Fate of developmental mechanisms of myocardial plasticity in the postnatal heart

SUMMARYWhether the heart’s organ-founding, progenitor cell gene regulatory networks (CPC-GRNs) are sustained after birth and can be therapeutically evoked for regeneration in response to disease, remains elusive. Here, we report a spatiotemporally resolved analysis of CPC-GRN deployment dynamics, through the pan-CPC-GRN gene Isl1. We show that the Isl1-CPC-GRNs that are deployed during early cardiogenesis and generate the cardiomyocyte majority from mesoderm, undergo programmed silencing through proteasome- and PRC2-mediated Isl1 repression, selectively in the arterial pole. In contrast, we identify a neural crest (CNC)-specific Wnt/β-catenin/Isl1-CPC-GRN that is deployed through the venous pole during cardiac growth and partitioning, and contributes a minority of cardiomyocytes which, in turn, expand massively to build ~10% of the biventricular myocardium. These “dorsal CNCs” continue to sporadically generate cardiomyocytes throughout postnatal growth which, however, are non-proliferative, suggesting that partitioning-like, fetal proliferation signals could be therapeutically targeted to evoke clonal expansion capacity in postnatal CNC-cardiomyocytes for heart regeneration.

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Discriminating the Single-cell Gene Regulatory Networks of Human Pancreatic Islets: A Novel Deep Learning Application

10.1101/2020.08.30.273839 ◽

2020 ◽

Author(s):

Turki Turki ◽

Y-h. Taguchi

Keyword(s):

Deep Learning ◽

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Metabolic Diseases ◽

Large Data ◽

Data Repositories ◽

Cell Gene Expression ◽

Gene Regulatory ◽

Cell Gene

AbstractAnalyzing single-cell pancreatic data would play an important role in understanding various metabolic diseases and health conditions. Due to the sparsity and noise present in such single-cell gene expression data, analyzing various functions related to the inference of gene regulatory networks, derived from single-cell data, remains difficult, thereby posing a barrier to the deepening of understanding of cellular metabolism. Since recent studies have led to the reliable inference of single-cell gene regulatory networks (SCGRNs), the challenge of discriminating between SCGRNs has now arisen. By accurately discriminating between SCGRNs (e.g., distinguishing SCGRNs of healthy pancreas from those of T2D pancreas), biologists would be able to annotate, organize, visualize, and identify common patterns of SCGRNs for metabolic diseases. Such annotated SCGRNs could play an important role in speeding up the process of building large data repositories. In this study, we aimed to contribute to the development of a novel deep learning (DL) application. First, we generated a dataset consisting of 224 SCGRNs belonging to both T2D and healthy pancreas and made it freely available. Next, we chose seven DL architectures, including VGG16, VGG19, Xception, ResNet50, ResNet101, DenseNet121, and DenseNet169, trained each of them on the dataset, and checked prediction based on a test set. We evaluated the DL architectures on an HP workstation platform with a single NVIDIA GeForce RTX 2080Ti GPU. Experimental results on the whole dataset, using several performance measures, demonstrated the superiority of VGG19 DL model in the automatic classification of SCGRNs, derived from the single-cell pancreatic data.

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Developmental maturation of the hematopoietic system controlled by a Lin28b-let-7-PRC1 axis

10.1101/2022.01.05.474696 ◽

2022 ◽

Author(s):

Dahai Wang ◽

Mayuri Tanaka-Yano ◽

Eleanor Meader ◽

Melissa Kinney ◽

Vivian Morris ◽

...

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Progenitor Cell ◽

B Lymphopoiesis ◽

Blood Disorders ◽

Hematopoietic Stem ◽

Repressor Complex ◽

Gene Regulatory ◽

Potential Impact ◽

Developmental Maturation

Hematopoiesis changes over life to meet the demands of maturation and aging. Here, we find that the definitive hematopoietic stem and progenitor cell (HSPC) compartment is remodeled from gestation into adulthood, a process regulated by the heterochronic Lin28b/let-7 axis. Native fetal and neonatal HSPCs distribute with a pro-lymphoid/erythroid bias with a shift toward myeloid output in adulthood. By mining transcriptomic data comparing juvenile and adult HSPCs and reconstructing coordinately activated gene regulatory networks, we uncover the Polycomb repressor complex 1 (PRC1) component Cbx2 as an effector of Lin28b/let-7 control of hematopoietic maturation. We find that juvenile Cbx2-/- hematopoietic tissues show impairment of B-lymphopoiesis and a precocious adult-like myeloid bias and that Cbx2/PRC1 regulates developmental timing of expression of key hematopoietic transcription factors. These findings define a novel mechanism of epigenetic regulation of HSPC output as a function of age with potential impact on age-biased pediatric and adult blood disorders.

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Multiview Graph Learning for single-cell RNA sequencing data

10.1101/2021.11.05.467476 ◽

2021 ◽

Author(s):

Abdullah Karaaslanli ◽

SATABDI SAHA ◽

Selin Aviyente ◽

Tapabrata Maiti

Keyword(s):

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Embryonic Stem ◽

Stem Cell Differentiation ◽

Joint Estimation ◽

Embryonic Stem Cell Differentiation ◽

Graph Learning ◽

Gene Regulatory ◽

Cell Gene

Characterizing the underlying topology of gene regulatory networks is one of the fundamental problems of systems biology. Ongoing developments in high throughput sequencing technologies has made it possible to capture the expression of thousands of genes at the single cell resolution. However, inherent cellular heterogeneity and high sparsity of the single cell datasets render void the application of regular Gaussian assumptions for constructing gene regulatory networks. Additionally, most algorithms aimed at single cell gene regulatory network reconstruction, estimate a single network ignoring group-level (cell-type) information present within the datasets. To better characterize single cell gene regulatory networks under different but related conditions we propose the joint estimation of multiple networks using multiview graph learning (mvGL). The proposed method is developed based on recent works in graph signal processing (GSP) for graph learning, where graph signals are assumed to be smooth over the unknown graph structure. Graphs corresponding to the different datasets are regularized to be similar to each other through a learned consensus graph. We further kernelize mvGL with the kernel selected to suit the structure of single cell data. An efficient algorithm based on prox-linear block coordinate descent is used to optimize mvGL. We study the performance of mvGL using synthetic data generated with a diverse set of parameters. We further show that mvGL successfully identifies well-established regulators in a mouse embryonic stem cell differentiation study and a cancer clinical study of medulloblastoma.

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SCGRNs: Novel supervised inference of single-cell gene regulatory networks of complex diseases

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2020.103656 ◽

2020 ◽

Vol 118 ◽

pp. 103656 ◽

Cited By ~ 1

Author(s):

Turki Turki ◽

Y-h. Taguchi

Keyword(s):

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Complex Diseases ◽

Gene Regulatory ◽

Cell Gene

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Probing T Cell Gene Regulatory Networks for Early Signs of Disease

Multiple Sclerosis Discovery Forum ◽

10.7493/msdf.10.24296.1 ◽

2015 ◽

Author(s):

Pat McCaffrey

Keyword(s):

T Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Early Signs ◽

Gene Regulatory ◽

Cell Gene

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Intrinsically Bayesian robust classifier for single-cell gene expression trajectories in gene regulatory networks

BMC Systems Biology ◽

10.1186/s12918-018-0549-y ◽

2018 ◽

Vol 12 (S3) ◽

Cited By ~ 6

Author(s):

Alireza Karbalayghareh ◽

Ulisses Braga-Neto ◽

Edward R. Dougherty

Keyword(s):

Gene Expression ◽

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Cell Gene Expression ◽

Gene Regulatory ◽

Cell Gene

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Discriminating the Single-cell Gene Regulatory Networks of Human Pancreatic Islets: A Novel Deep Learning Application

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2021.104257 ◽

2021 ◽

pp. 104257

Author(s):

Turki Turki ◽

Y-h. Taguchi

Keyword(s):

Deep Learning ◽

Single Cell ◽

Pancreatic Islets ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Human Pancreatic Islets ◽

Gene Regulatory ◽

Cell Gene

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Scanpy for analysis of large-scale single-cell gene expression data

10.1101/174029 ◽

2017 ◽

Cited By ~ 9

Author(s):

F. Alexander Wolf ◽

Philipp Angerer ◽

Fabian J. Theis

Keyword(s):

Gene Expression ◽

Single Cell ◽

Gene Expression Data ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Large Scale ◽

Expression Data ◽

Cell Gene Expression ◽

Gene Regulatory ◽

Cell Gene

We present Scanpy, a scalable toolkit for analyzing single-cell gene expression data. It includes preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing and simulation of gene regulatory networks. The Python-based implementation efficiently deals with datasets of more than one million cells and enables easy interfacing of advanced machine learning packages. Code is available fromhttps://github.com/theislab/scanpy.

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