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Cell-free circulating tumor RNAs in plasma as the potential prognostic biomarkers in colorectal cancer.
BACKGROUND: Cell free RNA (cfRNA) contains transcript fragments from multiple cell types, making it useful for cancer detection in clinical settings. However, the pathophysiological origins of cfRNAs in plasma from colorectal cancer (CRC) patients remain unclear. METHODS: To identify the tissue-specific contributions of cfRNAs transcriptomic profile, we used a published single-cell transcriptomics profile to deconvolute cell type abundance among paired plasma samples from CRC patients who underwent tumor-ablative surgery. We further validated the differentially expressed cfRNAs in 5 pairs of CRC tumor samples and adjacent tissue samples as well as 3 additional CRC tumor samples using RNA-sequencing. RESULTS: The transcriptomic component from intestinal secretory cells was significantly decreased in the in-house post-surgical cfRNA. The HPGD, PACS1, and TDP2 expression was consistent across cfRNA and tissue samples. Using the Cancer Genome Atlas (TCGA) CRC datasets, we were able to classify the patients into two groups with significantly different survival outcomes. CONCLUSIONS: The three-gene signature holds promise in applying minimal residual disease (MRD) testing, which involves profiling remnants of cancer cells after or during treatment. Biomarkers identified in the present study need to be validated in a larger cohort of samples in order to ascertain their possible use in early diagnosis of CRC.
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Evolution of pallium, hippocampus, and cortical cell types revealed by single-cell transcriptomics in reptiles.
Computations in the mammalian cortex are carried out by glutamatergic and γ-aminobutyric acid-releasing (GABAergic) neurons forming specialized circuits and areas. Here we asked how these neurons and areas evolved in amniotes. We built a gene expression atlas of the pallium of two reptilian species using large-scale single-cell messenger RNA sequencing. The transcriptomic signature of glutamatergic neurons in reptilian cortex suggests that mammalian neocortical layers are made of new cell types generated by diversification of ancestral gene-regulatory programs. By contrast, the diversity of reptilian cortical GABAergic neurons indicates that the interneuron classes known in mammals already existed in the common ancestor of all amniotes.
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Single-cell RNA transcriptome reveals the intra-tumoral heterogeneity and regulators underlying tumor progression in metastatic pancreatic ductal adenocarcinoma.
Pancreatic ductal adenocarcinoma (PDAC) is the most frequent and aggressive pancreatic tumor characterized by high metastatic risk and special tumor microenvironment. To comprehensively delineate the complex intra-tumoral heterogeneity and the underlying mechanism during metastatic lesions malignant progression, single-cell RNA sequencing (scRNA-seq) was employed. PCA and TSNE were used for dimensionality reduction analysis and cell clustering. Find All Markers function was used to calculate differential genes in each cluster, and Do Heatmap function was used to plot the distribution of differential genes in each cluster. GSVA was employed to assign pathway activity estimates to individual cells. Lineage trajectory progression was inferred by monocle. CNV status was inferred to compare the heterogeneity among patients and subtypes by infercnv. Ligand-receptor interactions were identified by CellPhoneDB, and regulons network of cells was analyzed by SCENIC. Through RNA-sequencing of 6236 individual cells from 5 liver metastatic PDAC lesions, 10 major cell clusters are identified by using unbiased clustering analysis of expression profiling and well-known cell markers. Cells with high CNV level were considered as malignant cells and pathway analyses were carried out to highlight intratumor heterogeneity in PDAC. Pseudotime trajectory analysis revealed that components of multiple tumor-related pathways and transcription factors (TFs) were differentially expressed along PDAC progression. The complex cellular communication suggested potential immunotherapeutic targets in PDAC. Regulon network identified multiple candidates for promising cell-specific transcriptional factors. Finally, metastatic-related genes expression levels and signaling pathways were validated in bulk RNA Sequencing data. This study contributed a comprehensive single-cell transcriptome atlas and contributed into novel insight of intratumor heterogeneity and molecular mechanism in metastatic PDAC.
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Single-cell genomic profile-based analysis of tissue differentiation in colorectal cancer.
Colorectal cancer (CRC) progression is associated with cancer cell dedifferentiation and sternness acquisition. Several methods have been developed to identify sternness signatures in CRCs. However, studies that directly measured the degree of dedifferentiation in CRC tissues are limited. It is unclear how the differentiation states change during CRC progression. To address this, we develop a method to analyze the tissue differentiation spectrum in colorectal cancer using normal gastrointestinal single-cell transcriptome data. Applying this method on 281 tumor samples from The Cancer Genome Atlas Colon Adenocarcinoma dataset, we identified three major CRC subtypes with distinct tissue differentiation pattern. We observed that differentiation states are closely correlated with anti-tumor immune response and patient outcomes in CRC. Highly dedifferentiated CRC samples escaped the immune surveillance and exhibited poor outcomes; mildly dedifferentiated CRC samples showed resistance to anti-tumor immune responses and had a worse survival rate; well-differentiated CRC samples showed sustained anti-tumor immune responses and had a good prognosis. Overall, the spectrum of tissue differentiation observed in CRCs can be used for future clinical risk stratification and subtype-based therapy selection.
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VBASS enables integration of single cell gene expression data in Bayesian association analysis of rare variants.
Rare or de novo variants have substantial contribution to human diseases, but the statistical power to identify risk genes by rare variants is generally low due to rarity of genotype data. Previous studies have shown that risk genes usually have high expression in relevant cell types, although for many conditions the identity of these cell types are largely unknown. Recent efforts in single cell atlas in human and model organisms produced large amount of gene expression data. Here we present VBASS, a Bayesian method that integrates single-cell expression and de novo variant (DNV) data to improve power of disease risk gene discovery. VBASS models disease risk prior as a function of expression profiles, approximated by deep neural networks. It learns the weights of neural networks and parameters of Gamma-Poisson likelihood models of DNV counts jointly from expression and genetics data. On simulated data, VBASS shows proper error rate control and better power than state-of-the-art methods. We applied VBASS to published datasets and identified more candidate risk genes with supports from literature or data from independent cohorts. VBASS can be generalized to integrate other types of functional genomics data in statistical genetics analysis.
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Immune infiltration associated C1q acts as a novel prognostic biomarker of cutaneous melanoma.
C1q (complement C1q A chain, complement C1q B chain, and complement C1q C chain) is a recognized component of the classical complement pathway that influences the prognosis of various cancers. However, the effects of C1q on cutaneous melanoma (SKCM) outcomes and immune infiltration remain unknown. Gene expression profiling interactive analysis 2 and the human protein atlas were used to evaluate differential expression of C1q mRNA and protein. The relationship between C1q expression and clinicopathological features was also examined. The genetic alterations of C1q and their impact on survival were analyzed using the cbioportal database. The Kaplan-Meier approach was used to assess the significance of C1q in individuals with SKCM. The cluster profiler R package and the cancer single-cell state atlas database were used to investigate the function and mechanism of C1q in SKCM. The relationship between C1q and immune cell infiltration was estimated using single-sample gene set enrichment analysis. C1q expression was increased, and predicted a favorable prognosis. High C1q expression correlated with clinicopathological T stage, pathological stage, overall survival, and disease specific survival events. Moreover, C1q genetic alterations range from 2.7% to 4%, with no impact on prognosis. According to the enrichment analysis, C1q and immune-related pathways were closely connected. The link between complement C1q B chain and the functional state of inflammation was determined using the cancer single-cell state atlas database. In particular, C1q expression was significantly associated with infiltration of most immune cells and checkpoints PDCD1, CD274, and HAVCR2. The results of this study suggest that C1q is associated with prognosis and immune cell infiltration, supporting its value as a diagnostic and prognostic biomarker.
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The osteoimmune cell atlas of peri-implantitis: A narrative scoping review.
BACKGROUND/AIM: Peri-implantitis represents a complex osteoimmune disorder characterized by dysregulated cellular ecosystems that drive chronic inflammation and accelerated peri-implant bone loss. MATERIALS AND METHODS: This article was designed as a narrative scoping review to map and integrate the available evidence from transcriptomics, single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, flow cytometry, and immunohistochemistry to construct a comprehensive cellular atlas of peri-implantitis. KEY FINDINGS: Current evidence suggests that peri-implantitis lesions contain expanded and activated neutrophil, macrophage, T-cell, and B-lineage compartments, together with stromal and vascular cell populations that may contribute to inflammatory amplification and osteoclastogenic signaling. Reported features include CXCR2+ neutrophil subsets with NETosis-associated activity, inflammatory macrophage states, altered Th17/Treg-related immune responses, and expanded B-cell/plasma-cell compartments forming organized adaptive immune niches. Proposed molecular mechanisms include NF-κB-related inflammatory activation, CXCL13-associated adaptive immune organization, NETosis-related pathways, and RANKL-mediated osteoclastogenic signaling. However, several of these pathways remain incompletely validated in human peri-implantitis tissue. Critical knowledge gaps include limited direct evidence for NLRP3 inflammasome activation, uncharacterized type I interferon signaling, unresolved hierarchies of cellular RANKL sources, and limited functional data for rare immune populations. CONCLUSIONS: The peri-implantitis osteoimmune microenvironment constitutes a highly organized, self-reinforcing inflammatory ecosystem. Progress toward precision therapeutic strategies requires spatial multiomics at subcellular resolution, longitudinal human studies, and functional ex vivo validation models to interrogate the self-sustaining circuits identified here.
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Single-nucleus transcriptomic profiling reveals cell type-specific responses of the soybean root tip to aluminum stress.
Aluminum (Al) toxicity resulting from soil acidification poses a major constraint on crop productivity. Although the root apex serves as the primary site for Al perception and adaptive remodeling, cell type-resolved responses to prolonged Al exposure remain poorly characterized. Here, we employed single-nucleus RNA sequencing (snRNA-seq) to generate a cell type-resolved transcriptomic atlas of the soybean (Glycine max) root tip under Al stress. We profiled a total of 79,201 high-quality nuclei and identified nine major root cell types. Our results reveal that Al stress elicits shared transcriptional responses across cell types while reshaping root cell type composition. Notably, the proportion of nuclei increased in the epidermal cell layer (from 34.16% to 70.41%). In contrast, a significant reduction in nuclear number was detected within the cortical cells (from 40.70% to 8.53%). Interestingly, microscopic examination further indicated a pronounced expansion of regions exhibiting patterns inconsistent with the decrease in cortical nuclei. Cell type-specific analyses identified a total of 20,544 differentially expressed genes (DEGs) and showed that epidermal cells primarily responded to Al stress by activating transcriptional programs associated with transport, cell wall reinforcement, and defense, accompanied by enhanced fatty acid biosynthesis and cuticle-related pathways. In contrast, cortical cells exhibited repression of cell cycle-related transcriptional programs, consistent with their reduced cell abundance and indicating that limitations in cell division are closely associated with cortical cell loss. Furthermore, untargeted metabolomic profiling supported an important role of lipid metabolism in the response to Al stress. Collectively, our study reveals cell type-specific response patterns of the soybean root tip to long-term Al stress at single-nucleus resolution and provides new insights into the cellular mechanisms underlying aluminum tolerance in crop roots.
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STAB: a spatio-temporal cell atlas of the human brain.
The human brain is the most complex organ consisting of billions of neuronal and non-neuronal cells that are organized into distinct anatomical and functional regions. Elucidating the cellular and transcriptome architecture underlying the brain is crucial for understanding brain functions and brain disorders. Thanks to the single-cell RNA sequencing technologies, it is becoming possible to dissect the cellular compositions of the brain. Although great effort has been made to explore the transcriptome architecture of the human brain, a comprehensive database with dynamic cellular compositions and molecular characteristics of the human brain during the lifespan is still not available. Here, we present STAB (a Spatio-Temporal cell Atlas of the human Brain), a database consists of single-cell transcriptomes across multiple brain regions and developmental periods. Right now, STAB contains single-cell gene expression profiling of 42 cell subtypes across 20 brain regions and 11 developmental periods. With STAB, the landscape of cell types and their regional heterogeneity and temporal dynamics across the human brain can be clearly seen, which can help to understand both the development of the normal human brain and the etiology of neuropsychiatric disorders. STAB is available at http://stab.comp-sysbio.org.
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Integrated multi-omic atlas reveals the hierarchy of spatiotemporal regulatory networks of mouse gastrulation.
Spatiotemporal coordination of cellular and molecular events is crucial for cell fate commitment during mouse gastrulation. However, the high-precision mechanisms governing the timing and spatial dynamics remain poorly understood. Here, we present a time-series single-cell multi-omic dataset of the gastrulating mouse embryos and construct a hierarchical gene regulatory landscape. Integrating this with real three-dimensional transcriptomic coordinate, we created ST-MAGIC and ST-MAGIC (+) atlas, dissecting the spatiotemporal logics of regulatory networks and signaling responsiveness underpinning the lineage commitment at gastrulation. Specifically, we delineated the multi-omic basis for left-right symmetry breaking events in the gastrula and also revealed the spatiotemporal molecular relay for axial mesendoderm lineage, where early and intermediate transcription factors first open the chromatin regions and setup the responsiveness to signaling, followed by terminal factors to consolidate the transcriptomic architecture. In summary, our study presents a spatiotemporal regulatory logic framework of mouse gastrulation for advancing our understanding of mammalian embryogenesis.