SUFI: an automated approach to spectral unmixing of fluorescent multiplex images captured in mouse and post-mortem human brain tissues

BMC Neuroscience

Software

Andrew E. Jaffe¹  Rahul A. Bharadwaj²  Madhavi Tippani²  Sang Ho Kwon²  Kristen R. Maynard²  Vijay Sadashivaiah²  Stephanie C. Page²  Svitlana V. Bach²  Joel E. Kleinman³  Thomas M. Hyde⁴ 

[1] Department of Genetic Medicine, McKusick-Nathans Institute, Johns Hopkins University School of Medicine, 21205, Baltimore, MD, USA;Department of Neuroscience, Johns Hopkins School of Medicine, 21205, Baltimore, MD, USA;Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 21205, Baltimore, MD, USA;Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, 21205, Baltimore, MD, USA;Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 21205, Baltimore, MD, USA;Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205, Baltimore, MD, USA;Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205, Baltimore, MD, USA;Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, 21205, Baltimore, MD, USA;Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205, Baltimore, MD, USA;Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, 21205, Baltimore, MD, USA;Department of Neurology, Johns Hopkins School of Medicine, 21205, Baltimore, MD, USA;

关键词: Linear unmixing;    Multispectral imaging;    Spectral unmixing;    Automatic unmixing;   

DOI  :  10.1186/s12868-022-00765-1

 received in 2021-07-19, accepted in 2022-12-06,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundMultispectral fluorescence imaging coupled with linear unmixing is a form of image data collection and analysis that allows for measuring multiple molecular signals in a single biological sample. Multiple fluorescent dyes, each measuring a unique molecule, are simultaneously measured and subsequently “unmixed” to provide a read-out for each molecular signal. This strategy allows for measuring highly multiplexed signals in a single data capture session, such as 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 post-mortem 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.ResultsThis 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 evaluate these remote sensing algorithms’ performances 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, which is used to quantify individual RNA transcripts at single cell resolution in intact tissues and perform differential expression analysis, and thereby provide an end-to-end solution for multispectral fluorescence image analysis and quantification.ConclusionsIn summary, we provide a robust, automated pipeline to assist biologists with improved spectral unmixing of multispectral fluorescence images.

【 授权许可】

CC BY   
© The Author(s) 2023

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