1
|
Liu L, Islam MZ, Liu X, Gupta M, Rozmus W, Mandal M, Tsui YY. Multi-wavelength multi-direction laser light scattering for cell characterization using machine learning-based methods. Cytometry A 2023; 103:796-806. [PMID: 37309309 DOI: 10.1002/cyto.a.24771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 05/18/2023] [Accepted: 06/09/2023] [Indexed: 06/14/2023]
Abstract
Cell identification and analysis play a crucial role in many biology- and health-related applications. The internal and surface structures of a cell are complex and many of the features are sub-micron in scale. Well-resolved images of these features cannot be obtained using optical microscopy. Previous studies have reported that the single-cell angular laser-light scattering patterns (ALSP) can be used for label-free cell identification and analysis. The ALSP can be affected by cell properties and the wavelength of the probing laser. Two cell properties, cell surface roughness and the number of mitochondria, are investigated in this study. The effects of probing laser wavelengths (blue, green, and red) and the directions of scattered light collection (forward, side, and backward) are studied to determine the optimum conditions for distinguishing the two cell properties. Machine learning (ML) analysis has been applied to ALSP obtained from numerical simulations. The results of ML analysis show that the backward scattering is the best direction for characterizing the surface roughness, while the forward scattering is the best direction for differentiating the number of mitochondria. The laser light having red or green wavelength is found to perform better than that having the blue wavelength in differentiating the surface roughness and the number of mitochondria. This study provides important insights into the effects of probing laser wavelength on gaining information about cells from their ALSP.
Collapse
Affiliation(s)
- Lina Liu
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
| | - Md Zahurul Islam
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
- Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - Xiaoxuan Liu
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
| | - Manisha Gupta
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
| | - Wojciech Rozmus
- Department of Physics, University of Alberta, Edmonton, Canada
| | - Mrinal Mandal
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
| | - Ying Yin Tsui
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Běhal J, Pirone D, Sirico D, Bianco V, Mugnano M, Del Giudice D, Cavina B, Kurelac I, Memmolo P, Miccio L, Ferraro P. On monocytes and lymphocytes biolens clustering by in flow holographic microscopy. Cytometry A 2023; 103:251-259. [PMID: 36028475 DOI: 10.1002/cyto.a.24685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 07/29/2022] [Accepted: 08/23/2022] [Indexed: 11/09/2022]
Abstract
Live cells act as biological lenses and can be employed as real-world optical components in bio-hybrid systems. Imaging at nanoscale, optical tweezers, lithography and also photonic waveguiding are some of the already proven functionalities, boosted by the advantage that cells are fully biocompatible for intra-body applications. So far, various cell types have been studied for this purpose, such as red blood cells, bacterial cells, stem cells and yeast cells. White Blood Cells (WBCs) play a very important role in the regulation of the human body activities and are usually monitored for assessing its health. WBCs can be considered bio-lenses but, to the best of our knowledge, characterization of their optical properties have not been investigated yet. Here, we report for the first time an accurate study of two model classes of WBCs (i.e., monocytes and lymphocytes) by means of a digital holographic microscope coupled with a microfluidic system, assuming WBCs bio-lens characteristics. Thus, quantitative phase maps for many WBCs have been retrieved in flow-cytometry (FC) by achieving a significant statistical analysis to prove the enhancement in differentiation among sphere-like bio-lenses according to their sizes (i.e., diameter d) exploiting intensity parameters of the modulated light in proximity of the cell optical axis. We show that the measure of the low intensity area (S: I z < I th z ) in a fixed plane, is a feasible parameter for cell clustering, while achieving robustness against experimental misalignments and allowing to adjust the measurement sensitivity in post-processing. 2D scatterplots of the identified parameters (d-S) show better differentiation respect to the 1D case. The results show that the optical focusing properties of WBCs allow the clustering of the two populations by means of a mere morphological analysis, thus leading to the new concept of cell-optical-fingerprint avoiding fluorescent dyes. This perspective can open new routes in biomedical sciences, such as the chance to find optical-biomarkers at single cell level for label-free diagnosis.
Collapse
Affiliation(s)
- Jaromír Běhal
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
- Department of Optics, Palacký University, Olomouc, Czech Republic
| | - Daniele Pirone
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
- DIETI, Department of Electrical Engineering and Information Technologies, University of Naples "Federico II", Naples, Italy
| | - Daniele Sirico
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
- Department of Chemical, Materials and Production Engineering of the University of Naples Federico II, Naples, Italy
| | - Vittorio Bianco
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
| | - Martina Mugnano
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
| | - Danila Del Giudice
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
- Department of Mathematics and Physics, University of Campania "L. Vanvitelli", Caserta, Italy
| | - Beatrice Cavina
- Department of Medical and Surgical Sciences (DIMEC), Centro di Studio e Ricerca sulle Neoplasie (CSR) Ginecologiche, Alma Mater Studiorum-University of Bologna, Bologna, Italy
- Centre for Applied Biomedical Research (CRBA), University of Bologna, Bologna, Italy
| | - Ivana Kurelac
- Department of Medical and Surgical Sciences (DIMEC), Centro di Studio e Ricerca sulle Neoplasie (CSR) Ginecologiche, Alma Mater Studiorum-University of Bologna, Bologna, Italy
- Centre for Applied Biomedical Research (CRBA), University of Bologna, Bologna, Italy
| | - Pasquale Memmolo
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
| | - Lisa Miccio
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
| | - Pietro Ferraro
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Naples, Italy
| |
Collapse
|
4
|
Cennamo N, Arcadio F, Capasso F, Maniglio D, Zeni L, Bossi AM. Non-Specific Responsive Nanogels and Plasmonics to Design MathMaterial Sensing Interfaces: The Case of a Solvent Sensor. SENSORS (BASEL, SWITZERLAND) 2022; 22:s222410006. [PMID: 36560375 PMCID: PMC9787685 DOI: 10.3390/s222410006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 12/02/2022] [Accepted: 12/14/2022] [Indexed: 06/01/2023]
Abstract
The combination of non-specific deformable nanogels and plasmonic optical probes provides an innovative solution for specific sensing using a generalistic recognition layer. Soft polyacrylamide nanogels that lack specific selectivity but are characterized by responsive behavior, i.e., shrinking and swelling dependent on the surrounding environment, were grafted to a gold plasmonic D-shaped plastic optical fiber (POF) probe. The nanogel-POF cyclically challenged with water or alcoholic solutions optically reported the reversible solvent-to-phase transitions of the nanomaterial, embodying a primary optical switch. Additionally, the non-specific nanogel-POF interface exhibited more degrees of freedom through which specific sensing was enabled. The real-time monitoring of the refractive index variations due to the time-related volume-to-phase transition effects of the nanogels enabled us to determine the environment's characteristics and broadly classify solvents. Hence the nanogel-POF interface was a descriptor of mathematical functions for substance identification and classification processes. These results epitomize the concept of responsive non-specific nanomaterials to perform a multiparametric description of the environment, offering a specific set of features for the processing stage and particularly suitable for machine and deep learning. Thus, soft MathMaterial interfaces provide the ground to devise devices suitable for the next generation of smart intelligent sensing processes.
Collapse
Affiliation(s)
- Nunzio Cennamo
- Department of Engineering, University of Campania Luigi Vanvitelli, Via Roma 29, 81031 Aversa, Italy
| | - Francesco Arcadio
- Department of Engineering, University of Campania Luigi Vanvitelli, Via Roma 29, 81031 Aversa, Italy
| | - Fiore Capasso
- Department of Engineering, University of Campania Luigi Vanvitelli, Via Roma 29, 81031 Aversa, Italy
| | - Devid Maniglio
- Department of Industrial Engineering, BIOtech Research Center, University of Trento, Via delle Regole 101, Mattarello, 38123 Trento, Italy
| | - Luigi Zeni
- Department of Engineering, University of Campania Luigi Vanvitelli, Via Roma 29, 81031 Aversa, Italy
| | - Alessandra Maria Bossi
- Department of Biotechnology, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
Běhal J, Borrelli F, Mugnano M, Bianco V, Capozzoli A, Curcio C, Liseno A, Miccio L, Memmolo P, Ferraro P. Developing a Reliable Holographic Flow Cyto-Tomography Apparatus by Optimizing the Experimental Layout and Computational Processing. Cells 2022; 11:2591. [PMID: 36010667 PMCID: PMC9406712 DOI: 10.3390/cells11162591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 08/17/2022] [Accepted: 08/18/2022] [Indexed: 11/16/2022] Open
Abstract
Digital Holographic Tomography (DHT) has recently been established as a means of retrieving the 3D refractive index mapping of single cells. To make DHT a viable system, it is necessary to develop a reliable and robust holographic apparatus in order that such technology can be utilized outside of specialized optics laboratories and operated in the in-flow modality. In this paper, we propose a quasi-common-path lateral-shearing holographic optical set-up to be used, for the first time, for DHT in a flow-cytometer modality. The proposed solution is able to withstand environmental vibrations that can severely affect the interference process. Furthermore, we have scaled down the system while ensuring that a full 360° rotation of the cells occurs in the field-of-view, in order to retrieve 3D phase-contrast tomograms of single cells flowing along a microfluidic channel. This was achieved by setting the camera sensor at 45° with respect to the microfluidic direction. Additional optimizations were made to the computational elements to ensure the reliable retrieval of 3D refractive index distributions by demonstrating an effective method of tomographic reconstruction, based on high-order total variation. The results were first demonstrated using realistic 3D numerical phantom cells to assess the performance of the proposed high-order total variation method in comparison with the gold-standard algorithm for tomographic reconstructions: namely, filtered back projection. Then, the proposed DHT system and the processing pipeline were experimentally validated for monocytes and mouse embryonic fibroblast NIH-3T3 cells lines. Moreover, the repeatability of these tomographic measurements was also investigated by recording the same cell multiple times and quantifying the ability to provide reliable and comparable tomographic reconstructions, as confirmed by a correlation coefficient greater than 95%. The reported results represent various steps forward in several key aspects of in-flow DHT, thus paving the way for its use in real-world applications.
Collapse
Affiliation(s)
- Jaromír Běhal
- Institute of Applied Sciences and Intelligent Systems, Italian National Research Council (CNR-ISASI), 80078 Pozzuoli, Italy
| | - Francesca Borrelli
- Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Università di Napoli Federico II, 80125 Napoli, Italy
| | - Martina Mugnano
- Institute of Applied Sciences and Intelligent Systems, Italian National Research Council (CNR-ISASI), 80078 Pozzuoli, Italy
| | - Vittorio Bianco
- Institute of Applied Sciences and Intelligent Systems, Italian National Research Council (CNR-ISASI), 80078 Pozzuoli, Italy
| | - Amedeo Capozzoli
- Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Università di Napoli Federico II, 80125 Napoli, Italy
| | - Claudio Curcio
- Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Università di Napoli Federico II, 80125 Napoli, Italy
| | - Angelo Liseno
- Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Università di Napoli Federico II, 80125 Napoli, Italy
| | - Lisa Miccio
- Institute of Applied Sciences and Intelligent Systems, Italian National Research Council (CNR-ISASI), 80078 Pozzuoli, Italy
| | - Pasquale Memmolo
- Institute of Applied Sciences and Intelligent Systems, Italian National Research Council (CNR-ISASI), 80078 Pozzuoli, Italy
| | - Pietro Ferraro
- Institute of Applied Sciences and Intelligent Systems, Italian National Research Council (CNR-ISASI), 80078 Pozzuoli, Italy
| |
Collapse
|
7
|
Dannhauser D, Rossi D, Palatucci AT, Rubino V, Carriero F, Ruggiero G, Ripaldi M, Toriello M, Maisto G, Netti PA, Terrazzano G, Causa F. Non-invasive and label-free identification of human natural killer cell subclasses by biophysical single-cell features in microfluidic flow. LAB ON A CHIP 2021; 21:4144-4154. [PMID: 34515262 DOI: 10.1039/d1lc00651g] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Natural killer (NK) cells are indicated as favorite candidates for innovative therapeutic treatment and are divided into two subclasses: immature regulatory NK CD56bright and mature cytotoxic NK CD56dim. Therefore, the ability to discriminate CD56dim from CD56bright could be very useful because of their higher cytotoxicity. Nowadays, NK cell classification is routinely performed by cytometric analysis based on surface receptor expression. Here, we present an in-flow, label-free and non-invasive biophysical analysis of NK cells through a combination of light scattering and machine learning (ML) for NK cell subclass classification. In this respect, to identify relevant biophysical cell features, we stimulated NK cells with interleukine-15 inducing a subclass transition from CD56bright to CD56dim. We trained our ML algorithm with sorted NK cell subclasses (≥86% accuracy). Next, we applied our NK cell classification algorithm to cells stimulated over time, to investigate the transition of CD56bright to CD56dim and their biophysical feature changes. Finally, we tested our approach on several proband samples, highlighting the potential of our measurement approach. We show a label-free way for the robust identification of NK cell subclasses based on biophysical features, which can be applied in both cell biology and cell therapy.
Collapse
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, 80125 Naples, Italy.
| | - Domenico Rossi
- Center for Advanced Biomaterials for Healthcare@CRIB, Istituto Italiano di Tecnologia, Largo Barsanti e Matteucci 53, 80125 Naples, Italy
| | - Anna Teresa Palatucci
- Dipartimento di Scienze (DiS), Università della Basilicata, Via dell'Ateneo Lucano 10, 85100 Potenza, Italy
| | - Valentina Rubino
- Dipartimento di Scienze Mediche Traslazionali, Università degli Studi di Napoli "Federico II", Naples, Italy
| | - Flavia Carriero
- Dipartimento di Scienze Mediche Traslazionali, Università degli Studi di Napoli "Federico II", Naples, Italy
| | - Giuseppina Ruggiero
- Dipartimento di Scienze Mediche Traslazionali, Università degli Studi di Napoli "Federico II", Naples, Italy
| | - Mimmo Ripaldi
- Dipartimento Oncologia AORN Santobono Pausilipon Hospital, Via Posillipo, 226, 80123, Naples, Italy
| | - Mario Toriello
- Dipartimento Oncologia AORN Santobono Pausilipon Hospital, Via Posillipo, 226, 80123, Naples, Italy
| | - Giovanna Maisto
- Dipartimento Oncologia AORN Santobono Pausilipon Hospital, Via Posillipo, 226, 80123, 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, 80125 Naples, Italy.
- Center for Advanced Biomaterials for Healthcare@CRIB, Istituto Italiano di Tecnologia, Largo Barsanti e Matteucci 53, 80125 Naples, Italy
| | - Giuseppe Terrazzano
- Dipartimento di Scienze (DiS), Università della Basilicata, Via dell'Ateneo Lucano 10, 85100 Potenza, 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.
| |
Collapse
|
8
|
Rossi D, Dannhauser D, Nastri BM, Ballini A, Fiorelli A, Santini M, Netti PA, Scacco S, Marino MM, Causa F, Boccellino M, Di Domenico M. New Trends in Precision Medicine: A Pilot Study of Pure Light Scattering Analysis as a Useful Tool for Non-Small Cell Lung Cancer (NSCLC) Diagnosis. J Pers Med 2021; 11:jpm11101023. [PMID: 34683164 PMCID: PMC8537600 DOI: 10.3390/jpm11101023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 10/10/2021] [Accepted: 10/11/2021] [Indexed: 11/16/2022] Open
Abstract
Simple Summary A reliable method for a fast diagnosis of non-small cell lung cancer (NSCLC) would greatly help in improving therapeutic success in personalized medicine approaches. Thus, in the present study, a new idea was proposed: a morphological single-cell analysis approach combined with a microfluidic device for liquid biopsy. The investigation of the NSCLC sample at different culturing times created the possibility of understanding the evolution of different cell types and their morphological changes, making the Circulating Tumour Cells (CTC) predominance against all other cell classes visible. Abstract Background: To date, in personalized medicine approaches, single-cell analyses such as circulating tumour cells (CTC) are able to reveal small structural cell modifications, and therefore can retrieve several biophysical cell properties, such as the cell dimension, the dimensional relationship between the nucleus and the cytoplasm and the optical density of cellular sub-compartments. On this basis, we present in this study a new morphological measurement approach for the detection of vital CTC from pleural washing in individual non-small cell lung cancer (NSCLC) patients. Materials and methods: After a diagnosis of pulmonary malignancy, pleural washing was collected from nine NSCLC patients. The collected samples were processed with a density gradient separation process. Light scattering analysis was performed on a single cell. The results of this analysis were used to obtain the cell’s biophysical pattern and, later on, as basis for Machine Learning (ML) on unknown samples. Results: Morphological single-cell analysis followed by ML show a predictive picture for an NSCLC patient, screening that it is possible to distinguish CTC from other cells. Moreover, we find that the proposed measurement approach was fast, reliable, label-free, identifying and count CTC in a biological fluid. Conclusions: Our findings demonstrate that CTC Biophysical Profile by Pure Light Scattering in NSCLC could be used as a promising diagnostic candidate in NSCLC patients.
Collapse
Affiliation(s)
- Domenico Rossi
- Center for Advanced Biomaterials for Healthcare@CRIB, Istituto Italiano di Tecnologia (IIT), Largo Barsanti e Matteucci 53, 80125 Naples, Italy; (D.R.); (P.A.N.)
| | - David Dannhauser
- Interdisciplinary Research Centre on Biomaterials (CRIB), Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università degli Studi di Napoli “Federico II”, Piazzale Tecchio 80, 80125 Naples, Italy; (D.D.); (F.C.)
| | - Bianca Maria Nastri
- Department of Experimental Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy;
| | - Andrea Ballini
- School of Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy
- Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (M.M.M.); (M.B.); (M.D.D.)
- Correspondence: (A.B.); (S.S.)
| | - Alfonso Fiorelli
- Department of Translational Medical and Surgical Science, Thoracic Surgery Unit, University of Campania “Luigi Vanvitelli”, 80131 Naples, Italy; (A.F.); (M.S.)
| | - Mario Santini
- Department of Translational Medical and Surgical Science, Thoracic Surgery Unit, University of Campania “Luigi Vanvitelli”, 80131 Naples, Italy; (A.F.); (M.S.)
| | - Paolo Antonio Netti
- Center for Advanced Biomaterials for Healthcare@CRIB, Istituto Italiano di Tecnologia (IIT), Largo Barsanti e Matteucci 53, 80125 Naples, Italy; (D.R.); (P.A.N.)
- Interdisciplinary Research Centre on Biomaterials (CRIB), Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università degli Studi di Napoli “Federico II”, Piazzale Tecchio 80, 80125 Naples, Italy; (D.D.); (F.C.)
| | - Salvatore Scacco
- Department of Basic Medical Sciences, Neurosciences and Sense Organs, University of Bari “Aldo Moro”, 70124 Bari, Italy
- Correspondence: (A.B.); (S.S.)
| | - Maria Michela Marino
- Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (M.M.M.); (M.B.); (M.D.D.)
| | - Filippo Causa
- Interdisciplinary Research Centre on Biomaterials (CRIB), Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università degli Studi di Napoli “Federico II”, Piazzale Tecchio 80, 80125 Naples, Italy; (D.D.); (F.C.)
| | - Mariarosaria Boccellino
- Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (M.M.M.); (M.B.); (M.D.D.)
| | - Marina Di Domenico
- Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (M.M.M.); (M.B.); (M.D.D.)
- Department of Biology, College of Science and Technology, Temple University, Philadelphia, PA 19121, USA
| |
Collapse
|
9
|
Pérez-Rodríguez S, Huang SA, Borau C, García-Aznar JM, Polacheck WJ. Microfluidic model of monocyte extravasation reveals the role of hemodynamics and subendothelial matrix mechanics in regulating endothelial integrity. BIOMICROFLUIDICS 2021; 15:054102. [PMID: 34548891 PMCID: PMC8443302 DOI: 10.1063/5.0061997] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 08/26/2021] [Indexed: 05/08/2023]
Abstract
Extravasation of circulating cells is an essential process that governs tissue inflammation and the body's response to pathogenic infection. To initiate anti-inflammatory and phagocytic functions within tissues, immune cells must cross the vascular endothelial barrier from the vessel lumen to the subluminal extracellular matrix. In this work, we present a microfluidic approach that enables the recreation of a three-dimensional, perfused endothelial vessel formed by human endothelial cells embedded within a collagen-rich matrix. Monocytes are introduced into the vessel perfusate, and we investigate the role of luminal flow and collagen concentration on extravasation. In vessels conditioned with the flow, increased monocyte adhesion to the vascular wall was observed, though fewer monocytes extravasated to the collagen hydrogel. Our results suggest that the lower rates of extravasation are due to the increased vessel integrity and reduced permeability of the endothelial monolayer. We further demonstrate that vascular permeability is a function of collagen hydrogel mass concentration, with increased collagen concentrations leading to elevated vascular permeability and increased extravasation. Collectively, our results demonstrate that extravasation of monocytes is highly regulated by the structural integrity of the endothelial monolayer. The microfluidic approach developed here allows for the dissection of the relative contributions of these cues to further understand the key governing processes that regulate circulating cell extravasation and inflammation.
Collapse
Affiliation(s)
| | - Stephanie A. Huang
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, North Carolina 27599, USA
| | | | | | | |
Collapse
|
10
|
Cho SY, Gong X, Koman VB, Kuehne M, Moon SJ, Son M, Lew TTS, Gordiichuk P, Jin X, Sikes HD, Strano MS. Cellular lensing and near infrared fluorescent nanosensor arrays to enable chemical efflux cytometry. Nat Commun 2021; 12:3079. [PMID: 34035262 PMCID: PMC8149711 DOI: 10.1038/s41467-021-23416-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 04/20/2021] [Indexed: 02/07/2023] Open
Abstract
Nanosensors have proven to be powerful tools to monitor single cells, achieving spatiotemporal precision even at molecular level. However, there has not been way of extending this approach to statistically relevant numbers of living cells. Herein, we design and fabricate nanosensor array in microfluidics that addresses this limitation, creating a Nanosensor Chemical Cytometry (NCC). nIR fluorescent carbon nanotube array is integrated along microfluidic channel through which flowing cells is guided. We can utilize the flowing cell itself as highly informative Gaussian lenses projecting nIR profiles and extract rich information. This unique biophotonic waveguide allows for quantified cross-correlation of biomolecular information with various physical properties and creates label-free chemical cytometer for cellular heterogeneity measurement. As an example, the NCC can profile the immune heterogeneities of human monocyte populations at attomolar sensitivity in completely non-destructive and real-time manner with rate of ~600 cells/hr, highest range demonstrated to date for state-of-the-art chemical cytometry.
Collapse
Affiliation(s)
- Soo-Yeon Cho
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Xun Gong
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Volodymyr B Koman
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Matthias Kuehne
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sun Jin Moon
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Manki Son
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tedrick Thomas Salim Lew
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Pavlo Gordiichuk
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Xiaojia Jin
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Hadley D Sikes
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Michael S Strano
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
| |
Collapse
|
11
|
Guo R, Barnea I, Shaked NT. Limited-angle tomographic phase microscopy utilizing confocal scanning fluorescence microscopy. BIOMEDICAL OPTICS EXPRESS 2021; 12:1869-1881. [PMID: 33996204 PMCID: PMC8086471 DOI: 10.1364/boe.419598] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 02/12/2021] [Accepted: 02/16/2021] [Indexed: 05/03/2023]
Abstract
We present a multimodal imaging technique, combining tomographic phase microscopy with limited angular projection range and number, and two-channel spinning-disk confocal scanning fluorescence microscopy. This technique allows high-accuracy 3D refractive index (RI) profiling of live cells in spite of the missing projections. The cellular outer shape and its interior organelles measured by the confocal fluorescence imaging not only specify the cell in molecular levels, but also provide the 3D distributions of the whole cell as well as its organelles. We take these additional 3D morphological details as constraints in Gerchberg-Papoulis-based optical diffraction tomography algorithm. We then obtain an accurate 3D RI tomogram, even with a sparse angular range having a small number of perspective projections, otherwise providing low-accuracy RI reconstruction. Then, we obtain both cellular molecular specificity and inner RI values of the cell and its organelles. We compare the reconstructed 3D RI profiles of various samples, demonstrating the superiority of the proposed technique.
Collapse
|
12
|
Miccio L, Cimmino F, Kurelac I, Villone MM, Bianco V, Memmolo P, Merola F, Mugnano M, Capasso M, Iolascon A, Maffettone PL, Ferraro P. Perspectives on liquid biopsy for label‐free detection of “circulating tumor cells” through intelligent lab‐on‐chips. VIEW 2020. [DOI: 10.1002/viw.20200034] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Affiliation(s)
- Lisa Miccio
- CNR‐ISASI Institute of Applied Sciences and Intelligent Systems E. Caianiello Pozzuoli Italy
- NEAPoLIS, Numerical and Experimental Advanced Program on Liquids and Interface Systems Joint Research Center CNR ‐ Università degli Studi di Napoli “Federico II” Napoli Italy
| | | | - Ivana Kurelac
- Dipartimento di Scienze Mediche e Chirurgiche Università di Bologna Bologna Italy
- Centro di Ricerca Biomedica Applicata (CRBA) Università di Bologna Bologna Italy
| | - Massimiliano M. Villone
- Dipartimento di Ingegneria Chimica dei Materiali e della Produzione Industriale Università degli Studi di Napoli “Federico II” Napoli Italy
- NEAPoLIS, Numerical and Experimental Advanced Program on Liquids and Interface Systems Joint Research Center CNR ‐ Università degli Studi di Napoli “Federico II” Napoli Italy
| | - Vittorio Bianco
- CNR‐ISASI Institute of Applied Sciences and Intelligent Systems E. Caianiello Pozzuoli Italy
- NEAPoLIS, Numerical and Experimental Advanced Program on Liquids and Interface Systems Joint Research Center CNR ‐ Università degli Studi di Napoli “Federico II” Napoli Italy
| | - Pasquale Memmolo
- CNR‐ISASI Institute of Applied Sciences and Intelligent Systems E. Caianiello Pozzuoli Italy
- NEAPoLIS, Numerical and Experimental Advanced Program on Liquids and Interface Systems Joint Research Center CNR ‐ Università degli Studi di Napoli “Federico II” Napoli Italy
| | - Francesco Merola
- CNR‐ISASI Institute of Applied Sciences and Intelligent Systems E. Caianiello Pozzuoli Italy
- NEAPoLIS, Numerical and Experimental Advanced Program on Liquids and Interface Systems Joint Research Center CNR ‐ Università degli Studi di Napoli “Federico II” Napoli Italy
| | - Martina Mugnano
- CNR‐ISASI Institute of Applied Sciences and Intelligent Systems E. Caianiello Pozzuoli Italy
- NEAPoLIS, Numerical and Experimental Advanced Program on Liquids and Interface Systems Joint Research Center CNR ‐ Università degli Studi di Napoli “Federico II” Napoli Italy
| | - Mario Capasso
- CEINGE Biotecnologie Avanzate Naples Italy
- Dipartimento di Medicina Molecolare e Biotecnologie Mediche Università degli Studi di Napoli Federico II Naples Italy
| | - Achille Iolascon
- CEINGE Biotecnologie Avanzate Naples Italy
- Dipartimento di Medicina Molecolare e Biotecnologie Mediche Università degli Studi di Napoli Federico II Naples Italy
| | - Pier Luca Maffettone
- Dipartimento di Ingegneria Chimica dei Materiali e della Produzione Industriale Università degli Studi di Napoli “Federico II” Napoli Italy
- NEAPoLIS, Numerical and Experimental Advanced Program on Liquids and Interface Systems Joint Research Center CNR ‐ Università degli Studi di Napoli “Federico II” Napoli Italy
| | - Pietro Ferraro
- CNR‐ISASI Institute of Applied Sciences and Intelligent Systems E. Caianiello Pozzuoli Italy
- NEAPoLIS, Numerical and Experimental Advanced Program on Liquids and Interface Systems Joint Research Center CNR ‐ Università degli Studi di Napoli “Federico II” Napoli Italy
| |
Collapse
|
13
|
Yamada H, Hirotsu A, Yamashita D, Yasuhiko O, Yamauchi T, Kayou T, Suzuki H, Okazaki S, Kikuchi H, Takeuchi H, Ueda Y. Label-free imaging flow cytometer for analyzing large cell populations by line-field quantitative phase microscopy with digital refocusing. BIOMEDICAL OPTICS EXPRESS 2020; 11:2213-2223. [PMID: 32341878 PMCID: PMC7173910 DOI: 10.1364/boe.389435] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 03/15/2020] [Accepted: 03/15/2020] [Indexed: 05/09/2023]
Abstract
We propose a line-field quantitative phase-imaging flow cytometer for analyzing large populations of label-free cells. Hydrodynamical focusing brings cells into the focus plane of an optical system while diluting the cell suspension, resulting in decreased throughput rate. To overcome the trade-off between throughput rate and in-focus imaging, our cytometer involves digitally extending the depth-of-focus on loosely hydrodynamically focusing cell suspensions. The cells outside the depth-of-focus range in the 70-µm diameter of the core flow were automatically digitally refocused after image acquisition. We verified that refocusing was successful with our cytometer through statistical analysis of image quality before and after digital refocusing.
Collapse
Affiliation(s)
- Hidenao Yamada
- Central Research Laboratory, Hamamatsu Photonics K.K., 5000 Hirakuchi, Hamakita-ku, Hamamatsu, Shizuoka-Pref., 434-8601, Japan
| | - Amane Hirotsu
- Department of Surgery, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka-Pref., 431-3192, Japan
| | - Daisuke Yamashita
- Central Research Laboratory, Hamamatsu Photonics K.K., 5000 Hirakuchi, Hamakita-ku, Hamamatsu, Shizuoka-Pref., 434-8601, Japan
| | - Osamu Yasuhiko
- Central Research Laboratory, Hamamatsu Photonics K.K., 5000 Hirakuchi, Hamakita-ku, Hamamatsu, Shizuoka-Pref., 434-8601, Japan
| | - Toyohiko Yamauchi
- Central Research Laboratory, Hamamatsu Photonics K.K., 5000 Hirakuchi, Hamakita-ku, Hamamatsu, Shizuoka-Pref., 434-8601, Japan
| | - Tsukasa Kayou
- Electron Tube Division, Hamamatsu Photonics K.K., 314-5 Shimokanzo, Iwata, Shizuoka-Pref., 438-0126, Japan
| | - Hiroaki Suzuki
- Electron Tube Division, Hamamatsu Photonics K.K., 314-5 Shimokanzo, Iwata, Shizuoka-Pref., 438-0126, Japan
| | - Shigetoshi Okazaki
- HAMAMATSU BioPhotonics Innovation Chair Preeminent Medical Photonics Education & Research Center, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka-Pref., 431-3192, Japan
| | - Hirotoshi Kikuchi
- Department of Surgery, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka-Pref., 431-3192, Japan
| | - Hiroya Takeuchi
- Department of Surgery, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka-Pref., 431-3192, Japan
| | - Yukio Ueda
- Central Research Laboratory, Hamamatsu Photonics K.K., 5000 Hirakuchi, Hamakita-ku, Hamamatsu, Shizuoka-Pref., 434-8601, Japan
| |
Collapse
|