Optical methodology for detecting histologically unapparent nanoscale consequences of genetic alterations in biological cells

Abstract

Recently, there has been a major thrust to understand biological processes at the nanoscale. Optical microscopy has been exceedingly useful in imaging cell microarchitecture. Characterization of cell organization at the nanoscale, however, has been stymied by the lack of practical means of cell analysis at these small scales. To address this need, we developed a microscopic spectroscopy technique, single-cell partial-wave spectroscopy (PWS), which provides insights into the statistical properties of the nanoscale architecture of biological cells beyond what conventional microscopy reveals. Coupled with the mesoscopic light transport theory, PWS quantifies the disorder strength of intracellular architecture. As an illustration of the potential of the technique, in the experiments with cell lines and an animal model of colon carcinogenesis we show that increase in the degree of disorder in cell nanoarchitecture parallels genetic events in the early stages of carcinogenesis in otherwise microscopically/histologically normal-appearing cells. These data indicate that this advance in single-cell optics represented by PWS may have significant biomedical applications.

Fig. 1.

PWS detects architectural changes in genetically modified HT29 cell lines that are otherwise histologically indistinguishable. (A) Steps involved in PWS signal acquisition and analysis. The spectrum of a typical reflection coefficient R(λ) obtained after removing the noise and the background reflection. (Inset) A typical back-scattering spectrum I(λ) from a single pixel of an HT29 cell image. (B) A representative p.d.f. P(<R>) of the mean fluctuating component 〈R〉 (circles) in HT29 cells. The solid lines are the fitted P(R) curve that follows the log-normal distributions (r² > 0.95). (C) The representative correlation decay C(Δk) computed from R(k). The logarithm of the autocorrelation function ln(C(Δk)) (circles) follows a linear dependence on (Δk)² (solid line) (r² = 0.98). (D) Representative cytological images (H&E staining) from 3 variants of HT29 cells. The nucleus and cytoplasm of a cell is marked for reference. The cytology images look similar for all of the 3 types of cells. (E) The representative pseudocolor PWS images from the 3 cell types shown in D: Color shows the magnitude of the L_d in a cell. These representative cells are chosen such that they correspond to the average L_d for each cell type. Each color map shows the entire cell. Although the cytology images shown in D appear similar, the PWS images are distinctly different from each other. To see the noise level, we also provide an L_d image of a pure glass that shows very low L_d relative to those of the cells. It is important to note that the HT29 cell, as an adenocarcinoma cell line, is dominated by a large nucleus. Hence, the PWS images indicate that the disorder is increased in the cell nucleus. However, the changes in disorder are not localized in the nucleus only and are present throughout the cytoplasm as is seen in cells with small nucleus-to-cytoplasm ratio. (F) Cells with a more aggressive malignant behavior have a higher intracellular disorder strength. The values of L_d and σ_Ld averaged over a cell, that is L_d^(c) and σ^(c), from 3 variants of HT29 cells plotted in L_d^(c),σ^(c) parameter space. As can be seen, for each cell type there is a separate regime in the parameter space that only slightly overlaps with the regimes of the other cell types. (Inset). The part of the parameter space for the EGFR and control HT29 cells with small L_d and σ_Ld values is amplified to show their separation in the parameter space. (G) The relative values of L_d^(g) from 3 variants of HT29 cells. The error bar represents the standard error of the mean. The average L_d^(g) is significantly decreased for the least-aggressive EGFR-knockdown cells compared with that of HT29 cells (P < 0.001) and significantly increased for the most-aggressive CSK-knockdown cells (P < 0.001). (H) The values of σ^(g) for the 3 cell types. σ^(g) is progressively increased from the least-aggressive EGFR-knockdown, to the control and to the most-aggressive CSK-knockdown cells (all P values < 0.001).

Fig. 2.

Histologically normal-appearing precancerous cells from the MIN-mouse model of intestinal carcinogenesis possess alterations in their nanoarchitecture detectable by PWS. (A) The bar graph shows that the L_d^(g) is significantly increased in the precancerous cells obtained from the MIN mice relative to that of the control wild-type animals (P < 0.001). (B) Comparison of σ^(g) in the wild-type and MIN mice. There is a significant increase in σ^(g) (P < 0.001) for the MIN mice compared with the control wild-type animals. (C) L_d and σ_Ld averaged over a cell [i.e., L_d^(c) and σ^(c)] for the cells obtained from the wild-type and the MIN-mice plotted in the [L_d^(c),σ^(c)] parameter space. Each point in this diagram corresponds to a single cell. Both the wild-type and MIN-mice cells have a separate regime in the parameter space with slight overlaps. (D) Comparison of the theoretical (true) L_d(L_d 〈Δn²〉 l_c) and the one calculated by using Eqs. 1 and 2 based on the FDTD simulations of light reflection from a 1D weakly disordered random media. The simulations were performed on a homogeneous dielectric slab with random additive refractive index fluctuations around the background refractive index n₀ = 1.38. L_d was obtained for the following parameters: (i) standard deviation of the refractive index fluctuations Δn between 0.01 and 0.05, (ii) the correlation length l_c between 5 and 45 nm, and (iii) the thickness of the sample L between 1.5 and 4 μm. As can be seen, the calculated and theoretical values of L_d agree well with each other (r² = 0.91), thereby confirming its agreement with the mesoscopic theory.