DOI: 10.25442/hku.19601602
title: Supporting data for "Deep Profiling of Cellular Light Scattering and Fractality: A new strategy for high-throughput Biophysical Cytometry and Its Applications"

description: Cell morphology is strongly indicative of the functions and states of individual cells. Despite its information-rich nature, cell morphology has not been widely conceived as an effective assaying readout, especially at the single-cell level. This is mainly due to the common tradeoff of imaging techniques between the throughput and information content, which leads to insufficient sensitivity and statistical power to draw the connection between morphological phenotypes and biologically relevant insights among large cell populations. Leveraging the emergent ultrafast optical imaging technologies, this work addressed this challenge by developing a new strategy of image-based single-cell morphological profiling, which consists of three unique attributes: First, the real-time and continuous high throughput (&gt;10,000 cells/sec) empowered by the laser-scanning imaging platforms allowed the large-scale and high-resolution single-cell image acquisition, which critically enables downstream in-depth cellular morphological analysis. Second, the imaging platforms also enabled quantitative phase imaging (QPI) – a powerful modality that reveals quantitative biophysical characteristics of cells. Particularly, QPI provided integrative knowledge of both cell morphology and light scattering properties based on Fourier Transform light scattering (FTLS) analysis. From this informative inspection of single-cell morphology, a wealth of phenotypes was harnessed to create a morphological profile (or catalog) for quantitative multidimensional characterization of cellular heterogeneity in a completely label-free manner. Third, by virtue of the subcellular resolution obtained in QPI, the complexity and irregularity of subcellular texture were evaluated statistically by fractal geometry, a pattern repeating its own organization at a smaller scale, which has been prevalently witnessed in cellular/subcellular morphology. Unlike most <em>fractal </em>applications in cell biology and clinical diagnosis, this work focused on comprehensive fractal analysis of <em>individual</em> cells down to the subcellular level (or termed <em>fractometry</em>), which gives deeper biophysical implications, i.e., subcellular dry-mass distribution and its fractal behavior. Furthermore, by establishing the correlation between fractal-related and morphological features, we also highlighted that better interpretability could be achieved to depict the architecture of cell texture in a fractal sense. This dataset includes three main experimental cases for the performance evaluation of this profiling approach. Significant differences among the FTLS- and fractal-derived features of lung cancer cell lines were found to distinguish the histological subtypes, which validated their applicability in cell type identification. We also showed that the variation of light scattering and fractal behavior shared consistent trends with cell cycle progression, thus could potentially offer valuable label-free markers for cell-state progression. With the enriched biophysical implications and the unprecedented statistical power, we anticipate that this new profiling strategy could accelerate the biological discovery in the context of cellular heterogeneity, as well as deeper understanding of how cell morphology encodes cell health and disease.