Cell-type deconvolution for bulk RNA-seq data using single-cell reference: a comparative analysis and recommendation guideline.

SummaryTo advance understanding of cellular heterogeneity in disease from single-cell sequencing data, we introduce residual principal-component analysis (ResidPCA), a robust method for identifying cell states that explicitly models cell-type heterogeneity. In simulations, ResidPCA achieved more than 4-fold higher accuracy than conventional PCA and over 3-fold higher accuracy than non-negative matrix factorization (NMF)-based methods in detecting states expressed across multiple cell types. Applied to single-cell RNA sequencing of light-stimulated mouse visual cortex cells, ResidPCA captured stimulus-driven variability with an accuracy more than 5-fold higher than NMF-based approaches. In single-nucleus datasets from an Alzheimer disease cohort, ResidPCA identified 44 chromatin accessibility-based states from single-nucleus ATAC-seq (snATAC-seq) and 42 transcriptional states from single-nucleus RNA-seq. Thirty snATAC-seq states were significantly enriched for Alzheimer disease heritability, often more so than established cell types such as microglia. The snATAC-seq state most significantly enriched for heritability further elucidates a recently implicated neuron-oligodendrocyte-microglial mechanistic axis, linking early amyloid production in neurons and oligodendrocytes with later microglial activation and immune response. These results highlight the ability of ResidPCA to uncover previously hidden biological variation in single-cell data and reveal disease-relevant cell states.

Meningiomas are the most common tumours that primarily arise in the central nervous system, but their intratumoural heterogeneity has not yet been thoroughly studied. We aimed to investigate the transcriptome characteristics and biological properties of ECM-remodeling meningioma cells. Single-cell RNA sequencing (ScRNA-seq) data from meningioma samples were acquired and used for analyses. We conducted comprehensive bioinformatics analyses, including screening for differentially expressed genes (DEGs), Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway and Gene Ontology (GO) term enrichment analyses, Gene Set Enrichment Analysis (GSEA), protein-protein interaction (PPI) analysis, and copy number variation (CNV) analysis on single-cell sequencing data from meningiomas. Eighteen cell types, including six meningioma subtypes, were identified in the data. ECM-remodeling meningioma cells (MGCs) were mainly distributed in brain-tumour interface tissues. KEGG and GO enrichment analyses revealed that 908 DEGs were mainly related to cell adhesion, extracellular matrix organization, and ECM-receptor interaction. GSEA analysis demonstrated that homophilic cell adhesion via plasma membrane adhesion molecules was significantly enriched (NES = 2.375, P < 0.001). CNV analysis suggested that ECM-remodeling MGCs showed considerably lower average CNV scores. ECM-remodeling MGCs predominantly localized at the brain-tumour interface area and adhere stably to the basement membrane with a lower degree of malignancy. This study provides novel insights into the malignancy of meningiomas.

Objectives Hypertrophic cardiomyopathy (HCM) is the most common hereditary cardiomyopathy and immune infiltration is considered an indispensable factor involved in its pathogenesis. In this study, we attempted to combine bulk sequencing and single-cell sequencing to map the immune infiltration-related genes in hypertrophic cardiomyopathy. Methods The GSE36961, GSE160997, and GSE122930 datasets were obtained from the Gene Expression Omnibus database. The compositional patterns of the 18 types of immune cell fraction and pathway enrichment score in control and HCM patients were estimated based on the GSE36961 cohort using xCell algorithm. The Weighted Gene Coexpression Network Analysis (WGCNA) was performed to identify genes associated with immune infiltration for hypertrophic cardiomyopathy. The area under the curve (AUC) value was obtained and used to evaluate the discriminatory ability of common immune-related DEGs. “NetworkAnalyst” platform was used to identify TF-gene and TF-miRNA interaction with identified common genes. Heat map was used to determine the association between common DEGs and various immune cells. Results Immune infiltration analysis by the xCell algorithm showed a higher level of CD8+ naive T cells, CD8+ T cells, as well as a lower level of activated dendritic cells (aDC), dendritic cells (DC), immature dendritic cells (iDC), conventional dendritic cells (cDC), macrophages, M1 macrophages, monocytes, and NKT cell in HCM compared with the control group in GSE36961 dataset. aDC, macrophages, and M1 macrophages were the top three discriminators between HCM and control groups with the area under the curve (AUC) of 0.907, 0.867, and 0.941. WGCNA analysis showed that 1258 immune-related genes were included in four different modules. Of these modules, the turquoise module showed a pivotal correlation with HCM. 13 common immune-related DEGs were found by intersecting common DEGs in GSE36961 and GSE160997 datasets with genes from the genes in turquoise module. 5 hub immune-related genes (S100A9, TYROBP, FCER1G, CD14, and S100A8) were identified by protein interaction network. Through analysis of single-cell sequencing data, S100a9, TYROBP, FCER1G, and S100a8 were mainly expressed by infiltrated M1 proinflammatory cells, especially Ccr2-M1 proinflammatory macrophage cells in the heart immune microenvironment while Cd14 was expressed by infiltrated M1 proinflammatory macrophage cells and M2 macrophages in transverse aortic constriction (TAC) mice at 1 week. Higher M2 macrophage and M1 proinflammatory macrophage infiltration as well as lower Ccr2-M1 proinflammatory macrophage and dendritic cells were shown in TAC 1week mice compared with sham mice. Conclusions There was a difference in immune infiltration between HCM patients and normal groups. aDC, macrophages, and M1 macrophages were the top three discriminator immune cell subsets between HCM and control groups. S100A9, TYROBP, FCER1G, CD14, and S100A8 were identified as potential biomarkers to discriminate HCM from the control group. S100a9, TYROBP, FCER1G, and S100a8 were mainly expressed by infiltrated M1 proinflammatory cells, especially Ccr2-M1 proinflammatory cells in the heart immune microenvironment while Cd14 was expressed by M2 macrophages in transverse aortic constriction (TAC) mice at 1 week.

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.

The combination of bulk and single-cell DNA sequencing data of the same tumor enables the inference of high-fidelity phylogenies that form the input to many important downstream analyses in cancer genomics. While many studies simultaneously perform bulk and single-cell sequencing, some studies have analyzed initial bulk data to identify which mutations to target in a follow-up single-cell sequencing experiment, thereby decreasing cost. Bulk data provide an additional untapped source of valuable information, composed of candidate phylogenies and associated clonal prevalence. Here, we introduce PhyDOSE, a method that uses this information to strategically optimize the design of follow-up single cell experiments. Underpinning our method is the observation that only a small number of clones uniquely distinguish one candidate tree from all other trees. We incorporate distinguishing features into a probabilistic model that infers the number of cells to sequence so as to confidently reconstruct the phylogeny of the tumor. We validate PhyDOSE using simulations and a retrospective analysis of a leukemia patient, concluding that PhyDOSE's computed number of cells resolves tree ambiguity even in the presence of typical single-cell sequencing errors. We also conduct a retrospective analysis on an acute myeloid leukemia cohort, demonstrating the potential to achieve similar results with a significant reduction in the number of cells sequenced. In a prospective analysis, we demonstrate the advantage of selecting cells to sequence across multiple biopsies and that only a small number of cells suffice to disambiguate the solution space of trees in a recent lung cancer cohort. In summary, PhyDOSE proposes cost-efficient single-cell sequencing experiments that yield high-fidelity phylogenies, which will improve downstream analyses aimed at deepening our understanding of cancer biology.

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.

Many species on Earth can carry up to two different versions of a given gene, with each of these ‘alleles’ having only a 50/50 chance of being transmitted to the next generation via sexual reproduction. Certain ‘selfish’ sequences, however, can hijack this process and increase their probability of being passed on to an offspring. Known as transmission distortion, this phenomenon may result in alleles spreading through the population even if they are detrimental to fertility. Transmission distortion has been detected in many species such as flies, mice and some plants. It can take place at various stages during reproduction; for example, the selfish alleles may become overrepresented among eggs or sperm. However, scientists need to study a large number of offspring or reproductive cells to be able to detect whether an allele is inherited more often than expected. This has made it difficult to determine whether transmission distortion also happens in humans, and research so far has resulted in conflicting conclusions. A recently published dataset of human sperm from 25 donors offered Carioscia, Weaver et al. the opportunity to examine this question. Every volunteer had produced between 969 and 3377 sperm cells, each with about 1% of their genome sequenced. Carioscia, Weaver et al. developed a computational method, which they named rhapsodi, that allowed them to ‘fill in the gaps’ and infer missing regions of the genome for each cell. To do so, they relied on the fact that sperm cells from a given individual are highly related to one another. With this more complete data at hand, it became possible to look for evidence of transmission distortion by searching for alleles that were overrepresented in sperm from a given donor. No selfish sequence could be detected in any of the 25 individuals, suggesting that human sperm may not be subject to pervasive transmission distortion. Signatures of selfish alleles detected in previous human studies may have therefore not resulted from this mechanism taking place at the sperm level. Instead, transmission distortion in humans could primarily target eggs or happen at later stages (for instance, if embryos carrying the selfish allele have better chances of survival). The ‘rhapsodi’ method developed by Carioscia, Weaver et al. should allow other scientists to work with datasets for which large portions of the genetic information is missing. It may therefore become easier for researchers to track selfish alleles which are difficult to detect, and to examine bigger, more diverse samples which also include individuals with known fertility challenges.

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

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.

Whole exome and genome sequencing, coupled with refined bioinformatic pipelines, have enabled improved diagnostic yields for individuals with Mendelian conditions and have led to the rapid identification of novel syndromes. For many Mendelian neurodevelopmental disorders (NDDs), there is a lack of pre-existing model systems for mechanistic work. Thus, it is critical for translational researchers to have an accessible phenotype- and genotype-informed approach for model system selection. Single-cell RNA sequencing data can be informative in such an approach, as it can indicate which cell types express a gene of interest at the highest levels across time. For Mendelian NDDs, such data for the developing human brain is especially useful. A valuable single-cell RNA sequencing dataset of the second trimester developing human brain was produced by Bhaduri et al in 2021, but access to these data can be limited by computing power and the learning curve of single-cell data analysis. To reduce these barriers for translational research on Mendelian NDDs, we have built the web-based tool, Neurodevelopment in Trimester 2 - VIsualization of Single cell Data Online Tool (NeuroTri2-VISDOT), for exploring this single-cell dataset, and we have employed it in several different settings to demonstrate its utility for the translational research community.

With the growing accumulation of scattered single-cell data and the rapid advancement of artificial intelligence (AI), there is a pressing need for a high-quality, well-organized, and AI-ready single-cell data resources to support large-scale model. Here, we present version 2.0 of human Ensemble Cell Atlas (hECA), a cell atlas incorporating both single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) data. It expands the scRNA-seq data collection to 10,831,024 human cells with unified labels, and adds the new modality of scATAC-seq profiles with 1,450,511 cells. The data cover 42 human organs and tissues. To ensure cross-dataset consistency and quality, we standardized gene expression and chromatin accessibility matrices, harmonized cellular metadata, and manually re-annotated cell types based on the unified Hierarchical Annotation Framework (uHAF). The strength of the dataset has been shown in pre-training the large generative cellular AI model scMulan. hECA2.0 provides a well-structured and ready-to-use data resource, serving as a robust data foundation for AI-driven single-cell research.

With the development of single-cell sequencing technology, it is a hot research topic to identify the cell types using single-cell sequencing data, and many single-cell clustering algorithms have been developed to study this issue. These methods capture partial information of single-cell sequencing data, and obtain the different performance on the same data set. Combining these different results into one can improve the accuracy and validity. Here, we proposed ECBN, Ensemble Clustering based on Bayesian Network. ECBN can ensemble several different results of state-of-the-art single cell clustering methods, such as Seurat, CIDR, SC3 and t-SNE+k-means, and generate a more optimal clustering result through Bayesian network. Experiments are carried on the 5 single cell data sets and compared with 4 individual single cell clustering methods and 3 integrative methods. The size of experiment data sets ranges from 822 to 3605 and the results show that our method can achieve good performance. Moreover, ECBN can also use the graphical regularization to lighten the limitation which is generated by the different basis results.

ObjectivePrevious studies have suggested a potential association between the platelet-to-lymphocyte ratio and disease activity in myasthenia gravis. However, the immunological mechanisms underlying this association remain insufficiently elucidated.MethodsA retrospective cohort of 229 patients with myasthenia gravis and a single-cell RNA sequencing dataset were analyzed to investigate the relationship between platelet-to-lymphocyte ratio and disease severity. Clinical associations were assessed using the Myasthenia Gravis Foundation of America classification and multivariable logistic regression, while single-cell RNA sequencing data were integrated to characterize immune alterations associated with elevated platelet-to-lymphocyte ratio.ResultsPatients with severe myasthenia gravis had longer disease duration and higher frequencies of bulbar symptoms, thymoma, and repetitive nerve stimulation positivity (all p < 0.001). Although median platelet-to-lymphocyte ratio values did not demonstrate significant groupwise differences (p = 0.108), multivariate analysis confirmed that an elevated platelet-to-lymphocyte ratio was independently associated with greater myasthenia gravis severity (adjusted odds ratio = 1.027, 95% confidence interval: 1.003–1.052, p = 0.034). Single-cell RNA sequencing revealed immune dysregulation in patients with a high platelet-to-lymphocyte ratio, characterized by increased platelets and neutrophils, reduced natural killer cells, and upregulation of platelet activation, cell–cell adhesion, and integrin-mediated signaling pathways, indicating a shift toward innate immune activation and impaired immune coordination.ConclusionElevated platelet-to-lymphocyte ratio independently predicts myasthenia gravis severity and may reflect immune dysregulation that contributes to disease progression and neuromuscular junction dysfunction.

Cell-to-cell communication (CCC) plays essential roles in multicellular organisms. the identification of CCC between cancer cells themselves and one between cancer cells and normal cells in tumor microenvironment contributes to the understanding of carcinogenesis, cancer development and metastasis. CCC is usually mediated by Ligand-Receptor Interactions (LRIs). In this manuscript, we developed an LRI-mediated CCC estimation framework (LRI-EnABCLG) by incorporating LRI collection, prediction and filtering, CCC inference and visualization. First, four LRI datasets were collected. Second, LRIs were predicted by a heterogeneous deep ensemble model. Third, LRIs were filtered by combining single-cell sequencing (scRNA-seq) data. Fourth, CCC was inferred by combining the filtered LRIs and scRNA-seq data. Finally, the proposed CCC prediction framework was applied to CCC analysis in colorectal tumor tissues. Our proposed LRI-EnABCLG model obtained better LRI prediction performance. Case study demonstrated that fibroblasts was more likely to communicate with colorectal cancer cells, which was in accord with the results from iTALK (a classical CCC analysis pipeline). We anticipate that this work can contribute to diagnosis and treatment of cancers.