4 research outputs found
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
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
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
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