scholarly journals Femtoliter Growth Channels: Bacteria Long-Term Growth Patterns Analysis on Single Cell Level

2012 ◽  
Vol 84 (8) ◽  
pp. 1407-1407
Author(s):  
A. Grünberger ◽  
C. Probst ◽  
W. Wiechert ◽  
D. Kohlheyer
Keyword(s):  
Single Cell ◽  
Growth Patterns ◽  
Single Cell Level ◽  
Cell Level

scholarly journals Long-term growth data of Escherichia coli at a single-cell level

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Yu Tanouchi ◽  
Anand Pai ◽  
Heungwon Park ◽  
Shuqiang Huang ◽  
Nicolas E. Buchler ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Single Cell ◽  
Single Cell Level ◽  
Growth Data ◽  
Cell Level

scholarly journals Long-Term In Vivo Imaging of Multiple Organs at the Single Cell Level

PLoS ONE ◽  
2013 ◽  
Vol 8 (1) ◽  
pp. e52087 ◽  
Author(s):  
Benny J. Chen ◽  
Yiqun Jiao ◽  
Ping Zhang ◽  
Albert Y. Sun ◽  
Geoffrey S. Pitt ◽  
...  
Keyword(s):  
Single Cell ◽  
In Vivo Imaging ◽  
Single Cell Level ◽  
Cell Level ◽  
Multiple Organs

PL02.3. A Phenotypic heterogeneity and plasticity as resistance mechanisms in Glioblastoma

2021 ◽  
Vol 23 (Supplement_2) ◽  
pp. ii1-ii1
Author(s):  
Y Y Yabo ◽  
A Oudin ◽  
A Skupin ◽  
P V Nazarov ◽  
S P Niclou ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Single Cell ◽  
Tumor Cells ◽  
Treatment Resistance ◽  
Resistance Mechanisms ◽  
Single Cell Level ◽  
Treatment Resistant ◽  
Cell Level ◽  
Transcriptomic Changes

Abstract BACKGROUND Glioblastomas are among the most heterogeneous tumors, which hampers patient stratification and development of effective therapies. Glioblastomas create a dynamic ecosystem, where heterogeneous tumor cells interact with the tumor microenvironment to establish different niches. Upon tumor growth, Glioblastoma cells manifest remarkable plasticity and respond flexibly to selective pressures by transiting towards states favorable to the new tumor microenvitonment. How this phenotypic plasticity contributes to treatment resistance is currently less clear. The exact nature of treatment resistant, tolerant and sensitive Glioblastoma cells remains unresolved. Further studies at the single cell level are needed to reveal transient and long-term signatures of the resistant states. MATERIAL AND METHODS To investigate long-term phenotypic changes upon treatment at the single cell level we performed single cell RNA-seq (scRNA-seq) on the longitudinal patient-derived xenograft (PDOX) models derived from Glioblastoma patient tumors prior and after the standard-of-care treatment. In addition, direct treatment of PDOXs with temozolomide combined with scRNA-seq allowed revealing short-term transcriptomic changes both in tumor cells and in the mouse-derived cells forming tumor microenvironment. Advanced computational algorithms, including reference-free deconvolution methods, were applied to reveal treatment resistance signatures and master regulators of the identified treatment-resistant subpopulations. RESULTS We show that PDOX models recapitulate all the major cell types and transcriptional programs reported in Glioblastoma patient samples, providing clinically relevant models for investigating treatment resistance signatures of tumor cells and associated tumor microenvironment. Analysis of treatment naïve and treated Glioblastomas at the single cell level revealed presence of pre-existing treatment resistant states as well as newly established resistant subpopulatons. Certain transcriptomic changes are preserved long term, regardless of the lack of genetic evolution of the tumor cells. CONCLUSION Phenotypic plasticity is an important factor contributing to resistance mechanisms in Glioblastoma. Key molecular regulators of tumor cell plasticity towards treatment resistance states represent novel targets for future combinatory treatments.

scholarly journals A single-cell level and connectome-derived computational model of the Drosophila brain

2018 ◽  
Author(s):  
Yu-Chi Huang ◽  
Cheng-Te Wang ◽  
Ta-Shun Su ◽  
Kuo-Wei Kao ◽  
Yen-Jen Lin ◽  
...  
Keyword(s):  
Single Cell ◽  
Computer Simulations ◽  
Network Model ◽  
Brain Network ◽  
Single Cell Level ◽  
Cell Level ◽  
Whole Brain ◽  
Stage Of Development ◽  
Model Animal

AbstractComputer simulations play an important role in testing hypotheses, integrating knowledge, and providing predictions of neural circuit functions. While considerable effort has been dedicated into simulating primate or rodent brains, the fruit fly (Drosophila melanogaster) is becoming a promising model animal in computational neuroscience for its small brain size, complex cognitive behavior, and abundancy of data available from genes to circuits. Moreover, several Drosophila connectome projects have generated a large number of neuronal images that account for a significant portion of the brain, making a systematic investigation of the whole brain circuit possible. Supported by FlyCircuit (http://www.flycircuit.tw), one of the largest Drosophila neuron image databases, we began a long-term project with the goal to construct a whole-brain spiking network model of the Drosophila brain. In this paper, we report the outcome of the first phase of the project. We developed the Flysim platform, which 1) identifies the polarity of each neuron arbor, 2) predicts connections between neurons, 3) translates morphology data from the database into physiology parameters for computational modeling, 4) reconstructs a brain-wide network model, which consists of 20,089 neurons and 1,044,020 synapses, and 5) performs computer simulations of the resting state. We compared the reconstructed brain network with a randomized brain network by shuffling the connections of each neuron. We found that the reconstructed brain can be easily stabilized by implementing synaptic short-term depression, while the randomized one exhibited seizure-like firing activity under the same treatment. Furthermore, the reconstructed Drosophila brain was structurally and dynamically more diverse than the randomized one and exhibited both Poisson-like and patterned firing activities. Despite being at its early stage of development, this single-cell level brain model allows us to study some of the fundamental properties of neural networks including network balance, critical behavior, long-term stability, and plasticity.

An artificial intelligent platform for live cell identification and the detection of cross-contamination (Preprint)

2019 ◽  
Author(s):  
Ruixin Wang ◽  
Dongni Wang ◽  
Dekai Kang ◽  
Xusen Guo ◽  
Chong Guo ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Line ◽  
Cell Lines ◽  
Cell Morphology ◽  
Single Cell Level ◽  
Cross Contamination ◽  
Cell Level ◽  
Cell Identification ◽  
Pure Cell ◽  
Microscopy Images

BACKGROUND In vitro human cell line models have been widely used for biomedical research to predict clinical response, identify novel mechanisms and drug response. However, one-fifth to one-third of cell lines have been cross-contaminated, which can seriously result in invalidated experimental results, unusable therapeutic products and waste of research funding. Cell line misidentification and cross-contamination may occur at any time, but authenticating cell lines is infrequent performed because the recommended genetic approaches are usually require extensive expertise and may take a few days. Conversely, the observation of live-cell morphology is a direct and real-time technique. OBJECTIVE The purpose of this study was to construct a novel computer vision technology based on deep convolutional neural networks (CNN) for “cell face” recognition. This was aimed to improve cell identification efficiency and reduce the occurrence of cell-line cross contamination. METHODS Unstained optical microscopy images of cell lines were obtained for model training (about 334 thousand patch images), and testing (about 153 thousand patch images). The AI system first trained to recognize the pure cell morphology. In order to find the most appropriate CNN model，we explored the key image features in cell morphology classification tasks using the classical CNN model-Alexnet. After that, a preferred fine-grained recognition model BCNN was used for the cell type identification (seven classifications). Next, we simulated the situation of cell cross-contamination and mixed the cells in pairs at different ratios. The detection of the cross-contamination was divided into two levels, whether the cells are mixed and what the contaminating cell is. The specificity, sensitivity, and accuracy of the model were tested separately by external validation. Finally, the segmentation model DialedNet was used to present the classification results at the single cell level. RESULTS The cell texture and density were the influencing factors that can be better recognized by the bilinear convolutional neural network (BCNN) comparing to AlexNet. The BCNN achieved 99.5% accuracy in identifying seven pure cell lines and 86.3% accuracy for detecting cross-contamination (mixing two of the seven cell lines). DilatedNet was applied to the semantic segment for analyzing in single-cell level and achieved an accuracy of 98.2%. CONCLUSIONS This study successfully demonstrated that cell lines can be morphologically identified using deep learning models. Only light-microscopy images and no reagents are required, enabling most labs to routinely perform cell identification tests.

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