scholarly journals A Finite Element Study of Micropipette Aspiration of Single Cells: Effect of Compressibility

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Amirhossein Jafari Bidhendi ◽  
Rami K. Korhonen
Keyword(s):  
Fluid Flow ◽  
Large Deformations ◽  
Large Strain ◽  
Single Cells ◽  
Material Model ◽  
Cell Types ◽  
Micropipette Aspiration ◽  
Bulk Relaxation ◽  
Optimal Material ◽  
Different Cell Types

Micropipette aspiration (MA) technique has been widely used to measure the viscoelastic properties of different cell types. Cells experience nonlinear large deformations during the aspiration procedure. Neo-Hookean viscohyperelastic (NHVH) incompressible and compressible models were used to simulate the creep behavior of cells in MA, particularly accounting for the effect of compressibility, bulk relaxation, and hardening phenomena under large strain. In order to find optimal material parameters, the models were fitted to the experimental data available for mesenchymal stem cells. Finally, through Neo-Hookean porohyperelastic (NHPH) material model for the cell, the influence of fluid flow on the aspiration length of the cell was studied. Based on the results, we suggest that the compressibility and bulk relaxation/fluid flow play a significant role in the deformation behavior of single cells and should be taken into account in the analysis of the mechanics of cells.

Comparison of Three Material Models to Predict the Time-Dependent Deformation of a Single Cell Under Micropipette Aspiration

2008 ◽  
Author(s):  
Ruogang Zhao ◽  
Kristine Wyss ◽  
Craig A. Simmons
Keyword(s):  
Experimental Data ◽  
Large Strain ◽  
Single Cells ◽  
Material Model ◽  
Micropipette Aspiration ◽  
Time Dependent ◽  
Large Strains ◽  
Fe Model ◽  
Whole Cells ◽  
Model Combining

Micropipette aspiration is an experimental technique that is used widely to measure the mechanical properties of single cells [1]. The viscoelastic properties of the probed cell are often estimated by fitting experimental data to a three-parameter standard linear solid (SLS) half-space model (e.g., [1]). However, this analytical model does not account for the large strains that can occur with micropipette aspiration. This limitation has motivated the development of numerical methods to interpret the experimental data. For example, Zhou [2] implemented a material model combining a hyperelastic neo-Hookean material and a viscoelastic SLS material in an axisymmetric finite element (FE) model to simulate large strain micropipette aspiration of a suspended cell. The time-dependent creep deformation of cells has also been described by power-law rheology [3]; this material model has been applied to micropipette aspiration of nuclei [4], but not whole cells.

scholarly journals Computational approaches towards reducing contamination in single-cell RNA-seq data

2020 ◽  
Author(s):  
Siamak Yousefi ◽  
Hao Chen ◽  
Jesse F. Ingels ◽  
Melinda S. McCarty ◽  
Arthur G. Centeno ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Real Life ◽  
Cell Types ◽  
Cell Capture ◽  
Rna Seq ◽  
Sequence Analyses ◽  
Cell Functions ◽  
Biological Interpretation ◽  
Different Cell Types

SUMMARYSingle cell RNA sequencing has enabled quantification of single cells and identification of different cell types and subtypes as well as cell functions in different tissues. Single cell RNA sequence analyses assume acquired RNAs correspond to cells, however, RNAs from contamination within the input data are also captured by these assays. The sequencing of background contamination as well as unwanted cells making their way to the final assay Potentially confound the correct biological interpretation of single cell transcriptomic data. Here we demonstrate two approaches to deal with background contamination as well as profiling of unwanted cells in the assays. We use three real-life datasets of whole-cell capture and nucleotide single-cell captures generated by Fluidigm and 10x technologies and show that these methods reduce the effect of contamination, strengthen clustering of cells and improves biological interpretation.

scholarly journals Mapping multicellular programs from single-cell profiles

2020 ◽  
Author(s):  
Livnat Jerby-Arnon ◽  
Aviv Regev
Keyword(s):  
Single Cell ◽  
Spatial Data ◽  
Spatial Information ◽  
Single Cells ◽  
Cell Types ◽  
Risk Genes ◽  
Health And Disease ◽  
Different Cell Types ◽  
Cellular Components ◽  
Acting In Concert

ABSTRACTTissue homeostasis relies on orchestrated multicellular circuits, where interactions between different cell types dynamically balance tissue function. While single-cell genomics identifies tissues’ cellular components, deciphering their coordinated action remains a major challenge. Here, we tackle this problem through a new framework of multicellular programs: combinations of distinct cellular programs in different cell types that are coordinated together in the tissue, thus forming a higher order functional unit at the tissue, rather than only cell, level. We develop the open-access DIALOGUE algorithm to systematically uncover such multi-cellular programs not only from spatial data, but even from tissue dissociated and profiled as single cells, e.g., by single-cell RNA-Seq. Tested on spatial transcriptomes from the mouse hypothalamus, DIALOGUE recovered spatial information, predicted the properties of a cell’s environment only based on its transcriptome, and identified multicellular programs that mark animal behavior. Applied to brain samples and colon biopsies profiled by scRNA-Seq, DIALOGUE identified multicellular configurations that mark Alzheimer’s disease and ulcerative colitis (UC), including a program spanning five cell types that is predictive of response to anti-TNF therapy in UC patients and enriched for UC risk genes from GWAS, each acting in different cell types, but all cells acting in concert. Taken together, our study provides a novel conceptual and methodological framework to unravel multicellular regulation in health and disease.

scholarly journals Unfolding and identification of membrane proteins from native cell membranes

2019 ◽  
Author(s):  
Nicola Galvanetto ◽  
Sourav Maity ◽  
Nina Ilieva ◽  
Zhongjie Ye ◽  
Alessandro Laio ◽  
...  
Keyword(s):  
Membrane Proteins ◽  
Single Molecule ◽  
Single Cells ◽  
Total Content ◽  
Cell Types ◽  
Single Cell Level ◽  
Cell Level ◽  
Single Molecule Force Spectroscopy ◽  
Native Cell ◽  
Different Cell Types

AbstractIs the mechanical unfolding of proteins just a technological feat applicable only to synthetic preparations or can it provide useful information even for real biological samples? Here, we describe a pipeline for analyzing native membranes based on high throughput single-molecule force spectroscopy. The protocol includes a technique for the isolation of the plasma membrane of single cells. Afterwards, one harvests hundreds of thousands SMSF traces from the sample. Finally, one characterizes and identifies the embedded membrane proteins. This latter step is the cornerstone of our approach and involves combining, within a Bayesian framework, the information of the shape of the SMFS Force-distance which are observed more frequently, with the information from Mass Spectrometry and from proteomic databases (Uniprot, PDB). We applied this method to four cell types where we classified the unfolding of 5-10% of their total content of membrane proteins. The ability to mechanically probe membrane proteins directly in their native membrane enables the phenotyping of different cell types with almost single-cell level of resolution.

scholarly journals Massively multiplex single-cell Hi-C

2016 ◽  
Author(s):  
Vijay Ramani ◽  
Xinxian Deng ◽  
Kevin L Gunderson ◽  
Frank J Steemers ◽  
Christine M Disteche ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
Cell Heterogeneity ◽  
Proof Of Concept ◽  
Chromosome Conformation ◽  
Large Numbers ◽  
Conformational Properties ◽  
Novel Method ◽  
Different Cell Types

AbstractWe present combinatorial single cell Hi-C, a novel method that leverages combinatorial cellular indexing to measure chromosome conformation in large numbers of single cells. In this proof-of-concept, we generate and sequence combinatorial single cell Hi-C libraries for two mouse and four human cell types, comprising a total of 9,316 single cells across 5 experiments. We demonstrate the utility of single-cell Hi-C data in separating different cell types, identify previously uncharacterized cell-to-cell heterogeneity in the conformational properties of mammalian chromosomes, and demonstrate that combinatorial indexing is a generalizable molecular strategy for single-cell genomics.

scholarly journals Massively parallel single cell lineage tracing using CRISPR/Cas9 induced genetic scars

2017 ◽  
Author(s):  
Bastiaan Spanjaard ◽  
Bo Hu ◽  
Nina Mitic ◽  
Jan Philipp Junker
Keyword(s):  
Single Cell ◽  
Computational Analysis ◽  
Systematic Approach ◽  
Single Cells ◽  
Cell Lineage ◽  
Transcriptome Profiling ◽  
Cell Types ◽  
Lineage Tracing ◽  
Lineage Trees ◽  
Different Cell Types

A key goal of developmental biology is to understand how a single cell transforms into a full-grown organism consisting of many different cell types. Single-cell RNA-sequencing (scRNA-seq) has become a widely-used method due to its ability to identify all cell types in a tissue or organ in a systematic manner 1–3. However, a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here, we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA-seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes, we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses, LINNAEUS (LINeage tracing by Nuclease-Activated Editing of Ubiquitous Sequences) can be used as a systematic approach for identifying the lineage origin of novel cell types, or of known cell types under different conditions.

scholarly journals The Heterocyst-Specific NsiR1 Small RNA Is an Early Marker of Cell Differentiation in Cyanobacterial Filaments

mBio ◽  
2014 ◽  
Vol 5 (3) ◽  
Author(s):  
Alicia M. Muro-Pastor
Keyword(s):  
Small Rna ◽  
Single Cells ◽  
Nitrogen Deficiency ◽  
Cell Types ◽  
Nitrogen Fixing ◽  
Vegetative Cells ◽  
Early Marker ◽  
Multicellular Organisms ◽  
Pcc 7120 ◽  
Different Cell Types

ABSTRACT Differentiation of single cells along filaments of cyanobacteria constitutes one of the simplest developmental patterns in nature. In response to nitrogen deficiency, certain cells located in a semiregular pattern along filaments differentiate into specialized nitrogen-fixing cells called heterocysts. The process involves the sequential activation of many genes whose expression takes place, either exclusively or at least more strongly, in those cells undergoing differentiation. In the model cyanobacterium Anabaena (Nostoc) sp. strain PCC 7120, increased transcription of hetR, considered the earliest detectable heterocyst-specific transcript, has been reported to occur in pairs or even in clusters of cells, thus making it difficult to identify prospective heterocysts during the early stages of differentiation, before any morphological change is detectable. The promoter of nsiR1 (nitrogen stress inducible RNA1), a heterocyst-specific small RNA, constitutes a minimal sequence promoting heterocyst-specific transcription. Using confocal fluorescence microscopy, I have analyzed expression of a gfp reporter transcriptionally fused to P nsiR1 . The combined analysis of green fluorescence (reporting transcriptional activity from P nsiR1 ) and red fluorescence (an indication of progress in the differentiation of individual cells) shows that expression of P nsiR1 takes place in single cells located in a semiregular pattern before any other morphological or fluorescence signature of differentiation can be observed, thus providing an early marker for cells undergoing differentiation. IMPORTANCE Cyanobacterial filaments containing heterocysts constitute an example of bacterial division of labor. When using atmospheric nitrogen, these filaments behave as multicellular organisms in which two different cell types (vegetative cells and nitrogen-fixing heterocysts) coexist and cooperate to achieve growth of the filament as a whole. The molecular basis governing the differentiation of individual vegetative cells, and thus the establishment of a one-dimensional pattern from cells that are apparently the same, remains one of the most intriguing aspects of this differentiation process. Recent evidence suggests that, at any given time, some cells in the filaments are more likely than others to become heterocysts when nitrogen limitation is encountered. The robust heterocyst-specific nsiR1 promoter, which is induced very early during differentiation, provides a valuable tool to analyze issues such as early candidacy or the possible role of transcriptional noise in determining the fate of specific cells in cyanobacterial filaments.

scholarly journals ProtAnno, an Automated Cell Type Annotation Tool for Single Cell Proteomics Data that Integrates Information from Multiple Reference Sources

2021 ◽  
Author(s):  
Wenxuan Deng ◽  
Biqing Zhu ◽  
Seyoung Park ◽  
Tomokazu S. Sumida ◽  
Avraham Unterman ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Earth Metal ◽  
Cell Types ◽  
Specific Cell ◽  
Cell Type ◽  
Proteomics Data ◽  
Data Annotation ◽  
Different Cell Types ◽  
Unique Molecular Identifier

Compared with sequencing-based global genomic profiling, cytometry labels targeted surface markers on millions of cells in parallel either by conjugated rare earth metal particles or Unique Molecular Identifier (UMI) barcodes. Correct annotation of these cells to specific cell types is a key step in the analysis of these data. However, there is no computational tool that automatically annotates single cell proteomics data for cell type inference. In this manuscript, we propose an automated single cell proteomics data annotation approach called ProtAnno to facilitate cell type assignments without laborious manual gating. ProtAnno is designed to incorporate information from annotated single cell RNA-seq (scRNA-seq), CITE-seq, and prior data knowledge (which can be imprecise) on biomarkers for different cell types. We have performed extensive simulations to demonstrate the accuracy and robustness of ProtAnno. For several single cell proteomics datasets that have been manually labeled, ProtAnno was able to correctly label most single cells. In summary, ProtAnno offers an accurate and robust tool to automate cell type annotations for large single cell proteomics datasets, and the analysis of such annotated cell types can offer valuable biological insights.

scholarly journals Comprehensive characterization of single-cell full-length isoforms in human and mouse with long-read sequencing

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Luyi Tian ◽  
Jafar S. Jabbari ◽  
Rachel Thijssen ◽  
Quentin Gouil ◽  
Shanika L. Amarasinghe ◽  
...  
Keyword(s):  
Ribosome Biogenesis ◽  
Single Cells ◽  
Transcript Level ◽  
Cell Types ◽  
Alternative Transcript ◽  
Long Read ◽  
Different Cell Types ◽  
Human And Mouse ◽  
Comprehensive Characterization

AbstractA modified Chromium 10x droplet-based protocol that subsamples cells for both short-read and long-read (nanopore) sequencing together with a new computational pipeline (FLAMES) is developed to enable isoform discovery, splicing analysis, and mutation detection in single cells. We identify thousands of unannotated isoforms and find conserved functional modules that are enriched for alternative transcript usage in different cell types and species, including ribosome biogenesis and mRNA splicing. Analysis at the transcript level allows data integration with scATAC-seq on individual promoters, improved correlation with protein expression data, and linked mutations known to confer drug resistance to transcriptome heterogeneity.

scholarly journals FSI Computation and Experimental Verification of Fluid Flow in Flexible Tubes

2020 ◽  
Vol 20 (3) ◽  
pp. 104-114
Author(s):  
Dominik Šedivý ◽  
Simona Fialová ◽  
Roman Klas ◽  
Michal Kotek
Keyword(s):  
Fluid Flow ◽  
Large Deformations ◽  
Computational Study ◽  
Experimental Testing ◽  
Material Model ◽  
Tube Material ◽  
Experimental Stand ◽  
Flexible Walls ◽  
Newtonian Liquids ◽  
Flexible Tubes

AbstractPresented paper is focused on the experimental and computational study of fluid flow in pipes with flexible walls. One possible real example of this phenomenon is the blood flow in arteries or their substitutes in the human body. The artery material itself should be understood as anisotropic and heterogeneous. Therefore, the experiment was carried out on the deforming tube, made of silicone (polydimethylsiloxane - PDMS). Obtained results and observed events were verified by numerical FSI simulations. Due to the large deformations occurring during loading of the tube, it was necessary to work with a dynamic mesh in the CFD part. Based on experimental testing of the tube material, a non-Hookean and Mooney-Rivlin material model were considered. Blood flowing in vessels is a heterogeneous liquid and exhibits non-Newtonian properties. In the real experimental stand has been somewhat simplified. Water, chosen as the liquid, belongs to the Newtonian liquids. The results show mainly comparisons of unsteady velocity profiles between the experiment and the numerical model.

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