Single-Cell RNA-Seq of Mouse Olfactory Bulb Reveals Cellular Heterogeneity and Activity-Dependent Molecular Census of Adult-Born Neurons

Tepe, Burak; Hill, Matthew C.; Pekarek, Brandon; Hunt, Patrick J.; Martin, Thomas J.; Martin, James F.; Arenkiel, Benjamin R.

doi:10.1016/j.celrep.2018.11.034

Cited by 128 publications

(171 citation statements)

References 85 publications

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“…Comparing the molecule counts versus cells table from Eng et al . with single‐cell RNA‐sequencing data from the same regions of mouse brain, the data are highly comparable. While the scRNA‐seq data measures about twice as many genes across the datasets, the total molecule count per cell and the number of genes observed in an individual cell are almost the same.…”

mentioning

confidence: 83%

A method for transcriptome‐wide gene expression quantification in intact tissues

Svensson

2019

Immunol Cell Biol

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mentioning

confidence: 83%

A method for transcriptome‐wide gene expression quantification in intact tissues

Svensson

2019

Immunol Cell Biol

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“…S16). Second, we obtained a list of 2,030 cell type specific marker genes identified in a recent single cell RNAseq study in the olfactory bulb 23 . Reassuringly, 55% of the unique SE genes identified by SPARK are in the marker list, while only 20% of the unique SE genes identified by SpatialDE are in the same list ( Fig.…”

Section: Olfactory Bulb Datamentioning

confidence: 99%

Statistical Analysis of Spatial Expression Pattern for Spatially Resolved Transcriptomic Studies

Sun

Zhu

Zhou

2019

Preprint

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Recent development of various spatially resolved transcriptomic techniques has enabled gene expression profiling on complex tissues with spatial localization information. Identifying genes that display spatial expression pattern in these studies is an important first step towards characterizing the spatial transcriptomic landscape. Detecting spatially expressed genes requires the development of statistical methods that can properly model spatial count data, provide effective type I error control, have sufficient statistical power, and are computationally efficient. Here, we developed such a method, SPARK. SPARK directly models count data generated from various spatial resolved transcriptomic techniques through generalized linear spatial models. With a new efficient penalized quasilikelihood based algorithm, SPARK is scalable to data sets with tens of thousands of genes measured on tens of thousands of samples. Importantly, SPARK relies on newly developed statistical formulas for hypothesis testing, producing well-calibrated p-values and yielding high statistical power. We illustrate the benefits of SPARK through extensive simulations and in-depth analysis of four published spatially resolved transcriptomic data sets. In the real data applications, SPARK is up to ten times more powerful than existing approaches. The high power of SPARK allows us to identify new genes and pathways that reveal new biology in the data that otherwise cannot be revealed by existing approaches.

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“…V-SVZ NSC-to-OB interneuron differentiation requires cells with a broad distribution of developmental states; however, the identities and the functions of key intermediate states and the gene expression programs pertinent to these transitions are unknown. While recent efforts have defined OB neuron subtypes (Eng et al, 2019; Tepe et al, 2018), classification of V-SVZ-derived OB neurons by fate-mapped scRNA-seq is still lacking.…”

Section: Introductionmentioning

confidence: 99%

“…Highly scalable scRNA-seq technologies (Klein et al, 2015; Macosko et al, 2015; Yuan and Sims, 2016; Zheng et al, 2016) have been applied to address cellular heterogeneity and temporal gene expression changes in various stem cell contexts including the V-SVZ field (Mizrak et al, 2019; Tepe et al, 2018; Zywitza et al, 2018); however these V-SVZ studies lacked genetically-induced fate-mapping reporters and could not link V-SVZ NSCs to their progeny in the olfactory bulb. Importantly, current high-throughput methods are unable to link live cell imaging and scRNA-seq data from the same cell, and cannot measure, for example, morphological features of individual cells.…”

Section: Introductionmentioning

confidence: 99%

Single-cell profiling and SCOPE-seq reveal the lineage dynamics of adult neurogenesis and NOTUM as a key V-SVZ regulator

Mizrak

Bayın

Yuan

et al. 2019

Preprint

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24Neural stem cells (NSCs) and their progeny reside in specialized niches in the adult mammalian 25 brain where they generate new neurons and glia throughout life. Adult NSCs of the ventricular-26 subventricular zone (V-SVZ) are prone to rapid exhaustion; thus timely, context-dependent 27 neurogenesis demands adaptive signaling among the vast number of neighboring progenitors 28 nestled between the ventricular surface and nearby blood vessels. To dissect adult neuronal 29 lineage progression and regulation, we profiled >56,000 V-SVZ and olfactory bulb (OB) cells by 30 single-cell RNA-sequencing (scRNA-seq). Our analyses revealed the diversity of V-SVZ-derived 31 OB neurons, the temporal dynamics of lineage progression, and a key intermediate NSC 32 population enriched for expression of Notum, which encodes a secreted WNT antagonist. Single 33 Cell Optical Phenotyping and Expression (SCOPE-seq), a technology linking live cell imaging with 34 2 scRNA-seq, uncovered dynamic control of cell size concomitant with NSC differentiation with35 Notum + NSCs at a critical size poised for cell division, and a preference of NOTUM surface binding 36 to neuronal precursors with active WNT signaling. Finally, in vivo pharmacological inhibition of 37 NOTUM significantly expanded neuronal precursor pools in the V-SVZ. Our findings highlight a 38 critical regulatory state during NSC activation marked by NOTUM, a secreted enzyme that 39 ensures efficient neurogenesis by preventing WNT signaling activation in NSC progeny. 40 41 INTRODUCTION 42Neurogenesis persists in two principal regions of the adult mouse brain: the subgranular zone of 43 the dentate gyrus and the V-SVZ located in the walls of the lateral ventricles (Doetsch et al., 1999; 44 Fiorelli et al., 2015; Mirzadeh et al., 2008). The V-SVZ contains both quiescent (qNSCs) and 45 actively dividing NSCs (aNSCs) (Codega et al., 2014; Mich et al., 2014) with intrinsic regional 46 identities defined during embryogenesis (Fuentealba et al., 2015), generating different OB 47 interneuron subtypes or glia depending on their location (Brill et al., 2009; Delgado and Lim, 2015; 48 Kohwi et al., 2005; Lopez-Juarez et al., 2013; Merkle et al., 2014; Merkle et al., 2007; Mizrak et 49 al., 2019; Ortega et al., 2013; Zweifel et al., 2018). V-SVZ qNSCs are specialized astrocytes with 50 radial morphology (reviewed in Kriegstein and Alvarez-Buylla, 2009; Mirzadeh et al., 2008), and 51 once activated divide symmetrically, resulting in their depletion over time (Obernier et al., 2018). 52 aNSCs give rise to transit-amplifying cells (TACs), which generate neuroblasts (NBs). NBs 53 migrate via the rostral migratory stream (RMS) to the OB (Figure 1A), where they terminally 54 differentiate into interneurons. V-SVZ NSC-to-OB interneuron differentiation requires cells with a 55 broad distribution of developmental states; however, the identities and the functions of key 56 intermediate states and the gene expression programs pertinent to these transitions are unknown. 57 While recent effort...

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Single-Cell RNA-Seq of Mouse Olfactory Bulb Reveals Cellular Heterogeneity and Activity-Dependent Molecular Census of Adult-Born Neurons

Cited by 128 publications

References 85 publications

A method for transcriptome‐wide gene expression quantification in intact tissues

A method for transcriptome‐wide gene expression quantification in intact tissues

Statistical Analysis of Spatial Expression Pattern for Spatially Resolved Transcriptomic Studies

Single-cell profiling and SCOPE-seq reveal the lineage dynamics of adult neurogenesis and NOTUM as a key V-SVZ regulator

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