Erysense, a Lab-on-a-Chip-Based Point-of-Care Device to Evaluate Red Blood Cell Flow Properties With Multiple Clinical Applications

in Frontiers in Physiology (2022), 13

In many medical disciplines, red blood cells are discovered to be biomarkers since they “experience” various conditions in basically all organs of the body. Classical examples are diabetes and ... [more ▼]

In many medical disciplines, red blood cells are discovered to be biomarkers since they “experience” various conditions in basically all organs of the body. Classical examples are diabetes and hypercholesterolemia. However, recently the red blood cell distribution width (RDW), is often referred to, as an unspecific parameter/marker (e.g., for cardiac events or in oncological studies). The measurement of RDW requires venous blood samples to perform the complete blood cell count (CBC). Here, we introduce Erysense, a lab-on-a-chip-based point-of-care device, to evaluate red blood cell flow properties. The capillary chip technology in combination with algorithms based on artificial neural networks allows the detection of very subtle changes in the red blood cell morphology. This flow-based method closely resembles in vivo conditions and blood sample volumes in the sub-microliter range are sufficient. We provide clinical examples for potential applications of Erysense as a diagnostic tool [here: neuroacanthocytosis syndromes (NAS)] and as cellular quality control for red blood cells [here: hemodiafiltration (HDF) and erythrocyte concentrate (EC) storage]. Due to the wide range of the applicable flow velocities (0.1–10 mm/s) different mechanical properties of the red blood cells can be addressed with Erysense providing the opportunity for differential diagnosis/judgments. Due to these versatile properties, we anticipate the value of Erysense for further diagnostic, prognostic, and theragnostic applications including but not limited to diabetes, iron deficiency, COVID-19, rheumatism, various red blood cell disorders and anemia, as well as inflammation-based diseases including sepsis. [less ▲]

A deep learning-based concept for high throughput image flow cytometry

in APPLIED PHYSICS LETTERS (2021), 118(12),

We propose a flow cytometry concept that combines a spatial optical modulation scheme and deep learning for lensless cell imaging. Inspired by auto-encoder techniques, an artificial neural network mimics ... [more ▼]

We propose a flow cytometry concept that combines a spatial optical modulation scheme and deep learning for lensless cell imaging. Inspired by auto-encoder techniques, an artificial neural network mimics the optical transfer function of a particular microscope and camera for certain types of cells once trained and reconstructs microscope images from simple waveforms that are generated by cells in microfluidic flow. This eventually enables the label-free detection of cells at high throughput while simultaneously providing their corresponding brightfield images. The present work focuses on the computational proof of concept of this method by mimicking the waveforms. Our suggested approach would require a minimum set of optical components such as a collimated light source, a slit mask, and a light sensor and could be easily integrated into a ruggedized lab-on-chip device. The method is benchmarked with a well-investigated dataset of red blood cell images. [less ▲]