Comparative Study of Prostate Cancer Biophysical and Migratory Characteristics via Iterative Mechanoelectrical Properties (iMEP) and Standard Migration Assays.

This study reveals a new microfluidic biosensor consisting of a multi-constriction microfluidic device with embedded electrodes for measuring the biophysical attributes of single cells. The biosensing platform called the iterative mechano-electrical properties (iMEP) analyzer captures electronic records of biomechanical and bioelectrical properties of cells. The iMEP assay is used in conjunction with standard migration assays, such as chemotaxis-based Boyden chamber and scratch wound healing assays, to evaluate the migratory behavior and biophysical properties of prostate cancer cells. The three cell lines evaluated in the study each represent a stage in the standard progression of prostate cancer, while the fourth cell line serves as a normal/healthy counterpart. Neither the scratch assay nor the chemotaxis assay could fully differentiate the four cell lines. Furthermore, there was not a direct correlation between wound healing rate or the migratory rate with the cells' metastatic potential. However, the iMEP assay, through its multiparametric dataset, could distinguish between all four cell line populations with p-value < 0.05. Further studies are needed to determine if iMEP signatures can be used for a wider range of human cells to assess the tumorigenicity of a cell population or the metastatic potential of cancer cells.

Scalable nanolaminated SERS multiwell cell culture assay.

This paper presents a new cell culture platform enabling label-free surface-enhanced Raman spectroscopy (SERS) analysis of biological samples. The platform integrates a multilayered metal-insulator-metal nanolaminated SERS substrate and polydimethylsiloxane (PDMS) multiwells for the simultaneous analysis of cultured cells. Multiple cell lines, including breast normal and cancer cells and prostate cancer cells, were used to validate the applicability of this unique platform. The cell lines were cultured in different wells. The Raman spectra of over 100 cells from each cell line were collected and analyzed after 12 h of introducing the cells to the assay. The unique Raman spectra of each cell line yielded biomarkers for identifying cancerous and normal cells. A kernel-based machine learning algorithm was used to extract the high-dimensional variables from the Raman spectra. Specifically, the nonnegative garrote on a kernel machine classifier is a hybrid approach with a mixed nonparametric model that considers the nonlinear relationships between the higher-dimension variables. The breast cancer cell lines and normal breast epithelial cells were distinguished with an accuracy close to 90%. The prediction rate between breast cancer cells and prostate cancer cells reached 94%. Four blind test groups were used to evaluate the prediction power of the SERS spectra. The peak intensities at the selected Raman shifts of the testing groups were selected and compared with the training groups used in the machine learning algorithm. The blind testing groups were correctly predicted 100% of the time, demonstrating the applicability of the multiwell SERS array for analyzing cell populations for cancer research.

Post-enrichment circulating tumor cell detection and enumeration via deformability impedance cytometry.

Circulating tumor cells (CTCs) in blood can provide valuable information when detecting, diagnosing, and monitoring cancer. This paper describes a system that consists of a constriction-based microfluidic sensor with embedded electrodes that can detect and enumerate cancer cells in blood. The biosensor measures impedance in terms of magnitude and phase at multiple frequencies as cells transit through the constriction channel. Cancer cells deform as they move through while blood cells remain intact, thus generating differential impedance profiles that can be used for detecting and counting CTCs. Two versions of this device are reported, one where the electrodes are embedded into the disposable microfluidic channel, and the other in which the disposable chip is externally fixed to a reusable substrate housing the electrodes. Both configurations were tested by spiking breast or prostate cancer cells into murine blood, and both detected all tumor cells passing through the narrow channels while being able to differentiate between the two cell lines. The chip in its current format has a throughput of 1 µL/min. While the throughput is scalable by integrating more constriction channels in parallel, the presented assay is intended for post-enrichment label-free enumeration and characterization of CTCs.

Refractive-Index-Insensitive Nanolaminated SERS Substrates for Label-Free Raman Profiling and Classification of Living Cancer Cells.

Surface-enhanced Raman spectroscopy (SERS) has emerged as an ultrasensitive molecular-fingerprint-based technique for label-free biochemical analysis of biological systems. However, for conventional SERS substrates, SERS enhancement factors (EFs) strongly depend on background refractive index (RI), which prevents reliable spatiotemporal SERS analysis of living cells consisting of different extra-/intracellular organelles with a heterogeneous distribution of local RI values between 1.30 and 1.60. Here, we demonstrate that nanolaminated SERS substrates can support uniform arrays of vertically oriented nanogap hot spots with large SERS EFs (>107) insensitive to background RI variations. Experimental and numerical studies reveal that the observed RI-insensitive SERS response is due to the broadband multiresonant optical properties of nanolaminated plasmonic nanostructures. As a proof-of-concept demonstration, we use RI-insensitive nanolaminated SERS substrates to achieve label-free Raman profiling and classification of living cancer cells with a high prediction accuracy of 96%. We envision that RI-insensitive high-performance nanolaminated SERS substrates can potentially enable label-free spatiotemporal biochemical analysis of living biological systems.

Biophysical phenotyping of cells via impedance spectroscopy in parallel cyclic deformability channels.

This paper describes a new microfluidic biosensor with capabilities of studying single cell biophysical properties. The chip contains four parallel sensing channels, where each channel includes two constriction regions separated by a relaxation region. All channels share a pair of electrodes to record the electrical impedance. Single cell impedance magnitudes and phases at different frequencies were obtained. The deformation and transition time information of cells passing through two sequential constriction regions were gained from the time points on impedance magnitude variations. Constriction channels separated by relaxation regions have been proven to improve the sensitivity of distinguishing single cells. The relaxation region between two sequential constriction channels provides extra time stamps that can be identified in the impedance plots. The new chip allows simultaneous measurement of the biophysical attributes of multiple cells in different channels, thereby increasing the overall throughput of the chip. Using the biomechanical parameters represented by the time stamps in the impedance results, breast cancer cells (MDA-MB-231) and the normal epithelial cells (MCF-10A) could be distinguished by 85%. The prediction accuracy at the single-cell level reached 97% when both biomechanical and bioelectrical parameters were utilized. While the new label-free assay has been tested to distinguish between normal and cancer cells, its application can be extended to include cell-drug interactions and circulating tumor cell detection in blood.

Kernel-Based Microfluidic Constriction Assay for Tumor Sample Identification.

A high-throughput multiconstriction microfluidic channels device can distinguish human breast cancer cell lines (MDA-MB-231, HCC-1806, MCF-7) from immortalized breast cells (MCF-10A) with a confidence level of ∼81-85% at a rate of 50-70 cells/min based on velocity increment differences through multiconstriction channels aligned in series. The results are likely related to the deformability differences between nonmalignant and malignant breast cells. The data were analyzed by the methods/algorithms of Ridge, nonnegative garrote on kernel machine (NGK), and Lasso using high-dimensional variables, including the cell sizes, velocities, and velocity increments. In kernel learning based methods, the prediction values of 10-fold cross-validations are used to represent the difference between two groups of data, where a value of 100% indicates the two groups are completely distinct and identifiable. The prediction value is used to represent the difference between two groups using the established algorithm classifier from high-dimensional variables. These methods were applied to heterogeneous cell populations prepared using primary tumor and adjacent normal tissue obtained from two patients. Primary breast cancer cells were distinguished from patient-matched adjacent normal cells with a prediction ratio of 70.07%-75.96% by the NGK method. Thus, this high-throughput multiconstriction microfluidic device together with the kernel learning method can be used to perturb and analyze the biomechanical status of cells obtained from small primary tumor biopsy samples. The resultant biomechanical velocity signatures identify malignancy and provide a new marker for evaluation in risk assessment.

Entrapment of Prostate Cancer Circulating Tumor Cells with a Sequential Size-Based Microfluidic Chip.

Circulating tumor cells (CTCs) are broadly accepted as an indicator for early cancer diagnosis and disease severity. However, there is currently no reliable method available to capture and enumerate all CTCs as most systems require either an initial CTC isolation or antibody-based capture for CTC enumeration. Many size-based CTC detection and isolation microfluidic platforms have been presented in the past few years. Here we describe a new size-based, multiple-row cancer cell entrapment device that captured LNCaP-C4-2 prostate cancer cells with >95% efficiency when in spiked mouse whole blood at ∼50 cells/mL. The capture ratio and capture limit on each row was optimized and it was determined that trapping chambers with five or six rows of micro constriction channels were needed to attain a capture ratio >95%. The device was operated under a constant pressure mode at the inlet for blood samples which created a uniform pressure differential across all the microchannels in this array. When the cancer cells deformed in the constriction channel, the blood flow temporarily slowed down. Once inside the trapping chamber, the cancer cells recovered their original shape after the deformation created by their passage through the constriction channel. The CTCs reached the cavity region of the trapping chamber, such that the blood flow in the constriction channel resumed. On the basis of this principle, the CTCs will be captured by this high-throughput entrapment chip (CTC-HTECH), thus confirming the potential for our CTC-HTECH to be used for early stage CTC enrichment and entrapment for clinical diagnosis using liquid biopsies.

Single-Cell Mechanical Characteristics Analyzed by Multiconstriction Microfluidic Channels.

A microfluidic device composed of variable numbers of multiconstriction channels is reported in this paper to differentiate a human breast cancer cell line, MDA-MB-231, and a nontumorigenic human breast cell line, MCF-10A. Differences between their mechanical properties were assessed by comparing the effect of single or multiple relaxations on their velocity profiles which is a novel measure of their deformation ability. Videos of the cells were recorded via a microscope using a smartphone, and imported to a tracking software to gain the position information on the cells. Our results indicated that a multiconstriction channel design with five deformation (50 µm in length, 10 µm in width, and 8 µm in height) separated by four relaxation (50 µm in length, 40 µm in width, and 30 µm in height) regions was superior to a single deformation design in differentiating MDA-MB-231 and MCF-10A cells. Velocity profile criteria can achieve a differentiation accuracy around 95% for both MDA-MB-231 and MCF-10A cells.