Identify Cell Types Research Articles

Single-cell insights into development of the bovine placenta†.

A central determinant of pregnancy success is proper development of the conceptus (embryo/fetus and associated extraembryonic membranes including the placenta). Although the gross morphology and histology of the bovine placenta have been well studied, the cellular and molecular mechanisms regulating placenta development and trophoblast differentiation and function remain essentially undefined. Here, single-cell transcriptome (scRNA-seq) analysis was performed on the day 17 bovine conceptus and chorion of day 24, 30, and 50 conceptuses (n = 3-4 samples per day) using the 10X Genomics platform. Bioinformatic analyses identified cell types and their ontogeny including trophoblast, mesenchyme, and immune cells. Loss of interferon tau-expressing trophoblast uninucleate cells occurred between days 17 and 30, whereas binucleate cells, identified based on expression of placental lactogen (CSH2) and specific pregnancy-associated glycoprotein genes (PAGs), first appeared on day 24. Several different types of uninucleate cells were present in day 24, 30, and 50 samples, but only one (day 24) or two types of binucleate cells (days 30 and 50). Cell trajectory analyses provided a conceptual framework for uninucleate cell development and binucleate cell differentiation, and bioinformatic analyses identified candidate transcription factors governing differentiation and function of the trophoblasts. The digital atlas of cell types in the developing bovine conceptus reported here serves as a resource to discover key genes and biological pathways regulating its development during the critical periods of implantation and placentation.

STGNNks: Identifying cell types in spatial transcriptomics data based on graph neural network, denoising auto-encoder, and [formula omitted]-sums clustering

Background:Spatial transcriptomics technologies fully utilize spatial location information, tissue morphological features, and transcriptional profiles. Integrating these data can greatly advance our understanding about cell biology in the morphological background. Methods:We developed an innovative spatial clustering method called STGNNks by combining graph neural network, denoising auto-encoder, and k-sums clustering. First, spatial resolved transcriptomics data are preprocessed and a hybrid adjacency matrix is constructed. Next, gene expressions and spatial context are integrated to learn spots’ embedding features by a deep graph infomax-based graph convolutional network. Third, the learned features are mapped to a low-dimensional space through a zero-inflated negative binomial (ZINB)-based denoising auto-encoder. Fourth, a k-sums clustering algorithm is developed to identify spatial domains by combining k-means clustering and the ratio-cut clustering algorithms. Finally, it implements spatial trajectory inference, spatially variable gene identification, and differentially expressed gene detection based on the pseudo-space-time method on six 10x Genomics Visium datasets. Results:We compared our proposed STGNNks method with five other spatial clustering methods, CCST, Seurat, stLearn, Scanpy and SEDR. For the first time, four internal indicators in the area of machine learning, that is, silhouette coefficient, the Davies-Bouldin index, the Caliniski-Harabasz index, and the S_Dbw index, were used to measure the clustering performance of STGNNks with CCST, Seurat, stLearn, Scanpy and SEDR on five spatial transcriptomics datasets without labels (i.e., Adult Mouse Brain (FFPE), Adult Mouse Kidney (FFPE), Human Breast Cancer (Block A Section 2), Human Breast Cancer (FFPE), and Human Lymph Node). And two external indicators including adjusted Rand index (ARI) and normalized mutual information (NMI) were applied to evaluate the performance of the above six methods on Human Breast Cancer (Block A Section 1) with real labels. The comparison experiments elucidated that STGNNks obtained the smallest Davies-Bouldin and S_Dbw values and the largest Silhouette Coefficient, Caliniski-Harabasz, ARI and NMI, significantly outperforming the above five spatial transcriptomics analysis algorithms. Furthermore, we detected the top six spatially variable genes and the top five differentially expressed genes in each cluster on the above five unlabeled datasets. And the pseudo-space-time tree plot with hierarchical layout demonstrated a flow of Human Breast Cancer (Block A Section 1) progress in three clades branching from three invasive ductal carcinoma regions to multiple ductal carcinoma in situ sub-clusters. Conclusion:We anticipate that STGNNks can efficiently improve spatial transcriptomics data analysis and further boost the diagnosis and therapy of related diseases. The codes are publicly available at https://github.com/plhhnu/STGNNks.

Abomasal RNA-seq reveals a strong local cellular response in suckling lambs with resistance against Haemonchus contortus

Santa Ines (SI) and Ile de France (IF) sheep are known to be resistant and susceptible to Haemonchus contortus infection, respectively. Several studies have shown some genes as potential biological markers for sheep resistance against gastrointestinal nematodes using molecular tools, including transcriptomic analysis. In this study, we sequenced the polyadenylated RNA of the abomasal tissue of SI and IF suckling lambs to identify mucosa-specific transcript alterations between breeds artificially infected with H. contortus. Naïve SI (n = 4) and IF (n = 4) lambs were artificially infected every other day, over a period of 52 days, from 14 to 66 days old, with a total of 5,400 H. contortus infective larvae. Fundic abomasal tissue samples were collected at 68 days old, and submitted to high-throughput RNA sequencing (RNA-seq). Differential expression analysis (P value < 0.001 and False Discovery Rate (FDR) < 0.05) between SI and IF samples identified 292 genes, most of which showed greater expression in SI lambs. To help annotate and assign possible function to differentially expressed genes (DEGs), we used previously available single-cell RNA-seq (scRNA-seq) data from ovine abomasal mucosa to putatively identify cell types and possible mechanisms involved in resistance to H. contortus. In particular, genes associated with endothelial and tuft cells showed the greatest increases in expression in SI relative to IF lambs. SI lambs had higher percentages of tuft cells than IF lambs in the fundic abomasal mucosa. Although we found innate immunity (cell-mediated in mucosa) acting as a protagonist in impairing H. contortus infection, a stronger acquired immune response was being modulated at an earlier stage by SI lambs. We suggest that the complex connection between innate and adaptive immunity is via cellular antigen processing and presentation (APP). Based on comparison with scRNA-seq data, SI lambs showed a robust APP mechanism characterized mainly by greater T cell APP, macrophage differentiation, and cytokine signalling. We identified potential mechanisms and markers to advance knowledge for selection of H. contortus resistance at a very early age, in SI as well as in other commercial sheep breeds.

Abstract P233: Single-cell Transcriptomic Characterization Of Aortic Lymphocyte Heterogeneity During Hypertension

Vascular inflammation is a hallmark of hypertension. T and B lymphocytes accumulate in the aortic wall during hypertension; however, it is unclear which subtypes or functions contribute to disease progression. Thus, we aimed to characterize T and B cell subtypes, activation states and phenotypes in hypertension. Angiotensin II infusion (0.7 mg/kg/day; s.c. for 28 days, n = 15) was used to induce hypertension in 12-week-old male C57BL/6 mice. Age-matched normotensive controls received vehicle infusion (0.5% NaCl, 0.1% acetic acid; s.c. for 28 days, n = 11). Systolic blood pressure (SBP) was measured weekly by tail-cuff plethysmography. Aortic stiffness and fibrosis were assessed at endpoint via high resolution ultrasound and picrosirius red-staining, respectively. Whole aortas (n = 3 per treatment group) were isolated and digested into single-cell suspensions and enriched for live, nucleated, metabolically active cells. Single-cell RNA sequencing was performed using Chromium 10x and NovaSeq genomics platforms. Bioinformatic analyses identified cell types, acquired molecular and biological activities and inferred gene regulatory networks. At endpoint, angiotensin II-treated mice had elevated SBP (179 ± 4 vs. 124 ± 2 mmHg; P &lt; 0.0001), with increased aortic pulse wave velocity (1.5 ± 0.2 vs 3.1 ± 0.3 m/s; P = 0.001) and collagen content (51 ± 2% vs. 60 ± 2% wall area; P = 0.02), compared to vehicle controls. Total aortic T cells and B cells increased by 16% (184 vs. 215 cells) and 74% (433 vs. 771 cells) in hypertension, respectively. These increases were attributed to the expansion of CD8+, regulatory T cells (Tregs), gamma-delta (GD) T cells, immature transitional B2 cells, naïve B2, memory B2, B1 B cells and plasma cells, with a reduction in regulatory B2 B cells (Bregs). Gene ontology analyses revealed both pro- and anti-inflammatory B and T cells were activated during hypertension. However, antigen receptor activity pathways were only enriched in GD T cells, B1 B cells and effector Bregs (running enrichment scores &gt; 0.2). Overall, these findings identify GD T cells, B1 B cells and effector Bregs as key lymphocyte subtypes that drive vascular inflammation during experimental hypertension and aortic stiffening.

Spatial transcriptomics drives a new era in plant research.

SUMMARYThe plant community lags far behind the animal and human fields concerning the application of single‐cell methodologies. This is primarily due to the challenges associated with plant tissue dissection and the limitations of the available technologies. However, recent advances in spatial transcriptomics enable the study of single‐cells derived from plant tissues from a spatial perspective. This technology is already successfully used to identify cell types, reconstruct cell‐fate lineages, and reveal cell‐to‐cell interactions. Future technological advancements will overcome the challenges in sample processing, data analysis, and the integration of multiple‐omics technologies. Thanks to spatial transcriptomics, we anticipate several plant research projects to significantly advance our understanding of critical aspects of plant biology.

The core role of macrophages in hepatocellular carcinoma: the definition of molecular subtypes and the prognostic risk system.

Background: In patients with hepatocellular carcinoma (HCC), the tumor microenvironment (TME) is resistant to immunotherapy because of its specificity. It is meaningful to explore the role of macrophage, which is one of the most abundant immune cells in the TME, in cellular communication and its effect on the prognosis and immunotherapy of HCC. Methods: Dimensionality reduction and clustering of the single-cell RNA-seq data from the GSE149614 dataset were carried out to identify the cellular composition of HCC. CellChat was used to analyze the communication between different cells. The specifically highly expressed genes of macrophages were extracted for univariate Cox regression analysis to obtain prognostic genes for HCC cluster analysis, and the risk system of macrophage-specifically highly expressed genes was developed by random forest analysis and multivariate Cox regression analysis. Prognosis, TME infiltration, potential responses to immunotherapy, and antineoplastic drugs were compared among molecular subtypes and between risk groups. Results: We found that HCC included nine identifiable cell types, of which macrophages had the highest communication intensity with each of the other eight cell types. Of the 179 specifically highly expressed genes of macrophage, 56 were significantly correlated with the prognosis of HCC, which classified HCC into three subtypes, which were reproducible and produced different survival outcomes, TME infiltration, and immunotherapy responses among the subtypes. In the integration of four macrophage-specifically highly expressed genes for the development of a risk system, the risk score was significantly involved in higher immune cell infiltration, poor prognosis, immunotherapy response rate, and sensitivity of six drugs. Conclusion: In this study, through single-cell RNA-seq data, we identified nine cell types, among which macrophage had the highest communication intensity with the rest of the cell types. Based on specifically highly expressed genes of macrophage, we successfully divided HCC patients into three clusters with distinct prognosis, TME, and therapeutic response. Additionally, a risk system was constructed, which provided a potential reference index for the prognostic target and preclinical individualized treatment of HCC.

LB1675 Uniquely identified cell type in atopic dermatitis may provoke and maintain the chronic itching sensation through interaction with dorsal root ganglion neuron

LB1675 Uniquely identified cell type in atopic dermatitis may provoke and maintain the chronic itching sensation through interaction with dorsal root ganglion neuron

ScSDSC: Self-supervised Deep Subspace Clustering for scRNA-seq Data

background: Single-cell RNA sequencing(scRNA-seq) data can identify heterogeneity between cells, thereby identifying cell types and discovering rare cell types. Clustering is often used to identify cell types, but the high noise and high dimension of scRNA-seq lead to the degradation of clustering performance and impact downstream analysis. Deep learning is widely used in this field, which provides promising performance in feature learning. objective: Most deep learning models only consider the relationship between genes, ignore the relationship between cells. We try to use the relationships between cells and the relationships between genes to construct clustering models. method: We proposed scSDSC: a deep subspace cluster architecture that considers the relationships between genes and cells at the same time. Similar to deep subspace clustering (DSC), we added a fully connected layer after the embedding layer to obtain the self-expression matrix. In addition, we also added a fully connected SoftMax layer to generate the pseudo-label and used the information carried by the pseudo-label for model training. Finally, the affinity matrix is obtained for spectral clustering. result: Experimental results on eight real datasets show that scSDSC outperforms existing methods in downstream analysis. conclusion: Our method plays an important role in improving clustering accuracy and downstream analysis.

LIDER: cell embedding based deep neural network classifier for supervised cell type identification.

Automatic cell type identification has been an urgent task for the rapid development of single-cell RNA-seq techniques. Generally, the current approach for cell type identification is to generate cell clusters by unsupervised clustering and later assign labels to each cell cluster with manual annotation. Here, we introduce LIDER (celL embeddIng based Deep nEural netwoRk classifier), a deep supervised learning method that combines cell embedding and deep neural network classifier for automatic cell type identification. Based on a stacked denoising autoencoder with a tailored and reconstructed loss function, LIDER identifies cell embedding and predicts cell types with a deep neural network classifier. LIDER was developed upon a stacked denoising autoencoder to learn encoder-decoder structures for identifying cell embedding. LIDER accurately identifies cell types by using stacked denoising autoencoder. Benchmarking against state-of-the-art methods across eight types of single-cell data, LIDER achieves comparable or even superior enhancement performance. Moreover, LIDER suggests comparable robust to batch effects. Our results show a potential in deep supervised learning for automatic cell type identification of single-cell RNA-seq data. The LIDER codes are available at https://github.com/ShiMGLab/LIDER.

Spatial transcriptomics reveal markers of histopathological changes in Duchenne muscular dystrophy mouse models

Duchenne muscular dystrophy is caused by mutations in the DMD gene, leading to lack of dystrophin. Chronic muscle damage eventually leads to histological alterations in skeletal muscles. The identification of genes and cell types driving tissue remodeling is a key step to developing effective therapies. Here we use spatial transcriptomics in two Duchenne muscular dystrophy mouse models differing in disease severity to identify gene expression signatures underlying skeletal muscle pathology and to directly link gene expression to muscle histology. We perform deconvolution analysis to identify cell types contributing to histological alterations. We show increased expression of specific genes in areas of muscle regeneration (Myl4, Sparc, Hspg2), fibrosis (Vim, Fn1, Thbs4) and calcification (Bgn, Ctsk, Spp1). These findings are confirmed by smFISH. Finally, we use differentiation dynamic analysis in the D2-mdx muscle to identify muscle fibers in the present state that are predicted to become affected in the future state.

Cellular heterogeneity of the developing worker honey bee (Apis mellifera) pupa: a single cell transcriptomics analysis.

It is estimated that animals pollinate 87.5% of flowering plants worldwide and that managed honey bees (Apis mellifera) account for 30-50% of this ecosystem service to agriculture. In addition to their important role as pollinators, honey bees are well-established insect models for studying learning and memory, behavior, caste differentiation, epigenetic mechanisms, olfactory biology, sex determination, and eusociality. Despite their importance to agriculture, knowledge of honey bee biology lags behind many other livestock species. In this study, we have used scRNA-Seq to map cell types to different developmental stages of the worker honey bee (prepupa at day 11 and pupa at day 15) and sought to determine their gene expression signatures. To identify cell-type populations, we examined the cell-to-cell network based on the similarity of the single-cells transcriptomic profiles. Grouping similar cells together we identified 63 different cell clusters of which 17 clusters were identifiable at both stages. To determine genes associated with specific cell populations or with a particular biological process involved in honey bee development, we used gene coexpression analysis. We combined this analysis with literature mining, the honey bee protein atlas, and gene ontology analysis to determine cell cluster identity. Of the cell clusters identified, 17 were related to the nervous system and sensory organs, 7 to the fat body, 19 to the cuticle, 5 to muscle, 4 to compound eye, 2 to midgut, 2 to hemocytes, and 1 to malpighian tubule/pericardial nephrocyte. To our knowledge, this is the first whole single-cell atlas of honey bees at any stage of development and demonstrates the potential for further work to investigate their biology at the cellular level.

ARGLRR: A Sparse Low-Rank Representation Single-Cell RNA-Sequencing Data Clustering Method Combined with a New Graph Regularization.

The development of single-cell transcriptome sequencing technologies has opened new ways to study biological phenomena at the cellular level. A key application of such technologies involves the employment of single-cell RNA sequencing (scRNA-seq) data to identify distinct cell types through clustering, which in turn provides evidence for revealing heterogeneity. Despite the promise of this approach, the inherent characteristics of scRNA-seq data, such as higher noise levels and lower coverage, pose major challenges to existing clustering methods and compromise their accuracy. In this study, we propose a method called Adjusted Random walk Graph regularization Sparse Low-Rank Representation (ARGLRR), a practical sparse subspace clustering method, to identify cell types. The fundamental low-rank representation (LRR) model is concerned with the global structure of data. To address the limited ability of the LRR method to capture local structure, we introduced adjusted random walk graph regularization in its framework. ARGLRR allows for the capture of both local and global structures in scRNA-seq data. Additionally, the imposition of similarity constraints into the LRR framework further improves the ability of the proposed model to estimate cell-to-cell similarity and capture global structural relationships between cells. ARGLRR surpasses other advanced comparison approaches on nine known scRNA-seq data sets judging by the results. In the normalized mutual information and Adjusted Rand Index metrics on the scRNA-seq data sets clustering experiments, ARGLRR outperforms the best-performing comparative method by 6.99% and 5.85%, respectively. In addition, we visualize the result using Uniform Manifold Approximation and Projection. Visualization results show that the usage of ARGLRR enhances the separation of different cell types within the similarity matrix.

A statistical framework to identify cell types whose genetically regulated proportions are associated with complex diseases.

Finding disease-relevant tissues and cell types can facilitate the identification and investigation of functional genes and variants. In particular, cell type proportions can serve as potential disease predictive biomarkers. In this manuscript, we introduce a novel statistical framework, cell-type Wide Association Study (cWAS), that integrates genetic data with transcriptomics data to identify cell types whose genetically regulated proportions (GRPs) are disease/trait-associated. On simulated and real GWAS data, cWAS showed good statistical power with newly identified significant GRP associations in disease-associated tissues. More specifically, GRPs of endothelial and myofibroblasts in lung tissue were associated with Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease, respectively. For breast cancer, the GRP of blood CD8+ T cells was negatively associated with breast cancer (BC) risk as well as survival. Overall, cWAS is a powerful tool to reveal cell types associated with complex diseases mediated by GRPs.

Glyoxal as an alternative fixative for single-cell RNA sequencing.

Single-cell RNA sequencing has become an important method to identify cell types, delineate the trajectories of cell differentiation in whole organisms, and understand the heterogeneity in cellular responses. Nevertheless, sample collection and processing remain a severe bottleneck for single-cell RNA sequencing experiments. Cell isolation protocols often lead to significant changes in the transcriptomes of cells, requiring novel methods to preserve cell states. Here, we developed and benchmarked protocols using glyoxal as a fixative for single-cell RNA sequencing applications. Using Drop-seq methodology, we detected a large number of transcripts and genes from glyoxal-fixed Drosophila cells after single-cell RNA sequencing. The effective glyoxal fixation of transcriptomes in Drosophila and human cells was further supported by a high correlation of gene expression data between glyoxal-fixed and unfixed samples. Accordingly, we also found highly expressed genes overlapping to a large extent between experimental conditions. These results indicated that our fixation protocol did not induce considerable changes in gene expression and conserved the transcriptome for subsequent single-cell isolation procedures. In conclusion, we present glyoxal as a suitable fixative for Drosophila cells and potentially cells of other species that allow high-quality single-cell RNA sequencing applications.

A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease

Kidney stone disease causes significant morbidity and increases health care utilization. In this work, we decipher the cellular and molecular niche of the human renal papilla in patients with calcium oxalate (CaOx) stone disease and healthy subjects. In addition to identifying cell types important in papillary physiology, we characterize collecting duct cell subtypes and an undifferentiated epithelial cell type that was more prevalent in stone patients. Despite the focal nature of mineral deposition in nephrolithiasis, we uncover a global injury signature characterized by immune activation, oxidative stress and extracellular matrix remodeling. We also identify the association of MMP7 and MMP9 expression with stone disease and mineral deposition, respectively. MMP7 and MMP9 are significantly increased in the urine of patients with CaOx stone disease, and their levels correlate with disease activity. Our results define the spatial molecular landscape and specific pathways contributing to stone-mediated injury in the human papilla and identify associated urinary biomarkers.

Single Cell Multiomics Identifies Cells and Genetic Networks Underlying Alveolar Capillary Dysplasia.

Rationale: Alveolar capillary dysplasia with misalignment of pulmonary veins (ACDMPV) is a lethal developmental disorder of lung morphogenesis caused by insufficiency of FOXF1 (forkhead box F1) transcription factor function. The cellular and transcriptional mechanisms by which FOXF1 deficiency disrupts human lung formation are unknown. Objectives: To identify cell types, gene networks, and cell-cell interactions underlying the pathogenesis of ACDMPV. Methods: We used single-nucleus RNA and assay for transposase-accessible chromatin sequencing, immunofluorescence confocal microscopy, and RNA in situ hybridization to identify cell types and molecular networks influenced by FOXF1 in ACDMPV lungs. Measurements and Main Results: Pathogenic single-nucleotide variants and copy-number variant deletions involving the FOXF1 gene locus in all subjects with ACDMPV (n = 6) were accompanied by marked changes in lung structure, including deficient alveolar development and a paucity of pulmonary microvasculature. Single-nucleus RNA and assay for transposase-accessible chromatin sequencing identified alterations in cell number and gene expression in endothelial cells (ECs), pericytes, fibroblasts, and epithelial cells in ACDMPV lungs. Distinct cell-autonomous roles for FOXF1 in capillary ECs and pericytes were identified. Pathogenic variants involving the FOXF1 gene locus disrupt gene expression in EC progenitors, inhibiting the differentiation or survival of capillary 2 ECs and cell-cell interactions necessary for both pulmonary vasculogenesis and alveolar type 1 cell differentiation. Loss of the pulmonary microvasculature was associated with increased VEGFA (vascular endothelial growth factor A) signaling and marked expansion of systemic bronchial ECs expressing COL15A1 (collagen type XV α 1 chain). Conclusions: Distinct FOXF1 gene regulatory networks were identified in subsets of pulmonary endothelial and fibroblast progenitors, providing both cellular and molecular targets for the development of therapies for ACDMPV and other diffuse lung diseases of infancy.

Identifying SARS-CoV-2 infected cells with scVDN.

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for understanding cellular heterogeneity and identifying cell types in virus-related research. However, direct identification of SARS-CoV-2-infected cells at the single-cell level remains challenging, hindering the understanding of viral pathogenesis and the development of effective treatments. In this study, we propose a deep learning framework, the single-cell virus detection network (scVDN), to predict the infection status of single cells. The scVDN is trained on scRNA-seq data from multiple nasal swab samples obtained from several contributors with varying cell types. To objectively evaluate scVDN's performance, we establish a model evaluation framework suitable for real experimental data. Our results demonstrate that scVDN outperforms four state-of-the-art machine learning models in identifying SARS-CoV-2-infected cells, even with extremely imbalanced labels in real data. Specifically, scVDN achieves a perfect AUC score of 1 in four cell types. Our findings have important implications for advancing virus research and improving public health by enabling the identification of virus-infected cells at the single-cell level, which is critical for diagnosing and treating viral infections. The scVDN framework can be applied to other single-cell virus-related studies, and we make all source code and datasets publicly available on GitHub at https://github.com/studentiz/scvdn.

The cholesterol transporter NPC1 is essential for epigenetic regulation and maturation of oligodendrocyte lineage cells

The intracellular cholesterol transporter NPC1 functions in late endosomes and lysosomes to efflux unesterified cholesterol, and its deficiency causes Niemann–Pick disease Type C, an autosomal recessive lysosomal disorder characterized by progressive neurodegeneration and early death. Here, we use single-nucleus RNA-seq on the forebrain of Npc1−/− mice at P16 to identify cell types and pathways affected early in pathogenesis. Our analysis uncovers significant transcriptional changes in the oligodendrocyte lineage during developmental myelination, accompanied by diminished maturation of myelinating oligodendrocytes. We identify upregulation of genes associated with neurogenesis and synapse formation in Npc1−/− oligodendrocyte lineage cells, reflecting diminished gene silencing by H3K27me3. Npc1−/− oligodendrocyte progenitor cells reproduce impaired maturation in vitro, and this phenotype is rescued by treatment with GSK-J4, a small molecule inhibitor of H3K27 demethylases. Moreover, mobilizing stored cholesterol in Npc1−/− mice by a single administration of 2-hydroxypropyl-β-cyclodextrin at P7 rescues myelination, epigenetic marks, and oligodendrocyte gene expression. Our findings highlight an important role for NPC1 in oligodendrocyte lineage maturation and epigenetic regulation, and identify potential targets for therapeutic intervention.

ScFED: Clustering Identifying Cell Types of scRNA-Seq Data Based on Feature Engineering Denoising.

Recently developed single-cell RNA-seq (scRNA-seq) technology has given researchers the chance to investigate single-cell level of disease development. Clustering is one of the most essential strategies for analyzing scRNA-seq data. Choosing high-quality feature sets can significantly enhance the outcomes of single-cell clustering and classification. But computationally burdensome and highly expressed genes cannot afford a stabilized and predictive feature set for technical reasons. In this study, we introduce scFED, a feature-engineered gene selection framework. scFED identifies prospective feature sets to eliminate the noise fluctuation. And fuse them with existing knowledge from the tissue-specific cellular taxonomy reference database (CellMatch) to avoid the influence of subjective factors. Then present a reconstruction approach for noise reduction and crucial information amplification. We apply scFED on four genuine single-cell datasets and compare it with other techniques. According to the results, scFED improves clustering, decreases dimension of the scRNA-seq data, improves cell type identification when combined with clustering algorithms, and has higher performance than other methods. Therefore, scFED offers certain benefits in scRNA-seq data gene selection.

Calcitonin Related Polypeptide Alpha Mediates Oral Cancer Pain.

Oral cancer patients suffer pain at the site of the cancer. Calcitonin gene related polypeptide (CGRP), a neuropeptide expressed by a subset of primary afferent neurons, promotes oral cancer growth. CGRP also mediates trigeminal pain (migraine) and neurogenic inflammation. The contribution of CGRP to oral cancer pain is investigated in the present study. The findings demonstrate that CGRP-immunoreactive (-ir) neurons and neurites innervate orthotopic oral cancer xenograft tumors in mice. Cancer increases anterograde transport of CGRP in axons innervating the tumor, supporting neurogenic secretion as the source of CGRP in the oral cancer microenvironment. CGRP antagonism reverses oral cancer nociception in preclinical oral cancer pain models. Single-cell RNA-sequencing is used to identify cell types in the cancer microenvironment expressing the CGRP receptor components, receptor activity modifying protein 1 Ramp1 and calcitonin receptor like receptor (CLR, encoded by Calcrl). Ramp1 and Calcrl transcripts are detected in cells expressing marker genes for Schwann cells, endothelial cells, fibroblasts and immune cells. Ramp1 and Calcrl transcripts are more frequently detected in cells expressing fibroblast and immune cell markers. This work identifies CGRP as mediator of oral cancer pain and suggests the antagonism of CGRP to alleviate oral cancer pain.