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Atlas of breast cancer infiltrated B-lymphocytes revealed by paired single-cell RNA-sequencing and antigen receptor profiling.
To gain mechanistic insights into the functions and developmental dynamics of tumor-infiltrated immune cells, especially B-lymphocytes, here we combine single-cell RNA-sequencing and antigen receptor lineage analysis to characterize a large number of triple-negative breast cancer infiltrated immune cells and report a comprehensive atlas of tumor-infiltrated B-lymphocytes. The single-cell transcriptional profiles reveal significant heterogeneity in tumor-infiltrated B-cell subgroups. The single-cell antigen receptor analyses demonstrate that compared with those in peripheral blood, tumor-infiltrated B-cells have more mature and memory B-cell characteristics, higher clonality, more class switching recombination and somatic hypermutations. Combined analyses suggest local differentiation of infiltrated memory B-cells within breast tumors. The B-cell signatures based on the single-cell RNA-sequencing results are significantly associated with improved survival in breast tumor patients. Functional analyses of tumor-infiltrated B-cell populations suggest that mechanistically, B-cell subgroups may contribute to immunosurveillance through various pathways. Further dissection of tumor-infiltrated B-cell populations will provide valuable clues for tumor immunotherapy.
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Single-Cell and Spatial Analysis of Emergent Organoid Platforms.
Organoids have emerged as a promising advancement of the two-dimensional (2D) culture systems to improve studies in organogenesis, drug discovery, precision medicine, and regenerative medicine applications. Organoids can self-organize as three-dimensional (3D) tissues derived from stem cells and patient tissues to resemble organs. This chapter presents growth strategies, molecular screening methods, and emerging issues of the organoid platforms. Single-cell and spatial analysis resolve organoid heterogeneity to obtain information about the structural and molecular cellular states. Culture media diversity and varying lab-to-lab practices have resulted in organoid-to-organoid variability in morphology and cell compositions. An essential resource is an organoid atlas that can catalog protocols and standardize data analysis for different organoid types. Molecular profiling of individual cells in organoids and data organization of the organoid landscape will impact biomedical applications from basic science to translational use.
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Cellular and Transcriptional Landscape of Human Hypoplastic Left Heart Syndrome.
Hypoplastic left heart syndrome (HLHS) is a congenital heart defect characterized by impaired development of the left ventricle, often managed through surgical palliation creating a single ventricle (SV). Failure of the anatomical right ventricle (RV) represents a common complication with high mortality. We used single-nucleus RNA sequencing to generate a map of the pediatric non-failing (NF) and failing (SysHF) SV. Fibroblasts and endocardial cells displayed the greatest transcriptional shifts between NF and SysHF. Notably, activated fibroblasts expanded in SysHF, and endocardial cells in NF demonstrated adaptive transcriptomic shifts absent from controls or SysHF samples. Ligand-target analysis predicted disease-state specific signaling from endocardial cells to fibroblasts: NRG3 signaling in NF and CCN2 signaling in SysHF. In silico perturbation predicted FOS, JUN, and STAT3 as regulators of fibroblast activation and endocardial adaptation. Finally, HLHS data was compared to adult human and murine RV failure to gain insight into shared pathological processes and the suitability of current animal models. These findings provide a comprehensive SV atlas and implicate cell non-autonomous signaling between endocardial cells and fibroblasts as drivers of SV systolic heart failure.
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Complementing tissue characterization by integrating transcriptome profiling from the Human Protein Atlas and from the FANTOM5 consortium.
Understanding the normal state of human tissue transcriptome profiles is essential for recognizing tissue disease states and identifying disease markers. Recently, the Human Protein Atlas and the FANTOM5 consortium have each published extensive transcriptome data for human samples using Illumina-sequenced RNA-Seq and Heliscope-sequenced CAGE. Here, we report on the first large-scale complex tissue transcriptome comparison between full-length versus 5'-capped mRNA sequencing data. Overall gene expression correlation was high between the 22 corresponding tissues analyzed (R > 0.8). For genes ubiquitously expressed across all tissues, the two data sets showed high genome-wide correlation (91% agreement), with differences observed for a small number of individual genes indicating the need to update their gene models. Among the identified single-tissue enriched genes, up to 75% showed consensus of 7-fold enrichment in the same tissue in both methods, while another 17% exhibited multiple tissue enrichment and/or high expression variety in the other data set, likely dependent on the cell type proportions included in each tissue sample. Our results show that RNA-Seq and CAGE tissue transcriptome data sets are highly complementary for improving gene model annotations and highlight biological complexities within tissue transcriptomes. Furthermore, integration with image-based protein expression data is highly advantageous for understanding expression specificities for many genes.
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A single-cell transcriptome atlas of the adult human retina.
The retina is a specialized neural tissue that senses light and initiates image processing. Although the functional organization of specific retina cells has been well studied, the molecular profile of many cell types remains unclear in humans. To comprehensively profile the human retina, we performed single-cell RNA sequencing on 20,009 cells from three donors and compiled a reference transcriptome atlas. Using unsupervised clustering analysis, we identified 18 transcriptionally distinct cell populations representing all known neural retinal cells: rod photoreceptors, cone photoreceptors, Müller glia, bipolar cells, amacrine cells, retinal ganglion cells, horizontal cells, astrocytes, and microglia. Our data captured molecular profiles for healthy and putative early degenerating rod photoreceptors, and revealed the loss of MALAT1 expression with longer post-mortem time, which potentially suggested a novel role of MALAT1 in rod photoreceptor degeneration. We have demonstrated the use of this retina transcriptome atlas to benchmark pluripotent stem cell-derived cone photoreceptors and an adult Müller glia cell line. This work provides an important reference with unprecedented insights into the transcriptional landscape of human retinal cells, which is fundamental to understanding retinal biology and disease.
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Epithelial pyroptosis-induced TREM1+ macrophages activate Th17 cells to accelerate oral mucosal inflammation.
Chronic inflammation of the oral mucosa could affect daily living and even threaten systemic health. Unlike periodontitis, oral lichen planus, a common oral chronic inflammatory disease, has diverse clinical manifestations and can progress to malignancy. Hence, this study aimed to investigate the mechanism of oral chronic inflammation using single-cell RNA sequencing (scRNA-seq), spatial transcriptome, a large clinical follow-up cohort with bulk RNA sequencing, cytological experiments, and multiplex immunohistochemistry. We found that epithelial pyroptosis-induced triggering receptor expressed on myeloid cell-1 (TREM1)+ macrophages activated pathogenic T helper cell 17 via interleukin-1β, to spur the inflammatory development of oral mucosal epithelium. Besides, we established a spatiotemporal interactional online database, Oral-Gut Axis Mucosal Immune Atlas (ORGUAMIA), to uncover the extensive pro-inflammatory role of epithelial pyroptosis-induced TREM1+ macrophages in chronic digestive tract disorders. In summary, this study highlights the role of epithelial pyroptosis-induced TREM1+ macrophages accelerating mucosal epithelial inflammation and offers ORGUAMIA as a tool for researchers using scRNA-seq and spatial transcriptome without technological barriers.
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A systems-based framework to computationally describe putative transcription factors and signaling pathways regulating glycan biosynthesis.
Glycosylation is a common posttranslational modification, and glycan biosynthesis is regulated by a set of glycogenes. The role of transcription factors (TFs) in regulating the glycogenes and related glycosylation pathways is largely unknown. In this work, we performed data mining of TF-glycogene relationships from the Cistrome Cancer database (DB), which integrates chromatin immunoprecipitation sequencing (ChIP-Seq) and RNA-Seq data to constitute regulatory relationships. In total, we observed 22,654 potentially significant TF-glycogene relationships, which include interactions involving 526 unique TFs and 341 glycogenes that span 29 the Cancer Genome Atlas (TCGA) cancer types. Here, TF-glycogene interactions appeared in clusters or so-called communities, suggesting that changes in single TF expression during both health and disease may affect multiple carbohydrate structures. Upon applying the Fisher's exact test along with glycogene pathway classification, we identified TFs that may specifically regulate the biosynthesis of individual glycan types. Integration with Reactome DB knowledge provided an avenue to relate cell-signaling pathways to TFs and cellular glycosylation state. Whereas analysis results are presented for all 29 cancer types, specific focus is placed on human luminal and basal breast cancer disease progression. Overall, the article presents a computational approach to describe TF-glycogene relationships, the starting point for experimental system-wide validation.
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Multi-omics analysis reveals AFAP1L1 as a prognostic biomarker and immune-related therapeutic target in glioma.
OBJECTIVES: Actin filament-associated protein 1 like 1 (AFAP1L1) is an adaptor protein lacking enzymatic and transcriptional activity, but the AFAP1L1 gene functions as an oncogene in colorectal cancer and gastric cancers. This study aims to investigate the role of AFAP1L1 in glioma and to explore changes in AFAP1L1 expression during glioma progression. METHODS: Clinical and transcriptomic data of glioma patients were downloaded from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), and the Gene Expression Omnibus (GEO) databases to analyze the associations between AFAP1L1 expression and glioma prognosis, somatic mutations, immune cell infiltration, and enriched signaling pathways. Western blotting and real-time polymerase chain reaction (PCR) were used to detect AFAP1L1 messenger RNA (mRNA) and protein expression in glioma patients. RESULTS: Patients with high AFAP1L1 expression had poorer prognosis, and AFAP1L1 was identified as an independent risk factor for glioma. In addition, glioma patients with high AFAP1L1 expression exhibited lower levels of somatic mutations, including amplification of oncogenes such as epidermal growth factor receptor and deletion of tumor suppressor genes such as cyclin-dependent kinase inhibitor 2A (CDKN2A). Estimation of STromal and Immune cells in Malignant Tumours using Expression (ESTIMATE) algorithm analysis showed that AFAP1L1 expression was positively correlated with the immune microenvironment. Tumor immune dysfunction and exclusion (TIDE) analysis indicated that glioma patients with high AFAP1L1 expression responded poorly to immunotherapy. Single cell analysis showed that AFAP1L1 expression was mainly concentrated in glioma cells. Enrichment analysis suggested that AFAP1L1 was potentially associated with small guanosine triphosphatases (GTPases), hypoxia-inducible factor-1 (HIF-1), focal adhesion, and mitogen-activated protein kinase (MAPK) signaling pathways. CONCLUSIONS: AFAP1L1 is a novel biomarker indicating glioma progression and a potential therapeutic target for glioma.
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Chemotherapy-refractory high-grade serous ovarian carcinoma: single-cell findings, TCGA-OV transcriptomic context, and a single-case MRI-transcriptome illustration.
BACKGROUND: Primary chemotherapy refractoriness remains a clinical challenge in high-grade serous ovarian carcinoma (HGSC) and may reflect pre-existing tumor-cell states and microenvironmental heterogeneity. We examined public single-cell and bulk transcriptomic resources, a barcode-matched MRI case, and an in vitro cisplatin-perturbation RT-qPCR experiment. METHODS: We reanalyzed GSE300897, comprising 19,156 annotated cells from nine treatment-naïve HGSC biopsy libraries (four chemotherapy-refractory and five chemotherapy-sensitive), with response summaries retained at the library/patient level. The Cancer Genome Atlas Ovarian Cancer (TCGA-OV) provided 421 unique primary tumors for cohort-scale transcriptomic and overall survival context. One TCGA-OV case (TCGA-24-1423) linked a multiparametric MRI examination to a tumor RNA-seq barcode. OVCAR-3 and Caov-3 cells were exposed to cisplatin (5 μM, 24 h), and IFIT1, MX1, SNAI1, and HIF1A expression was measured by RT-qPCR. RESULTS: Across the nine GSE300897 libraries, no cancer-cell program passed Benjamini-Hochberg (BH) q < 0.05. Refractory libraries showed a lower type-I interferon (IFN-I) score (-0.15 log1p(CP10k)) and epithelial-to-mesenchymal transition (EMT) balance (-0.16), with a small increase in hypoxia (+0.05). In TCGA-OV, none of the six displayed program scores reached BH q < 0.05 in age- and stage-adjusted Cox models; IFN-I tertiles separated nominally by Kaplan-Meier analysis (log-rank p = 0.029), whereas continuous IFN-I was not significant after adjustment (HR = 0.96 per SD, BH q = 0.927). In TCGA-24-1423, the paired transcriptome showed an IFN-I score at the 86.8th TCGA-OV percentile, with IFIT1 and MX1 at the 87.5th and 93.7th percentiles. RT-qPCR showed lower IFIT1/MX1 and higher SNAI1/HIF1A after acute cisplatin exposure in both cell lines (all Holm-adjusted p < 0.05). CONCLUSION: These findings support a conservative, hypothesis-generating interpretation of chemotherapy response-associated transcriptional heterogeneity in HGSC. Reduced IFN-I and altered cell-state programs were directional features of the small refractory single-cell cohort, whereas TCGA-OV provided context rather than independent response validation. The MRI-linked case demonstrates imaging-transcriptome pairing but does not establish an imaging biomarker. The RT-qPCR experiment demonstrates cisplatin-responsive changes in selected genes but models acute drug perturbation rather than the pretreatment clinical refractory state.
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Spatiotemporal single-cell analysis elucidates the cellular and molecular dynamics of human cornea aging.
BACKGROUND: The human cornea is a transparent and uniquely ordered optical-biological system. Precise coordination of its cellular mechanisms is essential to maintain its transparency and functionality. However, the spatial, cellular and molecular architecture of the human cornea and its intercellular interactions during aging have not been elucidated. METHODS: We performed single-cell RNA sequencing (scRNA-seq) and single-cell SpaTial Enhanced REsolution Omics-sequencing (scStereo-seq) analysis in corneal tissue from eight eyes of donors aged 33-88 years to elucidate the spatiotemporal cellular and molecular dynamics of human cornea aging. Immunofluorescence staining and Western blotting were performed to validate the findings. RESULTS: Spatiotemporal single-cell analysis revealed the complex cellular landscape, spatial organization and intercellular communication within the human cornea. The subpopulations of major cell types of the cornea were elucidated with precise spatial positions. In particular, we identified novel subpopulations, mapped the spatial positioning of limbal stem cells within the limbal niche, and delineated the interactions between major cell types. We observed that three basal cell subsets migrate centripetally from the peripheral to the central cornea with age, suggesting the "spatiotemporal centripetal pattern" as a novel paradigm for the age-related migration of corneal epithelial cells. Furthermore, we elucidated the age-related, region-specific molecular and functional characteristics of the corneal endothelium, demonstrating differential metabolic capacities and functional properties between the peripheral and central regions. CONCLUSIONS: As the first comprehensive spatiotemporal atlas, our work provides a valuable resource for understanding tissue homeostasis in the human cornea and advances research on corneal pathology, transplantation, senescence and regenerative medicine in the context of corneal aging.