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

2020 ◽  
Vol 466 (1-2) ◽  
pp. 1-11
Author(s):  
Malini Mukherjee ◽  
Jennifer DeRiso ◽  
Madhusudhana Janga ◽  
Eric Fogarty ◽  
Kameswaran Surendran
Keyword(s):  
Gene Expression ◽  
Cell Fate ◽  
Principal Cell ◽  
Cell Gene Expression ◽  
Cell Gene

scholarly journals Resolution of Cell Fate Decisions Revealed by Single-Cell Gene Expression Analysis from Zygote to Blastocyst

2010 ◽  
Vol 18 (4) ◽  
pp. 675-685 ◽  
Author(s):  
Guoji Guo ◽  
Mikael Huss ◽  
Guo Qing Tong ◽  
Chaoyang Wang ◽  
Li Li Sun ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Fate ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Cell Fate Decisions ◽  
Cell Gene Expression ◽  
Cell Gene

scholarly journals Transcription factor TFCP2L1 patterns cells in the mouse kidney collecting ducts

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Max Werth ◽  
Kai M Schmidt-Ott ◽  
Thomas Leete ◽  
Andong Qiu ◽  
Christian Hinze ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Notch Signaling ◽  
Salt Water ◽  
Collecting Duct ◽  
Cell Types ◽  
Nephron Segments ◽  
Collecting Ducts ◽  
Atpase Subunits ◽  
Central Characteristic ◽  
Salt And Pepper

Although most nephron segments contain one type of epithelial cell, the collecting ducts consists of at least two: intercalated (IC) and principal (PC) cells, which regulate acid-base and salt-water homeostasis, respectively. In adult kidneys, these cells are organized in rosettes suggesting functional interactions. Genetic studies in mouse revealed that transcription factor Tfcp2l1 coordinates IC and PC development. Tfcp2l1 induces the expression of IC specific genes, including specific H+-ATPase subunits and Jag1. Jag1 in turn, initiates Notch signaling in PCs but inhibits Notch signaling in ICs. Tfcp2l1 inactivation deletes ICs, whereas Jag1 inactivation results in the forfeiture of discrete IC and PC identities. Thus, Tfcp2l1 is a critical regulator of IC-PC patterning, acting cell-autonomously in ICs, and non-cell-autonomously in PCs. As a result, Tfcp2l1 regulates the diversification of cell types which is the central characteristic of 'salt and pepper' epithelia and distinguishes the collecting duct from all other nephron segments.

Serrate and Notch specify cell fates in the heart field by suppressing cardiomyogenesis

Development ◽  
2000 ◽  
Vol 127 (17) ◽  
pp. 3865-3876
Author(s):  
M.S. Rones ◽  
K.A. McLaughlin ◽  
M. Raffin ◽  
M. Mercola
Keyword(s):  
Gene Expression ◽  
Notch Signaling ◽  
Cell Fate ◽  
Notch Pathway ◽  
Cell Types ◽  
Myocardial Cell ◽  
Dominant Negative ◽  
Cell Fates ◽  
Cell Fate Decisions ◽  
Heart Field

Notch signaling mediates numerous developmental cell fate decisions in organisms ranging from flies to humans, resulting in the generation of multiple cell types from equipotential precursors. In this paper, we present evidence that activation of Notch by its ligand Serrate apportions myogenic and non-myogenic cell fates within the early Xenopus heart field. The crescent-shaped field of heart mesoderm is specified initially as cardiomyogenic. While the ventral region of the field forms the myocardial tube, the dorsolateral portions lose myogenic potency and form the dorsal mesocardium and pericardial roof (Raffin, M., Leong, L. M., Rones, M. S., Sparrow, D., Mohun, T. and Mercola, M. (2000) Dev. Biol., 218, 326–340). The local interactions that establish or maintain the distinct myocardial and non-myocardial domains have never been described. Here we show that Xenopus Notch1 (Xotch) and Serrate1 are expressed in overlapping patterns in the early heart field. Conditional activation or inhibition of the Notch pathway with inducible dominant negative or active forms of the RBP-J/Suppressor of Hairless [Su(H)] transcription factor indicated that activation of Notch feeds back on Serrate1 gene expression to localize transcripts more dorsolaterally than those of Notch1, with overlap in the region of the developing mesocardium. Moreover, Notch pathway activation decreased myocardial gene expression and increased expression of a marker of the mesocardium and pericardial roof, whereas inhibition of Notch signaling had the opposite effect. Activation or inhibition of Notch also regulated contribution of individual cells to the myocardium. Importantly, expression of Nkx2. 5 and Gata4 remained largely unaffected, indicating that Notch signaling functions downstream of heart field specification. We conclude that Notch signaling through Su(H) suppresses cardiomyogenesis and that this activity is essential for the correct specification of myocardial and non-myocardial cell fates.

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 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/.

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