Akbar Proity Nayeeb, Blümel Reinhold
Department of Physics, Wesleyan University, Middletown, Connecticut, United States of America.
PLoS One. 2025 May 6;20(5):e0320697. doi: 10.1371/journal.pone.0320697. eCollection 2025.
Infrared (IR) microspectroscopy stands as a transformative clinical tool for analyzing single biological cells in biopsy samples, offering critical insights into their chemical composition. In this study, we further develop a recently proposed inverse scattering algorithm that accurately reconstructs the dielectric properties of single cells, considering both scattering and absorption. We demonstrate the method's effectiveness using spherical model cells filled with six organic test substances: polymethyl methacrylate (PMMA), polycarbonate (PC), polydimethylsiloxane (PDMS), polyetherimide (PEI), polyethylene terephthalate (PET), and polystyrene (PS). The permittivity values of these substances, reconstructed from their extinction efficiencies and known refractive indexes from the literature, show excellent agreement with experimental data. Our comparative analysis of the basis sets for the reconstruction algorithm reveals that using dielectric functions leads to more accurate results compared to anti-symmetrized Lorentzians. We find that compared to other methods in the literature on PMMA spheres, our approach yields reconstructions of significantly higher quality. These findings not only enhance reconstruction accuracy but also advance the potential of IR microspectroscopy for clinical cytology, where precise molecular analysis is crucial for disease diagnosis and monitoring at the cellular level.
红外(IR)显微光谱法是一种用于分析活检样本中单个生物细胞的变革性临床工具,能为其化学成分提供关键见解。在本研究中,我们进一步开发了一种最近提出的逆散射算法,该算法在考虑散射和吸收的情况下,能准确重建单个细胞的介电特性。我们使用填充有六种有机测试物质的球形模型细胞来证明该方法的有效性,这六种物质分别是聚甲基丙烯酸甲酯(PMMA)、聚碳酸酯(PC)、聚二甲基硅氧烷(PDMS)、聚醚酰亚胺(PEI)、聚对苯二甲酸乙二酯(PET)和聚苯乙烯(PS)。根据这些物质的消光效率和文献中已知的折射率重建的介电常数与实验数据显示出极佳的一致性。我们对重建算法基组的比较分析表明,与反对称洛伦兹函数相比,使用介电函数能得到更准确的结果。我们发现,与文献中关于PMMA球体的其他方法相比,我们的方法能产生质量显著更高的重建结果。这些发现不仅提高了重建精度,还提升了红外显微光谱法在临床细胞学中的潜力,在临床细胞学中,精确的分子分析对于细胞水平的疾病诊断和监测至关重要。