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A single-cell transcriptomic atlas of the pigtail macaque placenta in late gestation.
BACKGROUND: The placenta is a complex organ with multiple immune and non-immune cell types that promote fetal tolerance and facilitate the transfer of nutrients and oxygen. The nonhuman primate (NHP) is a key experimental model for studying human pregnancy complications, in part due to similarities in placental structure, which makes it essential to understand how single-cell populations compare across the human and NHP maternal-fetal interface. We constructed a single-cell RNA-Seq (scRNA-Seq) atlas of the placenta from the pigtail macaque (Macaca nemestrina) in the third trimester, comprising three different tissues at the maternal-fetal interface: the chorionic villi (placental disc), chorioamniotic membranes, and the maternal decidua. Each tissue was separately dissociated into single cells and processed through the 10X Genomics and Seurat pipeline, followed by aggregation, unsupervised clustering, and cluster annotation. Next, we determined the maternal-fetal origins of cell populations and analyzed single-cell RNA trajectory, Gene Ontology enrichment, and cell-cell communication. RESULTS: Single-cell populations in the pigtail macaque were strikingly similar in their identity and frequency to those found in the human placenta, including cells from trophoblast, stromal cell, immune, and macrophage lineages. An advantage of our approach was the deep sequencing of three tissues at the maternal-fetal interface, which yielded a rich diversity of common and rare single-cell populations. CONCLUSIONS: Single-cell populations from the third-trimester pigtail macaque placenta are highly concordant with those from the human placenta. This scRNA-Seq atlas provides a powerful resource for understanding experimental perturbations on the NHP placenta.
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Lamprey 3D single-cell transcriptomics reveals ancestral and specialized features of the vertebrate brain.
The lamprey occupies a pivotal position for elucidating vertebrate brain evolution. Using spatial transcriptomics and single-nucleus RNA sequencing, we generated a three-dimensional molecular atlas of the lamprey brain, identifying 209 distinct cell clusters across 14 regions. Cross-species comparisons revealed broad conservation of regional spatial architecture, defining an ancestral organizational blueprint. Within this conserved framework, however, marked lineage-specific divergence emerged. We observed extensive neuronal specialization across vertebrate lineages, accompanied by regulatory shifts associated with spatial reorganization and functional diversification of neuronal populations. Additionally, our results suggest that a cerebellum-like architecture predates the jawed vertebrate cerebellum. Together, these findings identified constraints on neural organization and detected cellular innovations driving evolutionary diversification.
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Single-cell transcriptomic integrated with machine learning reveals human retinal cell-specific biomarkers in diabetic retinopathy.
BACKGROUND: Diabetic retinopathy (DR) is a leading cause of vision impairment worldwide, yet the cell-type-specific molecular alterations associated with disease progression remain incompletely understood. This study aimed to characterize transcriptional changes across retinal cell types in diabetes and DR and identify candidate disease-associated biomarkers using single-cell transcriptomics and machine-learning approaches. METHODS: We generated a single-cell RNA sequencing (scRNA-seq) atlas comprising 297 121 high-quality retinal cells from 20 eyes of 13 Chinese donors, including non-diabetic controls (NON), diabetes without retinopathy (DM), and DR samples. Following quality control, batch correction, clustering, and cell-type annotation, differential expression analyses were performed across disease states within each retinal cell type. Candidate biomarkers were further prioritized using a machine-learning framework incorporating L1-regularized logistic regression, recursive feature elimination with cross-validation, and stability selection. RESULTS: We identified 10 major retinal cell populations and characterized extensive cell-type-specific transcriptional alterations associated with diabetes and DR. Pathway enrichment analyses consistently highlighted immune activation, oxidative stress, neurodegeneration, and synaptic dysfunction across multiple retinal cell types. A total of 707 cell-type-specific candidate marker genes were identified, providing a comprehensive resource for investigating disease-associated molecular mechanisms and potential therapeutic targets. CONCLUSIONS: This study establishes a single-cell transcriptomic atlas of the Chinese diabetic retina and reveals cell-type-specific molecular signatures associated with DR progression. These findings provide biological insights into retinal disease mechanisms and nominate candidate biomarkers for future functional and translational studies.
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Single-cell transcriptome analysis reveals liver injury induced by glyphosate in mice.
BACKGROUND: Glyphosate (GLY), as the active ingredient of the most widely used herbicide worldwide, is commonly detected in the environment and living organisms, including humans. Its toxicity and carcinogenicity in mammals remain controversial. Several studies have demonstrated the hepatotoxicity of GLY; however, the underlying cellular and molecular mechanisms are still largely unknown. METHODS: Using single-cell RNA sequencing (scRNA-seq), immunofluorescent staining, and in vivo animal studies, we analyzed the liver tissues from untreated and GLY-treated mice. RESULTS: We generated the first scRNA-seq atlas of GLY-exposed mouse liver. GLY induced varied cell composition, shared or cell-type-specific transcriptional alterations, and dysregulated cell-cell communication and thus exerted hepatotoxicity effects. The oxidative stress and inflammatory response were commonly upregulated in several cell types. We also observed activation and upregulated phagocytosis in macrophages, as well as proliferation and extracellular matrix overproduction in hepatic stellate cells. CONCLUSIONS: Our study provides a comprehensive single-cell transcriptional picture of the toxic effect of GLY in the liver, which offers novel insights into the molecular mechanisms of the GLY-associated hepatotoxicity.
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Single-cell transcriptome sequencing (scRNA-seq) reveals dynamic gene expression trajectories regulating vascular cell senescence in Hylocereus undatus.
Fruit is highly susceptible to postharvest quality deterioration, and changes at the cellular level in the vascular bundle, a key hub for substance transport within the fruit, have a significant impact on the senescence process. Single-cell transcriptome sequencing (scRNA-seq) enables the precise analysis of gene expression dynamics in vascular cells across different stages of postharvest freshness and senescence. Utilizing scRNA-seq, this study developed a cellular atlas of the Hylocereus undatus (H. undatus) pericarp. Among the 13 cell clusters identified in the pericarp, vascular cells were predominantly localized in cluster 10, and the cell number within this cluster showed a significant decline with senescence. At a resolution of 0.2, two subpopulations of vascular cells were subdivided and identified, and their dynamics during the senescence process were analysed. In addition, through pseudo-timing trajectory analysis, two senescence hub regulators, HuCEP and HuSCPL48, were screened out by the pericarp vascular senescence gene regulatory network constructed by SCODE based on connectivity. In particular, genes related to mass transport, such as HuAAE3, and related to energy metabolism, such as HuNAD7, were significantly upregulated. At the same time, in the later stages of senescence, genes related to specific subsets of vascular cell activation protein degradation such as HuATG8CL and oxidative stress such as HuPMA4 were significantly up-regulated, leading to a stress response to fruit physiological decline. This study explored the dynamic changes in gene expression during vascular cell senescence.
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Time-series single-cell transcriptomics reveals pervasive daily rhythmicity and nocturnal spermatogenesis in the zebrafish testis.
The vertebrate testis has long been regarded as lacking strong cellular daily rhythmicity. Here, we use time-series single-cell RNA sequencing to construct a temporal atlas of the adult zebrafish testis. We identify previously unknown cell-type-specific markers and previously unidentified testicular subtypes and reveal that Sertoli cells, spermatogonia, and spermatids exhibit robust oscillations in key circadian clock genes. In contrast, Leydig cells and spermatocytes primarily exhibit rhythmic expression of key functional genes. We show that intercellular communication networks also exhibit daily rhythmicity. Pseudotime trajectory analysis demonstrates a notable nocturnal preference for germ cell progression. Disrupting circadian input via constant light reduced germ cell numbers and impaired fertility. Genetic ablation of clock1a arrests spermatogonial differentiation, whereas mutation of the rhythmic spermatocyte gene hmgb1a substantially reduces meiotic cells. Together, our findings redefine the testis as a highly rhythmic organ in which a precisely timed, nocturnally coordinated cellular symphony drives spermatogenesis.
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Single-cell transcriptomic analysis unveils dysregulated macrophage-podocyte crosstalk in membranous nephropathy.
BACKGROUND: Membranous nephropathy (MN) is an antibody-mediated glomerular disease, but the cellular networks driving injury remain poorly defined. METHODS: We integrated the cationic bovine serum albumin (cBSA)-induced mouse MN model with single-cell RNA sequencing (scRNA-seq) to profile renal cells. Bioinformatic analyses included clustering, differential expression, pathway enrichment, and cell-cell communication inference (CellChat). RESULTS: We successfully established a cBSA-induced MN mouse model exhibiting characteristic pathological features, including glomerular basement membrane thickening and IgG deposition. Single-cell transcriptomics revealed a profoundly remodeled renal cellular landscape in MN, characterized by immune activation (increases in macrophages, B cells, T cells, and NK cells) and concomitant tubular injury (significant loss of proximal tubule cells). We identified an imbalanced adaptive immune response: T follicular helper (Tfh) cells expanded, but plasma cells decreased, suggesting disrupted differentiation. Pathway analysis indicated metabolic reprogramming in B cells, ER stress in plasma cells, and pro-inflammatory activation in macrophages. Crucially, cell-cell communication analysis uncovered a rewired pathogenic macrophage-podocyte axis. This axis was defined by a dual imbalance: a gain-of-function in injury-promoting signals (Spp1-integrin) coupled with a loss-of-function in homeostatic signals (collagen IV). CONCLUSION: This study provides a comprehensive cellular atlas of MN and delineates a dysregulated macrophage-podocyte crosstalk as a key driver of glomerular injury, highlighting this axis as a promising target for therapeutic intervention.
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Decoding neurodegeneration one cell at a time.
Neurodegenerative diseases are characterized by protein misfolding and the selective vulnerability of specific neuronal subtypes. This selective vulnerability presents a paradox; most neurodegenerative disease genes are expressed broadly throughout the brain, and some ubiquitously, but only certain types of neurons are lost while others are resistant. The molecular basis for selective neuronal vulnerability has remained a mystery, but recent genomics technological innovations are starting to provide mechanistic insights. Here, we review how single-cell genomics techniques - single-cell transcriptomics, single-cell epigenomics, and spatial transcriptomics - advance our molecular understanding of selective vulnerability and neurodegeneration across Alzheimer disease, Parkinson disease, amyotrophic lateral sclerosis, frontotemporal dementia, and Huntington disease. Together, these approaches reveal the cell types affected in disease, define disease-associated molecular states, nominate candidate determinants of vulnerability and degeneration, and situate degenerating neurons within their local tissue context. Continued development and application of these techniques, including single-cell perturbation screens, will expand descriptive atlases of relevant cell types in health and disease and identify causal mechanisms, revealing the molecular basis of vulnerability and degeneration and informing therapeutic development.
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An integrated reference atlas of human skeletal muscle.
BACKGROUND: Single-cell and single-nucleus RNA sequencing have transformed our understanding of human skeletal muscle biology, yet reproducibility and cross-study comparison remain limited by the lack of a unified reference framework and consistent cell-type annotation. METHODS: We systematically searched for scRNA-seq and snRNA-seq datasets from adult human skeletal muscle. Seven eligible studies were retrieved and harmonised. We benchmarked multiple integration strategies to construct a joint reference atlas and derived modality-aware marker panels. Selected findings were validated by immunofluorescence in muscle biopsies. FINDINGS: We generated a harmonised atlas comprising 122,000 cells and 630,000 nuclei from 88 healthy individuals and resolved 17 major skeletal muscle cell populations, spanning mononuclear compartments and multinucleated myofibers. Cross-modality analysis identified tissue- and modality-aware marker panels and nominated both established and previously unrecognised markers. NOVA1 emerged as a selective marker of fibro-adipogenic progenitors and was validated at the transcript and protein levels. Focusing on myonuclei, pseudotime modelling reconstructed differentiation trajectories from quiescent muscle stem cells to mature type I and type II myofibers and revealed lineage-specific programs, including transient activation of protocadherin-γ genes during type I myofiber differentiation. We further provide an interactive web application for marker-based cell-type prediction using the reference atlas. INTERPRETATION: This integrated reference atlas and accompanying annotation tool establish a standardised framework for human muscle transcriptomics, promoting consistent cell-type assignment and providing a baseline for future studies of muscle development, ageing, and disease. FUNDING: Else Kröner-Fresenius-Stiftung and the German Research Foundation.
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NEXT-scASV: a Nextflow pipeline for allele-specific variant calling from single-cell RNA-seq data.
The rapid accumulation of single-cell sequencing data presents major computational challenges in reproducibility, scaling, and handling data characteristics such as sparsity and technical variations, which complicate even basic analyses. The next level of complexity is the allele-specific analysis, focused on identifying differential gene expression or regulation between homologous chromosomes by estimating the allelic imbalance of read counts at individual single-nucleotide variants. Here, we present NEXT-scASV, a scalable Nextflow pipeline for calling allele-specific variants (ASVs) from 5' single-cell RNA sequencing data. NEXT-scASV automates the entire process-from read alignment and quality control to variant calling and statistical evaluation of the allelic imbalance-within a containerized environment, ensuring reproducibility and ease of deployment across platforms. NEXT-scASV is able to perform de novo ASV detection from single-cell sequencing data without prior genotyping. Its modular design allows for massive parallelization, efficiently handling the scale of modern atlas-level studies. We validate NEXT-scASV on a dataset of 135,000 peripheral blood mononuclear cells from 57 donors, demonstrating that it processes large-scale data efficiently, completing analysis in a week on a cluster with one node and 100 threads. Crucially, the pipeline reliably identifies ASVs even in rare cell populations (e.g., gdT GZMBhi and memory B IGHMhi cells), which remains elusive for bulk analyses. We also successfully detect allele-specific regulation of long non-coding RNAs and other lowly expressed, cell type-specific genes. Genes linked to detected ASVs show a high concordance (80%) with previously reported eQTLs. This strong validation confirms that NEXT-scASV produces biologically relevant results, making it a powerful tool for uncovering allele-specific regulation in large-scale, complex single-cell studies.