SUFI: An automated approach to spectral unmixing of fluorescent biological images

Multispectral fluorescence imaging coupled with linear unmixing is a form of image data collection and analysis that uses multiple fluorescent dyes - each measuring a specific biological signal - that are simultaneously measured and subsequently "unmixed" to provide a read-out for each individual signal. This strategy allows for measuring multiple signals in a single data capture session - for example, multiple proteins or RNAs in tissue slices or cultured cells, but can often result in mixed signals and bleed-through problems across dyes. Existing spectral unmixing algorithms are limited in scope, throughput, and availability, and often require manual intervention to extract spectral signatures. We therefore developed an intuitive, automated, and flexible package called SUFI: spectral unmixing of fluorescent images (https://github.com/LieberInstitute/SUFI). This package unmixes multispectral fluorescence images by automating the extraction of spectral signatures using Vertex Component Analysis, and then performs one of three unmixing algorithms derived from remote sensing. We demonstrate these remote sensing algorithms performance on four unique biological datasets and compare the results to unmixing results obtained using ZEN Black software (Zeiss). We lastly integrate our unmixing pipeline into the computational tool dotdotdot that is used to quantify individual RNA transcripts at single cell resolution in intact tissues and perform differential expression analysis of smFISH data, and thereby provide a one-stop solution for multispectral fluorescence image analysis and quantification. In summary, we provide a robust, automated pipeline to assist biologists with improved spectral unmixing of multispectral fluorescence images. Author summaryIn the age of rapidly advancing imaging technologies and the widespread adoption of multiplex fluorescent experiments in diverse biological models, multispectral fluorescence imaging has emerged as a powerful technique that allows researchers to observe and study several elements within a single sample - each tagged with a different fluorescent dye. Using several fluorescent probes within the same sample provides a higher level of information but leads to mixed signals. Spectral unmixing is a computational technique that can resolve these mixed signals into individual channels. Existing spectral unmixing tools solve this problem to some extent, but their availability, applicability, and throughput is limited and often requires manual intervention. In order to address this challenge, we developed a robust, flexible, and automated pipeline called SUFI (fluorescence image spectral unmixing). We demonstrate the flexibility of SUFI using four types of biological data and compare the spectral unmixing performance against widely used ZEN Black software from Zeiss. We further provide tools and resources so that the SUFI pipeline can be readily adopted by the scientific community to unmix diverse types of multiplex fluorescent biological data at scale.