© 2014 Nature America, Inc. All rights reserved.
news and views measures express performance as a single number for a given data set, so they are well suited for optimizing user parameters. The most revealing biophysical measure is the familiar plot of meansquare displacement versus time. What are the results of all this? There is no ‘universally best’ method, but there are general principles for improving performance such as to tune the parameters in the algorithm carefully, make use of knowledge about particle motion and use multiple frames to find linkage. The latter two principles imply that it is useful to integrate a preliminary form of the analysis step into the linkage step. As the trajectories in Figure 1 suggest, algorithms must accommodate random walks, motion much smoother than random walks, and transitions. The generality and apparent circularity of the conclusions may be frustrating to users, but two facts make the situation less frustrating. First, users can ask more narrowly focused questions than the organizers did and can get more focused answers. The problem is simplified when the user is primarily interested in one scenario and a particular range of number densities and SNRs. The problem is further simplified when the question is narrowed: what approach is significantly better than what the lab is using now? Is method A better than method B? Second, the software
that the organizers used to generate test data sets and analyze performance is available and can be used by particle-tracking labs to optimize the user parameters for a particular algorithm and situation, as described in the article. Furthermore, developers of analysis programs can use the competition software as a standardized ‘test bed’ to see how their algorithms would have fared in the competition. Reviewers can legitimately ask whether a new algorithm has been tested on these data sets. One limitation of the competition is that it did not include the simple approach used in early SPT work as a control, though method 13 is close. Another limitation is the lack of error bars for the performance measures. It was impractical to require error bars in the competition, but when a lab is focused on a narrower range of situations and algorithms, the lab could run multiple simulations and generate error bars. The organizers’ software makes such calculations practical. These limitations are minor in light of what has been accomplished. The organizers and competitors have set a new standard in the field. One hopes for a rematch in, say, five years. COMPETING FINANCIAL INTERESTS The author declares no competing financial interests. 1. Chenouard, N. et al. Nat. Methods 11, 281–289 (2014).
Genome editing 101: let’s go digital
npg
Ryan Forster & Dirk Hockemeyer
An approach is described that simplifies the isolation of rare human pluripotent stem cells engineered to carry precise disease-relevant mutations. The correction of genetic mutations in human cells using site-specific nucleases (SSNs) became a reality in 2005 when Urnov et al. demonstrated that engineered zinc-finger nucleases can be used to ‘edit’ the human genome with single-base precision1. However, the inherent challenge of designing zincfinger nucleases delayed the implementation of SSNs as a standard research tool until recently. This changed when transcription activator– like effector nucleases (TALENs) and the RNA-guided nuclease Cas9, two novel and orthogonal strategies to engineer site-specific
nucleases, became available2,3. By following a set of very simple rules, a researcher can rapidly generate SSNs directed to almost any site in the human genome. As a result, human pluripotent stem cells (hPSCs) can now be genetically engineered to carry reporter genes, to overexpress disease-relevant genes, to carry corrected versions of these alleles at their endogenous locus or to harbor any other desired genetic manipulation4–6. Applying this level of control to genetically modify hPSCs, which can be differentiated into almost any cell type of the human
Ryan Forster and Dirk Hockemeyer are in the Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California, USA. e-mail: [email protected]
248 | VOL.11 NO.3| MARCH 2014 | nature methods
body, is an exciting new way to study human disease in the cell type most relevant to the disease. When SSNs are used to introduce or repair disease-relevant point mutations in hPSCs7, the resulting pair of otherwise isogenic cell lines will differ exclusively at one disease-relevant position, simplifying phenotypic analysis of an induced pluripotent stem cell (iPSC) disease model and overcoming challenges associated with the genetic heterogeneity of patient-derived hPSCs. Considering the ease by which SSNs can now be generated and tested, the next step is the broad implementation of this gene-correction approach in stem cell research. However, the uncertainty regarding offtarget modifications to the genome by the nuclease is an abiding challenge. What is needed are techniques that allow for the lowest effective level of expression of the nuclease, thereby minimizing the probability of point mutations being introduced outside the gene of interest. The development of such tools has been limited by the infeasibility of screening for the extremely rare on-target events that occur when the nucleases are expressed at critically low levels. In this issue of Nature Methods, Miyaoka et al.8 report an elegant new protocol that facilitates the isolation of rare modified hPSC clones, which they generated using genome editing, from populations of thousands of cells. The basic idea is simple: instead of genotyping a large number of iPSC clones to detect edited cells, Miyaoka et al. divide genome-edited iPSCs into small pools of cells with a stochastic distribution of correctly edited cells. Next they employ droplet digital PCR (ddPCR) to quantify genomeediting frequencies in these pools of cells. The pool with the highest editing frequency is iteratively subcloned into further pools until it yields a homogenous population of edited cells (Fig. 1). The protocol has some clever components. First, the researchers combined ddPCR with a TaqMan PCR detection assay using allele-specific probes that distinguish between the native and mutagenized DNA sequences. This approach provides a sensitive and, importantly, quantitative method to detect rare gene-editing events, offering an alternative to previously described strategies9. The engineering constraints inherent to making a precise change at a specific locus sometimes limit the resulting suitable SSNs to those of extremely low efficiencies. Miyaoka et al.8 demonstrate that their protocol can be used to isolate edited hPSC clones when using TALENs with genomeediting efficiencies as low as 0.023%. Second, the procedure is fully compatible with a 96-well
news and views
npg
© 2014 Nature America, Inc. All rights reserved.
Mutagenesis
Figure 1 | Isolation of rare engineered mutant human pluripotent stem cell clones by iterative subcloning and ddPCR screening. Adapted from ref. 8.
tissue culture system, making it a cost effective and scalable technique that can be used in a smaller laboratory setting. Although SSNs are potent tools in hPSC research, unwanted nuclease activity elsewhere in the genome must be considered during the experimental design. A high frequency of offtarget cuts is correlated with high expression of the nuclease, making it desirable to expose a cell to SSNs as little as possible. Miyaoka et al.8 do not directly test whether their method would allow researchers to titrate SSNs to sufficiently low levels to edit the genome entirely without off-target effects. However, by predicting and examining the most likely off-target sites upon expression of several nucleases, they suggest that the protocol—and the low level of nuclease expression it enables—might circumvent the generation of off-target point mutations. The most direct test of this hypothesis would be comprehensive whole-genome sequencing, which still needs to be performed. It is probable that reducing nuclease expression will lower the frequency off-target cuts; however, that most likely will not fully eliminate these events. Furthermore, this methodology does not address the genetic
drift that is inherent to the hPSC tissue culture system, which becomes exacerbated through serial single-cell passaging. As we now have powerful methods at hand that could streamline large-scale production of isogenic cell lines, it may be useful to think about how to implement these tools in a way that robustly links the genetic modification to a specific cellular phenotype. This is straightforward for gene knockouts, which can be generated with independent nucleases or in which phenotypes can be complemented by reintroducing the wildtype gene. However, when SSNs are used to generate otherwise isogenic cell lines—at a locus for which often only one nuclease can be designed and which may upon modification present a subtle phenotype—this is more challenging. The questions here are: How many independent clones must be analyzed to confidently avoid the effects caused by recurring off-target cuts or by the inherent heterogeneities in stem cell behavior? Can we use whole-genome sequencing to diagnose and later predict off-target sites? How many genomes do we have to sequence for a given SSN and experiment? An alternative and perhaps easier approach to assuage these concerns might be to further extend the design capabilities and safety features of the existing nucleases. This could be achieved by employing a new orthogonal Cas9-CRISPR (clustered, regularly interspaced, short palindromic repeats)-like protein that can be used as an SSN but that recognizes a different protospaceradjacent motif10,11. Such an orthogonal system could be used to identify the common phenotype of the same point mutation generated with a different set of nucleases. COMPETING FINANCIAL INTERESTS The authors declare no competing financial interests. 1. Urnov, F.D. et al. Nature 435, 646–651 (2005). 2. Miller, J.C. et al. Nat. Biotechnol. 29, 143–148 (2011). 3. Jinek, M. et al. Science 337, 816–821 (2012). 4. Hockemeyer, D. et al. Nat. Biotechnol. 27, 851–857 (2009). 5. Hockemeyer, D. et al. Nat. Biotechnol. 29, 731–734 (2011). 6. Mali, P. et al. Science 339, 823–826 (2013). 7. Soldner, F. et al. Cell 146, 318–331 (2011). 8. Miyaoka, Y. et al. Nat. Methods 11, 291–293 (2014). 9. Merkert, S. et al. Stem Cell Reports 2, 107–118 (2014). 10. Hou, Z. et al. Proc. Natl. Acad. Sci. USA 110, 15644–15649 (2013). 11. Esvelt, K.M. et al. Nat. Methods 10, 1116–1121 (2013).
nature methods | VOL.11 NO.3 | MARCH 2014 | 249
news and views measures express performance as a single number for a given data set, so they are well suited for optimizing user parameters. The most revealing biophysical measure is the familiar plot of meansquare displacement versus time. What are the results of all this? There is no ‘universally best’ method, but there are general principles for improving performance such as to tune the parameters in the algorithm carefully, make use of knowledge about particle motion and use multiple frames to find linkage. The latter two principles imply that it is useful to integrate a preliminary form of the analysis step into the linkage step. As the trajectories in Figure 1 suggest, algorithms must accommodate random walks, motion much smoother than random walks, and transitions. The generality and apparent circularity of the conclusions may be frustrating to users, but two facts make the situation less frustrating. First, users can ask more narrowly focused questions than the organizers did and can get more focused answers. The problem is simplified when the user is primarily interested in one scenario and a particular range of number densities and SNRs. The problem is further simplified when the question is narrowed: what approach is significantly better than what the lab is using now? Is method A better than method B? Second, the software
that the organizers used to generate test data sets and analyze performance is available and can be used by particle-tracking labs to optimize the user parameters for a particular algorithm and situation, as described in the article. Furthermore, developers of analysis programs can use the competition software as a standardized ‘test bed’ to see how their algorithms would have fared in the competition. Reviewers can legitimately ask whether a new algorithm has been tested on these data sets. One limitation of the competition is that it did not include the simple approach used in early SPT work as a control, though method 13 is close. Another limitation is the lack of error bars for the performance measures. It was impractical to require error bars in the competition, but when a lab is focused on a narrower range of situations and algorithms, the lab could run multiple simulations and generate error bars. The organizers’ software makes such calculations practical. These limitations are minor in light of what has been accomplished. The organizers and competitors have set a new standard in the field. One hopes for a rematch in, say, five years. COMPETING FINANCIAL INTERESTS The author declares no competing financial interests. 1. Chenouard, N. et al. Nat. Methods 11, 281–289 (2014).
Genome editing 101: let’s go digital
npg
Ryan Forster & Dirk Hockemeyer
An approach is described that simplifies the isolation of rare human pluripotent stem cells engineered to carry precise disease-relevant mutations. The correction of genetic mutations in human cells using site-specific nucleases (SSNs) became a reality in 2005 when Urnov et al. demonstrated that engineered zinc-finger nucleases can be used to ‘edit’ the human genome with single-base precision1. However, the inherent challenge of designing zincfinger nucleases delayed the implementation of SSNs as a standard research tool until recently. This changed when transcription activator– like effector nucleases (TALENs) and the RNA-guided nuclease Cas9, two novel and orthogonal strategies to engineer site-specific
nucleases, became available2,3. By following a set of very simple rules, a researcher can rapidly generate SSNs directed to almost any site in the human genome. As a result, human pluripotent stem cells (hPSCs) can now be genetically engineered to carry reporter genes, to overexpress disease-relevant genes, to carry corrected versions of these alleles at their endogenous locus or to harbor any other desired genetic manipulation4–6. Applying this level of control to genetically modify hPSCs, which can be differentiated into almost any cell type of the human
Ryan Forster and Dirk Hockemeyer are in the Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California, USA. e-mail: [email protected]
248 | VOL.11 NO.3| MARCH 2014 | nature methods
body, is an exciting new way to study human disease in the cell type most relevant to the disease. When SSNs are used to introduce or repair disease-relevant point mutations in hPSCs7, the resulting pair of otherwise isogenic cell lines will differ exclusively at one disease-relevant position, simplifying phenotypic analysis of an induced pluripotent stem cell (iPSC) disease model and overcoming challenges associated with the genetic heterogeneity of patient-derived hPSCs. Considering the ease by which SSNs can now be generated and tested, the next step is the broad implementation of this gene-correction approach in stem cell research. However, the uncertainty regarding offtarget modifications to the genome by the nuclease is an abiding challenge. What is needed are techniques that allow for the lowest effective level of expression of the nuclease, thereby minimizing the probability of point mutations being introduced outside the gene of interest. The development of such tools has been limited by the infeasibility of screening for the extremely rare on-target events that occur when the nucleases are expressed at critically low levels. In this issue of Nature Methods, Miyaoka et al.8 report an elegant new protocol that facilitates the isolation of rare modified hPSC clones, which they generated using genome editing, from populations of thousands of cells. The basic idea is simple: instead of genotyping a large number of iPSC clones to detect edited cells, Miyaoka et al. divide genome-edited iPSCs into small pools of cells with a stochastic distribution of correctly edited cells. Next they employ droplet digital PCR (ddPCR) to quantify genomeediting frequencies in these pools of cells. The pool with the highest editing frequency is iteratively subcloned into further pools until it yields a homogenous population of edited cells (Fig. 1). The protocol has some clever components. First, the researchers combined ddPCR with a TaqMan PCR detection assay using allele-specific probes that distinguish between the native and mutagenized DNA sequences. This approach provides a sensitive and, importantly, quantitative method to detect rare gene-editing events, offering an alternative to previously described strategies9. The engineering constraints inherent to making a precise change at a specific locus sometimes limit the resulting suitable SSNs to those of extremely low efficiencies. Miyaoka et al.8 demonstrate that their protocol can be used to isolate edited hPSC clones when using TALENs with genomeediting efficiencies as low as 0.023%. Second, the procedure is fully compatible with a 96-well
news and views
npg
© 2014 Nature America, Inc. All rights reserved.
Mutagenesis
Figure 1 | Isolation of rare engineered mutant human pluripotent stem cell clones by iterative subcloning and ddPCR screening. Adapted from ref. 8.
tissue culture system, making it a cost effective and scalable technique that can be used in a smaller laboratory setting. Although SSNs are potent tools in hPSC research, unwanted nuclease activity elsewhere in the genome must be considered during the experimental design. A high frequency of offtarget cuts is correlated with high expression of the nuclease, making it desirable to expose a cell to SSNs as little as possible. Miyaoka et al.8 do not directly test whether their method would allow researchers to titrate SSNs to sufficiently low levels to edit the genome entirely without off-target effects. However, by predicting and examining the most likely off-target sites upon expression of several nucleases, they suggest that the protocol—and the low level of nuclease expression it enables—might circumvent the generation of off-target point mutations. The most direct test of this hypothesis would be comprehensive whole-genome sequencing, which still needs to be performed. It is probable that reducing nuclease expression will lower the frequency off-target cuts; however, that most likely will not fully eliminate these events. Furthermore, this methodology does not address the genetic
drift that is inherent to the hPSC tissue culture system, which becomes exacerbated through serial single-cell passaging. As we now have powerful methods at hand that could streamline large-scale production of isogenic cell lines, it may be useful to think about how to implement these tools in a way that robustly links the genetic modification to a specific cellular phenotype. This is straightforward for gene knockouts, which can be generated with independent nucleases or in which phenotypes can be complemented by reintroducing the wildtype gene. However, when SSNs are used to generate otherwise isogenic cell lines—at a locus for which often only one nuclease can be designed and which may upon modification present a subtle phenotype—this is more challenging. The questions here are: How many independent clones must be analyzed to confidently avoid the effects caused by recurring off-target cuts or by the inherent heterogeneities in stem cell behavior? Can we use whole-genome sequencing to diagnose and later predict off-target sites? How many genomes do we have to sequence for a given SSN and experiment? An alternative and perhaps easier approach to assuage these concerns might be to further extend the design capabilities and safety features of the existing nucleases. This could be achieved by employing a new orthogonal Cas9-CRISPR (clustered, regularly interspaced, short palindromic repeats)-like protein that can be used as an SSN but that recognizes a different protospaceradjacent motif10,11. Such an orthogonal system could be used to identify the common phenotype of the same point mutation generated with a different set of nucleases. COMPETING FINANCIAL INTERESTS The authors declare no competing financial interests. 1. Urnov, F.D. et al. Nature 435, 646–651 (2005). 2. Miller, J.C. et al. Nat. Biotechnol. 29, 143–148 (2011). 3. Jinek, M. et al. Science 337, 816–821 (2012). 4. Hockemeyer, D. et al. Nat. Biotechnol. 27, 851–857 (2009). 5. Hockemeyer, D. et al. Nat. Biotechnol. 29, 731–734 (2011). 6. Mali, P. et al. Science 339, 823–826 (2013). 7. Soldner, F. et al. Cell 146, 318–331 (2011). 8. Miyaoka, Y. et al. Nat. Methods 11, 291–293 (2014). 9. Merkert, S. et al. Stem Cell Reports 2, 107–118 (2014). 10. Hou, Z. et al. Proc. Natl. Acad. Sci. USA 110, 15644–15649 (2013). 11. Esvelt, K.M. et al. Nat. Methods 10, 1116–1121 (2013).
nature methods | VOL.11 NO.3 | MARCH 2014 | 249
