scholarly journals Single-cell transcriptomic resolution of stem cells and their developmental trajectories in the hippocampus reveals epigenetic control of cell state perseverance

2021 ◽  
Author(s):  
Adrián Salas-Bastos ◽  
Martin Treppner ◽  
Josip S. Herman ◽  
Dimitrios Koutsogiannis ◽  
Harald Binder ◽  
...  
Keyword(s):  
Single Cell ◽  
Developmental Trajectories ◽  
Loss Of Function ◽  
Epigenetic Factor ◽  
Proliferation And Differentiation ◽  
Cornu Ammonis ◽  
Stem Cell Populations ◽  
Cell Trajectories ◽  
Conceptual Research ◽  
Radial Axis

Despite conceptual research on hippocampus development and the application of single-cell-resolved technologies, the nature and maturation of its diverse progenitor populations are unexplored. The chromatin modifier DOT1L balances progenitor proliferation and differentiation, and conditional loss-of-function mice featured impaired hippocampus development. We applied single-cell RNA sequencing on DOT1L-mutant mice and explored cell trajectories in the E16.5 hippocampus. We resolved in our data five distinct neural stem cell populations with the developmental repertoire to specifically generate the cornu ammonis (CA) 1 field and the dentate gyrus (DG). Within the two developing CA1- and CA3-fields, we identified two distinct maturation states and we thus propose CA1- and CA3-differentiation along the radial axis. In the developing hippocampus, DOT1L is primarily involved in the proper development of CA3 and the DG, and it serves as a state-preserving epigenetic factor that orchestrates the expression of several important transcription factors that impact neuronal differentiation and maturation.

scholarly journals Reversed graph embedding resolves complex single-cell developmental trajectories

2017 ◽  
Author(s):  
Xiaojie Qiu ◽  
Qi Mao ◽  
Ying Tang ◽  
Li Wang ◽  
Raghav Chawla ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Developmental Trajectories ◽  
Single Cells ◽  
Graph Embedding ◽  
Developmental Trajectory ◽  
Loss Of Function ◽  
Cell Fate Decisions ◽  
Principal Graph ◽  
Cell Trajectories

AbstractOrganizing single cells along a developmental trajectory has emerged as a powerful tool for understanding how gene regulation governs cell fate decisions. However, learning the structure of complex single-cell trajectories with two or more branches remains a challenging computational problem. We present Monocle 2, which uses reversed graph embedding to reconstruct single-cell trajectories in a fully unsupervised manner. Monocle 2 learns an explicit principal graph to describe the data, greatly improving the robustness and accuracy of its trajectories compared to other algorithms. Monocle 2 uncovered a new, alternative cell fate in what we previously reported to be a linear trajectory for differentiating myoblasts. We also reconstruct branched trajectories for two studies of blood development, and show that loss of function mutations in key lineage transcription factors diverts cells to alternative branches on the a trajectory. Monocle 2 is thus a powerful tool for analyzing cell fate decisions with single-cell genomics.

scholarly journals STREAM: Single-cell Trajectories Reconstruction, Exploration And Mapping of omics data

2018 ◽  
Author(s):  
Huidong Chen ◽  
Luca Albergante ◽  
Jonathan Y Hsu ◽  
Caleb A Lareau ◽  
Giosue` Lo Bosco ◽  
...  
Keyword(s):  
Single Cell ◽  
De Novo ◽  
Developmental Trajectories ◽  
Cellular Heterogeneity ◽  
State Transitions ◽  
Omics Data ◽  
Transcriptomic Data ◽  
Lineage Differentiation ◽  
Cell Trajectories

AbstractSingle-cell transcriptomic assays have enabled the de novo reconstruction of lineage differentiation trajectories, along with the characterization of cellular heterogeneity and state transitions. Several methods have been developed for reconstructing developmental trajectories from single-cell transcriptomic data, but efforts on analyzing single-cell epigenomic data and on trajectory visualization remain limited. Here we present STREAM, an interactive pipeline capable of disentangling and visualizing complex branching trajectories from both single-cell transcriptomic and epigenomic data.

scholarly journals Self-assembling Manifolds in Single-cell RNA Sequencing Data

2018 ◽  
Author(s):  
Alexander J. Tarashansky ◽  
Yuan Xue ◽  
Pengyang Li ◽  
Stephen R. Quake ◽  
Bo Wang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Developmental Trajectories ◽  
Cell Types ◽  
Selection Strategy ◽  
Sequencing Data ◽  
Biologically Relevant ◽  
Self Assembling ◽  
Single Cell Rna Sequencing ◽  
Stem Cell Populations

AbstractSingle-cell RNA sequencing has spurred the development of computational methods that enable researchers to classify cell types, delineate developmental trajectories, and measure molecular responses to external perturbations. Many of these technologies rely on their ability to detect genes whose cell-to-cell variations arise from the biological processes of interest rather than transcriptional or technical noise. However, for datasets in which the biologically relevant differences between cells are subtle, identifying these genes is a challenging task. We present the self-assembling manifold (SAM) algorithm, an iterative soft feature selection strategy to quantify gene relevance and improve dimensionality reduction. We demonstrate its advantages over other state-of-the-art methods with experimental validation in identifying novel stem cell populations of Schistosoma, a prevalent parasite that infects hundreds of millions of people. Extending our analysis to a total of 56 datasets, we show that SAM is generalizable and consistently outperforms other methods in a variety of biological and quantitative benchmarks.

scholarly journals Self-assembling manifolds in single-cell RNA sequencing data

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Alexander J Tarashansky ◽  
Yuan Xue ◽  
Pengyang Li ◽  
Stephen R Quake ◽  
Bo Wang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Developmental Trajectories ◽  
Cell Types ◽  
Selection Strategy ◽  
Sequencing Data ◽  
Biologically Relevant ◽  
Self Assembling ◽  
Single Cell Rna Sequencing ◽  
Stem Cell Populations

Single-cell RNA sequencing has spurred the development of computational methods that enable researchers to classify cell types, delineate developmental trajectories, and measure molecular responses to external perturbations. Many of these technologies rely on their ability to detect genes whose cell-to-cell variations arise from the biological processes of interest rather than transcriptional or technical noise. However, for datasets in which the biologically relevant differences between cells are subtle, identifying these genes is challenging. We present the self-assembling manifold (SAM) algorithm, an iterative soft feature selection strategy to quantify gene relevance and improve dimensionality reduction. We demonstrate its advantages over other state-of-the-art methods with experimental validation in identifying novel stem cell populations of Schistosoma mansoni, a prevalent parasite that infects hundreds of millions of people. Extending our analysis to a total of 56 datasets, we show that SAM is generalizable and consistently outperforms other methods in a variety of biological and quantitative benchmarks.

scholarly journals ATRT-13. DIFFERENT CELLS OF ORIGIN PAVE THE WAY FOR MOLECULAR HETEROGENEITY IN RHABDOID TUMORS

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii278-iii278
Author(s):  
Monika Graf ◽  
Marta Interlandi ◽  
Natalia Moreno ◽  
Dörthe Holdhof ◽  
Viktoria Melcher ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Patterns ◽  
Time Frame ◽  
Precursor Cells ◽  
Genetically Engineered ◽  
Molecular Heterogeneity ◽  
Loss Of Function ◽  
Cellular Origin ◽  
Cells Of Origin ◽  
Rhabdoid Tumors

Abstract Rhabdoid tumors (RT) are rare but highly aggressive pediatric neoplasms. These tumors carry homozygous loss-of-function alterations of SMARCB1 in almost all cases with an otherwise low mutational load. RT arise at different intracranial (ATRT) as well as extracranial (MRT) anatomical sites. Three main molecular subgroups (ATRT-SHH, ATRT-TYR, ATRT-MYC) have been characterized for ATRT which are epigenetically and clinically diverse, while MRT show remarkable similarities with ATRT-MYC distinct from ATRT-SHH and ATRT-TYR. Even though there are hypotheses about various cells of origin among RT subgroups, precursor cells of RT have not yet been identified. Previous studies on the temporal control of SMARCB1 knockout in genetically engineered mouse models have unveiled a tight vulnerable time frame during embryogenesis with regard to the susceptibility of precursor cells to result in RT. In this study, we employed single-cell RNA sequencing to describe the intra- and intertumoral heterogeneity of murine ATRT-SHH and -MYC as well as extracranial MYC tumor cells. We defined subgroup-specific tumor markers for all RT classes but also observed a notable overlap of gene expression patterns in all MYC subgroups. By comparing these single-cell transcriptomes with available single-cell maps of early embryogenesis, we gained first insights into the cellular origin of RT. Finally, unsupervised clustering of published human RT methylation data and healthy control tissues confirmed the existence of different cells of origin for intracranial SHH tumors and MYC tumors independent of their anatomical localizations.

scholarly journals Single-cell spatial transcriptomic analysis reveals common and divergent features of developing postnatal granule cerebellar cells and medulloblastoma

BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Wenqin Luo ◽  
Guan Ning Lin ◽  
Weichen Song ◽  
Yu Zhang ◽  
Huadong Lai ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Granule Cells ◽  
Cerebellar Granule Cells ◽  
Granule Neurons ◽  
Cerebellar Granule ◽  
Granule Neuron ◽  
Proliferation And Differentiation ◽  
Single Cell Rna Sequencing ◽  
Cerebellar Cells

Abstract Background Cerebellar neurogenesis involves the generation of large numbers of cerebellar granule neurons (GNs) throughout development of the cerebellum, a process that involves tight regulation of proliferation and differentiation of granule neuron progenitors (GNPs). A number of transcriptional regulators, including Math1, and the signaling molecules Wnt and Shh have been shown to have important roles in GNP proliferation and differentiation, and deregulation of granule cell development has been reported to be associated with the pathogenesis of medulloblastoma. While the progenitor/differentiation states of cerebellar granule cells have been broadly investigated, a more detailed association between developmental differentiation programs and spatial gene expression patterns, and how these lead to differential generation of distinct types of medulloblastoma remains poorly understood. Here, we provide a comparative single-cell spatial transcriptomics analysis to better understand the similarities and differences between developing granule and medulloblastoma cells. Results To acquire an enhanced understanding of the precise cellular states of developing cerebellar granule cells, we performed single-cell RNA sequencing of 24,919 murine cerebellar cells from granule neuron-specific reporter mice (Math1-GFP; Dcx-DsRed mice). Our single-cell analysis revealed that there are four major states of developing cerebellar granule cells, including two subsets of granule progenitors and two subsets of differentiating/differentiated granule neurons. Further spatial transcriptomics technology enabled visualization of their spatial locations in cerebellum. In addition, we performed single-cell RNA sequencing of 18,372 cells from Patched+/− mutant mice and found that the transformed granule cells in medulloblastoma closely resembled developing granule neurons of varying differentiation states. However, transformed granule neuron progenitors in medulloblastoma exhibit noticeably less tendency to differentiate compared with cells in normal development. Conclusion In sum, our study revealed the cellular and spatial organization of the detailed states of cerebellar granule cells and provided direct evidence for the similarities and discrepancies between normal cerebellar development and tumorigenesis.

scholarly journals An epigenetic GPI anchor defect impairs TLR4 signaling in the B cell transdifferentiation model for primary human monocytes BLaER1

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Julia Wegner ◽  
Thomas Zillinger ◽  
Thais Schlee-Guimaraes ◽  
Eva Bartok ◽  
Martin Schlee
Keyword(s):  
Single Cell ◽  
Human Monocyte ◽  
Surface Expression ◽  
Human Monocytes ◽  
Innate Immune Responses ◽  
Loss Of Function ◽  
Cell Cloning ◽  
Gpi Anchor ◽  
Single Cell Cloning ◽  
Antigen Presenting

AbstractAntigen-presenting myeloid cells like monocytes detect invading pathogens via pattern recognition receptors (PRRs) and initiate adaptive and innate immune responses. As analysis of PRR signaling in primary human monocytes is hampered by their restricted expandability, human monocyte models like THP-1 cells are commonly used for loss-of-function studies, such as with CRISPR-Cas9 editing. A recently developed transdifferentiation cell culture system, BLaER1, enables lineage conversion from malignant B cells to monocytes and was found superior to THP-1 in mimicking PRR signaling, thus being the first model allowing TLR4 and inflammasome pathway analysis. Here, we identified an important caveat when investigating TLR4-driven signaling in BLaER1 cells. We show that this model contains glycosylphosphatidylinositol (GPI) anchor-deficient cells, which lack CD14 surface expression when differentiated to monocytes, resulting in diminished LPS/TLR4 but not TLR7/TLR8 responsiveness. This GPI anchor defect is caused by epigenetic silencing of PIGH, leading to a random distribution of intact and PIGH-deficient clones after single-cell cloning. Overexpressing PIGH restored GPI-anchored protein (including CD14) expression and LPS responsiveness. When studying CD14- or other GPI-anchored protein-dependent pathways, researchers should consider this anomaly and ensure equal GPI-anchored protein expression when comparing cells that have undergone single-cell cloning, e. g. after CRISPR-Cas9 editing.

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