Biophysical Informatics Approach For Quantifying Phenotypic Heterogeneity In Cancer Cell Migration In Confined Microenvironments

2021 ◽  
Author(s):  
Xingjian Zhang ◽  
Michael Mak
Keyword(s):  
Single Cell ◽  
Cancer Cell ◽  
Cancer Cell Line ◽  
Phenotypic Heterogeneity ◽  
Geometric Constraints ◽  
Supplementary Information ◽  
Wide Channel ◽  
Transcriptomics Data ◽  
Confined Environment ◽  
External Stimuli

Abstract Motivation Cancer cell heterogeneity can manifest genetically and phenotypically. Bioinformatics methods have been used to analyze complex genomics and transcriptomics data, but have not been well-established for analyzing biophysical data of phenotypically heterogeneous tumor cells. Here, we take an informatics approach to analyze the biophysical data of MDA-MB-231 cells, a widely used breast cancer cell line, during their spontaneous migration through confined environments. Experimentally, we vary the constriction microchannel geometries (wide channel, short constriction, and long constriction) and apply drug treatments. We find that cells in the short constriction are similar in morphology to the cells in the wide channel. However, their fluorescence profiles are comparable to those in the long constriction. We demonstrate that the cell migratory phenotype is correlated more to mitochondria in a non-confined environment and more to actin in a confined environment. We demonstrate that the cells' migratory phenotypes are altered by ciliobrevin D, a dynein inhibitor, in both confined and non-confined environments. Overall, our approach elucidates phenotypic heterogeneity in cancer cells under confined microenvironments at single-cell resolution. Results Here, we apply a bioinformatics approach to a single cell invasion assay. We demonstrate that this method can determine distinctions in morphology, cytoskeletal activities, and mitochondrial activities under various geometric constraints and for cells of different speeds. Our approach can be readily adapted to various heterogeneity studies for different types of input biophysical data. In addition, this approach can be applied to studies related to biophysical changes due to differences in external stimuli, such as treatment effects on cellular and subcellular activities, at single-cell resolution. Finally, as similar bioinformatics methods have been widely applied in studies of genetic heterogeneity, biophysical information extracted using this approach can be analyzed together with the genetic data to relate genetic and phenotypic heterogeneity. Availability The data that support the findings of this study are available from the corresponding author upon reasonable request. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Microfluidic Single-Cell Proteomics Assay Chip: Lung Cancer Cell Line Case Study

Micromachines ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1147
Author(s):  
Yugyung Jung ◽  
Minkook Son ◽  
Yu Ri Nam ◽  
Jongchan Choi ◽  
James R. Heath ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Single Cell ◽  
Cancer Cells ◽  
Cell Line ◽  
Cancer Cell ◽  
Cancer Cell Line ◽  
Lung Cancer Cell Line ◽  
Cancer Drugs ◽  
Lung Cancer Cell ◽  
Anti Cancer

Cancer is a dynamic disease involving constant changes. With these changes, cancer cells become heterogeneous, resulting in varying sensitivity to chemotherapy. The heterogeneity of cancer cells plays a key role in chemotherapy resistance and cancer recurrence. Therefore, for effective treatment, cancer cells need to be analyzed at the single-cell level by monitoring various proteins and investigating their heterogeneity. We propose a microfluidic chip for a single-cell proteomics assay that is capable of analyzing complex cellular signaling systems to reveal the heterogeneity of cancer cells. The single-cell assay chip comprises (i) microchambers (n = 1376) for manipulating single cancer cells, (ii) micropumps for rapid single-cell lysis, and (iii) barcode immunosensors for detecting nine different secretory and intracellular proteins to reveal the correlation among cancer-related proteins. Using this chip, the single-cell proteomics of a lung cancer cell line, which may be easily masked in bulk analysis, were evaluated. By comparing changes in the level of protein secretion and heterogeneity in response to combinations of four anti-cancer drugs, this study suggests a new method for selecting the best combination of anti-cancer drugs. Subsequent preclinical and clinical trials should enable this platform to become applicable for patient-customized therapies.

scholarly journals cgCorrect: A method to correct for confounding cell-cell variation due to cell growth in single-cell transcriptomics

2016 ◽  
Author(s):  
Thomas Blasi ◽  
Florian Buettner ◽  
Michael K. Strasser ◽  
Carsten Marr ◽  
Fabian J. Theis
Keyword(s):  
Gene Expression ◽  
Steady State ◽  
Cell Growth ◽  
Single Cell ◽  
Cell Size ◽  
Computational Analysis ◽  
Simulated Data ◽  
Supplementary Information ◽  
Mrna Transcript ◽  
Transcriptomics Data

AbstractMotivation: Accessing gene expression at the single cell level has unraveled often large heterogeneity among seemingly homogeneous cells, which remained obscured in traditional population based approaches. The computational analysis of single-cell transcriptomics data, however, still imposes unresolved challenges with respect to normalization, visualization and modeling the data. One such issue are differences in cell size, which introduce additional variability into the data, for which appropriate normalization techniques are needed. Otherwise, these differences in cell size may obscure genuine heterogeneities among cell populations and lead to overdispersed steady-state distributions of mRNA transcript numbers.Results: We present cgCorrect, a statistical framework to correct for differences in cell size that are due to cell growth in single-cell transcriptomics data. We derive the probability for the cell growth corrected mRNA transcript number given the measured, cell size dependent mRNA transcript number, based on the assumption that the average number of transcripts in a cell increases proportional to the cell’s volume during cell cycle. cgCorrect can be used for both data normalization, and to analyze steady-state distributions used to infer the gene expression mechanism. We demonstrate its applicability on both simulated data and single-cell quantitative real-time PCR data from mouse blood stem and progenitor cells. We show that correcting for differences in cell size affects the interpretation of the data obtained by typically performed computational analysis.Availability: A Matlab implementation of cgCorrect is available at http://icb.helmholtz-muenchen.de/cgCorrectSupplementary information: Supplementary information are available online. The simulated data set is available at http://icb.helmholtz-muenchen.de/cgCorrect

Single Cell Electrophysiological Analysis via Sub-Micrometer Openings

2005 ◽  
Author(s):  
Sha Li ◽  
Liwei Lin
Keyword(s):  
Cervical Cancer ◽  
Hela Cell ◽  
Yeast Cell ◽  
Single Cell ◽  
Cell Line ◽  
Cancer Cell ◽  
Cancer Cell Line ◽  
Cervical Cancer Cell ◽  
Cervical Cancer Cell Line ◽  
Electrophysiological System

Single cell electrophysiological analyses were demonstrated via sub-micrometer openings using micro-and nano-machining technologies. In the prototype demonstration, a 6GΩ seal resistance was achieved on a HeLa cell, a cervical cancer cell line with size between 10~20μm, using the microfabricated electrophysiological system with opening size of 500nm in diameter and the measured electrolyte pipette resistance is 200kΩ. In a second experiment on a vacuolar yeast cell of 3~5μm in size, using a device with 800nm opening, a 500MΩ seal resistance was achieved and the measured electrolyte pipette resistance is 1MΩ.

scholarly journals Cerebro: interactive visualization of scRNA-seq data

2019 ◽  
Vol 36 (7) ◽  
pp. 2311-2313 ◽  
Author(s):  
Roman Hillje ◽  
Pier Giuseppe Pelicci ◽  
Lucilla Luzi
Keyword(s):  
Single Cell ◽  
Effective Interaction ◽  
Three Dimensional ◽  
R Package ◽  
Direct Access ◽  
Supplementary Information ◽  
Marker Genes ◽  
Transcriptomics Data ◽  
Or Gene ◽  
Access To Data

Abstract Despite the growing availability of sophisticated bioinformatic methods for the analysis of single-cell RNA-seq data, few tools exist that allow biologists without extensive bioinformatic expertise to directly visualize and interact with their own data and results. Here, we present Cerebro (cell report browser), a Shiny- and Electron-based standalone desktop application for macOS and Windows which allows investigation and inspection of pre-processed single-cell transcriptomics data without requiring bioinformatic experience of the user. Through an interactive and intuitive graphical interface, users can (i) explore similarities and heterogeneity between samples and cell clusters in two-dimensional or three-dimensional projections such as t-SNE or UMAP, (ii) display the expression level of single genes or gene sets of interest, (iii) browse tables of most expressed genes and marker genes for each sample and cluster and (iv) display trajectories calculated with Monocle 2. We provide three examples prepared from publicly available datasets to show how Cerebro can be used and which are its capabilities. Through a focus on flexibility and direct access to data and results, we think Cerebro offers a collaborative framework for bioinformaticians and experimental biologists that facilitates effective interaction to shorten the gap between analysis and interpretation of the data. Availability and implementation The Cerebro application, additional documentation, and example datasets are available at https://github.com/romanhaa/Cerebro. Similarly, the cerebroApp R package is available at https://github.com/romanhaa/cerebroApp. All components are released under the MIT License. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals In Vitro Anticancer Drug Sensitivity Sensing through Single-Cell Raman Spectroscopy

Biosensors ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 286
Author(s):  
Jingkai Wang ◽  
Kaicheng Lin ◽  
Huijie Hu ◽  
Xingwang Qie ◽  
Wei E. Huang ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Line ◽  
Anticancer Drugs ◽  
Cancer Cell ◽  
Anticancer Drug ◽  
Metabolic Activity ◽  
Drug Sensitivity ◽  
Cancer Cell Line ◽  
Sensitivity Testing

Traditional in vitro anticancer drug sensitivity testing at the population level suffers from lengthy procedures and high false positive rates. To overcome these defects, we built a confocal Raman microscopy sensing system and proposed a single-cell approach via Raman-deuterium isotope probing (Raman-DIP) as a rapid and reliable in vitro drug efficacy evaluation method. Raman-DIP detected the incorporation of deuterium into the cell, which correlated with the metabolic activity of the cell. The human non-small cell lung cancer cell line HCC827 and human breast cancer cell line MCF-7 were tested against eight different anticancer drugs. The metabolic activity of cancer cells could be detected as early as 12 h, independent of cell growth. Incubation of cells in 30% heavy water (D2O) did not show any negative effect on cell viability. Compared with traditional methods, Raman-DIP could accurately determine the drug effect, meanwhile, it could reduce the testing period from 72–144 h to 48 h. Moreover, the heterogeneity of cells responding to anticancer drugs was observed at the single-cell level. This proof-of-concept study demonstrated the potential of Raman-DIP to be a reliable tool for cancer drug discovery and drug susceptibility testing.

VeloViz: RNA velocity-informed embeddings for visualizing cellular trajectories

2021 ◽  
Author(s):  
Lyla Atta ◽  
Arpan Sahoo ◽  
Jean Fan
Keyword(s):  
Single Cell ◽  
Simulated Data ◽  
Supplementary Information ◽  
Intermediate Cell ◽  
Velocity Analysis ◽  
Cell State ◽  
Genome Wide ◽  
Transcriptomics Data ◽  
Reliable Representation ◽  
State Changes

Abstract Motivation Single-cell transcriptomics profiling technologies enable genome-wide gene expression measurements in individual cells but can currently only provide a static snapshot of cellular transcriptional states. RNA velocity analysis can help infer cell state changes using such single-cell transcriptomics data. To interpret these cell state changes inferred from RNA velocity analysis as part of underlying cellular trajectories, current approaches rely on visualization with principal components, t-distributed stochastic neighbor embedding and other 2D embeddings derived from the observed single-cell transcriptional states. However, these 2D embeddings can yield different representations of the underlying cellular trajectories, hindering the interpretation of cell state changes. Results We developed VeloViz to create RNA velocity-informed 2D and 3D embeddings from single-cell transcriptomics data. Using both real and simulated data, we demonstrate that VeloViz embeddings are able to capture underlying cellular trajectories across diverse trajectory topologies, even when intermediate cell states may be missing. By considering the predicted future transcriptional states from RNA velocity analysis, VeloViz can help visualize a more reliable representation of underlying cellular trajectories. Availability and implementation Source code is available on GitHub (https://github.com/JEFworks-Lab/veloviz) and Bioconductor (https://bioconductor.org/packages/veloviz) with additional tutorials at https://JEF.works/veloviz/. Datasets used can be found on Zenodo (https://doi.org/10.5281/zenodo.4632471). Supplementary information Supplementary data are available at Bioinformatics online.

Ligation of fas antigen stimulates chemoline secretion in pancreatic cancer cell line PANC-1

2001 ◽  
Vol 120 (5) ◽  
pp. A336-A336
Author(s):  
M SHIMADA ◽  
A ANDOH ◽  
Y ARAKI ◽  
Y FUJIYAMA ◽  
T BAMBA
Keyword(s):  
Pancreatic Cancer ◽  
Cell Line ◽  
Cancer Cell ◽  
Pancreatic Cancer Cell ◽  
Pancreatic Cancer Cell Line ◽  
Cancer Cell Line ◽  
Fas Antigen
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