Single-cell time-lapse imaging of the dynamic control of NF-κB signalling

The transcription factor NF-κB (nuclear factor κB) regulates critical cellular processes including the inflammatory response, apoptosis and the cell cycle. Over the past 20 years many of the components of the NF-κB signalling pathway have been elucidated along with their functions. Recent research in this field has focused on the dynamic regulation and network control of this system. With key roles in so many important cellular processes, it is critical that NF-κB signalling is tightly regulated. Recently, single-cell imaging and mathematical modelling have identified that the timing of cellular responses may play an important role in the regulation of this pathway. p65/RelA (RelA) has been shown to translocate between the nucleus and cytoplasm with varying oscillatory patterns in different cell lines leading to differences in transcriptional outputs from NF-κB-regulated genes. Variations in the timing or persistence of these movements may control the maintenance and differential expression of NF-κB-regulated genes.

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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 ◽

10.1016/j.ymeth.2017.10.003 ◽

2018 ◽

Vol 133 ◽

pp. 81-90 ◽

Cited By ~ 11

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

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The structure of the human cell cycle

10.1101/2021.02.11.430845 ◽

2021 ◽

Author(s):

Wayne Stallaert ◽

Katarzyna M. Kedziora ◽

Colin D. Taylor ◽

Tarek M. Zikry ◽

Holly K. Sobon ◽

...

Keyword(s):

Cell Cycle ◽

Single Cell ◽

Human Cell ◽

Time Lapse ◽

Underlying Structure ◽

Cell Cycle Exit ◽

Multiple Points ◽

Cell Cycles ◽

Single Cell Imaging ◽

Proliferative Cell

ABSTRACTThe human cell cycle is conventionally depicted as a five-phase model consisting of four proliferative phases (G1, S, G2, M) and a single state of arrest (G0). However, recent studies show that individual cells can take different paths through the cell cycle and exit into distinct arrest states, thus necessitating an update to the canonical model. We combined time lapse microscopy, highly multiplexed single cell imaging and manifold learning to determine the underlying “structure” of the human cell cycle under multiple growth and arrest conditions. By visualizing the cell cycle as a complete biological process, we identified multiple points of divergence from the proliferative cell cycle into distinct states of arrest, revealing multiple mechanisms of cell cycle exit and re-entry and the molecular routes to senescence, endoreduplication and polyploidy. These findings enable the visualization and comparison of alternative cell cycles in development and disease.One-sentence summaryA systems-level view of single-cell states reveals the underlying architecture of the human cell cycle

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Time-lapse Imaging Reveals Dynamic Relocalization of PP1γ throughout the Mammalian Cell Cycle

Molecular Biology of the Cell ◽

10.1091/mbc.e02-07-0376 ◽

2003 ◽

Vol 14 (1) ◽

pp. 107-117 ◽

Cited By ~ 103

Author(s):

Laura Trinkle-Mulcahy ◽

Paul D. Andrews ◽

Sasala Wickramasinghe ◽

Judith Sleeman ◽

Alan Prescott ◽

...

Keyword(s):

Cell Cycle ◽

Cell Lines ◽

Fluorescent Protein ◽

Temporal Distribution ◽

Time Lapse ◽

Cellular Processes ◽

Mammalian Cell Cycle ◽

Hela Cell Lines ◽

Spatio Temporal ◽

Time Lapse Imaging

Protein phosphatase 1 (PP1) is a ubiquitous serine/threonine phosphatase that regulates many cellular processes, including cell division. When transiently expressed as fluorescent protein (FP) fusions, the three PP1 isoforms, α, β/δ, and γ1, are active phosphatases with distinct localization patterns. We report here the establishment and characterization of HeLa cell lines stably expressing either FP-PP1γ or FP alone. Time-lapse imaging reveals dynamic targeting of FP-PP1γ to specific sites throughout the cell cycle, contrasting with the diffuse pattern observed for FP alone. FP-PP1γ shows a nucleolar accumulation during interphase. On entry into mitosis, it localizes initially at kinetochores, where it exchanges rapidly with the diffuse cytoplasmic pool. A dramatic relocalization of PP1 to the chromosome-containing regions occurs at the transition from early to late anaphase, and by telophase FP-PP1γ also accumulates at the cleavage furrow and midbody. The changing spatio-temporal distribution of PP1γ revealed using the stable PP1 cell lines implicates it in multiple processes, including nucleolar function, the regulation of chromosome segregation and cytokinesis.

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Automated Tracking of Root for Confocal Time-lapse Imaging of Cellular Processes

BIO-PROTOCOL ◽

10.21769/bioprotoc.2245 ◽

2017 ◽

Vol 7 (8) ◽

Cited By ~ 5

Author(s):

Mehdi Doumane ◽

Claire Lionnet ◽

Vincent Bayle ◽

Yvon Jaillais ◽

Marie-Cécile Caillaud

Keyword(s):

Time Lapse ◽

Automated Tracking ◽

Cellular Processes ◽

Time Lapse Imaging

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Neural network control of focal position during time-lapse microscopy of cells

10.1101/233940 ◽

2017 ◽

Author(s):

Ling Wei ◽

Elijah Roberts

Keyword(s):

Neural Network ◽

High Throughput ◽

Time Lapse ◽

Network Control ◽

Live Cell ◽

Yeast Cells ◽

Batch Size ◽

Neural Network Approach ◽

Cellular Processes ◽

Focal Position

AbstractLive-cell microscopy is quickly becoming an indispensable technique for studying the dynamics of cellular processes. Maintaining the specimen in focus during image acquisition is crucial for high-throughput applications, especially for long experiments or when a large sample is being continuously scanned. Automated focus control methods are often expensive, imperfect, or ill-adapted to a specific application and are a bottleneck for widespread adoption of high-throughput, live-cell imaging. Here, we demonstrate a neural network approach for automatically maintaining focus during bright-field microscopy. Z-stacks of yeast cells growing in a microfluidic device were collected and used to train a convolutional neural network to classify images according to their z-position. We studied the effect on prediction accuracy of the various hyperparameters of the neural network, including downsampling, batch size, and z-bin resolution. The network was able to predict the z-position of an image with ±1 μm accuracy, outperforming human annotators. Finally, we used our neural network to control microscope focus in real-time during a 24 hour growth experiment. The method robustly maintained the correct focal position compensating for 40 μm of focal drift and was insensitive to changes in the field of view. Only ~100 annotated z-stacks were required to train the network making our method quite practical for custom autofocus applications.

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Time-Lapse Imaging of Necroptosis and DAMP Release at Single-Cell Resolution

Methods in Molecular Biology - Live Cell Imaging ◽

10.1007/978-1-0716-1258-3_29 ◽

2021 ◽

pp. 353-363

Author(s):

Shin Murai ◽

Yoshitaka Shirasaki ◽

Hiroyasu Nakano

Keyword(s):

Single Cell ◽

Time Lapse ◽

Time Lapse Imaging

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Lineage analysis reveals an endodermal contribution to the vertebrate pituitary

Science ◽

10.1126/science.aba4767 ◽

2020 ◽

Vol 370 (6515) ◽

pp. 463-467 ◽

Cited By ~ 1

Author(s):

Peter Fabian ◽

Kuo-Chang Tseng ◽

Joanna Smeeton ◽

Joseph J. Lancman ◽

P. Duc Si Dong ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Endocrine Cell ◽

Time Lapse ◽

Cell Types ◽

Lineage Tracing ◽

Single Cell Rna Sequencing ◽

Growth Metabolism ◽

Time Lapse Imaging ◽

Lineage Analysis

Vertebrate sensory organs arise from epithelial thickenings called placodes. Along with neural crest cells, cranial placodes are considered ectodermal novelties that drove evolution of the vertebrate head. The anterior-most placode generates the endocrine lobe [adenohypophysis (ADH)] of the pituitary, a master gland controlling growth, metabolism, and reproduction. In addition to known ectodermal contributions, we use lineage tracing and time-lapse imaging in zebrafish to identify an endodermal contribution to the ADH. Single-cell RNA sequencing of the adult pituitary reveals similar competency of endodermal and ectodermal epithelia to generate all endocrine cell types. Further, endoderm can generate a rudimentary ADH-like structure in the near absence of ectodermal contributions. The fish condition supports the vertebrate pituitary arising through interactions of an ancestral endoderm-derived proto-pituitary with newly evolved placodal ectoderm.

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