Alexander disease (AxD) is a rare and severe neurodegenerative disorder caused by mutations in glial fibrillary acidic protein (GFAP). While the exact disease mechanism remains unknown, previous studies suggest that mutant GFAP influences many cellular processes, including cytoskeleton stability, mechanosensing, metabolism, and proteasome function. While most studies have primarily focused on GFAP-expressing astrocytes, GFAP is also expressed by radial glia and neural progenitor cells, prompting questions about the impact of GFAP mutations on central nervous system (CNS) development. In this study, we observed impaired differentiation of astrocytes and neurons in co-cultures of astrocytes and neurons, as well as in neural organoids, both generated from AxD patient-derived induced pluripotent stem (iPS) cells with a GFAPR239C mutation. Leveraging single-cell RNA sequencing (scRNA-seq), we identified distinct cell populations and transcriptomic differences between the mutant GFAP cultures and a corrected isogenic control. These findings were supported by results obtained with immunocytochemistry and proteomics. In co-cultures, the GFAPR239C mutation resulted in an increased abundance of immature cells, while in unguided neural organoids and cortical organoids, we observed altered lineage commitment and reduced abundance of astrocytes. Gene expression analysis revealed increased stress susceptibility, cytoskeletal abnormalities, and altered extracellular matrix and cell-cell communication patterns in the AxD cultures, which also exhibited higher cell death after stress. Overall, our results point to altered cell differentiation in AxD patient-derived iPS-cell models, opening new avenues for AxD research.

• MeSH
• Alexander Disease * genetics   pathology   metabolism   MeSH
• Astrocytes * metabolism   pathology   MeSH
• Cell Differentiation * physiology   MeSH
• Glial Fibrillary Acidic Protein * metabolism   genetics   MeSH
• Induced Pluripotent Stem Cells * metabolism   MeSH
• Coculture Techniques MeSH
• Cells, Cultured MeSH
• Humans MeSH
• Mutation MeSH
• Neural Stem Cells metabolism   MeSH
• Neurons metabolism   pathology   MeSH
• Organoids metabolism   pathology   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

Single-cell RNA sequencing (scRNA-seq) methods are widely used in life sciences, including immunology. Typical scRNA-seq analysis pipelines quantify the abundance of particular transcripts without accounting for alternative splicing. However, a well-established pan-leukocyte surface marker, CD45, encoded by the PTPRC gene, presents alternatively spliced variants that define different immune cell subsets. Information about some of the splicing patterns in particular cells in the scRNA-seq data can be obtained using isotype-specific DNA oligo-tagged anti-CD45 antibodies. However, this requires generation of an additional sequencing DNA library. Here, we present IDEIS, an easy-to-use software for CD45 isoform quantification that uses single-cell transcriptomic data as the input. We showed that IDEIS accurately identifies canonical human CD45 isoforms in datasets generated by 10× Genomics 5' sequencing assays. Moreover, we used IDEIS to determine the specificity of the Ptprc splicing pattern in mouse leukocyte subsets.

• MeSH
• Alternative Splicing MeSH
• Single-Cell Analysis methods   MeSH
• Leukocyte Common Antigens * genetics   metabolism   MeSH
• Leukocytes metabolism   immunology   MeSH
• Humans MeSH
• Mice MeSH
• Protein Isoforms genetics   MeSH
• Sequence Analysis, RNA methods   MeSH
• Software * MeSH
• Gene Expression Profiling methods   MeSH
• Transcriptome MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Mice MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH

Advances in single-cell level analytical techniques, especially cytometric approaches, have led to profound innovation in biomedical research, particularly in the field of clinical immunology. This has resulted in an expansion of high-dimensional data, posing great challenges for comprehensive and unbiased analysis. Conventional manual analysis is thus becoming untenable to handle these challenges. Furthermore, most newly developed computational methods lack flexibility and interoperability, hampering their accessibility and usability. Here, we adapted Seurat, an R package originally developed for single-cell RNA sequencing (scRNA-seq) analysis, for high-dimensional flow cytometric data analysis. Based on a 20-marker antibody panel and analyses of T-cell profiles in both adult blood and cord blood (CB), we showcased the robust capacity of Seurat in flow cytometric data analysis, which was further validated by Spectre, another high-dimensional cytometric data analysis package, and conventional manual analysis. Importantly, we identified a unique CD8+ T-cell population defined as CD8+CD45RA+CD27+CD161+ T cell that was predominantly present in CB. We characterised its IFN-γ-producing and potential cytotoxic properties using flow cytometry experiments and scRNA-seq analysis from a published dataset. Collectively, we identified a unique human CB CD8+CD45RA+CD27+CD161+ T-cell subset and demonstrated that Seurat, a widely used package for scRNA-seq analysis, possesses great potential to be repurposed for cytometric data analysis. This facilitates an unbiased and thorough interpretation of complicated high-dimensional data using a single analytical pipeline and opens a novel avenue for data-driven investigation in clinical immunology.

• MeSH
• Single-Cell Analysis methods   MeSH
• Tumor Necrosis Factor Receptor Superfamily, Member 7 metabolism   immunology   MeSH
• Leukocyte Common Antigens * metabolism   immunology   MeSH
• CD8-Positive T-Lymphocytes * immunology   MeSH
• Adult MeSH
• Fetal Blood * immunology   cytology   MeSH
• Immunophenotyping methods   MeSH
• NK Cell Lectin-Like Receptor Subfamily B immunology   metabolism   MeSH
• Humans MeSH
• Flow Cytometry * methods   MeSH
• Software MeSH
• T-Lymphocyte Subsets immunology   metabolism   MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Publication type
• Journal Article MeSH

Non-small cell lung carcinoma (NSCLC) represents the majority of lung cancer cases, comprising approximately 85 % of the total. The five-year survival rate for NSCLC patients remains discouragingly low. Recently, immunotherapy has emerged as a promising approach. Nevertheless, only a minority of patients experience considerable benefits from these treatments. This highlights the critical need for effective biomarkers that can predict both patient prognosis and response to immunotherapy. CD8+ T cells play a crucial role in cancer immunotherapy. Their presence within tumours is generally indicative of a favourable prognosis and increased efficacy of immunotherapy. This study was undertaken to identify and authenticate a novel biomarker signature based on CD8+ T-cell marker genes, to prognosticate therapeutic responses in individuals afflicted with NSCLC. This in-depth study was based on a total of 1,200 samples, which included four NSCLC specimens analysed through single-cell RNA sequencing (scRNA-seq), 1,000 NSCLC samples obtained from The Cancer Genome Atlas (TCGA) and 196 NSCLC specimens collected from the GSE37745 cohort. In patients with NSCLC, those presenting a favourable risk profile demonstrated notable elevations in specific immune cells while concurrently exhibiting reductions in other types. CD8+ T cells, with their established role in inducing apoptosis in cancer cells, have emerged as crucial predictors and modulators of treatment strategies for NSCLC patients. The combination of single-cell and bulk RNA sequencing has produced a biomarker signature, emphasizing the CD8+ T cells' crucial role in NSCLC prognosis and treatment.

• MeSH
• CD8-Positive T-Lymphocytes * immunology   MeSH
• Immunotherapy * methods   MeSH
• Middle Aged MeSH
• Humans MeSH
• Biomarkers, Tumor MeSH
• Lung Neoplasms * immunology   therapy   MeSH
• Carcinoma, Non-Small-Cell Lung * immunology   therapy   MeSH
• Prognosis MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

Biological age is typically estimated using biomarkers whose states have been observed to correlate with chronological age. A persistent limitation of such aging clocks is that it is difficult to establish how the biomarker states are related to the mechanisms of aging. Somatic mutations could potentially form the basis for a more fundamental aging clock since the mutations are both markers and drivers of aging and have a natural timescale. Cell lineage trees inferred from these mutations reflect the somatic evolutionary process, and thus, it has been conjectured, the aging status of the body. Such a timer has been impractical thus far, however, because detection of somatic variants in single cells presents a significant technological challenge. Here, we show that somatic mutations detected using single-cell RNA sequencing (scRNA-seq) from thousands of cells can be used to construct a cell lineage tree whose structure correlates with chronological age. De novo single-nucleotide variants (SNVs) are detected in human peripheral blood mononuclear cells using a modified protocol. A default model based on penalized multiple regression of chronological age on 31 metrics characterizing the phylogenetic tree gives a Pearson correlation of 0.81 and a median absolute error of ~4 years between predicted and chronological ages. Testing of the model on a public scRNA-seq dataset yields a Pearson correlation of 0.85. In addition, cell tree age predictions are found to be better predictors of certain clinical biomarkers than chronological age alone, for instance glucose, albumin levels, and leukocyte count. The geometry of the cell lineage tree records the structure of somatic evolution in the individual and represents a new modality of aging timer. In addition to providing a numerical estimate of "cell tree age," it unveils a temporal history of the aging process, revealing how clonal structure evolves over life span. Cell Tree Rings complements existing aging clocks and may help reduce the current uncertainty in the assessment of geroprotective trials.

• MeSH
• Biomarkers MeSH
• Longevity MeSH
• Phylogeny MeSH
• Leukocytes, Mononuclear * MeSH
• Humans MeSH
• Aging * genetics   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

In the past decade, single-cell transcriptomics has helped to uncover new cell types and states and led to the construction of a cellular compendium of health and disease. Despite this progress, some difficult-to-sequence cells remain absent from tissue atlases. Eosinophils-elusive granulocytes that are implicated in a plethora of human pathologies1-5-are among these uncharted cell types. The heterogeneity of eosinophils and the gene programs that underpin their pleiotropic functions remain poorly understood. Here we provide a comprehensive single-cell transcriptomic profiling of mouse eosinophils. We identify an active and a basal population of intestinal eosinophils, which differ in their transcriptome, surface proteome and spatial localization. By means of a genome-wide CRISPR inhibition screen and functional assays, we reveal a mechanism by which interleukin-33 (IL-33) and interferon-γ (IFNγ) induce the accumulation of active eosinophils in the inflamed colon. Active eosinophils are endowed with bactericidal and T cell regulatory activity, and express the co-stimulatory molecules CD80 and PD-L1. Notably, active eosinophils are enriched in the lamina propria of a small cohort of patients with inflammatory bowel disease, and are closely associated with CD4+ T cells. Our findings provide insights into the biology of eosinophils and highlight the crucial contribution of this cell type to intestinal homeostasis, immune regulation and host defence. Furthermore, we lay a framework for the characterization of eosinophils in human gastrointestinal diseases.

• MeSH
• Single-Cell Gene Expression Analysis MeSH
• B7-1 Antigen metabolism   MeSH
• Eosinophils * classification   cytology   immunology   metabolism   MeSH
• Inflammatory Bowel Diseases immunology   MeSH
• Immunity * MeSH
• Interferon-gamma MeSH
• Interleukin-33 MeSH
• Colitis * immunology   pathology   MeSH
• Humans MeSH
• Mice MeSH
• Proteome MeSH
• Intestines * immunology   pathology   MeSH
• T-Lymphocytes MeSH
• Transcriptome MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Mice MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH

The role of glia in amyotrophic lateral sclerosis (ALS) is undeniable. Their disease-related activity has been extensively studied in the spinal cord, but only partly in the brain. We present herein a comprehensive study of glia in the cortex of SOD1(G93A) mice-a widely used model of ALS. Using single-cell RNA sequencing (scRNA-seq) and immunohistochemistry, we inspected astrocytes, microglia, and oligodendrocytes, in four stages of the disease, respecting the factor of sex. We report minimal changes of glia throughout the disease progression and regardless of sex. Pseudobulk and single-cell analyses revealed subtle disease-related transcriptional alterations at the end-stage in microglia and oligodendrocytes, which were supported by immunohistochemistry. Therefore, our data support the hypothesis that the SOD1(G93A) mouse cortex does not recapitulate the disease in patients, and we recommend the use of a different model for future studies of the cortical ALS pathology.

• MeSH
• Amyotrophic Lateral Sclerosis * genetics   pathology   MeSH
• Spinal Cord pathology   MeSH
• Disease Models, Animal MeSH
• Motor Neurons pathology   MeSH
• Mice, Transgenic MeSH
• Mice MeSH
• Neuroglia * pathology   MeSH
• Superoxide Dismutase-1 * genetics   MeSH
• Superoxide Dismutase genetics   MeSH
• Animals MeSH
• Check Tag
• Mice MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Oligodendrocytes (OL) have been for decades considered a passive, homogenous population of cells that provide support to neurons, and show a limited response to pathological stimuli. This view has been dramatically changed by the introduction of powerful transcriptomic methods that have uncovered a broad spectrum of OL populations that co-exist within the healthy central nervous system (CNS) and also across a variety of diseases. Specifically, single-cell and single-nucleus RNA-sequencing (scRNA-seq, snRNA-seq) have been used to reveal OL variations in maturation, myelination and immune status. The newly discovered immunomodulatory role suggests that OL may serve as targets for future therapies. In this review, we summarize the current understanding of OL heterogeneity in mammalian CNS as revealed by scRNA-seq and snRNA-seq. We provide a list of key studies that identify consensus marker genes defining the currently known OL populations. This resource can be used to standardize analysis of OL related datasets and improve their interpretation, ultimately leading to a better understanding of OL functions in health and disease.

• Publication type
• Journal Article MeSH
• Review MeSH

High-Risk neuroblastoma (NB) survival rate is still <50%, despite treatments being more and more aggressive. The biggest hurdle liable to cancer therapy failure is the drug resistance by tumor cells that is likely due to the intra-tumor heterogeneity (ITH). To investigate the link between ITH and therapy resistance in NB, we performed a single cell RNA sequencing (scRNAseq) of etoposide and cisplatin resistant NB and their parental cells. Our analysis showed a clear separation of resistant and parental cells for both conditions by identifying 8 distinct tumor clusters in etoposide-resistant/parental and 7 in cisplatin-resistant/parental cells. We discovered that drug resistance can affect NB cell identities; highlighting the bi-directional ability of adrenergic-to-mesenchymal transition of NB cells. The biological processes driving the identified resistant cell subpopulations revealed genes such as (BARD1, BRCA1, PARP1, HISTH1 axis, members of RPL family), suggesting a potential drug resistance due to the acquisition of DNA repair mechanisms and to the modification of the drug targets. Deconvolution analysis of bulk RNAseq data from 498 tumors with cell subpopulation signatures showed that the transcriptional heterogeneity of our cellular models reflected the ITH of NB tumors and allowed the identification of clusters associated with worse/better survival. Our study demonstrates the distinct cell populations characterized by genes involved in different biological processes can have a role in NB drug treatment failure. These findings evidence the importance of ITH in NB drug resistance studies and the chance that scRNA-seq analysis offers in the identification of genes and pathways liable for drug resistance.

• Publication type
• Journal Article MeSH

BACKGROUND: Ubiquitin ligases (Ub-ligases) are essential intracellular enzymes responsible for the regulation of proteome homeostasis, signaling pathway crosstalk, cell differentiation and stress responses. Individual Ub-ligases exhibit their unique functions based on the nature of their substrates. They create a complex regulatory network with alternative and feedback pathways to maintain cell homeostasis, being thus important players in many physiological and pathological conditions. However, the functional classification of Ub-ligases needs to be revised and extended. METHODS: In the current study, we used a novel semantic biclustering technique for expression profiling of Ub-ligases and ubiquitination-related genes in the murine gastrointestinal tract (GIT). We accommodated a general framework of the algorithm for finding tissue-specific gene expression clusters in GIT. In order to test identified clusters in a biological system, we used a model of epithelial regeneration. For this purpose, a dextran sulfate sodium (DSS) mouse model, following with in situ hybridization, was used to expose genes with possible compensatory features. To determine cell-type specific distribution of Ub-ligases and ubiquitination-related genes, principal component analysis (PCA) and Uniform Manifold Approximation and Projection technique (UMAP) were used to analyze the Tabula Muris scRNA-seq data of murine colon followed by comparison with our clustering results. RESULTS: Our established clustering protocol, that incorporates the semantic biclustering algorithm, demonstrated the potential to reveal interesting expression patterns. In this manner, we statistically defined gene clusters consisting of the same genes involved in distinct regulatory pathways vs distinct genes playing roles in functionally similar signaling pathways. This allowed us to uncover the potentially redundant features of GIT-specific Ub-ligases and ubiquitination-related genes. Testing the statistically obtained results on the mouse model showed that genes clustered to the same ontology group simultaneously alter their expression pattern after induced epithelial damage, illustrating their complementary role during tissue regeneration. CONCLUSIONS: An optimized semantic clustering protocol demonstrates the potential to reveal a readable and unique pattern in the expression profiling of GIT-specific Ub-ligases, exposing ontologically relevant gene clusters with potentially redundant features. This extends our knowledge of ontological relationships among Ub-ligases and ubiquitination-related genes, providing an alternative and more functional gene classification. In a similar way, semantic cluster analysis could be used for studding of other enzyme families, tissues and systems.

• MeSH
• Gastrointestinal Tract metabolism   MeSH
• Humans MeSH
• Mice MeSH
• Semantics * MeSH
• Cluster Analysis MeSH
• Ubiquitin genetics   metabolism   MeSH
• Ubiquitin-Protein Ligases * genetics   MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Mice MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH