A microfluidic device for label-free, physical capture of circulating tumor cell clusters

Journal name:
Nature Methods
Volume:
12,
Pages:
685–691
Year published:
DOI:
doi:10.1038/nmeth.3404
Received
Accepted
Published online

Abstract

Cancer cells metastasize through the bloodstream either as single migratory circulating tumor cells (CTCs) or as multicellular groupings (CTC clusters). Existing technologies for CTC enrichment are designed to isolate single CTCs, and although CTC clusters are detectable in some cases, their true prevalence and significance remain to be determined. Here we developed a microchip technology (the Cluster-Chip) to capture CTC clusters independently of tumor-specific markers from unprocessed blood. CTC clusters are isolated through specialized bifurcating traps under low–shear stress conditions that preserve their integrity, and even two-cell clusters are captured efficiently. Using the Cluster-Chip, we identified CTC clusters in 30–40% of patients with metastatic breast or prostate cancer or with melanoma. RNA sequencing of CTC clusters confirmed their tumor origin and identified tissue-derived macrophages within the clusters. Efficient capture of CTC clusters will enable the detailed characterization of their biological properties and role in metastasis.

At a glance

Figures

  1. Design and operation of the Cluster-Chip.
    Figure 1: Design and operation of the Cluster-Chip.

    (a) The chip captures CTC clusters from unprocessed whole blood, whereas single cells pass through. (b) SEM micrographs of the Cluster-Chip show multiple rows of shifted triangular pillars that form consecutive cluster traps (left) and a high-magnification image of a cluster trap (right). Scale bars, 60 μm. (c) Image of a working Cluster-Chip. Blood from a single inlet is uniformly distributed over 4,096 parallel trapping paths and then collected at a single outlet. Close up (inset) shows a CTC cluster–trapping region with part of the microfluidic distribution and collection networks. The size of the glass slide is 3 inch × 1.5 inch. (d) A two-cell LNCaP (human prostate adenocarcinoma) cluster captured on the Cluster-Chip (top) and schematic explaining the dynamic balance responsible for capture (bottom). Forces acting on the cell cluster are drag forces (FD) due to fluid flow, reaction forces (FR) from the pillars and frictional forces (FF) including the effect of cell adhesion. (e) Finite-element analysis comparing the cell cluster dynamics in the Cluster-Chip (left) and in a filter with a single equivalent opening (right). Individual cells are 15 μm wide, and the openings (w) are 12 μm wide.

  2. Characterization of the Cluster-Chip using cell lines spiked in whole blood.
    Figure 2: Characterization of the Cluster-Chip using cell lines spiked in whole blood.

    (a) Procedure used to test for aggregation of single GFP-labeled cells (green) on the chip. Scale bar, 60 μm. (b) Capture efficiency at different flow speeds, measured at 4 °C to minimize cell adhesion, using artificial clusters of MDA-MB-231 cells spiked in whole blood. 100% capture efficiency corresponds to cases in which no clusters were detected in the waste (Online Methods). (c) Comparison of cell cluster size distribution in unprocessed (input) and captured populations. The experiment was performed at room temperature to maximize the capture efficiency of small clusters. Error bars, s.e.m. (n = 3 independent experiments). (d) Comparison of MDA-MB-231 cluster capture efficiency using the Cluster-Chip or membrane filters operated under different pressures. Effective whole-blood processing rate for each condition is noted in parentheses. Error bars, s.e.m. (n = 3 independent experiments). (e) Comparison of cluster capture efficiency of Cluster-Chip with immunoaffinity-based HB-Chip for three human breast cancer cell lines. Surface EpCAM expression is highest in MCF7 and lowest in LBX1. Error bars, s.e.m. (n = 4 (MCF7); n = 3 (MDA-MB-231 and LBX1)). The capture efficiencies for each cell type are normalized by the mean Cluster-Chip capture efficiency.

  3. Release of captured clusters from the Cluster-Chip.
    Figure 3: Release of captured clusters from the Cluster-Chip.

    (a) Experimental setup (top) and steps of the CTC cluster release process (bottom). The bulk of the blood sample is continuously rocked at room temperature and is cooled only when processed by the Cluster-Chip. (b) Release efficiency of MDA-MB-231 clusters from the chip as a function of the reverse flow rate and the processing temperature. (c) Nonspecific binding of leukocytes on the Cluster-Chip when the sample is processed at room temperature (top) and at 4 °C (bottom). Leukocytes were fixed with 4% PFA and stained with DAPI. (d) Left, images of the product released in solution from the Cluster-Chip operated at room temperature (top) and at 4 °C (bottom). Right, relative purity of released cell clusters against contaminating blood cells when Cluster-Chip is operated at room temperature and 4 °C. Scale bars, 60 μm.

  4. Capture of CTC clusters from blood samples of patients with metastatic cancer.
    Figure 4: Capture of CTC clusters from blood samples of patients with metastatic cancer.

    (a) Representative images of three CTC clusters isolated from patients with metastatic breast cancer. Left, bright-field and fluorescence images of a live CTC cluster stained for common breast cancer surface markers. Center, SEM micrograph of a fixed CTC cluster. Inset is ~2.1× magnified. Right, fluorescence image of a highly deformable CTC cluster stained for cytokeratin. Note that this CTC cluster is not split but is highly strained even under slow flow in the Cluster-Chip. (b) Percentage of patients with CTC clusters in breast, melanoma and prostate cancer. (c) Size distribution of CTC clusters isolated from breast, melanoma and prostate cancer patients. The box plots show the 25th, 50th and 75th percentiles for each disease type. Scale bars, 20 μm.

  5. Immunocytochemical and molecular characterization of patient CTC clusters.
    Figure 5: Immunocytochemical and molecular characterization of patient CTC clusters.

    (a) Images of a Ki67 and a Ki67+ CTC cluster stained with cytokeratin (red), Ki67 (yellow), CD45 (green) and DAPI (nuclei, blue). Arrows indicate Ki67+ cells within the CTC cluster.The bar graphs show the percentage of Ki67+ CTC clusters in this patient (left) and the percentage of Ki67+ cells within CTC clusters (right). (b) Image of a CTC cluster associated with a white blood cell (WBC). Cells were stained with cytokeratin (red), CD45 (green) and DAPI (nuclei, blue). (c) Images of WBC (top) and WBC+ (bottom) CTC clusters released from the Cluster-Chip and live stained with Texas red–conjugated antibodies against CD45, CD14 and CD16 (red). (d) Heat map showing expression levels of transcripts associated with CTCs, macrophages and monocytes, T cells, B cells, natural killer (NK) cells, hematopoietic stem cells (HSCs), granulocytes and platelets in 15 CTC clusters isolated at a single time point from a patient with metastatic breast cancer. RPM, reads per million. Scale bars, 20 μm.

  6. Finite-element analysis of cell cluster dynamics in different cluster trap architectures.
    Supplementary Fig. 1: Finite-element analysis of cell cluster dynamics in different cluster trap architectures.

    (a) Cluster-Chip (b) Filter (c) A structure identical to the Cluster-Chip except that one of the openings is closed d) A filter formed by two triangular pillars. In all cases except the Cluster-Chip, cluster larger than the opening size passes due to its elastic behavior. The cluster being missed points to the importance of split flow fields simultaneously effecting the cluster in the Cluster-Chip and proves that the capture in the Cluster-Chip is not simply due to filtering. In all simulations, the opening widths and diameters of individual cells are 14 μm.

  7. Probability of chip-mediated artifact cluster formation from single cells on the Cluster-Chip.
    Supplementary Fig. 2: Probability of chip-mediated artifact cluster formation from single cells on the Cluster-Chip.

    The calculated probabilities are based on simplifying assumptions on chip geometry and cell adhesion. We assume a blood sample with 1000 CTCs. In addition, each of the 4096 parallel cluster traps on the Cluster-Chip is assumed to have equal probability of receiving CTCs and whenever multiple CTCs go into the same trap, a CTC-cluster is formed. We used Poisson approximation to calculate probability of forming a CTC-cluster composed of a specific number of cells. Our model confirms that it is extremely unlikely to form artifact CTC clusters on the Cluster-Chip.

  8. Experimental setup used to quantify the capture efficiency of the Cluster-Chip.
    Supplementary Fig. 3: Experimental setup used to quantify the capture efficiency of the Cluster-Chip.

    A smaller version of the Cluster-Chip is connected to a large microfluidic waste chamber, which permits to accurately identify and analyze non-captured cell clusters in whole blood. Both the chip and waste chamber are imaged using fluorescence microscopy to calculate the cell cluster capture efficiency.

  9. Distribution of captured small and large clusters on the Cluster-Chip.
    Supplementary Fig. 4: Distribution of captured small and large clusters on the Cluster-Chip.

    Normalized distribution of (a) 2-cell clusters (b) 3-cell clusters (c) clusters containing 4 or more cells across the Cluster-Chip. We used simulated whole blood samples spiked with artificial clusters of MDA-MB-231 breast cancer cell line. The Cluster-Chip was operated at 2.5 ml/hr and was on a thermoelectric cooler set at 4°C.

  10. Characterization of MDA-MB-231 cell cluster size distribution before spiking.
    Supplementary Fig. 5: Characterization of MDA-MB-231 cell cluster size distribution before spiking.

    We prepare clusters of fluorescently labeled MDA-MB-231 cancer cells and deposit the suspension on an ultra-low attachment culture dish. Using fluorescence microscopy, we count the number of cells within each cluster and then spike into whole blood in order to prepare a simulated sample containing characterized cluster population for quantitative device testing.

  11. Intact and damaged cell clusters captured using a membrane filter.
    Supplementary Fig. 6: Intact and damaged cell clusters captured using a membrane filter.

    Fluorescent and brightftield images of intact and damaged MDA-MB-231 cell clusters when captured using a polycarbonate track-etched membrane filter operated under 1.5 psi.

  12. Viability of cell clusters released from the Cluster-Chip.
    Supplementary Fig. 7: Viability of cell clusters released from the Cluster-Chip.

    The Cluster-Chip is operated at 4°C and the clusters were released at 250 ml/hr reverse flow rate (A) Fluorescent images of MDA-MB-231 clusters treated with two-color viability (green)/cytotoxicity (red) assay. The cells are divided in two batches: Control and cell clusters captured and released from Cluster-Chip (B) Percentage of viable clusters captured and released using Cluster-Chip against the control. Scale bar 100 μm.

  13. Images of CTC -clusters isolated from a breast cancer patient and released in solution.
    Supplementary Fig. 8: Images of CTC -clusters isolated from a breast cancer patient and released in solution.

    CTC-clusters isolated from a patient with metastatic breast cancer using the Cluster-Chip operated at 4°C and released with reverse flow. CTC clusters are not fixed and live stained for common breast cancer surface markers. Scale bar 20 μm.

  14. Correlation between the number of CTCs and CTC clusters in patients.
    Supplementary Fig. 9: Correlation between the number of CTCs and CTC clusters in patients.

    Comparison of number of CTC clusters isolated using the Cluster-Chip and number of single CTCs isolated using CTC-iChip from the same patient at the same timepoint. Among the 19 cases studied, we found no correlation between the numbers of CTC-clusters and that of single CTCs.

  15. Proliferative index of single CTCs isolated from a breast cancer patient.
    Supplementary Fig. 10: Proliferative index of single CTCs isolated from a breast cancer patient.

    Representative images of a Ki67-negative (-) and a Ki67-positive (+) CTC stained with antibodies against wide spectrum cytokeratin (CK, red), Ki67 (yellow) and CD45 (green). Nuclei are stained with DAPI (blue). The bar graph shows the mean percentage of Ki67-positive cells isolated from a breast cancer patient across multiple time points. All together, 439 single CTCs were stained and 162 resulted positive for Ki67.

  16. Heat map showing expression levels of CTC clusters isolated from a patient.
    Supplementary Fig. 11: Heat map showing expression levels of CTC clusters isolated from a patient.

    Heatmap showing expression levels of representative epithelial-to-mesenchymal transition (EMT) genes in released CTC-clusters and healthy donor white blood cells (WBCs). Epithelial markers E-cadherin (CDH1), EpCAM and Mucin1 (MUC1), as well as mesenchymal markers Vimentin (VIM), Fibronectin1 (FN1), N-cadherin (CDH2) and TWIST1 are shown.

Videos

  1. Real-time capture of cell clusters in whole blood on the Cluster-Chip
    Video 1: Real-time capture of cell clusters in whole blood on the Cluster-Chip
    Real-time video of the Cluster-Chip processing a simulated sample at 2.5 ml/hr. The sample is prepared by spiking artificially formed clusters of fluorescently labeled MDA-MB-231 cancer cells into whole blood samples collected from a healthy donor. Unlabeled blood cells flowing through the chip are not visible in the fluorescent channel but can be noticed as they obscure the fluorescence emission from captured clusters.

Accession codes

Primary accessions

Gene Expression Omnibus

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Author information

  1. Present address: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.

    • A Fatih Sarioglu
  2. These authors contributed equally to this work.

    • A Fatih Sarioglu &
    • Nicola Aceto

Affiliations

  1. Center for Engineering in Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.

    • A Fatih Sarioglu,
    • Nikola Kojic,
    • Mahnaz Zeinali,
    • Bashar Hamza,
    • Shannon L Stott,
    • Ravi Kapur &
    • Mehmet Toner
  2. Cancer Center, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.

    • A Fatih Sarioglu,
    • Nicola Aceto,
    • Maria C Donaldson,
    • Mahnaz Zeinali,
    • Amanda Engstrom,
    • Huili Zhu,
    • Tilak K Sundaresan,
    • David T Miyamoto,
    • Xi Luo,
    • Aditya Bardia,
    • Ben S Wittner,
    • Sridhar Ramaswamy,
    • Toshi Shioda,
    • David T Ting,
    • Shannon L Stott,
    • Shyamala Maheswaran &
    • Daniel A Haber
  3. Department of Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.

    • A Fatih Sarioglu,
    • Nikola Kojic,
    • Shyamala Maheswaran &
    • Mehmet Toner
  4. Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.

    • Nicola Aceto,
    • Aditya Bardia,
    • Sridhar Ramaswamy,
    • David T Ting,
    • Shannon L Stott &
    • Daniel A Haber
  5. Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.

    • David T Miyamoto
  6. Howard Hughes Medical Institute, Chevy Chase, Maryland, USA.

    • Daniel A Haber

Contributions

A.F.S., N.A., S.M., D.A.H. and M.T. designed the research, analyzed the data and prepared the manuscript. N.K. performed computer simulations. M.C.D., M.Z. and A.E. processed clinical samples, performed immunofluorescence staining and scanning. B.H. manufactured devices for clinical studies. H.Z. and T.S. performed amplification and RNA sequencing. T.K.S., D.T.M., X.L. and A.B. provided clinical samples. B.S.W. performed statistical analysis on the RNA sequencing data. S.R., D.T.T., S.L.S. and R.K. commented on the manuscript.

Competing financial interests

A.F.S. and M.T. are inventors on a patent Massachusetts General Hospital filed to protect the Cluster-Chip technology.

Corresponding author

Correspondence to:

Author details

Supplementary information

Supplementary Figures

  1. Supplementary Figure 1: Finite-element analysis of cell cluster dynamics in different cluster trap architectures. (243 KB)

    (a) Cluster-Chip (b) Filter (c) A structure identical to the Cluster-Chip except that one of the openings is closed d) A filter formed by two triangular pillars. In all cases except the Cluster-Chip, cluster larger than the opening size passes due to its elastic behavior. The cluster being missed points to the importance of split flow fields simultaneously effecting the cluster in the Cluster-Chip and proves that the capture in the Cluster-Chip is not simply due to filtering. In all simulations, the opening widths and diameters of individual cells are 14 μm.

  2. Supplementary Figure 2: Probability of chip-mediated artifact cluster formation from single cells on the Cluster-Chip. (82 KB)

    The calculated probabilities are based on simplifying assumptions on chip geometry and cell adhesion. We assume a blood sample with 1000 CTCs. In addition, each of the 4096 parallel cluster traps on the Cluster-Chip is assumed to have equal probability of receiving CTCs and whenever multiple CTCs go into the same trap, a CTC-cluster is formed. We used Poisson approximation to calculate probability of forming a CTC-cluster composed of a specific number of cells. Our model confirms that it is extremely unlikely to form artifact CTC clusters on the Cluster-Chip.

  3. Supplementary Figure 3: Experimental setup used to quantify the capture efficiency of the Cluster-Chip. (287 KB)

    A smaller version of the Cluster-Chip is connected to a large microfluidic waste chamber, which permits to accurately identify and analyze non-captured cell clusters in whole blood. Both the chip and waste chamber are imaged using fluorescence microscopy to calculate the cell cluster capture efficiency.

  4. Supplementary Figure 4: Distribution of captured small and large clusters on the Cluster-Chip. (198 KB)

    Normalized distribution of (a) 2-cell clusters (b) 3-cell clusters (c) clusters containing 4 or more cells across the Cluster-Chip. We used simulated whole blood samples spiked with artificial clusters of MDA-MB-231 breast cancer cell line. The Cluster-Chip was operated at 2.5 ml/hr and was on a thermoelectric cooler set at 4°C.

  5. Supplementary Figure 5: Characterization of MDA-MB-231 cell cluster size distribution before spiking. (133 KB)

    We prepare clusters of fluorescently labeled MDA-MB-231 cancer cells and deposit the suspension on an ultra-low attachment culture dish. Using fluorescence microscopy, we count the number of cells within each cluster and then spike into whole blood in order to prepare a simulated sample containing characterized cluster population for quantitative device testing.

  6. Supplementary Figure 6: Intact and damaged cell clusters captured using a membrane filter. (413 KB)

    Fluorescent and brightftield images of intact and damaged MDA-MB-231 cell clusters when captured using a polycarbonate track-etched membrane filter operated under 1.5 psi.

  7. Supplementary Figure 7: Viability of cell clusters released from the Cluster-Chip. (104 KB)

    The Cluster-Chip is operated at 4°C and the clusters were released at 250 ml/hr reverse flow rate (A) Fluorescent images of MDA-MB-231 clusters treated with two-color viability (green)/cytotoxicity (red) assay. The cells are divided in two batches: Control and cell clusters captured and released from Cluster-Chip (B) Percentage of viable clusters captured and released using Cluster-Chip against the control. Scale bar 100 μm.

  8. Supplementary Figure 8: Images of CTC -clusters isolated from a breast cancer patient and released in solution. (377 KB)

    CTC-clusters isolated from a patient with metastatic breast cancer using the Cluster-Chip operated at 4°C and released with reverse flow. CTC clusters are not fixed and live stained for common breast cancer surface markers. Scale bar 20 μm.

  9. Supplementary Figure 9: Correlation between the number of CTCs and CTC clusters in patients. (85 KB)

    Comparison of number of CTC clusters isolated using the Cluster-Chip and number of single CTCs isolated using CTC-iChip from the same patient at the same timepoint. Among the 19 cases studied, we found no correlation between the numbers of CTC-clusters and that of single CTCs.

  10. Supplementary Figure 10: Proliferative index of single CTCs isolated from a breast cancer patient. (294 KB)

    Representative images of a Ki67-negative (-) and a Ki67-positive (+) CTC stained with antibodies against wide spectrum cytokeratin (CK, red), Ki67 (yellow) and CD45 (green). Nuclei are stained with DAPI (blue). The bar graph shows the mean percentage of Ki67-positive cells isolated from a breast cancer patient across multiple time points. All together, 439 single CTCs were stained and 162 resulted positive for Ki67.

  11. Supplementary Figure 11: Heat map showing expression levels of CTC clusters isolated from a patient. (197 KB)

    Heatmap showing expression levels of representative epithelial-to-mesenchymal transition (EMT) genes in released CTC-clusters and healthy donor white blood cells (WBCs). Epithelial markers E-cadherin (CDH1), EpCAM and Mucin1 (MUC1), as well as mesenchymal markers Vimentin (VIM), Fibronectin1 (FN1), N-cadherin (CDH2) and TWIST1 are shown.

Video

  1. Video 1: Real-time capture of cell clusters in whole blood on the Cluster-Chip (7.33 MB, Download)
    Real-time video of the Cluster-Chip processing a simulated sample at 2.5 ml/hr. The sample is prepared by spiking artificially formed clusters of fluorescently labeled MDA-MB-231 cancer cells into whole blood samples collected from a healthy donor. Unlabeled blood cells flowing through the chip are not visible in the fluorescent channel but can be noticed as they obscure the fluorescence emission from captured clusters.

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  1. Supplementary Text and Figures (797 KB)

    Supplementary Figures 1–11 and Supplementary Table 1

Additional data