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Blue-conversion of organic dyes produces artifacts in multicolor fluorescence imaging.

ABSTRACT: Multicolor fluorescence imaging is a powerful tool visualizing the spatiotemporal relationship among biomolecules. Here, we report that commonly employed organic dyes exhibit a blue-conversion phenomenon, which can produce severe multicolor image artifacts leading to false-positive colocalization by invading predefined spectral windows, as demonstrated in the case study using EGFR and Tensin2. These multicolor image artifacts become much critical in localization-based superresolution microscopy as the blue-converted dyes are photoactivatable. We provide a practical guideline for the use of organic dyes for multicolor imaging to prevent artifacts derived by blue-conversion.

SUBMITTER: Kim DH

PROVIDER: S-EPMC8246296 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Blue-conversion of organic dyes produces artifacts in multicolor fluorescence imaging.

Kim Do-Hyeon DH Chang Yeonho Y Park Soyeon S Jeong Min Gyu MG Kwon Yonghoon Y Zhou Kai K Noh Jungeun J Choi Yun-Kyu YK Hong Triet Minh TM Chang Young-Tae YT Ryu Sung Ho SH

Chemical science 20210518 25

Multicolor fluorescence imaging is a powerful tool visualizing the spatiotemporal relationship among biomolecules. Here, we report that commonly employed organic dyes exhibit a blue-conversion phenomenon, which can produce severe multicolor image artifacts leading to false-positive colocalization by invading predefined spectral windows, as demonstrated in the case study using EGFR and Tensin2. These multicolor image artifacts become much critical in localization-based superresolution microscopy ...[more]

PMID: 34257864

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Project description:Imaging neural activity in a behaving animal presents unique challenges in part because motion from an animal's movement creates artifacts in fluorescence intensity time-series that are difficult to distinguish from neural signals of interest. One approach to mitigating these artifacts is to image two channels simultaneously: one that captures an activity-dependent fluorophore, such as GCaMP, and another that captures an activity-independent fluorophore such as RFP. Because the activity-independent channel contains the same motion artifacts as the activity-dependent channel, but no neural signals, the two together can be used to identify and remove the artifacts. However, existing approaches for this correction, such as taking the ratio of the two channels, do not account for channel-independent noise in the measured fluorescence. Here, we present Two-channel Motion Artifact Correction (TMAC), a method which seeks to remove artifacts by specifying a generative model of the two channel fluorescence that incorporates motion artifact, neural activity, and noise. We use Bayesian inference to infer latent neural activity under this model, thus reducing the motion artifact present in the measured fluorescence traces. We further present a novel method for evaluating ground-truth performance of motion correction algorithms by comparing the decodability of behavior from two types of neural recordings; a recording that had both an activity-dependent fluorophore and an activity-independent fluorophore (GCaMP and RFP) and a recording where both fluorophores were activity-independent (GFP and RFP). A successful motion correction method should decode behavior from the first type of recording, but not the second. We use this metric to systematically compare five models for removing motion artifacts from fluorescent time traces. We decode locomotion from a GCaMP expressing animal 20x more accurately on average than from control when using TMAC inferred activity and outperforms all other methods of motion correction tested, the best of which were ~8x more accurate than control.

Dataset Information

Blue-conversion of organic dyes produces artifacts in multicolor fluorescence imaging.

Publications

Blue-conversion of organic dyes produces artifacts in multicolor fluorescence imaging.

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