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Li SS, Xue CD, Li YJ, Chen XM, Zhao Y, Qin KR. Microfluidic characterization of single-cell biophysical properties and the applications in cancer diagnosis. Electrophoresis 2024; 45:1212-1232. [PMID: 37909658 DOI: 10.1002/elps.202300177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 09/25/2023] [Accepted: 10/16/2023] [Indexed: 11/03/2023]
Abstract
Single-cell biophysical properties play a crucial role in regulating cellular physiological states and functions, demonstrating significant potential in the fields of life sciences and clinical diagnostics. Therefore, over the last few decades, researchers have developed various detection tools to explore the relationship between the biophysical changes of biological cells and human diseases. With the rapid advancement of modern microfabrication technology, microfluidic devices have quickly emerged as a promising platform for single-cell analysis offering advantages including high-throughput, exceptional precision, and ease of manipulation. Consequently, this paper provides an overview of the recent advances in microfluidic analysis and detection systems for single-cell biophysical properties and their applications in the field of cancer. The working principles and latest research progress of single-cell biophysical property detection are first analyzed, highlighting the significance of electrical and mechanical properties. The development of data acquisition and processing methods for real-time, high-throughput, and practical applications are then discussed. Furthermore, the differences in biophysical properties between tumor and normal cells are outlined, illustrating the potential for utilizing single-cell biophysical properties for tumor cell identification, classification, and drug response assessment. Lastly, we summarize the limitations of existing microfluidic analysis and detection systems in single-cell biophysical properties, while also pointing out the prospects and future directions of their applications in cancer diagnosis and treatment.
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Affiliation(s)
- Shan-Shan Li
- School of Mechanical Engineering, Dalian University of Technology, Dalian, Liaoning, P. R. China
| | - Chun-Dong Xue
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, Liaoning, P. R. China
| | - Yong-Jiang Li
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, Liaoning, P. R. China
| | - Xiao-Ming Chen
- School of Optoelectronic Engineering and Instrumentation Science, Dalian University of Technology, Dalian, Liaoning, P. R. China
| | - Yan Zhao
- Department of Stomach Surgery, Cancer Hospital of Dalian University of Technology, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, P. R. China
| | - Kai-Rong Qin
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, Liaoning, P. R. China
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Salahi A, Honrado C, Moore J, Adair S, Bauer TW, Swami NS. Supervised learning on impedance cytometry data for label-free biophysical distinction of pancreatic cancer cells versus their associated fibroblasts under gemcitabine treatment. Biosens Bioelectron 2023; 231:115262. [PMID: 37058962 PMCID: PMC10134450 DOI: 10.1016/j.bios.2023.115262] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 02/14/2023] [Accepted: 03/23/2023] [Indexed: 04/03/2023]
Abstract
Chemotherapy failure in pancreatic cancer patients is widely attributed to cancer cell reprogramming towards drug resistance by cancer associated fibroblasts (CAFs), which are the abundant cell type in the tumor microenvironment. Association of drug resistance to specific cancer cell phenotypes within multicellular tumors can advance isolation protocols for enabling cell-type specific gene expression markers to identify drug resistance. This requires the distinction of drug resistant cancer cells versus CAFs, which is challenging since permeabilization of CAF cells during drug treatment can cause non-specific uptake of cancer cell-specific stains. Cellular biophysical metrics, on the other hand, can provide multiparametric information to assess the gradual alteration of target cancer cells towards drug resistance, but these phenotypes need to be distinguished versus CAFs. Using pancreatic cancer cells and CAFs from a metastatic patient-derived tumor that exhibits cancer cell drug resistance under CAF co-culture, the biophysical metrics from multifrequency single-cell impedance cytometry are utilized for distinction of the subpopulation of viable cancer cells versus CAFs, before and after gemcitabine treatment. This is accomplished through supervised machine learning after training the model using key impedance metrics for cancer cells and CAFs from transwell co-cultures, so that an optimized classifier model can recognize each cell type and predict their respective proportions in multicellular tumor samples, before and after gemcitabine treatment, as validated by their confusion matrix and flow cytometry assays. In this manner, an aggregate of the distinguishing biophysical metrics of viable cancer cells after gemcitabine treatment in co-cultures with CAFs can be used in longitudinal studies, to classify and isolate the drug resistant subpopulation for identifying markers.
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Affiliation(s)
- Armita Salahi
- Electrical & Computer Engineering, University of Virginia, Charlottesville, VA, 22904, USA
| | - Carlos Honrado
- Electrical & Computer Engineering, University of Virginia, Charlottesville, VA, 22904, USA.
| | - John Moore
- Electrical & Computer Engineering, University of Virginia, Charlottesville, VA, 22904, USA
| | - Sara Adair
- Surgery, School of Medicine, University of Virginia, Charlottesville, USA
| | - Todd W Bauer
- Surgery, School of Medicine, University of Virginia, Charlottesville, USA
| | - Nathan S Swami
- Electrical & Computer Engineering, University of Virginia, Charlottesville, VA, 22904, USA; Chemistry, University of Virginia, Charlottesville, USA.
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Torres-Castro K, Jarmoshti J, Xiao L, Rane A, Salahi A, Jin L, Li X, Caselli F, Honrado C, Swami NS. Multichannel impedance cytometry downstream of cell separation by deterministic lateral displacement to quantify macrophage enrichment in heterogeneous samples. ADVANCED MATERIALS TECHNOLOGIES 2023; 8:2201463. [PMID: 37706194 PMCID: PMC10497222 DOI: 10.1002/admt.202201463] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Indexed: 09/15/2023]
Abstract
The integration of on-chip biophysical cytometry downstream of microfluidic enrichment for inline monitoring of phenotypic and separation metrics at single-cell sensitivity can allow for active control of separation and its application to versatile sample sets. We present integration of impedance cytometry downstream of cell separation by deterministic lateral displacement (DLD) for enrichment of activated macrophages from a heterogeneous sample, without the problems of biased sample loss and sample dilution caused by off-chip analysis. This required designs to match cell/particle flow rates from DLD separation into the confined single-cell impedance cytometry stage, the balancing of flow resistances across the separation array width to maintain unidirectionality, and the utilization of co-flowing beads as calibrated internal standards for inline assessment of DLD separation and for impedance data normalization. Using a heterogeneous sample with un-activated and activated macrophages, wherein macrophage polarization during activation causes cell size enlargement, on-chip impedance cytometry is used to validate DLD enrichment of the activated subpopulation at the displaced outlet, based on the multiparametric characteristics of cell size distribution and impedance phase metrics. This hybrid platform can monitor separation of specific subpopulations from cellular samples with wide size distributions, for active operational control and enhanced sample versatility.
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Affiliation(s)
- Karina Torres-Castro
- Electrical Engineering, University of Virginia, Charlottesville, Virginia-22904, USA
| | - Javad Jarmoshti
- Electrical Engineering, University of Virginia, Charlottesville, Virginia-22904, USA
| | - Li Xiao
- Orthopedics, School of Medicine, University of Virginia, Virginia-22904, USA
| | - Aditya Rane
- Chemistry, University of Virginia, Charlottesville, Virginia-22904, USA
| | - Armita Salahi
- Electrical Engineering, University of Virginia, Charlottesville, Virginia-22904, USA
| | - Li Jin
- Orthopedics, School of Medicine, University of Virginia, Virginia-22904, USA
| | - Xudong Li
- Orthopedics, School of Medicine, University of Virginia, Virginia-22904, USA
| | | | - Carlos Honrado
- Electrical Engineering, University of Virginia, Charlottesville, Virginia-22904, USA
| | - Nathan S. Swami
- Electrical Engineering, University of Virginia, Charlottesville, Virginia-22904, USA
- Chemistry, University of Virginia, Charlottesville, Virginia-22904, USA
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Huang C, Jiang Y, Li Y, Zhang H. Droplet Detection and Sorting System in Microfluidics: A Review. MICROMACHINES 2022; 14:mi14010103. [PMID: 36677164 PMCID: PMC9867185 DOI: 10.3390/mi14010103] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/23/2022] [Accepted: 12/26/2022] [Indexed: 05/26/2023]
Abstract
Since being invented, droplet microfluidic technologies have been proven to be perfect tools for high-throughput chemical and biological functional screening applications, and they have been heavily studied and improved through the past two decades. Each droplet can be used as one single bioreactor to compartmentalize a big material or biological population, so millions of droplets can be individually screened based on demand, while the sorting function could extract the droplets of interest to a separate pool from the main droplet library. In this paper, we reviewed droplet detection and active sorting methods that are currently still being widely used for high-through screening applications in microfluidic systems, including the latest updates regarding each technology. We analyze and summarize the merits and drawbacks of each presented technology and conclude, with our perspectives, on future direction of development.
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Affiliation(s)
- Can Huang
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77842, USA
| | - Yuqian Jiang
- State Key Laboratory of Food Nutrition and Safety, College of Food Science and Engineering, Tianjin University of Science and Technology, Tianjin 300457, China
| | - Yuwen Li
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77842, USA
| | - Han Zhang
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77842, USA
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