RT Journal Article SR Electronic T1 SUFI: An automated approach to spectral unmixing of fluorescent multiplex images captured in mouse and postmortem human brain tissues JF bioRxiv FD Cold Spring Harbor Laboratory SP 2021.01.28.428639 DO 10.1101/2021.01.28.428639 A1 Vijay Sadashivaiah A1 Madhavi Tippani A1 Stephanie C. Page A1 Sang Ho. Kwon A1 Svitlana V. Bach A1 Rahul A. Bharadwaj A1 Thomas M. Hyde A1 Joel E. Kleinman A1 Andrew E. Jaffe A1 Kristen R. Maynard YR 2021 UL http://biorxiv.org/content/early/2021/07/09/2021.01.28.428639.abstract AB 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 not optimized for challenging biological specimens such as postmortem human brain tissue, 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.Competing Interest StatementThe authors have declared no competing interest.