Circulating Tumor Cells E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Chemical Analysis: A Series of Monographs on Analytical Chemistry and Its Applications
- Sprache: Englisch
Introduces the reader to Circulating Tumor Cells (CTCs), their isolation method and analysis, and commercially available platforms * Presents the historical perspective and the overview of the field of circulating tumor cells (CTCs) * Discusses the state-of-art methods for CTC isolation, ranging from the macro- to micro-scale, from positive concentration to negative depletion, and from biological-property-enabled to physical-property-based approaches * Details commercially available CTC platforms * Describes post-isolation analysis and clinical translation * Provides a glossary of scientific terms related to CTCs
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Veröffentlichungsjahr: 2016
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Table of Contents
Cover
Title Page
Copyright
List of Contributors
Foreword
References
Preface
Part I: Introduction
Chapter 1: Circulating Tumor Cells and Historic Perspectives
1.1 Early Studies on Cancer Dormancy Led to the Development of a Sensitive Assay for CTCs (1970–1998)
1.2 Modern Era for Counting CTCs: 1998–2007
1.3 Proof of Malignancy of CTCs
1.4 New Experiments Involving CTCs
1.5 Clinical Cancer Dormancy
1.6 Human Epidermal Growth Factor Receptor 2 (HER2) Gene Amplification can be Acquired as Breast Cancer Progresses
1.7 uPAR and HER2 Co-amplification
1.8 Epithelial–Mesenchymal Transition (EMT)
1.9 New Instruments to Capture CTCs
1.10 Genotypic Analyses
1.11 Conclusions
References
Chapter 2: Introduction to Microfluidics
2.1 Introduction
2.2 Scaling Law
2.3 Device Fabrication
2.4 Functional Components in Microfluidic Devices
2.5 Concluding Remarks
References
Part II: Isolation Methods
Chapter 3: Ensemble-decision Aliquot Ranking (eDAR) for CTC Isolation and Analysis
3.1 Overview of eDAR
3.2 Individual Components and Analytical Performance of eDAR
3.3 Application and Downstream Analyses of eDAR
3.4 Conclusion and Perspective
References
Chapter 4: Sinusoidal Microchannels with High Aspect Ratios for CTC Selection and Analysis
4.1 Introduction
4.2 Parallel Arrays of High-Aspect-Ratio, Sinusoidal Microchannels for CTC Selection
4.3 Clinical Applications of Sinusoidal CTC Microchip
4.4 Conclusion
Acknowledgments
References
Chapter 5: Cell Separation Using Inertial Microfluidics
5.1 Introduction
5.2 Device Fabrication and System Setup
5.3 Inertial Focusing in Microfluidics
5.4 Cancer Cell Separation in Straight Microchannels
5.5 Cancer Cell Separation in Spiral Microchannels
5.6 Conclusions
References
Chapter 6: Morphological Characteristics of CTCs and the Potential for Deformability-Based Separation
6.1 Introduction
6.2 Limitations of Antibody-based CTC Separation Methods
6.3 Morphological and Biophysical Differences Between CTCs and Hematological Cells
6.4 Historical and Recent Methods in CTC Separation Based on Biophysical Properties
6.5 Microfluidic Ratchet for Deformability-Based Separation of CTCs
6.6 Resettable Cell Trap for Deformability-based Separation of CTCs
6.7 Summary
References
Chapter 7: Microfabricated Filter Membranes for Capture and Characterization of Circulating Tumor Cells (CTCs)
7.1 Introduction
7.2 Size-based Enrichment of Circulating Tumor Cells
7.3 Comparison Between Size-based CTC Isolation and Affinity-based Isolation
7.4 Characterization of CTCs Captured by Microfilters
7.5 Conclusion
References
Chapter 8: Miniaturized Nuclear Magnetic Resonance Platform for Rare Cell Detection and Profiling
8.1 Introduction
8.2 μNMR Technology
8.3 Clinical Application of μNMR for CTC Detection and Profiling
8.4 Conclusion
References
Chapter 9: Nanovelcro Cell-Affinity Assay for Detecting and Characterizing Circulating Tumor Cells
9.1 Introduction
9.2 Proof-of-Concept Demonstration of NanoVelcro Cell-Affinity Substrates
9.3 First-Generation NanoVelcro Chips for CTC Enumeration
9.4 Second-Generation NanoVelcro-LMD Technology for Single CTC Isolation
9.5 Third-Generation Thermoresponsive NanoVelcro Chips
9.6 Conclusions and Future Perspectives
Acknowledgment
References
Chapter 10: Acoustophoresis in Tumor Cell Enrichment
10.1 Introduction
10.2 Factors Determining Acoustophoresis Cell Separation
10.3 Acoustophoresis System for Separating Cells
10.4 Acoustophoresis Platform for Clinical Sample Processing
10.5 Unperturbed Cell Survival and Phenotype after Microchip Acoustophoresis
10.6 Summary
References
Chapter 11: Photoacoustic Flow Cytometry for Detection and Capture of Circulating Melanoma Cells
11.1 Introduction
11.2 Current Methods for Detection and Capture of CMCs
11.3 Discussion
11.4 Future Work
References
Chapter 12: Selectin-Mediated Targeting of Circulating Tumor Cells for Isolation and Treatment
12.1 Introduction
12.2 CTC Capture by E-selectin
12.3 Applications for E-selectin in Cancer Diagnosis and Treatment
12.4 Conclusions
References
Chapter 13: Aptamer-Enabled Tumor Cell Isolation
13.1 Introduction
13.2 Aptamers and their Biomedical Applications
13.3 Aptamer-based Tumor Cell Isolation
13.4 Conclusion and Outlook
References
Chapter 14: Depletion of Normal Cells for CTC Enrichment
14.1 Introduction
14.2 Estimates of Number and Type of Cells in Blood
14.3 Summary of Examples of Negative Depletion
14.4 Types of Cells Observed After Depletion of Normal Cells
14.5 Incomplete Depletion of Normal Cells
14.6 Conclusion
References
Part III: Post-Isolation Analysis and Clinical Translation
Chapter 15: Tumor Heterogeneity and Single-cell Analysis of CTCs
15.1 Introduction
15.2 Tumor Heterogeneity
15.3 Single-Cell Analysis of CTCs and CTC Heterogeneity
15.4 Gene Expression Analysis
15.5 Mutational Analysis
15.6 Conclusion: Clinical Implications and Future Perspectives
References
Chapter 16: Single-Cell Molecular Profiles and Biophysical Assessment of Circulating Tumor Cells
16.1 Introduction
16.2 Methods
16.3 CTC Applications
16.4 Conclusions
References
Chapter 17: Directing Circulating Tumor Cell Technologies Into Clinical Practice
17.1 Introduction
17.2 Defining Biomarkers
17.3 The Technology
17.4 Translating Technology
17.5 Conclusions
References
Part IV: Commercialization
Chapter 18: DEPArray™ Technology for Single CTC Analysis
18.1 Challenges in Molecular Profiling of CTCs
18.2 DEPArray
™
Technology Solution
18.3 DEPArray
™
for Single Tumor Cell Analysis
18.4 Clinical Significance in Single CTC Profiling
18.5 Conclusion
References
Chapter 19: CELLSEARCH® Instrument, Features, and Usage
19.1 Introduction
19.2 Principles of CELLSEARCH®
19.3 EpCAM Density and CTC Capture
19.4 Clinical Applications of CELLSEARCH® CTCs
19.5 Beyond EpCAM Capture
19.6 Discussion
References
Part V: Glossary
Circulating Tumor Cell Glossary
References
Index
End User License Agreement
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Guide
Cover
Table of Contents
Foreword
Preface
Part I: Introduction
Begin Reading
List of Illustrations
Chapter 2: Introduction to Microfluidics
Figure 2.1 Fluid flow velocity profile in a microchannel under a pressure gradient.
Figure 2.2 Schematics of photolithography and etching. (a)–(c) show the processes of photolithography using a positive photoresist. (d)–(f) show the processes of etching and the removal of photoresist.
Figure 2.3 Schematic of a micropump with an external actuator.
Chapter 3: Ensemble-decision Aliquot Ranking (eDAR) for CTC Isolation and Analysis
Figure 3.1 General workflow of eDAR having four individual steps.
Figure 3.2 Sample preparation of eDAR [7]. Typically, 2 ml of whole blood was incubated on a rocker at room temperature for 30 min with the chosen antibodies. After incubation, the sample was diluted to 14 ml with Isoton buffer and centrifuged for 10 min to remove the supernatant containing free antibodies.
Figure 3.3 Schematic and images showing the general concept of eDAR and the notion of virtual aliquots [7]. (a) Overview of the microfluidic chip. The labeled blood sample flows through the main channel of the microfluidic chip with desired aliquots actively sorted into the cell capture chamber. (b) A high-speed camera image of whole blood aliquoted into a continuous stream of droplets surrounded by silicone oil. (c) Laser-induced fluorescence triggers the sorting of an aliquot containing a CTC (shown in yellow) to the collection channel, which is confirmed by the second detection window.
Figure 3.4 Aliquot ranking system [7]. (a) The setup for aliquot ranking is comprised of two excitation lasers and four APDs. APDs 1, 2, and 3 collect fluorescence at three wavelength regions (560–590, 500–550, 640–690 nm) from the aliquot to determine the presence or absence of a single CTC. APD 4 confirms the sorting of the desired aliquot. (b) A segment of the APD traces at the three different colors from a breast cancer sample, showing aliquots positive for EpCAM (top trace) and Her2 (second trace) that was correctly sorted (bottom trance). (c) The S/N ratio for each single CTC detected in an aliquot of blood for both antibody markers from a breast cancer sample. (d) The S/N ratio for EpCAM-labeled single CTCs as a function of flow rate and signal bin time.
Figure 3.5 General scheme and effectiveness of the aliquot sorting [7]. (a) The solenoid piston is pushed down to stop the flow through the collection channel. (b) The solenoid piston is released to allow flow through the collection channel. (c) When the sorting scheme was configured as “off,” APD traces from the first detection window (black) and the second detection area (gray) show that no cells were collected. (d) When the sorting scheme was applied, APD traces from the first (black) and the second (gray) detection area show that all the CTCs were collected.
Figure 3.6 Microfluidic chip and hydrodynamic switching scheme based on the usage of an off-chip solenoid [18]. (a) General structure of the microfluidic chip and the configuration of the eDAR platform. The normally closed (NC) solenoid was connected to the center port on the left side channel. The bottom left channel was to collect sorted aliquots and transfer them to the subsequent purification area, which had 20,000 microslits. The area marked with a dashed box is further explained in (b)–(d). (b) The fluidic condition when no positive aliquot was ranked. (c) The blood flow was switched to the CTC collection channel by opening the solenoid, and the sorted aliquot was confirmed by the second APD. (d) The blood flow was switched back after the aliquot was sorted. (e) The segment of APD data from a pancreatic cancer sample that shows two events triggered the sorting, which then were confirmed by the second detection window. (f) The distribution of transit time at flow rates of 40 and 80 µl/min, respectively. (g) A plot shows the fastest average transit time was about 4 ms when the flow rate was 90 µl/min.
Figure 3.7 The on-chip purification scheme based on planar filter [7]. (a) The schematic drawing shows the structure of the cell capture chamber, which can trap the CTCs and further purify it by removing all the RBCs and most of the WBCs. (b) The bright field image shows the four MCF-7 cells isolated by eDAR and trapped on the polycarbonate filter. (c) Fluorescence images of the captured CTCs on the filtration membrane labeled with anti-EpCAM-PE. (d) Fluorescence images of the captured CTCs on the filtration membrane labeled with anti-Her2-Alexa647.
Figure 3.8 Microslits for the on-chip purification scheme used in eDAR [18]. (a) The sorted aliquots were further purified through the array of microslits. Objects in yellow represent CTCs; red and gray objects represent RBCs and WBCs, respectively. The curved arrows show the flow paths across the microslits. (b) The 3D model of the microslits with a 5 µm width. (c) Fluorescence (left) and bright field (right) images of five MCF-7 cells captured via eDAR. (d) Fluorescence (left) and bright field (right) images of two MDA-MB-231 cells captured via eDAR.
Figure 3.9 Multicolor fluorescence images for the CTCs trapped on eDAR microchip after the secondary labeling step [7, 18]. (a) Images of three CTCs isolated from a breast cancer sample; scale bar is 20 µm. (b) Images from a CTC labeled with fluorescent antibodies against the breast cancer stem cell marker (CD44+/CD24–). (c) Two SKBr-3 cells were captured by eDAR and further stained with additional markers.
Figure 3.10 Characterization and analytical performance of eDAR [18]. (a) The recovery and sorting efficiency value versus different flow rates. (b) The recovery ratio of MCF-7 cells spiked into whole blood. (c) The recovery ratio of 300 MCF-7 cells spiked into 1, 5, and 10 ml of whole-blood aliquots. (d) The recovery ratio of our selection schemes of four breast cancer cell lines spiked into whole blood.
Figure 3.11 Clinical results for CTCs isolated in blood samples from patients with metastatic breast cancer [7]. A side-by-side study was performed to compare the clinical results from 20 breast cancer samples analyzed by CellSearch and eDAR.
Figure 3.12 The distribution of 15 control samples and 10 pancreatic cancer samples analyzed by the second generation of eDAR, as well as the distribution of 16 pancreatic cancer samples analyzed by the first generation of eDAR [18]. O shows the average values for each data set; X shows the minimum and maximum values we found in each data set.
Figure 3.13 General scheme and procedure of the sequential immunostaining and photobleaching tests performed on the eDAR microchip [36]. (a) The in-line labeling system coupled to the current eDAR system. A peristaltic pump delivered the labeling reagents and washing buffer. The crossbars in this Figure mean that the corresponding ports were closed during the experiments. (b) The general process flow of the sequential immunostaining and photobleaching experiment.
Figure 3.14 Sequential immunostaining and photobleaching results for eight breast cancer cells trapped on an eDAR chip [36]. The Hoechst nuclear stain was used as positive control marker and CD45 was used to exclude the potential interference from WBCs. Eight protein markers were studied, including EpCAM/cytokeratin, MUC1/Her2, CDD44/CD24, and CD166/EGFR. Scale bar represents 20 µm.
Figure 3.15 Schematic and data illustrating the flow detection platform. (a) Depiction of the microfluidics and optics [38]. (b) CTC detection and identification scheme using APD signals. A typical CTC event at 183 ms is positive for EpCAM (yellow signal) and cytokeratin (red signal), but negative for CD45 (green signal). (c) A MCF-7 cell imaged in the microfluidic channel filled with whole blood. The top panel shows a bright field image of the blood in the microchannel. The dashed circle shows the location of the MCF-7 cell that is not visible beneath the many blood cells. The gray dashed lines show the location of the microchannel walls. Fluorescence images of the same location show the MCF-7 labeled with both anti-EpCAM-PE and anti-Cytoekratin-Alexa647.
Figure 3.16 Clinical results from the “simplified” eDAR system and CellSearch [38]. Our method found a median of 90 CTCs per 7.5 ml of blood compared to a median of 0 for the CellSearch system. The dashed line is a simple threshold (38 counts/7.5 ml) set based on the range of detected CTCs in healthy donors' blood. The solid line is the threshold (33 counts/7.5 ml) set using the mean background level plus two times its standard deviation. The dotted line is the threshold (63 counts/7.5 ml) determined by
Z
-test with a 95% confidence level.
Figure 3.17 Side-by-side clinical results for regular CTCs and circulating cells with EpCAM
+
/CD44
+
/CD24
−
expression from the CTC flow detection system and CellSearch method [38]. Normal CTCs were determined by both flow detection and CellSearch method. The average number of the EpCAM
+
/CD44
+
/CD24
−
cells for these 30 breast cancer samples is 150 cells/7.5 ml.
Figure 3.18 Comparison of the CTC enumeration results from the same set of breast cancer patients using eDAR and flow detection system [38]. The left part is the box plots that show the smallest observation, lower quartile, median, upper quartile, and the largest observation of the two data sets (eDAR vs flow detection), respectively. The right part shows the histogram of the two data sets.
Chapter 4: Sinusoidal Microchannels with High Aspect Ratios for CTC Selection and Analysis
Figure 4.1 (Left) Schematics of the first-generation, high-throughput CTC selection device showing: (a) A scaled AutoCAD diagram of the sinusoid capture channels with bright-field optical micrographs; (b) the integrated conductivity sensor consisting of cylindrical Pt electrodes that were 75 µm in diameter with a 50 µm gap; (c) single port exit where the device tapers from 100 µm wide to 50 µm while the depth tapers from 150 to 80 µm over a 2.5 mm region that ends 2.5 mm from the Pt electrodes; (d) micrograph taken at 5× magnification showing the sinusoidal cell capture channels; and (e) 3D projection of the topology of the device obtained at 2.5 µm resolution using noncontact optical profilometry (arrows indicate the Pt electrode conduits). Adapted from Adams
et al
. [48]. Copyright (2008) American Chemical Society. (Right) Second-generation sinusoidal device: (f) Schematic operation of the CTC selection module with 50 parallel, sinusoidal microchannels and inlet/outlet channels arranged in the
Z
-configuration. The large arrow indicates the sample flow direction through the selection channels. (g) SEM of the selection bed showing high-aspect-ratio (∼25 × 150 µm,
w
×
d
), sinusoidal microchannels and the output channel. (h) Composite fluorescence images of a CTC stained with DAPI, CK8/19, and CD45. (i) Three WBCs staining positively for DAPI and CD45 and negatively for CD8/19. (j) Fluid dynamics simulation results showing the distribution of flow velocities and shear stress in microfluidic selection channels.
Figure 4.2 (a) High-precision micromilling of a brass substrate using solid carbide bits on the order of 500 to 50 µm or smaller in diameter. (b) A completed brass master mold that can be used for. (c) repeated microreplication by hot embossing the mold master into a thermoplastic substrate. Reproduced with permission of Springer, from Hupert
et al
. [60].
Figure 4.3 SEMs of a CTC selection device shown in Figure 4.1f hot embossed into COC thermoplastic. (a) Low-resolution SEM showing a series of sinusoidal, high-aspect-ratio channels. (b) High-resolution SEM of one channel with the
inset
showing surface roughness due to milling. For reference, the marks left by the milling bit have an average roughness of 115 nm and mean peak height of 290 nm when measured vertically along the channel wall and 55 and 200 nm when measured horizontally, respectively, while the typical average roughness of the polymer used for embossing is <20 nm [49]. (c) SEM of the channel prior to thermal fusion bonding of the cover plate. (d) SEM of a channel following thermal fusion bonding of the cover plate. The cover plate is also made from COC.
Figure 4.4 Pristine polymer is activated via UV light to generate carboxylic acid moieties to which Abs are covalently linked via EDC/NHS chemistry.
Figure 4.5 Chemical structures of (a) PMMA and (b) COC.
Figure 4.6 Sinusoidal channels fabricated in PMMA or COC were UV activated, thermal fusion bonded, and labeled with Cy3-oligonucleotides covalently attached via EDC chemistry to serve as fluorescent reporters of successful activation. (a) Channels were cut along their length to expose Cy3-oligonucleotides immobilized along sidewalls. 5× fluorescent images of controls and Cy3-oligonucleotides are shown for (b,c) PMMA and (d,e) COC, respectively. 20× fluorescence images for (f) PMMA and (g) COC are presented along with (h) line plots as indicated by the thick, dotted lines. Controls were Cy3-labeled oligonucleotides immobilized without the EDC coupling agent. Only control images are scaled to the same intensity as their counterparts.
Figure 4.7 Cy3-oligonucleotides immobilized on UV and UV/thermal planar substrates for (a,b) PMMA and (c,d) COC, respectively. (e,f) Fluorescently labeled streptavidin was immobilized on UV and UV/thermal COC, respectively. Images of Cy3-oligonucleotides immobilized within UV-modified and thermal-fusion-bonded (g) PMMA and (h) COC microchannels. All fluorescence images of Cy3-labeled oligonucleotides are scaled to the same intensity. Fluorescent streptavidin images are scaled independently.
Figure 4.8 Box plots presenting CTCs and WBCs selected in UV-PMMA (
N
= 5) and UV-COC (
N
= 4) chips from blood samples secured from patient-derived xenograft mouse models for pancreatic ductal adenocarcinoma (PDAC). Data are normalized to 1 ml. Lower and upper edges of box show 25th and 75th percentiles, respectively. Solid line in box represents median, and solid diamond represents mean. Bars show maximum and minimum values (range).
Figure 4.9 Recovery of MCF-7 cells via anti-EpCAM monoclonal Abs in 35 and 50 µm wide channels that are either straight or sinusoid. In all configurations, optimal recovery occurs at 2 mm/s linear velocity. Recovery increased by ∼30% due to decreasing channel width and increased by ∼20% due to channel curvature.
Figure 4.10 Three-dimensional computational fluid dynamics simulations conducted on blood flow through the sinusoid cell isolation channel utilizing the Carreau model for blood's non-Newtonian viscosity. Shown are: (a) Longitudinal velocity profile of blood flow; (b) cross-sectional velocity streamlines of weak Dean flow on the order of 0.1 µm/s; (c) cross-sectional centrifugal forces acting on a 16 µm CTC due to the channel's radius of curvature, where positive forces act from left to right as shown by the force vector; (d) cross section of non-Newtonian viscosity profile generating fluidic drag that opposes the centrifugal forces; and (e) cross-sectional centrifugal velocities obtained by balancing centrifugal and drag forces, where positive velocity is a left-to-right motion as shown by the velocity vector. Note that negative centrifugal velocities in (e) are due to the velocity streamlines in (b).
Figure 4.11 The centrifugal velocity minus the axial velocity (Eq. 4.5) experienced by a CTC traversing through sinusoidal microfluidic channels. is critical to force CTCs out of laminar streamlines and toward microchannel surfaces and scales with the CTC's size and the fluid's forward linear velocity squared.
Figure 4.12 Schematic of the Chang–Hammer model describing (a) a cell rolling along a surface coated with recognition elements such as Abs. (b) The probability of cell adherence is governed by the relative motion of the cell's antigens with the surface elements.
Figure 4.13 (a) Schematic of flow fields generated in a micropillar device (0.65 mm/s), and a close-up of a pillar with a roll distance of approximately 75 μm. From Battle
et al
. [96]. (b) An SEM of the high-aspect ratio sinusoid channel (2 mm/s) with a period of 750 μm, 125 μm radius of curvature, and an effective roll distance of >300 μm. (c) The recovery of CTCs (EpCAM expression = 49,700 molecules/cell) for different roll lengths and translational velocities according to the Chang-Hammer kinetic model.
Figure 4.14 Recovery of the MCF-7 CTC surrogates (cell line), which expresses high levels of EpCAM and is commonly used to assess recovery, and clinical CTCs, which vary significantly in EpCAM expression. Cell recoveries were determined by the Chang–Hammer model assuming a 100 µm rolling distance and various translational velocities.
Figure 4.15 Time to process 7.5 ml of blood as a function of microchannel depth and channel number for a channel width of 25 µm and a linear flow velocity of 2 mm/s. AR = aspect ratio.
Figure 4.16 Comparison of different inlet/outlet geometries for a CTC selection device. (a) Results of computer simulations for the distribution of flow velocities within the CTC isolation bed with 50 microchannels arranged in the
Z
-configuration or 51 microchannels with triangular inlets and outlets. Pictures of devices filled with blood for (b) the triangular configuration and (c) the
Z
-configuration.
Figure 4.17 (a) Schematic diagram and (b) electrical representation of a
Z
-configuration network with three parallel channels. In (a), gray regions represent fluidic channels and black arrows represent the direction of flow. Jackson
et al
. [61].
Figure 4.18 (a) Various stages of filling of a 320-channel
Z
-configuration device (20-mm-long parallel channels) with a dye solution. (b) Numerical simulation results showing the distribution of flow velocities for different configurations of the CTC selection beds arranged in a
Z
-configuration. (c) Average linear velocities of fluid in 16 groups of 20 adjacent sinusoidal, high-aspect-ratio microchannels based on the results shown in (a) (filled bars) and theoretical values obtained via the network analysis model (empty bars). (d) Distribution of cells selected in 20- mm-long microchannels.
Figure 4.19 (a, b) AutoCAD renderings of
Z
-configuration cell isolation units with 250 parallel channels that utilize straight and tapered inlet and outlet channels. The
Z
-configuration with tapered inlets and outlets offers (c) constant linear velocity throughout the parallel array and (d) controlled shear stress throughout the tapered inlet and outlet channels to ensure mechanical stability of the CTCs.
Figure 4.20 Selection and enumeration of CTCs via immunophenotyping. (a) Fluorescence images of various selected cells from a metastatic PDAC patient: (i) a CTC; (ii) two white blood cells; and (iii) a cluster of CTCs. (b, f, j) CTC marker for cytokeratin 8/19 (red) with b, j positive for this marker and f negative for this marker; and (c, g, k) leukocyte antigen marker CD45 (green) with c, k negative for this marker and g positive for this marker. Micrographs (i–l) are of an aggregate of six CTCs captured in the HT-CTC module. This aggregate showed positive for cytokeratins 8/19 (j) and negative for leukocyte marker CD45 (k). Bars are 10 µm. (Bottom) Also shown is a box plot from CTCs isolated from five healthy donors, five locally resectable PDAC patients, and seven metastatic PDAC patients.
Figure 4.21 (a) Fluorescence image of two CTCs isolated from the blood of a patient with metastatic prostate cancer. CTCs demonstrated positive staining for DAPI (blue) and cytokeratins (red) and negative staining for CD45. (b) Summary of CTC isolation data for patients with prostate cancer. (c) Results showing the molecular profiling of EpCAM(+) CTCs. In this case, the total RNA was extracted from the CTCs with the mRNA reverse transcribed into cDNA using T20 primers and subjected to PCR with gene specific primers followed by gel electrophoresis.
Figure 4.22 (a) Schematic of the PCR/LDR assay. (b) Capillary electropherograms for separated LDR products generated from CTCs isolated from a metastatic colorectal cancer blood sample. LDRs were carried out in 20 µl using a commercial Taq Ligase. LDR mixture contained: discriminating and common primers (4 nM each), DNA template 0.6–1 ng (3–5 fmol), 40 units of Taq DNA ligase. Thermocycling conditions were 94 °C for 1 min and 59 °C for 4 min cycled 20 times. Common primers were Cy5 labeled for detection.
Chapter 5: Cell Separation Using Inertial Microfluidics
Figure 5.1 Schematic illustration of inertial focusing in different channel geometries. (a) Two inertial lift forces with opposite direction and orthogonal to the flow direction act to equilibrate microparticles near walls. (b) Inertial migration and final equilibrium positions in rectilinear microchannel with circular cross section. (c) Inertial migration and final equilibrium positions in rectilinear microchannel with square cross section. (d) Inertial migration and final equilibrium positions in rectilinear microchannel with rectangular cross section. (e) Inertial focusing in curved microchannel.
Figure 5.2 (a) Schematic of the device concept for size-based separation. Mixture of particles first flows through a high-AR channel (upstream segment) where they migrate to their equilibrium positions centered at the channel side walls. Once all particles fully focus into stable positions, the channel expands into a low-AR channel (downstream segment), which modifies the inertial lift and shifts the equilibrium positions to the center of the top and bottom walls. Due to strong dependence of migration velocity on particle size, large particles refocus much faster than small particles, leading to complete separation at the outlet. (b) Fluorescent images demonstrating progressive inertial migration of 20 µm (green) and 9.94 µm microparticles (red) leading to complete separation at the outlet. (c) Lateral distance
d
m
as a function of downstream length indicating the migration trajectory of each sized particles. (d) Fluorescent images of microparticles collected from inlet and outlets (e) Concentrations of microparticles from inlet sample and outlet samples indicate successful separation.
Figure 5.3 (a) FWHM as a function of blood hematocrit. The inset shows average intensity obtained from five measurements for each hematocrit value. (b) Bright-field image illustrating focusing of diluted blood (Ht = 0.8%) at ∼35 mm downstream and color inversion of the 100-frame-stacked image. (c) Fluorescence image showing HPET cells flowing in the central outlet. (d) Image illustrating that most of RBCs were collected from side outlets (#1 and #3), which appear to be darker gray, while the central outlet (#2) is clear. Reproduced with permission of the Royal Society of Chemistry.
Figure 5.4 (a) CFD-ACE simulation of a curved rectangular channel, velocity and pressure distribution at the inlet where the flow is laminar and the curvature has not been introduced; (b) velocity and pressure distribution at the center of the curved channel, which indicated the shift of the center of maximum velocity toward the concave wall. (c) Intensity plot of 20-µm-diameter particles across the width of the channel at the end of each loop in the spiral (loop1 being the innermost loop and loop4 being the outermost). The two inset Figure show the fluorescent images of the 20 µm polystyrene particles at the innermost loop (loop1) of the spiral device (500 µm × 110 µm) and at loop4 focused in a single stream near the inner channel wall.
Figure 5.5 (a) Photograph of the five-loop spiral microchannel with two inlets and eight outlets fabricated in PDMS. (b) Bright-field and epifluorescent images illustrating the distribution of the bigger∼15-µm-diameter SH-SY5Y cells (pseudo-colored green) and the smaller ∼8-µm-diameter C6 glioma cells at the inlet and the first two outlets of the spiral microchannel (scale bar 100 µm). (c) Image of the spiral sorting device. (d) Bright-field image of the outlet system.(e) Normalized focusing position of particles (
x
is the distance of the focused stream from the inner channel wall, and
w
is the width of the channel) as function of
De
. (f) Bright-field images of the stained blood samples after they were collected from each outlet of the design2 and centrifuged. Inlet has all the cells present. Arrows indicate the white blood cells. (g) outlets 2 and 3 have RBCs and platelets. (h) Outlet1 has majority of WBCs (neutrophils, eosinophils, and monocytes), some platelets, and very little RBCs; and (i) outlet 4 has only diluted plasma and platelets.
Figure 5.6 (a) Viability plot for each of the HPET, LNCap, and DU-145 cell lines in spiral sorting device (b) Fluorescent image of the focused streams of HPET cells. Phase contrast images of HPET cells at outlets 2 and 3 (c) and outlet1 (d). (e) Schematic of the principle of CTC isolation and enrichment by spiral microchannel with trapezoidal cross section. (f) The effect of the WBC concentration on the performance of spiral microchannel and the final purity and the histogram plot indicating separation efficiency for different cell lines (∼>80%). (g) Phase contrast micrographs of control (unsorted) and sorted MDA-MB-231 cells stained using the trypan blue dye indicating high cell viability.
Chapter 6: Morphological Characteristics of CTCs and the Potential for Deformability-Based Separation
Figure 6.1 Difference in size, shape, and nuclear volume of cultured cancer cells and CTCs. Cultured cancer cells (a–d) and CTCs from patients with prostate cancer (e–l) were stained for cytokeratin (green) and with DAPI nuclear stain (pink). The cells are imaged at the same scale, indicated by the yellow line that represents a length of 5 µm.
Figure 6.2 Diameter of cultured cancer cells and CTCs from patients with prostate cancer. CTCs are significantly smaller than cultured cells (
p
< 0.001), with an average diameter of 7.97 µm. This is in contrast to the cultured tumor cells that have an average diameter of 10.38 µm.
Figure 6.3 Cytological characteristics of CTCs. (a) An elongation factor was determined by bisecting the cell with perpendicular major and minor axes. The elongation factor represents the ratio of major and minor axes. CTCs exhibit an attenuated elongation, even compared to cultured tumor cells. (b) The nuclear-to-cytoplasmic (N:C) exhibited a high degree of intra- and interpatient variation but the median N:C ratio of CTCs was greater than that of cultured cancer cells.
Figure 6.4 Microfluidic ratchet mechanism. (a) When cells are deformed through a funnel-shaped constriction, the applied pressure required for their transit is dependent on the difference between the radius of the leading edge (
R
a
) and the trailing edge (
R
b
) of the cell. Therefore, since
R
b
is constrained by the funnel constriction when transiting in the forward direction, less pressure is required for deforming the cell in the forward direction than in the reverse. (b) By oscillating the applied pressure back and forth, large cells fail to transit the channel but to not clog the filter pore. Conversely, despite the oscillation of pressure, cells that deform through the constriction do not transit backward through the funnel pore.
Figure 6.5 Microfluidic ratchet device. (a) Photograph of the microfluidic device during the processing of whole blood. Blood is directed to the sample inlet and migrates to the sorting area, where CTCs are separated from white blood cells and each cell time is directed to a different outlet. (b) The sorting is achieved by aligning funnel-shaped micropores into rows, where each row has an incrementally smaller pore size. Thus, the different cell types are vertically displaced and CTCs can be captured from the rows with pore sizes less than 6 µm.
Figure 6.6 Validation of the microfluidic ratchet device. Leukocytes in whole blood were labeled by Hoechst dye and UM-UC13 cancer cells by Calcein-AM Green. When the two cell populations were mixed and sorted by microfluidic ratchet, (a) tumor cell capture efficiency and (b) tumor sample enrichment were assessed over a range of applied forward pressure.
Figure 6.7 Proliferative capacity of cultured cells enriched by the microfluidic ratchet device. Cancer cells that are enriched by the microfluidic ratchet device appear to demonstrate the same ability to proliferate, over 10 days, as normal cultured cells. While they are initially circularized due to treatment with trypsin, prior to sorting, the cancer cells re-establish their normal elongated shape as they grow in culture.
Figure 6.8 Resettable cell trap microfluidic device. (a) Whole blood is applied to a sample inlet and is directed into an array of parallel cell traps for sorting. White blood cells are flushed from the traps into the waste reservoir while CTCs are released from the trap into the collection reservoir. (b) The cell trap operates by the inflation of a flexible diaphragm that can constrict to specifically capture CTCs while permitting transit of white blood cells (top panel). The trap can then be relaxed to release all of the remaining adsorbed cells (bottom panel).
Figure 6.9 Enrichment and yield of UM-UC13 cancer cells doped into whole blood and processed using the resettable cell trap device. The graphs indicate the degree of cancer cell enrichment and yield (a) following filtration of cells through a single cell trap, (b) following filtration through three sequential cell traps and (c) filtration through four or five sequential cell traps.
Chapter 7: Microfabricated Filter Membranes for Capture and Characterization of Circulating Tumor Cells (CTCs)
Figure 7.1 Brief illustration of existing microfabricated filter membrane structures for capture and characterization of CTCs. (a) Track-etched polycarbonate filter, (b) parylene C filter with evenly distributed pore arrays, (c) 3D bilayer membrane filter, (d) slot-shaped pore filter membrane, (e) flexible micro spring array (FMSA), (f) three-dimensional palladium filter, (g) microfilter with conical-shaped holes, and (h) VyCap microsieves.
Chapter 8: Miniaturized Nuclear Magnetic Resonance Platform for Rare Cell Detection and Profiling
Figure 8.1 Principle of μNMR for cellular detection. Magnetic nanoparticles shorten the transverse relaxation time of water protons. The NMR signal from the magnetically labeled samples (b) decays faster than that from the nonlabeled samples (a). The shortening of relaxation time provides the sensing mechanism of μNMR.
Figure 8.2 Magnetic nanoparticles synthesized for high transverse relaxivity (
r
2
). (a) Transmission electron microscope (TEM) images of manganese-doped magnetite (MnFe
2
O
4
) particles. Smaller particles (left) were used as a seed to grow bigger particles (right). Adapted from Lee and Weissleder [11]. (b) TEM image of Fe-core and Fe
3
O
4
-shell (Fe/Fe
3
O
4
) particles. Yoon
et al
. [29]. Reproduced with permission of John Wiley and Sons, Inc. (c) High-resolution TEM image of an Fe/MnFe
2
O
4
shell particle. Note that the shell is polycrystalline with a grain size of ∼3 nm. (d) Plot of
r
2
as a function of magnetization and diameter of MNPs. The dotted line indicates the bulk magnetization value of MnFe
2
O
4
. Because of their stronger magnetization, Fe-core MNPs achieved higher
r
2
than ferrite-based MNPs. Fe/MnFe
2
O
4
MNPs assumed the highest
r
2
. CLIO, cross-linked iron oxide nanoparticle; MION, monocrystalline iron oxide nanoparticle.
Figure 8.3 Bioorthogonal nanoparticle detection (BOND). (a) Reaction schematic. Trans-cyclooctene (TCO) is conjugated with antibody, and tetrazine (Tz) with MNP. TCO-Tz coupling is based on the Diels–Alder cycloaddition. (b) Cells are prelabeled with TCO–antibodies and targeted with Tz-MNPs. The antibody provides sites for multiple MNP binding. (c, d) Compared to the direct targeting with MNP–antibody conjugates, BOND method achieves higher MNP loading on target cells as confirmed by fluorescent (c) and μNMR (d) measurements.
Figure 8.4 μNMR system. (a) A solenoidal coil embedded in a microfluidic system. The entire bore of the coil is available to samples, which maximizes the filling factor (≈1). (Right) The embedded coil design enhanced the NMR signal by more than 350%, compared to a similar coil wrapped around a tube. (b) A solenoidal coil was integrated with a sophisticated microfluidic system. MNP labeling, sample washing and NMR detection can be performed on-chip. (c) Schematic of the μNMR electronic components.
Figure 8.5 μNMR designed for clinical applications. (a) Self-adjusting algorithm in μNMR to compensate a temperature-dependent fluctuation of magnetic field from the permanent magnet. FID, free induction decay; FFT, fast Fourier transformation. (b) Plot of
T
2
value changes from the same sample measurement with and without the self-adjusting algorithm. (c) μNMR system for point-of-care operation, featuring automatic system configuration and a smartphone interface.
Figure 8.6 Profiling of tumor cells using the μNMR platform. Single fine-needle aspirates were obtained from patients and were tagged via the BOND strategy for μNMR detection. The profiling results indicate a high degree of heterogeneity in protein expression both across the different patient samples and even with the same tumor.
Figure 8.7 Quad-marker μNMR. TCO-modified antibodies are added to the whole blood to target CTCs. Red blood cells are then lysed, and Tz-modified MNPs were added for MNP labeling. The preparation process requires only 30 min.
Figure 8.8 Sensitive detection of CTCs using quad-marker μNMR. (a) CTCs were detected using quad-marker μNMR (top) and CellSearch (bottom), a gold standard assay, from the peripheral blood of advanced-stage ovarian cancer patients. Thirteen patients were positive for CTCs from the quad-marker μNMR measurements, but only one patent was positive from CellSearch assay. (b) Comparison of detection sensitivities of quad-marker μNMR and CellSearch using spiked cells in whole blood. With quad-μNMR, the average recovery rate was 38% across the various cell concentrations assessed. Similar experiments with CellSearch showed an average recovery rate of 9.1%. (c) Comparison of detection sensitivities of quad-marker and EpCAM μNMR. The quad-marker assay outperformed the single-marker one in the same μNMR setup.
Figure 8.9 Comparison of EpCAM-only and quad-marker assay using μNMR. Across all cancer cell lines with different EpCAM expression levels, the quad-marker assay (striped bars) showed higher NMR signal than the EpCAM-only assay (solid bars). All measurements were performed in triplicate. The error bar indicates the standard deviation.
Figure 8.10 Comparison of molecular profiles between CTCs and biopsy from the site of metastases. Each column represents a patient sample. The expression of a given marker is indicated with positive (+) or negative (−) signs. In each subject, concordant test result (positive in both CTC and biopsy, or negative in both CTC and biopsy) is shown in gray, and discordant test result in white.
Chapter 9: Nanovelcro Cell-Affinity Assay for Detecting and Characterizing Circulating Tumor Cells
Figure 9.1 The evolution of the three generations of NanoVelcro CTC chips. The first-generation chip, composed of a silicon nanowire substrate (SiNS) chip with an overlaid microfluidic chaotic mixer, provides a high-sensitivity method that outperforms the FDA-approved CellSearch™ in CTC enumeration from clinical blood samples. The second-generation polymer nanosubstrate chip specializes in single CTC isolation, which can then be subjected to single-cell genotyping. The third-generation thermoresponsive NanoVelcro chip utilizes a capture-and-release mechanism at 37 °C and 4 °C, respectively; the temperature-induced conformational change alters the accessibility of the captured cells and allows for rapid CTC purification with desired viability and cellular integrity.
Figure 9.2 NanoVelcro cell-affinity substrates. (a) A SiNS covered with anti-EpCAM is used to improve the efficiency of CTC capture in comparison to (b) a flat silicon substrate that is covered with anti-EpCAM. SiNS increases efficiency compared to an unstructured silicon substrate due to Velcro-like interactions between the anti-EpCAM-coated SiNS and the surfaces of the target cells. (c) To increase CTC detection, biotinylated anti-EpCAM is added to the SiNS. (d) Images of the MCF7 cells captured on the SiNS using SEM.
Figure 9.3 A collection of nanostructures employed for CTC capture. (a) Diagram of graphene oxide chip [71] components and conjugation between nanosheets and EpCAM antibodies. GMBS cross-linker binds to PL-PEG-NH2 on nanosheets, which adhere to the gold pattern. NeutrAvidin connects to the GMBS and biotinylated EpCAM. Zhang
et al
. [71]. Reproduced with permission of Nature Publishing Group. (b) Diagram of cell capture [72] using a cell-capture agent on the manganese dioxide surface. Yoon
et al
. [72]. Reproduced with permission of John Wiley and Sons, Inc. (c) Diagram of HCT116 cell capture [67] on SFG, Tf-SFG, and Tf-NMSFG following a 5 min incubation period. Park
et al
. [67]. Reproduced with permission of John Wiley and Sons, Inc. (d) Schematic diagram of the synthesis [73] of leukocyte-inspired particles. Huang
et al
. [73]. Reproduced with permission of John Wiley and Sons, Inc. To replicate the surface of the microvilli of leukocytes, nanofibers were coated with anti-EpCAM. (e) Magnified SEM image of captured cell on Au-SiNW1 and Au-SiNW2 substrates [66].
Figure 9.4 First-generation NanoVelcro CTC chip for detection and enumeration of CTCs in cancer patients' blood. (a) and (b) The NanoVelcro CTC chip contains a NanoVelcro substrate with specific pattern of indentations, and the chip is covered with a PDMS chaotic mixer. (c) The three-color ICC protocol allows captured CTCs (DAPI
+
/CK
+
/CD45
−
, sizes >6 µm) to be differentiated from WBCs (DAPI
+
/CK
−
/CD45
+
, sizes <12 µm). (d) A fluorescence micrograph of one captured prostate cancer CTC along with a nonspecifically captured WBC.
Figure 9.5 Clinical significance of first-generation NanoVelcro CTC chip. (a) A graph of CTC count comparison between the original NanoVelcro chips and CellSearch
TM
assay, based on samples from 26 prostate cancer patients. Wang
et al
. [55]. Reproduced with permission of John Wiley and Sons, Inc. (b) A graph of a prostate cancer patient's responses to treatment and progression by recording the sequential change in CTC and PSA. Lu
et al
. [56]. Reproduced with permission of Elsevier.
Figure 9.6 Capture and release of CTCs in the presence of aptamer-based capture agents. A visual representation of the interactions involved in the capture of the CTC, and the use of enzymes to release NSCLC CTCs from the SiNS grafted with aptamers.
Figure 9.7 Second-generation NanoVelcro CTC chip for single-CTC isolation, followed by mutational analyses. (a) The PDMS chaotic mixer is layered on top of a NanoVelcro chip that contains PLGA nanofibers. (b) For the binding of biotinylated capture agents (i.e., anti-EpCAM for prostate and pancreatic cancer), streptavidin is conjugated to PLGA nanofibers. (c) An image of the electrospun PLGA nanofibers by using SEM. (d) The graphic illustration of LMD-based single-CTC isolation. (e) The process to isolate single CTCs consists of (i) identification of CTC, (ii) isolation of the selected CTC using laser dissection, followed by (iii), and (iv) discharge of CTC from the silicon substrate into a 200 µl PCR tube. (f) Results of single-CTC WGA and gel electrophoresis after amplification in PCR with BRAF-specific primer. Through Sanger sequencing, further affirmation is gained because of the display of melanoma CTCs exhibiting the unique BRAF
V600E
mutation. (g) Pancreatic CTCs and the KRAS
G12V
mutation present.
Figure 9.8 Second-generation NanoVelcro CTC chip for single CTC isolation, followed by whole exome sequencing. (a) Using the LCM system for single-CTC isolation requires recognition of CTC, identification of UV dissection route and IR sticky finger positions, UV laser dissection, and collection of the identified CTC on the LCM cap. (b) The Circos plots symbolize Exome-Seq's coverage areas. From the outside to inside, the rings represent CTC-2, CTC-1, pooled CTCs (CTCp), and WBC. The ring on the outside is the human reference genome's karyotype. (c) The mutual mutations between CTCs and WBCs are compared with mutations that CTCs have in common. Zhao
et al
. [59]. Adapted with permission of John Wiley and Sons, Inc.
Figure 9.9 Operation mechanism of third-generation thermoresponsive NanoVelcro chip. (a) The thermoresponsive NanoVelcro system that is used to capture and discharge CTCs at 37 °C (left) and 4 °C (right). (b) The polymer brush configuration changes based on temperature, and the shifts will change anti-EpCAM's approach toward the capture and discharge of CTCs on the NanoVelcro.
Chapter 10: Acoustophoresis in Tumor Cell Enrichment
Figure 10.1 The fundamental separation mechanism in free-flow acoustophoresis. Larger objects will experience a stronger acoustophoretic force (
F
1
) and migrate faster to the channel center as compared to a smaller sized object (
F
2
) having the same mechanical properties.
Figure 10.2 Schematic of the cross section, transverse to the flow of the acoustophoresis microchannel. Vibrations (black arrows) from the piezoceramic actuator are transmitted via the bulk material of the chip to the interior walls of the flow chamber. When the frequency of oscillation matches the resonance condition determined by the channel width and the sound velocity in the liquid, a resonance builds up (solid and dashed black lines). Cells suspended in the liquid experience a force (gray arrows and solid gray line) toward the center of the channel. Dimensions are not to scale.
Figure 10.3 Simulated trajectories of identical particles undergoing acoustophoresis with different starting positions in height along the sidewalls. The rainbow-colored surface indicates the flow velocity distribution of the suspending liquid ranging from zero (blue) on the walls to high (red) in the central part of the flow. Yellow rectangles demark the regions of the flow that will exit through the side's outlet.
Figure 10.4 Schematic of the experimental setup for acoustophoretic cell separation. Sample was drawn from a test tube via a common side's inlet and laminated near the walls of the separation channel, while cell-free medium injected through the central inlet occupied the central part of the flow. Cells were subjected to ultrasound in the separation channel and moved toward the channel center at a rate determined by their acoustic properties and size. Two sample loops were used to collect cells from the side's and center outlet, respectively.
Figure 10.5 Analysis of the effects of retention time, cancer cell number, and diversity on acoustophoresis-mediated enrichment of cancer cells: Fluorescently labeled cultured prostate cancer cells were spiked into RBC lysed control blood (diluted 1:10). The cells were run through an acoustophoresis microfluidic chip in the presence of ultrasound. The cell content in the outlet fractions was analyzed by flow cytometry. The tumor cell capture efficiency and WBC depletion efficiency for acoustophoresis were determined for different total flow rates under constant voltage, for (a) fixed DU145 cells spiked in blood. (b) A dilution series with decreasing number of DU145 cells and constant level of WBCs was performed at a flow rate of 560 µl/min. (c) Acoustophoresis of three different prostate cancer cell lines (fixed in PFA) spiked in RBC lysed blood at the total flow rate of 560 µl/min. Data displayed as mean ± SD,
n
= 5.
Figure 10.6 Photo of the assembled second-generation acoustophoresis cell separation chip. White arrows indicate inlets and outlets accessible from the backside of the base plate. Piezoceramic actuators resonant at 2 and 5 MHz were glued to the backside of the chip for controlling prealignment and separation, respectively.
Figure 10.7 A schematic of the external fluidics for the acoustophoresis cell separation platform. The outputs of a pressure terminal were connected to the sample reservoirs, one for each inlet or outlet. When pressurized, an aqueous suspension of cells (filled and open circles) from the sample input tube entered the chip through the prealignment channel. The separated cells were collected in two test tubes. The pressures in each container were set using a feedback control loop, taking as input the readings from the three thermal flow sensors.
Figure 10.8 The plot shows that the larger (7 µm) beads appeared in the central outlet at lower voltage amplitudes than the smaller (5 µm) beads. Operating the microsystem with active acoustic prealignment (PA) of the particles before separation allowed a steeper transition slope from side's to central outlet. As a result, a vast majority of the larger beads could be collected in the central outlet, while the smaller beads exited through the side's outlet.
Figure 10.9 Samples of prostate cancer cells spiked in RBC lysed blood were run through a pressure-driven acoustophoretic microfluidic chip. The central outlet cancer cell recovery, that is, the relative amount of cells collected in the central outlet to the total amount of collected cells, was measured by flow cytometry. (a) The graph illustrates how prealignment of cells improves cell separation of PFA-fixed prostate cancer cells DU145 (black) and WBC (gray). Cells were processed through the microchip with acoustic prealignment turned on (solid) and off (dashed). (b) Three different PFA-fixed prostate cancer cell lines (DU145, PC3, and LNCaP) were separated from blood cells by acoustophoresis (prealignment on). (c) Acoustic separation of DU145 cells (2.5 × 10
5
/ml) spiked in increasing concentrations of WBC with active cell prealignment. (d) Viable nonfixed DU145 cells spiked in blood were separated by acoustophoresis (prealignment on). All data is presented as mean, max, and min values,
n
= 4.
Chapter 11: Photoacoustic Flow Cytometry for Detection and Capture of Circulating Melanoma Cells
Figure 11.1 The photoacoustic flowmeter consists of one or more laser sources irradiating a flow chamber through which blood cells are circulated. Light-absorbing particles emit photoacoustic waves indicating their presence, while other cells pass silently. Cell throughput is high, as thousands or even millions of normal cells can pass through the laser beam at any time as they generate no acoustic signal.
Figure 11.2 (a) Typical photoacoustic waves generated from CMCs. (b) The pigmented cell generating the photoacoustic wave.
Figure 11.3 Optical detection of photoacoustic waves from pigmented cells is shown here. A HeNe laser provided a probe beam that reflected off of the detection chamber via a prism that was in contact with the flowing medium. The reflected light was detected by a photodiode. The change in reflectance of the probe beam as shown on an oscilloscope indicated any photoacoustic waves generated in melanoma cells.
Figure 11.4 (a) The optical detection setup is shown here. (b) Pigmented melanoma cells in the microcuvette are shown here.
Figure 11.5 (a) The photoacoustic flowmeter separates continuous flow of blood cells with air bubbles. The resulting blood cell suspension droplets are irradiated by laser light. Droplets that contain CTCs generate photoacoustic waves that are sensed by an acoustic transducer. (b) The irradiated droplet contains a CMC and generates photoacoustic waves that are restricted to the droplet due to the acoustic impedance mismatch with the air separating it from other droplets.
Figure 11.6 (a) The photoacoustic flowmeter uses a computer-controlled scanning stage that sends droplets into a 96-well plate. (b) The droplets are monitored by a blue laser probe beam. The probe beam checks when droplets separate from the flow chamber, triggering the computer controller to advance the well plate to a vacant well in preparation for the next droplet.
Figure 11.7 Samples are centrifuged in order to separate blood into its components. The buffy coat, containing leukocytes and possibly CMCs, is taken and processed in the photoacoustic flow cytometer (PAFC).
Figure 11.8 Captured droplets are deposited onto microscope slides for capture and isolation. (1) A micropipette is positioned near a melanoma cell, indicated by a black arrow. (2) The melanoma cell is captured within the micropipette. (3) The micropipette is moved near a group of cytophilic isolation wells. There are four wells labeled a, b, c, and d. (4) The melanoma cell is deposited in well b.
Figure 11.9 (a) A bar graph shows the results of processing 10 ml blood samples from 20 melanoma patients. All but two showed CMCs. Sixteen healthy human controls showed no photoacoustic waveforms, indicating they were free of CMCs (not shown). (b) Bright-field images of two captured CMCs are shown, with pigmented structures indicated by dark shadows. Formalin-enhanced melanin fluorescence at 490 nm is shown for the same captured CMCs, indicating the dark shadows are melanin.
Figure 11.10 (a) Cultured melanoma cells are shown here after photoacoustic capture and immunohistochemical staining. Green fluorescence indicates MART-1 for melanoma, blue indicates DAPI, while red indicates CD-45 for leukocytes. (b) The same region under bright-field microscopy shows pigmented structures corresponding to the MART-1-positive cells.
Chapter 12: Selectin-Mediated Targeting of Circulating Tumor Cells for Isolation and Treatment
Figure 12.1 Leukocyte adhesion cascade.
Figure 12.2 Number of CTCs captured from the blood of cancer patients, along with the results of samples collected from healthy patients. Smooth tube device, halloysite-coated device, and CellSearch methods were compared in parallel experiments, data from [81].
Figure 12.3 Patient samples were collected from three breast cancer patients (Br1, Br2, and Br3), two prostate cancer patients (Pr1 and Pr2), one renal cancer patient, and one colon cancer patient (Re1 and Co1, respectively). Each tube of whole blood was split into three aliquots and treated with vehicle control, 20% peak plasma concentration (PPC), or 100% PPC of the appropriate drug. (a) CTC counts of patient samples treated with chemotherapeutic drugs. (b) Example micrographs of two patient samples. Patients showed varied responses to the drugs as shown in the line graphs. Pr2 shows significant response to both drugs, which can be seen in the top set of fluorescent images as a reduction in the number of cancer cells in the treatment images compared the control. Re1 shows no sensitivity to docetaxel, but is sensitive doxorubicin, which is observed in the lower set of fluorescent images as no reduction in green cells and a reduction in the number of cancer cells, respectively. Larger cells typically represent cancer cells. For more detail refer to original color format (blue: DAPI, green: EpCAM, red: CD45). DT, docetaxel; DOX, doxorubicin; MTX, mitoxantrone. Error bars represent standard error of the mean. *
p
< 0.05, **
p
< 0.01, ***
p
< 0.001; scale bar = 50 µm.
Figure 12.4 (a) Schematic of procedure for
in vivo
liposome experiments. (b) Flow cytometry of COLO205 cells
in vitro
(L), recovered from cardiac puncture of ES-liposome-treated mice (c), recovered from ES/TRAIL-liposome-treated mice (R). SSL, side-scattered light; FSC, forward-scattered light. (c) Number of viable cells recovered from blood of mice compared by treatments.
n
= 3 for all samples. Bars represent the mean ± SD in each treatment group. *
p
< 0.01, **
p
< 0.001, ***
p
< 0.0001 (one-way ANOVA with Tukey posttest). (d) Representative micrographs of cells recovered from mouse blood. Scale bar = 20 µm. (e) Leukocytes functionalized with fluorescent ES/TRAIL liposomes recovered during cardiac puncture. Scale bar = 50 µm. ES/TRAIL liposomes bound to leukocytes in the circulation of mice and successfully killed COLO205 cells in the circulation of mice.
Figure 12.5 Functions and uses for E-selectin in cancer treatment.
Chapter 13: Aptamer-Enabled Tumor Cell Isolation
Figure 13.1 Schematic of the cell-SELEX process. (from “ssDNA library” at the right, counterclockwise) A library of single-stranded DNA (ssDNA) is created and then incubated with target cells. Unbound DNAs are washed away, and the bound DNAs are retained with cells, followed by thermal denaturation to release them. The eluted DNAs are subjected to control cells and those bound DNAs are removed with the cells. The remaining DNAs are specific to the target cells, but not to control cells. They are amplified by PCR, followed by many rounds of the same selection/amplification cycles. The final PCR product is sequenced, and the aptamer's DNA sequence is then identified.
Figure 13.2 (a) Picture of a 3″ × 1″ microfluidic device, consisting of eight parallel channels with single inlet and outlet. Insert: micrograph of herringbone grooves inside channels. (b) Microstructure of circular micropillars, and (c) elliptical micropillars, inside a channel of microfluidic device.
Figure 13.3 (a) Schematic of surface modification and cell capture. (b) Representative image of a couple of target cells stained in red among a large number of control cells stained in blue. (c) Image of the same cell mixture after the capture experiment, showing significant enrichment of the target cells.
Figure 13.4 (a) Schematic of cell capture using AuNP–aptamers that were immobilized onto channel surfaces. Since multiple aptamers on the AuNP can simultaneously bind with a few receptors on the cell surface, cell capture efficiency would be enhanced. (b and c) Comparison in the capture efficiency of target cells in red between (b) with aptamer alone and (c) using AuNP–aptamers.
Figure 13.5 (a) Schematic of an ensemble of antibodies and aptamers immobilized on the channel surfaces and the multivalent interactions of one cell with the ensemble. The drawing is not to scale. (b and c) Comparison in the capture efficiency of target cells in green between (b) with antibody alone and (c) using antibody–aptamer ensemble.
Chapter 14: Depletion of Normal Cells for CTC Enrichment
Figure 14.1 Range of concentration of various types of blood cells.
Figure 14.2 (a) A photograph of the QMS system, while (b) presents a diagram of the quadrupole magnet on the left-hand side and a computer-generated field map on the RHS.
Figure 14.3 A matrix of the various combination of staining patterns on peripheral blood cells from breast cancer, BC, and squamous cell carcinoma of the head and neck, SCCHN, patients after an enrichment through depletion of normal cells.
Figure 14.4 Flow cytometry plots of normal blood (a) and metastatic breast cancer patient blood (b). The first column on the right is side scatter, SSC, versus forward scatter, FSC, (i.e., a measure of granularity versus size), the middle plots are histograms of unstained (red), and CD45 stained, prior to, blue, and after magnetic depletion, black. The last plots on the right are side scatter versus CD45 expression of the samples after depletion targeting CD45. The depletion on both samples was on the order of 2.5 log
10
.
Figure 14.5 Typical flow cytometry data from peripheral blood from a metastatic breast cancer patient after immunomagnetic enrichment. Cells that were not magnetically captured were stained with calcein to distinguish viable cells from dead cells and other events. The cells were also stained with anti-CD45 PE/Cy7 and anti-CD3, CD14, CD16, and CD19 PerCP antibodies. The largest portion of cells after magnetic enrichment are granulocytes, most of which are neutrophils (CD45
+
, CD16hi, solid box). A smaller fraction of remaining cells are lymphocytes (CD45
+
, CD3
+
or CD19
+
, dashed box). The ratio of granulocytes to lymphocytes is higher after magnetic enrichment than before magnetic enrichment, suggesting that magnetic enrichment captures more lymphocytes than granulocytes.
Figure 14.6 Confocal images of enriched samples from a metastatic breast cancer patient. The columns of images, from left to right, correspond to cells stained for DAPI, CD45, cytokeratins, and EpCAM. The color arrows correspond to cells highly positive for all markers, green, weak for CD45, positive of other markers, pink, and cells positive for EpCAM, negative for CK and CD45, white.
Chapter 16: Single-Cell Molecular Profiles and Biophysical Assessment of Circulating Tumor Cells
Figure 16.1 The schematic illustrating the origin of circulating tumor cells (CTCs) and metastasis. Primary tumors shed CTCs into the blood stream and subsequently colonize in the distant organs form secondary tumors and metastasis. The metastatic CTCs may home in to the primary tumors.
Figure 16.2 Components of CTCs' mechanical phenotype and their predicted association with cancer prognosis. (a, left), Stiffer and more rigid cells (dark gray) are less indented by an AFM tip than softer metastatic cells (light gray). More adhesive cells pull the tip back stronger. Smoother topography of a cell membrane is found in cancerous cells. (Right) A force plot cycle with the corresponding tip–cell interactions. Elasticity is calculated from a slope of the withdraw trace (the Young's modulus, Pa), adhesion from a depth of a negative dip (nN), and deformation from a depth of tip indentation (nm). (b) Functionalization of an amine activated tip with an Ab molecule attached via a PEG18 linker equipped with –SH or –NH
2
specific cross-linkers.
Figure 16.3 The schematic illustrations of development of metastatic castration-resistant prostate cancer (mCRPC) and epithelial-to-mesenchymal transition (EMT). (a) The schematic flow of mCRPC development. (b) During EMT, cells undergo dramatic molecular and phenotypic changes. Some of EMT-related genes are upregulated and some are downregulated. The morphological modifications of the cells are apparent externally. These lead to a hypothesis that EMT molecular profiles and biophysical modifications are potential biomarkers for mCRPC. ADT, androgen deprivation therapy.
Figure 16.4 The prostate cancer patient recruitment and pathological status of patients. CTCs from 17 patients and 8 patients were subject to AFM nanomechanical parameter assessment and microfluidic qRT-PCR, respectively. CS: castration-sensitive; CR: castration-resistant; CR-IS: castration-resistant but immunotherapy-sensitive; Pt: patient.
