Single-cell sequencing-based technologies will revolutionize whole-organism science

Exploring the latent space of transcriptomic data with topic modeling

The availability of high-dimensional transcriptomic datasets is increasing at a tremendous pace, together with the need for suitable computational tools. Clustering and dimensionality reduction methods are popular go-to methods to identify basic structures in these datasets. At the same time, different topic modeling techniques have been developed to organize the deluge of available data of natural language using their latent topical structure. This paper leverages the statistical analogies between text and transcriptomic datasets to compare different topic modeling methods when applied to gene expression data. Specifically, we test their accuracy in the specific task of discovering and reconstructing the tissue structure of the human transcriptome and distinguishing healthy from cancerous tissues. We examine the properties of the latent space recovered by different methods, highlight their differences, and their pros and cons across different tasks. We focus in particular on how different statistical priors can affect the results and their interpretability. Finally, we show that the latent topic space can be a useful low-dimensional embedding space, where a basic neural network classifier can annotate transcriptomic profiles with high accuracy.

Single-cell technology for cell-based drug delivery and pharmaceutical research

Leveraging the capacity to precisely manipulate and analyze individual cells, single-cell technology has rapidly become an indispensable tool in the advancement of cell-based drug delivery systems and innovative cell therapies. This technology offers powerful means to address cellular heterogeneity and significantly enhance therapeutic efficacy. Recent breakthroughs in techniques such as single-cell electroporation, mechanical perforation, and encapsulation, particularly when integrated with microfluidics and bioelectronics, have led to remarkable improvements in drug delivery efficiency, reductions in cytotoxicity, and more precise targeting of therapeutic effects. Moreover, single-cell analyses, including advanced sequencing and high-resolution sensing, offer profound insights into complex disease mechanisms, the development of drug resistance, and the intricate processes of stem cell differentiation. This review summarizes the most significant applications of these single-cell technologies, highlighting their impact on the landscape of modern biomedicine. Furthermore, it provides a forward-looking perspective on future research directions aimed at further optimizing drug delivery strategies and enhancing therapeutic outcomes in the treatment of various diseases.

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

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

Using Single-Cell RNA sequencing with Drosophila, Zebrafish, and mouse models for studying Alzheimer's and Parkinson's disease

Alzheimer's and Parkinson's disease are the most common neurodegenerative diseases, significantly affecting the elderly with no current cure available. With the rapidly aging global population, advancing research on these diseases becomes increasingly critical. Both disorders are often studied using model organisms, which enable researchers to investigate disease phenotypes and their underlying molecular mechanisms. In this review, we critically discuss the strengths and limitations of using Drosophila, zebrafish, and mice as models for Alzheimer's and Parkinson's research. A focus is the application of single-cell RNA sequencing, which has revolutionized the field by providing novel insights into the cellular and transcriptomic landscapes characterizing these diseases. We assess how combining animal disease modeling with high-throughput sequencing and computational approaches has advanced the field of Alzheimer's and Parkinson's disease research. Thereby, we highlight the importance of integrative multidisciplinary approaches to further our understanding of disease mechanisms and thus accelerating the development of successful therapeutic interventions.

Cardiac endothelial cells and cardiomyocytes alter their communication properties in diabetic mice

OBJECTIVE

We aimed to explore the heterogeneities and communication properties of cardiac CMs and ECs in diabetes.

METHODS

GSE213337 dataset was retrieved from NCBI Gene Expression Omnibus, containing the single-cell RNA sequencing data of hearts from the control and streptozotocin-induced diabetic mice. Cell cluster analysis was performed to identify the cell atlas. Data of CMs and ECs were extracted individually for re-cluster analysis, functional enrichment analysis and trajectory analysis. Cell communication analysis was conducted to explore the altered signals and significant ligand-receptor interactions.

RESULTS

Eleven cell types were identified in the heart tissue. CMs were re-clustered into four subclusters, and cluster 4 was dominant in diabetic condition and enriched in cellular energy metabolism processes. ECs were re-clustered into six subclusters, and clusters 2, 4 and 5 were dominant in the diabetic condition and mainly enriched in cellular energy metabolism and lipid transport processes. The cellular communication network was altered in the diabetic heart. ECs dominated the overall signaling and notably increased the ANGPTL and SEMA4 signals in the diabetic heart. Four significant ligand-receptor pairs implicating the two signals contributed to the communication between ECs and other cell types, including Angptl1-(Itga1 + Itgb1), Angptl4-Cdh5, Angptl4-Sdc3, and Sema4a-(Nrp + Plxna2). The ligand Angptl4 engaged in ECs-CMs communication in a paracrine manner.

CONCLUSION

Single-cell sequencing analysis revealed heterogeneities of ECs and CMs in diabetes, Angptl4-Cdh5 and Angptl4-Sdc3 were involved in the communication between ECs and CMs in diabetes.

Chromosomal Instability and Clonal Heterogeneity in Breast Cancer: From Mechanisms to Clinical Applications

BACKGROUND

Chromosomal instability (CIN) and clonal heterogeneity (CH) are fundamental hallmarks of breast cancer that drive tumor evolution, disease progression, and therapeutic resistance. Understanding the mechanisms underlying these phenomena is essential for improving cancer diagnosis, prognosis, and treatment strategies.

METHODS

In this review, we provide a comprehensive overview of the biological processes contributing to CIN and CH, highlighting their molecular determinants and clinical relevance.

RESULTS

We discuss the latest advances in detection methods, including single-cell sequencing and other high-resolution techniques, which have enhanced our ability to characterize intratumoral heterogeneity. Additionally, we explore how CIN and CH influence treatment responses, their potential as therapeutic targets, and their role in shaping the tumor immune microenvironment, which has implications for immunotherapy effectiveness.

CONCLUSIONS

By integrating recent findings, this review underscores the impact of CIN and CH on breast cancer progression and their translational implications for precision medicine.

Novel Molecular Methods in Soft Tissue Sarcomas: From Diagnostics to Theragnostics

Soft tissue sarcomas (STSs) are a diverse group of malignant tumors derived from mesenchymal tissues [...].

Cervical function in pregnancy and disease: new insights from single-cell analysis

The uterine cervix plays an essential role in regulating fertility, maintaining pregnancy, remodeling in preparation for parturition, and protecting the reproductive tract from infection. A compromise in cervical function contributes to adverse clinical outcomes. Understanding molecular events that drive the multifunctional and temporally defined roles of the cervix is necessary to effectively treat infertility, reproductive tract infections, preterm birth, labor dystocia, and cervical cancer. The application of single-cell technologies to study cervical pathophysiology, while in its infancy, underscores the potential of these approaches in developing clinically relevant biomarkers of disease and preventative therapies. This review focuses on insights gained from single-cell transcriptomic studies in human and mouse cervical tissue and highlights outstanding questions in the field. One collective advance from single-cell analysis is the dynamic plasticity of cervical epithelial cells during the reproductive cycle in health and disease. Single-cell comparisons between upper and lower regions of the reproductive tract also highlight the distinct and divergent immunological responses elicited in the cervix during the reproductive lifespan. These findings may reconcile prior controversies in the role of proinflammatory mediators during parturition. In addition to providing obstetric insights, single-cell technologies elucidate the molecular pathways that drive cervical cancer progression. Thus far, these technologies have uncovered cellular heterogeneity in the tumor microenvironment and have identified potential cancer stem cells. While single-cell technology alone will not uncover all the molecular underpinnings contributing to preterm birth or cervical cancer, the insights derived from this valuable technology will accelerate our understanding of cervical biology in health and disease, which ultimately will help develop biomarkers for disease prediction and prevention therapies.

The establishment and regulation of human germ cell lineage

The specification of primordial germ cells (PGCs) during early embryogenesis initiates the development of the germ cell lineage that ensures the perpetuation of genetic and epigenetic information from parents to offspring. Defects in germ cell development may lead to infertility or birth defects. Historically, our understanding of human PGCs (hPGCs) regulation has primarily been derived from studies in mice, given the ethical restrictions and practical limitations of human embryos at the stage of PGC specification. However, recent studies have increasingly highlighted significant mechanistic differences for PGC development in humans and mice. The past decade has witnessed the establishment of human pluripotent stem cell (hPSC)-derived hPGC-like cells (hPGCLCs) as new models for studying hPGC fate specification and differentiation. In this review, we systematically summarize the current hPSC-derived models for hPGCLC induction, and how these studies uncover the regulatory machinery for human germ cell fate specification and differentiation, forming the basis for reconstituting gametogenesis in vitro from hPSCs for clinical applications and disease modeling.

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

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

Post-stroke depression: exploring gut microbiota-mediated barrier dysfunction through immune regulation

Post-stroke depression (PSD) is one of the most common and devastating neuropsychiatric complications in stroke patients, affecting more than one-third of survivors of ischemic stroke (IS). Despite its high incidence, PSD is often overlooked or undertreated in clinical practice, and effective preventive measures and therapeutic interventions remain limited. Although the exact mechanisms of PSD are not fully understood, emerging evidence suggests that the gut microbiota plays a key role in regulating gut-brain communication. This has sparked great interest in the relationship between the microbiota-gut-brain axis (MGBA) and PSD, especially in the context of cerebral ischemia. In addition to the gut microbiota, another important factor is the gut barrier, which acts as a frontline sensor distinguishing between beneficial and harmful microbes, regulating inflammatory responses and immunomodulation. Based on this, this paper proposes a new approach, the microbiota-immune-barrier axis, which is not only closely related to the pathophysiology of IS but may also play a critical role in the occurrence and progression of PSD. This review aims to systematically analyze how the gut microbiota affects the integrity and function of the barrier after IS through inflammatory responses and immunomodulation, leading to the production or exacerbation of depressive symptoms in the context of cerebral ischemia. In addition, we will explore existing technologies that can assess the MGBA and potential therapeutic strategies for PSD, with the hope of providing new insights for future research and clinical interventions.

A comprehensive cell atlas of fall armyworm (Spodoptera frugiperda) larval gut and fat body via snRNA-Seq

The midgut and fat body of insects control key physiological processes, including growth, digestion, metabolism, and stress response. Single-nucleus RNA sequencing (snRNA-seq) is a promising way to reveal organ complexity at the cellular level, yet data for lepidopteran insects are lacking. We utilized snRNA-seq to assess cellular diversity in the midgut and fat body of Spodoptera frugiperda. Our study identified 20 distinct clusters in the midgut, including enterocytes, enteroendocrine, stem-like cells, and muscle cells, and 27 clusters in the fat body, including adipocytes, hemocytes, and epithelial cells. This dataset, containing all identified cell types in midgut and fat body, is valuable for characterizing the cellular composition of these organs and uncovering new cell-specific biomarkers. This cellular atlas enhances our understanding of cellular heterogeneity of fat and midgut, serving as a basis for future functional and comparative analyses. As the first snRNA-seq study on the midgut and fat body of S. frugiperda, it will also support future research, contribute to lepidopteran studies, and aid in developing targeted pest control strategies.

A single droplet dispensing system for high-throughput screening and reliable recovery of rare events

Microfluidic droplet sorting has emerged as a powerful technique for a broad spectrum of biomedical applications ranging from single cell analysis to high-throughput drug screening, biomarker detection and tissue engineering. However, the controlled and reliable retrieval of selected droplets for further off-chip analysis and processing is a significant challenge in droplet sorting, particularly in high-throughput applications with low expected hit rates. In this study, we present a microfluidic platform capable of sorting and dispensing individual droplets with minimal loss rates. We demonstrate our direct transfer mechanism by placing selected droplets containing hybridoma cells into microwells, eliminating the need for manual and often lossy handling steps. Sorted droplets are dispensed via a novel 3D-printed dispensing nozzle, enabling precise and controlled placement of selected single droplets into individual wells without affecting the microfluidic sorting flow. The sorting and transfer process is monitored in real time, which provides feedback and quality control of the entire workflow. Our integrated microfluidic system holds great potential for applications requiring high-throughput droplet sorting with minimal sample loss and precise dispensing into microwells, such as screening for therapeutical antibodies or monoclonal cells.

scSFCL:Deep clustering of scRNA-seq data with subspace feature confidence learning

The rapid development of single-cell RNA sequencing(scRNA-seq) technology has spawned a variety of single-cell clustering methods. These methods combine statistics and bioinformatics to reveal differences in gene expression between cells and the diversity of cell types. Deep exploration of single-cell data is more challenging due to the high dimensionality, sparsity and noise of scRNA-seq data. Discriminative attribute information is often difficult to be fully utilised, while traditional clustering methods may not accurately capture the diversity of cell types. Therefore, a deep clustering method is proposed for scRNA-seq data based on subspace feature confidence learning called scSFCL. By dividing the subspace based on kernel density, discriminative feature subsets are filtered. The feature confidence of the subset is learned by combining the graph convolutional network (GCN) with weighting. Also, scSFCL facilitates the complementary fusion of generic structural and idiosyncratic information through a mutually supervised clustering that integrates GCN and a denoising variational autoencoder based on zero-inflated negative binomials (DVAE-ZINB). By validation on multiple scRNA-seq datasets, it is shown that the clustering performance of scSFCL is significantly improved compared with traditional methods, providing an effective solution for deep clustering of scRNA-seq data.

Mapping Human Uterine Disorders Through Single-Cell Transcriptomics

Disruptions in uterine tissue function contribute to disorders such as endometriosis, adenomyosis, endometrial cancer, and fibroids, which all significantly impact health and fertility. Advances in transcriptomics, particularly single-cell RNA sequencing, have revolutionized uterine biological research by revealing the cellular heterogeneity and molecular mechanisms underlying disease states. Single-cell RNA sequencing and spatial transcriptomics have mapped endometrial and myometrial cellular landscapes, which helped to identify critical cell types, signaling pathways, and phase-specific dynamics. Said transcriptomic technologies also identified stromal and immune cell dysfunctions, such as fibroblast-to-myofibroblast transitions and impaired macrophage activity, which drive fibrosis, chronic inflammation, and lesion persistence in endometriosis. For endometrial cancer, scRNA-seq uncovered tumor microenvironmental complexities, identifying cancer-associated fibroblast subtypes and immune cell profiles contributing to progression and therapeutic resistance. Similarly, studies on adenomyosis highlighted disrupted signaling pathways, including Wnt and VEGF, and novel progenitor cell populations linked to tissue invasion and neuroinflammation, while single-cell approaches characterized smooth muscle and fibroblast subpopulations in uterine fibroids, elucidating their roles in extracellular matrix remodeling and signaling pathways like ERK and mTOR. Despite challenges such as scalability and reproducibility, single-cell transcriptomic approaches may have potential applications in biomarker discovery, therapeutic target identification, and personalized medicine in gynecological disorders.

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.

An improved bacterial single-cell RNA-seq reveals biofilm heterogeneity

In contrast to mammalian cells, bacterial cells lack mRNA polyadenylated tails, presenting a hurdle in isolating mRNA amidst the prevalent rRNA during single-cell RNA-seq. This study introduces a novel method, ribosomal RNA-derived cDNA depletion (RiboD), seamlessly integrated into the PETRI-seq technique, yielding RiboD-PETRI. This innovative approach offers a cost-effective, equipment-free, and high-throughput solution for bacterial single-cell RNA sequencing (scRNA-seq). By efficiently eliminating rRNA reads and substantially enhancing mRNA detection rates (up to 92%), our method enables precise exploration of bacterial population heterogeneity. Applying RiboD-PETRI to investigate biofilm heterogeneity, distinctive subpopulations marked by unique genes within biofilms were successfully identified. Notably, PdeI, a marker for the cell-surface attachment subpopulation, was observed to elevate cyclic diguanylate (c-di-GMP) levels, promoting persister cell formation. Thus, we address a persistent challenge in bacterial single-cell RNA-seq regarding rRNA abundance, exemplifying the utility of this method in exploring biofilm heterogeneity. Our method effectively tackles a long-standing issue in bacterial scRNA-seq: the overwhelming abundance of rRNA. This advancement significantly enhances our ability to investigate the intricate heterogeneity within biofilms at unprecedented resolution.

Droplet-based single-cell sequencing: Strategies and applications

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

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

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

π-HuB: the proteomic navigator of the human body

The human body contains trillions of cells, classified into specific cell types, with diverse morphologies and functions. In addition, cells of the same type can assume different states within an individual's body during their lifetime. Understanding the complexities of the proteome in the context of a human organism and its many potential states is a necessary requirement to understanding human biology, but these complexities can neither be predicted from the genome, nor have they been systematically measurable with available technologies. Recent advances in proteomic technology and computational sciences now provide opportunities to investigate the intricate biology of the human body at unprecedented resolution and scale. Here we introduce a big-science endeavour called π-HuB (proteomic navigator of the human body). The aim of the π-HuB project is to (1) generate and harness multimodality proteomic datasets to enhance our understanding of human biology; (2) facilitate disease risk assessment and diagnosis; (3) uncover new drug targets; (4) optimize appropriate therapeutic strategies; and (5) enable intelligent healthcare, thereby ushering in a new era of proteomics-driven phronesis medicine. This ambitious mission will be implemented by an international collaborative force of multidisciplinary research teams worldwide across academic, industrial and government sectors.

Epigenetics-targeted drugs: current paradigms and future challenges

Epigenetics governs a chromatin state regulatory system through five key mechanisms: DNA modification, histone modification, RNA modification, chromatin remodeling, and non-coding RNA regulation. These mechanisms and their associated enzymes convey genetic information independently of DNA base sequences, playing essential roles in organismal development and homeostasis. Conversely, disruptions in epigenetic landscapes critically influence the pathogenesis of various human diseases. This understanding has laid a robust theoretical groundwork for developing drugs that target epigenetics-modifying enzymes in pathological conditions. Over the past two decades, a growing array of small molecule drugs targeting epigenetic enzymes such as DNA methyltransferase, histone deacetylase, isocitrate dehydrogenase, and enhancer of zeste homolog 2, have been thoroughly investigated and implemented as therapeutic options, particularly in oncology. Additionally, numerous epigenetics-targeted drugs are undergoing clinical trials, offering promising prospects for clinical benefits. This review delineates the roles of epigenetics in physiological and pathological contexts and underscores pioneering studies on the discovery and clinical implementation of epigenetics-targeted drugs. These include inhibitors, agonists, degraders, and multitarget agents, aiming to identify practical challenges and promising avenues for future research. Ultimately, this review aims to deepen the understanding of epigenetics-oriented therapeutic strategies and their further application in clinical settings.

Single-cell transcriptomics: a new frontier in plant biotechnology research

Single-cell transcriptomic techniques have ushered in a new era in plant biology, enabling detailed analysis of gene expression at the resolution of individual cells. This review delves into the transformative impact of these technologies on our understanding of plant development and their far-reaching implications for plant biotechnology. We present a comprehensive overview of the latest advancements in single-cell transcriptomics, emphasizing their application in elucidating complex cellular processes and developmental pathways in plants. By dissecting the heterogeneity of cell populations, single-cell technologies offer unparalleled insights into the intricate regulatory networks governing plant growth, differentiation, and response to environmental stimuli. This review covers the spectrum of single-cell approaches, from pioneering techniques such as single-cell RNA sequencing (scRNA-seq) to emerging methodologies that enhance resolution and accuracy. In addition to showcasing the technological innovations, we address the challenges and limitations associated with single-cell transcriptomics in plants. These include issues related to sample preparation, cell isolation, data complexity, and computational analysis. We propose strategies to mitigate these challenges, such as optimizing protocols for protoplast isolation, improving computational tools for data integration, and developing robust pipelines for data interpretation. Furthermore, we explore the practical applications of single-cell transcriptomics in plant biotechnology. These applications span from improving crop traits through precise genetic modifications to enhancing our understanding of plant-microbe interactions. The review also touches on the potential for single-cell approaches to accelerate breeding programs and contribute to sustainable agriculture. This review concludes with a forward-looking perspective on the future impact of single-cell technologies in plant research. We foresee these tools becoming essential in plant biotechnology, spurring innovations that tackle global challenges in food security and environmental sustainability. This review serves as a valuable resource for researchers, providing a roadmap from sample preparation to data analysis and highlighting the transformative potential of single-cell transcriptomics in plant biotechnology.

Explainable deep neural networks for predicting sample phenotypes from single-cell transcriptomics

Recent advances in single-cell RNA-Sequencing (scRNA-Seq) technologies have revolutionized our ability to gather molecular insights into different phenotypes at the level of individual cells. The analysis of the resulting data poses significant challenges, and proper statistical methods are required to analyze and extract information from scRNA-Seq datasets. Sample classification based on gene expression data has proven effective and valuable for precision medicine applications. However, standard classification schemas are often not suitable for scRNA-Seq due to their unique characteristics, and new algorithms are required to effectively analyze and classify samples at the single-cell level. Furthermore, existing methods for this purpose have limitations in their usability. Those reasons motivated us to develop singleDeep, an end-to-end pipeline that streamlines the analysis of scRNA-Seq data training deep neural networks, enabling robust prediction and characterization of sample phenotypes. We used singleDeep to make predictions on scRNA-Seq datasets from different conditions, including systemic lupus erythematosus, Alzheimer's disease and coronavirus disease 2019. Our results demonstrate strong diagnostic performance, validated both internally and externally. Moreover, singleDeep outperformed traditional machine learning methods and alternative single-cell approaches. In addition to prediction accuracy, singleDeep provides valuable insights into cell types and gene importance estimation for phenotypic characterization. This functionality provided additional and valuable information in our use cases. For instance, we corroborated that some interferon signature genes are consistently relevant for autoimmunity across all immune cell types in lupus. On the other hand, we discovered that genes linked to dementia have relevant roles in specific brain cell populations, such as APOE in astrocytes.

PAGE-based transfer learning from single-cell to bulk sequencing enhances model generalization for sepsis diagnosis

Sepsis, caused by infections, sparks a dangerous bodily response. The transcriptional expression patterns of host responses aid in the diagnosis of sepsis, but the challenge lies in their limited generalization capabilities. To facilitate sepsis diagnosis, we present an updated version of single-cell Pair-wise Analysis of Gene Expression (scPAGE) using transfer learning method, scPAGE2, dedicated to data fusion between single-cell and bulk transcriptome. Compared to scPAGE, the upgrade to scPAGE2 featured ameliorated Differentially Expressed Gene Pairs (DEPs) for pretraining a model in single-cell transcriptome and retrained it using bulk transcriptome data to construct a sepsis diagnostic model, which effectively transferred cell-layer information from single-cell to bulk transcriptome. Seven datasets across three transcriptome platforms and fluorescence-activated cell sorting (FACS) were used for performance validation. The model involved four DEPs, showing robust performance across next-generation sequencing and microarray platforms, surpassing state-of-the-art models with an average AUROC of 0.947 and an average AUPRC of 0.987. Analysis of scRNA-seq data reveals higher cell proportions with JAM3-PIK3AP1 expression in sepsis monocytes, decreased ARG1-CCR7 in B and T cells. Elevated IRF6-HP in sepsis monocytes confirmed by both scRNA-seq and an independent cohort using FACS. Both the superior performance of the model and the in vitro validation of IRF6-HP in monocytes emphasize that scPAGE2 is effective and robust in the construction of sepsis diagnostic model. We additionally applied scPAGE2 to acute myeloid leukemia and demonstrated its superior classification performance. Overall, we provided a strategy to improve the generalizability of classification model that can be adapted to a broad range of clinical prediction scenarios.

Advances in analysis of atmospheric ultrafine particles and application in air quality, climate, and health research

There is growing interest in the contribution of ultrafine particles to air quality, climate, and human health. Ultrafine particles are of central significance for the influence of radiative forcing of climate change by involving in the formation of clouds and precipitation. Moreover, exposure to ultrafine particles can enhance the disease burden. The determination of those effects of ultrafine particles strongly depends on their chemical composition and physicochemical properties. This review focuses on the advanced techniques for the characterization of chemical composition and physicochemical properties of ultrafine particles in the past five years. The current analytical methodologies are broadly classified into electron and X-ray microscopy, optical spectroscopy and microscopy, electrical mobility, and mass spectrometry, and then described and discussed its operation principle, advantages, and limitations. Besides measurements, application of the state-of-the-art techniques is briefly reviewed to help us to promote a better understanding of atmospheric ultrafine particles relevant to air quality, climate, and health.

Construction and Validation of a Novel T/NK-Cell Prognostic Signature for Pancreatic Cancer Based on Single-Cell RNA Sequencing

BACKGROUND

Evidence with regards to the distinction between primary and metastatic tumors in pancreatic cancer and driving factors for metastases remains limited.

METHODS

Single-cell RNA sequencing (scRNA-seq) was conducted on metastatic pancreatic cancer. Bioinformatics analysis on relevant sequencing data was used to construct a risk model to predict patient prognosis. Furthermore, immune infiltration and metabolic differences were assessed. The biological function of key differential genes was evaluated.

RESULTS

Paired primary and metastatic tumor tissues from 3 pancreatic cancer patients were collected and conducted scRNA-seq. Subsequently, the T/NK cell subgroup was the most different cell type between primary tumors and liver metastases and was selected for further analysis. Eventually, 6 specifically expressed genes of T/NK cells (B2M, ZFP36L2, ANXA1, ARL4C, TSPYL2, FYN) were used constructing the prognostic model. The stability of this model was validated by an external cohort. Meanwhile, different immune infiltration abundances occurred between high and low risk groups stratified by the model. The high-risk group had a stronger metabolic capability.

CONCLUSIONS

A novel prognostic T/NK-cell signature for pancreatic cancer was constructed based on scRNA-seq data and externally validated. The involved key genes may play a role in multiple metabolic pathways of metastasis and affect the tumor immune microenvironment.

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.

Applications of single-cell analysis in immunotherapy for lung cancer: Current progress, new challenges and expectations

BACKGROUND

Lung cancer is a prevalent form of cancer worldwide, presenting a substantial risk to human well-being. Lung cancer is classified into two main types: non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). The advancement of tumor immunotherapy, specifically immune checkpoint inhibitors and adaptive T-cell therapy, has encountered substantial obstacles due to the rapid progression of SCLC and the metastasis, recurrence, and drug resistance of NSCLC. These challenges are believed to stem from the tumor heterogeneity of lung cancer within the tumor microenvironment.

AIM OF REVIEW

This review aims to comprehensively explore recent strides in single-cell analysis, a robust sequencing technology, concerning its application in the realm of tumor immunotherapy for lung cancer. It has been effectively integrated with transcriptomics, epigenomics, genomics, and proteomics for various applications. Specifically, these techniques have proven valuable in mapping the transcriptional activity of tumor-infiltrating lymphocytes in patients with NSCLC, identifying circulating tumor cells, and elucidating the heterogeneity of the tumor microenvironment.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The review emphasizes the paramount significance of single-cell analysis in mapping the immune cells within NSCLC patients, unveiling circulating tumor cells, and elucidating the tumor microenvironment heterogeneity. Notably, these advancements highlight the potential of single-cell analysis to revolutionize lung cancer immunotherapy by characterizing immune cell fates, improving therapeutic strategies, and identifying promising targets or prognostic biomarkers. It is potential to unravel the complexities within the tumor microenvironment and enhance treatment strategies marks a significant step towards more effective therapies and improved patient outcomes.

Mcadet: A feature selection method for fine-resolution single-cell RNA-seq data based on multiple correspondence analysis and community detection

Single-cell RNA sequencing (scRNA-seq) data analysis faces numerous challenges, including high sparsity, a high-dimensional feature space, and biological noise. These challenges hinder downstream analysis, necessitating the use of feature selection methods to identify informative genes, and reduce data dimensionality. However, existing methods for selecting highly variable genes (HVGs) exhibit limited overlap and inconsistent clustering performance across benchmark datasets. Moreover, these methods often struggle to accurately select HVGs from fine-resolution scRNA-seq datasets and minority cell types, which are more difficult to distinguish, raising concerns about the reliability of their results. To overcome these limitations, we propose a novel feature selection framework for scRNA-seq data called Mcadet. Mcadet integrates Multiple Correspondence Analysis (MCA), graph-based community detection, and a novel statistical testing approach. To assess the effectiveness of Mcadet, we conducted extensive evaluations using both simulated and real-world data, employing unbiased metrics for comparison. Our results demonstrate the superior performance of Mcadet in the selection of HVGs in scenarios involving fine-resolution scRNA-seq datasets and datasets containing minority cell populations. Overall, we demonstrate that Mcadet enhances the reliability of selected HVGs, although the impact of HVG selection on various downstream analyses varies and needs to be further investigated.

Single-cell transcriptional atlas of human breast cancers and model systems

BACKGROUND

Breast cancer's complex transcriptional landscape requires an improved understanding of cellular diversity to identify effective treatments. The study of genetic variations among breast cancer subtypes at single-cell resolution has potential to deepen our insights into cancer progression.

METHODS

In this study, we amalgamate single-cell RNA sequencing data from patient tumours and matched lymph metastasis, reduction mammoplasties, breast cancer patient-derived xenografts (PDXs), PDX-derived organoids (PDXOs), and cell lines resulting in a diverse dataset of 117 samples with 506 719 total cells. These samples encompass hormone receptor positive (HR+), human epidermal growth factor receptor 2 positive (HER2+), and triple-negative breast cancer (TNBC) subtypes, including isogenic model pairs. Herein, we delineated similarities and distinctions across models and patient samples and explore therapeutic drug efficacy based on subtype proportions.

RESULTS

PDX models more closely resemble patient samples in terms of tumour heterogeneity and cell cycle characteristics when compared with TNBC cell lines. Acquired drug resistance was associated with an increase in basal-like cell proportions within TNBC PDX tumours as defined with SCSubtype and TNBCtype cell typing predictors. All patient samples contained a mixture of subtypes; compared to primary tumours HR+ lymph node metastases had lower proportions of HER2-Enriched cells. PDXOs exhibited differences in metabolic-related transcripts compared to PDX tumours. Correlative analyses of cytotoxic drugs on PDX cells identified therapeutic efficacy was based on subtype proportion.

CONCLUSIONS

We present a substantial multimodel dataset, a dynamic approach to cell-wise sample annotation, and a comprehensive interrogation of models within systems of human breast cancer. This analysis and reference will facilitate informed decision-making in preclinical research and therapeutic development through its elucidation of model limitations, subtype-specific insights and novel targetable pathways.

KEY POINTS

Patient-derived xenografts models more closely resemble patient samples in tumour heterogeneity and cell cycle characteristics when compared with cell lines. 3D organoid models exhibit differences in metabolic profiles compared to their in vivo counterparts. A valuable multimodel reference dataset that can be useful in elucidating model differences and novel targetable pathways.

Graph domain adaptation-based framework for gene expression enhancement and cell type identification in large-scale spatially resolved transcriptomics

Spatially resolved transcriptomics (SRT) technologies facilitate gene expression profiling with spatial resolution in a naïve state. Nevertheless, current SRT technologies exhibit limitations, manifesting as either low transcript detection sensitivity or restricted gene throughput. These constraints result in diminished precision and coverage in gene measurement. In response, we introduce SpaGDA, a sophisticated deep learning-based graph domain adaptation framework for both scenarios of gene expression imputation and cell type identification in spatially resolved transcriptomics data by impartially transferring knowledge from reference scRNA-seq data. Systematic benchmarking analyses across several SRT datasets generated from different technologies have demonstrated SpaGDA's superior effectiveness compared to state-of-the-art methods in both scenarios. Further applied to three SRT datasets of different biological contexts, SpaGDA not only better recovers the well-established knowledge sourced from public atlases and existing scientific literature but also yields a more informative spatial expression pattern of genes. Together, these results demonstrate that SpaGDA can be used to overcome the challenges of current SRT data and provide more accurate insights into biological processes or disease development. The SpaGDA is available in https://github.com/shenrb/SpaGDA.

Binomial models uncover biological variation during feature selection of droplet-based single-cell RNA sequencing

Effective analysis of single-cell RNA sequencing (scRNA-seq) data requires a rigorous distinction between technical noise and biological variation. In this work, we propose a simple feature selection model, termed "Differentially Distributed Genes" or DDGs, where a binomial sampling process for each mRNA species produces a null model of technical variation. Using scRNA-seq data where cell identities have been established a priori, we find that the DDG model of biological variation outperforms existing methods. We demonstrate that DDGs distinguish a validated set of real biologically varying genes, minimize neighborhood distortion, and enable accurate partitioning of cells into their established cell-type groups.

Suppression of the JAK/STAT pathway inhibits neuroinflammation in the line 61-PFF mouse model of Parkinson's disease

Parkinson's disease (PD) is characterized by neuroinflammation, progressive loss of dopaminergic neurons, and accumulation of α-synuclein (α-Syn) into insoluble aggregates called Lewy pathology. The Line 61 α-Syn mouse is an established preclinical model of PD; Thy-1 is used to promote human α-Syn expression, and features of sporadic PD develop at 9-18 months of age. To accelerate the PD phenotypes, we injected sonicated human α-Syn preformed fibrils (PFFs) into the striatum, which produced phospho-Syn (p-α-Syn) inclusions in the substantia nigra pars compacta and significantly increased MHC Class II-positive immune cells. Additionally, there was enhanced infiltration and activation of innate and adaptive immune cells in the midbrain. We then used this new model, Line 61-PFF, to investigate the effect of inhibiting the JAK/STAT signaling pathway, which is critical for regulation of innate and adaptive immune responses. After administration of the JAK1/2 inhibitor AZD1480, immunofluorescence staining showed a significant decrease in p-α-Syn inclusions and MHC Class II expression. Flow cytometry showed reduced infiltration of CD4+ T-cells, CD8+ T-cells, CD19+ B-cells, dendritic cells, macrophages, and endogenous microglia into the midbrain. Importantly, single-cell RNA-Sequencing analysis of CD45+ cells from the midbrain identified 9 microglia clusters, 5 monocyte/macrophage (MM) clusters, and 5 T-cell (T) clusters, in which potentially pathogenic MM4 and T3 clusters were associated with neuroinflammatory responses in Line 61-PFF mice. AZD1480 treatment reduced cell numbers and cluster-specific expression of the antigen-presentation genes H2-Eb1, H2-Aa, H2-Ab1, and Cd74 in the MM4 cluster and proinflammatory genes such as Tnf, Il1b, C1qa, and C1qc in the T3 cluster. Together, these results indicate that inhibiting the JAK/STAT pathway suppresses the activation and infiltration of innate and adaptive cells, reducing neuroinflammation in the Line 61-PFF mouse model.

scVSC: Deep Variational Subspace Clustering for Single-Cell Transcriptome Data

Single-cell RNA sequencing (scRNA-seq) is a potent advancement for analyzing gene expression at the individual cell level, allowing for the identification of cellular heterogeneity and subpopulations. However, it suffers from technical limitations that result in sparse and heterogeneous data. Here, we propose scVSC, an unsupervised clustering algorithm built on deep representation neural networks. The method incorporates the variational inference into the subspace model, which imposes regularization constraints on the latent space and further prevents overfitting. In a series of experiments across multiple datasets, scVSC outperforms existing state-of-the-art unsupervised and semi-supervised clustering tools regarding clustering accuracy and running efficiency. Moreover, the study indicates that scVSC could visually reveal the state of trajectory differentiation, accurately identify differentially expressed genes, and further discover biologically critical pathways.

Single-Cell RNA-Sequencing: Opening New Horizons for Breast Cancer Research

Breast cancer is the most prevalent malignant tumor among women with high heterogeneity. Traditional techniques frequently struggle to comprehensively capture the intricacy and variety of cellular states and interactions within breast cancer. As global precision medicine rapidly advances, single-cell RNA sequencing (scRNA-seq) has become a highly effective technique, revolutionizing breast cancer research by offering unprecedented insights into the cellular heterogeneity and complexity of breast cancer. This cutting-edge technology facilitates the analysis of gene expression profiles at the single-cell level, uncovering diverse cell types and states within the tumor microenvironment. By dissecting the cellular composition and transcriptional signatures of breast cancer cells, scRNA-seq provides new perspectives for understanding the mechanisms behind tumor therapy, drug resistance and metastasis in breast cancer. In this review, we summarized the working principle and workflow of scRNA-seq and emphasized the major applications and discoveries of scRNA-seq in breast cancer research, highlighting its impact on our comprehension of breast cancer biology and its potential for guiding personalized treatment strategies.

scSemiProfiler: Advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning

Single-cell sequencing is a crucial tool for dissecting the cellular intricacies of complex diseases. Its prohibitive cost, however, hampers its application in expansive biomedical studies. Traditional cellular deconvolution approaches can infer cell type proportions from more affordable bulk sequencing data, yet they fall short in providing the detailed resolution required for single-cell-level analyses. To overcome this challenge, we introduce "scSemiProfiler", an innovative computational framework that marries deep generative models with active learning strategies. This method adeptly infers single-cell profiles across large cohorts by fusing bulk sequencing data with targeted single-cell sequencing from a few rigorously chosen representatives. Extensive validation across heterogeneous datasets verifies the precision of our semi-profiling approach, aligning closely with true single-cell profiling data and empowering refined cellular analyses. Originally developed for extensive disease cohorts, "scSemiProfiler" is adaptable for broad applications. It provides a scalable, cost-effective solution for single-cell profiling, facilitating in-depth cellular investigation in various biological domains.

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.

Deciphering brain cellular and behavioral mechanisms: Insights from single-cell and spatial RNA sequencing

The brain is a complex computing system composed of a multitude of interacting neurons. The computational outputs of this system determine the behavior and perception of every individual. Each brain cell expresses thousands of genes that dictate the cell's function and physiological properties. Therefore, deciphering the molecular expression of each cell is of great significance for understanding its characteristics and role in brain function. Additionally, the positional information of each cell can provide crucial insights into their involvement in local brain circuits. In this review, we briefly overview the principles of single-cell RNA sequencing and spatial transcriptomics, the potential issues and challenges in their data processing, and their applications in brain research. We further outline several promising directions in neuroscience that could be integrated with single-cell RNA sequencing, including neurodevelopment, the identification of novel brain microstructures, cognition and behavior, neuronal cell positioning, molecules and cells related to advanced brain functions, sleep-wake cycles/circadian rhythms, and computational modeling of brain function. We believe that the deep integration of these directions with single-cell and spatial RNA sequencing can contribute significantly to understanding the roles of individual cells or cell types in these specific functions, thereby making important contributions to addressing critical questions in those fields. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Development RNA in Disease and Development > RNA in Disease.

Advances in spatial transcriptomics and its applications in cancer research

Malignant tumors have increasing morbidity and high mortality, and their occurrence and development is a complicate process. The development of sequencing technologies enabled us to gain a better understanding of the underlying genetic and molecular mechanisms in tumors. In recent years, the spatial transcriptomics sequencing technologies have been developed rapidly and allow the quantification and illustration of gene expression in the spatial context of tissues. Compared with the traditional transcriptomics technologies, spatial transcriptomics technologies not only detect gene expression levels in cells, but also inform the spatial location of genes within tissues, cell composition of biological tissues, and interaction between cells. Here we summarize the development of spatial transcriptomics technologies, spatial transcriptomics tools and its application in cancer research. We also discuss the limitations and challenges of current spatial transcriptomics approaches, as well as future development and prospects.

Single cell proteomics by mass spectrometry reveals deep epigenetic insight into the actions of an orphan histone deacetylase inhibitor

Epigenetic programming has been shown to play a role in nearly every human system and disease where anyone has thought to look. However, the levels of heterogeneity at which epigenetic or epiproteomic modifications occur at single cell resolution across a population remains elusive. While recent advances in sequencing technology have allowed between 1 and 3 histone post-translational modifications to be analyzed in each single cell, over twenty separate chemical PTMs are known to exist, allowing thousands of possible combinations. Single cell proteomics by mass spectrometry (SCP) is an emerging technology in which hundreds or thousands of proteins can be directly quantified in typical human cells. As the proteins detected and quantified by SCP are heavily biased toward proteins of highest abundance, chromatin proteins are an attractive target for analysis. To this end, I applied SCP to the analysis of cancer cells treated with mocetinostat, a class specific histone deacetylase inhibitor. I find that 16 PTMs can be confidently identified and localized with high site specificity in single cells. In addition, the high abundance of histone proteins allows higher throughput methods to be utilized for SCP than previously described. While quantitative accuracy suffers when analyzing more than 700 cells per day, 9 histone proteins can be measured in single cells analyzed at even 3,500 cells per day, a throughput 10-fold greater than any previous report. In addition, the unbiased global approach utilized herein identifies a previously uncharacterized response to this drug through the S100-A8/S100-A9 protein complex partners. This response is observed in nearly every cell of the over 1,000 analyzed in this study, regardless of the relative throughput of the method utilized. While limitations exist in the methods described herein, current technologies can easily improve upon the results presented here to allow comprehensive analysis of histone PTMs to be performed in any mass spectrometry lab. All raw and processed data described in this study has been made publicly available through the ProteomeXchange/MASSIVE repository system as MSV000093434.

A body-brain circuit that regulates body inflammatory responses

The body-brain axis is emerging as a principal conductor of organismal physiology. It senses and controls organ function1,2, metabolism3 and nutritional state4-6. Here we show that a peripheral immune insult strongly activates the body-brain axis to regulate immune responses. We demonstrate that pro-inflammatory and anti-inflammatory cytokines communicate with distinct populations of vagal neurons to inform the brain of an emerging inflammatory response. In turn, the brain tightly modulates the course of the peripheral immune response. Genetic silencing of this body-brain circuit produced unregulated and out-of-control inflammatory responses. By contrast, activating, rather than silencing, this circuit affords neural control of immune responses. We used single-cell RNA sequencing, combined with functional imaging, to identify the circuit components of this neuroimmune axis, and showed that its selective manipulation can effectively suppress the pro-inflammatory response while enhancing an anti-inflammatory state. The brain-evoked transformation of the course of an immune response offers new possibilities in the modulation of a wide range of immune disorders, from autoimmune diseases to cytokine storm and shock.

Label-free single-cell analysis in microdroplets using a light-scattering-based optofluidic chip

Droplet-based single-cell analysis is a very powerful tool for studying phenotypic and genomic heterogeneity at single-cell resolution for a variety of biological problems. In conventional two-phase droplet microfluidics, due to the mismatch in optical properties between oil and aqueous phases, light scattering mainly happens at the oil/water interface that disables light-scattering-based cell analysis confined in microdroplets. Detection and analysis of cells in microdroplets thus mostly rely on the fluorescence labeling of cell samples, which may suffer from complex operation, cytotoxicity, and low fluorescence stability. In this work, we propose a novel light-scattering-based droplet screening (LSDS) that can effectively detect and characterize single cells confined in droplets by adjusting the optical properties of droplets in a multiangle optofluidic chip. Theoretical and simulated calculations suggest that refractive index (RI) matching in droplet two-phase materials can reduce or eliminate droplets' scattered signals (background signal), enabling the differentiation of scattered signals from single cells and particles within droplets. Furthermore, by using a set of multiangle (from -145° to 140°) optical fibers integrated into the optofluidic chip, the scattered light properties of droplets with the RI ranging from 1.334 to 1.429 are measured. We find that the smaller the RI and size of microparticles inside droplets are, the smaller the RI difference between two-phase materials Δn is required. Especially, when Δn is smaller than 0.02, single cells in droplets can be detected and analyzed solely based on light scattering. This capability allows to accurately detect droplets containing one single cell and one single gel bead, a typical droplet encapsulation for single-cell sequencing. Altogether, this work provides a powerful platform for high-throughput label-free single-cell analysis in microdroplets for diverse single-cell related biological assays.

Single-Cell Sequencing Technology and Its Application in the Study of Central Nervous System Diseases

The mammalian central nervous system consists of a large number of cells, which contain not only different types of neurons, but also a large number of glial cells, such as astrocytes, oligodendrocytes, and microglia. These cells are capable of performing highly refined electrophysiological activities and providing the brain with functions such as nutritional support, information transmission and pathogen defense. The diversity of cell types and individual differences between cells have brought inspiration to the study of the mechanism of central nervous system diseases. In order to explore the role of different cells, a new technology, single-cell sequencing technology has emerged to perform specific analysis of high-throughput cell populations, and has been continuously developed. Single-cell sequencing technology can accurately analyze single-cell expression in mixed-cell populations and collect cells from different spatial locations, time stages and types. By using single-cell sequencing technology to compare gene expression profiles of normal and diseased cells, it is possible to discover cell subsets associated with specific diseases and their associated genes. Therefore, scientists can understand the development process, related functions and disease state of the nervous system from an unprecedented depth. In conclusion, single-cell sequencing technology provides a powerful technology for the discovery of novel therapeutic targets for central nervous system diseases.

Advancing skeletal health and disease research with single-cell RNA sequencing

Orthopedic conditions have emerged as global health concerns, impacting approximately 1.7 billion individuals worldwide. However, the limited understanding of the underlying pathological processes at the cellular and molecular level has hindered the development of comprehensive treatment options for these disorders. The advent of single-cell RNA sequencing (scRNA-seq) technology has revolutionized biomedical research by enabling detailed examination of cellular and molecular diversity. Nevertheless, investigating mechanisms at the single-cell level in highly mineralized skeletal tissue poses technical challenges. In this comprehensive review, we present a streamlined approach to obtaining high-quality single cells from skeletal tissue and provide an overview of existing scRNA-seq technologies employed in skeletal studies along with practical bioinformatic analysis pipelines. By utilizing these methodologies, crucial insights into the developmental dynamics, maintenance of homeostasis, and pathological processes involved in spine, joint, bone, muscle, and tendon disorders have been uncovered. Specifically focusing on the joint diseases of degenerative disc disease, osteoarthritis, and rheumatoid arthritis using scRNA-seq has provided novel insights and a more nuanced comprehension. These findings have paved the way for discovering novel therapeutic targets that offer potential benefits to patients suffering from diverse skeletal disorders.

Novel Isolating Approaches to Circulating Tumor Cell Enrichment Based on Microfluidics: A Review

Circulating tumor cells (CTCs), derived from the primary tumor and carrying genetic information, contribute significantly to the process of tumor metastasis. The analysis and detection of CTCs can be used to assess the prognosis and treatment response in patients with tumors, as well as to help study the metastatic mechanisms of tumors and the development of new drugs. Since CTCs are very rare in the blood, it is a challenging problem to enrich CTCs efficiently. In this paper, we provide a comprehensive overview of microfluidics-based enrichment devices for CTCs in recent years. We explore in detail the methods of enrichment based on the physical or biological properties of CTCs; among them, physical properties cover factors such as size, density, and dielectric properties, while biological properties are mainly related to tumor-specific markers on the surface of CTCs. In addition, we provide an in-depth description of the methods for enrichment of single CTCs and illustrate the importance of single CTCs for performing tumor analyses. Future research will focus on aspects such as improving the separation efficiency, reducing costs, and increasing the detection sensitivity and accuracy.

A novel subtype based on driver methylation-transcription in lung adenocarcinoma

AIMS

To identify driver methylation genes and a novel subtype of lung adenocarcinoma (LUAD) by multi-omics and elucidate its molecular features and clinical significance.

METHODS

We collected LUAD patients from public databases, and identified driver methylation genes (DMGs) by MethSig and MethylMix algrothms. And novel driver methylation multi-omics subtypes were identified by similarity network fusion (SNF). Furthermore, the prognosis, tumor microenvironment (TME), molecular features and therapy efficiency among subtypes were comprehensively evaluated.

RESULTS

147 overlapped driver methylation were identified and validated. By integrating the mRNA expression and methylation of DMGs using SNF, four distinct patterns, termed as S1-S4, were characterized by differences in prognosis, biological features, and TME. The S2 subtype showed unfavorable prognosis. By comparing the characteristics of the DMGs subtypes with the traditional subtypes, S3 was concentrated in proximal-inflammatory (PI) subtype, and S4 was consisted of terminal respiratory unit (TRU) subtype and PI subtype. By analyzing TME and epithelial mesenchymal transition (EMT) features, increased immune infiltration and higher expression of immune checkpoint genes were found in S3 and S4. While S4 showed higher EMT score and expression of EMT associated genes, indicating S4 may not be as immunosensitive as the S3. Additionally, S3 had lower TIDE and higher IPS score, indicating its increased sensitivity to immunotherapy.

CONCLUSION

The driver methylation-related subtypes of LUAD demonstrate prognostic predictive ability that could help inform treatment response and provide complementary information to the existing subtypes.

Unveiling heterogeneity in MSCs: exploring marker-based strategies for defining MSC subpopulations

Mesenchymal stem/stromal cells (MSCs) represent a heterogeneous cell population distributed throughout various tissues, demonstrating remarkable adaptability to microenvironmental cues and holding immense promise for disease treatment. However, the inherent diversity within MSCs often leads to variability in therapeutic outcomes, posing challenges for clinical applications. To address this heterogeneity, purification of MSC subpopulations through marker-based isolation has emerged as a promising approach to ensure consistent therapeutic efficacy. In this review, we discussed the reported markers of MSCs, encompassing those developed through candidate marker strategies and high-throughput approaches, with the aim of explore viable strategies for addressing the heterogeneity of MSCs and illuminate prospective research directions in this field.

DNA lesion bypass and the stochastic dynamics of transcription-coupled repair

DNA base damage is a major source of oncogenic mutations and disruption to gene expression. The stalling of RNA polymerase II (RNAP) at sites of DNA damage and the subsequent triggering of repair processes have major roles in shaping the genome-wide distribution of mutations, clearing barriers to transcription, and minimizing the production of miscoded gene products. Despite its importance for genetic integrity, key mechanistic features of this transcription-coupled repair (TCR) process are controversial or unknown. Here, we exploited a well-powered in vivo mammalian model system to explore the mechanistic properties and parameters of TCR for alkylation damage at fine spatial resolution and with discrimination of the damaged DNA strand. For rigorous interpretation, a generalizable mathematical model of DNA damage and TCR was developed. Fitting experimental data to the model and simulation revealed that RNA polymerases frequently bypass lesions without triggering repair, indicating that small alkylation adducts are unlikely to be an efficient barrier to gene expression. Following a burst of damage, the efficiency of transcription-coupled repair gradually decays through gene bodies with implications for the occurrence and accurate inference of driver mutations in cancer. The reinitation of transcription from the repair site is not a general feature of transcription-coupled repair, and the observed data is consistent with reinitiation never taking place. Collectively, these results reveal how the directional but stochastic activity of TCR shapes the distribution of mutations following DNA damage.

Suppression of the JAK/STAT Pathway Inhibits Neuroinflammation in the Line 61-PFF Mouse Model of Parkinson's Disease

Parkinson's disease (PD) is characterized by neuroinflammation, progressive loss of dopaminergic neurons, and accumulation of a-synuclein (a-Syn) into insoluble aggregates called Lewy pathology. The Line 61 a-Syn mouse is an established preclinical model of PD; Thy-1 is used to promote human a-Syn expression, and features of sporadic PD develop at 9-18 months of age. To accelerate the PD phenotypes, we injected sonicated human a-Syn preformed fibrils (PFFs) into the striatum, which produced phospho-Syn (p-a-Syn) inclusions in the substantia nigra pars compacta and significantly increased MHC Class II-positive immune cells. Additionally, there was enhanced infiltration and activation of innate and adaptive immune cells in the midbrain. We then used this new model, Line 61-PFF, to investigate the effect of inhibiting the JAK/STAT signaling pathway, which is critical for regulation of innate and adaptive immune responses. After administration of the JAK1/2 inhibitor AZD1480, immunofluorescence staining showed a significant decrease in p-a-Syn inclusions and MHC Class II expression. Flow cytometry showed reduced infiltration of CD4+ T-cells, CD8+ T-cells, CD19+ B-cells, dendritic cells, macrophages, and endogenous microglia into the midbrain. Importantly, single-cell RNA-Sequencing analysis of CD45+ cells from the midbrain identified 9 microglia clusters, 5 monocyte/macrophage (MM) clusters, and 5 T-cell (T) clusters, in which potentially pathogenic MM4 and T3 clusters were associated with neuroinflammatory responses in Line 61-PFF mice. AZD1480 treatment reduced cell numbers and cluster-specific expression of the antigen-presentation genes H2-Eb1, H2-Aa, H2-Ab1, and Cd74 in the MM4 cluster and proinflammatory genes such as Tnf, Il1b, C1qa, and C1qc in the T3 cluster. Together, these results indicate that inhibiting the JAK/STAT pathway suppresses the activation and infiltration of innate and adaptive cells, reducing neuroinflammation in the Line 61-PFF mouse model.

What can we learn when fitting a simple telegraph model to a complex gene expression model?

In experiments, the distributions of mRNA or protein numbers in single cells are often fitted to the random telegraph model which includes synthesis and decay of mRNA or protein, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by crucial biological mechanisms such as feedback regulation, non-exponential gene inactivation durations, and multiple gene activation pathways. Here we investigate the dynamical properties of four relatively complex gene expression models by fitting their steady-state mRNA or protein number distributions to the simple telegraph model. We show that despite the underlying complex biological mechanisms, the telegraph model with three effective parameters can accurately capture the steady-state gene product distributions, as well as the conditional distributions in the active gene state, of the complex models. Some effective parameters are reliable and can reflect realistic dynamic behaviors of the complex models, while others may deviate significantly from their real values in the complex models. The effective parameters can also be applied to characterize the capability for a complex model to exhibit multimodality. Using additional information such as single-cell data at multiple time points, we provide an effective method of distinguishing the complex models from the telegraph model. Furthermore, using measurements under varying experimental conditions, we show that fitting the mRNA or protein number distributions to the telegraph model may even reveal the underlying gene regulation mechanisms of the complex models. The effectiveness of these methods is confirmed by analysis of single-cell data for E. coli and mammalian cells. All these results are robust with respect to cooperative transcriptional regulation and extrinsic noise. In particular, we find that faster relaxation speed to the steady state results in more precise parameter inference under large extrinsic noise.