Lamin A/C functions independently from mechanical signaling during adipogenesis

AbstractMesenchymal stem cells (MSC) maintain the musculoskeletal system by differentiating into multiple cell types including osteocytes and adipocytes. Mechanical signals, including strain and low intensity vibration (LIV), are important regulators of MSC differentiation. Lamin A/C is a vital protein for nuclear architecture that supports chromatin organization, as well as mechanical integrity and mechano-sensitivity of the nucleus in MSCs. Here, we investigated whether Lamin A/C and mechano-responsiveness are functionally coupled during adipogenesis. Lamin depletion in MSCs using siRNA increased nuclear area, height and volume and decreased circularity and stiffness, while phosphorylation of focal adhesions and dynamic substrate strain in response to LIV remained intact. Lamin A/C depletion decelerates adipogenesis as reflected by delayed appearance of key biomarkers (e.g., adiponectin/ADIPOQ). Based on RNA-seq data, reduced Lamin A/C levels decrease the activation of the adipocyte transcriptome that is normally observed in response to adipogenic cues mediating differentiation of MSCs. Mechanical stimulation via daily LIV application reduced the expression levels of ADIPOQ in both control and Lamin A/C depleted cells. Yet, treatment with LIV did not induce major transcriptome changes in either control or Lamin A/C depleted MSCs, suggesting that the biological effects of LIV on adipogenesis may not occur at the transcriptional level. We conclude that while Lamin A/C activation is essential for normal adipogenesis, it is dispensible for activation of focal adhesions by dynamic vibration induced mechanical signals.

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Lamin A/C Is Dispensable to Mechanical Repression of Adipogenesis

International Journal of Molecular Sciences ◽

10.3390/ijms22126580 ◽

2021 ◽

Vol 22 (12) ◽

pp. 6580

Author(s):

Matthew Goelzer ◽

Amel Dudakovic ◽

Melis Olcum ◽

Buer Sen ◽

Engin Ozcivici ◽

...

Keyword(s):

Cell Structure ◽

Nuclear Architecture ◽

Focal Adhesions ◽

Protein Translation ◽

Lamin A ◽

Transcriptional Level ◽

Rna Seq ◽

Interferon Signaling ◽

Regulate Protein ◽

Low Intensity Vibration

Mesenchymal stem cells (MSCs) maintain the musculoskeletal system by differentiating into multiple lineages, including osteoblasts and adipocytes. Mechanical signals, including strain and low-intensity vibration (LIV), are important regulators of MSC differentiation via control exerted through the cell structure. Lamin A/C is a protein vital to the nuclear architecture that supports chromatin organization and differentiation and contributes to the mechanical integrity of the nucleus. We investigated whether lamin A/C and mechanoresponsiveness are functionally coupled during adipogenesis in MSCs. siRNA depletion of lamin A/C increased the nuclear area, height, and volume and decreased the circularity and stiffness. Lamin A/C depletion significantly decreased markers of adipogenesis (adiponectin, cellular lipid content) as did LIV treatment despite depletion of lamin A/C. Phosphorylation of focal adhesions in response to mechanical challenge was also preserved during loss of lamin A/C. RNA-seq showed no major adipogenic transcriptome changes resulting from LIV treatment, suggesting that LIV regulation of adipogenesis may not occur at the transcriptional level. We observed that during both lamin A/C depletion and LIV, interferon signaling was downregulated, suggesting potentially shared regulatory mechanism elements that could regulate protein translation. We conclude that the mechanoregulation of adipogenesis and the mechanical activation of focal adhesions function independently from those of lamin A/C.

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Gene families with stochastic exclusive gene choice underlie cell adhesion in mammalian cells

10.1101/2020.08.24.264747 ◽

2020 ◽

Author(s):

Mikhail Iakovlev ◽

Simone Faravelli ◽

Attila Becskei

Keyword(s):

Cell Adhesion ◽

Mammalian Cells ◽

Gene Families ◽

Cell Types ◽

Cell Receptor ◽

Odorant Receptors ◽

Rna Seq ◽

Receptor Isoforms ◽

Multiple Cell ◽

Large Repertoire

ABSTRACTExclusive stochastic gene choice combines precision with diversity. This regulation enables most T-cells to express exactly one T-cell receptor isoform chosen from a large repertoire, and to react precisely against diverse antigens. Some cells express two receptor isoforms, revealing the stochastic nature of this process. A similar regulation of odorant receptors and protocadherins enable cells to recognize odors and confer individuality to cells in neuronal interaction networks, respectively. We explored whether genes in other families are expressed exclusively by analyzing single cell RNA-seq data with a simple metric. Chromosomal segments and families are more likely to express genes concurrently than exclusively, possibly due to the evolutionary and biophysical aspects of shared regulation. Nonetheless, gene families with exclusive gene choice were detected in multiple cell types, most of them are membrane proteins involved in ion transport and cell adhesion, suggesting the coordination of these two functions. Thus, stochastic exclusive expression extends beyond the prototypical families, permitting precision in gene choice to be combined with the diversity of intercellular interactions.

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Splatter: simulation of single-cell RNA sequencing data

10.1101/133173 ◽

2017 ◽

Cited By ~ 8

Author(s):

Luke Zappia ◽

Belinda Phipson ◽

Alicia Oshlack

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Real Data ◽

Cell Types ◽

Rna Seq ◽

Sequencing Data ◽

Sequencing Technologies ◽

Simulation Based ◽

Single Cell Rna Sequencing ◽

Multiple Cell

AbstractAs single-cell RNA sequencing technologies have rapidly developed, so have analysis methods. Many methods have been tested, developed and validated using simulated datasets. Unfortunately, current simulations are often poorly documented, their similarity to real data is not demonstrated, or reproducible code is not available.Here we present the Splatter Bioconductor package for simple, reproducible and well-documented simulation of single-cell RNA-seq data. Splatter provides an interface to multiple simulation methods including Splat, our own simulation, based on a gamma-Poisson distribution. Splat can simulate single populations of cells, populations with multiple cell types or differentiation paths.

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DeeReCT-TSS: A novel meta-learning-based method annotates TSS in multiple cell types based on DNA sequences and RNA-seq data

10.1101/2021.07.14.452328 ◽

2021 ◽

Author(s):

Juexiao Zhou ◽

Bin Zhang ◽

Haoyang Li ◽

Longxi Zhou ◽

Zhongxiao Li ◽

...

Keyword(s):

Dna Sequences ◽

Cell Types ◽

Rna Seq ◽

Sources Of Information ◽

Genome Wide ◽

A Genome ◽

Meta Learning ◽

Multiple Cell ◽

Different Cell Types ◽

Genome Scale

The accurate annotation of TSSs and their usage is critical for the mechanistic understanding of gene regulation under different biological contexts. To fulfill this, specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner. Various computational tools have also been developed for in silico prediction of TSSs solely based on genomic sequences. Most of these tools have drastic false positive predictions when applied on the genome-scale. Here, we present DeeReCT-TSS, a deep-learning-based method that is capable of TSSs identification across the whole genome based on DNA sequences and conventional RNA-seq data. We show that by effectively incorporating these two sources of information, DeeReCT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell types. Furthermore, we develop a meta-learning-based extension for simultaneous transcription start site (TSS) annotation on 10 cell types, which enables the identification of cell-type-specific TSS. Finally, we demonstrate the high precision of DeeReCT-TSS on two independent datasets from the ENCODE project by correlating our predicted TSSs with experimentally defined TSS chromatin states.

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DIANA-miRGen v4: indexing promoters and regulators for more than 1500 microRNAs

Nucleic Acids Research ◽

10.1093/nar/gkaa1060 ◽

2020 ◽

Vol 49 (D1) ◽

pp. D151-D159

Author(s):

Nikos Perdikopanis ◽

Georgios K Georgakilas ◽

Dimitris Grigoriadis ◽

Vasilis Pierros ◽

Ioannis Kavakiotis ◽

...

Keyword(s):

Critical Role ◽

Cell Types ◽

Mirna Biogenesis ◽

Transcriptional Level ◽

Pathological State ◽

Transcription Start Sites ◽

Multiple Cell ◽

Underlying Mechanisms ◽

Cap Analysis ◽

User Friendly

Abstract Deregulation of microRNA (miRNA) expression plays a critical role in the transition from a physiological to a pathological state. The accurate miRNA promoter identification in multiple cell types is a fundamental endeavor towards understanding and characterizing the underlying mechanisms of both physiological as well as pathological conditions. DIANA-miRGen v4 (www.microrna.gr/mirgenv4) provides cell type specific miRNA transcription start sites (TSSs) for over 1500 miRNAs retrieved from the analysis of >1000 cap analysis of gene expression (CAGE) samples corresponding to 133 tissues, cell lines and primary cells available in FANTOM repository. MiRNA TSS locations were associated with transcription factor binding site (TFBSs) annotation, for >280 TFs, derived from analyzing the majority of ENCODE ChIP-Seq datasets. For the first time, clusters of cell types having common miRNA TSSs are characterized and provided through a user friendly interface with multiple layers of customization. DIANA-miRGen v4 significantly improves our understanding of miRNA biogenesis regulation at the transcriptional level by providing a unique integration of high-quality annotations for hundreds of cell specific miRNA promoters with experimentally derived TFBSs.

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Dynamic reorganization of nuclear architecture during human cardiogenesis

10.1101/222877 ◽

2017 ◽

Cited By ~ 5

Author(s):

Paul A. Fields ◽

Vijay Ramani ◽

Giancarlo Bonora ◽

Gurkan Yardimci ◽

Alessandro Bertero ◽

...

Keyword(s):

Pluripotent Stem Cells ◽

Three Dimensional ◽

Nuclear Architecture ◽

Cell Types ◽

Cardiac Differentiation ◽

Dimensional Structure ◽

Rna Seq ◽

Dynamic Nature ◽

Chromosomal Architecture

AbstractWhile chromosomal architecture varies among cell types, little is known about how this organization is established or its role in development. We integrated Hi-C, RNA-seq and ATAC-seq during cardiac differentiation from human pluripotent stem cells to generate a comprehensive profile of chromosomal architecture. We identified active and repressive domains that are dynamic during cardiogenesis and recapitulate in vivo cardiomyocytes. During differentiation, heterochromatic regions condense in cis. In contrast, many cardiac-specific genes, such as TTN (titin), decompact and transition to an active compartment coincident with upregulation. Moreover, we identify a network of genes, including TTN, that share the heart-specific splicing factor, RBM20, and become associated in trans during differentiation, suggesting the existence of a 3D nuclear splicing factory. Our results demonstrate both the dynamic nature in nuclear architecture and provide insights into how developmental genes are coordinately regulated.One Sentence SummaryThe three-dimensional structure of the human genome is dynamically regulated both globally and locally during cardiogenesis.

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A probabilistic model-based bi-clustering method for single-cell transcriptomic data analysis

10.1101/181362 ◽

2017 ◽

Cited By ~ 2

Author(s):

Sha Cao ◽

Tao Sheng ◽

Xin Chen ◽

Qin Ma ◽

Chi Zhang

Keyword(s):

Single Cell ◽

Expression Patterns ◽

Cell Types ◽

Computational Techniques ◽

Rna Seq ◽

Transcriptomic Data ◽

Large Numbers ◽

A Cell ◽

Multiple Cell ◽

Cell Data

AbstractWe present here novel computational techniques for tackling four problems related to analyses of single-cell RNA-Seq data: (1) a mixture model for coping with multiple cell types in a cell population; (2) a truncated model for handling the unquantifiable errors caused by large numbers of zeros or low-expression values; (3) a bi-clustering technique for detection of sub-populations of cells sharing common expression patterns among subsets of genes; and (4) detection of small cell sub-populations with distinct expression patterns. Through case studies, we demonstrated that these techniques can derive high-resolution information from single-cell data that are not feasible using existing techniques.

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Three-dimensional spatial transcriptomics uncovers cell type dynamics in the rheumatoid arthritis synovium

10.1101/2020.12.10.420463 ◽

2020 ◽

Author(s):

Sanja Vickovic ◽

Denis Schapiro ◽

Konstantin Carlberg ◽

Britta Lötstedt ◽

Ludvig Larsson ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Three Dimensional ◽

Cell Types ◽

Molecular Profiling ◽

Rheumatoid Arthritis Synovium ◽

Cellular Interactions ◽

Rna Seq ◽

Cell Type ◽

Multiple Cell ◽

Cell Type Specific

AbstractThe inflamed rheumatic joint is a highly heterogeneous and complex tissue with dynamic recruitment and expansion of multiple cell types that interact in multifaceted ways within a localized area. Rheumatoid arthritis synovium has primarily been studied either by immunostaining or by molecular profiling after tissue homogenization. Here, we use Spatial Transcriptomics to study local cellular interactions at the site of chronic synovial inflammation. We report comprehensive spatial RNA-seq data coupled to quantitative and cell type-specific chemokine-driven dynamics at and around organized structures of infiltrating leukocyte cells in the synovium.

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Comparative analysis of commercially available single-cell RNA sequencing platforms for their performance in complex human tissues

10.1101/541433 ◽

2019 ◽

Cited By ~ 8

Author(s):

Yue J. Wang ◽

Jonathan Schug ◽

Jerome Lin ◽

Zhiping Wang ◽

Andrew Kossenkov ◽

...

Keyword(s):

Single Cell ◽

Single Cells ◽

Cell Types ◽

Human Islets ◽

Rna Seq ◽

Complex Biological System ◽

Multiple Cell ◽

Performance Statistics ◽

Sequencing Platforms ◽

Cell Fractions

ABSTRACTThe past five years have witnessed a tremendous growth of single-cell RNA-seq methodologies. Currently, there are three major commercial platforms for single-cell RNA-seq: Fluidigm C1, Clontech iCell8 (formerly Wafergen) and 10x Genomics Chromium. Here, we provide a systematic comparison of the throughput, sensitivity, cost and other performance statistics for these three platforms using single cells from primary human islets. The primary human islets represent a complex biological system where multiple cell types coexist, with varying cellular abundance, diverse transcriptomic profiles and differing total RNA contents. We apply standard pipelines optimized for each system to derive gene expression matrices. We further evaluate the performance of each system by benchmarking single-cell data with bulk RNA-seq data from sorted cell fractions. Our analyses can be generalized to a variety of complex biological systems and serve as a guide to newcomers to the field of single-cell RNA-seq when selecting platforms.

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A growth factor–expressing macrophage subpopulation orchestrates regenerative inflammation via GDF-15

Journal of Experimental Medicine ◽

10.1084/jem.20210420 ◽

2021 ◽

Vol 219 (1) ◽

Author(s):

Andreas Patsalos ◽

Laszlo Halasz ◽

Miguel A. Medina-Serpas ◽

Wilhelm K. Berger ◽

Bence Daniel ◽

...

Keyword(s):

Growth Factors ◽

Growth Factor ◽

Muscle Regeneration ◽

Tissue Repair ◽

Cell Types ◽

Transcriptional Level ◽

Loss Of Function ◽

Repair Gene ◽

Multiple Cell ◽

Inflammatory Macrophages

Muscle regeneration is the result of the concerted action of multiple cell types driven by the temporarily controlled phenotype switches of infiltrating monocyte–derived macrophages. Pro-inflammatory macrophages transition into a phenotype that drives tissue repair through the production of effectors such as growth factors. This orchestrated sequence of regenerative inflammatory events, which we termed regeneration-promoting program (RPP), is essential for proper repair. However, it is not well understood how specialized repair-macrophage identity develops in the RPP at the transcriptional level and how induced macrophage–derived factors coordinate tissue repair. Gene expression kinetics–based clustering of blood circulating Ly6Chigh, infiltrating inflammatory Ly6Chigh, and reparative Ly6Clow macrophages, isolated from injured muscle, identified the TGF-β superfamily member, GDF-15, as a component of the RPP. Myeloid GDF-15 is required for proper muscle regeneration following acute sterile injury, as revealed by gain- and loss-of-function studies. Mechanistically, GDF-15 acts both on proliferating myoblasts and on muscle-infiltrating myeloid cells. Epigenomic analyses of upstream regulators of Gdf15 expression identified that it is under the control of nuclear receptors RXR/PPARγ. Finally, immune single-cell RNA-seq profiling revealed that Gdf15 is coexpressed with other known muscle regeneration–associated growth factors, and their expression is limited to a unique subpopulation of repair-type macrophages (growth factor–expressing macrophages [GFEMs]).

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