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2022 ◽  
Vol 13 (1) ◽  
Hanbing Song ◽  
Hannah N. W. Weinstein ◽  
Paul Allegakoen ◽  
Marc H. Wadsworth ◽  
Jamie Xie ◽  

AbstractProstate cancer is the second most common malignancy in men worldwide and consists of a mixture of tumor and non-tumor cell types. To characterize the prostate cancer tumor microenvironment, we perform single-cell RNA-sequencing on prostate biopsies, prostatectomy specimens, and patient-derived organoids from localized prostate cancer patients. We uncover heterogeneous cellular states in prostate epithelial cells marked by high androgen signaling states that are enriched in prostate cancer and identify a population of tumor-associated club cells that may be associated with prostate carcinogenesis. ERG-negative tumor cells, compared to ERG-positive cells, demonstrate shared heterogeneity with surrounding luminal epithelial cells and appear to give rise to common tumor microenvironment responses. Finally, we show that prostate epithelial organoids harbor tumor-associated epithelial cell states and are enriched with distinct cell types and states from their parent tissues. Our results provide diagnostically relevant insights and advance our understanding of the cellular states associated with prostate carcinogenesis.

2022 ◽  
Vol 23 (1) ◽  
Maria Mircea ◽  
Mazène Hochane ◽  
Xueying Fan ◽  
Susana M. Chuva de Sousa Lopes ◽  
Diego Garlaschelli ◽  

AbstractThe ability to discover new cell phenotypes by unsupervised clustering of single-cell transcriptomes has revolutionized biology. Currently, there is no principled way to decide whether a cluster of cells contains meaningful subpopulations that should be further resolved. Here, we present phiclust (ϕclust), a clusterability measure derived from random matrix theory that can be used to identify cell clusters with non-random substructure, testably leading to the discovery of previously overlooked phenotypes.

Development ◽  
2022 ◽  
Joana Esteves de Lima ◽  
Cédrine Blavet ◽  
Marie-Ange Bonnin ◽  
Estelle Hirsinger ◽  
Emmanuelle Havis ◽  

The location and regulation of fusion events within skeletal muscles during development remain unknown. Using the fusion marker myomaker (Mymk), named TMEM8C in chicken, as a readout of fusion, we identified a co-segregation of TMEM8C-positive cells and MYOG-positive cells in single-cell RNA-sequencing datasets of limbs from chicken embryos. We found that TMEM8C transcripts, MYOG transcripts and the fusion-competent MYOG-positive cells were preferentially regionalized in central regions of foetal muscles. We also identified a similar regionalization for the NOTCH ligand JAGGED2 along with an absence of NOTCH activity in TMEM8C+ fusion-competent myocytes. NOTCH function in myoblast fusion had not been addressed so far. We analysed the consequences of NOTCH inhibition for TMEM8C expression and myoblast fusion during foetal myogenesis in chicken embryos. NOTCH inhibition increased myoblast fusion and TMEM8C expression and released the HEYL transcriptional repressor from the TMEM8C regulatory regions. These results identify a regionalization of TMEM8C-dependent fusion and a molecular mechanism underlying the fusion-inhibiting effect of NOTCH in foetal myogenesis. The modulation of NOTCH activity in the fusion zone could regulate the flux of fusion events.

2022 ◽  
Vol 8 ◽  
Wenzhou Liu ◽  
Yanbo Chen ◽  
Gang Zeng ◽  
Shuting Yang ◽  
Tao Yang ◽  

Objective: Osteoarthritis (OA) is the most common chronic degenerative joint disease, which represents the leading cause of age-related disability. Here, this study aimed to depict the intercellular heterogeneity of OA synovial tissues.Methods: Single-cell RNA sequencing (scRNA-seq) data were preprocessed and quality controlled by the Seurat package. Cell cluster was presented and cell types were annotated based on the mRNA expression of corresponding marker genes by the SingleR package. Cell-cell communication was assessed among different cell types. After integrating the GSE55235 and GSE55457 datasets, differentially expressed genes were identified between OA and normal synovial tissues. Then, differentially expressed marker genes were overlapped and their biological functions were analyzed.Results: Totally, five immune cell subpopulations were annotated in OA synovial tissues including macrophages, dendritic cells, T cells, monocytes and B cells. Pseudo-time analysis revealed the underlying evolution process in the inflammatory microenvironment of OA synovial tissue. There was close crosstalk between five cell types according to the ligand-receptor network. The genetic heterogeneity was investigated between OA and normal synovial tissues. Furthermore, functional annotation analysis showed the intercellular heterogeneity across immune cells in OA synovial tissues.Conclusion: This study offered insights into the heterogeneity of OA, which provided in-depth understanding of the transcriptomic diversities within synovial tissue. This transcriptional heterogeneity may improve our understanding on OA pathogenesis and provide potential molecular therapeutic targets for OA.

2022 ◽  
Vol 13 (1) ◽  
Peter Fabian ◽  
Kuo-Chang Tseng ◽  
Mathi Thiruppathy ◽  
Claire Arata ◽  
Hung-Jhen Chen ◽  

AbstractThe cranial neural crest generates a huge diversity of derivatives, including the bulk of connective and skeletal tissues of the vertebrate head. How neural crest cells acquire such extraordinary lineage potential remains unresolved. By integrating single-cell transcriptome and chromatin accessibility profiles of cranial neural crest-derived cells across the zebrafish lifetime, we observe progressive and region-specific establishment of enhancer accessibility for distinct fates. Neural crest-derived cells rapidly diversify into specialized progenitors, including multipotent skeletal progenitors, stromal cells with a regenerative signature, fibroblasts with a unique metabolic signature linked to skeletal integrity, and gill-specific progenitors generating cell types for respiration. By retrogradely mapping the emergence of lineage-specific chromatin accessibility, we identify a wealth of candidate lineage-priming factors, including a Gata3 regulatory circuit for respiratory cell fates. Rather than multilineage potential being established during cranial neural crest specification, our findings support progressive and region-specific chromatin remodeling underlying acquisition of diverse potential.

2022 ◽  
Vol 13 (1) ◽  
Georgios Theocharidis ◽  
Beena E. Thomas ◽  
Debasree Sarkar ◽  
Hope L. Mumme ◽  
William J. R. Pilcher ◽  

AbstractDiabetic foot ulceration (DFU) is a devastating complication of diabetes whose pathogenesis remains incompletely understood. Here, we profile 174,962 single cells from the foot, forearm, and peripheral blood mononuclear cells using single-cell RNA sequencing. Our analysis shows enrichment of a unique population of fibroblasts overexpressing MMP1, MMP3, MMP11, HIF1A, CHI3L1, and TNFAIP6 and increased M1 macrophage polarization in the DFU patients with healing wounds. Further, analysis of spatially separated samples from the same patient and spatial transcriptomics reveal preferential localization of these healing associated fibroblasts toward the wound bed as compared to the wound edge or unwounded skin. Spatial transcriptomics also validates our findings of higher abundance of M1 macrophages in healers and M2 macrophages in non-healers. Our analysis provides deep insights into the wound healing microenvironment, identifying cell types that could be critical in promoting DFU healing, and may inform novel therapeutic approaches for DFU treatment.

2022 ◽  
Vol 13 (1) ◽  
K. H. Brian Lam ◽  
Alberto J. Leon ◽  
Weili Hui ◽  
Sandy Che-Eun Lee ◽  
Ihor Batruch ◽  

AbstractGlioblastoma is an aggressive form of brain cancer with well-established patterns of intra-tumoral heterogeneity implicated in treatment resistance and progression. While regional and single cell transcriptomic variations of glioblastoma have been recently resolved, downstream phenotype-level proteomic programs have yet to be assigned across glioblastoma’s hallmark histomorphologic niches. Here, we leverage mass spectrometry to spatially align abundance levels of 4,794 proteins to distinct histologic patterns across 20 patients and propose diverse molecular programs operational within these regional tumor compartments. Using machine learning, we overlay concordant transcriptional information, and define two distinct proteogenomic programs, MYC- and KRAS-axis hereon, that cooperate with hypoxia to produce a tri-dimensional model of intra-tumoral heterogeneity. Moreover, we highlight differential drug sensitivities and relative chemoresistance in glioblastoma cell lines with enhanced KRAS programs. Importantly, these pharmacological differences are less pronounced in transcriptional glioblastoma subgroups suggesting that this model may provide insights for targeting heterogeneity and overcoming therapy resistance.

2022 ◽  
Vol 12 (1) ◽  
Syed Taufiqul Islam ◽  
Yoshihito Kurashige ◽  
Erika Minowa ◽  
Koki Yoshida ◽  
Durga Paudel ◽  

AbstractThe epithelial cell rests of Malassez (ERM) are essential in preventing ankylosis between the alveolar bone and the tooth (dentoalveolar ankylosis). Despite extensive research, the mechanism by which ERM cells suppress ankylosis remains uncertain; perhaps its varied population is to reason. Therefore, in this study, eighteen unique clones of ERM (CRUDE) were isolated using the single-cell limiting dilution and designated as ERM 1–18. qRT-PCR, ELISA, and western blot analyses revealed that ERM-2 and -3 had the highest and lowest amelogenin expression, respectively. Mineralization of human periodontal ligament fibroblasts (HPDLF) was reduced in vitro co-culture with CRUDE ERM, ERM-2, and -3 cells, but recovered when an anti-amelogenin antibody was introduced. Transplanted rat molars grown in ERM-2 cell supernatants produced substantially less bone than those cultured in other cell supernatants; inhibition was rescued when an anti-amelogenin antibody was added to the supernatants. Anti-Osterix antibody staining was used to confirm the development of new bones. In addition, next-generation sequencing (NGS) data were analysed to discover genes related to the distinct roles of CRUDE ERM, ERM-2, and ERM-3. According to this study, amelogenin produced by ERM cells helps to prevent dentoalveolar ankylosis and maintain periodontal ligament (PDL) space, depending on their clonal diversity.

2022 ◽  
Vol 11 ◽  
Dingju Wei ◽  
Meng Xu ◽  
Zhihua Wang ◽  
Jingjing Tong

Metabolic reprogramming is one of the hallmarks of malignant tumors, which provides energy and material basis for tumor rapid proliferation, immune escape, as well as extensive invasion and metastasis. Blocking the energy and material supply of tumor cells is one of the strategies to treat tumor, however tumor cell metabolic heterogeneity prevents metabolic-based anti-cancer treatment. Therefore, searching for the key metabolic factors that regulate cell cancerous change and tumor recurrence has become a major challenge. Emerging technology––single-cell metabolomics is different from the traditional metabolomics that obtains average information of a group of cells. Single-cell metabolomics identifies the metabolites of single cells in different states by mass spectrometry, and captures the molecular biological information of the energy and substances synthesized in single cells, which provides more detailed information for tumor treatment metabolic target screening. This review will combine the current research status of tumor cell metabolism with the advantages of single-cell metabolomics technology, and explore the role of single-cell sequencing technology in searching key factors regulating tumor metabolism. The addition of single-cell technology will accelerate the development of metabolism-based anti-cancer strategies, which may greatly improve the prognostic survival rate of cancer patients.

2022 ◽  
Vol 12 ◽  
Xin Duan ◽  
Wei Wang ◽  
Minghui Tang ◽  
Feng Gao ◽  
Xudong Lin

Identifying the phenotypes and interactions of various cells is the primary objective in cellular heterogeneity dissection. A key step of this methodology is to perform unsupervised clustering, which, however, often suffers challenges of the high level of noise, as well as redundant information. To overcome the limitations, we proposed self-diffusion on local scaling affinity (LSSD) to enhance cell similarities’ metric learning for dissecting cellular heterogeneity. Local scaling infers the self-tuning of cell-to-cell distances that are used to construct cell affinity. Our approach implements the self-diffusion process by propagating the affinity matrices to further improve the cell similarities for the downstream clustering analysis. To demonstrate the effectiveness and usefulness, we applied LSSD on two simulated and four real scRNA-seq datasets. Comparing with other single-cell clustering methods, our approach demonstrates much better clustering performance, and cell types identified on colorectal tumors reveal strongly biological interpretability.

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