Abstract
Recent advances in fluorescence microscopy have yielded an abundance of high-dimensional spectrally rich datasets that cannot always be adequately explored through conventional three-color visualization methods. While computational image processing techniques allow researchers to derive spectral characteristics of their datasets that cannot be visualized directly, there are still limitations in how to best visually display these resulting rich spectral data. Data sonification has the potential to provide a novel way for researchers to intuitively perceive these characteristics auditorily through direct interaction with the raw multi-channel data. The human ear is well tuned to detect subtle differences in sound that could represent discrete changes in fluorescence spectra. We present a proof of concept implementation of a functional data sonification workflow for analysis of fluorescence microscopy data as an FIJI ImageJ plugin and evaluate its utility with various hyperspectral microscopy datasets. Additionally, we provide a framework for prototyping and testing new sonification methods and a mathematical model to point out scenarios where vision-based spectral analysis fails and sonification-based approaches would not. With this first reported practical application of sonification to biological fluorescence microscopy and supporting computational tools for further exploration, we discuss the current advantages and disadvantages of sonification over conventional spectral visualization approaches. We also discuss where further efforts in spectral sonification need to go to maximize its practical biological applications.
Highlights
Use of spectral information in microscopy The increased availability of microscopes with multiple spectral channels and multi-colored fluorescent molecular markers has allowed life science researchers to generate datasets of higher spectral complexity than ever before from their optical imaging systems[1]
The ear has finer resolution when sensing complex, non-sinusoidal tones[15]. This behavior makes the ear a substantially more optimal sensor for spectrally rich signals than the eye, because, as we demonstrate, Acquisition and interpretation of spectral data As fluorescence microscopy datasets increase in richness and dimensional complexity, designing flexible tools for researchers to explore these larger and denser datasets that strike the appropriate balance between intuitive functionality and analytic effectiveness is becoming increasingly important
We produce a hyperspectral image in which every pixel has a different spectrum due to different relative fluorophore concentration or emission wavelengths, but all appear identical to a three channel detector with overlapping spectra such as the eye
Summary
Use of spectral information in microscopy The increased availability of microscopes with multiple spectral channels and multi-colored fluorescent molecular markers has allowed life science researchers to generate datasets of higher spectral complexity than ever before from their optical imaging systems[1]. By selectively placing different markers throughout a specimen, a researcher can construct detailed visual narratives of different biological processes based on the spectral variations throughout the data[2,3,4,5]. This approach, called multiplexing, allows for functional readout of multiple genetic functions spatially and temporally, a feature unique to fluorescence imaging. Calibration Volume ‘B’ contains the cascading 30px gradient with low background noise This volume tests a particular synth’s signal-noise discrimination with low intensity homogeneous background noise across all channels
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