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Kim H, Kim S, Lim H, Chung AJ. Expanding CAR-T cell immunotherapy horizons through microfluidics. LAB ON A CHIP 2024; 24:1088-1120. [PMID: 38174732 DOI: 10.1039/d3lc00622k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Chimeric antigen receptor (CAR)-T cell therapies have revolutionized cancer treatment, particularly in hematological malignancies. However, their application to solid tumors is limited, and they face challenges in safety, scalability, and cost. To enhance current CAR-T cell therapies, the integration of microfluidic technologies, harnessing their inherent advantages, such as reduced sample consumption, simplicity in operation, cost-effectiveness, automation, and high scalability, has emerged as a powerful solution. This review provides a comprehensive overview of the step-by-step manufacturing process of CAR-T cells, identifies existing difficulties at each production stage, and discusses the successful implementation of microfluidics and related technologies in addressing these challenges. Furthermore, this review investigates the potential of microfluidics-based methodologies in advancing cell-based therapy across various applications, including solid tumors, next-generation CAR constructs, T-cell receptors, and the development of allogeneic "off-the-shelf" CAR products.
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Affiliation(s)
- Hyelee Kim
- Department of Bioengineering, Korea University, 02841 Seoul, Republic of Korea
- Interdisciplinary Program in Precision Public Health (PPH), Korea University, 02841 Seoul, Republic of Korea.
| | - Suyeon Kim
- Department of Bioengineering, Korea University, 02841 Seoul, Republic of Korea
- Interdisciplinary Program in Precision Public Health (PPH), Korea University, 02841 Seoul, Republic of Korea.
| | - Hyunjung Lim
- Interdisciplinary Program in Precision Public Health (PPH), Korea University, 02841 Seoul, Republic of Korea.
| | - Aram J Chung
- Department of Bioengineering, Korea University, 02841 Seoul, Republic of Korea
- Interdisciplinary Program in Precision Public Health (PPH), Korea University, 02841 Seoul, Republic of Korea.
- School of Biomedical Engineering, Korea University, 02841 Seoul, Republic of Korea.
- MxT Biotech, 04785 Seoul, Republic of Korea
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Wu T, Tan JHL, Sin W, Luah YH, Tan SY, Goh M, Birnbaum ME, Chen Q, Cheow LF. Cell Granularity Reflects Immune Cell Function and Enables Selection of Lymphocytes with Superior Attributes for Immunotherapy. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2302175. [PMID: 37544893 PMCID: PMC10558660 DOI: 10.1002/advs.202302175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 07/20/2023] [Indexed: 08/08/2023]
Abstract
In keeping with the rule of "form follows function", morphological aspects of a cell can reflect its role. Here, it is shown that the cellular granularity of a lymphocyte, represented by its intrinsic side scatter (SSC), is a potent indicator of its cell state and function. The granularity of a lymphocyte increases from naïve to terminal effector state. High-throughput cell-sorting yields a SSChigh population that can mediate immediate effector functions, and a highly prolific SSClow population that can give rise to the replenishment of the memory pool. CAR-T cells derived from the younger SSClow population possess desirable attributes for immunotherapy, manifested by increased naïve-like cells and stem cell memory (TSCM )-like cells together with a balanced CD4/CD8 ratio, as well as enhanced target-killing in vitro and in vivo. Altogether, lymphocyte segregation based on biophysical properties is an effective approach for label-free selection of cells that share collective functions and can have important applications for cell-based immunotherapies.
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Affiliation(s)
- Tongjin Wu
- Department of Biomedical EngineeringFaculty of EngineeringNational University of SingaporeSingapore117583Singapore
- Institute for Health Innovation and TechnologyNational University of SingaporeSingapore117599Singapore
| | - Joel Heng Loong Tan
- Institute of Molecular and Cell Biology (IMCB)Agency for ScienceTechnology and Research (A*STAR)Singapore138673Singapore
| | - Wei‐Xiang Sin
- Critical Analytics for Manufacturing of Personalized MedicineSingapore‐MIT Alliance for Research and TechnologySingapore138602Singapore
| | - Yen Hoon Luah
- Department of Biomedical EngineeringFaculty of EngineeringNational University of SingaporeSingapore117583Singapore
- Institute for Health Innovation and TechnologyNational University of SingaporeSingapore117599Singapore
- Critical Analytics for Manufacturing of Personalized MedicineSingapore‐MIT Alliance for Research and TechnologySingapore138602Singapore
| | - Sue Yee Tan
- Institute of Molecular and Cell Biology (IMCB)Agency for ScienceTechnology and Research (A*STAR)Singapore138673Singapore
| | - Myra Goh
- Institute of Molecular and Cell Biology (IMCB)Agency for ScienceTechnology and Research (A*STAR)Singapore138673Singapore
| | - Michael E. Birnbaum
- Critical Analytics for Manufacturing of Personalized MedicineSingapore‐MIT Alliance for Research and TechnologySingapore138602Singapore
- Department of Biological EngineeringMassachusetts Institute of TechnologyCambridgeMA02139USA
| | - Qingfeng Chen
- Institute of Molecular and Cell Biology (IMCB)Agency for ScienceTechnology and Research (A*STAR)Singapore138673Singapore
| | - Lih Feng Cheow
- Department of Biomedical EngineeringFaculty of EngineeringNational University of SingaporeSingapore117583Singapore
- Institute for Health Innovation and TechnologyNational University of SingaporeSingapore117599Singapore
- Critical Analytics for Manufacturing of Personalized MedicineSingapore‐MIT Alliance for Research and TechnologySingapore138602Singapore
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Cioffi G, Dannhauser D, Rossi D, Netti PA, Causa F. Unknown cell class distinction via neural network based scattering snapshot recognition. BIOMEDICAL OPTICS EXPRESS 2023; 14:5060-5074. [PMID: 37854558 PMCID: PMC10581789 DOI: 10.1364/boe.492028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 07/25/2023] [Accepted: 07/31/2023] [Indexed: 10/20/2023]
Abstract
Neural network-based image classification is widely used in life science applications. However, it is essential to extrapolate a correct classification method for unknown images, where no prior knowledge can be utilised. Under a closed set assumption, unknown images will be inevitably misclassified, but this can be genuinely overcome choosing an open-set classification approach, which first generates an in-distribution of identified images to successively discriminate out-of-distribution images. The testing of such image classification for single cell applications in life science scenarios has yet to be done but could broaden our expertise in quantifying the influence of prediction uncertainty in deep learning. In this framework, we implemented the open-set concept on scattering snapshots of living cells to distinguish between unknown and known cell classes, targeting four different known monoblast cell classes and a single tumoral unknown monoblast cell line. We also investigated the influence on experimental sample errors and optimised neural network hyperparameters to obtain a high unknown cell class detection accuracy. We discovered that our open-set approach exhibits robustness against sample noise, a crucial aspect for its application in life science. Moreover, the presented open-set based neural network reveals measurement uncertainty out of the cell prediction, which can be applied to a wide range of single cell classifications.
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Affiliation(s)
- Gaia Cioffi
- Interdisciplinary Research Centre on Biomaterials (CRIB) and Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università degli Studi di Napoli “Federico II”, Piazzale Tecchio 80, 80125 Naples, Italy
| | - David Dannhauser
- Interdisciplinary Research Centre on Biomaterials (CRIB) and Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università degli Studi di Napoli “Federico II”, Piazzale Tecchio 80, 80125 Naples, Italy
| | - Domenico Rossi
- Center for Advanced Biomaterials for Healthcare@CRIB, Istituto Italiano di Tecnologia, Largo Barsanti e Matteucci 53, 80125 Naples, Italy
| | - Paolo A. Netti
- Interdisciplinary Research Centre on Biomaterials (CRIB) and Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università degli Studi di Napoli “Federico II”, Piazzale Tecchio 80, 80125 Naples, Italy
- Center for Advanced Biomaterials for Healthcare@CRIB, Istituto Italiano di Tecnologia, Largo Barsanti e Matteucci 53, 80125 Naples, Italy
| | - Filippo Causa
- Interdisciplinary Research Centre on Biomaterials (CRIB) and Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università degli Studi di Napoli “Federico II”, Piazzale Tecchio 80, 80125 Naples, Italy
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Dudaie M, Barnea I, Nissim N, Shaked NT. On-chip label-free cell classification based directly on off-axis holograms and spatial-frequency-invariant deep learning. Sci Rep 2023; 13:12370. [PMID: 37524884 PMCID: PMC10390541 DOI: 10.1038/s41598-023-38160-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 07/04/2023] [Indexed: 08/02/2023] Open
Abstract
We present a rapid label-free imaging flow cytometry and cell classification approach based directly on raw digital holograms. Off-axis holography enables real-time acquisition of cells during rapid flow. However, classification of the cells typically requires reconstruction of their quantitative phase profiles, which is time-consuming. Here, we present a new approach for label-free classification of individual cells based directly on the raw off-axis holographic images, each of which contains the complete complex wavefront (amplitude and quantitative phase profiles) of the cell. To obtain this, we built a convolutional neural network, which is invariant to the spatial frequencies and directions of the interference fringes of the off-axis holograms. We demonstrate the effectiveness of this approach using four types of cancer cells. This approach has the potential to significantly improve both speed and robustness of imaging flow cytometry, enabling real-time label-free classification of individual cells.
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Affiliation(s)
- Matan Dudaie
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, 69978, Tel Aviv, Israel
| | - Itay Barnea
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, 69978, Tel Aviv, Israel
| | - Noga Nissim
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, 69978, Tel Aviv, Israel
| | - Natan T Shaked
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, 69978, Tel Aviv, Israel.
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Su Z, Chen Z, Ma K, Chen H, Ho JWK. Molecular determinants of intrinsic cellular stiffness in health and disease. Biophys Rev 2022; 14:1197-1209. [PMID: 36345276 PMCID: PMC9636357 DOI: 10.1007/s12551-022-00997-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 09/11/2022] [Indexed: 10/14/2022] Open
Abstract
In recent years, the role of intrinsic biophysical features, especially cellular stiffness, in diverse cellular and disease processes is being increasingly recognized. New high throughput techniques for the quantification of cellular stiffness facilitate the study of their roles in health and diseases. In this review, we summarized recent discovery about how cellular stiffness is involved in cell stemness, tumorigenesis, and blood diseases. In addition, we review the molecular mechanisms underlying the gene regulation of cellular stiffness in health and disease progression. Finally, we discussed the current understanding on how the cytoskeleton structure and the regulation of these genes contribute to cellular stiffness, highlighting where the field of cellular stiffness is headed.
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Affiliation(s)
- Zezhuo Su
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, SAR China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong, SAR China
| | - Zhenlin Chen
- Department of Biomedical Engineering, College of Engineering, City University of Hong Kong, Kowloon, Hong Kong, SAR China
| | - Kun Ma
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, SAR China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong, SAR China
| | - Huaying Chen
- School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, Shenzhen, 518055 China
| | - Joshua W. K. Ho
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, SAR China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong, SAR China
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Dannhauser D, Rossi D, De Gregorio V, Netti PA, Terrazzano G, Causa F. Single cell classification of macrophage subtypes by label-free cell signatures and machine learning. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220270. [PMID: 36177192 PMCID: PMC9515641 DOI: 10.1098/rsos.220270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 09/08/2022] [Indexed: 06/16/2023]
Abstract
Pro-inflammatory (M1) and anti-inflammatory (M2) macrophage phenotypes play a fundamental role in the immune response. The interplay and consequently the classification between these two functional subtypes is significant for many therapeutic applications. Albeit, a fast classification of macrophage phenotypes is challenging. For instance, image-based classification systems need cell staining and coloration, which is usually time- and cost-consuming, such as multiple cell surface markers, transcription factors and cytokine profiles are needed. A simple alternative would be to identify such cell types by using single-cell, label-free and high throughput light scattering pattern analyses combined with a straightforward machine learning-based classification. Here, we compared different machine learning algorithms to classify distinct macrophage phenotypes based on their optical signature obtained from an ad hoc developed wide-angle static light scattering apparatus. As the main result, we were able to identify unpolarized macrophages from M1- and M2-polarized phenotypes and distinguished them from naive monocytes with an average accuracy above 85%. Therefore, we suggest that optical single-cell signatures within a lab-on-a-chip approach along with machine learning could be used as a fast, affordable, non-invasive macrophage phenotyping tool to supersede resource-intensive cell labelling.
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Affiliation(s)
- David Dannhauser
- Interdisciplinary Research Centre on Biomaterials (CRIB) and Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università degli Studi di Napoli ‘Federico II’, Piazzale Tecchio 80, Naples 80125, Italy
| | - Domenico Rossi
- Center for Advanced Biomaterials for Healthcare@CRIB, Istituto Italiano di Tecnologia, Largo Barsanti e Matteucci 53, Naples 80125, Italy
| | - Vincenza De Gregorio
- Interdisciplinary Research Centre on Biomaterials (CRIB) and Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università degli Studi di Napoli ‘Federico II’, Piazzale Tecchio 80, Naples 80125, Italy
- Dipartimento di Biologia, Università degli Studi di Napoli ‘Federico II’, Complesso Universitario di Monte S Angelo, Naples, Italy
| | - Paolo Antonio Netti
- Interdisciplinary Research Centre on Biomaterials (CRIB) and Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università degli Studi di Napoli ‘Federico II’, Piazzale Tecchio 80, Naples 80125, Italy
- Center for Advanced Biomaterials for Healthcare@CRIB, Istituto Italiano di Tecnologia, Largo Barsanti e Matteucci 53, Naples 80125, Italy
| | - Giuseppe Terrazzano
- Dipartimento di Scienze (DiS), Università della Basilicata, Via dell'Ateneo Lucano 10, Potenza 85100, Italy
| | - Filippo Causa
- Interdisciplinary Research Centre on Biomaterials (CRIB) and Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università degli Studi di Napoli ‘Federico II’, Piazzale Tecchio 80, Naples 80125, Italy
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Szittner Z, Péter B, Kurunczi S, Székács I, Horváth R. Functional blood cell analysis by label-free biosensors and single-cell technologies. Adv Colloid Interface Sci 2022; 308:102727. [DOI: 10.1016/j.cis.2022.102727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/25/2022] [Accepted: 06/27/2022] [Indexed: 11/01/2022]
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