Extracting cancer cell line electrochemical parameters at the single cell level using a microfabricated device

2009 ◽  
Vol 4 (2) ◽  
pp. 216-223 ◽  
Author(s):  
Jassim A. Alqabandi ◽  
Ussama M. Abdel-Motal ◽  
Kamal Youcef-Toumi
Keyword(s):  
Single Cell ◽  
Cell Line ◽  
Cancer Cell ◽  
Cancer Cell Line ◽  
Single Cell Level ◽  
Cell Level ◽  
Electrochemical Parameters

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.

scholarly journals Microfluidic Analyzer Enabling Quantitative Measurements of Specific Intracellular Proteins at the Single-Cell Level

Micromachines ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 588 ◽  
Author(s):  
Lixing Liu ◽  
Beiyuan Fan ◽  
Diancan Wang ◽  
Xiufeng Li ◽  
Yeqing Song ◽  
...  
Keyword(s):  
Adenoid Cystic Carcinoma ◽  
Single Cell ◽  
Cell Line ◽  
Oral Carcinoma ◽  
Parental Cell Line ◽  
Single Cell Level ◽  
Cell Level ◽  
Salivary Adenoid Cystic Carcinoma ◽  
Adenoid Cystic ◽  
Intracellular Proteins

This paper presents a microfluidic instrument capable of quantifying single-cell specific intracellular proteins, which are composed of three functioning modules and two software platforms. Under the control of a LabVIEW platform, a pressure module flushed cells stained with fluorescent antibodies through a microfluidic module with fluorescent intensities quantified by a fluorescent module and translated into the numbers of specific intracellular proteins at the single-cell level using a MATLAB platform. Detection ranges and resolutions of the analyzer were characterized as 896.78–6.78 × 105 and 334.60 nM for Alexa 488, 314.60–2.11 × 105 and 153.98 nM for FITC, and 77.03–5.24 × 104 and 37.17 nM for FITC-labelled anti-beta-actin antibodies. As a demonstration, the numbers of single-cell beta-actins of two paired oral tumor cell types and two oral patient samples were quantified as: 1.12 ± 0.77 × 106/cell (salivary adenoid cystic carcinoma parental cell line (SACC-83), ncell = 13,689) vs. 0.90 ± 0.58 × 105/cell (salivary adenoid cystic carcinoma lung metastasis cell line (SACC-LM), ncell = 15,341); 0.89 ± 0.69 × 106/cell (oral carcinoma cell line (CAL 27), ncell = 7357) vs. 0.93 ± 0.69 × 106/cell (oral carcinoma lymphatic metastasis cell line (CAL 27-LN2), ncell = 6276); and 0.86 ± 0.52 × 106/cell (patient I) vs. 0.85 ± 0.58 × 106/cell (patient II). These results (1) validated the developed analyzer with a throughput of 10 cells/s and a processing capability of ~10,000 cells for each cell type, and (2) revealed that as an internal control in cell analysis, the expressions of beta-actins remained stable in oral tumors with different malignant levels.

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.

scholarly journals Optimized Data-Independent Acquisition Approach for Proteomic Analysis at Single-Cell Level

2021 ◽  
Author(s):  
Yuefan Wang ◽  
Tung-Shing Mamie Lih ◽  
Lijun Chen ◽  
Yuanwei Xu ◽  
Morgan D. Kuczler ◽  
...  
Keyword(s):  
Single Cell ◽  
Proteomic Analysis ◽  
Single Cell Analysis ◽  
Cancer Cell Line ◽  
Cellular Heterogeneity ◽  
Single Cell Level ◽  
Cell Level ◽  
Data Independent Acquisition ◽  
Peptide Precursors ◽  
Proteomic Study

Abstract Background: Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. Methods: We report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. Results: We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1,500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. Conclusions: Our results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

scholarly journals Fusion transcript detection using spatial transcriptomics

2020 ◽  
Author(s):  
Stefanie Friedrich ◽  
Erik LL Sonnhammer
Keyword(s):  
Single Cell ◽  
Spatial Information ◽  
Cancer Cell Line ◽  
Fusion Transcript ◽  
Single Cell Level ◽  
Cell Level ◽  
Cancer Data ◽  
Fusion Transcripts ◽  
Tissue Sections ◽  
Transcript Detection

Abstract Background Fusion transcripts are involved in tumourigenesis and play a crucial role in tumour heterogeneity, tumour evolution and cancer treatment resistance. However, fusion transcripts have not been studied at high spatial resolution in tissue sections due to the lack of full-length transcripts with spatial information. New high-throughput technologies like spatial transcriptomics measure the transcriptome of tissue sections on almost single-cell level. While this technique does not allow for direct detection of fusion transcripts, we show that they can be inferred using the relative poly(A) tail abundance of the involved parental genes. Method We present a new method STfusion, which uses spatial transcriptomics to infer the presence and absence of poly(A) tails. A fusion transcript lacks a poly(A) tail for the 5´ gene and has an elevated number of poly(A) tails for the 3´ gene. Its expression level is defined by the upstream promoter of the 5´ gene. STfusion measures the difference between the observed and expected number of poly(A) tails with a novel C-score. Results We verified the STfusion ability to predict fusion transcripts on HeLa cells with known fusions. STfusion and C-score applied to clinical prostate cancer data revealed the spatial distribution of the cis-SAGe SLC45A3-ELK4 in 12 tissue sections with almost single-cell resolution. The cis-SAGe occurred in disease areas, e.g. inflamed, prostatic intraepithelial neoplastic, or cancerous areas, and occasionally in normal glands. Conclusions STfusion detects fusion transcripts in cancer cell line and clinical tissue data, and distinguishes chimeric transcripts from chimeras caused by trans-splicing events. With STfusion and the use of C-scores, fusion transcripts can be spatially localised in clinical tissue sections on almost single cell level. Keywords Fusion transcript detection, Spatial Transcriptomics, gene fusion, cis-SAGE, oncogene

DNA-fueled molecular machine for label-free and non-enzymatic ultrasensitive detection of telomerase activity

The Analyst ◽  
2016 ◽  
Vol 141 (16) ◽  
pp. 4855-4858 ◽  
Author(s):  
Panpan Sun ◽  
Xiang Ran ◽  
Chaoqun Liu ◽  
Chaoying Liu ◽  
Fang Pu ◽  
...  
Keyword(s):  
Single Cell ◽  
Cancer Cell ◽  
Telomerase Activity ◽  
Molecular Machine ◽  
Ultrasensitive Detection ◽  
Single Cell Level ◽  
Label Free ◽  
Enzymatic Method ◽  
Cell Level ◽  
Cell Extracts

A label-free and non-enzymatic method based on DNA-fueled molecular machine has been introduced for ultrasensitive detection of telomerase activity in cancer cell extracts even at the single-cell level.

scholarly journals Monitoring Bip promoter activation during cancer cell growth by bioluminescence imaging at the single-cell level

2016 ◽  
Vol 2 (6) ◽  
Author(s):  
Tomohisa Horibe ◽  
Aya Torisawa ◽  
Ryohsuke Kurihara ◽  
Ryutaro Akiyoshi ◽  
Yoko Hatta-Ohashi ◽  
...  
Keyword(s):  
Cell Growth ◽  
Single Cell ◽  
Cancer Cell ◽  
Bioluminescence Imaging ◽  
Single Cell Level ◽  
Promoter Activation ◽  
Cell Level ◽  
Cancer Cell Growth

scholarly journals Fusion transcript detection using spatial transcriptomics

2020 ◽  
Author(s):  
Stefanie Friedrich ◽  
Erik LL Sonnhammer
Keyword(s):  
Single Cell ◽  
Spatial Information ◽  
Direct Detection ◽  
Cancer Cell Line ◽  
Fusion Transcript ◽  
Single Cell Level ◽  
Cell Level ◽  
Cancer Data ◽  
Fusion Transcripts ◽  
Tissue Sections

Abstract Background Fusion transcripts are involved in tumourigenesis and play a crucial role in tumour heterogeneity, tumour evolution and cancer treatment resistance. However, fusion transcripts have not been studied at high spatial resolution in tissue sections due to the lack of full-length transcripts with spatial information. New high-throughput technologies like spatial transcriptomics measure the transcriptome of tissue sections on almost single-cell level. While this technique does not allow for direct detection of fusion transcripts, we show that they can be inferred using the relative poly(A) tail abundance of the involved parental genes. Method We present a new method STfusion, which uses spatial transcriptomics to infer the presence and absence of poly(A) tails. A fusion transcript lacks a poly(A) tail for the 5´ gene and has an elevated number of poly(A) tails for the 3´ gene. Its expression level is defined by the upstream promoter of the 5´ gene. STfusion measures the difference between the observed and expected number of poly(A) tails with a novel C-score. Results We verified the STfusion ability to predict fusion transcripts on HeLa cells with known fusions. STfusion and C-score applied to clinical prostate cancer data revealed the spatial distribution of the cis-SAGe SLC45A3-ELK4 in 12 tissue sections with almost single-cell resolution. The cis-SAGe occurred in disease areas, e.g. inflamed, prostatic intraepithelial neoplastic, or cancerous areas, and occasionally in normal glands. Conclusions STfusion detects fusion transcripts in cancer cell line and clinical tissue data, and distinguishes chimeric transcripts from chimeras caused by trans-splicing events. With STfusion and the use of C-scores, fusion transcripts can be spatially localised in clinical tissue sections on almost single cell level.

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