TABLE OF CONTENTS
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| April 2018 Volume 15, Issue 4 |
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 | Editorial This Month Correspondence Research Highlights Technology Feature News and Views Analysis Brief Communications Articles Application Note |  | Advertisement |  |  |  |  Nikon's all-new A1R HD confocal features a High Definition 1K resonant scanner, which delivers high resolution images at ultra-high speed. The new scanner also provides 4x the field of view at the same resolution usually generated by a normal 512x512 scanner. Learn more | | | | |
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Nature Neuroscience: Poster on Cerebral Organoids Emerging three-dimensional culture methods enable differentiated human stem cells to form into brain organoids or assembloids, which can be used to study evolution, development, and disease. Available to download free online Produced with support from: STEMCELL Technologies | | |
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Editorial | |
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| CRISPR off-targets: a reassessment pp229 - 230 doi:10.1038/nmeth.4664 There was insufficient data to support the claim of unexpected off-target effects due to CRISPR in a paper published in Nature Methods. More work is needed to determine whether such events occur in vivo. |
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This Month | |
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| The Author File: Shai Shen-Orr p231 Vivien Marx doi:10.1038/nmeth.4659 Comparing single-cell trajectories with a new tool and what happens when aversion turns into love. |
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| Points of Significance: Statistics versus machine learning pp233 - 234 Danilo Bzdok, Naomi Altman and Martin Krzywinski doi:10.1038/nmeth.4642 Statistics draws population inferences from a sample, and machine learning finds generalizable predictive patterns. |
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Correspondence | |
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| Response to “Unexpected mutations after CRISPR-Cas9 editing in vivo” pp235 - 236 Lauryl M J Nutter, Jason D Heaney, K C Kent Lloyd, Stephen A Murray, John R Seavitt et al. doi:10.1038/nmeth.4559 See also: Correspondence by Lareau et al. | Correspondence by Wilson et al. | Correspondence by Lescarbeau et al. | Correspondence by Kim et al. |
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| Response to “Unexpected mutations after CRISPR-Cas9 editing in vivo” pp236 - 237 Christopher J Wilson, Tim Fennell, Anne Bothmer, Morgan L Maeder, Deepak Reyon et al. doi:10.1038/nmeth.4552 See also: Correspondence by Lareau et al. | Correspondence by Lescarbeau et al. | Correspondence by Kim et al. | Correspondence by Nutter et al. |
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| Response to “Unexpected mutations after CRISPR-Cas9 editing in vivo” p237 Reynald M Lescarbeau, Bradley Murray, Thomas M Barnes and Nessan Bermingham doi:10.1038/nmeth.4553 See also: Correspondence by Lareau et al. | Correspondence by Wilson et al. | Correspondence by Kim et al. | Correspondence by Nutter et al. |
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| Response to “Unexpected mutations after CRISPR-Cas9 editing in vivo” pp238 - 239 Caleb A Lareau, Kendell Clement, Jonathan Y Hsu, Vikram Pattanayak, J Keith Joung et al. doi:10.1038/nmeth.4541 See also: Correspondence by Wilson et al. | Correspondence by Lescarbeau et al. | Correspondence by Kim et al. | Correspondence by Nutter et al. |
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| Response to “Unexpected mutations after CRISPR-Cas9 editing in vivo” pp239 - 240 Sang-Tae Kim, Jeongbin Park, Daesik Kim, Kyoungmi Kim, Sangsu Bae et al. doi:10.1038/nmeth.4554 See also: Correspondence by Lareau et al. | Correspondence by Wilson et al. | Correspondence by Lescarbeau et al. | Correspondence by Nutter et al. |
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Research Highlights | |
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Technology Feature | |
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| How to pull the blanket off dormant cancer cells pp249 - 252 Vivien Marx doi:10.1038/nmeth.4640 When asleep, cancer cells can evade chemo. When they wake up, they can cause cancer recurrence. By deciphering dormancy cues, labs explore how to break this cycle. |
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News and Views | |
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| A deep (learning) dive into a cell pp253 - 254 Kristin Branson doi:10.1038/nmeth.4658 An interpretable, deep neural network produces mechanistic hypotheses on how genetic interactions contribute to whole-cell phenotypes. See also: Article by Ma et al. |
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Analysis | |
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| Bias, robustness and scalability in single-cell differential expression analysis pp255 - 261 Charlotte Soneson and Mark D Robinson doi:10.1038/nmeth.4612 An extensive evaluation of differential expression methods applied to single-cell expression data, using uniformly processed public data in the new conquer resource. |
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Brief Communications | |
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| Quantitative mapping and minimization of super-resolution optical imaging artifacts pp263 - 266 Sian Culley, David Albrecht, Caron Jacobs, Pedro Matos Pereira, Christophe Leterrier et al. doi:10.1038/nmeth.4605 This paper reports an approach to map errors in super-resolution images, based on quantitative comparison to diffraction-limited equivalents. |
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| Alignment of single-cell trajectories to compare cellular expression dynamics pp267 - 270 Ayelet Alpert, Lindsay S Moore, Tania Dubovik and Shai S Shen-Orr doi:10.1038/nmeth.4628 cellAlign enables quantitative comparisons of expression dynamics within and between single-cell trajectories based on single-cell RNA-seq or mass cytometry data. |
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| On the design of CRISPR-based single-cell molecular screens pp271 - 274 Andrew J Hill, Jose L McFaline-Figueroa, Lea M Starita, Molly J Gasperini, Kenneth A Matreyek et al. doi:10.1038/nmeth.4604 CRISPR-based single-cell pooled screens that use linked barcodes suffer from lost sensitivity due to lentiviral template switching. The barcode-free CROP-seq design circumvents this problem. |
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| A hybridization-chain-reaction-based method for amplifying immunosignals pp275 - 278 Rui Lin, Qiru Feng, Peng Li, Ping Zhou, Ruiyu Wang et al. doi:10.1038/nmeth.4611 isHCR allows multiplexed, sensitive detection of immunostained proteins in cultured cells, as well as in dense and cleared tissue. |
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| Identifying the favored mutation in a positive selective sweep pp279 - 282 Ali Akbari, Joseph J Vitti, Arya Iranmehr, Mehrdad Bakhtiari, Pardis C Sabeti et al. doi:10.1038/nmeth.4606 The iSAFE software accurately identifies the favored mutation within a positive selective sweep region of the genome. |
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