scholarly journals Segmentation and tracking of stem cells in time lapse microscopy to quantify dynamic behavioral changes during spheroid formation

2015 ◽  
Vol 87 (6) ◽  
pp. 491-502 ◽  
Author(s):  
Ching-Fen Jiang ◽  
Shan-hui Hsu ◽  
Ka-Pei Tsai ◽  
Ming-Hong Tsai
Keyword(s):  
Stem Cells ◽  
Time Lapse ◽  
Behavioral Changes ◽  
Spheroid Formation ◽  
Time Lapse Microscopy ◽  
Segmentation And Tracking

scholarly journals Comparsion of Migration Pattern between Young and Senescent Mesenchymal Stem Cells in Time Lapse Microscopy

2015 ◽  
Vol 108 (2) ◽  
pp. 456a-457a
Author(s):  
Ching-Fen Jiang ◽  
Shan-hui Hsu ◽  
Ka-Pei Tsai ◽  
Jia-Yin Li
Keyword(s):  
Stem Cells ◽  
Mesenchymal Stem Cells ◽  
Time Lapse ◽  
Migration Pattern ◽  
Time Lapse Microscopy

scholarly journals The migratory and mitotic behaviour of glioma stem cells in vitro: Optimisation of live-cell time-lapse microscopy

2011 ◽  
Vol 9 (7) ◽  
pp. 514-515
Author(s):  
James Barnett ◽  
Omar Pathmanaban ◽  
Ian Kamaly-Asl ◽  
Brian Bigger
Keyword(s):  
Stem Cells ◽  
Time Lapse ◽  
Live Cell ◽  
Glioma Stem Cells ◽  
Time Lapse Microscopy

Automated segmentation and tracking of non-rigid objects in time-lapse microscopy videos of polymorphonuclear neutrophils

2015 ◽  
Vol 20 (1) ◽  
pp. 34-51 ◽  
Author(s):  
Susanne Brandes ◽  
Zeinab Mokhtari ◽  
Fabian Essig ◽  
Kerstin Hünniger ◽  
Oliver Kurzai ◽  
...  
Keyword(s):  
Time Lapse ◽  
Polymorphonuclear Neutrophils ◽  
Automated Segmentation ◽  
Time Lapse Microscopy ◽  
Rigid Objects ◽  
Segmentation And Tracking

scholarly journals Exact Bayesian lineage tree-based inference identifies Nanog negative autoregulation in mouse embryonic stem cells

2016 ◽  
Author(s):  
Justin Feigelman ◽  
Stefan Ganscha ◽  
Simon Hastreiter ◽  
Michael Schwarzfischer ◽  
Adam Filipczyk ◽  
...  
Keyword(s):  
Stem Cells ◽  
Embryonic Stem Cells ◽  
Model Selection ◽  
Embryonic Stem ◽  
Time Lapse ◽  
Cell Populations ◽  
Time Lapse Microscopy ◽  
Stochastic Inference ◽  
Proliferating Cell ◽  
Selection Of

AbstractThe autoregulatory motif of Nanog, a heterogeneously expressed core pluripotency factor in mouse embryonic stem cells, remains debated. Although recent time-lapse microscopy data provide the unparalleled ability to monitor Nanog expression at the single-cell level, the extraction of mechanistic knowledge is precluded by the lack of inference techniques suitable for noisy, incomplete and heterogeneous data obtained from proliferating cell populations.This work identifies Nanog’s autoregulatory motif from quantified time-lapse fluorescence line-age trees with STILT (Stochastic Inference on Lineage Trees), a novel particle-filter based algorithm for exact Bayesian parameter inference and model selection of stochastic models. We first verify STILT’s ability to accurately infer parameters and select the correct autoregulatory motif from simulated data. We then apply STILT to time-lapse microscopy movies of a fluorescent Nanog fusion protein reporter and reject the possibility of positive autoregulation. Finally, we use STILT for experimental design, perform in silico overexpression simulations, and experimentally validate model predictions via exogenous Nanog overexpression. We finally conclude that the protein expression dynamics and overexpression experiments strongly suggest a weak negative feedback from the protein on the DNA activation rate.We find that a simple autoregulatory mechanism can explain the observed heterogeneous Nanog dynamics. This finding has implications on the understanding of the core pluripotency network, such as supporting the ability of mESC populations to diversify their proteomic profile to respond to a spectrum of differentiation cues. Beyond this application STILT constitutes a generally applicable fully Bayesian approach for model selection of gene regulatory models on the basis of time-lapse imaging data of proliferating cell populations. STILT is freely available at: http://www.imsb.ethz.ch/research/claassen/Software/stilt—stochastic-inference-on-lineage-trees.html

Experience of using time-lapse microscopy in IVF and ICSI programs

2020 ◽  
pp. 47-50
Author(s):  
N. V. Saraeva ◽  
N. V. Spiridonova ◽  
M. T. Tugushev ◽  
O. V. Shurygina ◽  
A. I. Sinitsyna
Keyword(s):  
Pregnancy Rate ◽  
Embryo Transfer ◽  
Selection Procedure ◽  
Time Lapse ◽  
Single Embryo Transfer ◽  
Main Group ◽  
Clinical Pregnancy ◽  
Control Group ◽  
Time Lapse Microscopy

In order to increase the pregnancy rate in the assisted reproductive technology, the selection of one embryo with the highest implantation potential it is very important. Time-lapse microscopy (TLM) is a tool for selecting quality embryos for transfer. This study aimed to assess the benefits of single-embryo transfer of autologous oocytes performed on day 5 of embryo incubation in a TLM-equipped system in IVF and ICSI programs. Single-embryo transfer following incubation in a TLM-equipped incubator was performed in 282 patients, who formed the main group; the control group consisted of 461 patients undergoing single-embryo transfer following a traditional culture and embryo selection procedure. We assessed the quality of transferred embryos, the rates of clinical pregnancy and delivery. The groups did not differ in the ratio of IVF and ICSI cycles, average age, and infertility factor. The proportion of excellent quality embryos for transfer was 77.0% in the main group and 65.1% in the control group (p = 0.001). In the subgroup with receiving eight and less oocytes we noted the tendency of receiving more quality embryos in the main group (р = 0.052). In the subgroup of nine and more oocytes the quality of the transferred embryos did not differ between two groups. The clinical pregnancy rate was 60.2% in the main group and 52.9% in the control group (p = 0.057). The delivery rate was 45.0% in the main group and 39.9% in the control group (p > 0.050).

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