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Geng X, Zhou ZA, Mi Y, Wang C, Wang M, Guo C, Qu C, Feng S, Kim I, Yu M, Ji H, Ren X. Glioma Single-Cell Biomechanical Analysis by Cyclic Conical Constricted Microfluidics. Anal Chem 2023; 95:15585-15594. [PMID: 37843131 DOI: 10.1021/acs.analchem.3c02434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Determining the grade of glioma is a critical step in choosing patients' treatment plans in clinical practices. The pathological diagnosis of patient's glioma samples requires extensive staining and imaging procedures, which are expensive and time-consuming. Current advanced uniform-width-constriction-channel-based microfluidics have proven to be effective in distinguishing cancer cells from normal tissues, such as breast cancer, ovarian cancer, prostate cancer, etc. However, the uniform-width-constriction channels can result in low yields on glioma cells with irregular morphologies and high heterogeneity. In this research, we presented an innovative cyclic conical constricted (CCC) microfluidic device to better differentiate glioma cells from normal glial cells. Compared with the widely used uniform-width-constriction microchannels, the new CCC configuration forces single cells to deform gradually and obtains the biophysical attributes from each deformation. The human-derived glioma cell lines U-87 and U-251, as well as the human-derived normal glial astrocyte cell line HA-1800 were selected as the proof of concept. The results showed that CCC channels can effectively obtain the biomechanical characteristics of different 12-25 μm glial cell lines. The patient glioma samples with WHO grades II, III, and IV were tested by CCC channels and compared between Elastic Net (ENet) and Lasso analysis. The results demonstrated that CCC channels and the ENet can successfully select critical biomechanical parameters to differentiate the grades of single-glioma cells. This CCC device can be potentially further applied to the extensive family of brain tumors at the single-cell level.
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Affiliation(s)
- Xin Geng
- Department of Neurosurgery, Shanxi Provincial People's Hospital, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan, Shanxi 030012, China
| | - Zi-Ang Zhou
- Department of Microelectronics, Tianjin University, Tianjin 300072, China
| | - Yang Mi
- Department of Neurosurgery, Shanxi Provincial People's Hospital, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan, Shanxi 030012, China
| | - Chunhong Wang
- Department of Neurosurgery, Shanxi Provincial People's Hospital, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan, Shanxi 030012, China
| | - Meng Wang
- Department of Neurosurgery, Shanxi Provincial People's Hospital, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan, Shanxi 030012, China
| | - Chenjia Guo
- Department of Pathology, Shanxi Provincial People's Hospital, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan, Shanxi 030012, China
| | - Chongxiao Qu
- Department of Pathology, Shanxi Provincial People's Hospital, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan, Shanxi 030012, China
| | - Shilun Feng
- Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
| | - Inyoung Kim
- Department of Statistics, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Miao Yu
- Department of Research and Development, Stedical Scientific, Carlsbad, California 92010, United States
| | - Hongming Ji
- Department of Neurosurgery, Shanxi Provincial People's Hospital, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan, Shanxi 030012, China
| | - Xiang Ren
- Department of Neurosurgery, Shanxi Provincial People's Hospital, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan, Shanxi 030012, China
- Department of Microelectronics, Tianjin University, Tianjin 300072, China
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2
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Ren X, Ellis BW, Ronan G, Blood SR, DeShetler C, Senapati S, March KL, Handberg E, Anderson D, Pepine C, Chang HC, Zorlutuna P. A multiplexed ion-exchange membrane-based miRNA (MIX·miR) detection platform for rapid diagnosis of myocardial infarction. LAB ON A CHIP 2021; 21:3876-3887. [PMID: 34546237 PMCID: PMC9115728 DOI: 10.1039/d1lc00685a] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Micro RNAs (miRNAs) have shown great potential as rapid and discriminating biomarkers for acute myocardial infarction (AMI) diagnosis. We have developed a multiplexed ion-exchange membrane-based miRNA (MIX·miR) preconcentration/sensing amplification-free platform for quantifying in parallel a panel of miRNAs, including miR-1, miR-208b, and miR-499, from the same plasma samples from: 1) reference subjects with no evident coronary artery disease (NCAD); 2) subjects with stable coronary artery disease (CAD); and 3) subjects experiencing ST-elevation myocardial infarction (STEMI) prior to (STEMI-pre) and following (STEMI-PCI) percutaneous coronary intervention. The picomolar limit of detection from raw plasma and 3-decade dynamic range of MIX·miR permits detection of the miRNA panel in untreated samples from disease patients and its precise standard curve, provided by large 0.1 to 1 V signals and eliminates individual sensor calibration. The use of molecular concentration feature reduces the assay time to less than 30 minutes and increases the detection sensitivity by bringing all targets close to the sensors. miR-1 was low for NCAD patients but more than one order of magnitude above the normal value for all samples from three categories (CAD, STEMI-pre, and STEMI-PCI) of patients with CAD. In fact, miR-1 expression levels of stable CAD, STEMI-pre and STEMI-PCI are each more than 10-fold higher than the previous class, in that order, well above the 95% confidence level of MIX·miR. Its overexpression estimate is significantly higher than the PCR benchmark. This suggests that, in contrast to protein biomarkers of myocardial injury, miR-1 appears to differentiate ischemia from both reperfusion injury and non-AMI CAD patients. The battery-operated MIX·miR can be a portable and low-cost AMI diagnostic device, particularly useful in settings where cardiac catheterization is not readily available to determine the status of coronary reperfusion.
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Affiliation(s)
- Xiang Ren
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA.
| | - Bradley W Ellis
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA.
| | - George Ronan
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA.
| | - Stuart Ryan Blood
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Cameron DeShetler
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Satyajyoti Senapati
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Keith L March
- Division of Cardiology, Department of Medicine in the College of Medicine, University of Florida, Gainesville, FL 32611, USA
| | - Eileen Handberg
- Division of Cardiology, Department of Medicine in the College of Medicine, University of Florida, Gainesville, FL 32611, USA
| | - David Anderson
- Division of Cardiology, Department of Medicine in the College of Medicine, University of Florida, Gainesville, FL 32611, USA
| | - Carl Pepine
- Division of Cardiology, Department of Medicine in the College of Medicine, University of Florida, Gainesville, FL 32611, USA
| | - Hsueh-Chia Chang
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA.
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Pinar Zorlutuna
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA.
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
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3
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Baum ZJ, Yu X, Ayala PY, Zhao Y, Watkins SP, Zhou Q. Artificial Intelligence in Chemistry: Current Trends and Future Directions. J Chem Inf Model 2021; 61:3197-3212. [PMID: 34264069 DOI: 10.1021/acs.jcim.1c00619] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The application of artificial intelligence (AI) to chemistry has grown tremendously in recent years. In this Review, we studied the growth and distribution of AI-related chemistry publications in the last two decades using the CAS Content Collection. The volume of both journal and patent publications have increased dramatically, especially since 2015. Study of the distribution of publications over various chemistry research areas revealed that analytical chemistry and biochemistry are integrating AI to the greatest extent and with the highest growth rates. We also investigated trends in interdisciplinary research and identified frequently occurring combinations of research areas in publications. Furthermore, topic analyses were conducted for journal and patent publications to illustrate emerging associations of AI with certain chemistry research topics. Notable publications in various chemistry disciplines were then evaluated and presented to highlight emerging use cases. Finally, the occurrence of different classes of substances and their roles in AI-related chemistry research were quantified, further detailing the popularity of AI adoption in the life sciences and analytical chemistry. In summary, this Review offers a broad overview of how AI has progressed in various fields of chemistry and aims to provide an understanding of its future directions.
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Affiliation(s)
- Zachary J Baum
- Chemical Abstracts Service, 2540 Olentangy River Road, Columbus, Ohio 43210, United States
| | - Xiang Yu
- Chemical Abstracts Service, 2540 Olentangy River Road, Columbus, Ohio 43210, United States
| | - Philippe Y Ayala
- Chemical Abstracts Service, 2540 Olentangy River Road, Columbus, Ohio 43210, United States
| | - Yanan Zhao
- Chemical Abstracts Service, 2540 Olentangy River Road, Columbus, Ohio 43210, United States
| | - Steven P Watkins
- Chemical Abstracts Service, 2540 Olentangy River Road, Columbus, Ohio 43210, United States
| | - Qiongqiong Zhou
- Chemical Abstracts Service, 2540 Olentangy River Road, Columbus, Ohio 43210, United States
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4
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Graybill PM, Bollineni RK, Sheng Z, Davalos RV, Mirzaeifar R. A constriction channel analysis of astrocytoma stiffness and disease progression. BIOMICROFLUIDICS 2021; 15:024103. [PMID: 33763160 PMCID: PMC7968935 DOI: 10.1063/5.0040283] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 02/23/2021] [Indexed: 05/12/2023]
Abstract
Studies have demonstrated that cancer cells tend to have reduced stiffness (Young's modulus) compared to their healthy counterparts. The mechanical properties of primary brain cancer cells, however, have remained largely unstudied. To investigate whether the stiffness of primary brain cancer cells decreases as malignancy increases, we used a microfluidic constriction channel device to deform healthy astrocytes and astrocytoma cells of grade II, III, and IV and measured the entry time, transit time, and elongation. Calculating cell stiffness directly from the experimental measurements is not possible. To overcome this challenge, finite element simulations of the cell entry into the constriction channel were used to train a neural network to calculate the stiffness of the analyzed cells based on their experimentally measured diameter, entry time, and elongation in the channel. Our study provides the first calculation of stiffness for grades II and III astrocytoma and is the first to apply a neural network analysis to determine cell mechanical properties from a constriction channel device. Our results suggest that the stiffness of astrocytoma cells is not well-correlated with the cell grade. Furthermore, while other non-central-nervous-system cell types typically show reduced stiffness of malignant cells, we found that most astrocytoma cell lines had increased stiffness compared to healthy astrocytes, with lower-grade astrocytoma having higher stiffness values than grade IV glioblastoma. Differences in nucleus-to-cytoplasm ratio only partly explain differences in stiffness values. Although our study does have limitations, our results do not show a strong correlation of stiffness with cell grade, suggesting that other factors may play important roles in determining the invasive capability of astrocytoma. Future studies are warranted to further elucidate the mechanical properties of astrocytoma across various pathological grades.
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Affiliation(s)
| | - R. K. Bollineni
- Department of Mechanical Engineering, Virginia Tech, Blacksburg, Virginia 24061, USA
| | - Z. Sheng
- Department of Internal Medicine, Virginia Tech Carilion School of Medicine and Virginia Tech Fralin Biomedical Research Institute, Roanoke, Virginia 24016, USA
| | - R. V. Davalos
- Authors to whom correspondence should be addressed: and
| | - R. Mirzaeifar
- Department of Mechanical Engineering, Virginia Tech, Blacksburg, Virginia 24061, USA
- Authors to whom correspondence should be addressed: and
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5
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Ghassemi P, Harris KS, Ren X, Foster BM, Langefeld CD, Kerr BA, Agah M. Comparative Study of Prostate Cancer Biophysical and Migratory Characteristics via Iterative Mechanoelectrical Properties (iMEP) and Standard Migration Assays. SENSORS AND ACTUATORS. B, CHEMICAL 2020; 321:128522. [PMID: 32863589 PMCID: PMC7455013 DOI: 10.1016/j.snb.2020.128522] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
This study reveals a new microfluidic biosensor consisting of a multi-constriction microfluidic device with embedded electrodes for measuring the biophysical attributes of single cells. The biosensing platform called the iterative mechano-electrical properties (iMEP) analyzer captures electronic records of biomechanical and bioelectrical properties of cells. The iMEP assay is used in conjunction with standard migration assays, such as chemotaxis-based Boyden chamber and scratch wound healing assays, to evaluate the migratory behavior and biophysical properties of prostate cancer cells. The three cell lines evaluated in the study each represent a stage in the standard progression of prostate cancer, while the fourth cell line serves as a normal/healthy counterpart. Neither the scratch assay nor the chemotaxis assay could fully differentiate the four cell lines. Furthermore, there was not a direct correlation between wound healing rate or the migratory rate with the cells' metastatic potential. However, the iMEP assay, through its multiparametric dataset, could distinguish between all four cell line populations with p-value < 0.05. Further studies are needed to determine if iMEP signatures can be used for a wider range of human cells to assess the tumorigenicity of a cell population or the metastatic potential of cancer cells.
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Affiliation(s)
- Parham Ghassemi
- The Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Koran S. Harris
- Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, North Carolina 27157, United States
| | - Xiang Ren
- The Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Brittni M. Foster
- Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, North Carolina 27157, United States
| | - Carl D. Langefeld
- Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, 27157, United States
| | - Bethany A. Kerr
- Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, North Carolina 27157, United States
| | - Masoud Agah
- The Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
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6
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Ren X, Nam W, Ghassemi P, Strobl JS, Kim I, Zhou W, Agah M. Scalable nanolaminated SERS multiwell cell culture assay. MICROSYSTEMS & NANOENGINEERING 2020; 6:47. [PMID: 34567659 PMCID: PMC8433130 DOI: 10.1038/s41378-020-0145-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 11/10/2019] [Accepted: 12/30/2019] [Indexed: 05/23/2023]
Abstract
This paper presents a new cell culture platform enabling label-free surface-enhanced Raman spectroscopy (SERS) analysis of biological samples. The platform integrates a multilayered metal-insulator-metal nanolaminated SERS substrate and polydimethylsiloxane (PDMS) multiwells for the simultaneous analysis of cultured cells. Multiple cell lines, including breast normal and cancer cells and prostate cancer cells, were used to validate the applicability of this unique platform. The cell lines were cultured in different wells. The Raman spectra of over 100 cells from each cell line were collected and analyzed after 12 h of introducing the cells to the assay. The unique Raman spectra of each cell line yielded biomarkers for identifying cancerous and normal cells. A kernel-based machine learning algorithm was used to extract the high-dimensional variables from the Raman spectra. Specifically, the nonnegative garrote on a kernel machine classifier is a hybrid approach with a mixed nonparametric model that considers the nonlinear relationships between the higher-dimension variables. The breast cancer cell lines and normal breast epithelial cells were distinguished with an accuracy close to 90%. The prediction rate between breast cancer cells and prostate cancer cells reached 94%. Four blind test groups were used to evaluate the prediction power of the SERS spectra. The peak intensities at the selected Raman shifts of the testing groups were selected and compared with the training groups used in the machine learning algorithm. The blind testing groups were correctly predicted 100% of the time, demonstrating the applicability of the multiwell SERS array for analyzing cell populations for cancer research.
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Affiliation(s)
- Xiang Ren
- Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24061 USA
| | - Wonil Nam
- Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24061 USA
| | - Parham Ghassemi
- Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24061 USA
| | - Jeannine S. Strobl
- Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24061 USA
| | - Inyoung Kim
- Department of Statistics, Virginia Tech, Blacksburg, VA 24061 USA
| | - Wei Zhou
- Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24061 USA
| | - Masoud Agah
- Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24061 USA
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7
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Bahcecioglu G, Basara G, Ellis BW, Ren X, Zorlutuna P. Breast cancer models: Engineering the tumor microenvironment. Acta Biomater 2020; 106:1-21. [PMID: 32045679 PMCID: PMC7185577 DOI: 10.1016/j.actbio.2020.02.006] [Citation(s) in RCA: 87] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 01/14/2020] [Accepted: 02/05/2020] [Indexed: 12/24/2022]
Abstract
The mechanisms behind cancer initiation and progression are not clear. Therefore, development of clinically relevant models to study cancer biology and drug response in tumors is essential. In vivo models are very valuable tools for studying cancer biology and for testing drugs; however, they often suffer from not accurately representing the clinical scenario because they lack either human cells or a functional immune system. On the other hand, two-dimensional (2D) in vitro models lack the three-dimensional (3D) network of cells and extracellular matrix (ECM) and thus do not represent the tumor microenvironment (TME). As an alternative approach, 3D models have started to gain more attention, as such models offer a platform with the ability to study cell-cell and cell-material interactions parametrically, and possibly include all the components present in the TME. Here, we first give an overview of the breast cancer TME, and then discuss the current state of the pre-clinical breast cancer models, with a focus on the engineered 3D tissue models. We also highlight two engineering approaches that we think are promising in constructing models representative of human tumors: 3D printing and microfluidics. In addition to giving basic information about the TME in the breast tissue, this review article presents the state-of-the-art tissue engineered breast cancer models. STATEMENT OF SIGNIFICANCE: Involvement of biomaterials and tissue engineering fields in cancer research enables realistic mimicry of the cell-cell and cell-extracellular matrix (ECM) interactions in the tumor microenvironment (TME), and thus creation of better models that reflect the tumor response against drugs. Engineering the 3D in vitro models also requires a good understanding of the TME. Here, an overview of the breast cancer TME is given, and the current state of the pre-clinical breast cancer models, with a focus on the engineered 3D tissue models is discussed. This review article is useful not only for biomaterials scientists aiming to engineer 3D in vitro TME models, but also for cancer researchers willing to use these models for studying cancer biology and drug testing.
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Affiliation(s)
- Gokhan Bahcecioglu
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Gozde Basara
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Bradley W Ellis
- Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Xiang Ren
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Pinar Zorlutuna
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, United States; Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN 46556, United States; Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556, United States; Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN 46556, United States.
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8
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Ghassemi P, Ren X, Foster BM, Kerr BA, Agah M. Post-enrichment circulating tumor cell detection and enumeration via deformability impedance cytometry. Biosens Bioelectron 2020; 150:111868. [PMID: 31767345 PMCID: PMC6957725 DOI: 10.1016/j.bios.2019.111868] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 11/07/2019] [Accepted: 11/08/2019] [Indexed: 02/05/2023]
Abstract
Circulating tumor cells (CTCs) in blood can provide valuable information when detecting, diagnosing, and monitoring cancer. This paper describes a system that consists of a constriction-based microfluidic sensor with embedded electrodes that can detect and enumerate cancer cells in blood. The biosensor measures impedance in terms of magnitude and phase at multiple frequencies as cells transit through the constriction channel. Cancer cells deform as they move through while blood cells remain intact, thus generating differential impedance profiles that can be used for detecting and counting CTCs. Two versions of this device are reported, one where the electrodes are embedded into the disposable microfluidic channel, and the other in which the disposable chip is externally fixed to a reusable substrate housing the electrodes. Both configurations were tested by spiking breast or prostate cancer cells into murine blood, and both detected all tumor cells passing through the narrow channels while being able to differentiate between the two cell lines. The chip in its current format has a throughput of 1 μL/min. While the throughput is scalable by integrating more constriction channels in parallel, the presented assay is intended for post-enrichment label-free enumeration and characterization of CTCs.
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Affiliation(s)
- Parham Ghassemi
- The Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, 24061, United States.
| | - Xiang Ren
- The Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, 24061, United States.
| | - Brittni M Foster
- Department of Cancer Biology, Wake Forest University School of Medicine, Winston-Salem, NC, 27157, United States.
| | - Bethany A Kerr
- Department of Cancer Biology, Wake Forest University School of Medicine, Winston-Salem, NC, 27157, United States.
| | - Masoud Agah
- The Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, 24061, United States.
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Ren X, Ghassemi P, Strobl JS, Agah M. Biophysical phenotyping of cells via impedance spectroscopy in parallel cyclic deformability channels. BIOMICROFLUIDICS 2019; 13:044103. [PMID: 31341524 PMCID: PMC6639115 DOI: 10.1063/1.5099269] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 07/09/2019] [Indexed: 05/19/2023]
Abstract
This paper describes a new microfluidic biosensor with capabilities of studying single cell biophysical properties. The chip contains four parallel sensing channels, where each channel includes two constriction regions separated by a relaxation region. All channels share a pair of electrodes to record the electrical impedance. Single cell impedance magnitudes and phases at different frequencies were obtained. The deformation and transition time information of cells passing through two sequential constriction regions were gained from the time points on impedance magnitude variations. Constriction channels separated by relaxation regions have been proven to improve the sensitivity of distinguishing single cells. The relaxation region between two sequential constriction channels provides extra time stamps that can be identified in the impedance plots. The new chip allows simultaneous measurement of the biophysical attributes of multiple cells in different channels, thereby increasing the overall throughput of the chip. Using the biomechanical parameters represented by the time stamps in the impedance results, breast cancer cells (MDA-MB-231) and the normal epithelial cells (MCF-10A) could be distinguished by 85%. The prediction accuracy at the single-cell level reached 97% when both biomechanical and bioelectrical parameters were utilized. While the new label-free assay has been tested to distinguish between normal and cancer cells, its application can be extended to include cell-drug interactions and circulating tumor cell detection in blood.
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Affiliation(s)
| | | | | | - Masoud Agah
- Author to whom correspondence should be addressed:. Telephone: (540) 231-2653
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10
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Yang D, Zhou Y, Zhou Y, Han J, Ai Y. Biophysical phenotyping of single cells using a differential multiconstriction microfluidic device with self-aligned 3D electrodes. Biosens Bioelectron 2019; 133:16-23. [PMID: 30903937 DOI: 10.1016/j.bios.2019.03.002] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2018] [Revised: 03/01/2019] [Accepted: 03/01/2019] [Indexed: 01/01/2023]
Abstract
Precise measurement of mechanical and electrical properties of single cells can yield useful information on the physiological and pathological state of cells. In this work, we develop a differential multiconstriction microfluidic device with self-aligned 3D electrodes to simultaneously characterize the deformability, electrical impedance and relaxation index of single cells at a high throughput manner (>430 cell/min). Cells are pressure-driven to flow through a series of sequential microfluidic constrictions, during which deformability, electrical impedance and relaxation index of single cells are extracted simultaneously from impedance spectroscopy measurements. Mechanical and electrical phenotyping of untreated, Cytochalasin B treated and N-Ethylmaleimide treated MCF-7 breast cancer cells demonstrate the ability of our system to distinguish different cell populations purely based on these biophysical properties. In addition, we quantify the classification of different cell types using a back propagation neural network. The trained neural network yields the classification accuracy of 87.8% (electrical impedance), 70.1% (deformability), 42.7% (relaxation index) and 93.3% (combination of electrical impedance, deformability and relaxation index) with high sensitivity (93.3%) and specificity (93.3%) for the test group. Furthermore, we have demonstrated the cell classification of a cell mixture using the presented biophysical phenotyping technique with the trained neural network, which is in quantitative agreement with the flow cytometric analysis using fluorescent labels. The developed concurrent electrical and mechanical phenotyping provide great potential for high-throughput and label-free single cell analysis.
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Affiliation(s)
- Dahou Yang
- Pillar of Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore
| | - Ying Zhou
- BioSystems and Micromechanics IRG (BioSyM), Singapore-MIT Alliance for Research and Technology (SMART) Centre, Singapore 138602, Singapore
| | - Yinning Zhou
- Pillar of Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore
| | - Jongyoon Han
- BioSystems and Micromechanics IRG (BioSyM), Singapore-MIT Alliance for Research and Technology (SMART) Centre, Singapore 138602, Singapore; Department of Electrical Engineering and Computer Science, and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ye Ai
- Pillar of Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore.
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11
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Chen Z, Zhang Z, Guo X, Memon K, Panhwar F, Wang M, Cao Y, Zhao G. Sensing Cell Membrane Biophysical Properties for Detection of High Quality Human Oocytes. ACS Sens 2019; 4:192-199. [PMID: 30584760 DOI: 10.1021/acssensors.8b01215] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Oocyte quality plays a crucial role in the early development and implantation of the embryos, and consequently has a profound impact on the accomplishment of assisted reproductive technology (ART). A simple and efficient method for detecting high-quality human oocytes is urgently needed. However, the clinically used morphological method is time-consuming, subjective, and inaccurate. To this end, we propose a practical and effective approach for detecting high-quality oocytes via on-chip measurement of the oocyte membrane permeability. We found that oocytes can be divided into two subpopulations (high-quality versus poor-quality oocytes) according to their membrane permeability differences, and as was further confirmed by subsequent in vitro fertilization (IVF) and development experiments (the blastocyst rates of high-quality and poor-quality oocytes were 60% and 0%, respectively). This approach shows great potentials in improving the success of ART, including both the fertilization and development rates, and thus it may have wide applications in the clinic.
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Affiliation(s)
- Zhongrong Chen
- Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, Anhui, China
| | - Zhiguo Zhang
- Reproductive Medicine Center, Department of Obstetrics and Gynecology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, Anhui, China
- Anhui Province Key Laboratory of Reproductive Health and Genetics, Anhui Provincial Engineering Technology Research Center for Biopreservation and Artificial Organs, Anhui Medical University, Hefei 230022, Anhui, China
| | - Xiaojie Guo
- Hefei Blood Center, Hefei 230031, Anhui, China
| | - Kashan Memon
- Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, Anhui, China
| | - Fazil Panhwar
- Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, Anhui, China
| | - Meng Wang
- Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, Anhui, China
| | - Yunxia Cao
- Reproductive Medicine Center, Department of Obstetrics and Gynecology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, Anhui, China
- Anhui Province Key Laboratory of Reproductive Health and Genetics, Anhui Provincial Engineering Technology Research Center for Biopreservation and Artificial Organs, Anhui Medical University, Hefei 230022, Anhui, China
| | - Gang Zhao
- Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, Anhui, China
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Boosting support vector machines for cancer discrimination tasks. Comput Biol Med 2018; 101:236-249. [DOI: 10.1016/j.compbiomed.2018.08.006] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Revised: 07/31/2018] [Accepted: 08/04/2018] [Indexed: 01/17/2023]
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