scholarly journals Single-cell transcriptomic profiling of progenitors of the oligodendrocyte lineage reveals transcriptional convergence during development

2017 ◽  
Author(s):  
Sueli Marques ◽  
Darya Vanichkina ◽  
David van Bruggen ◽  
Elisa M. Floriddia ◽  
Hermany Munguba ◽  
...  
Keyword(s):  
Single Cell ◽  
Lineage Tracing ◽  
Cell Level ◽  
Transcriptional Profiles ◽  
Cell Lineages ◽  
Distinct Cell ◽  
The Central Nervous System ◽  
Oligodendrocyte Precursor ◽  
Brain And Spinal Cord ◽  
Transcriptional Landscape

SummaryPdgfra+ oligodendrocyte precursor cells (OPCs) arise in distinct specification waves during embryogenesis in the central nervous system (CNS). It is unclear whether there is a correlation between these waves and different transcriptional oligodendrocyte (OL) states at adult stages. Here we present a bulk and single-cell transcriptomics resource providing insights on how transitions between these states occur. We show that E13.5 Pdgfra+ populations are not OPCs, exhibiting instead hallmarks of neural progenitors. A subset of these progenitors, which we refer as pre-OPCs, rewires their transcriptional landscape, converging into indistinguishable OPC states at E17.5 and post-natal stages. P7 brain and spinal cord OPCs present similar transcriptional profiles at the single-cell level, indicating that OPC states are not region-specific. Postnatal OPC progeny of E13.5 Pdgfra+ have electrophysiological and transcriptional profiles similar to OPCs derived from subsequent specification waves. In addition, lineage tracing indicates that a subset of E13.5 Pdgfra+ cells also originate cells of the pericyte lineage. In summary, our results indicate that embryonic Pdgfra+ cells are diverse and give rise at post-natal stages to distinct cell lineages, including OPCs with convergent transcriptional profiles in different CNS regions.

scholarly journals Single-cell Sequencing Reveals Brain/Spinal Cord Oligodendrocyte Precursor Heterogeneity and Requirement for mTOR in Cholesterol Biosynthesis and Myelin Maintenance

2020 ◽  
Author(s):  
Luipa Khandker ◽  
Marisa A. Jeffries ◽  
Yun-Juan Chang ◽  
Marie L. Mather ◽  
Jennifer N. Bourne ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Cholesterol Biosynthesis ◽  
Adult Brain ◽  
Cell Analysis ◽  
Sequencing Studies ◽  
Oligodendrocyte Precursor ◽  
Brain And Spinal Cord ◽  
The Brain

AbstractBrain and spinal cord oligodendroglia have distinct functional characteristics, and cell autonomous loss of individual genes can result in different regional phenotypes. However, sequencing studies to date have not revealed distinctions between brain and spinal cord oligodendroglia. Using single-cell analysis of oligodendroglia during myelination, we demonstrate that brain and spinal cord precursors are transcriptionally distinct, defined predominantly by cholesterol biosynthesis. We further identify mechanistic target of rapamycin (mTOR) as a major regulator promoting cholesterol biosynthesis in oligodendroglia. Oligodendroglial-specific loss of mTOR compromises cholesterol biosynthesis in both the brain and spinal cord. Importantly, mTOR loss has a greater impact on cholesterol biosynthesis in spinal cord oligodendroglia that corresponds with more pronounced developmental deficits. However, loss of mTOR in brain oligodendroglia ultimately results in oligodendrocyte death, spontaneous demyelination, and impaired axonal function, demonstrating that mTOR is required for myelin maintenance in the adult brain.

scholarly journals Integrated profiling of single cell epigenomic and transcriptomic landscape of Parkinson’s disease mouse brain

2020 ◽  
Author(s):  
Jixing Zhong ◽  
Gen Tang ◽  
Jiacheng Zhu ◽  
Xin Qiu ◽  
Weiying Wu ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Single Cell ◽  
Early Stage ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Rna Seq ◽  
Cell Level ◽  
Distinct Cell ◽  
Single Nucleus

AbstractParkinson’s disease (PD) is a neurodegenerative disease leading to the impairment of execution of movement. PD pathogenesis has been largely investigated, but either restricted in bulk level or at certain cell types, which failed to capture cellular heterogeneity and intrinsic interplays among distinct cell types. To overcome this, we applied single-nucleus RNA-seq and single cell ATAC-seq on cerebellum, midbrain and striatum of PD mouse and matched control. With 74,493 cells in total, we comprehensively depicted the dysfunctions under PD pathology covering proteostasis, neuroinflammation, calcium homeostasis and extracellular neurotransmitter homeostasis. Besides, by multi-omics approach, we identified putative biomarkers for early stage of PD, based on the relationships between transcriptomic and epigenetic profiles. We located certain cell types that primarily contribute to PD early pathology, narrowing the gap between genotypes and phenotypes. Taken together, our study provides a valuable resource to dissect the molecular mechanism of PD pathogenesis at single cell level, which could facilitate the development of novel methods regarding diagnosis, monitoring and practical therapies against PD at early stage.

scholarly journals Single-Cell Signature Explorer for comprehensive visualization of single cell signatures across scRNA-seq datasets

2019 ◽  
Vol 47 (21) ◽  
pp. e133-e133 ◽  
Author(s):  
Frédéric Pont ◽  
Marie Tosolini ◽  
Jean J Fournié
Keyword(s):  
Lung Cancer ◽  
Single Cell ◽  
High Throughput ◽  
Metabolic Pathways ◽  
Single Cell Level ◽  
Cell Level ◽  
Human Pbmc ◽  
Adult Testis ◽  
Cell Lineages ◽  
Gene Feature

Abstract The momentum of scRNA-seq datasets prompts for simple and powerful tools exploring their meaningful signatures. Here we present Single-Cell_Signature_Explorer (https://sites.google.com/site/fredsoftwares/products/single-cell-signature-explorer), the first method for qualitative and high-throughput scoring of any gene set-based signature at the single cell level and its visualization using t-SNE or UMAP. By scanning datasets for single or combined signatures, it rapidly maps any multi-gene feature, exemplified here with signatures of cell lineages, biological hallmarks and metabolic pathways in large scRNAseq datasets of human PBMC, melanoma, lung cancer and adult testis.

scholarly journals Single-cell transcriptomics identifies divergent developmental lineage trajectories during human pituitary development

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Shu Zhang ◽  
Yueli Cui ◽  
Xinyi Ma ◽  
Jun Yong ◽  
Liying Yan ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Developmental Trajectories ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Human Pituitary ◽  
Pituitary Development ◽  
Physiological Processes ◽  
Distinct Cell ◽  
Transcriptional Landscape

Abstract The anterior pituitary gland plays a central role in regulating various physiological processes, including body growth, reproduction, metabolism and stress response. Here, we perform single-cell RNA-sequencing (scRNA-seq) of 4113 individual cells from human fetal pituitaries. We characterize divergent developmental trajectories with distinct transitional intermediate states in five hormone-producing cell lineages. Corticotropes exhibit an early intermediate state prior to full differentiation. Three cell types of the PIT-1 lineage (somatotropes, lactotropes and thyrotropes) segregate from a common progenitor coexpressing lineage-specific transcription factors of different sublineages. Gonadotropes experience two multistep developmental trajectories. Furthermore, we identify a fetal gonadotrope cell subtype expressing the primate-specific hormone chorionic gonadotropin. We also characterize the cellular heterogeneity of pituitary stem cells and identify a hybrid epithelial/mesenchymal state and an early-to-late state transition. Here, our results provide insights into the transcriptional landscape of human pituitary development, defining distinct cell substates and subtypes and illustrating transcription factor dynamics during cell fate commitment.

scholarly journals Lineage tracing on transcriptional landscapes links state to fate during differentiation

2018 ◽  
Author(s):  
Caleb Weinreb ◽  
Alejo Rodriguez-Fraticelli ◽  
Fernando Camargo ◽  
Allon M Klein
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Cell Types ◽  
Lineage Tracing ◽  
Cell Potential ◽  
Monocyte Differentiation ◽  
Cell Assays ◽  
Transcriptional Landscape ◽  
Developmental Hierarchy

AbstractA challenge in stem cell biology is to associate molecular differences among progenitor cells with their capacity to generate mature cell types. Though the development of single cell assays allows for the capture of progenitor cell states in great detail, these assays cannot definitively link cell states to their long-term fate. Here, we use expressed DNA barcodes to clonally trace single cell transcriptomes dynamically during differentiation and apply this approach to the study of hematopoiesis. Our analysis identifies functional boundaries of cell potential early in the hematopoietic hierarchy and locates them on a continuous transcriptional landscape. We reconstruct a developmental hierarchy showing separate ontogenies for granulocytic subtypes and two routes to monocyte differentiation that leave a persistent imprint on mature cells. Finally, we use our approach to benchmark methods of dynamic inference from single-cell snapshots, and provide evidence of strong early fate biases dependent on cellular properties hidden from single-cell RNA sequencing.

scholarly journals Single-cell transcriptomics of the mouse gonadal soma reveals the establishment of sexual dimorphism in distinct cell lineages

2018 ◽  
Author(s):  
Isabelle Stévant ◽  
Françoise Kühne ◽  
Andy Greenfield ◽  
Marie-Christine Chaboissier ◽  
Emmanouil T. Dermitzakis ◽  
...  
Keyword(s):  
Time Series ◽  
Sex Determination ◽  
Single Cell ◽  
Cell Fate ◽  
Somatic Cells ◽  
Supporting Cells ◽  
Single Population ◽  
Cell Lineages ◽  
Multipotent Progenitors ◽  
Distinct Cell

SummarySex determination is a unique process that allows the study of multipotent progenitors and their acquisition of sex-specific fates during differentiation of the gonad into a testis or an ovary. Using time-series single-cell RNA sequencing (scRNA-seq) on ovarian Nr5a1-GFP+ somatic cells during sex determination, we identified a single population of early progenitors giving rise to both pre-granulosa cells and potential steroidogenic precursor cells. By comparing time-series scRNA-seq of XX and XY somatic cells, we demonstrate that the supporting cells emerge from the early progenitors with a non-sex-specific transcriptomic program, before pre-granulosa and Sertoli cells acquire their sex-specific identity. In XX and XY steroidogenic precursors similar transcriptomic profiles underlie the acquisition of cell fate, but with a delay in XX cells. Our data provide a novel framework, at single-cell resolution, for further interrogation of the molecular and cellular basis of mammalian sex determination.

scholarly journals Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity

2018 ◽  
Author(s):  
Longqi Liu ◽  
Chuanyu Liu ◽  
Andrés Quintero ◽  
Liang Wu ◽  
Yue Yuan ◽  
...  
Keyword(s):  
Single Cell ◽  
Target Genes ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Cell Populations ◽  
Single Cell Level ◽  
Complex Cell ◽  
Cell Level ◽  
Distinct Cell ◽  
Cell Variation

AbstractIntegrative analysis of multi-omics layers at single cell level is critical for accurate dissection of cell-to-cell variation within certain cell populations. Here we report scCAT-seq, a technique for simultaneously assaying chromatin accessibility and the transcriptome within the same single cell. We show that the combined single cell signatures enable accurate construction of regulatory relationships between cis-regulatory elements and the target genes at single-cell resolution, providing a new dimension of features that helps direct discovery of regulatory patterns specific to distinct cell identities. Moreover, we generated the first single cell integrated maps of chromatin accessibility and transcriptome in human pre-implantation embryos and demonstrated the robustness of scCAT-seq in the precise dissection of master transcription factors in cells of distinct states during embryo development. The ability to obtain these two layers of omics data will help provide more accurate definitions of “single cell state” and enable the deconvolution of regulatory heterogeneity from complex cell populations.

scholarly journals Developmental landscape of  human forebrain at a single-cell level unveils early waves of oligodendrogenesis

2021 ◽  
Author(s):  
David van Bruggen ◽  
Fabio Baldivia Pohl ◽  
Christoffer Mattsson Langseth ◽  
Petra Kukanja ◽  
Hower Lee ◽  
...  
Keyword(s):  
Single Cell ◽  
Network Inference ◽  
Regulatory Mechanisms ◽  
Oligodendrocyte Precursor Cells ◽  
Second Trimester ◽  
Rna Seq ◽  
Cell Level ◽  
Human Central Nervous System ◽  
Oligodendrocyte Precursor

Oligodendrogenesis in the human central nervous system has been mainly observed at the second trimester of gestation, a much later developmental stage compared to mouse. Here we characterize the transcriptomic neural diversity in the human forebrain at post conceptual weeks (PCW) 8 to 10, using single-cell RNA-Seq. We find evidence of the emergence of a first wave of oligodendrocyte lineage cells as early as PCW 8, which we also confirm at the epigenomic level with single-cell ATAC-Seq. Using regulatory network inference, we predict key transcriptional events leading to the specification of oligodendrocyte precursor cells (OPCs). Moreover, by profiling the spatial expression of fifty key genes using In Situ Sequencing (ISS), we identify regions in the human ventral fetal forebrain where oligodendrogenesis first occurs. Our results indicate evolutionary conservation of the first wave of oligodendrogenesis between mouse and human and describe regulatory mechanisms required for human OPC specification.

scholarly journals Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts

2020 ◽  
Author(s):  
Jeffrey J. Quinn ◽  
Matthew G. Jones ◽  
Ross A. Okimoto ◽  
Shigeki Nanjo ◽  
Michelle M. Chan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Single Cell ◽  
Cancer Progression ◽  
Cell Lineage ◽  
Lineage Tracing ◽  
Rna Seq ◽  
Disease Etiology ◽  
Cell Lineages ◽  
Transient Events

AbstractCancer progression is characterized by rare, transient events which are nonetheless highly consequential to disease etiology and mortality. Detailed cell phylogenies can recount the history and chronology of these critical events – including metastatic seeding. Here, we applied our Cas9-based lineage tracer to study the subclonal dynamics of metastasis in a lung cancer xenograft mouse model, revealing the underlying rates, routes, and drivers of metastasis. We report deeply resolved phylogenies for tens of thousands of metastatically disseminated cancer cells. We observe surprisingly diverse metastatic phenotypes, ranging from metastasis-incompetent to aggressive populations. These phenotypic distinctions result from pre-existing, heritable, and characteristic differences in gene expression, and we demonstrate that these differentially expressed genes can drive invasiveness. Furthermore, metastases transit via diverse, multidirectional tissue routes and seeding topologies. Our work demonstrates the power of tracing cancer progression at unprecedented resolution and scale.One Sentence SummarySingle-cell lineage tracing and RNA-seq capture diverse metastatic behaviors and drivers in lung cancer xenografts in mice.

scholarly journals Single-cell lineage tracing by integrating CRISPR-Cas9 mutations with transcriptomic data

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Hamim Zafar ◽  
Chieh Lin ◽  
Ziv Bar-Joseph
Keyword(s):  
Single Cell ◽  
Cell Lineage ◽  
Lineage Tracing ◽  
Expression Data ◽  
Random Mutation ◽  
Novel Technologies ◽  
Cell Lineages ◽  
Gene Sets ◽  
Lineage Tree ◽  
Differentiation Pathways

Abstract Recent studies combine two novel technologies, single-cell RNA-sequencing and CRISPR-Cas9 barcode editing for elucidating developmental lineages at the whole organism level. While these studies provided several insights, they face several computational challenges. First, lineages are reconstructed based on noisy and often saturated random mutation data. Additionally, due to the randomness of the mutations, lineages from multiple experiments cannot be combined to reconstruct a species-invariant lineage tree. To address these issues we developed a statistical method, LinTIMaT, which reconstructs cell lineages using a maximum-likelihood framework by integrating mutation and expression data. Our analysis shows that expression data helps resolve the ambiguities arising in when lineages are inferred based on mutations alone, while also enabling the integration of different individual lineages for the reconstruction of an invariant lineage tree. LinTIMaT lineages have better cell type coherence, improve the functional significance of gene sets and provide new insights on progenitors and differentiation pathways.

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