Trajectory-based differential expression analysis for single-cell sequencing data

From single-omics to interactomics: How can ligand-induced perturbations modulate single-cell phenotypes?

Cells suffer from perturbations by different stimuli, which, consequently, rise to individual alterations in their profile and function that may end up affecting the tissue as a whole. This is no different if we consider the effect of a therapeutic agent on a biological system. As cells are exposed to external ligands their profile can change at different single-omics levels. Detecting how these changes take place through different sequencing technologies is key to a better understanding of the effects of therapeutic agents. Single-cell RNA-sequencing stands out as one of the most common approaches for cell profiling and perturbation analysis. As a result, single-cell transcriptomics data can be integrated with other omics data sources, such as proteomics and epigenomics data, to clarify the perturbation effects and mechanism at the cell level. Appropriate computational tools are key to process and integrate the available information. This chapter focuses on the recent advances on ligand-induced perturbation and single-cell omics computational tools and algorithms, their current limitations, and how the deluge of data can be used to improve the current process of drug research and development.

In vivo labeling reveals continuous trafficking of TCF-1+ T cells between tumor and lymphoid tissue

Improving the efficacy of immune checkpoint therapies will require a better understanding of how immune cells are recruited and sustained in tumors. Here, we used the photoconversion of the tumor immune cell compartment to identify newly entering lymphocytes, determine how they change over time, and investigate their egress from the tumor. Combining single-cell transcriptomics and flow cytometry, we found that while a diverse mix of CD8 T cell subsets enter the tumor, all CD8 T cells retained within this environment for more than 72 h developed an exhausted phenotype, revealing the rapid establishment of this program. Rather than forming tumor-resident populations, non-effector subsets, which express TCF-1 and include memory and stem-like cells, were continuously recruited into the tumor, but this recruitment was balanced by concurrent egress to the tumor-draining lymph node. Thus, the TCF-1+ CD8 T cell niche in tumors is highly dynamic, with the circulation of cells between the tumor and peripheral lymphoid tissue to bridge systemic and intratumoral responses.

Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution

Tumor evolution is driven by the progressive acquisition of genetic and epigenetic alterations that enable uncontrolled growth and expansion to neighboring and distal tissues. The study of phylogenetic relationships between cancer cells provides key insights into these processes. Here, we introduced an evolving lineage-tracing system with a single-cell RNA-seq readout into a mouse model of Kras;Trp53(KP)-driven lung adenocarcinoma and tracked tumor evolution from single-transformed cells to metastatic tumors at unprecedented resolution. We found that the loss of the initial, stable alveolar-type2-like state was accompanied by a transient increase in plasticity. This was followed by the adoption of distinct transcriptional programs that enable rapid expansion and, ultimately, clonal sweep of stable subclones capable of metastasizing. Finally, tumors develop through stereotypical evolutionary trajectories, and perturbing additional tumor suppressors accelerates progression by creating novel trajectories. Our study elucidates the hierarchical nature of tumor evolution and, more broadly, enables in-depth studies of tumor progression.

CyTOF® for the Masses

Mass cytometry has revolutionized immunophenotyping, particularly in exploratory settings where simultaneous breadth and depth of characterization of immune populations is needed with limited samples such as in preclinical and clinical tumor immunotherapy. Mass cytometry is also a powerful tool for single-cell immunological assays, especially for complex and simultaneous characterization of diverse intratumoral immune subsets or immunotherapeutic cell populations. Through the elimination of spectral overlap seen in optical flow cytometry by replacement of fluorescent labels with metal isotopes, mass cytometry allows, on average, robust analysis of 60 individual parameters simultaneously. This is, however, associated with significantly increased complexity in the design, execution, and interpretation of mass cytometry experiments. To address the key pitfalls associated with the fragmentation, complexity, and analysis of data in mass cytometry for immunologists who are novices to these techniques, we have developed a comprehensive resource guide. Included in this review are experiment and panel design, antibody conjugations, sample staining, sample acquisition, and data pre-processing and analysis. Where feasible multiple resources for the same process are compared, allowing researchers experienced in flow cytometry but with minimal mass cytometry expertise to develop a data-driven and streamlined project workflow. It is our hope that this manuscript will prove a useful resource for both beginning and advanced users of mass cytometry.

Single-cell RNA sequencing of the retina in a model of retinitis pigmentosa reveals early responses to degeneration in rods and cones

Background

In inherited retinal disorders such as retinitis pigmentosa (RP), rod photoreceptor-specific mutations cause primary rod degeneration that is followed by secondary cone death and loss of high-acuity vision. Mechanistic studies of retinal degeneration are challenging because of retinal heterogeneity. Moreover, the detection of early cone responses to rod death is especially difficult due to the paucity of cones in the retina. To resolve heterogeneity in the degenerating retina and investigate events in both types of photoreceptors during primary rod degeneration, we utilized droplet-based single-cell RNA sequencing in an RP mouse model, rd10.

Results

Using trajectory analysis, we defined two consecutive phases of rod degeneration at P21, characterized by the early transient upregulation of Egr1 and the later induction of Cebpd. EGR1 was the transcription factor most significantly associated with the promoters of differentially regulated genes in Egr1-positive rods in silico. Silencing Egr1 affected the expression levels of two of these genes in vitro. Degenerating rods exhibited changes associated with metabolism, neuroprotection, and modifications to synapses and microtubules. Egr1 was also the most strongly upregulated transcript in cones. Its upregulation in cones accompanied potential early respiratory dysfunction and changes in signaling pathways. The expression pattern of EGR1 in the retina was dynamic during degeneration, with a transient increase of EGR1 immunoreactivity in both rods and cones during the early stages of their degenerative processes.

Conclusion

Our results identify early and late changes in degenerating rd10 rod photoreceptors and reveal early responses to rod degeneration in cones not expressing the disease-causing mutation, pointing to mechanisms relevant for secondary cone degeneration. In addition, our data implicate EGR1 as a potential key regulator of early degenerative events in rods and cones, providing a potential broad target for modulating photoreceptor degeneration.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01280-9.

OBIF: an omics-based interaction framework to reveal molecular drivers of synergy

Bioactive molecule library screening may empirically identify effective combination therapies, but molecular mechanisms underlying favorable drug–drug interactions often remain unclear, precluding further rational design. In the absence of an accepted systems theory to interrogate synergistic responses, we introduce Omics-Based Interaction Framework (OBIF) to reveal molecular drivers of synergy through integration of statistical and biological interactions in synergistic biological responses. OBIF performs full factorial analysis of feature expression data from single versus dual exposures to identify molecular clusters that reveal synergy-mediating pathways, functions and regulators. As a practical demonstration, OBIF analyzed transcriptomic and proteomic data of a dyad of immunostimulatory molecules that induces synergistic protection against influenza A and revealed unanticipated NF-κB/AP-1 cooperation that is required for antiviral protection. To demonstrate generalizability, OBIF analyzed data from a diverse array of Omics platforms and experimental conditions, successfully identifying the molecular clusters driving their synergistic responses. Hence, unlike existing synergy quantification and prediction methods, OBIF is a phenotype-driven systems model that supports multiplatform interrogation of synergy mechanisms.

Human distal airways contain a multipotent secretory cell that can regenerate alveoli

The human lung differs substantially from its mouse counterpart, resulting in a distinct distal airway architecture affected by disease pathology in chronic obstructive pulmonary disease. In humans, the distal branches of the airway interweave with the alveolar gas-exchange niche, forming an anatomical structure known as the respiratory bronchioles. Owing to the lack of a counterpart in mouse, the cellular and molecular mechanisms that govern respiratory bronchioles in the human lung remain uncharacterized. Here we show that human respiratory bronchioles contain a unique secretory cell population that is distinct from cells in larger proximal airways. Organoid modelling reveals that these respiratory airway secretory (RAS) cells act as unidirectional progenitors for alveolar type 2 cells, which are essential for maintaining and regenerating the alveolar niche. RAS cell lineage differentiation into alveolar type 2 cells is regulated by Notch and Wnt signalling. In chronic obstructive pulmonary disease, RAS cells are altered transcriptionally, corresponding to abnormal alveolar type 2 cell states, which are associated with smoking exposure in both humans and ferrets. These data identify a distinct progenitor in a region of the human lung that is not found in mouse that has a critical role in maintaining the gas-exchange compartment and is altered in chronic lung disease.

QPCTL regulates macrophage and monocyte abundance and inflammatory signatures in the tumor microenvironment

The enzyme glutaminyl-peptide cyclotransferase-like protein (QPCTL) catalyzes the formation of pyroglutamate residues at the NH₂-terminus of proteins, thereby influencing their biological properties. A number of studies have implicated QPCTL in the regulation of chemokine stability. Furthermore, QPCTL activity has recently been shown to be critical for the formation of the high-affinity SIRPα binding site of the CD47 “don’t-eat-me” protein. Based on the latter data, interference with QPCTL activity —and hence CD47 maturation—may be proposed as a means to promote anti-tumor immunity. However, the pleiotropic activity of QPCTL makes it difficult to predict the effects of QPCTL inhibition on the tumor microenvironment (TME). Using a syngeneic mouse melanoma model, we demonstrate that QPCTL deficiency alters the intra-tumoral monocyte-to-macrophage ratio, results in a profound increase in the presence of pro-inflammatory cancer-associated fibroblasts (CAFs) relative to immunosuppressive TGF-β1-driven CAFs, and leads to an increased IFN and decreased TGF-β transcriptional response signature in tumor cells. Importantly, the functional relevance of the observed TME remodeling is demonstrated by the synergy between QPCTL deletion and anti PD-L1 therapy, sensitizing an otherwise refractory melanoma model to anti-checkpoint therapy. Collectively, these data provide support for the development of strategies to interfere with QPCTL activity as a means to promote tumor-specific immunity.

Genomic and Epigenomic Landscape of Juvenile Myelomonocytic Leukemia

Simple Summary

Juvenile myelomonocytic leukemia (JMML) is a rare pediatric myelodysplastic/myeloproliferative neoplasm characterized by the constitutive activation of the RAS pathway. In spite of the recent progresses in the molecular characterization of JMML, this disease is still a clinical challenge due to its heterogeneity, difficult diagnosis, poor prognosis, and the lack of curative treatment options other than hematopoietic stem cell transplantation (HSCT). In this review, we will provide a detailed overview of the genetic and epigenetic alterations occurring in JMML, and discuss their clinical relevance in terms of disease prognosis and risk of relapse after HSCT. We will also present the most recent advances on novel preclinical and clinical therapeutic approaches directed against JMML molecular targets. Finally, we will outline future research perspectives to further explore the oncogenic mechanism driving JMML leukemogenesis and progression, with special attention to the application of single-cell next-generation sequencing technologies.

Abstract

Juvenile myelomonocytic leukemia (JMML) is a rare myelodysplastic/myeloproliferative neoplasm of early childhood. Most of JMML patients experience an aggressive clinical course of the disease and require hematopoietic stem cell transplantation, which is currently the only curative treatment. JMML is characterized by RAS signaling hyperactivation, which is mainly driven by mutations in one of five genes of the RAS pathway, including PTPN11, KRAS, NRAS, NF1, and CBL. These driving mutations define different disease subtypes with specific clinico-biological features. Secondary mutations affecting other genes inside and outside the RAS pathway contribute to JMML pathogenesis and are associated with a poorer prognosis. In addition to these genetic alterations, JMML commonly presents aberrant epigenetic profiles that strongly correlate with the clinical outcome of the patients. This observation led to the recent publication of an international JMML stratification consensus, which defines three JMML clinical groups based on DNA methylation status. Although the characterization of the genomic and epigenomic landscapes in JMML has significantly contributed to better understand the molecular mechanisms driving the disease, our knowledge on JMML origin, cell identity, and intratumor and interpatient heterogeneity is still scarce. The application of new single-cell sequencing technologies will be critical to address these questions in the future.

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application

Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through  GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.

Single-cell transcriptomics reveals expression profiles of Trypanosoma brucei sexual stages

Early diverging lineages such as trypanosomes can provide clues to the evolution of sexual reproduction in eukaryotes. In Trypanosoma brucei, the pathogen that causes Human African Trypanosomiasis, sexual reproduction occurs in the salivary glands of the insect host, but analysis of the molecular signatures that define these sexual forms is complicated because they mingle with more numerous, mitotically-dividing developmental stages. We used single-cell RNA-sequencing (scRNAseq) to profile 388 individual trypanosomes from midgut, proventriculus, and salivary glands of infected tsetse flies allowing us to identify tissue-specific cell types. Further investigation of salivary gland parasite transcriptomes revealed fine-scale changes in gene expression over a developmental progression from putative sexual forms through metacyclics expressing variant surface glycoprotein genes. The cluster of cells potentially containing sexual forms was characterized by high level transcription of the gamete fusion protein HAP2, together with an array of surface proteins and several genes of unknown function. We linked these expression patterns to distinct morphological forms using immunofluorescence assays and reporter gene expression to demonstrate that the kinetoplastid-conserved gene Tb927.10.12080 is exclusively expressed at high levels by meiotic intermediates and gametes. Further experiments are required to establish whether this protein, currently of unknown function, plays a role in gamete formation and/or fusion.

Mapping the developing human cardiac endothelium at single-cell resolution identifies MECOM as a regulator of arteriovenous gene expression

AIMS

Coronary vasculature formation is a critical event during cardiac development, essential for heart function throughout perinatal and adult life. However, current understanding of coronary vascular development has largely been derived from transgenic mouse models. The aim of this study was to characterize the transcriptome of the human foetal cardiac endothelium using single-cell RNA sequencing (scRNA-seq) to provide critical new insights into the cellular heterogeneity and transcriptional dynamics that underpin endothelial specification within the vasculature of the developing heart.

METHODS AND RESULTS

We acquired scRNA-seq data of over 10 000 foetal cardiac endothelial cells (ECs), revealing divergent EC subtypes including endocardial, capillary, venous, arterial, and lymphatic populations. Gene regulatory network analyses predicted roles for SMAD1 and MECOM in determining the identity of capillary and arterial populations, respectively. Trajectory inference analysis suggested an endocardial contribution to the coronary vasculature and subsequent arterialization of capillary endothelium accompanied by increasing MECOM expression. Comparative analysis of equivalent data from murine cardiac development demonstrated that transcriptional signatures defining endothelial subpopulations are largely conserved between human and mouse. Comprehensive characterization of the transcriptional response to MECOM knockdown in human embryonic stem cell-derived EC (hESC-EC) demonstrated an increase in the expression of non-arterial markers, including those enriched in venous EC.

CONCLUSIONS

scRNA-seq of the human foetal cardiac endothelium identified distinct EC populations. A predicted endocardial contribution to the developing coronary vasculature was identified, as well as subsequent arterial specification of capillary EC. Loss of MECOM in hESC-EC increased expression of non-arterial markers, suggesting a role in maintaining arterial EC identity.

scInTime: A Computational Method Leveraging Single-Cell Trajectory and Gene Regulatory Networks to Identify Master Regulators of Cellular Differentiation

Trajectory inference (TI) or pseudotime analysis has dramatically extended the analytical framework of single-cell RNA-seq data, allowing regulatory genes contributing to cell differentiation and those involved in various dynamic cellular processes to be identified. However, most TI analysis procedures deal with individual genes independently while overlooking the regulatory relations between genes. Integrating information from gene regulatory networks (GRNs) at different pseudotime points may lead to more interpretable TI results. To this end, we introduce scInTime-an unsupervised machine learning framework coupling inferred trajectory with single-cell GRNs (scGRNs) to identify master regulatory genes. We validated the performance of our method by analyzing multiple scRNA-seq data sets. In each of the cases, top-ranking genes predicted by scInTime supported their functional relevance with corresponding signaling pathways, in line with the results of available functional studies. Overall results demonstrated that scInTime is a powerful tool to exploit pseudotime-series scGRNs, allowing for a clear interpretation of TI results toward more significant biological insights.

Temporal transcriptomic analysis using TrendCatcher identifies early and persistent neutrophil activation in severe COVID-19

Studying temporal gene expression shifts during disease progression provides important insights into the biological mechanisms that distinguish adaptive and maladaptive responses. Existing tools for the analysis of time course transcriptomic data are not designed to optimally identify distinct temporal patterns when analyzing dynamic differentially expressed genes (DDEGs). Moreover, there are not enough methods to assess and visualize the temporal progression of biological pathways mapped from time course transcriptomic data sets. In this study, we developed an open-source R package TrendCatcher (https://github.com/jaleesr/TrendCatcher), which applies the smoothing spline ANOVA model and break point searching strategy, to identify and visualize distinct dynamic transcriptional gene signatures and biological processes from longitudinal data sets. We used TrendCatcher to perform a systematic temporal analysis of COVID-19 peripheral blood transcriptomes, including bulk and single-cell RNA-Seq time course data. TrendCatcher uncovered the early and persistent activation of neutrophils and coagulation pathways, as well as impaired type I IFN (IFN-I) signaling in circulating cells as a hallmark of patients who progressed to severe COVID-19, whereas no such patterns were identified in individuals receiving SARS-CoV-2 vaccinations or patients with mild COVID-19. These results underscore the importance of systematic temporal analysis to identify early biomarkers and possible pathogenic therapeutic targets.

Reconstruction of dynamic regulatory networks reveals signaling-induced topology changes associated with germ layer specification

Elucidating regulatory relationships between transcription factors (TFs) and target genes is fundamental to understanding how cells control their identity and behavior. Unfortunately, existing computational gene regulatory network (GRN) reconstruction methods are imprecise, computationally burdensome, and fail to reveal dynamic regulatory topologies. Here, we present Epoch, a reconstruction tool that uses single-cell transcriptomics to accurately infer dynamic networks. We apply Epoch to identify the dynamic networks underpinning directed differentiation of mouse embryonic stem cells (ESCs) guided by multiple signaling pathways, and we demonstrate that modulating these pathways drives topological changes that bias cell fate potential. We also find that Peg3 rewires the pluripotency network to favor mesoderm specification. By integrating signaling pathways with GRNs, we trace how Wnt activation and PI3K suppression govern mesoderm and endoderm specification, respectively. Finally, we identify regulatory circuits of patterning and axis formation that distinguish in vitro and in vivo mesoderm specification.

Single-nucleus transcriptomes reveal evolutionary and functional properties of cell types in the Drosophila accessory gland

Many traits responsible for male reproduction evolve quickly, including gene expression phenotypes in germline and somatic male reproductive tissues. Rapid male evolution in polyandrous species is thought to be driven by competition among males for fertilizations and conflicts between male and female fitness interests that manifest in postcopulatory phenotypes. In Drosophila, seminal fluid proteins secreted by three major cell types of the male accessory gland and ejaculatory duct are required for female sperm storage and use, and influence female postcopulatory traits. Recent work has shown that these cell types have overlapping but distinct effects on female postcopulatory biology, yet relatively little is known about their evolutionary properties. Here, we use single-nucleus RNA-Seq of the accessory gland and ejaculatory duct from Drosophila melanogaster and two closely related species to comprehensively describe the cell diversity of these tissues and their transcriptome evolution for the first time. We find that seminal fluid transcripts are strongly partitioned across the major cell types, and expression of many other genes additionally defines each cell type. We also report previously undocumented diversity in main cells. Transcriptome divergence was found to be heterogeneous across cell types and lineages, revealing a complex evolutionary process. Furthermore, protein adaptation varied across cell types, with potential consequences for our understanding of selection on male postcopulatory traits.

CellRank for directed single-cell fate mapping

Computational trajectory inference enables the reconstruction of cell state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank (https://cellrank.org) for single-cell fate mapping in diverse scenarios, including regeneration, reprogramming and disease, for which direction is unknown. Our approach combines the robustness of trajectory inference with directional information from RNA velocity, taking into account the gradual and stochastic nature of cellular fate decisions, as well as uncertainty in velocity vectors. On pancreas development data, CellRank automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. Applied to lineage-traced cellular reprogramming data, predicted fate probabilities correctly recover reprogramming outcomes. CellRank also predicts a new dedifferentiation trajectory during postinjury lung regeneration, including previously unknown intermediate cell states, which we confirm experimentally.

CellRank infers directed cell state transitions and cell fates incorporating RNA velocity information into a graph based Markov process.

Deep learning-based brain transcriptomic signatures associated with the neuropathological and clinical severity of Alzheimer's disease

Brain tissue gene expression from donors with and without Alzheimer's disease has been used to help inform the molecular changes associated with the development and potential treatment of this disorder. Here, we use a deep learning method to analyse RNA-seq data from 1114 brain donors from the Accelerating Medicines Project for Alzheimer's Disease consortium to characterize post-mortem brain transcriptome signatures associated with amyloid-β plaque, tau neurofibrillary tangles and clinical severity in multiple Alzheimer's disease dementia populations. Starting from the cross-sectional data in the Religious Orders Study and Memory and Aging Project cohort (n = 634), a deep learning framework was built to obtain a trajectory that mirrors Alzheimer's disease progression. A severity index was defined to quantitatively measure the progression based on the trajectory. Network analysis was then carried out to identify key gene (index gene) modules present in the model underlying the progression. Within this data set, severity indexes were found to be very closely correlated with all Alzheimer's disease neuropathology biomarkers (R ∼ 0.5, P < 1e-11) and global cognitive function (R = -0.68, P < 2.2e-16). We then applied the model to additional transcriptomic data sets from different brain regions (MAYO, n = 266; Mount Sinai Brain Bank, n = 214), and observed that the model remained significantly predictive (P < 1e-3) of neuropathology and clinical severity. The index genes that significantly contributed to the model were integrated with Alzheimer's disease co-expression regulatory networks, resolving four discrete gene modules that are implicated in vascular and metabolic dysfunction in different cell types, respectively. Our work demonstrates the generalizability of this signature to frontal and temporal cortex measurements and additional brain donors with Alzheimer's disease, other age-related neurological disorders and controls, and revealed that the transcriptomic network modules contribute to neuropathological and clinical disease severity. This study illustrates the promise of using deep learning methods to analyse heterogeneous omics data and discover potentially targetable molecular networks that can inform the development, treatment and prevention of neurodegenerative diseases like Alzheimer's disease.

Single-nucleus chromatin accessibility profiling highlights distinct astrocyte signatures in progressive supranuclear palsy and corticobasal degeneration

Tauopathies such as progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD) exhibit characteristic neuronal and glial inclusions of hyperphosphorylated Tau (pTau). Although the astrocytic pTau phenotype upon neuropathological examination is the most guiding feature in distinguishing both diseases, regulatory mechanisms controlling their transitions into disease-specific states are poorly understood to date. Here, we provide accessible chromatin data of more than 45,000 single nuclei isolated from the frontal cortex of PSP, CBD, and control individuals. We found a strong association of disease-relevant molecular changes with astrocytes and demonstrate that tauopathy-relevant genetic risk variants are tightly linked to astrocytic chromatin accessibility profiles in the brains of PSP and CBD patients. Unlike the established pathogenesis in the secondary tauopathy Alzheimer disease, microglial alterations were relatively sparse. Transcription factor (TF) motif enrichments in pseudotime as well as modeling of the astrocytic TF interplay suggested a common pTau signature for CBD and PSP that is reminiscent of an inflammatory immediate-early response. Nonetheless, machine learning models also predicted discriminatory features, and we observed marked differences in molecular entities related to protein homeostasis between both diseases. Predicted TF involvement was supported by immunofluorescence analyses in postmortem brain tissue for their highly correlated target genes. Collectively, our data expand the current knowledge on risk gene involvement (e.g., MAPT, MAPK8, and NFE2L2) and molecular pathways leading to the phenotypic changes associated with CBD and PSP.

A multidimensional coding architecture of the vagal interoceptive system

Interoception, the ability to timely and precisely sense changes inside the body, is critical for survival^1-4. Vagal sensory neurons (VSNs) form an important body-to-brain connection, navigating visceral organs along the rostral-caudal axis of the body and crossing the surface-lumen axis of organs into appropriate tissue layers^5,6. The brain can discriminate numerous body signals through VSNs, but the underlying coding strategy remains poorly understood. Here we show that VSNs code visceral organ, tissue layer and stimulus modality-three key features of an interoceptive signal-in different dimensions. Large-scale single-cell profiling of VSNs from seven major organs in mice using multiplexed projection barcodes reveals a 'visceral organ' dimension composed of differentially expressed gene modules that code organs along the body's rostral-caudal axis. We discover another 'tissue layer' dimension with gene modules that code the locations of VSN endings along the surface-lumen axis of organs. Using calcium-imaging-guided spatial transcriptomics, we show that VSNs are organized into functional units to sense similar stimuli across organs and tissue layers; this constitutes a third 'stimulus modality' dimension. The three independent feature-coding dimensions together specify many parallel VSN pathways in a combinatorial manner and facilitate the complex projection of VSNs in the brainstem. Our study highlights a multidimensional coding architecture of the mammalian vagal interoceptive system for effective signal communication.

Single-cell transcriptomic analysis of zebrafish cranial neural crest reveals spatiotemporal regulation of lineage decisions during development

Neural crest (NC) cells migrate throughout vertebrate embryos to give rise to a huge variety of cell types, but when and where lineages emerge and their regulation remain unclear. We have performed single-cell RNA sequencing (RNA-seq) of cranial NC cells from the first pharyngeal arch in zebrafish over several stages during migration. Computational analysis combining pseudotime and real-time data reveals that these NC cells first adopt a transitional state, becoming specified mid-migration, with the first lineage decisions being skeletal and pigment, followed by neural and glial progenitors. In addition, by computationally integrating these data with RNA-seq data from a transgenic Wnt reporter line, we identify gene cohorts with similar temporal responses to Wnts during migration and show that one, Atp6ap2, is required for melanocyte differentiation. Together, our results show that cranial NC cell lineages arise progressively and uncover a series of spatially restricted cell interactions likely to regulate such cell-fate decisions.

Tatarakis et al. provide a single-cell transcriptomic timeline of cranial neural crest (NC) development in zebrafish and address long-standing questions surrounding the integration of NC cell migration and lineage specification. They find that lineages are specified mid-migration. These fate decisions correspond to shifts in Wnt signaling, and lineages rapidly segregate.

MultiMAP: dimensionality reduction and integration of multimodal data

Multimodal data is rapidly growing in many fields of science and engineering, including single-cell biology. We introduce MultiMAP, a novel algorithm for dimensionality reduction and integration. MultiMAP can integrate any number of datasets, leverages features not present in all datasets, is not restricted to a linear mapping, allows the user to specify the influence of each dataset, and is extremely scalable to large datasets. We apply MultiMAP to single-cell transcriptomics, chromatin accessibility, methylation, and spatial data and show that it outperforms current approaches. On a new thymus dataset, we use MultiMAP to integrate cells along a temporal trajectory. This enables quantitative comparison of transcription factor expression and binding site accessibility over the course of T cell differentiation, revealing patterns of expression versus binding site opening kinetics.

HTLV-1 infection promotes excessive T cell activation and transformation into adult T cell leukemia/lymphoma

Human T cell leukemia virus type 1 (HTLV-1) mainly infects CD4+ T cells and induces chronic, persistent infection in infected individuals, with some developing adult T cell leukemia/lymphoma (ATL). HTLV-1 alters cellular differentiation, activation, and survival; however, it is unknown whether and how these changes contribute to the malignant transformation of infected cells. In this study, we used single-cell RNA-sequencing and T cell receptor-sequencing to investigate the differentiation and HTLV-1-mediated transformation of T cells. We analyzed 87,742 PBMCs from 12 infected and 3 uninfected individuals. Using multiple independent bioinformatics methods, we demonstrated the seamless transition of naive T cells into activated T cells, whereby HTLV-1-infected cells in an activated state further transformed into ATL cells, which are characterized as clonally expanded, highly activated T cells. Notably, the greater the activation state of ATL cells, the more they acquire Treg signatures. Intriguingly, the expression of HLA class II genes in HTLV-1-infected cells was uniquely induced by the viral protein Tax and further upregulated in ATL cells. Functional assays revealed that HTLV-1-infected cells upregulated HLA class II molecules and acted as tolerogenic antigen-presenting cells to induce anergy of antigen-specific T cells. In conclusion, our study revealed the in vivo mechanisms of HTLV-1-mediated transformation and immune escape at the single-cell level.

Systematic temporal analysis of peripheral blood transcriptomes using TrendCatcher identifies early and persistent neutrophil activation as a hallmark of severe COVID-19

Studying temporal gene expression shifts during disease progression provides important insights into the biological mechanisms that distinguish adaptive and maladaptive responses. Existing tools for the analysis of time course transcriptomic data are not designed to optimally identify distinct temporal patterns when analyzing dynamic differentially expressed genes (DDEGs). Moreover, there is a lack of methods to assess and visualize the temporal progression of biological pathways mapped from time course transcriptomic datasets. In this study, we developed an open-source R package TrendCatcher (https://github.com/jaleesr/TrendCatcher), which applies the smoothing spline ANOVA model and break point searching strategy to identify and visualize distinct dynamic transcriptional gene signatures and biological processes from longitudinal datasets. We used TrendCatcher to perform a systematic temporal analysis of COVID-19 peripheral blood transcriptomes, including bulk RNA-seq and scRNA-seq time course data. TrendCatcher uncovered the early and persistent activation of neutrophils and coagulation pathways as well as impaired type I interferon (IFN-I) signaling in circulating cells as a hallmark of patients who progressed to severe COVID-19, whereas no such patterns were identified in individuals receiving SARS-CoV-2 vaccinations or patients with mild COVID-19. These results underscore the importance of systematic temporal analysis to identify early biomarkers and possible pathogenic therapeutic targets.

Dynamic regulatory networks of T cell trajectory dissect transcriptional control of T cell state transition

T cells exhibit heterogeneous functional states, which correlate with responsiveness to immune checkpoint blockade and prognosis of tumor patients. However, the molecular regulatory mechanisms underlying the dynamic process of T cell state transition remain largely unknown. Based on single-cell transcriptome data of T cells in non-small cell lung cancer, we combined cell states and pseudo-times to propose a pipeline to construct dynamic regulatory networks for dissecting the process of T cell dysfunction. Candidate regulators at different stages were revealed in the process of tumor-infiltrating T cell dysfunction. Through comparing dynamic networks across the T cell state transition, we revealed frequent regulatory interaction rewiring and further refined critical regulators mediating each state transition. Several known regulators were identified, including TCF7, EOMES, ID2, and TOX. Notably, one of the critical regulators, TSC22D3, was frequently identified in the state transitions from the intermediate state to the pre-dysfunction and dysfunction state, exerting diverse roles in each state transition by regulatory interaction rewiring. Moreover, higher expression of TSC22D3 was associated with the clinical outcome of tumor patients. Our study embedded transcription factors (TFs) within the temporal dynamic networks, providing a comprehensive view of dynamic regulatory mechanisms controlling the process of T cell state transition.

A Comprehensive Survey of Statistical Approaches for Differential Expression Analysis in Single-Cell RNA Sequencing Studies

Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput sequencing technique for studying gene expressions at the cell level. Differential Expression (DE) analysis is a major downstream analysis of scRNA-seq data. DE analysis the in presence of noises from different sources remains a key challenge in scRNA-seq. Earlier practices for addressing this involved borrowing methods from bulk RNA-seq, which are based on non-zero differences in average expressions of genes across cell populations. Later, several methods specifically designed for scRNA-seq were developed. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to comprehensively study the performance of DE analysis methods. Here, we provide a review and classification of different DE approaches adapted from bulk RNA-seq practice as well as those specifically designed for scRNA-seq. We also evaluate the performance of 19 widely used methods in terms of 13 performance metrics on 11 real scRNA-seq datasets. Our findings suggest that some bulk RNA-seq methods are quite competitive with the single-cell methods and their performance depends on the underlying models, DE test statistic(s), and data characteristics. Further, it is difficult to obtain the method which will be best-performing globally through individual performance criterion. However, the multi-criteria and combined-data analysis indicates that DECENT and EBSeq are the best options for DE analysis. The results also reveal the similarities among the tested methods in terms of detecting common DE genes. Our evaluation provides proper guidelines for selecting the proper tool which performs best under particular experimental settings in the context of the scRNA-seq.

Klf5 establishes bi-potential cell fate by dual regulation of ICM and TE specification genes

Early blastomeres of mouse preimplantation embryos exhibit bi-potential cell fate, capable of generating both embryonic and extra-embryonic lineages in blastocysts. Here we identify three major two-cell-stage (2C)-specific endogenous retroviruses (ERVs) as the molecular hallmark of this bi-potential plasticity. Using the long terminal repeats (LTRs) of all three 2C-specific ERVs, we identify Krüppel-like factor 5 (Klf5) as their major upstream regulator. Klf5 is essential for bi-potential cell fate; a single Klf5-overexpressing embryonic stem cell (ESC) generates terminally differentiated embryonic and extra-embryonic lineages in chimeric embryos, and Klf5 directly induces inner cell mass (ICM) and trophectoderm (TE) specification genes. Intriguingly, Klf5 and Klf4 act redundantly during ICM specification, whereas Klf5 deficiency alone impairs TE specification. Klf5 is regulated by multiple 2C-specific transcription factors, particularly Dux, and the Dux/Klf5 axis is evolutionarily conserved. The 2C-specific transcription program converges on Klf5 to establish bi-potential cell fate, enabling a cell state with dual activation of ICM and TE genes.

Using multiple 2C-specific ERV cell fate markers, Kinisu et al. identify Klf5 as a key transcription factor that confers a 2C-like developmental potential and activates ICM and TE specification genes. Klf5 and Klf4 act redundantly for ICM and TE specification in mouse preimplantation embryos.

Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments

MOTIVATION

The negative binomial distribution has been shown to be a good model for counts data from both bulk and single-cell RNA-sequencing (RNA-seq). Gaussian process (GP) regression provides a useful non-parametric approach for modelling temporal or spatial changes in gene expression. However, currently available GP regression methods that implement negative binomial likelihood models do not scale to the increasingly large datasets being produced by single-cell and spatial transcriptomics.

RESULTS

The GPcounts package implements GP regression methods for modelling counts data using a negative binomial likelihood function. Computational efficiency is achieved through the use of variational Bayesian inference. The GP function models changes in the mean of the negative binomial likelihood through a logarithmic link function and the dispersion parameter is fitted by maximum likelihood. We validate the method on simulated time course data, showing better performance to identify changes in over-dispersed counts data than methods based on Gaussian or Poisson likelihoods. To demonstrate temporal inference, we apply GPcounts to single-cell RNA-seq datasets after pseudotime and branching inference. To demonstrate spatial inference, we apply GPcounts to data from the mouse olfactory bulb to identify spatially variable genes and compare to two published GP methods. We also provide the option of modelling additional dropout using a zero-inflated negative binomial. Our results show that GPcounts can be used to model temporal and spatial counts data in cases where simpler Gaussian and Poisson likelihoods are unrealistic.

AVAILABILITY AND IMPLEMENTATION

GPcounts is implemented using the GPflow library in Python and is available at https://github.com/ManchesterBioinference/GPcounts along with the data, code and notebooks required to reproduce the results presented here. The version used for this paper is archived at https://doi.org/10.5281/zenodo.5027066.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-Cell RNAseq Profiling of Human γδ T Lymphocytes in Virus-Related Cancers and COVID-19 Disease

The detailed characterization of human γδ T lymphocyte differentiation at the single-cell transcriptomic (scRNAseq) level in tumors and patients with coronavirus disease 2019 (COVID-19) requires both a reference differentiation trajectory of γδ T cells and a robust mapping method for additional γδ T lymphocytes. Here, we incepted such a method to characterize thousands of γδ T lymphocytes from (n = 95) patients with cancer or adult and pediatric COVID-19 disease. We found that cancer patients with human papillomavirus-positive head and neck squamous cell carcinoma and Epstein-Barr virus-positive Hodgkin's lymphoma have γδ tumor-infiltrating T lymphocytes that are more prone to recirculate from the tumor and avoid exhaustion. In COVID-19, both TCRVγ9 and TCRVγnon9 subsets of γδ T lymphocytes relocalize from peripheral blood mononuclear cells (PBMC) to the infected lung tissue, where their advanced differentiation, tissue residency, and exhaustion reflect T cell activation. Although severe COVID-19 disease increases both recruitment and exhaustion of γδ T lymphocytes in infected lung lesions but not blood, the anti-IL6R therapy with Tocilizumab promotes γδ T lymphocyte differentiation in patients with COVID-19. PBMC from pediatric patients with acute COVID-19 disease display similar γδ T cell lymphopenia to that seen in adult patients. However, blood γδ T cells from children with the COVID-19-related multisystem inflammatory syndrome are not lymphodepleted, but they are differentiated as in healthy PBMC. These findings suggest that some virus-induced memory γδ T lymphocytes durably persist in the blood of adults and could subsequently infiltrate and recirculate in tumors.

Phased differentiation of γδ T and T CD8 tumor-infiltrating lymphocytes revealed by single-cell transcriptomics of human cancers

γδ T lymphocytes diverge from conventional T CD8 lymphocytes for ontogeny, homing, and antigen specificity, but whether their differentiation in tumors also deviates was unknown. Using innovative analyses of our original and ~150 published single-cell RNA sequencing datasets validated by phenotyping of human tumors and murine models, here we present the first high-resolution view of human γδ T cell differentiation in cancer. While γδ T lymphocytes prominently encompass TCRVγ9 cells more differentiated than T CD8 in healthy donor’s blood, a different scenario is unveiled in tumors. Solid tumors and lymphomas are infiltrated by a majority of TCRVγnon9 γδ T cells which are quantitatively correlated and remarkably aligned with T CD8 for differentiation, exhaustion, gene expression profile, and response to immune checkpoint therapy. This cancer-wide association is critical for developing cancer immunotherapies.

Accurate Single-Cell Clustering through Ensemble Similarity Learning

Single-cell sequencing provides novel means to interpret the transcriptomic profiles of individual cells. To obtain in-depth analysis of single-cell sequencing, it requires effective computational methods to accurately predict single-cell clusters because single-cell sequencing techniques only provide the transcriptomic profiles of each cell. Although an accurate estimation of the cell-to-cell similarity is an essential first step to derive reliable single-cell clustering results, it is challenging to obtain the accurate similarity measurement because it highly depends on a selection of genes for similarity evaluations and the optimal set of genes for the accurate similarity estimation is typically unknown. Moreover, due to technical limitations, single-cell sequencing includes a larger number of artificial zeros, and the technical noise makes it difficult to develop effective single-cell clustering algorithms. Here, we describe a novel single-cell clustering algorithm that can accurately predict single-cell clusters in large-scale single-cell sequencing by effectively reducing the zero-inflated noise and accurately estimating the cell-to-cell similarities. First, we construct an ensemble similarity network based on different similarity estimates, and reduce the artificial noise using a random walk with restart framework. Finally, starting from a larger number small size but highly consistent clusters, we iteratively merge a pair of clusters with the maximum similarities until it reaches the predicted number of clusters. Extensive performance evaluation shows that the proposed single-cell clustering algorithm can yield the accurate single-cell clustering results and it can help deciphering the key messages underlying complex biological mechanisms.

The Prognostic Model Based on Tumor Cell Evolution Trajectory Reveals a Different Risk Group of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide, and heterogeneity of HCC is the major barrier in improving patient outcome. To stratify HCC patients with different degrees of malignancy and provide precise treatment strategies, we reconstructed the tumor evolution trajectory with the help of scRNA-seq data and established a 30-gene prognostic model to identify the malignant state in HCC. Patients were divided into high-risk and low-risk groups. C-index and receiver operating characteristic (ROC) curve confirmed the excellent predictive value of this model. Downstream analysis revealed the underlying molecular and functional characteristics of this model, including significantly higher genomic instability and stronger proliferation/progression potential in the high-risk group. In summary, we established a novel prognostic model to overcome the barriers caused by HCC heterogeneity and provide the possibility of better clinical management for HCC patients to improve their survival outcomes.

Interplay between the EMT transcription factors ZEB1 and ZEB2 regulates hematopoietic stem and progenitor cell differentiation and hematopoietic lineage fidelity

The ZEB2 transcription factor has been demonstrated to play important roles in hematopoiesis and leukemic transformation. ZEB1 is a close family member of ZEB2 but has remained more enigmatic concerning its roles in hematopoiesis. Here, we show using conditional loss-of-function approaches and bone marrow (BM) reconstitution experiments that ZEB1 plays a cell-autonomous role in hematopoietic lineage differentiation, particularly as a positive regulator of monocyte development in addition to its previously reported important role in T-cell differentiation. Analysis of existing single-cell (sc) RNA sequencing (RNA-seq) data of early hematopoiesis has revealed distinctive expression differences between Zeb1 and Zeb2 in hematopoietic stem and progenitor cell (HSPC) differentiation, with Zeb2 being more highly and broadly expressed than Zeb1 except at a key transition point (short-term HSC [ST-HSC]➔MPP1), whereby Zeb1 appears to be the dominantly expressed family member. Inducible genetic inactivation of both Zeb1 and Zeb2 using a tamoxifen-inducible Cre-mediated approach leads to acute BM failure at this transition point with increased long-term and short-term hematopoietic stem cell numbers and an accompanying decrease in all hematopoietic lineage differentiation. Bioinformatics analysis of RNA-seq data has revealed that ZEB2 acts predominantly as a transcriptional repressor involved in restraining mature hematopoietic lineage gene expression programs from being expressed too early in HSPCs. ZEB1 appears to fine-tune this repressive role during hematopoiesis to ensure hematopoietic lineage fidelity. Analysis of Rosa26 locus–based transgenic models has revealed that Zeb1 as well as Zeb2 cDNA-based overexpression within the hematopoietic system can drive extramedullary hematopoiesis/splenomegaly and enhance monocyte development. Finally, inactivation of Zeb2 alone or Zeb1/2 together was found to enhance survival in secondary MLL-AF9 acute myeloid leukemia (AML) models attesting to the oncogenic role of ZEB1/2 in AML.

This study shows that the closely related transcription factors ZEB1 and ZEB2 cooperate to restrain myeloid and lymphoid differentiation programs in hematopoietic stem and progenitor cells, ensuring fidelity of differentiation in multiple lineages.

Single-cell RNA-seq unravels alterations of the human spermatogonial stem cell compartment in patients with impaired spermatogenesis

Despite the high incidence of male infertility, only 30% of infertile men receive a causative diagnosis. To explore the regulatory mechanisms governing human germ cell function in normal and impaired spermatogenesis (crypto), we performed single-cell RNA sequencing (>30,000 cells). We find major alterations in the crypto spermatogonial compartment with increased numbers of the most undifferentiated spermatogonia (PIWIL4⁺). We also observe a transcriptional switch within the spermatogonial compartment driven by increased and prolonged expression of the transcription factor EGR4. Intriguingly, the EGR4-regulated chromatin-associated transcriptional repressor UTF1 is downregulated at transcriptional and protein levels. This is associated with changes in spermatogonial chromatin structure and fewer A_dark spermatogonia, characterized by tightly compacted chromatin and serving as reserve stem cells. These findings suggest that crypto patients are disadvantaged, as fewer cells safeguard their germline’s genetic integrity. These identified spermatogonial regulators will be highly interesting targets to uncover genetic causes of male infertility.

Crypto(zoospermic) men show increased number of PIWIL4⁺/EGR4⁺ spermatogonia

Crypto undifferentiated spermatogonia over-activate the EGR4 regulatory network

The predicted EGR4 target UTF1 is downregulated in crypto spermatogonia

Crypto testes show reduced numbers of UTF1⁺ A_dark reserve spermatogonia

Identify differential genes and cell subclusters from time-series scRNA-seq data using scTITANS

Time-series single-cell RNA sequencing (scRNA-seq) provides a breakthrough in modern biology by enabling researchers to profile and study the dynamics of genes and cells based on samples obtained from multiple time points at an individual cell resolution. However, cell asynchrony and an additional dimension of multiple time points raises challenges in the effective use of time-series scRNA-seq data for identifying genes and cell subclusters that vary over time. However, no effective tools are available. Here, we propose scTITANS (https://github.com/ZJUFanLab/scTITANS), a method that takes full advantage of individual cells from all time points at the same time by correcting cell asynchrony using pseudotime from trajectory inference analysis. By introducing a time-dependent covariate based on time-series analysis method, scTITANS performed well in identifying differentially expressed genes and cell subclusters from time-series scRNA-seq data based on several example datasets. Compared to current attempts, scTITANS is more accurate, quantitative, and capable of dealing with heterogeneity among cells and making full use of the timing information hidden in biological processes. When extended to broader research areas, scTITANS will bring new breakthroughs in studies with time-series single cell RNA sequencing data.

Single-cell transcriptomic analysis of bloodstream Trypanosoma brucei reconstructs cell cycle progression and developmental quorum sensing

Developmental steps in the trypanosome life-cycle involve transition between replicative and non-replicative forms specialised for survival in, and transmission between, mammalian and tsetse fly hosts. Here, using oligopeptide-induced differentiation in vitro, we model the progressive development of replicative 'slender' to transmissible 'stumpy' bloodstream form Trypanosoma brucei and capture the transcriptomes of 8,599 parasites using single cell transcriptomics (scRNA-seq). Using this framework, we detail the relative order of biological events during asynchronous development, profile dynamic gene expression patterns and identify putative regulators. We additionally map the cell cycle of proliferating parasites and position stumpy cell-cycle exit at early G1 before progression to a distinct G0 state. A null mutant for one transiently elevated developmental regulator, ZC3H20 is further analysed by scRNA-seq, identifying its point of failure in the developmental atlas. This approach provides a paradigm for the dissection of differentiation events in parasites, relevant to diverse transitions in pathogen biology.

Application of single-cell transcriptomics to kinetoplastid research

Kinetoplastid parasites are responsible for both human and animal diseases across the globe where they have a great impact on health and economic well-being. Many species and life cycle stages are difficult to study due to limitations in isolation and culture, as well as to their existence as heterogeneous populations in hosts and vectors. Single-cell transcriptomics (scRNA-seq) has the capacity to overcome many of these difficulties, and can be leveraged to disentangle heterogeneous populations, highlight genes crucial for propagation through the life cycle, and enable detailed analysis of host–parasite interactions. Here, we provide a review of studies that have applied scRNA-seq to protozoan parasites so far. In addition, we provide an overview of sample preparation and technology choice considerations when planning scRNA-seq experiments, as well as challenges faced when analysing the large amounts of data generated. Finally, we highlight areas of kinetoplastid research that could benefit from scRNA-seq technologies.

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.

Single-nucleus transcriptome analysis of human brain immune response in patients with severe COVID-19

BACKGROUND

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, has been associated with neurological and neuropsychiatric illness in many individuals. We sought to further our understanding of the relationship between brain tropism, neuro-inflammation, and host immune response in acute COVID-19 cases.

METHODS

Three brain regions (dorsolateral prefrontal cortex, medulla oblongata, and choroid plexus) from 5 patients with severe COVID-19 and 4 controls were examined. The presence of the virus was assessed by western blot against viral spike protein, as well as viral transcriptome analysis covering > 99% of SARS-CoV-2 genome and all potential serotypes. Droplet-based single-nucleus RNA sequencing (snRNA-seq) was performed in the same samples to examine the impact of COVID-19 on transcription in individual cells of the brain.

RESULTS

Quantification of viral spike S1 protein and viral transcripts did not detect SARS-CoV-2 in the postmortem brain tissue. However, analysis of 68,557 single-nucleus transcriptomes from three distinct regions of the brain identified an increased proportion of stromal cells, monocytes, and macrophages in the choroid plexus of COVID-19 patients. Furthermore, differential gene expression, pseudo-temporal trajectory, and gene regulatory network analyses revealed transcriptional changes in the cortical microglia associated with a range of biological processes, including cellular activation, mobility, and phagocytosis.

CONCLUSIONS

Despite the absence of detectable SARS-CoV-2 in the brain at the time of death, the findings suggest significant and persistent neuroinflammation in patients with acute COVID-19.

Single-Cell RNA Sequencing Approaches for Tracing T Cell Development

T cell development occurs in the thymus, where uncommitted progenitors are directed into a range of sublineages with distinct functions. The goal is to generate a TCR repertoire diverse enough to recognize potential pathogens while remaining tolerant of self. Decades of intensive research have characterized the transcriptional programs controlling critical differentiation checkpoints at the population level. However, greater precision regarding how and when these programs orchestrate differentiation at the single-cell level is required. Single-cell RNA sequencing approaches are now being brought to bear on this question, to track the identity of cells and analyze their gene expression programs at a resolution not previously possible. In this review, we discuss recent advances in the application of these technologies that have the potential to yield unprecedented insight to T cell development.

Extensive transcriptional and chromatin changes underlie astrocyte maturation in vivo and in culture

Astrocytes have essential functions in brain homeostasis that are established late in differentiation, but the mechanisms underlying the functional maturation of astrocytes are not well understood. Here we identify extensive transcriptional changes that occur during murine astrocyte maturation in vivo that are accompanied by chromatin remodelling at enhancer elements. Investigating astrocyte maturation in a cell culture model revealed that in vitro-differentiated astrocytes lack expression of many mature astrocyte-specific genes, including genes for the transcription factors Rorb, Dbx2, Lhx2 and Fezf2. Forced expression of these factors in vitro induces distinct sets of mature astrocyte-specific transcripts. Culturing astrocytes in a three-dimensional matrix containing FGF2 induces expression of Rorb, Dbx2 and Lhx2 and improves astrocyte maturity based on transcriptional and chromatin profiles. Therefore, extrinsic signals orchestrate the expression of multiple intrinsic regulators, which in turn induce in a modular manner the transcriptional and chromatin changes underlying astrocyte maturation.

A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples

Pseudotime analysis with single-cell RNA-sequencing (scRNA-seq) data has been widely used to study dynamic gene regulatory programs along continuous biological processes. While many computational methods have been developed to infer the pseudo-temporal trajectories of cells within a biological sample, methods that compare pseudo-temporal patterns with multiple samples (or replicates) across different experimental conditions are lacking. Lamian is a comprehensive and statistically-rigorous computational framework for differential multi-sample pseudotime analysis. It can be used to identify changes in a biological process associated with sample covariates, such as different biological conditions, and also to detect changes in gene expression, cell density, and topology of a pseudotemporal trajectory. Unlike existing methods that ignore sample variability, Lamian draws statistical inference after accounting for cross-sample variability and hence substantially reduces sample-specific false discoveries that are not generalizable to new samples. Using both simulations and real scRNA-seq data, including an analysis of differential immune response programs between COVID-19 patients with different disease severity levels, we demonstrate the advantages of Lamian in decoding cellular gene expression programs in continuous biological processes.

Single-cell transcriptomics reveals immune response of intestinal cell types to viral infection

Human intestinal epithelial cells form a primary barrier protecting us from pathogens, yet only limited knowledge is available about individual contribution of each cell type to mounting an immune response against infection. Here, we developed a framework combining single-cell RNA-Seq and highly multiplex RNA FISH and applied it to human intestinal organoids infected with human astrovirus, a model human enteric virus. We found that interferon controls the infection and that astrovirus infects all major cell types and lineages and induces expression of the cell proliferation marker MKI67. Intriguingly, each intestinal epithelial cell lineage exhibits a unique basal expression of interferon-stimulated genes and, upon astrovirus infection, undergoes an antiviral transcriptional reprogramming by upregulating distinct sets of interferon-stimulated genes. These findings suggest that in the human intestinal epithelium, each cell lineage plays a unique role in resolving virus infection. Our framework is applicable to other organoids and viruses, opening new avenues to unravel roles of individual cell types in viral pathogenesis.

How to Get Started with Single Cell RNA Sequencing Data Analysis

Over the last 5 years, single cell methods have enabled the monitoring of gene and protein expression, genetic, and epigenetic changes in thousands of individual cells in a single experiment. With the improved measurement and the decreasing cost of the reactions and sequencing, the size of these datasets is increasing rapidly. The critical bottleneck remains the analysis of the wealth of information generated by single cell experiments. In this review, we give a simplified overview of the analysis pipelines, as they are typically used in the field today. We aim to enable researchers starting out in single cell analysis to gain an overview of challenges and the most commonly used analytical tools. In addition, we hope to empower others to gain an understanding of how typical readouts from single cell datasets are presented in the published literature.

Current progress and potential opportunities to infer single-cell developmental trajectory and cell fate

Rapid technological advances in transcriptomics and lineage tracing technologies provide new opportunities to understand organismal development at the single-cell level. Building on these advances, various computational methods have been proposed to infer developmental trajectories and to predict cell fate. These methods have unveiled previously uncharacterized transitional cell types and differentiation processes. Importantly, the ability to recover cell states and trajectories has been evolving hand-in-hand with new technologies and diverse experimental designs; more recent methods can capture complex trajectory topologies and infer short- and long-term cell fate dynamics. Here, we summarize and categorize the most recent and popular computational approaches for trajectory inference based on the information they leverage and describe future challenges and opportunities for the development of new methods for reconstructing differentiation trajectories and inferring cell fates.

Retinoic acid signaling is critical during the totipotency window in early mammalian development

Totipotent cells hold enormous potential for regenerative medicine. Thus, the development of cellular models recapitulating totipotent-like features is of paramount importance. Cells resembling the totipotent cells of early embryos arise spontaneously in mouse embryonic stem (ES) cell cultures. Such '2-cell-like-cells' (2CLCs) recapitulate 2-cell-stage features and display expanded cell potential. Here, we used 2CLCs to perform a small-molecule screen to identify new pathways regulating the 2-cell-stage program. We identified retinoids as robust inducers of 2CLCs and the retinoic acid (RA)-signaling pathway as a key component of the regulatory circuitry of totipotent cells in embryos. Using single-cell RNA-seq, we reveal the transcriptional dynamics of 2CLC reprogramming and show that ES cells undergo distinct cellular trajectories in response to RA. Importantly, endogenous RA activity in early embryos is essential for zygotic genome activation and developmental progression. Overall, our data shed light on the gene regulatory networks controlling cellular plasticity and the totipotency program.

scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured

A pressing challenge in single-cell transcriptomics is to benchmark experimental protocols and computational methods. A solution is to use computational simulators, but existing simulators cannot simultaneously achieve three goals: preserving genes, capturing gene correlations, and generating any number of cells with varying sequencing depths. To fill this gap, we propose scDesign2, a transparent simulator that achieves all three goals and generates high-fidelity synthetic data for multiple single-cell gene expression count-based technologies. In particular, scDesign2 is advantageous in its transparent use of probabilistic models and its ability to capture gene correlations via copulas.

Interfacing Seurat with the R tidy universe

Motivation

Seurat is one of the most popular software suites for the analysis of single-cell RNA sequencing data. Considering the popularity of the tidyverse ecosystem, which offers a large set of data display, query, manipulation, integration and visualization utilities, a great opportunity exists to interface the Seurat object with the tidyverse. This interface gives the large data science community of tidyverse users the possibility to operate with familiar grammar.

Results

To provide Seurat with a tidyverse-oriented interface without compromising efficiency, we developed tidyseurat, a lightweight adapter to the tidyverse. Tidyseurat displays cell information as a tibble abstraction, allowing intuitively interfacing Seurat with dplyr, tidyr, ggplot2 and plotly packages powering efficient data manipulation, integration and visualization. Iterative analyses on data subsets are enabled by interfacing with the popular nest-map framework.

Availability and implementation

The software is freely available at cran.r-project.org/web/packages/tidyseurat and github.com/stemangiola/tidyseurat.

Supplementary information

Supplementary data are available at Bioinformatics online.

Integrating molecular, histopathological, neuroimaging and clinical neuroscience data with NeuroPM-box

Understanding and treating heterogeneous brain disorders requires specialized techniques spanning genetics, proteomics, and neuroimaging. Designed to meet this need, NeuroPM-box is a user-friendly, open-access, multi-tool cross-platform software capable of characterizing multiscale and multifactorial neuropathological mechanisms. Using advanced analytical modeling for molecular, histopathological, brain-imaging and/or clinical evaluations, this framework has multiple applications, validated here with synthetic (N > 2900), in-vivo (N = 911) and post-mortem (N = 736) neurodegenerative data, and including the ability to characterize: (i) the series of sequential states (genetic, histopathological, imaging or clinical alterations) covering decades of disease progression, (ii) concurrent intra-brain spreading of pathological factors (e.g., amyloid, tau and alpha-synuclein proteins), (iii) synergistic interactions between multiple biological factors (e.g., toxic tau effects on brain atrophy), and (iv) biologically-defined patient stratification based on disease heterogeneity and/or therapeutic needs. This freely available toolbox ( neuropm-lab.com/neuropm-box.html ) could contribute significantly to a better understanding of complex brain processes and accelerating the implementation of Precision Medicine in Neurology.