Single-cell transcriptional diversity is a hallmark of developmental potential

Single-cell genomics revolutionizes plant development studies across scales

Understanding the development of tissues, organs and entire organisms through the lens of single-cell genomics has revolutionized developmental biology. Although single-cell transcriptomics has been pioneered in animal systems, from an experimental perspective, plant development holds some distinct advantages: cells do not migrate in relation to one another, and new organ formation (of leaves, roots, flowers, etc.) continues post-embryonically from persistent stem cell populations known as meristems. For a time, plant studies lagged behind animal or cell culture-based, single-cell approaches, largely owing to the difficulty in dissociating plant cells from their rigid cell walls. Recent intensive development of single-cell and single-nucleus isolation techniques across plant species has opened up a wide range of experimental approaches. This has produced a rapidly expanding diversity of information across tissue types and species, concomitant with the creative development of methods. In this brief Spotlight, we highlight some of the technical developments and how they have led to profiling single-cell genomics in various plant organs. We also emphasize the contribution of single-cell genomics in revealing developmental trajectories among different cell types within plant organs. Furthermore, we present efforts toward comparative analysis of tissues and organs at a single-cell level. Single-cell genomics is beginning to generate comprehensive information relating to how plant organs emerge from stem cell populations.

Single-Cell Transcriptome Integration Analysis Reveals the Correlation Between Mesenchymal Stromal Cells and Fibroblasts

Background: Mesenchymal stromal cells (MSCs) and fibroblasts show similar morphology, surface marker expression, and proliferation, differentiation, and immunomodulatory capacities. These similarities not only blur their cell identities but also limit their application. Methods: We performed single-cell transcriptome sequencing of the human umbilical cord and foreskin MSCs (HuMSCs and FSMSCs) and extracted the single-cell transcriptome data of the bone marrow and adipose MSCs (BMSCs and ADMSCs) from the Gene Expression Omnibus (GEO) database. Then, we performed quality control, batch effect correction, integration, and clustering analysis of the integrated single-cell transcriptome data from the HuMSCs, FMSCs, BMSCs, and ADMSCs. The cell subsets were annotated based on the surface marker phenotypes for the MSCs (CD105 ⁺ , CD90 ⁺, CD73 ⁺, CD45 ^-, CD34 ^-, CD19 ^-, HLA-DRA ^-, and CD11b ^-), fibroblasts (VIM ⁺, PECAM1 ^-, CD34 ^-, CD45 ^-, EPCAM ^-, and MYH11 ^-), and pericytes (CD146 ⁺, PDGFRB ⁺, PECAM1 ^-, CD34 ^-, and CD45 ^-). The expression levels of common fibroblast markers (ACTA2, FAP, PDGFRA, PDGFRB, S100A4, FN1, COL1A1, POSTN, DCN, COL1A2, FBLN2, COL1A2, DES, and CDH11) were also analyzed in all cell subsets. Finally, the gene expression profiles, differentiation status, and the enrichment status of various gene sets and regulons were compared between the cell subsets. Results: We demonstrated 15 distinct cell subsets in the integrated single-cell transcriptome sequencing data. Surface marker annotation demonstrated the MSC phenotype in 12 of the 15 cell subsets. C10 and C14 subsets demonstrated both the MSC and pericyte phenotypes. All 15 cell subsets demonstrated the fibroblast phenotype. C8, C12, and C13 subsets exclusively demonstrated the fibroblast phenotype. We identified 3,275 differentially expressed genes, 305 enriched gene sets, and 34 enriched regulons between the 15 cell subsets. The cell subsets that exclusively demonstrated the fibroblast phenotype represented less primitive and more differentiated cell types. Conclusion: Cell subsets with the MSC phenotype also demonstrated the fibroblast phenotype, but cell subsets with the fibroblast phenotype did not necessarily demonstrate the MSC phenotype, suggesting that MSCs represented a subclass of fibroblasts. We also demonstrated that the MSCs and fibroblasts represented highly heterogeneous populations with distinct cell subsets, which could be identified based on the differentially enriched gene sets and regulons that specify proliferating, differentiating, metabolic, and/or immunomodulatory functions.

Patterning the embryonic pulmonary mesenchyme

Smooth muscle guides the morphogenesis of several epithelia during organogenesis, including the mammalian airways. However, it remains unclear how airway smooth muscle differentiation is spatiotemporally patterned and whether it originates from transcriptionally distinct mesenchymal progenitors. Using single-cell RNA-sequencing of embryonic mouse lungs, we show that the pulmonary mesenchyme contains a continuum of cell identities, but no transcriptionally distinct progenitors. Transcriptional variability correlates with spatially distinct sub-epithelial and sub-mesothelial mesenchymal compartments that are regulated by Wnt signaling. Live-imaging and tension-sensors reveal compartment-specific migratory behaviors and cortical forces and show that sub-epithelial mesenchyme contributes to airway smooth muscle. Reconstructing differentiation trajectories reveals early activation of cytoskeletal and Wnt signaling genes. Consistently, Wnt activation induces the earliest stages of smooth muscle differentiation and local accumulation of mesenchymal F-actin, which influences epithelial morphology. Our single-cell approach uncovers the principles of pulmonary mesenchymal patterning and identifies a morphogenetically active mesenchymal layer that sculpts the airway epithelium.

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The embryonic lung mesenchyme is organized into spatially distinct compartments

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Migratory behaviors and cortical forces differ between compartments

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Diffusion analysis recapitulates airway smooth muscle differentiation

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The early stages of smooth muscle differentiation influence airway branching

Transcriptome Analysis Revealed the Symbiosis Niche of 3D Scaffolds to Accelerate Bone Defect Healing

Three dimension (3D) printed scaffolds have been shown to be superior in promoting tissue repair, but the cell-level specific regulatory network activated by 3D printing scaffolds with different material components to form a symbiosis niche have not been systematically revealed. Here, three typical 3D printed scaffolds, including natural polymer hydrogel (gelatin-methacryloyl, GelMA), synthetic polymer material (polycaprolactone, PCL), and bioceramic (β-tricalcium phosphate, β-TCP), are fabricated to explore the regulating effect of the symbiotic microenvironment during bone healing. Enrichment analysis show that hydrogel promotes tissue regeneration and reconstruction by improving blood vessel generation by enhancing oxygen transport and red blood cell development. The PCL scaffold regulates cell proliferation and differentiation by promoting cellular senescence, cell cycle and deoxyribonucleic acid (DNA) replication pathways, accelerating the process of endochondral ossification, and the formation of callus. The β-TCP scaffold can specifically enhance the expression of osteoclast differentiation and extracellular space pathway genes to promote the differentiation of osteoclasts and promote the process of bone remodeling. In these processes, specific biomaterial properties can be used to guide cell behavior and regulate molecular network in the symbiotic microenvironment to reduce the barriers of regeneration and repair.

A single-cell Arabidopsis root atlas reveals developmental trajectories in wild-type and cell identity mutants

In all multicellular organisms, transcriptional networks orchestrate organ development. The Arabidopsis root, with its simple structure and indeterminate growth, is an ideal model for investigating the spatiotemporal transcriptional signatures underlying developmental trajectories. To map gene expression dynamics across root cell types and developmental time, we built a comprehensive, organ-scale atlas at single-cell resolution. In addition to estimating developmental progressions in pseudotime, we employed the mathematical concept of optimal transport to infer developmental trajectories and identify their underlying regulators. To demonstrate the utility of the atlas to interpret new datasets, we profiled mutants for two key transcriptional regulators at single-cell resolution, shortroot and scarecrow. We report transcriptomic and in vivo evidence for tissue trans-differentiation underlying a mixed cell identity phenotype in scarecrow. Our results support the atlas as a rich community resource for unraveling the transcriptional programs that specify and maintain cell identity to regulate spatiotemporal organ development.

A single-cell Arabidopsis root atlas reveals developmental trajectories in wild-type and cell identity mutants

In all multicellular organisms, transcriptional networks orchestrate organ development. The Arabidopsis root, with its simple structure and indeterminate growth, is an ideal model for investigating the spatiotemporal transcriptional signatures underlying developmental trajectories. To map gene expression dynamics across root cell types and developmental time, we built a comprehensive, organ-scale atlas at single-cell resolution. In addition to estimating developmental progressions in pseudotime, we employed the mathematical concept of optimal transport to infer developmental trajectories and identify their underlying regulators. To demonstrate the utility of the atlas to interpret new datasets, we profiled mutants for two key transcriptional regulators at single-cell resolution, shortroot and scarecrow. We report transcriptomic and in vivo evidence for tissue trans-differentiation underlying a mixed cell identity phenotype in scarecrow. Our results support the atlas as a rich community resource for unraveling the transcriptional programs that specify and maintain cell identity to regulate spatiotemporal organ development.

Signature-scoring methods developed for bulk samples are not adequate for cancer single-cell RNA sequencing data

Quantifying the activity of gene expression signatures is common in analyses of single-cell RNA sequencing data. Methods originally developed for bulk samples are often used for this purpose without accounting for contextual differences between bulk and single-cell data. More broadly, these methods have not been benchmarked. Here we benchmark five such methods, including single sample gene set enrichment analysis (ssGSEA), Gene Set Variation Analysis (GSVA), AUCell, Single Cell Signature Explorer (SCSE), and a new method we developed, Jointly Assessing Signature Mean and Inferring Enrichment (JASMINE). Using cancer as an example, we show cancer cells consistently express more genes than normal cells. This imbalance leads to bias in performance by bulk-sample-based ssGSEA in gold standard tests and down sampling experiments. In contrast, single-cell-based methods are less susceptible. Our results suggest caution should be exercised when using bulk-sample-based methods in single-cell data analyses, and cellular contexts should be taken into consideration when designing benchmarking strategies.

Integrative molecular and clinical profiling of acral melanoma links focal amplification of 22q11.21 to metastasis

Acral melanoma, the most common melanoma subtype among non-White individuals, is associated with poor prognosis. However, its key molecular drivers remain obscure. Here, we perform integrative genomic and clinical profiling of acral melanomas from 104 patients treated in North America (n = 37) or China (n = 67). We find that recurrent, late-arising focal amplifications of cytoband 22q11.21 are a leading determinant of inferior survival, strongly associated with metastasis, and linked to downregulation of immunomodulatory genes associated with response to immune checkpoint blockade. Unexpectedly, LZTR1 - a known tumor suppressor in other cancers - is a key candidate oncogene in this cytoband. Silencing of LZTR1 in melanoma cell lines causes apoptotic cell death independent of major hotspot mutations or melanoma subtypes. Conversely, overexpression of LZTR1 in normal human melanocytes initiates processes associated with metastasis, including anchorage-independent growth, formation of spheroids, and an increase in MAPK and SRC activities. Our results provide insights into the etiology of acral melanoma and implicate LZTR1 as a key tumor promoter and therapeutic target.

Hif-1a suppresses ROS-induced proliferation of cardiac fibroblasts following myocardial infarction

We report that cardiac fibroblasts (CFs) and mesenchymal progenitors are more hypoxic than other cardiac interstitial populations, express more hypoxia-inducible factor 1α (HIF-1α), and exhibit increased glycolytic metabolism. CF-specific deletion of Hif-1a resulted in decreased HIF-1 target gene expression and increased mesenchymal progenitors in uninjured hearts and increased CF activation without proliferation following sham injury, as demonstrated using single-cell RNA sequencing (scRNA-seq). After myocardial infarction (MI), however, there was ∼50% increased CF proliferation and excessive scarring and contractile dysfunction, a scenario replicated in 3D engineered cardiac microtissues. CF proliferation was associated with higher reactive oxygen species (ROS) as occurred also in wild-type mice treated with the mitochondrial ROS generator MitoParaquat (MitoPQ). The mitochondrial-targeted antioxidant MitoTEMPO rescued Hif-1a mutant phenotypes. Thus, HIF-1α in CFs provides a critical braking mechanism against excessive post-ischemic CF activation and proliferation through regulation of mitochondrial ROS. CFs are potential cellular targets for designer antioxidant therapies in cardiovascular disease.

Hubness reduction improves clustering and trajectory inference in single-cell transcriptomic data

MOTIVATION

Single-cell RNA-seq (scRNAseq) datasets are characterized by large ambient dimensionality, and their analyses can be affected by various manifestations of the dimensionality curse. One of these manifestations is the hubness phenomenon, i.e. existence of data points with surprisingly large incoming connectivity degree in the datapoint neighbourhood graph. Conventional approach to dampen the unwanted effects of high dimension consists in applying drastic dimensionality reduction. It remains unexplored if this step can be avoided thus retaining more information than contained in the low-dimensional projections, by correcting directly hubness.

RESULTS

We investigated hubness in scRNAseq data. We show that hub cells do not represent any visible technical or biological bias. The effect of various hubness reduction methods is investigated with respect to the clustering, trajectory inference and visualization tasks in scRNAseq datasets. We show that hubness reduction generates neighbourhood graphs with properties more suitable for applying machine learning methods; and that it outperforms other state-of-the-art methods for improving neighbourhood graphs. As a consequence, clustering, trajectory inference and visualization perform better, especially for datasets characterized by large intrinsic dimensionality. Hubness is an important phenomenon characterizing data point neighbourhood graphs computed for various types of sequencing datasets. Reducing hubness can be beneficial for the analysis of scRNAseq data with large intrinsic dimensionality in which case it can be an alternative to drastic dimensionality reduction.

AVAILABILITY AND IMPLEMENTATION

The code used to analyze the datasets and produce the figures of this article is available from https://github.com/sysbio-curie/schubness.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Interconnected high-dimensional landscapes of epithelial-mesenchymal plasticity and stemness in cancer

Establishing macrometastases at distant organs is a highly challenging process for cancer cells, with extremely high attrition rates. A very small percentage of disseminated cells have the ability to dynamically adapt to their changing micro-environments through reversibly switching to another phenotype, aiding metastasis. Such plasticity can be exhibited along one or more axes-epithelial-mesenchymal plasticity (EMP) and cancer stem cells (CSCs) being the two most studied, and often tacitly assumed to be synonymous. Here, we review the emerging concepts related to EMP and CSCs across multiple cancers. Both processes are multi-dimensional in nature; for instance, EMP can be defined on morphological, molecular and functional changes, which may or may not be synchronized. Similarly, self-renewal, multi-lineage potential, and resistance to anoikis and/or therapy may not all occur simultaneously in CSCs. Thus, understanding the complexity in defining EMP and CSCs is essential if we are to understand their contribution to cancer metastasis. This will require a more comprehensive understanding of the non-linearity of these processes. These processes are dynamic, reversible, and semi-independent in nature; cells traverse the inter-connected high-dimensional EMP and CSC landscapes in diverse paths, each of which may exhibit a distinct EMP-CSC coupling. Our proposed model offers a potential unifying framework for elucidating the coupled decision-making along these dimensions and highlights a key set of open questions to be answered.

Landscape of cancer-associated fibroblasts identifies the secreted biglycan as a protumor and immunosuppressive factor in triple-negative breast cancer

Cancer-associated fibroblasts (CAFs) are essential for tumor microenvironment remodeling and correlate with tumor progression. However, interactions between CAFs and tumor cells and immune cells in triple-negative breast cancer (TNBC) are still poorly explored. Here, we investigate the role of CAFs in TNBC and potential novel mediators of their functions. The clustering of classic markers was applied to estimate the relative abundance of CAFs in TNBC cohorts. Primary fibroblasts were isolated from normal and tumor samples. The RNA and culture medium of fibroblasts were subjected to RNA sequencing and mass spectrometry to explore the upregulated signatures in CAFs. Microdissection and single-cell RNA sequencing datasets were used to examine the expression profiles. CAFs were associated with hallmark signalings and immune components in TNBC. Clustering based on CAF markers in the literature revealed different CAF infiltration groups in TNBC: low, medium and high. Most of the cancer hallmark signaling pathways were enriched in the high CAF infiltration group. Furthermore, RNA sequencing and mass spectrometry identified biglycan (BGN), a soluble secreted protein, as upregulated in CAFs compared to normal cancer-adjacent fibroblasts (NAFs). The expression of biglycan was negatively correlated with CD8 + T cells. Biglycan indicated poor prognostic outcomes and might be correlated with the immunosuppressive tumor microenvironment (TME). In conclusion, CAFs play an essential role in tumor progression and the TME. We identified an extracellular protein, biglycan, as a prognostic marker and potential therapeutic target in TNBC.

Single-Cell Transcriptome Profiling Reveals Multicellular Ecosystem of Nucleus Pulposus during Degeneration Progression

Although degeneration of the nucleus pulposus (NP) is a major contributor to intervertebral disc degeneration (IVDD) and low back pain, the underlying molecular complexity and cellular heterogeneity remain poorly understood. Here, a comprehensive single-cell resolution transcript landscape of human NP is reported. Six novel human NP cells (NPCs) populations are identified by their distinct molecular signatures. The potential functional differences among NPC subpopulations are analyzed. Predictive transcripts, transcriptional factors, and signal pathways with respect to degeneration grades are explored. It is reported that fibroNPCs is the subpopulation for end-stage degeneration. CD90+NPCs are observed to be progenitor cells in degenerative NP tissues. NP-infiltrating immune cells comprise a previously unrecognized diversity of cell types, including granulocytic myeloid-derived suppressor cells (G-MDSCs). Integrin αM (CD11b) and oxidized low density lipoprotein receptor 1 (OLR1) as surface markers of NP-derived G-MDSCs are uncovered. The G-MDSCs are found to be enriched in mildly degenerated (grade II and III) NP tissues compared to severely degenerated (grade IV and V) NP tissues. Their immunosuppressive function and alleviation effects on NPCs' matrix degradation are revealed in vitro. Collectively, this study reveals the NPC-type complexity and phenotypic characteristics in NP, thereby providing new insights and clues for IVDD treatment.

Single-cell RNA Sequencing Reveals How the Aryl Hydrocarbon Receptor Shapes Cellular Differentiation Potency in the Mouse Colon

Despite recent progress recognizing the importance of aryl hydrocarbon receptor (Ahr)-dependent signaling in suppressing colon tumorigenesis, its role in regulating colonic crypt homeostasis remains unclear. To assess the effects of Ahr on intestinal epithelial cell heterogeneity and functional phenotypes, we utilized single-cell transcriptomics and advanced analytic strategies to generate a high-quality atlas for colonic intestinal crypts from wild-type and intestinal-specific Ahr knockout mice. Here we observed the promotive effects of Ahr deletion on Foxm1-regulated genes in crypt-associated canonical epithelial cell types and subtypes of goblet cells and deep crypt-secretory cells. We also show that intestinal Ahr deletion elevated single-cell entropy (a measure of differentiation potency or cell stemness) and RNA velocity length (a measure of the rate of cell differentiation) in noncycling and cycling Lgr5+ stem cells. In general, intercellular signaling cross-talk via soluble and membrane-bound factors was perturbed in Ahr-null colonocytes. Taken together, our single-cell RNA sequencing analyses provide new evidence of the molecular function of Ahr in modulating putative stem cell driver genes, cell potency lineage decisions, and cell-cell communication in vivo. PREVENTION RELEVANCE: Our mouse single-cell RNA sequencing analyses provide new evidence of the molecular function of Ahr in modulating colonic stemness and cell-cell communication in vivo. From a cancer prevention perspective, Ahr should be considered a therapeutic target to recalibrate remodeling of the intestinal stem cell niche.

Genetic and Transcriptional Contributions to Relapse in Normal Karyotype Acute Myeloid Leukemia

To better understand clonal and transcriptional adaptations after relapse in patients with acute myeloid leukemia (AML), we collected presentation and relapse samples from six normal karyotype AML cases. We performed enhanced whole-genome sequencing to characterize clonal evolution, and deep-coverage single-cell RNA sequencing on the same samples, which yielded 142,642 high-quality cells for analysis. Identifying expressed mutations in individual cells enabled us to discriminate between normal and AML cells, to identify coordinated changes in the genome and transcriptome, and to identify subclone-specific cell states. We quantified the coevolution of genetic and transcriptional heterogeneity during AML progression, and found that transcriptional changes were significantly correlated with genetic changes. However, transcriptional adaptation sometimes occurred independently, suggesting that clonal evolution does not represent all relevant biological changes. In three cases, we identified cells at diagnosis that likely seeded the relapse. Finally, these data revealed a conserved relapse-enriched leukemic cell state bearing markers of stemness, quiescence, and adhesion. SIGNIFICANCE: These data enabled us to identify a relapse-enriched leukemic cell state with distinct transcriptional properties. Detailed case-by-case analyses elucidated the complex ways in which the AML genome, transcriptome, and immune microenvironment interact to evade chemotherapy. These analyses provide a blueprint for evaluating these factors in larger cohorts.This article is highlighted in the In This Issue feature, p. 1.

Differential pre-malignant programs and microenvironment chart distinct paths to malignancy in human colorectal polyps

Colorectal cancers (CRCs) arise from precursor polyps whose cellular origins, molecular heterogeneity, and immunogenic potential may reveal diagnostic and therapeutic insights when analyzed at high resolution. We present a single-cell transcriptomic and imaging atlas of the two most common human colorectal polyps, conventional adenomas and serrated polyps, and their resulting CRC counterparts. Integrative analysis of 128 datasets from 62 participants reveals adenomas arise from WNT-driven expansion of stem cells, while serrated polyps derive from differentiated cells through gastric metaplasia. Metaplasia-associated damage is coupled to a cytotoxic immune microenvironment preceding hypermutation, driven partly by antigen-presentation differences associated with tumor cell-differentiation status. Microsatellite unstable CRCs contain distinct non-metaplastic regions where tumor cells acquire stem cell properties and cytotoxic immune cells are depleted. Our multi-omic atlas provides insights into malignant progression of colorectal polyps and their microenvironment, serving as a framework for precision surveillance and prevention of CRC.

Single-cell quantification of a broad RNA spectrum reveals unique noncoding patterns associated with cell types and states

The ability to interrogate total RNA content of single cells would enable better mapping of the transcriptional logic behind emerging cell types and states. However, current single-cell RNA-sequencing (RNA-seq) methods are unable to simultaneously monitor all forms of RNA transcripts at the single-cell level, and thus deliver only a partial snapshot of the cellular RNAome. Here we describe Smart-seq-total, a method capable of assaying a broad spectrum of coding and noncoding RNA from a single cell. Smart-seq-total does not require splitting the RNA content of a cell and allows the incorporation of unique molecular identifiers into short and long RNA molecules for absolute quantification. It outperforms current poly(A)-independent total RNA-seq protocols by capturing transcripts of a broad size range, thus enabling simultaneous analysis of protein-coding, long-noncoding, microRNA, and other noncoding RNA transcripts from single cells. We used Smart-seq-total to analyze the total RNAome of human primary fibroblasts, HEK293T, and MCF7 cells, as well as that of induced murine embryonic stem cells differentiated into embryoid bodies. By analyzing the coexpression patterns of both noncoding RNA and mRNA from the same cell, we were able to discover new roles of noncoding RNA throughout essential processes, such as cell cycle and lineage commitment during embryonic development. Moreover, we show that independent classes of short-noncoding RNA can be used to determine cell-type identity.

Cancer stem cells: advances in biology and clinical translation-a Keystone Symposia report

The test for the cancer stem cell (CSC) hypothesis is to find a target expressed on all, and only CSCs in a patient tumor, then eliminate all cells with that target that eliminates the cancer. That test has not yet been achieved, but CSC diagnostics and targets found on CSCs and some other cells have resulted in a few clinically relevant therapies. However, it has become apparent that eliminating the subset of tumor cells characterized by self-renewal properties is essential for long-term tumor control. CSCs are able to regenerate and initiate tumor growth, recapitulating the heterogeneity present in the tumor before treatment. As great progress has been made in identifying and elucidating the biology of CSCs as well as their interactions with the tumor microenvironment, the time seems ripe for novel therapeutic strategies that target CSCs to find clinical applicability. On May 19-21, 2021, researchers in cancer stem cells met virtually for the Keystone eSymposium "Cancer Stem Cells: Advances in Biology and Clinical Translation" to discuss recent advances in the understanding of CSCs as well as clinical efforts to target these populations.

Deciphering the spatial-temporal transcriptional landscape of human hypothalamus development

The hypothalamus comprises various nuclei and neuronal subpopulations that control fundamental homeostasis and behaviors. However, spatiotemporal molecular characterization of hypothalamus development in humans is largely unexplored. Here, we revealed spatiotemporal transcriptome profiles and cell-type characteristics of human hypothalamus development and illustrated the molecular diversity of neural progenitors and the cell-fate decision, which is programmed by a combination of transcription factors. Different neuronal and glial fates are sequentially produced and showed spatial developmental asynchrony. Moreover, human hypothalamic gliogenesis occurs at an earlier stage of gestation and displays distinctive transcription profiles compared with those in mouse. Notably, early oligodendrocyte cells in humans exhibit different gene patterns and interact with neuronal cells to regulate neuronal maturation by Wnt, Hippo, and integrin signals. Overall, our study provides a comprehensive molecular landscape of human hypothalamus development at early- and mid-embryonic stages and a foundation for understanding its spatial and functional complexity.

Single-Cell RNA-Seq Analysis Reveals the Acquisition of Cancer Stem Cell Traits and Increase of Cell-Cell Signaling during EMT Progression

Intermediate cell states (ICSs) during the epithelial-mesenchymal transition (EMT) are emerging as a driving force of cancer invasion and metastasis. ICSs typically exhibit hybrid epithelial/mesenchymal characteristics as well as cancer stem cell (CSC) traits including proliferation and drug resistance. Here, we analyze several single-cell RNA-seq (scRNA-seq) datasets to investigate the relation between several axes of cancer progression including EMT, CSC traits, and cell-cell signaling. To accomplish this task, we integrate computational methods for clustering and trajectory inference with analysis of EMT gene signatures, CSC markers, and cell-cell signaling pathways, and highlight conserved and specific processes across the datasets. Our analysis reveals that "standard" measures of pluripotency often used in developmental contexts do not necessarily correlate with EMT progression and expression of CSC-related markers. Conversely, an EMT circuit energy that quantifies the co-expression of epithelial and mesenchymal genes consistently increases along EMT trajectories across different cancer types and anatomical locations. Moreover, despite the high context specificity of signal transduction across different cell types, cells undergoing EMT always increased their potential to send and receive signals from other cells.

Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration

Skeletal muscle repair is driven by the coordinated self-renewal and fusion of myogenic stem and progenitor cells. Single-cell gene expression analyses of myogenesis have been hampered by the poor sampling of rare and transient cell states that are critical for muscle repair, and do not inform the spatial context that is important for myogenic differentiation. Here, we demonstrate how large-scale integration of single-cell and spatial transcriptomic data can overcome these limitations. We created a single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 88 publicly available single-cell (scRNAseq) and single-nucleus (snRNAseq) RNA-sequencing datasets. The resulting dataset includes more than 365,000 cells and spans a wide range of ages, injury, and repair conditions. Together, these data enabled identification of the predominant cell types in skeletal muscle, and resolved cell subtypes, including endothelial subtypes distinguished by vessel-type of origin, fibro-adipogenic progenitors defined by functional roles, and many distinct immune populations. The representation of different experimental conditions and the depth of transcriptome coverage enabled robust profiling of sparsely expressed genes. We built a densely sampled transcriptomic model of myogenesis, from stem cell quiescence to myofiber maturation, and identified rare, transitional states of progenitor commitment and fusion that are poorly represented in individual datasets. We performed spatial RNA sequencing of mouse muscle at three time points after injury and used the integrated dataset as a reference to achieve a high-resolution, local deconvolution of cell subtypes. We also used the integrated dataset to explore ligand-receptor co-expression patterns and identify dynamic cell-cell interactions in muscle injury response. We provide a public web tool to enable interactive exploration and visualization of the data. Our work supports the utility of large-scale integration of single-cell transcriptomic data as a tool for biological discovery.

Lysergic acid diethylamide induces increased signalling entropy in rats' prefrontal cortex

Psychedelic drugs are gaining attention from the scientific community as potential new compounds for the treatment of psychiatric diseases such as mood and substance use disorders. The 5‐HT_2A receptor has been identified as the main molecular target, and early studies pointed to an effect on the expression of neuroplasticity genes. Analysing RNA‐seq data from the prefrontal cortex of rats chronically treated with lysergic acid diethylamide (LSD), we describe the psychedelic‐induced rewiring of gene co‐expression networks, which become less centralised but more complex, with an overall increase in signalling entropy typical of highly plastic systems. Intriguingly, signalling entropy mirrors, at the molecular level, the increased brain entropy reported through neuroimaging studies in human, suggesting the underlying mechanisms of higher‐order phenomena. Moreover, from the analysis of network topology, we identify potential transcriptional regulators and propose the involvement of different cell types in psychedelics’ activity.

Dynamic transcriptional reprogramming leads to immunotherapeutic vulnerabilities in myeloma

While there is extensive evidence for genetic variation as a basis for treatment resistance, other sources of variation result from cellular plasticity. Using multiple myeloma as an example of an incurable lymphoid malignancy, we show how cancer cells modulate lineage restriction, adapt their enhancer usage and employ cell-intrinsic diversity for survival and treatment escape. By using single-cell transcriptome and chromatin accessibility profiling, we show that distinct transcriptional states co-exist in individual cancer cells and that differential transcriptional regulon usage and enhancer rewiring underlie these alternative transcriptional states. We demonstrate that exposure to standard treatment further promotes transcriptional reprogramming and differential enhancer recruitment while simultaneously reducing developmental potential. Importantly, treatment generates a distinct complement of actionable immunotherapy targets, such as CXCR4, which can be exploited to overcome treatment resistance. Our studies therefore delineate how to transform the cellular plasticity that underlies drug resistance into immuno-oncologic therapeutic opportunities.

Spatially confined sub-tumor microenvironments in pancreatic cancer

Intratumoral heterogeneity is a critical frontier in understanding how the tumor microenvironment (TME) propels malignant progression. Here, we deconvolute the human pancreatic TME through large-scale integration of histology-guided regional multiOMICs with clinical data and patient-derived preclinical models. We discover "subTMEs," histologically definable tissue states anchored in fibroblast plasticity, with regional relationships to tumor immunity, subtypes, differentiation, and treatment response. "Reactive" subTMEs rich in complex but functionally coordinated fibroblast communities were immune hot and inhabited by aggressive tumor cell phenotypes. The matrix-rich "deserted" subTMEs harbored fewer activated fibroblasts and tumor-suppressive features yet were markedly chemoprotective and enriched upon chemotherapy. SubTMEs originated in fibroblast differentiation trajectories, and transitory states were notable both in single-cell transcriptomics and in situ. The intratumoral co-occurrence of subTMEs produced patient-specific phenotypic and computationally predictable heterogeneity tightly linked to malignant biology. Therefore, heterogeneity within the plentiful, notorious pancreatic TME is not random but marks fundamental tissue organizational units.

Integrated computational analyses reveal novel insights into the stromal microenvironment of SHH-subtype medulloblastoma

Medulloblastoma is the most common malignant brain tumour of childhood. While our understanding of this disease has progressed substantially in recent years, the role of tumour microenvironment remains unclear. Given the increasing role of microenvironment-targeted therapeutics in other cancers, this study was aimed at further exploring its role in medulloblastoma. Multiple computational techniques were used to analyze open-source bulk and single cell RNA seq data from primary samples derived from all subgroups of medulloblastoma. Gene expression is used to infer stromal subpopulations, and network-based approaches are used to identify potential therapeutic targets. Bulk data was obtained from 763 medulloblastoma samples and single cell data from an additional 7241 cells from 23 tumours. Independent bulk (285 tumours) and single cell (32,868 cells from 29 tumours) validation cohorts were used to verify results. The SHH subgroup was found to be enriched in stromal activity, including the epithelial-to-mesenchymal transition, while group 3 is comparatively stroma-suppressed. Several receptor and ligand candidates underlying this difference are identified which we find to correlate with metastatic potential of SHH medulloblastoma. Additionally, a biologically active gradient is detected within SHH medulloblastoma, from "stroma-active" to "stroma-suppressed" cells which may have relevance to targeted therapy. This study serves to further elucidate the role of the stromal microenvironment in SHH-subgroup medulloblastoma and identify novel treatment possibilities for this challenging disease.

Integrating single-cell genomics pipelines to discover mechanisms of stem cell differentiation

Pluripotent stem cells underpin a growing sector that leverages their differentiation potential for research, industry, and clinical applications. This review evaluates the landscape of methods in single-cell transcriptomics that are enabling accelerated discovery in stem cell science. We focus on strategies for scaling stem cell differentiation through multiplexed single-cell analyses, for evaluating molecular regulation of cell differentiation using new analysis algorithms, and methods for integration and projection analysis to classify and benchmark stem cell derivatives against in vivo cell types. By discussing the available methods, comparing their strengths, and illustrating strategies for developing integrated analysis pipelines, we provide user considerations to inform their implementation and interpretation.

Scikit-Dimension: A Python Package for Intrinsic Dimension Estimation

Dealing with uncertainty in applications of machine learning to real-life data critically depends on the knowledge of intrinsic dimensionality (ID). A number of methods have been suggested for the purpose of estimating ID, but no standard package to easily apply them one by one or all at once has been implemented in Python. This technical note introduces scikit-dimension, an open-source Python package for intrinsic dimension estimation. The scikit-dimension package provides a uniform implementation of most of the known ID estimators based on the scikit-learn application programming interface to evaluate the global and local intrinsic dimension, as well as generators of synthetic toy and benchmark datasets widespread in the literature. The package is developed with tools assessing the code quality, coverage, unit testing and continuous integration. We briefly describe the package and demonstrate its use in a large-scale (more than 500 datasets) benchmarking of methods for ID estimation for real-life and synthetic data.

Single-cell analysis of cell identity in the Arabidopsis root apical meristem: insights and opportunities

A fundamental question in developmental biology is how the progeny of stem cells become differentiated tissues. The Arabidopsis root is a tractable model to address this question due to its simple organization and defined cell lineages. In particular, the zone of dividing cells at the root tip-the root apical meristem-presents an opportunity to map the gene regulatory networks underlying stem cell niche maintenance, tissue patterning, and cell identity acquisition. To identify molecular regulators of these processes, studies over the last 20 years employed global profiling of gene expression patterns. However, these technologies are prone to information loss due to averaging gene expression signatures over multiple cell types and/or developmental stages. Recently developed high-throughput methods to profile gene expression at single-cell resolution have been successfully applied to plants. Here, we review insights from the first published single-cell mRNA sequencing and chromatin accessibility datasets generated from Arabidopsis roots. These studies successfully reconstruct developmental trajectories, phenotype cell identity mutants at unprecedented resolution, and reveal cell type-specific responses to environmental stimuli. The experimental insight gained from Arabidopsis paves the way to profile roots from additional species.

Integrated spatial multiomics reveals fibroblast fate during tissue repair

In the skin, tissue injury results in fibrosis in the form of scars composed of dense extracellular matrix deposited by fibroblasts. The therapeutic goal of regenerative wound healing has remained elusive, in part because principles of fibroblast programming and adaptive response to injury remain incompletely understood. Here, we present a multimodal -omics platform for the comprehensive study of cell populations in complex tissue, which has allowed us to characterize the cells involved in wound healing across both time and space. We employ a stented wound model that recapitulates human tissue repair kinetics and multiple Rainbow transgenic lines to precisely track fibroblast fate during the physiologic response to skin injury. Through integrated analysis of single cell chromatin landscapes and gene expression states, coupled with spatial transcriptomic profiling, we are able to impute fibroblast epigenomes with temporospatial resolution. This has allowed us to reveal potential mechanisms controlling fibroblast fate during migration, proliferation, and differentiation following skin injury, and thereby reexamine the canonical phases of wound healing. These findings have broad implications for the study of tissue repair in complex organ systems.

The landscape of tumor cell states and ecosystems in diffuse large B cell lymphoma

Biological heterogeneity in diffuse large B cell lymphoma (DLBCL) is partly driven by cell-of-origin subtypes and associated genomic lesions, but also by diverse cell types and cell states in the tumor microenvironment (TME). However, dissecting these cell states and their clinical relevance at scale remains challenging. Here, we implemented EcoTyper, a machine-learning framework integrating transcriptome deconvolution and single-cell RNA sequencing, to characterize clinically relevant DLBCL cell states and ecosystems. Using this approach, we identified five cell states of malignant B cells that vary in prognostic associations and differentiation status. We also identified striking variation in cell states for 12 other lineages comprising the TME and forming cell state interactions in stereotyped ecosystems. While cell-of-origin subtypes have distinct TME composition, DLBCL ecosystems capture clinical heterogeneity within existing subtypes and extend beyond cell-of-origin and genotypic classes. These results resolve the DLBCL microenvironment at systems-level resolution and identify opportunities for therapeutic targeting (https://ecotyper.stanford.edu/lymphoma).

Delineating spatiotemporal and hierarchical development of human fetal innate lymphoid cells

Whereas the critical roles of innate lymphoid cells (ILCs) in adult are increasingly appreciated, their developmental hierarchy in early human fetus remains largely elusive. In this study, we sorted human hematopoietic stem/progenitor cells, lymphoid progenitors, putative ILC progenitor/precursors and mature ILCs in the fetal hematopoietic, lymphoid and non-lymphoid tissues, from 8 to 12 post-conception weeks, for single-cell RNA-sequencing, followed by computational analysis and functional validation at bulk and single-cell levels. We delineated the early phase of ILC lineage commitment from hematopoietic stem/progenitor cells, which mainly occurred in fetal liver and intestine. We further unveiled interleukin-3 receptor as a surface marker for the lymphoid progenitors in fetal liver with T, B, ILC and myeloid potentials, while IL-3RA- lymphoid progenitors were predominantly B-lineage committed. Notably, we determined the heterogeneity and tissue distribution of each ILC subpopulation, revealing the proliferating characteristics shared by the precursors of each ILC subtype. Additionally, a novel unconventional ILC2 subpopulation (CRTH2- CCR9+ ILC2) was identified in fetal thymus. Taken together, our study illuminates the precise cellular and molecular features underlying the stepwise formation of human fetal ILC hierarchy with remarkable spatiotemporal heterogeneity.

Single-cell multimodal glioma analyses identify epigenetic regulators of cellular plasticity and environmental stress response

Glioma intratumoral heterogeneity enables adaptation to challenging microenvironments and contributes to therapeutic resistance. We integrated 914 single-cell DNA methylomes, 55,284 single-cell transcriptomes and bulk multi-omic profiles across 11 adult IDH mutant or IDH wild-type gliomas to delineate sources of intratumoral heterogeneity. We showed that local DNA methylation disorder is associated with cell-cell DNA methylation differences, is elevated in more aggressive tumors, links with transcriptional disruption and is altered during the environmental stress response. Glioma cells under in vitro hypoxic and irradiation stress increased local DNA methylation disorder and shifted cell states. We identified a positive association between genetic and epigenetic instability that was supported in bulk longitudinally collected DNA methylation data. Increased DNA methylation disorder associated with accelerated disease progression and recurrently selected DNA methylation changes were enriched for environmental stress response pathways. Our work identified an epigenetically facilitated adaptive stress response process and highlights the importance of epigenetic heterogeneity in shaping therapeutic outcomes.

Dach1 Extends Artery Networks and Protects Against Cardiac Injury

[Figure: see text].

Single-cell ATAC and RNA sequencing reveal pre-existing and persistent cells associated with prostate cancer relapse

Prostate cancer is heterogeneous and patients would benefit from methods that stratify those who are likely to respond to systemic therapy. Here, we employ single-cell assays for transposase-accessible chromatin (ATAC) and RNA sequencing in models of early treatment response and resistance to enzalutamide. In doing so, we identify pre-existing and treatment-persistent cell subpopulations that possess regenerative potential when subjected to treatment. We find distinct chromatin landscapes associated with enzalutamide treatment and resistance that are linked to alternative transcriptional programs. Transcriptional profiles characteristic of persistent cells are able to stratify the treatment response of patients. Ultimately, we show that defining changes in chromatin and gene expression in single-cell populations from pre-clinical models can reveal as yet unrecognized molecular predictors of treatment response. This suggests that the application of single-cell methods with high analytical resolution in pre-clinical models may powerfully inform clinical decision-making.

Disrupting biological sensors of force promotes tissue regeneration in large organisms

Tissue repair and healing remain among the most complicated processes that occur during postnatal life. Humans and other large organisms heal by forming fibrotic scar tissue with diminished function, while smaller organisms respond with scarless tissue regeneration and functional restoration. Well-established scaling principles reveal that organism size exponentially correlates with peak tissue forces during movement, and evolutionary responses have compensated by strengthening organ-level mechanical properties. How these adaptations may affect tissue injury has not been previously examined in large animals and humans. Here, we show that blocking mechanotransduction signaling through the focal adhesion kinase pathway in large animals significantly accelerates wound healing and enhances regeneration of skin with secondary structures such as hair follicles. In human cells, we demonstrate that mechanical forces shift fibroblasts toward pro-fibrotic phenotypes driven by ERK-YAP activation, leading to myofibroblast differentiation and excessive collagen production. Disruption of mechanical signaling specifically abrogates these responses and instead promotes regenerative fibroblast clusters characterized by AKT-EGR1.

Humans and other large mammals heal wounds by forming fibrotic scar tissue with diminished function. Here, the authors show that disrupting mechanotransduction through the focal adhesion kinase pathway in large animals accelerates healing, prevents fibrosis, and enhances skin regeneration.

Aged skeletal stem cells generate an inflammatory degenerative niche

Loss of skeletal integrity during ageing and disease is associated with an imbalance in the opposing actions of osteoblasts and osteoclasts1. Here we show that intrinsic ageing of skeletal stem cells (SSCs)2 in mice alters signalling in the bone marrow niche and skews the differentiation of bone and blood lineages, leading to fragile bones that regenerate poorly. Functionally, aged SSCs have a decreased bone- and cartilage-forming potential but produce more stromal lineages that express high levels of pro-inflammatory and pro-resorptive cytokines. Single-cell RNA-sequencing studies link the functional loss to a diminished transcriptomic diversity of SSCs in aged mice, which thereby contributes to the transformation of the bone marrow niche. Exposure to a youthful circulation through heterochronic parabiosis or systemic reconstitution with young haematopoietic stem cells did not reverse the diminished osteochondrogenic activity of aged SSCs, or improve bone mass or skeletal healing parameters in aged mice. Conversely, the aged SSC lineage promoted osteoclastic activity and myeloid skewing by haematopoietic stem and progenitor cells, suggesting that the ageing of SSCs is a driver of haematopoietic ageing. Deficient bone regeneration in aged mice could only be returned to youthful levels by applying a combinatorial treatment of BMP2 and a CSF1 antagonist locally to fractures, which reactivated aged SSCs and simultaneously ablated the inflammatory, pro-osteoclastic milieu. Our findings provide mechanistic insights into the complex, multifactorial mechanisms that underlie skeletal ageing and offer prospects for rejuvenating the aged skeletal system.

Transcriptomic entropy benchmarks stem cell-derived cardiomyocyte maturation against endogenous tissue at single cell level

The immaturity of pluripotent stem cell (PSC)-derived tissues has emerged as a universal problem for their biomedical applications. While efforts have been made to generate adult-like cells from PSCs, direct benchmarking of PSC-derived tissues against in vivo development has not been established. Thus, maturation status is often assessed on an ad-hoc basis. Single cell RNA-sequencing (scRNA-seq) offers a promising solution, though cross-study comparison is limited by dataset-specific batch effects. Here, we developed a novel approach to quantify PSC-derived cardiomyocyte (CM) maturation through transcriptomic entropy. Transcriptomic entropy is robust across datasets regardless of differences in isolation protocols, library preparation, and other potential batch effects. With this new model, we analyzed over 45 scRNA-seq datasets and over 52,000 CMs, and established a cross-study, cross-species CM maturation reference. This reference enabled us to directly compare PSC-CMs with the in vivo developmental trajectory and thereby to quantify PSC-CM maturation status. We further found that our entropy-based approach can be used for other cell types, including pancreatic beta cells and hepatocytes. Our study presents a biologically relevant and interpretable metric for quantifying PSC-derived tissue maturation, and is extensible to numerous tissue engineering contexts.

Single-cell resolution of lineage trajectories in the Arabidopsis stomatal lineage and developing leaf

Dynamic cell identities underlie flexible developmental programs. The stomatal lineage in the Arabidopsis leaf epidermis features asynchronous and indeterminate divisions that can be modulated by environmental cues. The products of the lineage, stomatal guard cells and pavement cells, regulate plant-atmosphere exchanges, and the epidermis as a whole influences overall leaf growth. How flexibility is encoded in development of the stomatal lineage and how cell fates are coordinated in the leaf are open questions. Here, by leveraging single-cell transcriptomics and molecular genetics, we uncovered models of cell differentiation within Arabidopsis leaf tissue. Profiles across leaf tissues identified points of regulatory congruence. In the stomatal lineage, single-cell resolution resolved underlying cell heterogeneity within early stages and provided a fine-grained profile of guard cell differentiation. Through integration of genome-scale datasets and spatiotemporally precise functional manipulations, we also identified an extended role for the transcriptional regulator SPEECHLESS in reinforcing cell fate commitment.

A comprehensive analysis of gene expression changes in a high replicate and open-source dataset of differentiating hiPSC-derived cardiomyocytes

We performed a comprehensive analysis of the transcriptional changes occurring during human induced pluripotent stem cell (hiPSC) differentiation to cardiomyocytes. Using single cell RNA-seq, we sequenced > 20,000 single cells from 55 independent samples representing two differentiation protocols and multiple hiPSC lines. Samples included experimental replicates ranging from undifferentiated hiPSCs to mixed populations of cells at D90 post-differentiation. Differentiated cell populations clustered by time point, with differential expression analysis revealing markers of cardiomyocyte differentiation and maturation changing from D12 to D90. We next performed a complementary cluster-independent sparse regression analysis to identify and rank genes that best assigned cells to differentiation time points. The two highest ranked genes between D12 and D24 (MYH7 and MYH6) resulted in an accuracy of 0.84, and the three highest ranked genes between D24 and D90 (A2M, H19, IGF2) resulted in an accuracy of 0.94, revealing that low dimensional gene features can identify differentiation or maturation stages in differentiating cardiomyocytes. Expression levels of select genes were validated using RNA FISH. Finally, we interrogated differences in cardiac gene expression resulting from two differentiation protocols, experimental replicates, and three hiPSC lines in the WTC-11 background to identify sources of variation across these experimental variables.

De novo generation of macrophage from placenta-derived hemogenic endothelium

Macrophages play pivotal roles in immunity, hematopoiesis, and tissue homeostasis. In mammals, macrophages have been shown to originate from yolk-sac-derived erythro-myeloid progenitors and aorta-gonad-mesonephros (AGM)-derived hematopoietic stem cells. However, whether macrophages can arise from other embryonic sites remains unclear. Here, using single-cell RNA sequencing, we profile the transcriptional landscape of mouse fetal placental hematopoiesis. We uncover and experimentally validate that a CD44⁺ subpopulation of placental endothelial cells (ECs) exhibits hemogenic potential. Importantly, lineage tracing using the newly generated Hoxa13 reporter line shows that Hoxa13-labeled ECs can produce placental macrophages, named Hofbauer cell (HBC)-like cells. Furthermore, we identify two subtypes of HBC-like cells, and cell-cell interaction analysis identifies their potential roles in angiogenesis and antigen presentation, separately. Our study provides a comprehensive understanding of placental hematopoiesis and highlights the placenta as a source of macrophages, which has important implications for both basic and translational research.

Ultra-fast scalable estimation of single-cell differentiation potency from scRNA-Seq data

Motivation

An important task in the analysis of single-cell RNA-Seq data is the estimation of differentiation potency, as this can help identify stem-or-multipotent cells in non-temporal studies or in tissues where differentiation hierarchies are not well established. A key challenge in the estimation of single-cell potency is the need for a fast and accurate algorithm, scalable to large scRNA-Seq studies profiling millions of cells.

Results

Here, we present a single-cell potency measure, called Correlation of Connectome and Transcriptome (CCAT), which can return accurate single-cell potency estimates of a million cells in minutes, a 100-fold improvement over current state-of-the-art methods. We benchmark CCAT against 8 other single-cell potency models and across 28 scRNA-Seq studies, encompassing over 2 million cells, demonstrating comparable accuracy than the current state-of-the-art, at a significantly reduced computational cost, and with increased robustness to dropouts.

Availability and implementation

CCAT is part of the SCENT R-package, freely available from https://github.com/aet21/SCENT.

Supplementary information

Supplementary data are available at Bioinformatics online.

Statistical mechanics meets single-cell biology

Single-cell omics is transforming our understanding of cell biology and disease, yet the systems-level analysis and interpretation of single-cell data faces many challenges. In this Perspective, we describe the impact that fundamental concepts from statistical mechanics, notably entropy, stochastic processes and critical phenomena, are having on single-cell data analysis. We further advocate the need for more bottom-up modelling of single-cell data and to embrace a statistical mechanics analysis paradigm to help attain a deeper understanding of single-cell systems biology.

CStreet: a computed Cell State trajectory inference method for time-series single-cell RNA sequencing data

MOTIVATION

The increasing amount of time-series single-cell RNA sequencing (scRNA-seq) data raises the key issue of connecting cell states (i.e., cell clusters or cell types) to obtain the continuous temporal dynamics of transcription, which can highlight the unified biological mechanisms involved in cell state transitions. However, most existing trajectory methods are specifically designed for individual cells, so they can hardly meet the needs of accurately inferring the trajectory topology of the cell state, which usually contains cells assigned to different branches.

RESULTS

Here, we present CStreet, a computed Cell State trajectory inference method for time-series scRNA-seq data. It uses time-series information to construct the k-nearest neighbors connections between cells within each time point and between adjacent time points. Then, CStreet estimates the connection probabilities of the cell states and visualizes the trajectory, which may include multiple starting points and paths, using a force-directed graph. By comparing the performance of CStreet with that of six commonly used cell state trajectory reconstruction methods on simulated data and real data, we demonstrate the high accuracy and high tolerance of CStreet.

AVAILABILITY AND IMPLEMENTATION

CStreet is written in Python and freely available on the web at https://github.com/TongjiZhanglab/CStreet and https://doi.org/10.5281/zenodo.4483205.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-cell transcriptomics defines heterogeneity of epicardial cells and fibroblasts within the infarcted murine heart

In the adult heart, the epicardium becomes activated after injury, contributing to cardiac healing by secretion of paracrine factors. Here, we analyzed by single-cell RNA sequencing combined with RNA in situ hybridization and lineage tracing of Wilms tumor protein 1-positive (WT1+) cells, the cellular composition, location, and hierarchy of epicardial stromal cells (EpiSC) in comparison to activated myocardial fibroblasts/stromal cells in infarcted mouse hearts. We identified 11 transcriptionally distinct EpiSC populations, which can be classified into three groups, each containing a cluster of proliferating cells. Two groups expressed cardiac specification markers and sarcomeric proteins suggestive of cardiomyogenic potential. Transcripts of hypoxia-inducible factor (HIF)-1α and HIF-responsive genes were enriched in EpiSC consistent with an epicardial hypoxic niche. Expression of paracrine factors was not limited to WT1+ cells but was a general feature of activated cardiac stromal cells. Our findings provide the cellular framework by which myocardial ischemia may trigger in EpiSC the formation of cardioprotective/regenerative responses.

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.

Applications of Single-Cell Omics in Tumor Immunology

The tumor microenvironment (TME) is an ecosystem that contains various cell types, including cancer cells, immune cells, stromal cells, and many others. In the TME, cancer cells aggressively proliferate, evolve, transmigrate to the circulation system and other organs, and frequently communicate with adjacent immune cells to suppress local tumor immunity. It is essential to delineate this ecosystem's complex cellular compositions and their dynamic intercellular interactions to understand cancer biology and tumor immunology and to benefit tumor immunotherapy. But technically, this is extremely challenging due to the high complexities of the TME. The rapid developments of single-cell techniques provide us powerful means to systemically profile the multiple omics status of the TME at a single-cell resolution, shedding light on the pathogenic mechanisms of cancers and dysfunctions of tumor immunity in an unprecedently resolution. Furthermore, more advanced techniques have been developed to simultaneously characterize multi-omics and even spatial information at the single-cell level, helping us reveal the phenotypes and functionalities of disease-specific cell populations more comprehensively. Meanwhile, the connections between single-cell data and clinical characteristics are also intensively interrogated to achieve better clinical diagnosis and prognosis. In this review, we summarize recent progress in single-cell techniques, discuss their technical advantages, limitations, and applications, particularly in tumor biology and immunology, aiming to promote the research of cancer pathogenesis, clinically relevant cancer diagnosis, prognosis, and immunotherapy design with the help of single-cell techniques.

The regulatory landscape of Arabidopsis thaliana roots at single-cell resolution

The scarcity of accessible sites that are dynamic or cell type-specific in plants may be due in part to tissue heterogeneity in bulk studies. To assess the effects of tissue heterogeneity, we apply single-cell ATAC-seq to Arabidopsis thaliana roots and identify thousands of differentially accessible sites, sufficient to resolve all major cell types of the root. We find that the entirety of a cell's regulatory landscape and its transcriptome independently capture cell type identity. We leverage this shared information on cell identity to integrate accessibility and transcriptome data to characterize developmental progression, endoreduplication and cell division. We further use the combined data to characterize cell type-specific motif enrichments of transcription factor families and link the expression of family members to changing accessibility at specific loci, resolving direct and indirect effects that shape expression. Our approach provides an analytical framework to infer the gene regulatory networks that execute plant development.

The regulatory landscape of Arabidopsis thaliana roots at single-cell resolution

The scarcity of accessible sites that are dynamic or cell type-specific in plants may be due in part to tissue heterogeneity in bulk studies. To assess the effects of tissue heterogeneity, we apply single-cell ATAC-seq to Arabidopsis thaliana roots and identify thousands of differentially accessible sites, sufficient to resolve all major cell types of the root. We find that the entirety of a cell's regulatory landscape and its transcriptome independently capture cell type identity. We leverage this shared information on cell identity to integrate accessibility and transcriptome data to characterize developmental progression, endoreduplication and cell division. We further use the combined data to characterize cell type-specific motif enrichments of transcription factor families and link the expression of family members to changing accessibility at specific loci, resolving direct and indirect effects that shape expression. Our approach provides an analytical framework to infer the gene regulatory networks that execute plant development.