chemotherapy, and immunotherapy (ICI, CAR T-cell-based therapies, and cancer vaccines) less effective. Single-cell RNA sequencing (scRNA-seq) also makes it possible to find cellular subclones and profile tumors, which opens up new diagnostic and therapeutic targets for better AML management. The HSCT process works better when genetic and transcriptional information about the patient and donor stem cells is collected. This saves time and lowers the risk of harmful side effects happening in the body.

How to view the female reproductive tract through single-cell looking glasses

Single-cell technologies have emerged as an unprecedented tool for biologists and clinicians, allowing them to assess organs and tissues at the level of individual cells. In the field of women's reproductive biology, single-cell studies have provided insights into the cellular and molecular processes that regulate reproductive and obstetrical functions in health and disease. The knowledge that these studies generate is helping clinicians to improve the understanding and diagnosis of infertility related issues or pregnancy complications and to find new avenues for their treatment. However, navigating the expansive landscape of this type of transcriptomic data analysis represents a pivotal challenge in current research. Single cell RNA sequencing involves isolating cells into droplets, reverse transcribing RNA to generate complementary DNA, with each droplet content uniquely labeled by a barcode. Upon sequencing the complementary DNAs, the barcodes enable the reassignment of sequencing reads to individual droplets, facilitating the reconstruction of the cellular landscape of the sample obtained from a tissue or organ and beyond. Researchers, equipped with the metaphorical 'single-cell glasses,' must adequately choose from a plethora of strategies to dissect and interpret cellular information. Sophisticated algorithms and the decision-making process are often underestimated, resulting in artefactual or cumbersome interpreted results. Computational biologists apply and innovate computational tools designed to process, model, and interpret expansive datasets. The ramifications of their work extend far beyond the realm of data processing; they give shape to the outcome of analyses, playing a pivotal role in drawing meaningful conclusions from the wealth of information garnered. In this review, we describe the wide variety of approaches and analytical steps available with enough detail to gain a concise picture of what a complete examination of a single-cell dataset would be. We commence with a discussion on key points in experimental design, highlighting crucial questions one should consider. Following this, we delve into the various preprocessing and quality control steps essential for any single-cell dataset. The subsequent section offers a detailed guide on constructing a single-cell atlas, exploring nuances such as differential characteristics in visualization and clustering techniques, as well as strategies for assigning identity to cell populations through gene marker annotations. Moving beyond the creation of an atlas, we explore methods for investigating pathological conditions. This involves conducting cell population comparison tests between conditions and analyzing specific cell-to-cell communications and cellular differentiation trajectories in both health and disease scenarios. This work aims to furnish a newcomer researcher and/or clinician with essential guidelines to embark on a single-cell adventure without succumbing to common pitfalls. By bridging the gap between theory and practice, it facilitates the translation of single-cell technologies into clinically relevant applications. Throughout the manuscript, practical examples of its usage in women's reproductive health studies are provided. Various sections delve into specific clinical scenarios, demonstrating how these guidelines can be instrumental in unraveling the molecular landscapes of diseases and physiological processes related to women's reproduction.

Single-cell sequencing reveals the same heterogeneity of neutrophils in heatstroke-induced lung and liver injury

Heatstroke (HS) is typically considered a sepsis-like syndrome caused by hyperthermia, often accompanied by multiple organ dysfunctions (MODS). To explore the mechanisms of MODS, we established a mouse model of HS by exposing mice to a hyperthermic and high-humidity environment. Then, we utilized single-cell RNA sequencing (scRNA-seq) to depict the cellular landscape of HS mice lung tissue and liver tissue. We found that the enhancement of neutrophil infiltration mediated by the "Cxcr2-Cxcl2″ receptor-ligand pair is a prominent feature of HS-induced lung injury. By effectively suppressing the recruitment of neutrophils in HS-induced lung injury, the application of Cxcr2 inhibitor held positive implications for improving HS-induced lung injury. In addition to the chemotactic effect of immune cells on neutrophils, we identified a subcluster of fibroblasts labeled as Col14a1+, which possessed notable chemotactic factor-secretion characteristics and likely exerted a role in the early stages of neutrophil infiltration. Furthermore, our study unveiled significant heterogeneity among neutrophils within the HS-induced lung injury. Particularly, Cd177 + neutrophils exhibited a dominant presence, characterized by heightened pro-inflammatory responses and oxidative stress. In heatstroke-induced liver injury, neutrophils exhibited similar heterogeneous characteristics. Cd177 + neutrophils exhibited an enhanced ability to produce neutrophil extracellular traps (NETs) while lowering the levels of NETs can significantly improve heatstroke-induced lung and liver injury. Additionally, our study identified Cebpe as a key transcriptional regulatory factor in Cd177 + neutrophil differentiation. Knockdown of the expression of Cebpe can suppress the Cd177 + neutrophil differentiation and decrease the expression levels of NETs. Our research indicated a common heterogeneity in neutrophils during MODS in HS. Cd177 + neutrophils contributed to organ damage in HS, and Cebpe may serve as a crucial intervention target in the treatment of HS.

Single-nucleus RNA sequencing reveals dynamic changes in the microenvironment of visceral adipose tissue and metabolic characteristics after cold exposure

Introduction

Visceral adipose tissue (VAT) plays a crucial role in regulating systemic metabolic balance. Excess accumulation of VAT is closely associated with various metabolic disorders, a process that involves the coordinated actions of multiple cell types within the tissue. Cold exposure, as a potential intervention, has been proposed to improve metabolic dysfunction. However, the heterogeneity of VAT and its comprehensive metabolic characteristics under cold exposure remain unclear.

Methods

We collected epididymal white adipose tissue (eWAT) of C57BL/6J mice after cold exposure at three different time points for single-nucleus RNA sequencing (snRNA-seq) analysis.

Results

We successfully identified ten major cell types in eWAT, enabling understanding of the dynamic changes in the eWAT microenvironment and its metabolic features during cold exposure. This study revealed that cold exposure for 1 day reduced cellular metabolic activity and intercellular communication in eWAT including receptor-ligand-based cell communication and metabolite-mediated interactions. However, after 14 days of cold acclimation, the metabolic activity of adipocytes was significantly enhanced, and intercellular metabolic communication was restored. Additionally, prolonged cold exposure promoted the formation of a distinct adipocyte subpopulation that may be associated with UCP1-independent thermogenesis. These changes may be a new homeostatic state established by VAT to adapt to the cold environment. The study also identified the importance of adipocytes, adipose stem and progenitor cells, myeloid cells, and endothelial cells in the process of cold adaptation.

Discussion

This research provides valuable insights into the cellular heterogeneity, adipocyte remodeling, and metabolic reprogramming in eWAT after cold exposure. It highlights the critical role of transcriptional dynamics in eWAT during cold exposure and provides new perspectives on the prevention and treatment of metabolic diseases.

Transient power-law behaviour following induction distinguishes between competing models of stochastic gene expression

What features of transcription can be learnt by fitting mathematical models of gene expression to mRNA count data? Given a suite of models, fitting to data selects an optimal one, thus identifying a probable transcriptional mechanism. Whilst attractive, the utility of this methodology remains unclear. Here, we sample steady-state, single-cell mRNA count distributions from parameters in the physiological range, and show they cannot be used to confidently estimate the number of inactive gene states, i.e. the number of rate-limiting steps in transcriptional initiation. Distributions from over 99% of the parameter space generated using models with 2, 3, or 4 inactive states can be well fit by one with a single inactive state. However, we show that for many minutes following induction, eukaryotic cells show an increase in the mean mRNA count that obeys a power law whose exponent equals the sum of the number of states visited from the initial inactive to the active state and the number of rate-limiting post-transcriptional processing steps. Our study shows that estimation of the exponent from eukaryotic data can be sufficient to determine a lower bound on the total number of regulatory steps in transcription initiation, splicing, and nuclear export.

Enhanced Integration of Single-Cell Multi-Omics Data Using Graph Attention Networks

The continuous advancement of single-cell multimodal omics (scMulti-omics) technologies offers unprecedented opportunities to measure various modalities, including RNA expression, protein abundance, gene perturbation, DNA methylation, and chromatin accessibility at single-cell resolution. These advances hold significant potential for breakthroughs by integrating diverse omics modalities. However, the data generated from different omics layers often face challenges due to high dimensionality, heterogeneity, and sparsity, which can adversely impact the accuracy and efficiency of data integration analyses. To address these challenges, we propose a high-precision analysis method called scMGAT (single-cell multiomics data analysis based on multihead graph attention networks). This method effectively coordinates reliable information across multiomics data sets using a multihead attention mechanism, allowing for better management of the heterogeneous characteristics inherent in scMulti-omics data. We evaluated scMGAT's performance on eight sets of real scMulti-omics data, including samples from both human and mouse. The experimental results demonstrate that scMGAT significantly enhances the quality of multiomics data and improves the accuracy of cell-type annotation compared to state-of-the-art methods. scMGAT is now freely accessible at https://github.com/Xingyu-Liao/scMGAT.

Application and Progression of Single-Cell RNA Sequencing in Diabetes Mellitus and Diabetes Complications

Diabetes is a systemic metabolic disorder primarily caused by insulin deficiency and insulin resistance, leading to chronic hyperglycemia. Prolonged diabetes can result in metabolic damage to multiple organs, including the heart, brain, liver, muscles, and adipose tissue, thereby causing various chronic fatal complications such as diabetic retinopathy, diabetic cardiomyopathy, and diabetic nephropathy. Single-cell RNA sequencing (scRNA-seq) has emerged as a valuable tool for investigating the cell diversity and pathogenesis of diabetes and identifying potential therapeutic targets in diabetes or diabetes complications. This review provides a comprehensive overview of recent applications of scRNA-seq in diabetes-related researches and highlights novel biomarkers and immunotherapy targets with cell-type information for diabetes and its associated complications.

Tumor Prognostic Risk Model Related to Monocytes/Macrophages in Hepatocellular Carcinoma Based on Machine Learning and Multi-Omics

Tumor-associated macrophages (TAMs) are crucial in hepatocellular carcinoma (HCC) development and invasion. This study explores monocyte/ macrophage-associated gene expression profiles in HCC, constructs a prognostic model based on these genes, and examines its relationship with drug resistance and immune therapy responses. Single-cell RNA sequencing(scRNA-seq) data from 10 HCC tissue biopsy samples, totaling 24,597 cells, were obtained from the GEO database to identify monocyte/macrophage-associated genes. A prognostic model was constructed and validated using external datasets and Western blot. Relationships between the model, clinical correlates, drug sensitivity, and immune therapy responses were investigated. From scRNA-seq data, 2,799 monocyte/macrophage marker genes were identified. Using the TCGA dataset, a prognostic model based on the single-gene UQCRH was constructed, stratifying patients into high-risk and low-risk groups based on overall survival rates. High-risk group patients showed reduced survival rates and higher UQCRH expression in tumor tissues. Western blot analysis further confirmed the elevated expression of UQCRH in HCC cell lines. Spatial transcriptomics analysis revealed that high UQCRH expression co-localized with malignant cells in the tumor tissue. Drug sensitivity analysis revealed that the high-risk group had lower sensitivity to sorafenib and axitinib. Immune therapy response analysis indicated poorer outcomes in the high-risk group, with more pronounced APC inhibition and a weaker IFN-II response. Clinical indicator analysis showed a positive correlation between high UQCRH expression and tumor invasion. Enrichment analysis of UQCRH and associated molecules indicated involvement in oxidative phosphorylation and mitochondrial electron transport. This study introduces a prognostic model for HCC patients based on monocyte/macrophage marker genes. The single-gene model predicts HCC patient survival and treatment outcomes, identifying high-risk individuals with varying drug sensitivities and immune suppression states.

Synergistic potential of stem cells and microfluidics in regenerative medicine

Regenerative medicine has immense potential to revolutionize healthcare by using regenerative capabilities of stem cells. Microfluidics, a cutting-edge technology, offers precise control over cellular microenvironments. The integration of these two fields provides a deep understanding of stem cell behavior and enables the development of advanced therapeutic strategies. This critical review explores the use of microfluidic systems to culture and differentiate stem cells with precision. We examined the use of microfluidic platforms for controlled nutrient supply, mechanical stimuli, and real-time monitoring, providing an unprecedented level of detail in studying cellular responses. The convergence of stem cells and microfluidics holds immense promise for tissue repair, regeneration, and personalized medicine. It offers a unique opportunity to revolutionize the approach to regenerative medicine, facilitating the development of advanced therapeutic strategies and enhancing healthcare outcomes.

The role of oxidative stress-mediated fibro-adipogenic progenitor senescence in skeletal muscle regeneration and repair

BACKGROUND

Stem cells play a pivotal role in tissue regeneration and repair. Skeletal muscle comprises two main stem cells: muscle stem cells (MuSCs) and fibro-adipogenic progenitors (FAPs). FAPs are essential for maintaining the regenerative milieu of muscle tissue and modulating the activation of muscle satellite cells. However, during acute skeletal muscle injury, the alterations and mechanisms of action of FAPs remain unclear.

METHODS

we employed the GEO database for bioinformatics analysis of skeletal muscle injury. A skeletal muscle injury model was established through cardiotoxin (CTX, 10µM, 50µL) injection into the tibialis anterior (TA) of C57BL/6 mice. Three days post-injury, we extracted the TA, isolated FAPs (CD31-CD45-PDGFRα+Sca-1+), and assessed the senescence phenotype through SA-β-Gal staining and Western blot. Additionally, we established a co-culture system to evaluate the capacity of FAPs to facilitate MuSCs differentiation. Finally, we alleviated the senescent of FAPs through in vitro (100 µM melatonin, 5 days) and in vivo (20 mg/kg/day melatonin, 15 days) administration experiments, confirming melatonin's pivotal role in the regeneration and repair processes of skeletal muscle.

RESULTS

In single-cell RNA sequencing analysis, we discovered the upregulation of senescence-related pathways in FAPs following injury. Immunofluorescence staining revealed the co-localization of FAPs and senescent markers in injured muscles. We established the CTX injury model and observed a reduction in the number of FAPs post-injury, accompanied by the manifestation of a senescent phenotype. Melatonin treatment was found to attenuate the injury-induced senescence of FAPs. Further co-culture experiments revealed that melatonin facilitated the restoration of FAPs' capacity to promote myoblast differentiation. Through GO and KEGG analysis, we found that the administration of melatonin led to the upregulation of AMPK pathway in FAPs, a pathway associated with antioxidant stress response. Finally, drug administration experiments corroborated that melatonin enhances skeletal muscle regeneration and repair by alleviating FAP senescence in vivo.

CONCLUSION

In this study, we first found FAPs underwent senescence and redox homeostasis imbalance after injury. Next, we utilized melatonin to enhance FAPs regenerative and repair capabilities by activating AMPK signaling pathway. Taken together, this work provides a novel theoretical foundation for treating skeletal muscle injury.

Single-cell RNA sequencing in autoimmune diseases: New insights and challenges

Autoimmune diseases involve a variety of cell types, yet the intricacies of their individual roles within molecular mechanisms and therapeutic strategies remain poorly understood. Single-cell RNA sequencing (scRNA-seq) offers detailed insights into transcriptional diversity at the single-cell level, significantly advancing research in autoimmune diseases. This article explores how scRNA-seq enhances the understanding of cellular heterogeneity and its potential applications in the etiology, diagnosis, treatment, and prognosis of autoimmune diseases. By revealing a comprehensive cellular landscape, scRNA-seq illuminates the functional regulation of different cell subtypes during disease progression. It aids in identifying diagnostic and prognostic markers, and analyzing cell communication networks to uncover potential therapeutic targets. Despite its valuable contributions, addressing the limitations of scRNA-seq is essential for making further advancements.

Intratumoral Injection of Cytotoxic Drugs Plus Hapten Elicits Significant Immune Responses in Sentinel Lymph Nodes: Implications for Cancer Immunotherapy

OBJECTIVES

Studying the sentinel lymph nodes (SLNs) to define the metastasis and the mechanisms of abscopal effect.

METHOD

Using scRNA-seq to investigate the cellular and genomic changes of SLNs before or after treatment of the primar pancreatic carcinoma site by intratumoral injection of cytotoxic chemotherapy drugs plus hapten.

RESULT

A very significant decrease of SLN carcinoma cells, but increased macrophages, monocytes, CD8 T-effector cells, and fibroblasts. At the genomic and functional levels, the above treatment increased the ability of angiogenesis, epithelial mesenchymal transition (EMT), and cancer-associated fibroblast (CAF) signaling. In addition, matricellular proteins were upregulated.

ALTERNATIVE CONCLUSION

Our study demonstrated a marked reduction in carcinoma cells within SLNs following treatment, alongside a notable increase in inflammatory and stromal cells, which suggested a dynamic reorganization of the lymph node microenvironment rather than straightforward tumor growth. The absence of significant enlargement of SLNs, despite the increase in cellular density, may be attributed to the remodeling effects induced by the treatment. The observed upregulation of angiogenic factors, EMT pathways, and CAF signaling highlighted a complex interaction between tumor and immune responses. These findings provided new insights into the abscopal effect, revealing how targeted therapies can modulate lymph node microenvironments to enhance local immune responses and potentially improve systemic antitumor efficacy.

Uncovering immune cell heterogeneity in hepatocellular carcinoma by combining single-cell RNA sequencing with T-cell receptor sequencing

BACKGROUND

Understanding the status and function of tumor-infiltrating immune cells is essential for improving immunotherapeutic effects and predicting the clinical response in human patients with carcinoma. However, little is known about tumor-infiltrating immune cells, and the corresponding research results in hepatocellular carcinoma (HCC) are limited.

AIM

To investigate potential biomarker genes that are important for the development of HCC and to understand how immune cell subsets react throughout this process.

METHODS

Using single-cell RNA sequencing and T-cell receptor sequencing, the heterogeneity and potential functions of immune cell subpopulations from HCC tissue and normal tissue adjacent to carcinoma, as well as their possible interactions, were analyzed.

RESULTS

Eight T-cell clusters from patients were analyzed and identified using bioinformatics, including six typical major T-cell clusters and two newly identified T-cell clusters, among which Fc epsilon receptor 1G+ T cells were characterized by the upregulation of Fc epsilon receptor 1G, tyrosine kinase binding protein, and T cell receptor delta constant, whereas metallothionein 1E+ T cells proliferated significantly in tumors. Differentially expressed genes, such as regulator of cell cycle, cysteine and serine rich nuclear protein 1, SMAD7 and metallothionein 1E, were identified as significantly upregulated in tumors and have potential as biomarkers. In association with T-cell receptor analysis, we inferred the clonal expansion characteristics of each T-cell cluster in HCC patients.

CONCLUSION

We identified lymphocyte subpopulations and potential biomarker genes critical for HCC development and revealed the clonal amplification of infiltrating T cells. These data provide valuable resources for understanding the response of immune cell subsets in HCC.

Role of CD4+ T cells in cancer immunity: a single-cell sequencing exploration of tumor microenvironment

Recent oncological research has intensely focused on the tumor immune microenvironment (TME), particularly the functions of CD4 + T lymphocytes. CD4+ T lymphocytes have been implicated in antigen presentation, cytokine release, and cytotoxicity, suggesting their contribution to the dynamics of the TME. Furthermore, the application of single-cell sequencing has yielded profound insights into the phenotypic diversity and functional specificity of CD4+ T cells in the TME. In this review, we discuss the current findings from single-cell analyses, emphasizing the heterogeneity of CD4+ T cell subsets and their implications in tumor immunology. In addition, we review the critical signaling pathways and molecular networks underpinning CD4+ T cell activities, thereby offering novel perspectives on therapeutic targets and strategies for cancer treatment and prognosis.

Integrative Multi-Omics Approaches for Identifying and Characterizing Biological Elements in Crop Traits: Current Progress and Future Prospects

The advancement of multi-omics tools has revolutionized the study of complex biological systems, providing comprehensive insights into the molecular mechanisms underlying critical traits across various organisms. By integrating data from genomics, transcriptomics, metabolomics, and other omics platforms, researchers can systematically identify and characterize biological elements that contribute to phenotypic traits. This review delves into recent progress in applying multi-omics approaches to elucidate the genetic, epigenetic, and metabolic networks associated with key traits in plants. We emphasize the potential of these integrative strategies to enhance crop improvement, optimize agricultural practices, and promote sustainable environmental management. Furthermore, we explore future prospects in the field, underscoring the importance of cutting-edge technological advancements and the need for interdisciplinary collaboration to address ongoing challenges. By bridging various omics platforms, this review aims to provide a holistic framework for advancing research in plant biology and agriculture.

Single-cell sequencing elucidates the mechanism of NUSAP1 in glioma and its diagnostic and prognostic significance

Background

Personalized precision medicine (PPPM) in cancer immunology and oncology is a rapidly advancing field with significant potential. Gliomas, known for their poor prognosis, rank among the most lethal brain tumors. Despite advancements, there remains a critical need for precise, individualized treatment strategies.

Methods

We conducted a comprehensive analysis of RNA-seq and microarray data from the TCGA and GEO databases, supplemented by single-cell RNA sequencing (scRNA-seq) data from glioma patients. By integrating single-cell sequencing analysis with foundational experiments, we investigated the molecular variations and cellular interactions within neural glioma cell subpopulations during tumor progression.

Results

Our single-cell sequencing analysis revealed distinct gene expression patterns across glioma cell subpopulations. Notably, differentiation trajectory analysis identified NUSAP1 as a key marker for the terminal subpopulation. We found that elevated NUSAP1 expression correlated with poor prognosis, prompting further investigation of its functional role through both cellular and animal studies.

Conclusions

NUSAP1-based risk models hold potential as predictive and therapeutic tools for personalized glioma treatment. In-depth exploration of NUSAP1's mechanisms in glioblastoma could enhance our understanding of its response to immunotherapy, suggesting that targeting NUSAP1 may offer therapeutic benefits for glioma patients.

Advancements in single-cell RNA sequencing and spatial transcriptomics: transforming biomedical research

In recent years, significant advancements in biochemistry, materials science, engineering, and computer-aided testing have driven the development of high-throughput tools for profiling genetic information. Single-cell RNA sequencing (scRNA-seq) technologies have established themselves as key tools for dissecting genetic sequences at the level of single cells. These technologies reveal cellular diversity and allow for the exploration of cell states and transformations with exceptional resolution. Unlike bulk sequencing, which provides population-averaged data, scRNA-seq can detect cell subtypes or gene expression variations that would otherwise be overlooked. However, a key limitation of scRNA-seq is its inability to preserve spatial information about the RNA transcriptome, as the process requires tissue dissociation and cell isolation. Spatial transcriptomics is a pivotal advancement in medical biotechnology, facilitating the identification of molecules such as RNA in their original spatial context within tissue sections at the single-cell level. This capability offers a substantial advantage over traditional single-cell sequencing techniques. Spatial transcriptomics offers valuable insights into a wide range of biomedical fields, including neurology, embryology, cancer research, immunology, and histology. This review highlights single-cell sequencing approaches, recent technological developments, associated challenges, various techniques for expression data analysis, and their applications in disciplines such as cancer research, microbiology, neuroscience, reproductive biology, and immunology. It highlights the critical role of single-cell sequencing tools in characterizing the dynamic nature of individual cells.

tagtango: an application to compare single-cell annotations

SUMMARY

In this article, we present tagtango, an innovative R package and web application designed for robust and intuitive comparison of single-cell clusters and annotations. It offers an interactive platform that simplifies the exploration of differences and similarities among different clustering and annotation methods. Leveraging single-cell data analysis and different visualizations, it allows researchers to dissect the underlying biological differences across groups. tagtango is a user-friendly application that is portable and works seamlessly across multiple operating systems.

AVAILABILITY AND IMPLEMENTATION

tagtango is freely available at https://github.com/bernibra/tagtango as an R package as well as an online web service at https://tagtango.unil.ch.

Gene Editing for Enhanced Swine Production: Current Advances and Prospects

Traditional pig breeding has improved production traits but faces limitations in genetic diversity, disease resistance, and environmental adaptation. Gene editing technologies, such as CRISPR/Cas9, base editing, and prime editing, enable precise genetic modifications, overcoming these limitations and expanding applications to biomedical research. Here, we reviewed the advancements in gene editing technologies in pigs and explored pathways toward optimized swine genetics for a resilient and adaptive livestock industry. This review synthesizes recent research on gene editing tools applied to pigs, focusing on CRISPR/Cas9 and its derivatives. It examines their impact on critical swine production traits and their role as human disease models. Significant advancements have been made in targeting genes for disease resistance, such as those conferring immunity to porcine reproductive and respiratory syndrome viruses. Additionally, gene-edited pigs are increasingly used as models for human diseases, demonstrating the technology's broader applications. However, challenges such as off-target effects, ethical concerns, and varying regulatory frameworks remain. Gene editing holds substantial potential for sustainable and productive livestock production by enhancing key traits and supporting biomedical applications. Addressing technical and ethical challenges through integrated approaches will be essential to realize its full potential, ensuring a resilient, ethical, and productive livestock sector for future generations.

Towards the Next Generation of Data-Driven Therapeutics Using Spatially Resolved Single-Cell Technologies and Generative AI

Recent advances in multi-omics and spatially resolved single-cell technologies have revolutionised our ability to profile millions of cellular states, offering unprecedented opportunities to understand the complex molecular landscapes of human tissues in both health and disease. These developments hold immense potential for precision medicine, particularly in the rational design of novel therapeutics for treating inflammatory and autoimmune diseases. However, the vast, high-dimensional data generated by these technologies present significant analytical challenges, such as distinguishing technical variation from biological variation or defining relevant questions that leverage the added spatial dimension to improve our understanding of tissue organisation. Generative artificial intelligence (AI), specifically variational autoencoder- or transformer-based latent variable models, provides a powerful and flexible approach to addressing these challenges. These models make inferences about a cell's intrinsic state by effectively identifying complex patterns, reducing data dimensionality and modelling the biological variability in single-cell datasets. This review explores the current landscape of single-cell and spatial multi-omics technologies, the application of generative AI in data analysis and modelling and their transformative impact on our understanding of autoimmune diseases. By combining spatial and single-cell data with advanced AI methodologies, we highlight novel insights into the pathogenesis of autoimmune disorders and outline future directions for leveraging these technologies to achieve the goal of AI-powered personalised medicine.

Single-Cell Analysis of Endothelial Cell Injury in IgA Nephropathy

BACKGROUND

The precise mechanisms responsible for renal injury in IgA nephropathy (IgAN) are not fully understood. Our study employed an extensive scRNA-seq analysis of kidney biopsies obtained from individuals with IgAN, with a specific emphasis on investigating the involvement of renal endothelial cells.

METHODS

We obtained data from the Gene Expression Omnibus database and conducted bioinformatics analysis, which included enrichment analysis of differentially expressed genes, AUCell analysis, and high-dimensional weighted gene co-expression network analysis (hdWGCNA). The results of these analyses were further validated using human renal glomerular endothelial cells (HRGECs).

RESULTS

The ScRNA-seq data uncovered notable variations in gene expression between IgAN and control kidney tissues. The enrichment analysis using AUCell demonstrated a high presence of adhesion molecules and components related to the mitogen-activated protein kinase signaling pathway within the renal endothelial cells. Furthermore, through hdWGCNA analysis, it was discovered that interleukin (IL)-6, Rac1, and cadherin exhibited associations with the renal endothelial cells. Stimulation of HRGECs with IL-6/IL-6 receptor resulted in a significant reduction in VE-cad expression while inhibiting Rac1 led to a substantial decrease in Rac1-GTP levels and an increase in VE-cad expression.

CONCLUSION

This study presents novel findings regarding the contribution of renal endothelial cells to the development of IgAN, as it demonstrates that IL-6 negatively regulates VE-cad expression in HRGECs via Rac1. These results highlight the significant involvement of renal endothelial cells in the pathogenesis of IgAN.

Single-cell multiomics to advance cell therapy

Single-cell RNA-sequencing (scRNAseq) was first introduced in 2009 and has evolved with many technological advancements over the last decade. Not only are there several scRNAseq platforms differing in many aspects, but there are also a large number of computational pipelines available for downstream analyses which are being developed at an exponential rate. Such computational data appear in many scientific publications in virtually every field of study; thus, investigators should be able to understand and interpret data in this rapidly evolving field. Here, we discuss key differences in scRNAseq platforms, crucial steps in scRNAseq experiments, standard downstream analyses and introduce newly developed multimodal approaches. We then discuss how single-cell omics has been applied to advance the field of cell therapy.

Single cell RNA sequencing improves the next generation of approaches to AML treatment: challenges and perspectives

Acute myeloid leukemia (AML) is caused by altered maturation and differentiation of myeloid blasts, as well as transcriptional/epigenetic alterations, all leading to excessive proliferation of malignant blood cells in the bone marrow. Tumor heterogeneity due to the acquisition of new somatic alterations leads to a high rate of resistance to current therapies or reduces the efficacy of hematopoietic stem cell transplantation (HSCT), thus increasing the risk of relapse and mortality. Single-cell RNA sequencing (scRNA-seq) will enable the classification of AML and guide treatment approaches by profiling patients with different facets of the same disease, stratifying risk, and identifying new potential therapeutic targets at the time of diagnosis or after treatment. ScRNA-seq allows the identification of quiescent stem-like cells, and leukemia stem cells responsible for resistance to therapeutic approaches and relapse after treatment. This method also introduces the factors and mechanisms that enhance the efficacy of the HSCT process. Generated data of the transcriptional profile of the AML could even allow the development of cancer vaccines and CAR T-cell therapies while saving valuable time and alleviating dangerous side effects of chemotherapy and HSCT in vivo. However, scRNA-seq applications face various challenges such as a large amount of data for high-dimensional analysis, technical noise, batch effects, and finding small biological patterns, which could be improved in combination with artificial intelligence models.

Revealing microRNA regulation in single cells

MicroRNAs (miRNAs) are key regulators of gene expression and control cellular functions in physiological and pathophysiological states. miRNAs play important roles in disease, stress, and development, and are now being investigated for therapeutic approaches. Alternative processing of miRNAs during biogenesis results in the generation of miRNA isoforms (isomiRs) which further diversify miRNA gene regulation. Single-cell RNA-sequencing (scsRNA-seq) technologies, together with computational strategies, enable exploration of miRNAs, isomiRs, and interacting RNAs at the cellular level. By integration with other miRNA-associated single-cell modalities, miRNA roles can be resolved at different stages of processing and regulation. In this review we discuss (i) single-cell experimental assays that measure miRNA and isomiR abundances, and (ii) computational methods for their analysis to investigate the mechanisms of miRNA biogenesis and post-transcriptional regulation.

Detection of Cancer Stem Cells from Patient Samples

The existence of cancer stem cells (CSCs) in various tumors has become increasingly clear in addition to their prominent role in therapy resistance, metastasis, and recurrence. For early diagnosis, disease progression monitoring, and targeting, there is a high demand for clinical-grade methods for quantitative measurement of CSCs from patient samples. Despite years of active research, standard measurement of CSCs has not yet reached clinical settings, especially in the case of solid tumors. This is because detecting this plastic heterogeneous population of cells is not straightforward. This review summarizes various techniques, highlighting their benefits and limitations in detecting CSCs from patient samples. In addition, methods designed to detect CSCs based on secreted and niche-associated signaling factors are reviewed. Spatial and single-cell methods for analyzing patient tumor tissues and noninvasive techniques such as liquid biopsy and in vivo imaging are discussed. Additionally, methods recently established in laboratories, preclinical studies, and clinical assays are covered. Finally, we discuss the characteristics of an ideal method as we look toward the future.

Deciphering cellular complexity: advances and future directions in single-cell protein analysis

Single-cell protein analysis has emerged as a powerful tool for understanding cellular heterogeneity and deciphering the complex mechanisms governing cellular function and fate. This review provides a comprehensive examination of the latest methodologies, including sophisticated cell isolation techniques (Fluorescence-Activated Cell Sorting (FACS), Magnetic-Activated Cell Sorting (MACS), Laser Capture Microdissection (LCM), manual cell picking, and microfluidics) and advanced approaches for protein profiling and protein-protein interaction analysis. The unique strengths, limitations, and opportunities of each method are discussed, along with their contributions to unraveling gene regulatory networks, cellular states, and disease mechanisms. The importance of data analysis and computational methods in extracting meaningful biological insights from the complex data generated by these technologies is also highlighted. By discussing recent progress, technological innovations, and potential future directions, this review emphasizes the critical role of single-cell protein analysis in advancing life science research and its promising applications in precision medicine, biomarker discovery, and targeted therapeutics. Deciphering cellular complexity at the single-cell level holds immense potential for transforming our understanding of biological processes and ultimately improving human health.

Single-cell sequencing in diabetic retinopathy: progress and prospects

Diabetic retinopathy is a major ocular complication of diabetes, characterized by progressive retinal microvascular damage and significant visual impairment in working-age adults. Traditional bulk RNA sequencing offers overall gene expression profiles but does not account for cellular heterogeneity. Single-cell RNA sequencing overcomes this limitation by providing transcriptomic data at the individual cell level and distinguishing novel cell subtypes, developmental trajectories, and intercellular communications. Researchers can use single-cell sequencing to draw retinal cell atlases and identify the transcriptomic features of retinal cells, enhancing our understanding of the pathogenesis and pathological changes in diabetic retinopathy. Additionally, single-cell sequencing is widely employed to analyze retinal organoids and single extracellular vesicles. Single-cell multi-omics sequencing integrates omics information, whereas stereo-sequencing analyzes gene expression and spatiotemporal data simultaneously. This review discusses the protocols of single-cell sequencing for obtaining single cells from retina and accurate sequencing data. It highlights the applications and advancements of single-cell sequencing in the study of normal retinas and the pathological changes associated with diabetic retinopathy. This underscores the potential of these technologies to deepen our understanding of the pathogenesis of diabetic retinopathy that may lead to the introduction of new therapeutic strategies.

Deterministic patterns in single-cell transcriptomic data

We report the existence of deterministic patterns in statistical plots of single-cell transcriptomic data. We develop a theory showing that the patterns are neither artifacts introduced by the measurement process nor due to underlying biological mechanisms. Rather they naturally emerge from finite sample size effects. The theory precisely predicts the patterns in data from multiplexed error-robust fluorescence in situ hybridization and five different types of single-cell sequencing platforms.