SEARCH · PubMed Health

Results for “Cell Lineage”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources

PubMed2025

LINNAEUS: Simultaneous Single-Cell Lineage Tracing and Cell Type Identification.

A key goal of biology is to understand the origin of the many cell types that can be observed during diverse processes such as development, regeneration, and disease. Single-cell RNA-sequencing (scRNA-seq) is commonly used to identify cell types in a tissue or organ. However, organizing the resulting taxonomy of cell types into lineage trees to understand the origins of cell states and relationships between cells remains challenging. Here we present LINNAEUS (Spanjaard et al, Nat Biotechnol 36:469-473. https://doi.org/10.1038/nbt.4124 , 2018; Hu et al, Nat Genet 54:1227-1237. https://doi.org/10.1038/s41588-022-01129-5 , 2022) (LINeage tracing by Nuclease-Activated Editing of Ubiquitous Sequences)-a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA-seq with computational analysis of lineage barcodes, generated by genome editing of transgenic reporter genes, LINNAEUS can be used to reconstruct organism-wide single-cell lineage trees. LINNAEUS provides a systematic approach for tracing the origin of novel cell types, or known cell types under different conditions.

Single-Cell Analysis↗

PubMed2026

LAML-Pro: joint maximum likelihood inference of cell genotypes and cell lineage trees.

MOTIVATION: Recent dynamic lineage tracing technologies use genome editing to induce heritable mutations, or edits, that accumulate across successive cell divisions. These edits are measured using single-cell sequencing or imaging, providing data to reconstruct cell lineages at single-cell resolution. Current computational approaches to infer cell lineage trees, or phylogenies, from these data perform two separate steps: (i) Identify each cell's edits (genotype) from the raw sequencing or imaging data; (ii) Infer a cell lineage tree from the cell genotypes. However, genotyping cells is an inexact process and genotype errors can yield an inaccurate lineage tree. For example, using fluorescence based-imaging to measure edits results in a high fraction (≈25%-50%) of uncertain or erroneous genotypes. RESULTS: We introduce Lineage Analysis via Maximum Likelihood with PRobabilistic Observations (LAML-Pro), an algorithm that jointly infers cell genotypes and a cell lineage tree. LAML-Pro is based on the Probabilistic Mixed-type Missing Observation (PMMO) model, which we derive to describe both the genome editing and genotype observation processes. LAML-Pro constructs lineage trees from thousands of cells in under an hour by leveraging the sparsity of transitions under the PMMO model. On simulated data, we demonstrate that LAML-Pro corrects genotype errors and infers substantially more accurate trees than existing methods which are vulnerable to genotype errors. Applied to data from two recent imaging-based lineage tracing systems, LAML-Pro reduces genotype errors by 5-fold and produces more spatially coherent lineage trees compared to existing methods. AVAILABILITY AND IMPLEMENTATION: LAML-Pro is implemented in C++ and is available as both a command-line interface and as a Python library at: github.com/raphael-group/LAML-Pro.

Cell Lineage↗

PubMed2026

LAML-Pro: Joint Maximum Likelihood Inference of Cell Genotypes and Cell Lineage Trees.

MOTIVATION: Recent dynamic lineage tracing technologies use genome editing to induce heritable mutations, or edits, that accumulate across successive cell divisions. These edits are measured using single-cell sequencing or imaging, providing data to reconstruct cell lineages at single-cell resolution. Current computational approaches to infer cell lineage trees, or phylogenies, from these data perform two separate steps: (1) Identify each cell's edits (genotype) from the raw sequencing or imaging data; (2) Infer a cell lineage tree from the cell genotypes. However, genotyping cells is an inexact process and genotype errors can yield an inaccurate lineage tree. For example, using fluorescence based-imaging to measure edits results in a high fraction (≈ 25-50%) of uncertain or erroneous genotypes. RESULTS: We introduce Lineage Analysis via Maximum Likelihood with PRobabilistic Observations (LAML-Pro), an algorithm that jointly infers cell genotypes and a cell lineage tree. LAML-Pro is based on the Probabilistic Mixed-type Missing Observation (PMMO) model, which we derive to describe both the genome editing and genotype observation processes. LAML-Pro constructs lineage trees from thousands of cells in under an hour by leveraging the sparsity of transitions under the PMMO model. On simulated data, we demonstrate that LAML-Pro corrects genotype errors and infers substantially more accurate trees than existing methods which are vulnerable to genotype errors. Applied to data from two recent imaging-based lineage tracing systems, LAML-Pro reduces genotype errors by 5-fold and produces more spatially coherent lineage trees compared to existing methods. AVAILABILITY AND IMPLEMENTATION: LAML-Pro is freely available at: github.com/raphael-group/LAML-Pro.

Journal Article↗

PubMed2025

CLADES: A Programmable Cascade of Genes for Cell Lineage Analysis and Manipulation.

In the Drosophila brain, neuronal diversity originates from approximately 100 neural stem cells, each dividing asymmetrically. Precise mapping of cell lineages at the single-cell resolution is crucial for understanding the mechanisms that direct neuronal specification. However, existing methods for high-resolution lineage tracing are notably time-consuming and labor-intensive. Here, we outline the best practices for lineage tracing using CLADES (cell lineage access driven by an edition sequence), a revolutionary approach to neuronal lineage tracing that addresses the limitations of previous methods. CLADES effectively traces the birth order of neurons using approximately 100 samples. The technique relies on a genetic cascade of reporter activations and deactivations that delineate lineage progression through color-coded markers. This system not only facilitates the detailed mapping of neuronal lineages but also holds the potential to be applied to tracking biological events and producing cell types for therapeutic purposes.

Animals↗

PubMed2025

Transcriptome-wide analysis reveals potential roles of CFD and ANGPTL4 in fibroblasts regulating B cell lineage for extracellular matrix-driven clustering and novel avenues for immunotherapy in breast cancer.

BACKGROUND: The remodeling of the extracellular matrix (ECM) plays a pivotal role in tumor progression and drug resistance. However, the compositional patterns of ECM in breast cancer and their underlying biological functions remain elusive. METHODS: Transcriptome and genome data of breast cancer patients from TCGA database was downloaded. Patients were classified into different clusters by using non-negative matrix factorization (NMF) based on signatures of ECM components and regulators. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify core genes related to ECM clusters. Additional 10 independent public cohorts including Metabric, SCAN_B, GSE12276, GSE16446, GSE19615, GSE20685, GSE21653, GSE58644, GSE58812, and GSE88770 were collected to construct Training or Testing cohort, following machine learning calculating ECM correlated index (ECI) for survival analysis. Pathway enrichment and correlation analysis were used to explore the relationship among ECM clusters, ECI and TME. Single-cell transcriptome data from GSE161529 was processed for uncovering the differences among ECM clusters. RESULTS: Using NMF, we identified three ECM clusters in the TCGA database: C1 (Neuron), C2 (ECM), and C3 (Immune). Subsequently, WGCNA was employed to pinpoint cluster-specific genes and develop a prognostic model. This model demonstrated robust predictive power for breast cancer patient survival in both the Training cohort (n = 5,392, AUC = 0.861) and the Testing cohort (n = 1,344, AUC = 0.711). Upon analyzing the tumor microenvironment (TME), we discovered that fibroblasts and B cell lineage were the core cell types associated with the ECM cluster phenotypes. Single-cell RNA sequencing data further revealed that angiopoietin like 4 (ANGPTL4)+ fibroblasts were specifically linked to the C2 phenotype, while complement factor D (CFD)+ fibroblasts characterized the other ECM clusters. CellChat analysis indicated that ANGPTL4+ and CFD+ fibroblasts regulate B cell lineage via distinct signaling pathways. Additionally, analysis using the Kaplan-Meier Plotter website showed that CFD was favorable for immunotherapy response, whereas ANGPTL4 negatively impacted the outcomes of cancer patients receiving immunotherapy. CONCLUSION: We identified distinct ECM clusters in breast cancer patients, irrespective of molecular subtypes. Additionally, we constructed an effective prognostic model based on these ECM clusters and recognized ANGPTL4+ and CFD+ fibroblasts as potential biomarkers for immunotherapy in breast cancer.

Humans↗

PubMed2025

Deciphering Cell Fate and Clonal Dynamics via Integrative Single-Cell Lineage Modeling.

Through natural or synthetic lineage barcodes, single-cell technologies now enable the joint measurement of molecular states and clonal identities, providing an unprecedented opportunity to study cell fate and dynamics. Yet, most computational methods for inferring cell development and differentiation rely exclusively on transcriptional similarity, overlooking the lineage information encoded by lineage barcodes. This limitation is exemplified by T cells, where subtle transcriptional differences mark divergent fates with distinct biological activity. Single-cell RNA and matched TCR sequencing is now ubiquitous in the analysis of clinical samples, where the TCR sequence provides an endogenous clonal barcode and could reveal clonal T cell responses. We present Clonotrace, a computational framework that jointly models gene expression and clonotype information to infer cell state transitions and fate biases with higher fidelity. While motivated by challenges in analyzing T cell populations, especially in the tumor microenvironment and immunotherapy settings, Clonotrace is broadly applicable to any lineage-barcoded single-cell dataset. Across diverse systems including T cells, hematopoietic differentiation, and cancer therapy resistance models, Clonotrace reveals differentiation hierarchies, distinguishes unipotent from multipotent states, and identifies candidate fate-determining genes driving lineage commitment.

Journal Article↗

PubMed2025

Deciphering Cell Fate and Clonal Dynamics via Integrative Single-Cell Lineage Modeling.

Journal Article↗

PubMed2025

Alternative pre-mRNA Splicing and Gene Expression Patterns in Midbrain Lineage Cells Carrying Familial Parkinson's Disease Mutations.

Parkinson's disease (PD) arises from genetic and environmental factors. Human genetics has identified mutations in ~20 inherited familial genes linked to monogenic forms of PD. To investigate the effects of individual familial PD mutations, human pluripotent embryonic stem cells (hPSCs) carrying 12 distinct familial PD mutations were differentiated into midbrain lineage cells, including dopaminergic (mDA) neurons. Global gene expression and pre-mRNA splicing patterns were analyzed in midbrain cultures carrying pathogenic PD mutations in the PRKN, SNCA, LRRK2, PINK1, DNAJC6, FBXO7, SYNJ1, DJ1, VPS13C, ATP13A2 and GBA1 genes. This analysis revealed that these familial PD mutations lead to pre-mRNA splicing changes linked to RNA splicing factors and to pathways controlling cell projections, cytoskeleton, GTPase regulation and others. Importantly, we have also shown that subsets of these splicing changes overlap with changes found in PD patient postmortem brains. Mutation-specific pre-mRNA isoforms may function as both diagnostic biomarkers for familial PD-associated genotypes and promising therapeutic targets.

Journal Article↗

PubMed2025

Genome-wide association meta-regression identifies stem cell lineage orchestration as a key driver of acne risk.

Over 85% of the population experience acne at some point in their lives, with its severity spanning a quantitative spectrum, from mild, transient outbreaks to more persistent, severe forms of the condition. Moderate to severe disease poses a substantial global burden arising from both the physical and psychological impacts of this highly visible condition. The analytical approach taken in this study aimed to address the impact of variation in the dichotomisation of acne case control status, driven by ascertainment and study design, on effect size estimates across independent genetic association studies of acne. Through a fixed intercept meta-regression framework, we combined evidence genome-wide for association with acne across studies in which case-control status had been ascertained in different settings, allowing for different severity threshold definitions. Across a combined sample of 73,997 cases and 1,103,940 controls of European, South Asian and African American ancestry we identify genetic variation at 165 genomic loci that influence acne risk. There is evidence for both shared and ancestry specific components to the genetic susceptibility to acne and for sex differences in the magnitude of effect of risk alleles at three loci. We observe that common genetic variation explains 13.4% of acne heritability on the liability scale. Consistent with the hypothesis that genetic risk primarily operates at the level of individual pilosebaceous units, a polygenic score derived from this case-control study of acne susceptibility is associated with both self-reported and clinically assessed acne severity in adolescence, further strengthening the link between genetic risk and disease severity. Prioritisation of causal genes at the identified acne risk loci, provides genetic validation of the targets of established and emerging acne therapies, including retinoid treatments. The identified acne risk loci are enriched for genes encoding downstream effectors of RXRA signalling, including SOX9 and components of the WNT and p53 pathways. Illustrating that the control of stem cell lineage plasticity and cellular fate are important mechanisms through which genetic variation influences acne susceptibility within the pilosebaceous unit.

Journal Article↗

PubMed2025

Tracking HIV persistence across T cell lineages during early ART-treated HIV-1-infection using a reservoir-marking humanized mouse model.

Human immunodeficiency virus (HIV) infection depletes CD4 T-cells, and long-term persistence of latent virus prevents full clearance of HIV even in the presence of effective antiretroviral therapy (ART), Here we present the HIV-1-induced lineage tracing (HILT) system, a model that irreversibly marks infected cells within a humanized mouse model, which detects rare latently infected cells. Immunodeficient mice transplanted with genetically modified hematopoietic stem cells develop a human immune system, in which CD4 T-cells contain a genetic switch that permanently labels cells infected by HIV-1 expressing cre-recombinase. Through single-cell RNA sequencing of HILT-marked cells during acute infection and post-ART treatment, we identify distinct CD4+ T-cell transcriptional lineages enriched in either active or latent infections. Comparative gene expression analysis highlights common pathways modulated in both states, including EIF2, Sirtuin, and protein ubiquitination. Critical regulators of these pathways, including JUN, BCL2, and MDM2, change to opposite directions in the two states, highlighting gene expression programs that may support HIV persistence across T-cell lineages and states.

Animals↗

PubMed2025

Multicolor Cell Lineage Tracing Using MAGIC Markers Strategies.

Multicolor MAGIC Markers strategies are useful lineage tracing tools to study brain development at a multicellular scale. In this chapter, we describe an in utero electroporation method to simultaneously label multiple neighboring progenitors and their respective progeny using these multicolor reporters. In utero electroporation enables the introduction of any gene of interest into embryonic neural progenitors lining the brain ventricles through a simple pipeline consisting of a micro-injection followed by the application of electrical pulses. Successful in utero electroporation requires a concise yet complete understanding of each step of the surgical protocol, spanning from the preoperative preparation to the postoperative care, as well as the MAGIC Markers tool outlined in this study. Besides a detailed protocol, we present non-integrative and integrative approaches to demonstrate the range of cell and lineage tracking possibilities of multicolored progenitors and their descent over time.

Cell Lineage↗

PubMed2026

Engineering the Vero Cell Lineage: Toward a Programmable Vaccine Manufacturing Platform.

Vero cells remain an indispensable continuous substrate for human viral vaccine manufacturing. Despite decades of empirical process optimization, intrinsic genomic instability, including segmental aneuploidy and dynamic chromatin rearrangements, continues to limit the durability of engineered phenotypes under sustained viral burden and bioreactor stress. Here, we review the expanding engineering toolkit for the Vero lineage across a three-layered functional framework: the membrane interface, cytoplasmic foundry, and nuclear blueprint, evaluating translational prospects at each level. Receptor transplantation and morphological reprogramming have broadened viral entry range and enabled suspension-adapted culture formats, while metabolic flux management and temporally controlled apoptosis modulation have addressed intracellular production bottlenecks, albeit often with trade-offs between productivity, biosafety, and long-term population stability. At the genomic level, targeted perturbations of transcriptional regulators and emerging epigenetic interventions offer more durable gains, yet expression drift, clonal heterogeneity, and karyotypic instability during extended passaging highlight the need for locus-level precision rather than constitutive trait installation. Looking forward, infection-responsive dynamic logic circuits and the systematic identification of Vero-specific genomic safe harbors could shift the paradigm toward a conditionally responsive manufacturing architecture. Collectively, these advances suggest a pathway for transitioning the Vero lineage from a passive, empirically optimized biological substrate into a conditionally responsive, genomically stable, and programmable platform for modern vaccine preparedness.

Vero cells↗

PubMed2026

Atherosclerotic plaque fibroblasts derive from adventitial and medial Pdgfra-lineage-positive cells and predominantly maintain fibroblast identity.

AIMS: Fibroblasts are mesenchymal cells in the healthy vascular adventitia. In atherosclerosis, single-cell sequencing datasets suggest fibroblasts are abundant in plaques. However, their identity, origin, and fate during plaque progression remain unclear, which we aim to unravel here. APPROACH AND RESULTS: To robustly define fibroblast identity, origin, and fate, we employed meta-analyses of 54 single-cell RNA sequencing libraries, including murine smooth muscle cell (Myh11) and endothelial cell (EC) (Cdh5) lineage reporter mice with and without atherosclerosis; human control and atherosclerotic arteries; and murine adventitia and atherosclerotic plaques processed separately from low-density lipoprotein (LDL) receptor knockout (Ldlr-/-) mice. These meta-analyses showed that murine and human plaque fibroblast identity was robustly defined by Pdgfra, Pi16, Cygb, and Serpinf1 mRNA. Ninety-five percent of plaque fibroblasts do not derive from the Myh11 lineage, while no Cdh5-lineage-positive cells were present in the fibroblast cluster. We identified five murine arterial fibroblast subsets in atherosclerotic murine aorta: progenitor fibroblasts, matrix fibroblasts, inflammatory fibroblasts, an EC-like fibroblast subset, detected in both adventitia and plaques, and Col5a3+ fibroblasts, unique to the adventitia. We next studied fibroblast identity, origin, and fate using pseudotime analysis and Pdgfra-CreERT2/tdTomato lineage reporter mice (Pdgfra Lin+). Healthy Pdgfra Lin+ reporter mice showed predominant adventitial tdTomato expression, and infrequent medial and intimal Pdgfra Lin+ cells co-expressing MYH11 and PECAM1, respectively. The Pdgfra Lin+ plaque area increased with diet duration. Pdgfra Lin+ cells largely maintain fibroblast identity in the plaque, while <10% co-express SMC markers (MYH11, SM22α), or contribute to ACTA2+ cap cells. ECs gaining mesenchymal markers are transcriptionally distinct from Cdh5-lineage-negative fibroblasts gaining EC markers. Plaque-resident EC-like fibroblasts displayed a mesenchymal-to-endothelial transition transcriptome, which was induced in human primary fibroblasts in vitro by starvation, and dampened or reversed by IL1B, TGFB1, TGFB3, and oxidized LDL. Cross-species integration showed that all murine plaque fibroblasts were conserved in human atherosclerosis, with one additional subset partially resembling murine subsets, and three human-specific subsets. Importantly, human fibroblast subsets differentially correlated to human plaque traits, with EC-like fibroblasts correlating to plaque instability. CONCLUSION: Our results indicate that 95% of plaque-residing fibroblasts are Myh11 Lin- Plaque fibroblasts have a dual origin, predominantly adventitial Pdgfra Lin+ progenitor fibroblasts, with a minor contribution from medial Pdgfra Lin+  Myh11+ SMCs. Most plaque fibroblasts maintain fibroblast identity. Murine plaque fibroblast subsets were conserved in human atherosclerosis. EC-like fibroblasts are linked to human plaque instability. Intervening in progenitor-to-specific fibroblast transitions could present a new avenue to promote plaque stability in atherosclerosis.

Atherosclerosis↗

PubMed2026

Bayesian inference of lineage trees by joint analysis of single-cell multimodal lineage-tracing data with BiLinT.

The advent of single-cell lineage-tracing technologies has enabled the simultaneous profiling of gene expression and lineage barcodes. However, accurate, high-resolution reconstruction of cell lineage trees remains challenging because most existing approaches treat these modalities separately and therefore fail to fully exploit their complementary information. Here we present BiLinT, a Bayesian framework that jointly models multimodal single-cell lineage-tracing data for lineage tree reconstruction. BiLinT integrates barcode evolution (a continuous-time Markov chain) with gene expression dynamics (an Ornstein-Uhlenbeck process) within a unified probabilistic model. Across synthetic and real data sets, BiLinT provides accurate lineage-tree reconstruction and reveals differentiation-associated clonal structure and developmental fate biases.

Journal Article↗

PubMed2025

Bayesian Phylogenetic Lineage Reconstruction with Loss of Heterozygosity Mutations Derived from Single-Cell RNA Sequencing.

Mutations are acquired frequently, such t`hat each cell's genome inscribes its history of cell divisions. Loss of heterozygosity (LOH) accumulates throughout the genome, offering large encoding capacity for phylogenetic inference of cell lineage.In this chapter, we demonstrate a method, using single-cell RNA sequencing, for reconstructing cell lineages from inferred LOH events in a Bayesian manner, annotating the lineage with cell phenotypes, and marking developmental time points based on X-chromosome inactivation. This type of retrospective analysis could be incorporated into scRNA-seq pipelines and was initially developed to investigate Emx1+ cortical projection neuron and glia lineages from C57Bl/6J (B6) and CAST/EiJ (CA) interstrain F1 mice, describing progenitor cells giving rise to multiple cortical cell types through stereotyped expansion and distinct waves of neurogenesis.

Animals↗

PubMed2025

Germline stem cell isolation, lineage tracing, and aging in a protochordate.

Germline stem cells (GSCs), the source of gametes, are the only stem cells capable of passing genes to future generations and are therefore considered units of natural selection. Yet, the factors that influence GSC fitness, and thus govern GSC competition, which exist in both protochordates and mammals, remain poorly understood. We studied how aging affects GSC fitness in the protochordate Botryllus schlosseri, an evolutionary crosspoint between invertebrates and vertebrates. GSCs were isolated and distinguished from developing and mature gametes using flow cytometry and scRNA-Seq, facilitated by a new PacBio genome assembly. Moreover, their function was validated through a novel lineage tracing approach that combines membrane-labeled GSC transplantation with scRNA-Seq. Leveraging our method to isolate them, single-cell transcriptomics showed significant age-related changes between young and old GSCs. Spermatids and sperm, however, showed minimal changes, suggesting that reproductive aging is governed by GSCs rather than by gametes. Reduced expressions of markers like DDX4 and PIWIL1 in aged GSCs mirrored trends in mammalian datasets, pointing to a conserved GSC-driven aging mechanism across chordate evolution. This study provides new techniques that lay the foundation to investigate further drivers of GSC fitness and highlights fertility-related genes as promising targets for therapies to preserve reproductive health.

Journal Article↗

PubMed2026

Hippocampal teneurin-4 knockdown promotes depression-like behavioral phenotypes by disrupting oligodendrocyte differentiation in mice.

Depression is one of the most prevalent mental disorders worldwide. The limited clinical efficacy of current antidepressants highlights identifying new therapeutic targets. Emerging evidence suggests that dysfunction of oligodendrocyte lineage cells contributes to the pathophysiology of depression. Teneurin-4 (Tenm4), a transmembrane protein that promotes oligodendrocyte differentiation and myelination, has been implicated in psychiatric disorders in genome-wide association studies; however, its causal role remains unclear. To determine whether Tenm4 contributes to depressive-like behavioral phenotypes, we examined Tenm4 protein expression in mice exposed to repeated forced swimming stress and generated hippocampal Tenm4 knockdown (Tenm4KD) mice. Chronic stress reduced Tenm4 expression levels in the hippocampus. Mice with hippocampus-specific Tenm4KD exhibited depressive-like behaviors, accompanied by reduced hippocampal myelin basic protein. Importantly, administration of clemastine, a myelin formation promoter, inhibited the reduction of myelin and attenuated depression-like behavioral phenotypes. Immunohistochemical analysis showed that Tenm4KD significantly decreased the number of mature oligodendrocyte cells and increased in the number of oligodendrocyte precursor cells, without changes in the total number of oligodendrocyte lineage cells. This study provides the first evidence that hippocampal Tenm4 deficiency induces depression-like behavior phenotypes through impaired oligodendrocyte differentiation and promoting demyelination. Our results identify Tenm4 as a molecular regulator of stress-induced behavioral phenotypes and suggest that it might represent a potential therapeutic target for mood disorders associated with demyelination.

Animals↗

PubMed2024

TGF-β controls alveolar type 1 epithelial cell plasticity and alveolar matrisome gene transcription in mice.

Premature birth disrupts normal lung development and places infants at risk for bronchopulmonary dysplasia (BPD), a disease disrupting lung health throughout the life of an individual and that is increasing in incidence. The TGF-β superfamily has been implicated in BPD pathogenesis, however, what cell lineage it impacts remains unclear. We show that TGFbr2 is critical for alveolar epithelial (AT1) cell fate maintenance and function. Loss of TGFbr2 in AT1 cells during late lung development leads to AT1-AT2 cell reprogramming and altered pulmonary architecture, which persists into adulthood. Restriction of fetal lung stretch and associated AT1 cell spreading through a model of oligohydramnios enhances AT1-AT2 reprogramming. Transcriptomic and proteomic analyses reveal the necessity of TGFbr2 expression in AT1 cells for extracellular matrix production. Moreover, TGF-β signaling regulates integrin transcription to alter AT1 cell morphology, which further impacts ECM expression through changes in mechanotransduction. These data reveal the cell intrinsic necessity of TGF-β signaling in maintaining AT1 cell fate and reveal this cell lineage as a major orchestrator of the alveolar matrisome.

Humans↗