Development and Degeneration of the Intervertebral Disc-Insights from Across Species.

Transcriptomic analysis across species is increasingly used to reveal conserved gene regulations which implicate crucial regulators. Cross-species analysis of single-cell RNA sequencing (scRNA-seq) data provides new opportunities to identify the cellular and molecular conservations, especially for cell types and cell type-specific gene regulations. However, few methods have been developed to analyze cross-species scRNA-seq data to uncover both molecular and cellular conservations. Here, we built a tool called CACIMAR, which can perform cross-species analysis of cell identities, markers, regulations, and interactions using scRNA-seq profiles. Based on the weighted sum models of the conserved features, we developed different conservation scores to measure the conservation of cell types, regulatory networks, and intercellular interactions. Using publicly available scRNA-seq data on retinal regeneration in mice, zebrafish, and chick, we demonstrated four main functions of CACIMAR. First, CACIMAR allows to identify conserved cell types even in evolutionarily distant species. Second, the tool facilitates the identification of evolutionarily conserved or species-specific marker genes. Third, CACIMAR enables the identification of conserved intracellular regulations, including cell type-specific regulatory subnetworks and regulators. Lastly, CACIMAR provides a unique feature for identifying conserved intercellular interactions. Overall, CACIMAR facilitates the identification of evolutionarily conserved cell types, marker genes, intracellular regulations, and intercellular interactions, providing insights into the cellular and molecular mechanisms of species evolution.

Cell type prediction is one of the most challenging goals in single-cell RNA sequencing (scRNA-seq) data. Existing methods use unsupervised learning to identify signature genes in each cluster, followed by a literature survey to look up those genes for assigning cell types. However, finding potential marker genes in each cluster is cumbersome, which impedes the systematic analysis of single-cell RNA sequencing data. To address this challenge, we proposed a framework based on regularized multi-task learning (RMTL) that enables us to simultaneously learn the subpopulation associated with a particular cell type. Learning the structure of subpopulations is treated as a separate task in the multi-task learner. Regularization is used to modulate the multi-task model (e.g., W1, W2, … Wt) jointly, according to the specific prior. For validating our model, we trained it with reference data constructed from a single-cell RNA sequencing experiment and applied it to a query dataset. We also predicted completely independent data (the query dataset) from the reference data which are used for training. We have checked the efficacy of the proposed method by comparing it with other state-of-the-art techniques well known for cell type detection. Results revealed that the proposed method performed accurately in detecting the cell type in scRNA-seq data and thus can be utilized as a useful tool in the scRNA-seq pipeline.

Anchored-fusion enables targeted fusion search in bulk and single-cell RNA sequencing data

Analysis of single-cell RNA sequencing (scRNA-seq) data of immune cells from the tumor microenvironment (TME) may identify tumor progression biomarkers. This study was designed to investigate the prognostic value of differentially expressed genes (DEGs) in intrahepatic cholangiocarcinoma (ICC) using scRNA-seq. We downloaded the scRNA-seq data of 33,991 cell samples, including 17,090 ICC cell samples and 16,901 ICC adjacent tissue cell samples regarded as normal cells. scRNA-seq data were processed and classified into 20 clusters. The immune cell clusters were extracted and processed again in the same way, and each type of immune cells was divided into several subclusters. In total, 337 marker genes of macrophages and 427 marker genes of B cells were identified by comparing ICC subclusters with normal subclusters. Finally, 659 DEGs were obtained by merging B cell and macrophage marker genes. ICC sample clinical information and gene expression data were downloaded. A nine-prognosis-related-gene (PRG) signature was established by analyzing the correlation between DEGs and overall survival in ICC. The robustness and validity of the signature were verified. Functional enrichment analysis revealed that the nine PRGs were mainly involved in tumor immune mechanisms. In conclusion, we established a PRG signature based on scRNA-seq data from immune cells of patients with ICC. This PRG signature not only reflects the TME immune status but also provides new biomarkers for ICC prognosis.

Childhood cancer survivors (CCSs) face lifelong side effects related to their treatment with chemotherapy. Anthracycline agents, such as doxorubicin (DOX), are important in the treatment of childhood cancers but are associated with cardiotoxicity. Cardiac toxicities represent a significant source of chronic disability that cancer survivors face; despite this, the chronic cardiotoxicity phenotype and how it relates to acute toxicity remains poorly defined. To address this critical knowledge gap, we studied the acute effect of DOX on murine cardiac nonmyocytes in vivo. Determination of the acute cellular effects of DOX on nonmyocytes, a cell pool with finite replicative capacity, provides a basis for understanding the pathogenesis of the chronic heart disease that CCSs face. To investigate the acute cellular effects of DOX, we present single-cell RNA sequencing (scRNAseq) data from homeostatic cardiac nonmyocytes and compare it with preexisting datasets, as well as a novel CyTOF datasets. SCANPY, a python-based single-cell analysis, was used to assess the heterogeneity of cells detected in scRNAseq and CyTOF. To further assist in CyTOF data annotation, joint analyses of scRNAseq and CyTOF data using an artificial neural network known as sparse autoencoder for clustering, imputation, and embedding (SAUCIE) are performed. Lastly, the panel is tested on a mouse model of acute DOX exposure at two time points (24 and 72 h) after the last dose of doxorubicin and examined with joint clustering. In sum, we report the first ever CyTOF study of cardiac nonmyocytes and characterize the effect of acute DOX exposure with scRNAseq and CyTOF.NEW & NOTEWORTHY We describe the first mass cytometry studies of murine cardiac nonmyocytes. The mass cytometry panel is compared with single-cell RNA sequencing data. Homeostatic cardiac nonmyocytes are characterized by mass cytometry to identify and quantify four major cell populations: endothelial cells, fibroblasts, leukocytes, and pericytes. The single-cell acute nonmyocyte response to doxorubicin is studied at 24 and 72 h after doxorubicin exposure given daily for 5 days at a dose of 4 mg/kg/day.

Chronic kidney disease (CKD) emerges as a substantial contributor to various cardiovascular disorders, including ischemic cardiomyopathy (ICM). However, the underlying molecular mechanisms linking CKD and ICM remain elusive. Our study aims to unravel these connections by integrating publicly available bulk and single-cell RNA sequencing (scRNA-seq) data. Expression profiles from two ICM datasets obtained from heart tissue and one CKD with Peripheral Blood Mononuclear Cell (CKD-PBMC) dataset were collected. We initiated by identifying shared differentially expressed genes (DEGs) between ICM and CKD. Subsequent functional enrichment analysis shed light on the mechanisms connecting CKD to ICM. Machine learning algorithms enabled the identification of 13 candidate genes, including AGRN, COL16A1, COL1A2, FAP, FRZB, GPX3, ITIH5, NFASC, PTN, SLC38A1, STARD7, THBS2, and VPS35. Their expression patterns in ICM were investigated via scRNA-seq data analysis. Notably, most of them were enriched in fibroblasts. COL16A1, COL1A2, PTN, and FAP were enriched in scar-formation fibroblasts, while GPX3 and THBS2 showed enrichment in angiogenesis fibroblasts. A Gaussian naïve Bayes model was developed for diagnosing CKD-related ICM, bolstered by SHapley Additive exPlanations interpretability and validated internally and externally. In conclusion, our investigation unveils the extracellular matrix's role in CKD and ICM interplay, identifies 13 candidate genes, and showcases their expression patterns in ICM. We also constructed a diagnostic model using 13 gene features and presented an innovative approach for managing CKD-related ICM through serum-based diagnostic strategies.

Osteoporosis is a common disease, especially among the elderly. This study aimed to comprehensively examine the roles of immune microenvironment in osteoporosis pathogenesis. Expression profiles of GSE35959, GSE7158, and GSE13850 datasets were used to analyze differential expression and identify hub genes related to immune features. Based on the single-cell RNA sequencing (scRNA-seq) data of an osteoporosis patient, different cell types were classified and the relation between immune environment and osteoporosis was explored. Twelve hub genes significantly associated with immune features were selected and 11 subgroups were defined using scRNA-seq data. The expression of two hub genes (CDKN1A and TEFM) was greatly altered during the transformation from mesenchymal stem cells (MSCs) to osteoblasts. Chemokines and chemokine receptors were differentially enriched in different cell types. CXCL12 was high-expressed in MSCs. This study emphasized that immune microenvironment played a critical role in the pathogenesis of osteoporosis. Chemokines and chemokine receptors can modify cell development and affect the interactions among different cell types, leading to unbalanced bone remodeling.

As of today (7 April 2020), more than 81,000 people around the world have died from the coronavirus disease 19 (COVID-19) pandemic. There is no approved drug or vaccine for COVID-19, although more than 10 clinical trials have been launched to test potential drugs. In an urgent response to this pandemic, I developed a bioinformatics pipeline to identify compounds and drug candidates to potentially treat COVID-19. This pipeline is based on publicly available single-cell RNA sequencing (scRNA-seq) data and the drug perturbation database "Library of Integrated Network-Based Cellular Signatures" (LINCS). I developed a ranking score system that prioritizes these drugs or small molecules. The four drugs with the highest total score are didanosine, benzyl-quinazolin-4-yl-amine, camptothecin, and RO-90-7501. In conclusion, I have demonstrated the utility of bioinformatics for identifying drugs than can be repurposed for potentially treating COVID-19 patients.

This chapter will review the statistical methods used in RNA sequencing data analysis, including bulk RNA sequencing and single-cell RNA sequencing. RNA sequencing data analysis has been widely used in biomedical and biological research to identify genes associated with certain conditions or diseases. Many statistical methods have been proposed to analyze bulk and single-cell RNA sequencing data. Several studies have compared the performance of different statistical methods for RNA sequencing data analysis through simulation studies and real data evaluations. This chapter will summarize the statistical methods and the evaluation results for comparing different statistical analysis methods used for RNA sequencing data analysis. It will cover the statistical models, model assumptions, and challenges encountered in the RNA sequencing data analysis. It is hoped that this chapter will help researchers learn more about the statistical perspective of the RNA sequencing data analysis and enable them to choose appropriate statistical analysis methods for their own RNA sequencing data analysis.

In the analysis of single-cell RNA-sequencing (scRNA-seq) data, how to effectively and accurately identify cell clusters from a large number of cell mixtures is still a challenge. Low-rank representation (LRR) method has achieved excellent results in subspace clustering. But in previous studies, most LRR-based methods usually choose the original data matrix as the dictionary. In addition, the methods based on LRR usually use spectral clustering algorithm to complete cell clustering. Therefore, there is a matching problem between the spectral clustering method and the affinity matrix, which is difficult to ensure the optimal effect of clustering. Considering the above two points, we propose the DLNLRR method to better identify the cell type. First, DLNLRR can update the dictionary during the optimization process instead of using the predefined fixed dictionary, so it can realize dictionary learning and LRR learning at the same time. Second, DLNLRR can realize subspace clustering without relying on spectral clustering algorithm, that is, we can perform clustering directly based on the low-rank matrix. Finally, we carry out a large number of experiments on real single-cell datasets and experimental results show that DLNLRR is superior to other scRNA-seq data analysis algorithms in cell type identification.

Single cell studies increasing reveal myriad cellular subtypes beyond those postulated or observed through optical and fluorescence microscopy as well as DNA sequencing studies. While gene sequencing at the single cell level offer a path towards illuminating, in totality, the different subtypes of cells present, the technique nevertheless does not offer answers concerning the functional repertoire of the cell, which is defined by the collection of RNA transcribed from the genome. Known as the transcriptome, transcribed RNA defines the function of the cell as proteins or effector RNA molecules, while the genome is the collection of all information endowed in the cell type, expressed or not. Thus, a particular cell state, lineage, cell fate or cellular differentiation is more fully depicted by transcriptomic analysis compared to delineating the genomic context at the single cell level. While conceptually sound and could be analysed by contemporary single cell RNA sequencing technology and data analysis pipelines, the relative instability of RNA in view of RNase in the environment would make sample preparation particularly challenging, where degradation of cellular RNA by extraneous factors could provide a misinterpretation of specific functions available to a cell type. Hence, RNA as the de facto functional molecule of the cell defining the proteomics landscape as well as effector RNA repertoire, meant that RNA transcriptomics at the single cell level is the way forward if the goal is to understand all available cell types, lineage, cell fate and cellular differentiation. Given that a cell state is defined by the functions encoded by functional molecules such as proteins and RNA, single cell RNA sequencing offers a larger contextual basis for understanding cellular decision making and functions, for example, proteins are increasingly known to work in concert with RNA effector molecules in enabling a function. Hence, providing a view of the diverse cell types and lineages present in a body, single cell RNA sequencing is only hampered by the high sensitivity required to analyse the small amount of RNA available in single cells, as well as the perennial problem of RNA studies: how to prevent or reduce RNA degradation by environmental RNase enzymes. Ability to reduce RNA degradation would provide the cell biologist a unique view of the functional landscape of different cells in the body through the language of RNA.

Background: Cancer stem cells (CSCs) play a key role in tumor initiation, progression, and resistance to conventional therapies. These cells possess the ability to self-renew and differentiate, contributing to tumor heterogeneity and recurrence. Preclinical data indicate that targeting cancer stem cell-specific pathways could lead to more effective treatments and prevent relapse, thereby improving outcomes. However, there is limited clinical data to support this, in part due to the challenges of measuring CSCs in the clinical setting. Single-cell RNA sequencing (scRNAseq) data in combination with stemness gene expression signatures were investigated as a novel approach to detect and assess changes in CSC numbers in response to treatment. Methods: Single-cell RNAseq datasets from two clinical trials were interrogated – OPPORTUNE, a window-of-opportunity trial evaluating anastrozole vs anastrozole plus pictilisib (PI3K inhibitor) in 75 patients with ER+ HER2- early breast cancer, and FELINE, a neoadjuvant trial which compared letrozole plus placebo with letrozole plus ribociclib (CDK4/6 inhibitor) in 120 patients with ER+ HER2- early breast cancer. The primary endpoint for OPPORTUNE was the inhibition of tumor cell proliferation as measured by Ki67. The primary endpoint for FELINE was the rate of preoperative endocrine prognostic index (PEPI) score 0 after neoadjuvant endocrine therapy. Thirty-four patients from the FELINE study had available scRNAseq data for analysis; this number was 62 patients for the OPPORTUNE study. Stemness gene expression signatures with published evidence of selectivity for breast CSCs were identified from the literature and their utility in detecting CSCs compared in the datasets. Subsequently, changes in CSC fraction with treatment was assessed. Results: Eight different stemness gene signatures were identified from the published literature. Among these, four (Kim_myc, benporath_es2, Bhattacharya_hESC, and Shats_consensus) exhibited selective expression in a minority of tumor cells, with the Shats consensus signature demonstrating the highest specificity to tumor cells over non-malignant cells. Utilizing a Gaussian mixture model, we estimated that approximately 4.5% of cells within a tumor are likely stem cells. In the OPPORTUNE study, higher stemness scores were observed in luminal B compared to luminal A tumors, but there was no association with PIK3CA mutation status. Changes to Ki67 were inversely associated with the CSC fraction – tumors with higher stemness scores were less likely to achieve a complete cell cycle arrest versus endocrine-sensitive tumors. Similarly, in the FELINE trial, non-responders in the letrozole arm showed a trend towards elevated stemness scores. Conclusion: The use of stemness gene expression signatures in scRNAseq data is a feasible method to assess changes in putative CSC fraction with treatment in breast cancer. Increased stemness scores were associated with more aggressive subtypes and resistance to treatment. Citation Format: Peter Hall,Alejandro Chibly, Peter Schmid, Sarah E Pinder, Arnie Purushotham, Alastair Thompson, Steven Gendreau. Evaluation of breast cancer stem cell gene expression signatures in single-cell RNA sequencing (scRNAseq) data from the OPPORTUNE and FELINE trials, and the association with treatment resistance [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr PS13-01.

Differential gene expression analysis is one of the significant efforts in single cell RNA sequencing (scRNAseq) analysis to discover the specific changes in expression levels of individual cell types. Since scRNAseq exhibits multimodality, large amounts of zero counts, and sparsity, it is different from the traditional bulk RNA sequencing (RNAseq) data. The new challenges of scRNAseq data promote the development of new methods for identifying differentially expressed (DE) genes. In this study, we proposed a new method, SigEMD, that combines a logistic regression model and a nonparametric method based on Earth Mover's Distance, to precisely and efficiently identify DE genes in scRNAseq data. The regression model is used to reduce the impact of large amounts of zero counts, and the nonparametric method is used to improve the sensitivity of detecting DE genes from multimodal scRNAseq data. By additionally employing gene interaction network information to adjust the final states of DE genes, we further reduce the false positives of calling DE genes. We used simulated data and real data to evaluate the detection accuracy of the proposed method and to compare its performance with those of other differential expression analysis methods. Results indicate that the proposed method has an overall powerful performance in terms of precision in detection, sensitivity, and specificity.

Background Crohn’s disease (CD) is an immune-mediated disease characterized by chronic, relapsing and remitting, or progressive inflammation of the digestive tract. One in 3 people with CD will develop intestinal fibrosis requiring surgery within 10 years of diagnosis. Biological therapy is effective at reducing inflammation in CD and early and aggressive treatment with biologics will reduce the incidence of fibrosis. However, in children, for whom a step-up approach to therapy is used, and populations that are refractory to biological therapy; fibrosis remains a serious concern. Treatments are urgently needed as currently there are no treatments that target intestinal fibrosis directly in CD. Mice deficient in the Src homology 2 domain-containing inositolpolyphosphate 5’-phosphatase (SHIP-/-) develop spontaneous CD-like ileal inflammation and fibrosis. Fibrosis is dependent on PI3Kp110δ activity. IL-4 and IL-13 lead to downstream activation of PI3Kp110δ. Recent work in our lab has shown through pharmacological inhibition that the development of fibrosis in SHIP-/- mice is dependenet on IL-13 signaling, but not IL-4. Aims Aim 1: To determine whether genetic ablation of IL-13 prevents ileal fibrosis in SHIP-/- mice Aim 2: To determine the cellular source of IL-13 in the distal ileum of SHIP-/- mice Aim 3: To determine the cellular source of IL-13 in the distal ileum of individuals with CD Methods SHIP-/- mice were crossed with IL-13 deficient mice (IL13-/-) to generate SHIP+/+IL13+/+, SHIP+/+IL13-/-, SHIP-/-IL13+/+, and SHIP-/-IL13-/- mice. Measurements of ileal fibrosis included gross and histopathology, muscle thickening, and collagen accumulation (Masson’s trichrome staining for fibrosis). Single cell RNA sequencing (snRNA-seq) was performed on the distal ileum of SHIP-/- mice to determine the cellular source of IL-13. Previously published scRNA-seq data were analyzed to determine the cellular source of IL-13 in individuals with CD. Results Genetic ablation of IL-13 in SHIP-/- mice (SHIP-/-IL13-/-) reduced the development of ileal fibrosis. Analysis of single cell RNA sequencing data (scRNA seq) has revealed mast cells as the cellular source of IL-13 in the distal ileum of people with CD. Conclusions Mast cell derived IL-13 may be driving the development of intestinal fibrosis in CD. Our data suggest that an antibody-based biologic targeting IL-13 may be an effective strategy to prevent fibrosis in people with Crohn’s Disease. Funding Agencies CAG, CIHR

Precise regulation of glucose metabolism-related genes is essential for early embryonic development. Although previous research has yielded detailed information on the biochemical processes, little is yet known of the dynamic gene expression profiles in glucose metabolism of preimplantation embryos at a single-cell resolution. In the present study, we performed integrated analysis of single-cell RNA sequencing (scRNA-seq) data of human preimplantation embryos that had been cultured in sequential medium. Different cells in the same embryo have similar gene expression patterns in glucose metabolism. During the switch from the cleavage to morula stage, the expression of glycolysis-related genes, such as glucose transporter genes (solute carrier family 2 (facilitated glucose transporter), member 1 (SLC2A1) and solute carrier family 2 (facilitated glucose transporter), member 3 (SLC2A3) and genes encoding hexokinase, phosphofructokinase, pyruvate kinase and lactate dehydrogenase, is increased. The genes involved in the pentose phosphate pathway are highly expressed at the cleavage stage, generating the reducing power to balance oxidative stress derived from biosynthesis. Expression of the genes involved in the biosynthesis of glycerophospholipids is increased after the morula stage. Nevertheless, the expression of tricarboxylic acid-related genes remains relatively unchanged during the preimplantation stages. In conclusion, we discovered that the gene expression profiles are dynamic according to glucose utilisation in the embryos at different stages, which contributes to our understanding of regulatory mechanisms of glucose metabolism-related genes in human preimplantation embryos.