Pre-processing visualization of hyperspectral fluorescent data with Spectrally Encoded Enhanced Representations

Wen Shi,Gianluca Turcatel,Masahiro Kitano,Hsiao J Chiang,Benjamin Steventon,Scott E Fraser,Francesco Cutrale,David Warburton,Cosimo Arnesano,Le A Trinh,Daniel E S Koo

doi:10.1038/s41467-020-14486-8

Abstract

Hyperspectral fluorescence imaging is gaining popularity for it enables multiplexing of spatio-temporal dynamics across scales for molecules, cells and tissues with multiple fluorescent labels. This is made possible by adding the dimension of wavelength to the dataset. The resulting datasets are high in information density and often require lengthy analyses to separate the overlapping fluorescent spectra. Understanding and visualizing these large multi-dimensional datasets during acquisition and pre-processing can be challenging. Here we present Spectrally Encoded Enhanced Representations (SEER), an approach for improved and computationally efficient simultaneous color visualization of multiple spectral components of hyperspectral fluorescence images. Exploiting the mathematical properties of the phasor method, we transform the wavelength space into information-rich color maps for RGB display visualization. We present multiple biological fluorescent samples and highlight SEER’s enhancement of specific and subtle spectral differences, providing a fast, intuitive and mathematical way to interpret hyperspectral images during collection, pre-processing and analysis.

Highlights

Hyperspectral fluorescence imaging is gaining popularity for it enables multiplexing of spatiotemporal dynamics across scales for molecules, cells and tissues with multiple fluorescent labels
Among the advantages of using multiple fluorophores is the capability to simultaneously follow differently labeled molecules, cells or tissues space- and time-wise. This is especially important in the field of biology where tissues, proteins and their functions within organisms are deeply intertwined, and there remain numerous unanswered questions regarding the relationship between individual components6. Fluorescence hyperspectral imaging (fHSI) empowers scientists with a more complete insight into biological systems with multiplexed information deriving from observation of the full spectrum for each point in the image[7]
One strategy is to construct fixed spectral envelopes from the first three components produced by principal component analysis (PCA) or independent component analysis (ICA), converting a hyperspectral image to a three-band visualization[10,11,12,13,14]

Summary

Introduction

Hyperspectral fluorescence imaging is gaining popularity for it enables multiplexing of spatiotemporal dynamics across scales for molecules, cells and tissues with multiple fluorescent labels This is made possible by adding the dimension of wavelength to the dataset. It is advantageous to perform an informed visualization of the spectral data during acquisition, especially for lengthy time-lapse recordings, and prior to performing analysis Such preprocessing visualization allows scientists to evaluate image collection parameters within the experimental pipeline as well as to choose the most appropriate processing method. Each spectrum is displayed with the most similar hue and saturation for tri-stimulus displays in order for the human eye to recognize details in the image[15] Another popular visualization technique is pixel-based image fusion, which preserves the spectral pairwise distances for the fused image in comparison to the input data[16]. These weights can be further optimized by implementing widely applied mathematical techniques, such as Bayesian inference[17], by using a filters-bank[18] for feature extraction[19] or by noise smoothing[20]

Methods

Results

Conclusion