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. 2012 Aug 9:2:96.
doi: 10.3389/fonc.2012.00096. eCollection 2012.

Quantification of cellular volume and sub-cellular density fluctuations: comparison of normal peripheral blood cells and circulating tumor cells identified in a breast cancer patient

Kevin G Phillips  1 , Anand Kolatkar, Kathleen J Rees, Rachel Rigg, Dena Marrinucci, Madelyn Luttgen, Kelly Bethel, Peter Kuhn, Owen J T McCarty
Affiliations

Quantification of cellular volume and sub-cellular density fluctuations: comparison of normal peripheral blood cells and circulating tumor cells identified in a breast cancer patient

Kevin G Phillips et al. Front Oncol. .

Abstract

Cancer metastasis, the leading cause of cancer-related deaths, is facilitated in part by the hematogenous transport of circulating tumor cells (CTCs) through the vasculature. Clinical studies have demonstrated that CTCs circulate in the blood of patients with metastatic disease across the major types of carcinomas, and that the number of CTCs in peripheral blood is correlated with overall survival in metastatic breast, colorectal, and prostate cancer. While the potential to monitor metastasis through CTC enumeration exists, the basic physical features of CTCs remain ill defined and moreover, the corresponding clinical utility of these physical parameters is unknown. To elucidate the basic physical features of CTCs we present a label-free imaging technique utilizing differential interference contrast (DIC) microscopy to measure cell volume and to quantify sub-cellular mass-density variations as well as the size of subcellular constituents from mass-density spatial correlations. DIC measurements were carried out on CTCs identified in a breast cancer patient using the high-definition (HD) CTC detection assay. We compared the biophysical features of HD-CTC to normal blood cell subpopulations including leukocytes, platelets (PLT), and red blood cells (RBCs). HD-CTCs were found to possess larger volumes, decreased mass-density fluctuations, and shorter-range spatial density correlations in comparison to leukocytes. Our results suggest that HD-CTCs exhibit biophysical signatures that might be used to potentially aid in their detection and to monitor responses to treatment in a label-free fashion. The biophysical parameters reported here can be incorporated into computational models of CTC-vascular interactions and in vitro flow models to better understand metastasis.

Keywords: breast cancer; cellular density; cellular volume; circulating tumor cells; differential interference contrast microscopy.

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Figures

Figure 1
Figure 1
Image segmentation of DIC image cubes. (A) En face DIC image, leukocyte, (B) corresponding cross sectional image through center of cell, (C) Hilbert transform of (B), (D) high pass filter applied to (C).
Figure 2
Figure 2
Fluctuation and spatial correlation analysis of axial DIC intensity profiles. (A) En face DIC image CTC. (B) Axial profiles of DIC intensity for pixels in the cell (black) and glass substrate (blue), (C) normalized DIC intensity profile (black, cell signal divided by glass signal) for pixels inside the cell and corresponding smooth fit (blue), (D) fluctuating part of the DIC intensity (normalized signal minus smooth fit), (E) autocorrelation function of the fluctuating part of the DIC signal, the correlation length Lc[μm] is defined as the first zero crossing, (F) histogram of the fluctuating part of the DIC intensity, the mass-density fluctuations are quantified through the standard deviation, σA[−], of the histogram.
Figure 3
Figure 3
En face and cross sectional post-processed DIC images of normal blood cell populations and HD-CTCs. Representative en face and sagittal Hilbert transformed DIC images of (A,B) PLT, (C,D) RBC, (E,F) leukocytes. (G) Merged fluorescence image of HD-CTCs and leukocytes from breast cancer patient, (H) corresponding DIC image, (I) corresponding Hilbert transformed DIC image, (J) corresponding cross sectional images at the t, top; m, middle; b, bottom locations denoted in (I). (K) Scatter plot of cell areas vs. corresponding volume for each cell type.
Figure 4
Figure 4
Fluctuation and spatial correlation maps: comparison of HD-CTCs and leukocytes. (A) Merged fluorescence image of HD-CTCs and leukocytes, (B) corresponding DIC images, (C) spatial correlation length map, (D) density amplitude fluctuation map, (E) histogram of DIC intensity of cells indicated with white arrows in (B). (F) HD-CTC and leukocyte LC histograms of cells indicated in (C). (G) HD-CTC and leukocyte σA histograms of cells indicated in (D).
Figure 5
Figure 5
Mass density fluctuation and spatial correlation metrics for normal blood cell subpopulations and HD-CTCs. (A) Scatter plot of spatial correlation length vs. amplitude fluctuations for HD-CTCs and leukocytes from breast cancer patient. (B) Box plot comparing population averages of the axial resolved DIC amplitude fluctuations averages of the axial resolved DIC intensity spatial correlation length among PLTs, RBCs, leukocytes, and HD-CTCs. (C) Box plot comparing population averages of the axial resolved DIC intensity spatial correlation length among PLTs, RBCs, leukocytes, and HD-CTCs. *Denotes p < 0.001 in comparison to leukocytes.
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