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@we_harm on our recent work to use RNA-targeting CRISPRs for high-throughput screens in human cells. We are very excited by the possibility of using these tools for new kinds of functional genomics & transcriptomics and welcome feedback on the study.https://twitter.com/we_harm/status/1211324632474771456 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Neville Sanjana proslijedio/la je Tweet
Congrats to Aaron and Yilan on our NCB review on RNA-targeting CRISPR systems!https://www.nature.com/articles/s41556-019-0454-7 …
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Neville Sanjana proslijedio/la je Tweet
To understand the brain, we must understand what it’s made of — its cells. Using the Patch-seq method, our researchers describe the electrical and morphological properties of transcriptomic cell types in mouse visual cortex.
#openscience#brainscience
https://www.biorxiv.org/content/10.1101/2020.02.03.932244v1 …pic.twitter.com/h89cevZ2bw
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Neville Sanjana proslijedio/la je Tweet
Single-cell nucleic acid sequencing has become an essential biomedical research tool. Now
@slavovLab@Northeastern makes a strong case that single-cell proteomics will yield another dimension of vital insights https://science.sciencemag.org/content/367/6477/512/tab-pdf …@ScienceMagazine w/@ScienceVisualspic.twitter.com/3H2HKQj2nf
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"Middling correlation" between RNA and protein in CCLE proteomics datasethttps://twitter.com/dnusinow/status/1220392307888754688 …
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Neville Sanjana proslijedio/la je Tweet
Inside today's paper: A whole spread of coronavirus stories, including the hunt for a vaccine to prevent infection, courtesy of yours truly and
@katie_thomas via@nytimes https://www.nytimes.com/2020/01/28/health/coronavirus-vaccine.html …pic.twitter.com/nykJlGUsDe
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Finally, thinking to the future, one thing that is evident is that there is a lot of room for new strategies to design efficient, PAM-flexible — or perhaps even PAM-independent — Cas9 enzymes. (15/15)
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Second, none of the three PAM flexible Cas9 mutants were capable of matching the activity of wild-type Cas9 at NGG PAM sites, so these mutations likely incur a fitness cost. (14/15)
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What to make of all this? First, multi-CRISPR pooled screens provide a controlled way to benchmark different genome editing tools. (13/15)
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Although there was only a partial rescue for nuclease activity (yield an intermediate level of activity between xCas9 and Cas9-NG), we found that — much to our surprise — xCas9-NG yielded a better transcriptional activator than either existing PAM-flexible enzyme. (12/15)pic.twitter.com/dY5ZYwmWV3
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Given this data, we wondered whether the lower activity of xCas9 could be rescued using the Cas9-NG mutations. So, we made a frankenzyme: xCas9-NG. (11/15)pic.twitter.com/14c8JFW89L
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At this point, you might have noticed that there is also a difference in the PAM-flexible Cas9 variants: Specifically, that Cas9-NG outperforms xCas9 at NGH PAMs. (H= not G) Indeed, this is the case across all of our screens: knock-out, CRISPRi, and CRISPRa. (10/15)pic.twitter.com/iqoAOJX27D
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We also detected Cas9-NG activity at unconventional NAD PAMs. Upon testing, we found that some (but not all) NAD target sites work with Cas9-NG. For example, only Cas9-NG can drive CD45 expression at the NAA target site shown in the last row below. (9/15)pic.twitter.com/JoPDgQYyZ3
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Since CRISPRi and CRISPRa require binding at the target site (but not cutting since they utilize nuclease-null dCas9), we wondered if results might be different for those applications. But they were broadly the same as what we’d seen with nuclease. (8/15)pic.twitter.com/43mTzteoWA
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And, in case you’re curious, if you deep sequence the target sites, DNA editing/indels are very well correlated (as expected) with protein loss across all enzymes: (7/15)pic.twitter.com/nkYFQGwtTq
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Overall, we found that PAM flexibility of Cas9 mutants comes at a cost of reduced editing efficacy. For example, here are several guides targeting the cell-surface marker CD46. Overall, wild-type Cas9 leads to highest knock-out. (6/15)pic.twitter.com/gUUMzyV4ov
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Thus, in a single screen, we could capture both target site/PAM preferences and also relative efficiency of each Cas9 variant. (5/15)pic.twitter.com/dDzP3XdJbn
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So, instead of a typical CRISPR pooled screen with a library of guide RNAs, we also had all 3 different Cas9 enzymes in the same pool (transduced separately). For the multi-CRISPR pooled screen, we added a 6nt barcode after the gRNA to encode enzyme & KO/i/a modality. (4/15)pic.twitter.com/FBbSnXgKJl
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First a little background: For gain-of-function screens, we were interested to know whether we could target smaller regions (and hence those containing few NGG PAMs) using the recent variants, xCas9 and Cas9-NG, which only requiring a single G at the target site. (3/15)
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First, this work was co-led by postdocs Mat Legut and Zharko Daniloski and aided by several others in our group. (Special shout-out to Xinhe Xue, who spent her PhD rotation helping us launch this project and then joined our lab!) (2/15)
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TWEETORIAL on our new preprint: A genome engineering shootout (!) between wild-type Cas9 and 2 different PAM-flexible CRISPR enzymes — xCas9 and Cas9-NG — across 3 different tasks: knock-out, CRISPRi, and CRISPRa (1/15) https://www.biorxiv.org/content/10.1101/2020.01.22.916064v1 …pic.twitter.com/VHjEPpFybH
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