Nanobodies (nAbs) are small, minimal antibodies that have distinct attributes that make them uniquely suited for certain biomedical research, diagnostic and therapeutic applications. Prominent uses include as intracellular antibodies or intrabodies to bind and deliver cargo to specific proteins and/or subcellular sites within cells, and as nanoscale immunolabels for enhanced tissue penetration and improved spatial imaging resolution. Here, we report the generation and validation of nAbs against a set of proteins prominently expressed at specific subcellular sites in mammalian brain neurons. We describe a novel hierarchical validation pipeline to systematically evaluate nAbs isolated by phage display for effective and specific use as intrabodies and immunolabels in mammalian cells including brain neurons. These nAbs form part of a robust toolbox for targeting proteins with distinct and highly spatially-restricted subcellular localization in mammalian brain neurons, allowing for visualization and/or modulation of structure and function at those sites.
All data generated or analyzed during this study are included in the manuscript and supporting files.
- James S Trimmer
- James S Trimmer
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Animal experimentation: All procedures involving llamas were performed at Triple J Farms of Kent Laboratories (Bellingham, WA) in strict accordance with the Guide for the Care and Use of Laboratory Animals of the NIH. All procedures involving rats were approved by the University of California, Davis, Institutional Animal Care and Use Committee (IACUC) under protocols 20485 and 21265 and were performed in strict accordance with the Guide for the Care and Use of Laboratory Animals of the NIH. All rats were maintained under standard light-dark cycles and allowed to feed and drink ad libitum. All procedures involving mice were approved by the Stanford University IACUC under protocol 18846 and were performed in strict accordance with the Guide for the Care and Use of Laboratory Animals of the NIH.
- Graeme W Davis, University of California, San Francisco, United States
© 2019, Dong et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Artificial neural networks could pave the way for efficiently simulating large-scale models of neuronal networks in the nervous system.
A key question in decision making is how humans arbitrate between competing learning and memory systems to maximize reward. We address this question by probing the balance between the effects, on choice, of incremental trial-and-error learning versus episodic memories of individual events. Although a rich literature has studied incremental learning in isolation, the role of episodic memory in decision making has only recently drawn focus, and little research disentangles their separate contributions. We hypothesized that the brain arbitrates rationally between these two systems, relying on each in circumstances to which it is most suited, as indicated by uncertainty. We tested this hypothesis by directly contrasting contributions of episodic and incremental influence to decisions, while manipulating the relative uncertainty of incremental learning using a well-established manipulation of reward volatility. Across two large, independent samples of young adults, participants traded these influences off rationally, depending more on episodic information when incremental summaries were more uncertain. These results support the proposal that the brain optimizes the balance between different forms of learning and memory according to their relative uncertainties and elucidate the circumstances under which episodic memory informs decisions.