Diffusion maps for high-dimensional single-cell analysis of differentiation data

The San Diego Nathan Shock Center: tackling the heterogeneity of aging

Understanding basic mechanisms of aging holds great promise for developing interventions that prevent or delay many age-related declines and diseases simultaneously to increase human healthspan. However, a major confounding factor in aging research is the heterogeneity of the aging process itself. At the organismal level, it is clear that chronological age does not always predict biological age or susceptibility to frailty or pathology. While genetics and environment are major factors driving variable rates of aging, additional complexity arises because different organs, tissues, and cell types are intrinsically heterogeneous and exhibit different aging trajectories normally or in response to the stresses of the aging process (e.g., damage accumulation). Tackling the heterogeneity of aging requires new and specialized tools (e.g., single-cell analyses, mass spectrometry-based approaches, and advanced imaging) to identify novel signatures of aging across scales. Cutting-edge computational approaches are then needed to integrate these disparate datasets and elucidate network interactions between known aging hallmarks. There is also a need for improved, human cell-based models of aging to ensure that basic research findings are relevant to human aging and healthspan interventions. The San Diego Nathan Shock Center (SD-NSC) provides access to cutting-edge scientific resources to facilitate the study of the heterogeneity of aging in general and to promote the use of novel human cell models of aging. The center also has a robust Research Development Core that funds pilot projects on the heterogeneity of aging and organizes innovative training activities, including workshops and a personalized mentoring program, to help investigators new to the aging field succeed. Finally, the SD-NSC participates in outreach activities to educate the general community about the importance of aging research and promote the need for basic biology of aging research in particular.

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.

Mapping the genetic architecture of human traits to cell types in the kidney identifies mechanisms of disease and potential treatments

The functional interpretation of genome-wide association studies (GWAS) is challenging due to the cell-type-dependent influences of genetic variants. Here, we generated comprehensive maps of expression quantitative trait loci (eQTLs) for 659 microdissected human kidney samples and identified cell-type-eQTLs by mapping interactions between cell type abundances and genotypes. By partitioning heritability using stratified linkage disequilibrium score regression to integrate GWAS with single-cell RNA sequencing and single-nucleus assay for transposase-accessible chromatin with high-throughput sequencing data, we prioritized proximal tubules for kidney function and endothelial cells and distal tubule segments for blood pressure pathogenesis. Bayesian colocalization analysis nominated more than 200 genes for kidney function and hypertension. Our study clarifies the mechanism of commonly used antihypertensive and renal-protective drugs and identifies drug repurposing opportunities for kidney disease.

Advances and opportunities in image analysis of bacterial cells and communities

The cellular morphology and sub-cellular spatial structure critically influence the function of microbial cells. Similarly, the spatial arrangement of genotypes and phenotypes in microbial communities has important consequences for cooperation, competition, and community functions. Fluorescence microscopy techniques are widely used to measure spatial structure inside living cells and communities, which often results in large numbers of images that are difficult or impossible to analyze manually. The rapidly evolving progress in computational image analysis has recently enabled the quantification of a large number of properties of single cells and communities, based on traditional analysis techniques and convolutional neural networks. Here, we provide a brief introduction to core concepts of automated image processing, recent software tools and how to validate image analysis results. We also discuss recent advances in image analysis of microbial cells and communities, and how these advances open up opportunities for quantitative studies of spatiotemporal processes in microbiology, based on image cytometry and adaptive microscope control.

Viral vector-mediated reprogramming of the fibroblastic tumor stroma sustains curative melanoma treatment

The tumor microenvironment (TME) is a complex amalgam of tumor cells, immune cells, endothelial cells and fibroblastic stromal cells (FSC). Cancer-associated fibroblasts are generally seen as tumor-promoting entity. However, it is conceivable that particular FSC populations within the TME contribute to immune-mediated tumor control. Here, we show that intratumoral treatment of mice with a recombinant lymphocytic choriomeningitis virus-based vaccine vector expressing a melanocyte differentiation antigen resulted in T cell-dependent long-term control of melanomas. Using single-cell RNA-seq analysis, we demonstrate that viral vector-mediated transduction reprogrammed and activated a Cxcl13-expressing FSC subset that show a pronounced immunostimulatory signature and increased expression of the inflammatory cytokine IL-33. Ablation of Il33 gene expression in Cxcl13-Cre-positive FSCs reduces the functionality of intratumoral T cells and unleashes tumor growth. Thus, reprogramming of FSCs by a self-antigen-expressing viral vector in the TME is critical for curative melanoma treatment by locally sustaining the activity of tumor-specific T cells.

Lymphocytic choriomeningitis virus (LCMV)-based viral vectors have been shown to induce potent antitumor immune responses. Here the authors show that a LCMV-based vaccine vector remodels the tumor-associated fibroblastic stroma, sustaining CD8+ T cell activation and reducing tumor growth in a preclinical model of melanoma.

Single-cell transcriptome and TCR profiling reveal activated and expanded T cell populations in Parkinson's disease

Given the chronic inflammatory nature of Parkinson's disease (PD), T cell immunity may be important for disease onset. Here, we performed single-cell transcriptome and TCR sequencing, and conducted integrative analyses to decode composition, function and lineage relationship of T cells in the blood and cerebrospinal fluid of PD. Combined expression and TCR-based lineage tracking, we discovered a large population of CD8⁺ T cells showing continuous progression from central memory to terminal effector T cells in PD patients. Additionally, we identified a group of cytotoxic CD4⁺ T cells (CD4 CTLs) remarkably expanded in PD patients, which derived from Th1 cells by TCR-based fate decision. Finally, we screened putative TCR-antigen pairs that existed in both blood and cerebrospinal fluid of PD patients to provide potential evidence for peripheral T cells to participate in neuronal degeneration. Our study provides valuable insights and rich resources for understanding the adaptive immune response in PD.

Single-nucleus RNA-seq2 reveals functional crosstalk between liver zonation and ploidy

Single-cell RNA-seq reveals the role of pathogenic cell populations in development and progression of chronic diseases. In order to expand our knowledge on cellular heterogeneity, we have developed a single-nucleus RNA-seq2 method tailored for the comprehensive analysis of the nuclear transcriptome from frozen tissues, allowing the dissection of all cell types present in the liver, regardless of cell size or cellular fragility. We use this approach to characterize the transcriptional profile of individual hepatocytes with different levels of ploidy, and have discovered that ploidy states are associated with different metabolic potential, and gene expression in tetraploid mononucleated hepatocytes is conditioned by their position within the hepatic lobule. Our work reveals a remarkable crosstalk between gene dosage and spatial distribution of hepatocytes.

The loss of RNA N6-adenosine methyltransferase Mettl14 in tumor-associated macrophages promotes CD8+ T cell dysfunction and tumor growth

Tumor-associated macrophages (TAMs) can dampen the antitumor activity of T cells, yet the underlying mechanism remains incompletely understood. Here, we show that C1q⁺ TAMs are regulated by an RNA N⁶-methyladenosine (m⁶A) program and modulate tumor-infiltrating CD8⁺ T cells by expressing multiple immunomodulatory ligands. Macrophage-specific knockout of an m⁶A methyltransferase Mettl14 drives CD8⁺ T cell differentiation along a dysfunctional trajectory, impairing CD8⁺ T cells to eliminate tumors. Mettl14-deficient C1q⁺ TAMs show a decreased m⁶A abundance on and a higher level of transcripts of Ebi3, a cytokine subunit. In addition, neutralization of EBI3 leads to reinvigoration of dysfunctional CD8⁺ T cells and overcomes immunosuppressive impact in mice. We show that the METTL14-m⁶A levels are negatively correlated with dysfunctional T cell levels in patients with colorectal cancer, supporting the clinical relevance of this regulatory pathway. Thus, our study demonstrates how an m⁶A methyltransferase in TAMs promotes CD8⁺ T cell dysfunction and tumor progression.

A Bayesian inference transcription factor activity model for the analysis of single-cell transcriptomes

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful experimental approach to study cellular heterogeneity. One of the challenges in scRNA-seq data analysis is integrating different types of biological data to consistently recognize discrete biological functions and regulatory mechanisms of cells, such as transcription factor activities and gene regulatory networks in distinct cell populations. We have developed an approach to infer transcription factor activities from scRNA-seq data that leverages existing biological data on transcription factor binding sites. The Bayesian inference transcription factor activity model (BITFAM) integrates ChIP-seq transcription factor binding information into scRNA-seq data analysis. We show that the inferred transcription factor activities for key cell types identify regulatory transcription factors that are known to mechanistically control cell function and cell fate. The BITFAM approach not only identifies biologically meaningful transcription factor activities, but also provides valuable insights into underlying transcription factor regulatory mechanisms.

Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry

Deep learning offers the potential to extract more than meets the eye from images captured by imaging flow cytometry. This protocol describes the application of deep learning to single-cell images to perform supervised cell classification and weakly supervised learning, using example data from an experiment exploring red blood cell morphology. We describe how to acquire and transform suitable input data as well as the steps required for deep learning training and inference using an open-source web-based application. All steps of the protocol are provided as open-source Python as well as MATLAB runtime scripts, through both command-line and graphic user interfaces. The protocol enables a flexible and friendly environment for morphological phenotyping using supervised and weakly supervised learning and the subsequent exploration of the deep learning features using multi-dimensional visualization tools. The protocol requires 40 h when training from scratch and 1 h when using a pre-trained model.

The triumphs and limitations of computational methods for scRNA-seq

The rapid progress of protocols for sequencing single-cell transcriptomes over the past decade has been accompanied by equally impressive advances in the computational methods for analysis of such data. As capacity and accuracy of the experimental techniques grew, the emerging algorithm developments revealed increasingly complex facets of the underlying biology, from cell type composition to gene regulation to developmental dynamics. At the same time, rapid growth has forced continuous reevaluation of the underlying statistical models, experimental aims, and sheer volumes of data processing that are handled by these computational tools. Here, I review key computational steps of single-cell RNA sequencing (scRNA-seq) analysis, examine assumptions made by different approaches, and highlight successes, remaining ambiguities, and limitations that are important to keep in mind as scRNA-seq becomes a mainstream technique for studying biology.

Advances and Opportunities in Single-Cell Transcriptomics for Plant Research

Single-cell approaches are quickly changing our view on biological systems by increasing the spatiotemporal resolution of our analyses to the level of the individual cell. The field of plant biology has fully embraced single-cell transcriptomics and is rapidly expanding the portfolio of available technologies and applications. In this review, we give an overview of the main advances in plant single-cell transcriptomics over the past few years and provide the reader with an accessible guideline covering all steps, from sample preparation to data analysis. We end by offering a glimpse of how these technologies will shape and accelerate plant-specific research in the near future.

Quantitative single-cell proteomics as a tool to characterize cellular hierarchies

Large-scale single-cell analyses are of fundamental importance in order to capture biological heterogeneity within complex cell systems, but have largely been limited to RNA-based technologies. Here we present a comprehensive benchmarked experimental and computational workflow, which establishes global single-cell mass spectrometry-based proteomics as a tool for large-scale single-cell analyses. By exploiting a primary leukemia model system, we demonstrate both through pre-enrichment of cell populations and through a non-enriched unbiased approach that our workflow enables the exploration of cellular heterogeneity within this aberrant developmental hierarchy. Our approach is capable of consistently quantifying ~1000 proteins per cell across thousands of individual cells using limited instrument time. Furthermore, we develop a computational workflow (SCeptre) that effectively normalizes the data, integrates available FACS data and facilitates downstream analysis. The approach presented here lays a foundation for implementing global single-cell proteomics studies across the world.

Unpaired data empowers association tests

Motivation

There is growing interest in the biomedical research community to incorporate retrospective data, available in healthcare systems, to shed light on associations between different biomarkers. Understanding the association between various types of biomedical data, such as genetic, blood biomarkers, imaging, etc. can provide a holistic understanding of human diseases. To formally test a hypothesized association between two types of data in Electronic Health Records (EHRs), one requires a substantial sample size with both data modalities to achieve a reasonable power. Current association test methods only allow using data from individuals who have both data modalities. Hence, researchers cannot take advantage of much larger EHR samples that includes individuals with at least one of the data types, which limits the power of the association test.

Results

We present a new method called the Semi-paired Association Test (SAT) that makes use of both paired and unpaired data. In contrast to classical approaches, incorporating unpaired data allows SAT to produce better control of false discovery and to improve the power of the association test. We study the properties of the new test theoretically and empirically, through a series of simulations and by applying our method on real studies in the context of Chronic Obstructive Pulmonary Disease. We are able to identify an association between the high-dimensional characterization of Computed Tomography chest images and several blood biomarkers as well as the expression of dozens of genes involved in the immune system.

Availability and implementation

Code is available on https://github.com/batmanlab/Semi-paired-Association-Test.

Supplementary information

Supplementary data are available at Bioinformatics online.

Single-cell molecular profiling provides a high-resolution map of basophil and mast cell development

BACKGROUND

Basophils and mast cells contribute to the development of allergic reactions. Whereas these mature effector cells are extensively studied, the differentiation trajectories from hematopoietic progenitors to basophils and mast cells are largely uncharted at the single-cell level.

METHODS

We performed multicolor flow cytometry, high-coverage single-cell RNA sequencing analyses, and cell fate assays to chart basophil and mast cell differentiation at single-cell resolution in mouse.

RESULTS

Analysis of flow cytometry data reconstructed a detailed map of basophil and mast cell differentiation, including a bifurcation of progenitors into two specific trajectories. Molecular profiling and pseudotime ordering of the single cells revealed gene expression changes during differentiation. Cell fate assays showed that multicolor flow cytometry and transcriptional profiling successfully predict the bipotent phenotype of a previously uncharacterized population of peritoneal basophil-mast cell progenitors.

CONCLUSIONS

A combination of molecular and functional profiling of bone marrow and peritoneal cells provided a detailed road map of basophil and mast cell development. An interactive web resource was created to enable the wider research community to explore the expression dynamics for any gene of interest.

Mammary gland development from a single cell 'omics view

Understanding the complexity and heterogeneity of mammary cell subpopulations is vital to delineate the mechanisms behind breast cancer development, progression and prevention. Increasingly sophisticated tools for investigating these cell subtypes has led to the development of a greater understanding of these cell subtypes, complex interplay of certain subtypes and their developmental potential. Of note, increasing accessibility and affordability of single cell technologies has led to a plethora of studies being published containing data from mammary cell subtypes and their differentiation potential in both mice and human data sets. Here, we review the different types of single cell technologies and how they have been used to improve our understanding of mammary gland development.

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.

Developmental bifurcation of human T follicular regulatory cells

Germinal centers (GCs) are anatomic structures where B cells undergo affinity maturation, leading to production of high-affinity antibodies. The balance between T follicular helper (T_FH) and regulatory (T_FR) cells is critical for adequate control of GC responses. The study of human T_FH and T_FR cell development has been hampered because of the lack of in vitro assays reproducing in vivo biology, along with difficult access to healthy human lymphoid tissues. We used a single-cell transcriptomics approach to study the maturation of T_FH and T_FR cells isolated from human blood, iliac lymph nodes (LNs), and tonsils. As independent tissues have distinct proportions of follicular T cells in different maturation states, we leveraged the heterogeneity to reconstruct the maturation trajectory for human T_FH and T_FR cells. We found that the dominant maturation of T_FR cells follows a bifurcated trajectory from precursor T_reg cells, with one arm of the bifurcation leading to blood T_FR cells and the other leading to the most mature GC T_FR cells. Overall, our data provide a comprehensive resource for the transcriptomics of different follicular T cell populations and their dynamic relationship across different tissues.

The neutrotime transcriptional signature defines a single continuum of neutrophils across biological compartments

Neutrophils are implicated in multiple homeostatic and pathological processes, but whether functional diversity requires discrete neutrophil subsets is not known. Here, we apply single-cell RNA sequencing to neutrophils from normal and inflamed mouse tissues. Whereas conventional clustering yields multiple alternative organizational structures, diffusion mapping plus RNA velocity discloses a single developmental spectrum, ordered chronologically. Termed here neutrotime, this spectrum extends from immature pre-neutrophils, largely in bone marrow, to mature neutrophils predominantly in blood and spleen. The sharpest increments in neutrotime occur during the transitions from pre-neutrophils to immature neutrophils and from mature marrow neutrophils to those in blood. Human neutrophils exhibit a similar transcriptomic pattern. Neutrophils migrating into inflamed mouse lung, peritoneum and joint maintain the core mature neutrotime signature together with new transcriptional activity that varies with site and stimulus. Together, these data identify a single developmental spectrum as the dominant organizational theme of neutrophil heterogeneity.

Hotspot identifies informative gene modules across modalities of single-cell genomics

Two fundamental aims that emerge when analyzing single-cell RNA-seq data are identifying which genes vary in an informative manner and determining how these genes organize into modules. Here, we propose a general approach to these problems, called "Hotspot," that operates directly on a given metric of cell-cell similarity, allowing for its integration with any method (linear or non-linear) for identifying the primary axes of transcriptional variation between cells. In addition, we show that when using multimodal data, Hotspot can be used to identify genes whose expression reflects alternative notions of similarity between cells, such as physical proximity in a tissue or clonal relatedness in a cell lineage tree. In this manner, we demonstrate that while Hotspot is capable of identifying genes that reflect nuanced transcriptional variability between T helper cells, it can also identify spatially dependent patterns of gene expression in the cerebellum as well as developmentally heritable expression programs during embryogenesis. Hotspot is implemented as an open-source Python package and is available for use at http://www.github.com/yoseflab/hotspot. A record of this paper's transparent peer review process is included in the supplemental information.

Delineating the heterogeneity of matrix-directed differentiation toward soft and stiff tissue lineages via single-cell profiling

The clinical utility of mesenchymal stromal/stem cells (MSCs) in mediating immunosuppressive effects and supporting regenerative processes is broadly established. However, the inherent heterogeneity of MSCs compromises its biomedical efficacy and reproducibility. To study how cellular variation affects fate decision-making processes, we perform single-cell RNA sequencing at multiple time points during bipotential matrix-directed differentiation toward soft- and stiff tissue lineages. In this manner, we identify distinctive MSC subpopulations that are characterized by their multipotent differentiation capacity and mechanosensitivity. Also, whole-genome screening highlights TPM1 as a potent mechanotransducer of matrix signals and regulator of cell differentiation. Thus, by introducing single-cell methodologies into mechanobiology, we delineate the complexity of adult stem cell responses to extracellular cues in tissue regeneration and immunomodulation.

Mesenchymal stromal/stem cells (MSCs) form a heterogeneous population of multipotent progenitors that contribute to tissue regeneration and homeostasis. MSCs assess extracellular elasticity by probing resistance to applied forces via adhesion, cytoskeletal, and nuclear mechanotransducers that direct differentiation toward soft or stiff tissue lineages. Even under controlled culture conditions, MSC differentiation exhibits substantial cell-to-cell variation that remains poorly characterized. By single-cell transcriptional profiling of nonconditioned, matrix-conditioned, and early differentiating cells, we identified distinct MSC subpopulations with distinct mechanosensitivities, differentiation capacities, and cell cycling. We show that soft matrices support adipogenesis of multipotent cells and early endochondral ossification of nonadipogenic cells, whereas intramembranous ossification and preosteoblast proliferation are directed by stiff matrices. Using diffusion pseudotime mapping, we outline hierarchical matrix-directed differentiation and perform whole-genome screening of mechanoresponsive genes. Specifically, top-ranked tropomyosin-1 is highly sensitive to stiffness cues both at RNA and protein levels, and changes in TPM1 expression determine the differentiation toward soft versus stiff tissue lineage. Consistent with actin stress fiber stabilization, tropomyosin-1 overexpression maintains YAP1 nuclear localization, activates YAP1 target genes, and directs osteogenic differentiation. Knockdown of tropomyosin-1 reversed YAP1 nuclear localization consistent with relaxation of cellular contractility, suppressed osteogenesis, activated early endochondral ossification genes after 3 d of culture in induction medium, and facilitated adipogenic differentiation after 1 wk. Our results delineate cell-to-cell variation of matrix-directed MSC differentiation and highlight tropomyosin-mediated matrix sensing.

Divergent clonal differentiation trajectories establish CD8+ memory T cell heterogeneity during acute viral infections in humans

The CD8⁺ T cell response to an antigen is composed of many T cell clones with unique T cell receptors, together forming a heterogeneous repertoire of effector and memory cells. How individual T cell clones contribute to this heterogeneity throughout immune responses remains largely unknown. In this study, we longitudinally track human CD8⁺ T cell clones expanding in response to yellow fever virus (YFV) vaccination at the single-cell level. We observed a drop in clonal diversity in blood from the acute to memory phase, suggesting that clonal selection shapes the circulating memory repertoire. Clones in the memory phase display biased differentiation trajectories along a gradient from stem cell to terminally differentiated effector memory fates. In secondary responses, YFV- and influenza-specific CD8⁺ T cell clones are poised to recapitulate skewed differentiation trajectories. Collectively, we show that the sum of distinct clonal phenotypes results in the multifaceted human T cell response to acute viral infections.

A genome-wide atlas of co-essential modules assigns function to uncharacterized genes

A central question in the post-genomic era is how genes interact to form biological pathways. Measurements of gene dependency across hundreds of cell lines have been used to cluster genes into 'co-essential' pathways, but this approach has been limited by ubiquitous false positives. In the present study, we develop a statistical method that enables robust identification of gene co-essentiality and yields a genome-wide set of functional modules. This atlas recapitulates diverse pathways and protein complexes, and predicts the functions of 108 uncharacterized genes. Validating top predictions, we show that TMEM189 encodes plasmanylethanolamine desaturase, a key enzyme for plasmalogen synthesis. We also show that C15orf57 encodes a protein that binds the AP2 complex, localizes to clathrin-coated pits and enables efficient transferrin uptake. Finally, we provide an interactive webtool for the community to explore our results, which establish co-essentiality profiling as a powerful resource for biological pathway identification and discovery of new gene functions.

An NKX2-1/ERK/WNT feedback loop modulates gastric identity and response to targeted therapy in lung adenocarcinoma

Cancer cells undergo lineage switching during natural progression and in response to therapy. NKX2-1 loss in human and murine lung adenocarcinoma leads to invasive mucinous adenocarcinoma (IMA), a lung cancer subtype that exhibits gastric differentiation and harbors a distinct spectrum of driver oncogenes. In murine BRAFV600E-driven lung adenocarcinoma, NKX2-1 is required for early tumorigenesis, but dispensable for established tumor growth. NKX2-1-deficient, BRAFV600E-driven tumors resemble human IMA and exhibit a distinct response to BRAF/MEK inhibitors. Whereas BRAF/MEK inhibitors drive NKX2-1-positive tumor cells into quiescence, NKX2-1-negative cells fail to exit the cell cycle after the same therapy. BRAF/MEK inhibitors induce cell identity switching in NKX2-1-negative lung tumors within the gastric lineage, which is driven in part by WNT signaling and FoxA1/2. These data elucidate a complex, reciprocal relationship between lineage specifiers and oncogenic signaling pathways in the regulation of lung adenocarcinoma identity that is likely to impact lineage-specific therapeutic strategies.

DTFLOW: Inference and Visualization of Single-cell Pseudotime Trajectory Using Diffusion Propagation

One of the major challenges in single-cell data analysis is the determination of cellular developmental trajectories using single-cell data. Although substantial studies have been conducted in recent years, more effective methods are still strongly needed to infer the developmental processes accurately. This work devises a new method, named DTFLOW, for determining the pseudo-temporal trajectories with multiple branches. DTFLOW consists of two major steps: a new method called Bhattacharyya kernel feature decomposition (BKFD) to reduce the data dimensions, and a novel approach named Reverse Searching on k-nearest neighbor graph (RSKG) to identify the multi-branching processes of cellular differentiation. In BKFD, we first establish a stationary distribution for each cell to represent the transition of cellular developmental states based on the random walk with restart algorithm, and then propose a new distance metric for calculating pseudotime of single cells by introducing the Bhattacharyya kernel matrix. The effectiveness of DTFLOW is rigorously examined by using four single-cell datasets. We compare the efficiency of DTFLOW with the published state-of-the-art methods. Simulation results suggest that DTFLOW has superior accuracy and strong robustness properties for constructing pseudotime trajectories. The Python source code of DTFLOW can be freely accessed at https://github.com/statway/DTFLOW.

CytoTree: an R/Bioconductor package for analysis and visualization of flow and mass cytometry data

BACKGROUND

The rapidly increasing dimensionality and throughput of flow and mass cytometry data necessitate new bioinformatics tools for analysis and interpretation, and the recently emerging single-cell-based algorithms provide a powerful strategy to meet this challenge.

RESULTS

Here, we present CytoTree, an R/Bioconductor package designed to analyze and interpret multidimensional flow and mass cytometry data. CytoTree provides multiple computational functionalities that integrate most of the commonly used techniques in unsupervised clustering and dimensionality reduction and, more importantly, support the construction of a tree-shaped trajectory based on the minimum spanning tree algorithm. A graph-based algorithm is also implemented to estimate the pseudotime and infer intermediate-state cells. We apply CytoTree to several examples of mass cytometry and time-course flow cytometry data on heterogeneity-based cytology and differentiation/reprogramming experiments to illustrate the practical utility achieved in a fast and convenient manner.

CONCLUSIONS

CytoTree represents a versatile tool for analyzing multidimensional flow and mass cytometry data and to producing heuristic results for trajectory construction and pseudotime estimation in an integrated workflow.

Connecting the Dots: Resolving the Bone Marrow Niche Heterogeneity

Single-cell sequencing approaches have transformed our understanding of stem cell systems, including hematopoiesis and its niche within the bone marrow. Recent reports examined the bone marrow microenvironment at single-cell resolution at steady state, following chemotherapy treatment, leukemic onset, and aging. These rapid advancements significantly informed our understanding of bone marrow niche heterogeneity. However, inconsistent representation and nomenclature among the studies hinder a comprehensive interpretation of this body of work. Here, we review recent reports interrogating bone marrow niche architecture and present an integrated overview of the published datasets.

Revealing lineage-related signals in single-cell gene expression using random matrix theory

Gene expression profiles of a cellular population, generated by single-cell RNA sequencing, contains rich information about biological state, including cell type, cell cycle phase, gene regulatory patterns, and location within the tissue of origin. A major challenge is to disentangle information about these different biological states from each other, including distinguishing from cell lineage, since the correlation of cellular expression patterns is necessarily contaminated by ancestry. Here, we use a recent advance in random matrix theory, discovered in the context of protein phylogeny, to identify differentiation or ancestry-related processes in single-cell data. Qin and Colwell [C. Qin, L. J. Colwell, Proc. Natl. Acad. Sci. U.S.A. 115, 690-695 (2018)] showed that ancestral relationships in protein sequences create a power-law signature in the covariance eigenvalue distribution. We demonstrate the existence of such signatures in scRNA-seq data and that the genes driving them are indeed related to differentiation and developmental pathways. We predict the existence of similar power-law signatures for cells along linear trajectories and demonstrate this for linearly differentiating systems. Furthermore, we generalize to show that the same signatures can arise for cells along tissue-specific spatial trajectories. We illustrate these principles in diverse tissues and organisms, including the mammalian epidermis and lung, Drosophila whole-embryo, adult Hydra, dendritic cells, the intestinal epithelium, and cells undergoing induced pluripotent stem cells (iPSC) reprogramming. We show how these results can be used to interpret the gradual dynamics of lineage structure along iPSC reprogramming. Together, we provide a framework that can be used to identify signatures of specific biological processes in single-cell data without prior knowledge and identify candidate genes associated with these processes.

Characterization of a common progenitor pool of the epicardium and myocardium

The mammalian heart is derived from multiple cell lineages; however, our understanding of when and how the diverse cardiac cell types arise is limited. We mapped the origin of the embryonic mouse heart at single-cell resolution using a combination of transcriptomic, imaging, and genetic lineage labeling approaches. This mapping provided a transcriptional and anatomic definition of cardiac progenitor types. Furthermore, it revealed a cardiac progenitor pool that is anatomically and transcriptionally distinct from currently known cardiac progenitors. Besides contributing to cardiomyocytes, these cells also represent the earliest progenitor of the epicardium, a source of trophic factors and cells during cardiac development and injury. This study provides detailed insights into the formation of early cardiac cell types, with particular relevance to the development of cell-based cardiac regenerative therapies.

Integrative Single-Cell RNA-Seq and ATAC-Seq Analysis of Human Developmental Hematopoiesis

Regulation of hematopoiesis during human development remains poorly defined. Here we applied single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) to over 8,000 human immunophenotypic blood cells from fetal liver and bone marrow. We inferred their differentiation trajectory and identified three highly proliferative oligopotent progenitor populations downstream of hematopoietic stem cells (HSCs)/multipotent progenitors (MPPs). Along this trajectory, we observed opposing patterns of chromatin accessibility and differentiation that coincided with dynamic changes in the activity of distinct lineage-specific transcription factors. Integrative analysis of chromatin accessibility and gene expression revealed extensive epigenetic but not transcriptional priming of HSCs/MPPs prior to their lineage commitment. Finally, we refined and functionally validated the sorting strategy for the HSCs/MPPs and achieved around 90% enrichment. Our study provides a useful framework for future investigation of human developmental hematopoiesis in the context of blood pathologies and regenerative medicine.

UniPath: a uniform approach for pathway and gene-set based analysis of heterogeneity in single-cell epigenome and transcriptome profiles

Recent advances in single-cell open-chromatin and transcriptome profiling have created a challenge of exploring novel applications with a meaningful transformation of read-counts, which often have high variability in noise and drop-out among cells. Here, we introduce UniPath, for representing single-cells using pathway and gene-set enrichment scores by a transformation of their open-chromatin or gene-expression profiles. The robust statistical approach of UniPath provides high accuracy, consistency and scalability in estimating gene-set enrichment scores for every cell. Its framework provides an easy solution for handling variability in drop-out rate, which can sometimes create artefact due to systematic patterns. UniPath provides an alternative approach of dimension reduction of single-cell open-chromatin profiles. UniPath's approach of predicting temporal-order of single-cells using their pathway enrichment scores enables suppression of covariates to achieve correct order of cells. Analysis of mouse cell atlas using our approach yielded surprising, albeit biologically-meaningful co-clustering of cell-types from distant organs. By enabling an unconventional method of exploiting pathway co-occurrence to compare two groups of cells, our approach also proves to be useful in inferring context-specific regulations in cancer cells. Available at https://reggenlab.github.io/UniPathWeb/.

Fully defined human pluripotent stem cell-derived microglia and tri-culture system model C3 production in Alzheimer's disease

Aberrant inflammation in the CNS has been implicated as a major player in the pathogenesis of human neurodegenerative disease. We developed a new approach to derive microglia from human pluripotent stem cells (hPSCs) and built a defined hPSC-derived tri-culture system containing pure populations of hPSC-derived microglia, astrocytes, and neurons to dissect cellular cross-talk along the neuroinflammatory axis in vitro. We used the tri-culture system to model neuroinflammation in Alzheimer's disease with hPSCs harboring the APP^SWE+/+ mutation and their isogenic control. We found that complement C3, a protein that is increased under inflammatory conditions and implicated in synaptic loss, is potentiated in tri-culture and further enhanced in APP^SWE+/+ tri-cultures due to microglia initiating reciprocal signaling with astrocytes to produce excess C3. Our study defines the major cellular players contributing to increased C3 in Alzheimer's disease and presents a broadly applicable platform to study neuroinflammation in human disease.

Differential regulation of β-catenin-mediated transcription via N- and C-terminal co-factors governs identity of murine intestinal epithelial stem cells

The homeostasis of the gut epithelium relies upon continuous renewal and proliferation of crypt-resident intestinal epithelial stem cells (IESCs). Wnt/β-catenin signaling is required for IESC maintenance, however, it remains unclear how this pathway selectively governs the identity and proliferative decisions of IESCs. Here, we took advantage of knock-in mice harboring transgenic β-catenin alleles with mutations that specifically impair the recruitment of N- or C-terminal transcriptional co-factors. We show that C-terminally-recruited transcriptional co-factors of β-catenin act as all-or-nothing regulators of Wnt-target gene expression. Blocking their interactions with β-catenin rapidly induces loss of IESCs and intestinal homeostasis. Conversely, N-terminally recruited co-factors fine-tune β-catenin's transcriptional output to ensure proper self-renewal and proliferative behaviour of IESCs. Impairment of N-terminal interactions triggers transient hyperproliferation of IESCs, eventually resulting in exhaustion of the self-renewing stem cell pool. IESC mis-differentiation, accompanied by unfolded protein response stress and immune infiltration, results in a process resembling aberrant "villisation" of intestinal crypts. Our data suggest that IESC-specific Wnt/β-catenin output requires selective modulation of gene expression by transcriptional co-factors.

Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples

Unraveling molecular regulatory networks underlying disease progression is critically important for understanding disease mechanisms and identifying drug targets. The existing methods for inferring gene regulatory networks (GRNs) rely mainly on time-course gene expression data. However, most available omics data from cross-sectional studies of cancer patients often lack sufficient temporal information, leading to a key challenge for GRN inference. Through quantifying the latent progression using random walks-based manifold distance, we propose a latent-temporal progression-based Bayesian method, PROB, for inferring GRNs from the cross-sectional transcriptomic data of tumor samples. The robustness of PROB to the measurement variabilities in the data is mathematically proved and numerically verified. Performance evaluation on real data indicates that PROB outperforms other methods in both pseudotime inference and GRN inference. Applications to bladder cancer and breast cancer demonstrate that our method is effective to identify key regulators of cancer progression or drug targets. The identified ACSS1 is experimentally validated to promote epithelial-to-mesenchymal transition of bladder cancer cells, and the predicted FOXM1-targets interactions are verified and are predictive of relapse in breast cancer. Our study suggests new effective ways to clinical transcriptomic data modeling for characterizing cancer progression and facilitates the translation of regulatory network-based approaches into precision medicine.

OCT4 expression in human embryonic stem cells: spatio-temporal dynamics and fate transitions

The improved in vitro regulation of human embryonic stem cell (hESC) pluripotency and differentiation trajectories is required for their promising clinical applications. The temporal and spatial quantification of the molecular interactions controlling pluripotency is also necessary for the development of successful mathematical and computational models. Here we use time-lapse experimental data of OCT4-mCherry fluorescence intensity to quantify the temporal and spatial dynamics of the pluripotency transcription factor OCT4 in a growing hESC colony in the presence and absence of BMP4. We characterise the internal self-regulation of OCT4 using the Hurst exponent and autocorrelation analysis, quantify the intra-cellular fluctuations and consider the diffusive nature of OCT4 evolution for individual cells and pairs of their descendants. We find that OCT4 abundance in the daughter cells fluctuates sub-diffusively, showing anti-persistent self-regulation. We obtain the stationary probability distributions governing hESC transitions amongst the different cell states and establish the times at which pro-fate cells (which later give rise to pluripotent or differentiated cells) cluster in the colony. By quantifying the similarities between the OCT4 expression amongst neighbouring cells, we show that hESCs express similar OCT4 to cells within their local neighbourhood within the first two days of the experiment and before BMP4 treatment. Our framework allows us to quantify the relevant properties of proliferating hESC colonies and the procedure is widely applicable to other transcription factors and cell populations.

Continuous human uterine NK cell differentiation in response to endometrial regeneration and pregnancy

Immune cell differentiation is critical for adequate tissue-specific immune responses to occur. Here, we studied differentiation of human uterine natural killer cells (uNK cells). These cells reside in a tissue undergoing constant regeneration and represent the major leukocyte population at the maternal-fetal interface. However, their physiological response during the menstrual cycle and in pregnancy remains elusive. By surface proteome and transcriptome analysis as well as using humanized mice, we identify a differentiation pathway of uNK cells in vitro and in vivo with sequential acquisition of killer cell immunoglobulin-like receptors and CD39. uNK cell differentiation occurred continuously in response to the endometrial regeneration and was driven by interleukin-15. Differentiated uNK cells displayed reduced proliferative capacity and immunomodulatory function including enhanced angiogenic capacity. By studying human uterus transplantation and monozygotic twins, we found that the uNK cell niche could be replenished from circulation and that it was under genetic control. Together, our study uncovers a continuous differentiation pathway of human NK cells in the uterus that is coupled to profound functional changes in response to local tissue regeneration and pregnancy.

Exponential-Family Embedding With Application to Cell Developmental Trajectories for Single-Cell RNA-Seq Data

Scientists often embed cells into a lower-dimensional space when studying single-cell RNA-seq data for improved downstream analyses such as developmental trajectory analyses, but the statistical properties of such nonlinear embedding methods are often not well understood. In this article, we develop the exponential-family SVD (eSVD), a nonlinear embedding method for both cells and genes jointly with respect to a random dot product model using exponential-family distributions. Our estimator uses alternating minimization, which enables us to have a computationally efficient method, prove the identifiability conditions and consistency of our method, and provide statistically principled procedures to tune our method. All these qualities help advance the single-cell embedding literature, and we provide extensive simulations to demonstrate that the eSVD is competitive compared to other embedding methods. We apply the eSVD via Gaussian distributions where the standard deviations are proportional to the means to analyze a single-cell dataset of oligodendrocytes in mouse brains. Using the eSVD estimated embedding, we then investigate the cell developmental trajectories of the oligodendrocytes. While previous results are not able to distinguish the trajectories among the mature oligodendrocyte cell types, our diagnostics and results demonstrate there are two major developmental trajectories that diverge at mature oligodendrocytes. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplementary materials.

Enabling single-cell trajectory network enrichment

Single-cell sequencing (scRNA-seq) technologies allow the investigation of cellular differentiation processes with unprecedented resolution. Although powerful software packages for scRNA-seq data analysis exist, systems biology-based tools for trajectory analysis are rare and typically difficult to handle. This hampers biological exploration and prevents researchers from gaining deeper insights into the molecular control of developmental processes. Here, to address this, we have developed Scellnetor; a network-constraint time-series clustering algorithm. It allows extraction of temporal differential gene expression network patterns (modules) that explain the difference in regulation of two developmental trajectories. Using well-characterized experimental model systems, we demonstrate the capacity of Scellnetor as a hypothesis generator to identify putative mechanisms driving haematopoiesis or mechanistically interpretable subnetworks driving dysfunctional CD8 T-cell development in chronic infections. Altogether, Scellnetor allows for single-cell trajectory network enrichment, which effectively lifts scRNA-seq data analysis to a systems biology level.

Opposing immune and genetic mechanisms shape oncogenic programs in synovial sarcoma

Synovial sarcoma (SyS) is an aggressive neoplasm driven by the SS18-SSX fusion, and is characterized by low T cell infiltration. Here, we studied the cancer-immune interplay in SyS using an integrative approach that combines single-cell RNA sequencing (scRNA-seq), spatial profiling and genetic and pharmacological perturbations. scRNA-seq of 16,872 cells from 12 human SyS tumors uncovered a malignant subpopulation that marks immune-deprived niches in situ and is predictive of poor clinical outcomes in two independent cohorts. Functional analyses revealed that this malignant cell state is controlled by the SS18-SSX fusion, is repressed by cytokines secreted by macrophages and T cells, and can be synergistically targeted with a combination of HDAC and CDK4/CDK6 inhibitors. This drug combination enhanced malignant-cell immunogenicity in SyS models, leading to induced T cell reactivity and T cell-mediated killing. Our study provides a blueprint for investigating heterogeneity in fusion-driven malignancies and demonstrates an interplay between immune evasion and oncogenic processes that can be co-targeted in SyS and potentially in other malignancies.

Kidney Single-cell Transcriptomes Predict Spatial Corticomedullary Gene Expression and Tissue Osmolality Gradients

BACKGROUND

Single-cell transcriptomes from dissociated tissues provide insights into cell types and their gene expression and may harbor additional information on spatial position and the local microenvironment. The kidney's cells are embedded into a gradient of increasing tissue osmolality from the cortex to the medulla, which may alter their transcriptomes and provide cues for spatial reconstruction.

METHODS

Single-cell or single-nuclei mRNA sequencing of dissociated mouse kidneys and of dissected cortex, outer, and inner medulla, to represent the corticomedullary axis, was performed. Computational approaches predicted the spatial ordering of cells along the corticomedullary axis and quantitated expression levels of osmo-responsive genes. In situ hybridization validated computational predictions of spatial gene-expression patterns. The strategy was used to compare single-cell transcriptomes from wild-type mice to those of mice with a collecting duct-specific knockout of the transcription factor grainyhead-like 2 (Grhl2^CD-/-), which display reduced renal medullary osmolality.

RESULTS

Single-cell transcriptomics from dissociated kidneys provided sufficient information to approximately reconstruct the spatial position of kidney tubule cells and to predict corticomedullary gene expression. Spatial gene expression in the kidney changes gradually and osmo-responsive genes follow the physiologic corticomedullary gradient of tissue osmolality. Single-nuclei transcriptomes from Grhl2^CD-/- mice indicated a flattened expression gradient of osmo-responsive genes compared with control mice, consistent with their physiologic phenotype.

CONCLUSIONS

Single-cell transcriptomics from dissociated kidneys facilitated the prediction of spatial gene expression along the corticomedullary axis and quantitation of osmotically regulated genes, allowing the prediction of a physiologic phenotype.

Gut CD4+ T cell phenotypes are a continuum molded by microbes, not by TH archetypes

CD4+ effector lymphocytes (Teff) are traditionally classified by the cytokines they produce. To determine the states that Teff cells actually adopt in frontline tissues in vivo, we applied single-cell transcriptome and chromatin analyses to colonic Teff cells in germ-free or conventional mice or in mice after challenge with a range of phenotypically biasing microbes. Unexpected subsets were marked by the expression of the interferon (IFN) signature or myeloid-specific transcripts, but transcriptome or chromatin structure could not resolve discrete clusters fitting classic helper T cell (TH) subsets. At baseline or at different times of infection, transcripts encoding cytokines or proteins commonly used as TH markers were distributed in a polarized continuum, which was functionally validated. Clones derived from single progenitors gave rise to both IFN-γ- and interleukin (IL)-17-producing cells. Most of the transcriptional variance was tied to the infecting agent, independent of the cytokines produced, and chromatin variance primarily reflected activities of activator protein (AP)-1 and IFN-regulatory factor (IRF) transcription factor (TF) families, not the canonical subset master regulators T-bet, GATA3 or RORγ.

Gradient of Developmental and Injury Response transcriptional states defines functional vulnerabilities underpinning glioblastoma heterogeneity

Glioblastomas harbor diverse cell populations, including rare glioblastoma stem cells (GSCs) that drive tumorigenesis. To characterize functional diversity within this population, we performed single-cell RNA sequencing on >69,000 GSCs cultured from the tumors of 26 patients. We observed a high degree of inter- and intra-GSC transcriptional heterogeneity that could not be fully explained by DNA somatic alterations. Instead, we found that GSCs mapped along a transcriptional gradient spanning two cellular states reminiscent of normal neural development and inflammatory wound response. Genome-wide CRISPR-Cas9 dropout screens independently recapitulated this observation, with each state characterized by unique essential genes. Further single-cell RNA sequencing of >56,000 malignant cells from primary tumors found that the majority organize along an orthogonal astrocyte maturation gradient yet retain expression of founder GSC transcriptional programs. We propose that glioblastomas grow out of a fundamental GSC-based neural wound response transcriptional program, which is a promising target for new therapy development.

A distinct innate immune signature marks progression from mild to severe COVID-19

Coronavirus disease 2019 (COVID-19) manifests with a range of severities, but immune signatures of mild and severe disease are still not fully understood. Here, we use mass cytometry and targeted proteomics to profile the innate immune response of patients with mild or severe COVID-19 and of healthy individuals. Sampling at different stages allows us to reconstruct a pseudo-temporal trajectory of the innate response. A surge of CD169+ monocytes associated with an IFN-γ+MCP-2+ signature rapidly follows symptom onset. At later stages, we observe a persistent inflammatory phenotype in patients with severe disease, dominated by high CCL3 and CCL4 abundance correlating with the re-appearance of CD16+ monocytes, whereas the response of mild COVID-19 patients normalizes. Our data provide insights into the dynamic nature of inflammatory responses in COVID-19 patients and identify sustained innate immune responses as a likely mechanism in severe patients, thus supporting the investigation of targeted interventions in severe COVID-19.

Sequential fate-switches in stem-like cells drive the tumorigenic trajectory from human neural stem cells to malignant glioma

Glioblastoma (GBM) is an incurable and highly heterogeneous brain tumor, originating from human neural stem/progenitor cells (hNSCs/hNPCs) years ahead of diagnosis. Despite extensive efforts to characterize hNSCs and end-stage GBM at bulk and single-cell levels, the de novo gliomagenic path from hNSCs is largely unknown due to technical difficulties in early-stage sampling and preclinical modeling. Here, we established two highly penetrant hNSC-derived malignant glioma models, which resemble the histopathology and transcriptional heterogeneity of human GBM. Integrating time-series analyses of whole-exome sequencing, bulk and single-cell RNA-seq, we reconstructed gliomagenic trajectories, and identified a persistent NSC-like population at all stages of tumorigenesis. Through trajectory analyses and lineage tracing, we showed that tumor progression is primarily driven by multi-step transcriptional reprogramming and fate-switches in the NSC-like cells, which sequentially generate malignant heterogeneity and induce tumor phenotype transitions. We further uncovered stage-specific oncogenic cascades, and among the candidate genes we functionally validated C1QL1 as a new glioma-promoting factor. Importantly, the neurogenic-to-gliogenic switch in NSC-like cells marks an early stage characterized by a burst of oncogenic alterations, during which transient AP-1 inhibition is sufficient to inhibit gliomagenesis. Together, our results reveal previously undercharacterized molecular dynamics and fate choices driving de novo gliomagenesis from hNSCs, and provide a blueprint for potential early-stage treatment/diagnosis for GBM.

Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data

The analysis of single-cell genomics data presents several statistical challenges, and extensive efforts have been made to produce methods for the analysis of this data that impute missing values, address sampling issues and quantify and correct for noise. In spite of such efforts, no consensus on best practices has been established and all current approaches vary substantially based on the available data and empirical tests. The k-Nearest Neighbor Graph (kNN-G) is often used to infer the identities of, and relationships between, cells and is the basis of many widely used dimensionality-reduction and projection methods. The kNN-G has also been the basis for imputation methods using, e.g., neighbor averaging and graph diffusion. However, due to the lack of an agreed-upon optimal objective function for choosing hyperparameters, these methods tend to oversmooth data, thereby resulting in a loss of information with regard to cell identity and the specific gene-to-gene patterns underlying regulatory mechanisms. In this paper, we investigate the tuning of kNN- and diffusion-based denoising methods with a novel non-stochastic method for optimally preserving biologically relevant informative variance in single-cell data. The framework, Denoising Expression data with a Weighted Affinity Kernel and Self-Supervision (DEWÄKSS), uses a self-supervised technique to tune its parameters. We demonstrate that denoising with optimal parameters selected by our objective function (i) is robust to preprocessing methods using data from established benchmarks, (ii) disentangles cellular identity and maintains robust clusters over dimension-reduction methods, (iii) maintains variance along several expression dimensions, unlike previous heuristic-based methods that tend to oversmooth data variance, and (iv) rarely involves diffusion but rather uses a fixed weighted kNN graph for denoising. Together, these findings provide a new understanding of kNN- and diffusion-based denoising methods. Code and example data for DEWÄKSS is available at https://gitlab.com/Xparx/dewakss/-/tree/Tjarnberg2020branch.

Regulation of immune responses by the airway epithelial cell landscape

The community of cells lining our airways plays a collaborative role in the preservation of immune homeostasis in the lung and provides protection from the pathogens and pollutants in the air we breathe. In addition to its structural attributes that provide effective mucociliary clearance of the lower airspace, the airway epithelium is an immunologically active barrier surface that senses changes in the airway environment and interacts with resident and recruited immune cells. Single-cell RNA-sequencing is illuminating the cellular heterogeneity that exists in the airway wall and has identified novel cell populations with unique molecular signatures, trajectories of differentiation and diverse functions in health and disease. In this Review, we discuss how our view of the airway epithelial landscape has evolved with the advent of transcriptomic approaches to cellular phenotyping, with a focus on epithelial interactions with the local neuronal and immune systems.

Emergence of Neuronal Diversity during Vertebrate Brain Development

Neurogenesis comprises many highly regulated processes including proliferation, differentiation, and maturation. However, the transcriptional landscapes underlying brain development are poorly characterized. We describe a developmental single-cell catalog of ∼220,000 zebrafish brain cells encompassing 12 stages from embryo to larva. We characterize known and novel gene markers for ∼800 clusters and provide an overview of the diversification of neurons and progenitors across these time points. We also introduce an optimized GESTALT lineage recorder that enables higher expression and recovery of Cas9-edited barcodes to query lineage segregation. Cell type characterization indicates that most embryonic neural progenitor states are transitory and transcriptionally distinct from neural progenitors of post-embryonic stages. Reconstruction of cell specification trajectories reveals that late-stage retinal neural progenitors transcriptionally overlap cell states observed in the embryo. The zebrafish brain development atlas provides a resource to define and manipulate specific subsets of neurons and to uncover the molecular mechanisms underlying vertebrate neurogenesis.

Adapted single-cell consensus clustering (adaSC3)

The analysis of single-cell RNA sequencing data is of great importance in health research. It challenges data scientists, but has enormous potential in the context of personalized medicine. The clustering of single cells aims to detect different subgroups of cell populations within a patient in a data-driven manner. Some comparison studies denote single-cell consensus clustering (SC3), proposed by Kiselev et al. (Nat Methods 14(5):483–486, 2017), as the best method for classifying single-cell RNA sequencing data. SC3 includes Laplacian eigenmaps and a principal component analysis (PCA). Our proposal of unsupervised adapted single-cell consensus clustering (adaSC3) suggests to replace the linear PCA by diffusion maps, a non-linear method that takes the transition of single cells into account. We investigate the performance of adaSC3 in terms of accuracy on the data sets of the original source of SC3 as well as in a simulation study. A comparison of adaSC3 with SC3 as well as with related algorithms based on further alternative dimension reduction techniques shows a quite convincing behavior of adaSC3.

DrivAER: Identification of driving transcriptional programs in single-cell RNA sequencing data

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) unfolds complex transcriptomic datasets into detailed cellular maps. Despite recent success, there is a pressing need for specialized methods tailored towards the functional interpretation of these cellular maps.

FINDINGS

Here, we present DrivAER, a machine learning approach for the identification of driving transcriptional programs using autoencoder-based relevance scores. DrivAER scores annotated gene sets on the basis of their relevance to user-specified outcomes such as pseudotemporal ordering or disease status. DrivAER iteratively evaluates the information content of each gene set with respect to the outcome variable using autoencoders. We benchmark our method using extensive simulation analysis as well as comparison to existing methods for functional interpretation of scRNA-seq data. Furthermore, we demonstrate that DrivAER extracts key pathways and transcription factors that regulate complex biological processes from scRNA-seq data.

CONCLUSIONS

By quantifying the relevance of annotated gene sets with respect to specified outcome variables, DrivAER greatly enhances our ability to understand the underlying molecular mechanisms.