Raman Flow Cytometry and Its Biomedical Applications
Abstract
:1. Introduction
2. Basics of Raman Spectroscopy
3. Development and Advantages of RFC
3.1. Development of RFC
3.2. Advantages of RFC
4. Biomedical Applications of RFC
4.1. Cancer Cells and Cancer Detection
4.2. Stem Cells and Cellular Therapy
4.3. Microbial Cells and Subpopulation Differentiation
4.4. Drug Discovery and Sensitivity Assessment (Cellular Level)
5. Current Challenges and Future Directions
5.1. Sensitivity
5.2. Throughput
5.3. Instrumental Design and Data Processing
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Raman Shift (cm−1) | Vibrational Mode and/or Assignments | Raman Shift (cm−1) | Vibrational Mode and/or Assignments |
---|---|---|---|
407 | Carbohydrates | 1124–1128 | Cytochrome C |
429 | Cholesterol ester | 1168–1172 | Lipids, Tyrosine |
490 | Glycogen, Chitin | 1240 | Phosphate (asymmetric) |
538 | Cholesterol ester | 1254–1270 | Lipids, Protein, Adenine, Cytosine, Guanine |
573 | Cytosine, Guanine, Tryptophan | 1313–1315 | Collagen, Lipid, Guanine |
595 | Phosphatidylserine | 1330 | DNA, Phospholipids, Purine |
621 | Phenylalanine | 1361 | Guanine |
663–671 | Guanine, Thymine in nucleotide, Tyrosine | 1386–1389 | CH3 band |
726 | Adenine, Peptidoglycan | 1421 | Peptidoglycan |
750 | Cytochrome C | 1480–1491 | Guanine, Adenine |
781 | Cytosine, Uracil in nucleotide | 1573–1582 | Amide II, Nucleic acid, Peptidoglycan |
845–860 | Proline, Tyrosine | 1583 | Cytochrome C |
913–915 | Glucose, Ribose | 1601–1607 | Phenylalanine, Tyrosine |
1001 | Phenylalanine | 1616 | Tyrosine |
1082 | Carbohydrates | 1655–1676 | Protein, Lipid, Unsaturated fatty acids |
1102 | Phosphate (symmetric) |
Flow Cytometry Type | Label | Throughput | Information Content | Biological Interference |
---|---|---|---|---|
Fluorescence Flow Cytometry | Label Reqiured (fluorescent dye) | 100, 000 eps | Univariate (up to 12) | Label Dependent, Photon Dependent |
Spontaneous Raman Flow Cytometry | Label Free | 1eps | Multivariate (20+) | Photon Dependent |
Coherent Raman Flow Cytometry | Label Free | 100 eps | Multivariate (15+) | Photon Dependent |
Sample Type | Biomolecules Detected | Applications | Raman Spectroscopy Type | Excitation Wavelength | Throughput | Reference |
---|---|---|---|---|---|---|
Cancer Cells | Carbon nanotubes (CNTs) | Human cervical cancer cells (HeLa) detection | Time-resolved Raman spectroscopy | 514 nm, 633 nm, and 785 nm | -- | [66] |
Proteins (specific cell surface markers CD19, CD20, and CD45) | Malignant B cells from LY10 lymphoma cell line and primary chronic lymphocytic leukemia cells detection | SERS | 638 nm | -- | [67] | |
Glycoprotein (neuropilin-1, NRP-1) | Cancerous and non-cancerous prostate cells detection and monitoring | SERS | 633 nm | -- | [68] | |
Proteins, lipids, saccharides (paramylon) and pigments (chlorophyll) | Blood samples (cancerous and other cells) detection | SRS microscopy | 790 nm and 1030 nm | 140 eps | [55] | |
Proteins, lipids, and nucleic acids | Human cancer cell lines (bladder (T24), lung (A549), renal (OSRC-2), and breast (MCF-7)) detection and sorting | Spontaneous Raman | 532 nm | 30 eps | [69] | |
Stem Cells | Proteins, lipids, and saccharides | High-integrity pluripotent stem cells (hiPSCs) analysis and sorting | SRS microscopy | 790.6 nm and 796.6 nm | 100 eps | [58] |
Proteins, lipids, and nucleic acids | Bone-derived mesenchymal stem cell (MSC) lines classification | Spontaneous Raman | 532 nm | -- | [70] | |
Proteins (myofibril proteins) and saccharides (glycogen) | Cardiomyocytes derived from human embryonic stem cells (hESCs) analysis and sorting | Spontaneous Raman microscopy | 785 nm | 10 eps | [71] | |
Microbe Cells | Lipids | Yeast cell subpopulations classification | Multiplex coherent anti-Stokes Raman scattering (MCARS) | 806 nm | 100 eps | [72] |
Lipids (triglycerides and lipid-related molecules) | Staphylococcus aureus identification and analysis | SRS | 1040 nm Stokes beam, 680–1300 nm pump beam | 11,000 eps | [73] | |
Saccharides (paramylon, a β-1,3-glucan) | Euglena gracilis identification and analysis | FT-CARS | 780 nm | 100 eps | [53] | |
Pigments (chlorophyll and astaxanthin) | Haematococcus lacustris identification and analysis | FT-CARS | 780 nm | 2000 eps | [54] |
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Xu, J.; Chen, H.; Wang, C.; Ma, Y.; Song, Y. Raman Flow Cytometry and Its Biomedical Applications. Biosensors 2024, 14, 171. https://doi.org/10.3390/bios14040171
Xu J, Chen H, Wang C, Ma Y, Song Y. Raman Flow Cytometry and Its Biomedical Applications. Biosensors. 2024; 14(4):171. https://doi.org/10.3390/bios14040171
Chicago/Turabian StyleXu, Jiayang, Hongyi Chen, Ce Wang, Yuting Ma, and Yizhi Song. 2024. "Raman Flow Cytometry and Its Biomedical Applications" Biosensors 14, no. 4: 171. https://doi.org/10.3390/bios14040171
APA StyleXu, J., Chen, H., Wang, C., Ma, Y., & Song, Y. (2024). Raman Flow Cytometry and Its Biomedical Applications. Biosensors, 14(4), 171. https://doi.org/10.3390/bios14040171