Motivation: Colocalization of structures in biomedical images can lead to insights into biological behaviors. One class of colocalization problems is examining an annular structure (disk-shaped such as a cell, vesicle or molecule) interacting with a network structure (vascular, neuronal, cytoskeletal, organellar). Examining colocalization events across conditions is often complicated by changes in density of both structure types, confounding traditional statistical approaches since colocalization cannot be normalized to the density of both structure types simultaneously. We have developed a technique to measure colocalization independent of structure density and applied it to characterizing intercellular colocation with blood vessel networks. This technique could be used to analyze colocalization of any annular structure with an arbitrarily shaped network structure. Results: We present the circular colocalization affinity with network structures test (CIRCOAST), a novel statistical hypothesis test to probe for enriched network colocalization in 2D z-projected multichannel images by using agent-based Monte Carlo modeling and image processing to generate the pseudo-null distribution of random cell placement unique to each image. This hypothesis test was validated by confirming that adipose-derived stem cells (ASCs) exhibit enriched colocalization with endothelial cells forming arborized networks in culture and then applied to show that locally delivered ASCs have enriched colocalization with murine retinal microvasculature in a model of diabetic retinopathy. We demonstrate that the CIRCOAST test provides superior power and type I error rates in characterizing intercellular colocalization compared to generic approaches that are confounded by changes in cell or vessel density. Availability and implementation: CIRCOAST source code available at: https://github.com/uva-peirce-cottler-lab/ARCAS. Supplementary information: Supplementary data are available at Bioinformatics online.

Bioinformatics (2019) https://doi.org/10.1093/bioinformatics/bty638, 35(3): 506–514. The publisher wishes to inform the reader that Figures 2, 5 and 6 appeared incorrectly in the above manuscript. Network area fraction dilated by cell radius determines the random cell colocation fraction. The mean ICF was calculated with the MCMRP over 10 000 trials with randomly selected parameters and displayed as a function of (A) cell diameter, (B) cell number, (C) network radius, (D) network fraction, (E) network length density and (F) cell-dilated network fraction (CDNF, N=2 500 images). Pearson correlation coefficient and associated 95% confidence interval and P-values are provided at the top of each scatterplot ASCs exhibit enriched colocalization with HUVECS network, while fluorescent microspheres (μSpheres) do not. (A) ASCs (red) co-cultured with HUVECS (green). (B) Distribution of simulated mean CIRCOAST P-values (blue) of random colocalization of ASC group compared to observed mean CIRCOAST P-value (red). (C) Fluorescent μSpheres seeded on a culture of HUVECs (scale bar 250 um). (D) Distribution of simulated mean CIRCOAST P-values (blue) of random colocalization from fluorescent μSpheres compared to actual mean P-value (red). (E) Circular cell density and (F) endothelial network density between groups. (G) Distribution of P-values (blue) derived from permuting CIRCOAST P-values in a Wilcox sum rank test between ASCs and μSpheres, with observed P-value (red) (N=6 wells, 3 images/well) Injected live and dead ASCs both exhibit enriched intercellular colocalization affinity with the vasculature. (A) Confocal image of retinal vasculature (green, preprocessed and thresholded) and injected with live DiI-labeled circular ASCs (red). (B) Distribution of simulated mean CIRCOAST P-values (blue) of random colocalization of ASC group compared to observed mean binomial P-value (red). (C) Dead DiI-labeled circular ASCs in the retinal vasculature (scale bars 150 um). (D) Distribution of simulated mean CIRCOAST P-values (blue) of random colocalization from dead cell group, compared to actual mean P-value (red). (E) Injected circular cell and (F) endothelial network density between study groups. (G) Distribution of permuted P-vales of Wilcox sum rank test of CIRCOAST P-values between study groups, with observed P-value (red) (N=6 mice, 3 images/mouse) The paper has been corrected online.

Techniques such as ballistocardiography (BCG) that can provide noninvasive long-term physiological monitoring have gained interest due to a growing recognition of adverse effects from poor sleep and sleep disorders. The noninvasive analysis of physiological signals (NAPS) system is a BCG-based monitoring system developed to measure heart rate, breathing rate, and musculoskeletal movement that shows promise as a general sleep analysis tool. Overnight sleep studies were conducted on 40 healthy subjects during a clinical trial at the University of Virginia. The NAPS system's measures of heart rate and breathing rate were compared to ECG, pulse oximetry, and respiratory inductance plethysmography (RIP). The subjects were split into a training dataset and a validation dataset, maintaining similar demographics in each set. The NAPS system accurately detected heart rate, averaged over the prescribed 30-s epochs, to within less than 2.72 beats per minute of ECG, and accurately detected breathing rate, averaged over the same epochs, to within 2.10 breaths per minute of RIP bands used in polysomnography.