Cellular Landscape Research Articles

MiLoPYP: self-supervised molecular pattern mining and particle localization in situ

Cryo-electron tomography allows the routine visualization of cellular landscapes in three dimensions at nanometer-range resolutions. When combined with single-particle tomography, it is possible to obtain near-atomic resolution structures of frequently occurring macromolecules within their native environment. Two outstanding challenges associated with cryo-electron tomography/single-particle tomography are the automatic identification and localization of proteins, tasks that are hindered by the molecular crowding inside cells, imaging distortions characteristic of cryo-electron tomography tomograms and the sheer size of tomographic datasets. Current methods suffer from low accuracy, demand extensive and time-consuming manual labeling or are limited to the detection of specific types of proteins. Here, we present MiLoPYP, a two-step dataset-specific contrastive learning-based framework that enables fast molecular pattern mining followed by accurate protein localization. MiLoPYP’s ability to effectively detect and localize a wide range of targets including globular and tubular complexes as well as large membrane proteins, will contribute to streamline and broaden the applicability of high-resolution workflows for in situ structure determination.

Intracellular spatial transcriptomic analysis toolkit (InSTAnT)

Imaging-based spatial transcriptomics technologies such as Multiplexed error-robust fluorescence in situ hybridization (MERFISH) can capture cellular processes in unparalleled detail. However, rigorous and robust analytical tools are needed to unlock their full potential for discovering subcellular biological patterns. We present Intracellular Spatial Transcriptomic Analysis Toolkit (InSTAnT), a computational toolkit for extracting molecular relationships from spatial transcriptomics data at single molecule resolution. InSTAnT employs specialized statistical tests and algorithms to detect gene pairs and modules exhibiting intriguing patterns of co-localization, both within individual cells and across the cellular landscape. We showcase the toolkit on five different datasets representing two different cell lines, two brain structures, two species, and three different technologies. We perform rigorous statistical assessment of discovered co-localization patterns, find supporting evidence from databases and RNA interactions, and identify associated subcellular domains. We uncover several cell type and region-specific gene co-localizations within the brain. Intra-cellular spatial patterns discovered by InSTAnT mirror diverse molecular relationships, including RNA interactions and shared sub-cellular localization or function, providing a rich compendium of testable hypotheses regarding molecular functions.

Deciphering the cellular and molecular landscapes of Wnt/β-catenin signaling in mouse embryonic kidney development

BackgroundThe Wnt/β-catenin signaling pathway is critical in kidney development, yet its specific effects on gene expression in different embryonic kidney cell types are not fully understood. MethodsWnt/β-catenin signaling was activated in mouse E12.5 kidneys in vitro using CHIR99021, with RNA sequencing performed afterward, and the results were compared to DMSO controls (dataset GSE131240). Differential gene expression in ureteric buds and cap mesenchyme following pathway activation (datasets GSE20325 and GSE39583) was analyzed. Single-cell RNA-seq data from the Mouse Cell Atlas was used to link differentially expressed genes (DEGs) with kidney cell types. β-catenin ChIP-seq data (GSE39837) identified direct transcriptional targets. ResultsActivation of Wnt/β-catenin signaling led to 917 significant DEGs, including the upregulation of Notum and Apcdd1 and the downregulation of Crym and Six2. These DEGs were involved in kidney development and immune response. Single-cell analysis identified 787 DEGs across nineteen cell subtypes, with Macrophage_Apoe high cells showing the most pronounced enrichment of Wnt/β-catenin-activated genes. Gene expression profiles in ureteric buds and cap mesenchyme differed significantly upon β-catenin manipulation, with cap mesenchyme showing a unique set of DEGs. Analysis of β-catenin ChIP-seq data revealed 221 potential direct targets, including Dpp6 and Fgf12. ConclusionThis study maps the complex gene expression driven by Wnt/β-catenin signaling in embryonic kidney cell types. The identified DEGs and β-catenin targets elucidate the molecular details of kidney development and the pathway's role in immune processes, providing a foundation for further research into Wnt/β-catenin signaling in kidney development and disease.

A physics-informed neural SDE network for learning cellular dynamics from time-series scRNA-seq data.

Learning cellular dynamics through reconstruction of the underlying cellular potential energy landscape (aka Waddington landscape) from time-series single-cell RNA sequencing (scRNA-seq) data is a current challenge. Prevailing data-driven computational methods can be hampered by the lack of physical principles to guide learning from complex data, resulting in reduced prediction accuracy and interpretability when applied to infer cell population dynamics. Here, we propose PI-SDE, a physics-informed neural stochastic differential equation (SDE) framework that combines the Hamilton-Jacobi (HJ) equation and neural SDE to learn cellular dynamics. Grounded in potential energy theory of biological systems, PI-SDE integrates the principle of least action by enforcing the HJ equation when reconstructing cellular potential energy function. This approach not only facilitates accurate predictions, but also improves interpretability, especially in the reconstructed potential energy landscape. Through benchmarking on two real scRNA-seq datasets, we demonstrate the importance of incorporating the HJ regularization term in dynamic inference, especially in predicting gene expression at held-out time points. Meanwhile, the learned potential energy landscape provides biologically interpretable insights into the process of cell differentiation. Our framework enhances model performance, while maintaining robustness and stability. PI-SDE software is available at https://github.com/QiJiang-QJ/PI-SDE.

Molecular and spatial signatures of human and rat corpus cavernosum physiopathological processes at single-cell resolution

The composition and cellular heterogeneity of the corpus cavernosum (CC) microenvironment have been characterized, but the spatial heterogeneity at the molecular level remains unexplored. In this study, we integrate single-cell RNA sequencing (scRNA-seq) and spatial transcriptome sequencing to comprehensively chart the spatial cellular landscape of the human and rat CC under normal and disease conditions. We observe differences in the proportions of cell subtypes and marker genes between humans and rats. Based on the analysis of the fibroblast (FB) niche, we also find that the enrichment scores of mechanical force signaling vary across different regions and correlate with the spatial distribution of FB subtypes. Invitro, the soft and hard extracellular matrix (ECM) induces the differentiation of FBs into apolipoprotein (APO)+ FBs and cartilage oligomeric matrix protein (COMP)+ FBs, respectively. In summary, our study provides a cross-species and physiopathological transcriptomic atlas of the CC, contributing to a further understanding of the molecular anatomy and regulation of penile erection.

Adenoid cystic carcinoma: insights from molecular characterization and therapeutic advances.

Adenoid cystic carcinoma (ACC) is a malignant tumor primarily originating from the salivary glands, capable of affecting multiple organs. Although ACC typically exhibits slow growth, it is notorious for its propensity for neural invasion, local recurrence, and distant metastasis, making it a particularly challenging cancer to treat. The complexity of ACC's histological and molecular features poses significant challenges to current treatment modalities, which often show limited effectiveness. Recent advancements in single-cell RNA-sequencing (scRNA-seq) have begun to unravel unprecedented insights into the heterogeneity and subpopulation diversity within ACC, revealing distinct cellular phenotypes and origins. This review delves into the intricate pathological and molecular characteristics of ACC, focusing on recent therapeutic advancements. We particularly emphasize the insights gained from scRNA-seq studies that shed light on the cellular landscape of ACC, underscoring its heterogeneity and pathobiology. Moreover, by integrating analyses from public databases, this review proposes novel perspectives for advancing treatment strategies in ACC. This review contributes to the academic understanding of ACC by proposing novel therapeutic approaches informed by cutting-edge molecular insights, paving the way for more effective, personalized therapeutic approaches for this challenging malignancy.

ScCAD: Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression data

Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for characterizing cellular landscapes within complex tissues. Large-scale single-cell transcriptomics holds great potential for identifying rare cell types critical to the pathogenesis of diseases and biological processes. Existing methods for identifying rare cell types often rely on one-time clustering using partial or global gene expression. However, these rare cell types may be overlooked during the clustering phase, posing challenges for their accurate identification. In this paper, we propose a Cluster decomposition-based Anomaly Detection method (scCAD), which iteratively decomposes clusters based on the most differential signals in each cluster to effectively separate rare cell types and achieve accurate identification. We benchmark scCAD on 25 real-world scRNA-seq datasets, demonstrating its superior performance compared to 10 state-of-the-art methods. In-depth case studies across diverse datasets, including mouse airway, brain, intestine, human pancreas, immunology data, and clear cell renal cell carcinoma, showcase scCAD’s efficiency in identifying rare cell types in complex biological scenarios. Furthermore, scCAD can correct the annotation of rare cell types and identify immune cell subtypes associated with disease, thereby offering valuable insights into disease progression.

Deciphering the cellular landscape and potential targets of nasopharyngeal and oral cancers using single-cell RNA sequencing.

Cellular heterogeneity in nasopharyngeal cancer (NPC) and oral cancer remains unclear. In the current study, using single-cell RNA sequencing techniques, we investigated the cellular landscape in NPC and oral cancers. We identified a diverse range of cell types within the tumor microenvironment (TME) and variations in cell infiltration between NPC and oral cancer. In oral cancer, we observed a predominant infiltration of epithelial cells, fibroblasts, and endothelial cells (ECs), while T cells were the main infiltrating cell population in NPCs. We further classified these infiltrating cells into subclusters. Additionally, we observed complex interactions among cells that led to distinct trajectories. In particular, a unique epithelial subcluster with high expression of major histocompatibility complex class II (MHC-II) molecules was correlated with a favorable outcome and infiltration of CD4+ T cells. In addition, MHC-II+ epithelial cells inhibited mouse tumor growth and promoted T-cell infiltration. Consequently, our findings provide a deep understanding of the TME showing a significant prognostic value and therapeutic potential.

Single-cell landscape of intratumoral heterogeneity and tumor microenvironment remolding in pre-nodal metastases of breast cancer

BackgroundThe metastasis of cancer cells is influenced by both their intrinsic characteristics and the tumor microenvironment (TME). However, the molecular mechanisms underlying pre-nodal metastases of breast cancer remain unclear.MethodsWe integrated a total of 216,963 cells from 54 samples across 6 single-cell datasets to profile the cellular landscape differences between primary tumors and pre-nodal metastases.ResultsWe revealed three distinct metastatic epithelial cell subtypes (Epi1, Epi2 and Epi3), which exhibited different metastatic mechanisms. Specifically, the marker gene KCNK15 of the Epi1 subtype exhibited increased gene expression along the cell differentiation trajectory and was specifically regulated by the transcription factor ASCL1. In the Epi3 subtype, we highlighted NR2F1 as a regulator targeting the marker gene MUCL1. Additionally, we found that the Epi2 and Epi3 subtypes shared some regulons, such as ZEB1 and NR2C1. Similarly, we identified specific subtypes of stromal and immune cells in the TME, and discovered that vascular cancer-associated fibroblasts might promote capillary formation through CXCL9+ macrophages in pre-nodal metastases. All three subtypes of metastatic epithelial cells were associated with poor prognosis.ConclusionsIn summary, this study dissects the intratumoral heterogeneity and remodeling of the TME in pre-nodal metastases of breast cancer, providing novel insights into the mechanisms underlying breast cancer metastasis.

Computational Analysis of Marker Genes in Alzheimer’s Disease across Multiple Brain Regions

Alzheimer’s disease (AD) is the most common cause of neurodegenerative dementia in the elderly, which is characterized by progressive cognitive impairment. Herein, we undertake a sophisticated computational analysis by integrating single-cell RNA sequencing (scRNA-seq) data from multiple brain regions significantly affected by the disease, including the entorhinal cortex, prefrontal cortex, superior frontal gyrus, and superior parietal lobe. Our pipeline combines datasets derived from the aforementioned tissues into a unified analysis framework, facilitating cross-regional comparisons to provide a holistic view of the impact of the disease on the cellular and molecular landscape of the brain. We employed advanced computational techniques such as batch effect correction, normalization, dimensionality reduction, clustering, and visualization to explore cellular heterogeneity and gene expression patterns across these regions. Our findings suggest that enabling the integration of data from multiple batches can significantly enhance our understanding of AD complexity, thereby identifying key molecular targets for potential therapeutic intervention. This study established a precedent for future research by demonstrating how existing data can be reanalysed in a coherent manner to elucidate the systemic nature of the disease and inform the development of more effective diagnostic tools and targeted therapies.

ESR1 Fusions Invoke Breast Cancer Subtype-Dependent Enrichment of Ligand-Independent Oncogenic Signatures and Phenotypes.

Breast cancer is a leading cause of female mortality and despite advancements in personalized therapeutics, metastatic disease largely remains incurable due to drug resistance. The estrogen receptor (ER, ESR1) is expressed in two-thirds of all breast cancer, and under endocrine stress, somatic ESR1 mutations arise in approximately 30% of cases that result in endocrine resistance. We and others reported ESR1 fusions as a mechanism of ER-mediated endocrine resistance. ER fusions, which retain the activation function 1- and DNA-binding domains, harbor ESR1 exons 1 to 6 fused to an in-frame gene partner resulting in loss of the ER ligand-binding domain (LBD). We demonstrate that in a no-special type (invasive ductal carcinoma [IDC]-NST) and an invasive lobular carcinoma (ILC) cell line, ER fusions exhibit robust hyperactivation of canonical ER signaling pathways independent of estradiol or antiendocrine therapies. We employ cell line models stably overexpressing ER fusions with concurrent endogenous ER knockdown to minimize endogenous ER influence. Cell lines exhibited shared transcriptomic enrichment in pathways known to be drivers of metastatic disease, notably MYC signaling. Cells expressing the 3' fusion partners SOX9 and YAP1 consistently demonstrated enhanced growth and cell survival. ILC cells expressing the DAB2 fusion led to enhanced growth, survival, and migration, phenotypes not appreciated in the IDC-NST DAB2 model. Herein, we report that cell line activity is subtype-, fusion-, and assay-specific, suggesting that LBD loss, the fusion partner, and the cellular landscape all influence fusion activities. Therefore, it will be critical to assess fusion frequency in the context of the clinicopathology.

A deep learning method that identifies cellular heterogeneity using nanoscale nuclear features

Cellular phenotypic heterogeneity is an important hallmark of many biological processes and understanding its origins remains a substantial challenge. This heterogeneity often reflects variations in the chromatin structure, influenced by factors such as viral infections and cancer, which dramatically reshape the cellular landscape. To address the challenge of identifying distinct cell states, we developed artificial intelligence of the nucleus (AINU), a deep learning method that can identify specific nuclear signatures at the nanoscale resolution. AINU can distinguish different cell states based on the spatial arrangement of core histone H3, RNA polymerase II or DNA from super-resolution microscopy images. With only a small number of images as the training data, AINU correctly identifies human somatic cells, human-induced pluripotent stem cells, very early stage infected cells transduced with DNA herpes simplex virus type 1 and even cancer cells after appropriate retraining. Finally, using AI interpretability methods, we find that the RNA polymerase II localizations in the nucleoli aid in distinguishing human-induced pluripotent stem cells from their somatic cells. Overall, AINU coupled with super-resolution microscopy of nuclear structures provides a robust tool for the precise detection of cellular heterogeneity, with considerable potential for advancing diagnostics and therapies in regenerative medicine, virology and cancer biology.

Single-cell RNA sequencing in endometrial cancer: exploring the epithelial cells and the microenvironment landscape.

Single-cell RNA sequencing (scRNA-seq) technology has emerged as a powerful tool for dissecting cellular heterogeneity and understanding the intricate biology of diseases, including cancer. Endometrial cancer (EC) stands out as the most prevalent gynecological malignancy in Europe and the second most diagnosed worldwide, yet its cellular complexity remains poorly understood. In this review, we explore the contributions of scRNA-seq studies to shed light on the tumor cells and cellular landscape of EC. We discuss the diverse tumoral and microenvironmental populations identified through scRNA-seq, highlighting the implications for understanding disease progression. Furthermore, we address potential limitations inherent in scRNA-seq studies, such as technical biases and sample size constraints, emphasizing the need for larger-scale research encompassing a broader spectrum of EC histological subtypes. Notably, a significant proportion of scRNA-seq analyses have focused on primary endometrioid carcinoma tumors, underscoring the need to incorporate additional histological and aggressive types to comprehensively capture the heterogeneity of EC. By critically evaluating the current state of scRNA-seq research in EC, this review underscores the importance of advancing towards more comprehensive studies to accelerate our understanding of this complex disease.

Beyond the Heartbeat: Single-Cell Omics Redefining Cardiovascular Research.

This review aims to explore recent advances in single-cell omics techniques as applied to various regions of the human heart, illuminating cellular diversity, regulatory networks, and disease mechanisms. We examine the contributions of single-cell transcriptomics, genomics, proteomics, epigenomics, and spatial transcriptomics in unraveling the complexity of cardiac tissues. Recent strides in single-cell omics technologies have revolutionized our understanding of the heart's cellular composition, cell type heterogeneity, and molecular dynamics. These advancements have elucidated pathological conditions as well as the cellular landscape in heart development. We highlight emerging applications of integrated single-cell omics, particularly for cardiac regeneration, disease modeling, and precision medicine, and emphasize the transformative potential of these technologies to advance cardiovascular research and clinical practice.

From Cell to Gene: Deciphering the Mechanism of Heart Failure With Single-Cell Sequencing.

Heart failure (HF) is a prevalent cardiovascular disease with significant morbidity and mortality rates worldwide. Due to the intricate structure of the heart, diverse cell types, and the complex pathogenesis of HF, further in-depth investigation into the underlying mechanismsis required. The elucidation of the heterogeneity of cardiomyocytes and the intercellular communication network is particularly important. Traditional high-throughput sequencing methods provide an average measure of gene expression, failing to capture the "heterogeneity" between cells and impacting the accuracy of gene function knowledge. In contrast, single-cell sequencing techniques allow for the amplification of the entire genome or transcriptome at the individual cell level, facilitating the examination of gene structure and expression with unparalleled precision. This approach offers valuable insights into disease mechanisms, enabling the identification of changes in cellular components and gene expressions during hypertrophy associated with HF. Moreover, it reveals distinct cell populations and their unique roles in the HF microenvironment, providing a comprehensive understanding of the cellular landscape that underpins HF pathogenesis. This review focuses on the insights provided by single-cell sequencing techniques into the mechanisms underlying HF and discusses the challenges encountered in current cardiovascular research.

The Current Molecular and Cellular Landscape of Chronic Obstructive Pulmonary Disease (COPD): A Review of Therapies and Efforts towards Personalized Treatment.

Chronic obstructive pulmonary disease (COPD) ranks as the third leading cause of global illness and mortality. It is commonly triggered by exposure to respiratory irritants like cigarette smoke or biofuel pollutants. This multifaceted condition manifests through an array of symptoms and lung irregularities, characterized by chronic inflammation and reduced lung function. Present therapies primarily rely on maintenance medications to alleviate symptoms, but fall short in impeding disease advancement. COPD's diverse nature, influenced by various phenotypes, complicates diagnosis, necessitating precise molecular characterization. Omics-driven methodologies, including biomarker identification and therapeutic target exploration, offer a promising avenue for addressing COPD's complexity. This analysis underscores the critical necessity of improving molecular profiling to deepen our comprehension of COPD and identify potential therapeutic targets. Moreover, it advocates for tailoring treatment strategies to individual phenotypes. Through comprehensive exploration-based molecular characterization and the adoption of personalized methodologies, innovative treatments may emerge that are capable of altering the trajectory of COPD, instilling optimism for efficacious disease-modifying interventions.

Aging human abdominal subcutaneous white adipose tissue at single cell resolution.

White adipose tissue (WAT) is a robust energy storage and endocrine organ critical for maintaining metabolic health as we age. Our aim was to identify cell-specific transcriptional aberrations that occur in WAT with aging. We leveraged full-length snRNA-Seq and histology to characterize the cellular landscape of human abdominal subcutaneous WAT in a prospective cohort of 10 younger (≤30 years) and 10 older individuals (≥65 years) balanced for sex and body mass index (BMI). The older group had greater cholesterol, very-low-density lipoprotein, triglycerides, thyroid stimulating hormone, and aspartate transaminase compared to the younger group (p < 0.05). We highlight that aging WAT is associated with adipocyte hypertrophy, increased proportions of lipid-associated macrophages and mast cells, an upregulation of immune responses linked to fibrosis in pre-adipocyte, adipocyte, and vascular populations, and highlight CXCL14 as a biomarker of these processes. We show that older WAT has elevated levels of senescence marker p16 in adipocytes and identify the adipocyte subpopulation driving this senescence profile. We confirm that these transcriptional and phenotypical changes occur without overt fibrosis and in older individuals that have comparable WAT insulin sensitivity to the younger individuals.

Abstract Tu037: Unraveling the Cellular Mechanisms of Cardiogenic Liver Disease

Background: Right heart failure (RHF) is associated with chronically elevated central venous pressures which can lead to hepatic dysfunction, fibrosis, and ultimately cirrhosis. Despite the prognostic importance of liver disease in RHF little is understood about the cellular and molecular changes that define cardiogenic liver disease. Filling this knowledge gap has the potential to lead to better diagnostic and therapeutic approaches for patients with heart failure and liver dysfunction. Aims: 1) To evaluate the key clinical and pathologic features of cardiogenic liver disease in humans 2) To explore shifts in the cellular landscape in the congested liver using human samples and a mouse model congestive hepatopathy. Methods: We identified 34 patients with heart failure who underwent liver biopsy due to concern for cardiogenic liver disease. These biopsy samples were stained for numerous cellular markers (macrophage subsets, activated stellate cells, endothelial cells, biliary cells) and compared to normal controls. Clinical data was also collected for the included patients. To model cardiogenic liver disease in a mouse we performed partial IVC ligation (pIVCL) to induce liver congestion and then assessed the liver response using flow cytometry, tissue imaging and mRNA analysis. Results: Liver biopsies from patients with cardiogenic liver disease demonstrated increased liver fibrosis, macrophage aggregates, and biliary metaplasia. The severity of biliary metaplasia was closely associated with the extent of liver congestion. Using multi-marker staining the metaplastic cells were found to be of hepatocyte origin. Interestingly, the liver niche containing metaplastic biliary cells was also enriched for recruited macrophages and activated stellate cells. Similar pathologic features were observed in the mouse model of liver congestion. Conclusions: In this study we identified biliary metaplasia and macrophage recruitment as key pathologic features of cardiogenic liver disease. Ongoing investigation of the interplay between macrophages and metaplastic biliary in humans and animal models of congestive hepatopathy may lead to the discovery of novel disease mechanisms and biomarkers for liver dysfunction in heart failure.

Comprehensive insights into the understanding of hypoxia in ameloblastoma.

Hypoxia is characterized by a disparity between supply and demand of oxygen. The association between hypoxia and head and neck tumors is a topic of significant interest. Tumors frequently encounter areas with inadequate oxygen supply, resulting in a hypoxic microenvironment. Ameloblastoma is one of the most common benign odontogenic tumors of the maxillofacial region. It is a slow-growing but locally invasive tumor with a high recurrence rate. The literature has demonstrated the correlation between hypoxia and ameloblastoma, revealing a discernible link between the heightened expression of hypoxic markers in low oxygen conditions. This association is intricately tied to the tumoral potential for invasion, progression, and malignant transformation. Hypoxia profoundly influences the molecular and cellular landscape within ameloblastic lesions. The present review sheds light on the mechanisms, implications, and emerging perspectives in understanding this intriguing association to clarify the dynamic relationship between hypoxia and ameloblastoma.

Altered lipid metabolism promoting cardiac fibrosis is mediated by CD34+ cell-derived FABP4+ fibroblasts

Hyperlipidemia and hypertension might play a role in cardiac fibrosis, in which a heterogeneous population of fibroblasts seems important. However, it is unknown whether CD34+ progenitor cells are involved in the pathogenesis of heart fibrosis. This study aimed to explore the mechanism of CD34+ cell differentiation in cardiac fibrosis during hyperlipidemia. Through the analysis of transcriptomes from 50,870 single cells extracted from mouse hearts and 76,851 single cells from human hearts, we have effectively demonstrated the evolving cellular landscape throughout cardiac fibrosis. Disturbances in lipid metabolism can accelerate the development of fibrosis. Through the integration of bone marrow transplantation models and lineage tracing, our study showed that hyperlipidemia can expedite the differentiation of non-bone marrow-derived CD34+ cells into fibroblasts, particularly FABP4+ fibroblasts, in response to angiotensin II. Interestingly, the partial depletion of CD34+ cells led to a notable reduction in triglycerides in the heart, mitigated fibrosis, and improved cardiac function. Furthermore, immunostaining of human heart tissue revealed colocalization of CD34+ cells and fibroblasts. Mechanistically, our investigation of single-cell RNA sequencing data through pseudotime analysis combined with in vitro cellular studies revealed the crucial role of the PPARγ/Akt/Gsk3β pathway in orchestrating the differentiation of CD34+ cells into FABP4+ fibroblasts. Through our study, we generated valuable insights into the cellular landscape of CD34+ cell-derived cells in the hypertrophic heart with hyperlipidemia, indicating that the differentiation of non-bone marrow-derived CD34+ cells into FABP4+ fibroblasts during this process accelerates lipid accumulation and promotes heart failure via the PPARγ/Akt/Gsk3β pathway.