Quantitative Analysis of Multiple Proteins of Different Invasive Tumor Cell Lines at the Same Single-Cell Level

Small ◽  
2018 ◽  
Vol 14 (17) ◽  
pp. 1703684 ◽  
Author(s):  
Xiangchun Zhang ◽  
Ru Liu ◽  
Qingming Shu ◽  
Qing Yuan ◽  
Gengmei Xing ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Tumor Cell ◽  
Single Cell ◽  
Cell Lines ◽  
Single Cell Level ◽  
Tumor Cell Lines ◽  
Cell Level ◽  
Invasive Tumor

Heterogeneity of radiation induced apoptosis in Ewing Tumor cell lines characterized on a single cell level

APOPTOSIS ◽  
2005 ◽  
Vol 10 (1) ◽  
pp. 177-184 ◽  
Author(s):  
S. K�nemann ◽  
T. B�lling ◽  
A. Kolkmeyer ◽  
D. Riesenbeck ◽  
S. Hesselmann ◽  
...  
Keyword(s):  
Tumor Cell ◽  
Single Cell ◽  
Cell Lines ◽  
Single Cell Level ◽  
Tumor Cell Lines ◽  
Cell Level ◽  
Radiation Induced ◽  
Induced Apoptosis ◽  
Ewing Tumor

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.

Analysis of Mitosis In Acute Myeloid Leukemia Using Live-Cell Imaging.

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 3363-3363
Author(s):  
Dominik Schnerch ◽  
Julia Felthaus ◽  
Lara Mentlein ◽  
Monika Engelhardt ◽  
Ralph M. Waesch
Keyword(s):  
Single Cell ◽  
Cell Line ◽  
Cell Lines ◽  
Genetic Instability ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Cyclin B ◽  
Single Cell Level ◽  
Cell Level ◽  
Mitotic Control

Abstract Abstract 3363 Proper mitotic control is a prerequisite to guarantee the equal distribution of the genetic material onto the two developing daughter cells. A mitotic key regulator is cyclin B. High levels of cyclin B facilitate entry into mitosis whereas its controlled degradation coordinates chromosome separation and cytokinesis. The latter events are coordinated by the anaphase- promoting complex / cyclosome (APC/C), a ubiquitin ligase that couples ubiquitin chains to cyclin B, mediating its proteasomal degradation. The regulation of the APC/C-activity by complex protein networks, such as the spindle assembly checkpoint, therefore presents the basis for an accurate mitosis. Mitotic errors give rise to daughter cells with an aberrant set of chromosomes and contribute to genetic instability. Genetic instability is a hallmark of cancer cells and plays an important role in the onset and progression of acute myeloid leukemia (AML). In rare cases, de novo AMLs present with multiple cytogenetic aberrations (complex karyotype). However, a larger number of patients develop karyotype deviations in the course of the disease, sometimes even under therapy, which comes along with an adverse prognosis. Understanding the biology that drives the gain and loss of genetic material therefore bears the potential of identifying new therapeutic targets. We compared a number of lymphoblastic and myeloid cell lines and found AML cell lines to be deficient in arresting at metaphase in the presence of the microtubule-disrupting agent nocodazole. Cyclin B was expressed at much lower levels in the AML cell line Kasumi-1 and did not accumulate following spindle disruption as observed in the lymphoblastic cell line DG-75. We could show that Kasumi-1 cells, when challenged with nocodazole, were not capable of properly maintaining chromatid-cohesion and underwent premature sister chromatid separation. These findings suggest that mitotic control mechanisms do not work tightly enough in AML cells to prevent chromosome separation in the presence of spindle disruption. We applied live-cell imaging to exactly characterize mitotic timing in Kasumi-1 cells at a single cell level. The expression of a GFP-tagged derivative of histone H2 served to visualize the nuclear envelope breakdown and anaphase onset. Detection of the latter events allowed the faithful measurement of mitotic timing. We could find a significant shortening of mitosis in Kasumi-1 cells as compared to the lymphoblastic cell line DG-75. In both AML cell lines and primary AML blasts we identified the spindle assembly checkpoint components BubR1 and Bub1 to be downregulated. Interestingly, re-expression of BubR1 in Kasumi-1 cells led to a significant stabilization of cyclin B on western blots. To address the question whether an increased expression of cyclin B leads to a more pronounced mitotic delay in the presence of spindle-disruption in AML cells is subject of current experiments. It was reported that different cell types can escape from a mitotic block as a consequence of cyclin B degradation. In the literature, this phenomenon was referred to as mitotic slippage and is known to drive genetic instability. To monitor cyclin B turnover and localization at a single cell level, we generated a chimeric cyclin B-molecule, SNAP-cyclin B, which can couple to a suitable fluorochrome in a self-labeling reaction after addition to the growth medium. In this system, the fluorescence intensity reflects the amount of chimeric cyclin B and allows the monitoring of APC/C-dependent proteolysis. In our current approaches we aim at studying cyclin B-turnover at a single cell level in AML cell lines as well as primary leukemia cells by using live-cell imaging before and after BubR1- and Bub1-rescue. An aberrant cell cycle control is found in most human malignancies and might be an important driving force in leukemogenesis. We hypothesize that BubR1, in concert with different other regulators, might lead to inaccuracies in mitotic control. This hypothesis is underlined by the shortened time to anaphase in Kasumi-1 cells and a decreased expression of cyclin B, both of which are characteristics of BubR1-depletion. Mitotic regulators are already targets in AML therapy and a deeper understanding of mitotic processes in AML might lead to improved approaches. Disclosures: No relevant conflicts of interest to declare.

Quantitative analysis “at the single cell level” of the novel CD28-CD11b-subpopulation of CD8+ T lymphocytes

1998 ◽  
Vol 16 ◽  
pp. S95 ◽  
Author(s):  
A. Lonati ◽  
S. Licenziati ◽  
M. Marcelli ◽  
D. Canaris ◽  
G. Pasolini ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
T Lymphocytes ◽  
Single Cell ◽  
Single Cell Level ◽  
The Novel ◽  
Cell Level ◽  
Cd8 T Lymphocytes

scholarly journals OPP Labeling Enables Total Protein Synthesis Quantification in CHO Production Cell Lines at the Single-Cell Level

2018 ◽  
Vol 13 (4) ◽  
pp. 1700492 ◽  
Author(s):  
Fabian Nagelreiter ◽  
Michael T. Coats ◽  
Gerald Klanert ◽  
Elisabeth Gludovacz ◽  
Nicole Borth ◽  
...  
Keyword(s):  
Protein Synthesis ◽  
Single Cell ◽  
Cell Lines ◽  
Total Protein ◽  
Single Cell Level ◽  
Cell Level ◽  
Production Cell

scholarly journals 2P323 Quantitative Analysis of Expressed Rare Messages in Single Cell Level using Gold Nano-Particles

2005 ◽  
Vol 45 (supplement) ◽  
pp. S200
Author(s):  
H. Kim ◽  
A. Kira ◽  
H. Kohno ◽  
K. Matsumura ◽  
K. Orita ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Single Cell ◽  
Nano Particles ◽  
Single Cell Level ◽  
Cell Level ◽  
Gold Nano Particles

scholarly journals Application of Imaging Flow Cytometry for the Characterization of Intracellular Attributes in Chinese Hamster Ovary Cell Lines at the Single‐Cell Level

2019 ◽  
Vol 14 (7) ◽  
pp. 1800675 ◽  
Author(s):  
Eva Pekle ◽  
Andrew Smith ◽  
Guglielmo Rosignoli ◽  
Christopher Sellick ◽  
C. M. Smales ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
Cell Lines ◽  
Chinese Hamster Ovary ◽  
Chinese Hamster ◽  
Ovary Cell ◽  
Single Cell Level ◽  
Cell Level ◽  
Imaging Flow Cytometry
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