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 Live-cell time-lapse imaging and single-cell tracking of in vitro cultured neural stem cells – Tools for analyzing dynamics of cell cycle, migration, and lineage selection

Methods ◽  
2018 ◽  
Vol 133 ◽  
pp. 81-90 ◽  
Author(s):  
Katja M. Piltti ◽  
Brian J. Cummings ◽  
Krystal Carta ◽  
Ayla Manughian-Peter ◽  
Colleen L. Worne ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Stem Cells ◽  
Neural Stem Cells ◽  
Single Cell ◽  
Cell Tracking ◽  
Time Lapse ◽  
Live Cell ◽  
Time Lapse Imaging

scholarly journals Methods and sensors for functional genomic studies of cell-cycle transitions in single cells

2020 ◽  
Vol 52 (10) ◽  
pp. 468-477
Author(s):  
Alexander C. Zambon ◽  
Tom Hsu ◽  
Seunghee Erin Kim ◽  
Miranda Klinck ◽  
Jennifer Stowe ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Imaging System ◽  
Single Cells ◽  
Cell Types ◽  
Cell Analysis ◽  
Mcf7 Cells ◽  
Genomic Studies ◽  
Cell Subpopulations

Much of our understanding of the regulatory mechanisms governing the cell cycle in mammals has relied heavily on methods that measure the aggregate state of a population of cells. While instrumental in shaping our current understanding of cell proliferation, these approaches mask the genetic signatures of rare subpopulations such as quiescent (G0) and very slowly dividing (SD) cells. Results described in this study and those of others using single-cell analysis reveal that even in clonally derived immortalized cancer cells, ∼1–5% of cells can exhibit G0 and SD phenotypes. Therefore to enable the study of these rare cell phenotypes we established an integrated molecular, computational, and imaging approach to track, isolate, and genetically perturb single cells as they proliferate. A genetically encoded cell-cycle reporter (K67p-FUCCI) was used to track single cells as they traversed the cell cycle. A set of R-scripts were written to quantify K67p-FUCCI over time. To enable the further study G0 and SD phenotypes, we retrofitted a live cell imaging system with a micromanipulator to enable single-cell targeting for functional validation studies. Single-cell analysis revealed HT1080 and MCF7 cells had a doubling time of ∼24 and ∼48 h, respectively, with high duration variability in G1 and G2 phases. Direct single-cell microinjection of mRNA encoding (GFP) achieves detectable GFP fluorescence within ∼5 h in both cell types. These findings coupled with the possibility of targeting several hundreds of single cells improves throughput and sensitivity over conventional methods to study rare cell subpopulations.

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.

Statistical single-cell analysis of cell cycle-dependent quantum dot cytotoxicity and cellular uptake using a microfluidic system

RSC Advances ◽  
2014 ◽  
Vol 4 (47) ◽  
pp. 24929-24934 ◽  
Author(s):  
Jing Wu ◽  
Haifang Li ◽  
Qiushui Chen ◽  
Xuexia Lin ◽  
Wu Liu ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Quantum Dot ◽  
Single Cell ◽  
Cellular Uptake ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Microfluidic System ◽  
Cell Analysis ◽  
System P ◽  
Cycle Dependent

The response of single cells in different cell cycle phases to QD cytotoxicity studied on a microfluidic device.

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 Cell-ACDC: a user-friendly toolset embedding state-of-the-art neural networks for segmentation, tracking and cell cycle annotations of live-cell imaging data

2021 ◽  
Author(s):  
Francesco Padovani ◽  
Benedikt Mairhoermann ◽  
Pascal Falter-Braun ◽  
Jette Lengefeld ◽  
Kurt M Schmoller
Keyword(s):  
Cell Cycle ◽  
Image Analysis ◽  
Deep Learning ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
State Of The Art ◽  
Single Cells ◽  
Live Cell ◽  
Cell Segmentation ◽  
User Friendly

Live-cell imaging is a powerful tool to study dynamic cellular processes on the level of single cells with quantitative detail. Microfluidics enables parallel high-throughput imaging, creating a downstream bottleneck at the stage of data analysis. Recent progress on deep learning image analysis dramatically improved cell segmentation and tracking. Nevertheless, manual data validation and correction is typically still required and broadly used tools spanning the complete range of live-cell imaging analysis, from cell segmentation to pedigree analysis and signal quantification, are still needed. Here, we present Cell-ACDC, a user-friendly graphical user-interface (GUI)-based framework written in Python, for segmentation, tracking and cell cycle annotation. We included two state-of-the-art and high-accuracy deep learning models for single-cell segmentation of yeast and mammalian cells implemented in the most used deep learning frameworks TensorFlow and PyTorch. Additionally, we developed and implemented a cell tracking method and embedded it into an intuitive, semi-automated workflow for label-free cell cycle annotation of single cells. The open-source and modularized nature of Cell-ACDC will enable simple and fast integration of new deep learning-based and traditional methods for cell segmentation or downstream image analysis. Source code: https://github.com/SchmollerLab/Cell_ACDC

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 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.

scholarly journals DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics

2021 ◽  
Vol 22 (3) ◽  
pp. 1399
Author(s):  
Salim Ghannoum ◽  
Waldir Leoncio Netto ◽  
Damiano Fantini ◽  
Benjamin Ragan-Kelley ◽  
Amirabbas Parizadeh ◽  
...  
Keyword(s):  
Single Cell ◽  
Biomarker Discovery ◽  
Enrichment Analysis ◽  
Myxoid Liposarcoma ◽  
R Package ◽  
Differential Analysis ◽  
A Cell ◽  
Reproducible Analysis ◽  
Transcriptomic Level ◽  
User Friendly

The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the transcriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in a network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a user-friendly computational pipeline using Jupyter notebooks. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation dataset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. R users can use the notebooks to understand the different steps of the pipeline and will guide them to explore their scRNA-seq data. We also provide a cloud version using Binder that allows the execution of the pipeline without the need of downloading R, Jupyter or any of the packages used by the pipeline. The cloud version can serve as a tutorial for training purposes, especially for those that are not R users or have limited programing skills. However, in order to do meaningful scRNA-seq analyses, all users will need to understand the implemented methods and their possible options and limitations.

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