PT  - JOURNAL ARTICLE
AU  - Niksa Praljak
AU  - Shamreen Iram
AU  - Utku Goreke
AU  - Gundeep Singh
AU  - Ailis Hill
AU  - Umut A. Gurkan
AU  - Michael Hinczewski
TI  - Integrating deep learning with microfluidics for biophysical classification of sickle red blood cells
AID  - 10.1101/2020.07.01.181545
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.07.01.181545
4099  - http://biorxiv.org/content/early/2020/07/04/2020.07.01.181545.short
4100  - http://biorxiv.org/content/early/2020/07/04/2020.07.01.181545.full
AB  - Sickle cell disease (SCD), a group of inherited blood disorders with significant morbidity and early mortality, affects a sizeable global demographic largely of African and Indian descent. It is manifested in a mutated form of hemoglobin that distorts the red blood cells into a characteristic sickle shape with altered biophysical properties. Sickle red blood cells (sRBCs) show heightened adhesive interactions with inflamed endothelium, triggering obstruction of blood vessels and painful vaso-occlusive crisis events. Numerous studies have reported microfluidic-assay-based disease monitoring tools which rely on quantifying adhesion characteristics of adhered sRBCs from high resolution channel images. The current workflow for analyzing images from these assays relies on manual cell counting and detailed morphological characterization by a specially trained worker, which is time and labor intensive. Moreover manual counts by different individuals are prone to artifacts due to user bias. We present here a standardized and reproducible image analysis workflow designed to tackle these issues, using a two part deep neural network architecture that works in tandem for automatic, fast and reliable segmentation and classification into subtypes of adhered cell images. Our training utilized an exhaustive data set of images generated by the SCD BioChip, a microfluidic assay which injects clinical whole blood samples into protein-functionalized microchannels, mimicking physiological conditions in the microvasculature. The automated image analysis performs robustly in comparison to human classification: accuracies were similar to or better than those of the trained personnel, while the overall analysis time was improved by two orders of magnitude.Competing Interest StatementThe authors have declared no competing interest.