scholarly journals Modular output circuits of the fastigial nucleus mediate diverse motor and nonmotor functions of the cerebellar vermis

2020 ◽  
Author(s):  
Hirofumi Fujita ◽  
Takashi Kodama ◽  
Sascha du Lac
Keyword(s):  
Gene Expression ◽  
Inferior Olive ◽  
Cell Types ◽  
Cerebellar Vermis ◽  
Projection Neurons ◽  
Specific Cell ◽  
Synaptic Connections ◽  
Cell Gene Expression ◽  
Output Neurons ◽  
Cell Gene

AbstractThe cerebellar vermis, long associated with axial motor control, has been implicated in a surprising range of neuropsychiatric disorders and cognitive and affective functions. Remarkably little is known, however, about the specific cell types and neural circuits responsible for these diverse functions. Here, using single-cell gene expression profiling and anatomical circuit analyses of vermis output neurons in the mouse fastigial (medial cerebellar) nucleus, we identify five major classes of glutamatergic projection neurons distinguished by gene expression, morphology, distribution, and input-output connectivity. Each fastigial cell type is connected with a specific set of Purkinje cells and inferior olive neurons and in turn innervates a distinct collection of downstream targets. Transsynaptic tracing indicates extensive disynaptic links with cognitive, affective, and motor forebrain circuits. These results indicate that diverse cerebellar vermis functions are mediated by modular synaptic connections of distinct fastigial cell types which differentially coordinate posturomotor, oromotor, positional-autonomic, orienting, and vigilance circuits.

scholarly journals Modular output circuits of the fastigial nucleus for diverse motor and nonmotor functions of the cerebellar vermis

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Hirofumi Fujita ◽  
Takashi Kodama ◽  
Sascha du Lac
Keyword(s):  
Gene Expression ◽  
Inferior Olive ◽  
Cell Types ◽  
Cerebellar Vermis ◽  
Projection Neurons ◽  
Specific Cell ◽  
Synaptic Connections ◽  
Cell Gene Expression ◽  
Output Neurons ◽  
Cell Gene

The cerebellar vermis, long associated with axial motor control, has been implicated in a surprising range of neuropsychiatric disorders and cognitive and affective functions. Remarkably little is known, however, about the specific cell types and neural circuits responsible for these diverse functions. Here, using single-cell gene expression profiling and anatomical circuit analyses of vermis output neurons in the mouse fastigial (medial cerebellar) nucleus, we identify five major classes of glutamatergic projection neurons distinguished by gene expression, morphology, distribution, and input-output connectivity. Each fastigial cell type is connected with a specific set of Purkinje cells and inferior olive neurons and in turn innervates a distinct collection of downstream targets. Transsynaptic tracing indicates extensive disynaptic links with cognitive, affective, and motor forebrain circuits. These results indicate that diverse cerebellar vermis functions could be mediated by modular synaptic connections of distinct fastigial cell types with posturomotor, oromotor, positional-autonomic, orienting, and vigilance circuits.

scholarly journals Inferring Relevant Cell Types for Complex Traits by Using Single-Cell Gene Expression

2017 ◽  
Vol 101 (5) ◽  
pp. 686-699 ◽  
Author(s):  
Diego Calderon ◽  
Anand Bhaskar ◽  
David A. Knowles ◽  
David Golan ◽  
Towfique Raj ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Complex Traits ◽  
Cell Types ◽  
Cell Gene Expression ◽  
Cell Gene

scholarly journals Foxi1 inactivation rescues loss of principal cell fate selection in Hes1-deficient kidneys but does not ensure maintenance of principal cell gene expression

2019 ◽  
Author(s):  
Malini Mukherjee ◽  
Jennifer DeRiso ◽  
Madhusudhana Janga ◽  
Eric Fogarty ◽  
Kameswaran Surendran
Keyword(s):  
Gene Expression ◽  
Notch Signaling ◽  
Cell Fate ◽  
Collecting Duct ◽  
Cell Types ◽  
Principal Cell ◽  
Intercalated Cell ◽  
Collecting Ducts ◽  
Cell Gene Expression ◽  
Cell Gene

AbstractThe distal nephron and collecting duct segments of the mammalian kidney consist of intercalated cell types intermingled among principal cell types. Notch signaling ensures that a sufficient number of cells select a principal instead of an intercalated cell fate. However, the precise mechanisms by which Notch signaling patterns the distal nephron and collecting duct cell fates is unknown. Here we observed that Hes1, a direct target of Notch signaling pathway, is required within the mouse developing collecting ducts for repression of Foxi1 expression, an essential intercalated cell specific transcription factor. Interestingly, inactivation of Foxi1 in Hes1-deficient collecting ducts rescues the deficiency in principal cell fate selection, overall urine concentrating deficiency, and reduces the occurrence of hydronephrosis. However, Foxi1 inactivation does not rescue the reduction in expression of all principal cell genes in the Hes1-deficient kidney collecting duct cells that select the principal cell fate. Additionally, suppression of Notch/Hes1 signaling in mature principal cells reduces principal cell gene expression without activating Foxi1. We conclude that Hes1 is a Notch signaling target that is essential for normal patterning of the collecting ducts with intermingled cell types by repressing Foxi1, and for maintenance of principal cell gene expression independent of repressing Foxi1.

scholarly journals In situelectro-sequencing in three-dimensional tissues

2021 ◽  
Author(s):  
Qiang Li ◽  
Zuwan Lin ◽  
Ren Liu ◽  
Xin Tang ◽  
Jiahao Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Three Dimensional ◽  
Cell Types ◽  
Developmental Trajectory ◽  
Single Cell Level ◽  
Cell Level ◽  
Cell Gene Expression ◽  
Cell Gene

AbstractPairwise mapping of single-cell gene expression and electrophysiology in intact three-dimensional (3D) tissues is crucial for studying electrogenic organs (e.g., brain and heart)1–5. Here, we introducein situelectro-sequencing (electro-seq), combining soft bioelectronics within situRNA sequencing to stably map millisecond-timescale cellular electrophysiology and simultaneously profile a large number of genes at single-cell level across 3D tissues. We appliedin situelectro-seq to 3D human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) patches, precisely registering the CM gene expression with electrophysiology at single-cell level, enabling multimodalin situanalysis. Such multimodal data integration substantially improved the dissection of cell types and the reconstruction of developmental trajectory from spatially heterogeneous tissues. Using machine learning (ML)-based cross-modal analysis,in situelectro-seq identified the gene-to-electrophysiology relationship over the time course of cardiac maturation. Further leveraging such a relationship to train a coupled autoencoder, we demonstrated the prediction of single-cell gene expression profile evolution using long-term electrical measurement from the same cardiac patch or 3D millimeter-scale cardiac organoids. As exemplified by cardiac tissue maturation,in situelectro-seq will be broadly applicable to create spatiotemporal multimodal maps and predictive models in electrogenic organs, allowing discovery of cell types and gene programs responsible for electrophysiological function and dysfunction.

Microfluidic single-cell transcriptomics: moving towards multimodal and spatiotemporal omics

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Shichao Lin ◽  
Yilong Liu ◽  
Mingxia Zhang ◽  
Xing Xu ◽  
Yingwen Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Cell Gene Expression ◽  
Cell Gene

Cells are the basic units of life with vast heterogeneity. Single-cell transcriptomics unveils cell-to-cell gene expression variabilities, discovers novel cell types, and uncovers the critical roles of cellular heterogeneity in...

scGMAI: a Gaussian mixture model for clustering single-cell RNA-Seq data based on deep autoencoder

2020 ◽  
Author(s):  
Bin Yu ◽  
Chen Chen ◽  
Ren Qi ◽  
Ruiqing Zheng ◽  
Patrick J Skillman-Lawrence ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rapid Development ◽  
Cell Types ◽  
Gaussian Mixture ◽  
Computational Techniques ◽  
Rna Seq ◽  
Fast Independent Component Analysis ◽  
Cell Gene Expression ◽  
Cell Gene

Abstract The rapid development of single-cell RNA sequencing (scRNA-Seq) technology provides strong technical support for accurate and efficient analyzing single-cell gene expression data. However, the analysis of scRNA-Seq is accompanied by many obstacles, including dropout events and the curse of dimensionality. Here, we propose the scGMAI, which is a new single-cell Gaussian mixture clustering method based on autoencoder networks and the fast independent component analysis (FastICA). Specifically, scGMAI utilizes autoencoder networks to reconstruct gene expression values from scRNA-Seq data and FastICA is used to reduce the dimensions of reconstructed data. The integration of these computational techniques in scGMAI leads to outperforming results compared to existing tools, including Seurat, in clustering cells from 17 public scRNA-Seq datasets. In summary, scGMAI is an effective tool for accurately clustering and identifying cell types from scRNA-Seq data and shows the great potential of its applicative power in scRNA-Seq data analysis. The source code is available at https://github.com/QUST-AIBBDRC/scGMAI/.

scholarly journals Prediction of single-cell gene expression for transcription factor analysis

GigaScience ◽  
2020 ◽  
Vol 9 (11) ◽  
Author(s):  
Fatemeh Behjati Ardakani ◽  
Kathrin Kattler ◽  
Tobias Heinen ◽  
Florian Schmidt ◽  
David Feuerborn ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Single Cell ◽  
Cell Types ◽  
Single Cell Level ◽  
Cell Level ◽  
Novel Approach ◽  
Cell Gene Expression ◽  
Specific Regulation ◽  
Cell Gene

Abstract Background Single-cell RNA sequencing is a powerful technology to discover new cell types and study biological processes in complex biological samples. A current challenge is to predict transcription factor (TF) regulation from single-cell RNA data. Results Here, we propose a novel approach for predicting gene expression at the single-cell level using cis-regulatory motifs, as well as epigenetic features. We designed a tree-guided multi-task learning framework that considers each cell as a task. Through this framework we were able to explain the single-cell gene expression values using either TF binding affinities or TF ChIP-seq data measured at specific genomic regions. TFs identified using these models could be validated by the literature. Conclusion Our proposed method allows us to identify distinct TFs that show cell type–specific regulation. This approach is not limited to TFs but can use any type of data that can potentially be used in explaining gene expression at the single-cell level to study factors that drive differentiation or show abnormal regulation in disease. The implementation of our workflow can be accessed under an MIT license via https://github.com/SchulzLab/Triangulate.

scholarly journals Single-cell analysis delineates a trajectory toward the human early otic lineage

2016 ◽  
Vol 113 (30) ◽  
pp. 8508-8513 ◽  
Author(s):  
Megan Ealy ◽  
Daniel C. Ellwanger ◽  
Nina Kosaric ◽  
Andres P. Stapper ◽  
Stefan Heller
Keyword(s):  
Gene Expression ◽  
Stem Cell ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Cell Types ◽  
Marker Genes ◽  
Cell Analysis ◽  
Cell Gene Expression ◽  
Neural Ectoderm ◽  
Cell Gene

Efficient pluripotent stem cell guidance protocols for the production of human posterior cranial placodes such as the otic placode that gives rise to the inner ear do not exist. Here we use a systematic approach including defined monolayer culture, signaling modulation, and single-cell gene expression analysis to delineate a developmental trajectory for human otic lineage specification in vitro. We found that modulation of bone morphogenetic protein (BMP) and WNT signaling combined with FGF and retinoic acid treatments over the course of 18 days generates cell populations that develop chronological expression of marker genes of non-neural ectoderm, preplacodal ectoderm, and early otic lineage. Gene expression along this differentiation path is distinct from other lineages such as endoderm, mesendoderm, and neural ectoderm. Single-cell analysis exposed the heterogeneity of differentiating cells and allowed discrimination of non-neural ectoderm and otic lineage cells from off-target populations. Pseudotemporal ordering of human embryonic stem cell and induced pluripotent stem cell-derived single-cell gene expression profiles revealed an initially synchronous guidance toward non-neural ectoderm, followed by comparatively asynchronous occurrences of preplacodal and otic marker genes. Positive correlation of marker gene expression between both cell lines and resemblance to mouse embryonic day 10.5 otocyst cells implied reasonable robustness of the guidance protocol. Single-cell trajectory analysis further revealed that otic progenitor cell types are induced in monolayer cultures, but further development appears impeded, likely because of lack of a lineage-stabilizing microenvironment. Our results provide a framework for future exploration of stabilizing microenvironments for efficient differentiation of stem cell-generated human otic cell types.

scholarly journals Feature extraction approach in single-cell gene expression profiling for cell-type marker identification

2019 ◽  
Author(s):  
Nigatu A. Adossa ◽  
Leif Schauser ◽  
Vivi G. Gregersen ◽  
Laura L. Elo
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Type I ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Gene Expression ◽  
Cell Type Specific ◽  
Cell Gene ◽  
Marker Identification

AbstractBackgroundRecent advances in single-cell gene expression profiling technology have revolutionized the understanding of molecular processes underlying developmental cell and tissue differentiation, enabling the discovery of novel cell-types and molecular markers that characterize developmental trajectories. Common approaches for identifying marker genes are based on pairwise statistical testing for differential gene expression between cell-types in heterogeneous cell populations, which is challenging due to unequal sample sizes and variance between groups resulting in little statistical power and inflated type I errors.ResultsWe developed an alternative feature extraction method, Marker gene Identification for Cell-type Identity (MICTI) that encodes the cell-type specific expression information to each gene in every single-cell. This approach identifies features (genes) that are cell-type specific for a given cell-type in heterogeneous cell population. To validate this approach, we used (i) simulated single cell RNA-seq data, (ii) human pancreatic islet single-cell RNA-seq data and (iii) a simulated mixture of human single-cell RNA-seq data related to immune cells, particularly B cells, CD4+ memory cells, CD8+ memory cells, dendritic cells, fibroblast cells, and lymphoblast cells. For all cases, we were able to identify established cell-type-specific markers.ConclusionsOur approach represents a highly efficient and fast method as an alternative to differential expression analysis for molecular marker identification in heterogeneous single-cell RNA-seq data.

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