Tissue dynamics spectroscopic imaging: functional imaging of heterogeneous cancer tissue

Abstract

Significance: Tumor heterogeneity poses a challenge for the chemotherapeutic treatment of cancer. Tissue dynamics spectroscopy captures dynamic contrast and can capture the response of living tissue to applied therapeutics, but the current analysis averages over the complicated spatial response of living biopsy samples.

Aim: To develop tissue dynamics spectroscopic imaging (TDSI) to map the heterogeneous spatial response of tumor tissue to anticancer drugs.

Approach: TDSI is applied to tumor spheroids grown from cell lines and to ex vivo living esophageal biopsy samples. Doppler fluctuation spectroscopy is performed on a voxel basis to extract spatial maps of biodynamic biomarkers. Functional images and bivariate spatial maps are produced using a bivariate color merge to represent the spatial distribution of pairs of signed drug-response biodynamic biomarkers.

Results: We have mapped the spatial variability of drug responses within biopsies and have tracked sample-to-sample variability. Sample heterogeneity observed in the biodynamic maps is associated with histological heterogeneity observed using inverted selective-plane illumination microscopy.

Conclusion: We have demonstrated the utility of TDSI as a functional imaging method to measure tumor heterogeneity and its potential for use in drug-response profiling.

Keywords: deep optical imaging; functional imaging; tissue-dynamics imaging; tumor spatial heterogeneity.

Fig. 1

(a) An OCI image of a human esophageal biopsy and differential spectrograms (defined below) for the two circled areas. At time $t=0$ (black line), 100 μL of $25\mu \mathrm{M}$ cisplatin and $25\mu \mathrm{M}$ fluorouracil (5fu) combined solution is added to the sample. The two regions have significantly different responses: region 1 shows enhancement in low frequency and suppression in high frequency (redshift), while region 2 has suppression across all frequencies. (b) The terminal spectra of regions 1 and 2, respectively, compared to the average sample baseline spectrum.

Fig. 2

A schematic of the interferometer with a Mach–Zehnder off-axis digital holography configuration. The camera is on the Fourier plane. Optical sections are reconstructed using a 2-D spatial Fourier transform. ND, neutral density filter; ${\mathrm{L}}_1-{\mathrm{L}}_7$, lenses; ${\mathrm{BS}}_1-{\mathrm{BS}}_3$, beam splitters; ${\mathrm{M}}_1-{\mathrm{M}}_5$, mirrors; FP, Fourier plane; IP, image plane; ${\mathrm{f}}_5={\mathrm{f}}_6=150\mathrm{mm}$; ${\mathrm{f}}_7=50\mathrm{mm}$. The translation stage defines the coherence gate for time-domain ranging.

Fig. 3

(a) A subset of the spectrogram masks used in the color merge. (b) Two maps of drug response of an esophageal sample “150903-14” (same as in Fig. 1) exposed to cisplatin and fluorouracil combination therapy under masks G0 and G1. (c) A “merged” bivariate color image with its 2-D color map.

Fig. 4

(a) Bivariate color representation of drug responses of two samples treated with different drugs, showing two univariate maps and a “merged” bivariate color map. The first sample was refreshed with DMSO, while the second sample was treated with $25\mu \mathrm{M}$ fluorouracil (5fu). (b) Global and regional spectrograms of sample “151208-6” from (a). The global spectrogram has a relatively weak response ($\max <10\%$), while the two circled areas have 30% to 60% enhancement or suppression. Drugs were added at $t=0$ (black line on spectrograms).

Fig. 5

More examples of bivariate TDS images showing sample-to-sample variability in drug responses. Masks are designated in the lower left corners of images, while lower right corners designate drug treatments. Drug abbreviations: DMSO, 0.1% DMSO in growth medium (used as a negative control); cisp, $25\mu \mathrm{M}$ cisplatin; 5fu: $25\mu \mathrm{M}$ fluorouracil; tax, $5\mu \mathrm{M}$ paclitaxel; carbo, $25\mu \mathrm{M}$ carboplatin. “+” indicates a combination of two drugs. The color map and scale are the same as in Fig. 3.

Fig. 6

Three types of samples that have different levels of same-mask heterogeneity and cross-mask heterogeneity, with scores on the right.

Fig. 7

Time-lapse TDS image of samples responding to drugs. Sample “170317-9”: response of a DLD spheroid sample treated with $10\mu \mathrm{M}$ nocodazole [same as in Fig. 3(d)] with the G2 mask, showing a silent core shortly after drug was added (0.7 h), which was “invaded” by the drug and later achieved a spatially homogeneous response (2.7 to 8.7 h). Sample “170606-15”: response of an esophageal biopsy sample in the control medium, also under the G2 mask.

Fig. 8

TDSI map, iSPIM 3-D-reconstructed image and H&E histology image for an esophageal biopsy sample. All scale bars are $100\mu \mathrm{m}$. (a) Bivariate colormap representing G0 (broadband inhibition) and G1 (blue shift). (b) Bivariate TDSI map of human esophageal biopsy responding to 5-fluorouracil (5-fu) with G0 and G2 as the two masks. The color map and color scale are the same as in Fig. 3. (c) The iSPIM image with DRAQ 5 (blue) and Eosin (pink) for the same sample in (a), and (d) histology image. The orientations of the images are not registered.