scholarly journals Optogenetic actuator/biosensor circuits for large-scale interrogation of ERK dynamics identify sources of MAPK signaling robustness.

2021 ◽  
Author(s):  
Coralie Dessauges ◽  
Jan Mikelson ◽  
Maciej Dobrzynski ◽  
Marc-Antoine Jacques ◽  
Agne Frismantiene ◽  
...  
Keyword(s):  
Single Cell ◽  
Negative Feedback ◽  
Large Scale ◽  
Mapk Signaling ◽  
Genetic Circuits ◽  
Oncogenic Signaling ◽  
Potent Inhibition ◽  
A Cell ◽  
Weak Node ◽  
Erk Activity

Measurements of single-cell ERK activity dynamics provide unique insights in the MAPK network topology. We built genetic circuits consisting of optogenetic actuators activating ERK from different nodes within the MAPK network together with an ERK biosensor to measure single-cell ERK dynamics. Evaluating ERK dynamics induced by different temporal optogenetic inputs, in response to a large number of perturbations, shows that the MAPK network is robust to downregulation of most of its nodes. This robustness emerges in part because of the ERK-RSK2-SOS negative feedback. Bypassing this feedback, by direct activation of the RAS/RAF/MEK/ERK submodule, or by RSK2 perturbation, breaks MAPK network robustness. Targeting the RSK2-mediated feedback in a ErbB2-dependent oncogenic signaling model greatly sensitizes ERK to MEK inhibition, allowing efficient ERK activity shutdown within a cell population. Thus, the RSK2-mediated negative feedback is a weak node of the MAPK network whose perturbation enables potent inhibition of ERK.

scholarly journals SIMLR: a tool for large-scale single-cell analysis by multi-kernel learning

2017 ◽  
Author(s):  
Bo Wang ◽  
Daniele Ramazzotti ◽  
Luca De Sano ◽  
Junjie Zhu ◽  
Emma Pierson ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Single Cell Analysis ◽  
R Package ◽  
Supplementary Information ◽  
Cell Analysis ◽  
Rna Seq ◽  
A Cell ◽  
Supplementary Material ◽  
Public Datasets

AbstractMotivationWe here present SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a cell-to-cell similarity measure from single-cell RNA-seq data. SIMLR can be effectively used to perform tasks such as dimension reduction, clustering, and visualization of heterogeneous populations of cells. SIMLR was benchmarked against state-of-the-art methods for these three tasks on several public datasets, showing it to be scalable and capable of greatly improving clustering performance, as well as providing valuable insights by making the data more interpretable via better a visualization.Availability and ImplementationSIMLR is available on GitHub in both R and MATLAB implementations. Furthermore, it is also available as an R package on [email protected] or [email protected] InformationSupplementary data are available at Bioinformatics online.

scholarly journals XCVATR: Detection and Characterization of Variant Impact on the Embeddings of Single -Cell and Bulk RNA-Sequencing Samples

2021 ◽  
Author(s):  
Arif Ozgun Harmanci ◽  
Akdes Serin Harmanci ◽  
Tiemo Klisch ◽  
Akash J Patel
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Joint Analysis ◽  
Rna Seq ◽  
Single Nucleotide Variants ◽  
Cell Counts ◽  
A Cell ◽  
Local Enrichment ◽  
Cell Cell

Gene expression profiling via RNA-sequencing has become standard for measuring and analyzing the gene activity in bulk and at single cell level. Increasing sample sizes and cell counts provides substantial information about transcriptional architecture of samples. In addition to quantification of expression at cellular level, RNA-seq can be used for detecting of variants, including single nucleotide variants and small insertions/deletions and also large variants such as copy number variants. The joint analysis of variants with transcriptional state of cells or samples can provide insight about impact of mutations. To provide a comprehensive method to jointly analyze the genetic variants and cellular states, we introduce XCVATR, a method that can identify variants, detect local enrichment of expressed variants within embedding of samples and cells. The embeddings provide information about cellular states among cells by defining a cell-cell distance metric. Unlike clustering algorithms, which depend on a cell-cell distance and use it to define clusters that explain cells globally, XCVATR detects the local enrichment of expressed variants in the embedding space such that embedding can be computed using any type of measurement or method, for example by PCA or tSNE of the expression levels. In other words, XCVATR searches patterns of association of each variant with the positions of cells in an embedding of the cells. XCVATR also visualizes the local clumps of small and large-scale variant calls in single cell and bulk RNA-sequencing datasets. We perform simulations and demonstrate that XCVATR can identify the enrichments of expressed variants and demonstrate its application on several single cell and bulk RNA-seq datasets.

scholarly journals DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics

2021 ◽  
Vol 22 (3) ◽  
pp. 1399
Author(s):  
Salim Ghannoum ◽  
Waldir Leoncio Netto ◽  
Damiano Fantini ◽  
Benjamin Ragan-Kelley ◽  
Amirabbas Parizadeh ◽  
...  
Keyword(s):  
Single Cell ◽  
Biomarker Discovery ◽  
Enrichment Analysis ◽  
Myxoid Liposarcoma ◽  
R Package ◽  
Differential Analysis ◽  
A Cell ◽  
Reproducible Analysis ◽  
Transcriptomic Level ◽  
User Friendly

The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the transcriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in a network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a user-friendly computational pipeline using Jupyter notebooks. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation dataset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. R users can use the notebooks to understand the different steps of the pipeline and will guide them to explore their scRNA-seq data. We also provide a cloud version using Binder that allows the execution of the pipeline without the need of downloading R, Jupyter or any of the packages used by the pipeline. The cloud version can serve as a tutorial for training purposes, especially for those that are not R users or have limited programing skills. However, in order to do meaningful scRNA-seq analyses, all users will need to understand the implemented methods and their possible options and limitations.

scholarly journals FlsnRNA-seq: protoplasting-free full-length single-nucleus RNA profiling in plants

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yanping Long ◽  
Zhijian Liu ◽  
Jinbu Jia ◽  
Weipeng Mo ◽  
Liang Fang ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Walls ◽  
Large Scale ◽  
Full Length ◽  
Cell Level ◽  
Root Cells ◽  
Rna Profiling ◽  
Different Types ◽  
Long Read ◽  
Single Nucleus

AbstractThe broad application of single-cell RNA profiling in plants has been hindered by the prerequisite of protoplasting that requires digesting the cell walls from different types of plant tissues. Here, we present a protoplasting-free approach, flsnRNA-seq, for large-scale full-length RNA profiling at a single-nucleus level in plants using isolated nuclei. Combined with 10x Genomics and Nanopore long-read sequencing, we validate the robustness of this approach in Arabidopsis root cells and the developing endosperm. Sequencing results demonstrate that it allows for uncovering alternative splicing and polyadenylation-related RNA isoform information at the single-cell level, which facilitates characterizing cell identities.

scholarly journals MEDALT: single-cell copy number lineage tracing enabling gene discovery

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Fang Wang ◽  
Qihan Wang ◽  
Vakul Mohanty ◽  
Shaoheng Liang ◽  
Jinzhuang Dou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Single Cell ◽  
Copy Number ◽  
Speciation Analysis ◽  
Population Based ◽  
Lineage Tracing ◽  
Breast Cancer Patients ◽  
Lineage Tree ◽  
History Of ◽  
A Cell

AbstractWe present a Minimal Event Distance Aneuploidy Lineage Tree (MEDALT) algorithm that infers the evolution history of a cell population based on single-cell copy number (SCCN) profiles, and a statistical routine named lineage speciation analysis (LSA), whichty facilitates discovery of fitness-associated alterations and genes from SCCN lineage trees. MEDALT appears more accurate than phylogenetics approaches in reconstructing copy number lineage. From data from 20 triple-negative breast cancer patients, our approaches effectively prioritize genes that are essential for breast cancer cell fitness and predict patient survival, including those implicating convergent evolution.The source code of our study is available at https://github.com/KChen-lab/MEDALT.

scholarly journals Elucidating the molecular determinants of Aβ aggregation with deep mutational scanning

2019 ◽  
Author(s):  
Vanessa E. Gray ◽  
Katherine Sitko ◽  
Floriane Z. Ngako Kameni ◽  
Miriam Williamson ◽  
Jason J. Stephany ◽  
...  
Keyword(s):  
Large Scale ◽  
Single Amino Acid ◽  
Alanine Scanning Mutagenesis ◽  
Aggregation Assay ◽  
Disease Etiology ◽  
Charged Amino Acids ◽  
Molecular Determinants ◽  
Aβ Aggregation ◽  
A Cell

AbstractDespite the importance of Aβ aggregation in Alzheimer’s disease etiology, our understanding of the sequence determinants of aggregation is sparse and largely derived from in vitro studies. For example, in vitro proline and alanine scanning mutagenesis of Aβ40 proposed core regions important for aggregation. However, we lack even this limited mutagenesis data for the more disease-relevant Aβ42. Thus, to better understand the molecular determinants of Aβ42 aggregation in a cell-based system, we combined a yeast DHFR aggregation assay with deep mutational scanning. We measured the effect of 791 of the 798 possible single amino acid substitutions on the aggregation propensity of Aβ42. We found that ~75% of substitutions, largely to hydrophobic residues, maintained or increased aggregation. We identified 11 positions at which substitutions, particularly to hydrophilic and charged amino acids, disrupted Aβ aggregation. These critical positions were similar but not identical to critical positions identified in previous Aβ mutagenesis studies. Finally, we analyzed our large-scale mutagenesis data in the context of different Aβ aggregate structural models, finding that the mutagenesis data agreed best with models derived from fibrils seeded using brain-derived Aβ aggregates.

scholarly journals How to make an oscillator

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Bas JHM Rosier ◽  
Tom FA de Greef
Keyword(s):  
Genetic Circuits ◽  
A Cell

A cell-free approach reveals how genetic circuits can produce robust oscillations of proteins and other components.

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