Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments

Abstract

Due to its specificity, fluorescence microscopy has become a quintessential imaging tool in cell biology. However, photobleaching, phototoxicity, and related artifacts continue to limit fluorescence microscopy’s utility. Recently, it has been shown that artificial intelligence (AI) can transform one form of contrast into another. We present phase imaging with computational specificity (PICS), a combination of quantitative phase imaging and AI, which provides information about unlabeled live cells with high specificity. Our imaging system allows for automatic training, while inference is built into the acquisition software and runs in real-time. Applying the computed fluorescence maps back to the quantitative phase imaging (QPI) data, we measured the growth of both nuclei and cytoplasm independently, over many days, without loss of viability. Using a QPI method that suppresses multiple scattering, we measured the dry mass content of individual cell nuclei within spheroids. In its current implementation, PICS offers a versatile quantitative technique for continuous simultaneous monitoring of individual cellular components in biological applications where long-term label-free imaging is desirable.

Deep Learning in Biomedical Optics

“…Second, digital fluorescence labeling has been demonstrated for transforming label‐free contrast to fluorescence labels [23,40–43] (Fig. 4f).…”

“…This is highlighted in [40], 3D multiplexed digital labeling using transmission brightfield or phase contrast images on multiple subcellular components are demonstrated, including nucleoli (PCC0.9), nuclear envelope, microtubules, actin filaments (PCC0.8), mitochondria, cell membrane, Endoplasmic reticulum, DNA+ (PCC0.7), DNA (PCC0.6), Actomyosin bundles, tight junctions (PCC0.5), Golgi apparatus (PCC0.2), and Desmosomes (PCC0.1). Recent advances further exploit other label‐free contrasts, including polarization [41], quantitative phase map [43], and reflectance phase‐contrast microscopy [42]. Beyond predicting fluorescence labels, recent advances further demonstrate multiplexed single‐cell profiling using the digitally predicted labels [42].…”

Label-free screening of brain tissue myelin content using phase imaging with computational specificity (PICS)

“…Here, we apply phase imaging with computational specificity (PICS), 52,53 a new microscopy technique that combines AI computation with quantitative data to extract precise molecular information. Specifically, we combine deep learning networks with SLIM data to define subtle myelin variations in brain tissue, a strategy undertaken for the first time to our knowledge.…”

Rapid label-free identification of pathogenic bacteria species from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network

“…Among various biomedical studies, bacteria has been investigated with QPI during growth (Ahn et al, 2020;Mir et al, 2011), while optically controlled in the presence of eukaryotic cells (Kemper et al, 2013), and upon the treatments of antibotics (Oh et al, 2020). In recent years, machine learning has been introduced to QPI (Jo et al, 2018;Rivenson et al, 2019b), enabling diverse applications including virtual staining (Rivenson et al, 2019a), virtual molecular imaging (Jo et al, 2020;Kandel et al, 2020), improvement of image quality (Kamilov et al, 2015;Ryu et al, 2019;Ryu et al, 2021), and a variety cell type classification (Chen et al, 2016;Rubin et al, 2019;Siu et al, 2020;Yoon et al, 2017). One noteworthy study realized efficient screening for anthrax spores using a handheld twodimensional (2D) QPI microscope and artificial neural network (ANN) (Jo et al, 2017).…”