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 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 analysis of circadian dynamics in tissue explants

2015 ◽  
Vol 26 (22) ◽  
pp. 3940-3945 ◽  
Author(s):  
Laura Lande-Diner ◽  
Jacob Stewart-Ornstein ◽  
Charles J. Weitz ◽  
Galit Lahav
Keyword(s):  
Circadian Clock ◽  
Single Cell ◽  
Cell Tracking ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Tissue Imaging ◽  
Wide Field ◽  
Isolated Cells ◽  
Peripheral Tissues

Tracking molecular dynamics in single cells in vivo is instrumental to understanding how cells act and interact in tissues. Current tissue imaging approaches focus on short-term observation and typically nonendogenous or implanted samples. Here we develop an experimental and computational setup that allows for single-cell tracking of a transcriptional reporter over a period of >1 wk in the context of an intact tissue. We focus on the peripheral circadian clock as a model system and measure the circadian signaling of hundreds of cells from two tissues. The circadian clock is an autonomous oscillator whose behavior is well described in isolated cells, but in situ analysis of circadian signaling in single cells of peripheral tissues is as-yet uncharacterized. Our approach allowed us to investigate the oscillatory properties of individual clocks, determine how these properties are maintained among different cells, and assess how they compare to the population rhythm. These experiments, using a wide-field microscope, a previously generated reporter mouse, and custom software to track cells over days, suggest how many signaling pathways might be quantitatively characterized in explant models.

scholarly journals Functional single cell proteomic profiling of cells with abnormal DNA damage response dynamics

2021 ◽  
Author(s):  
Pin-Rui Su ◽  
Li You ◽  
Cecile Beerens ◽  
Karel Bezstarosti ◽  
Jeroen Demmers ◽  
...  
Keyword(s):  
Dna Damage ◽  
Single Cell ◽  
Dna Damage Response ◽  
Cell Tracking ◽  
Single Cells ◽  
Proteomic Profiling ◽  
Response Dynamics ◽  
Isolated Cells ◽  
Cellular Dynamics ◽  
Damage Response

Tumor heterogeneity is an important source of cancer therapy resistance. Single cell proteomics has the potential to decipher protein content leading to heterogeneous cellular phenotypes. Single-Cell ProtEomics by Mass Spectrometry (SCoPE-MS) is a recently developed, promising, unbiased proteomic profiling techniques, which allows profiling several tens of single cells for >1000 proteins per cell. However, a method to link single cell proteomes with cellular behaviors is needed to advance this type of profiling technique. Here, we developed a microscopy-based functional single cell proteomic profiling technology, called FUNpro, to link the proteome of individual cells with phenotypes of interest, even if the phenotypes are dynamic or the cells of interest are sparse. FUNpro enables one i) to screen thousands of cells with subcellular resolution and monitor (intra)cellular dynamics using a custom-built microscope, ii) to real-time analyze (intra)cellular dynamics of individual cells using an integrated cell tracking algorithm, iii) to promptly isolate the cells displaying phenotypes of interest, and iv) to single cell proteomically profile the isolated cells. We applied FUNpro to proteomically profile a newly identified small subpopulation of U2OS osteosarcoma cells displaying an abnormal, prolonged DNA damage response (DDR) after ionizing radiation (IR). With this, we identified PDS5A and PGAM5 proteins contributing to the abnormal DDR dynamics and helping the cells survive after IR.

scholarly journals Single‐Cell Tracking: A New Microengineered Platform for 4D Tracking of Single Cells in a Stem‐Cell‐Based In Vitro Morphogenesis Model (Adv. Mater. 24/2020)

2020 ◽  
Vol 32 (24) ◽  
pp. 2070182
Author(s):  
Pinak Samal ◽  
Philipp Maurer ◽  
Clemens Blitterswijk ◽  
Roman Truckenmüller ◽  
Stefan Giselbrecht
Keyword(s):  
Stem Cell ◽  
Single Cell ◽  
Cell Tracking ◽  
Single Cells ◽  
In Vitro Morphogenesis

scholarly journals Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

2019 ◽  
Author(s):  
Erick Moen ◽  
Enrico Borba ◽  
Geneva Miller ◽  
Morgan Schwartz ◽  
Dylan Bannon ◽  
...  
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
Live Cell Imaging ◽  
Cell Tracking ◽  
Cell Imaging ◽  
Single Cells ◽  
Live Cell ◽  
Imaging Data ◽  
Cell Nuclei ◽  
Data Types

AbstractLive-cell imaging experiments have opened an exciting window into the behavior of living systems. While these experiments can produce rich data, the computational analysis of these datasets is challenging. Single-cell analysis requires that cells be accurately identified in each image and subsequently tracked over time. Increasingly, deep learning is being used to interpret microscopy image with single cell resolution. In this work, we apply deep learning to the problem of tracking single cells in live-cell imaging data. Using crowdsourcing and a human-in-the-loop approach to data annotation, we constructed a dataset of over 11,000 trajectories of cell nuclei that includes lineage information. Using this dataset, we successfully trained a deep learning model to perform cell tracking within a linear programming framework. Benchmarking tests demonstrate that our method achieves state-of-the-art performance on the task of cell tracking with respect to multiple accuracy metrics. Further, we show that our deep learning-based method generalizes to perform cell tracking for both fluorescent and brightfield images of the cell cytoplasm, despite having never been trained on those data types. This enables analysis of live-cell imaging data collected across imaging modalities. A persistent cloud deployment of our cell tracker is available at http://www.deepcell.org.

scholarly journals Microfluidics 3D gel-island chip for single cell isolation and lineage-dependent drug responses study

Lab on a Chip ◽  
2016 ◽  
Vol 16 (13) ◽  
pp. 2504-2512 ◽  
Author(s):  
Zhixiong Zhang ◽  
Yu-Chih Chen ◽  
Yu-Heng Cheng ◽  
Yi Luan ◽  
Euisik Yoon
Keyword(s):  
Single Cell ◽  
Drug Response ◽  
Cell Tracking ◽  
Single Cells ◽  
Cell Isolation ◽  
Microfluidic Platform ◽  
Drug Responses ◽  
3D Microenvironment ◽  
Single Cell Isolation

This paper reports a novel gel-island microfluidic platform enabling single-cell tracking in biomimetic 3D microenvironment for investigating heterogeneous drug response of single cells.

scholarly journals Voltage-gated ion channels mediate the electrotaxis of glioblastoma cells in a hybrid PMMA/PDMS microdevice

2020 ◽  
Author(s):  
Hsieh-Fu Tsai ◽  
Camilo IJspeert ◽  
Amy Q. Shen
Keyword(s):  
Machine Learning ◽  
Ion Channels ◽  
Single Cell ◽  
Cell Tracking ◽  
Cancer Metastasis ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Patient Specific ◽  
Glioblastoma Cells ◽  
Voltage Gated

Transformed astrocytes in the most aggressive form cause glioblastoma, the most common cancer in central nervous system with high mortality. The physiological electric field by neuronal local field potentials and tissue polarity may guide the infiltration of glioblastoma cells through the electrotaxis process. However, microenvironments with multiplex gradients are difficult to create. In this work, we have developed a hybrid microfluidic platform to study glioblastoma electrotaxis in controlled microenvironments with high through-put quantitative analysis by a machine learning-powered single cell tracking software. By equalizing the hydrostatic pressure difference between inlets and outlets of the microchannel, uniform single cells can be seeded reliably inside the microdevice. The electrotaxis of two glioblastoma models, T98G and U-251MG, require optimal laminin-containing extracellular matrix and exhibits opposite directional and electro-alignment tendencies. Calcium signaling is a key contributor in glioblastoma pathophysiology but its role in glioblastoma electrotaxis is still an open question. Anodal T98G electrotaxis and cathodal U-251MG electrotaxis require the presence of extracellular calcium cations. U-251MG electrotaxis is dependent on the P/Q-type voltage-gated calcium channel (VGCC) and T98G is dependent on the R-type VGCC. U-251MG and T98G electrotaxis are also mediated by A-type (rapidly inactivating) voltage-gated potassium channels and acid-sensing sodium channels. The involvement of multiple ion channels suggests that the glioblastoma electrotaxis is complex and patient-specific ion channel expression can be critical to develop personalized therapeutics to fight against cancer metastasis. The hybrid microfluidic design and machine learning-powered single cell analysis provide a simple and flexible platform for quantitative investigation of complicated biological systems.

scholarly journals pcnaDeep: A Fast and Robust Single-Cell Tracking Method Using Deep-Learning Mediated Cell Cycle Profiling

2021 ◽  
Author(s):  
Yifan Gui ◽  
Shuang Shuang Xie ◽  
Yanan Wang ◽  
Ping Wang ◽  
Renzhi Yao ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Deep Learning ◽  
Single Cell ◽  
Cell Tracking ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Time Lapse ◽  
Molecular Control ◽  
A Cell ◽  
User Friendly

Motivation: Computational methods that track single-cells and quantify fluorescent biosensors in time-lapse microscopy images have revolutionised our approach in studying the molecular control of cellular decisions. One barrier that limits the adoption of single-cell analysis in biomedical research is the lack of efficient methods to robustly track single-cells over cell division events. Results: Here, we developed an application that automatically tracks and assigns mother-daughter relationships of single-cells. By incorporating cell cycle information from a well-established fluorescent cell cycle reporter, we associate mitosis relationships enabling high fidelity long-term single-cell tracking. This was achieved by integrating a deep-learning based fluorescent PCNA signal instance segmentation module with a cell tracking and cell cycle resolving pipeline. The application offers a user-friendly interface and extensible APIs for customized cell cycle analysis and manual correction for various imaging configurations. Availability and Implementation: pcnaDeep is an open-source Python application under the Apache 2.0 licence. The source code, documentation and tutorials are available at https://github.com/chan-labsite/PCNAdeep.

scholarly journals Antibiotics modulate attractive interactions in bacterial colonies affecting survivability under combined treatment

2020 ◽  
Author(s):  
Tom Cronenberg ◽  
Marc Hennes ◽  
Isabelle Wielert ◽  
Berenike Maier
Keyword(s):  
Cell Motility ◽  
Single Cell ◽  
Antibiotic Treatment ◽  
Cell Tracking ◽  
Single Cells ◽  
Combined Treatment ◽  
Cellular Functions ◽  
Mutant Strains ◽  
A Cell ◽  
Cell Volumes

AbstractBiofilm formation protects bacteria from antibiotics. Very little is known about the response of biofilm-dwelling bacteria to antibiotics at the single cell level. Here, we developed a cell-tracking approach to investigate how antibiotics affect structure and dynamics of colonies formed by the human pathogen Neisseria gonorrhoeae. Antibiotics targeting different cellular functions enlarge the cell volumes and modulate within-colony motility. Focusing on azithromycin and ceftriaxone, we identify changes in type 4 pilus (T4P) mediated cell-to-cell attraction as the molecular mechanism for different effects on motility. By using strongly attractive mutant strains, we reveal that the survivability under ceftriaxone treatment depends on motility. Combining our results, we find that sequential treatment with azithromycin and ceftriaxone is synergistic. Taken together, we demonstrate that antibiotics modulate T4P-mediated attractions and hence cell motility and colony fluidity.Author SummaryAggregation into colonies and biofilms can enhance bacterial survivability under antibiotic treatment. Aggregation requires modulation of attractive forces between neighboring bacteria, yet the link between attraction and survivability is poorly characterized. Here, we quantify these attractive interactions and show that different antibiotics enhance or inhibit them. Live-cell tracking of single cells in spherical colonies enables us to correlate attractive interactions with single cell motility and colony fluidity. Even moderate changes in cell-to-cell attraction caused by antibiotics strongly impact on within-colony motility. Vice versa, we reveal that motility correlates with survivability under antibiotic treatment. In summary, we demonstrate a link between cellular attraction, colony fluidity, and survivability with the potential to optimize the treatment strategy of commonly used drug combinations.

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.

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