Single cell characterization of a synthetic bacterial clock with a hybrid feedback loop containing dCas9-sgRNA

AbstractGenetic networks that generate oscillations in gene expression activity are found in a wide range of organisms throughout all kingdoms of life. Oscillatory dynamics facilitates the temporal orchestration of metabolic and growth processes inside cells and organisms, as well as the synchronization of such processes with periodically occurring changes in the environment. Synthetic oscillator gene circuits such as the ‘repressilator’ can perform similar functions in bacteria. Until recently, such circuits were mainly based on a relatively small set of well-characterized transcriptional repressors and activators. A promising, sequence-programmable alternative for gene regulation is given by CRISPR interference (CRISPRi), which enables transcriptional repression of nearly arbitrary gene targets directed by short guide RNA molecules. In order to demonstrate the use of CRISPRi in the context of dynamic gene circuits, we here replaced one of the nodes of a repressilator circuit by the RNA-guided dCas9 protein. Using single cell experiments in microfluidic reactors we show that this system displays robust relaxation oscillations over multiple periods and over the time course of several days. Through statistical analysis of the single cell data, the potential for the circuit to act as a synthetic pacemaker for cellular processes is evaluated. The use of CRISPRi in the context of an oscillator circuit is found to have profound effects on its dynamics. Specifically, irreversible binding of dCas9-sgRNA appears to prolong the period of the oscillator. Further, we demonstrate that the oscillator affects cellular growth, leading to variations in growth rate with the oscillator’s frequency.

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High resolution AKT signaling in individual cells

10.1101/373993 ◽

2018 ◽

Cited By ~ 1

Author(s):

Sean M. Gross ◽

Mark A. Dane ◽

Elmar Bucher ◽

Laura M. Heiser

Keyword(s):

Single Cell ◽

Intracellular Signaling ◽

Time Course ◽

Dynamic Range ◽

Individual Cell ◽

Akt Signaling ◽

Live Cells ◽

Theoretic Approach ◽

Wide Range ◽

Igf I

AbstractCells sense and respond to their environment by activating distinct intracellular signaling pathways, however an individual cell’s ability to faithfully transmit and discriminate environmental signals is thought to be limited. To assess the fidelity of signal transmission in the PI3K-AKT signaling pathway, we first developed an optimized genetically encoded sensor that had an increased dynamic range and reduced variation under basal conditions. We then used this reporter to track responses to varying doses of IGF-I in live cells and found that signaling responses from individual cells overlapped across a wide range of IGF-I doses, suggesting limited transmission accuracy. However, further analysis of individual cell traces revealed that responses were constant over time without stochastic fluctuations. We devised a new information theoretic approach to calculate the channel capacity using variance of the single cell time course data‐‐rather than population-level variance as has been previously used—and predicted that cells were capable of discriminating multiple growth factor doses. We validated these predictions by tracking individual cell responses to multiple IGF-I doses and found that cells can accurately distinguish at least four different IGF-I concentrations, as demonstrated by their distinct responses. Furthermore, we found a similar discriminatory ability to pathway inhibition, as assessed by responses to the PI3K inhibitor alpelisib. Our studies indicate that cells can faithfully transmit an IGF-I input into a down-stream signaling response and that heterogeneous responses result from variation in the input-output relation across the population. These observations reveal the importance of viewing each cell as having its own communication channel and underscore the importance of understanding responses at the single cell level.

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Alteration of microtubule polymerization modulates arteriolar vasomotor tone

AJP Heart and Circulatory Physiology ◽

10.1152/ajpheart.1999.277.1.h100 ◽

1999 ◽

Vol 277 (1) ◽

pp. H100-H106 ◽

Cited By ~ 24

Author(s):

Steven H. Platts ◽

Jeff C. Falcone ◽

William T. Holton ◽

Michael A. Hill ◽

Gerald A. Meininger

Keyword(s):

Time Course ◽

Contractile Function ◽

Vasomotor Tone ◽

Microtubule Depolymerization ◽

Similar Time ◽

Cellular Processes ◽

Endothelial Denudation ◽

Wide Range ◽

Intracellular Ca ◽

Cytoskeletal Elements

Microtubules are important cytoskeletal elements that have been shown to play a major role in many cellular processes because of their mechanical properties and/or their participation in various cell signaling pathways. We tested the hypothesis that depolymerization of microtubules would alter vascular smooth muscle (VSM) tone and hence contractile function. In our studies, isolated cremaster arterioles exhibited significant vasoconstriction that developed over a 20- to 40-min period when they were treated with microtubule depolymerizing drugs colchicine (10 μM), nocodazole (10 μM), or demecolcine (10 μM). Immunofluorescent labeling of microtubules in cultured rat VSM revealed that both colchicine and nocodazole caused microtubule depolymerization over a similar time course. The vasoconstriction was maintained over a wide range of intraluminal pressures (30–170 cmH2O). The increased tone was not affected by endothelial denudation, suggesting that it was due to an effect on VSM. Microtubule depolymerization with demecolcine or colchicine had no effect on VSM intracellular Ca2+ concentration ([Ca2+]i). These data indicate that microtubules significantly interact with processes leading to the expression of vasomotor tone. The mechanism responsible for the effect of microtubules on vasomotor tone appears to be independent of both the endothelium and an increase in VSM [Ca2+]i.

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FORKS: Finding Orderings Robustly using k-means and Steiner trees

10.1101/132811 ◽

2017 ◽

Cited By ~ 1

Author(s):

Mayank Sharma ◽

Huipeng Li ◽

Debarka Sengupta ◽

Shyam Prabhakar ◽

Jayadeva

Keyword(s):

Single Cell ◽

Time Course ◽

Time Estimation ◽

Steiner Trees ◽

Rna Seq ◽

Estimation Algorithms ◽

Starting Point ◽

Wide Range ◽

Benchmark Datasets ◽

Memory Efficient

AbstractRecent advances in single cell RNA-seq technologies have provided researchers with unprecedented details of transcriptomic variation across individual cells. However, it has not been straightforward to infer differentiation trajectories from such data, due to the parameter-sensitivity of existing methods. Here, we present Finding Orderings Robustly using k-means and Steiner trees (FORKS), an algorithm that pseudo-temporally orders cells and thereby infers bifurcating state trajectories. FORKS, which is a generic method, can be applied to both single-cell and bulk differentiation data. It is a semi-supervised approach, in that it requires the user to specify the starting point of the time course. We systematically benchmarked FORKS and eight other pseudo-time estimation algorithms on six benchmark datasets, and found it to be more accurate, more reproducible, and more memory-efficient than existing methods for pseudo-temporal ordering. Another major advantage of our approach is its robustness – FORKS can be used with default parameter settings on a wide range of datasets.

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UniverSC: a flexible cross-platform single-cell data processing pipeline

10.21203/rs.3.rs-244461/v1 ◽

2021 ◽

Author(s):

S. Kelly ◽

Kai Battenberg ◽

Nicola Hetherington ◽

Makoto Hayashi ◽

Aki Minoda

Keyword(s):

Single Cell ◽

Sequencing Analysis ◽

Command Line ◽

Rna Molecules ◽

Processing Pipeline ◽

Wide Range ◽

Single Cell Rna Sequencing ◽

Command Line Tool ◽

Cross Platform ◽

Cell Data

Abstract Single-cell RNA-sequencing analysis to quantify RNA molecules in individual cells has become popular owing to the large amount of information one can obtain from each experiment. We have developed UniverSC (https://github.com/minoda-lab/universc), a universal single-cell processing tool that supports any UMI-based platform. Our command-line tool enables consistent and comprehensive integration, comparison, and evaluation across data generated from a wide range of platforms.

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UniverSC: a flexible cross-platform single-cell data processing pipeline

10.1101/2021.01.19.427209 ◽

2021 ◽

Author(s):

S. Thomas Kelly ◽

Kai Battenberg ◽

Nicola A. Hetherington ◽

Makoto Hayashi ◽

Aki Minoda

Keyword(s):

Single Cell ◽

Sequencing Analysis ◽

Command Line ◽

Rna Molecules ◽

Processing Pipeline ◽

Wide Range ◽

Single Cell Rna Sequencing ◽

Command Line Tool ◽

Cross Platform ◽

Cell Data

AbstractSingle-cell RNA-sequencing analysis to quantify RNA molecules in individual cells has become popular owing to the large amount of information one can obtain from each experiment. We have developed UniverSC (https://github.com/minoda-lab/universc), a universal single-cell processing tool that supports any UMI-based platform. Our command-line tool enables consistent and comprehensive integration, comparison, and evaluation across data generated from a wide range of platforms.

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Single-Cell and Neuronal Network Alterations in an In Vitro Model of Fragile X Syndrome

Cerebral Cortex ◽

10.1093/cercor/bhz068 ◽

2019 ◽

Vol 30 (1) ◽

pp. 31-46 ◽

Cited By ~ 2

Author(s):

Anastasiya Moskalyuk ◽

Sebastiaan Van De Vijver ◽

Peter Verstraelen ◽

Winnok H De Vos ◽

R Frank Kooy ◽

...

Keyword(s):

Single Cell ◽

In Vitro Model ◽

Ex Vivo ◽

Fragile X ◽

Network Development ◽

Cellular Processes ◽

Wide Range ◽

Clinical Consequences ◽

Vitro Model

Abstract The Fragile X mental retardation protein (FMRP) is involved in many cellular processes and it regulates synaptic and network development in neurons. Its absence is known to lead to intellectual disability, with a wide range of comorbidities including autism. Over the past decades, FMRP research focused on abnormalities both in glutamatergic and GABAergic signaling, and an altered balance between excitation and inhibition has been hypothesized to underlie the clinical consequences of absence of the protein. Using Fmrp knockout mice, we studied an in vitro model of cortical microcircuitry and observed that the loss of FMRP largely affected the electrophysiological correlates of network development and maturation but caused less alterations in single-cell phenotypes. The loss of FMRP also caused a structural increase in the number of excitatory synaptic terminals. Using a mathematical model, we demonstrated that the combination of an increased excitation and reduced inhibition describes best our experimental observations during the ex vivo formation of the network connections.

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Single-cell and neuronal network alterations in an in vitro model of Fragile X syndrome

10.1101/366997 ◽

2018 ◽

Cited By ~ 1

Author(s):

Anastasiya Moskalyuk ◽

R. Frank Kooy ◽

Michele Giugliano

Keyword(s):

Single Cell ◽

Ex Vivo ◽

Fragile X ◽

Network Development ◽

Cellular Processes ◽

Wide Range ◽

Clinical Consequences ◽

Vitro Model ◽

Cell Phenotypes

AbstractThe Fragile X mental retardation protein (FMRP) is involved in many cellular processes and it regulates synaptic and network development in neurons. Its absence is known to lead to intellectual disability, with a wide range of co-morbidities including autism. Over the past decades, FMRP research focused on abnormalities both in glutamatergic and GABAergic signalling, and an altered balance between excitation and inhibition has been hypothesised to underlie the clinical consequences of absence of the protein. Using FMRP knockout mice, we studied an in vitro model of cortical microcircuitry and observed that the loss of FMRP largely affected the electrophysiological correlates of network development and maturation but caused less alterations in single-cell phenotypes. Using a mathematical model, we demonstrated that the combination of an increased excitation and reduced inhibition describes best predicts our experimental observations during the ex vivo formation of the network connections.

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Multiplexed PCR-Free Detection of MicroRNAs in Single Cancer Cells Using a DNA-Barcoded Microtrough Array Chip

Micromachines ◽

10.3390/mi10040215 ◽

2019 ◽

Vol 10 (4) ◽

pp. 215 ◽

Cited By ~ 1

Author(s):

Nayi Wang ◽

Yao Lu ◽

Zhuo Chen ◽

Rong Fan

Keyword(s):

Single Cell ◽

High Throughput ◽

Single Cells ◽

Pcr Amplification ◽

Intratumor Heterogeneity ◽

Rna Molecules ◽

Multiplexed Pcr ◽

Microrna Detection ◽

Wide Range ◽

Health And Disease

MicroRNAs are a class of small RNA molecules that regulate the expression of mRNAs in a wide range of biological processes and are implicated in human health and disease such as cancers. How to measure microRNA profiles in single cells with high throughput is essential to the development of cell-based assays for interrogating microRNA-mediated intratumor heterogeneity and the design of new lab tests for diagnosis and monitoring of cancers. Here, we report on an in situ hybridization barcoding workflow implemented in a sub-nanoliter microtrough array chip for high-throughput and multiplexed microRNA detection at the single cell level. The microtroughs are used to encapsulate single cells that are fixed, permeabilized, and pre-incubated with microRNA detection probes, each of which consists of a capture strand complementary to specific microRNA and a unique reporter strand that can be photocleaved in the microtroughs and subsequently detected by an array of DNA barcodes patterned on the bottom of the microtroughs. In this way, the measurement of reporter strands released from single cells is a surrogate for detecting single-cell microRNA profiles. This approach permits direct measurement of microRNAs without PCR amplification owing to the small volume (<1 nL) of microtroughs. It offers high throughput and high multiplexing capability for evaluating microRNA heterogeneity in single cells, representing a new approach toward microRNA-based diagnosis and monitoring of complex human diseases.

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Unravelling the Network of Nuclear Matrix Metalloproteinases for Targeted Drug Design

Biology ◽

10.3390/biology9120480 ◽

2020 ◽

Vol 9 (12) ◽

pp. 480

Author(s):

Anastasia S. Frolova ◽

Anastasiia I. Petushkova ◽

Vladimir A. Makarov ◽

Surinder M. Soond ◽

Andrey A. Zamyatnin

Keyword(s):

Matrix Metalloproteinases ◽

Tumor Cells ◽

Cancer Progression ◽

Nuclear Matrix ◽

Nuclear Translocation ◽

Cell Types ◽

Cellular Growth ◽

Cellular Processes ◽

Wide Range ◽

Cellular Apoptosis

Matrix metalloproteinases (MMPs) are zinc-dependent endopeptidases that are responsible for the degradation of a wide range of extracellular matrix proteins, which are involved in many cellular processes to ensure the normal development of tissues and organs. Overexpression of MMPs has been observed to facilitate cellular growth, migration, and metastasis of tumor cells during cancer progression. A growing number of these proteins are being found to exist in the nuclei of both healthy and tumor cells, thus highlighting their localization as having a genuine purpose in cellular homeostasis. The mechanism underlying nuclear transport and the effects of MMP nuclear translocation have not yet been fully elucidated. To date, nuclear MMPs appear to have a unique impact on cellular apoptosis and gene regulation, which can have effects on immune response and tumor progression, and thus present themselves as potential therapeutic targets in certain types of cancer or disease. Herein, we highlight and evaluate what progress has been made in this area of research, which clearly has some value as a specific and unique way of targeting the activity of nuclear matrix metalloproteinases within various cell types.

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Clustering Single-Cell RNA-Seq Data with Regularized Gaussian Graphical Model

Genes ◽

10.3390/genes12020311 ◽

2021 ◽

Vol 12 (2) ◽

pp. 311

Author(s):

Zhenqiu Liu

Keyword(s):

Single Cell ◽

Free Parameter ◽

Graphical Model ◽

Expression Patterns ◽

Information Criterion ◽

Log P ◽

Rna Seq ◽

Clustering Methods ◽

Wide Range ◽

Free Parameters

Single-cell RNA-seq (scRNA-seq) is a powerful tool to measure the expression patterns of individual cells and discover heterogeneity and functional diversity among cell populations. Due to variability, it is challenging to analyze such data efficiently. Many clustering methods have been developed using at least one free parameter. Different choices for free parameters may lead to substantially different visualizations and clusters. Tuning free parameters is also time consuming. Thus there is need for a simple, robust, and efficient clustering method. In this paper, we propose a new regularized Gaussian graphical clustering (RGGC) method for scRNA-seq data. RGGC is based on high-order (partial) correlations and subspace learning, and is robust over a wide-range of a regularized parameter λ. Therefore, we can simply set λ=2 or λ=log(p) for AIC (Akaike information criterion) or BIC (Bayesian information criterion) without cross-validation. Cell subpopulations are discovered by the Louvain community detection algorithm that determines the number of clusters automatically. There is no free parameter to be tuned with RGGC. When evaluated with simulated and benchmark scRNA-seq data sets against widely used methods, RGGC is computationally efficient and one of the top performers. It can detect inter-sample cell heterogeneity, when applied to glioblastoma scRNA-seq data.

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