Classification of low quality cells from single-cell RNA-seq data

Neuron-Glia-Ratio-Like Approach Evidenced for Limited Variability and In-Aggregate Circadian Shifts in Cortical Cell-Specific Transcriptomes

Regardless of shifts in levels of individual transcripts, it remains elusive whether natural variability in cell-specific transcriptomes within the cerebral cortex is limited in aggregate. It is also unclear whether cortical cell-specific transcriptomes might change dynamically in absence of cell number changes. Total variation in neuron- and glia-specific in-aggregate transcriptomes could be identified in a model-free way via glia-neuron ratio approach, by univariate median-to-median ratios comparing integral levels of cell-specific transcripts within a tissue sample. When deleterious, regenerative or developmental events affecting cortical cell numbers were subtle, median-to-median ratios demonstrated within-group variability not exceeding <20-25% in most cases. These levels of total variability might be explained in part by limited (~5-10%) circadian and stress-induced shifts in cell-specific cortical transcriptomes. Relevant in-aggregate transcriptomic alterations were identified after shifts in cell numbers induced by well-validated deleterious events including ischemia, traumatic injury, microglia's activation/depletion or specific mutations. Cortical median-to-median ratios also follow naturally occurring changes in the numbers of excitatory, inhibitory neurons and glial cells during perinatal brain development. These findings characterize cortical cell-specific transcriptomes as subjects to circadian shifts and lifetime events, urging the importance of reporting full details on an origin of any transcriptomic sample collected in vivo.

Single-nuclei transcriptomics enable detection of somatic variants in patient brain tissue

Somatic variants are a major cause of human disease, including neurological disorders like focal epilepsies, but can be challenging to study due to their mosaicism in bulk tissue biopsies. Coupling single-cell genotype and transcriptomic data has potential to provide insight into the role somatic variants play in disease etiology, such as by determining what cell types are affected or how the mutations affect gene expression. Here, we asked whether commonly used single-nucleus 3'- or 5'-RNA-sequencing assays can be used to derive single-nucleus genotype data for a priori known variants that are located near to either end of a transcript. To that end, we compared performance of commercially available single-nuclei 3'- and 5'- gene expression kits using resected brain samples from three pediatric patients with focal epilepsy. We quantified the ability to detect genetic variants in single-nucleus datasets depending on distance from the transcript end. Finally, we demonstrated the ability to identify affected cell types in a patient with a RHEB somatic variant causing an epilepsy-associated cortical malformation. Our results demonstrate that single-nuclei 3' or 5'-RNA-sequencing data can be used to identify known somatic variants in single-nuclei when they are expressed within proximity to a transcript end.

Selective advantage of epigenetically disrupted cancer cells via phenotypic inertia

The evolution of established cancers is driven by selection of cells with enhanced fitness. Subclonal mutations in numerous epigenetic regulator genes are common across cancer types, yet their functional impact has been unclear. Here, we show that disruption of the epigenetic regulatory network increases the tolerance of cancer cells to unfavorable environments experienced within growing tumors by promoting the emergence of stress-resistant subpopulations. Disruption of epigenetic control does not promote selection of genetically defined subclones or favor a phenotypic switch in response to environmental changes. Instead, it prevents cells from mounting an efficient stress response via modulation of global transcriptional activity. This "transcriptional numbness" lowers the probability of cell death at early stages, increasing the chance of long-term adaptation at the population level. Our findings provide a mechanistic explanation for the widespread selection of subclonal epigenetic-related mutations in cancer and uncover phenotypic inertia as a cellular trait that drives subclone expansion.

Colorectal Cancer Metastases in the Liver Establish Immunosuppressive Spatial Networking between Tumor-Associated SPP1+ Macrophages and Fibroblasts

PURPOSE

The liver is the most frequent metastatic site for colorectal cancer. Its microenvironment is modified to provide a niche that is conducive for colorectal cancer cell growth. This study focused on characterizing the cellular changes in the metastatic colorectal cancer (mCRC) liver tumor microenvironment (TME).

EXPERIMENTAL DESIGN

We analyzed a series of microsatellite stable (MSS) mCRCs to the liver, paired normal liver tissue, and peripheral blood mononuclear cells using single-cell RNA sequencing (scRNA-seq). We validated our findings using multiplexed spatial imaging and bulk gene expression with cell deconvolution.

RESULTS

We identified TME-specific SPP1-expressing macrophages with altered metabolism features, foam cell characteristics, and increased activity in extracellular matrix (ECM) organization. SPP1+ macrophages and fibroblasts expressed complementary ligand-receptor pairs with the potential to mutually influence their gene-expression programs. TME lacked dysfunctional CD8 T cells and contained regulatory T cells, indicative of immunosuppression. Spatial imaging validated these cell states in the TME. Moreover, TME macrophages and fibroblasts had close spatial proximity, which is a requirement for intercellular communication and networking. In an independent cohort of mCRCs in the liver, we confirmed the presence of SPP1+ macrophages and fibroblasts using gene-expression data. An increased proportion of TME fibroblasts was associated with the worst prognosis in these patients.

CONCLUSIONS

We demonstrated that mCRC in the liver is characterized by transcriptional alterations of macrophages in the TME. Intercellular networking between macrophages and fibroblasts supports colorectal cancer growth in the immunosuppressed metastatic niche in the liver. These features can be used to target immune-checkpoint-resistant MSS tumors.

Single-Cell Transcriptomic Profiling of Müller Glia in the rd10 Retina

Müller glia are the principal macroglia of the retina and support retinal neurons both in health and disease. In retinitis pigmentosa (RP), a highly heterogeneous inherited retinal disorder, the most common form of pathology involves primary rod degeneration, followed by secondary cone death. To investigate Müller glia responses to rod degeneration, we performed droplet-based single-cell RNA sequencing in the rd10 mouse model of RP during primary rod degeneration. We confirmed known MG behavior on gliosis, metabolic, and immune functions. Pde6brd10 Müller glia also exhibited an increased expression of histocompatibility complex members, which might arise from a novel immune function of Müller glia in RP. We also describe a possible decrease in glial lipid biogenesis, which might affect degenerating photoreceptors.

Multi-omic analyses in immune cell development with lessons learned from T cell development

Traditionally, flow cytometry has been the preferred method to characterize immune cells at the single-cell level. Flow cytometry is used in immunology mostly to measure the expression of identifying markers on the cell surface, but-with good antibodies-can also be used to assess the expression of intracellular proteins. The advent of single-cell RNA-sequencing has paved the road to study immune development at an unprecedented resolution. Single-cell RNA-sequencing studies have not only allowed us to efficiently chart the make-up of heterogeneous tissues, including their most rare cell populations, it also increasingly contributes to our understanding how different omics modalities interplay at a single cell resolution. Particularly for investigating the immune system, this means that these single-cell techniques can be integrated to combine and correlate RNA and protein data at the single-cell level. While RNA data usually reveals a large heterogeneity of a given population identified solely by a combination of surface protein markers, the integration of different omics modalities at a single cell resolution is expected to greatly contribute to our understanding of the immune system.

Bulk and Single-Cell RNA-Seq Analyses for Studies of Spermatogonia

Robust methods have been developed that leverage next-generation sequencing (NGS) to measure abundance of all mRNAs (RNA-seq) in samples as small as individual cells in order to study the testicular transcriptome in mammals. In this chapter, we present robust options for implementing bioinformatics workflows for the analysis of bulk RNA-seq from aggregate samples of hundreds to millions of cells and single-cell RNA-seq from individual cells. We also provide detailed protocols for using the R packages DESeq2 and Seurat, important parameters for successful implementation, and considerations for drawing conclusions from the results.

scAN1.0: A reproducible and standardized pipeline for processing 10X single cell RNAseq data

Single cell transcriptomics has recently seen a surge in popularity, leading to the need for data analysis pipelines that are reproducible, modular, and interoperable across different systems and institutions.To meet this demand, we introduce scAN1.0, a processing pipeline for analyzing 10X single cell RNA sequencing data. scAN1.0 is built using the Nextflow DSL2 and can be run on most computational systems. The modular design of Nextflow pipelines enables easy integration and evaluation of different blocks for specific analysis steps.We demonstrate the usefulness of scAN1.0 by showing its ability to examine the impact of the mapping step during the analysis of two datasets: (i) a 10X scRNAseq of a human pituitary gonadotroph tumor dataset and (ii) a murine 10X scRNAseq acquired on CD8 T cells during an immune response.

Stemness-related genes revealed by single-cell profiling of naïve and stimulated human CD34+ cells from CB and mPB

BACKGROUND

Hematopoietic stem cells (HSCs) from different sources show varied repopulating capacity, and HSCs lose their stemness after long-time ex vivo culture. A deep understanding of these phenomena may provide helpful insights for HSCs.

METHODS

Here, we applied single-cell RNA-seq (scRNA-seq) to analyse the naïve and stimulated human CD34+ cells from cord blood (CB) and mobilised peripheral blood (mPB).

RESULTS

We collected over 16 000 high-quality single-cell data to construct a comprehensive inference map and characterised the HSCs under a quiescent state on the hierarchy top. Then, we compared HSCs in CB with those in mPB and HSCs of naïve samples to those of cultured samples, and identified stemness-related genes (SRGs) associated with cell source (CS-SRGs) and culture time (CT-SRGs), respectively. Interestingly, CS-SRGs and CT-SRGs share genes enriched in the signalling pathways such as mRNA catabolic process, translational initiation, ribonucleoprotein complex biogenesis and cotranslational protein targeting to membrane, suggesting dynamic protein translation and processing may be a common requirement for stemness maintenance. Meanwhile, CT-SRGs are enriched in pathways involved in glucocorticoid and corticosteroid response that affect HSCs homing and engraftment. In contrast, CS-SRGs specifically contain genes related to purine and ATP metabolic process, which is crucial for HSC homeostasis in the stress settings. Particularly, when CT-SRGs are used as reference genes for the construction of the development trajectory of CD34+ cells, lymphoid and myeloid lineages are clearly separated after HSCs/MPPs. Finally, we presented an application through a small-scale drug screening using Connectivity Map (CMap) against CT-SRGs. A small molecule, cucurbitacin I, was found to efficiently expand HSCs ex vivo while maintaining its stemness.

CONCLUSIONS

Our findings provide new perspectives for understanding HSCs, and the strategy to identify candidate molecules through SRGs may be applicable to study other stem cells.

Benchmarking full-length transcript single cell mRNA sequencing protocols

BACKGROUND

Single cell mRNA sequencing technologies have transformed our understanding of cellular heterogeneity and identity. For sensitive discovery or clinical marker estimation where high transcript capture per cell is needed only plate-based techniques currently offer sufficient resolution.

RESULTS

Here, we present a performance evaluation of four different plate-based scRNA-seq protocols. Our evaluation is aimed towards applications taxing high gene detection sensitivity, reproducibility between samples, and minimum hands-on time, as is required, for example, in clinical use. We included two commercial kits, NEBNext® Single Cell/ Low Input RNA Library Prep Kit (NEB®), SMART-seq® HT kit (Takara®), and the non-commercial protocols Genome & Transcriptome sequencing (G&T) and SMART-seq3 (SS3). G&T delivered the highest detection of genes per single cell. SS3 presented the highest gene detection per single cell at the lowest price. Takara® kit presented similar high gene detection per single cell, and high reproducibility between samples, but at the absolute highest price. NEB® delivered a lower detection of genes but remains an alternative to more expensive commercial kits.

CONCLUSION

For the tested kits we found that ease-of-use came at higher prices. Takara can be selected for its ease-of-use to analyse a few samples, but we recommend the cheaper G&T-seq or SS3 for laboratories where a substantial sample flow can be expected.

Biology-inspired data-driven quality control for scientific discovery in single-cell transcriptomics

BACKGROUND

Quality control (QC) of cells, a critical first step in single-cell RNA sequencing data analysis, has largely relied on arbitrarily fixed data-agnostic thresholds applied to QC metrics such as gene complexity and fraction of reads mapping to mitochondrial genes. The few existing data-driven approaches perform QC at the level of samples or studies without accounting for biological variation.

RESULTS

We first demonstrate that QC metrics vary with both tissue and cell types across technologies, study conditions, and species. We then propose data-driven QC (ddqc), an unsupervised adaptive QC framework to perform flexible and data-driven QC at the level of cell types while retaining critical biological insights and improved power for downstream analysis. ddqc applies an adaptive threshold based on the median absolute deviation on four QC metrics (gene and UMI complexity, fraction of reads mapping to mitochondrial and ribosomal genes). ddqc retains over a third more cells when compared to conventional data-agnostic QC filters. Finally, we show that ddqc recovers biologically meaningful trends in gradation of gene complexity among cell types that can help answer questions of biological interest such as which cell types express the least and most number of transcripts overall, and ribosomal transcripts specifically.

CONCLUSIONS

ddqc retains cell types such as metabolically active parenchymal cells and specialized cells such as neutrophils which are often lost by conventional QC. Taken together, our work proposes a revised paradigm to quality filtering best practices-iterative QC, providing a data-driven QC framework compatible with observed biological diversity.

Application of Single-Cell RNA Sequencing in Ovarian Development

The ovary is a female reproductive organ that plays a key role in fertility and the maintenance of endocrine homeostasis, which is of great importance to women's health. It is characterized by a high heterogeneity, with different cellular subpopulations primarily containing oocytes, granulosa cells, stromal cells, endothelial cells, vascular smooth muscle cells, and diverse immune cell types. Each has unique and important functions. From the fetal period to old age, the ovary experiences continuous structural and functional changes, with the gene expression of each cell type undergoing dramatic changes. In addition, ovarian development strongly relies on the communication between germ and somatic cells. Compared to traditional bulk RNA sequencing techniques, the single-cell RNA sequencing (scRNA-seq) approach has substantial advantages in analyzing individual cells within an ever-changing and complicated tissue, classifying them into cell types, characterizing single cells, delineating the cellular developmental trajectory, and studying cell-to-cell interactions. In this review, we present single-cell transcriptome mapping of the ovary, summarize the characteristics of the important constituent cells of the ovary and the critical cellular developmental processes, and describe key signaling pathways for cell-to-cell communication in the ovary, as revealed by scRNA-seq. This review will undoubtedly improve our understanding of the characteristics of ovarian cells and development, thus enabling the identification of novel therapeutic targets for ovarian-related diseases.

Single nuclei profiling identifies cell specific markers of skeletal muscle aging, frailty, and senescence

Aging is accompanied by a loss of muscle mass and function, termed sarcopenia, which causes numerous morbidities and economic burdens in human populations. Mechanisms implicated in age-related sarcopenia or frailty include inflammation, muscle stem cell depletion, mitochondrial dysfunction, and loss of motor neurons, but whether there are key drivers of sarcopenia are not yet known. To gain deeper insights into age-related muscle loss, we performed transcriptome profiling on lower limb muscle biopsies from 72 young, elderly, and frail human subjects using bulk RNA-seq (N = 72) and single-nuclei RNA-seq (N = 17). This combined approach revealed changes in gene expression that occur with age and frailty in multiple cell types comprising mature skeletal muscle. Notably, we found increased expression of the genes MYH8 and PDK4, and decreased expression of the gene IGFN1, in aged muscle. We validated several key genes changes in fixed human muscle tissue using digital spatial profiling. We also identified a small population of nuclei that express CDKN1A, present only in aged samples, consistent with p21cip1-driven senescence in this subpopulation. Overall, our findings identify unique cellular subpopulations in aged and sarcopenic skeletal muscle, which will facilitate the development of new therapeutic strategies to combat age-related frailty.

Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications

The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.

Single-cell CRISPR immune screens reveal immunological roles of tumor intrinsic factors

Genetic screens are widely exploited to develop novel therapeutic approaches for cancer treatment. With recent advances in single-cell technology, single-cell CRISPR screen (scCRISPR) platforms provide opportunities for target validation and mechanistic studies in a high-throughput manner. Here, we aim to establish scCRISPR platforms which are suitable for immune-related screens involving multiple cell types. We integrated two scCRISPR platforms, namely Perturb-seq and CROP-seq, with both in vitro and in vivo immune screens. By leveraging previously generated resources, we optimized experimental conditions and data analysis pipelines to achieve better consistency between results from high-throughput and individual validations. Furthermore, we evaluated the performance of scCRISPR immune screens in determining underlying mechanisms of tumor intrinsic immune regulation. Our results showed that scCRISPR platforms can simultaneously characterize gene expression profiles and perturbation effects present in individual cells in different immune screen conditions. Results from scCRISPR immune screens also predict transcriptional phenotype associated with clinical responses to cancer immunotherapy. More importantly, scCRISPR screen platforms reveal the interactive relationship between targeting tumor intrinsic factors and T cell-mediated antitumor immune response which cannot be easily assessed by bulk RNA-seq. Collectively, scCRISPR immune screens provide scalable and reliable platforms to elucidate molecular determinants of tumor immune resistance.

Single-cell RNA-sequencing data analysis reveals a highly correlated triphasic transcriptional response to SARS-CoV-2 infection

Single-cell RNA sequencing (scRNA-seq) is currently one of the most powerful techniques available to study the transcriptional response of thousands of cells to an external perturbation. Here, we perform a pseudotime analysis of SARS-CoV-2 infection using publicly available scRNA-seq data from human bronchial epithelial cells and colon and ileum organoids. Our results reveal that, for most genes, the transcriptional response to SARS-CoV-2 infection follows a non-linear pattern characterized by an initial and a final down-regulatory phase separated by an intermediate up-regulatory stage. A correlation analysis of transcriptional profiles suggests a common mechanism regulating the mRNA levels of most genes. Interestingly, genes encoded in the mitochondria or involved in translation exhibited distinct pseudotime profiles. To explain our results, we propose a simple model where nuclear export inhibition of nsp1-sensitive transcripts will be sufficient to explain the transcriptional shutdown of SARS-CoV-2 infected cells.

Fibroblastic reticular cells mitigate acute GvHD via MHCII-dependent maintenance of regulatory T cells

Acute graft versus host disease (aGvHD) is a life-threatening complication of allogeneic hematopoietic cell transplantation (allo-HCT) inflicted by alloreactive T cells primed in secondary lymphoid organs (SLOs) and subsequent damage to aGvHD target tissues. In recent years, Treg transfer and/or expansion has emerged as a promising therapy to modulate aGvHD. However, cellular niches essential for fostering Tregs to prevent aGvHD have not been explored. Here, we tested whether and to what extent MHC class II (MHCII) expressed on Ccl19+ fibroblastic reticular cells (FRCs) shape the donor CD4+ T cell response during aGvHD. Animals lacking MHCII expression on Ccl19-Cre-expressing FRCs (MHCIIΔCcl19) showed aberrant CD4+ T cell activation in the effector phase, resulting in exacerbated aGvHD that was associated with significantly reduced expansion of Foxp3+ Tregs and invariant NK T (iNKT) cells. Skewed Treg maintenance in MHCIIΔCcl19 mice resulted in loss of protection from aGvHD provided by adoptively transferred donor Tregs. In contrast, although FRCs upregulated costimulatory surface receptors, and although they degraded and processed exogenous antigens after myeloablative irradiation, FRCs were dispensable to activate alloreactive CD4+ T cells in 2 mouse models of aGvHD. In summary, these data reveal an immunoprotective, MHCII-mediated function of FRC niches in secondary lymphoid organs (SLOs) after allo-HCT and highlight a framework of cellular and molecular interactions that regulate CD4+ T cell alloimmunity.

Single-cell immune profiling reveals thymus-seeding populations, T cell commitment, and multilineage development in the human thymus

T cell development in the mouse thymus has been studied extensively, but less is known regarding T cell development in the human thymus. We used a combination of single-cell techniques and functional assays to perform deep immune profiling of human T cell development, focusing on the initial stages of prelineage commitment. We identified three thymus-seeding progenitor populations that also have counterparts in the bone marrow. In addition, we found that the human thymus physiologically supports the development of monocytes, dendritic cells, and NK cells, as well as limited development of B cells. These results are an important step toward monitoring and guiding regenerative therapies in patients after hematopoietic stem cell transplantation.

Differential effects of periodontal microbiome on the rheumatoid factor induction during rheumatoid arthritis pathogenesis

Association between exposure to periodontal bacteria and development of autoantibodies related to rheumatoid arthritis (RA) has been widely accepted; however, direct causal relationship between periodontal bacteria and rheumatoid factor (RF) is currently not fully understood. We investigated whether periodontal bacteria could affect RF status. Patients with preclinical, new-onset, or chronic RA underwent periodontal examination, and investigation of subgingival microbiome via 16S rRNA sequencing. Degree of arthritis and RF induction was examined in collagen-induced arthritis (CIA) mice that were orally inoculated with different periodontal bacteria species. Subsequently, single-cell RNA sequencing analysis of the mouse spleen cells was performed. Patients with preclinical RA showed an increased abundance of the Porphyromonadacae family in the subgingival microbiome compared to those with new-onset or chronic RA, despite comparable periodontitis severity among them. Notably, a distinct subgingival microbial community was found between patients with high-positive RF and those with negative or low-positive RF (p=0.022). Oral infections with the periodontal pathogens P. gingivalis and Treponema denticola in CIA mice similarly enhanced arthritis score, but resulted in different levels of RF induction. Genes related to B cell receptor signaling, B cell proliferation, activation, and differentiation, and CD4⁺ T cell costimulation and cytokine production were involved in the differential induction of RF in mice exposed to different bacteria. In summary, periodontal microbiome might shape RF status by affecting the humoral immune response during RA pathogenesis.

Dysregulation of neuroprotective astrocytes, a spectrum of microglial activation states, and altered hippocampal neurogenesis are revealed by single-cell RNA sequencing in prion disease

Prion diseases are neurodegenerative disorders with long asymptomatic incubation periods, followed by a rapid progression of cognitive and functional decline culminating in death. The complexity of intercellular interactions in the brain is challenging to unravel and the basis of disease pathobiology remains poorly understood. In this study, we employed single cell RNA sequencing (scRNAseq) to produce an atlas of 147,536 single cell transcriptomes from cortex and hippocampus of mice infected with prions and showing clinical signs. We identified transcriptionally distinct populations and sub-populations of all the major brain cell-types. Disease-related transcription was highly specific to not only overarching cell-types, but also to sub-populations of glia and neurons. Most striking was an apparent decrease in relative frequency of astrocytes expressing genes that are required for brain homeostasis such as lipid synthesis, glutamate clearance, synaptic modulation and regulation of blood flow. Additionally, we described a spectrum of microglial activation states that suggest delineation of phagocytic and neuroinflammatory functions in different cell subsets. Differential responses of immature and mature neuron populations were also observed, alongside abnormal hippocampal neurogenesis. Our scRNAseq library provides a new layer of knowledge on single cell gene expression in prion disease, and is a basis for a more detailed understanding of cellular interplay that leads to neurodegeneration.

Intermediate cells of in vitro cellular reprogramming and in vivo tissue regeneration require desmoplakin

Amphibians and fish show considerable regeneration potential via dedifferentiation of somatic cells into blastemal cells. In terms of dedifferentiation, in vitro cellular reprogramming has been proposed to share common processes with in vivo tissue regeneration, although the details are elusive. Here, we identified the cytoskeletal linker protein desmoplakin (Dsp) as a common factor mediating both reprogramming and regeneration. Our analysis revealed that Dsp expression is elevated in distinct intermediate cells during in vitro reprogramming. Knockdown of Dsp impedes in vitro reprogramming into induced pluripotent stem cells and induced neural stem/progenitor cells as well as in vivo regeneration of zebrafish fins. Notably, reduced Dsp expression impairs formation of the intermediate cells during cellular reprogramming and tissue regeneration. These findings suggest that there is a Dsp-mediated evolutionary link between cellular reprogramming in mammals and tissue regeneration in lower vertebrates and that the intermediate cells may provide alternative approaches for mammalian regenerative therapy.

The primordial differentiation of tumor-specific memory CD8+ T cells as bona fide responders to PD-1/PD-L1 blockade in draining lymph nodes

Blocking PD-1/PD-L1 signaling transforms cancer therapy and is assumed to unleash exhausted tumor-reactive CD8⁺ T cells in the tumor microenvironment (TME). However, recent studies have also indicated that the systemic tumor-reactive CD8⁺ T cells may respond to PD-1/PD-L1 immunotherapy. These discrepancies highlight the importance of further defining tumor-specific CD8⁺ T cell responders to PD-1/PD-L1 blockade. Here, using multiple preclinical tumor models, we revealed that a subset of tumor-specific CD8⁺ cells in the tumor draining lymph nodes (TdLNs) was not functionally exhausted but exhibited canonical memory characteristics. TdLN-derived tumor-specific memory (T_TSM) cells established memory-associated epigenetic program early during tumorigenesis. More importantly, TdLN-T_TSM cells exhibited superior anti-tumor therapeutic efficacy after adoptive transfer and were characterized as bona fide responders to PD-1/PD-L1 blockade. These findings highlight that TdLN-T_TSM cells could be harnessed to potentiate anti-tumor immunotherapy.

Single-cell and single-nuclei RNA sequencing as powerful tools to decipher cellular heterogeneity and dysregulation in neurodegenerative diseases

Neurodegenerative diseases affect millions of people worldwide and there are currently no cures. Two types of common neurodegenerative diseases are Alzheimer's (AD) and Parkinson's disease (PD). Single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq) have become powerful tools to elucidate the inherent complexity and dynamics of the central nervous system at cellular resolution. This technology has allowed the identification of cell types and states, providing new insights into cellular susceptibilities and molecular mechanisms underlying neurodegenerative conditions. Exciting research using high throughput scRNA-seq and snRNA-seq technologies to study AD and PD is emerging. Herein we review the recent progress in understanding these neurodegenerative diseases using these state-of-the-art technologies. We discuss the fundamental principles and implications of single-cell sequencing of the human brain. Moreover, we review some examples of the computational and analytical tools required to interpret the extensive amount of data generated from these assays. We conclude by highlighting challenges and limitations in the application of these technologies in the study of AD and PD.

Single-Cell Transcriptomic Profiling in Inherited Retinal Degeneration Reveals Distinct Metabolic Pathways in Rod and Cone Photoreceptors

The cellular mechanisms underlying hereditary photoreceptor degeneration are still poorly understood. The aim of this study was to systematically map the transcriptional changes that occur in the degenerating mouse retina at the single cell level. To this end, we employed single-cell RNA-sequencing (scRNA-seq) and retinal degeneration-1 (rd1) mice to profile the impact of the disease mutation on the diverse retinal cell types during early post-natal development. The transcriptome data allowed to annotate 43,979 individual cells grouped into 20 distinct clusters. We further characterized cluster-specific metabolic and biological changes in individual cell types. Our results highlight Ca2+-signaling as relevant to hereditary photoreceptor degeneration. Although metabolic reprogramming in retina, known as the 'Warburg effect', has been documented, further metabolic changes were noticed in rd1 mice. Such metabolic changes in rd1 mutation was likely regulated through mitogen-activated protein kinase (MAPK) pathway. By combining single-cell transcriptomes and immunofluorescence staining, our study revealed cell type-specific changes in gene expression, as well as interplay between Ca2+-induced cell death and metabolic pathways.

From multitude to singularity: An up-to-date overview of scRNA-seq data generation and analysis

Single cell RNA sequencing (scRNA-seq) is today a common and powerful technology in biomedical research settings, allowing to profile the whole transcriptome of a very large number of individual cells and reveal the heterogeneity of complex clinical samples. Traditionally, cells have been classified by their morphology or by expression of certain proteins in functionally distinct settings. The advent of next generation sequencing (NGS) technologies paved the way for the detection and quantitative analysis of cellular content. In this context, transcriptome quantification techniques made their advent, starting from the bulk RNA sequencing, unable to dissect the heterogeneity of a sample, and moving to the first single cell techniques capable of analyzing a small number of cells (1-100), arriving at the current single cell techniques able to generate hundreds of thousands of cells. As experimental protocols have improved rapidly, computational workflows for processing the data have also been refined, opening up to novel methods capable of scaling computational times more favorably with the dataset size and making scRNA-seq much better suited for biomedical research. In this perspective, we will highlight the key technological and computational developments which have enabled the analysis of this growing data, making the scRNA-seq a handy tool in clinical applications.

Single-cell RNA sequencing analysis of T helper cell differentiation and heterogeneity

Single-cell transcriptomics has emerged as a powerful tool to investigate cells' biological landscape and focus on the expression profile of individual cells. Major advantage of this approach is an analysis of highly complex and heterogeneous cell populations, such as a specific subpopulation of T helper cells that are known to differentiate into distinct subpopulations. The need for distinguishing the specific expression profile is even more important considering the T cell plasticity. However, importantly, the universal pipelines for single-cell analysis are usually not sufficient for every cell type. Here, the aims are to analyze the diversity of T cell phenotypes employing classical in vitro cytokine-mediated differentiation of human T cells isolated from human peripheral blood by single-cell transcriptomic approach with support of labelled antibodies and a comprehensive bioinformatics analysis using combination of Seurat, Nebulosa, GGplot and others. The results showed high expression similarities between Th1 and Th17 phenotype and very distinct Th2 expression profile. In a case of Th2 highly specific marker genes SPINT2, TRIB3 and CST7 were expressed. Overall, our results demonstrate how donor difference, Th plasticity and cell cycle influence the expression profiles of distinct T cell populations. The results could help to better understand the importance of each step of the analysis when working with T cell single-cell data and observe the results in a more practical way by using our analyzed datasets.

The Continued Absence of Functional Germline Stem Cells in Adult Ovaries

Ovaries are central to development, fertility, and reproduction of women. A particularly interesting feature of ovaries is their accelerated aging compared to other tissues, leading to loss of function far before other organs senesce. The limited pool of ovarian follicles is generated before birth and once exhausted, menopause will inevitably commence around the age of 50 years marking the end of fertility. Yet, there are reports suggesting the presence of germline stem cells and neo-oogenesis in adult human ovaries. These observations have fueled a long debate, created experimental fertility treatments, and opened business opportunities. Our recent analysis of cell types in the ovarian cortex of women of fertile age could not find evidence of germline stem cells. Like before, our work has been met with critique suggesting methodological shortcomings. We agree that excellence starts with methods and welcome discussion on the pros and cons of different protocols. In this commentary, we discuss the recent re-interpretation of our work.

Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data

Single-cell RNA-sequencing (scRNA-seq) enables researchers to quantify transcriptomes of thousands of cells simultaneously and study transcriptomic changes between cells. scRNA-seq datasets increasingly include multisubject, multicondition experiments to investigate cell-type-specific differential states (DS) between conditions. This can be performed by first identifying the cell types in all the subjects and then by performing a DS analysis between the conditions within each cell type. Naïve single-cell DS analysis methods that treat cells statistically independent are subject to false positives in the presence of variation between biological replicates, an issue known as the pseudoreplicate bias. While several methods have already been introduced to carry out the statistical testing in multisubject scRNA-seq analysis, comparisons that include all these methods are currently lacking. Here, we performed a comprehensive comparison of 18 methods for the identification of DS changes between conditions from multisubject scRNA-seq data. Our results suggest that the pseudobulk methods performed generally best. Both pseudobulks and mixed models that model the subjects as a random effect were superior compared with the naïve single-cell methods that do not model the subjects in any way. While the naïve models achieved higher sensitivity than the pseudobulk methods and the mixed models, they were subject to a high number of false positives. In addition, accounting for subjects through latent variable modeling did not improve the performance of the naïve methods.

Single-cell sequencing: A cutting edge tool in molecular medical research

The rapid development of advanced high throughput technologies and introduction of high resolution "omics" data through analysis of biological molecules has revamped medical research. Single-cell sequencing in recent years, is in fact revolutionising the field by providing a deeper, spatio-temporal analyses of individual cells within tissues and their relevance to disease. Like conventional sequencing, the single-cell approach deciphers the sequence of nucleotides in a given Deoxyribose Nucleic Acid (DNA), Ribose Nucleic Acid (RNA), Micro Ribose Nucleic Acid (miRNA), epigenetically modified DNA or chromatin DNA; however, the unit of analyses is changed to single cells rather than the entire tissue. Further, a large number of single cells analysed from a single tissue generate a unique holistic perception capturing all kinds of perturbations across different cells in the tissue that increases the precision of data. Inherently, execution of the technique generates a large amount of data, which is required to be processed in a specific manner followed by customised bioinformatic analysis to produce meaningful results. The most crucial role of single-cell sequencing technique is in elucidating the inter-cell genetic, epigenetic, transcriptomic and proteomic heterogeneity in health and disease. The current review presents a brief overview of this cutting-edge technology and its applications in medical research.

Celda: a Bayesian model to perform co-clustering of genes into modules and cells into subpopulations using single-cell RNA-seq data

Single-cell RNA-seq (scRNA-seq) has emerged as a powerful technique to quantify gene expression in individual cells and to elucidate the molecular and cellular building blocks of complex tissues. We developed a novel Bayesian hierarchical model called Cellular Latent Dirichlet Allocation (Celda) to perform co-clustering of genes into transcriptional modules and cells into subpopulations. Celda can quantify the probabilistic contribution of each gene to each module, each module to each cell population and each cell population to each sample. In a peripheral blood mononuclear cell dataset, Celda identified a subpopulation of proliferating T cells and a plasma cell which were missed by two other common single-cell workflows. Celda also identified transcriptional modules that could be used to characterize unique and shared biological programs across cell types. Finally, Celda outperformed other approaches for clustering genes into modules on simulated data. Celda presents a novel method for characterizing transcriptional programs and cellular heterogeneity in scRNA-seq data.

Probing cell identity hierarchies by fate titration and collision during direct reprogramming

Despite the therapeutic promise of direct reprogramming, basic principles concerning fate erasure and the mechanisms to resolve cell identity conflicts remain unclear. To tackle these fundamental questions, we established a single-cell protocol for the simultaneous analysis of multiple cell fate conversion events based on combinatorial and traceable reprogramming factor expression: Collide-seq. Collide-seq revealed the lack of a common mechanism through which fibroblast-specific gene expression loss is initiated. Moreover, we found that the transcriptome of converting cells abruptly changes when a critical level of each reprogramming factor is attained, with higher or lower levels not contributing to major changes. By simultaneously inducing multiple competing reprogramming factors, we also found a deterministic system, in which titration of fates against each other yields dominant or colliding fates. By investigating one collision in detail, we show that reprogramming factors can disturb cell identity programs independent of their ability to bind their target genes. Taken together, Collide-seq has shed light on several fundamental principles of fate conversion that may aid in improving current reprogramming paradigms.

Comparative single-cell transcriptional atlases of Babesia species reveal conserved and species-specific expression profiles

Babesia is a genus of apicomplexan parasites that infect red blood cells in vertebrate hosts. Pathology occurs during rapid replication cycles in the asexual blood stage of infection. Current knowledge of Babesia replication cycle progression and regulation is limited and relies mostly on comparative studies with related parasites. Due to limitations in synchronizing Babesia parasites, fine-scale time-course transcriptomic resources are not readily available. Single-cell transcriptomics provides a powerful unbiased alternative for profiling asynchronous cell populations. Here, we applied single-cell RNA sequencing to 3 Babesia species (B. divergens, B. bovis, and B. bigemina). We used analytical approaches and algorithms to map the replication cycle and construct pseudo-synchronized time-course gene expression profiles. We identify clusters of co-expressed genes showing "just-in-time" expression profiles, with gradually cascading peaks throughout asexual development. Moreover, clustering analysis of reconstructed gene curves reveals coordinated timing of peak expression in epigenetic markers and transcription factors. Using a regularized Gaussian graphical model, we reconstructed co-expression networks and identified conserved and species-specific nodes. Motif analysis of a co-expression interactome of AP2 transcription factors identified specific motifs previously reported to play a role in DNA replication in Plasmodium species. Finally, we present an interactive web application to visualize and interactively explore the datasets.

Single-cell sequencing reveals novel cellular heterogeneity in uterine leiomyomas

STUDY QUESTION

What are the cellular composition and single-cell transcriptomic differences between myometrium and leiomyomas as defined by single-cell RNA sequencing?

SUMMARY ANSWER

We discovered cellular heterogeneity in smooth muscle cells (SMCs), fibroblast and endothelial cell populations in both myometrium and leiomyoma tissues.

WHAT IS KNOWN ALREADY

Previous studies have shown the presence of SMCs, fibroblasts, endothelial cells and immune cells in myometrium and leiomyomas. However, there is no information on the cellular heterogeneity in these tissues and the transcriptomic differences at the single-cell level between these tissues.

STUDY DESIGN, SIZE, DURATION

We collected five leiomyoma and five myometrium samples from a total of eight patients undergoing hysterectomy. We then performed single-cell RNA sequencing to generate a cell atlas for both tissues. We utilized our single-cell sequencing data to define cell types, compare cell types by tissue type (leiomyoma versus myometrium) and determine the transcriptional changes at a single-cell resolution between leiomyomas and myometrium. Additionally, we performed MED12-variant analysis at the single-cell level to determine the genotype heterogeneity within leiomyomas.

PARTICIPANTS/MATERIALS, SETTING, METHODS

We collected five MED12-variant positive leiomyomas and five myometrium samples from a total of eight patients. We then performed single-cell RNA sequencing on freshly isolated single-cell preparations. Histopathological assessment confirmed the identity of the samples. Sanger sequencing was performed to confirm the presence of the MED12 variant in leiomyomas.

MAIN RESULTS AND ROLE OF CHANCE

Our data revealed previously unknown heterogeneity in the SMC, fibroblast cell and endothelial cell populations of myometrium and leiomyomas. We discovered the presence of two different lymphatic endothelial cell populations specific to uterine leiomyomas. We showed that both myometrium and MED12-variant leiomyomas are relatively similar in cellular composition but differ in cellular transcriptomic profiles. We found that fibroblasts influence the leiomyoma microenvironment through their interactions with endothelial cells, immune cells and SMCs. Variant analysis at the single-cell level revealed the presence of both MED12 variants as well as the wild-type MED12 allele in SMCs of leiomyomatous tissue. These results indicate genotype heterogeneity of cellular composition within leiomyomas.

LARGE SCALE DATA

The datasets are available in the NCBI Gene Expression Omnibus (GEO) using GSE162122.

LIMITATIONS, REASONS FOR CAUTION

Our study focused on MED12-variant positive leiomyomas for single-cell RNA sequencing analyses. Leiomyomas carrying other genetic rearrangements may differ in their cellular composition and transcriptomic profiles.

WIDER IMPLICATIONS FOR THE FINDINGS

Our study provides a cellular atlas for myometrium and MED12-variant positive leiomyomas as defined by single-cell RNA sequencing. Our analysis provides significant insight into the differences between myometrium and leiomyomas at the single-cell level and reveals hitherto unknown genetic heterogeneity in multiple cell types within human leiomyomas. Our results will be important for future studies into the origin and growth of human leiomyomas.

STUDY FUNDING/COMPETING INTEREST(S)

This work was supported by funding from the National Institute of Child Health and Human Development (HD098580 and HD088629). The authors declare no competing interests.

Gruffi: an algorithm for computational removal of stressed cells from brain organoid transcriptomic datasets

Organoids enable in vitro modeling of complex developmental processes and disease pathologies. Like most 3D cultures, organoids lack sufficient oxygen supply and therefore experience cellular stress. These negative effects are particularly prominent in complex models, such as brain organoids, and can affect lineage commitment. Here, we analyze brain organoid and fetal single‐cell RNA sequencing (scRNAseq) data from published and new datasets, totaling about 190,000 cells. We identify a unique stress signature in the data from all organoid samples, but not in fetal samples. We demonstrate that cell stress is limited to a defined subpopulation of cells that is unique to organoids and does not affect neuronal specification or maturation. We have developed a computational algorithm, Gruffi, which uses granular functional filtering to identify and remove stressed cells from any organoid scRNAseq dataset in an unbiased manner. We validated our method using six additional datasets from different organoid protocols and early brains, and show its usefulness to other organoid systems including retinal organoids. Our data show that the adverse effects of cell stress can be corrected by bioinformatic analysis for improved delineation of developmental trajectories and resemblance to in vivo data.

Distinct functional properties of murine perinatal and adult adipose progenitor subpopulations

Adult white adipose tissue (WAT) harbors distinct mesenchymal stromal cell subpopulations that differentially affect WAT function and plasticity. Here we unveil the cellular landscape of the perinatal epididymal WAT primordium using single-cell transcriptomics in male mice. We reveal that adipocyte precursor cells and fibro-inflammatory progenitors (FIPs) emerge as functionally distinct PDGFRβ+ subpopulations within the epididymal WAT anlagen prior to adipocyte accrual. We further identify important molecular and functional differences between perinatal and adult FIPs, including differences in their pro-inflammatory response, adipogenic capacity and anti-adipogenic behavior. Notably, we find that transient overexpression of Pparg in PDGFRβ+ cells only during postnatal days 0.5 to 7.5 in male mice leads to hyperplastic WAT development, durable progenitor cell reprogramming, and protection against pathologic WAT remodeling and glucose intolerance in adult-onset obesity. Thus, factors that alter the adipogenic capacity of perinatal adipose progenitors can have long-lasting effects on progenitor plasticity, tissue expandability and metabolic health into adulthood.

ZetaSuite: computational analysis of two-dimensional high-throughput data from multi-target screens and single-cell transcriptomics

Two-dimensional high-throughput data have become increasingly common in functional genomics studies, which raises new challenges in data analysis. Here, we introduce a new statistic called Zeta, initially developed to identify global splicing regulators from a two-dimensional RNAi screen, a high-throughput screen coupled with high-throughput functional readouts, and ZetaSuite, a software package to facilitate general application of the Zeta statistics. We compare our approach with existing methods using multiple benchmarked datasets and then demonstrate the broad utility of ZetaSuite in processing public data from large-scale cancer dependency screens and single-cell transcriptomics studies to elucidate novel biological insights.

Identification and implication of tissue-enriched ligands in epithelial-endothelial crosstalk during pancreas development

Development of the pancreas is driven by an intrinsic program coordinated with signals from other cell types in the epithelial environment. These intercellular communications have been so far challenging to study because of the low concentration, localized production and diversity of the signals released. Here, we combined scRNAseq data with a computational interactomic approach to identify signals involved in the reciprocal interactions between the various cell types of the developing pancreas. This in silico approach yielded 40,607 potential ligand-target interactions between the different main pancreatic cell types. Among this vast network of interactions, we focused on three ligands potentially involved in communications between epithelial and endothelial cells. BMP7 and WNT7B, expressed by pancreatic epithelial cells and predicted to target endothelial cells, and SEMA6D, involved in the reverse interaction. In situ hybridization confirmed the localized expression of Bmp7 in the pancreatic epithelial tip cells and of Wnt7b in the trunk cells. On the contrary, Sema6d was enriched in endothelial cells. Functional experiments on ex vivo cultured pancreatic explants indicated that tip cell-produced BMP7 limited development of endothelial cells. This work identified ligands with a restricted tissular and cellular distribution and highlighted the role of BMP7 in the intercellular communications contributing to vessel development and organization during pancreas organogenesis.

In vitro atlas of dorsal spinal interneurons reveals Wnt signaling as a critical regulator of progenitor expansion

Restoring sensation after injury or disease requires a reproducible method for generating large quantities of bona fide somatosensory interneurons. Toward this goal, we assess the mechanisms by which dorsal spinal interneurons (dIs; dI1-dI6) can be derived from mouse embryonic stem cells (mESCs). Using two developmentally relevant growth factors, retinoic acid (RA) and bone morphogenetic protein (BMP) 4, we recapitulate the complete in vivo program of dI differentiation through a neuromesodermal intermediate. Transcriptional profiling reveals that mESC-derived dIs strikingly resemble endogenous dIs, with the correct molecular and functional signatures. We further demonstrate that RA specifies dI4-dI6 fates through a default multipotential state, while the addition of BMP4 induces dI1-dI3 fates and activates Wnt signaling to enhance progenitor proliferation. Constitutively activating Wnt signaling permits the dramatic expansion of neural progenitor cultures. These cultures retain the capacity to differentiate into diverse populations of dIs, thereby providing a method of increasing neuronal yield.

Analysis of Gene Expression and TCR/B Cell Receptor Profiling of Immune Cells in Primary Sjögren's Syndrome by Single-Cell Sequencing

Primary Sjögren's syndrome (pSS) is a chronic autoimmune disease that is estimated to affect 35 million people worldwide and is characterized by lymphocytic infiltration, elevated circulating autoantibodies, and proinflammatory cytokines. The key immune cell subset changes and the TCR/BCR repertoire alterations in pSS patients remain unclear. In this study, we sought to comprehensively characterize the transcriptional changes in PBMCs of pSS patients by single-cell RNA sequencing and single-cell V(D)J sequencing. Naive CD8⁺ T cells and mucosal-associated invariant T cells were markedly decreased but regulatory T cells were increased in pSS patients. There were a large number of differentially expressed genes shared by multiple subpopulations of T cells and B cells. Abnormal signaling pathways, including Ag processing and presentation, the BCR signaling pathway, the TCR signaling pathway, and Epstein-Barr virus infection, were highly enriched in pSS patients. Moreover, there were obvious differences in the CD30, FLT3, IFN-II, IL-1, IL-2, IL-6, IL-10, RESISTIN, TGF-β, TNF, and VEGF signaling networks between pSS patients and healthy controls. Single-cell TCR and BCR repertoire analysis showed that there was a lower diversity of T cells in pSS patients than in healthy controls; however, there was no significant difference in the degree of clonal expansion, CDR3 length distribution, or degree of sequence sharing. Notably, our results further emphasize the functional importance of αβ pairing in determining Ag specificity. In conclusion, our analysis provides a comprehensive single-cell map of gene expression and TCR/BCR profiles in pSS patients for a better understanding of the pathogenesis, diagnosis, and treatment of pSS.

Comparison of human skin- and nerve-derived Schwann cells reveals many similarities and subtle genomic and functional differences

Skin is an easily accessible tissue and a rich source of Schwann cells (SCs). Toward potential clinical application of autologous SC therapies, we aim to improve the reliability and specificity of our protocol to obtain SCs from small skin samples. As well, to explore potential functional distinctions between skin-derived SCs (Sk-SCs) and nerve-derived SCs (N-SCs), we used single-cell RNA-sequencing and a series of in vitro and in vivo assays. Our results showed that Sk-SCs expressed typical SC markers. Single-cell sequencing of Sk- and N-SCs revealed an overwhelming overlap in gene expression with the exception of HLA genes which were preferentially up-regulated in Sk-SCs. In vitro, both cell types exhibited similar levels of proliferation, migration, uptake of myelin debris and readily associated with neurites when co-cultured with human iPSC-induced motor neurons. Both exhibited ensheathment of multiple neurites and early phase of myelination, especially in N-SCs. Interestingly, dorsal root ganglion (DRG) neurite outgrowth assay showed substantially more complexed neurite outgrowth in DRGs exposed to Sk-SC conditioned media compared to those from N-SCs. Multiplex ELISA array revealed shared growth factor profiles, but Sk-SCs expressed a higher level of VEGF. Transplantation of Sk- and N-SCs into injured peripheral nerve in nude rats and NOD-SCID mice showed close association of both SCs to regenerating axons. Myelination of rodent axons was observed infrequently by N-SCs, but absent in Sk-SC xenografts. Overall, our results showed that Sk-SCs share near-identical properties to N-SCs but with subtle differences that could potentially enhance their therapeutic utility.

Integration of computational analysis and spatial transcriptomics in single-cell study

Recent advances of single-cell transcriptomics technologies and allied computational methodologies have revolutionized molecular cell biology. Meanwhile, pioneering explorations in spatial transcriptomics have opened avenues to address fundamental biological questions in health and diseases. Here, we review the technical attributes of single-cell RNA sequencing and spatial transcriptomics, and the core concepts of computational data analysis. We further highlight the challenges in the application of data integration methodologies and the interpretation of the biological context of the findings.

SAREV: A review on statistical analytics of single-cell RNA sequencing data

Due to the development of next-generation RNA sequencing (NGS) technologies, there has been tremendous progress in research involving determining the role of genomics, transcriptomics and epigenomics in complex biological systems. However, scientists have realized that information obtained using earlier technology, frequently called 'bulk RNA-seq' data, provides information averaged across all the cells present in a tissue. Relatively newly developed single cell (scRNA-seq) technology allows us to provide transcriptomic information at a single-cell resolution. Nevertheless, these high-resolution data have their own complex natures and demand novel statistical data analysis methods to provide effective and highly accurate results on complex biological systems. In this review, we cover many such recently developed statistical methods for researchers wanting to pursue scRNA-seq statistical and computational research as well as scientific research about these existing methods and free software tools available for their generated data. This review is certainly not exhaustive due to page limitations. We have tried to cover the popular methods starting from quality control to the downstream analysis of finding differentially expressed genes and concluding with a brief description of network analysis.

Single-Cell RNA Sequencing Reveals Cellular and Transcriptional Changes Associated With Traumatic Brain Injury

Traumatic brain injury (TBI) is currently a substantial public health problem and one of the leading causes of morbidity and mortality worldwide. However, the cellular and transcriptional changes in TBI at single-cell level have not been well characterized. In this study, we reanalyzed a single-cell RNA sequencing (scRNA-seq) dataset of mouse hippocampus to identify the key cellular and transcriptional changes associated with TBI. Specifically, we found that oligodendrocytes were the most abundant cell type in mouse hippocampus, and detected an expanded astrocyte population, which was significantly activated in TBI. The enhanced activity of inflammatory response-related pathways in the astrocytes of TBI samples suggested that the astrocytes, along with microglia, which were the major brain-resident immune cells, were responsible for inflammation in the acute phase of TBI. Hormone secretion, transport, and exocytosis were found upregulated in the excitatory neurons of TBI, which gave us a hint that excitatory neurons might excessively transport and excrete glutamate in response to TBI. Moreover, the ependymal subpopulation C0 was TBI-specific and characterized by downregulated cilium movement, indicating that the attenuated activity of cilium movement following TBI might decrease cerebrospinal fluid flow. Furthermore, we observed that downregulated genes in response to candesartan treatment were preferentially expressed in excitatory neurons and were related to pathways like neuronal systems and neuroactive ligand-receptor interaction, indicating that candesartan might promote recovery of neurons after traumatic brain injury via mediating neuroactive ligand-receptor interactions and reducing excitotoxicity. In conclusion, our study identified key cell types in TBI, which improved our understanding of the cellular and transcriptional changes after TBI and offered an insight into the molecular mechanisms that could serve as therapeutic targets.

Metabolic reprogramming of skeletal muscle by resident macrophages points to CSF1R inhibitors as muscular dystrophy therapeutics

The role of tissue-resident macrophages during tissue regeneration or fibrosis is not well understood, mainly due to the lack of a specific marker for their identification. Here, we identified three populations of skeletal muscle-resident myelomonocytic cells: a population of macrophages positive for lymphatic vessel endothelial receptor 1 (LYVE1) and T cell membrane protein 4 (TIM4 or TIMD4), a population of LYVE1^-TIM4^- macrophages, and a population of cells likely representing dendritic cells that were positive for CD11C and major histocompatibility complex class II (MHCII). Using a combination of parabiosis and lineage-tracing experiments, we found that, at steady state, TIM4^- macrophages were replenished from the blood, whereas TIM4⁺ macrophages locally self-renewed [self-renewing resident macrophages (SRRMs)]. We further showed that Timd4 could be reliably used to distinguish SRRMs from damage-induced infiltrating macrophages. Using a colony-stimulating factor 1 receptor (CSF1R) inhibition/withdrawal approach to specifically deplete SRRMs, we found that SRRMs provided a nonredundant function in clearing damage-induced apoptotic cells early after extensive acute injury. In contrast, in chronic mild injury as seen in a mouse model of Duchenne muscular dystrophy, depletion of both TIM4^-- and TIM4⁺-resident macrophage populations through long-term CSF1R inhibition changed muscle fiber composition from damage-sensitive glycolytic fibers toward damage-resistant glycolytic-oxidative fibers, thereby protecting muscle against contraction-induced injury both ex vivo and in vivo. This work reveals a previously unidentified role for resident macrophages in modulating tissue metabolism and may have therapeutic potential given the ongoing clinical testing of CSF1R inhibitors.

The Development of Mechanical Allodynia in Diabetic Rats Revealed by Single-Cell RNA-Seq

Mechanical allodynia (MA) is the main reason that patients with diabetic peripheral neuropathy (DPN) seek medical advice. It severely debilitates the quality of life. Investigating hyperglycemia-induced changes in neural transcription could provide fundamental insights into the complex pathogenesis of painful DPN (PDPN). Gene expression profiles of physiological dorsal root ganglia (DRG) have been studied. However, the transcriptomic changes in DRG neurons in PDPN remain largely unexplored. In this study, by single-cell RNA sequencing on dissociated rat DRG, we identified five physiological neuron types and a novel neuron type MAAC (Fxyd7⁺/Atp1b1⁺) in PDPN. The novel neuron type originated from peptidergic neuron cluster and was characterized by highly expressing genes related to neurofilament and cytoskeleton. Based on the inferred gene regulatory networks, we found that activated transcription factors Hobx7 and Larp1 in MAAC could enhance Atp1b1 expression. Moreover, we constructed the cellular communication network of MAAC and revealed its receptor-ligand pairs for transmitting signals with other cells. Our molecular investigation at single-cell resolution advances the understanding of the dynamic peripheral neuron changes and underlying molecular mechanisms during the development of PDPN.

Single-cell RNA sequencing of mitotic-arrested prospermatogonia with DAZL::GFP chickens and revealing unique epigenetic reprogramming of chickens

Background

Germ cell mitotic arrest is conserved in many vertebrates, including birds, although the time of entry or exit into quiescence phase differs. Mitotic arrest is essential for the normal differentiation of male germ cells into spermatogonia and accompanies epigenetic reprogramming and meiosis inhibition from embryonic development to post-hatch. However, mitotic arrest was not well studied in chickens because of the difficulty in obtaining pure germ cells from relevant developmental stage.

Results

We performed single-cell RNA sequencing to investigate transcriptional dynamics of male germ cells during mitotic arrest in DAZL::GFP chickens. Using differentially expressed gene analysis and K-means clustering to analyze cells at different developmental stages (E12, E16, and hatch), we found that metabolic and signaling pathways were regulated, and that the epigenome was reprogrammed during mitotic arrest. In particular, we found that histone H3K9 and H3K14 acetylation (by HDAC2) and DNA demethylation (by DNMT3B and HELLS) led to a transcriptionally permissive chromatin state. Furthermore, we found that global DNA demethylation occurred gradually after the onset of mitotic arrest, indicating that the epigenetic-reprogramming schedule of the chicken genome differs from that of the mammalian genome. DNA hypomethylation persisted after hatching, and methylation was slowly re-established 3 weeks later.

Conclusions

We found a unique epigenetic-reprogramming schedule of mitotic-arrested chicken prospermatogonia and prolonged hypomethylation after hatching. This will provide a foundation for understanding the process of germ-cell epigenetic regulation in several species for which this process is not clearly described. Our findings on the biological processes related to sex-specific differentiation of prospermatogonia could help studying germline development in vitro more elaborately.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40104-022-00712-4.

Alveolar Basal Cells Differentiate towards Secretory Epithelial- and Aberrant Basaloid-like Cells In Vitro

In idiopathic pulmonary fibrosis (IPF), keratin (KRT)17+/KRT5+ basal and KRT17+/KRT5- aberrant basaloid cells are atypically present within the alveolar space. We previously described the fibrosis-enriched outgrowth of alveolar basal cells from peripheral fibrotic lung tissue. Using single cell RNA sequencing (scRNA-seq), we here characterize the transcriptome of these cultured alveolar basal cells under different culture conditions.

METHODS

Fibrotic peripheral lung tissue pieces were placed in DMEM growth medium. Outgrown cells were analysed by scRNA-seq, TaqMan-PCR or immunofluorescence (IF) either directly or after medium change to an epithelial cell specific medium (Cnt-PR-A).

RESULTS

A fraction of alveolar basal cells cultured in DMEM growth medium showed close transcriptomic similarities to IPF basal cells. However, although they expressed KRT5, the transcriptome of the majority of cells matched best to the transcriptome of recently described KRT17+/KRT5- aberrant basaloid cells, co-expressing the canonical basal cell marker KRT17 and mesenchymal cell marker (VIM, FN1). A smaller fraction of cells matched best to secretory epithelial cells. Two differentiation gradients from basal to aberrant basaloid-like cells and basal to secretory epithelial-like cells were apparent. Interestingly, these differentiation paths seemed reversed when the cell culture medium was changed to Cnt-PR-A.

CONCLUSIONS

Our results suggest that cultured alveolar basal cells have the capacity to differentiate towards secretory epithelial-like cells and to aberrant basaloid-like cells. However, due to the persistent expression of KRT5, a complete differentiation towards aberrant basaloid cells did not seem to be achieved in our culture conditions. Importantly, differentiation seemed reversible by changing the cells microenvironment. Determining specific factors influencing these differentiation paths may help to define novel drug targets for IPF therapy.

scAmpi-A versatile pipeline for single-cell RNA-seq analysis from basics to clinics

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique to decipher tissue composition at the single-cell level and to inform on disease mechanisms, tumor heterogeneity, and the state of the immune microenvironment. Although multiple methods for the computational analysis of scRNA-seq data exist, their application in a clinical setting demands standardized and reproducible workflows, targeted to extract, condense, and display the clinically relevant information. To this end, we designed scAmpi (Single Cell Analysis mRNA pipeline), a workflow that facilitates scRNA-seq analysis from raw read processing to informing on sample composition, clinically relevant gene and pathway alterations, and in silico identification of personalized candidate drug treatments. We demonstrate the value of this workflow for clinical decision making in a molecular tumor board as part of a clinical study.

Cellular responses in the FGF10-mediated improvement of hindlimb regenerative capacity in Xenopus laevis revealed by single-cell transcriptomics

Xenopus laevis tadpoles possess regenerative capacity in their hindlimb buds at early developmental stages (stages ~52-54); they can regenerate complete hindlimbs with digits after limb bud amputation. However, they gradually lose their regenerative capacity as metamorphosis proceeds. Tadpoles in late developmental stages regenerate fewer digits (stage ~56), or only form cartilaginous spike without digits or joints (stage ~58 or later) after amputation. Previous studies have shown that administration of fibroblast growth factor 10 (FGF10) in late-stage (stage 56) tadpole hindlimb buds after amputation can improve their regenerative capacity, which means that the cells responding to FGF10 signaling play an important role in limb bud regeneration. In this study, we performed single-cell RNA sequencing (scRNA-seq) of hindlimb buds that were amputated and administered FGF10 by implanting FGF10-soaked beads at a late stage (stage 56), and explored cell clusters exhibiting a differential gene expression pattern compared with that in controls treated with phosphate-buffered saline. The scRNA-seq data showed expansion of fgf8-expressing cells in the cluster of the apical epidermal cap of FGF10-treated hindlimb buds, which was reported previously, indicating that the administration of FGF10 was successful. On analysis, in addition to the epidermal cluster, a subset of myeloid cells and a newly identified cluster of steap4-expressing cells showed remarkable differences in their gene expression profiles between the FGF10- or phosphate-buffered saline-treatment conditions, suggesting a possible role of these clusters in improving the regenerative capacity of hindlimbs via FGF10 administration.