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. 2020 Jul;25(7):1-13.
doi: 10.1117/1.JBO.25.7.071206.

Selecting optimal spectral bands for improved detection of autofluorescent biomarkers in multiphoton microscopy

Björn-Ole Meyer  1 , M. Pilar J. Stella  2 , Bjørn Holst  3 , Boye S. Nielsen  3 , Kim Holmstrøm  3 , Peter E. Andersen  4 , Dominik Marti  4
Affiliations

Selecting optimal spectral bands for improved detection of autofluorescent biomarkers in multiphoton microscopy

Björn-Ole Meyer et al. J Biomed Opt. 2020 Jul.

Abstract

Significance: In multiphoton microscopy, two-photon excited fluorescence (TPEF) spectra carry valuable information on morphological and functional biological features. For measuring these biomarkers, separation of different parts of the fluorescence spectrum into channels is typically achieved by the use of optical band pass filters. However, spectra from different biomarkers can be unknown or overlapping, creating a crosstalk in between the channels. Previously, establishing these channels relied on prior knowledge or heuristic testing.

Aim: The presented method aims to provide spectral bands with optimal separation between groups of specimens expressing different biomarkers.

Approach: We have developed a system capable of resolving TPEF with high spectral resolution for the characterization of biomarkers. In addition, an algorithm is created to simulate and optimize optical band pass filters for fluorescence detection channels. To demonstrate the potential improvements in cell and tissue classification using these optimized channels, we recorded spectrally resolved images of cancerous (HT29) and normal epithelial colon cells (FHC), cultivated in 2D layers and in 3D to form spheroids. To provide an example of an application, we relate the results with the widely used redox ratio.

Results: We show that in the case of two detection channels, our system and algorithm enable the selection of optimized band pass filters without the need of knowing involved fluorophores. An improvement of 31,5% in separating different 2D cell cultures is achieved, compared to using established spectral bands that assume NAD(P)H and FAD as main contributors of autofluorescence. The compromise is a reduced SNR in the images.

Conclusions: We show that the presented method has the ability to improve imaging contrast and can be used to tailor a given label-free optical imaging system using optical band pass filters targeting a specific biomarker or application.

Keywords: biomarkers; fluorescence spectrum; functional imaging; hyperspectral imaging; multiphoton microscopy; redox ratio; spectroscopy; two-photon excitation fluorescence.

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Figures

Fig. 1
Fig. 1
Setup of the custom-built two-photon fluorescence microscope. As excitation source, we use a mode-locked Ti:sapphire laser with a center wavelength at 785 nm for illumination. The emitted fluorescence light is collected in epi-detection by the same objective and then focused into a fiber. The signals can be detected by a PMT for quick imaging or a spectrometer for hyperspectral imaging.
Fig. 2
Fig. 2
Infographic visualizing the underlying algorithm. (a) Two input spectra (orange and dark green) are separated into spectral bands. (b) The ratios of the power falling into these spectral bands (yellow and light green) are calculated separately for each spectrum, and the two ratios are then divided to quantify the separation. (c) The separation is compared against the geometric mean of the four band’s powers, which serves as a measure for the expected SNR of the entire system.
Fig. 3
Fig. 3
(a) Cancerous (HT29) and (b) normal (FHC) epithelial colon cells are grown as 3D spheroids. Spheroid formation is observed using a commercial OCT system (TELESTO-II, THORLABS), and approximate TPEFM imaging locations are marked (arrow).
Fig. 4
Fig. 4
(a) TPEFM images of cancerous (HT29) and normal (FHC) cells in 2D cultures (PMT detection and entire visible spectrum). (b) Corresponding TPEF spectra summed over the full respective images, normalized to the area underneath the curve for better visual comparison.
Fig. 5
Fig. 5
Simulations of spectral bands on data from literature. (a) Spectra of NAD(P)H and FAD are normalized using the PMF and shown as used for the simulation. Spectral bands of bandpass filters (410 to 490 nm and 510 to 650 nm, circle) and improved spectral bands (401 to 483 nm and 489 to 641 nm, triangle) are marked. Additionally, spectral bands with a high separation and reduced relative signal collection efficiency are marked (square). (b) Simulated combinations of spectral bands are plotted with their separation (scaling the contrast of a system) and their relative signal collection efficiency (scaling the SNR of a system). The combinations of spectral bands shown in (a) are marked and favorable combinations yielding a maximal achievable separation are highlighted.
Fig. 6
Fig. 6
TPEF spectra from different cancer models are investigated and spectral bands with optimized separation are visualized. Autofluorescence spectra of cancerous (HT29) and noncancerous (FHC) cell cultures grown in (a) 2D and (c) spheroid cancer models are shown normalized using the PMF as used for the simulation. Spectral bands of bandpass filters (410 to 490 nm and 510 to 650 nm, circle) and improved spectral bands (triangle) are marked for the investigation of cancer models. Simulated combinations of spectral bands are plotted for (b) 2D and (d) spheroid cancer models with their separation (scaling the contrast of a system) and their relative signal collection efficiency (scaling the SNR of a system). The combinations of spectral bands shown in (a) and (c) are marked and favorable combinations of spectral bands yielding a maximal achievable separation between the measured spectra are highlighted.
Fig. 7
Fig. 7
(a) and (b) Color-coded images are calculated from hyperspectral images of a 2D co-culture (HT29 and FHC cells). Images are composed by integrating over (a) initial spectral bands: 410 to 490 nm (cyan), 510 to 650 nm (red) and (b) optimized spectral bands: 407 to 450 nm (cyan), 535 to 650 nm (red). (c) and (d) Ratiometric images are calculated by dividing the red channel by the sum of both channels for (c) the initial and (d) optimized bands. To improve SNR, a 2×2 binning is applied.
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