1
|
Gouzou D, Taimori A, Haloubi T, Finlayson N, Wang Q, Hopgood JR, Vallejo M. Applications of machine learning in time-domain fluorescence lifetime imaging: a review. Methods Appl Fluoresc 2024; 12:022001. [PMID: 38055998 PMCID: PMC10851337 DOI: 10.1088/2050-6120/ad12f7] [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: 06/30/2023] [Revised: 09/25/2023] [Accepted: 12/06/2023] [Indexed: 12/08/2023]
Abstract
Many medical imaging modalities have benefited from recent advances in Machine Learning (ML), specifically in deep learning, such as neural networks. Computers can be trained to investigate and enhance medical imaging methods without using valuable human resources. In recent years, Fluorescence Lifetime Imaging (FLIm) has received increasing attention from the ML community. FLIm goes beyond conventional spectral imaging, providing additional lifetime information, and could lead to optical histopathology supporting real-time diagnostics. However, most current studies do not use the full potential of machine/deep learning models. As a developing image modality, FLIm data are not easily obtainable, which, coupled with an absence of standardisation, is pushing back the research to develop models which could advance automated diagnosis and help promote FLIm. In this paper, we describe recent developments that improve FLIm image quality, specifically time-domain systems, and we summarise sensing, signal-to-noise analysis and the advances in registration and low-level tracking. We review the two main applications of ML for FLIm: lifetime estimation and image analysis through classification and segmentation. We suggest a course of action to improve the quality of ML studies applied to FLIm. Our final goal is to promote FLIm and attract more ML practitioners to explore the potential of lifetime imaging.
Collapse
Affiliation(s)
- Dorian Gouzou
- Dorian Gouzou and Marta Vallejo are with Institute of Signals, Sensors and Systems, School of Engineering and Physical Sciences, Heriot Watt University, Edinburgh, EH14 4AS, United Kingdom
| | - Ali Taimori
- Tarek Haloubi, Ali Taimori, and James R. Hopgood are with Institute for Imaging, Data and Communication, School of Engineering, University of Edinburgh, Edinburgh, EH9 3FG, United Kingdom
| | - Tarek Haloubi
- Tarek Haloubi, Ali Taimori, and James R. Hopgood are with Institute for Imaging, Data and Communication, School of Engineering, University of Edinburgh, Edinburgh, EH9 3FG, United Kingdom
| | - Neil Finlayson
- Neil Finlayson is with Institute for Integrated Micro and Nano Systems, School of Engineering, University ofEdinburgh, Edinburgh EH9 3FF, United Kingdom
| | - Qiang Wang
- Qiang Wang is with Centre for Inflammation Research, University of Edinburgh, Edinburgh, EH16 4TJ, United Kingdom
| | - James R Hopgood
- Tarek Haloubi, Ali Taimori, and James R. Hopgood are with Institute for Imaging, Data and Communication, School of Engineering, University of Edinburgh, Edinburgh, EH9 3FG, United Kingdom
| | - Marta Vallejo
- Dorian Gouzou and Marta Vallejo are with Institute of Signals, Sensors and Systems, School of Engineering and Physical Sciences, Heriot Watt University, Edinburgh, EH14 4AS, United Kingdom
| |
Collapse
|
2
|
Selig M, Poehlman L, Lang NC, Völker M, Rolauffs B, Hart ML. Prediction of six macrophage phenotypes and their IL-10 content based on single-cell morphology using artificial intelligence. Front Immunol 2024; 14:1336393. [PMID: 38239351 PMCID: PMC10794337 DOI: 10.3389/fimmu.2023.1336393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 12/14/2023] [Indexed: 01/22/2024] Open
Abstract
Introduction The last decade has led to rapid developments and increased usage of computational tools at the single-cell level. However, our knowledge remains limited in how extracellular cues alter quantitative macrophage morphology and how such morphological changes can be used to predict macrophage phenotype as well as cytokine content at the single-cell level. Methods Using an artificial intelligence (AI) based approach, this study determined whether (i) accurate macrophage classification and (ii) prediction of intracellular IL-10 at the single-cell level was possible, using only morphological features as predictors for AI. Using a quantitative panel of shape descriptors, our study assessed image-based original and synthetic single-cell data in two different datasets in which CD14+ monocyte-derived macrophages generated from human peripheral blood monocytes were initially primed with GM-CSF or M-CSF followed by polarization with specific stimuli in the presence/absence of continuous GM-CSF or M-CSF. Specifically, M0, M1 (GM-CSF-M1, TNFα/IFNγ-M1, GM-CSF/TNFα/IFNγ-M1) and M2 (M-CSF-M2, IL-4-M2a, M-CSF/IL-4-M2a, IL-10-M2c, M-CSF/IL-10-M2c) macrophages were examined. Results Phenotypes were confirmed by ELISA and immunostaining of CD markers. Variations of polarization techniques significantly changed multiple macrophage morphological features, demonstrating that macrophage morphology is a highly sensitive, dynamic marker of phenotype. Using original and synthetic single-cell data, cell morphology alone yielded an accuracy of 93% for the classification of 6 different human macrophage phenotypes (with continuous GM-CSF or M-CSF). A similarly high phenotype classification accuracy of 95% was reached with data generated with different stimuli (discontinuous GM-CSF or M-CSF) and measured at a different time point. These comparably high accuracies clearly validated the here chosen AI-based approach. Quantitative morphology also allowed prediction of intracellular IL-10 with 95% accuracy using only original data. Discussion Thus, image-based machine learning using morphology-based features not only (i) classified M0, M1 and M2 macrophages but also (ii) classified M2a and M2c subtypes and (iii) predicted intracellular IL-10 at the single-cell level among six phenotypes. This simple approach can be used as a general strategy not only for macrophage phenotyping but also for prediction of IL-10 content of any IL-10 producing cell, which can help improve our understanding of cytokine biology at the single-cell level.
Collapse
Affiliation(s)
- Mischa Selig
- G.E.R.N. Research Center for Tissue Replacement, Regeneration & Neogenesis, Department of Orthopedics and Trauma Surgery, Faculty of Medicine, Medical Center-Albert-Ludwigs-University of Freiburg, Freiburg im Breisgau, Germany
| | - Logan Poehlman
- G.E.R.N. Research Center for Tissue Replacement, Regeneration & Neogenesis, Department of Orthopedics and Trauma Surgery, Faculty of Medicine, Medical Center-Albert-Ludwigs-University of Freiburg, Freiburg im Breisgau, Germany
| | - Nils C Lang
- G.E.R.N. Research Center for Tissue Replacement, Regeneration & Neogenesis, Department of Orthopedics and Trauma Surgery, Faculty of Medicine, Medical Center-Albert-Ludwigs-University of Freiburg, Freiburg im Breisgau, Germany
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Marita Völker
- G.E.R.N. Research Center for Tissue Replacement, Regeneration & Neogenesis, Department of Orthopedics and Trauma Surgery, Faculty of Medicine, Medical Center-Albert-Ludwigs-University of Freiburg, Freiburg im Breisgau, Germany
| | - Bernd Rolauffs
- G.E.R.N. Research Center for Tissue Replacement, Regeneration & Neogenesis, Department of Orthopedics and Trauma Surgery, Faculty of Medicine, Medical Center-Albert-Ludwigs-University of Freiburg, Freiburg im Breisgau, Germany
| | - Melanie L Hart
- G.E.R.N. Research Center for Tissue Replacement, Regeneration & Neogenesis, Department of Orthopedics and Trauma Surgery, Faculty of Medicine, Medical Center-Albert-Ludwigs-University of Freiburg, Freiburg im Breisgau, Germany
| |
Collapse
|
3
|
Neto NGB, Suku M, Hoey DA, Monaghan MG. 2P-FLIM unveils time-dependent metabolic shifts during osteogenic differentiation with a key role of lactate to fuel osteogenesis via glutaminolysis identified. Stem Cell Res Ther 2023; 14:364. [PMID: 38087380 PMCID: PMC10717614 DOI: 10.1186/s13287-023-03606-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 12/06/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Human mesenchymal stem cells (hMSCs) utilize discrete biosynthetic pathways to self-renew and differentiate into specific cell lineages, with undifferentiated hMSCs harbouring reliance on glycolysis and hMSCs differentiating towards an osteogenic phenotype relying on oxidative phosphorylation as an energy source. METHODS In this study, the osteogenic differentiation of hMSCs was assessed and classified over 14 days using a non-invasive live-cell imaging modality-two-photon fluorescence lifetime imaging microscopy (2P-FLIM). This technique images and measures NADH fluorescence from which cellular metabolism is inferred. RESULTS During osteogenesis, we observe a higher dependence on oxidative phosphorylation (OxPhos) for cellular energy, concomitant with an increased reliance on anabolic pathways. Guided by these non-invasive observations, we validated this metabolic profile using qPCR and extracellular metabolite analysis and observed a higher reliance on glutaminolysis in the earlier time points of osteogenic differentiation. Based on the results obtained, we sought to promote glutaminolysis further by using lactate, to improve the osteogenic potential of hMSCs. Higher levels of mineral deposition and osteogenic gene expression were achieved when treating hMSCs with lactate, in addition to an upregulation of lactate metabolism and transmembrane cellular lactate transporters. To further clarify the interplay between glutaminolysis and lactate metabolism in osteogenic differentiation, we blocked these pathways using BPTES and α-CHC respectively. A reduction in mineralization was found after treatment with BPTES and α-CHC, demonstrating the reliance of hMSC osteogenesis on glutaminolysis and lactate metabolism. CONCLUSION In summary, we demonstrate that the osteogenic differentiation of hMSCs has a temporal metabolic profile and shift that is observed as early as day 3 of cell culture using 2P-FLIM. Furthermore, extracellular lactate is shown as an essential metabolite and metabolic fuel to ensure efficient osteogenic differentiation and as a signalling molecule to promote glutaminolysis. These findings have significant impact in the use of 2P-FLIM to discover potent approaches towards bone tissue engineering in vitro and in vivo by engaging directly with metabolite-driven osteogenesis.
Collapse
Affiliation(s)
- Nuno G B Neto
- Department of Mechanical, Manufacturing and Biomedical Engineering, Trinity College Dublin, Parsons Building, Dublin 2, Ireland
- Trinity Centre for Biomedical Engineering, Trinity College Dublin, Dublin, Ireland
| | - Meenakshi Suku
- Department of Mechanical, Manufacturing and Biomedical Engineering, Trinity College Dublin, Parsons Building, Dublin 2, Ireland
- CURAM SFI Research Centre for Medical Devices, National University of Ireland, Galway, Ireland
- Trinity Centre for Biomedical Engineering, Trinity College Dublin, Dublin, Ireland
| | - David A Hoey
- Department of Mechanical, Manufacturing and Biomedical Engineering, Trinity College Dublin, Parsons Building, Dublin 2, Ireland
- CURAM SFI Research Centre for Medical Devices, National University of Ireland, Galway, Ireland
- Advanced Materials for Bioengineering Research (AMBER), Centre, Trinity College Dublin and Royal College of Surgeons in Ireland, Dublin, Ireland
- Trinity Centre for Biomedical Engineering, Trinity College Dublin, Dublin, Ireland
| | - Michael G Monaghan
- Department of Mechanical, Manufacturing and Biomedical Engineering, Trinity College Dublin, Parsons Building, Dublin 2, Ireland.
- CURAM SFI Research Centre for Medical Devices, National University of Ireland, Galway, Ireland.
- Advanced Materials for Bioengineering Research (AMBER), Centre, Trinity College Dublin and Royal College of Surgeons in Ireland, Dublin, Ireland.
- Trinity Centre for Biomedical Engineering, Trinity College Dublin, Dublin, Ireland.
| |
Collapse
|
4
|
Monaghan MG, Borah R, Thomsen C, Browne S. Thou shall not heal: Overcoming the non-healing behaviour of diabetic foot ulcers by engineering the inflammatory microenvironment. Adv Drug Deliv Rev 2023; 203:115120. [PMID: 37884128 DOI: 10.1016/j.addr.2023.115120] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 08/01/2023] [Accepted: 10/23/2023] [Indexed: 10/28/2023]
Abstract
Diabetic foot ulcers (DFUs) are a devastating complication for diabetic patients that have debilitating effects and can ultimately lead to limb amputation. Healthy wounds progress through the phases of healing leading to tissue regeneration and restoration of the barrier function of the skin. In contrast, in diabetic patients dysregulation of these phases leads to chronic, non-healing wounds. In particular, unresolved inflammation in the DFU microenvironment has been identified as a key facet of chronic wounds in hyperglyceamic patients, as DFUs fail to progress beyond the inflammatory phase and towards resolution. Thus, control over and modulation of the inflammatory response is a promising therapeutic avenue for DFU treatment. This review discusses the current state-of-the art regarding control of the inflammatory response in the DFU microenvironment, with a specific focus on the development of biomaterials-based delivery strategies and their cargos to direct tissue regeneration in the DFU microenvironment.
Collapse
Affiliation(s)
- Michael G Monaghan
- Department of Mechanical, Manufacturing and Biomedical Engineering, Trinity College Dublin, Dublin 2, Ireland; Advanced Materials and BioEngineering Research (AMBER), Centre at Trinity College Dublin and the Royal College of Surgeons in Ireland, Dublin 2, Ireland; CÚRAM, Centre for Research in Medical Devices, National University of Ireland, H91 W2TY Galway, Ireland; Trinity Centre for Biomedical Engineering, Trinity College Dublin, Dublin 2, Ireland
| | - Rajiv Borah
- Department of Mechanical, Manufacturing and Biomedical Engineering, Trinity College Dublin, Dublin 2, Ireland; Advanced Materials and BioEngineering Research (AMBER), Centre at Trinity College Dublin and the Royal College of Surgeons in Ireland, Dublin 2, Ireland; Trinity Centre for Biomedical Engineering, Trinity College Dublin, Dublin 2, Ireland
| | - Charlotte Thomsen
- Department of Mechanical, Manufacturing and Biomedical Engineering, Trinity College Dublin, Dublin 2, Ireland; Tissue Engineering Research Group, Department of Anatomy and Regenerative Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Shane Browne
- CÚRAM, Centre for Research in Medical Devices, National University of Ireland, H91 W2TY Galway, Ireland; Trinity Centre for Biomedical Engineering, Trinity College Dublin, Dublin 2, Ireland; Tissue Engineering Research Group, Department of Anatomy and Regenerative Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland.
| |
Collapse
|
5
|
Barroso M, Monaghan MG, Niesner R, Dmitriev RI. Probing organoid metabolism using fluorescence lifetime imaging microscopy (FLIM): The next frontier of drug discovery and disease understanding. Adv Drug Deliv Rev 2023; 201:115081. [PMID: 37647987 PMCID: PMC10543546 DOI: 10.1016/j.addr.2023.115081] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 04/20/2023] [Accepted: 08/24/2023] [Indexed: 09/01/2023]
Abstract
Organoid models have been used to address important questions in developmental and cancer biology, tissue repair, advanced modelling of disease and therapies, among other bioengineering applications. Such 3D microenvironmental models can investigate the regulation of cell metabolism, and provide key insights into the mechanisms at the basis of cell growth, differentiation, communication, interactions with the environment and cell death. Their accessibility and complexity, based on 3D spatial and temporal heterogeneity, make organoids suitable for the application of novel, dynamic imaging microscopy methods, such as fluorescence lifetime imaging microscopy (FLIM) and related decay time-assessing readouts. Several biomarkers and assays have been proposed to study cell metabolism by FLIM in various organoid models. Herein, we present an expert-opinion discussion on the principles of FLIM and PLIM, instrumentation and data collection and analysis protocols, and general and emerging biosensor-based approaches, to highlight the pioneering work being performed in this field.
Collapse
Affiliation(s)
- Margarida Barroso
- Department of Molecular and Cellular Physiology, Albany Medical College, Albany, NY 12208, USA
| | - Michael G Monaghan
- Department of Mechanical, Manufacturing and Biomedical Engineering, Trinity College Dublin, Dublin 02, Ireland
| | - Raluca Niesner
- Dynamic and Functional In Vivo Imaging, Freie Universität Berlin and Biophysical Analytics, German Rheumatism Research Center, Berlin, Germany
| | - Ruslan I Dmitriev
- Tissue Engineering and Biomaterials Group, Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, C. Heymanslaan 10, 9000 Ghent, Belgium; Ghent Light Microscopy Core, Ghent University, 9000 Ghent, Belgium.
| |
Collapse
|
6
|
Hegarty C, Neto N, Cahill P, Floudas A. Computational approaches in rheumatic diseases - Deciphering complex spatio-temporal cell interactions. Comput Struct Biotechnol J 2023; 21:4009-4020. [PMID: 37649712 PMCID: PMC10462794 DOI: 10.1016/j.csbj.2023.08.005] [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: 04/04/2023] [Revised: 08/04/2023] [Accepted: 08/04/2023] [Indexed: 09/01/2023] Open
Abstract
Inflammatory arthritis, including rheumatoid (RA), and psoriatic (PsA) arthritis, are clinically and immunologically heterogeneous diseases with no identified cure. Chronic inflammation of the synovial tissue ushers loss of function of the joint that severely impacts the patient's quality of life, eventually leading to disability and life-threatening comorbidities. The pathogenesis of synovial inflammation is the consequence of compounded immune and stromal cell interactions influenced by genetic and environmental factors. Deciphering the complexity of the synovial cellular landscape has accelerated primarily due to the utilisation of bulk and single cell RNA sequencing. Particularly the capacity to generate cell-cell interaction networks could reveal evidence of previously unappreciated processes leading to disease. However, there is currently a lack of universal nomenclature as a result of varied experimental and technological approaches that discombobulates the study of synovial inflammation. While spatial transcriptomic analysis that combines anatomical information with transcriptomic data of synovial tissue biopsies promises to provide more insights into disease pathogenesis, in vitro functional assays with single-cell resolution will be required to validate current bioinformatic applications. In order to provide a comprehensive approach and translate experimental data to clinical practice, a combination of clinical and molecular data with machine learning has the potential to enhance patient stratification and identify individuals at risk of arthritis that would benefit from early therapeutic intervention. This review aims to provide a comprehensive understanding of the effect of computational approaches in deciphering synovial inflammation pathogenesis and discuss the impact that further experimental and novel computational tools may have on therapeutic target identification and drug development.
Collapse
Affiliation(s)
- Ciara Hegarty
- Translational Immunology lab, School of Biotechnology, Dublin City University, Dublin, Ireland
| | - Nuno Neto
- Trinity Centre for Biomedical Engineering, Trinity College Dublin, Ireland
| | - Paul Cahill
- Vascular Biology lab, School of Biotechnology, Dublin City University, Dublin, Ireland
| | - Achilleas Floudas
- Translational Immunology lab, School of Biotechnology, Dublin City University, Dublin, Ireland
| |
Collapse
|
7
|
Sun Z, Wu X, Tao R, Zhang T, Liu X, Wang J, Wan H, Zheng S, Zhao X, Zhang Z, Yang P. Prediction of IDH mutation status of glioma based on terahertz spectral data. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 295:122629. [PMID: 36958244 DOI: 10.1016/j.saa.2023.122629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 02/07/2023] [Accepted: 03/12/2023] [Indexed: 06/18/2023]
Abstract
Gliomas are the most common type of primary tumor in the central nervous system in adults. Isocitrate dehydrogenase (IDH) mutation status is an important molecular biomarker for adult diffuse gliomas. In this study, we were aiming to predict IDH mutation status based on terahertz time-domain spectroscopy technology. Ninety-two frozen sections of glioma tissue from nine patients were included, and terahertz spectroscopy data were obtained. Through Least Absolute Shrinkage and Selection Operator (LASSO), Principal component analysis (PCA), and Random forest (RF) algorithms, a predictive model for predicting IDH mutation status in gliomas was established based on the terahertz spectroscopy dataset with an AUC of 0.844. These results indicate that gliomas with different IDH mutation status have different terahertz spectral features, and the use of terahertz spectroscopy can establish a predictive model of IDH mutation status, providing a new way for glioma research.
Collapse
Affiliation(s)
- Zhiyan Sun
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xianhao Wu
- School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
| | - Rui Tao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Tianyao Zhang
- School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China; Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
| | - Xing Liu
- Department of Pathology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jiangfei Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Haibin Wan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Shaowen Zheng
- School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
| | - Xiaoyan Zhao
- School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China; Shunde Innovation School, University of Science and Technology Beijing, Foshan, China.
| | - Zhaohui Zhang
- School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China; Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China.
| | - Pei Yang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| |
Collapse
|
8
|
Hu L, Ter Hofstede B, Sharma D, Zhao F, Walsh AJ. Comparison of phasor analysis and biexponential decay curve fitting of autofluorescence lifetime imaging data for machine learning prediction of cellular phenotypes. FRONTIERS IN BIOINFORMATICS 2023; 3:1210157. [PMID: 37455808 PMCID: PMC10342207 DOI: 10.3389/fbinf.2023.1210157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 06/21/2023] [Indexed: 07/18/2023] Open
Abstract
Introduction: Autofluorescence imaging of the coenzymes reduced nicotinamide (phosphate) dinucleotide (NAD(P)H) and oxidized flavin adenine dinucleotide (FAD) provides a label-free method to detect cellular metabolism and phenotypes. Time-domain fluorescence lifetime data can be analyzed by exponential decay fitting to extract fluorescence lifetimes or by a fit-free phasor transformation to compute phasor coordinates. Methods: Here, fluorescence lifetime data analysis by biexponential decay curve fitting is compared with phasor coordinate analysis as input data to machine learning models to predict cell phenotypes. Glycolysis and oxidative phosphorylation of MCF7 breast cancer cells were chemically inhibited with 2-deoxy-d-glucose and sodium cyanide, respectively; and fluorescence lifetime images of NAD(P)H and FAD were obtained using a multiphoton microscope. Results: Machine learning algorithms built from either the extracted lifetime values or phasor coordinates predict MCF7 metabolism with a high accuracy (∼88%). Similarly, fluorescence lifetime images of M0, M1, and M2 macrophages were acquired and analyzed by decay fitting and phasor analysis. Machine learning models trained with features from curve fitting discriminate different macrophage phenotypes with improved performance over models trained using only phasor coordinates. Discussion: Altogether, the results demonstrate that both curve fitting and phasor analysis of autofluorescence lifetime images can be used in machine learning models for classification of cell phenotype from the lifetime data.
Collapse
Affiliation(s)
| | | | | | | | - Alex J. Walsh
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
| |
Collapse
|
9
|
Chariyev-Prinz F, Szojka A, Neto N, Burdis R, Monaghan MG, Kelly DJ. An assessment of the response of human MSCs to hydrostatic pressure in environments supportive of differential chondrogenesis. J Biomech 2023; 154:111590. [PMID: 37163962 DOI: 10.1016/j.jbiomech.2023.111590] [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: 09/26/2022] [Revised: 01/31/2023] [Accepted: 04/11/2023] [Indexed: 05/12/2023]
Abstract
Mechanical stimulation can modulate the chondrogenic differentiation of stem/progenitor cells and potentially benefit tissue engineering (TE) of functional articular cartilage (AC). Mechanical cues like hydrostatic pressure (HP) are often applied to cell-laden scaffolds, with little optimization of other key parameters (e.g. cell density, biomaterial properties) known to effect lineage commitment. In this study, we first sought to establish cell seeding densities and fibrin concentrations supportive of robust chondrogenesis of human mesenchymal stem cells (hMSCs). High cell densities (15*106 cells/ml) were more supportive of sGAG deposition on a per cell basis, while collagen deposition was higher at lower seeding densities (5*106 cells/ml). Employment of lower fibrin (2.5 %) concentration hydrogels supported more robust chondrogenesis of hMSCs, with higher collagen type II and lower collagen type X deposition compared to 5 % hydrogels. The application of HP to hMSCs maintained in identified chondro-inductive culture conditions had little effect on overall levels of cartilage-specific matrix production. However, if hMSCs were first temporally primed with TGF-β3 before its withdrawal, they responded to HP by increased sGAG production. The response to HP in higher cell density cultures was also associated with a metabolic shift towards glycolysis, which has been linked with a mature chondrocyte-like phenotype. These results suggest that mechanical stimulation may not be necessary to engineer functional AC grafts using hMSCs if other culture conditions have been optimised. However, such bioreactor systems can potentially be employed to better understand how engineered tissues respond to mechanical loading in vivo once removed from in vitro culture environments.
Collapse
Affiliation(s)
- Farhad Chariyev-Prinz
- Trinity Centre for Biomedical Engineering, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland; Department of Mechanical, Manufacturing and Biomedical Engineering, School of Engineering, Trinity College Dublin, Dublin, Ireland
| | - Alex Szojka
- Department of Surgery, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Nuno Neto
- Trinity Centre for Biomedical Engineering, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland; Department of Mechanical, Manufacturing and Biomedical Engineering, School of Engineering, Trinity College Dublin, Dublin, Ireland
| | - Ross Burdis
- Trinity Centre for Biomedical Engineering, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland; Department of Mechanical, Manufacturing and Biomedical Engineering, School of Engineering, Trinity College Dublin, Dublin, Ireland
| | - Michael G Monaghan
- Trinity Centre for Biomedical Engineering, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland; Department of Mechanical, Manufacturing and Biomedical Engineering, School of Engineering, Trinity College Dublin, Dublin, Ireland; Advanced Materials and Bioengineering Research Centre (AMBER), Royal College of Surgeons in Ireland and Trinity College Dublin, Dublin, Ireland
| | - Daniel J Kelly
- Trinity Centre for Biomedical Engineering, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland; Department of Mechanical, Manufacturing and Biomedical Engineering, School of Engineering, Trinity College Dublin, Dublin, Ireland; Advanced Materials and Bioengineering Research Centre (AMBER), Royal College of Surgeons in Ireland and Trinity College Dublin, Dublin, Ireland.
| |
Collapse
|
10
|
Vishnyakova P, Nikonova E, Jumaniyazova E, Solovyev I, Kirillova A, Farmakovskaya M, Savitsky A, Shirshin E, Sukhikh G, Fatkhudinov T. Fluorescence lifetime imaging microscopy as an instrument for human sperm assessment. Biochem Biophys Res Commun 2023; 645:10-16. [PMID: 36669422 DOI: 10.1016/j.bbrc.2023.01.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 01/03/2023] [Accepted: 01/07/2023] [Indexed: 01/11/2023]
Abstract
Mammalian spermatozoa are highly energized cells in which most of the proteins and activated signaling cascades are involved in the metabolic pathways. Flavin adenine dinucleotide (FAD) has one of the most important roles in the correct functional activity of spermatozoa since it acts as a cofactor for flavoenzymes, critical for proper metabolism and predominantly located in mitochondria. Non-invasive, vital and non-traumatic examination of sperm FAD level and microenvironment could be performed by fluorescence lifetime imaging microscopy (FLIM). In this study, we assessed the metabolic status of spermatozoa from healthy donors and found that FLIM could be used to segregate and separate the male germ cells according to the type of metabolic activity which corresponds with spermatozoa motility measured in standard spermogram tests.
Collapse
Affiliation(s)
- Polina Vishnyakova
- National Medical Research Center for Obstetrics, Gynecology and Perinatology Named after Academician V.I. Kulakov of Ministry of Healthcare of Russian Federation, Moscow, Russia; Peoples' Friendship University of Russia (RUDN University), Moscow, Russia.
| | - Elena Nikonova
- Laboratory of Clinical Biophotonics, Biomedical Science and Technology Park, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Enar Jumaniyazova
- Peoples' Friendship University of Russia (RUDN University), Moscow, Russia
| | - Ilya Solovyev
- A.N. Bach Institute of Biochemistry, Research Center of Biotechnology of the Russian Academy of Sciences, Moscow, Russia
| | - Anastasia Kirillova
- National Medical Research Center for Obstetrics, Gynecology and Perinatology Named after Academician V.I. Kulakov of Ministry of Healthcare of Russian Federation, Moscow, Russia; Center of Life Sciences, Skolkovo Institute of Science and Technology (Skoltech), Skolkovo, Russia
| | - Maria Farmakovskaya
- National Medical Research Center for Obstetrics, Gynecology and Perinatology Named after Academician V.I. Kulakov of Ministry of Healthcare of Russian Federation, Moscow, Russia
| | - Alexander Savitsky
- A.N. Bach Institute of Biochemistry, Research Center of Biotechnology of the Russian Academy of Sciences, Moscow, Russia
| | - Evgeny Shirshin
- Faculty of Physics, M. V. Lomonosov Moscow State University, Moscow, Russia
| | - Gennady Sukhikh
- National Medical Research Center for Obstetrics, Gynecology and Perinatology Named after Academician V.I. Kulakov of Ministry of Healthcare of Russian Federation, Moscow, Russia
| | - Timur Fatkhudinov
- Peoples' Friendship University of Russia (RUDN University), Moscow, Russia; A.P. Avtsyn Research Institute of Human Morphology, Moscow, Russian Federation
| |
Collapse
|