Reconstruction of complex single-cell trajectories using CellRouter

Rocha, Edroaldo Lummertz da; Rowe, R. Grant; Lundin, Vanessa; Malleshaiah, Mohan; Jha, Deepak Kumar; Rambo, Carlos R.; Li, Hu; North, Trista E.; Collins, James J.; Daley, George Q.

doi:10.1038/s41467-018-03214-y

Cited by 84 publications

(59 citation statements)

References 32 publications

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“…Pregranulosa cells or their precursors (supporting somatic cells) were identified within three distinct clusters on the basis of the elevated expression of Wnt4 and Wnt6 (Supplementary Figure 1d) (Jameson et al, 2012, McLaren, 2000). We also identified 5 ovarian somatic cells with their classic makers including mesothelial cells ( Lhx9 and Upk3b ) (Kanamori-Katayama et al, 2011, Mazaud et al, 2002), interstitial cells ( Col1a2 and Bgn ) (Piprek et al, 2018), endothelial cells ( Pecam1 and Kdr ) (Brennan et al, 2002, Jeays-Ward et al, 2003) and two contaminative somatic cell populations from blood, immune cells ( Cd52 and Car2 ) and erythroid cells ( Alas2 and Alad ) (Harigae et al, 2003, Lummertz da Rocha et al, 2018) (Supplementary Figure 1d). Thereby providing evidence of the heterogeneity of all somatic cell populations during the time frame analysed.…”

Section: Resultsmentioning

confidence: 99%

Dissecting the initiation of female meiosis in the mouse at single-cell resolution

Wang

Zhang

et al. 2019

Preprint

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ABSTRACTGerm cell meiosis is one of the most finely orchestrated events during gametogenesis with distinct developmental patterns in males and females. However, in mammals, the molecular mechanisms involved in this process remain not well known. Here, we report detailed transcriptome analyses of cell populations present in the mouse female gonadal ridges (E11.5) and the embryonic ovaries from E12.5 to E14.5 using single cell RNA sequencing (scRNA seq). These periods correspond with the initiation and progression of meiosis throughout the first stage of prophase I. We identified 13 transcriptionally distinct cell populations and 7 transcriptionally distinct germ cell subclusters that correspond to mitotic (3 clusters) and meiotic (4 clusters) germ cells. By comparing the signature gene expression pattern of 4 meiotic germ cell clusters, we found that the 4 cell clusters correspond to different cell status en route to meiosis progression, and therefore, our research here characterized detailed transcriptome dynamics during meiotic prophase I. Reconstructing the progression of meiosis along pseudotime, we identified several new genes and molecular pathways with potential critical roles in the mitosis/meiosis transition and early meiotic progression. Last, the heterogeneity within somatic cell populations was also discussed and different cellular states were identified. Our scRNA seq analysis here represents a new important resource for deciphering the molecular pathways driving meiosis initiation and progression in female germ cells and ovarian somatic cells.

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Section: Resultsmentioning

confidence: 99%

Dissecting the initiation of female meiosis in the mouse at single-cell resolution

Wang

Zhang

et al. 2019

Preprint

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“…To examine the causal relationships governing subcluster specific functional changes, we next systematically examined the TF dynamics underlying subcluster-specific cell state transitions towards AD. To this aim we used CellRouter 64 to predict TFs that drive transitions from control to AD subclusters. Briefly, CellRouter constructs path trajectories across single cell subpopulations and builds gene regulatory networks (GRNs) using mutual information, a metric drawn from information theory that measures the amount of information one random variable gives about another.…”

Section: Resultsmentioning

confidence: 99%

“…CellRouter is a single-cell RNA sequencing algorithm used to identify gene regulatory changes along transitions between user-defined cell states 64 . CellRouter analysis was run using the pipeline provided by the original authors (https://github.com/edroaldo/CellRouter) as of 14th December 2018.…”

Section: Cell Router Analysesmentioning

confidence: 99%

A single cell brain atlas in human Alzheimer’s disease

Grubman

Chew

Ouyang

et al. 2019

Preprint

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26Alzheimer's disease (AD) is a heterogeneous disease that is largely dependent on the complex 27 cellular microenvironment in the brain. This complexity impedes our understanding of how 28 individual cell types contribute to disease progression and outcome. To characterize the 29 molecular and functional cell diversity in the human AD brain we utilized single nuclei RNA-30 seq in AD and control patient brains in order to map the landscape of cellular heterogeneity in 31 AD. We detail gene expression changes at the level of cells and cell subclusters, highlighting 32 specific cellular contributions to global gene expression patterns between control and 33 Alzheimer's patient brains. We observed distinct cellular regulation of APOE which was 34 repressed in oligodendrocyte progenitor cells (OPCs) and astrocyte AD subclusters, and highly 35 enriched in a microglial AD subcluster. In addition, oligodendrocyte and microglia AD 36 subclusters show discordant expression of APOE. Integration of transcription factor regulatory 37 modules with downstream GWAS gene targets revealed subcluster-specific control of AD cell 38 fate transitions. For example, this analysis uncovered that astrocyte diversity in AD was under 39 the control of transcription factor EB (TFEB), a master regulator of lysosomal function and 40 which initiated a regulatory cascade containing multiple AD GWAS genes. These results 41 establish functional links between specific cellular sub-populations in AD, and provide new 42 insights into the coordinated control of AD GWAS genes and their cell-type specific 43 contribution to disease susceptibility. Finally, we created an interactive reference web resource 44 which will facilitate brain and AD researchers to explore the molecular architecture of subtype 45 and AD-specific cell identity, molecular and functional diversity at the single cell level. 46Highlights 47 • We generated the first human single cell transcriptome in AD patient brains 48 • Our study unveiled 9 clusters of cell-type specific and common gene expression 49 patterns between control and AD brains, including clusters of genes that present 50 properties of different cell types (i.e. astrocytes and oligodendrocytes) 51 • Our analyses also uncovered functionally specialized sub-cellular clusters: 5 52 microglial clusters, 8 astrocyte clusters, 6 neuronal clusters, 6 oligodendrocyte 53 clusters, 4 OPC and 2 endothelial clusters, each enriched for specific ontological 54 gene categories 55 • Our analyses found manifold AD GWAS genes specifically associated with one 56 cell-type, and sets of AD GWAS genes co-ordinately and differentially regulated 57 between different brain cell-types in AD sub-cellular clusters 58 • We mapped the regulatory landscape driving transcriptional changes in AD brain, 59 and identified transcription factor networks which we predict to control cell fate 60 transitions between control and AD sub-cellular clusters 61 • Finally, we provide an interactive web-resource that allows the user to further 62 visua...

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“…It has been a major challenge to illuminate the dynamic mechanisms of cellular programs governing fate transitions from single-cell data that lacks temporal information (Trapnell, 2015). The current methods have mainly focused on identifying trajectories between the most phenotypically distant cell states, and they are usually less robust in reconstructing trajectories from early states towards intermediate or transitory cell states [e.g., Wishbone (Setty et al, 2016), Diffusion Pseudotime (Haghverdi et al, 2016), Cycler (Gut et al, 2015), and CellRouter (Lummertz da Rocha et al, 2018)]. Some of the methods have focused on gaining insights into the regulatory mechanisms driving cell differentiation [e.g., Monocle (Trapnell et al, 2014), ERA (Kafri et al, 2013), Waterfall (Shin et al, 2015), and PIDC (Chan et al, 2017)], and they seem not to consider how discontinuous, stochastic fate transition events are driven by the dynamic nature of the developmental landscape (which can change in response to activity of gene regulatory networks and extracellular signals) and reflected in the observed increased transcriptional heterogeneity at transition points.…”

Section: Introductionmentioning

confidence: 99%

Revealing Dynamic Mechanisms of Cell Fate Decisions From Single-Cell Transcriptomic Data

Zhang

Nie

2019

Front. Genet.

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Cell fate decisions play a pivotal role in development, but technologies for dissecting them are limited. We developed a multifunction new method, Topographer, to construct a "quantitative" Waddington's landscape of single-cell transcriptomic data. This method is able to identify complex cell-state transition trajectories and to estimate complex cell-type dynamics characterized by fate and transition probabilities. It also infers both marker gene networks and their dynamic changes as well as dynamic characteristics of transcriptional bursting along the cell-state transition trajectories. Applying this method to single-cell RNA-seq data on the differentiation of primary human myoblasts, we not only identified three known cell types, but also estimated both their fate probabilities and transition probabilities among them. We found that the percent of genes expressed in a bursty manner is significantly higher at (or near) the branch point (~97%) than before or after branch (below 80%), and that both gene-gene and cell-cell correlation degrees are apparently lower near the branch point than away from the branching. Topographer allows revealing of cell fate mechanisms in a coherent way at three scales: cell lineage (macroscopic), gene network (mesoscopic), and gene expression (microscopic).

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Reconstruction of complex single-cell trajectories using CellRouter

Cited by 84 publications

References 32 publications

Dissecting the initiation of female meiosis in the mouse at single-cell resolution

Dissecting the initiation of female meiosis in the mouse at single-cell resolution

A single cell brain atlas in human Alzheimer’s disease

Revealing Dynamic Mechanisms of Cell Fate Decisions From Single-Cell Transcriptomic Data

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