The effect of Cytochalasin D on F-Actin behavior of single-cell electroendocytosis using multi-chamber micro cell chip

We have developed a polymer MEMS sensor for measuring mechanical forces generated by single adherent cells. Mechanical forces are known to play a role in cell regulation, and measuring these forces is an important step in understanding cellular mechanotransduction. The sensor consists of four polystyrene microcantilever beams with cell adhesion pads at each end. Finite element analysis was used to guide the design of a compound cantilever to allow measurement of forces in multiple directions. The device was evaluated by measuring forces generated by WS-1 human skin fibroblasts. A single cell was placed on the sensor using a custom micromanipulator. Forces were calculated by optically measuring the deflection of each probe during cell attachment and spreading. Measurements were performed on normal cells and those treated with cytochalasin D to disrupt the actin cytoskeleton. Cytochalasin D treated cells showed a significant decrease in force. This device can be used to evaluate the mechanical response of cells to a variety of chemical, mechanical, and other environmental stimuli.

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Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state

Nature Biotechnology ◽

10.1038/nbt.3383 ◽

2015 ◽

Vol 33 (11) ◽

pp. 1165-1172 ◽

Cited By ~ 447

Author(s):

Assaf Rotem ◽

Oren Ram ◽

Noam Shoresh ◽

Ralph A Sperling ◽

Alon Goren ◽

...

Keyword(s):

Single Cell ◽

Chromatin State ◽

Cell Chip ◽

Cell Subpopulations

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Cis-topic modelling of single-cell epigenomes

10.1101/370346 ◽

2018 ◽

Cited By ~ 8

Author(s):

Carmen Bravo González-Blas ◽

Liesbeth Minnoye ◽

Dafni Papasokrati ◽

Sara Aibar ◽

Gert Hulselmans ◽

...

Keyword(s):

Single Cell ◽

Binding Sites ◽

Bioconductor Package ◽

Rna Seq ◽

Probabilistic Framework ◽

Cell Type ◽

Regulatory Motifs ◽

Cell Chip ◽

Heterogeneous Samples ◽

Cell Type Specific

AbstractSingle-cell epigenomics provides new opportunities to decipher genomic regulatory programs from heterogeneous samples and dynamic processes. We present a probabilistic framework called cisTopic, to simultaneously discover “cis-regulatory topics” and stable cell states from sparse single-cell epigenomics data. After benchmarking cisTopic on single-cell ATAC-seq data, single-cell DNA methylation data, and semi-simulated single-cell ChIP-seq data, we use cisTopic to predict regulatory programs in the human brain and validate these by aligning them with co-expression networks derived from single-cell RNA-seq data. Next, we performed a time-series single-cell ATAC-seq experiment after SOX10 perturbations in melanoma cultures, where cisTopic revealed dynamic regulatory topics driven by SOX10 and AP-1. Finally, machine learning and enhancer modelling approaches allowed to predict cell type specific SOX10 and SOX9 binding sites based on topic specific co-regulatory motifs. cisTopic is available as an R/Bioconductor package at http://github.com/aertslab/cistopic.

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Single-cell ChIP-seq imputation with SIMPA by leveraging bulk ENCODE data

10.1101/2019.12.20.883983 ◽

2019 ◽

Author(s):

Steffen Albrecht ◽

Tommaso Andreani ◽

Miguel A. Andrade-Navarro ◽

Jean-Fred Fontaine

Keyword(s):

Machine Learning ◽

Transcription Factors ◽

Single Cell ◽

Cell Types ◽

Genomic Region ◽

Imputation Method ◽

Data Imputation ◽

Learning Models ◽

Cell Chip ◽

Machine Learning Models

AbstractSingle-cell ChIP-seq analysis is challenging due to data sparsity. We present SIMPA (https://github.com/salbrec/SIMPA), a single-cell ChIP-seq data imputation method leveraging predictive information within bulk ENCODE data to impute missing protein-DNA interacting regions of target histone marks or transcription factors. Machine learning models trained for each single cell, each target, and each genomic region enable drastic improvement in cell types clustering and genes identification.

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