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This post was contributed by Richi Sakaguchi from Kyoto University, and Marcus N. Leiwe and Takeshi Imai from Kyushu University.

Stochastic multicolor labeling is a powerful solution for discriminating between neurons for light microscopy-based neuronal reconstruction. To achieve stochastic multicolor labeling, Brainbow used the Cre-loxP system to express one of the three fluorescent protein (XFP) genes in a transgene. When multiple copies of the transgene cassette are introduced, stochasticity will result in a combinatorial expression of these three genes with different copy numbers, producing dozens of color hues (Livet et al., 2007; Cai et al., 2013). However, the brightness of Brainbow was inherently low. This is because the stochastic and combinatorial expression of fluorescent proteins is only possible at low copy number ranges, resulting in low fluorescent protein level.

Autophagy (Greek for “self-eating”) is a process by which cytoplasmic material, including organelles, are targeted to lysosomes for degradation. Autophagy is a dynamic process which involves autophagosome synthesis, delivery of materials to be degraded to the lysosome, and degradation of autophagic substrates inside the lysosome. Historically, methods for studying autophagy focused on counting the number of autophagosomes. This approach, however, has inherent limitations because it turns a dynamic process into a static measurement and it provides limited information about what materials or organelles are being targeted for autophagy. The development of several fluorescent autophagy reporters now allows for the measurement of autophagic flux, or the changes in autophagic activity, and are a more reliable indicator of autophagic activity. The aim of this post is to provide an overview of four autophagy biosensors currently available from Addgene.

This post was contributed by Rory Macneil, founder of Research Space.

There are many types of electronic lab notebooks (ELNs), each with its own pros and cons. All ELNs have the virtue of liberating data from paper into an electronic environment and hence making it searchable and shareable, but some ELNs provide better support for reproducibility than others.

Unfortunately, most ELNs are designed as closed ecosystems and act as data silos because they limit connectivity with other tools and resources. Thus, it’s difficult to get data out of the ELN. This means that reproducibility is limited in two ways – only data inside the ELN is reproducible, and only the highly restricted number of people who can access the ELN have the ability to attempt to reproduce the research that produced the data.

The holidays may be over, but signs of holiday spirit still linger on around the Addgene office as we peruse the #DeckTheLab submissions. We asked all of the Addgenies to pick their favorites out of the bunch and here’s what ended up on top.

Addgene has so much to celebrate! As we enter this new growth phase of the organization and the expansion of our impact, I’d like to take a moment and honor all of the success we have achieved and the opportunities we have for the future. At Addgene we will never ever stop planning, perfecting, and learning, but there are a lot of things we actually don’t have to worry about and that is something to recognize!