Single-cell Transcriptomics Reveals the Divergent Developmental Lineage Trajectories during Human Pituitary Development
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PROVIDER: PRJNA597874 | ENA |
REPOSITORIES: ENA
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Project description:The mammalian cerebral cortex contains an extraordinary diversity of cell types that emerge through the implementation of different developmental programs. Delineating when and how cellular diversification occurs is particularly challenging for cortical inhibitory neurons, as they represent a relatively small proportion of all cortical cells, migrate tangentially from their embryonic origin to the cerebral cortex, and have a protracted development. Here we combine single-cell RNA sequencing and spatial transcriptomics to characterize the emergence of neuronal diversity among somatostatin-expressing (SST+) cells, the most diverse subclass of inhibitory neurons in the mouse cerebral cortex. We found that SST+ inhibitory neurons segregate during embryonic stages into long-range projection (LRP) neurons and two types of interneurons, Martinotti cells and non-Martinotti cells, following distinct developmental trajectories. Two main subtypes of LRP neurons and several subtypes of interneurons are readily distinguishable in the embryo, although interneuron diversity is further refined during early postanal life. Our results suggest that the timing for cellular diversification is unique for different subtypes of SST+ neurons and particularly divergent for LRP neurons and interneurons. Thus, the diversification of SST+ inhibitory neurons involves a temporal cascade of unique molecular programs driving their divergent developmental trajectories.
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Project description:Biologists rely on morphology, function, and specific markers to define the differentiation status of cells. Transcript profiling has expanded the repertoire of these markers by providing the snapshot of cellular status that reflects the activity of all genes. However, such data have been used only to assess relative similarities and differences of these cells. Here we show that principal component analysis (PCA) of global gene expression profiles map cells in multidimensional transcript profile space and the positions of differentiating cells progress in a stepwise manner along trajectories starting from undifferentiated embryonic stem (ES) cells located in the apex. We present three cell lineage trajectories, which represent the differentiation of ES cells into the first three lineages in mammalian development: primitive endoderm, trophoblast, and primitive ectoderm/neural ectoderm. The positions of the cells along these trajectories seem to reflect the developmental potency of cells and can be used as a scale for the potential of cells. Indeed, we show that embryonic germ (EG) cells and induced pluripotent (iPS) cells are mapped near the origin of the trajectories, whereas mouse embryo fibroblast (MEF) and fibroblast cell lines are mapped near the far end of the trajectories. We propose that this method can be used as the non-operational semi-quantitative definition of cell differentiation status and developmental potency. Furthermore, the global expression profiles of cell lineages provide a framework for the future study of in vitro and in vivo cell differentiation. Keywords: cell type comparison design,reference design,replicate design,time series design Most of the cells and RNA samples used in this study were described in detail previously (See paper's citation associated with this dataset). To maximize the uniformity of the microarray data, all the samples, including ones analyzed by DNA microarray previously, were hybridized to the same platform (the NIA Mouse 44K Microarray manufactured by Agilent Technologies: AMADID #015087). The intensity of each gene feature per array was extracted from scanned microarray images using Feature Extraction Software V9.5.
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Project description:T cells activated by chronic antigen exposure in the setting of viral infections or cancer can adopt an exhausted T cell (TEx) state, characterized by reduced effector function and proliferative capacity, and the upregulation of inhibitory receptors. Here, we generate a single-cell multi-omic atlas of T cell exhaustion in chronic viral infection that redefines the phenotypic diversity and molecular regulation of TEx states. Longitudinal analysis of the T cell response identifies an early effector phenotype that is epigenetically primed for TEx differentiation. However, clonal T cell trajectories defined using paired single-cell RNA and T cell receptor (scRNA/TCR-seq) sequencing reveal divergent differentiation trajectories among clones that recognize shared antigens, resulting in TEx- or effector memory-biased clone behaviors. Multi-organ clonal analysis reveals that T cell clone behaviors are sensitive to the tissue environment, and the liver niche preclude the development of effector memory phenotypes and induce exaggerated exhaustion. Finally, we show that divergent clonal trajectories are driven by differences in TCR affinity, and that high-affinity T cell clones preferentially adopt the divergent fate, while low-affinity clones adopt effector memory fates that are deleted in high antigen niches. These findings reveal heterogeneity in clonal T cell responses to chronic antigen and link TCR signal strength and epigenetic programming to TEx cell fates and persistence, which may be manipulated for cancer immunotherapy.
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Project description:Biologists rely on morphology, function, and specific markers to define the differentiation status of cells. Transcript profiling has expanded the repertoire of these markers by providing the snapshot of cellular status that reflects the activity of all genes. However, such data have been used only to assess relative similarities and differences of these cells. Here we show that principal component analysis (PCA) of global gene expression profiles map cells in multidimensional transcript profile space and the positions of differentiating cells progress in a stepwise manner along trajectories starting from undifferentiated embryonic stem (ES) cells located in the apex. We present three cell lineage trajectories, which represent the differentiation of ES cells into the first three lineages in mammalian development: primitive endoderm, trophoblast, and primitive ectoderm/neural ectoderm. The positions of the cells along these trajectories seem to reflect the developmental potency of cells and can be used as a scale for the potential of cells. Indeed, we show that embryonic germ (EG) cells and induced pluripotent (iPS) cells are mapped near the origin of the trajectories, whereas mouse embryo fibroblast (MEF) and fibroblast cell lines are mapped near the far end of the trajectories. We propose that this method can be used as the non-operational semi-quantitative definition of cell differentiation status and developmental potency. Furthermore, the global expression profiles of cell lineages provide a framework for the future study of in vitro and in vivo cell differentiation. Keywords: cell type comparison design,reference design,replicate design,time series design
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Project description:Pituitary neuroendocrine tumors (PitNETs) account for the second most common intracranial tumors. Currently, there is still a lack of comprehensive understanding regarding the molecular mechanisms associated with lineage or clinical features of PitNETs, which hinders development of effective treatment strategies. Herein, we performed in-depth proteome profiling of 68 PitNET tumors and 4 pituitary glands. We systematically investigated the common and specific aberrant biological mechanisms in TPIT (ACTH/SCA) and PIT1 (TSH/PRL/GH) lineages. Our findings revealed that Wnt signaling pathway and MAPK signaling pathway played crucial regulatory roles in both lineages, while lipid metabolism abnormalities were more prominent in TPIT lineage, and decreased cell adhesion was more significant in PIT1 lineage. Based on these findings, we delved into the interactions between drugs and proteins to explore reliable drug targets and treatment options. We identified 17 potential drug targets and 51 FDA-approved drugs in TPIT lineage, and 19 potential drug targets and 97 FDA-approved drugs in PIT1 lineage, respectively. Additionally, we reclassified the samples based on 4 important clinical phenotypes: invasiveness, recurrence, high incidence in women of childbearing age, and tumor stiffness. By combining artificial intelligence (AI) and bioinformatics analysis, we further investigated the related mechanistic information. Our results highlighted the close association of abnormal neuronal activity with tumor invasiveness, as well as the intimate correlation between cell adhesion alterations and the other 3 clinical phenotypes. Overall, our study provides valuable insights into both of lineage-based and clinical feature-based pathophysiological mechanisms of PitNETs, and offers novel reference for future targeted therapies.
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