scholarly journals 3D magnetically controlled spatiotemporal probing and actuation of collagen networks from a single cell perspective

Lab on a Chip ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 3850-3862
Author(s):  
Daphne O. Asgeirsson ◽  
Michael G. Christiansen ◽  
Thomas Valentin ◽  
Luca Somm ◽  
Nima Mirkhani ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Extracellular Matrix ◽  
Image Analysis ◽  
Matrix Models ◽  
Single Cell ◽  
Physical Modeling ◽  
Single Cells ◽  
Field Control

Rod-shaped magnetic microprobes are employed to assess and actuate extracellular matrix models in 3D from the perspective of single cells. To achieve this, our method combines magnetic field control, physical modeling, and image analysis.

scholarly journals Single-cell motion of magnetotactic bacteria in microfluidic confinement: interplay between surface interaction and magnetic torque

2021 ◽  
Author(s):  
Agnese Codutti ◽  
Mohammad A. Charsooghi ◽  
Elisa Cerdá-Doñate ◽  
Hubert M. Taïeb ◽  
Tom Robinson ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Single Cell ◽  
Cell Tracking ◽  
Brownian Particle ◽  
Single Cells ◽  
Natural Habitat ◽  
Magnetotactic Bacteria ◽  
Particle Model ◽  
Complex Environments ◽  
Magnetic Torque

AbstractSwimming microorganisms often experience complex environments in their natural habitat. The same is true for microswimmers in envisioned biomedical applications. The simple aqueous conditions typically studied in the lab differ strongly from those found in these environments and often exclude the effects of small volume confinement or the influence that external fields have on their motion. In this work, we investigate magnetically steerable microswimmers, specifically magnetotactic bacteria, in strong spatial confinement and under the influence of an external magnetic field. We trap single cells in micrometer-sized microfluidic chambers and track and analyze their motion, which shows a variety of different trajectories, depending on the chamber size and the strength of the magnetic field. Combining these experimental observations with simulations using a variant of an active Brownian particle model, we explain the variety of trajectories by the interplay between the wall interactions and the magnetic torque. We also analyze the pronounced cell-to-cell heterogeneity, which makes single-cell tracking essential for an understanding of the motility patterns. In this way, our work establishes a basis for the analysis and prediction of microswimmer motility in more complex environments.

scholarly journals Exploring endocytic compartment morphology with systematic genetics and single cell image analysis

2019 ◽  
Author(s):  
Mojca Mattiazzi Usaj ◽  
Nil Sahin ◽  
Helena Friesen ◽  
Carles Pons ◽  
Matej Usaj ◽  
...  
Keyword(s):  
Image Analysis ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Endocytic Pathway ◽  
Cellular Heterogeneity ◽  
Incomplete Penetrance ◽  
Coat Proteins ◽  
Mutant Phenotypes ◽  
Membrane Components

ABSTRACTEndocytosis is a conserved process that mediates the internalization of nutrients and plasma membrane components, including receptors, for sorting to endosomes and the vacuole (lysosome). We combined systematic yeast genetics, high-content screening, and neural network-based image analysis of single cells to screen for genes that influence the morphology of four main endocytic compartments: coat proteins, actin patches, late endosome, and vacuole. This unbiased approach identified 17 mutant phenotypes and ∼1600 genes whose perturbation affected at least one of the four compartments. Numerous mutants were associated with multiple phenotypes, indicating that morphological pleiotropy is often seen within the endocytic pathway. Morphological profiles based on the 17 aberrant phenotypes were highly correlated for functionally related genes, enabling prediction of gene function. Incomplete penetrance was prevalent, and single-cell analysis enabled exploration of the mechanisms underlying cellular heterogeneity, which include replicative age, organelle inheritance, and stress response.

scholarly journals An image-based assay to quantify changes in proliferation and viability upon drug treatment in 3D microenvironments

2018 ◽  
Author(s):  
Vasanth S. Murali ◽  
Bo-Jui Chang ◽  
Reto Fiolka ◽  
Gaudenz Danuser ◽  
Murat Can Cobanoglu ◽  
...  
Keyword(s):  
Image Analysis ◽  
Drug Treatment ◽  
Single Cell ◽  
Cell Fate ◽  
High Throughput ◽  
Single Cells ◽  
3D Culture ◽  
Melanoma Cells ◽  
Critical Parameters ◽  
3D Collagen

AbstractBackgroundEvery biological experiment requires a choice of throughput balanced against physiological relevance. Most primary drugs screens neglect critical parameters such as microenvironmental conditions, cell-cell heterogeneity, and specific readouts of cell fate for the sake of throughput.MethodsHere we describe a methodology to quantify proliferation and viability of single cells in 3D culture conditions by leveraging automated microscopy and image analysis to facilitate reliable and high-throughput measurements. We detail experimental conditions that can be adjusted to increase either throughput or robustness of the assay, and we provide a stand alone image analysis program for users who wish to implement this 3D drug screening assay in high throughput.ResultsWe demonstrate this approach by evaluating a combination of RAF and MEK inhibitors on melanoma cells, showing that cells cultured in 3D collagen-based matrices are more sensitive than cells grown in 2D culture, and that cell proliferation is much more sensitive than cell viability. We also find that cells grown in 3D cultured spheroids exhibit equivalent sensitivity to single cells grown in 3D collagen, suggesting that for the case of melanoma, a 3D single cell model may be equally effective for drug identification as 3D spheroids models. The single cell resolution of this approach enables stratification of heterogeneous populations of cells into differentially responsive subtypes upon drug treatment, which we demonstrate by determining the effect of RAK/MEK inhibition on melanoma cells co-cultured with fibroblasts. Furthermore, we show that spheroids grown from single cells exhibit dramatic heterogeneity to drug response, suggesting that heritable drug resistance can arise stochastically in single cells but be retained by subsequent generations.ConclusionIn summary, image-based analysis renders cell fate detection robust, sensitive, and high-throughput, enabling cell fate evaluation of single cells in more complex microenvironmental conditions.

scholarly journals Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow

Micromachines ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 311 ◽  
Author(s):  
Iordania Constantinou ◽  
Michael Jendrusch ◽  
Théo Aspert ◽  
Frederik Görlitz ◽  
André Schulze ◽  
...  
Keyword(s):  
Image Analysis ◽  
Single Cell ◽  
Cell Imaging ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Microfluidic Devices ◽  
Complex Data ◽  
Shot Classification ◽  
Single Cell Imaging ◽  
Small Channels

Single-cell analysis commonly requires the confinement of cell suspensions in an analysis chamber or the precise positioning of single cells in small channels. Hydrodynamic flow focusing has been broadly utilized to achieve stream confinement in microchannels for such applications. As imaging flow cytometry gains popularity, the need for imaging-compatible microfluidic devices that allow for precise confinement of single cells in small volumes becomes increasingly important. At the same time, high-throughput single-cell imaging of cell populations produces vast amounts of complex data, which gives rise to the need for versatile algorithms for image analysis. In this work, we present a microfluidics-based platform for single-cell imaging in-flow and subsequent image analysis using variational autoencoders for unsupervised characterization of cellular mixtures. We use simple and robust Y-shaped microfluidic devices and demonstrate precise 3D particle confinement towards the microscope slide for high-resolution imaging. To demonstrate applicability, we use these devices to confine heterogeneous mixtures of yeast species, brightfield-image them in-flow and demonstrate fully unsupervised, as well as few-shot classification of single-cell images with 88% accuracy.

scholarly journals Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting

2018 ◽  
Author(s):  
Alex X Lu ◽  
Oren Z Kraus ◽  
Sam Cooper ◽  
Alan M Moses
Keyword(s):  
Image Analysis ◽  
Single Cell ◽  
Single Cells ◽  
Training Data ◽  
High Quality ◽  
Feature Representations ◽  
Starting Point ◽  
Microscopy Images ◽  
Quality Features ◽  
Paired Cell

AbstractCellular microscopy images contain rich insights about biology. To extract this information, researchers use features, or measurements of the patterns of interest in the images. Here, we introduce a convolutional neural network (CNN) to automatically design features for fluorescence microscopy. We use a self-supervised method to learn feature representations of single cells in microscopy images without labelled training data. We train CNNs on a simple task that leverages the inherent structure of microscopy images and controls for variation in cell morphology and imaging: given one cell from an image, the CNN is asked to predict the fluorescence pattern in a second different cell from the same image. We show that our method learns high-quality features that describe protein expression patterns in single cells both yeast and human microscopy datasets. Moreover, we demonstrate that our features are useful for exploratory biological analysis, by capturing high-resolution cellular components in a proteome-wide cluster analysis of human proteins, and by quantifying multi-localized proteins and single-cell variability. We believe paired cell inpainting is a generalizable method to obtain feature representations of single cells in multichannel microscopy images.Author SummaryTo understand the cell biology captured by microscopy images, researchers use features, or measurements of relevant properties of cells, such as the shape or size of cells, or the intensity of fluorescent markers. Features are the starting point of most image analysis pipelines, so their quality in representing cells is fundamental to the success of an analysis. Classically, researchers have relied on features manually defined by imaging experts. In contrast, deep learning techniques based on convolutional neural networks (CNNs) automatically learn features, which can outperform manually-defined features at image analysis tasks. However, most CNN methods require large manually-annotated training datasets to learn useful features, limiting their practical application. Here, we developed a new CNN method that learns high-quality features for single cells in microscopy images, without the need for any labeled training data. We show that our features surpass other comparable features in identifying protein localization from images, and that our method can generalize to diverse datasets. By exploiting our method, researchers will be able to automatically obtain high-quality features customized to their own image datasets, facilitating many downstream analyses, as we highlight by demonstrating many possible use cases of our features in this study.

In-Situ Dual Beam (FIBSEM) Techniques for Probe Pad Deposition and Dielectric Integrity Inspection in 0.2 μm Technology DRAM Single Cells

1999 ◽  
Author(s):  
Gunnar Zimmermann ◽  
Richard Chapman
Keyword(s):  
Electron Beam ◽  
Single Cell ◽  
Single Cells ◽  
Ion Beam ◽  
Gate Oxide ◽  
Ion Milling ◽  
Dual Beam ◽  
Sem Imaging ◽  
Innovative Techniques

Abstract Dual beam FIBSEM systems invite the use of innovative techniques to localize IC fails both electrically and physically. For electrical localization, we present a quick and reliable in-situ FIBSEM technique to deposit probe pads with very low parasitic leakage (Ipara < 4E-11A at 3V). The probe pads were Pt, deposited with ion beam assistance, on top of highly insulating SiOx, deposited with electron beam assistance. The buried plate (n-Band), p-well, wordline and bitline of a failing and a good 0.2 μm technology DRAM single cell were contacted. Both cells shared the same wordline for direct comparison of cell characteristics. Through this technique we electrically isolated the fail to a single cell by detecting leakage between the polysilicon wordline gate and the cell diffusion. For physical localization, we present a completely in-situ FIBSEM technique that combines ion milling, XeF2 staining and SEM imaging. With this technique, the electrically isolated fail was found to be a hole in the gate oxide at the bad cell.

scholarly journals Visually precise, low-damage, single-cell spatial manipulation with single-pixel resolution

2021 ◽  
Vol 12 (11) ◽  
pp. 4111-4118
Author(s):  
Qi Zhang ◽  
Yunlong Shao ◽  
Boye Li ◽  
Yuanyuan Wu ◽  
Jingying Dong ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Pixel Resolution ◽  
Single Pixel

We achieved the low-damage spatial puncture of single cells at specific visual points with an accuracy of <65 nm.

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