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Spatial Location of SPP1+TAMs and Mutually Exclusive Subsets Based on Single-Cell and Transcriptome Data.
To clarify locations and mutually exclusive subsets of SPP1+tumor associated macrophages (SPP1+TAMs) in lung adenocarcinoma (LUAD). Analyze the polarization type, mutually exclusive cell subset, and spatial location of SPP1+TAMs based on the transcriptome data of the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) database, single-cell and spatial transcriptome data of the Gene Expression Omnibus database (GEO), immunohistochemical data of the Human Protein Atlas (HPA) database and local LUAD to provide a scientific basis for precision treatment of LUAD. Secreted phosphoprotein-1 (SPP1) was identified as the central regulatory gene of LUAD. The single-cell transcriptome data showed that SPP1 is up-regulated in invasive lung carcinoma (ILC), while macrophage receptor with collagenous structure (MARCO) is up-regulated in adenocarcinoma in situ (AIS). MARCO+M2 macrophages and SPP1+M2 macrophages clustered in different positions in the uniform manifold approximation and projection (UMAP) to form mutually exclusive cell subsets. SPP1+M2 plays a major role in ILC and MARCO+M2 plays a major role in AIS. The spatial transcriptome data showed that some SPP1+M2 were clustered at the edge of the tumor, and this particular spatial location may increase tumor invasiveness. Further spatial distance analysis showed that the distance was greater from the SPP1+M2 to alveolar type 2 epithelial cell (AT2) than from the MARCO+M2. Immunohistochemical analysis showed that SPP1 expression was up-regulated in the tumor or ILC group and notably higher in inflammatory cells. In summary, SPP1+M2 and MARCO+M2 form mutually exclusive cell subsets, and MARCO+M2 and SPP1+M2 synergistically promote different stages of tumor progression.
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Single-cell transcriptomes reveal spatiotemporal heat stress response in maize roots.
Plant roots perceive heat stress (HS) and adapt their architecture accordingly, which in turn influence the yield in crops. Investigating their heterogeneity and cell type-specific response to HS is essential for improving crop resilience. Here, we generate single-cell transcriptional landscape of maize (Zea mays) roots in response to HS. We characterize 15 cell clusters corresponding to 9 major cell types and identify cortex as the main root cell type responsive to HS with the most differentially expressed genes and its trajectory being preferentially affected upon HS. We find that cortex size strongly correlated with heat tolerance that is experimentally validated by using inbred lines and genetic mutation analysis of one candidate gene in maize, providing potential HS tolerance indicator and targets for crop improvement. Moreover, interspecies comparison reveals conserved root cell types and core markers in response to HS in plants, which are experimentally validated. These results provide a universal atlas for unraveling the transcriptional programs that specify and maintain the cell identity of maize roots in response to HS at a cell type-specific level.
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Annotation of cell types in single-cell sequencing for cardiovascular disease: Concepts, workflows, challenges, and best practices.
Single-cell and single-nucleus RNA sequencing (scRNA-seq and snRNA-seq) have transformed cardiovascular biology by resolving cellular heterogeneity and disease-specific cell states. The interpretive power of these technologies, however, hinges critically on accurate cell-type annotation, the assignment of biologically meaningful labels to clusters or individual cells. Mis-annotation risks systematic bias in mechanistic inference, obscures rare populations, and undermines cross-study reproducibility. This Review systematically integrates current conceptual frameworks, computational methodologies, and domain-specific best practices for cell-type annotation in cardiovascular single-cell research. We first examine the biological principles that underpin annotation, including the definition of cell types, the distinction between cell states and transitional phenotypes, considerations of granularity, and ontology mapping. We then delineate comprehensive analytical workflows spanning preprocessing and quality control (empty droplet filtering, doublet detection, normalization, and batch correction), dimensionality reduction (PCA, UMAP, t-SNE), and graph-based clustering. Turning to annotation strategies, we survey manual marker-based curation with canonical cardiovascular markers, reference-correlation methods (SingleR), supervised machine-learning classifiers (CellTypist, SingleCellNet, Garnett, scBERT), reference-mapping and label-transfer platforms (Azimuth, scArches, Symphony), and hybrid probabilistic frameworks (scANVI). We also summarize the software ecosystems of R/Seurat and Python/Scanpy/scvi-tools. Benchmarking evidence indicates that annotation accuracy depends more on reference data quality than on algorithmic sophistication. SingleR, CellTypist, and Azimuth have emerged as leading performers, whereas ensemble consensus approaches that integrate two or more independent methods enhance robustness. We further highlight cardiovascular-specific challenges, including modality-dependent differences in cardiomyocyte representation between scRNA-seq and snRNA-seq, vascular and stromal heterogeneity, immune cell tissue adaptation, disease-induced transitional cell states, and barriers to cross-species translation. Finally, we present a ten-step best-practice workflow, a comprehensive reporting and reproducibility checklist, and illustrative case studies drawn from the Adult Human Heart Atlas and CardioAtlas. We offer practical recommendations and discuss future directions encompassing multimodal integration, spatial transcriptomics, and AI-assisted annotation, all aimed at ensuring reproducible and interpretable annotations across laboratories [1-5].
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An atlas of neural crest lineages along the posterior developing zebrafish at single-cell resolution.
Neural crest cells (NCCs) are vertebrate stem cells that give rise to various cell types throughout the developing body in early life. Here, we utilized single-cell transcriptomic analyses to delineate NCC-derivatives along the posterior developing vertebrate, zebrafish, during the late embryonic to early larval stage, a period when NCCs are actively differentiating into distinct cellular lineages. We identified several major NCC/NCC-derived cell-types including mesenchyme, neural crest, neural, neuronal, glial, and pigment, from which we resolved over three dozen cellular subtypes. We dissected gene expression signatures of pigment progenitors delineating into chromatophore lineages, mesenchyme cells, and enteric NCCs transforming into enteric neurons. Global analysis of NCC derivatives revealed they were demarcated by combinatorial hox gene codes, with distinct profiles within neuronal cells. From these analyses, we present a comprehensive cell-type atlas that can be utilized as a valuable resource for further mechanistic and evolutionary investigations of NCC differentiation.
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Single-cell transcriptomics across 2,534 microbial species reveals functional heterogeneity in the rumen microbiome.
Deciphering the activity of individual microbes within complex communities and environments remains a challenge. Here we describe the development of microbiome single-cell transcriptomics using droplet-based single-cell RNA sequencing and pangenome-based computational analysis to characterize the functional heterogeneity of the rumen microbiome. We generated a microbial genome database (the Bovine Gastro Microbial Genome Map) as a functional reference map for the construction of a single-cell transcriptomic atlas of the rumen microbiome. The atlas includes 174,531 microbial cells and 2,534 species, of which 172 are core active species grouped into 12 functional clusters. We detected single-cell-level functional roles, including a key role for Basfia succiniciproducens in the carbohydrate metabolic niche of the rumen microbiome. Furthermore, we explored functional heterogeneity and reveal metabolic niche trajectories driven by biofilm formation pathway genes within B. succiniciproducens. Our results provide a resource for studying the rumen microbiome and illustrate the diverse functions of individual microbial cells that drive their ecological niche stability or adaptation within the ecosystem.
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Single-nuclei RNA sequencing reveals obesity-associated rewiring of estrogen signaling in endometrioid adenocarcinoma.
Endometrioid adenocarcinoma has one of the strongest body mass index associations of any solid tumor. This is widely attributed to amplification of estrogen-driven signaling via increased production of unopposed estrogen in peripheral adipose tissue. However, there is little evidence as to whether this is a pure dosage effect or if persistent signaling leads to feedback on the execution of that signaling. Using single-nucleus RNA sequencing of primary tumors from postmenopausal patients with normal and obese body mass indices, we identified coordinated transcriptional remodeling across tumor epithelial, immune, and stromal compartments. Network-level analysis of tumor epithelial cells revealed that ESR1-associated co-expression modules in tumors from patients with obesity were distinct across two independent patient cohorts and transcriptomic modalities. These network constructions were then shown to be enriched for estrogen receptor targets using existing chromatin immunoprecipitation data from endometrioid adenocarcinoma cell lines and normal endometrial tissue, as well as RNA-seq following exogenous exposure to estrogen for the cell lines. Thus, this study provides a single-nuclei atlas of endometrioid adenocarcinoma and suggests that obesity likely both amplifies estrogenic signaling and qualitatively reshapes its regulatory context.
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Transcriptomic Analysis of the Ocular Posterior Segment Completes a Cell Atlas of the Human Eye.
Although the visual system extends through the brain, most vision loss originates from defects in the eye. Its central element is the neural retina, which senses light, processes visual signals, and transmits them to the rest of the brain through the optic nerve (ON). Surrounding the retina are numerous other structures, conventionally divided into anterior and posterior segments. Here we used high-throughput single nucleus RNA sequencing (snRNA-seq) to classify and characterize cells in the extraretinal components of the posterior segment: ON, optic nerve head (ONH), peripheral sclera, peripapillary sclera (PPS), choroid, and retinal pigment epithelium (RPE). Defects in each of these tissues are associated with blinding diseases - for example, glaucoma (ONH and PPS), optic neuritis (ON), retinitis pigmentosa (RPE), and age-related macular degeneration (RPE and choroid). From ∼151,000 single nuclei, we identified 37 transcriptomically distinct cell types, including multiple types of astrocytes, oligodendrocytes, fibroblasts, and vascular endothelial cells. Our analyses revealed a differential distribution of many cell types among distinct structures. Together with our previous analyses of the anterior segment and retina, the new data complete a "Version 1" cell atlas of the human eye. We used this atlas to map the expression of >180 genes associated with the risk of developing glaucoma, which is known to involve ocular tissues in both anterior and posterior segments as well as neural retina. Similar methods can be used to investigate numerous additional ocular diseases, many of which are currently untreatable.
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Beaconet: A Reference-Free Method for Integrating Multiple Batches of Single-Cell Transcriptomic Data in Original Molecular Space.
Integrating multiple single-cell datasets is essential for the comprehensive understanding of cell heterogeneity. Batch effect is the undesired systematic variations among technologies or experimental laboratories that distort biological signals and hinder the integration of single-cell datasets. However, existing methods typically rely on a selected dataset as a reference, leading to inconsistent integration performance using different references, or embed cells into uninterpretable low-dimensional feature space. To overcome these limitations, a reference-free method, Beaconet, for integrating multiple single-cell transcriptomic datasets in original molecular space by aligning the global distribution of each batch using an adversarial correction network is presented. Through extensive comparisons with 13 state-of-the-art methods, it is demonstrated that Beaconet can effectively remove batch effect while preserving biological variations and is superior to existing unsupervised methods using all possible references in overall performance. Furthermore, Beaconet performs integration in the original molecular feature space, enabling the characterization of cell types and downstream differential expression analysis directly using integrated data with gene-expression features. Additionally, when applying to large-scale atlas data integration, Beaconet shows notable advantages in both time- and space-efficiencies. In summary, Beaconet serves as an effective and efficient batch effect removal tool that can facilitate the integration of single-cell datasets in a reference-free and molecular feature-preserved mode.
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Single-cell atlas of the small intestine throughout the human lifespan demonstrates unique features of fetal immune cells.
Proper development of mucosal immunity is critical for human health. Over the past decade, it has become evident that in humans, this process begins in utero. However, there are limited data on the unique features and functions of fetal mucosal immune cells. To address this gap, we integrated several single-cell ribonucleic acid sequencing datasets of the human small intestine (SI) to create an SI transcriptional atlas throughout the human life span, ranging from the first trimester to adulthood, with a focus on immune cells. Fetal SI displayed a complex immune landscape comprising innate and adaptive immune cells that exhibited distinct transcriptional programs from postnatal samples, especially compared with pediatric and adult samples. We identified shifts in myeloid populations across gestation and progression of memory T-cell states throughout the human lifespan. In particular, there was a marked shift of memory T cells from those with stem-like properties in the fetal samples to fully differentiated cells with a high expression of activation and effector function genes in adult samples, with neonatal samples containing both features. Finally, we demonstrate that the SI developmental atlas can be used to elucidate improper trajectories linked to mucosal diseases by implicating developmental abnormalities underlying necrotizing enterocolitis, a severe intestinal complication of prematurity. Collectively, our data provide valuable resources and important insights into intestinal immunity that will facilitate regenerative medicine and disease understanding.
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Phenotype Scoring of Population Scale Single-Cell Data Dissects Alzheimer's Disease Complexity.
The complexity of Alzheimer's disease (AD) manifests in diverse clinical phenotypes, including cognitive impairment and neuropsychiatric symptoms (NPSs). However, the etiology of these phenotypes remains elusive. To address this, the PsychAD project generated a population-level single-nucleus RNA-seq dataset comprising over 6 million nuclei from the prefrontal cortex of 1,494 individual brains, covering a variety of AD-related phenotypes that capture cognitive impairment, severity of pathological lesions, and the presence of NPSs. Leveraging this dataset, we developed a deep learning framework, called Phenotype Associated Single Cell encoder (PASCode), to score single-cell phenotype associations, and identified ∼1.5 million phenotype associate cells (PACs). We compared PACs within 27 distinct brain cell subclasses and prioritized cell subpopulations and their expressed genes across various AD phenotypes, including the upregulation of a reactive astrocyte subtype with neuroprotective function in AD resilient donors. Additionally, we identified PACs that link multiple phenotypes, including a subpopulation of protoplasmic astrocytes that alter their gene expression and regulation in AD donors with depression. Uncovering the cellular and molecular mechanisms underlying diverse AD phenotypes has the potential to provide valuable insights towards the identification of novel diagnostic markers and therapeutic targets. All identified PACs, along with cell type and gene expression information, are summarized into an AD-phenotypic single-cell atlas for the research community.