Biology subjects

Chang, J.-B.

Publications and source records attributed to Chang, J.-B..

4 recordsLinked to original sources

bioRxiv2022

IMPASTO: Multiplexed cyclic imaging without signal removal via self-supervised neural unmixing

Spatially resolved proteomics requires a highly multiplexed imaging modality. Cyclic imaging techniques, which repeat staining, imaging, and signal erasure, have been adopted for this purpose. However, due to tissue distortion, it is challenging to obtain high fluorescent signal intensities and complete signal erasure in thick tissue with cyclic imaging techniques. Here, we propose an "erasureless" cyclic imaging method named IMPASTO. In IMPASTO, specimens are iteratively stained and imaged without signal erasure. Then, images from two consecutive rounds are unmixed to retrieve the images of single proteins through self-supervised machine learning without any prior training. Using IMPASTO, we demonstrate 30-plex imaging from brain slices in 10 rounds, and when used in combination with spectral unmixing, in five rounds. We show that IMPASTO causes negligible tissue distortion and demonstrate 3D multiplexed imaging of brain slices. Further, we show that IMPASTO can shorten the signal removal processes of existing cyclic imaging techniques.

bioengineering↗

bioRxiv2022

Statistically unbiased prediction enables accurate denoising of voltage imaging data

Here we report SUPPORT (Statistically Unbiased Prediction utilizing sPatiOtempoRal information in imaging daTa), a self-supervised learning method for removing Poisson-Gaussian noise in voltage imaging data. SUPPORT is based on the insight that a pixel value in voltage imaging data is highly dependent on its spatially neighboring pixels in the same time frame, even when its temporally adjacent frames do not provide useful information for statistical prediction. Such spatiotemporal dependency is captured and utilized to accurately denoise voltage imaging data in which the existence of the action potential in a time frame cannot be inferred by the information in other frames. Through simulation and experiments, we show that SUPPORT enables precise denoising of voltage imaging data while preserving the underlying dynamics in the scene.

neuroscience↗

bioRxiv2021

Whole-ExM: Expansion microscopy imaging of all anatomical structures of whole larval zebrafish

Nanoscale resolution imaging of whole vertebrates is required for a systematic understanding of human diseases, but this has yet to be realized. Expansion microscopy (ExM) is an attractive option for achieving this goal, but the expansion of whole vertebrates has not been demonstrated due to the difficulty of expanding hard body components. Here, we demonstrate whole-body ExM, which enables nanoscale resolution imaging of anatomical structures, proteins, and endogenous fluorescent proteins (FPs) of whole zebrafish larvae and mouse embryos by expanding them fourfold. We first show that post-digestion decalcification and digestion kinetics matching are critical steps in the expansion of whole vertebrates. Then, whole-body ExM is combined with the improved pan-protein labeling approach to demonstrate the three-dimensional super-resolution imaging of antibody- or FP-labeled structures and all major anatomical structures surrounding them. We also show that whole-body ExM enables visualization of the nanoscale details of neuronal structures across the entire body.

developmental biology↗

bioRxiv2021

PICASSO: Ultra-multiplexed fluorescence imaging of biomolecules through single-round imaging and blind source unmixing

Ultra-multiplexed fluorescence imaging of biomolecules is essential to studying heterogeneous biological systems. However, this is challenging due to fluorophores spectral overlap and variation of the emission spectra. Here, we propose a strategy termed PICASSO, which enables more than 15-colour multiplexed imaging of thick tissue slices through a single imaging process and blind unmixing without reference spectra measurement. We show that PICASSO can be used to achieve a high multiplexing capability in diverse applications, such as 3D protein imaging, expansion microscopy, tissue clearing, imaging of clinical specimens, and cyclic immunofluorescence imaging. PICASSO only requires an equal number of images as the number of fluorophores, enabling such a high level of multiplexed imaging even with bandpass filter-based microscopy. As such, PICASSO would be a useful tool for the study of cancer, the immune system, and the brain, as well as for the diagnosis of cancer, as it enables ultra-multiplexed imaging of diverse specimens with minimum instrumental requirements and experimental processes.

neuroscience↗