Abstract
Cancers consist of a heterogeneous populations of cells that may
respond differently to treatment through drug-resistant
sub-populations. The scarcity of these resistant sub-populations makes
it challenging to understand how to counter their resistance. We report
a label-free microfluidic approach to separate cancer cells treated
with chemotherapy into sub-populations enriched in chemoresistant and
chemosensitive cells based on the differences in cellular stiffness.
The sorting approach enabled analysis of the molecular distinctions
between resistant and sensitive cells. Consequently, the role of
multiple mechanisms of drug resistance was identified, including
decreased sensitivity to apoptosis, enhanced metabolism, and extrusion
of drugs, and, for the first time, the role of estrogen receptor in
drug resistance of leukemia cells. To validate these findings, several
inhibitors for the identified resistance pathways were tested with
chemotherapy to increase cytotoxicity sevenfold. Thus, microfluidic
sorting can identify molecular mechanisms of drug resistance to examine
heterogeneous responses of cancers to therapies.
Introduction
Chemotherapy is one of the most common modalities of cancer
treatment^[42]1,[43]2, but its use is complicated by innate and
acquired resistance of cancer cells to commonly used anticancer
drugs^[44]3. To address the problem of drug resistance, modern genomic,
proteomic, and functional analytical techniques have identified novel
genes and signaling networks that determine the responsiveness of
tumors to a particular drug treatment^[45]1,[46]2,[47]4,[48]5. These
approaches interrogate clinical samples as a whole and identify
molecular signatures and genotypes that predict overall responses to
certain drugs. However, determination and prediction of drug response
for individual patients is stymied due to complexities caused by cancer
cell heterogeneity^[49]1,[50]2,[51]4,[52]5. Resistance to treatment of
a small subset of cancer cells can have a crucial role in cancer
progression and disease recurrence in multiple malignancies^[53]6. The
small population of resistant cells can elude chemotherapy in many ways
and thus their specific study is needed to identify effectual
treatments in precision medicine^[54]7,[55]8. Since drug-sensitive
cells can be orders of magnitude more prevalent than the resistant
cells, methods to sort and isolate resistant cells for their study
distinct from sensitive cells may enable the discovery of resistance
biomarkers and the prediction of alternative treatments to circumvent
drug resistance^[56]9,[57]10. Although fluorescent labels of viability
or apoptosis can be used to isolate sensitive and resistant cells,
labeling cells with fluorescent tags is time consuming and may alter
the properties of the cells and interfere with downstream analyses. For
instance, fluorescently labeled caspase inhibitor assay (FLICA)-based
reagents not only detect, but also irreversibly inhibit caspase
activity, which substantially alters biology of probed cells and
seriously limits their use for future studies^[58]11,[59]12. Therefore,
new technologies for label-free functional testing of cells are needed
to scrutinize heterogeneous response to drugs.
The biophysical properties of cell responses have been effectively
exploited previously for sorting and enhanced detection of numerous
malignant cells in microfluidic platforms^[60]13–[61]16, as well as for
sorting cells by viability^[62]17. In this article, a microfluidic
device has been used to sort drug-resistant and sensitive leukemia
cells by differences in their stiffness that result after treatment
with chemotherapy, which was previously identified as an early
biophysical response of cells to toxic agents^[63]17–[64]20. Separated
populations were tested to determine their differential gene expression
in response to chemotherapy. The microchannel device uses periodic
diagonal ridges oriented skew to the direction of fluid flow to
compress and sort cells by stiffness and is shown to be highly accurate
to separate apoptotic cells^[65]25,[66]26. The schematic of the process
is shown in Fig. [67]1a and a micrograph of the device is shown in
Fig. [68]1b. Flowing cells are translated perpendicular to the channel
axis based on cell biomechanical properties as shown in Fig. [69]1c.
Fig. 1. Experimental setup and cell sorting using ridge based microfluidic
device.
[70]Fig. 1
[71]Open in a new tab
a Schematic diagram of the experimental setup showing the sorting of
drug-treated cells using microfluidic device and subsequent
characterization of gene expression and phenotypic characteristics; b
optical micrograph of a three-outlet device; c representative
trajectories of resistant and sensitive cells flowing inside the device
As a proof of concept, the chemotherapeutic agent daunorubicin was
applied to the leukemia cell lines K562 and Jurkat, and a small
population of surviving (resistant) cells was isolated using
microfluidics. Gene expression differences between sensitive and
resistant cells were determined using the quantitative polymerase chain
reaction (qPCR). On the basis of a network analysis of gene
expression data, several molecular pathways were identified as
significant to resistance. Inhibitors of these resistance pathways were
then confirmed to increase the cytotoxicity of daunorubicin. Cell
stiffness was thus identified as a biomarker that can be used to
isolate and study resistant cells. Biophysical sorting introduces a
novel opportunity to examine the heterogeneous response of cells to
therapies to better address drug resistance and design effective
precision treatments against cancer cells.
Results and discussion
Characterization of chemotherapy-treated and -untreated cells
AFM analysis was conducted on both untreated and daunorubicin-treated
K562 and Jurkat cell populations. Cells were treated with 1 µM and 2 µM
daunorubicin for ~2 h. The Young’s modulus of K562 and Jurkat cells
before and after drug treatment are shown in Fig. [72]2a and b,
respectively. The average Young’s modulus of untreated K562 and Jurkat
cells were 0.42 ± 0.38 and 0.29 ± 0.21 kPa, respectively. After 2 µM
drug treatment the average Young’s modulus increased threefold to
1.51 ± 1.29 and 1.10 ± 1.08 kPa, respectively (p-value < 0.001). The
increase of stiffness after the application of chemotherapy is
consistent with several previously reported
studies^[73]18,[74]21–[75]23. The stiffness of a cell is associated to
the apoptotic response of cells, and include the dynamic changes in the
actin cytoskeleton, reduction in cytoplasmic constituents, and
cross-linking of the membrane with cytoskeletal
structures^[76]18,[77]22,[78]24.
Fig. 2. Stiffness of untreated and daunorubicin-treated cells are presented
by Young’s moduli.
[79]Fig. 2
[80]Open in a new tab
The stiffness of individual cells is shown by individual dots (N = 25
for each cell type). The bars and shaded regions are representing mean
and standard error of mean (SEM), respectively. a The stiffness of
untreated K562 cells was significantly lower (p < 0.005) than the
stiffness of treated cells for both concentrations of daunorubicin; and
b the stiffness of untreated Jurkat cells was also significantly lower
(p < 0.001) than stiffness of treated cells for both concentrations of
daunorubicin
Untreated and daunorubicin-treated cells were separately flowed through
the microfluidics device at various flow rates to optimize their
trajectories for sorting experiments. Video microscopy showed that
untreated cells followed flow streamlines consistent with cell softness
and resulted in a net negative transverse displacement with respect to
the direction of fluid flow^[81]25,[82]26. Untreated (soft) and
chemotreated (stiff) cells migrated to opposite sides of the ridged
microchannel and sorted according to their differences in mechanical
stiffness^[83]14.
Sorting of cell mixtures treated and untreated with chemotherapy
K562 and Jurkat cells were treated with 1 µM and 2 µM daunorubicin for
2 h and stained with red cell tracker. Untreated cells were stained
with green cell tracker, and mixed with treated cells of the same type
at a ratio of 1:1. The mixture was sorted using the three-outlet
microfluidic device at a flow rate of 0.03 ml/min, which was determined
to optimize differential trajectories^[84]17. The device processed ~500
cells/s. The purity of the sorted cells for untreated and
daunorubicin-treated cells was dependent on the concentration of
daunorubicin for both K562 and Jurkat of cells. For K562 cells and 1 µM
drug concentration, the purity was 82.7% of untreated and 80.0% of
treated cells in soft and stiff outlet, respectively. For 2 µM
concentration, the purity increased to 93.7% and 89.5% in soft and
stiff outlet (Fig. [85]3a–c), corresponding to an enrichment factor of
14.34 and 9.64, respectively, which is consistent with a larger
stiffness difference between treated and untreated cells. In the study
of Jurkat cells, the purity of recovered untreated and
daunorubicin-treated populations of cells (at 2 µM) was found to be 93%
and 89% (Fig. [86]3d, e) with enrichment factor of 13.6 and 9.0,
respectively.
Fig. 3. The data obtained by sorting untreated and daunorubicin-treated cells
using 3-outlet microfluidic devices.
[87]Fig. 3
[88]Open in a new tab
Flow cytometry data from a inlet; b soft outlet; and c stiff outlet;
the summarized data of sorted K562 and Jurkat cells at outlets d purity
of untreated cells in soft outlets and chemotherapy-treated cells in
stiff outlets; e enrichment of untreated and chemotherapy-treated cells
in soft and stiff outlets, respectively, with error bars showing
standard deviation (N = 2). Stiffness of sorted cells from three
outlets f statistical difference in stiffness of K562 cells among three
outlets were significant (p < 0.005 between any two outlets, N = 20);
and g statistical difference in stiffness of Jurkat cells among three
outlets were significant (p < 0.001 between any two outlets, N = 20)
Stiffness of sorted cells
To validate that the sorting mechanism was dependent upon mechanical
stiffness, AFM analysis was performed on the sorted cells. After
sorting mixtures of untreated and daunorubicin (2 µM)-treated cells,
the average Young’s modulus of the cells collected from soft outlets
was significantly lower compared to the stiffer outlets
(p-value < 0.01), as shown in Fig. [89]3f and g.
Assessment of apoptosis of sorted cells
K562 cells treated with 2 µM daunorubicin for 2 h were mixed with
untreated cells at a ratio of 1:1, and sorted using the 3-outlet
microfluidic device. Viability of the cells collected from soft outlet
was found to be 94.7% as untreated cells were primarily directed to
this outlet. The stiff outlet was found to be enriched for
daunorubicin-treated cells and showed a viability of only 7.3%, shown
in Fig. [90]4a. Representative results obtained from flow cytometry
analysis are shown in Fig. S[91]1. For the lower concentration of 1 µM,
cell viability was 84.9% and 20.15% in soft and stiff outlets,
respectively. Similar results of Jurkat cell viability was observed and
shown in Fig. [92]4a. By reducing the concentration of daunorubicin,
the decrease in stiffness difference resulted in a decrease in purity
of cells in both the outlets and consequently, a lower viability
difference in the sorted the cells. The sensitivity and specificity of
the device is shown in Table [93]S1 and the diagnostic odd ratio (DOR)
is shown in Fig. S[94]2.
Fig. 4. Analysis of sorted cells from soft and stiff outlets for K562 and
Jurkat cells using flow cytometry.
[95]Fig. 4
[96]Open in a new tab
a Viability analysis using EthD-1 stain; b activity of Caspase-3/7
gene; c Expression of ABCB1 gene. Error bars are showing standard
deviation for all the figures (N = 2)
Treatment with daunorubicin also induced apoptosis, which was confirmed
by increased caspase-3/7 activity (Fig. S[97]3). Daunorubicin causes
DNA synthesis inhibition, free radical formation, and lipid
peroxidation, DNA binding and the accumulation of DNA damage via the
inhibition of topoisomerase II^[98]27. For both K562 and Jurkat cells,
the percentage of cells that showed caspsase-3/7 activity was
equivalent to the percentage of nonviable cells, indicating that the
nonviable cells followed an apoptotic cell death upon treatment with
daunorubicin (Fig. [99]4b).
Assessment of ABCB1 expression of sorted cells
ABCB1, a member of the ABC-transporter family responsible for the
extrusion of some drugs, is associated with multidrug resistance of
cancer cells through aberrant expression of its product
MDR1^[100]28,[101]29. ABCB1 is reported to have low expression in K562
cells which was also observed in this study (Fig. [102]4c).
However, after treating the cells with both the 1 µM and 2 µM
daunorubicin, the expression of ABCB1 increased markedly. Untreated and
chemotreated cells were mixed at 1:1 ratio and sorted. For K562 cells
treated with 2 µM daunorubicin, the ABCB1 expression was detected in
8.95% and 95.45% cells from soft and stiff outlets, respectively (Fig.
S[103]4). Similar experiments were performed with Jurkat cells and
ABCB1 was expressed in over 40% untreated Jurkat cells. The Jurkat
cells treated with 2 µM of daunorubicin and subsequently sorted showed
47.25% and 96.25% cells ABCB1 expression in soft and stiff outlets,
respectively. The results are summarized in Fig. [104]4c.
Sorting of sensitive and resistant cells after drug treatment
K562 cells were treated with a lower dose of daunorubicin (50 nM) for
15 h which resulted in survival of a minority of cells (<15%). The
treated cells were then sorted through a 5-outlet device (Fig. [105]1b)
and analyzed using flow cytometry shown in Fig. [106]5a and b. The
5-outlet design was used to increase the fractionation of the sorted
populations to result in both improved sensitivity and
specificity^[107]17. The stiffest outlet (stiff 1) had only nonviable
cells, whereas the next outlet, stiff 2, had 99.7% nonviable cells with
an enrichment factor of 59.1. Evaluating the viable cells, the softest
outlet soft 1 enriched viable cells to 96.3% purity with an enrichment
factor of 143.52. The next softer outlet, soft 2, had 81.8% viable
cells with an enrichment of 25.27. The activity of caspase-3/7 was also
observed on sorted cells shown in Fig. [108]5c and the expression of
apoptosis markers was consistent with viability results.
Daunorubicin-treated K562 cells collected from soft 1 outlet were also
significantly softer than the cells from the stiff 1 outlet, as
measured with AFM (Fig. [109]5d). This result indicates that minority
populations of viable, chemotherapy-resistant cells can be enriched to
high purity. A comparison of the improvement in accuracy of the
3-outlet and 5-outlet devices using a DOR analysis^[110]17 is shown in
Fig. S[111]2.
Fig. 5. Viability and stiffness analysis of sorted K562 cells after
daunorubicin treatment.
[112]Fig. 5
[113]Open in a new tab
Viability analysis performed with EthD-1 of sorted K562 cells after
treatment with daunorubicin using 5-outlet device, showing the a inlet
and b outlets. c Apoptotic marker showing caspase-3/7 activity at
different outlets. d Stiffness of treated K562 cells after separation
to two different outlets (p < 0.00001, N = 25)
Gene expression analysis
To observe whether gene expression differences were present between
chemotherapy-resistant and sensitive cells, a comparative qPCR analysis
was performed upon cells from stiff and soft outlets. The results of
gene expressions are summarized in Fig. S[114]5. Apoptosis related
genes Casp-3 and Casp-7 were overexpressed in the cells collected from
stiff outlet, indicating that the number of nonviable cells were higher
in the stiff outlet compared to the soft one^[115]30,[116]31. Also, the
higher expression of KRT-19, a member of keratin family, was observed
in the cells collected from soft outlet^[117]31,[118]32. The keratins
are intermediate filament proteins primarily accountable for the
structural integrity of epithelial cells. Change in the BCL2 gene was
not observed in the cell population after chemotreatment (Fig.
S[119]5), but overexpression of this gene in the small subset of soft
cells suggested increased resistance to apoptotic cell
death^[120]33–[121]35. From the similarity of the expression profile of
cells at inlet and stiff outlets in Fig. S[122]5, we highlight that the
sorting allowed us to discover gene expression differences between
sensitive and resistant cells separately not observable in the bulk
analysis of treated cells.
To understand the underlying mechanism for heterogeneity of
responsiveness of cells in the soft and stiff outlets, an array of
genes related to cancer drug resistance and metabolism was analyzed.
Among 84 examined genes related to drug resistance from the PCR Array,
27 differentially expressed genes were identified, of which 24 were
upregulated and 3 downregulated in soft (resistant) cells
(Table [123]S3). A sizable group of upregulated genes (9/24) is formed
by members of the CYP supergene family that encode cytochrome P450
monooxygenases (CYP450). CYP450 enzymes are responsible for 80% of
phase I drug oxidation reactions^[124]36, and these reactions can
activate or inactivate numerous anticancer drugs. For instance, CYP3A4,
which metabolizes about half of all marketed drugs, was found
upregulated in soft/resistant cells. CYP3A4 and CYP2C8, which are both
upregulated in soft cells, are major CYP450 enzymes responsible for
oxidation and inactivation of anticancer drug taxol^[125]37.
The finding of upregulated CYP1A1 and CYP1A2 in soft (resistant) cells
is consistent with the findings^[126]38 that CYP450 genes are
significantly upregulated in doxorubicin-resistant cells developed from
a MCF-7 breast cancer cell line. Upregulation of antiapoptotic gene
BCL2 and downregulation of pro-apoptotic gene BAX, detected in soft
(resistant) vs. stiff (sensitive) cells, are known to desensitize
various cancer cells to apoptosis, which is consistent with a
drug-resistant phenotype^[127]39. Another gene identified as
upregulated in soft (resistant) cells is CDKN1A, which encodes a
cyclin-dependent kinase inhibitor p21^(WAf1/CIP1). Upregulation of
CDKN1A was previously shown to protect colon cancer cells against
apoptosis induced by doxorubicin (a structural analog of daunorubicin)
through inhibition of caspase-3 activation^[128]40. Interestingly,
upregulation of CDKN1A also reportedly inhibits apoptosis induced in
chronic myelogenous leukemia (CML) cells upon treatment with targeted
therapeutic agent imatinib^[129]41.
Upregulation of transporter gene ABCC1 was detected in soft (resistant)
cells relative to stiff cells. This gene encodes multidrug
resistance-associated protein 1 (MRP1), an ABC transporter with
unusually broad substrate specificity^[130]42. MRP1 is capable of
extruding a wide variety of neutral hydrophobic compounds and
contributes in this way to the defense against xenobiotics, endogenous
toxic metabolites, and oxidative stress.
EGFR was found upregulated in soft (resistant) cells. Deregulation of
EGFR through various mechanisms, including overexpression of EGFR gene,
has been demonstrated in various solid tumors and associated with poor
prognosis in some tumor types^[131]43. EGF also acts as a survival
factor, and deregulated EGFR-signaling inhibits apoptosis through
downstream effectors PI3K/Akt and MEK/Erk^[132]44. Inhibition of EGFR
would be reasonably expected to reverse the observed deregulation of
Bcl-2 and Bax and increase drug sensitivity of resistant cells^[133]45.
The inverse association between CYP3A4 and ABCB1/MDR1 is intriguing.
Specifically, resistance to daunorubicin in leukemia cells is
associated with upregulation of ABCB1 gene^[134]46, while the soft
(resistant) cells in our study display downregulation of the ABCB1
gene, which encodes for the multidrug resistance protein 1 (MDR1,
P-glycoprotein). However, downregulation of MDR1 in soft cells does not
necessarily imply increased sensitivity to daunorubicin because
increased expression of CYP3A4 is likely enhancing metabolism and
inactivation of the drug in soft cells. Most compounds that are
substrates of CYP3A4 are also substrates or inhibitors of MDR1^[135]47.
In addition, both CYP3A4 and ABCB1 gene encoding for MDR1 protein are
transcriptionally regulated by pregnane X receptor (PXR) that acts as a
xenobiotic sensor regulating various phase I and phase II drug
metabolizing enzymes and transporters^[136]48. Thus, the downregulated
ABCB1 gene does not imply increased sensitivity of cells to MDR1
substrates as these agents can be metabolized and inactivated by
upregulated CYP3A4. Consequently, upregulation of CYP3A4 may counteract
downregulation of MDR1, as both these resistance factors are known to
protect cells against many of the same cytotoxic agents.
To identify high-level regulators of gene expression that can be
responsible for the observed differences in gene expression between
soft (resistant) and stiff (sensitive) cells, we performed various
systems level analyses that included interactome analysis, network
building and topological scoring with pathway enrichment analysis.
Interactome analysis identified 127 transcription factors in the entire
MetaCore human protein interaction network (N = 40,221 network objects)
that display significant overconnectivity to 27 genes differentially
expressed between soft and stiff cells (Supplemental File [137]1). At
least some of these 127 overconnected transcription factors, selected
with stringent criteria (FDR = 0.001), represent regulator genes likely
involved in gene expression changes observed between soft and stiff
cells. Of them, three transcription factors: ESR1, NF-kB, and PPARA are
overconnected, as well as upregulated, in the soft vs. stiff cells.
The implied relevance of ESR1 (estrogen receptor 1; ERα) in soft cells
is unexpected considering the fact that this receptor is usually
involved in sex-specific tissue cancers (breast, ovarian, endometrial,
and prostate). Building networks from 27 differentially expressed genes
using the “Transcription Regulation” algorithm produced a prioritized
list of 30 transcription factor-centric networks (Supplemental
File [138]2). Intriguingly, the network centered on ESR1 (Fig. [139]6a)
scores among the top of these prioritized transcription factor-centric
networks. This finding further implicates estrogen receptor 1 in the
observed gene expression changes. Regarding ESR1, a prior gene
expression study of chronic myelogenous leukemia (CML) has uncovered
that genes upregulated in CML were significantly enriched with genes
regulated by estrogen receptor. The effect of estrogen signaling was
seen in a data set that included CML specimens from men and women of
various ages. In addition, the enrichment for ERα-regulated genes was
seen in a subset of known CML-associated genes. Taken together, these
findings implied the role of estrogen receptor activity in CML, even in
men and postmenopausal women^[140]49.
Fig. 6. Gene expression analysis of resistant and sensitive cells.
Fig. 6
[141]Open in a new tab
a Transcription factor-centric network around ESR1 built from genes
differentially expressed between soft and stiff cells. Red circle:
upregulated gene; blue circle: downregulated gene; green edge:
transcriptional activation; red edge: transcriptional repression; b
custom map produced from genes differentially expressed between soft
and stiff cells and topologically relevant genes for differentially
expressed set. Edges represent direct transcriptional regulation or
influence on expression between map objects. Red thermometer: genes
upregulated in soft (resistant) cells; blue thermometer: genes
downregulated in soft (resistant) cells; yellow thermometer: genes
topologically relevant to set of upregulated genes in soft (resistant)
cells (for additional legend:
[142]https://portal.genego.com/legends/MetaCoreQuickReferenceGuide.pdf)
Upregulation of genes NFKB1 and NFKB2, encoding NF-κB family members
p100/52 and p105/50, was found in soft cells. Activation of NF-κB
signaling is known to occur upon treatment with anticancer drugs and
cause resistance in cancer cells^[143]50, but upregulation of
NFKBIB^[144]51 and NFKBIE^[145]52 genes in soft cells, which both
encode inhibitors of NF-κB signaling, lowers the confidence in the role
of NF-κB signaling in resistance of soft cells. Nevertheless,
activation of NF-κB signaling is supported by results of interactome
analysis and transcription regulation network building (Supplemental
Files [146]1 and 2).
Interestingly, soft cells in our study also displayed upregulation of
ESR2 gene that encodes ERβ receptor. Significant enrichment of pathway
map “Regulation of actin cytoskeleton by Rho GTPases” further supports
the role of differences in actin cytoskeleton between soft and stiff
cells in our study (Fig. S[147]6).
Taken together, the data suggest that activation of ESR1 (ERα) can be
directly or indirectly involved in several differences in gene
expression observed between soft and stiff cells (Fig. [148]6b). The
expression signature of ER-signaling has been found in CML
specimens^[149]49, and activation of ER-signaling reportedly decreased
stiffness of osteoblasts and endothelial cells^[150]53,[151]54. Other
differences in gene expression can be attributed to genes identified as
topologically relevant or overconnected to the set of our
differentially expressed genes (Fig. [152]6b; Supplemental
File [153]1). Our results suggest a possible role of multiple
mechanisms involved in the drug resistance of soft cells, including
decreased sensitivity to apoptosis, enhanced phase I metabolism of
anticancer drugs and their enhanced extrusion from cells by an ABC
transporter. Nevertheless, ERα seems to be critically involved in at
least some of these diverse mechanisms and its modulation may at least
in part decrease drug resistance and increase cell stiffness, which can
result in cancer cells that display less resistant/less aggressive
phenotype. Additional details of the gene expression data are described
in the Supplement “Gene Expression Pathways” section.
Experimental validation of the role of ESR1, NF-κB, and CYP3A4 in the
resistance of K562 cells
To experimentally validate the pathways identified by gene expression
and in silico analyses as potentially responsible for drug resistance
of K562 cells, including ESR1, NF-κB, CYP3A4, and PXR, products of
genes were inhibited by treatment of cells with low-molecular weight
inhibitors at concentrations/times that did not influence cell
viability. Cells with inhibited ESR1, NF-κB, or CYP3A4 activity
subsequently displayed significantly increased sensitivity to cytotoxic
effects of daunorubicin (Fig. [154]7), reducing the number of surviving
cells up to sevenfold, and supporting our prediction of the role of
identified pathways in drug resistance of leukemia K562 cells. The
control experiments of the nontoxic antagonist treatments are shown in
Fig. S[155]7. Thus, the microfluidic sorting of resistant cells can
potentially solve the unmet challenge in individualized therapy to
choose supporting agents in combination therapy with improved activity
against dysregulated pathways in leukemic cells to produce long-lasting
remissions^[156]55.
Fig. 7. Viability of inhibitors and chemotherapy treated K562 cells.
Fig. 7
[157]Open in a new tab
Average viability with standard deviation of inhibitors and
chemotherapy-treated K562 cells after (N = 3), with p-values are
calculated with respect to only daunorubicin-treated cells in which *
represents p < 0.05 and ** represents p < 0.0005
Here we showed that differential cell stiffness can be an effective
biomarker for rapid and non-destructive separation of resistant cells
from sensitive leukemia cells after chemotherapy treatment for
comparative analysis of their genetic/phenotypic properties and
determination of the underlying mechanisms through network analyses.
The purity of the isolated resistant cells was over 95%. Thus,
microfluidics processing can examine gene expression differences
between sensitive and resistant cells accurately in spite of the small
initial percentages of resistant cells. The plausible mechanisms
related to drug resistance were identified. The roles of estrogen
receptor signaling, NF-κB signaling and CYP3A4 activity in resistance
of K562 cells have been experimentally validated by testing their
inhibitors in combination with chemotherapy to reduce drug resistance.
As a result, cell sorting by biophysical properties can be used to
examine heterogeneous responses of cells to chemotherapy treatments
with possible future application in precision medicine approaches to
improve chemotherapy selection and use.
Materials and methods
Fabrication of microfluidic device
The microfluidic device was fabricated using replica molding of
polydimethylsiloxane (PDMS) on a SU-8 patterned silicon
wafer^[158]13–[159]15. All devices tested were designed in AutoCAD and
simulated the flow trajectories in ANSYS Fluent. The molds for the
device were fabricated on a silicon wafer by spin coating SU-8 2007
(SU-8 2007, Microchem Corp.) using a two-layer photolithography
process. The dimensions of the molds, particularly the ridge heights,
were measured with profilometry (Dektak 150 profiler) and optical
microscopy. Several device parameters influence cell trajectories,
which include ridge gap distance, number of ridges, inter-ridge
spacing, and angle of ridges. The effects of ridge angles, ridge gap,
ridge spacing, and number of ridges was studied
previously^[160]13–[161]15. The ridge angle, ridge number, ridge
spacing, and ridge gap were chosen to be 30°, 14 ridges, 200 µm, and
9 µm, respectively^[162]17. The ridge gap was chosen to be small enough
to compress the cells sufficiently without clogging the device and
compares to an average cell diameter of 15 µm. Three and five outlet
devices were tested to evaluate the accuracy of fractionation of the
heterogeneous cells to isolate target cell types. The mold pattern was
translated to polydimethylsiloxane (PDMS), inlet and outlet holes were
punched with biopsy punch, and the chip bonded to
glass^[163]13,[164]14,[165]17. To prevent non-specific cell adhesion to
the microfluidic channel walls, the device was coated with bovine serum
albumin (Sigma Aldrich) at a concentration of 10 mg/ml and incubated at
4 °C overnight.
Cell culture and treatments
Jurkat (CRL-1990) and K562 (CCL-243) cells were purchased from ATCC.
The cells were cultured and maintained in RPMI-1640 medium (Sigma) with
the addition of 10% FBS and 1% penicillin streptomycin. All cells were
incubated at 37 °C in humidified air with 5% CO[2]. Cells were expanded
to 80% confluency in non-treated cell culture flasks over two days.
Cells were treated with daunorubicin at concentrations of 0.05 µM,
1 µM, and 2 µM for 2 and 15 h of exposure.
Experimental setup
The accuracy of sorting was tested by mixing fluorescently labeled
daunorubicin-treated cells with untreated cells, sorting using the
microfluidic device, and analyzing the outlets using flow cytometric
analysis. Gene expression differences of resistant cells were
determined by treating all cells with daunorubicin and sorting using
microfluidics. In all cases, cells were washed and resuspended in PBS
at a concentration of ~1 million cells/ml and infused into the
microfluidic device using a syringe pump (PHD 2000, Harvard Apparatus)
at specified flow rates. The device flow was formed by three inlet
streams, which included two sheath streams to hydrodynamically focus
the stream containing the cells. The cell trajectories were observed by
an inverted bright-field microscope (Eclipse Ti, Nikon) and recorded by
high-speed camera (Phantom v7.3, Vision Research) at a frame rate of
2000 frames per second^[166]13,[167]17. The stiffness of cells before
and after separation experiments was measured using atomic force
microscopy (AFM, MFP-3D, Asylum Research^[168]56,[169]57) in
suspension-like condition to retain rounded morphology and similar
indentation range as microfluidics. To improve cell stability during
the AFM measurement, a monolayer of Cell-Tak (BD Biosciences) was
applied to gently attach cells to the glass substrate. Beaded silicon
nitride cantilevers (spring constant 37.1 pN/nm) were used to indent
the center of the cells at a rate of 1.5 μm/s. Sufficient force was
applied to achieve at least 5 μm deformation such that compression was
in close comparison with the microfluidic experiments. Cell Young’s
modulus values were calculated from the force-indentation curves and
fit to a Hertzian model to compute the average Young’s modulus. One-way
analysis of variance (ANOVA) was performed between Young’s modulus of
chemotherapy-treated and -untreated cells to determine statistical
significance.
Flow cytometry analysis
To differentiate in flow cytometry, cells were labeled with 2 µM with
CellTracker™ red (chemotherapy-treated cells) and green (untreated
cells) (Molecular Probes Inc.) for ~1 h in 37 °C. After loading the
cells with the dye, the accuracy of sorting could be quantified. From
flow cytometry results, the enrichment factor was calculated from that
using the following equation:
[MATH: Numberofgreencells∕NumberofredcellsOutlet
Numberofgreencells∕NumberofredcellsInlet.
:MATH]
The sensitivity and specificity at different outlets, as well as the
DOR was determined as described previously^[170]17,[171]57. The
sensitivity measures the proportion of positive cells that are
correctly identified and specificity measures the proportion of
negatives that are correctly identified from a sample. A confusion
matrix was used to determine condition positive outcomes, which were
untreated cells in the in the soft outlet and treated cells in stiff
outlet, whereas negative outcomes were defined as untreated cells in
stiff outlet and treated cells in soft outlet, resulting in frequencies
of true positives, false positives, false negatives, and true negatives
(TP, FP, FN, and TN, respectively). Cell viability of microfluidic
sorted cells and control cells that were not processed by microfluidics
was tested by flow cytometry analysis using 2 µM ethidium homodimer-1
(EthD-1) (Molecular Probes Inc.)^[172]17,[173]58,[174]59. EthD-1 is a
cell impermeable nucleic acid stain that shows strong fluorescence at
635 nm when bound to DNA in dead cells with disintegrated cell
membranes. Apoptosis was determined by flow cytometry analysis through
activation of caspase-3/7 using Cell Event Caspase-3/7 Green flow
cytometry assay kit (Invitrogen)^[175]60,[176]61. To determine the
expression of the ABC-transporter protein ABCB1, cells were incubated
with P-glycoprotein antibody (UIC2) conjugated with PE (ThermoFisher
Scientific) for 1 h, washed, and resuspended in PBS to analyze by flow
cytometry^[177]28,[178]62,[179]63. Flow cytometry analysis was
performed using BD Accuri C6 flow cytometer (BD Biosciences).
qPCR
For chemosensitive and chemoresistant cells, ~10,000 cells were
collected from stiff and soft outlets and total cellular RNA was
isolated using a RNA isolation kit (Macherey-Nagel) according to the
instructions provided by the manufacturer. Thereafter, RNA was
reverse-transcribed to cDNA using the kit purchased from ThermoFisher
Scientific (Catalog number 4387406) following the manufacturer’s
instruction. qPCR was used to analyze expression of transporter,
apoptosis, structural integrity, and resistance related genes using the
2×TaqMan® PreAmp Master Mix (Applied Biosystems, PN 4391128) and
Fluidigm Biomark system. The primers were designed using Primer 3 Plus
website
([180]http://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi)
and “BLAT” tool from UCSC genome browser website
([181]http://genome.ucsc.edu/). The list of primers is given in
Table [182]S2.
In addition, expression of genes related to apoptosis, xenobiotic
metabolism and drug resistance was investigated using a commercially
available PCR array ExProfile™ Human Cancer Drug Resistance &
Metabolism Related Gene qPCR Array (Catalog Number: QG007-B6,
Genecopedia, Inc.), using the StepOnePlus^TM instrument from Applied
Biosystems. The array tested for expression of 84 genes relevant to
drug resistance and drug metabolism, as well as endogenous control
genes. GAPDH gene was used for normalization of gene expression. Soft
and stiff sub-populations separated from the same daunorubicin-treated
populations of K562 cells were analyzed as matched-pairs. Genes that
were selected as differentially expressed showed a two-tailed
p-value < 0.05 using the matched-pair t-test and an absolute fold
change (FC) values ≥ 1.5. The use of multiplicity correction by FDR
found that q-values ≤ 0.074 for all genes selected as significantly
differentially expressed.
Identification of resistance pathways with systems biology analysis of gene
expression
Differentially expressed genes were analyzed with MetacoreTM suite v
6.31 build 68930 (Thomson Reuters) using the following approaches: (i)
enrichment analysis in GeneGO canonical “Pathway Maps” functional
ontology, (ii) build networks using “Transcription Regulation”
algorithm, and (iii) interactome overconnectivity (one-step) analysis
for transcription factors. One-way analysis of variance (ANOVA) was
performed between soft and stiff outlets to determine statistical
significance in gene expressions. The topological significance analysis
(TSA) of gene expression profile was performed using online tool
provided by GeneGO, Inc.
([183]http://topology.genego.com/zcgi/topology_scoring.cgi). Enrichment
analysis was employed to identify GeneGO signaling pathway maps
significantly enriched by differentially expressed genes.
Transcriptional regulation network building tool produces networks with
central transcription factors connected to several differentially
expressed genes largely one-step away. One-step interactome analysis
for transcription factors determines the relative connectivity of all
known transcription factors from MetaCore global interactome to the set
of differentially expressed genes and identifies direct neighboring
transcription factors with significant connectivity^[184]64. TSA
further extends one-step interactome analysis and identifies “hidden
nodes” as genes that occupy topologically significant positions in
MetaCore global interactome with respect to differentially expressed
genes (without limiting to local interaction
neighborhood)^[185]65,[186]66. Topologically significant genes
(p < 0.01) were identified for all genes upregulated in soft cells
relative to stiff cells using the “transcriptional activation paths
from all nodes” algorithm and subsequently mapped to GeneGO human
signaling pathway maps as described above. Multiple testing correction
was performed using false discovery rate with the adaptive threshold
set to permit no more than one pathway incorrectly predicted as
significantly enriched. Insight generated from analytical approaches
described above was used to build a custom signaling map representing
simplified model that could explain changes in gene expression observed
between soft and stiff cells (Map Editor tool in MetaCore; Thomson
Reuters).
Effect of ESR1, NF-κB, and CYP3A4 on the resistance of K562 cells
The effect of inhibition of genes identified as likely involved in
differences between soft and stiff cells on anticancer activity of
daunorubicin was tested using specific small molecule inhibitors. K562
cells were treated with caffeic acid phenethylester (inhibitor of
NF-κB, 5 µM), fulvestrant (selective estrogen receptor downregulator,
1 µM), clarithromycin (CYP3A4 inhibitor, 25 µM), and ketoconazole (PXR
inhibitor, 1 µM) (Abcam) for 15 h and then the viability of cells was
assessed. Concentrations of these inhibitors were selected based on
preliminary results showing no significant loss of viability in K562
cells treated with the inhibitors alone at these concentrations. For
combined treatment with all four inhibitors together, the concentration
was optimized for caffeic acid phenethylester, fulvestrant,
clarithromycin, and ketoconazole as 1.2, 0.25, 6.2 and 0.25 µM,
respectively. Cells have been experimentally validated by testing
antagonists in combination with chemotherapy to reduce drug resistance.
As a result, cell sorting by biophysical properties can be used to
examine heterogeneous responses of cells to chemotherapy treatments
with possible future application in precision medicine approaches to
improve chemotherapy selection and use.
Disclaimer
The views expressed in this paper are those of the author(s) and do not
represent official EPA policy.
Electronic supplementary material
[187]Supplemental Materials^ (3.4MB, docx)
Acknowledgements