ddSeeker: a tool for processing Bio-Rad ddSEQ single cell RNA-seq data

Modulation of Neuronal Excitability and Plasticity by BHLHE41 Conveys Lithium Non-Responsiveness

Many bipolar disorder (BD) patients are non-responsive to lithium. The mechanisms underlying lithium (non-)responsiveness are largely unknown. By using gene-set enrichment analysis methods, we found that core clock gene-sets are significantly associated with lithium response. Among the top hits was BHLHE41, a modulator of the molecular clock and homeostatic sleep. Since BHLHE41 and its paralog BHLHE40 are functionally redundant, we assessed chronic lithium response in double-knockout mutant mice (DKO). We demonstrated that DKOs are non-responsive to lithium's effect in various behavioral tasks. Cellular assays and patch clamp recordings revealed lowered excitability and reduced lithium-response in prefrontal cortical layer 2/3 DKO neurons and on hippocampal long-term potentiation. Single-cell RNA sequencing identified that lithium deregulated mitochondrial respiration, cation channel and postsynapse associated gene-sets specifically in upper layer excitatory neurons. Our findings show that lithium acts in a highly cell-specific way on neuronal metabolism and excitability and modulates synaptic plasticity depending on BHLHE40/41.

Creeping fat exhibits distinct Inflammation-specific adipogenic preadipocytes in Crohn's disease

Creeping fat (CrF) is an extraintestinal manifestation observed in patients with Crohn's disease (CD). It is characterized by the accumulation of mesenteric adipose tissue (MAT) that wraps around the intestinal wall. Although the role of CrF in CD is still debated, multiple studies have highlighted a correlation between CrF and inflammation, as well as fibrostenosais of the intestine, which contributes to the worsening of CD symptoms. However, the mechanism underlying the potential role of CrF in the development of Crohn's fibrosis remains an enigma. This study aimed to analyze CrF comprehensively using single-cell RNA sequencing analysis. The data was compared with transcriptomic data from adipose tissue in other disease conditions, such as ulcerative colitis, lymphedema, and obesity. Our analysis classified two lineages of preadipocyte (PAC) clusters responsible for adipogenesis and fibrosis in CrF. Committed PACs in CrF showed increased cytokine expression in response to bacterial stimuli, potentially worsening inflammation in patients with CD. We also observed an increase in fibrotic activity in PAC clusters in CrF. Co-analyzing the data from patients with lymphedema, we found that pro-fibrotic PACs featured upregulated pentraxin-3 expression, suggesting a potential target for the treatment of fibrosis in CrF. Furthermore, PACs in CrF exhibited a distinct increase in cell-to-cell communication via cytokines related to inflammation and fibrosis, such as CCL, LIGHT, PDGF, MIF, and SEMA3. Interestingly, these interactions also increased in PACs of the lymphedema, whereas the increased MIF signal of PACs was found to be a distinct characteristic of CrF. In immune cell clusters in CrF, we observed high immune activity of pro-inflammatory macrophages, antigen-presenting macrophages, B cells, and IgG+ plasma cells. Finally, we have demonstrated elevated IgG+ plasma cell infiltration and increased pentraxin-3 protein levels in the fibrotic regions of CrF in CD patients when compared to MAT from both UC patients and healthy individuals. These findings provide new insights into the transcriptomic features related to the inflammation of cells in CrF and suggest potential targets for attenuating fibrosis in CD.

Essential procedures of single-cell RNA sequencing in multiple myeloma and its translational value

Multiple myeloma (MM) is a malignant neoplasm characterized by clonal proliferation of abnormal plasma cells. In many countries, it ranks as the second most prevalent malignant neoplasm of the hematopoietic system. Although treatment methods for MM have been continuously improved and the survival of patients has been dramatically prolonged, MM remains an incurable disease with a high probability of recurrence. As such, there are still many challenges to be addressed. One promising approach is single-cell RNA sequencing (scRNA-seq), which can elucidate the transcriptome heterogeneity of individual cells and reveal previously unknown cell types or states in complex tissues. In this review, we outlined the experimental workflow of scRNA-seq in MM, listed some commonly used scRNA-seq platforms and analytical tools. In addition, with the advent of scRNA-seq, many studies have made new progress in the key molecular mechanisms during MM clonal evolution, cell interactions and molecular regulation in the microenvironment, and drug resistance mechanisms in target therapy. We summarized the main findings and sequencing platforms for applying scRNA-seq to MM research and proposed broad directions for targeted therapies based on these findings.

Spermiogenesis alterations in the absence of CTCF revealed by single cell RNA sequencing

CTCF is an architectonic protein that organizes the genome inside the nucleus in almost all eukaryotic cells. There is evidence that CTCF plays a critical role during spermatogenesis as its depletion produces abnormal sperm and infertility. However, defects produced by its depletion throughout spermatogenesis have not been fully characterized. In this work, we performed single cell RNA sequencing in spermatogenic cells with and without CTCF. We uncovered defects in transcriptional programs that explain the severity of the damage in the produced sperm. In the early stages of spermatogenesis, transcriptional alterations are mild. As germ cells go through the specialization stage or spermiogenesis, transcriptional profiles become more altered. We found morphology defects in spermatids that support the alterations in their transcriptional profiles. Altogether, our study sheds light on the contribution of CTCF to the phenotype of male gametes and provides a fundamental description of its role at different stages of spermiogenesis.

A flexible cross-platform single-cell data processing pipeline

Single-cell RNA-sequencing analysis to quantify the RNA molecules in individual cells has become popular, as it can obtain a large amount of information from each experiment. We introduce UniverSC ( https://github.com/minoda-lab/universc ), a universal single-cell RNA-seq data processing tool that supports any unique molecular identifier-based platform. Our command-line tool, docker image, and containerised graphical application enables consistent and comprehensive integration, comparison, and evaluation across data generated from a wide range of platforms. We also provide a cross-platform application to run UniverSC via a graphical user interface, available for macOS, Windows, and Linux Ubuntu, negating one of the bottlenecks with single-cell RNA-seq analysis that is data processing for researchers who are not bioinformatically proficient.

Expression of Lineage Transcription Factors Identifies Differences in Transition States of Induced Human Oligodendrocyte Differentiation

Oligodendrocytes (OLs) are critical for myelination and are implicated in several brain disorders. Directed differentiation of human-induced OLs (iOLs) from pluripotent stem cells can be achieved by forced expression of different combinations of the transcription factors SOX10 (S), OLIG2 (O), and NKX6.2 (N). Here, we applied quantitative image analysis and single-cell transcriptomics to compare different transcription factor (TF) combinations for their efficacy towards robust OL lineage conversion. Compared with S alone, the combination of SON increases the number of iOLs and generates iOLs with a more complex morphology and higher expression levels of myelin-marker genes. RNA velocity analysis of individual cells reveals that S generates a population of oligodendrocyte-precursor cells (OPCs) that appear to be more immature than those generated by SON and to display distinct molecular properties. Our work highlights that TFs for generating iOPCs or iOLs should be chosen depending on the intended application or research question, and that SON might be beneficial to study more mature iOLs while S might be better suited to investigate iOPC biology.

High-Dimensional Single-Cell Transcriptomics in Melanoma and Cancer Immunotherapy

Recent advances in single-cell transcriptomics have greatly improved knowledge of complex transcriptional programs, rapidly expanding our knowledge of cellular phenotypes and functions within the tumour microenvironment and immune system. Several new single-cell technologies have been developed over recent years that have enabled expanded understanding of the mechanistic cells and biological pathways targeted by immunotherapies such as immune checkpoint inhibitors, which are now routinely used in patient management with high-risk early-stage or advanced melanoma. These technologies have method-specific strengths, weaknesses and capabilities which need to be considered when utilising them to answer translational research questions. Here, we provide guidance for the implementation of single-cell transcriptomic analysis platforms by reviewing the currently available experimental and analysis workflows. We then highlight the use of these technologies to dissect the tumour microenvironment in the context of cancer patients treated with immunotherapy. The strategic use of single-cell analytics in clinical settings are discussed and potential future opportunities are explored with a focus on their use to rationalise the design of novel immunotherapeutic drug therapies that will ultimately lead to improved cancer patient outcomes.

WASP: a versatile, web-accessible single cell RNA-Seq processing platform

Background

The technology of single cell RNA sequencing (scRNA-seq) has gained massively in popularity as it allows unprecedented insights into cellular heterogeneity as well as identification and characterization of (sub-)cellular populations. Furthermore, scRNA-seq is almost ubiquitously applicable in medical and biological research. However, these new opportunities are accompanied by additional challenges for researchers regarding data analysis, as advanced technical expertise is required in using bioinformatic software.

Results

Here we present WASP, a software for the processing of Drop-Seq-based scRNA-Seq data. Our software facilitates the initial processing of raw reads generated with the ddSEQ or 10x protocol and generates demultiplexed gene expression matrices including quality metrics. The processing pipeline is realized as a Snakemake workflow, while an R Shiny application is provided for interactive result visualization. WASP supports comprehensive analysis of gene expression matrices, including detection of differentially expressed genes, clustering of cellular populations and interactive graphical visualization of the results. The R Shiny application can be used with gene expression matrices generated by the WASP pipeline, as well as with externally provided data from other sources.

Conclusions

With WASP we provide an intuitive and easy-to-use tool to process and explore scRNA-seq data. To the best of our knowledge, it is currently the only freely available software package that combines pre- and post-processing of ddSEQ- and 10x-based data. Due to its modular design, it is possible to use any gene expression matrix with WASP’s post-processing R Shiny application. To simplify usage, WASP is provided as a Docker container. Alternatively, pre-processing can be accomplished via Conda, and a standalone version for Windows is available for post-processing, requiring only a web browser.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07469-6.

Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

BACKGROUND

Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation.

RESULTS

Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluated methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5' v1 and 3' v3 methods. We demonstrate that these methods have fewer dropout events, which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures.

CONCLUSION

Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

Unveiling functional heterogeneity in breast cancer multicellular tumor spheroids through single-cell RNA-seq

Heterogeneity is an intrinsic characteristic of cancer. Even in isogenic tumors, cell populations exhibit differential cellular programs that overall supply malignancy and decrease treatment efficiency. In this study, we investigated the functional relationship among cell subtypes and how this interdependency can promote tumor development in a cancer cell line. To do so, we performed single-cell RNA-seq of MCF7 Multicellular Tumor Spheroids as a tumor model. Analysis of single-cell transcriptomes at two-time points of the spheroid growth, allowed us to dissect their functional relationship. As a result, three major robust cellular clusters, with a non-redundant complementary composition, were found. Meanwhile, one cluster promotes proliferation, others mainly activate mechanisms to invade other tissues and serve as a reservoir population conserved over time. Our results provide evidence to see cancer as a systemic unit that has cell populations with task stratification with the ultimate goal of preserving the hallmarks in tumors.

Single-Cell RNA Sequencing of Hematopoietic Stem and Progenitor Cells Treated with Gemcitabine and Carboplatin

Treatments that include gemcitabine and carboplatin induce dose-limiting myelosuppression. The understanding of how human bone marrow is affected on a transcriptional level leading to the development of myelosuppression is required for the implementation of personalized treatments in the future. In this study, we treated human hematopoietic stem and progenitor cells (HSPCs) harvested from a patient with chronic myelogenous leukemia (CML) with gemcitabine/carboplatin. Thereafter, scRNA-seq was performed to distinguish transcriptional effects induced by gemcitabine/carboplatin. Gene expression was calculated and evaluated among cells within and between samples compared to untreated cells. Cell cycle analysis showed that the treatments effectively decrease cell proliferation, indicated by the proportion of cells in the G2M-phase dropping from 35% in untreated cells to 14.3% in treated cells. Clustering and t-SNE showed that cells within samples and between treated and untreated samples were affected differently. Enrichment analysis of differentially expressed genes showed that the treatments influence KEGG pathways and Gene Ontologies related to myeloid cell proliferation/differentiation, immune response, cancer, and the cell cycle. The present study shows the feasibility of using scRNA-seq and chemotherapy-treated HSPCs to find genes, pathways, and biological processes affected among and between treated and untreated cells. This indicates the possible gains of using single-cell toxicity studies for personalized medicine.

Single-nucleus RNA-seq identifies divergent populations of FSHD2 myotube nuclei

FSHD is characterized by the misexpression of DUX4 in skeletal muscle. Although DUX4 upregulation is thought to be the pathogenic cause of FSHD, DUX4 is lowly expressed in patient samples, and analysis of the consequences of DUX4 expression has largely relied on artificial overexpression. To better understand the native expression profile of DUX4 and its targets, we performed bulk RNA-seq on a 6-day differentiation time-course in primary FSHD2 patient myoblasts. We identify a set of 54 genes upregulated in FSHD2 cells, termed FSHD-induced genes. Using single-cell and single-nucleus RNA-seq on myoblasts and differentiated myotubes, respectively, we captured, for the first time, DUX4 expressed at the single-nucleus level in a native state. We identified two populations of FSHD myotube nuclei based on low or high enrichment of DUX4 and FSHD-induced genes ("FSHD-Lo" and "FSHD Hi", respectively). FSHD-Hi myotube nuclei coexpress multiple DUX4 target genes including DUXA, LEUTX and ZSCAN4, and also upregulate cell cycle-related genes with significant enrichment of E2F target genes and p53 signaling activation. We found more FSHD-Hi nuclei than DUX4-positive nuclei, and confirmed with in situ RNA/protein detection that DUX4 transcribed in only one or two nuclei is sufficient for DUX4 protein to activate target genes across multiple nuclei within the same myotube. DUXA (the DUX4 paralog) is more widely expressed than DUX4, and depletion of DUXA suppressed the expression of LEUTX and ZSCAN4 in late, but not early, differentiation. The results suggest that the DUXA can take over the role of DUX4 to maintain target gene expression. These results provide a possible explanation as to why it is easier to detect DUX4 target genes than DUX4 itself in patient cells and raise the possibility of a self-sustaining network of gene dysregulation triggered by the limited DUX4 expression.

Chromosomal mosaicism in human blastocysts: the ultimate diagnostic dilemma

BACKGROUND

Trophectoderm (TE) biopsy and next generation sequencing (NGS) are currently the preferred techniques for preimplantation genetic testing for aneuploidies (PGT-A). Although this approach delivered important improvements over previous testing strategies, increased sensitivity has also prompted a rise in diagnoses of uncertain clinical significance. This includes reports of chromosomal mosaicism, suggesting the presence of karyotypically distinct cells within a single TE biopsy. Given that PGT-A relies on the chromosomal constitution of the biopsied cells being representative of the entire embryo, the prevalence and clinical implications of blastocyst mosaicism continue to generate considerable controversy.

OBJECTIVE AND RATIONALE

The objective of this review was to evaluate existing scientific evidence regarding the prevalence and impact of chromosomal mosaicism in human blastocysts. We discuss insights from a biological, technical and clinical perspective to examine the implications of this diagnostic dilemma for PGT-A.

SEARCH METHODS

The PubMed and Google Scholar databases were used to search peer-reviewed publications using the following terms: ‘chromosomal mosaicism’, ‘human’, ‘embryo’, ‘blastocyst’, ‘implantation’, ‘next generation sequencing’ and ‘clinical management’ in combination with other keywords related to the subject area. Relevant articles in the English language, published until October 2019 were critically discussed.

OUTCOMES

Chromosomal mosaicism predominately results from errors in mitosis following fertilization. Although it appears to be less pervasive at later developmental stages, establishing the true prevalence of mosaicism in human blastocysts remains exceedingly challenging. In a clinical context, blastocyst mosaicism can only be reported based on a single TE biopsy and has been ascribed to 2–13% of embryos tested using NGS. Conversely, data from NGS studies disaggregating whole embryos suggests that mosaicism may be present in up to ~50% of blastocysts. However, differences in testing and reporting strategies, analysis platforms and the number of cells sampled inherently overshadow current data, while added uncertainties emanate from technical artefacts. Moreover, laboratory factors and aspects of in vitro culture generate further variability. Outcome data following the transfer of blastocysts diagnosed as mosaic remain limited. Current studies suggest that the transfer of putative mosaic embryos may lead to healthy live births, but also results in significantly reduced ongoing pregnancy rates compared to the transfer of euploid blastocysts. Observations that a subset of mosaic blastocysts has the capacity to develop normally have sparked discussions regarding the ability of embryos to self-correct. However, there is currently no direct evidence to support this assumption. Nevertheless, the exclusion of mosaic blastocysts results in fewer embryos available for transfer, which may inevitably compromise treatment outcomes.

WIDER IMPLICATIONS

Chromosomal mosaicism in human blastocysts remains a perpetual diagnostic and clinical dilemma in the context of PGT-A. This review offers an important scientific resource, informing about the challenges, risks and value of diagnosing mosaicism. Elucidating these uncertainties will ultimately pave the way towards improved clinical and patient management.

Single-Cell Transcriptomics: A High-Resolution Avenue for Plant Functional Genomics

Plant function is the result of the concerted action of single cells in different tissues. Advances in RNA-seq technologies and tissue processing allow us now to capture transcriptional changes at single-cell resolution. The incredible potential of single-cell RNA-seq lies in the novel ability to study and exploit regulatory processes in complex tissues based on the behaviour of single cells. Importantly, the independence from reporter lines allows the analysis of any given tissue in any plant. While there are challenges associated with the handling and analysis of complex datasets, the opportunities are unique to generate knowledge of tissue functions in unprecedented detail and to facilitate the application of such information by mapping cellular functions and interactions in a plant cell atlas.

Single-Cell Genomics

BACKGROUND

Single-cell genomics is an approach to investigate cell heterogeneity and to identify new molecular features correlated with clinical outcomes. This approach allows identification of the complexity of cell diversity in a sample without the loss of information that occurs when multicellular or bulk tissue samples are analyzed.

CONTENT

The first single-cell RNA-sequencing study was published in 2009, and since then many more studies and single-cell sequencing methods have been published. These studies have had a major impact on several fields, including microbiology, neurobiology, cancer, and developmental biology. Recently, improvements in reliability and the development of commercial single-cell isolation platforms are opening the potential of this technology to the clinical laboratory.

SUMMARY

In this review we provide an overview of the current state of single-cell genomics. We describe opportunities in clinical research and medical applications.