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Dhillon AK, Sharma A, Yadav V, Singh R, Ahuja T, Barman S, Siddhanta S. Raman spectroscopy and its plasmon-enhanced counterparts: A toolbox to probe protein dynamics and aggregation. WILEY INTERDISCIPLINARY REVIEWS. NANOMEDICINE AND NANOBIOTECHNOLOGY 2024; 16:e1917. [PMID: 37518952 DOI: 10.1002/wnan.1917] [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: 06/29/2022] [Revised: 06/22/2023] [Accepted: 07/06/2023] [Indexed: 08/01/2023]
Abstract
Protein unfolding and aggregation are often correlated with numerous diseases such as Alzheimer's, Parkinson's, Huntington's, and other debilitating neurological disorders. Such adverse events consist of a plethora of competing mechanisms, particularly interactions that control the stability and cooperativity of the process. However, it remains challenging to probe the molecular mechanism of protein dynamics such as aggregation, and monitor them in real-time under physiological conditions. Recently, Raman spectroscopy and its plasmon-enhanced counterparts, such as surface-enhanced Raman spectroscopy (SERS) and tip-enhanced Raman spectroscopy (TERS), have emerged as sensitive analytical tools that have the potential to perform molecular studies of functional groups and are showing significant promise in probing events related to protein aggregation. We summarize the fundamental working principles of Raman, SERS, and TERS as nondestructive, easy-to-perform, and fast tools for probing protein dynamics and aggregation. Finally, we highlight the utility of these techniques for the analysis of vibrational spectra of aggregation of proteins from various sources such as tissues, pathogens, food, biopharmaceuticals, and lastly, biological fouling to retrieve precise chemical information, which can be potentially translated to practical applications and point-of-care (PoC) devices. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Diagnostic Tools > Diagnostic Nanodevices Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.
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Affiliation(s)
| | - Arti Sharma
- Department of Chemistry, Indian Institute of Technology Delhi, New Delhi, India
| | - Vikas Yadav
- Department of Chemistry, Indian Institute of Technology Delhi, New Delhi, India
| | - Ruchi Singh
- Department of Chemistry, Indian Institute of Technology Delhi, New Delhi, India
| | - Tripti Ahuja
- Department of Chemistry, Indian Institute of Technology Delhi, New Delhi, India
| | - Sanmitra Barman
- Center for Advanced Materials and Devices (CAMD), BML Munjal University, Haryana, India
| | - Soumik Siddhanta
- Department of Chemistry, Indian Institute of Technology Delhi, New Delhi, India
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Wurm LM, Fischer B, Neuschmelting V, Reinecke D, Fischer I, Croner RS, Goldbrunner R, Hacker MC, Dybaś J, Kahlert UD. Rapid, label-free classification of glioblastoma differentiation status combining confocal Raman spectroscopy and machine learning. Analyst 2023; 148:6109-6119. [PMID: 37927114 DOI: 10.1039/d3an01303k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2023]
Abstract
Label-free identification of tumor cells using spectroscopic assays has emerged as a technological innovation with a proven ability for rapid implementation in clinical care. Machine learning facilitates the optimization of processing and interpretation of extensive data, such as various spectroscopy data obtained from surgical samples. The here-described preclinical work investigates the potential of machine learning algorithms combining confocal Raman spectroscopy to distinguish non-differentiated glioblastoma cells and their respective isogenic differentiated phenotype by means of confocal ultra-rapid measurements. For this purpose, we measured and correlated modalities of 1146 intracellular single-point measurements and sustainingly clustered cell components to predict tumor stem cell existence. By further narrowing a few selected peaks, we found indicative evidence that using our computational imaging technology is a powerful approach to detect tumor stem cells in vitro with an accuracy of 91.7% in distinct cell compartments, mainly because of greater lipid content and putative different protein structures. We also demonstrate that the presented technology can overcome intra- and intertumoral cellular heterogeneity of our disease models, verifying the elevated physiological relevance of our applied disease modeling technology despite intracellular noise limitations for future translational evaluation.
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Affiliation(s)
- Lennard M Wurm
- Department of Neurosurgery, University Hospital Düsseldorf and Medical Faculty Heinrich-Heine University, Düsseldorf, Germany
- Department of Neurosurgery, University Hospital Cologne, Cologne, Germany
| | - Björn Fischer
- Institute of Pharmaceutics and Biopharmaceutics, University of Düsseldorf, Düsseldorf, Germany
- FISCHER GmbH, Raman Spectroscopic Services, 40667 Meerbusch, Germany
| | | | - David Reinecke
- Department of Neurosurgery, University Hospital Cologne, Cologne, Germany
| | - Igor Fischer
- Department of Neurosurgery, University Hospital Düsseldorf and Medical Faculty Heinrich-Heine University, Düsseldorf, Germany
| | - Roland S Croner
- Clinic of General- Visceral-, Vascular and Transplantation Surgery, Department of Molecular and Experimental Surgery, University Hospital Magdeburg and Medical Faculty Otto-von-Guericke University, Magdeburg, Germany.
| | - Roland Goldbrunner
- Department of Neurosurgery, University Hospital Cologne, Cologne, Germany
| | - Michael C Hacker
- Institute of Pharmaceutics and Biopharmaceutics, University of Düsseldorf, Düsseldorf, Germany
| | - Jakub Dybaś
- Jagiellonian Center for Experimental Therapeutics, Jagiellonian University, Krakow, Poland
| | - Ulf D Kahlert
- Clinic of General- Visceral-, Vascular and Transplantation Surgery, Department of Molecular and Experimental Surgery, University Hospital Magdeburg and Medical Faculty Otto-von-Guericke University, Magdeburg, Germany.
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LaLone V, Smith D, Diaz-Espinosa J, Rosania GR. Quantitative Raman chemical imaging of intracellular drug-membrane aggregates and small molecule drug precipitates in cytoplasmic organelles. Adv Drug Deliv Rev 2023; 202:115107. [PMID: 37769851 PMCID: PMC10841539 DOI: 10.1016/j.addr.2023.115107] [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: 07/16/2023] [Revised: 09/20/2023] [Accepted: 09/25/2023] [Indexed: 10/02/2023]
Abstract
Raman confocal microscopes have been used to visualize the distribution of small molecule drugs within different subcellular compartments. This visualization allows the discovery, characterization, and detailed analysis of the molecular transport phenomena underpinning the Volume of Distribution - a key parameter governing the systemic pharmacokinetics of small molecule drugs. In the specific case of lipophilic small molecules with large Volumes of Distribution, chemical imaging studies using Raman confocal microscopes have revealed how weakly basic, poorly soluble drug molecules can accumulate inside cells by forming stable, supramolecular complexes in association with cytoplasmic membranes or by precipitating out within organelles. To study the self-assembly and function of the resulting intracellular drug inclusions, Raman chemical imaging methods have been developed to measure and map the mass, concentration, and ionization state of drug molecules at a microscopic, subcellular level. Beyond the field of drug delivery, Raman chemical imaging techniques relevant to the study of microscopic drug precipitates and drug-lipid complexes which form inside cells are also being developed by researchers with seemingly unrelated scientific interests. Highlighting advances in data acquisition, calibration methods, and computational data management and analysis tools, this review will cover a decade of technological developments that enable the conversion of spectral signals obtained from Raman confocal microscopes into new discoveries and information about previously unknown, concentrative drug transport pathways driven by soluble-to-insoluble phase transitions occurring within the cytoplasmic organelles of eukaryotic cells.
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Affiliation(s)
- Vernon LaLone
- Cambium Analytica Research Laboratories, Traverse City, MI, United States
| | - Doug Smith
- Cambium Analytica Research Laboratories, Traverse City, MI, United States
| | - Jennifer Diaz-Espinosa
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI, United States
| | - Gus R Rosania
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI, United States.
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Mozafari N, Mozafari N, Dehshahri A, Azadi A. Knowledge Gaps in Generating Cell-Based Drug Delivery Systems and a Possible Meeting with Artificial Intelligence. Mol Pharm 2023; 20:3757-3778. [PMID: 37428824 DOI: 10.1021/acs.molpharmaceut.3c00162] [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] [Indexed: 07/12/2023]
Abstract
Cell-based drug delivery systems are new strategies in targeted delivery in which cells or cell-membrane-derived systems are used as carriers and release their cargo in a controlled manner. Recently, great attention has been directed to cells as carrier systems for treating several diseases. There are various challenges in the development of cell-based drug delivery systems. The prediction of the properties of these platforms is a prerequisite step in their development to reduce undesirable effects. Integrating nanotechnology and artificial intelligence leads to more innovative technologies. Artificial intelligence quickly mines data and makes decisions more quickly and accurately. Machine learning as a subset of the broader artificial intelligence has been used in nanomedicine to design safer nanomaterials. Here, how challenges of developing cell-based drug delivery systems can be solved with potential predictive models of artificial intelligence and machine learning is portrayed. The most famous cell-based drug delivery systems and their challenges are described. Last but not least, artificial intelligence and most of its types used in nanomedicine are highlighted. The present Review has shown the challenges of developing cells or their derivatives as carriers and how they can be used with potential predictive models of artificial intelligence and machine learning.
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Affiliation(s)
- Negin Mozafari
- Department of Pharmaceutics, School of Pharmacy, Shiraz University of Medical Sciences, 71468 64685 Shiraz, Iran
| | - Niloofar Mozafari
- Design and System Operations Department, Regional Information Center for Science and Technology, 71946 94171 Shiraz, Iran
| | - Ali Dehshahri
- Department of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, 71468 64685 Shiraz, Iran
- Pharmaceutical Sciences Research Centre, Shiraz University of Medical Sciences, 71468 64685 Shiraz, Iran
| | - Amir Azadi
- Department of Pharmaceutics, School of Pharmacy, Shiraz University of Medical Sciences, 71468 64685 Shiraz, Iran
- Pharmaceutical Sciences Research Centre, Shiraz University of Medical Sciences, 71468 64685 Shiraz, Iran
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LaLone V, Aizenshtadt A, Goertz J, Skottvoll FS, Mota MB, You J, Zhao X, Berg HE, Stokowiec J, Yu M, Schwendeman A, Scholz H, Wilson SR, Krauss S, Stevens MM. Quantitative chemometric phenotyping of three-dimensional liver organoids by Raman spectral imaging. CELL REPORTS METHODS 2023; 3:100440. [PMID: 37159662 PMCID: PMC10162950 DOI: 10.1016/j.crmeth.2023.100440] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 02/06/2023] [Accepted: 03/08/2023] [Indexed: 05/11/2023]
Abstract
Confocal Raman spectral imaging (RSI) enables high-content, label-free visualization of a wide range of molecules in biological specimens without sample preparation. However, reliable quantification of the deconvoluted spectra is needed. Here we develop an integrated bioanalytical methodology, qRamanomics, to qualify RSI as a tissue phantom calibrated tool for quantitative spatial chemotyping of major classes of biomolecules. Next, we apply qRamanomics to fixed 3D liver organoids generated from stem-cell-derived or primary hepatocytes to assess specimen variation and maturity. We then demonstrate the utility of qRamanomics for identifying biomolecular response signatures from a panel of liver-altering drugs, probing drug-induced compositional changes in 3D organoids followed by in situ monitoring of drug metabolism and accumulation. Quantitative chemometric phenotyping constitutes an important step in developing quantitative label-free interrogation of 3D biological specimens.
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Affiliation(s)
- Vernon LaLone
- Department of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London, London SW7 2AZ, UK
- Hybrid Technology Hub-Centre of Excellence, Imperial College London, London SW7 2AZ, UK
| | - Aleksandra Aizenshtadt
- Hybrid Technology Hub-Centre of Excellence, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, P.O. Box 1112, Blindern, 0317 Oslo, Norway
| | - John Goertz
- Department of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London, London SW7 2AZ, UK
| | - Frøydis Sved Skottvoll
- Hybrid Technology Hub-Centre of Excellence, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, P.O. Box 1112, Blindern, 0317 Oslo, Norway
- Department of Chemistry, University of Oslo, P.O. Box 1033, Blindern, 0315 Oslo, Norway
| | - Marco Barbero Mota
- Department of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London, London SW7 2AZ, UK
| | - Junji You
- Department of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London, London SW7 2AZ, UK
| | - Xiaoyu Zhao
- Department of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London, London SW7 2AZ, UK
| | - Henriette Engen Berg
- Department of Chemistry, University of Oslo, P.O. Box 1033, Blindern, 0315 Oslo, Norway
| | - Justyna Stokowiec
- Hybrid Technology Hub-Centre of Excellence, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, P.O. Box 1112, Blindern, 0317 Oslo, Norway
| | - Minzhi Yu
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Anna Schwendeman
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hanne Scholz
- Hybrid Technology Hub-Centre of Excellence, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, P.O. Box 1112, Blindern, 0317 Oslo, Norway
- Department of Transplant Medicine, Oslo University Hospital, Oslo, Norway
- Institute for Surgical Research, Oslo University Hospital, Oslo, Norway
| | - Steven Ray Wilson
- Hybrid Technology Hub-Centre of Excellence, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, P.O. Box 1112, Blindern, 0317 Oslo, Norway
- Department of Chemistry, University of Oslo, P.O. Box 1033, Blindern, 0315 Oslo, Norway
| | - Stefan Krauss
- Hybrid Technology Hub-Centre of Excellence, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, P.O. Box 1112, Blindern, 0317 Oslo, Norway
- Department of Immunology and Transfusion Medicine, Oslo University Hospital, P.O. Box 4950, Nydalen, 0424 Oslo, Norway
| | - Molly M. Stevens
- Department of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London, London SW7 2AZ, UK
- Hybrid Technology Hub-Centre of Excellence, Imperial College London, London SW7 2AZ, UK
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Desa DE, Qian T, Skala MC. Label-free optical imaging and sensing for quality control of stem cell manufacturing. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2023; 25:100435. [PMID: 37885458 PMCID: PMC10602581 DOI: 10.1016/j.cobme.2022.100435] [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] [Indexed: 12/15/2022]
Abstract
Human stem cells provide emerging methods for drug screening, disease modeling, and personalized patient therapies. To meet this growing demand for scale-up, stem cell manufacturing methods must be streamlined with continuous monitoring technologies and automated feedback to optimize growth conditions for high production and consistency. Label-free optical imaging and sensing, including multiphoton microscopy, Raman spectroscopy, and low-cost methods such as phase and transmitted light microscopy, can provide rapid, repeatable, and non-invasive monitoring of stem cells throughout cell differentiation and maturation. Machine learning algorithms trained on label-free optical imaging and sensing features could identify viable cells and predict optimal manufacturing conditions. These techniques have the potential to streamline stem cell manufacturing and accelerate their use in regenerative medicine.
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Affiliation(s)
- Danielle E Desa
- Morgridge Institute for Research, 330 N. Orchard St., Madison, WI 53715, United States
| | - Tongcheng Qian
- Morgridge Institute for Research, 330 N. Orchard St., Madison, WI 53715, United States
| | - Melissa C Skala
- Morgridge Institute for Research, 330 N. Orchard St., Madison, WI 53715, United States
- Department of Biomedical Engineering, University of Wisconsin-Madison, 1550 Engineering Dr., Madison, WI 53706, United States
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Huang C, Jiang Y, Li Y, Zhang H. Droplet Detection and Sorting System in Microfluidics: A Review. MICROMACHINES 2022; 14:mi14010103. [PMID: 36677164 PMCID: PMC9867185 DOI: 10.3390/mi14010103] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/23/2022] [Accepted: 12/26/2022] [Indexed: 05/26/2023]
Abstract
Since being invented, droplet microfluidic technologies have been proven to be perfect tools for high-throughput chemical and biological functional screening applications, and they have been heavily studied and improved through the past two decades. Each droplet can be used as one single bioreactor to compartmentalize a big material or biological population, so millions of droplets can be individually screened based on demand, while the sorting function could extract the droplets of interest to a separate pool from the main droplet library. In this paper, we reviewed droplet detection and active sorting methods that are currently still being widely used for high-through screening applications in microfluidic systems, including the latest updates regarding each technology. We analyze and summarize the merits and drawbacks of each presented technology and conclude, with our perspectives, on future direction of development.
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Affiliation(s)
- Can Huang
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77842, USA
| | - Yuqian Jiang
- State Key Laboratory of Food Nutrition and Safety, College of Food Science and Engineering, Tianjin University of Science and Technology, Tianjin 300457, China
| | - Yuwen Li
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77842, USA
| | - Han Zhang
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77842, USA
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Kim W, Park E, Yoo HS, Park J, Jung YM, Park JH. Recent Advances in Monitoring Stem Cell Status and Differentiation Using Nano-Biosensing Technologies. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:nano12172934. [PMID: 36079970 PMCID: PMC9457759 DOI: 10.3390/nano12172934] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 08/22/2022] [Accepted: 08/23/2022] [Indexed: 05/14/2023]
Abstract
In regenerative medicine, cell therapies using various stem cells have received attention as an alternative to overcome the limitations of existing therapeutic methods. Clinical applications of stem cells require the identification of characteristics at the single-cell level and continuous monitoring during expansion and differentiation. In this review, we recapitulate the application of various stem cells used in regenerative medicine and the latest technological advances in monitoring the differentiation process of stem cells. Single-cell RNA sequencing capable of profiling the expression of many genes at the single-cell level provides a new opportunity to analyze stem cell heterogeneity and to specify molecular markers related to the branching of differentiation lineages. However, this method is destructive and distorted. In addition, the differentiation process of a particular cell cannot be continuously tracked. Therefore, several spectroscopic methods have been developed to overcome these limitations. In particular, the application of Raman spectroscopy to measure the intrinsic vibration spectrum of molecules has been proposed as a powerful method that enables continuous monitoring of biochemical changes in the process of the differentiation of stem cells. This review provides a comprehensive overview of current analytical methods employed for stem cell engineering and future perspectives of nano-biosensing technologies as a platform for the in situ monitoring of stem cell status and differentiation.
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Affiliation(s)
- Wijin Kim
- Department of Biomedical Science, Kangwon National University, Chuncheon 24341, Gangwon-do, Korea
| | - Eungyeong Park
- Department of Chemistry, Kangwon National University, Chuncheon 24341, Gangwon-do, Korea
| | - Hyuk Sang Yoo
- Department of Biomedical Science, Kangwon National University, Chuncheon 24341, Gangwon-do, Korea
- Kangwon Radiation Convergence Research Support Center, Kangwon National University, Chuncheon 24341, Gangwon-do, Korea
| | - Jongmin Park
- Department of Chemistry, Kangwon National University, Chuncheon 24341, Gangwon-do, Korea
| | - Young Mee Jung
- Department of Chemistry, Kangwon National University, Chuncheon 24341, Gangwon-do, Korea
- Kangwon Radiation Convergence Research Support Center, Kangwon National University, Chuncheon 24341, Gangwon-do, Korea
- Correspondence: (Y.M.J.); (J.H.P.); Tel.: +82-33-250-8495 (Y.M.J.); +82-33-250-6566 (J.H.P.)
| | - Ju Hyun Park
- Department of Biomedical Science, Kangwon National University, Chuncheon 24341, Gangwon-do, Korea
- Correspondence: (Y.M.J.); (J.H.P.); Tel.: +82-33-250-8495 (Y.M.J.); +82-33-250-6566 (J.H.P.)
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Kukolj T, Lazarević J, Borojević A, Ralević U, Vujić D, Jauković A, Lazarević N, Bugarski D. A Single-Cell Raman Spectroscopy Analysis of Bone Marrow Mesenchymal Stem/Stromal Cells to Identify Inter-Individual Diversity. Int J Mol Sci 2022; 23:4915. [PMID: 35563306 PMCID: PMC9103070 DOI: 10.3390/ijms23094915] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 04/21/2022] [Accepted: 04/25/2022] [Indexed: 12/15/2022] Open
Abstract
The heterogeneity of stem cells represents the main challenge in regenerative medicine development. This issue is particularly pronounced when it comes to the use of primary mesenchymal stem/stromal cells (MSCs) due to a lack of identification markers. Considering the need for additional approaches in MSCs characterization, we applied Raman spectroscopy to investigate inter-individual differences between bone marrow MSCs (BM-MSCs). Based on standard biological tests, BM-MSCs of analyzed donors fulfill all conditions for their characterization, while no donor-related specifics were observed in terms of BM-MSCs morphology, phenotype, multilineage differentiation potential, colony-forming capacity, expression of pluripotency-associated markers or proliferative capacity. However, examination of BM-MSCs at a single-cell level by Raman spectroscopy revealed that despite similar biochemical background, fine differences in the Raman spectra of BM-MSCs of each donor can be detected. After extensive principal component analysis (PCA) of Raman spectra, our study revealed the possibility of this method to diversify BM-MSCs populations, whereby the grouping of cell populations was most prominent when cell populations were analyzed in pairs. These results indicate that Raman spectroscopy, as a label-free assay, could have a huge potential in understanding stem cell heterogeneity and sorting cell populations with a similar biochemical background that can be significant for the development of personalized therapy approaches.
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Affiliation(s)
- Tamara Kukolj
- Group for Hematology and Stem Cells, Institute for Medical Research, National Institute of Republic of Serbia, University of Belgrade, 11129 Belgrade, Serbia; (A.J.); (D.B.)
| | - Jasmina Lazarević
- Center for Solid State Physics and New Materials, Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, 11080 Belgrade, Serbia; (J.L.); (U.R.); (N.L.)
| | - Ana Borojević
- Mother and Child Health Care Institute of Serbia ‘’Dr Vukan Čupić’’, 11000 Belgrade, Serbia; (A.B.); (D.V.)
| | - Uroš Ralević
- Center for Solid State Physics and New Materials, Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, 11080 Belgrade, Serbia; (J.L.); (U.R.); (N.L.)
| | - Dragana Vujić
- Mother and Child Health Care Institute of Serbia ‘’Dr Vukan Čupić’’, 11000 Belgrade, Serbia; (A.B.); (D.V.)
- School of Medicine, University of Belgrade, 11000 Belgrade, Serbia
| | - Aleksandra Jauković
- Group for Hematology and Stem Cells, Institute for Medical Research, National Institute of Republic of Serbia, University of Belgrade, 11129 Belgrade, Serbia; (A.J.); (D.B.)
| | - Nenad Lazarević
- Center for Solid State Physics and New Materials, Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, 11080 Belgrade, Serbia; (J.L.); (U.R.); (N.L.)
| | - Diana Bugarski
- Group for Hematology and Stem Cells, Institute for Medical Research, National Institute of Republic of Serbia, University of Belgrade, 11129 Belgrade, Serbia; (A.J.); (D.B.)
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Tomita S, Ishihara S, Kurita R. A polymer-based chemical tongue for the non-invasive monitoring of osteogenic stem-cell differentiation by pattern recognition of serum-supplemented spent media. J Mater Chem B 2022; 10:7581-7590. [DOI: 10.1039/d2tb00606e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The development of non-invasive techniques to characterize cultured cells is invaluable not only to ensure the reproducibility of cell research, but also for quality assurance of industrial cell products for...
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