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Chengshun Jiang, Jie Deng, Wanwan Gan, Jiaqi Zou, Tongkai Cai, Hao Yin, Yongbing Cao. Raman spectroscopy combined with multiple technologies for label-free identification of immune cells: An overview[J]. Journal of Pharmaceutical Analysis. doi: 10.1016/j.jpha.2025.101468
Citation: Chengshun Jiang, Jie Deng, Wanwan Gan, Jiaqi Zou, Tongkai Cai, Hao Yin, Yongbing Cao. Raman spectroscopy combined with multiple technologies for label-free identification of immune cells: An overview[J]. Journal of Pharmaceutical Analysis. doi: 10.1016/j.jpha.2025.101468
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Raman spectroscopy combined with multiple technologies for label-free identification of immune cells: An overview

doi: 10.1016/j.jpha.2025.101468

  • a. Institute of Vascular Diseases, Shanghai TCM-Integrated Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200086, China;

  • b. Department of Physiology and Pharmacology, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 210009, China;

  • c. Shanghai Diacart Biomedical Science and Technology Limited Company, Shanghai, 201203, China

Funds:

This paper was supported by the Science and Technology Innovation Plan of Shanghai Science and Technology Commission, China (Grant No. 22S21901400).

  • Received Date: Apr. 07, 2025
  • Accepted Date: Oct. 09, 2025
  • Rev Recd Date: Oct. 03, 2025
  • Available Online: Oct. 13, 2025
    Abstract
  • Traditional immune cell identification and sorting methods rely on antibodies and fluorophores, which may compromise cell viability and functionality. Raman spectroscopy, a label-free and highly sensitive technique, enables precise differentiation of immune cell subtypes based on their intrinsic biochemical composition. When integrated with chemometrics, microfluidics, and machine learning (ML)/deep learning (DL) approaches, Raman spectroscopy significantly enhances the accuracy, throughput, and efficiency of immune cell sorting. This review systematically analyzes the principles and advantages of these integrated strategies and explores their potential applications in immunological research, clinical diagnostics, and precision medicine, paving the way for non-invasive and high-efficiency immune cell analysis.
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  • [1]
    Y. Wang, H. Zhang, C. Liu, et al., Immune checkpoint modulators in cancer immunotherapy: Recent advances and emerging concepts, J. Hematol. Oncol. 15 (2022), 111.
    [2]
    D.J. Baker, Z. Arany, J.A. Baur, et al., CAR T therapy beyond cancer: The evolution of a living drug, Nature 619 (2023) 707-715.
    [3]
    B. Kruse, A.C. Buzzai, N. Shridhar, et al., CD4+ T cell-induced inflammatory cell death controls immune-evasive tumours, Nature 618 (2023) 1033-1040.
    [4]
    J. Ochando, W.J.M. Mulder, J.C. Madsen, et al., Trained immunity: Basic concepts and contributions to immunopathology, Nat. Rev. Nephrol. 19 (2023) 23-37.
    [5]
    X.Y. Zhao, G.Y. Liu, Y.T. Sui, et al., Denoising method for Raman spectra with low signal-to-noise ratio based on feature extraction, Spectrochim. Acta A Mol. Biomol. Spectrosc. 250 (2021), 119374.
    [6]
    X. Tang, Q. Wu, L. Shang, et al., Raman cell sorting for single-cell research, Front. Bioeng. Biotechnol. 12 (2024), 1389143.
    [7]
    L. Zhu, J. Li, J. Pan, et al., Precise identification of glioblastoma micro-infiltration at cellular resolution by Raman spectroscopy, Adv. Sci. 11 (2024), 2401014.
    [8]
    X. Chen, Y. Zhang, S. Ye, et al., Time-resolved Raman spectroscopy for monitoring the structural evolution of materials during rapid compression, Rev. Sci. Instrum. 94 (2023), 123901.
    [9]
    M.M. Schmidt, A.G. Brolo, N.C. Lindquist, Single-molecule surface-enhanced Raman spectroscopy: Challenges, opportunities, and future directions, ACS Nano (2024).
    [10]
    T.D. Wu, S. Madireddi, P.E. de Almeida, et al., Peripheral T cell expansion predicts tumour infiltration and clinical response, Nature 579 (2020) 274-278.
    [11]
    S.K. Paidi, P. Raj, R. Bordett, et al., Raman and quantitative phase imaging allow morpho-molecular recognition of malignancy and stages of B-cell acute lymphoblastic leukemia, Biosens. Bioelectron. 190 (2021), 113403.
    [12]
    N. Pavillon, N.I. Smith, Immune cell type, cell activation, and single cell heterogeneity revealed by label-free optical methods, Sci. Rep. 9 (2019), 17054.
    [13]
    N. Feuerer, J. Marzi, E.M. Brauchle, et al., Lipidome profiling with Raman microspectroscopy identifies macrophage response to surface topographies of implant materials, Proc. Natl. Acad. Sci. USA 118 (2021), e2113694118.
    [14]
    K. Hiramatsu, T. Ideguchi, Y. Yonamine, et al., High-throughput label-free molecular fingerprinting flow cytometry. Sci Adv. 16 (2019), eaau0241.
    [15]
    K. Miyake, S. Shibata, S. Yoshikawa, et al., Basophils and their effector molecules in allergic disorders, Allergy 76 (2021) 1693-1706.
    [16]
    C.H. Koh, S. Lee, M. Kwak, et al., CD8 T-cell subsets: Heterogeneity, functions, and therapeutic potential, Exp. Mol. Med. 55 (2023) 2287-2299.
    [17]
    N. Ohkura, S. Sakaguchi, Transcriptional and epigenetic basis of Treg cell development and function: Its genetic anomalies or variations in autoimmune diseases, Cell Res. 30 (2020) 465-474.
    [18]
    T. Inoue, T. Kurosaki, Memory B cells, Nat. Rev. Immunol. 24 (2024) 5-17.
    [19]
    E. Vivier, L. Rebuffet, E. Narni-Mancinelli, et al., Natural killer cell therapies, Nature 626 (2024) 727-736.
    [20]
    A. Herault, J. Mak, J. de la Cruz-Chuh, et al., NKG2D-bispecific enhances NK and CD8+ T cell antitumor immunity, Cancer Immunol. Immunother. 73 (2024), 209.
    [21]
    G. Ning, X. Liao, H. Jiang, Integrated analyses reveal CST7 and DUSP5 regulate Th2 cells differentiation to promote chronic HBV infection, Genes Immun. 25 (2024) 423-433.
    [22]
    I.E. Osadare, L. Xiong, I, Rubio et al., Raman Spectroscopy Profiling of Splenic T-Cells in Sepsis and Endotoxemia in Mice. Int. J. Mol. Sci. 24(2023),12027.
    [23]
    M. Notzel, M. Mahamid, R. Kronstein-Wiedemann, et al., Raman spectroscopy of optically trapped living human T cell subsets and monocytes, Int. J. Mol. Sci. 25 (2024), 9557.
    [24]
    N. Chaudhary, T.N.Q. Nguyen, D. Cullen, et al., Discrimination of immune cell activation using Raman micro-spectroscopy in an in-vitro & ex-vivo model, Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 248 (2021), 119118.
    [25]
    N. McReynolds, F.G.M. Cooke, M. Chen, et al., Multimodal discrimination of immune cells using a combination of Raman spectroscopy and digital holographic microscopy, Sci. Rep. 7 (2017), 43631.
    [26]
    A. Borek-Dorosz, A.M. Nowakowska, P. Leszczenko, et al., Raman-based spectrophenotyping of the most important cells of the immune system, J. Adv. Res. 41 (2022) 191-203.
    [27]
    N. Feuerer, D.A. Carvajal Berrio, F. Billing, et al., Raman microspectroscopy identifies biochemical activation fingerprints in THP-1- and PBMC-derived macrophages, Biomedicines 10 (2022), 989.
    [28]
    P. Laskowska, A. Borek-Dorosz, A.M. Nowakowska, et al., p1374: Raman spectroscopy studies of immune response elements, HemaSphere 7 (2023), e3122415.
    [29]
    N. Pavillon, N.I. Smith, Non-invasive monitoring of T cell differentiation through Raman spectroscopy, Sci. Rep. 13 (2023), 3129.
    [30]
    J. Gala de Pablo, M. Lindley, K. Hiramatsu, et al., High-throughput Raman flow cytometry and beyond, Acc. Chem. Res. 54 (2021) 2132-2143.
    [31]
    J. Picot, C.L. Guerin, C. Le Van Kim, et al., Flow cytometry: Retrospective, fundamentals and recent instrumentation, Cytotechnology 64 (2012) 109-130.
    [32]
    H. Kanno, K. Hiramatsu, H. Mikami, et al., High-throughput fluorescence lifetime imaging flow cytometry, Nat. Commun. 15 (2024), 7376.
    [33]
    A. Kumanovics, A.A. Sadighi Akha, Flow cytometry for B-cell subset analysis in immunodeficiencies, J. Immunol. Methods. 509 (2022), 113327.
    [34]
    W. Ma, S. Wei, S. Long, et al., Dynamic evaluation of blood immune cells predictive of response to immune checkpoint inhibitors in NSCLC by multicolor spectrum flow cytometry, Front. Immunol. 14 (2023), 1206631.
    [35]
    J.R. Brestoff, J.L. Frater, Contemporary challenges in clinical flow cytometry: Small samples, big data, little time, J. Appl. Lab. Med. 7 (2022) 931-944.
    [36]
    J. Hunka, J.T. Riley, G.F. Debes, Approaches to overcome flow cytometry limitations in the analysis of cells from veterinary relevant species, BMC Vet. Res. 16 (2020), 83.
    [37]
    S. Oh, S.H. Jung, H. Seo, et al., Magnetic activated cell sorting (MACS) pipette tip for immunomagnetic bacteria separation, Sens. Actuat. B-Chem. 272 (2018) 324-330.
    [38]
    Z. Wang, S. Ahmed, M. Labib, et al., Efficient recovery of potent tumour-infiltrating lymphocytes through quantitative immunomagnetic cell sorting, Nat. Biomed. Eng. 6 (2022) 108-117.
    [39]
    B.A. Sutermaster, E.M. Darling, Considerations for high-yield, high-throughput cell enrichment: Fluorescence versus magnetic sorting, Sci. Rep. 9 (2019), 227.
    [40]
    M. Legut, N.E. Sanjana, Immunomagnetic cell sorting, Nat. Biomed. Eng. 3 (2019) 759-760.
    [41]
    M. Frenea-Robin, J. Marchalot, Basic principles and recent advances in magnetic cell separation, Magnetochemistry 8 (2022), 11.
    [42]
    H. Hayrapetyan, T. Tran, E. Tellez-Corrales, et al., Enzyme-linked immunosorbent assay: Types and applications, Methods Mol. Biol. 2612 (2023) 1-17.
    [43]
    S. Zhang, A. Garcia-D’Angeli, J.P. Brennan, et al., Predicting detection limits of enzyme-linked immunosorbent assay (ELISA) and bioanalytical techniques in general, Analyst 139 (2014) 439-445.
    [44]
    E.A. Beebe, M.T. Orr, Assessment of antigen-specific cellular immunogenicity using intracellular cytokine staining, ELISpot, and culture supernatants. Vaccine Adjuvants. Springer New York, (2016), 13–320.
    [45]
    F. Brosseron, K. Marcus, C. May, Isolating peripheral lymphocytes by density gradient centrifugation and magnetic cell sorting, Meth. Mol Biol 1295 (2015) 33-42.
    [46]
    N. Lu, H.M. Tay, C. Petchakup, et al., Label-free microfluidic cell sorting and detection for rapid blood analysis, Lab Chip 23 (2023) 1226-1257.
    [47]
    J. Liu, W. Hu, Y. Han, et al., Recent advances in mass spectrometry imaging of single cells, Anal. Bioanal. Chem. 415 (2023) 4093-4110.
    [48]
    D.K. Singh, C.C. Ahrens, W. Li, et al., Label-free fingerprinting of tumor cells in bulk flow using inline digital holographic microscopy, Biomed. Opt. Express 8 (2017), 536.
    [49]
    R. Kato, T.A. Yano, T. Tanaka, Single-cell infrared vibrational analysis by optical trapping mid-infrared photothermal microscopy, Analyst 148 (2023) 1285-1290.
    [50]
    N. Musso, A. Romano, P.G. Bonacci, et al., Label-free enrichment of circulating tumor plasma cells: Future potential applications of dielectrophoresis in multiple myeloma, Int. J. Mol. Sci. 23 (2022), 12052.
    [51]
    M.A. Sirotin, M.N. Romodina, E.V. Lyubin, et al., Single-cell all-optical coherence elastography with optical tweezers, Biomed. Opt. Express 13 (2022), 14.
    [52]
    Y.T. Hsiao, C.N. Tsai, C.Y. Cheng, et al., Molecularly specific and functional live cell imaging by label-free interference microscopy, ACS Photonics 9 (2022) 2237-2245.
    [53]
    J. Mukherjee, D. Chaturvedi, S. Mishra, et al., Microfluidic technology for cell biology-related applications: A review, J. Biol. Phys. 50 (2024) 1-27.
    [54]
    D.L. Lezzar, F.W. Lam, R. Huerta, et al., A high-throughput microfluidic device based on controlled incremental filtration to enable centrifugation-free, low extracorporeal volume leukapheresis, Sci. Rep. 12 (2022), 13798.
    [55]
    L.A.N. Julius, D. Akgul, G. Krishnan, et al., Portable dielectrophoresis for biology: ADEPT facilitates cell trapping, separation, and interactions, Microsyst. Nanoeng. 10 (2024), 29.
    [56]
    J. Yao, K. Zhao, J. Lou, et al., Recent advances in dielectrophoretic manipulation and separation of microparticles and biological cells, Biosensors 14 (2024), 417.
    [57]
    J. Yang, Y. Huang, X.B. Wang, et al., Differential analysis of human leukocytes by dielectrophoretic field-flow-fractionation, Biophys. J. 78 (2000) 2680-2689.
    [58]
    M.E.P. Emmerich, A.S. Sinnigen, P. Neubauer, et al., Dielectrophoretic separation of blood cells, Biomed. Microdevices 24 (2022), 30.
    [59]
    T. Xu, Y. Li, X. Han, et al., Versatile, facile and low-cost single-cell isolation, culture and sequencing by optical tweezer-assisted pool-screening, Lab Chip 23(2022), 125-135.
    [60]
    A.M. Malinowska, J. van Mameren, E.J.G. Peterman, et al., Introduction to optical tweezers: Background, system designs, and applications. Single Molecule Analysis. Springer US, (2023), pp –28.
    [61]
    J. Verduijn, L. Van der Meeren, D.V. Krysko, et al., Deep learning with digital holographic microscopy discriminates apoptosis and necroptosis, Cell Death Discov. 7 (2021), 229.
    [62]
    P. Gao, C. Yuan, Resolution enhancement of digital holographic microscopy via synthetic aperture: A review, Light. Adv. Manuf. 3 (2022) 105-120.
    [63]
    H. Zhang, J. Zhang, C. Yuan, et al., Recent advances in mass spectrometry imaging combined with artificial intelligence for spatially clarifying molecular profiles: Toward biomedical applications, Trac Trends Anal. Chem. 178 (2024), 117834.
    [64]
    Y. Zhang, H. Zong, C. Zong, et al., Fluorescence-detected mid-infrared photothermal microscopy, J. Am. Chem. Soc. 143 (2021) 11490-11499.
    [65]
    J.S. Park, I.B. Lee, H.M. Moon, et al., Label-free and live cell imaging by interferometric scattering microscopy, Chem. Sci. 9 (2018) 2690-2697.
    [66]
    T. Pan, T. Gao, X. Fan, et al., Development of a cost-effective confocal Raman microscopy with high sensitivity, Talanta 281 (2025), 126754.
    [67]
    N. Topfer, M.M. Muller, M. Dahms, et al., Raman spectroscopy reveals LPS-induced changes of biomolecular composition in monocytic THP-1 cells in a label-free manner, Integr. Biol. 11 (2019) 87-98.
    [68]
    A. Pistiki, A. Ramoji, O. Ryabchykov, et al., Biochemical Analysis of Leukocytes after In Vitro and In Vivo Activation with Bacterial and Fungal Pathogens Using Raman Spectroscopy, Int J Mol Sci 2021, 22(19), 10481.
    [69]
    C.V. Raman, K.S. Krishnan, A new type of secondary radiation, Nature 121 (1928) 501-502.
    [70]
    K. Liu, Q. Zhao, B. Li, et al., Raman spectroscopy: A novel technology for gastric cancer diagnosis, Front. Bioeng. Biotechnol. 10 (2022), 856591.
    [71]
    R.R. Jones, D.C. Hooper, L. Zhang, et al., Raman techniques: Fundamentals and frontiers, Nanoscale Res. Lett. 14 (2019), 231.
    [72]
    D. Wang, P. He, Z. Wang, et al., Advances in single cell Raman spectroscopy technologies for biological and environmental applications, Curr. Opin. Biotechnol. 64 (2020) 218-229.
    [73]
    G.P.S. Smith, C.M. McGoverin, S.J. Fraser, et al., Raman imaging of drug delivery systems, Adv. Drug Deliv. Rev. 89 (2015) 21-41.
    [74]
    Y. Yu, Y. Tang, K. Chu, et al., High-resolution low-power hyperspectral line-scan imaging of fast cellular dynamics using azo-enhanced Raman scattering probes, J. Am. Chem. Soc. 144 (2022) 15314-15323.
    [75]
    G. Das, R.L. Rocca, T. Lakshmikanth, et al., Monitoring human leukocyte antigen class I molecules by micro-Raman spectroscopy at single-cell level, J. Biomed. Opt. 15 (2010), 027007.
    [76]
    N. Chaudhary, C. Wynne, A.D. Meade, A review of applications of Raman spectroscopy in immunology, Biomed. Spectrosc. Ima. 923–931.
    [77]
    J.C. Greig, W.J. Tipping, D. Graham, et al., New insights into lipid and fatty acid metabolism from Raman spectroscopy, Analyst 149 (2024) 4789-4810.
    [78]
    S.K. Paidi, J. Rodriguez Troncoso, P. Raj, et al., Raman spectroscopy and machine learning reveals early tumor microenvironmental changes induced by immunotherapy, Cancer Res 81 (2021) 5745-5755.
    [79]
    P. Laskowska, A. Borek-Dorosz, A.M. Nowakowska, et al., Raman spectroscopy imaging in evaluation of immune cells activation status, Blood 142 (2023), 7157.
    [80]
    B. Kann, H.L. Offerhaus, M. Windbergs, et al., Raman microscopy for cellular investigations: From single cell imaging to drug carrier uptake visualization, Adv. Drug Deliv. Rev. 89 (2015) 71-90.
    [81]
    A.Z. Samuel, K. Sugiyama, M. Ando, et al., Direct imaging of intracellular RNA, DNA, and liquid-liquid phase separated membraneless organelles with Raman microspectroscopy, Commun. Biol. 5 (2022), 1383.
    [82]
    A. Borek-Dorosz, A. Pieczara, J. Orleanska, et al., Raman microscopy reveals how cell inflammation activates glucose and lipid metabolism, Biochim. Biophys. Acta Mol. Cell Res. 1871 (2024), 119575.
    [83]
    A. Roth, A. Tannert, N. Ziller, et al., Quantification of polystyrene uptake by different cell lines using fluorescence microscopy and label-free visualization of intracellular polystyrene particles by Raman microspectroscopic imaging, Cells 13 (2024), 454.
    [84]
    M. Magdy, A conceptual overview of surface-enhanced Raman scattering (SERS), Plasmonics 18 (2023) 803-809.
    [85]
    C. Jiang, Y. Cao, F. Lu, Application of surface-enhanced Raman scattering technique for biomacromolecular detection, Vib. Spectrosc. 133 (2024), 103713.
    [86]
    D. Yilmaz, M. Culha, Monitoring lipopolysaccharide-induced macrophage polarization by surface-enhanced Raman scattering, Mikrochim. Acta 191 (2024), 548.
    [87]
    H. Zhang, L. Yang, M. Zhang, et al., A statistical route to robust SERS quantitation beyond the single-molecule level, Nano Lett. 24 (2024) 11116-11123.
    [88]
    D. Rist, T. DePalma, E. Stagner, et al., Cancer cell targeting, magnetic sorting, and SERS detection through cell surface receptors, ACS Sens. 8 (2023) 4636-4645.
    [89]
    C. Hoppener, J. Aizpurua, H. Chen, et al., Tip-enhanced Raman scattering, Nat. Rev. Method. Primer. 4 (2024), 47.
    [90]
    Y. Cao, M. Sun, Tip-enhanced Raman spectroscopy, Rev. Phys. 8 (2022), 100067.
    [91]
    D. Mrdenovic, W. Ge, N. Kumar, et al., Nanoscale chemical imaging of human cell membranes using tip-enhanced Raman spectroscopy, Angew. Chem. Int. Ed 61 (2022), e202210288.
    [92]
    C. Zhang, D. Zhang, J. Cheng, Coherent Raman Scattering Microscopy in Biology and Medicine, Annu. Rev. Biomed Eng. 17 (2015) 415-445.
    [93]
    H. Hu, J. Bai, G. Xia, et al., Improved baseline correction method based on polynomial fitting for Raman spectroscopy, Photonic Sens. 8 (2018) 332-340.
    [94]
    G. Zhang, H. Hao, Y. Wang, et al., Optimized adaptive Savitzky-Golay filtering algorithm based on deep learning network for absorption spectroscopy, Spectrochim. ACTA. A. 263 (2021), 120187.
    [95]
    J. Wahl, M. Sjodahl, K. Ramser, Single-step preprocessing of Raman spectra using convolutional neural networks, Appl. Spectrosc. 74 (2020) 427-438.
    [96]
    J. Basumatary, D. Gangopadhyay, A. Nath, et al., Studies of temperature dependent Raman spectroscopy of two nematic liquid crystalline compounds of homologous series, Spectrochim. ACTA. A. 300 (2023), 122898.
    [97]
    Y.J. Lee, H.J. Ahn, G.J. Lee, et al., Investigation of biochemical property changes in activation-induced CD8+ T cell apoptosis using Raman spectroscopy, J. Biomed. Opt. 20 (2015), 75001.
    [98]
    Z. Xia, Y. Chen, C. Xu, Multiview PCA: A methodology of feature extraction and dimension reduction for high-order data, IEEE. T. Cybernetics. 52 (2022) 11068-11080.
    [99]
    S. Zhao, B. Zhang, J. Yang, et al., Linear discriminant analysis, Nat. Rev. Method. Primer. 4 (2024), 70.
    [100]
    J.L. Godoy, J.R. Vega, J.L. Marchetti, Relationships between pca and PLS-regression, Chemom. Intell. Lab. 130 (2014) 182-191.
    [101]
    C. Ma, L. Zhai, J. Ding, et al., Raman spectroscopy combined with partial least squares (PLS) based on hybrid spectral preprocessing and backward interval PLS (biPLS) for quantitative analysis of four PAHs in oil sludge, Spectrochim. ACTA. A. 310 (2024), 123953.
    [102]
    Y. Chen, Reference-related component analysis: A new method inheriting the advantages of PLS and PCA for separating interesting information and reducing data dimension, Chemom. Intell. Lab. 156 (2016) 196-202.
    [103]
    L. Tang, S. Peng, Y. Bi, et al., A new method combining LDA and PLS for dimension reduction, PLoS One 9 (2014), e96944.
    [104]
    K.L. Brown, O.Y. Palyvoda, J.S. Thakur, et al., Raman spectroscopic differentiation of activated versus non-activated T lymphocytes: An in vitro study of an acute allograft rejection model, J. Immunol. Methods. 340 (2009) 48-54.
    [105]
    M. Chen, N. McReynolds, E.C. Campbell, et al., The use of wavelength modulated Raman spectroscopy in label-free identification of T lymphocyte subsets, natural killer cells and dendritic cells, PLoS One 10 (2015), e0125158.
    [106]
    I.W. Schie, J. Ruger, A.S. Mondol, et al., High-throughput screening Raman spectroscopy platform for label-free cellomics, Anal. Chem. 90 (2018) 2023-2030.
    [107]
    A.R.B. Ribeiro, E.C.O. Silva, P.M.C. Araujo, et al., Application of Raman spectroscopy for characterization of the functional polarization of macrophages into M1 and M2 cells, Spectrochim. Acta A Mol. Biomol. Spectrosc. 265 (2022), 120328.
    [108]
    T. Ichimura, L.D. Chiu, K. Fujita, et al., Non-label immune cell state prediction using Raman spectroscopy, Sci. Rep. 6 (2016), 37562.
    [109]
    I. Pastrana-Otero, S. Majumdar, A.E. Gilchrist, et al., Identification of the differentiation stages of living cells from the six most immature murine hematopoietic cell populations by multivariate analysis of single-cell Raman spectra, Anal. Chem. 94 (2022) 11999-12007.
    [110]
    Z. Jiang, H. Shi, X. Tang, et al., Recent advances in droplet microfluidics for single-cell analysis, Trac. Trend. Anal. Chem. 159 (2023), 116932.
    [111]
    S. Lin, D. Feng, X. Han, et al., Microfluidic platform for omics analysis on single cells with diverse morphology and size: A review, Anal. Chim. Acta 1294 (2024), 342217.
    [112]
    W. Zhou, Y. Yan, Q. Guo, et al., Microfluidics applications for high-throughput single cell sequencing, J. Nanobiotechnol. 19 (2021), 312.
    [113]
    Y. Lyu, X. Yuan, A. Glidle, et al., Automated Raman based cell sorting with 3D microfluidics, Lab Chip 20 (2020) 4235-4245.
    [114]
    X. Wang, Y. Xin, L. Ren, et al., Positive dielectrophoresis-based Raman-activated droplet sorting for culture-free and label-free screening of enzyme function in vivo, Sci. Adv. 6 (2020), eabb3521.
    [115]
    J. Zhou, A. Wei, A. Bertsch, et al., High precision, high throughput generation of droplets containing single cells, Lab. Chip. 22 (2022) 4841-4848.
    [116]
    H. Li, T. Peng, Y. Zhong, et al., pH regulator on digital microfluidics with pico-dosing technique, Biosensors 13 (2023), 951.
    [117]
    K. Ko, H. Yoo, S. Han, et al., Surface-enhanced Raman spectroscopy with single cell manipulation by microfluidic dielectrophoresis, Analyst 149 (2024) 5649-5656.
    [118]
    C. Nie, I. Shaw, C. Chen, Application of microfluidic technology based on surface-enhanced Raman scattering in cancer biomarker detection: A review, J. Pharm. Anal. 13 (2023) 1429-1451.
    [119]
    R. Muddiman, S. Harkin, M. Butler, et al., Broadband CARS Hyperspectral Classification of Single Immune Cells. J Biophotonics. 18 (2025), e202400382.
    [120]
    K. Guo, Z. Song, J. Zhou, et al., An artificial intelligence-assisted digital microfluidic system for multistate droplet control, Microsyst. Nanoeng. 10 (2024), 138.
    [121]
    A. Falamas, D. Cuibus, N. Tosa, et al., Toward microfluidic SERS and EC-SERS applications via tunable gold films over nanospheres, Discov. Nano 18 (2023), 73.
    [122]
    K. Nicinski, J. Krajczewski, A. Kudelski, et al., Detection of circulating tumor cells in blood by shell-isolated nanoparticle - enhanced Raman spectroscopy (SHINERS) in microfluidic device, Sci. Rep. 9 (2019), 9267.
    [123]
    F.M. Siemers, J. Bajorath, Differences in learning characteristics between support vector machine and Random Forest models for compound classification revealed by Shapley value analysis, Sci. Rep. 13 (2023), 5983.
    [124]
    F.M. Shiri, T. Perumal, N. Mustapha, et al., A comprehensive overview and comparative analysis on deep learning models: CNN, RNN, LSTM, GRU, (2023): 2305.17473.
    [125]
    S. Shetab Boushehri, K. Essig, N.K. Chlis, et al., Explainable machine learning for profiling the immunological synapse and functional characterization of therapeutic antibodies, Nat. Commun. 14 (2023), 7888.
    [126]
    S. Lu, Y. Huang, W.X. Shen, et al., Raman spectroscopic deep learning with signal aggregated representations for enhanced cell phenotype and signature identification, PNAS Nexus 3 (2024), pgae268.
    [127]
    S. Badillo, B. Banfai, F. Birzele, et al., An introduction to machine learning, Clin. Pharmacol. Ther. 107 (2020) 871-885.
    [128]
    L. Zhang, C. Li, D. Peng, et al., Raman spectroscopy and machine learning for the classification of breast cancers, Spectrochim. Acta A Mol. Biomol. Spectrosc. 264 (2022), 120300.
    [129]
    A. Maguire, I. Vega-Carrascal, J. Bryant, et al., Competitive evaluation of data mining algorithms for use in classification of leukocyte subtypes with Raman microspectroscopy, Analyst 140 (2015) 2473-2481.
    [130]
    I.W. Schie, R. Kiselev, C. Krafft, et al., Rapid acquisition of mean Raman spectra of eukaryotic cells for a robust single cell classification, Analyst 141 (2016) 6387-6395.
    [131]
    J. Hatwell, M.M. Gaber, R.M.A. Azad, CHIRPS: Explaining Random Forest classification, Artif. Intell. Rev. 53 (2020) 5747-5788.
    [132]
    B. Aybey, S. Zhao, B. Brors, et al., Immune cell type signature discovery and Random Forest classification for analysis of single cell gene expression datasets, Front. Immunol. 14 (2023), 1194745.
    [133]
    H. Zhan, L. Cheng, H. Li, et al., Integrated analyses delineate distinctive immunological pathways and diagnostic signatures for Behcet’s disease by leveraging gene microarray data, Immunol. Res. 71 (2023) 860-872.
    [134]
    N. Arend, A. Pittner, A. Ramoji, et al., Detection and differentiation of bacterial and fungal infection of neutrophils from peripheral blood using Raman spectroscopy, Anal. Chem. 92 (2020) 10560-10568.
    [135]
    R. Dunne, R. Reguant, P. Ramarao-Milne, et al., Thresholding Gini variable importance with a single-trained Random Forest: An empirical Bayes approach, Comput. Struct. Biotec. 21 (2023) 4354-4360.
    [136]
    O. Elharrouss, Y. Akbari, N. Almadeed, et al., Backbones-review: Feature extractor networks for deep learning and deep reinforcement learning approaches in computer vision, Comput. Sci. Rev. 53 (2024), 100645.
    [137]
    Y. Zhu, H. chen, W. Meng, et al., A wide kernel CNN-LSTM-based transfer learning method with domain adaptability for rolling bearing fault diagnosis with a small dataset, Adv. Mech. Eng. 14 (2022), 168781322211357.
    [138]
    J. Li, X. Wang, S. Min, et al., Raman spectroscopy combined with convolutional neural network for the sub-types classification of breast cancer and critical feature visualization, Comput. Meth. Prog. Biomed. 255 (2024), 108361.
    [139]
    C. Zhang, M. Wang, H. Wu, et al., Real-time monitoring of single dendritic cell maturation using deep learning-assisted surface-enhanced Raman spectroscopy, Theranostics 14 (2024) 6818-6830.
    [140]
    R.K. Gupta, M. Chen, G.P.A. Malcolm, et al., Label-free optical hemogram of granulocytes enhanced by artificial neural networks, Opt. Express 27 (2019) 13706-13720.
    [141]
    Z. Wang, Y. Li, J. Zhai, et al., Deep learning-based Raman spectroscopy qualitative analysis algorithm: A convolutional neural network and transformer approach, Talanta 275 (2024), 126138.
    [142]
    S.M. Al-Selwi, M.F. Hassan, S.J. Abdulkadir, et al., RNN-LSTM: From applications to modeling techniques and beyond: Systematic review, J. King. Saud. Univ-Com. 36 (2024), 102068.
    [143]
    Y. Chen, Z. Fu, Multi-Step Ahead Forecasting of the Energy Consumed by the Residential and Commercial Sectors in the United States Based on a Hybrid CNN-BiLSTM Model, Sustainability 15 (2023), 1895.
    [144]
    Kandadi, Thirupathi, Shankarlingam, G., DRAWBACKS OF LSTM ALGORITHM: A CASE STUDY. https://ssrn.com/abstract=5080605 or https://doi.org/10.2139/ssrn.5080605. (Accessed 3 January 2025).
    [145]
    M. Cuperlovic-Culf, T. Nguyen-Tran, S.A.L. Bennett, Machine learning and hybrid methods for metabolic pathway modeling. Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology. Springer US, (2022), pp 17–439.
    [146]
    J. Lu, J. Chen, L. Huang, et al., Rapid identification of species, antimicrobial-resistance genotypes and phenotypes of gram-positive cocci using long short-term memory Raman spectra methods, Adv. Intell. Syst. 5 (2023), 2200235.
    [147]
    A. Anjikar, N.P. Rao, R. Paneerselvam, et al., Deep Learning in Biomedical Applications of Raman Spectroscopy. B.S. Gerstman, Biological and Medical Physics, Biomedical Engineering, Springer, Singapore, 2024; pp. 209-247.
    [148]
    R.K. Halder, M.N. Uddin, M.A. Uddin, et al., Enhancing K-nearest neighbor algorithm: A comprehensive review and performance analysis of modifications, J. Big. Data. 11 (2024), 113.
    [149]
    W. Zhang, Machine learning approaches to predicting company bankruptcy, J. Risk. Financ. Manag. 6 (2017) 364-374.
    [150]
    M. Beckmann, N.F.F. Ebecken, B.S.L. Pires de Lima, A KNN undersampling approach for data balancing, J. Intell. Learn. Syst. Appl. 7 (2015) 104-116.
    [151]
    J. Ren, Q. Gao, X. Zhou, et al., Identification of gene markers associated with COVID-19 severity and recovery in different immune cell subtypes, Biology 12 (2023), 947.
    [152]
    F.U. Ciloglu, A.M. Saridag, I.H. Kilic, et al., Identification of methicillin-resistant Staphylococcus aureus bacteria using surface-enhanced Raman spectroscopy and machine learning techniques, Analyst 145 (2020) 7559-7570.
    [153]
    C. Bentejac, A. Csorgő, G. Martinez-Munoz, A comparative analysis of gradient boosting algorithms, Artif. Intell. Rev. 54 (2021) 1937-1967.
    [154]
    M. Lin, X. Zhu, T. Hua, T, et al., Detection of Ionospheric Scintillation Based on XGBoost Model Improved by SMOTE-ENN Technique, Remote Sensing 13 (2021), 2577.
    [155]
    D. Boldini, F. Grisoni, D. Kuhn, et al., Practical guidelines for the use of gradient boosting for molecular property prediction, J. Cheminformatics. 15 (2023), 73.
    [156]
    Z. Liu, Y. Xue, C. Yang, et al., Rapid identification and drug resistance screening of respiratory pathogens based on single-cell Raman spectroscopy, Front. Microbiol. 14 (2023), 1065173.
    [157]
    K. Liu, B. Liu, Y. Zhang, et al., Building an ensemble learning model for gastric cancer cell line classification via rapid Raman spectroscopy, Comput. Struct. Biotechnol. J. 21 (2022) 802-811.
    [158]
    K.M. Ting, Precision and recall. C. Sammut, G.I. Webb, Encyclopedia of Machine Learning. Springer, Boston, 2011, pp. 781
    [159]
    D.J. Hand, P. Christen, N. Kirielle, F*: An interpretable transformation of the F-measure, Mach. Learn. 110 (2021) 451-456.
    [160]
    M.G. Fernandez-Manteca, A.A. Ocampo-Sosa, C. Ruiz de Alegria-Puig, et al., Automatic classification of Candida species using Raman spectroscopy and machine learning, Spectrochim. Acta. A. 290 (2023), 122270.
    [161]
    S. Cipolla, J. Gondzio, Training very large scale nonlinear SVMs using Alternating Direction Method of Multipliers coupled with the Hierarchically Semi-Separable kernel approximations, EURO J. Comput. Optim. 10 (2022), 100046.
    [162]
    Y. Akhiat, Y. Manzali, M. Chahhou, et al., A new noisy Random Forest based method for feature selection, Cybern. Inf. Technol. 21 (2021) 10-28.
    [163]
    C. Jung, M. Abuhamad, D. Mohaisen, et al., WBC image classification and generative models based on convolutional neural network, BMC Med. Imaging 22 (2022), 94.
    [164]
    Utkarsh, P.K. Jain, Predicting bentonite swelling pressure: Optimized XGBoost versus neural networks, Sci. Rep. 14 (2024), 17533.
    [165]
    C.P.Y. Lau, W. Ma, K.Y. Law, et al., Development of deep learning algorithms to discriminate giant cell tumors of bone from adjacent normal tissues by confocal Raman spectroscopy, Analyst 147 (2022) 1425-1439.
    [166]
    B. Liu, K. Liu, J. Sun, et al., Classification of pathogenic bacteria by Raman spectroscopy combined with variational auto-encoder and deep learning, J. Biophotonics 16 (2023), e202200270.
    [167]
    Z.E. Fitri, L.N.Y. Syahputri, A.M.N. Imron, Classification of white blood cell abnormalities for early detection of myeloproliferative neoplasms syndrome based on K-nearest neighborr, Sci. J. Inform. 7 (2020) 136-142.
    [168]
    A. Torang, P. Gupta, D.J. Klinke 2nd, An elastic-net logistic regression approach to generate classifiers and gene signatures for types of immune cells and T helper cell subsets, BMC Bioinformatics 20 (2019), 433.
    [169]
    Y. Xu, R. Goodacre, On splitting training and validation set: A comparative study of cross-validation, bootstrap and systematic sampling for estimating the generalization performance of supervised learning, J. Anal. Test. 2 (2018) 249-262.
    [170]
    Y. Chen, Z. Yan, Y. Zhu, A comprehensive survey for generative data augmentation, Neurocomputing 600 (2024), 128167.
    [171]
    N. Bayat, D.D. Davey, M. Coathup, et al., White blood cell classification using multi-attention data augmentation and regularization, Big Data Cogn. Comput. 6 (2022), 122.
    [172]
    J. Hu, Y. Zou, B. Sun, et al., Raman spectrum classification based on transfer learning by a convolutional neural network: Application to pesticide detection, Spectrochim. Acta A Mol. Biomol. Spectrosc. 265 (2022), 120366.
    [173]
    V.S. Mahalle, N.M. Kandoi, S.B. Patil, Transfer learning by fine-tuning pre-trained convolutional neural network architectures for image recognition. Data Science and Big Data Analytics. Springer Nature Singapore, (2024), pp 73–287.
    [174]
    N. Blake, R. Gaifulina, M. Isabelle, et al., System transferability of Raman-based oesophageal tissue classification using modern machine learning to support multi-centre clinical diagnostics, BJC Rep. 2 (2024), 52.
    [175]
    W. Zhang, X. Li, H. Ma, et al., Transfer learning using deep representation regularization in remaining useful life prediction across operating conditions, Reliab. Eng. Syst. Safe. 211 (2021), 107556.
    [176]
    R. Schiemer, M. Rudt, J. Hubbuch, Generative data augmentation and automated optimization of convolutional neural networks for process monitoring, Front. Bioeng. Biotechnol. 12 (2024), 1228846.
    [177]
    M.H. Rahman, R. Sikder, M. Tripathi, et al., Machine learning-assisted Raman spectroscopy and SERS for bacterial pathogen detection: Clinical, food safety, and environmental applications, Chemosensors 12 (2024), 140.
    [178]
    X. Hu, M. Zhu, Z. Feng, et al., Manifold-based Shapley explanations for high dimensional correlated features, Neural Netw. 180 (2024), 106634.
    [179]
    X. Deng, K. Milligan, A. Brolo, et al., Radiation treatment response and hypoxia biomarkers revealed by machine learning assisted Raman spectroscopy in tumour cells and xenograft tissues, Analyst 147 (2022) 5091-5104.
    [180]
    N. Ibtehaz, M.E.H. Chowdhury, A. Khandakar, et al., RamanNet: A generalized neural network architecture for Raman spectrum analysis, Neural Comput. Appl. 35 (2023) 18719-18735.
    [181]
    X. Xiang, H. Yu, Y. Wang, et al., Stable local interpretable model-agnostic explanations based on a variational autoencoder, Appl. Intell. 53 (2023) 28226-28240.
    [182]
    J. Contreras, A. Winterfeld, J. Popp, et al., Spectral zones-based SHAP/LIME: Enhancing interpretability in spectral deep learning models through grouped feature analysis, Anal. Chem. 96 (2024) 15588-15597.
    [183]
    V. Vimbi, N. Shaffi, M. Mahmud, Interpreting artificial intelligence models: A systematic review on the application of LIME and SHAP in Alzheimer’s disease detection, Brain Inform. 11 (2024), 10.
    [184]
    S. Banabilah, M. Aloqaily, E. Alsayed, et al., Federated learning review: Fundamentals, enabling technologies, and future applications, Inform. Process. Manag. 59 (2022), 103061.
    [185]
    Y. Zhang, Y. Lu, F. Liu, A systematic survey for differential privacy techniques in federated learning, J. Inf. Secur. 14 (2023) 111-135.
    [186]
    C. Wu, F. Wu, L. Lyu, et al., A federated graph neural network framework for privacy-preserving personalization, Nat. Commun. 13 (2022), 3091.
    [187]
    A. Kumar, M.R. Islam, S.M. Zughaier, et al., Precision classification and quantitative analysis of bacteria biomarkers via surface-enhanced Raman spectroscopy and machine learning, Spectrochim. Acta A Mol. Biomol. Spectrosc. 320 (2024), 124627.
    [188]
    J. Xia, J. Li, X. Wang, et al., Enhanced cancer classification and critical feature visualization using Raman spectroscopy and convolutional neural networks, Spectrochim. Acta A Mol. Biomol. Spectrosc. 326 (2025), 125242.
    [189]
    J.W. Chan, Recent advances in laser tweezers Raman spectroscopy (LTRS) for label-free analysis of single cells, J. Biophotonics 6 (2013) 36-48.
    [190]
    M. Navas-Moreno, J.W. Chan, Laser tweezers Raman microspectroscopy of single cells and biological particles. Cellular Heterogeneity. Springer New York, (2018), pp 19–257.
    [191]
    P. Zhang, L. Ren, X. Zhang, et al., Raman-activated cell sorting based on dielectrophoretic single-cell trap and release, Anal. Chem. 87 (2015) 2282-2289.
    [192]
    X. Wang, L. Ren, Z. Diao, et al., Robust spontaneous Raman flow cytometry for single-cell metabolic phenome profiling via pDEP-DLD-RFC, Adv. Sci. 10 (2023), e2207497.
    [193]
    N. Pavillon, E.L. Lim, A. Tanaka, et al., Non-invasive detection of regulatory T cells with Raman spectroscopy, Sci. Rep. 14 (2024), 14025.
    [194]
    Z. Chen, F. Lin, Y. Gao, et al., FOXP3 and RORγt: Transcriptional regulation of Treg and Th17, Int. Immunopharmacol. 11 (2011) 536-542.
    [195]
    T. Kamala, An optimized immunomagnetic bead-based negative selection protocol for CD4 T-cell isolation from mouse lymph nodes and spleen, Scand. J. Immunol. 67 (2008) 285-294.
    [196]
    J. Reed, S.A. Wetzel, CD4+ T cell differentiation and activation. Immunotoxicity Testing. Springer New York, (2018), pp 35–351.
    [197]
    A. Schmidt, S. Elias, R.N. Joshi, et al., In vitro differentiation of human CD4+FOXP3+ induced regulatory T cells (iTregs) from Naive CD4+ T cells using a TGF-β-containing protocol, J. Vis. Exp. (2016), 55015.
    [198]
    T. Ouyang, W. Pedrycz, N.J. Pizzi, Rule-based modeling with DBSCAN-based information granules, IEEE. T. Cyberntics. 51 (2021) 3653-3663.
    [199]
    R. Wang, J. Lu, Y. Lu, et al., Discrete and parameter-free multiple kernel k-means, IEEE. T. Image Process. 31 (2022) 2796-2808.
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