Dawson, T. R. | bioRxiv Science

bioRxiv2023

Culture conditions greatly impact the levels of vesicular and extravesicular Ago2 and RNA in extracellular vesicle preparations.

Extracellular vesicle (EV)-carried miRNAs can influence gene expression and functional phenotypes in recipient cells. Argonaute 2 (Ago2) is a key miRNA-binding protein that has been identified in EVs and could influence RNA silencing. However, Ago2 is in a non-vesicular form in serum and can be an EV contaminant. In addition, RNA-binding proteins (RBPs), including Ago2, and RNAs are often minor EV components whose sorting into EVs may be regulated by cell signaling state. To determine the conditions that influence detection of RBPs and RNAs in EVs, we evaluated the effect of growth factors, oncogene signaling, serum, and cell density on the vesicular and nonvesicular content of Ago2, other RBPs, and RNA in small EV (SEV) preparations. Media components affected both the intravesicular and extravesicular levels of RBPs and miRNAs in EVs, with serum contributing strongly to extravesicular miRNA contamination. Furthermore, isolation of EVs from hollow fiber bioreactors revealed complex preparations, with multiple EV-containing peaks and a large amount of extravesicular Ago2/RBPs. Finally, KRAS mutation impacts the detection of intra- and extra-vesicular Ago2. These data indicate that multiple cell culture conditions and cell states impact the presence of RBPs in EV preparations, some of which can be attributed to serum contamination.

cancer biology↗

bioRxiv2021

Generating accurate 3D gaze vectors using synchronized eye tracking and motion capture

Assessing gaze behaviour during real-world tasks is difficult; dynamic bodies moving through dynamic worlds make gaze analysis difficult. Current approaches involve laborious coding of pupil positions. In settings where motion capture and mobile eye tracking are used concurrently in naturalistic tasks, it is critical that data collection be simple, efficient, and systematic. One solution is to combine eye tracking with motion capture to generate 3D gaze vectors. When combined with tracked or known object locations, 3D gaze vector generation can be automated. Here we use combined eye and motion capture and explore how linear regression models generate accurate 3D gaze vectors. We compare spatial accuracy of models derived from four short calibration routines across three pupil data inputs: the efficacy of calibration routines were assessed, a validation task requiring short fixations on taskrelevant locations, and a naturalistic object interaction task to bridge the gap between laboratory and "in the wild" studies. Further, we generated and compared models using spherical and cartesian coordinate systems and monocular (Left or Right) or binocular data. All calibration routines performed similarly, with the best performance (i.e., sub-centimetre errors) coming from the naturalistic task trials when the participant is looking at an object in front of them. We found that spherical coordinate systems generate the most accurate gaze vectors with no differences in accuracy when using monocular or binocular data. Overall, we recommend one-minute calibration routines using binocular pupil data combined with a spherical world coordinate system to produce the highest quality gaze vectors.

neuroscience↗