Dataset Information

Kernel-Based Microfluidic Constriction Assay for Tumor Sample Identification.

ABSTRACT: A high-throughput multiconstriction microfluidic channels device can distinguish human breast cancer cell lines (MDA-MB-231, HCC-1806, MCF-7) from immortalized breast cells (MCF-10A) with a confidence level of ?81-85% at a rate of 50-70 cells/min based on velocity increment differences through multiconstriction channels aligned in series. The results are likely related to the deformability differences between nonmalignant and malignant breast cells. The data were analyzed by the methods/algorithms of Ridge, nonnegative garrote on kernel machine (NGK), and Lasso using high-dimensional variables, including the cell sizes, velocities, and velocity increments. In kernel learning based methods, the prediction values of 10-fold cross-validations are used to represent the difference between two groups of data, where a value of 100% indicates the two groups are completely distinct and identifiable. The prediction value is used to represent the difference between two groups using the established algorithm classifier from high-dimensional variables. These methods were applied to heterogeneous cell populations prepared using primary tumor and adjacent normal tissue obtained from two patients. Primary breast cancer cells were distinguished from patient-matched adjacent normal cells with a prediction ratio of 70.07%-75.96% by the NGK method. Thus, this high-throughput multiconstriction microfluidic device together with the kernel learning method can be used to perturb and analyze the biomechanical status of cells obtained from small primary tumor biopsy samples. The resultant biomechanical velocity signatures identify malignancy and provide a new marker for evaluation in risk assessment.

SUBMITTER: Ren X

PROVIDER: S-EPMC6830436 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Kernel-Based Microfluidic Constriction Assay for Tumor Sample Identification.

Ren Xiang X Ghassemi Parham P Kanaan Yasmine M YM Naab Tammey T Copeland Robert L RL Dewitty Robert L RL Kim Inyoung I Strobl Jeannine S JS Agah Masoud M

ACS sensors 20180718 8

A high-throughput multiconstriction microfluidic channels device can distinguish human breast cancer cell lines (MDA-MB-231, HCC-1806, MCF-7) from immortalized breast cells (MCF-10A) with a confidence level of ∼81-85% at a rate of 50-70 cells/min based on velocity increment differences through multiconstriction channels aligned in series. The results are likely related to the deformability differences between nonmalignant and malignant breast cells. The data were analyzed by the methods/algorith ...[more]

PMID: 29979037

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Project description:Identification of HLA class I ligands from the tumor surface (ligandome or immunopeptidome) is essential for designing T-cell mediated cancer therapeutic approaches. However, the sensitivity of the process for isolating MHC-I restricted tumor-specific peptides has been the major limiting factor for reliable tumor antigen characterization, making clear the need for technical improvement. Here, we describe our work from the fabrication and development of a microfluidic-based chip (PeptiCHIP) and its use to identify and characterize tumor-specific ligands on clinically relevant human samples. Specifically, we assessed the potential of immobilizing a pan-HLA antibody on solid surfaces via well-characterized streptavidin-biotin chemistry, overcoming the limitations of the cross-linking chemistry used to prepare the affinity matrix with the desired antibodies in the immunopeptidomics workflow. Furthermore, to address the restrictions related to the handling and the limited availability of tumor samples, we further developed the concept toward the implementation of a microfluidic through-flow system. Thus, the biotinylated pan-HLA antibody was immobilized on streptavidin-functionalized surfaces, and immune-affinity purification (IP) was carried out on customized microfluidic pillar arrays made of thiol-ene polymer. Compared to the standard methods reported in the field, our methodology reduces the amount of antibody and the time required for peptide isolation. In this work, we carefully examined the specificity and robustness of our customized technology for immunopeptidomics workflows. We tested this platform by immunopurifying HLA-I complexes from 1 × 106 cells both in a widely studied B-cell line and in patients-derived ex vivo cell cultures, instead of 5 × 108 cells as required in the current technology. After the final elution in mild acid, HLA-I-presented peptides were identified by tandem mass spectrometry and further investigated by in vitro methods. These results highlight the potential to exploit microfluidics-based strategies in immunopeptidomics platforms and in personalized immunopeptidome analysis from cells isolated from individual tumor biopsies to design tailored cancer therapeutic vaccines. Moreover, the possibility to integrate multiple identical units on a single chip further improves the throughput and multiplexing of these assays with a view to clinical needs.

Dataset Information

Kernel-Based Microfluidic Constriction Assay for Tumor Sample Identification.

Publications

Kernel-Based Microfluidic Constriction Assay for Tumor Sample Identification.

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