scholarly journals Single-Cell Phenotypic Analysis and Digital Molecular Detection Linkable by a Hydrogel Bead-Based Platform

2021 ◽  
Author(s):  
Yanzhe Zhu ◽  
Jing Li ◽  
Xingyu Lin ◽  
Xiao Huang ◽  
Michael R. Hoffmann
Keyword(s):  
Single Cell ◽  
Molecular Detection ◽  
Phenotypic Analysis ◽  
Hydrogel Bead

scholarly journals A hydrogel beads based platform for single-cell phenotypic analysis and digital molecular detection

2019 ◽  
Author(s):  
Yanzhe Zhu ◽  
Jing Li ◽  
Xingyu Lin ◽  
Xiao Huang ◽  
Michael R. Hoffmann
Keyword(s):  
Single Cell ◽  
Molecular Detection ◽  
Basic Research ◽  
Genotypic Differences ◽  
Phenotypic Analysis ◽  
Hydrogel Beads ◽  
Research Areas ◽  
Hydrogel Network ◽  
Digital Polymerase Chain Reaction ◽  
Environment Evolution

AbstractMicrofluidic platforms integrating phenotyping and genotyping approaches have the potential to advance the understanding of single cell genotype-to-phenotype correlations. These correlations can play a key role in tackling antibiotic heteroresistance, cancer cell heterogeneity, and other related fundamental problems. Herein, we report a novel platform that enables both high-throughput digital molecular detection and single-cell phenotypic analysis, utilizing nanoliter-sized biocompatible polyethylene glycol hydrogel beads produced by a convenient and disposable centrifugal droplet generation device. The hydrogel beads have been demonstrated enhanced thermal stability, and achieved uncompromised efficiencies in digital polymerase chain reaction, digital loop-mediated isothermal amplification, and single cell phenotyping. The crosslinked hydrogel network highlights the prospective linkage of various subsequent molecular analyses to address the genotypic differences between cellular subpopulations exhibiting distinct phenotypes. Our platform shows great potential for applications in clinical practice and medical research, and promises new perspectives in mechanism elucidation of environment-evolution interaction and other basic research areas.

scholarly journals Single-cell analysis for drug development using convex lens-induced confinement imaging

BioTechniques ◽  
2019 ◽  
Vol 67 (5) ◽  
pp. 210-217 ◽  
Author(s):  
Ndeye Khady Thiombane ◽  
Nicolas Coutin ◽  
Daniel Berard ◽  
Radin Tahvildari ◽  
Sabrina Leslie ◽  
...  
Keyword(s):  
Single Cell ◽  
Drug Response ◽  
Drug Exposure ◽  
Cellular Proliferation ◽  
New Technologies ◽  
Detection Sensitivity ◽  
Yeast Cells ◽  
Sample Population ◽  
Phenotypic Analysis ◽  
Convex Lens

New technologies have powered rapid advances in cellular imaging, genomics and phenotypic analysis in life sciences. However, most of these methods operate at sample population levels and provide statistical averages of aggregated data that fail to capture single-cell heterogeneity, complicating drug discovery and development. Here we demonstrate a new single-cell approach based on convex lens-induced confinement (CLiC) microscopy. We validated CLiC on yeast cells, demonstrating subcellular localization with an enhanced signal-to-noise and fluorescent signal detection sensitivity compared with traditional imaging. In the live-cell CLiC assay, cellular proliferation times were consistent with flask culture. Using methotrexate, we provide drug response data showing a fivefold cell size increase following drug exposure. Taken together, CLiC enables high-quality imaging of single-cell drug response and proliferation for extended observation periods.

scholarly journals High-Content Image-Based Single-Cell Phenotypic Analysis for the Testicular Toxicity Prediction Induced by Bisphenol A and Its Analogs Bisphenol S, Bisphenol AF, and Tetrabromobisphenol A in a Three-Dimensional Testicular Cell Co-culture Model

2019 ◽  
Vol 173 (2) ◽  
pp. 313-335 ◽  
Author(s):  
Lei Yin ◽  
Jacob Steven Siracusa ◽  
Emily Measel ◽  
Xueling Guan ◽  
Clayton Edenfield ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Three Dimensional ◽  
Testicular Cell ◽  
Phenotypic Analysis ◽  
Bisphenol S ◽  
Cytoskeletal Structure ◽  
Culture Model ◽  
Bisphenol Af

Abstract Emerging data indicate that structural analogs of bisphenol A (BPA) such as bisphenol S (BPS), tetrabromobisphenol A (TBBPA), and bisphenol AF (BPAF) have been introduced into the market as substitutes for BPA. Our previous study compared in vitro testicular toxicity using murine C18-4 spermatogonial cells and found that BPAF and TBBPA exhibited higher spermatogonial toxicities as compared with BPA and BPS. Recently, we developed a novel in vitro three-dimensional (3D) testicular cell co-culture model, enabling the classification of reproductive toxic substances. In this study, we applied the testicular cell co-culture model and employed a high-content image (HCA)-based single-cell analysis to further compare the testicular toxicities of BPA and its analogs. We also developed a machine learning (ML)-based HCA pipeline to examine the complex phenotypic changes associated with testicular toxicities. We found dose- and time-dependent changes in a wide spectrum of adverse endpoints, including nuclear morphology, DNA synthesis, DNA damage, and cytoskeletal structure in a single-cell-based analysis. The co-cultured testicular cells were more sensitive than the C18 spermatogonial cells in response to BPA and its analogs. Unlike conventional population-averaged assays, single-cell-based assays not only showed the levels of the averaged population, but also revealed changes in the sub-population. Machine learning-based phenotypic analysis revealed that treatment of BPA and its analogs resulted in the loss of spatial cytoskeletal structure, and an accumulation of M phase cells in a dose- and time-dependent manner. Furthermore, treatment of BPAF-induced multinucleated cells, which were associated with altered DNA damage response and impaired cellular F-actin filaments. Overall, we demonstrated a new and effective means to evaluate multiple toxic endpoints in the testicular co-culture model through the combination of ML and high-content image-based single-cell analysis. This approach provided an in-depth analysis of the multi-dimensional HCA data and provided an unbiased quantitative analysis of the phenotypes of interest.

scholarly journals An Optically Decodable Bead Array for Linking Imaging and Sequencing with Single-Cell Resolution

2018 ◽  
Author(s):  
Jinzhou Yuan ◽  
Jenny Sheng ◽  
Peter A. Sims
Keyword(s):  
Single Cell ◽  
Expression Profiling ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Living Cells ◽  
Live Cell ◽  
Proof Of Concept ◽  
Rna Seq ◽  
Phenotypic Analysis

AbstractOptically decodable beads link the identity of an analyte or sample to a measurement through an optical barcode, enabling libraries of biomolecules to be captured on beads in solution and decoded by fluorescence. This approach has been foundational to microarray, sequencing, and flow-based expression profiling technologies. We have combined microfluidics with optically decodable beads to link phenotypic analysis of living cells to sequencing. As a proof-of-concept, we applied this to demonstrate an accurate and scalable tool for connecting live cell imaging to single-cell RNA-Seq called Single Cell Optical Phenotyping and Expression (SCOPE-Seq).

Abstract 977: Phenotypic analysis of single-cell breast cancer inhibition data reveals insights into EMT

2017 ◽  
Author(s):  
William S. Chen ◽  
Nevena Zivanovic ◽  
Dana Pe'er ◽  
Bernd Bodenmiller ◽  
Smita Krishnaswamy
Keyword(s):  
Breast Cancer ◽  
Single Cell ◽  
Phenotypic Analysis ◽  
Inhibition Data ◽  
Breast Cancer Inhibition ◽  
Cancer Inhibition

Abstract 219: A high throughput single-cell workflow for paired genomic and phenotypic analysis

2020 ◽  
Author(s):  
Dalia Dhingra ◽  
Pedro Mendez ◽  
Aik Ooi ◽  
Shu Wang ◽  
Saurabh Gulati ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Phenotypic Analysis

MEMS FOR CELLULAR FORCE MEASUREMENTS AND MOLECULAR DETECTION

2004 ◽  
Vol 01 (01) ◽  
pp. 23-32 ◽  
Author(s):  
YU SUN ◽  
BRADLEY J. NELSON
Keyword(s):  
Single Cell ◽  
Molecular Detection ◽  
Force Measurement ◽  
Quantitative Information ◽  
Force Measurements ◽  
Mems Technology

MEMS technology and devices have proven their importance in facilitating single cell studies by providing quantitative information on cellular and sub-cellular levels. This paper reviews existing techniques for cellular and sub-cellular force measurement and molecular detection using MEMS-based devices. Literature on these techniques and sample devices is reviewed. The significance and limitations of various approaches are analyzed.

scholarly journals Microfluidic particle zipper enables controlled loading of droplets with distinct particle types

Lab on a Chip ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 2465-2472 ◽  
Author(s):  
Cyrille L. Delley ◽  
Adam R. Abate
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Hydrogel Bead ◽  
Distinct Particle

We describe a microfluidic particle zipper which enables hydrogel bead pairing at high throughput for single-cell genomic applications.

Live single-cell video-mass spectrometry for cellular and subcellular molecular detection and cell classification

2008 ◽  
Vol 43 (12) ◽  
pp. 1692-1700 ◽  
Author(s):  
Hajime Mizuno ◽  
Naohiro Tsuyama ◽  
Takanori Harada ◽  
Tsutomu Masujima
Keyword(s):  
Mass Spectrometry ◽  
Single Cell ◽  
Molecular Detection ◽  
Cell Classification

scholarly journals Show me your neighbours, and I’ll tell you what you are – cellular microenvironment matters

2017 ◽  
Author(s):  
Timea Toth ◽  
Tamas Balassa ◽  
Norbert Bara ◽  
Ferenc Kovacs ◽  
Andras Kriston ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Cell Cultures ◽  
Cell Biology ◽  
Single Cell Level ◽  
Cell Level ◽  
Phenotypic Analysis ◽  
Additional Information ◽  
A Cell ◽  
Local Properties

AbstractTo answer major questions of cell biology, it is essential to understand cellular complexity. Modern automated microscopes produce vast amounts of images routinely, making manual analysis nearly impossible. Due to their efficiency, machine learning-based analysis software have become essential tools to perform single-cell-level phenotypic analysis of large imaging datasets. However, an important limitation of such methods is that they do not use the information gained from the cellular micro- and macroenvironment: the algorithmic decision is based solely on the local properties of the cell of interest. Here, we present how various microenvironmental features contribute to identifying a cell and how such additional information can improve single-cell-level phenotypic image analysis. The proposed methodology was tested for different sizes of Euclidean and nearest neighbour-based cellular environments both on tissue sections and cell cultures. Our experimental data verify that the microenvironment of a cell largely determines its entity. This effect was found to be especially strong for established tissues, while it was somewhat weaker in the case of cell cultures. Our analysis shows that combining local cellular features with the properties of the cell's microenvironment significantly improves the accuracy of machine learning-based phenotyping.

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