A method to quantitatively characterize the formation and dissociation of tumor cell clusters using light transmission aggregometry.

Abstract

In this paper, we have modified the workflow of the traditional light transmission aggregometry (LTA) protocol to characterize tumor cell clusters in vitro in a quantifiable and multifaceted manner. Circulating tumor cell (CTC) clusters have high metastatic potential compared to single tumor cells traveling in the bloodstream. Thus, engineering new therapeutic strategies that specifically target this CTC population is essential. To accomplish this, quantifiable methods to characterize their therapeutic effect on tumor cell clusters is a prerequisite. The method presented here enables the user to precisely quantify the dissociation of cancer cell clusters in the presence of clinically relevant fibrinolytic agents, such as alteplase and tenecteplase. The efficacy of the fibrinolytic agents can be quantified using this in vitro assay, prior to conducting preclinical studies. Here, we have obtained the fibrinolytic activity data in terms of lag time to the initiation of tumor cell dissociation, time to 25% dissociation, and trend of dissociation over time. To validate the assay, cell counts and phase-contrast microscopy images were recorded over time. Further, we explored an LTA-assisted preparation of platelet-tumor-cell clusters of calibrated size for potential downstream testing/applications. To assess whether the assay is applicable to characterize the dissociation of cancer cell clusters in the presence of platelets, we added low (50 000 platelets·μL^-1), normal (200 000 platelets·μL^-1) and high (450 000 platelets·μL^-1) concentrations of platelets to the tumor cell clusters. In addition to dissociation parameters, microcopy images were recorded over time to validate the assay and enabled the enumeration of clusters and single cells. The correlative light electron microscopy (CLEM) technique was utilized to visualize the morphology and composition of platelet-tumor cell clusters.