Sebastian, J. A. | Search bioRxiv

bioRxiv2022

High-frequency quantitative ultrasound to assess the acoustic properties of engineeredtissues in vitro

Acoustic properties of biomaterials and engineered tissues reflect their structure and cellularity. High-frequency ultrasound (US) can non-invasively characterize and monitor these properties with sub-millimetre resolution. We present an approach to estimate the acoustic properties of cell-laden hydrogels that accounts for frequency-dependent effects of attenuation in coupling media, hydrogel thickness, and interfacial transmission/reflection coefficients of US waves, all of which can bias attenuation estimates. Cell-seeded fibrin hydrogel disks were raster-scanned using a 40 MHz US transducer. Thickness, speed of sound, acoustic impedance, and acoustic attenuation coefficients were determined from the difference in the time-of-flight and ratios of the magnitudes of US signals, interfacial transmission/reflection coefficients, and acoustic properties of the coupling media. With this approach, hydrogel thickness was accurately measured by US, with excellent agreement to confocal microscopy (r2 = 0.97). Accurate thickness measurement enabled acoustic property measurements that were independent of hydrogel thickness, despite up to 60% reduction in thickness due to cell-mediated contraction. Notably, acoustic attenuation coefficients increased with increasing cell concentration (p<0.001), reflecting hydrogel cellularity independent of contracted hydrogel thickness. This approach enables accurate measurement of the intrinsic acoustic properties of biomaterials and engineered tissues to provide new insights into their structure and cellularity.

bioengineering↗

bioRxiv2018

Label-free assessment of red blood cell storage lesions by deep learning

Blood transfusion is a life-saving clinical procedure. With millions of units needed globally each year, it is a growing concern to improve product quality and recipient outcomes.\n\nStored red blood cells (RBCs) undergo continuous degradation, leading to structural and biochemical changes. To analyze RBC storage lesions, complex biochemical and biophysical assays are often employed.\n\nWe demonstrate that label-free imaging flow cytometry and deep learning can characterize RBC morphologies during 42-day storage, replacing the current practice of manually quantifying a blood smear from stored blood units. Based only on bright field and dark field images, our model achieved 90% accuracy in classifying six different RBC morphologies associated with storage lesions versus human-curated manual examination. A model fitted to the deep learning-extracted features revealed a pattern of morphological changes within the aging blood unit that allowed predicting the expiration date of stored blood using solely morphological assessment.\n\nDeep learning and label-free imaging flow cytometry could therefore be applied to reduce complex laboratory procedures and facilitate robust and objective characterization of blood samples.

bioinformatics↗