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Differential detection of tumor cells using a combination of cell rolling, multivalent binding, and multiple antibodies.

ABSTRACT: Effective quantification and in situ identification of circulating tumor cells (CTCs) in blood are still elusive because of the extreme rarity and heterogeneity of the cells. In our previous studies, we developed a novel platform that captures tumor cells at significantly improved efficiency in vitro using a unique biomimetic combination of two physiological processes: E-selectin-induced cell rolling and poly(amidoamine) (PAMAM) dendrimer-mediated strong multivalent binding. Herein, we have engineered a novel multifunctional surface, on the basis of the biomimetic cell capture, through optimized incorporation of multiple antibodies directed to cancer cell-specific surface markers, such as epithelial cell adhesion molecule (EpCAM), human epidermal growth factor receptor-2 (HER-2), and prostate specific antigen (PSA). The surfaces were tested using a series of tumor cells, MDA-PCa-2b, MCF-7, and MDA-MB-361, both in mixture in vitro and after being spiked into human blood. Our multifunctional surface demonstrated highly efficient capture of tumor cells in human blood, achieving up to 82% capture efficiency (?10-fold enhancement than a surface with the antibodies alone) and up to 90% purity. Furthermore, the multipatterned antibodies allowed differential capturing of the tumor cells. These results support that our multifunctional surface has great potential as an effective platform that accommodates virtually any antibodies, which will likely lead to clinically significant, differential detection of CTCs that are rare and highly heterogeneous.

SUBMITTER: Myung JH

PROVIDER: S-EPMC4066911 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

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Differential detection of tumor cells using a combination of cell rolling, multivalent binding, and multiple antibodies.

Myung Ja Hye JH Gajjar Khyati A KA Chen Jihua J Molokie Robert E RE Hong Seungpyo S

Analytical chemistry 20140603 12

Effective quantification and in situ identification of circulating tumor cells (CTCs) in blood are still elusive because of the extreme rarity and heterogeneity of the cells. In our previous studies, we developed a novel platform that captures tumor cells at significantly improved efficiency in vitro using a unique biomimetic combination of two physiological processes: E-selectin-induced cell rolling and poly(amidoamine) (PAMAM) dendrimer-mediated strong multivalent binding. Herein, we have engi ...[more]

PMID: 24892731

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Project description:Tumor heterogeneity provides a complex challenge to cancer treatment and is a critical component of therapeutic response, disease recurrence, and patient survival. Single-cell RNA-sequencing (scRNA-seq) technologies have revealed the prevalence of intratumor and intertumor heterogeneity. Computational techniques are essential to quantify the differences in variation of these profiles between distinct cell types, tumor subtypes, and patients to fully characterize intratumor and intertumor molecular heterogeneity. In this study, we adapted our algorithm for pathway dysregulation, Expression Variation Analysis (EVA), to perform multivariate statistical analyses of differential variation of expression in gene sets for scRNA-seq. EVA has high sensitivity and specificity to detect pathways with true differential heterogeneity in simulated data. EVA was applied to several public domain scRNA-seq tumor datasets to quantify the landscape of tumor heterogeneity in several key applications in cancer genomics such as immunogenicity, metastasis, and cancer subtypes. Immune pathway heterogeneity of hematopoietic cell populations in breast tumors corresponded to the amount of diversity present in the T-cell repertoire of each individual. Cells from head and neck squamous cell carcinoma (HNSCC) primary tumors had significantly more heterogeneity across pathways than cells from metastases, consistent with a model of clonal outgrowth. Moreover, there were dramatic differences in pathway dysregulation across HNSCC basal primary tumors. Within the basal primary tumors, there was increased immune dysregulation in individuals with a high proportion of fibroblasts present in the tumor microenvironment. These results demonstrate the broad utility of EVA to quantify intertumor and intratumor heterogeneity from scRNA-seq data without reliance on low-dimensional visualization. SIGNIFICANCE: This study presents a robust statistical algorithm for evaluating gene expression heterogeneity within pathways or gene sets in single-cell RNA-seq data.

Dataset Information

Differential detection of tumor cells using a combination of cell rolling, multivalent binding, and multiple antibodies.

Publications

Differential detection of tumor cells using a combination of cell rolling, multivalent binding, and multiple antibodies.

Similar Datasets

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