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Abstract 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. 

Abstract Serial block face scanning electron microscopy (SBF‐SEM), also referred to as serial block‐face electron microscopy, is an advanced ultrastructural imaging technique that enables three‐dimensional visualization that provides largerx‐ andy‐axis ranges than other volumetric EM techniques. While SEM is first introduced in the 1930s, SBF‐SEM is developed as a novel method to resolve the 3D architecture of neuronal networks across large volumes with nanometer resolution by Denk and Horstmann in 2004. Here, the authors provide an accessible overview of the advantages and challenges associated with SBF‐SEM. Beyond this, the applications of SBF‐SEM in biochemical domains as well as potential future clinical applications are briefly reviewed. Finally, the alternative forms of artificial intelligence‐based segmentation which may contribute to devising a feasible workflow involving SBF‐SEM, are also considered.