Biological and Medical Importance of Cellular Heterogeneity Deciphered by Single-Cell RNA Sequencing

Advances in single-cell long-read sequencing technologies

With an increase in accuracy and throughput of long-read sequencing technologies, they are rapidly being assimilated into the single-cell sequencing pipelines. For transcriptome sequencing, these techniques provide RNA isoform-level information in addition to the gene expression profiles. Long-read sequencing technologies not only help in uncovering complex patterns of cell-type specific splicing, but also offer unprecedented insights into the origin of cellular complexity and thus potentially new avenues for drug development. Additionally, single-cell long-read DNA sequencing enables high-quality assemblies, structural variant detection, haplotype phasing, resolving high-complexity regions, and characterization of epigenetic modifications. Given that significant progress has primarily occurred in single-cell RNA isoform sequencing (scRiso-seq), this review will delve into these advancements in depth and highlight the practical considerations and operational challenges, particularly pertaining to downstream analysis. We also aim to offer a concise introduction to complementary technologies for single-cell sequencing of the genome, epigenome and epitranscriptome. We conclude by identifying certain key areas of innovation that may drive these technologies further and foster more widespread application in biomedical science.

Single-cell combined with transcriptome sequencing to explore the molecular mechanism of cell communication in idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a common, chronic, and progressive lung disease that severely impacts human health and survival. However, the intricate molecular underpinnings of IPF remains elusive. This study aims to delve into the nuanced molecular interplay of cellular interactions in IPF, thereby laying the groundwork for innovative therapeutic approaches in the clinical field of IPF. Sophisticated bioinformatics methods were employed to identify crucial biomarkers essential for the progression of IPF. The GSE122960 single-cell dataset was obtained from the Gene Expression Omnibus (GEO) compendium, and intercellular communication potentialities were scrutinized via CellChat. The random survival forest paradigm was established using the GSE70866 dataset. Quintessential genes were selected through Kaplan-Meier (KM) curves, while immune infiltration examinations, functional enrichment critiques and nomogram paradigms were inaugurated. Analysis of intercellular communication revealed an intimate potential connections between macrophages and various cell types, pinpointing five cardinal genes influencing the trajectory and prognosis of IPF. The nomogram paradigm, sculpted from these seminal genes, exhibits superior predictive prowess. Our research meticulously identified five critical genes, confirming their intimate association with the prognosis, immune infiltration and transcriptional governance of IPF. Interestingly, we discerned these genes' engagement with the EPITHELIAL_MESENCHYMAL_TRANSITION signalling pathway, which may enhance our understanding of the molecular complexity of IPF.

Single-Cell Sequencing Reveals MYOF-Enriched Monocyte/Macrophage Subcluster as a Favorable Prognostic Factor in Sepsis

This research utilized single-cell RNA sequencing to map the immune cell landscape in sepsis, revealing 28 distinct cell clusters and categorizing them into nine major types. Delving into the monocyte/macrophage subclusters, 12 unique subclusters are identified and pathway enrichment analyses are conducted using KEGG and GO, discovering enriched pathways such as oxidative phosphorylation and antigen processing. Further GSVA and AUCell assessments show varied activation of interferon pathways, especially in subclusters 4 and 11. The clinical correlation analysis reveals genes significantly linked to survival outcomes. Additionally, cellular differentiation in these subclusters is explored. Building on these insights, the differential gene expression within these subclusters is specifically scrutinized, which reveal MYOF as a key gene with elevated expression levels in the survivor group. This finding is further supported by in-depth pathway enrichment analysis and the examination of cellular differentiation trajectories, where MYOF's role became evident in the context of immune response regulation and sepsis progression. Validating the role of the MYOF gene in sepsis, a dose-dependent response to LPS in THP-1 cells and C57 mice is observed. Finally, inter-cellular communications are analyzed, particularly focusing on the MYOF+Mono/Macro subcluster, which indicates a pivotal role in immune regulation and potential therapeutic targeting.

Modular dual-color BiAD sensors for locus-specific readout of epigenome modifications in single cells

Dynamic changes in the epigenome at defined genomic loci play crucial roles during cellular differentiation and disease development. Here, we developed dual-color bimolecular anchor detector (BiAD) sensors for high-sensitivity readout of locus-specific epigenome modifications by fluorescence microscopy. Our BiAD sensors comprise an sgRNA/dCas9 complex as anchor and double chromatin reader domains as detector modules, both fused to complementary parts of a split IFP2.0 fluorophore, enabling its reconstitution upon binding of both parts in close proximity. In addition, a YPet fluorophore is recruited to the sgRNA to mark the genomic locus of interest. With these dual-color BiAD sensors, we detected H3K9me2/3 and DNA methylation and their dynamic changes upon RNAi or inhibitor treatment with high sensitivity at endogenous genomic regions. Furthermore, we showcased locus-specific H3K36me2/3 readout as well as H3K27me3 and H3K9me2/3 enrichment on the inactive X chromosome, highlighting the broad applicability of our dual-color BiAD sensors for single-cell epigenome studies.

Leveraging cross-source heterogeneity to improve the performance of bulk gene expression deconvolution

A main limitation of bulk transcriptomic technologies is that individual measurements normally contain contributions from multiple cell populations, impeding the identification of cellular heterogeneity within diseased tissues. To extract cellular insights from existing large cohorts of bulk transcriptomic data, we present CSsingle, a novel method designed to accurately deconvolve bulk data into a predefined set of cell types using a scRNA-seq reference. Through comprehensive benchmark evaluations and analyses using diverse real data sets, we reveal the systematic bias inherent in existing methods, stemming from differences in cell size or library size. Our extensive experiments demonstrate that CSsingle exhibits superior accuracy and robustness compared to leading methods, particularly when dealing with bulk mixtures originating from cell types of markedly different cell sizes, as well as when handling bulk and single-cell reference data obtained from diverse sources. Our work provides an efficient and robust methodology for the integrated analysis of bulk and scRNA-seq data, facilitating various biological and clinical studies.

Viral vector-mediated transgene delivery with novel recombinase systems for targeting neuronal populations defined by multiple features

A comprehensive understanding of neuronal diversity and connectivity is essential for understanding the anatomical and cellular mechanisms that underlie functional contributions. With the advent of single-cell analysis, growing information regarding molecular profiles leads to the identification of more heterogeneous cell types. Therefore, the need for additional orthogonal recombinase systems is increasingly apparent, as heterogeneous tissues can be further partitioned into increasing numbers of specific cell types defined by multiple features. Critically, new recombinase systems should work together with pre-existing systems without cross-reactivity in vivo. Here, we introduce novel site-specific recombinase systems based on ΦC31 bacteriophage recombinase for labeling multiple cell types simultaneously and a novel viral strategy for versatile and robust intersectional expression of any transgene. Together, our system will help researchers specifically target different cell types with multiple features in the same animal.

Single-cell sequencing highlights heterogeneity and malignant progression in actinic keratosis and cutaneous squamous cell carcinoma

Cutaneous squamous cell carcinoma (cSCC) is the second most frequent of the keratinocyte-derived malignancies with actinic keratosis (AK) as a precancerous lesion. To comprehensively delineate the underlying mechanisms for the whole progression from normal skin to AK to invasive cSCC, we performed single-cell RNA sequencing (scRNA-seq) to acquire the transcriptomes of 138,982 cells from 13 samples of six patients including AK, squamous cell carcinoma in situ (SCCIS), cSCC, and their matched normal tissues, covering comprehensive clinical courses of cSCC. We identified diverse cell types, including important subtypes with different gene expression profiles and functions in major keratinocytes. In SCCIS, we discovered the malignant subtypes of basal cells with differential proliferative and migration potential. Differentially expressed genes (DEGs) analysis screened out multiple key driver genes including transcription factors along AK to cSCC progression. Immunohistochemistry (IHC)/immunofluorescence (IF) experiments and single-cell ATAC sequencing (scATAC-seq) data verified the expression changes of these genes. The functional experiments confirmed the important roles of these genes in regulating cell proliferation, apoptosis, migration, and invasion in cSCC tumor. Furthermore, we comprehensively described the tumor microenvironment (TME) landscape and potential keratinocyte-TME crosstalk in cSCC providing theoretical basis for immunotherapy. Together, our findings provide a valuable resource for deciphering the progression from AK to cSCC and identifying potential targets for anticancer treatment of cSCC.

Chemical tagging mass spectrometry: an approach for single-cell omics

Single-cell (SC) analysis offers new insights into the study of fundamental biological phenomena and cellular heterogeneity. The superior sensitivity, high throughput, and rich chemical information provided by mass spectrometry (MS) allow MS to emerge as a leading technology for molecular profiling of SC omics, including the SC metabolome, lipidome, and proteome. However, issues such as ionization suppression, low concentration, and huge span of dynamic concentrations of SC components lead to poor MS response for certain types of molecules. It is noted that chemical tagging/derivatization has been adopted in SCMS analysis, and this strategy has been proven an effective solution to circumvent these issues in SCMS analysis. Herein, we review the basic principle and general strategies of chemical tagging/derivatization in SCMS analysis, along with recent applications of chemical derivatization to single-cell metabolomics and multiplexed proteomics, as well as SCMS imaging. Furthermore, the challenges and opportunities for the improvement of chemical derivatization strategies in SCMS analysis are discussed.

Single-Cell RNA-Sequencing in Astrocyte Development, Heterogeneity, and Disease

Astrocytes are the most plentiful cell type in the central nervous system (CNS) and perform complicated functions in health and disease. It is obvious that different astrocyte subpopulations, or activation states, are relevant with specific genomic programs and functions. In recent years, the emergence of new technologies such as single-cell RNA sequencing (scRNA-seq) has made substantial advance in the characterization of astrocyte heterogeneity, astrocyte developmental trajectory, and its role in CNS diseases which has had a significant impact on neuroscience. In this review, we present an overview of astrocyte development, heterogeneity, and its essential role in the physiological and pathological environments of the CNS. We focused on the critical role of single-cell sequencing in revealing astrocyte development, heterogeneity, and its role in different CNS diseases.

Application of single-cell RNA sequencing in head and neck squamous cell carcinoma

Single-cell RNA sequencing has been broadly applied to head and neck squamous cell carcinoma (HNSCC) for characterizing the heterogeneity and genomic mutations of HNSCC benefiting from the advantage of single-cell resolution. We summarized most of the current studies and aimed to explore their research methods and ideas, as well as how to transform them into clinical applications. Through single-cell RNA sequencing, we found the differences in tumor cells' expression programs and differentiation tracks. The studies of immune microenvironment allowed us to distinguish immune cell subpopulations, the extensive expression of immune checkpoints, and the complex crosstalk network between immune cells and non-immune cells. For cancer-associated fibroblasts (CAFs), single-cell RNA sequencing had made an irreplaceable contribution to the exploration of their differentiation status, specific CAFs markers, and the interaction with tumor cells and immune cells. In addition, we demonstrated in detail how single-cell RNA sequencing explored the HNSCC epithelial-to-mesenchymal transition (EMT) model and the mechanism of drug resistance, as well as its clinical value.

Computational approaches in rheumatic diseases - Deciphering complex spatio-temporal cell interactions

Inflammatory arthritis, including rheumatoid (RA), and psoriatic (PsA) arthritis, are clinically and immunologically heterogeneous diseases with no identified cure. Chronic inflammation of the synovial tissue ushers loss of function of the joint that severely impacts the patient's quality of life, eventually leading to disability and life-threatening comorbidities. The pathogenesis of synovial inflammation is the consequence of compounded immune and stromal cell interactions influenced by genetic and environmental factors. Deciphering the complexity of the synovial cellular landscape has accelerated primarily due to the utilisation of bulk and single cell RNA sequencing. Particularly the capacity to generate cell-cell interaction networks could reveal evidence of previously unappreciated processes leading to disease. However, there is currently a lack of universal nomenclature as a result of varied experimental and technological approaches that discombobulates the study of synovial inflammation. While spatial transcriptomic analysis that combines anatomical information with transcriptomic data of synovial tissue biopsies promises to provide more insights into disease pathogenesis, in vitro functional assays with single-cell resolution will be required to validate current bioinformatic applications. In order to provide a comprehensive approach and translate experimental data to clinical practice, a combination of clinical and molecular data with machine learning has the potential to enhance patient stratification and identify individuals at risk of arthritis that would benefit from early therapeutic intervention. This review aims to provide a comprehensive understanding of the effect of computational approaches in deciphering synovial inflammation pathogenesis and discuss the impact that further experimental and novel computational tools may have on therapeutic target identification and drug development.

Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing

Spatial transcriptomics (ST) technologies are rapidly becoming the extension of single-cell RNA sequencing (scRNAseq), holding the potential of profiling gene expression at a single-cell resolution while maintaining cellular compositions within a tissue. Having both expression profiles and tissue organization enables researchers to better understand cellular interactions and heterogeneity, providing insight into complex biological processes that would not be possible with traditional sequencing technologies. Data generated by ST technologies are inherently noisy, high-dimensional, sparse, and multi-modal (including histological images, count matrices, etc.), thus requiring specialized computational tools for accurate and robust analysis. However, many ST studies currently utilize traditional scRNAseq tools, which are inadequate for analyzing complex ST datasets. On the other hand, many of the existing ST-specific methods are built upon traditional statistical or machine learning frameworks, which have shown to be sub-optimal in many applications due to the scale, multi-modality, and limitations of spatially resolved data (such as spatial resolution, sensitivity, and gene coverage). Given these intricacies, researchers have developed deep learning (DL)-based models to alleviate ST-specific challenges. These methods include new state-of-the-art models in alignment, spatial reconstruction, and spatial clustering, among others. However, DL models for ST analysis are nascent and remain largely underexplored. In this review, we provide an overview of existing state-of-the-art tools for analyzing spatially resolved transcriptomics while delving deeper into the DL-based approaches. We discuss the new frontiers and the open questions in this field and highlight domains in which we anticipate transformational DL applications.

Cellular heterogeneity in disease severity and clinical outcome: Granular understanding of immune response is key

During an infectious disease progression, it is crucial to understand the cellular heterogeneity underlying the differential immune response landscape that will augment the precise information of the disease severity modulators, leading to differential clinical outcome. Patients with COVID-19 display a complex yet regulated immune profile with a heterogeneous array of clinical manifestation that delineates disease severity sub-phenotypes and worst clinical outcomes. Therefore, it is necessary to elucidate/understand/enumerate the role of cellular heterogeneity during COVID-19 disease to understand the underlying immunological mechanisms regulating the disease severity. This article aims to comprehend the current findings regarding dysregulation and impairment of immune response in COVID-19 disease severity sub-phenotypes and relate them to a wide array of heterogeneous populations of immune cells. On the basis of the findings, it suggests a possible functional correlation between cellular heterogeneity and the COVID-19 disease severity. It highlights the plausible modulators of age, gender, comorbidities, and hosts' genetics that may be considered relevant in regulating the host response and subsequently the COVID-19 disease severity. Finally, it aims to highlight challenges in COVID-19 disease that can be achieved by the application of single-cell genomics, which may aid in delineating the heterogeneity with more granular understanding. This will augment our future pandemic preparedness with possibility to identify the subset of patients with increased diseased severity.

Integration of Bulk RNA Sequencing and Single-Cell RNA Sequencing to Reveal Uveal Melanoma Tumor Heterogeneity and Cells Related to Survival

Molecular classification based on transcriptional characteristics is often used to study tumor heterogeneity. Human cancer has different cell populations with distinct transcription in tumors, and their heterogeneity is the focus of tumor therapy. Our purpose was to explore the tumor heterogeneity of uveal melanoma (UM) through RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq). Based on the consensus clustering assays of the prognosis-related immune gene set, the immune subtype (IS) of UM and its corresponding immune characteristics were comprehensively analyzed. The heterogeneous cell groups and corresponding marker genes of UM were identified from GSE138433 using scRNA-seq analysis. Pseudotime trajectory analysis and SCENIC analysis were conducted to explore the trajectory of cell differentiation and the regulatory network of single-cell transcription factors (TFs). Based on 37 immune gene sets, UM was divided into two different immune subtypes (IS1 and IS2). The two kinds of ISs have different characteristics in prognosis, immune-related molecules, immune score, and immune cell infiltration. According to 11,988 cells of scRNA-seq data from six UM samples, 11 cell clusters and 10 cell types were identified. The subsets of C1, C4, C5, C8, and C9 were related to the prognosis of UM, and different TF–target gene regulatory networks were involved. These five cell subsets differentiated into 3 different states. Our results provided valuable information about the heterogeneity of UM tumors and the expression patterns of TFs in different cell types.

A deep matrix factorization based approach for single-cell RNA-seq data clustering

The rapid development of single-cell sequencing technologies makes it possible to analyze cellular heterogeneity at the single-cell level. Cell clustering is one of the most fundamental and common steps in the heterogeneity analysis. However, due to the high noise level, high dimensionality and high sparsity, accurate cell clustering is still challengeable. Here, we present DeepCI, a new clustering approach for scRNA-seq data. Using two autoencoders to obtain cell embedding and gene embedding, DeepCI can simultaneously learn cell low-dimensional representation and clustering. In addition, the recovered gene expression matrix can be obtained by the matrix multiplication of cell and gene embedding. To evaluate the performance of DeepCI, we performed it on several real scRNA-seq datasets for clustering and visualization analysis. The experimental results show that DeepCI obtains the overall better performance than several popular single cell analysis methods. We also evaluated the imputation performance of DeepCI by a dedicated experiment. The corresponding results show that the imputed gene expression of known specific marker gene can greatly improve the accuracy of cell type classification.

Cancer Risk and Mutational Patterns Following Organ Transplantation

The rapid development of medical technology and widespread application of immunosuppressive drugs have improved the success rate of organ transplantation significantly. However, the use of immunosuppressive agents increases the frequency of malignancy greatly. With the prospect of “precision medicine” for tumors and development of next-generation sequencing technology, more attention has been paid to the application of high-throughput sequencing technology in clinical oncology research, which is mainly applied to the early diagnosis of tumors and analysis of tumor-related genes. All generations of cancers carry somatic mutations, meanwhile, significant differences were observed in mutational signatures across tumors. Systematic sequencing of cancer genomes from patients after organ transplantation can reveal DNA damage and repair processes in exposed cancer cells and their precursors. In this review, we summarize the application of high-throughput sequencing and organoids in the field of organ transplantation, the mutational patterns of cancer genomes, and propose a new research strategy for understanding the mechanism of cancer following organ transplantation.

Molecular analysis of vascular gene expression

A State of the Art lecture entitled "Molecular Analysis of Vascular Gene Expression" was presented at the ISTH Congress in 2021. Endothelial cells (ECs) form a critical interface between the blood and underlying tissue environment, serving as a reactive barrier to maintain tissue homeostasis. ECs play an important role in not only coagulation, but also in the response to inflammation by connecting these two processes in the host defense against pathogens. Furthermore, ECs tailor their behavior to the needs of the microenvironment in which they reside, resulting in a broad display of EC phenotypes. While this heterogeneity has been acknowledged for decades, the contributing molecular mechanisms have only recently started to emerge due to technological advances. These include high-throughput sequencing combined with methods to isolate ECs directly from their native tissue environment, as well as sequencing samples at a high cellular resolution. In addition, the newest technologies simultaneously quantitate and visualize a multitude of RNA transcripts directly in tissue sections, thus providing spatial information. Understanding how ECs function in (patho)physiological conditions is crucial to develop new therapeutics as many diseases can directly affect the endothelium. Of particular relevance for thrombotic disorders, EC dysfunction can lead to a procoagulant, proinflammatory phenotype with increased vascular permeability that can result in coagulopathy and tissue damage, as seen in a number of infectious diseases, including sepsis and coronavirus disease 2019. In light of the current pandemic, we will summarize relevant new data on the latter topic presented during the 2021 ISTH Congress.

Fibroblasts orchestrate cellular crosstalk in the heart through the ECM

Cell communication is needed for organ function and stress responses, especially in the heart. Cardiac fibroblasts, cardiomyocytes, immune cells, and endothelial cells comprise the major cell types in ventricular myocardium that together coordinate all functional processes. Critical to this cellular network is the non-cellular extracellular matrix (ECM) that provides structure and harbors growth factors and other signaling proteins that affect cell behavior. The ECM is not only produced and modified by cells within the myocardium, largely cardiac fibroblasts, it also acts as an avenue for communication among all myocardial cells. In this Review, we discuss how the development of therapeutics to combat cardiac diseases, specifically fibrosis, relies on a deeper understanding of how the cardiac ECM is intertwined with signaling processes that underlie cellular activation and behavior.

Tracing PRX1+ cells during molar formation and periodontal ligament reconstruction

Neural crest-derived mesenchymal stem cells (MSCs) are known to play an essential function during tooth and skeletal development. PRX1⁺ cells constitute an important MSC subtype that is implicated in osteogenesis. However, their potential function in tooth development and regeneration remains elusive. In the present study, we first assessed the cell fate of PRX1⁺ cells during molar development and periodontal ligament (PDL) formation in mice. Furthermore, single-cell RNA sequencing analysis was performed to study the distribution of PRX1⁺ cells in PDL cells. The behavior of PRX1⁺ cells during PDL reconstruction was investigated using an allogeneic transplanted tooth model. Although PRX1⁺ cells are spatial specific and can differentiate into almost all types of mesenchymal cells in first molars, their distribution in third molars is highly limited. The PDL formation is associated with a high number of PRX1⁺ cells; during transplanted teeth PDL reconstruction, PRX1⁺ cells from the recipient alveolar bone participate in angiogenesis as pericytes. Overall, PRX1⁺ cells are a key subtype of dental MSCs involved in the formation of mouse molar and PDL and participate in angiogenesis as pericytes during PDL reconstruction after tooth transplantation.

Single cell RNA sequencing approaches to cardiac development and congenital heart disease

The development of single cell RNA sequencing technologies has accelerated the ability of scientists to understand healthy and disease states of the cardiovascular system. Congenital heart defects occur in approximately 40,000 births each year and 1 out of 4 children are born with critical congenital heart disease requiring surgical interventions and a lifetime of monitoring. An understanding of how the normal heart develops and how each cell contributes to normal and pathological anatomy is an important goal in pediatric cardiovascular research. Single cell sequencing has provided the tools to increase the ability to discover rare cell types and novel genes involved in normal cardiac development. Knowledge of gene expression of single cells within cardiac tissue has contributed to the understanding of how each cell type contributes to the anatomic structures of the heart. In this review, we summarize how single cell RNA sequencing has been utilized to understand cardiac developmental processes and congenital heart disease. We discuss the advantages and disadvantages of whole cell versus single nuclei RNA sequencing and describe the approaches to analyze the interactomes, transcriptomes, and differentiation trajectory from single cell data. We summarize the currently available single cell RNA sequencing technologies and technical aspects of performing single cell analysis and how to overcome common obstacles. We also review data from the recently published human and mouse fetal heart atlases and advancements that have occurred within the field due to the application of these single cell tools. Finally we highlight the potential for single cell technologies to uncover novel mechanisms of disease pathogenesis by leveraging findings from genome wide association studies.

Advances in single-cell sequencing: insights from organ transplantation

Single-cell RNA sequencing (scRNA-seq) is a comprehensive technical tool to analyze intracellular and intercellular interaction data by whole transcriptional profile analysis. Here, we describe the application in biomedical research, focusing on the immune system during organ transplantation and rejection. Unlike conventional transcriptome analysis, this method provides a full map of multiple cell populations in one specific tissue and presents a dynamic and transient unbiased method to explore the progression of allograft dysfunction, starting from the stress response to final graft failure. This promising sequencing technology remarkably improves individualized organ rejection treatment by identifying decisive cellular subgroups and cell-specific interactions.

Heterogeneity of immune cells in human atherosclerosis revealed by scRNA-Seq

Immune cells in atherosclerosis include T, B, natural killer (NK) and NKT cells, macrophages, monocytes, dendritic cells (DCs), neutrophils and mast cells. Advances in single cell RNA sequencing (sRNA-Seq) have refined our understanding of immune cell subsets. Four recent studies have used scRNA-Seq of immune cells in human atherosclerotic lesions and peripheral blood mononuclear cells (PBMCs), some including cell surface phenotypes revealed by oligonucleotide-tagged antibodies, which confirmed known and identified new immune cell subsets and identified genes significantly upregulated in PBMCs from HIV+ subjects with atherosclerosis compared to PBMCs from matched HIV+ subjects without atherosclerosis. The ability of scRNA-Seq to identify cell types is greatly augmented by adding cell surface phenotype using antibody sequencing. In this review we summarize the latest data obtained by scRNA-Seq on plaques and human PBMCs in human subjects with atherosclerosis.

Single-cell RNA sequencing in human lung cancer: Applications, challenges, and pathway towards personalized therapy

Lung cancer is one of the most prevalent human cancers, and single-cell RNA sequencing (scRNA-seq) has been widely used to study human lung cancer at the cellular, genetic, and molecular level. Even though there are published reviews, which summarized the applications of scRNA-seq in human cancers like breast cancer, there is lack of a comprehensive review, which could effectively highlight the broad use of scRNA-seq in studying lung cancer. This review, therefore, was aimed to summarize the various applications of scRNA-seq in human lung cancer research based on the findings from different published in vitro, in vivo, and clinical studies. The review would first briefly outline the concept and principle of scRNA-seq, followed by the discussion on the applications of scRNA-seq in studying human lung cancer. Finally, the challenges faced when using scRNA-seq to study human lung cancer would be discussed, and the potential applications and challenges of scRNA-seq to facilitate the development of personalized cancer therapy in the future would be explored.

Single-cell RNA sequencing: A new opportunity for retinal research

Single-cell RNA sequencing (scRNA-seq) is a technology for single-cell transcriptome analysis that can be used to characterize complex dynamics of various retinal cell types. It provides deep scrutiny into the gene expression character of diverse cell types, lending insight into all the biological processes being carried out. The scRNA-seq is an alternative to regular RNA-seq, which does not achieve cellular heterogeneity. The retina, is a part of the central nervous system (CNS) and consists of six types of neurons and several types of glial cells. Studying retinal cell heterogeneity is important for understanding retinal diseases. Currently, scRNA-seq is employed to assess retina development and retinal disease pathogenesis and has improved our understanding of the relationship between the retina, its visual pathways, and the brain. Moreover, this technology provides new ideas on the sensitivity and molecular mechanisms of cell subtypes involved in retinal-related diseases. The application of scRNA-seq technology has given us a deeper understanding of the latest advancements and challenges in retinal development and diseases. We advocate scRNA-seq as one of the important tools for developing novel therapies for retinal diseases. This article is categorized under: RNA Methods > RNA Analyses in Cells RNA in Disease and Development > RNA in Development RNA in Disease and Development > RNA in Disease.

Single-cell transcriptomic analysis of small and large wounds reveals the distinct spatial organization of regenerative fibroblasts

Wound-induced hair follicle neogenesis (WIHN) has been an important model to study hair follicle regeneration during wound repair. However, the cellular and molecular components of the dermis that make large wounds more regenerative are not fully understood. Here, we compare and contrast recently published scRNA-seq data of small scarring wounds to wounds that regenerate in hope to elucidate the role of fibroblasts lineages in WIHN. Our analysis revealed an over-representation of the newly identified upper wound fibroblasts in regenerative wound conditions, which express the retinoic acid binding protein Crabp1. This regenerative cell type shares a similar gene signature to the murine papillary fibroblast lineage, which are necessary to support hair follicle morphogenesis and homeostasis. RNA velocity analysis comparing scarring and regenerating wounds revealed the divergent trajectories towards upper and lower wound fibroblasts and that the upper populations were closely associated with the specialized dermal papilla. We also provide analyses and explanation reconciling the inconsistency between the histological lineage tracing and the scRNA-seq data from recent reports investigating large wounds. Finally, we performed a computational test to map the spatial location of upper wound fibroblasts in large wounds which revealed that upper peripheral fibroblasts might harbour equivalent regenerative competence as those in the centre. Overall, our scRNA-seq reanalysis combining multiple samples suggests that upper wound fibroblasts are required for hair follicle regeneration and that papillary fibroblasts may migrate from the wound periphery to the centre during wound re-epithelialization. Moreover, data from this publication are made available on our searchable web resource: https://skinregeneration.org/.