Ageing and sources of transcriptional heterogeneity

2019 ◽  
Vol 400 (7) ◽  
pp. 867-878 ◽  
Author(s):  
Chrysa Nikopoulou ◽  
Swati Parekh ◽  
Peter Tessarz
Keyword(s):  
Gene Expression ◽  
New Technologies ◽  
Biological Function ◽  
Recent Literature ◽  
Cellular Heterogeneity ◽  
Transcriptional Noise ◽  
Single Cell Sequencing ◽  
Sequencing Technologies ◽  
Recent Developments ◽  
Cell To Cell Variability

Abstract Cellular heterogeneity is an important contributor to biological function and is employed by cells, tissues and organisms to adapt, compensate, respond, defend and/or regulate specific processes. Research over the last decades has revealed that transcriptional noise is a major driver for cell-to-cell variability. In this review we will discuss sources of transcriptional variability, in particular bursting of gene expression and how it could contribute to cellular states and fate decisions. We will highlight recent developments in single cell sequencing technologies that make it possible to address cellular heterogeneity in unprecedented detail. Finally, we will review recent literature, in which these new technologies are harnessed to address pressing questions in the field of ageing research, such as transcriptional noise and cellular heterogeneity in the course of ageing.

scholarly journals Transcriptional noise and cellular heterogeneity in mammalian macrophages

2006 ◽  
Vol 361 (1467) ◽  
pp. 495-506 ◽  
Author(s):  
S Ramsey ◽  
A Ozinsky ◽  
A Clark ◽  
K.D Smith ◽  
P de Atauri ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Frequency ◽  
Cellular Heterogeneity ◽  
Frequency Noise ◽  
Scaling Effects ◽  
Transcriptional Noise ◽  
Expression Variability ◽  
Genome Complexity ◽  
Cell Variation ◽  
Gene Expression Levels

Transcriptional noise is known to play a crucial role in heterogeneity in bacteria and yeast. Mammalian macrophages are known to exhibit cell-to-cell variation in their responses to pathogens, but the source of this heterogeneity is not known. We have developed a detailed stochastic model of gene expression that takes into account scaling effects due to cell size and genome complexity. We report the results of applying this model to simulating gene expression variability in mammalian macrophages, demonstrating a possible molecular basis for heterogeneity in macrophage signalling responses. We note that the nature of predicted transcriptional noise in macrophages is different from that in yeast and bacteria. Some molecular interactions in yeast and bacteria are thought to have evolved to minimize the effects of the high-frequency noise observed in these species. Transcriptional noise in macrophages results in slow changes to gene expression levels and would not require the type of spike-filtering circuits observed in yeast and bacteria.

Y RNAs: recent developments

2013 ◽  
Vol 4 (2) ◽  
pp. 103-110 ◽  
Author(s):  
Adam E. Hall ◽  
Carly Turnbull ◽  
Tamas Dalmay
Keyword(s):  
Molecular Mechanisms ◽  
Biological Function ◽  
Recent Literature ◽  
Chromosomal Dna ◽  
Cellular Processes ◽  
Recent Developments ◽  
Rna Fragments ◽  
High Conservation ◽  
Non Coding Rnas ◽  
Insight Into

AbstractNon-coding RNAs have emerged as key regulators in diverse cellular processes. Y RNAs are ∼100-nucleotide-long non-coding RNAs that show high conservation in metazoans. Human Y RNAs are known to bind to the Ro60 and La proteins to form the Ro ribonucleoprotein complex. Their main biological function appears to be in mediating the initiation of chromosomal DNA replication, regulating the autoimmune protein Ro60, and generating smaller RNA fragments following cellular stress, although the precise molecular mechanisms underlying these functions remain elusive. Here, we aim to review the most recent literature on Y RNAs and gain insight into the function of these intriguing molecules.

scholarly journals Discovering Cellular Mitochondrial Heteroplasmy Heterogeneity with Single Cell RNA and ATAC Sequencing

Biology ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 503
Author(s):  
Aidan S. Marshall ◽  
Nick S. Jones
Keyword(s):  
Next Generation Sequencing ◽  
Single Cell ◽  
Cellular Heterogeneity ◽  
Open Problems ◽  
Single Cell Sequencing ◽  
Sequencing Technologies ◽  
Single Cell Rna Sequencing ◽  
Mitochondrial Heteroplasmy ◽  
Mitochondrial Heterogeneity ◽  
Generation Sequencing

Next-generation sequencing technologies have revolutionised the study of biological systems by enabling the examination of a broad range of tissues. Its application to single-cell genomics has generated a dynamic and evolving field with a vast amount of research highlighting heterogeneity in transcriptional, genetic and epigenomic state between cells. However, compared to these aspects of cellular heterogeneity, relatively little has been gleaned from single-cell datasets regarding cellular mitochondrial heterogeneity. Single-cell sequencing techniques can provide coverage of the mitochondrial genome which allows researchers to probe heteroplasmies at the level of the single cell, and observe interactions with cellular function. In this review, we give an overview of two popular single-cell modalities—single-cell RNA sequencing and single-cell ATAC sequencing—whose throughput and widespread usage offers researchers the chance to probe heteroplasmy combined with cell state in detailed resolution across thousands of cells. After summarising these technologies in the context of mitochondrial research, we give an overview of recent methods which have used these approaches for discovering mitochondrial heterogeneity. We conclude by highlighting current limitations of these approaches and open problems for future consideration.

scholarly journals Cobolt: Joint analysis of multimodal single-cell sequencing data

2021 ◽  
Author(s):  
Boying Gong ◽  
Yun Zhou ◽  
Elizabeth Purdom
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Chromatin Accessibility ◽  
Joint Analysis ◽  
Sequencing Data ◽  
Multiple Modalities ◽  
Single Cell Sequencing ◽  
Sequencing Technologies ◽  
Variational Autoencoder ◽  
Cell Gene Expression

AbstractSingle-cell measurements of different cellular features or modalities from cells from the same system allow for a comprehensive understanding of a biological process. While the most common single-cell sequencing technologies require separate input cells for different modalities, there are a growing number of platforms that allow for measuring several modalities on a single cell. We present a novel method, Cobolt, for analyzing such multi-modality single-cell sequencing datasets. Cobolt jointly models the multiple modalities via a novel application of Multimodal Variational Autoencoder (MVAE) to a hierarchical generative model. We first demonstrate its performance on data from the multi-modality platform SNARE-seq, consisting of measurements of gene expression and chromatin accessibility on the same cells. We then illustrate the ability of Cobolt to integrate multi-modality platforms with single-modality platforms by jointly analyzing a SNARE-seq dataset, a single-cell gene expression dataset, and a single-cell chromatin accessibility dataset. We compared Cobolt with current options for analyzing such datasets and show that Cobolt provides robust and flexible results for integration of single-cell data on multiple modalities.

scholarly journals RNA Modifications and RNA Metabolism in Neurological Disease Pathogenesis

2021 ◽  
Vol 22 (21) ◽  
pp. 11870
Author(s):  
Biswanath Chatterjee ◽  
Che-Kun James Shen ◽  
Pritha Majumder
Keyword(s):  
Gene Expression ◽  
Stress Responses ◽  
Neurological Diseases ◽  
Rna Metabolism ◽  
Cellular Heterogeneity ◽  
Rna Modifications ◽  
Sequencing Technologies ◽  
Physiological Processes ◽  
Non Coding Rna ◽  
The Stability

The intrinsic cellular heterogeneity and molecular complexity of the mammalian nervous system relies substantially on the dynamic nature and spatiotemporal patterning of gene expression. These features of gene expression are achieved in part through mechanisms involving various epigenetic processes such as DNA methylation, post-translational histone modifications, and non-coding RNA activity, amongst others. In concert, another regulatory layer by which RNA bases and sugar residues are chemically modified enhances neuronal transcriptome complexity. Similar RNA modifications in other systems collectively constitute the cellular epitranscriptome that integrates and impacts various physiological processes. The epitranscriptome is dynamic and is reshaped constantly to regulate vital processes such as development, differentiation and stress responses. Perturbations of the epitranscriptome can lead to various pathogenic conditions, including cancer, cardiovascular abnormalities and neurological diseases. Recent advances in next-generation sequencing technologies have enabled us to identify and locate modified bases/sugars on different RNA species. These RNA modifications modulate the stability, transport and, most importantly, translation of RNA. In this review, we discuss the formation and functions of some frequently observed RNA modifications—including methylations of adenine and cytosine bases, and isomerization of uridine to pseudouridine—at various layers of RNA metabolism, together with their contributions to abnormal physiological conditions that can lead to various neurodevelopmental and neurological disorders.

Recent Developments of Ambulatory Assessment Methods

2014 ◽  
Vol 25 (4) ◽  
pp. 279-287 ◽  
Author(s):  
Stefan Hey ◽  
Panagiota Anastasopoulou ◽  
André Bideaux ◽  
Wilhelm Stork
Keyword(s):  
Real Time ◽  
Information And Communication Technologies ◽  
New Technologies ◽  
Psychological Research ◽  
Ambulatory Assessment ◽  
Emotional States ◽  
Recent Developments ◽  
Potential Applications ◽  
Information And Communication ◽  
Physiological Sensors

Ambulatory assessment of emotional states as well as psychophysiological, cognitive and behavioral reactions constitutes an approach, which is increasingly being used in psychological research. Due to new developments in the field of information and communication technologies and an improved application of mobile physiological sensors, various new systems have been introduced. Methods of experience sampling allow to assess dynamic changes of subjective evaluations in real time and new sensor technologies permit a measurement of physiological responses. In addition, new technologies facilitate the interactive assessment of subjective, physiological, and behavioral data in real-time. Here, we describe these recent developments from the perspective of engineering science and discuss potential applications in the field of neuropsychology.

3D Printing in Pharmaceuticals: Regulatory Perspective

2019 ◽  
Vol 24 (42) ◽  
pp. 5081-5083 ◽  
Author(s):  
Mohd. A. Mirza ◽  
Zeenat Iqbal
Keyword(s):  
New Technologies ◽  
Pharmaceutical Product ◽  
Regulatory Agencies ◽  
Major Research ◽  
Data Bases ◽  
Drug Products ◽  
Regulatory Perspective ◽  
Regulatory Guidelines ◽  
Recent Developments ◽  
Technical Issues

Background: The last few decades have witnessed enormous advancements in the field of Pharmaceutical drug, design and delivery. One of the recent developments is the advent of 3DP technology. It has earlier been successfully employed in fields like aerospace, architecture, tissue engineering, biomedical research, medical device and others, has recently forayed into the pharmaceutical industry.Commonly understood as an additive manufacturing technology, 3DP aims at delivering customized drug products and is the most acceptable form of“personalized medicine”. Methods: Data bases and search engines of regulatory agencies like USFDA and EMA have been searched thoroughly for relevant guidelines and approved products. Other portals like PubMed and Google Scholar were also ferreted for any relevant repository of publications are referred to wherever required. Results: So far only one pharmaceutical product has been approved in this category by USFDA and stringent regulatory agencies are working over the drafting of guidelines and technical issues. Major research of this category belongs to the academic domain. Conclusion: It is also implicit to such new technologies that there would be numerous challenges and doubts before these are accepted as safe and efficacious. The situation demands concerted and cautious efforts to bring in foolproof regulatory guidelines which would ultimately lead to the success of this revolutionary technology.

Mass Spectrometry for Proteomics and Recent Developments in ESI, MALDI and other Ionization Methodologies

2019 ◽  
Vol 16 (4) ◽  
pp. 267-276
Author(s):  
Qurat ul Ain Farooq ◽  
Noor ul Haq ◽  
Abdul Aziz ◽  
Sara Aimen ◽  
Muhammad Inam ul Haq
Keyword(s):  
Mass Spectrometry ◽  
Electrospray Ionization ◽  
New Technologies ◽  
Protein Complexes ◽  
Imaging Techniques ◽  
Desorption Electrospray Ionization ◽  
High Throughput Analysis ◽  
Enhanced Sensitivity ◽  
Recent Developments ◽  
The Way

Background: Mass spectrometry is a tool used in analytical chemistry to identify components in a chemical compound and it is of tremendous importance in the field of biology for high throughput analysis of biomolecules, among which protein is of great interest. Objective: Advancement in proteomics based on mass spectrometry has led the way to quantify multiple protein complexes, and proteins interactions with DNA/RNA or other chemical compounds which is a breakthrough in the field of bioinformatics. Methods: Many new technologies have been introduced in electrospray ionization (ESI) and Matrixassisted Laser Desorption/Ionization (MALDI) techniques which have enhanced sensitivity, resolution and many other key features for the characterization of proteins. Results: The advent of ambient mass spectrometry and its different versions like Desorption Electrospray Ionization (DESI), DART and ELDI has brought a huge revolution in proteomics research. Different imaging techniques are also introduced in MS to map proteins and other significant biomolecules. These drastic developments have paved the way to analyze large proteins of >200kDa easily. Conclusion: Here, we discuss the recent advancement in mass spectrometry, which is of great importance and it could lead us to further deep analysis of the molecules from different perspectives and further advancement in these techniques will enable us to find better ways for prediction of molecules and their behavioral properties.

scholarly journals Cellular Heterogeneity–Adjusted cLonal Methylation (CHALM) improves prediction of gene expression

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jianfeng Xu ◽  
Jiejun Shi ◽  
Xiaodong Cui ◽  
Ya Cui ◽  
Jingyi Jessica Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Cellular Heterogeneity ◽  
Biological Functions ◽  
Global Correlation ◽  
Differentially Methylated Genes ◽  
Promoter Dna Methylation ◽  
Functional Consequences ◽  
Promoter Dna ◽  
Underlying Mechanisms

AbstractPromoter DNA methylation is a well-established mechanism of transcription repression, though its global correlation with gene expression is weak. This weak correlation can be attributed to the failure of current methylation quantification methods to consider the heterogeneity among sequenced bulk cells. Here, we introduce Cell Heterogeneity–Adjusted cLonal Methylation (CHALM) as a methylation quantification method. CHALM improves understanding of the functional consequences of DNA methylation, including its correlations with gene expression and H3K4me3. When applied to different methylation datasets, the CHALM method enables detection of differentially methylated genes that exhibit distinct biological functions supporting underlying mechanisms.

Feedback-Induced Variations of Distribution in a Representative Gene Model

2015 ◽  
Vol 25 (07) ◽  
pp. 1540008
Author(s):  
Peijiang Liu ◽  
Zhanjiang Yuan ◽  
Lifang Huang ◽  
Tianshou Zhou
Keyword(s):  
Gene Expression ◽  
Power Law ◽  
Positive Feedback ◽  
Bimodal Distribution ◽  
Stochastic Bifurcation ◽  
Protein Distribution ◽  
Identical Cell ◽  
Induced Changes ◽  
Gene Switching ◽  
Cell To Cell Variability

Gene expression is inherently noisy, implying that the number of mRNAs or proteins is not invariant rather than follows a distribution. This distribution can not only provide the exact information on the dynamics of gene expression but also describe cell-to-cell variability in a genetically identical cell population. Here, we systematically investigate a two-state model of gene expression, a model paradigm used to study expression dynamics, focusing on the effect of feedback on the type of mRNA or protein distribution. If there is no feedback, then the distribution may be bimodal, power-law tailed, or Poisson-like, depending on gene switching rates. However, we find that feedback can tune or change the type of the distribution in each case and tends to unimodalize the distribution as its strength increases. Specifically, positive feedback can change not only a power-law tailed distribution into a bimodal or Poisson-like distribution but also a bimodal distribution into a Poisson-like distribution (implying that stochastic bifurcation can take place). In addition, it can make a Poisson-like distribution become more peaked but does not change the type of this distribution. In contrast to positive feedback, negative feedback has less influence on the shape of the distributions except for the bimodal case. In all cases, the noise-feedback curve used extensively in previous studies cannot well reflect the feedback-induced changes in the shape of distributions. Feedback-induced variations in distribution would be important for cell survival in fluctuating environments.

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