Numerous protocols have been described for producing neural retina from human pluripotent stem cells (hPSCs), many of which are based on the culture of 3D organoids. Although nearly all such methods yield at least partial segments of retinal structure with a mature appearance, variabilities exist within and between organoids that can change over a protracted time course of differentiation. Adding to this complexity are potential differences in the composition and configuration of retinal organoids when viewed across multiple differentiations and hPSC lines. In an effort to understand better the current capabilities and limitations of these cultures, we generated retinal organoids from 16 hPSC lines and monitored their appearance and structural organization over time by light microscopy, immunocytochemistry, metabolic imaging and electron microscopy. We also employed optical coherence tomography and 3D imaging techniques to assess and compare whole or broad regions of organoids to avoid selection bias. Results from this study led to the development of a practical staging system to reduce inconsistencies in retinal organoid cultures and increase rigor when utilizing them in developmental studies, disease modeling and transplantation.

Human pluripotent stem cell (hPSC)-derived cardiomyocytes provide a promising regenerative cell therapy for cardiovascular patients and an important model system to accelerate drug discovery. However, cost-effective and time-efficient platforms must be developed to evaluate the quality of hPSC-derived cardiomyocytes during biomanufacturing. Here, we develop a non-invasive label-free live cell imaging platform to predict the efficiency of hPSC differentiation into cardiomyocytes. Autofluorescence imaging of metabolic co-enzymes is performed under varying differentiation conditions (cell density, concentration of Wnt signaling activator) across five hPSC lines. Live cell autofluorescence imaging and multivariate classification models provide high accuracy to separate low (< 50%) and high (≥ 50%) differentiation efficiency groups (quantified by cTnT expression on day 12) within 1 day after initiating differentiation (area under the receiver operating characteristic curve, 0.91). This non-invasive and label-free method could be used to avoid batch-to-batch and line-to-line variability in cell manufacturing from hPSCs.

Thermal ablation of soft tissue changes the tissue microstructure, and consequently induces changes to its acoustic properties. While B-mode ultrasound provides high-resolution and high-frame-rate images of ablative therapeutic procedures, it is not particularly effective at delineating boundaries of ablated regions due to poor contrast in echogenicity between ablated and surrounding normal tissue. Quantitative Ultrasound (QUS) techniques can provide quantitative estimates of acoustic properties such as backscatter and attenuation coefficients and differentiate ablated and unablated regions more effectively, with the potential for monitoring minimally invasive thermal therapies. In this study, a previously introduced attenuation estimation method was used to create quantitative attenuation coefficient maps for 11 microwave ablation procedures performed on refrigerated ex-vivo bovine liver. The attenuation images correlate well with the pathological images of the ablated region. The mean attenuation coefficient for regions of interest (ROIs) drawn inside and outside the ablated zones were 0.9 (±0.2) and 0.45 (±0.15) dB/cm/MHz, respectively. These estimates agree with reported values in the literature and establish the usefulness of noninvasive attenuation imaging for monitoring therapeutic procedures in the liver.

The ultrasonic attenuation coefficient is an important parameter that has been studied extensively in Quantitative Ultrasound and Tissue Characterization. There are various methods described in the literature that estimate this parameter by measuring either spectral difference (i.e., decay) or spectral shift of the backscattered echo signal. Under ideal conditions, i.e., in the absence of abrupt changes in tissue backscattering, Spectral Difference methods can produce estimates with high accuracy and precision. On the other hand, diffraction-corrected Spectral Shift methods (e.g., the Hybrid method) are better suited for application in practical settings using clinical ultrasound scanners. However, current Spectral Shift methods use inefficient frequency shift estimators that ultimately degrade the quality of attenuation coefficient estimates. In this paper, a probabilistic model of the backscattered radiofrequency (RF) echo is used to derive the Cramér-Rao lower bound (CRLB) on estimation variance of the spectral centroid. Next, an efficient correlation-based shift estimator is presented that achieves the CRLB. Used in conjunction with a well-characterized reference phantom to correct for diffraction and other system-related effects, this estimator greatly improves the accuracy and precision of Spectral-Shift attenuation estimation. A theoretical analysis of this method is provided, and its performance is quantitatively compared with that of the Hybrid method using simulated and experimental phantom studies. A minimum of 3-fold reduction in the standard deviation of attenuation coefficient estimates is observed using the new method.