Single-Cell Sequencing Reveals Differential Cell Types in Skin Tissues of Liaoning Cashmere Goats and Key Genes Related Potentially to the Fineness of Cashmere Fiber

Cashmere fineness is one of the important factors determining cashmere quality; however, our understanding of the regulation of cashmere fineness at the cellular level is limited. Here, we used single-cell RNA sequencing and computational models to identify 13 skin cell types in Liaoning cashmere goats. We also analyzed the molecular changes in the development process by cell trajectory analysis and revealed the maturation process in the gene expression profile in Liaoning cashmere goats. Weighted gene co-expression network analysis explored hub genes in cell clusters related to cashmere formation. Secondary hair follicle dermal papilla cells (SDPCs) play an important role in the growth and density of cashmere. ACTA2, a marker gene of SDPCs, was selected for immunofluorescence (IF) and Western blot (WB) verification. Our results indicate that ACTA2 is mainly expressed in SDPCs, and WB results show different expression levels. COL1A1 is a highly expressed gene in SDPCs, which was verified by IF and WB. We then selected CXCL8 of SDPCs to verify and prove the differential expression in the coarse and fine types of Liaoning cashmere goats. Therefore, the CXCL8 gene may regulate cashmere fineness. These genes may be involved in regulating the fineness of cashmere in goat SDPCs; our research provides new insights into the mechanism of cashmere growth and fineness regulation by cells.

Single-cell RNA Sequencing Technology Revealed the Pivotal Role of Fibroblast Heterogeneity in Ang II-Induced Abdominal Aortic Aneurysms

Aim: The mechanism of abdominal aortic aneurysm (AAA) has not been fully elucidated. In this study, we aimed to map the cellular heterogeneity, molecular alteration, and functional transformation of angiotensin (Ang) II-induced AAA in mice based on single-cell RNA sequencing (sc-RNA seq) technology.Method: Single-cell RNA sequencing was performed on suprarenal abdominal aorta from male APOE-/- C57BL/6 mice of Ang II-induced AAA and shame models. Immunohistochemistry was used to determine the pathophysiological characteristics of AAA, and sc-RNA seq was used to determine the heterogeneity and phenotypic transformation of all cell types. A single-cell trajectory was performed to predict the differentiation of fibroblasts. Finally ligand–receptor analysis was used to evaluate intercellular communication between fibroblasts and smooth muscle cells.Results: More than 27,000 cells were isolated and 25 clusters representing 8 types of cells were identified, including fibroblasts, macrophages, endothelial cells, smooth muscle cells, T lymphocytes, B lymphocytes, granulocytes, and natural killer cells. During AAA progression, the function and phenotype of different type cells altered separately. The pro-inflammatory function of inflammatory cells was enhanced. The proliferation phenotype degreased while pro-inflammatory, regeneration and damage-related phenotypes increased in endothelial cells. Smooth muscle cells also transformed from contractile to secretory phenotype. The alterations of fibroblasts were the most conspicuous according sub-group clustering analysis. Single-cell trajectory revealed the critical reprogramming genes of fibroblasts mainly enriched in regulation of immune system. Finally, the ligand–receptor analysis confirmed that increases in secondary collagen synthesis led by fibroblasts were one of the most prominent characteristics of Ang II-induced AAA.Conclusion: Our study revealed the cellular heterogeneity of Ang II-induced AAA. Fibroblasts may play a central role in Ang II-induced AAA progression according multiple biological functions including immune regulation and extracellular matrix metabolic balance. Our study may provide us with a different perspective on the etiology and pathogenesis of AAA.

NR0B2 regulation during Primary Sclerosing Cholangitis defines a metabolic and pre-malignant reprogramming of Cholangiocyte

SUMMARYPrimary Sclerosing Cholangitis (PSC) is an idiopathic, cholestatic liver disease that is characterized by persistent, progressive, biliary inflammation leading to cirrhosis. These patients present higher risk for developing bile duct cancers.Biomedical text-mining related to PSC symptoms like: biliary inflammation, biliary fibrosis, biliary cholestasis was initiated to collect gene associations with this pathophysiology. The text mining work was integrated in distinct omics data such as human transcriptome of PSC liver, Farnesoid X receptor (FXR) functional liver transcriptome and liver single cell transcriptome of the Abcb4-/- model of PSC. A molecular network implicated in abnormal hepatobiliary system physiology was built and confirming a major implication of Nr0b2 and its associated nuclear receptors like FXR in a metabolic cascade that could influence immune response. TNFRSF12A/TWEAK receptor, was found up regulated in PSC liver independently of FXR regulation and TWEAK signaling is known for its implication in pre-conditioning niche of cholangiocarcinoma. NR0B2 deregulation in PSC liver was found independent of gender, age and body mass index surrogates. At single cell level, Nr0b2 up regulation was found in cholangiocytes but not in hepatocytes. In affected cholangiocytes, the cell trajectory built on Nr0b2 expression, revealed implication of several metabolic pathways for detoxification like sulfur, glutathione derivative and monocarboxylic acid metabolisms. On this cell trajectory it was discovered some molecules potentially implicated in carcinogenesis like: GSTA3, ID2 and mainly TMEM45A a transmembrane molecule from golgi apparatus considered as oncogene in several cancers. All together, these observations found in humanPSC liver and in its murine models allowed to highlight an independent deregulation of NR0B2 with a metabolic and premalignant reprogramming of cholangiocytes.

Stromal androgen and hedgehog signaling regulates stem cell niches in pubertal prostate development

Stromal androgen-receptor (AR) action is essential for prostate development, morphogenesis, and regeneration. However, mechanisms underlying how stromal AR maintains the cell niche in support of pubertal prostatic epithelial growth are unknown. Here, using advanced mouse genetic tools, we demonstrate that selective deletion of stromal AR expression in prepubescent Shh responsive Gli1-expressing cells significantly impedes pubertal prostate epithelial growth and development. Single-cell transcriptomic analyses showed that AR loss in these prepubescent Gli1-expressing cells dysregulates androgen-signaling initiated stromal-epithelial paracrine interactions, leading to growth retardation of pubertal prostate epithelia and significant development defects. Specifically, AR loss elevates Shh-signaling activation in both prostatic stromal and adjacent epithelial cells, directly inhibiting prostatic epithelial growth. Single-cell trajectory analyses further identified aberrant differentiation fates of prostatic epithelial cells directly altered by stromal AR deletion. In vivo recombination of AR-deficient stromal Gli1-lineage cells with wild-type prostatic epithelial cells failed to develop normal prostatic epithelia. These data demonstrate novel mechanisms underlying how stromal AR-signaling facilitates Shh-mediated cell niches in pubertal prostatic epithelial growth and development.

LISA2: Learning Complex Single-Cell Trajectory and Expression Trends

Single-cell transcriptional and epigenomics profiles have been applied in a variety of tissues and diseases for discovering new cell types, differentiation trajectories, and gene regulatory networks. Many methods such as Monocle 2/3, URD, and STREAM have been developed for tree-based trajectory building. Here, we propose a fast and flexible trajectory learning method, LISA2, for single-cell data analysis. This new method has two distinctive features: (1) LISA2 utilizes specified leaves and root to reduce the complexity for building the developmental trajectory, especially for some special cases such as rare cell populations and adjacent terminal cell states; and (2) LISA2 is applicable for both transcriptomics and epigenomics data. LISA2 visualizes complex trajectories using 3D Landmark ISOmetric feature MAPping (L-ISOMAP). We apply LISA2 to simulation and real datasets in cerebellum, diencephalon, and hematopoietic stem cells including both single-cell transcriptomics data and single-cell assay for transposase-accessible chromatin data. LISA2 is efficient in estimating single-cell trajectory and expression trends for different kinds of molecular state of cells.

EGR1 dysregulation defines an inflammatory and leukemic program in cell trajectory of human-aged hematopoietic stem cells (HSC)

Background During aging, hematopoietic stem cells (HSC) lose progressively both their self-renewal and differentiation potential. The precise molecular mechanisms of this phenomenon are not well established. To uncover the molecular events underlying this event, we have performed a bioinformatics analysis of 650 single-cell transcriptomes. Methods Single-cell transcriptome analyses of expression heterogeneity, cell cycle, and cell trajectory in human cell compartment enriched in hematopoietic stem cell compartment were investigated in the bone marrow according to the age of the donors. Identification of aging-related nodules was identified by weighted correlation network analysis in this primitive compartment. Results The analysis of single-cell transcriptomes allowed to uncover a major upregulation of EGR1 in human-aged lineage−CD34+CD38− cells which present cell cycle dysregulation with reduction of G2/M phase according to less expression of CCND2 during S phase. EGR1 upregulation in aging hematopoietic stem cells was found to be independent of cell cycle phases and gender. EGR1 expression trajectory in aged HSC highlighted a signature enriched in hematopoietic and immune disorders with the best induction of AP-1 complex and quiescence regulators such as EGR1, BTG2, JUNB, and NR41A. Sonic Hedgehog-related TMEM107 transmembrane molecule followed also EGR1 cell trajectory. EGR1-dependent gene weighted network analysis in human HSC-associated IER2 target protein-specific regulators of PP2A activity, IL1B, TNFSF10 ligands, and CD69, SELP membrane molecules in old HSC module with immune and leukemogenic signature. In contrast, for young HSC which were found with different cell cycle phase progression, its specific module highlighted upregulation of HIF1A hypoxic factor, PDE4B immune marker, DRAK2 (STK17B) T cell apoptosis regulator, and MYADM myeloid-associated marker. Conclusion EGR1 was found to be connected to the aging of human HSC and highlighted a specific cell trajectory contributing to the dysregulation of an inflammatory and leukemia-related transcriptional program in aged human HSCs. EGR1 and its program were found to be connected to the aging of human HSC with dissociation of quiescence property and cell cycle phase progression in this primitive hematopoietic compartment.

The Trifecta of Single-Cell, Systems-Biology, and Machine-Learning Approaches

Together, single-cell technologies and systems biology have been used to investigate previously unanswerable questions in biomedicine with unparalleled detail. Despite these advances, gaps in analytical capacity remain. Machine learning, which has revolutionized biomedical imaging analysis, drug discovery, and systems biology, is an ideal strategy to fill these gaps in single-cell studies. Machine learning additionally has proven to be remarkably synergistic with single-cell data because it remedies unique challenges while capitalizing on the positive aspects of single-cell data. In this review, we describe how systems-biology algorithms have layered machine learning with biological components to provide systems level analyses of single-cell omics data, thus elucidating complex biological mechanisms. Accordingly, we highlight the trifecta of single-cell, systems-biology, and machine-learning approaches and illustrate how this trifecta can significantly contribute to five key areas of scientific research: cell trajectory and identity, individualized medicine, pharmacology, spatial omics, and multi-omics. Given its success to date, the systems-biology, single-cell omics, and machine-learning trifecta has proven to be a potent combination that will further advance biomedical research.