CellRank for directed single-cell fate mapping

Single-Cell and Spatial Transcriptome Analysis of Dermal Fibroblast Development in Perinatal Mouse Skin: Dynamic Lineage Differentiation and Key Driver Genes

Several single-cell RNA studies of developing mouse skin have elucidated the molecular and cellular processes involved in skin development. However, they have primarily focused on either the fetal or early postnatal period, leaving a gap in our understanding of skin development. In this study, we conducted a comprehensive time-series analysis by combining single-cell RNA-sequencing datasets collected at different stages of development (embryonic days 13.5, 14.5, and 16.5 and postnatal days 0, 2, and 4) and validated our findings through multipanel in situ spatial transcriptomics. Our analysis indicated that embryonic fibroblasts exhibit heterogeneity from a very early stage and that the rapid determination of each lineage occurs within days after birth. The expression of putative key driver genes, including Hey1, Ebf1, Runx3, and Sox11 for the dermal papilla trajectory; Lrrc15 for the dermal sheath trajectory; Zfp536 and Nrn1 for the papillary fibroblast trajectory; and Lrrn4cl and Mfap5 for the fascia fibroblast trajectory, was detected in the corresponding, spatially identified cell types. Finally, cell-to-cell interaction analysis indicated that the dermal papilla lineage is the primary source of the noncanonical Wnt pathway during skin development. Together, our study provides a transcriptomic reference for future research in the field of skin development and regeneration.

Aging atlas reveals cell-type-specific effects of pro-longevity strategies

Organismal aging involves functional declines in both somatic and reproductive tissues. Multiple strategies have been discovered to extend lifespan across species. However, how age-related molecular changes differ among various tissues and how those lifespan-extending strategies slow tissue aging in distinct manners remain unclear. Here we generated the transcriptomic Cell Atlas of Worm Aging (CAWA, http://mengwanglab.org/atlas ) of wild-type and long-lived strains. We discovered cell-specific, age-related molecular and functional signatures across all somatic and germ cell types. We developed transcriptomic aging clocks for different tissues and quantitatively determined how three different pro-longevity strategies slow tissue aging distinctively. Furthermore, through genome-wide profiling of alternative polyadenylation (APA) events in different tissues, we discovered cell-type-specific APA changes during aging and revealed how these changes are differentially affected by the pro-longevity strategies. Together, this study offers fundamental molecular insights into both somatic and reproductive aging and provides a valuable resource for in-depth understanding of the diversity of pro-longevity mechanisms.

Single cell RNA analysis uncovers the cell differentiation and functionalization for air breathing of frog lung

The evolution and development of vertebrate lungs have been widely studied due to their significance in terrestrial adaptation. Amphibians possess the most primitive lungs among tetrapods, underscoring their evolutionary importance in bridging the transition from aquatic to terrestrial life. However, the intricate process of cell differentiation during amphibian lung development remains poorly understood. Using single-cell RNA sequencing, we identify 13 cell types in the developing lungs of a land-dwelling frog (Microhyla fissipes). We elucidate the differentiation trajectories and mechanisms of mesenchymal cells, identifying five cell fates and their respective driver genes. Using temporal dynamics analyses, we reveal the gene expression switches of epithelial cells, which facilitate air breathing during metamorphosis. Furthermore, by integrating the published data from another amphibian and two terrestrial mammals, we illuminate both conserved and divergent cellular repertoires during the evolution of tetrapod lungs. These findings uncover the frog lung cell differentiation trajectories and functionalization for breathing in air and provide valuable insights into the cell-type evolution of vertebrate lungs.

Decoding the complexity of delayed wound healing following Enterococcus faecalis infection

Wound infections are highly prevalent and can lead to delayed or failed healing, causing significant morbidity and adverse economic impacts. These infections occur in various contexts, including diabetic foot ulcers, burns, and surgical sites. Enterococcus faecalis is often found in persistent non-healing wounds, but its contribution to chronic wounds remains understudied. To address this, we employed single-cell RNA sequencing (scRNA-seq) on infected wounds in comparison to uninfected wounds in a mouse model. Examining over 23,000 cells, we created a comprehensive single-cell atlas that captures the cellular and transcriptomic landscape of these wounds. Our analysis revealed unique transcriptional and metabolic alterations in infected wounds, elucidating the distinct molecular changes associated with bacterial infection compared to the normal wound healing process. We identified dysregulated keratinocyte and fibroblast transcriptomes in response to infection, jointly contributing to an anti-inflammatory environment. Notably, E. faecalis infection prompted a premature, incomplete epithelial-mesenchymal transition in keratinocytes. Additionally, E. faecalis infection modulated M2-like macrophage polarization by inhibiting pro-inflammatory resolution in vitro, in vivo, and in our scRNA-seq atlas. Furthermore, we discovered macrophage crosstalk with neutrophils, which regulates chemokine signaling pathways, while promoting anti-inflammatory interactions with endothelial cells. Overall, our findings offer new insights into the immunosuppressive role of E. faecalis in wound infections.

Single-cell multiomics profiling reveals heterogeneous transcriptional programs and microenvironment in DSRCTs

Desmoplastic small round cell tumor (DSRCT) is a rare, aggressive sarcoma driven by the EWSR1::WT1 chimeric transcription factor. Despite this unique oncogenic driver, DSRCT displays a polyphenotypic differentiation of unknown causality. Using single-cell multi-omics on 12 samples from five patients, we find that DSRCT tumor cells cluster into consistent subpopulations with partially overlapping lineage- and metabolism-related transcriptional programs. In vitro modeling shows that high EWSR1::WT1 DNA-binding activity associates with most lineage-related states, in contrast to glycolytic and profibrotic states. Single-cell chromatin accessibility analysis suggests that EWSR1::WT1 binding site variability may drive distinct lineage-related transcriptional programs, supporting some level of cell-intrinsic plasticity. Spatial transcriptomics reveals that glycolytic and profibrotic states specifically localize within hypoxic niches at the periphery of tumor cell islets, suggesting an additional role of tumor cell-extrinsic microenvironmental cues. We finally identify a single-cell transcriptomics-derived epithelial signature associated with improved patient survival, highlighting the clinical relevance of our findings.

CellWalker2: multi-omic discovery of hierarchical cell type relationships and their associations with genomic annotations

CellWalker2 is a graph diffusion-based method for single-cell genomics data integration. It extends the CellWalker model by incorporating hierarchical relationships between cell types, providing estimates of statistical significance, and adding data structures for analyzing multi-omics data so that gene expression and open chromatin can be jointly modeled. Our open-source software enables users to annotate cells using existing ontologies and to probabilistically match cell types between two or more contexts, including across species. CellWalker2 can also map genomic regions to cell ontologies, enabling precise annotation of elements derived from bulk data, such as enhancers, genetic variants, and sequence motifs. Through simulation studies, we show that CellWalker2 performs better than existing methods in cell type annotation and mapping. We then use data from the brain and immune system to demonstrate CellWalker2's ability to discover cell type-specific regulatory programs and both conserved and divergent cell type relationships in complex tissues.

Spatial transition tensor of single cells

Spatial transcriptomics and messenger RNA splicing encode extensive spatiotemporal information for cell states and transitions. The current lineage-inference methods either lack spatial dynamics for state transition or cannot capture different dynamics associated with multiple cell states and transition paths. Here we present spatial transition tensor (STT), a method that uses messenger RNA splicing and spatial transcriptomes through a multiscale dynamical model to characterize multistability in space. By learning a four-dimensional transition tensor and spatial-constrained random walk, STT reconstructs cell-state-specific dynamics and spatial state transitions via both short-time local tensor streamlines between cells and long-time transition paths among attractors. Benchmarking and applications of STT on several transcriptome datasets via multiple technologies on epithelial-mesenchymal transitions, blood development, spatially resolved mouse brain and chicken heart development, indicate STT's capability in recovering cell-state-specific dynamics and their associated genes not seen using existing methods. Overall, STT provides a consistent multiscale description of single-cell transcriptome data across multiple spatiotemporal scales.

Causal gene regulatory analysis with RNA velocity reveals an interplay between slow and fast transcription factors

Single-cell expression dynamics, from differentiation trajectories or RNA velocity, have the potential to reveal causal links between transcription factors (TFs) and their target genes in gene regulatory networks (GRNs). However, existing methods either overlook these expression dynamics or necessitate that cells be ordered along a linear pseudotemporal axis, which is incompatible with branching trajectories. We introduce Velorama, an approach to causal GRN inference that represents single-cell differentiation dynamics as a directed acyclic graph of cells, constructed from pseudotime or RNA velocity measurements. Additionally, Velorama enables the estimation of the speed at which TFs influence target genes. Applying Velorama, we uncover evidence that the speed of a TF's interactions is tied to its regulatory function. For human corticogenesis, we find that slow TFs are linked to gliomas, while fast TFs are associated with neuropsychiatric diseases. We expect Velorama to become a critical part of the RNA velocity toolkit for investigating the causal drivers of differentiation and disease.

Reconstructing developmental trajectories using latent dynamical systems and time-resolved transcriptomics

The snapshot nature of single-cell transcriptomics presents a challenge for studying the dynamics of cell fate decisions. Metabolic labeling and splicing can provide temporal information at single-cell level, but current methods have limitations. Here, we present a framework that overcomes these limitations: experimentally, we developed sci-FATE2, an optimized method for metabolic labeling with increased data quality, which we used to profile 45,000 embryonic stem (ES) cells differentiating into neural tube identities. Computationally, we developed a two-stage framework for dynamical modeling: VelvetVAE, a variational autoencoder (VAE) for velocity inference that outperforms all other tools tested, and VelvetSDE, a neural stochastic differential equation (nSDE) framework for simulating trajectory distributions. These recapitulate underlying dataset distributions and capture features such as decision boundaries between alternative fates and fate-specific gene expression. These methods recast single-cell analyses from descriptions of observed data to models of the dynamics that generated them, providing a framework for investigating developmental fate decisions.

SCTC: inference of developmental potential from single-cell transcriptional complexity

Inferring the developmental potential of single cells from scRNA-Seq data and reconstructing the pseudo-temporal path of cell development are fundamental but challenging tasks in single-cell analysis. Although single-cell transcriptional diversity (SCTD) measured by the number of expressed genes per cell has been widely used as a hallmark of developmental potential, it may lead to incorrect estimation of differentiation states in some cases where gene expression does not decrease monotonously during the development process. In this study, we propose a novel metric called single-cell transcriptional complexity (SCTC), which draws on insights from the economic complexity theory and takes into account the sophisticated structure information of scRNA-Seq count matrix. We show that SCTC characterizes developmental potential more accurately than SCTD, especially in the early stages of development where cells typically have lower diversity but higher complexity than those in the later stages. Based on the SCTC, we provide an unsupervised method for accurate, robust, and transferable inference of single-cell pseudotime. Our findings suggest that the complexity emerging from the interplay between cells and genes determines the developmental potential, providing new insights into the understanding of biological development from the perspective of complexity theory.

Scalable integration of multiomic single-cell data using generative adversarial networks

MOTIVATION

Single-cell profiling has become a common practice to investigate the complexity of tissues, organs, and organisms. Recent technological advances are expanding our capabilities to profile various molecular layers beyond the transcriptome such as, but not limited to, the genome, the epigenome, and the proteome. Depending on the experimental procedure, these data can be obtained from separate assays or the very same cells. Yet, integration of more than two assays is currently not supported by the majority of the computational frameworks avaiable.

RESULTS

We here propose a Multi-Omic data integration framework based on Wasserstein Generative Adversarial Networks suitable for the analysis of paired or unpaired data with a high number of modalities (>2). At the core of our strategy is a single network trained on all modalities together, limiting the computational burden when many molecular layers are evaluated.

AVAILABILITY AND IMPLEMENTATION

Source code of our framework is available at https://github.com/vgiansanti/MOWGAN.

sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data

Class-switch recombination (CSR) is an integral part of B cell maturation. Here we present sciCSR (pronounced 'scissor', single-cell inference of class-switch recombination), a computational pipeline that analyzes CSR events and dynamics of B cells from single-cell RNA sequencing (scRNA-seq) experiments. Validated on both simulated and real data, sciCSR re-analyzes scRNA-seq alignments to differentiate productive heavy-chain immunoglobulin transcripts from germline 'sterile' transcripts. From a snapshot of B cell scRNA-seq data, a Markov state model is built to infer the dynamics and direction of CSR. Applying sciCSR on severe acute respiratory syndrome coronavirus 2 vaccination time-course scRNA-seq data, we observe that sciCSR predicts, using data from an earlier time point in the collected time-course, the isotype distribution of B cell receptor repertoires of subsequent time points with high accuracy (cosine similarity ~0.9). Using processes specific to B cells, sciCSR identifies transitions that are often missed by conventional RNA velocity analyses and can reveal insights into the dynamics of B cell CSR during immune response.

PhyloVelo enhances transcriptomic velocity field mapping using monotonically expressed genes

Single-cell RNA sequencing (scRNA-seq) is a powerful approach for studying cellular differentiation, but accurately tracking cell fate transitions can be challenging, especially in disease conditions. Here we introduce PhyloVelo, a computational framework that estimates the velocity of transcriptomic dynamics by using monotonically expressed genes (MEGs) or genes with expression patterns that either increase or decrease, but do not cycle, through phylogenetic time. Through integration of scRNA-seq data with lineage information, PhyloVelo identifies MEGs and reconstructs a transcriptomic velocity field. We validate PhyloVelo using simulated data and Caenorhabditis elegans ground truth data, successfully recovering linear, bifurcated and convergent differentiations. Applying PhyloVelo to seven lineage-traced scRNA-seq datasets, generated using CRISPR-Cas9 editing, lentiviral barcoding or immune repertoire profiling, demonstrates its high accuracy and robustness in inferring complex lineage trajectories while outperforming RNA velocity. Additionally, we discovered that MEGs across tissues and organisms share similar functions in translation and ribosome biogenesis.

Single-cell immune repertoire analysis

Single-cell T cell and B cell antigen receptor-sequencing data analysis can potentially perform in-depth assessments of adaptive immune cells that inform on understanding immune cell development to tracking clonal expansion in disease and therapy. However, it has been extremely challenging to analyze and interpret T cells and B cells and their adaptive immune receptor repertoires at the single-cell level due to not only the complexity of the data but also the underlying biology. In this Review, we delve into the computational breakthroughs that have transformed the analysis of single-cell T cell and B cell antigen receptor-sequencing data.

Dissection and integration of bursty transcriptional dynamics for complex systems

RNA velocity estimation is a potentially powerful tool to reveal the directionality of transcriptional changes in single-cell RNA-sequencing data, but it lacks accuracy, absent advanced metabolic labeling techniques. We developed an approach, TopicVelo, that disentangles simultaneous, yet distinct, dynamics by using a probabilistic topic model, a highly interpretable form of latent space factorization, to infer cells and genes associated with individual processes, thereby capturing cellular pluripotency or multifaceted functionality. Focusing on process-associated cells and genes enables accurate estimation of process-specific velocities via a master equation for a transcriptional burst model accounting for intrinsic stochasticity. The method obtains a global transition matrix by leveraging cell topic weights to integrate process-specific signals. In challenging systems, this method accurately recovers complex transitions and terminal states, while our use of first-passage time analysis provides insights into transient transitions. These results expand the limits of RNA velocity, empowering future studies of cell fate and functional responses.

Single-nucleus and spatial landscape of the sub-ventricular zone in human glioblastoma

The sub-ventricular zone (SVZ) is the most well-characterized neurogenic area in the mammalian brain. We previously showed that in 65% of patients with glioblastoma (GBM), the SVZ is a reservoir of cancer stem-like cells that contribute to treatment resistance and emergence of recurrence. Here, we built a single-nucleus RNA-sequencing-based microenvironment landscape of the tumor mass (T_Mass) and the SVZ (T_SVZ) of 15 GBM patients and 2 histologically normal SVZ (N_SVZ) samples as controls. We identified a mesenchymal signature in the T_SVZ of GBM patients: tumor cells from the T_SVZ relied on the ZEB1 regulatory network, whereas tumor cells in the T_Mass relied on the TEAD1 regulatory network. Moreover, the T_SVZ microenvironment was predominantly characterized by tumor-supportive microglia, which spatially co-exist and establish heterotypic interactions with tumor cells. Lastly, differential gene expression analyses, predictions of ligand-receptor and incoming/outgoing interactions, and functional assays revealed that the IL-1β/IL-1RAcP and Wnt-5a/Frizzled-3 pathways are therapeutic targets in the T_SVZ microenvironment. Our data provide insights into the biology of the SVZ in GBM patients and identify specific targets of this microenvironment.

The post-septic peripheral myeloid compartment reveals unexpected diversity in myeloid-derived suppressor cells

Introduction

Sepsis engenders distinct host immunologic changes that include the expansion of myeloid-derived suppressor cells (MDSCs). These cells play a physiologic role in tempering acute inflammatory responses but can persist in patients who develop chronic critical illness.

Methods

Cellular Indexing of Transcriptomes and Epitopes by Sequencing and transcriptomic analysis are used to describe MDSC subpopulations based on differential gene expression, RNA velocities, and biologic process clustering.

Results

We identify a unique lineage and differentiation pathway for MDSCs after sepsis and describe a novel MDSC subpopulation. Additionally, we report that the heterogeneous response of the myeloid compartment of blood to sepsis is dependent on clinical outcome.

Discussion

The origins and lineage of these MDSC subpopulations were previously assumed to be discrete and unidirectional; however, these cells exhibit a dynamic phenotype with considerable plasticity.

Hodge Decomposition of Single-Cell RNA Velocity

RNA velocity has the ability to capture the cell dynamic information in the biological processes; yet, a comprehensive analysis of the cell state transitions and their associated chemical and biological processes remains a gap. In this work, we provide the Hodge decomposition, coupled with discrete exterior calculus (DEC), to unveil cell dynamics by examining the decomposed curl-free, divergence-free, and harmonic components of the RNA velocity field in a low dimensional representation, such as a UMAP or a t-SNE representation. Decomposition results show that the decomposed components distinctly reveal key cell dynamic features such as cell cycle, bifurcation, and cell lineage differentiation, regardless of the choice of the low-dimensional representations. The consistency across different representations demonstrates that the Hodge decomposition is a reliable and robust way to extract these cell dynamic features, offering unique analysis and insightful visualization of single-cell RNA velocity fields.

Modeling gene expression cascades during cell state transitions

During cellular processes such as differentiation or response to external stimuli, cells exhibit dynamic changes in their gene expression profiles. Single-cell RNA sequencing (scRNA-seq) can be used to investigate these dynamic changes. To this end, cells are typically ordered along a pseudotemporal trajectory which recapitulates the progression of cells as they transition from one cell state to another. We infer transcriptional dynamics by modeling the gene expression profiles in pseudotemporally ordered cells using a Bayesian inference approach. This enables ordering genes along transcriptional cascades, estimating differences in the timing of gene expression dynamics, and deducing regulatory gene interactions. Here, we apply this approach to scRNA-seq datasets derived from mouse embryonic forebrain and pancreas samples. This analysis demonstrates the utility of the method to derive the ordering of gene dynamics and regulatory relationships critical for proper cellular differentiation and maturation across a variety of developmental contexts.

RNA velocity prediction via neural ordinary differential equation

RNA velocity is a crucial tool for unraveling the trajectory of cellular responses. Several approaches, including ordinary differential equations and machine learning models, have been proposed to interpret velocity. However, the practicality of these methods is constrained by underlying assumptions. In this study, we introduce SymVelo, a dual-path framework that effectively integrates high- and low-dimensional information. Rigorous benchmarking and extensive studies demonstrate that SymVelo is capable of inferring differentiation trajectories in developing organs, analyzing gene responses to stimulation, and uncovering transcription dynamics. Moreover, the adaptable architecture of SymVelo enables customization to accommodate intricate data and diverse modalities in forthcoming research, thereby providing a promising avenue for advancing our understanding of cellular behavior.

Th17 Cells Secrete TWEAK to Trigger Epithelial-Mesenchymal Transition and Promote Colorectal Cancer Liver Metastasis

Liver metastasis is the leading cause of mortality in patients with colorectal cancer. Given the significance of both epithelial-mesenchymal transition (EMT) of tumor cells and the immune microenvironment in colorectal cancer liver metastasis (CRLM), the interplay between them could hold the key for developing improved treatment options. We employed multiomics analysis of 130 samples from 18 patients with synchronous CRLM integrated with external datasets to comprehensively evaluate the interaction between immune cells and EMT of tumor cells in liver metastasis. Single-cell RNA sequencing analysis revealed distinct distributions of nonmalignant cells between primary tumors from patients with metastatic colorectal cancer (mCRC) and non-metastatic colorectal cancer, showing that Th17 cells were predominantly enriched in the primary lesion of mCRC. TWEAK, a cytokine secreted by Th17 cells, promoted EMT by binding to receptor Fn14 on tumor cells, and the TWEAK-Fn14 interaction enhanced tumor migration and invasion. In mouse models, targeting Fn14 using CRISPR-induced knockout or lipid nanoparticle-encapsulated siRNA alleviated metastasis and prolonged survival. Mice lacking Il17a or Tnfsf12 (encoding TWEAK) exhibited fewer metastases compared with wild-type mice, while cotransfer of Th17 with tumor cells promoted liver metastasis. Higher TWEAK expression was associated with a worse prognosis in patients with colorectal cancer. In addition, CD163L1+ macrophages interacted with Th17 cells, recruiting Th17 via the CCL4-CCR5 axis. Collectively, this study unveils the role of immune cells in the EMT process and identifies TWEAK secreted by Th17 as a driver of CRLM.

SIGNIFICANCE

TWEAK secreted by Th17 cells promotes EMT by binding to Fn14 on colorectal cancer cells, suggesting that blocking the TWEAK-Fn14 interaction may be a promising therapeutic approach to inhibit liver metastasis.

Powerful and accurate detection of temporal gene expression patterns from multi-sample multi-stage single-cell transcriptomics data with TDEseq

We present a non-parametric statistical method called TDEseq that takes full advantage of smoothing splines basis functions to account for the dependence of multiple time points in scRNA-seq studies, and uses hierarchical structure linear additive mixed models to model the correlated cells within an individual. As a result, TDEseq demonstrates powerful performance in identifying four potential temporal expression patterns within a specific cell type. Extensive simulation studies and the analysis of four published scRNA-seq datasets show that TDEseq can produce well-calibrated p-values and up to 20% power gain over the existing methods for detecting temporal gene expression patterns.

Gene trajectory inference for single-cell data by optimal transport metrics

Single-cell RNA sequencing has been widely used to investigate cell state transitions and gene dynamics of biological processes. Current strategies to infer the sequential dynamics of genes in a process typically rely on constructing cell pseudotime through cell trajectory inference. However, the presence of concurrent gene processes in the same group of cells and technical noise can obscure the true progression of the processes studied. To address this challenge, we present GeneTrajectory, an approach that identifies trajectories of genes rather than trajectories of cells. Specifically, optimal transport distances are calculated between gene distributions across the cell-cell graph to extract gene programs and define their gene pseudotemporal order. Here we demonstrate that GeneTrajectory accurately extracts progressive gene dynamics in myeloid lineage maturation. Moreover, we show that GeneTrajectory deconvolves key gene programs underlying mouse skin hair follicle dermal condensate differentiation that could not be resolved by cell trajectory approaches. GeneTrajectory facilitates the discovery of gene programs that control the changes and activities of biological processes.

Myeloid-T cell interplay and cell state transitions associated with checkpoint inhibitor response in melanoma

BACKGROUND

The treatment of melanoma, the deadliest form of skin cancer, has greatly benefited from immunotherapy. However, many patients do not show a durable response, which is only partially explained by known resistance mechanisms.

METHODS

We performed single-cell RNA sequencing of tumor immune infiltrates and matched peripheral blood mononuclear cells of 22 checkpoint inhibitor (CPI)-naive stage III-IV metastatic melanoma patients. After sample collection, the same patients received CPI treatment, and their response was assessed.

FINDINGS

CPI responders showed high levels of classical monocytes in peripheral blood, which preferentially transitioned toward CXCL9-expressing macrophages in tumors. Trajectories of tumor-infiltrating CD8+ T cells diverged at the level of effector memory/stem-like T cells, with non-responder cells progressing into a state characterized by cellular stress and apoptosis-related gene expression. Consistently, predicted non-responder-enriched myeloid-T/natural killer cell interactions were primarily immunosuppressive, while responder-enriched interactions were supportive of T cell priming and effector function.

CONCLUSIONS

Our study illustrates that the tumor immune microenvironment prior to CPI treatment can be indicative of response. In perspective, modulating the myeloid and/or effector cell compartment by altering the described cell interactions and transitions could improve immunotherapy response.

FUNDING

This research was funded by Roche Pharma Research and Early Development.

Dissecting the spatiotemporal diversity of adult neural stem cells

Adult stem cells are important for tissue turnover and regeneration. However, in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here, we dissected the diversity of neural stem cells in the adult zebrafish brain, an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single-cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze the regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells, with some subtypes being restricted to a single brain region, while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation, with different states being involved in proliferative and non-proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for the functional manipulation of neural stem cell subtypes.

CD200+ fibroblasts form a pro-resolving mesenchymal network in arthritis

Fibroblasts are important regulators of inflammation, but whether fibroblasts change phenotype during resolution of inflammation is not clear. Here we use positron emission tomography to detect fibroblast activation protein (FAP) as a means to visualize fibroblast activation in vivo during inflammation in humans. While tracer accumulation is high in active arthritis, it decreases after tumor necrosis factor and interleukin-17A inhibition. Biopsy-based single-cell RNA-sequencing analyses in experimental arthritis show that FAP signal reduction reflects a phenotypic switch from pro-inflammatory MMP3+/IL6+ fibroblasts (high FAP internalization) to pro-resolving CD200+DKK3+ fibroblasts (low FAP internalization). Spatial transcriptomics of human joints indicates that pro-resolving niches of CD200+DKK3+ fibroblasts cluster with type 2 innate lymphoid cells, whereas MMP3+/IL6+ fibroblasts colocalize with inflammatory immune cells. CD200+DKK3+ fibroblasts stabilized the type 2 innate lymphoid cell phenotype and induced resolution of arthritis via CD200-CD200R1 signaling. Taken together, these data suggest a dynamic molecular regulation of the mesenchymal compartment during resolution of inflammation.

Gastrulation-stage gene expression in Nipbl+/- mouse embryos foreshadows the development of syndromic birth defects

In animal models, Nipbl deficiency phenocopies gene expression changes and birth defects seen in Cornelia de Lange syndrome, the most common cause of which is Nipbl haploinsufficiency. Previous studies in Nipbl+/- mice suggested that heart development is abnormal as soon as cardiogenic tissue is formed. To investigate this, we performed single-cell RNA sequencing on wild-type and Nipbl+/- mouse embryos at gastrulation and early cardiac crescent stages. Nipbl+/- embryos had fewer mesoderm cells than wild-type and altered proportions of mesodermal cell subpopulations. These findings were associated with underexpression of genes implicated in driving specific mesodermal lineages. In addition, Nanog was found to be overexpressed in all germ layers, and many gene expression changes observed in Nipbl+/- embryos could be attributed to Nanog overexpression. These findings establish a link between Nipbl deficiency, Nanog overexpression, and gene expression dysregulation/lineage misallocation, which ultimately manifest as birth defects in Nipbl+/- animals and Cornelia de Lange syndrome.

Mapping single-cell developmental potential in health and disease with interpretable deep learning

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cell fate in developmental systems. However, identifying the molecular hallmarks of potency - the capacity of a cell to differentiate into other cell types - has remained challenging. Here, we introduce CytoTRACE 2, an interpretable deep learning framework for characterizing potency and differentiation states on an absolute scale from scRNA-seq data. Across 31 human and mouse scRNA-seq datasets encompassing 28 tissue types, CytoTRACE 2 outperformed existing methods for recovering experimentally determined potency levels and differentiation states covering the entire range of cellular ontogeny. Moreover, it reconstructed the temporal hierarchy of mouse embryogenesis across 62 timepoints; identified pan-tissue expression programs that discriminate major potency levels; and facilitated discovery of cellular phenotypes in cancer linked to survival and immunotherapy resistance. Our results illuminate a fundamental feature of cell biology and provide a broadly applicable platform for delineating single-cell differentiation landscapes in health and disease.

CD169+ classical monocyte as an important participant in Graves' ophthalmopathy through CXCL12-CXCR4 axis

Patients with Graves' disease (GD) can develop Graves' ophthalmopathy (GO), but the underlying pathological mechanisms driving this development remain unclear. In our study, which included patients with GD and GO, we utilized single-cell RNA sequencing (scRNA-seq) and multiplatform analyses to investigate CD169+ classical monocytes, which secrete proinflammatory cytokines and are expanded through activated interferon signaling. We found that CD169+ clas_mono was clinically significant in predicting GO progression and prognosis, and differentiated into CD169+ macrophages that promote inflammation, adipogenesis, and fibrosis. Our murine model of early-stage GO showed that CD169+ classical monocytes accumulated in orbital tissue via the Cxcl12-Cxcr4 axis. Further studies are needed to investigate whether targeting circulating monocytes and the Cxcl12-Cxcr4 axis could alleviate GO progression.

CD4+ T cell activation distinguishes response to anti-PD-L1+anti-CTLA4 therapy from anti-PD-L1 monotherapy

Cancer patients often receive a combination of antibodies targeting programmed death-ligand 1 (PD-L1) and cytotoxic T lymphocyte antigen-4 (CTLA4). We conducted a window-of-opportunity study in head and neck squamous cell carcinoma (HNSCC) to examine the contribution of anti-CTLA4 to anti-PD-L1 therapy. Single-cell profiling of on- versus pre-treatment biopsies identified T cell expansion as an early response marker. In tumors, anti-PD-L1 triggered the expansion of mostly CD8+ T cells, whereas combination therapy expanded both CD4+ and CD8+ T cells. Such CD4+ T cells exhibited an activated T helper 1 (Th1) phenotype. CD4+ and CD8+ T cells co-localized with and were surrounded by dendritic cells expressing T cell homing factors or antibody-producing plasma cells. T cell receptor tracing suggests that anti-CTLA4, but not anti-PD-L1, triggers the trafficking of CD4+ naive/central-memory T cells from tumor-draining lymph nodes (tdLNs), via blood, to the tumor wherein T cells acquire a Th1 phenotype. Thus, CD4+ T cell activation and recruitment from tdLNs are hallmarks of early response to anti-PD-L1 plus anti-CTLA4 in HNSCC.

Single-cell division tracing and transcriptomics reveal cell types and differentiation paths in the regenerating lung

Understanding the molecular and cellular processes involved in lung epithelial regeneration may fuel the development of therapeutic approaches for lung diseases. We combine mouse models allowing diphtheria toxin-mediated damage of specific epithelial cell types and parallel GFP-labeling of functionally dividing cells with single-cell transcriptomics to characterize the regeneration of the distal lung. We uncover cell types, including Krt13+ basal and Krt15+ club cells, detect an intermediate cell state between basal and goblet cells, reveal goblet cells as actively dividing progenitor cells, and provide evidence that adventitial fibroblasts act as supporting cells in epithelial regeneration. We also show that diphtheria toxin-expressing cells can persist in the lung, express specific inflammatory factors, and transcriptionally resemble a previously undescribed population in the lungs of COVID-19 patients. Our study provides a comprehensive single-cell atlas of the distal lung that characterizes early transcriptional and cellular responses to concise epithelial injury, encompassing proliferation, differentiation, and cell-to-cell interactions.

MYT1L deficiency impairs excitatory neuron trajectory during cortical development

Mutations that reduce the function of MYT1L, a neuron-specific transcription factor, are associated with a syndromic neurodevelopmental disorder. Furthermore, MYT1L is routinely used as a proneural factor in fibroblast-to-neuron transdifferentiation. MYT1L has been hypothesized to play a role in the trajectory of neuronal specification and subtype specific maturation, but this hypothesis has not been directly tested, nor is it clear which neuron types are most impacted by MYT1L loss. In this study, we profiled 313,335 nuclei from the forebrains of wild-type and MYT1L-deficient mice at two developmental stages: E14 at the peak of neurogenesis and P21, when neurogenesis is complete, to examine the role of MYT1L levels in the trajectory of neuronal development. We found that MYT1L deficiency significantly disrupted the relative proportion of cortical excitatory neurons at E14 and P21. Significant changes in gene expression were largely concentrated in excitatory neurons, suggesting that transcriptional effects of MYT1L deficiency are largely due to disruption of neuronal maturation programs. Most effects on gene expression were cell autonomous and persistent through development. In addition, while MYT1L can both activate and repress gene expression, the repressive effects were most sensitive to haploinsufficiency, and thus more likely mediate MYT1L syndrome. These findings illuminate the intricate role of MYT1L in orchestrating gene expression dynamics during neuronal development, providing insights into the molecular underpinnings of MYT1L syndrome.

Multi-condition and multi-modal temporal profile inference during mouse embryonic development

The emergence of single-cell time-series datasets enables modeling of changes in various types of cellular profiles over time. However, due to the disruptive nature of single-cell measurements, it is impossible to capture the full temporal trajectory of a particular cell. Furthermore, single-cell profiles can be collected at mismatched time points across different conditions (e.g., sex, batch, disease) and data modalities (e.g., scRNA-seq, scATAC-seq), which makes modeling challenging. Here we propose a joint modeling framework, Sunbear, for integrating multi-condition and multi-modal single-cell profiles across time. Sunbear can be used to impute single-cell temporal profile changes, align multi-dataset and multi-modal profiles across time, and extrapolate single-cell profiles in a missing modality. We applied Sunbear to reveal sex-biased transcription during mouse embryonic development and predict dynamic relationships between epigenetic priming and transcription for cells in which multi-modal profiles are unavailable. Sunbear thus enables the projection of single-cell time-series snapshots to multi-modal and multi-condition views of cellular trajectories.

An atlas of epithelial cell states and plasticity in lung adenocarcinoma

Understanding the cellular processes that underlie early lung adenocarcinoma (LUAD) development is needed to devise intervention strategies1. Here we studied 246,102 single epithelial cells from 16 early-stage LUADs and 47 matched normal lung samples. Epithelial cells comprised diverse normal and cancer cell states, and diversity among cancer cells was strongly linked to LUAD-specific oncogenic drivers. KRAS mutant cancer cells showed distinct transcriptional features, reduced differentiation and low levels of aneuploidy. Non-malignant areas surrounding human LUAD samples were enriched with alveolar intermediate cells that displayed elevated KRT8 expression (termed KRT8+ alveolar intermediate cells (KACs) here), reduced differentiation, increased plasticity and driver KRAS mutations. Expression profiles of KACs were enriched in lung precancer cells and in LUAD cells and signified poor survival. In mice exposed to tobacco carcinogen, KACs emerged before lung tumours and persisted for months after cessation of carcinogen exposure. Moreover, they acquired Kras mutations and conveyed sensitivity to targeted KRAS inhibition in KAC-enriched organoids derived from alveolar type 2 (AT2) cells. Last, lineage-labelling of AT2 cells or KRT8+ cells following carcinogen exposure showed that KACs are possible intermediates in AT2-to-tumour cell transformation. This study provides new insights into epithelial cell states at the root of LUAD development, and such states could harbour potential targets for prevention or intervention.

Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer

Molecular stratification using gene-level transcriptional data has identified subtypes with distinctive genotypic and phenotypic traits, as exemplified by the consensus molecular subtypes (CMS) in colorectal cancer (CRC). Here, rather than gene-level data, we make use of gene ontology and biological activation state information for initial molecular class discovery. In doing so, we defined three pathway-derived subtypes (PDS) in CRC: PDS1 tumors, which are canonical/LGR5+ stem-rich, highly proliferative and display good prognosis; PDS2 tumors, which are regenerative/ANXA1+ stem-rich, with elevated stromal and immune tumor microenvironmental lineages; and PDS3 tumors, which represent a previously overlooked slow-cycling subset of tumors within CMS2 with reduced stem populations and increased differentiated lineages, particularly enterocytes and enteroendocrine cells, yet display the worst prognosis in locally advanced disease. These PDS3 phenotypic traits are evident across numerous bulk and single-cell datasets, and demark a series of subtle biological states that are currently under-represented in pre-clinical models and are not identified using existing subtyping classifiers.

Nuclear morphology is shaped by loop-extrusion programs

It is well established that neutrophils adopt malleable polymorphonuclear shapes to migrate through narrow interstitial tissue spaces1-3. However, how polymorphonuclear structures are assembled remains unknown4. Here we show that in neutrophil progenitors, halting loop extrusion-a motor-powered process that generates DNA loops by pulling in chromatin5-leads to the assembly of polymorphonuclear genomes. Specifically, we found that in mononuclear neutrophil progenitors, acute depletion of the loop-extrusion loading factor nipped-B-like protein (NIPBL) induced the assembly of horseshoe, banded, ringed and hypersegmented nuclear structures and led to a reduction in nuclear volume, mirroring what is observed during the differentiation of neutrophils. Depletion of NIPBL also induced cell-cycle arrest, activated a neutrophil-specific gene program and conditioned a loss of interactions across topologically associating domains to generate a chromatin architecture that resembled that of differentiated neutrophils. Removing NIPBL resulted in enrichment for mega-loops and interchromosomal hubs that contain genes associated with neutrophil-specific enhancer repertoires and an inflammatory gene program. On the basis of these observations, we propose that in neutrophil progenitors, loop-extrusion programs produce lineage-specific chromatin architectures that permit the packing of chromosomes into geometrically confined lobular structures. Our data also provide a blueprint for the assembly of polymorphonuclear structures, and point to the possibility of engineering de novo nuclear shapes to facilitate the migration of effector cells in densely populated tumorigenic environments.

Sirpα on tumor-associated myeloid cells restrains antitumor immunity in colorectal cancer independent of its interaction with CD47

Immunosuppressive myeloid cells hinder immunotherapeutic efficacy in tumors, but the precise mechanisms remain undefined. Here, by performing single-cell RNA sequencing in colorectal cancer tissues, we found tumor-associated macrophages and granulocytic myeloid-derived suppressor cells increased most compared to their counterparts in normal tissue and displayed the highest immune-inhibitory signatures among all immunocytes. These cells exhibited significantly increased expression of immunoreceptor tyrosine-based inhibitory motif-bearing receptors, including SIRPA. Notably, Sirpa-/- mice were more resistant to tumor progression than wild-type mice. Moreover, Sirpα deficiency reprogramed the tumor microenvironment through expansion of TAM_Ccl8hi and gMDSC_H2-Q10hi subsets showing strong antitumor activity. Sirpa-/- macrophages presented strong phagocytosis and antigen presentation to enhance T cell activation and proliferation. Furthermore, Sirpa-/- macrophages facilitated T cell recruitment via Syk/Btk-dependent Ccl8 secretion. Therefore, Sirpα deficiency enhances innate and adaptive immune activation independent of expression of CD47 and Sirpα blockade could be a promising strategy to improve cancer immunotherapy efficacy.

Cell fate decision in erythropoiesis: Insights from multiomics studies

Every second, the body produces 2 million red blood cells through a process called erythropoiesis. Erythropoiesis is hierarchical in that it results from a series of cell fate decisions whereby hematopoietic stem cells progress toward the erythroid lineage. Single-cell transcriptomic and proteomic approaches have revolutionized the way we understand erythropoiesis, revealing it to be a gradual process that underlies a progressive restriction of fate potential driven by quantitative changes in lineage-specifying transcription factors. Despite these major advances, we still know very little about what cell fate decision entails at the molecular level. Novel approaches that simultaneously measure additional properties in single cells, including chromatin accessibility, transcription factor binding, and/or cell surface proteins are being developed at a fast pace, providing the means to exciting new advances in the near future. In this review, we briefly summarize the main findings obtained from single-cell studies of erythropoiesis, highlight outstanding questions, and suggest recent technological advances to address them.

Human surface ectoderm and amniotic ectoderm are sequentially specified according to cellular density

Mechanisms specifying amniotic ectoderm and surface ectoderm are unresolved in humans due to their close similarities in expression patterns and signal requirements. This lack of knowledge hinders the development of protocols to accurately model human embryogenesis. Here, we developed a human pluripotent stem cell model to investigate the divergence between amniotic and surface ectoderms. In the established culture system, cells differentiated into functional amnioblast-like cells. Single-cell RNA sequencing analyses of amnioblast differentiation revealed an intermediate cell state with enhanced surface ectoderm gene expression. Furthermore, when the differentiation started at the confluent condition, cells retained the expression profile of surface ectoderm. Collectively, we propose that human amniotic ectoderm and surface ectoderm are specified along a common nonneural ectoderm trajectory based on cell density. Our culture system also generated extraembryonic mesoderm-like cells from the primed pluripotent state. Together, this study provides an integrative understanding of the human nonneural ectoderm development and a model for embryonic and extraembryonic human development around gastrulation.

Single-cell transcriptomics is revolutionizing the improvement of plant biotechnology research: recent advances and future opportunities

Single-cell approaches are a promising way to obtain high-resolution transcriptomics data and have the potential to revolutionize the study of plant growth and development. Recent years have seen the advent of unprecedented technological advances in the field of plant biology to study the transcriptional information of individual cells by single-cell RNA sequencing (scRNA-seq). This review focuses on the modern advancements of single-cell transcriptomics in plants over the past few years. In addition, it also offers a new insight of how these emerging methods will expedite advance research in plant biotechnology in the near future. Lastly, the various technological hurdles and inherent limitations of single-cell technology that need to be conquered to develop such outstanding possible knowledge gain is critically analyzed and discussed.

Gastrulation-stage gene expression in Nipbl+/- mouse embryos foreshadows the development of syndromic birth defects

In animal models, Nipbl -deficiency phenocopies gene expression changes and birth defects seen in Cornelia de Lange Syndrome (CdLS), the most common cause of which is Nipbl -haploinsufficiency. Previous studies in Nipbl +/- mice suggested that heart development is abnormal as soon as cardiogenic tissue is formed. To investigate this, we performed single-cell RNA-sequencing on wildtype (WT) and Nipbl +/- mouse embryos at gastrulation and early cardiac crescent stages. Nipbl +/- embryos had fewer mesoderm cells than WT and altered proportions of mesodermal cell subpopulations. These findings were associated with underexpression of genes implicated in driving specific mesodermal lineages. In addition, Nanog was found to be overexpressed in all germ layers, and many gene expression changes observed in Nipbl +/- embryos could be attributed to Nanog overexpression. These findings establish a link between Nipbl -deficiency, Nanog overexpression, and gene expression dysregulation/lineage misallocation, which ultimately manifest as birth defects in Nipbl +/- animals and CdLS.

Teaser

Gene expression changes during gastrulation of Nipbl -deficient mice shed light on early origins of structural birth defects.

Molecularly stratified hypothalamic astrocytes are cellular foci for obesity

Astrocytes safeguard the homeostasis of the central nervous system1,2. Despite their prominent morphological plasticity under conditions that challenge the brain's adaptive capacity3-5, the classification of astrocytes, and relating their molecular make-up to spatially devolved neuronal operations that specify behavior or metabolism, remained mostly futile6,7. Although it seems unexpected in the era of single-cell biology, the lack of a major advance in stratifying astrocytes under physiological conditions rests on the incompatibility of 'neurocentric' algorithms that rely on stable developmental endpoints, lifelong transcriptional, neurotransmitter, and neuropeptide signatures for classification6-8 with the dynamic functional states, anatomic allocation, and allostatic plasticity of astrocytes1. Simplistically, therefore, astrocytes are still grouped as 'resting' vs. 'reactive', the latter referring to pathological states marked by various inducible genes3,9,10. Here, we introduced a machine learning-based feature recognition algorithm that benefits from the cumulative power of published single-cell RNA-seq data on astrocytes as a reference map to stepwise eliminate pleiotropic and inducible cellular features. For the healthy hypothalamus, this walk-back approach revealed gene regulatory networks (GRNs) that specified subsets of astrocytes, and could be used as landmarking tools for their anatomical assignment. The core molecular censuses retained by astrocyte subsets were sufficient to stratify them by allostatic competence, chiefly their signaling and metabolic interplay with neurons. Particularly, we found differentially expressed mitochondrial genes in insulin-sensing astrocytes and demonstrated their reciprocal signaling with neurons that work antagonistically within the food intake circuitry. As a proof-of-concept, we showed that disrupting Mfn2 expression in astrocytes reduced their ability to support dynamic circuit reorganization, a time-locked feature of satiety in the hypothalamus, thus leading to obesity in mice. Overall, our results suggest that astrocytes in the healthy brain are fundamentally more heterogeneous than previously thought and topologically mirror the specificity of local neurocircuits.

Lineage-specific intolerance to oncogenic drivers restricts histological transformation

Lung adenocarcinoma (LUAD) and small cell lung cancer (SCLC) are thought to originate from different epithelial cell types in the lung. Intriguingly, LUAD can histologically transform into SCLC after treatment with targeted therapies. In this study, we designed models to follow the conversion of LUAD to SCLC and found that the barrier to histological transformation converges on tolerance to Myc, which we implicate as a lineage-specific driver of the pulmonary neuroendocrine cell. Histological transformations are frequently accompanied by activation of the Akt pathway. Manipulating this pathway permitted tolerance to Myc as an oncogenic driver, producing rare, stem-like cells that transcriptionally resemble the pulmonary basal lineage. These findings suggest that histological transformation may require the plasticity inherent to the basal stem cell, enabling tolerance to previously incompatible oncogenic driver programs.

Resolving cellular dynamics using single-cell temporal transcriptomics

Cellular dynamics, the transition of a cell from one state to another, is central to understanding developmental processes and disease progression. Single-cell transcriptomics has been pushing the frontiers of cellular dynamics studies into a genome-wide and single-cell level. While most single-cell RNA sequencing approaches are disruptive and only provide a snapshot of cell states, the dynamics of a cell could be reconstructed by either exploiting temporal information hiding in the transcriptomics data or integrating additional information. In this review, we describe these approaches, highlighting their underlying principles, key assumptions, and the rationality to interpret the results as models. We also discuss the recently emerging nondisruptive live-cell transcriptomics methods, which are highly complementary to the computational models for their assumption-free nature.

Studying temporal dynamics of single cells: expression, lineage and regulatory networks

Learning how multicellular organs are developed from single cells to different cell types is a fundamental problem in biology. With the high-throughput scRNA-seq technology, computational methods have been developed to reveal the temporal dynamics of single cells from transcriptomic data, from phenomena on cell trajectories to the underlying mechanism that formed the trajectory. There are several distinct families of computational methods including Trajectory Inference (TI), Lineage Tracing (LT), and Gene Regulatory Network (GRN) Inference which are involved in such studies. This review summarizes these computational approaches which use scRNA-seq data to study cell differentiation and cell fate specification as well as the advantages and limitations of different methods. We further discuss how GRNs can potentially affect cell fate decisions and trajectory structures.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12551-023-01090-5.

A time- and single-cell-resolved model of murine bone marrow hematopoiesis

The paradigmatic hematopoietic tree model is increasingly recognized to be limited, as it is based on heterogeneous populations largely defined by non-homeostatic assays testing cell fate potentials. Here, we combine persistent labeling with time-series single-cell RNA sequencing to build a real-time, quantitative model of in vivo tissue dynamics for murine bone marrow hematopoiesis. We couple cascading single-cell expression patterns with dynamic changes in differentiation and growth speeds. The resulting explicit linkage between molecular states and cellular behavior reveals widely varying self-renewal and differentiation properties across distinct lineages. Transplanted stem cells show strong acceleration of differentiation at specific stages of erythroid and neutrophil production, illustrating how the model can quantify the impact of perturbations. Our reconstruction of dynamic behavior from snapshot measurements is akin to how a kinetoscope allows sequential images to merge into a movie. We posit that this approach is generally applicable to understanding tissue-scale dynamics at high resolution.

Representing and extracting knowledge from single-cell data

Single-cell analysis is currently one of the most high-resolution techniques to study biology. The large complex datasets that have been generated have spurred numerous developments in computational biology, in particular the use of advanced statistics and machine learning. This review attempts to explain the deeper theoretical concepts that underpin current state-of-the-art analysis methods. Single-cell analysis is covered from cell, through instruments, to current and upcoming models. The aim of this review is to spread concepts which are not yet in common use, especially from topology and generative processes, and how new statistical models can be developed to capture more of biology. This opens epistemological questions regarding our ontology and models, and some pointers will be given to how natural language processing (NLP) may help overcome our cognitive limitations for understanding single-cell data.

Sfrp1 inhibits lung fibroblast invasion during transition to injury-induced myofibroblasts

BACKGROUND

Fibroblast-to-myofibroblast conversion is a major driver of tissue remodelling in organ fibrosis. Distinct lineages of fibroblasts support homeostatic tissue niche functions, yet their specific activation states and phenotypic trajectories during injury and repair have remained unclear.

METHODS

We combined spatial transcriptomics, multiplexed immunostainings, longitudinal single-cell RNA-sequencing and genetic lineage tracing to study fibroblast fates during mouse lung regeneration. Our findings were validated in idiopathic pulmonary fibrosis patient tissues in situ as well as in cell differentiation and invasion assays using patient lung fibroblasts. Cell differentiation and invasion assays established a function of SFRP1 in regulating human lung fibroblast invasion in response to transforming growth factor (TGF)β1.

MEASUREMENTS AND MAIN RESULTS

We discovered a transitional fibroblast state characterised by high Sfrp1 expression, derived from both Tcf21-Cre lineage positive and negative cells. Sfrp1 + cells appeared early after injury in peribronchiolar, adventitial and alveolar locations and preceded the emergence of myofibroblasts. We identified lineage-specific paracrine signals and inferred converging transcriptional trajectories towards Sfrp1 + transitional fibroblasts and Cthrc1 + myofibroblasts. TGFβ1 downregulated SFRP1 in noninvasive transitional cells and induced their switch to an invasive CTHRC1+ myofibroblast identity. Finally, using loss-of-function studies we showed that SFRP1 modulates TGFβ1-induced fibroblast invasion and RHOA pathway activity.

CONCLUSIONS

Our study reveals the convergence of spatially and transcriptionally distinct fibroblast lineages into transcriptionally uniform myofibroblasts and identifies SFRP1 as a modulator of TGFβ1-driven fibroblast phenotypes in fibrogenesis. These findings are relevant in the context of therapeutic interventions that aim at limiting or reversing fibroblast foci formation.