Biophysical Profiling of Sickle Cell Disease Using Holographic Cytometry and Deep Learning

Deep learning ResNet34 model-assisted diagnosis of sickle cell disease via microcolumn isoelectric focusing

Traditional methods for sickle cell disease (SCD) screening can be inaccurate and misleading, and the early and accurate diagnosis of SCD is crucial for effective management and treatment. Although microcolumn isoelectric focusing (mIEF) is effective, the hemoglobinopathies must be accurately identified, wherein skilled personnel are required to analyse the bands in mIEF. Further automating and standardizing the diagnostic methods via AI to identify abnormal Hbs would be a useful endeavor. In this study, we propose a novel approach for SCD diagnosis by integrating the high throughput capability of ResNet34 in image analysis, as a deep learning convolutional neural network, for the precise separation of Hb variants using mIEF. Initially, SCD blood samples were subjected to mIEF and the resulting patterns were then captured as digital images. The sensitivity and specificity of the mIEF analysis were 100% and 97.8%, respectively, with a 99.39% accuracy. Comparison with HPLC showed a strong linear correlation (R2 = 0.9934), good agreement with the Bland-Altman plot (average difference ± 1.96 SD, bias = 9.89%) and a 100% match with the DNA analysis. Subsequently, the mIEF images were then input into the ResNet34 model, pre-trained on a large dataset, for feature extraction and classification. The integration of ResNet34 with mIEF demonstrated promising results in terms of precision (90.1%) and accuracy in distinguishing between the various SCD conditions. Overall, the proposed method offers a more effective, automated, and reduced cost approach for SCD diagnosis, which could potentially streamline diagnostic workflows and mitigate the subjectivity and variability inherent in manual assessments.

Utilizing quantitative phase microscopy to localize fluorescence in three dimensions via the transport of intensity equation

We demonstrate the use of a novel, to the best of our knowledge, localization algorithm for digitally refocusing fluorescence images from a three-dimensional cell culture. Simultaneous phase and fluorescence intensity images are collected through a multimodal system that combines digital holography via quantitative phase microscopy (QPM) and fluorescence microscopy. Defocused fluorescence images are localized to a specific z-plane within the three-dimensional (3D) matrix using the transport of intensity equation (TIE) and depth-resolved information derived from the QPM measurements. This technique is applied to cells stained with different fluorescent tags suspended in 3D collagen hydrogel cultures. Experimental findings demonstrate the localization of defocused images, facilitating the analysis and comparison of cells within the hydrogel matrix. This method holds promise for comprehensive cellular imaging of fluorescence labeling in three-dimensional environments, enabling detailed investigations into cellular behavior and interactions.

Computing Sickle Erythrocyte Health Index on quantitative phase imaging and machine learning

Sickle cell disease (SCD) is a genetic disorder characterized by abnormal hemoglobin and deformation of red blood cells (RBCs), leading to complications and reduced life expectancy. This study developed an in vitro assessment, the Sickle Erythrocyte Health Index, using quantitative phase imaging (QPI) and machine learning to model the health of RBCs in people with SCD. The health index combines assessment of cell deformation, sickle-shaped classification, and membrane flexibility to evaluate erythrocyte health. Using QPI and image processing, the percentage of sickle-shaped cells and membrane flexibility were quantified. Statistically significant differences were observed between individuals with and without SCD, indicating the impact of underlying pathophysiology on erythrocyte health. Additionally, sodium metabisulfite led to an increase in sickle-shaped cells and a decrease in flexibility in the sickle cell blood samples. Based on these findings, two approaches were used to calculate the Sickle Erythrocyte Health Index: one using hand-crafted features and one using learned features from deep learning models. Both indices showed significant differences between non-SCD and SCD groups and sensitivity to changes induced by sodium metabisulfite. The Sickle Erythrocyte Health Index has important clinical implications for SCD management and could be used by providers when making treatment decisions. Further research is warranted to evaluate the clinical utility and applicability of the Sickle Erythrocyte Health Index in diverse patient populations.