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A region-delineated snRNA-seq atlas of mouse spinal cord across lifespan resolves the interaction of normative aging programs with SOD1-G93A ALS.
Aging is the strongest risk factor for amyotrophic lateral sclerosis (ALS), yet how normative aging programs intersect with disease mechanisms remain unclear. Here we generated a lifespan-resolved, cell type- and region-specific single-nucleus RNA-sequencing atlas of the mouse spinal cord spanning embryonic development through advanced age in WT mice and end-stage disease in the SOD1-G93A ALS model. This resource enabled systematic comparison of physiological aging trajectories with disease-associated transcriptional changes across spinal cord cell types and rostrocaudal regions. We found that SOD1-G93A transcript and protein states differed markedly across spinal regions during disease onset and progression, and these molecular patterns paralleled the relative resilience of cervical regions and the heightened vulnerability of lumbar regions to degeneration in this transgenic mouse model. Prior to disease onset, we identified reduced ubiquitin expression that primed region-specific disruption of proteostasis in the SOD1-G93A spinal cord. Despite these disease-associated changes, aging-related transcriptional programs were largely preserved across most cell types, arguing against a global acceleration of aging in ALS. Instead, microglia emerged as a key exception, exhibiting accelerated and rewired aging- and disease-associated gene expression modules regulated by MITF and NRF2. Together, these findings provide an anatomically, cellularly, and temporally resolved framework for understanding how aging programs interact with disease-specific pathways to shape regional dysfunction and neurodegeneration in ALS.
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Comprehensive Analysis of CRIP1 Expression in Acute Myeloid Leukemia.
Acute myeloid leukemia (AML) is a highly heterogeneous hematological malignancy that imposes great challenges in terms of drug resistance and relapse. Previous studies revealed heterogeneous leukemia cells and their relevant gene markers, such as CRIP1 as clinically prognostic in t (8;21) AML patients. However, the expression and role of CRIP1 in AML are poorly understood. We used the single-cell RNA sequencing and gene expression data from t (8;21) AML patients to analyze the immune and regulation networks of CRIP1. Two independent cohorts from GSE37642 and The Cancer Genome Atlas (TCGA) datasets were employed as validation cohorts. In addition, the methylation data from TCGA were used to analyze the methylation effect of the CRIP1 expression. Gene expression profile from t (8;21) AML patients showed that the CRIP1-high group exhibited an enrichment of immune-related pathways, including tumor necrosis factor (TNF)α signaling via nuclear factor kappa B (NFκB) pathways. Further studies using CIBERSORT showed that the CRIP1-high group had a significantly higher infiltration of exhausted CD8 T cells and activated mast cells. The CRIP1 expression was validated in the GSE37642-GPL96, GSE37642-GPL570, and TCGA datasets. In addition, with the methylation data, four CpG probes of CRIP1 (cg07065217, cg04411625, cg25682097, and 11763800) were identified as negatively associated with the CRIP1 gene expression in AML patients. Our data provide a comprehensive overview of the regulation of CRIP1 expression in AML patients. The evaluation of the TNFα-NFκB signaling pathway as well as the immune heterogeneity might provide new insights for exploring improvements in AML treatment.
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A universal tool for predicting differentially active features in single-cell and spatial genomics data.
With the growing complexity of single-cell and spatial genomics data, there is an increasing importance of unbiased and efficient exploratory data analysis tools. One common exploratory data analysis step is the prediction of genes with different levels of activity in a subset of cells or locations inside a tissue. We previously developed singleCellHaystack, a method for predicting differentially expressed genes from single-cell transcriptome data, without relying on comparisons between clusters of cells. Here we present an update to singleCellHaystack, which is now a universally applicable method for predicting differentially active features: (1) singleCellHaystack now accepts continuous features that can be RNA or protein expression, chromatin accessibility or module scores from single-cell, spatial and even bulk genomics data, and (2) it can handle 1D trajectories, 2-3D spatial coordinates, as well as higher-dimensional latent spaces as input coordinates. Performance has been drastically improved, with up to ten times reduction in computational time and scalability to millions of cells, making singleCellHaystack a suitable tool for exploratory analysis of atlas level datasets. singleCellHaystack is available as packages in both R and Python.
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A Single Cell Atlas of the Newt Iris During Lens Regeneration.
Iris pigmented epithelial (IPE) cells transdifferentiate to lens epithelial cells (LECs) during Wolffian lens regeneration in newts. Single cell RNA sequencing was used at multiple timepoints to further our understanding of this process and the cells involved in it. All major cell types present in and adjacent to the iris were identified including IPE cells, macrophages, non-pigmented ciliary epithelial cells, pigmented ciliary epithelial cells, and stroma-residing fibroblasts, endothelial cells, iridophores, and melanocytes. In the intact iris, IPE cell subpopulations were characterized by the expression of the dorsoventral genes TBX5 and VAX2, and newly identified markers LTBP2, CHRM3, and NTN1. During regeneration, IPE heterogeneity was correlated with functional states such as the cell cycle, migration, and lens vesicle formation. Pseudotime trajectory analysis revealed new insights into transcriptional and reprogramming factors during the IPE-to-LEC conversion and built a molecular and genetic blueprint of newt lens regeneration. Macrophages were identified as tissue-resident and underwent polarization from M1 early to M2 late during lens regeneration, an event that correlated temporally with the IPE-to-LEC reprogramming. Overall, this atlas provides data and analysis for iris cell types, IPE subpopulations, IPE cell states, gene expression changes as IPE cells reprogram to LECs, macrophage identity and function, and cell-to-cell interactions during newt lens regeneration.
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A novel pyroptosis-related gene signature predicts the prognosis of glioma through immune infiltration.
BACKGROUND: Glioma is the most common primary intracranial tumour and has a very poor prognosis. Pyroptosis, also known as inflammatory necrosis, is a type of programmed cell death that was discovered in recent years. The expression and role of pyroptosis-related genes in gliomas are still unclear. METHODS: In this study, we analysed the RNA-seq and clinical information of glioma patients from The Cancer Genome Atlas (TCGA) database and Chinese Glioma Genome Atlas (CGGA) database. To investigate the prognosis and immune microenvironment of pyroptosis-related genes in gliomas, we constructed a risk model based on the TCGA cohort. The patients in the CGGA cohort were used as the validation cohort. RESULTS: In this study, we identified 34 pyroptosis-related differentially expressed genes (DEGs) in glioma. By clustering these DEGs, all glioma cases can be divided into two clusters. Survival analysis showed that the overall survival time of Cluster 1 was significantly higher than that of Cluster 2. Using the TCGA cohort as the training set, a 10-gene risk model was constructed through univariate Cox regression analysis and LASSO Cox regression analysis. According to the risk score, gliomas were divided into high-risk and low-risk groups. Survival analysis showed that the low-risk group had a longer survival time than the high-risk group. The above results were verified in the CGGA validation cohort. To verify that the risk model was independent of other clinical features, the distribution and the Kaplan-Meier survival curves associated with risk scores were performed. Combined with the characteristics of the clinical cases, the risk score was found to be an independent factor predicting the overall survival of patients with glioma. The analysis of single sample Gene Set Enrichment Analysis (ssGSEA) showed that compared with the low-risk group, the high-risk group had immune cell and immune pathway activities that were significantly upregulated. CONCLUSION: We established 10 pyroptosis-related gene markers that can be used as independent clinical predictors and provide a potential mechanism for the treatment of glioma.
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Loss of Krüppel-like factor 9 deregulates both physiological gene expression and development.
Krüppel-like factor 9 (Klf9) is a ubiquitously expressed transcription factor that is a feedforward regulator of multiple stress-responsive and endocrine signaling pathways. We previously described how loss of Klf9 function affects the transcriptome of zebrafish larvae sampled at a single time point 5 days post-fertilization (dpf). However, klf9 expression oscillates diurnally, and the sampled time point corresponded to its expression nadir. To determine if the transcriptomic effects of the klf9-/- mutation vary with time of day, we performed bulk RNA-seq on 5 dpf zebrafish embryos sampled at three timepoints encompassing the predawn peak and midmorning nadir of klf9 expression. We found that while the major effects of the klf9-/- mutation that we reported previously are robust to time of day, the mutation has additional effects that manifest only at the predawn time point. We used a published single-cell atlas of zebrafish development to associate the effects of the klf9-/- mutation with different cell types and found that the mutation increased mRNA associated with digestive organs (liver, pancreas, and intestine) and decreased mRNA associated with differentiating neurons and blood. Measurements from confocally-imaged larvae suggest that overrepresentation of liver mRNA in klf9-/- mutants is due to development of enlarged livers.
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A Drosophila single-cell 3D spatiotemporal multi-omics atlas unveils panoramic key regulators of cell-type differentiation.
The development of a multicellular organism is a highly intricate process tightly regulated by numerous genes and pathways in both spatial and temporal manners. Here, we present Flysta3D-v2, a comprehensive multi-omics atlas of the model organism Drosophila spanning its developmental lifespan from embryo to pupa. Our datasets encompass 3D single-cell spatial transcriptomic, single-cell transcriptomic, and single-cell chromatin accessibility information. Through the integration of multimodal data, we generated developmentally continuous in silico 3D models of the entire organism. We further constructed tissue development trajectories that uncover the detailed profiles of cell-type differentiation. With a focus on the midgut, we identified transcription factors involved in midgut cell-type regulation and validated exex as a key regulator of copper cell development. This extensive atlas provides a rich resource and serves as a systematic platform for studying Drosophila development with integrated single-cell data at ultra-high spatiotemporal resolution.
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Single-Cell RNA Sequencing Identifies Crucial Genes Influencing the Polarization of Tumor-Associated Macrophages in Liver Cancer.
BACKGROUND: In the context of hepatocellular carcinoma (HCC), tumor-associated macrophages (TAMs) are pivotal for the immunosuppressive nature of the tumor microenvironment (TME). This investigation delves into the functional transformations of TAMs within the TME by leveraging single-cell transcriptomics to pinpoint critical genes influencing TAM subset polarization. METHODS: We procured single-cell and bulk transcriptomic data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), implementing quality assurance, dimensional reduction, clustering, and annotation on the single-cell sequencing data. To examine cellular interactions, CellChat was utilized, while single-cell regulatory network inference and clustering (SCENIC) was applied to deduce transcription factors (TFs) and their associated targets. Through gene enrichment, survival, and immune infiltration correlation analyses, we sought to pinpoint and validate influential genes. A TAM model under HCC conditions was then established to confirm the expression levels of these key genes. RESULTS: Our analysis encompassed 74,742 cells and 23,110 genes. Through postdimensional reduction and clustering, we identified seven distinct cell types and nine TAM subtypes. Analysis via CellChat highlighted a predominance of M2-phenotype-inclined TAM subsets within the tumor's core. SCENIC pinpointed the transcription factor PRDM1 and its target genes as pivotal in this region. Further analysis indicated these genes' involvement in macrophage polarization. Employing trajectory analysis, survival analysis, and immune infiltration correlation, we scrutinized and validated genes likely directing M2 polarization. Experimental validation confirmed PRDM1's heightened expression in TAMs conditioned by HCC. CONCLUSIONS: Our findings suggest the PRDM1 gene is a key regulator of M2 macrophage polarization, contributing to the immunosuppressive TME in HCC.
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Multi-omics and single-cell sequencing analyses reveal the potential significance of circadian pathways in cancer therapy.
BACKGROUND: Circadian rhythm disturbance is an independent risk factor for cancer. However, few studies have been reported on circadian rhythm related genes (CRGs) in cancer, so it is important to further explore the impact of CRGs in pan-cancer. RESEARCH DESIGN AND METHODS: The Cancer Genome Atlas database was used to collect cancer-related data such as copy number variation, single nucleotide variants, methylation, and survival differences. Immunohistochemistry (IHC) was used to verify the expression of circadian rhythm hub genes. The circadian pathway scores (CRS) were calculated using single-sample gene enrichment analysis. TIMER and GEPIA databases were used for immune-cell integration and assessment. Single-cell sequencing data was used to evaluate the abundance of CRS in tumor microenvironment cells. RESULTS: In this study, we found that the expression of circadian pathway varies between tumors. CSNK1E was significantly up-regulated in most tumors and CRY2 was significantly down-regulated in most tumors. The protein interaction network suggested CRY2 as the core gene and IHC verified its significant low expression in KIRC. In addition, CRGs were found to be protective factors in most tumors and have the potential to act as specific immune markers in different tumors. CRS was significantly lower in abundance in most tumors. CRS was significantly associated with overall survival in tumor patients and associated with the expression of many immune cells in the tumor immune microenvironment. CRS is significantly associated with tumor mutational burden and microsatellite instability scores in most tumors and may serve as a potential immunotherapeutic marker. CONCLUSIONS: The circadian rhythm pathway may be a breakthrough point in regulating the tumor microenvironment meanwhile a suitable immunotherapy method in the future.
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Single-cell DNA methylome and 3D genome atlas of the human subcutaneous adipose tissue.
Human subcutaneous adipose tissue (SAT) contains a diverse array of cell-types; however, the epigenomic landscape among the SAT cell-types has remained elusive. Our integrative analysis of single-cell resolution DNA methylation and chromatin conformation profiles (snm3C-seq), coupled with matching RNA expression (snRNA-seq), systematically cataloged the epigenomic, 3D topology, and transcriptomic dynamics across the SAT cell-types. We discovered that the SAT CG methylation (mCG) landscape is characterized by pronounced hyper-methylation in myeloid cells and hypo-methylation in adipocytes and adipose stem and progenitor cells (ASPCs), driving nearly half of the 705,063 detected differentially methylated regions (DMRs). In addition to the enriched cell-type-specific transcription factor binding motifs, we identified TET1 and DNMT3A as plausible candidates for regulating cell-type level mCG profiles. Furthermore, we observed that global mCG profiles closely correspond to SAT lineage, which is also reflected in cell-type-specific chromosome compartmentalization. Adipocytes, in particular, display significantly more short-range chromosomal interactions, facilitating the formation of complex local 3D genomic structures that regulate downstream transcriptomic activity, including those associated with adipogenesis. Finally, we discovered that variants in cell-type level DMRs and A compartments significantly predict and are enriched for variance explained in abdominal obesity. Together, our multimodal study characterizes human SAT epigenomic landscape at the cell-type resolution and links partitioned polygenic risk of abdominal obesity to SAT epigenome.