Dataset Information

Nanomotors Sense Local Physicochemical Heterogeneities in Tumor Microenvironments*.

ABSTRACT: The invasion of cancer is brought about by continuous interaction of malignant cells with their surrounding tissue microenvironment. Investigating the remodeling of local extracellular matrix (ECM) by invading cells can thus provide fundamental insights into the dynamics of cancer progression. In this paper, we use an active untethered nanomechanical tool, realized as magnetically driven nanomotors, to locally probe a 3D tissue culture environment. We observed that nanomotors preferentially adhere to the cancer-proximal ECM and magnitude of the adhesive force increased with cell lines of higher metastatic ability. We experimentally confirmed that sialic acid linkage specific to cancer-secreted ECM makes it differently charged, which causes this adhesion. In an assay consisting of both cancerous and non-cancerous epithelia, that mimics the in vivo histopathological milieu of a malignant breast tumor, we find that nanomotors preferentially decorate the region around the cancer cells.

SUBMITTER: Dasgupta D

PROVIDER: S-EPMC7756332 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Nanomotors Sense Local Physicochemical Heterogeneities in Tumor Microenvironments*.

Dasgupta Debayan D Pally Dharma D Saini Deepak K DK Bhat Ramray R Ghosh Ambarish A

Angewandte Chemie (International ed. in English) 20201025 52

The invasion of cancer is brought about by continuous interaction of malignant cells with their surrounding tissue microenvironment. Investigating the remodeling of local extracellular matrix (ECM) by invading cells can thus provide fundamental insights into the dynamics of cancer progression. In this paper, we use an active untethered nanomechanical tool, realized as magnetically driven nanomotors, to locally probe a 3D tissue culture environment. We observed that nanomotors preferentially adhe ...[more]

PMID: 32918839

Similar Datasets

Project description:While it has been established that a number of microenvironment components can affect the likelihood of metastasis, the link between microenvironment and tumor cell phenotypes is poorly understood. Here we have examined microenvironment control over two different tumor cell motility phenotypes required for metastasis. By high-resolution multiphoton microscopy of mammary carcinoma in mice, we detected two phenotypes of motile tumor cells, different in locomotion speed. Only slower tumor cells exhibited protrusions with molecular, morphological, and functional characteristics associated with invadopodia. Each region in the primary tumor exhibited either fast- or slow-locomotion. To understand how the tumor microenvironment controls invadopodium formation and tumor cell locomotion, we systematically analyzed components of the microenvironment previously associated with cell invasion and migration. No single microenvironmental property was able to predict the locations of tumor cell phenotypes in the tumor if used in isolation or combined linearly. To solve this, we utilized the support vector machine (SVM) algorithm to classify phenotypes in a nonlinear fashion. This approach identified conditions that promoted either motility phenotype. We then demonstrated that varying one of the conditions may change tumor cell behavior only in a context-dependent manner. In addition, to establish the link between phenotypes and cell fates, we photoconverted and monitored the fate of tumor cells in different microenvironments, finding that only tumor cells in the invadopodium-rich microenvironments degraded extracellular matrix (ECM) and disseminated. The number of invadopodia positively correlated with degradation, while the inhibiting metalloproteases eliminated degradation and lung metastasis, consistent with a direct link among invadopodia, ECM degradation, and metastasis. We have detected and characterized two phenotypes of motile tumor cells in vivo, which occurred in spatially distinct microenvironments of primary tumors. We show how machine-learning analysis can classify heterogeneous microenvironments in vivo to enable prediction of motility phenotypes and tumor cell fate. The ability to predict the locations of tumor cell behavior leading to metastasis in breast cancer models may lead towards understanding the heterogeneity of response to treatment.

Dataset Information

Nanomotors Sense Local Physicochemical Heterogeneities in Tumor Microenvironments*.

Publications

Nanomotors Sense Local Physicochemical Heterogeneities in Tumor Microenvironments*.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure