scholarly journals P8‐14: Development of an optimal protocol for molecular profiling of tumor cells in malignant pleural effusions at single‐cell level

Respirology ◽  
2021 ◽  
Vol 26 (S3) ◽  
pp. 292-292
Keyword(s):  
Single Cell ◽  
Tumor Cells ◽  
Molecular Profiling ◽  
Single Cell Level ◽  
Pleural Effusions ◽  
Cell Level ◽  
Malignant Pleural Effusions ◽  
Optimal Protocol

scholarly journals Development of an optimal protocol for molecular profiling of tumor cells in pleural effusions at single‐cell level

2021 ◽  
Author(s):  
Ikuko Takeda Nakamura ◽  
Masachika Ikegami ◽  
Nobuhiko Hasegawa ◽  
Takuo Hayashi ◽  
Toshihide Ueno ◽  
...  
Keyword(s):  
Single Cell ◽  
Tumor Cells ◽  
Molecular Profiling ◽  
Single Cell Level ◽  
Pleural Effusions ◽  
Cell Level ◽  
Optimal Protocol

scholarly journals Heterogeneity of PIK3CA mutational status at the single cell level in circulating tumor cells from metastatic breast cancer patients

2014 ◽  
Vol 9 (4) ◽  
pp. 749-757 ◽  
Author(s):  
Marta Pestrin ◽  
Francesca Salvianti ◽  
Francesca Galardi ◽  
Francesca De Luca ◽  
Natalie Turner ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Metastatic Breast Cancer ◽  
Single Cell ◽  
Cancer Patients ◽  
Tumor Cells ◽  
Metastatic Breast ◽  
Single Cell Level ◽  
Breast Cancer Patients ◽  
Cell Level ◽  
Mutational Status

scholarly journals Combined Molecular Genetic and Cytogenetic Analysis from Single Cells after Isothermal Whole-Genome Amplification

2011 ◽  
Vol 57 (7) ◽  
pp. 1032-1041 ◽  
Author(s):  
Thomas Kroneis ◽  
Jochen B Geigl ◽  
Amin El-Heliebi ◽  
Martina Auer ◽  
Peter Ulz ◽  
...  
Keyword(s):  
Single Cell ◽  
Tumor Cells ◽  
Whole Genome Amplification ◽  
Single Cells ◽  
Molecular Genetic ◽  
Target Cells ◽  
Whole Genome ◽  
Single Cell Level ◽  
Cell Level ◽  
Genome Amplification

BACKGROUND Analysis of chromosomal aberrations or single-gene disorders from rare fetal cells circulating in the blood of pregnant women requires verification of the cells' genomic identity. We have developed a method enabling multiple analyses at the single-cell level that combines verification of the genomic identity of microchimeric cells with molecular genetic and cytogenetic diagnosis. METHODS We used a model system of peripheral blood mononuclear cells spiked with a colon adenocarcinoma cell line and immunofluorescence staining for cytokeratin in combination with DNA staining with the nuclear dye TO-PRO-3 in a preliminary study to define candidate microchimeric (tumor) cells in Cytospin preparations. After laser microdissection, we performed low-volume on-chip isothermal whole-genome amplification (iWGA) of single and pooled cells. RESULTS DNA fingerprint analysis of iWGA aliquots permitted successful identification of all analyzed candidate microchimeric cell preparations (6 samples of pooled cells, 7 samples of single cells). Sequencing of 3 single-nucleotide polymorphisms was successful at the single-cell level for 20 of 32 allelic loci. Metaphase comparative genomic hybridization (mCGH) with iWGA products of single cells showed the gains and losses known to be present in the genomic DNA of the target cells. CONCLUSIONS This method may be instrumental in cell-based noninvasive prenatal diagnosis. Furthermore, the possibility to perform mCGH with amplified DNA from single cells offers a perspective for the analysis of nonmicrochimeric rare cells exhibiting genomic alterations, such as circulating tumor cells.

scholarly journals Characteristics and requirements of the interaction between human monocytes and tumor cells on the single cell level

Blood ◽  
1983 ◽  
Vol 61 (2) ◽  
pp. 390-396 ◽  
Author(s):  
MG Golightly ◽  
DG Fischer ◽  
C Ohlander ◽  
HS Koren
Keyword(s):  
Tumor Cell ◽  
Single Cell ◽  
Tumor Cells ◽  
Autologous Serum ◽  
Target Cells ◽  
Single Cell Level ◽  
Human Monocytes ◽  
Blood Monocytes ◽  
Cell Level ◽  
Effector Cells

Abstract Highly purified (97%-99%) and viable (99%) peripheral blood monocytes obtained by EDTA-reversible adherence to autologous-serum-precoated plastic surfaces could rapidly lyse a variety of tumor cells in a 3–4 hr 51Cr release assay. Using these monocytes as effectors, a short-term agarose/conjugate assay was utilized, permitting us to examine the interaction between fresh human monocytes and neoplastic target cells on a single cell level. That the tumor-bound effector cells were indeed monocytes was confirmed by employing the monocyte-specific monoclonal antibody 61D3, which stained 95%-99% of the mononuclear cells bound to conjugated and killed K562 tumor targets. The binding of monocytes to target cells appeared to be temperature dependent and was extremely rapid, reaching a plateau after 5 min at 30 degrees C. Our findings demonstrated for the first time that only a proportion of human blood monocytes can bind to a particular target cell and that only a fraction of the binding cells have the intrinsic potential to kill those neoplastic targets. The proportion of monocytes capable of binding and killing varies between individuals and also depends on the tumor cell used, indicating heterogeneity in the monocyte and tumor cell populations. The highest proportion of monocytes bind to the human erythromyeloid leukemia K562 cell line (13%-50%). The frequency of monocytes capable of killing K562 tumor cells is relatively low (7%- 13%). The system described here should be useful to study the heterogeneity of mononuclear phagocytes and to analyze the molecular basis of the interaction between those effector cells and neoplastic target cells.

scholarly journals Convolutional Neural Network Can Recognize Drug Resistance of Single Cancer Cells

2020 ◽  
Vol 21 (9) ◽  
pp. 3166
Author(s):  
Kiminori Yanagisawa ◽  
Masayasu Toratani ◽  
Ayumu Asai ◽  
Masamitsu Konno ◽  
Hirohiko Niioka ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Single Cell ◽  
Tumor Cells ◽  
Antitumor Drugs ◽  
Single Cell Level ◽  
Cell Level ◽  
Single Cancer ◽  
Deep Learning Model

It is known that single or isolated tumor cells enter cancer patients’ circulatory systems. These circulating tumor cells (CTCs) are thought to be an effective tool for diagnosing cancer malignancy. However, handling CTC samples and evaluating CTC sequence analysis results are challenging. Recently, the convolutional neural network (CNN) model, a type of deep learning model, has been increasingly adopted for medical image analyses. However, it is controversial whether cell characteristics can be identified at the single-cell level by using machine learning methods. This study intends to verify whether an AI system could classify the sensitivity of anticancer drugs, based on cell morphology during culture. We constructed a CNN based on the VGG16 model that could predict the efficiency of antitumor drugs at the single-cell level. The machine learning revealed that our model could identify the effects of antitumor drugs with ~0.80 accuracies. Our results show that, in the future, realizing precision medicine to identify effective antitumor drugs for individual patients may be possible by extracting CTCs from blood and performing classification by using an AI system.

scholarly journals A New Era in Liquid Biopsy: From Genotype to Phenotype

2019 ◽  
Vol 66 (1) ◽  
pp. 89-96 ◽  
Author(s):  
Shana O Kelley ◽  
Klaus Pantel
Keyword(s):  
Single Cell ◽  
Tumor Cells ◽  
Liquid Biopsy ◽  
Molecular Profile ◽  
Molecular Testing ◽  
Single Cell Level ◽  
Cell Level ◽  
Intact Cells ◽  
Cancer Management ◽  
Phenotypic Information

Abstract BACKGROUND Liquid biopsy, in which tumor cells and tumor-derived biomolecules are collected from the circulation, is an attractive strategy for the management of cancer that allows the serial monitoring of patients during treatment. The analysis of circulating DNA produced by tumors provides a means to collect genotypic information about the molecular profile of a patient's cancer. Phenotypic information, which may be highly relevant for therapeutic selection, is ideally derived from intact cells, necessitating the analysis of circulating tumor cells (CTCs). CONTENT Recent advances in profiling CTCs at the single-cell level are providing new ways to collect critical phenotypic information. Analysis of secreted proteins, surface proteins, and intracellular RNAs for CTCs at the single-cell level is now possible and provides a means to quantify molecular markers that are involved with the mechanism of action of the newest therapeutics. We review the latest technological advances in this area along with related breakthroughs in high-purity CTC capture and in vivo profiling approaches, and we also present a perspective on how genotypic and phenotypic information collected via liquid biopsies is being used in the clinic. SUMMARY Over the past 5 years, the use of liquid biopsy has been adopted in clinical medicine, representing a major paradigm shift in how molecular testing is used in cancer management. The first tests to be used are genotypic measurements of tumor mutations that affect therapeutic effectiveness. Phenotypic information is also clinically relevant and essential for monitoring proteins and RNA sequences that are involved in therapeutic response.

Single-cell level point mutation analysis of circulating tumor cells through droplet microfluidics

2021 ◽  
Author(s):  
Shihui Qiu ◽  
Chuanjie Shen ◽  
Xiaoyu Jian ◽  
Yunxing Lu ◽  
Zhaoduo Tong ◽  
...  
Keyword(s):  
Single Cell ◽  
Tumor Cells ◽  
Point Mutation ◽  
Circulating Tumor Cells ◽  
Mutation Analysis ◽  
Droplet Microfluidics ◽  
Single Cell Level ◽  
Cell Level

scholarly journals Identifying tumor cells at the single cell level

2021 ◽  
Author(s):  
Jan Dohmen ◽  
Artem Baranovskii ◽  
Bora Uyar ◽  
Jonathan Ronen ◽  
Vedran Franke ◽  
...  
Keyword(s):  
Single Cell ◽  
Tumor Cells ◽  
Biomarker Discovery ◽  
Single Cell Analysis ◽  
High Sensitivity ◽  
Simple Problem ◽  
Cancer Therapies ◽  
Single Cell Level ◽  
Cell Level

Tumors are highly complex tissues composed of cancerous cells, surrounded by a heterogeneous cellular microenvironment. Tumor response to treatments is governed by an interaction of cancer cell intrinsic factors with external influences of the tumor microenvironment. Disentangling the heterogeneity within a tumor is a crucial step in developing and utilization of effective cancer therapies. Single cell sequencing has the potential to revolutionize personalized medicine. In cancer therapy it enables an effective characterization of the complete heterogeneity within the tumor. A governing challenge in cancer single cell analysis is cell annotation, the assignment of a particular cell type or a cell state to each sequenced cell. We propose Ikarus, a machine learning pipeline aimed at solving a perceived simple problem, distinguishing tumor cells from normal cells at the single cell level. Automatic characterization of tumor cells is a critical limiting step for a multitude of research, clinical, and commercial applications. Automatic characterization of tumor cells would expedite neoantigen prediction, automatic characterization of tumor cell states, it would greatly facilitate cancer biomarker discovery. Such a tool can be used for automatic annotation of histopathological data, profiled using multichannel immunofluorescence or spatial sequencing. We have tested ikarus on multiple single cell datasets to ascertain that it achieves high sensitivity and specificity in multiple experimental contexts.

scholarly journals Characteristics and requirements of the interaction between human monocytes and tumor cells on the single cell level

Blood ◽  
1983 ◽  
Vol 61 (2) ◽  
pp. 390-396
Author(s):  
MG Golightly ◽  
DG Fischer ◽  
C Ohlander ◽  
HS Koren
Keyword(s):  
Tumor Cell ◽  
Single Cell ◽  
Tumor Cells ◽  
Autologous Serum ◽  
Target Cells ◽  
Single Cell Level ◽  
Human Monocytes ◽  
Blood Monocytes ◽  
Cell Level ◽  
Effector Cells

Highly purified (97%-99%) and viable (99%) peripheral blood monocytes obtained by EDTA-reversible adherence to autologous-serum-precoated plastic surfaces could rapidly lyse a variety of tumor cells in a 3–4 hr 51Cr release assay. Using these monocytes as effectors, a short-term agarose/conjugate assay was utilized, permitting us to examine the interaction between fresh human monocytes and neoplastic target cells on a single cell level. That the tumor-bound effector cells were indeed monocytes was confirmed by employing the monocyte-specific monoclonal antibody 61D3, which stained 95%-99% of the mononuclear cells bound to conjugated and killed K562 tumor targets. The binding of monocytes to target cells appeared to be temperature dependent and was extremely rapid, reaching a plateau after 5 min at 30 degrees C. Our findings demonstrated for the first time that only a proportion of human blood monocytes can bind to a particular target cell and that only a fraction of the binding cells have the intrinsic potential to kill those neoplastic targets. The proportion of monocytes capable of binding and killing varies between individuals and also depends on the tumor cell used, indicating heterogeneity in the monocyte and tumor cell populations. The highest proportion of monocytes bind to the human erythromyeloid leukemia K562 cell line (13%-50%). The frequency of monocytes capable of killing K562 tumor cells is relatively low (7%- 13%). The system described here should be useful to study the heterogeneity of mononuclear phagocytes and to analyze the molecular basis of the interaction between those effector cells and neoplastic target cells.

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