Mechanisms of radial glia progenitor cell lineage progression

Beattie, Robert; Hippenmeyer, Simon

doi:10.1002/1873-3468.12906

Cited by 105 publications

(98 citation statements)

References 169 publications

(228 reference statements)

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“…Cells within each cell type cluster can be further divided into cell states. One such example are the radial glia, which differentiate to produce both neurons and astrocytes, but have been shown to switch their preference from the former to the latter at a later stage (27–29). Figure 5A shows how matrix factorization has captured two subpoplations within the radial glia cluster, one present exclusively in postnatal day 0 and the other in postnatal day 5.…”

Section: Resultsmentioning

confidence: 99%

Untangling biological factors influencing trajectory inference from single cell data

Charrout

Reinders

Mahfouz

2020

Preprint

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Advances in single-cell RNA sequencing over the past decade has shifted the discussion of cell identity towards the transcriptional state of the cell. While the incredible resolution provided by singlecell RNA sequencing has led to great advances in unravelling tissue heterogeneity and inferring cell differentiation dynamics, it raises the question of which sources of variation are important for determining cellular identity. Here we show that confounding biological sources of variation, most notably the cell cycle, can distort the inference of differentiation trajectories. We show that by factorizing single cell data into distinct sources of variation, we can select a relevant set of factors that constitute the core regulators for trajetory inference, while filtering out confounding sources of variation (e.g. cell cycle) which can perturb the inferred trajectory. Script are available publicly on https://github.com/mochar/cell_variation. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 single cell | pseudotime | factor analysis | trajectory inference S ingle cell RNA-sequencing enables quantitative gene ex-1 pression profiling of individual cells. From an RNA view-2 point, these cells live in a high-dimensional space defined by the 3 expression of their genes. A critical step when analyzing such 4data is the identification of cells in order to find and label the 5 cell types present in the data. This is often achieved by group-6 ing together cells with similar expression profiles by applying 7 a clustering method. The resulting cell clusters are thus sepa-8 rated from one another by a set of genes uniquely expressed or 9 silenced in a subset of clusters. These so-called marker genes 10 can then be used for identification by cross-referencing with 11 known marker genes or marker genes found in other studies. 12This clustering-based approach for cell identification relies on 13 the general presumption that the measured expression lev-14 els are reflective of the cell's identity, which may be violated 15 due to shared transcriptional programs between two or more 16 types. Large variations within cell type clusters due to many 17 exclusive programs may also pose a problem as it can become 18 hard to discern between cell types and cell states (1). More 19 generally, sources of variation that contribute significantly to 20 the cell-cell distances in gene space, yet do not reflect the cell 21 type, can be detrimental to the identification task. These can 22 vary from small transient changes e.g. cell communication, 23 up to complex shifts in the cell's regulatory state such as the 24 cell cycle, which has been reported to contribute a substantial 25 portion of the gene expression variance (2). Moreover, cell 26 identification is often preceded by a gene filtering step whereby 27 genes with low variance (and therefore little information) are 28 discarded to ease the computational burden in downstream 29 analysis. Depending on the normalization and filtering criteria 30 used, gene filtering can lead to a lower dimensional space that 31 f...

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Section: Resultsmentioning

confidence: 99%

Untangling biological factors influencing trajectory inference from single cell data

Charrout

Reinders

Mahfouz

2020

Preprint

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“…While the MADM‐based lineage analysis revealed definitive quantitative ontogeny of neocortical excitatory neurons and glial cells (Gao et al . ), the cellular and molecular mechanisms dictating neural progenitor cell lineage progression are not well understood (Beattie and Hippenmeyer ). Major progress has been made in classifying cell‐types based on single cell transcriptome analysis (Lein et al .…”

Section: Radial Glial Progenitors Generate Cell‐type Diversity In Thementioning

confidence: 99%

“…; Vasistha et al . ), outer subventricular zone (SVZ) radial glial progenitors (oRGs aka basal RGs or bRGs) (Beattie and Hippenmeyer ), a subset of glial lineages and adult SVZ stem cells (Bayraktar et al . ).…”

mentioning

confidence: 99%

“…NESCs initially amplify their pool in fast cell cycle divisions before they transform into radial glial progenitors (RGPs) (Taverna et al 2014). RGPs have been demonstrated to be the main source in the developing cortex for the vast majority of cortical excitatory neurons, transient amplifying progenitors such as intermediate progenitors (IPs) (Kowalczyk et al 2009;Vasistha et al 2014), outer subventricular zone (SVZ) radial glial progenitors (oRGs aka basal RGs or bRGs) (Beattie and Hippenmeyer 2017), a subset of glial lineages and adult SVZ stem cells (Bayraktar et al 2015). The apical processes of RGPs serve as a scaffold for nascent cortical neurons, which migrate from the ventricular zones (VZ)/SVZ through the intermediate zone, in order to reach the cortical plate (CP) (Evsyukova et al 2013;Hippenmeyer 2014).…”

mentioning

confidence: 99%

“…Upon completion of neurogenesis, a defined fraction of individual RGPs proceed to gliogenesis whereby about 1 in 6 neurogenic RGPs produce gliaastrocytes and/or oligodendrocytesindicating a coupling between gliogenesis and neurogenesis at a predictable rate. While the MADM-based lineage analysis revealed definitive quantitative ontogeny of neocortical excitatory neurons and glial cells (Gao et al 2014), the cellular and molecular mechanisms dictating neural progenitor cell lineage progression are not well understood (Beattie and Hippenmeyer 2017). Major progress has been made in classifying cell-types based on single cell transcriptome analysis (Lein et al 2017;Luo et al 2017;Zeng and Sanes 2017), however it remains elusive which neuronal and glial cell types arise from an individual progenitor cell.…”

mentioning

confidence: 99%

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Epigenetic cues modulating the generation of cell‐type diversity in the cerebral cortex

Amberg

Laukoter

Hippenmeyer

2018

Journal of Neurochemistry

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The cerebral cortex is composed of a large variety of distinct cell‐types including projection neurons, interneurons, and glial cells which emerge from distinct neural stem cell lineages. The vast majority of cortical projection neurons and certain classes of glial cells are generated by radial glial progenitor cells in a highly orchestrated manner. Recent studies employing single cell analysis and clonal lineage tracing suggest that neural stem cell and radial glial progenitor lineage progression are regulated in a profound deterministic manner. In this review we focus on recent advances based mainly on correlative phenotypic data emerging from functional genetic studies in mice. We establish hypotheses to test in future research and outline a conceptual framework how epigenetic cues modulate the generation of cell‐type diversity during cortical development.

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Wnt/β‐catenin signaling in brain development and mental disorders: keeping TCF7L2 in mind

et al. 2019

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Canonical Wnt signaling, which is transduced by β‐catenin and lymphoid enhancer factor 1/T cell‐specific transcription factors (LEF1/TCFs), regulates many aspects of metazoan development and tissue renewal. Although much evidence has associated canonical Wnt/β‐catenin signaling with mood disorders, the mechanistic links are still unknown. Many components of the canonical Wnt pathway are involved in cellular processes that are unrelated to classical canonical Wnt signaling, thus further blurring the picture. The present review critically evaluates the involvement of classical Wnt/β‐catenin signaling in developmental processes that putatively underlie the pathology of mental illnesses. Particular attention is given to the roles of LEF1/TCFs, which have been discussed surprisingly rarely in this context. Highlighting recent discoveries, we propose that alterations in the activity of LEF1/TCFs, and particularly of transcription factor 7‐like 2 (TCF7L2), result in defects previously associated with neuropsychiatric disorders, including imbalances in neurogenesis and oligodendrogenesis, the functional disruption of thalamocortical circuitry and dysfunction of the habenula.

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Mechanisms of radial glia progenitor cell lineage progression

Cited by 105 publications

References 169 publications

Untangling biological factors influencing trajectory inference from single cell data

Untangling biological factors influencing trajectory inference from single cell data

Epigenetic cues modulating the generation of cell‐type diversity in the cerebral cortex

Wnt/β‐catenin signaling in brain development and mental disorders: keeping TCF7L2 in mind

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