bioRxiv Science⌕ Search

Biology subjects

Zervantonakis, I.

Publications and source records attributed to Zervantonakis, I..

6 recordsLinked to original sources

Hydrogel array patterning using 3D-printed microfluidic inserts to control cell-cell and cell-ECM interactions

By shaping biochemical gradients and extracellular matrix cues within the local microenvironment, cellular spatial organization plays a critical role in regulating tissue development, homeostasis, and disease progression. Microfluidic platforms are highly suitable for the study of these cell-cell and cell-matrix interactions as they precisely control cell arrangement and gradients compared to conventional experimental systems. Cells are often embedded within hydrogels to improve physiological relevance by enabling matrix-mediated signaling. However, many designs restrict the number and arrangement of hydrogels or generate gradients in only one dimension, limiting their ability to recapitulate complex tissue architectures. To address this need, we introduce a 3D printed microfluidic insert compatible with microplates that allows patterning of up to ten unique hydrogel arrays in two dimensions and generation of parallel or orthogonal concentration gradients. We first develop a physics-based computational model of hydrogel filling to define design parameters that ensure robust hydrogel patterning. We then establish perpendicular concentration gradients on timescales relevant to biological experiments. Furthermore, we demonstrate high cell viability in our 3D-printed devices and control of fibroblast migration across multiple patterned hydrogels. Finally, we monitor the recruitment of primary human monocyte towards cell-free and fibroblast-seeded 3D collagen matrices. Our microfluidic insert platform is compatible with high-throughput automation workflows and allows for interrogation of spatially variant signals that regulate cell migration and cell-cell signaling in physiologically-relevant 3D microenvironments.

bioengineering↗

Cancer-cell derived S100A11 promotes macrophage recruitment in ER+ breast cancer

Macrophages are pivotal in driving breast tumor development, progression, and resistance to treatment, particularly in estrogen receptor-positive (ER+) tumors, where they infiltrate the tumor microenvironment (TME) influenced by cancer cell-secreted factors. By analyzing single-cell RNA-sequencing data from 25 ER+ tumors, we elucidated interactions between cancer cells and macrophages, correlating macrophage density with epithelial cancer cell density. We identified that S100A11, a previously unexplored factor in macrophage-cancer crosstalk, predicts high macrophage density and poor outcomes in ER+ tumors. We found that recombinant S100A11 enhances macrophage infiltration and migration in a dose-dependent manner. Additionally, in 3D models, we showed that S100A11 expression levels in ER+ cancer cells predict macrophage infiltration patterns. Neutralizing S100A11 decreased macrophage recruitment, both in cancer cell lines and in a clinically relevant patient-derived organoid model, underscoring its role as a paracrine regulator of cancer-macrophage interactions in the pro-tumorigenic TME. This study offers novel insights into the interplay between macrophages and cancer cells in ER+ breast tumors, highlighting S100A11 as a potential therapeutic target to modulate the macrophage-rich tumor microenvironment.

systems biology↗

Digital Light Processing 3D printing for biological applications of polydimethylsiloxane-based microfluidics

Soft lithography microfluidics offer many benefits over conventional biological assays; however, the impact this field is inhibited by the lack of widespread adoption of this technology in part due to prohibitive cost and fabrication time. Recent improvements in three-dimensional (3D) printing technologies such as digital light processing (DLP) printing offer a cost-effective and rapid prototyping solution to microfluidic fabrication. Limited information is available about how 3D printing parameters and resin cytocompatibility impact the performance of 3D printed molds for fabrication of polydimethylsiloxane (PDMS)-based microfluidics for cellular studies. Using a low-cost, commercially available DLP 3D printer, we assess the cytocompatibility of several resins, optimize printer settings and characterize minimum feature size of our system. We demonstrate the applications of DLP printing for soft lithography microfluidics by developing four assays to characterize cell viability, drug response, establish concentration gradients, and monitor live-cell 3D invasion into a hydrogel.

bioengineering↗

Paracrine CSF1 signaling regulates macrophage migration dynamics towards ovarian cancer cells in a 3D microfluidic model that recapitulates in vivo infiltration patterns in patient-derived xenograft models

Ovarian cancer is the second most deadly gynecologic cancer in the United States, and tumorassociated macrophages in the ovarian cancer microenvironment are the most abundant immune cell type and are associated poor survival. Here, we utilize three-dimensional microfluidic assays to investigate the dynamics of macrophage infiltration towards ovarian cancer cells. Experimental results demonstrate that both ovarian cancer cell lines and patient-derived xenograft models promote the infiltration of macrophages into a 3D collagen type I extracellular matrix. Additionally, blocking CSF1 signaling reduced the number of recruited macrophages as well as migration speed, while macrophage recruitment was enhanced by addition of recombinant CSF1. We further demonstrated that results obtained with our microfluidic model are consistent with the recruitment of macrophages in vivo by patient-derived xenograft models, and that a xenograft model with high CSF1 expression showed an enhanced ability to recruit macrophages both in vitro and in vivo. These results highlight the role of CSF1 signaling in ovarian cancer, as well as the utility of microfluidic models in recapitulating the 3D ovarian cancer microenvironment.

bioengineering↗

Large-scale Characterization of Drug Responses of Clinically Relevant Proteins in Cancer Cell Lines

Perturbation biology is a powerful approach to developing quantitative models of cellular behaviors and gaining mechanistic insights into disease development. In recent years, large-scale resources for phenotypic and mRNA responses of cancer cell lines to perturbations have been generated. However, similar large-scale protein response resources are not available, resulting in a critical knowledge gap for elucidating oncogenic mechanisms and developing effective cancer therapies. Here we generated and compiled perturbed expression profiles of ~210 clinically relevant proteins in >12,000 cancer cell-line samples in response to >150 drug compounds using reverse-phase protein arrays. We show that integrating protein response signals substantially increases the predictive power for drug sensitivity and aids in gaining insights into mechanisms of drug resistance. We build a systematic map of protein-drug connectivity and develop an open-access, user-friendly data portal for community use. Our study provides a valuable information resource for a broad range of quantitative modeling and biomedical applications. HighlightsO_LIA large collection of cancer cell line protein responses to drug perturbations C_LIO_LIPerturbed protein responses greatly increase predictive power for drug sensitivity C_LIO_LIBuild a systematic map of protein-drug connectivity based on response profiles C_LIO_LIDevelop a user-friendly, interactive data portal for community use C_LI

systems biology↗

A SNAI2-PEAK1 stromal axis drives progression and lapatinib resistance in HER2-positive breast cancer by supporting a cytokine expression profile that converges on PI3K/Akt signaling

Intercellular mechanisms by which the stromal microenvironment contributes to solid tumor progression and targeted therapy resistance remain poorly understood, presenting significant clinical hurdles. PEAK1 (Pseudopodium-Enriched Atypical Kinase One) is an actin cytoskeleton- and focal adhesion-associated pseudokinase that promotes cell state plasticity and cancer metastasis by mediating growth factor-integrin signaling crosstalk. Here, we determined that stromal PEAK1 expression predicts poor outcomes in HER2-positive breast cancers high in SNAI2 expression and enriched for MSC content. Notably, we identified that mesenchymal stem cells (MSCs) and cancer-associated fibroblasts (CAFs) express high PEAK1 protein levels and MSCs require PEAK1 to potentiate tumorigenesis, lapatinib resistance and metastasis of HER2-positive breast cancer cells. Analysis of PEAK1-dependent secreted factors from MSCs revealed a CCL4-, INHBA- and GDF5-focused network that converged on PI3K/Akt signaling. In this regard, we observed that MSC expression of PEAK1 is required for sustained Akt phosphorylation in neighboring HER2-positive breast cancer cells following lapatinib treatment. Finally, we uncovered a significant correlation between INHBA and PEAK1 expression levels in breast cancer, and that INHBA is an excellent predictor of disease relapse and decreased survival in HER2-positive tumors enriched for PEAK1 expression and MSC content. Importantly, we provide the first evidence that PEAK1 promotes tumorigenic phenotypes through a previously unrecognized SNAI2-PEAK1-INHBA-PI3K/Akt stromal to tumor cell signaling axis. These results establish a new, targetable intercellular mechanism that may be leveraged to improve targeted therapy responses and patient outcomes in breast cancer and other stroma-rich malignancies.

cancer biology↗