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

Advances in single-cell RNA sequencing (scRNA-seq) technologies in the past 10 years have had a transformative effect on biomedical research, enabling the profiling and analysis of the transcriptomes of single cells at unprecedented resolution and throughput. Specifically, scRNA-seq has facilitated the identification of novel or rare cell types, the analysis of single-cell trajectory construction and stem or progenitor cell differentiation, and the comparison of healthy and disease-related tissues at single-cell resolution. These applications have been critical in advances in cardiovascular research in the past decade as evidenced by the generation of cell atlases of mammalian heart and blood vessels and the elucidation of mechanisms involved in cardiovascular development and stem or progenitor cell differentiation. In this Review, we summarize the currently available scRNA-seq technologies and analytical tools and discuss the latest findings using scRNA-seq that have substantially improved our knowledge on the development of the cardiovascular system and the mechanisms underlying cardiovascular diseases. Furthermore, we examine emerging strategies that integrate multimodal single-cell platforms, focusing on future applications in cardiovascular precision medicine that use single-cell omics approaches to characterize cell-specific responses to drugs or environmental stimuli and to develop effective patient-specific therapeutics.

Single-Cell RNA Sequencing Unveils the Hallmarks of Populations at Different Stages of HTLV-1 Infection

Recent advances in single-cell RNA sequencing (scRNA-seq) technology have enabled the acquisition of RNA at the single-cell level, which showed that the expression level of genes is highly variable across and within the cell types. Even well-known housekeeping genes showed high expression variance in a single condition and within the same cell types. Previous studies made efforts to identify stably expressed genes and use them as a yardstick for robust gene expression normalization. On the other hand, drugs were shown to be less effective on genes with high expression variance. Thus, identifying both stably and variably expressed genes is an important task, especially at the single-cell level. In this study, using the Kullback-Leibler divergence method, we proposed a metric to measure the expression stability of each gene. Using private scRNA-seq data composed of 25 severe COVID-19 patients and 40 healthy individuals, we identified variably expressed genes specific to COVID-19-infected patients and healthy cohorts.

Recent advancements in single-cell RNA sequencing (scRNA-seq) technology have enabled the comprehensive profiling of gene expression patterns at the single-cell level, offering unprecedented insights into cellular diversity and heterogeneity within plant tissues. In this study, we present a systematic approach to construct a plant single-cell database, scPlantDB, which is publicly available at https://biobigdata.nju.edu.cn/scplantdb. We integrated single-cell transcriptomic profiles from 67 high-quality datasets across 17 plant species, comprising approximately 2.5 million cells. The data underwent rigorous collection, manual curation, strict quality control and standardized processing from public databases. scPlantDB offers interactive visualization of gene expression at the single-cell level, facilitating the exploration of both single-dataset and multiple-dataset analyses. It enables systematic comparison and functional annotation of markers across diverse cell types and species while providing tools to identify and compare cell types based on these markers. In summary, scPlantDB serves as a comprehensive database for investigating cell types and markers within plant cell atlases. It is a valuable resource for the plant research community.

The recent advances in spatial transcriptomics have brought unprecedented opportunities to understand the cellular heterogeneity in the spatial context. However, the current limitations of spatial technologies hamper the exploration of cellular localizations and interactions at single-cell level. Here, we present spatial transcriptomics deconvolution by topic modeling (STRIDE), a computational method to decompose cell types from spatial mixtures by leveraging topic profiles trained from single-cell transcriptomics. STRIDE accurately estimated the cell-type proportions and showed balanced specificity and sensitivity compared to existing methods. We demonstrated STRIDE’s utility by applying it to different spatial platforms and biological systems. Deconvolution by STRIDE not only mapped rare cell types to spatial locations but also improved the identification of spatially localized genes and domains. Moreover, topics discovered by STRIDE were associated with cell-type-specific functions and could be further used to integrate successive sections and reconstruct the three-dimensional architecture of tissues. Taken together, STRIDE is a versatile and extensible tool for integrated analysis of spatial and single-cell transcriptomics and is publicly available at https://github.com/wanglabtongji/STRIDE.

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.

Single-cell RNA sequencing (scRNA-seq) technology has emerged as a powerful tool for studying complex biological systems and diseases with high resolution. However, generating sufficient high quality scRNA-seq samples for accurate downstream analyses and ensuring reproducibility remains a challenge. Previous methods often produce low-quality samples or a limited number of useful specific cell subpopulations. To address this issue, we propose a novel Efficient Single-Cell RNA-seq Flow Diffusion (named ESCFD) method based on the Diffusion Model, capable of synthesizing large-scale, high-quality scRNA-seq samples and generating either ''holistic'' or specific cellular subpopulations within a single framework. A pre-guidance mechanism is designed for synthesizing specific cellular subpopulations of high quality, and a post-guidance mechanism was designed to accelerate the sampling of scRNA-seq samples. The ESCFD can efficiently synthesize large-scale and high-quality scRNA-seq samples for various downstream tasks, surpassing previous work with faster sampling speed. Experimental results demonstrate that the ESCFD model achieves state-of-the-art performance in cell classification and cell distribution similarity when evaluated on three scRNA-seq benchmarks, and verified the effectiveness of accelerated sampling. Visualization experiments also reveal the ESCFD's ability to synthesize specific cellular subpopulations. This innovative approach has the potential to significantly advance our understanding of complex biological systems and accelerate the development of novel therapeutic interventions and personalized medicine strategies.

Demystifying tolerance

Immunotherapy has made great strides in the treatment of lung cancer, but a significant proportion of patients still do not respond to treatment. Therefore, the identification of novel targets is crucial to improving the response to immunotherapy. The tumor microenvironment (TME) is a complex niche composed of diverse pro-tumor molecules and cell populations, making the function and mechanism of a unique cell subset difficult to understand. However, the advent of single-cell RNA sequencing (scRNA-seq) technology has made it possible to identify cellular markers and understand their potential functions and mechanisms in the TME. In this review, we highlight recent advances emerging from scRNA-seq studies in lung cancer, with a particular focus on stromal cells. We elucidate the cellular developmental trajectory, phenotypic remodeling, and cell interactions during tumor progression. Our review proposes predictive biomarkers and novel targets for lung cancer immunotherapy based on cellular markers identified through scRNA-seq. The identification of novel targets could help improve the response to immunotherapy. The use of scRNA-seq technology could provide new strategies to understand the TME and develop personalized immunotherapy for lung cancer patients.

Neurodegenerative diseases (NDs) involve the progressive loss of neuronal structure or function in the brain and spinal cord. Despite their diverse etiologies, NDs manifest similar pathologies. Emerging research identifies vascular defects as a previously neglected hallmark of NDs. The development and popularization of single-cell RNA sequencing (scRNA-seq) technologies have significantly advanced our understanding of brain vascular cell types and their molecular characteristics, including gene expression changes at the single-cell level in NDs. These unprecedented insights deepen our understanding of the pathogenic mechanisms underlying NDs. However, the occurrence and role of vascular defects in disease progression remain largely unexplored. In this paper, we systematically summarize recent advances in the structure and organization of the central nervous system vasculature in mice, healthy individuals, and patients with NDs, focussing primarily on disease-specific alterations in vascular cell types or subtypes. Combining scRNA-seq with pathology evidence, we propose that vascular defects, characterized by disruptions in cell types and structural integrity, may serve as common early features of NDs. Finally, we discuss several pathways through which vascular defects in NDs lead to neuronal degeneration. A deeper understanding of the causes and contributions of vascular defects to NDs aids in elucidating the pathogenic mechanisms and developing meaningful therapeutic interventions.

Osteosarcoma (OS) stands as the most prevalent primary bone cancer in children and adolescents, and its limited treatment options often result in unsatisfactory outcomes, particularly for metastatic cases. The tumor microenvironment (TME) has been recognized as a crucial determinant in OS progression. However, the intercellular dynamics between high TP53-expressing OS cells and neighboring cell types within the TME are yet to be thoroughly understood. In our study, we harnessed the single-cell RNA sequencing (scRNA-seq) technology in combination with the computational tool-Cellchat, aiming to elucidate the intercellular communication networks present within OS. Through meticulous quantitative inference and subsequent analysis of these networks, we succeeded in identifying significant signaling pathways connecting high TP53-expressing OS cells with proximate cell types, namely Macrophages, Monocytes, Endothelial Cells, and PVLs. This research brings forth a nuanced understanding of the intricate patterns and coordination involved in the TME's intercellular communication signals. These findings not only provide profound insights into the molecular mechanisms underpinning OS but also indicate potential therapeutic targets that could revolutionize treatment strategies.

Regulatory T cells (Tregs) expressing the transcription factor FoxP3 are essential for maintaining immunological balance and are a significant component of the immunosuppressive tumor microenvironment (TME). Single-cell RNA sequencing (ScRNA-seq) technology has shown that Tregs exhibit significant plasticity and functional diversity in various tumors within the TME. This results in Tregs playing a dual role in the TME, which is not always centered around supporting tumor progression as typically believed. Abundant data confirms the anti-tumor activities of Tregs and their correlation with enhanced patient prognosis in specific types of malignancies. In this review, we summarize the potential anti-tumor actions of Tregs, including suppressing tumor-promoting inflammatory responses and boosting anti-tumor immunity. In addition, this study outlines the spatial and temporal variations in Tregs function to emphasize that their predictive significance in malignancies may change. It is essential to comprehend the functional diversity and potential anti-tumor effects of Tregs to improve tumor therapy strategies.

Tumor Heterogeneity-Based Sscr-Score Predicts Prognosis and Reveals Mechanism of SORL1 in Restraining CLL Progression

Appendiceal adenocarcinoma (AA) is an understudied gastrointestinal malignancy. Treatment is guided by its proximal counterpart, colorectal cancer (CRC), despite recent studies demonstrating AA's unique mutational landscape and poor response to CRC chemotherapy. In this study, we describe AA on a single-cell level and uncover features highlighting the contrast between AA and CRC; we believe these findings will support AA as a unique disease entity and encourage further disease-specific focus. Three patients with peritoneal metastases from AA and one from CRC profiled with 5' single-cell RNA sequencing. Traditional k-means clustering analysis of >30 000 cells revealed three canonical compartments and 11 major cell types. AA samples were mostly comprised of stromal cells (56%), while healthy appendix samples had significantly more immune and epithelial cells. Strikingly, fibroblasts were the most abundant cell type in AA with cancer-associated fibroblasts from the mucinous AA tumors showing a distinct profile from goblet cell AA or CRC. Pseudobulk analysis comparing tumor cells from AA with normal appendiceal epithelial cells demonstrated up-regulation of a diverse range of oncogenic pathways including inflammatory, epithelial-mesenchymal transition, and angiogenesis. As the first application of single-cell technology to AA these data provide insight into the intratumor heterogeneity of AA and highlight the important contribution of the tumor microenvironment in this orphan disease. These results also reinforce multiple observations that AA is a unique disease entity from CRC and targeting the tumor microenvironment should be considered as a therapeutic strategy.

The tumor microenvironment (TME) not only influences cancer progression but also has been shown to have a significant effect on the prognosis. One of the major TME components is cancer-associated fibroblasts (CAFs), of which several subtypes have been identified in tumor tissues. Although single-cell RNA sequencing (scRNA-seq) technology is a powerful tool for investigating the proportions and composition of cells in tissues, large-scale datasets are needed to analyze small cell populations. Here, we constructed an integrated scRNA-seq dataset for non-small cell lung cancer (NSCLC) by compiling publicly available data and observed differences in TME heterogeneity and cellular composition between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). In particular, we identified significant differences in CAF subtypes between LUAD and LUSC. Inflammatory CAFs (iCAFs) were predominantly found in LUAD, whereas myofibroblastic CAFs (mCAFs) were more common in LUSC. A coculture analysis of lung fibroblasts confirmed that LUAD cells induced the iCAF phenotype and, in contrast, that LUSC cells promoted myofibroblastic differentiation. A correlation analysis with the prognosis identified mCAFs as poor prognostic factors in both LUAD and LUSC, but iCAFs were found to be a poor prognostic factor only in LUSC, with the opposite pattern observed in LUAD. This work highlights important considerations regarding the CAF subtype dominance in LUAD and LUSC, which may be related to the prognosis.