Visualisation of tumour cells in bone in vivo at single-cell resolution

The skeleton is a common site for the establishment of distant metastases. Once cancers occupy bone, the prognosis is poor as disease recurrence and visceral spread is imminent. Understanding the pathways and cellular interactions, which regulate tumour cell seeding, dormancy and growth in bone, is pertinent to improving outcomes for patients with advanced cancers. Advances in imaging techniques have facilitated the development of the concept that the behavior of bone marrow resident cells dictates the fate of tumour cells upon arrival in bone. This review summarises recent findings achieved through intravital imaging. It highlights the importance of developing both longitudinal static and acute dynamic data to develop our understanding of tumour cell engraftment, dormancy, activation and the subsequent establishment of metastases. We also describe how imaging techniques have developed our knowledge of the elements that make up the complex bone microenvironment which tumour cells interact with to survive and grow. We also discuss how through combining these imaging insights with single cell RNA sequencing data, we are entering a new era of research which has the power to define the cell-cell interactions which control tumour cell growth in bone.

Culture shock: microglial heterogeneity, activation, and disrupted single-cell microglial networks in vitro

BACKGROUND

Microglia, the resident immune cells of the brain, play a critical role in numerous diseases, but are a minority cell type and difficult to genetically manipulate in vivo with viral vectors and other approaches. Primary cultures allow a more controlled setting to investigate these cells, but morphological and transcriptional changes upon removal from their normal brain environment raise many caveats from in vitro studies.

METHODS

To investigate whether cultured microglia recapitulate in vivo microglial signatures, we used single-cell RNA sequencing (scRNAseq) to compare microglia freshly isolated from the brain to primary microglial cultures. We performed cell population discovery, differential expression analysis, and gene co-expression module analysis to compare signatures between in vitro and in vivo microglia. We constructed causal predictive network models of transcriptional regulators from the scRNAseq data and identified a set of potential key drivers of the cultured phenotype. To validate this network analysis, we knocked down two of these key drivers, C1qc and Prdx1, in primary cultured microglia and quantified changes in microglial activation markers.

RESULTS

We found that, although often assumed to be a relatively homogenous population of cells in culture, in vitro microglia are a highly heterogeneous population consisting of distinct subpopulations of cells with transcriptional profiles reminiscent of macrophages and monocytes, and are marked by transcriptional programs active in neurodegeneration and other disease states. We found that microglia in vitro presented transcriptional activation of a set of "culture shock genes" not found in freshly isolated microglia, characterized by strong upregulation of disease-associated genes including Apoe, Lyz2, and Spp1, and downregulation of homeostatic microglial markers, including Cx3cr1, P2ry12, and Tmem119. Finally, we found that cultured microglia prominently alter their transcriptional machinery modulated by key drivers from the homeostatic to activated phenotype. Knockdown of one of these drivers, C1qc, resulted in downregulation of microglial activation genes Lpl, Lyz2, and Ccl4.

CONCLUSIONS

Overall, our data suggest that when removed from their in vivo home environment, microglia suffer a severe case of "culture shock", drastically modulating their transcriptional regulatory network state from homeostatic to activated through upregulation of modules of culture-specific genes. Consequently, cultured microglia behave as a disparate cell type that does not recapitulate the homeostatic signatures of microglia in vivo. Finally, our predictive network model discovered potential key drivers that may convert activated microglia back to their homeostatic state, allowing for more accurate representation of in vivo states in culture. Knockdown of key driver C1qc partially attenuated microglial activation in vitro, despite C1qc being only weakly upregulated in culture. This suggests that even genes that are not strongly differentially expressed across treatments or preparations may drive downstream transcriptional changes in culture.

Understanding initiation and progression of hepatocellular carcinoma through single cell sequencing

Unsatisfied clinical outcome drives to better understand hepatic carcinogenesis, microenvironment and escape of immune surveillance in hepatocellular carcinoma (HCC). Single cell RNA sequencing (scRNA-Seq) has generated enormous data to pinpoint pathophysiologic alterations in tumor microenvironment (TME) or trace lineage development in cancer stem cells (CSCs), circulating tumor cells (CTCs), and subsets of immune cells, such as exhausting T cells, tumor-associated macrophages (TAMs), dendritic cells or other lineages. New insights have significantly advanced current understanding in progression, poor responses to molecular-targeted therapeutics or immune checkpoint inhibitors, metastasis in both basic research and clinical practice. The present review intends to cover a basic workflow of the scRNA-seq technology, existing limitations and improvement areas. Moreover, in-depth understanding in TME, exhausting T cells, CSCs, CTCs, tumor-associated macrophages, dendritic cells in HCC facilitates implementation of personalized and precise therapy in an era of availability with an array of systemic regimens.

Current insights in mouse iNKT and MAIT cell development using single cell transcriptomics data

Innate T (T_inn) cells are a collection of T cells with important regulatory functions that have a crucial role in immunity towards tumors, bacteria, viruses, and in cell-mediated autoimmunity. In mice, the two main αβ T_inn cell subsets include the invariant NKT (iNKT) cells that recognize glycolipid antigens presented by non-polymorphic CD1d molecules and the mucosal associated invariant T (MAIT) cells that recognize vitamin B metabolites presented by the non-polymorphic MR1 molecules. Due to their ability to promptly secrete large quantities of cytokines either after T cell antigen receptor (TCR) activation or upon exposure to tissue- and antigen-presenting cell-derived cytokines, T_inn cells are thought to act as a bridge between the innate and adaptive immune systems and have the ability to shape the overall immune response. Their swift response reflects the early acquisition of helper effector programs during their development in the thymus, independently of pathogen exposure and prior to taking up residence in peripheral tissues. Several studies recently profiled, in an unbiased manner, the transcriptomes of mouse thymic iNKT and MAIT cells at the single cell level. Based on these data, we re-examine in this review how T_inn cells develop in the mouse thymus and undergo effector differentiation.

Novel insights into adipose tissue heterogeneity

When normalized to volume, adipose tissue is comprised mainly of large lipid metabolizing and storing cells called adipocytes. Strikingly, the numerical representation of non-adipocytes, composed of a wide variety of cell types found in the so-called stromal vascular fraction (SVF), outnumber adipocytes by far. Besides its function in energy storage, adipose tissue has emerged as a versatile organ that regulates systemic metabolism and has therefore constituted an attractive target for the treatment of metabolic diseases. Recent high-resolution single cells/nucleus RNA seq data exemplify an intriguingly profound diversity of both adipocytes and SVF cells in all adipose depots, and the current data, while limited, demonstrate the significance of the intra-tissue cell composition in shaping the overall functionality of this tissue. Due to the complexity of adipose tissue, our understanding of the biological relevance of this heterogeneity and plasticity is fractional. Therefore, establishing atlases of adipose tissue cell heterogeneity is the first step towards generating an understanding of these functionalities. In this review, we will describe the current knowledge on adipose tissue cell composition and the heterogeneity of single-cell RNA sequencing, including the technical limitations.

Preparation of Single Pineal Cells

The isolation of single cells from the pineal gland plays an essential role in understanding the complex nature of such processes as differentiation, metabolism, and cell-cell communication within the pineal gland. This procedure is the portal to single-cell RNA sequencing, which produces the transcriptome of individual cells. As such, single-cell RNA sequencing is critical to the continued development of knowledge of the pineal cell physiology. This chapter describes a simple procedure for isolating individual cells. Starting with the incubation of whole tissue in an enzyme preparation, which dissociates the pineal gland into small pieces, it continues with gentle trituration and then isolation of single cells through filtration. The procedure takes less than 2 h.

Single cell RNA sequencing techniques and applications in research of ovary development and related diseases

The ovary is a highly organized composite of germ cells and various types of somatic cells, whose communications dictate ovary development to generate functional oocytes. The differences between individual cells might have profound effects on ovary functions. Single cell RNA sequencing techniques are promising approaches to explore the cell type composition of organisms, the dynamics of transcriptomes, the regulatory network between genes and the signaling pathways between cell types at the single cell resolution. In this review, we provide an overview of the currently available single cell RNA sequencing techniques including Smart-seq2 and Drop-seq, as well as their applications in biological and clinical research to provide a better understanding on the molecular mechanisms underlying ovary development and associated diseases.

Transcription factor network analysis based on single cell RNA-seq identifies that Trichostatin-a reverses docetaxel resistance in prostate Cancer

Background

Overcoming drug resistance is critical for increasing the survival rate of prostate cancer (PCa). Docetaxel is the first cytotoxic chemotherapeutical approved for treatment of PCa. However, 99% of PCa patients will develop resistance to docetaxel within 3 years. Understanding how resistance arises is important to increasing PCa survival.

Methods

In this study, we modeled docetaxel resistance using two PCa cell lines: DU145 and PC3. Using the Passing Attributes between Networks for Data Assimilation (PANDA) method to model transcription factor (TF) activity networks in both sensitive and resistant variants of the two cell lines. We identified edges and nodes shared by both PCa cell lines that composed a shared TF network that modeled changes which occur during acquisition of docetaxel resistance in PCa. We subjected the shared TF network to connectivity map analysis (CMAP) to identify potential drugs that could disrupt the resistant networks. We validated the candidate drug in combination with docetaxel to treat docetaxel-resistant PCa in both in vitro and in vivo models.

Results

In the final shared TF network, 10 TF nodes were identified as the main nodes for the development of docetaxel resistance. CMAP analysis of the shared TF network identified trichostatin A (TSA) as a candidate adjuvant to reverse docetaxel resistance. In cell lines, the addition of TSA to docetaxel enhanced cytotoxicity of docetaxel resistant PCa cells with an associated reduction of the IC50 of docetaxel on the resistant cells. In the PCa mouse model, combination of TSA and docetaxel reduced tumor growth and final weight greater than either drug alone or vehicle.

Conclusions

We identified a shared TF activity network that drives docetaxel resistance in PCa. We also demonstrated a novel combination therapy to overcome this resistance. This study highlights the usage of novel application of single cell RNA-sequencing and subsequent network analyses that can reveal novel insights which have the potential to improve clinical outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-09048-0.

Traditional chinese medicine syndromes classification associates with tumor cell and microenvironment heterogeneity in colorectal cancer: a single cell RNA sequencing analysis

BACKGROUND

Colorectal cancer (CRC) is one of the common gastrointestinal malignancies, tumor heterogeneity is the main cause of refractory CRC. Syndrome differentiation is the premise of individualized treatment of traditional Chinese medicine (TCM), but TCM syndrome lacks objective identification in CRC. This study is to investigate the correlation and significance of tumor heterogeneity and TCM syndromes classification in CRC.

METHODS

In this study, we using scRNA-seq technology, investigate the significance of tumor heterogeneity in TCM syndromes classification on CRC.

RESULTS

The results showed that 662 cells isolated from 11 primary CRC tumors are divided into 14 different cell clusters, and each cell subtype and its genes have different functions and signal transduction pathways, indicating significant heterogeneity. CRC tumor cell clusters have different proportions in Excess, Deficiency and Deficiency-Excess syndromes, and have their own characteristic genes, gene co-expression networks, gene functional interpretations as well as monocle functional evolution. Moreover, there were significant differences between the high expressions of MUC2, REG4, COL1A2, POSTN, SDPR, GPX1, ELF3, KRT8, KRT18, KRT19, FN1, SERPINE1, TCF4 and ZEB1 genes in Excess and Deficiency syndrome classification in CRC (P < 0.01).

CONCLUSIONS

The Excess and Deficiency syndromes classification may be related to tumor heterogeneity and its microenvironment in CRC.

Spatial mapping of cancer tissues by OMICS technologies

Spatial mapping of heterogeneity in gene expression in cancer tissues can improve our understanding of cancers and help in the rapid detection of cancers with high accuracy and reliability. Significant advancements have been made in recent years in OMICS technologies, which possess the strong potential to be applied in the spatial mapping of biopsy tissue samples and their molecular profiling to a single-cell level. The clinical application of OMICS technologies in spatial profiling of cancer tissues is also advancing. The current review presents recent advancements and prospects of applying OMICS technologies to the spatial mapping of various analytes in cancer tissues. We benchmark the current state of the art in the field to advance existing OMICS technologies for high throughput spatial profiling. The factors taken into consideration include spatial resolution, types of biomolecules, number of different biomolecules that can be detected from the same assay, labeled versus label-free approaches, and approximate time required for each assay. Further advancements are still needed for the widespread application of OMICs technologies in performing fast and high throughput spatial mapping of cancer tissues as well as their effective use in research and clinical applications.

Significance of intratumoral infiltration of B cells in cancer immunotherapy: From a single cell perspective

Immunotherapy for cancer has provided new treatment approaches for malignant tumors, but there are low rates of response and high rates of resistance. The most recent sequencing method which is called single-cell RNA sequencing(scRNA-seq) determines the transcriptome at the single cell level, which allows high-resolution dynamic monitoring of the tumor microenvironment (TME) during immunotherapy. As an important part of humoral immunity, tumor-infiltrated B cells have been reported to have distinct functions in anti-tumor immunity, which are characterized by their RNA transcriptome, membrane surface receptors, and immunoglobulin secretion, suggesting great immunotherapeutic effects. On the basis of the important roles of B cells in immunotherapy reported in recent publications, we discuss the tumor-infiltrated B cells' subpopulations, differentiation trajectory, and interactions with other cells in the TME in this review, hoping to illustrate its significance in potential clinical application as biomarkers and therapeutic targets.

SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data

BACKGROUND

Recent studies have demonstrated the utility of scRNA-seq SNVs to distinguish tumor from normal cells, characterize intra-tumoral heterogeneity, and define mutation-associated expression signatures. In addition to cancer studies, SNVs from single cells have been useful in studies of transcriptional burst kinetics, allelic expression, chromosome X inactivation, ploidy estimations, and haplotype inference.

RESULTS

To aid these types of studies, we have developed a tool, SCReadCounts, for cell-level tabulation of the sequencing read counts bearing SNV reference and variant alleles from barcoded scRNA-seq alignments. Provided genomic loci and expected alleles, SCReadCounts generates cell-SNV matrices with the absolute variant- and reference-harboring read counts, as well as cell-SNV matrices of expressed Variant Allele Fraction (VAFRNA) suitable for a variety of downstream applications. We demonstrate three different SCReadCounts applications on 59,884 cells from seven neuroblastoma samples: (1) estimation of cell-level expression of known somatic mutations and RNA-editing sites, (2) estimation of cell- level allele expression of biallelic SNVs, and (3) a discovery mode assessment of the reference and each of the three alternative nucleotides at genomic positions of interest that does not require prior SNV information. For the later, we applied SCReadCounts on the coding regions of KRAS, where it identified known and novel somatic mutations in a low-to-moderate proportion of cells. The SCReadCounts read counts module is benchmarked against the analogous modules of GATK and Samtools. SCReadCounts is freely available ( https://github.com/HorvathLab/NGS ) as 64-bit self-contained binary distributions for Linux and MacOS, in addition to Python source.

CONCLUSIONS

SCReadCounts supplies a fast and efficient solution for estimation of cell-level SNV expression from scRNA-seq data. SCReadCounts enables distinguishing cells with monoallelic reference expression from those with no gene expression and is applicable to assess SNVs present in only a small proportion of the cells, such as somatic mutations in cancer.

Single-cell RNA-seq reveals functionally distinct biomaterial degradation-related macrophage populations

Macrophages play crucial roles in host tissue reaction to biomaterials upon implantation in vivo. However, the complexity of biomaterial degradation-related macrophage subpopulations that accumulate around the implanted biomaterials in situ is not fully understood. Here, using single cell RNA-seq, we analyze the transcriptome profiles of the various cell types around the scaffold to map the scaffold-induced reaction, in an unbiased approach. This enables mapping of all biomaterial degradation-associated cells at high resolution, revealing distinct subpopulations of tissue-resident macrophages as the major cellular sources of biomaterial degradation in situ. We also find that scaffold architecture can affect the mechanotransduction and catabolic activity of specific material degradation-related macrophage subpopulations in an Itgav-Mapk1-Stat3 dependent manner, eventually leading to differences in scaffold degradation rate in vivo. Our work dissects unanticipated aspects of the cellular and molecular basis of biomaterial degradation at the single-cell level, and provides a conceptual framework for developing functional tissue engineering scaffolds in future.

Single cell RNA sequencing (scRNA-Seq) deciphering pathological alterations in streptozotocin-induced diabetic retinas

Diabetic retinopathy (DR) is an irreversible and progressive diabetic complication leading to visual impairment, even blindness. Due to the delicate and complicated structure of the retina, the pathology of DR has not been completely elucidated yet. We constructed a transcriptome atlas of >14,000 single cells from healthy and streptozotocin (STZ)-induced diabetic murine retinas to decipher pathological alterations of DR. We found four stress-inducible genes Cirbp, Rmb3, Mt1 and Mt2 commonly induced in most types of retinal cells. Bipolar cells were little affected on both number and gene expression. Diabetes increased expression of inflammatory factor genes in retinal microglia, and stimulated expression of immediate early genes (IEGs) in retinal astrocytes. A large number of genes were deregulated in diabetic vascular endothelial cells (ECs), and the differentially expressed genes were paired to the pathways functioning in metabolism, shear stress and vascular permeability. These pathways were mapped by more deregulated genes in a subpopulation of ECs specifically presented in diabetic retinas (diabetic retinal ECs, DRECs). Moreover, several inflammation pathways were activated in DRECs, and the most significant one is the IL-17 signaling pathway. According to the EC markers, DRECs were mainly capillary ECs, confirmed by immunofluorescent staining of S100a9, a target gene of the IL-17 signaling pathway. This study deciphered pathological alterations of DR, and provided clues for potential targets for DR therapy.

Study of cellular heterogeneity and differential dynamics of autophagy in human embryonic kidney development by single-cell RNA sequencing

Background

Autophagy is believed to participate in embryonic development, but whether the expression of autophagy-associated genes undergoes changes during the development of human embryonic kidneys remains unknown.

Methods

In this work, we identified 36,151 human renal cells from embryonic kidneys of 9–18 gestational weeks in 16 major clusters by single-cell RNA sequencing (scRNA-seq), and detected 1350 autophagy-related genes in all fetal renal cells. The abundance of each cell cluster in Wilms tumor samples from scRNA-seq and GDC TARGET WT datasets was detected by CIBERSORTx. R package Monocle 3 was used to determine differentiation trajectories. Cyclone tool of R package scran was applied to calculate the cell cycle scores. R package SCENIC was used to investigate the transcriptional regulons. The FindMarkers tool from Seurat was used to calculate DEGs. GSVA was used to perform gene set enrichment analyses. CellphoneDB was utilized to analyze intercellular communication.

Results

It was found that cells in the 13th gestational week showed the lowest transcriptional level in each cluster in all stages. Nephron progenitors could be divided into four subgroups with diverse levels of autophagy corresponding to different SIX2 expressions. SSBpod (podocyte precursors) could differentiate into four types of podocytes (Pod), and autophagy-related regulation was involved in this process. Pseudotime analysis showed that interstitial progenitor cells (IPCs) potentially possessed two primitive directions of differentiation to interstitial cells with different expressions of autophagy. It was found that NPCs, pretubular aggregates and interstitial cell clusters had high abundance in Wilms tumor as compared with para-tumor samples with active intercellular communication.

Conclusions

All these findings suggest that autophagy may be involved in the development and cellular heterogeneity of early human fetal kidneys. In addition, part of Wilms tumor cancer cells possess the characteristics of some fetal renal cell clusters.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02154-w.

Whole-genome methylation analysis of testicular germ cells from cryptozoospermic men points to recurrent and functionally relevant DNA methylation changes

BACKGROUND

Several studies have reported an association between male infertility and aberrant sperm DNA methylation patterns, in particular in imprinted genes. In a recent investigation based on whole methylome and deep bisulfite sequencing, we have not found any evidence for such an association, but have demonstrated that somatic DNA contamination and genetic variation confound methylation studies in sperm of severely oligozoospermic men. To find out whether testicular germ cells (TGCs) of such patients might carry aberrant DNA methylation, we compared the TGC methylomes of four men with cryptozoospermia (CZ) and four men with obstructive azoospermia, who had normal spermatogenesis and served as controls (CTR).

RESULTS

There was no difference in DNA methylation at the whole genome level or at imprinted regions between CZ and CTR samples. However, using stringent filters to identify group-specific methylation differences, we detected 271 differentially methylated regions (DMRs), 238 of which were hypermethylated in CZ (binominal test, p < 2.2 × 10-16). The DMRs were enriched for distal regulatory elements (p = 1.0 × 10-6) and associated with 132 genes, 61 of which are differentially expressed at various stages of spermatogenesis. Almost all of the 67 DMRs associated with the 61 genes (94%) are hypermethylated in CZ (63/67, p = 1.107 × 10-14). As judged by single-cell RNA sequencing, 13 DMR-associated genes, which are mainly expressed during meiosis and spermiogenesis, show a significantly different pattern of expression in CZ patients. In four of these genes, the promoter is hypermethylated in CZ men, which correlates with a lower expression level in these patients. In the other nine genes, eight of which downregulated in CZ, germ cell-specific enhancers may be affected.

CONCLUSIONS

We found that impaired spermatogenesis is associated with DNA methylation changes in testicular germ cells at functionally relevant regions of the genome. We hypothesize that the described DNA methylation changes may reflect or contribute to premature abortion of spermatogenesis and therefore not appear in the mature, motile sperm.

Tissue-specific smooth muscle cell subtypes identified by transcriptional profiling

Smooth muscle cells (SMCs) are specialized cells present in many organs where they serve diverse tissue-specific functions. Using the Tabula Muris compendium of single-cell RNA sequencing data, we extracted individual SMC transcriptomes from eight mouse tissues to investigate the transcriptomic landscape of tissue-specific SMCs. We identified marker genes, signaling pathways, and biological processes enriched in tissue-specific SMCs, and inferred potential ligand-receptor interaction between SMC and other cell types. Our analysis also identified sex differences in SMC gene expression in different tissues. Lastly, we used unsupervised clustering to identify novel SMC subtypes based on their downstream targets of transcription factors. Our results highlight the variable SMC phenotypes and underscore this cell's remarkable adaptability to contribute to diverse tissue function.

Progress in the production of haematopoietic stem and progenitor cells from human pluripotent stem cells

Cell therapies are currently used to treat many haematological diseases. These treatments range from the long-term reconstitution of the entire haematopoietic system using the most potent haematopoietic stem cells (HSCs) to the short-term rescue with mature functional end cells such as oxygen-carrying red blood cells and cells of the immune system that can fight infection and repair tissue. Limitations in supply and the risk of transmitting infection has prompted the design of protocols to produce some of these cell types from human pluripotent stem cells (hPSCs). Although it has proven challenging to generate the most potent HSCs directly from hPSCs, significant progress has been made in the development of differentiation protocols that can successfully produce haematopoietic progenitor cells and most of the mature cell lineages. We review the key steps used in the production of haematopoietic stem and progenitor cells (HSPCs) from hPSCs and the cell surface markers and reporter strategies that have been used to define specific transitions. Most studies have relied on the use of known markers that define HSPC production in vivo but more recently single cell RNA sequencing has allowed a less biased approach to their characterisation. Transcriptional profiling has identified new markers for naïve and committed hPSC-derived HSPC populations and trajectory analyses has provided novel insights into their lineage potential. Direct comparison of in vitro- and in vivo-derived RNA single cell sequencing datasets has highlights similarities and differences between the two systems and this deeper understanding will be key to the design and the tracking of improved and more efficient differentiation protocols.

Tracing colonic embryonic transcriptional profiles and their reactivation upon intestinal damage

We lack a holistic understanding of the genetic programs orchestrating embryonic colon morphogenesis and governing damage response in the adult. A window into these programs is the transcriptomes of the epithelial and mesenchymal cell populations in the colon. Performing unbiased single-cell transcriptomic analyses of the developing mouse colon at different embryonic stages (embryonic day 14.5 [E14.5], E15.5, and E18.5), we capture cellular and molecular profiles of the stages before, during, and after the appearance of crypt structures, as well as in a model of adult colitis. The data suggest most adult lineages are established by E18.5. We find embryonic-specific gene expression profiles and cell populations that reappear in response to tissue damage. Comparison of the datasets from mice and human colitis suggests the processes are conserved. In this study, we provide a comprehensive single-cell atlas of the developing mouse colon and evidence for the reactivation of embryonic genes in disease.

Human umbilical cord mesenchymal stromal cells attenuate pulmonary fibrosis via regulatory T cell through interaction with macrophage

BACKGROUND

Pulmonary fibrosis (PF) is a growing clinical problem with limited therapeutic options. Human umbilical cord mesenchymal stromal cell (hucMSC) therapy is being investigated in clinical trials for the treatment of PF patients. However, little is known about the underlying molecular and cellular mechanisms of hucMSC therapy on PF. In this study, the molecular and cellular behavior of hucMSC was investigated in a bleomycin-induced mouse PF model.

METHODS

The effect of hucMSCs on mouse lung regeneration was determined by detecting Ki67 expression and EdU incorporation in alveolar type 2 (AT2) and lung fibroblast cells. hucMSCs were transfected to express the membrane localized GFP before transplant into the mouse lung. The cellular behavior of hucMSCs in mouse lung was tracked by GFP staining. Single cell RNA sequencing was performed to investigate the effects of hucMSCs on gene expression profiles of macrophages after bleomycin treatment.

RESULTS

hucMSCs could alleviate collagen accumulation in lung and decrease the mortality of mouse induced by bleomycin. hucMSC transplantation promoted AT2 cell proliferation and inhibited lung fibroblast cell proliferation. By using single cell RNA sequencing, a subcluster of interferon-sensitive macrophages (IFNSMs) were identified after hucMSC infusion. These IFNSMs elevate the secretion of CXCL9 and CXCL10 following hucMSC infusion and recruit more Treg cells to the injured lung.

CONCLUSIONS

Our study establishes a link between hucMSCs, macrophage, Treg, and PF. It provides new insights into how hucMSCs interact with macrophage during the repair process of bleomycin-induced PF and play its immunoregulation function.

scLink: Inferring Sparse Gene Co-expression Networks from Single-cell Expression Data

A system-level understanding of the regulation and coordination mechanisms of gene expression is essential for studying the complexity of biological processes in health and disease. With the rapid development of single-cell RNA sequencing technologies, it is now possible to investigate gene interactions in a cell type-specific manner. Here we propose the scLink method, which uses statistical network modeling to understand the co-expression relationships among genes and construct sparse gene co-expression networks from single-cell gene expression data. We use both simulation and real data studies to demonstrate the advantages of scLink and its ability to improve single-cell gene network analysis. The scLink R package is available at https://github.com/Vivianstats/scLink.

Single-cell classification using graph convolutional networks

BACKGROUND

Analyzing single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in this area is the identification of cell types. With the availability of a huge amount of single cell sequencing data and discovering more and more cell types, classifying cells into known cell types has become a priority nowadays. Several methods have been introduced to classify cells utilizing gene expression data. However, incorporating biological gene interaction networks has been proved valuable in cell classification procedures.

RESULTS

In this study, we propose a multimodal end-to-end deep learning model, named sigGCN, for cell classification that combines a graph convolutional network (GCN) and a neural network to exploit gene interaction networks. We used standard classification metrics to evaluate the performance of the proposed method on the within-dataset classification and the cross-dataset classification. We compared the performance of the proposed method with those of the existing cell classification tools and traditional machine learning classification methods.

CONCLUSIONS

Results indicate that the proposed method outperforms other commonly used methods in terms of classification accuracy and F1 scores. This study shows that the integration of prior knowledge about gene interactions with gene expressions using GCN methodologies can extract effective features improving the performance of cell classification.

Applications and analytical tools of cell communication based on ligand-receptor interactions at single cell level

BACKGROUND

Cellular communication is an essential feature of multicellular organisms. Binding of ligands to their homologous receptors, which activate specific cell signaling pathways, is a basic type of cellular communication and intimately linked to many degeneration processes leading to diseases.

MAIN BODY

This study reviewed the history of ligand-receptor and presents the databases which store ligand-receptor pairs. The recently applications and research tools of ligand-receptor interactions for cell communication at single cell level by using single cell RNA sequencing have been sorted out.

CONCLUSION

The summary of the advantages and disadvantages of analysis tools will greatly help researchers analyze cell communication at the single cell level. Learning cell communication based on ligand-receptor interactions by single cell RNA sequencing gives way to developing new target drugs and personalizing treatment.

Revealing consensus gene pathways associated with respiratory functions and disrupted by PM2.5 nitrate exposure at bulk tissue and single cell resolution

BACKGROUND

Nitrate is a major pollutant component in ambient PM_2.5. It is known that chronic exposure to PM_2.5 NO₃^- damages respiratory functions. We aim to explore the underlying toxicological mechanism at single cell resolution.

METHODS

We systematically conducted exposure experiments on forty C57BL/6 mice, assessed respiratory functions, and profiled lung transcriptome. . Afterward, we estimated the cell type compositions from RNA-seq data using deconvolution analysis. The genes and pathways associated with respiratory function and dysregulated by to PM_2.5 NO₃^- exposure were characterized at bulk-tissue and single-cell resolution.

RESULTS

PM_2.5 NO₃^- exposure did not significantly modify the cell type composition in lung, but profoundly altered the gene expression within each cell type. At ambient concentration (22 μg/m³), exposure significantly (FDR<10%) altered 95 genes' expression. Among the genes associated with respiratory functions, a large fraction (74.6-91.7%) were significantly perturbed by PM_2.5 NO₃^- exposure. For example, among the 764 genes associated with peak expiratory flow (PEF), 608 (79.6%) were affected by exposure (p = 1.92e-345). Pathways known to play role in lung disease pathogenesis, including circadian rhythms, sphingolipid metabolism, immune response and lysosome, were found significantly associated with respiratory functions and disrupted by PM_2.5 NO₃^- exposure.

CONCLUSIONS

This study extended our knowledge of PM_2.5 NO₃^- exposure's effect to the levels of lung gene expression, pathways, lung cell type composition and cell specific transcriptome. At single cell resolution, we provided insights in toxicological mechanism of PM_2.5 NO₃^- exposure and subsequent pulmonary disease risks.

GLI3 Is Associated With Neuronal Differentiation in SHH-Activated and WNT-Activated Medulloblastoma

Glioma-associated oncogene homolog 3 (GLI3), whose main function is to inhibit GLI1, has been associated with neuronal differentiation in medulloblastoma. However, it is not clear what molecular subtype(s) show increased GLI3 expression. GLI3 levels were assessed by immunohistochemistry in 2 independent cohorts, including a total of 88 cases, and found to be high in both WNT- and SHH-activated medulloblastoma. Analysis of bulk mRNA expression data and single cell RNA sequencing studies confirmed that GLI1 and GLI3 are highly expressed in SHH-activated medulloblastoma, whereas GLI3 but not GLI1 is highly expressed in WNT-activated medulloblastoma. Immunohistochemical analysis has shown that GLI3 is expressed inside the neuronal differentiated nodules of SHH-activated medulloblastoma, whereas GLI1/2 are expressed in desmoplastic areas. In contrast, GLI3 is diffusely expressed in WNT-activated medulloblastoma, whereas GLI1 is suppressed. Our data suggest that GLI3 may be a master regulator of neuronal differentiation and morphology in these subgroups.

Illuminating Neural Circuits in Alzheimer's Disease

Alzheimer's disease (AD) is the most common neurodegenerative disorder and there is currently no cure. Neural circuit dysfunction is the fundamental mechanism underlying the learning and memory deficits in patients with AD. Therefore, it is important to understand the structural features and mechanisms underlying the deregulated circuits during AD progression, by which new tools for intervention can be developed. Here, we briefly summarize the most recently established cutting-edge experimental approaches and key techniques that enable neural circuit tracing and manipulation of their activity. We also discuss the advantages and limitations of these approaches. Finally, we review the applications of these techniques in the discovery of circuit mechanisms underlying β-amyloid and tau pathologies during AD progression, and as well as the strategies for targeted AD treatments.

Defining the Homo- and Heterodimerization Propensities of Metabotropic Glutamate Receptors

The eight metabotropic glutamate receptors (mGluRs) serve critical modulatory roles throughout the nervous system. The molecular diversity of mGluRs is thought to be further expanded by the formation of heterodimers, but the co-expression of mGluR subtypes at the cellular level and the relative propensities of heterodimer formation are not well known. Here, we analyze single-cell RNA sequencing data and find that cortical pyramidal cells express multiple mGluR subtypes with distinct profiles for different receptor combinations. We then develop quantitative, fluorescence-based assays to define the relative homo- and heterodimer propensities across group-I, -II, and -III mGluRs. We find a strong preference for heterodimerization in a number of cases, including mGluR2 with mGluR3, which we confirm in frontal cortex using in situ RNA hybridization and co-immunoprecipitation. Together, our findings support the biological relevance of mGluR heterodimerization and highlight the complex landscape of mGluR populations in the brain.

Single cell RNA sequencing detects persistent cell type- and methylmercury exposure paradigm-specific effects in a human cortical neurodevelopmental model

The developing human brain is uniquely vulnerable to methylmercury (MeHg) resulting in lasting effects especially in developing cortical structures. Here we assess by single-cell RNA sequencing (scRNAseq) persistent effects of developmental MeHg exposure in a differentiating cortical human-induced pluripotent stem cell (hiPSC) model which we exposed to in vivo relevant and non-cytotoxic MeHg (0.1 and 1.0 μM) concentrations. The cultures were exposed continuously for 6 days either once only during days 4-10, a stage representative of neural epithelial- and radial glia cells, or twice on days 4-10 and days 14-20, a somewhat later stage which includes intermediate precursors and early postmitotic neurons. After the completion of MeHg exposure the cultures were differentiated further until day 38 and then assessed for persistent MeHg-induced effects by scRNAseq. We report subtle, but significant changes in the population size of different cortical cell types/stages and cell cycle. We also observe MeHg-dependent differential gene expression and altered biological processes as determined by Gene Ontology analysis. Our data demonstrate that MeHg results in changes in gene expression in human developing cortical neurons that manifest well after cessation of exposure and that these changes are cell type-, developmental stage-, and exposure paradigm-specific.

Boosting scRNA-seq data clustering by cluster-aware feature weighting

Background

The rapid development of single-cell RNA sequencing (scRNA-seq) enables the exploration of cell heterogeneity, which is usually done by scRNA-seq data clustering. The essence of scRNA-seq data clustering is to group cells by measuring the similarities among genes/transcripts of cells. And the selection of features for cell similarity evaluation is of great importance, which will significantly impact clustering effectiveness and efficiency.

Results

In this paper, we propose a novel method called CaFew to select genes based on cluster-aware feature weighting. By optimizing the clustering objective function, CaFew obtains a feature weight matrix, which is further used for feature selection. The genes have large weights in at least one cluster or the genes whose weights vary greatly in different clusters are selected. Experiments on 8 real scRNA-seq datasets show that CaFew can obviously improve the clustering performance of existing scRNA-seq data clustering methods. Particularly, the combination of CaFew with SC3 achieves the state-of-art performance. Furthermore, CaFew also benefits the visualization of scRNA-seq data.

Conclusion

CaFew is an effective scRNA-seq data clustering method due to its gene selection mechanism based on cluster-aware feature weighting, and it is a useful tool for scRNA-seq data analysis.

Reduced apoptosis of monocytes and macrophages is associated with their persistence in wounds of diabetic mice

Monocytes and macrophages (Mo/MΦ) rapidly accumulate in skin wounds after injury, then disappear as healing progresses. However, the mechanisms underlying their ultimate fate in wounds remain to be elucidated. Here, we show that apoptosis of Mo/MΦ parallels their reduction as wound healing progresses in non-diabetic mice. scRNAseq analysis confirmed enriched apoptosis GO pathways on day 6 post-injury in wound Mo/MΦ from non-diabetic mice. In contrast, there was significantly less Mo/MΦ apoptosis in wounds from diabetic mice, particularly in the pro-inflammatory Ly6C+ population, which may contribute to persistent Mo/MΦ accumulation and chronic inflammation. scRNAseq analysis implicated TNF, MAPK, Jak-STAT, and FoxO signaling pathways in promoting wound Mo/MΦ apoptosis in non-diabetic mice while cell proliferation related pathways appeared to be activated in diabetic mice. These novel findings indicate that reduced apoptosis is a contributor to persistent Mo/MΦ accumulation in diabetic wounds. These findings also highlight pathways that may regulate Mo/MΦ apoptosis during wound healing, which could be targeted to help resolve inflammation and improve healing.

Single cell analyses to understand the immune continuum in atherosclerosis

Atherosclerosis is initiated by the accumulation of lipids in the arterial wall that trigger a complex and poorly understood network of inflammatory processes. At the same time, recent clinical findings reveal that targeting specific immune alterations in patients with cardiovascular disease (CVD) represents a promising approach to preventing recurrent cardiovascular events. In order to achieve these tailored therapies, it is critical to resolve the heterogenous environment of the atherosclerotic lesion and decipher the complex structural and functional changes which immune cells undergo throughout disease progression. Recently, single-cell approaches including single cell mass cytometry by time of flight (CyTOF), single cell RNA sequencing (scRNA-seq) and Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) have emerged as valuable tools in resolving cellular plasticity within atherosclerotic lesions. In this review, we will discuss the most important insights that have been gleaned from the application of these single-cell approaches to validated experimental models of atherosclerosis. Additionally, as clinical progress in treatment of the disease depends on the translation of discoveries to human tissues, we will also examine the challenges associated with the application of single-cell approaches to human vascular tissue and the discoveries made by the initial efforts in this direction. Finally, we will analyze the advantages and limitations of dissociative single-cell approaches and how novel in-situ technologies could advance the field by allowing for the investigation of individual cells while preserving the heterogenous architecture of the atherosclerotic lesion.

Resolving Human Lactation Heterogeneity Using Single Milk-Derived Cells, a Resource at the Ready

Single cell RNA sequencing (scRNAseq) of human milk-derived cells (HMDCs) makes highly detailed analyses of the biology of human lactation possible. We explore this powerful application as an exciting tool to inspect the cellular composition of human milk. We point out some important challenges unique to this approach and highlight the importance of collaborations between biologists and well-trained bioinformaticians to utilize these data to their maximum potential. We extend this focus by discussing the first two such studies that describe HMDCs via scRNAseq and a variety of important questions in the field that warrant attention through further research. The stage is set to apply scRNAseq in human lactation biology, potentially leading to new insights regarding the molecular and cellular diversity of human secretory mammary epithelial cells.

Digital Cell Sorter (DCS): a cell type identification, anomaly detection, and Hopfield landscapes toolkit for single-cell transcriptomics

MOTIVATION

Analysis of singe cell RNA sequencing (scRNA-seq) typically consists of different steps including quality control, batch correction, clustering, cell identification and characterization, and visualization. The amount of scRNA-seq data is growing extremely fast, and novel algorithmic approaches improving these steps are key to extract more biological information. Here, we introduce: (i) two methods for automatic cell type identification (i.e., without expert curator) based on a voting algorithm and a Hopfield classifier, (ii) a method for cell anomaly quantification based on isolation forest, and (iii) a tool for the visualization of cell phenotypic landscapes based on Hopfield energy-like functions. These new approaches are integrated in a software platform that includes many other state-of-the-art methodologies and provides a self-contained toolkit for scRNA-seq analysis.

RESULTS

We present a suite of software elements for the analysis of scRNA-seq data. This Python-based open source software, Digital Cell Sorter (DCS), consists in an extensive toolkit of methods for scRNA-seq analysis. We illustrate the capability of the software using data from large datasets of peripheral blood mononuclear cells (PBMC), as well as plasma cells of bone marrow samples from healthy donors and multiple myeloma patients. We test the novel algorithms by evaluating their ability to deconvolve cell mixtures and detect small numbers of anomalous cells in PBMC data.

AVAILABILITY

The DCS toolkit is available for download and installation through the Python Package Index (PyPI). The software can be deployed using the Python import function following installation. Source code is also available for download on Zenodo: DOI 10.5281/zenodo.2533377.

SUPPLEMENTARY INFORMATION

Supplemental Materials are available at PeerJ online.

Fibroblast and Myofibroblast Subtypes: Single Cell Sequencing

The stroma constitutes the structural framework of an organ and plays crucial roles in health and following organ damage. The major player of the stroma with respect to extracellular matrix deposition, maintenance, and remodeling is the fibroblast and its activated derivative, the myofibroblast. It has long been recognized that there is considerable variability to the fibroblast phenotype. The recent advent of new single cell "omics" technologies has revolutionized our understanding and appreciation of cellular heterogeneity of fibroblasts been revolutionized. With these tools, the nature and defining characteristics of the cells comprising the stroma is finally being defined not just through a few markers, but by taking a wholistic look at transcriptional programs. It is now apparent that stromal cells are not only transcriptionally diverse, but also functionally, epigenetically, and spatially heterogeneous. Studying populations at single cell resolution has enabled identification of new clusters of cells with unique transcriptional signatures. Whether these clusters truly represent distinct subpopulations or different states of the same population remains to be clarified. In this chapter, we first describe a procedure for purification and preparation of a single cell suspension from tissue samples (in this case the heart) for single cell RNA sequencing. We also describe preparation of high-quality tissue sections for spatial transcriptomics. Secondly, we outline a workflow for computational analysis of single cell RNA sequencing and spatial transcriptomics data, as well as integrating them together, to explore the heterogeneity within fibroblasts/myofibroblasts and identify different subtypes and their locations in the heart.

Single Cell RNA Sequencing of Human Milk-Derived Cells Reveals Sub-Populations of Mammary Epithelial Cells with Molecular Signatures of Progenitor and Mature States: a Novel, Non-invasive Framework for Investigating Human Lactation Physiology

Cells in human milk are an untapped source, as potential "liquid breast biopsies", of material for investigating lactation physiology in a non-invasive manner. We used single cell RNA sequencing (scRNA-seq) to identify milk-derived mammary epithelial cells (MECs) and their transcriptional signatures in women with diet-controlled gestational diabetes (GDM) with normal lactation. Methodology is described for coordinating milk collections with single cell capture and library preparation via cryopreservation, in addition to scRNA-seq data processing and analyses of MEC transcriptional signatures. We comprehensively characterized 3740 cells from milk samples from two mothers at two weeks postpartum. Most cells (>90%) were luminal MECs (luMECs) expressing lactalbumin alpha and casein beta and positive for keratin 8 and keratin 18. Few cells were keratin 14⁺ basal MECs and a small immune cell population was present (<10%). Analysis of differential gene expression among clusters identified six potentially distinct luMEC subpopulation signatures, suggesting the potential for subtle functional differences among luMECs, and included one cluster that was positive for both progenitor markers and mature milk transcripts. No expression of pluripotency markers POU class 5 homeobox 1 (POU5F1, encoding OCT4) SRY-box transcription factor 2 (SOX2) or nanog homeobox (NANOG), was observed. These observations were supported by flow cytometric analysis of MECs from mature milk samples from three women with diet-controlled GDM (2-8 mo postpartum), indicating a negligible basal/stem cell population (epithelial cell adhesion molecule (EPCAM)^-/integrin subunit alpha 6 (CD49f)⁺, 0.07%) and a small progenitor population (EPCAM⁺/CD49f⁺, 1.1%). We provide a computational framework for others and future studies, as well as report the first milk-derived cells to be analyzed by scRNA-seq. We discuss the clinical potential and current limitations of using milk-derived cells as material for characterizing human mammary physiology.

Investigating transcriptome-wide sex dimorphism by multi-level analysis of single-cell RNA sequencing data in ten mouse cell types

BACKGROUND

It is a long established fact that sex is an important factor that influences the transcriptional regulatory processes of an organism. However, understanding sex-based differences in gene expression has been limited because existing studies typically sequence and analyze bulk tissue from female or male individuals. Such analyses average cell-specific gene expression levels where cell-to-cell variation can easily be concealed. We therefore sought to utilize data generated by the rapidly developing single cell RNA sequencing (scRNA-seq) technology to explore sex dimorphism and its functional consequences at the single cell level.

METHODS

Our study included scRNA-seq data of ten well-defined cell types from the brain and heart of female and male young adult mice in the publicly available tissue atlas dataset, Tabula Muris. We combined standard differential expression analysis with the identification of differential distributions in single cell transcriptomes to test for sex-based gene expression differences in each cell type. The marker genes that had sex-specific inter-cellular changes in gene expression formed the basis for further characterization of the cellular functions that were differentially regulated between the female and male cells. We also inferred activities of transcription factor-driven gene regulatory networks by leveraging knowledge of multidimensional protein-to-genome and protein-to-protein interactions and analyzed pathways that were potential modulators of sex differentiation and dimorphism.

RESULTS

For each cell type in this study, we identified marker genes with significantly different mean expression levels or inter-cellular distribution characteristics between female and male cells. These marker genes were enriched in pathways that were closely related to the biological functions of each cell type. We also identified sub-cell types that possibly carry out distinct biological functions that displayed discrepancies between female and male cells. Additionally, we found that while genes under differential transcriptional regulation exhibited strong cell type specificity, six core transcription factor families responsible for most sex-dimorphic transcriptional regulation activities were conserved across the cell types, including ASCL2, EGR, GABPA, KLF/SP, RXRα, and ZF.

CONCLUSIONS

We explored novel gene expression-based biomarkers, functional cell group compositions, and transcriptional regulatory networks associated with sex dimorphism with a novel computational pipeline. Our findings indicated that sex dimorphism might be widespread across the transcriptomes of cell types, cell type-specific, and impactful for regulating cellular activities.

Activin A does not drive post-traumatic heterotopic ossification

Heterotopic ossification (HO), the formation of ectopic bone in soft tissues, has been extensively studied in its two primary forms: post-traumatic HO (tHO) typically found in patients who have experienced musculoskeletal or neurogenic injury and in fibrodysplasia ossificans progressiva (FOP), where it is genetically driven. Given that in both diseases HO arises via endochondral ossification, the molecular mechanisms behind both diseases have been postulated to be manifestations of similar pathways including those activated by BMP/TGFβ superfamily ligands. A significant step towards understanding the molecular mechanism by which HO arises in FOP was the discovery that FOP causing ACVR1 variants trigger HO in response to activin A, a ligand that does not activate signaling from wild type ACVR1, and that is not inherently osteogenic in wild type settings. The physiological significance of this finding was demonstrated by showing that activin A neutralizing antibodies stop HO in two different genetically accurate mouse models of FOP. In order to explore the role of activin A in tHO, we performed single cell RNA sequencing and compared the expression of activin A as well as other BMP pathway genes in tHO and FOP HO. We show that activin A is expressed in response to injury in both settings, but by different types of cells. Given that wild type ACVR1 does not transduce signal when engaged by activin A, we hypothesized that inhibition of activin A will not block tHO. Nonetheless, as activin A was expressed in tHO lesions, we tested its inhibition and compared it with inhibition of BMPs. We show here that anti-activin A does not block tHO, whereas agents such as antibodies that neutralize ACVR1 or ALK3-Fc (which blocks osteogenic BMPs) are beneficial, though not completely curative. These results demonstrate that inhibition of activin A should not be considered as a therapeutic strategy for ameliorating tHO.

A robust nonlinear low-dimensional manifold for single cell RNA-seq data

BACKGROUND

Modern developments in single-cell sequencing technologies enable broad insights into cellular state. Single-cell RNA sequencing (scRNA-seq) can be used to explore cell types, states, and developmental trajectories to broaden our understanding of cellular heterogeneity in tissues and organs. Analysis of these sparse, high-dimensional experimental results requires dimension reduction. Several methods have been developed to estimate low-dimensional embeddings for filtered and normalized single-cell data. However, methods have yet to be developed for unfiltered and unnormalized count data that estimate uncertainty in the low-dimensional space. We present a nonlinear latent variable model with robust, heavy-tailed error and adaptive kernel learning to estimate low-dimensional nonlinear structure in scRNA-seq data.

RESULTS

Gene expression in a single cell is modeled as a noisy draw from a Gaussian process in high dimensions from low-dimensional latent positions. This model is called the Gaussian process latent variable model (GPLVM). We model residual errors with a heavy-tailed Student's t-distribution to estimate a manifold that is robust to technical and biological noise found in normalized scRNA-seq data. We compare our approach to common dimension reduction tools across a diverse set of scRNA-seq data sets to highlight our model's ability to enable important downstream tasks such as clustering, inferring cell developmental trajectories, and visualizing high throughput experiments on available experimental data.

CONCLUSION

We show that our adaptive robust statistical approach to estimate a nonlinear manifold is well suited for raw, unfiltered gene counts from high-throughput sequencing technologies for visualization, exploration, and uncertainty estimation of cell states.

Immune cell infiltration features and related marker genes in lung cancer based on single-cell RNA-seq

PURPOSE

Immune cells in the immune microenvironment of lung cancer have a great impact on the development of lung cancer. Our purpose was to analyze the immune cell infiltration features and related marker genes for lung cancer.

METHODS

Single cell RNA sequencing data of 11,485 lung cancer cells were retrieved from the Gene Expression Omnibus. After quality control and data normalization, cell clustering was performed using the Seurat package. Based on the marker genes of each cell type from the CellMarker database, each cell was divided into G1, G2M, and S phases. Then, differential expression and functional enrichment analyses were performed. CIBERSORT was used to reconstruct immune cell types.

RESULTS

Following cell filtering, highly variable genes were identified for all cells. 14 cell types were clustered. Among them, CD4 + T cell, B cell, plasma cell, natural killer cell and cancer stem cell were the top five cell types. Up-regulated genes were mainly enriched in immune-related biological processes and pathways. Using CIBERSORT, we identified the significantly higher fractions of naïve B cell, memory CD4 + T cell, T follicular helper cell, T regulatory helper cell and M1 macrophage in lung cancer tissues compared to normal tissues. Furthermore, the fractions of resting NK cell, monocyte, M0 macrophage, resting mast cell, eosinophil and neutrophil were significantly lower in tumor tissues than normal tissues.

CONCLUSION

Our findings dissected the immune cell infiltration features and related marker genes for lung cancer, which might provide novel insights for the immunotherapy of lung cancer.

Single-Cell RNA Sequencing Reveals mRNA Splice Isoform Switching during Kidney Development

BACKGROUND

During mammalian kidney development, nephron progenitors undergo a mesenchymal-to-epithelial transition and eventually differentiate into the various tubular segments of the nephron. Recently, Drop-seq single-cell RNA sequencing technology for measuring gene expression from thousands of individual cells identified the different cell types in the developing kidney. However, that analysis did not include the additional layer of heterogeneity that alternative mRNA splicing creates.

METHODS

Full transcript length single-cell RNA sequencing characterized the transcriptomes of 544 individual cells from mouse embryonic kidneys.

RESULTS

Gene expression levels measured with full transcript length single-cell RNA sequencing identified each cell type. Further analysis comprehensively characterized splice isoform switching during the transition between mesenchymal and epithelial cellular states, which is a key transitional process in kidney development. The study also identified several putative splicing regulators, including the genes Esrp1/2 and Rbfox1/2.

CONCLUSIONS

Discovery of the sets of genes that are alternatively spliced as the fetal kidney mesenchyme differentiates into tubular epithelium will improve our understanding of the molecular mechanisms that drive kidney development.

Lrig1 expression prospectively identifies stem cells in the ventricular-subventricular zone that are neurogenic throughout adult life

BACKGROUND

Leucine-rich repeats and immunoglobulin-like domains 1 (Lrig1) regulates stem cell quiescence. As a marker, it identifies stem cells in multiple organs of the mouse. We had detected Lrig1 expression in cultured Id1^high neural stem cells obtained from the lateral walls lining the lateral ventricles of the adult mouse brain. Thus, we investigated whether Lrig1 expression also identifies stem cells in that region in vivo.

METHODS

Publicly available single cell RNA sequencing datasets were analyzed with Seurat and Monocle. The Lrig1+ cells were lineage traced in vivo with a novel non-disruptive co-translational Lrig1^T2A-iCreERT2 reporter mouse line.

RESULTS

Analysis of single cell RNA sequencing datasets suggested Lrig1 was highly expressed in the most primitive stem cells of the neurogenic lineage in the lateral wall of the adult mouse brain. In support of their neurogenic stem cell identity, cell cycle entry was only observed in two morphologically distinguishable Lrig1+ cells that could also be induced into activation by Ara-C infusion. The Lrig1+ neurogenic stem cells were observed throughout the lateral wall. Neuroblasts and neurons were lineage traced from Lrig1+ neurogenic stem cells at 1 year after labeling.

CONCLUSIONS

We identified Lrig1 as a marker of long-term neurogenic stem cells in the lateral wall of the mouse brain. Lrig1 expression revealed two morphotypes of the Lrig1+ cells that function as long-term neurogenic stem cells. The spatial distribution of the Lrig1+ neurogenic stem cells suggested all subtypes of the adult neurogenic stem cells were labeled.

A single cell transcriptome atlas of the developing zebrafish hindbrain

Segmentation of the vertebrate hindbrain leads to the formation of rhombomeres, each with a distinct anteroposterior identity. Specialised boundary cells form at segment borders that act as a source or regulator of neuronal differentiation. In zebrafish, there is spatial patterning of neurogenesis in which non-neurogenic zones form at boundaries and segment centres, in part mediated by Fgf20 signalling. To further understand the control of neurogenesis, we have carried out single cell RNA sequencing of the zebrafish hindbrain at three different stages of patterning. Analyses of the data reveal known and novel markers of distinct hindbrain segments, of cell types along the dorsoventral axis, and of the transition of progenitors to neuronal differentiation. We find major shifts in the transcriptome of progenitors and of differentiating cells between the different stages analysed. Supervised clustering with markers of boundary cells and segment centres, together with RNA-seq analysis of Fgf-regulated genes, has revealed new candidate regulators of cell differentiation in the hindbrain. These data provide a valuable resource for functional investigations of the patterning of neurogenesis and the transition of progenitors to neuronal differentiation.

Single cell RNA-seq reveals the landscape of tumor and infiltrating immune cells in nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) is one of the most malignant tumors in Southern China and southeast Asia, which is characterized by a dense lymphocyte infiltration and a poor prognosis. The emergence of single-cell sequencing represents a powerful tool to resolve tumor heterogeneity and delineate the complex communication among the tumor cells with neighboring stromal and immune cells in the tumor microenvironment (TME). Here, we performed single cell RNA-seq and analyzed tumor cells together with the infiltrating immune cells from three NPC tumor tissues. In our study, the malignant cells display the intra- and inter-tumoral heterogeneity among the individual patients. Analysis of the immune cells reveal the heterogeneous composition of the distinct immune cells and the various functional states of T cells in NPC tumors. Additionally, coupled with the reconstruct of the T cell receptor (TCR) sequences from immune cells full-length single-cell sequence data, we identify the diverse T cell clonotypes and expansion distribution in individual tumors. Overall, we firstly reveal the landscape of tumor and infiltrating immune cells in nasopharyngeal cancer. These results provide deeper insights on the mechanisms of tumor clearance by immune cells in the surrounding microenvironment, which will be helpful in improving the targeted and immune therapies for NPC.

Alginate oligosaccharides improve germ cell development and testicular microenvironment to rescue busulfan disrupted spermatogenesis

Rationale: Busulfan is currently an indispensable anti-cancer drug, particularly for children, but the side effects on male reproduction are so serious that critical drug management is needed to minimize any negative impact. Meanwhile, alginate oligosaccharides (AOS) are natural products with many consequent advantages, that have attracted a great deal of pharmaceutical attention. In the current investigation, we performed single-cell RNA sequencing on murine testes treated with busulfan and/or AOS to define the mitigating effects of AOS on spermatogenesis at the single cell level.

Methods: Testicular cells (in vivo) were examined by single cell RNA sequencing analysis, histopathological analysis, immunofluorescence staining, and Western blotting. Testes samples (ex vivo) underwent RNA sequencing analysis. Blood and testicular metabolomes were determined by liquid chromatography-mass spectrometry (LC/MS).

Results: We found that AOS increased murine sperm concentration and motility, and rescued busulfan disrupted spermatogenesis through improving (i) the proportion of germ cells, (ii) gene expression important for spermatogenesis, and (iii) transcriptional factors in vivo. Furthermore, AOS promoted the ex vivo expression of genes important for spermatogenesis. Finally, our results showed that AOS improved blood and testis metabolomes as well as the gut microbiota to support the recovery of spermatogenesis.

Conclusions: AOS could be used to improve fertility in patients undergoing chemotherapy and to combat other factors that induce infertility in humans.

Single cell transcriptome dynamics from pluripotency to FLK1+ mesoderm

Hemangiogenic progenitors generating blood and endothelial cells are specified from FLK1-expressing (FLK1⁺) mesoderm by the transcription factor ETV2. FLK1⁺ mesoderm also contributes to smooth muscle and cardiomyocytes. However, the developmental process of FLK1⁺ mesoderm generation and its allocation to various cell fates remain obscure. Recent single cell RNA-sequencing studies of early embryos or in vitro-differentiated human embryonic stem (ES) cells have provided unprecedented information on the spatiotemporal resolution of cells in embryogenesis. These snapshots, however, lack information on continuous dynamic developmental processes. Here, we performed single cell RNA sequencing of in vitro-differentiated mouse ES cells to capture the continuous developmental process leading to hemangiogenesis. We found that hemangiogenic progenitors from ES cells develop through intermediate gastrulation stages, which are gradually specified by 'relay'-like highly overlapping transcription factor modules. Moreover, the transcriptional program of the Flk1⁺ mesoderm was maintained in the smooth muscle lineage, suggesting that smooth muscle is the default fate of Flk1⁺ mesoderm. We also identified the SRC kinase contributing to ETV2-mediated activation of the hemangiogenic program. This continuous transcriptome map will facilitate both basic and applied studies of mesoderm development.

Single-cell sequencing and its applications in head and neck cancer

Head and neck squamous cell carcinoma (HNSCC), like many tumors, is characterized by significant intra-tumoral heterogeneity, namely transcriptional, genetic, and epigenetic differences that define distinct cellular subpopulations. While it has been established that intra-tumoral heterogeneity may have prognostic significance in HNSCC, we are only beginning to describe and define such heterogeneity at a cellular resolution. Recent advances in single-cell sequencing technologies have been critical in this regard, opening new avenues in our understanding of more nuanced tumor biology by identifying distinct cellular subpopulations, dissecting signaling within the tumor microenvironment, and characterizing cellular genomic mutations and copy number aberrations. The combined effect of these insights is likely to be robust and meaningful changes in existing diagnostic and treatment algorithms through the application of novel biomarkers as well as targeted therapeutics. Here, we review single-cell technological and computational advances at the genomic, transcriptomic, and epigenomic levels, and discuss their applications in cancer research and clinical practice, with a specific focus on HNSCC.

Applications of single cell RNA sequencing to research of stem cells

Stem cells (SCs) with their self-renewal and pluripotent differentiation potential, show great promise for therapeutic applications to some refractory diseases such as stroke, Parkinsonism, myocardial infarction, and diabetes. Furthermore, as seed cells in tissue engineering, SCs have been applied widely to tissue and organ regeneration. However, previous studies have shown that SCs are heterogeneous and consist of many cell subpopulations. Owing to this heterogeneity of cell states, gene expression is highly diverse between cells even within a single tissue, making precise identification and analysis of biological properties difficult, which hinders their further research and applications. Therefore, a defined understanding of the heterogeneity is a key to research of SCs. Traditional ensemble-based sequencing approaches, such as microarrays, reflect an average of expression levels across a large population, which overlook unique biological behaviors of individual cells, conceal cell-to-cell variations, and cannot understand the heterogeneity of SCs radically. The development of high throughput single cell RNA sequencing (scRNA-seq) has provided a new research tool in biology, ranging from identification of novel cell types and exploration of cell markers to the analysis of gene expression and predicating developmental trajectories. scRNA-seq has profoundly changed our understanding of a series of biological phenomena. Currently, it has been used in research of SCs in many fields, particularly for the research of heterogeneity and cell subpopulations in early embryonic development. In this review, we focus on the scRNA-seq technique and its applications to research of SCs.

Single cell analysis of autism patient with bi-allelic NRXN1-alpha deletion reveals skewed fate choice in neural progenitors and impaired neuronal functionality

We generated human iPS derived neural stem cells and differentiated cells from healthy control individuals and an individual with autism spectrum disorder carrying bi-allelic NRXN1-alpha deletion. We investigated the expression of NRXN1-alpha during neural induction and neural differentiation and observed a pivotal role for NRXN1-alpha during early neural induction and neuronal differentiation. Single cell RNA-seq pinpointed neural stem cells carrying NRXN1-alpha deletion shifting towards radial glia-like cell identity and revealed higher proportion of differentiated astroglia. Furthermore, neuronal cells carrying NRXN1-alpha deletion were identified as immature by single cell RNA-seq analysis, displayed significant depression in calcium signaling activity and presented impaired maturation action potential profile in neurons investigated with electrophysiology. Our observations propose NRXN1-alpha plays an important role for the efficient establishment of neural stem cells, in neuronal differentiation and in maturation of functional excitatory neuronal cells.

Polled Digital Cell Sorter (p-DCS): Automatic identification of hematological cell types from single cell RNA-sequencing clusters

BACKGROUND

Single cell RNA sequencing (scRNA-seq) brings unprecedented opportunities for mapping the heterogeneity of complex cellular environments such as bone marrow, and provides insight into many cellular processes. Single cell RNA-seq has a far larger fraction of missing data reported as zeros (dropouts) than traditional bulk RNA-seq, and unsupervised clustering combined with Principal Component Analysis (PCA) can be used to overcome this limitation. After clustering, however, one has to interpret the average expression of markers on each cluster to identify the corresponding cell types, and this is normally done by hand by an expert curator.

RESULTS

We present a computational tool for processing single cell RNA-seq data that uses a voting algorithm to automatically identify cells based on approval votes received by known molecular markers. Using a stochastic procedure that accounts for imbalances in the number of known molecular signatures for different cell types, the method computes the statistical significance of the final approval score and automatically assigns a cell type to clusters without an expert curator. We demonstrate the utility of the tool in the analysis of eight samples of bone marrow from the Human Cell Atlas. The tool provides a systematic identification of cell types in bone marrow based on a list of markers of immune cell types, and incorporates a suite of visualization tools that can be overlaid on a t-SNE representation. The software is freely available as a Python package at https://github.com/sdomanskyi/DigitalCellSorter .

CONCLUSIONS

This methodology assures that extensive marker to cell type matching information is taken into account in a systematic way when assigning cell clusters to cell types. Moreover, the method allows for a high throughput processing of multiple scRNA-seq datasets, since it does not involve an expert curator, and it can be applied recursively to obtain cell sub-types. The software is designed to allow the user to substitute the marker to cell type matching information and apply the methodology to different cellular environments.