Recent developments in microfluidic devices, nanoparticle chemistry, fluorescent microscopy, and biochemical techniques such as genetic identification and antibody capture have provided easier and more sensitive platforms for detecting and diagnosing diseases as well as providing new fundamental insight into disease progression. These advancements have led to the development of new technology and assays capable of easy and early detection of pathogenicity as well as the enhancement of the drug discovery and development pipeline. While some studies have focused on treatment, many of these technologies have found initial success in laboratories as a precursor for clinical applications. This review highlights the current and future progress of microfluidic techniques geared toward the timely and inexpensive diagnosis of disease including technologies aimed at high-throughput single cell analysis for drug development. It also summarizes novel microfluidic approaches to characterize fundamental cellular behavior and heterogeneity.

Cell-penetrating peptides (CPPs) have emerged as a powerful tool for the delivery of otherwise impermeable cargoes into intact cells. Recent efforts to improve the delivery capability of peptides have mainly focused on the identity of the CPP; however, there is evidence that the identity of the cargo itself affects the uptake. The goal of this work was to investigate how the characteristics of a peptide cargo, including net charge and length, either enhance or diminish the internalization efficiency of the CPP/cargo complex. A small library of CPP/cargo complexes were synthesized consisting of structured and unstructured CPPs with cargoes of net positive, negative, or neutral charge and lengths of 4 or 8 amino acids. Cargoes with a net positive charge were found to enhance the overall uptake of the complexes while net neutral and negatively charged cargoes diminished uptake. Conversely, the net length of the cargo had no significant effect on uptake of the CPP/cargo complexes. Microcopy images confirmed the increased uptake of the positively charged cargoes; however, an increase in punctate regions with the addition of a cargo was also observed. The effects of the net positively charged cargoes were confirmed with both structured and unstructured CPPs, which demonstrated similar trends of an increase in uptake with the addition of positively charged residues. These findings demonstrate that the net charge of cargoes impacts the uptake of the complex, which can be considered in the future when designing peptide-based reporters or therapeutics.

Breast cancer tumorigenesis and response to therapy is regulated by cancer cell interactions with the tumor microenvironment (TME). Breast cancer signaling to the surrounding TME results in a heterogeneous and diverse tumor microenvironment, which includes the production of cancer-associated fibroblasts, macrophages, adipocytes, and stem cells. The secretory profile of these cancer-associated cell types results in elevated chemokines and growth factors that promote cell survival and proliferation within the tumor. Current co-culture approaches mostly rely on transwell chambers to study intercellular signaling between adipose-derived stem cells (ASCs) and cancer cells; however, these methods are limited to endpoint measurements and lack dynamic control. In this study, a 4-channel, “flow-free” microfluidic device was developed to co-culture triple-negative MDA-MB-231 breast cancer cells and ASCs to study intercellular communication between two distinct cell types found in the TME. The device consists of two layers: a top PDMS layer with four imprinted channels coupled with a bottom agarose slab enclosed in a Plexiglas chamber. For dynamic co-culture, the device geometry contained two centered, flow-free channels, which were supplied with media from two outer flow channels via orthogonal diffusion through the agarose. Continuous fresh media was provided to the cell culture channel via passive diffusion without creating any shearing effect on the cells. The device geometry also allowed for the passive diffusion of cytokines and growth factors between the two cell types cultured in parallel channels to initiate cell-to-cell crosstalk. The device was used to show that MDA-MB-231 cells co-cultured with ASCs exhibited enhanced growth, a more aggressive morphology, and polarization toward the ASCs. The MDA-MB-231 cells were found to exhibit a greater degree of resistance to the drug paclitaxel when co-cultured with ASCs when compared to single culture studies. This microfluidic device is an ideal platform to study intercellular communication for many types of cells during co-culture experiments and allows for new investigations into stromal cell-mediated drug resistance in the tumor microenvironment.

Even though the majority of breast cancers respond well to primary therapy, a large percentage of patients relapse with metastatic disease, for which there is no treatment. In metastasis, a tumor sheds a small number of cancerous cells, termed circulating tumor cells (CTCs), into the local vasculature, from where they spread throughout the body to form new tumors. As CTCs move through the circulatory system, they experience physiological forces not present in the initial tumor environment, namely, fluid shear stress (FSS). Evidence suggests that CTCs respond to FSS by adopting a more aggressive phenotype; however, to date single-cell morphological changes have not been quantified to support this observation. Furthermore, the methodology of previous studies involves inducing FSS by flowing cells through the tubing, which lacks a precise and tunable control of FSS. Here, a microfluidic approach is used for isolating and characterizing the biophysical response of single breast cancer cells to conditions experienced in the circulatory system during metastasis. To evaluate the single-cell response of multiple breast cancer types, two model circulating tumor cell lines, MDA-MB-231 and MCF7, were challenged with FSS at precise magnitudes and durations. As expected, both MDA-MB-231 and MCF7 cells exhibited greater deformability due to increasing duration and magnitudes of FSS. However, wide variations in single-cell responses were observed. MCF7 cells were found to rapidly deform but reach a threshold value after 5 min of FSS, while MDA-MB-231 cells were observed to deform at a slower rate but with a larger threshold of deformation. This behavioral diversity suggests the presence of distinct cell subpopulations with different phenotypes.

High-throughput droplet microfluidic devices with fluorescence detection systems provide several advantages over conventional end-point cytometric techniques due to their ability to isolate single cells and investigate complex intracellular dynamics. While there have been significant advances in the field of experimental droplet microfluidics, the development of complementary software tools has lagged. Existing quantification tools have limitations including interdependent hardware platforms or challenges analyzing a wide range of high-throughput droplet microfluidic data using a single algorithm. To address these issues, an all-in-one Python algorithm called FluoroCellTrack was developed and its wide-range utility was tested on three different applications including quantification of cellular response to drugs, droplet tracking, and intracellular fluorescence. The algorithm imports all images collected using bright field and fluorescence microscopy and analyzes them to extract useful information. Two parallel steps are performed where droplets are detected using a mathematical Circular Hough Transform (CHT) while single cells (or other contours) are detected by a series of steps defining respective color boundaries involving edge detection, dilation, and erosion. These feature detection steps are strengthened by segmentation and radius/area thresholding for precise detection and removal of false positives. Individually detected droplet and contour center maps are overlaid to obtain encapsulation information for further analyses. FluoroCellTrack demonstrates an average of a ~92–99% similarity with manual analysis and exhibits a significant reduction in analysis time of 30 min to analyze an entire cohort compared to 20 h required for manual quantification.