Isolation and Preparation of Embryonic Zebrafish Retinal Cells for Single-Cell RNA Sequencing.

Recent technological advances in single-cell RNA sequencing (scRNA-Seq) have enabled scientists to answer novel questions in biology with unparalleled precision. Indeed, in the field of ocular development and regeneration, scRNA-Seq studies have resulted in a number of exciting discoveries that have begun to revolutionize the way we think about these processes. Despite the widespread success of scRNA-Seq, many scientists are wary to perform scRNA-Seq experiments due to the uncertainty of obtaining high-quality viable cell populations that are necessary for the generation of usable data that enable rigorous computational analyses. Here, we describe methodology to reproducibility generate high-quality single-cell suspensions from embryonic zebrafish eyes. These single-cell suspensions served as inputs to the 10× Genomics v3.1 system and yielded high-quality scRNA-Seq data in proof-of-principle studies. In describing methodology to quantitatively assess cell yields, cell viability, and other critical quality control parameters, this protocol can serve as a useful starting point for others in designing their scRNA-Seq experiments in the zebrafish eye and in other developing or regenerating tissues in zebrafish or other model systems.

Experimental Framework for Assessing Mouse Retinal Regeneration Through Single-Cell RNA-Sequencing.

Retinal degenerative diseases including age-related macular degeneration and glaucoma are estimated to currently affect more than 14 million people in the United States, with an increased prevalence of retinal degenerations in aged individuals. An expanding aged population who are living longer forecasts an increased prevalence and economic burden of visual impairments. Improvements to visual health and treatment paradigms for progressive retinal degenerations slow vision loss. However, current treatments fail to remedy the root cause of visual impairments caused by retinal degenerations-loss of retinal neurons. Stimulation of retinal regeneration from endogenous cellular sources presents an exciting treatment avenue for replacement of lost retinal cells. In multiple species including zebrafish and Xenopus, Müller glial cells maintain a highly efficient regenerative ability to reconstitute lost cells throughout the organism's lifespan, highlighting potential therapeutic avenues for stimulation of retinal regeneration in humans. Here, we describe how the application of single-cell RNA-sequencing (scRNA-seq) has enhanced our understanding of Müller glial cell-derived retinal regeneration, including the characterization of gene regulatory networks that facilitate/inhibit regenerative responses. Additionally, we provide a validated experimental framework for cellular preparation of mouse retinal cells as input into scRNA-seq experiments, including insights into experimental design and analyses of resulting data.

Single-cell sequencing analysis revealed that NEAT1 was a potential biomarker and therapeutic target of prostate cancer.

BACKGROUND: Prostate cancer (PCa) usually manifests atypical symptoms in the early stage, and once symptoms appear, most PCa patients have developed to the advanced stage, failing to undergo radical surgery. In this study, PCa occurrence-related biomarkers were explored based on single-cell RNA sequencing (scRNA-seq) data. METHODS: scRNA-seq data of prostate normal (Normal), benign prostatic hyperplasia (BPH), and PCa (Tumor) samples were acquired from the Gene Expression Omnibus (GEO). Cellular subsets associated with PCa occurrence were obtained using cell annotation. Additionally, the mRNA expression of nuclear enriched abundant transcript 1 (NEAT1) was detected by quantitative real-time PCR (qRT-PCR). The effects of NEAT1 on cell proliferation and apoptosis were analyzed by 5-ethynyl-2-deoxyuridine (EdU) and flow cytometry. Subsequently, cell-derived xenograft (CDX) models were constructed and divided into the LV-NC and LV-shNEAT1 groups. After the tumor tissues of CDX model mice in each group were extracted, the cell growth and Ki67 expression were observed separately using hematoxylin-eosin (H&E) staining and immunohistochemistry (IHC). RESULTS: Ten cellular subsets were obtained via cell annotation, and significantly differential changes were observed between Basal intermediate and Luminal during the course of BPH to PCa. NEAT1-Luminal was highly recruited in the Tumor group with low stemness and high malignancy scores. Matrix metallopeptidase 7 (MMP7)- keratin 17 (KRT17)-Basal intermediate had high ratios in the Tumor group with low stemness and high malignancy scores. The results of pseudotime analysis revealed that NEAT1-Luminal in the Tumor group were consistently distributed with tumor stage cells. In vitro assays showed that NEAT1 expression was elevated in PCa cells, and NEAT1 knockdown could inhibit cell proliferation and induce apoptosis. CDX assays indicated that silencing NEAT1 could reduce the growth rate of PCa tumor volume in CDX model mice. H&E staining results showed that nuclei of tumor cells were reduced and exhibited lighter color in the LV-shNEAT1 group compared with the LV-NC group. IHC results showed that Ki67 positivity was significantly lower in the LV-shNEAT1 group than in the LV-NC group. CONCLUSION: NEAT1 expression is increased in PCa, and NEAT1 can be a potential biomarker and therapeutic target for PCa.

Visualizing scRNA-Seq data at population scale with GloScope.

Increasingly, scRNA-Seq studies explore cell populations across different samples and the effect of sample heterogeneity on organism's phenotype. However, relatively few bioinformatic methods have been developed which adequately address the variation between samples for such population-level analyses. We propose a framework for representing the entire single-cell profile of a sample, which we call a GloScope representation. We implement GloScope on scRNA-Seq datasets from study designs ranging from 12 to over 300 samples and demonstrate how GloScope allows researchers to perform essential bioinformatic tasks at the sample-level, in particular visualization and quality control assessment.

SRSF1 Is Crucial for Maintaining Satellite Cell Homeostasis During Skeletal Muscle Growth and Regeneration.

BACKGROUND: The splicing factor SRSF1 emerges as a mater regulator of cell proliferation, displaying high expression in actively proliferative satellite cells (SCs). In SRSF1 knockout mice (KO) generated via MyoD-Cre, early mortality and muscle atrophy are observed during postnatal muscle growth. Despite these findings, the precise mechanisms through which SRSF1 loss influences SCs' functions and its role in muscle regeneration remain to be elucidated. METHODS: To unravel the exact mechanisms underlying the impact of SRSF1 deficiency SC functions, we employed single-cell RNA sequencing (scRNA-seq) on a mononuclear cell suspension isolated from the newborn diaphragm of KO and control mice. Concurrently, we subjected diaphragm muscles to RNA-seq analysis to identify dysregulated splicing events associated with SRSF1 deletion. For the analysis of the effect of SRSF1 deletion on muscle regeneration, we generated mice with inducible SC-specific Srsf1 ablation through Pax7-CreER. SRSF1 ablation was induced by intraperitoneal injection of tamoxifen. Using cardiotoxin-induced muscle injury, we examined the consequences of SRSF1 depletion on SC function through HE staining, immunostaining and EdU incorporation assay. C2C12 myoblasts and isolated myoblasts were employed to assess stem cell function and senescence. RESULTS: Utilizing scRNA-seq analysis, we observed a noteworthy increase in activated and proliferating myoblasts when SRSF1 was absent. This increase was substantial, with the proportion rising from 28.68% in the control group to 77.06% in the knockout group. However, these myoblasts experienced mitotic abnormalities in the absence of SRSF1, resulting in cell cycle arrest and the onset of cellular senescence. In the knockout mice, the proportion of Pax7+ cells within improper niche positioning increased significantly to 25% compared to 12% in the control cells (n ≥ 10, p < 0.001). Furthermore, there was an observation of persistent cell cycle exit specifically in the Pax7+ cells deficient in SRSF1 (n = 6, p < 0.001). SRSF1 plays a pivotal role in regulating the splicing of Fgfr1op2, favouring the full-length isoform crucial for mitotic spindle organization. Disrupting SRSF1 in C2C12 and primary myoblasts results in multipolar spindle formation (p < 0.001) and dysregulated splicing of Fgfr1op2 and triggers cellular senescence. Consequently, adult SCs lacking SRSF1 initially activate upon injury but face substantial challenge in proliferation (n = 4, p < 0.001), leading to a failure in muscle regeneration. CONCLUSIONS: SRSF1 plays a critical role in SCs by ensuring proper splicing, maintaining mitotic progression and preventing premature senescence. These findings underscore the significant role of SRSF1 in controlling SC proliferation during skeletal muscle growth and regeneration.

Comprehensive Analysis of scRNA-Seq and Bulk RNA-Seq Reveals Transcriptional Signatures of Macrophages in Intrahepatic Cholestasis of Pregnancy.

Purpose: Intrahepatic cholestasis of pregnancy (ICP) is a disorder that characterized by maternal pruritus, abnormal liver function, and an elevation in total bile acid concentrations during pregnancy. Immune factors have been recognized as playing a vital role in the mechanism of ICP. However, the underlying mechanisms regulating dysfunctional immune cells and immune genes remain to be fully elucidated. Patients and Methods: Single-cell RNA sequencing and bulk RNA sequencing data of the placenta were downloaded from the SRA database. The AUCell package, Monocle package and SCENIC package were utilized to explored immune cell activity, cell trajectory and transcription factor, respectively. GO, KEGG, and GSEA were employed to explore potential biological mechanisms. Cell-cell communications were further investigated using the CellChat package. RT-PCR, and Western blot were used to verify the gene expression in placenta. Results: In placenta cells, macrophages were found to be significantly increased in ICP. Additionally, macrophages exhibited the highest immune gene score and were divided into four subclusters (MF1-4). Our analysis revealed significant elevations in MF2, associated with LPS response and antigen presentation, and MF4, associated with TNF and cytokine production. MF3 displayed an anti-inflammatory phenotype. MF1, closely related to ribosomes and proteins, exhibited a sharp decrease. Although ICP maintained an anti-inflammatory state, macrophage trajectories showed a gradual progression toward inflammation. Subsequently, we confirmed that cytokine- and chemokine-related signaling pathways were emphasized in macrophages. Within the CXCL signaling pathway, the increased expression of CXCL1 in macrophages can interact with CXCR2 in neutrophils, potentially inducing macrophage infiltration, stimulating neutrophil chemotaxis, and leading to an inflammatory response and cellular damage. Conclusion: In conclusion, we firstly revealed the transcriptional signatures of macrophages in ICP and discovered a tendency toward an inflammatory state. This study also provides new evidence that the CXCL1-CXCR2 axis may play an important role in the pathogenesis of ICP.

YY1 knockout in pro-B cells impairs lineage commitment, enabling unusual hematopoietic lineage plasticity.

During B-cell development, cells progress through multiple developmental stages, with the pro-B-cell stage defining commitment to the B-cell lineage. YY1 is a ubiquitous transcription factor that is capable of both activation and repression functions. We found here that knockout of YY1 at the pro-B-cell stage eliminates B lineage commitment. YY1 knockout pro-B cells can generate T lineage cells in vitro using the OP9-DL4 feeder system and in vivo after injection into sublethally irradiated Rag1-/- mice. These T lineage-like cells lose their B lineage transcript profile and gain a T-cell lineage profile. Single-cell RNA-seq experiments showed that as YY1 knockout pro-B cells transition into T lineage cells in vitro, various cell clusters adopt transcript profiles representing a multiplicity of hematopoietic lineages, indicating unusual lineage plasticity. In addition, YY1 KO pro-B cells in vivo can give rise to other hematopoietic lineages in vivo. Evaluation of RNA-seq, scRNA-seq, ChIP-seq, and scATAC-seq data indicates that YY1 controls numerous chromatin-modifying proteins leading to increased accessibility of alternative lineage genes in YY1 knockout pro-B cells. Given the ubiquitous nature of YY1 and its dual activation and repression functions, YY1 may regulate commitment in multiple cell lineages.

Automated single-cell omics end-to-end framework with data-driven batch inference.

To facilitate single-cell multi-omics analysis and improve reproducibility, we present single-cell pipeline for end-to-end data integration (SPEEDI), a fully automated end-to-end framework for batch inference, data integration, and cell-type labeling. SPEEDI introduces data-driven batch inference and transforms the often heterogeneous data matrices obtained from different samples into a uniformly annotated and integrated dataset. Without requiring user input, it automatically selects parameters and executes pre-processing, sample integration, and cell-type mapping. It can also perform downstream analyses of differential signals between treatment conditions and gene functional modules. SPEEDI's data-driven batch-inference method works with widely used integration and cell-typing tools. By developing data-driven batch inference, providing full end-to-end automation, and eliminating parameter selection, SPEEDI improves reproducibility and lowers the barrier to obtaining biological insight from these valuable single-cell datasets. The SPEEDI interactive web application can be accessed at https://speedi.princeton.edu/. A record of this paper's transparent peer review process is included in the supplemental information.

Single-cell transcriptome analysis of the mouse lungs during the injury and recovery periods after lipopolysaccharide administration.

OBJECTIVE: This study sought to investigate the cellular and molecular alterations during the injury and recovery periods of ALI and develop effective treatments for ALI. METHODS: Pulmonary histology at 1, 3, 6, and 9 days after lipopolysaccharide administration mice were assessed. An unbiased single-cell RNA sequencing was performed in alveoli tissues from injury (day 3) and recovery (day 6) mice after lipopolysaccharide administration. The roles of Fpr2 and Dpp4 in ALI were assessed. RESULTS: The most severe lung injury occurred on day 3, followed by recovery entirely on day 9 after lipopolysaccharide administration. The numbers of Il1a+ neutrophils, monocytes/macrophages, and Cd4+ and Cd8+ T cells significantly increased at day 3 after LPS administration; subsequently, the number of Il1a+ neutrophils greatly decreased, the numbers of monocytes/macrophages and Cd4+ and Cd8+ T cells continuously increased, and the number of resident alveolar macrophages significantly increased at day 6. The interactions between monocytes/macrophages and pneumocytes during the injury period were enhanced by the Cxcl10/Dpp4 pair, and inhibiting Dpp4 improved ALI significantly, while inhibiting Fpr2 did not. CONCLUSIONS: Our results offer valuable insights into the cellular and molecular mechanisms underlying its progression and identify Dpp4 as an effective therapeutic target for ALI.

Identification of KLHL17 as a prognostic marker for prostate cancer based on single-cell sequencing and in vitro/in vivo experiments.

BACKGROUND: Prostate cancer (PCa) is a leading cause of cancer-related mortality among men, characterized by significant heterogeneity that complicates diagnosis and treatment. METHODS AND RESULTS: To enhance our understanding of PCa, we utilized single-cell RNA sequencing (scRNA-seq) data to identify distinct malignant epithelial cell subpopulations and their molecular characteristics. By integrating scRNA-seq data with bulk RNA-seq data, we constructed a prognostic risk score model. The influence of key genes identified in the risk score on PCa was validated through both in vitro and in vivo experiments. Our study revealed eight unique malignant epithelial cell clusters, each exhibiting distinct molecular characteristics and biological functions. KEGG and GO enrichment analyses highlighted their involvement in various pathways. The prognostic risk score model demonstrated strong predictive power for patient outcomes, particularly in predicting progression-free survival (PFS). Notably, KLHL17, identified as a high-risk gene, was found to significantly impact PCa cell proliferation, migration, invasion, and apoptosis upon knockdown. This finding was further validated in vivo using a subcutaneous xenograft tumor model, where reduced KLHL17 expression led to decreased tumor growth. CONCLUSION: Our research provides a comprehensive analysis of PCa heterogeneity and highlights KLHL17 as a potential therapeutic target.

Identification and evaluation of candidate COVID-19 critical genes and medicinal drugs related to plasma cells.

The ongoing COVID-19 pandemic, caused by the SARS-CoV-2 virus, represents one of the most significant global health crises in recent history. Despite extensive research into the immune mechanisms and therapeutic options for COVID-19, there remains a paucity of studies focusing on plasma cells. In this study, we utilized the DESeq2 package to identify differentially expressed genes (DEGs) between COVID-19 patients and controls using datasets GSE157103 and GSE152641. We employed the xCell algorithm to perform immune infiltration analyses, revealing notably elevated levels of plasma cells in COVID-19 patients compared to healthy individuals. Subsequently, we applied the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm to identify COVID-19 related plasma cell module genes. Further, positive cluster biomarker genes for plasma cells were extracted from single-cell RNA sequencing data (GSE171524), leading to the identification of 122 shared genes implicated in critical biological processes such as cell cycle regulation and viral infection pathways. We constructed a robust protein-protein interaction (PPI) network comprising 89 genes using Cytoscape, and identified 20 hub genes through cytoHubba. These genes were validated in external datasets (GSE152418 and GSE179627). Additionally, we identified three potential small molecules (GSK-1070916, BRD-K89997465, and idarubicin) that target key hub genes in the network, suggesting a novel therapeutic approach. These compounds were characterized by their ability to down-regulate AURKB, KIF11, and TOP2A effectively, as evidenced by their low free binding energies determined through computational analyses using cMAP and AutoDock. This study marks the first comprehensive exploration of plasma cells' role in COVID-19, offering new insights and potential therapeutic targets. It underscores the importance of a systematic approach to understanding and treating COVID-19, expanding the current body of knowledge and providing a foundation for future research.

Tracking changes in functionality and morphology of repopulated microglia in young and old mice.

BACKGROUND: Microglia (MG) are myeloid cells of the central nervous system that support homeostasis and instigate neuroinflammation in pathologies. Single-cell RNA sequencing (scRNA-seq) revealed the functional heterogeneity of MG in mouse brains. Microglia are self-renewing cells and inhibition of colony-stimulating factor 1 receptor (CSF1R) signaling depletes microglia which rapidly repopulate. The functions of repopulated microglia are poorly known. METHODS: We combined scRNA-seq, bulk RNA-seq, immunofluorescence, and confocal imaging to study the functionalities and morphology of repopulated microglia. RESULTS: A CSRF1R inhibitor (BLZ-945) depleted microglia within 21 days and a number of microglia was fully restored within 7 days, as confirmed by TMEM119 staining and flow cytometry. ScRNA-seq and computational analyses demonstrate that repopulated microglia originated from preexisting progenitors and reconstituted functional clusters but upregulated inflammatory genes. Percentages of proliferating, immature microglia displaying inflammatory gene expression increased in aging mice. Morphometric analysis of MG cell body and branching revealed a distinct morphology of repopulated MG, particularly in brains of old mice. We demonstrate that with aging some repopulated MG fail to reach the homeostatic phenotype. These differences may contribute to the deterioration of MG protective functions with age.

A combined analysis of bulk RNA-seq and scRNA-seq was performed to investigate the molecular mechanisms associated with the occurrence of myocardial infarction.

BACKGROUND: Myocardial infarction (MI) induces complex transcriptional changes across diverse cardiac cell types. Single-cell RNA sequencing (scRNA-seq) provides an unparalleled ability to discern cellular diversity during infarction, yet the veracity of these discoveries necessitates confirmation. This investigation sought to elucidate MI mechanisms by integrating scRNA-seq and bulk RNA-seq data. METHODS: Publicly available scRNA-seq (GSE136088) and bulk RNA-seq (GSE153485) data from mice MI models were analyzed. Cell types were annotated, and differential expression analysis conducted. Bulk RNA-seq underwent quality control, principal component analysis, and differential expression analysis. RESULTS: In scRNA-seq data, the comparison between MI and sham groups unveiled a reduction in endothelial cell populations, but macrophages and monocytes increased. Within fibroblast subgroups, three distinct categories were discerned, with two exhibiting upregulation in MI. Notably, endothelial cells exhibited an elevated expression of genes associated with apoptosis and ferroptosis. In bulk RNA-seq analysis, distinct patterns emerged when comparing MI and sham groups. Specifically, six genes linked to endothelial ferroptosis exhibited heightened expression in MI group, thereby corroborating the scRNA-seq findings. Moreover, the examination of isolated cardiac macrophages from mice MI model revealed increased expression of Spp1, Col1a2, Col3a1, Ctsd, and Lgals3 compared to sham group, thus substantiating the dysregulation of macrophage apoptosis-related proteins following MI. CONCLUSION: MI altered the transcriptomic landscapes of cardiac cells with increased expression of apoptotic genes. Moreover, the upregulation of macrophage apoptosis marker was confirmed within MI models. The presence of endothelial cell depletion and ferroptosis in MI has been demonstrated.

Single-cell RNA sequencing data locate ALDH1A2-mediated retinoic acid synthetic pathway to glomerular parietal epithelial cells.

Aldehyde dehydrogenase 1, family member A2, is a retinoic acid-synthesizing enzyme encoded by Aldh1a2 in mice and ALDH1A2 in humans. This enzyme is indispensable for kidney development, but its role in kidney physiology and pathophysiology remains to be fully defined. In this review, we mined single-cell and single-nucleus RNA sequencing databases of mouse and human kidneys and found that glomerular parietal epithelial cells (PECs) express a full set of genes encoding proteins needed for cellular vitamin A uptake, intracellular transport, and metabolism into retinoic acid. In particular, Aldh1a2/ALDH1A2 mRNAs are selectively enriched in mouse and human PECs. Aldh1a2 expression in PECs is greatly increased in a mouse model of anti-glomerular basement membrane glomerulonephritis and moderately induced in a mouse model of ischemia-reperfusion acute kidney injury. Aldh1a2 expression in PECs is substantially repressed in a chronic kidney disease mouse model combining diabetes, hypertension, and partial nephrectomy and is moderately repressed in mouse models of focal segmental glomerulosclerosis and diabetic nephropathy. Single-nucleus RNA sequencing data show that ALDH1A2 mRNA expression in PECs is diminished in patients with chronic kidney disease associated with diabetes, hypertension and polycystic kidney disease. In addition to data mining, we also performed Spearman's rank correlation coefficient analyses and identified gene transcripts correlated with Aldh1a2/ALDH1A2 transcripts in mouse PECs and PEC subtypes, and in human PECs of healthy subjects and patients with AKI or CKD. Furthermore, we conducted Gene Ontology pathway analyses and identified the biological pathways enriched among these Aldh1a2/ALDH1A2-correlated genes. Our data mining and analyses led us to hypothesize that ALDH1A2-mediated retinoic acid synthesis in PECs plays a yet-undefined role in the kidney and that its dysregulation mediates injury. Conditional, PEC-selective Aldh1a2 knockout, RNA silencing and transgenic mouse models will be useful tools to test this hypothesis. Clinical studies on genetics, epigenetics, expression and functions of ALDH1A2 and other genes needed for retinoic acid biosynthesis and signaling are also warranted.

MultiSC: a deep learning pipeline for analyzing multiomics single-cell data.

Single-cell technologies enable researchers to investigate cell functions at an individual cell level and study cellular processes with higher resolution. Several multi-omics single-cell sequencing techniques have been developed to explore various aspects of cellular behavior. Using NEAT-seq as an example, this method simultaneously obtains three kinds of omics data for each cell: gene expression, chromatin accessibility, and protein expression of transcription factors (TFs). Consequently, NEAT-seq offers a more comprehensive understanding of cellular activities in multiple modalities. However, there is a lack of tools available for effectively integrating the three types of omics data. To address this gap, we propose a novel pipeline called MultiSC for the analysis of MULTIomic Single-Cell data. Our pipeline leverages a multimodal constraint autoencoder (single-cell hierarchical constraint autoencoder) to integrate the multi-omics data during the clustering process and a matrix factorization-based model (scMF) to predict target genes regulated by a TF. Moreover, we utilize multivariate linear regression models to predict gene regulatory networks from the multi-omics data. Additional functionalities, including differential expression, mediation analysis, and causal inference, are also incorporated into the MultiSC pipeline. Extensive experiments were conducted to evaluate the performance of MultiSC. The results demonstrate that our pipeline enables researchers to gain a comprehensive view of cell activities and gene regulatory networks by fully leveraging the potential of multiomics single-cell data. By employing MultiSC, researchers can effectively integrate and analyze diverse omics data types, enhancing their understanding of cellular processes.

Disulfiram ameliorates bone loss in ovariectomized mice by suppressing osteoclastogenesis.

INTRODUCTION: Disulfiram (DSF), known as an anti-alcoholism drug, has been reported to suppress osteoclast differentiation in vitro; however, it remains uncertain whether DSF is effective in preventing osteoclastogenesis in vivo. This study aimed to investigate the effect of DSF administration in osteoporotic mice and its contribution to osteoclastogenesis in vivo. MATERIALS AND METHODS: The bone phenotype of ovariectomized mice, both treated and untreated with DSF, was examined using microcomputed tomography analysis. Osteoclastic and osteoblastic parameters were assessed through bone morphometric analysis. The direct effect of DSF on osteoblastogenesis in vitro was evaluated via a primary osteoblast culture experiment. The expression of genes related to DSF targets (Nup85, Ccr2, and Ccr5) in osteoclast-lineage cells was examined using scRNA-seq analysis and flow cytometry analysis using the bone marrow cells from ovariectomized mice. The impact of DSF on osteoclast-lineage cells was assessed using primary cultures of osteoclasts. RESULTS: DSF administration ameliorated ovariectomy-induced bone loss and mitigated the increase of osteoclasts without affecting osteoblastogenesis. The scRNA-seq data revealed that osteoclast precursor cells expressed Nup85, Ccr2, and Ccr5. CCR2 and CCR5-positive cells in osteoclast precursor cells within bone marrow increased following ovariectomy, and this increase was canceled by DSF administration. Finally, we found that DSF had a significant inhibitory effect on osteoclastogenesis in the early stage by suppressing Tnfrsf11a expression. CONCLUSION: This study demonstrates that DSF could be a candidate for osteoporosis therapies because it suppresses osteoclastogenesis from an early stage in vivo.

A Strategy to Compare Single-Cell RNA Sequencing Data Sets Provides Phenotypic Insight into Cellular Heterogeneity Underlying Biological Similarities and Differences Between Samples.

Single-cell RNA sequencing (scRNA-seq) allows for an unbiased assessment of cellular phenotypes by enabling the extraction of transcriptomic data. An important question in downstream analysis is how to evaluate biological similarities and differences between samples in high dimensional space. This becomes especially complex when there is cellular heterogeneity within the samples. Here, we present scCompare, a computational pipeline for comparison of scRNA-seq data sets. Phenotypic identities from a known data set are transferred onto another data set using correlation-based mapping to average transcriptomic signatures from each cluster of cells' annotated phenotype. Statistically derived lower cutoffs for phenotype inclusivity allow for cells to be unmapped if they are distinct from the known phenotypes, facilitating potential novel cell type detection. In a comparison of our tool using scRNA-seq data sets from human peripheral blood mononuclear cells (PBMCs), we show that scCompare outperforms single-cell variational inference (scVI) in higher precision and sensitivity for most of the cell types. scCompare was used on a cardiomyocyte data set where it confirmed the discovery of a distinct cluster of cells that differed between the 2 protocols for differentiation. Further use of scCompare on cell atlas data sets revealed insights into the cellular heterogeneity underpinning biological diversity between samples. In addition, we used a cell atlas to better understand the effect of key parameters used in the scCompare pipeline. We envision that scCompare will be of value to the research community when comparing large scRNA-seq data sets.

Leveraging single-cell transcriptomic data to uncover immune suppressive cancer cell subsets in triple-negative canine breast cancers.

Introduction: Single-cell RNA sequencing (scRNA-seq) has become an essential tool for uncovering the complexities of various physiological and immunopathological conditions in veterinary medicine. However, there is currently limited information on immune-suppressive cancer subsets in canine breast cancers. In this study, we aimed to identify and characterize immune-suppressive subsets of triple-negative canine breast cancer (TNBC) by utilizing integrated scRNA-seq data from published datasets. Methods: Published scRNA-seq datasets, including data from six groups of 30 dogs, were subjected to integrated bioinformatic analysis. Results: Immune modulatory TNBC subsets were identified through functional enrichment analysis using immune-suppressive gene sets, including those associated with anti-inflammatory and M2-like macrophages. Key immune-suppressive signaling, such as viral infection, angiogenesis, and leukocyte chemotaxis, was found to play a role in enabling TNBC to evade immune surveillance. In addition, interactome analysis revealed significant interactions between distinct subsets of cancer cells and effector T cells, suggesting potential T-cell suppression. Discussion: The present study demonstrates a versatile and scalable approach to integrating and analyzing scRNA-seq data, which successfully identified immune-modulatory subsets of canine TNBC. It also revealed potential mechanisms through which TNBC promotes immune evasion in dogs. These findings are crucial for advancing the understanding of the immune pathogenesis of canine TNBC and may aid in the development of new immune-based therapeutic strategies.

Single cell RNA sequencing provides novel cellular transcriptional profiles and underlying pathogenesis of presbycusis.

Age-related hearing loss (ARHL) or presbycusis is associated with irreversible progressive damage in the inner ear, where the sound is transduced into electrical signal; but the detailed mechanism remains unclear. Here, we sought to determine the potential molecular mechanism involved in the pathogeneses of ARHL with bioinformatics methods. A single-cell transcriptome sequencing study was performed on the cochlear samples from young and aged mice. Detection of identified cell type marker allowed us to screen 18 transcriptional clusters, including myeloid cells, epithelial cells, B cells, endothelial cells, fibroblasts, T cells, inner pillar cells, neurons, inner phalangeal cells, and red blood cells. Cell-cell communications were analyzed between young and aged cochlear tissue samples by using the latest integration algorithms Cellchat. A total of 56 differentially expressed genes were screened between the two groups. Functional enrichment analysis showed these genes were mainly involved in immune, oxidative stress, apoptosis, and metabolic processes. The expression levels of crucial genes in cochlear tissues were further verified by immunohistochemistry. Overall, this study provides new theoretical support for the development of clinical therapeutic drugs.