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A scRNA-seq reference contrasting living and early post-mortem human retina across diverse donor states.
BACKGROUND: Current human retina studies predominantly utilize post-mortem tissue, and the sample accessibility constraints make the characterization of the living human retina at single-cell resolution a challenge. Although single-nucleus RNA-seq expands the utility of frozen samples, it provides a nuclear-centric view, potentially missing key cytoplasmic information and transient biological processes. Thus, it is important to generate resources directly from living human retinal tissue to complement existing datasets. METHODS: We profiled 106,829 single cells from nine unfrozen human retina samples. Living samples were collected within 10 min of therapeutic enucleation and four postmortem samples were collected within 6 h. After standardized dissociation, single-cell transcriptomes were generated using 10x Genomics 3' RNA-seq and applied scVI to generate batch-corrected integrated atlas. Major cell types and subtypes were annotated through iterative Leiden clustering, canonical markers. Subsequent analyses included differential expression comparisons between cell states and regulon activity profiling to further characterize cellular identities and regulatory networks. Transcriptional dynamics were assessed using RNA velocity, and cell-cell signaling pathways were inferred with CellChat. Key findings were validated in independent samples from two additional donors (four samples) using the identical workflow. RESULTS: We contribute to establishing a reference for retinal cell type proportions and cellular states. Our analysis revealed ELF1-mlCone, a distinct cluster of mlCone photoreceptors identified by distinct transcriptional features. The presence and transcriptional features of this cluster were validated in independent samples. Additionally, by comparing living and post-mortem samples, our study highlights differences in transcriptional dynamics: living tissue preserved coherent RNA velocity streams, enabling clear dynamic state transitions, while post-mortem tissue exhibited disorganized patterns. These findings suggest that using living tissue can improve the capture of active cellular states and transitions. CONCLUSIONS: Our atlas provides a single-cell reference contrasting living versus early postmortem human retina, integrating cell type composition, transcriptional diversity, and functional insights. It may serve as a useful resource for retinal research and for understanding aspects of human retinal biology, particularly given its inclusion of living tissue and diverse pathological states.
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Single-Cell Transcriptomic Profile Associated with Sub-Subtype A6 and CRF63-02A6 HIV-1 Strain Infection.
We present the single-cell transcriptomic analysis of peripheral blood mononuclear cells (PBMC) from individuals during acute HIV-1 infection caused by viral strains circulating in Russia and the Former Soviet Union (FSU) countries. Using 10x Genomics single-cell RNA sequencing (scRNA-seq) on the Illumina NextSeq 550 platform, we have analyzed scRNA-seq data from three treatment-naive patients (viral load > 1 × 106 copies/mL, estimated infection duration ≤ 4 weeks) and three healthy donors. Data integration (Seurat, Harmony), automated cell-type annotation (CellTypist), and GeneOntology (GO) enrichment analysis for highly expressed and low-expressed genes revealed a profound reorganization of transcriptional programs across key immune populations, including memory CD4+ and CD8+ T cells, non-classical monocytes and natural killer cells (NK-cells). We observed signatures of hyperactivation of pro-inflammatory pathways (NF-kB, TNF, and type I/II interferon signaling), upregulation of genes associated with cellular migration (CXCR4, CCR7) and metabolic adaptation (oxidative phosphorylation components), alongside a mixed pro- and anti-apoptotic expression profile. Notably, our data pointed to a pronounced dysregulation of the TGF-β and mTOR signaling cascades, disrupted intercellular communication networks-particularly between cytotoxic cells and their regulators-altered expression of genes implicated in disease progression (OLR1, SERPINB2, COPS9) and viral persistence control (NEAT1, NAF1). This work provides an initial single-cell transcriptional atlas characterizing early immune responses to HIV-1 sub-subtypes A6 and CRF63_02A6, the predominant drivers of the HIV epidemic across the FSU region.
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Single-nucleus RNA sequencing reveals a spatiotemporal pattern of H2S signaling in Chinese cabbage.
Plants rely on gasotransmitters like hydrogen sulfide (H2S) to coordinate stress adaptation, yet how H2S orchestrates spatiotemporal responses across cell types remains unknown. Here, we generated a high-resolution single-nucleus RNA sequencing (snRNA-seq) atlas of Chinese cabbage (Brassica rapa L. ssp. pekinensis) leaves, capturing the dynamic transcriptional response to H2S over the course of 24 h. We identified 18 transcriptionally distinct clusters corresponding to nine major leaf cell types. H2S induces globally coordinated transcriptional reprogramming, prominently featuring the broad activation of defense responses across cell types. We identified two H2S-induced genes that enhance drought tolerance. BrHSP23.6, initially confined to guard cells, becomes globally expressed under H2S fumigation and is required for H2S-mediated enhancement of photosynthetic performance and drought tolerance. Similarly, BrNTF2, a de novo induced nuclear transport factor, is essential for H2S-dependent stomatal regulation and drought resilience. In parallel, H2S spatiotemporally activates hormone and sulfur metabolic pathways. Our study deciphers a spatiotemporal pattern of H2S signaling at single-cell resolution, providing a new framework for understanding how gasotransmitters orchestrate plant adaptation.
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scDenorm: a denormalization tool for integrating single-cell transcriptomics data.
Integrating single-cell omics data at an atlas scale enhances our understanding of cell types and disease mechanisms. However, the integration of data processed by different normalization methods can lead to biases, such as unexpected batch effects and gene expression distortion, leading to misinterpretations in downstream analysis. To address these challenges, we present scDenorm, an algorithm that reverts delta-method normalized single-cell omics data to raw counts, preserving the integrity of the original measurements and ensuring consistent data processing during integration. We evaluated scDenorm's performance on large-scale datasets and benchmarked its impact on data integration and downstream analysis across 3 datasets.
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Single-cell transcriptomics unveils pyroptosis-related immune microenvironment dynamics and prognostic modeling in esophageal squamous cell carcinoma.
BACKGROUND: Esophageal cancer remains a leading cause of global cancer mortality with limited therapeutic efficacy. This study aims to characterize pyroptosis-related immune mechanisms in esophageal cancer and develop a clinical prediction model to improve prognostic evaluation. METHODS: Single-cell RNA sequencing data from seven esophageal cancer tissues and one normal control were integrated to construct a cellular atlas. Immune subpopulations were isolated, and pyroptosis-associated differentially expressed genes were identified. We intersected these genes with pyroptosis-related genes(PRG), identifying 13 key pyroptosis genes. Computational analyses included pyroptosis scoring, subcluster re-analysis, pathway enrichment, and intercellular communication mapping. Pseudotime trajectory analysis was applied to high-pyroptosis immune subsets. A clinical prognostic model incorporating PRG was validated using bulk sequencing data, followed by immune infiltration quantification and chemotherapy response assessment. RESULTS: By integrating single-cell and bulk transcriptomic datasets, we found that pyroptosis-related genes were enriched in immune-inflammatory pathways in ESCC immune cells. B cells showed extensive interactions within the ESCC immune microenvironment. The identified pyroptosis-related genes appeared to be involved in immune-cell state transitions and intercellular communication. A two-gene pyroptosis-related prognostic model based on GSDMB and CYCS showed moderate predictive performance in the TCGA training cohort. However, its performance in the external GSE53625 validation cohort was limited, indicating that the model should be interpreted as an exploratory prognostic signature rather than a clinically established prediction tool. A nomogram integrating the risk score with pathological stage was further constructed to explore its potential clinical applicability.
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Galaxy as a gateway to bioinformatics: Multi-Interface Galaxy Hands-on Training Suite (MIGHTS) for scRNA-seq.
BACKGROUND: Bioinformatics is fundamental to biomedical sciences, but its mastery presents a steep learning curve for bench biologists and clinicians. Learning to code while analyzing data is difficult. The curve may be flattened by separating these two aspects and providing intermediate steps for budding bioinformaticians. Single-cell analysis is in great demand from biologists and biomedical scientists, as evidenced by the proliferation of training events, materials, and collaborative global efforts like the Human Cell Atlas. However, iterative analyses lacking reinstantiation, coupled with unstandardized pipelines, have made effective single-cell training a moving target. FINDINGS: To address these challenges, we present a Multi-Interface Galaxy Hands-on Training Suite (MIGHTS) for single-cell RNA sequencing (scRNA-seq) analysis, which offers parallel analytical methods using a graphical interface (buttons) or code. With clear, interoperable materials, MIGHTS facilitates smooth transitions between environments. Bridging the biologist-programmer gap, MIGHTS emphasizes interdisciplinary communication for effective learning at all levels. Real-world data analysis in MIGHTS promotes critical thinking and best practices, while FAIR data principles ensure validation of results. MIGHTS is freely available, hosted on the Galaxy Training Network, and leverages Galaxy interfaces for analyses in both settings. Given the ongoing popularity of Python-based (Scanpy) and R-based (Seurat & Monocle) scRNA-seq analyses, MIGHTS enables analyses using both. CONCLUSIONS: MIGHTS consists of 11 tutorials, including recordings, slide decks, and interactive visualizations, and a demonstrated track record of sustainability via regular updates and community collaborations. Parallel pathways in MIGHTS enable concurrent training of scientists at any programming level, addressing the heterogeneous needs of novice bioinformaticians.
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Combined lineage tracing and scRNA-seq reveals unexpected first heart field predominance of human iPSC differentiation.
During mammalian development, the left and right ventricles arise from early populations of cardiac progenitors known as the first and second heart fields, respectively. While these populations have been extensively studied in non-human model systems, their identification and study in vivo human tissues have been limited due to the ethical and technical limitations of accessing gastrulation-stage human embryos. Human-induced pluripotent stem cells (hiPSCs) present an exciting alternative for modeling early human embryogenesis due to their well-established ability to differentiate into all embryonic germ layers. Here, we describe the development of a TBX5/MYL2 lineage tracing reporter system that allows for the identification of FHF- progenitors and their descendants including left ventricular cardiomyocytes. Furthermore, using single-cell RNA sequencing (scRNA-seq) with oligonucleotide-based sample multiplexing, we extensively profiled differentiating hiPSCs across 12 timepoints in two independent iPSC lines. Surprisingly, our reporter system and scRNA-seq analysis revealed a predominance of FHF differentiation using the small molecule Wnt-based 2D differentiation protocol. We compared this data with existing murine and 3D cardiac organoid scRNA-seq data and confirmed the dominance of left ventricular cardiomyocytes (>90%) in our hiPSC-derived progeny. Together, our work provides the scientific community with a powerful new genetic lineage tracing approach as well as a single-cell transcriptomic atlas of hiPSCs undergoing cardiac differentiation.
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Exploiting RAG1-deficient chickens to construct a single-cell transcriptional atlas of chicken heterophils during inflammation.
INTRODUCTION: Avian heterophils naturally lack myeloperoxidase (MPO), making chicken a unique evolutionary model for investigating MPO-independent innate immune strategies. However, the molecular programs governing heterophil maturation and activation remain largely unexplored. METHODS: Using single-cell RNA sequencing in a RAG1-deficient chicken model, we mapped heterophil maturation during emergency granulopoiesis and identified eight transcriptionally distinct subsets (H0-H7). We inferred a developmental trajectory spanning transcriptionally distinct heterophil subsets, from early subsets enriched for genes associated with translational activity, oxidative phosphorylation, and survival-related programs to terminal subsets enriched for effector-associated gene signatures. RESULTS AND DISCUSSION: Unlike mammalian neutrophils, heterophils did not sequentially deploy canonical granule gene expression; instead, maturation was marked by progressive MMP9 upregulation. Transcriptional profiling revealed a prominent enrichment of nitric oxide signaling pathway, with basal level of NADPH oxidase expression. Trajectory analysis indicated that cells transitioned from interferon-stimulated gene (ISG)-dominant states to effector profiles capable of degranulation, phagocytosis, and extracellular trap formation. Terminal subsets showed exhibited transcriptional signatures associated with enhanced extracellular matrix remodeling, acute phase responses, and nutritional immunity. Together, these findings provide a detailed single-cell atlas of avian heterophils, offering valuable evolutionary insights into the developmental trajectories and transcriptional heterogeneity of innate immune cells in a naturally MPO-independent species.
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Identification of Drug-resistant Cell Subpopulations in Colorectal Cancer Through Single-cell Analysis and Exploration of Potential Therapeutic Strategies.
INTRODUCTION: The therapeutic efficacy of Colorectal Cancer (CRC) is often compromised by resistance to the standard chemotherapy agent oxaliplatin. METHODS: This study obtained single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database. Differentially Expressed Genes (DEGs) between resistant and sensitive epithelial subpopulations were identified, followed by enrichment analysis. Pseudotemporal trajectory and cell-cell communication were analyzed using Monocle2 and CellChat, respectively. The candidate drug was predicted by Connectivity Map (cMAP) analysis. External validation included assessment of the EpC2 signature in an oxaliplatin-resistant cell line dataset (GSE76092), survival analysis using The Cancer Genome Atlas (TCGA) cohorts, and re-analysis of the GSE179784 dataset to assess the reproducibility of EpC2-like subpopulations and their DNA Damage Repair (DDR) scores. RESULTS: Cell subpopulations were divided into 10 clusters. Among them, epithelial cells comprised 5 subpopulations, with EPC2 identified as a potential oxaliplatin-resistant subset. DEGs were enriched in the TNF and IL-17 pathways. External validation confirmed the enrichment of EpC2 in resistant cell lines and its association with poor survival. Pseudotemporal trajectory revealed that epithelial cells underwent state transitions, forming two distinct branches. The resistant group exhibited enrichment in RNA splicing and NF-κB pathways. Cell-cell communication analysis revealed interactions involving MDK- NCL and PPIA-BSG. Dasatinib was predicted as a candidate drug. DISCUSSION: We identified an oxaliplatin-resistant subpopulation of Epithelial Cells (EpC2) in CRC, elucidated its multi-layered resistance mechanisms, and integrated multi- omics and cMAP database analyses to predict a potential intervention drug. CONCLUSION: This study provided potential therapeutic possibilities for oxaliplatin resistance, contributing to CRC treatment.
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Insights into speckled eggs provided by single-cell transcriptomics in high-yield laying hens.
Eggshell quality is a major concern in the poultry industry. The speckle phenotype in eggshell affects eggshell color, thickness, and Haugh Unit (HU) and significantly affects the hatchability rate. The rate of speckled eggs increases during the late-laying period, which can lead to notable economic losses. Eggshell speckles have medium heritability; however, the genetic architecture of speckled eggshells remains unclear. In this study, the chickens were divided into two groups based on the rate of egg speckling. Uterine tissues were collected and analyzed using 10X Genomics single-cell RNA sequencing to delineate the molecular signatures of the various cell types. A total of 10 cell types were identified: immune (B, T, monocyte macrophages, and plasma cells), endothelial, epithelial, stromal, neuroendocrine, serous, and protoporphyrin IX pigment cells. The trends of cell type distributions were consistent in two groups. Functional enrichment analysis showed that unique biological processes, such as IgA production and Toll-like C-type lectin receptor signaling pathways, were enriched in immune cells, whereas TGF-beta, Wnt, and NOD-like receptor signaling pathways were enriched in epithelial cells. Subsequently, we identified differentially expressed genes in the two groups, among which SPP1 (secreted phosphoprotein 1) was down-regulated in all cell types in the speckled group. The cellular interaction signals of speckled group were significantly enhanced compared to the control group. The cell proliferation and differentiation pathways mediated by WNT, SPP1, and BMP were inhibited and the interaction related to cell migration was activated in the speckled group. This study explored cell heterogeneity, extended prior findings by adding single-cell resolution atlas, and identified cell type specific patterns that can guide future functional studies of speckled eggs.