Delineating the decision-making mechanisms underlying choice between drug and nondrug rewards remains a challenge. This study adopts an original approach to probe these mechanisms by comparing response latencies during sampling versus choice trials. While lengthening of latencies during choice is predicted in a deliberative choice model (DCM), the race-like response competition mechanism postulated by the Sequential choice model (SCM) predicts a shortening of latencies during choice compared to sampling. Here, we tested these predictions by conducting a retrospective analysis of cocaine-versus-saccharin choice experiments conducted in our laboratory. We found that rats engage deliberative decision-making mechanisms after limited training, but adopt a SCM-like response selection mechanism after more extended training, while their behavior is presumably habitual. Thus, the DCM and SCM may not be general models of choice, as initially formulated, but could be dynamically engaged to control choice behavior across early and extended training. Investigating the decision-making mechanisms underlying choice between drug and nondrug rewards is essential to understand how their alterations can contribute to substance use disorders. Over the past decade, a growing research effort has been made to model this choice situation in animals, particularly in rodents . OverallPrevious research aimed at testing the validity of different models of animal choice, including behavioral ecology-inspired models, have proposed that the decision-making mechanisms underlying choice should be, in theory, inferable from a detailed analysis of response latencies . One sucThe sequential choice model (SCM) makes a prediction in direct opposition to the DCM. This model explains choice without postulating any explicit deliberation and/or valuation processes. Specifically, when facing different options, animals would consider each option sequentially, not simultaneously, and would decide whether to accept or reject it with no consideration of the other option available. It is hypothesized that such sequential decision process has evolved as an adaptation to the natural ‘reward ecology’ of most animals. Unlike in laboratory choice settings, successive encounters with different rewards are the rule in natural environments while simultaneous encounters are the exception . Based oThus, the DCM and SCM models make opposite testable predictions about the differences between choice and sampling latencies  . InteresThe goal of the present study is to test these predictions by conducting a systematic retrospective analysis of all cocaine versus saccharin choice experiments that have been conducted in rats in our laboratory over the past 12 years. This allowed us to probe the decision-making mechanisms underlying choice between drug and nondrug rewards as a function of prior training (limited or extended).We first assessed distributions of response latencies and preference in experiments without prior instrumental training before choice testing . We assuBoth decision-making models predict that the relative latency of each response option when encountered sequentially  should predict which one will be selected when encountered simultaneously . That is, the fastest the response for an option during sampling, the more likely this option will be selected during choice. We first tested this prediction by correlating preference with two estimates of the relative response latency for cocaine versus saccharin during sampling trials; the latency ratio (LR) and the proportion of winning latencies (WL). The LR was computed by dividing the mean saccharin sampling latency by the sum of saccharin and cocaine mean latencies. The WL was computed by estimating the probability that each of the six cocaine sampling latencies was shorter  than each of the six saccharin sampling latencies when compared two by two. LR and WL scores close to one indicate faster cocaine sampling latencies. We found that both LR and WL were positively correlated with the percentage of cocaine choice  rats. Among the 31 rats included in the analysis, 29 expressed a preference for saccharin . In thisWe next assessed preference and response latencies during sampling and choice in experiments including prior instrumental training before choice testing . We firsAs expected from prior studies , the majThe DCM and SCM make opposite predictions when comparing sampling and choice latencies . To testIn SP rats, the distribution of sampling and choice latencies on saccharin trials did not significantly differ, although there was a trend toward longer choice latencies . AccordiAlthough the SCM prediction was only partially validated, the results above suggest that after more extended training, choice behavior does not anymore involve a deliberation over options value, a finding in agreement with prior studies showing that preference for the nondrug reward is under habitual control . We nextIn the second experiment, water-restricted rats were offered a choice between water and cocaine. Water was then devalued by satiation. Although water preference was initially insensitive to water devaluation, rats eventually reversed their preference in favor of cocaine following repeated devaluation training . SamplinThe aim of this study was to determine the decision-making processes underlying choice by comparing sampling and choice response latencies, after limited or extended training. An increase in latencies during choice is predicted from the DCM whereas the SCM predicts a shortening of latencies during choice compared to sampling. Here we tested these different predictions in a systematic retrospective analysis of all the choice experiments conducted in the laboratory over the past 12 years. Our analysis shows a lengthening of choice latencies after limited training, suggesting the involvement of a deliberative process. However, when prior training is included before choice testing, we observed similar choice and sampling latencies on saccharin trials and a shortening of choice latencies on cocaine trials. These results are partially consistent with the SCM, but inconsistent with the DCM. Finally, additional analyses in two independent experiments assessing habitual control of choice behavior confirm that the engagement of deliberative or valuation processes can be inferred from the comparison of sampling and choice response latencies.In agreement with previous experiments , resultsContrary to all prior studies testing predictions of the SCM , analysiIn the data set with prior instrumental training, the shortening of cocaine response latencies during choice is consistent with the SCM. Indeed, the race-like competition process implies that only the shortest latencies should be expressed during choice trials, the selection of one option excluding the opportunity to select the alternative option . This crThe floor effect for saccharin response latencies cannot be excluded, but is also likely to occur . In the Although cocaine was the preferred option in CP rats, we were able to observe a shortening of latencies at choice following extended training. Interestingly, this difference was mainly driven by the long sampling latencies since choice latencies were more homogenous between rewards and groups of rats. This finding is in agreement with the SCM, which assumes that while sampling latencies for each option are expressed without cross-censorship from the alternative option, choice latencies are censored by the selection of the alternative option. In CP and IND rats, cocaine sampling latencies are quite long, a finding consistent with prior studies from our lab that remains to be elucidated. However, during choice trials, selection of saccharin censors longer cocaine response latencies, thereby biasing the distribution of cocaine choice latencies in favor of the shortest latencies, more similar to saccharin latencies. Interestingly the gap between cocaine choice and sampling latencies was more pronounced in IND rats compared to CP rats. This result further supports the SCM, which predicts that trimming of the leftward side of the distribution of latencies would be more pronounced when the overlap between response distributions is stronger . Since rLike habit, the automatic selection process of option based on the response latency implied by the SCM is oblivious to the options’ value. The SCM assumes that during sequential encounters, each option is processed independently and choice results from a race between the mechanisms generating response latencies for each available option . Thus, aA key question raised by our findings is whether the differences between sampling and choice latencies plausibly reflect operation of different decision-making mechanisms (race-like versus deliberative model) or different operations of the same decision-making mechanism. Likewise, it would be interesting to determine whether decision-making mechanisms or operations differ in the groups without and with prior training, when the behavior is presumably goal-directed and habitual, respectively. The interplay between goal-directed and habitual behavior is often described as dichotomous (i.e. based on separate decision-making mechanisms) but there is now a consensus on the need to consider it as a gradient , which mTo conclude, this systematic analysis has begun to probe the decision-making processes underlying choice between sweet water and cocaine in rats. It shows that when simultaneously facing these two options, rats first engage a deliberative strategy but after more extended training, rats adopt an automated SCM-like response selection strategy, in the absence of deliberation. Clearly, more research is needed to dissect the precise decision-making mechanisms underlying choice between drug and nondrug rewards in our discrete-trial choice procedure. However, these results suggest that the comparison of sampling and choice latencies could be used to infer the involvement of deliberative processes during choice.The data analyzed in this study have been obtained from previous experiments conducted in our laboratory over the past 12 years. All experimental subjects were male adult Wistar rats . Rats were housed in groups of two or three and maintained in a temperature-controlled vivarium (23° C) with a 12 hr reverse light–dark cycle. Testing occurred during the dark phase of the cycle and water and food were available ad libitum in all experiments. All experiments were carried out in accordance with institutional and international standards of care and use of laboratory animals . All experiments have been approved by the Committee of the Veterinary Services Gironde, agreement number B33-063-5.In the first set of experiments, a total of 31 rats were directly tested in the choice schedule without prior operant training  under a fixed-ratio (FR) schedule of saccharin and cocaine self-administration on alternate daily sessions, 6 days a week, before choice testing .Each daily choice session consisted of 12 trials, spaced by 10 min inter-trials intervals, and divided into two successive phases, sampling and choice. The sampling phase was composed of four sampling trials in which each lever and thus each response option was presented alternatively and sequentially. If rats responded twice within 5 min on the available lever, they were rewarded by the corresponding reward. Reward delivery was signaled by the immediate retraction of the lever and illumination of a cue-light above it. If rats failed to complete the response requirement within 5 min, the lever was retracted until the next trial 10 min later. The choice phase consisted of eight choice trials during which the two response options were presented simultaneously. Specifically, each choice trial began with the simultaneous presentation of both levers S and C and rats could select one of the two levers by responding twice consecutively on it to obtain the corresponding reward. Reward delivery was signaled by the simultaneous retraction of both levers and illumination of the cue-light above the selected lever. If rats failed to respond on either lever within 5 min, both levers were retracted and no cue-light and reward was delivered. Importantly, 10 min inter-trial intervals were set to minimize the direct pharmacological effects of cocaine on subsequent trials and prevent rats from choosing under the influence of the drug . A schemIn the W/O training set, rats were first trained in the discrete-trials choice schedule with a FR1 for 10 sessions before testing with the final FR2 schedule for five sessions.Only choice experiments that were conducted under similar conditions  and that resulted in a stable preference within 5–10 choice sessions  were included in the present analysis . The lasSampling and choice latencies for each response option were analyzed for each individual rat over the last three stable choice sessions. In total, there were six sampling latencies per option and per rat and 24 choice latencies, with a variable number of responses for cocaine and saccharin, depending on the rat’s preference. For convenience, performance during choice was expressed in percent of cocaine choices. Response latencies corresponded to the time to complete the FR2 requirement from trial onset . When a rat failed to respond within 5 min after trial onset (omission), it was assigned a maximal response latency of 300 s. However, omissions occur rarely.To estimate the relative speed at which the cocaine and saccharin options are selected during sampling trials, we computed for each individual rat the latency ratio (LR) and the proportion of winning latencies (WL). The LR was computed by dividing the mean saccharin sampling latency by the sum of saccharin and cocaine mean latencies. Thus, LR values close to zero indicate faster saccharin sampling latencies while LR values close to one indicate faster cocaine sampling latencies. The WL was computed by estimating the probability that each of the six cocaine sampling latencies was shorter  than each of the six saccharin sampling latencies when compared two by two. The WL values ranged from 0  to 100% . Correlation analyses were conducted between these two measures of relative latencies and the percentage of cocaine choices.To determine whether and to what extent, cocaine and saccharin sampling latencies could predict rat’s preference profile, a LDA model  was trained on the mean cocaine and saccharin sampling latencies to classify the preference of individual rats as SP, IND, or CP. Rats were considered as SP, IND, or CP if their preference was below 33.3%, between 33.3% and 66.6% or above 66.6%, respectively. LDA models were trained on 90% of the data set and used to classify rats’ preference in the remaining 10% . To account for the unbalanced number of SP, IND, and CP rats, we performed the analysis on the same number of rats in each preference group by randomly sampling subjects in the SP and IND groups based on the number of CP rats (N = 19). This analysis was performed on 50 random selections of SP and IND rats and the performance across all 50 repetitions was averaged to determine the model accuracy. The same analysis was conducted with the preference group identities shuffled to determine the model accuracy expected from chance. To assess whether decoding accuracy significantly departed from chance, a permutation test was conducted.To test predictions of the different decision-making models, we compared the distributions of sampling and choice latencies for each response option. Note that the number of choice latencies (i.e. max. 24) was larger than the number of sampling latencies (six per option) and that among choice trials, the number of saccharin responses was disproportionally higher than cocaine responses because of preference for saccharin in the majority of rats. To avoid any selection bias related to differences in preference, choice and sampling responses for saccharin were compared in SP (<33.3% cocaine choice) and IND rats (33.3–66.6% cocaine choice) whereas choice and sampling responses for cocaine were compared in CP (>66.6% cocaine choice) and IND rats. Note that the IND group comprises comparable numbers of cocaine and saccharin choice latencies allowing for analysis of both reward responses in this subgroup of rats.To confirm whether the engagement of deliberative process could be inferred from an analysis of response latencies, choice and sampling latencies were compared in two independent experiments testing preference sensitivity to devaluation of the nondrug reward . In the In the second experiment, 28 male Wistar rats were water-restricted and allowed to choose between water and cocaine, as described above. Water was then devalued with 1 hr water access immediately before the choice session and during every inter-trial intervals of choice sessions (satiation sessions). We have shown that preference was initially insensitive to water devaluation but gradually reversed toward cocaine across repeated cycles of satiation and privation sessions  . Water sA detailed description of the methods for these two experiments is available in Linear regressions were tested with the Spearman's rank correlation test. Mean sampling and choice latencies were compared within subject using the non-parametric Wilcoxon test. The effect sizes were estimated with the rank-biserial correlation. Following the LDA, a permutation test was conducted to determine whether the decoding accuracy significantly departed from chance. All statistical analyses were conducted on Python. In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:Decision-making mechanisms underlying choice between drug and nondrug rewards have not yet been delineated. In this manuscript, a retrospective analysis reveals that, in a drug-choice setting, preference in rats appears to be driven initially by deliberative processes, but after extended training shifts to more automatic selection processes.Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by Shelly Flagel as the Reviewing Editor and Michael Taffe as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Gavan McNally (Reviewer #1); Stephanie Groman (Reviewer #2).Thank you for submitting your article "Probing the decision-making mechanisms underlying choice between drug and nondrug rewards in rats" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife but that some conclusions require a modest amount of revisions and/or additional new data, as they do with your paper, we are asking that the manuscript be revised to either limit claims to those supported by data in hand, or to explicitly state that the relevant conclusions require additional supporting data.We would like to draw your attention to changes in our policy on revisions we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, when editors judge that a submitted work as a whole belongs in eLife, either of which would be linked to the original paper.Our expectation is that the authors will eventually carry out the additional experiments and report on how they affect the relevant conclusions either in a preprint on bioRxiv or medRxiv, or if appropriate, as a Research Advance in Summary:In this manuscript the authors perform a retrospective analysis in attempt to delineate the role of goal-directed versus habitual mechanisms underlying choice between drug and non-drug rewards. Specifically, the authors utilized data generated in their laboratory to assess cocaine-versus-saccharin choice following limited and extended training paradigms. A sequential choice model was used to assess the prediction that increased latencies during choice reflects goal-directed control; whereas no change in latencies reflects habitual control. Based on this model, the authors report that rats engage in goal-directed control after limited training, and adopt more habitual responding after extended training. The authors conclude that the sequential choice model is specific to habitual choice.We know already from decades of work that choice response times are almost always log-normally distributed . The question here is whether differences in the mean and dispersion of these distributions can be used to derive insights into nature of the decision-making mechanism – a deliberative comparison versus a race model – and how this may differ for rats that prefer cocaine over saccharin and how this might be altered by more extended training. These questions are framed in terms of the differences between goal-directed and habitual behavior, which may be less compelling than considering the significance of the data in their own right.While the Reviewers appreciate the approach and conceptual framework described in this manuscript, they are all in agreement that additional data and analyses are needed to better support the claims surrounding goal-directed versus habitual control of reward-seeking behavior. For example, an independent evaluation of whether the target behavior is in fact goal-directed or habitual seems necessary to support such claims. Additional concerns raised by the Reviewers and suggestions for improvement our included below.Essential revisions:1. Much, if not all, of the absolute differences between latencies in sample and choice phases appear to be carried by the sample rather than the choice phase. Choice latencies for cocaine preferring rats, saccharin preferring rats, and the indifferent rats are all very similar. In contrast, the sampling latencies for cocaine preferring rats and the indifferent rats are longer. Why is this the case? The authors seem more concerned with the choice side of the experiment being different, not the sample phase. Is this predicted by the models being tested? Why would an SCM-like model would predict the difference being in the sample phase. Either way, the authors should be clearer about where the difference is expected to lie and why the sample phase is so obviously different in some conditions but the choice phase so similar.different decision making mechanisms (race model versus deliberative processing) or different operation of the same decision-making mechanism. The answer is difficult to derive from the data and modelling provided. The authors frame the differences in response time as being uniquely predicted or explained by different forms of choice. The models that the authors are using are closely linked to, and intellectually derived from, models of human choice reaction time. The most successful of these models are the diffusion model (DDM) . Diffusion Decision Model: Current Issues and History. Trends in Cognitive Sciences 20, 260-281) and the linear ballistic accumulator (LBA) . The simplest complete model of choice response time: linear ballistic accumulation. Cognitive Psychology 57, 153-178.2008).2. The major issue with this manuscript is whether the differences between response latencies in the sample versus choice phases plausibly reflect operation of Even though the DDM and LBA adopt different architectures to each other , they are intended to explain the same data. Of relevance, the same model (a DDM or an LBA) can explain differences in both the response distribution and the mean response time via changes in the starting point of evidence accumulation, rate of evidence accumulation, and/or the boundary or threshold at which evidence is translated into choice behavior. So, for either a difference accumulator model (DDM) or a race model (LBA), the difference between sampling and choice performance could reflect changes in how the model is operating between these two phases, including a change in the starting point of the decision [bias], a change in rate of accumulation [evidence], a change in threshold [caution] or collapsing boundary scenario, rather than reflecting operation of a completely different decision-making mechanism.Another option could be to acknowledge this possibility and discuss it. e.g., does it really matter if it is a qualitatively different decision-making process or different operation of the same decision-making mechanism? The action-habit distinction doesn't live or die by reaction/response time data, this distinction is almost certainly far less absolute than often portrayed in the addiction literature, and it is generally intended as an account of what is learned rather than an account of how that learning is translated into behaviour (even if an S-R mechanism provides an account of both). Response time data likely reflect something different- about how what has been learned is translated into behaviour. The third, marginally more difficult but more interesting option, would be to explore these issues formally and to move beyond simple descriptive or LDA analyses of response time distributions. The LBA has a full analytical solution and there are reasonable approximations for the DDM. Formal modelling of choice response times  could indicate whether a single decision-making mechanism (LBA or DDM or something else) can explain response times under both sample and choice conditions or not. This is a standard approach in cognitive modelling. This would be compelling if it showed the dissociation the authors argue – i.e. one model cannot be fit to both sample and choice datasets for all animals. However, if one model can expain both, then formal modelling would show which decision making parameters change between the sample and choice conditions for cocaine v sacc v ind animals to putatively cause the differences in response times observed. Either way, more formal modelling would provide a platform towards identification of those specific features of the decision-making mechanisms that are being affected.3. The use of response times between sampling and choice behavior as a proxy for assessing habitual vs. goal-directed behaviors is quite clever and the predictions are straightforward. However, the authors do not provide any data to validate this metric. For example, are there data in these animals using more traditional tests of habit  that could be used to support their argument that behavior is habitual when sampling latencies are equivalent to choice latencies? Even using devaluation procedures there is variation across subjects, so perhaps the authors could correlate the difference score with responses during devaluation? These additional data would better support the utility of this task and analytic approach.4. Most animals prefer saccharin and perform more saccharin-seeking responses; thus, the outcomes are not matched. Would cocaine RT become faster with more equivalent practice? This model should be applied perhaps in the first instance to responding for two natural rewards, where devaluation could confirm the nature of control and directly test the ideas from the model.5. The stated aim of the study was to determine the decision-making processes underlying choice, but the data presented do not directly address this aim. It may predict choice, but not how that choice is arrived at? To address this, the model should be applied perhaps in the first instance to responding for two natural rewards, where devaluation could confirm the nature of control and directly test the ideas from the model.6. The authors state that they can't rule out a floor effect, but this is an important possibility that if true would completely change the interpretation of the findings. More discussion of the implications is warranted.eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by Shelly Flagel as the Reviewing Editor and Michael Taffe as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Stephanie Mary Groman (Reviewer #2).Thank you for submitting your article "Probing the decision-making mechanisms underlying choice between drug and nondrug rewards in rats" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission. Although we generally try to avoid multiple rounds when the authors are unwilling to adjust their claims that reviewers evaluated as insufficiently supported, we believe another opportunity to revise in light of these significant concerns is justified here.Essential revisions:1. While the authors were generally responsive to prior concerns, additional revisions are needed to temper the claims surrounding goal-directed versus habitual control of reward-seeking behavior. As noted by all of the reviewers previously, the reported procedures do not allow the unequivocal identification of the psychological bases of action vs. habit. Thus, in lieu of additional data or analyses, the authors need to re-frame the manuscript and moderate their conclusions. The reviewers believe that the importance of these data is independent of the action vs. habit distinction, and feel that the impact of the manuscript will be greater if alternative interpretations are further considered.Reviewer #1:I remain of the view that this is a fascinating and potentially important manuscript. However, I am remain as unsure as to what it really means. There are differences in RTs, but as noted by all reviewers previously, the procedures simply do not allow unequivocal identification of the psychological bases for these differences . This is somewhat unfortunate as the manuscript is framed in terms of these differences. As indicated previously, I think the importance of these data is independent of the kinds of action/habit distinctions the authors are trying to draw. Likewise, the modelling is novel but does not really solve the problem of whether different decision mechanisms for choice are involved or whether the same mechanism is operating in different ways. For these reasons, I find myself in much the same place after reading the revision as I did the original manuscript: intrigued, seeking to be supportive, but quite unsure as to what it means.Reviewer #2:The authors have addressed my previous comments and I have no additional concerns. This is an excellent manuscript with fantastic results – well done. Essential revisions:1. Much, if not all, of the absolute differences between latencies in sample and choice phases appear to be carried by the sample rather than the choice phase. Choice latencies for cocaine preferring rats, saccharin preferring rats, and the indifferent rats are all very similar. In contrast, the sampling latencies for cocaine preferring rats and the indifferent rats are longer. Why is this the case? The authors seem more concerned with the choice side of the experiment being different, not the sample phase. Is this predicted by the models being tested? Why would an SCM-like model would predict the difference being in the sample phase. Either way, the authors should be clearer about where the difference is expected to lie and why the sample phase is so obviously different in some conditions but the choice phase so similar.This is an interesting issue that we now better explain in the Discussion . Briefly, the fact that latency differences mainly appear during the sampling phase and not during the choice phase is a direct consequence of the response latency-based censorship among options hypothesized by the SCM. Indeed, while sampling latencies for each option are expressed without censorship by the alternative option, choice latencies are systematically censored by selection of the alternative option, thereby biasing their distribution in favor of the shortest choice latencies. This response latency-based censorship also inevitably results in a homogenization of choice latencies across preference groups.Now, the differences in sampling latencies between the different groups of rats  were in fact due to the type of options sampled that were considered for analysis, as now better explained in the text . Regardless of the groups, sampling latencies for cocaine were longer than sampling latencies for saccharin. In Figure 3 , this difference between options appears as a difference between groups because for the purpose of our analysis, we only compare the sampling and choice latencies of the option that is preferred  or chosen (saccharin or cocaine in indifferent rats) during choice trials. The reason why sampling latencies for cocaine are longer than sampling latencies for saccharin has not been elucidated yet. To paraphrase Newton, we “feign no hypotheses” here.2. The major issue with this manuscript is whether the differences between response latencies in the sample versus choice phases plausibly reflect operation of different decision making mechanisms (race model versus deliberative processing) or different operation of the same decision-making mechanism. The answer is difficult to derive from the data and modelling provided. The authors frame the differences in response time as being uniquely predicted or explained by different forms of choice. The models that the authors are using are closely linked to, and intellectually derived from, models of human choice reaction time. The most successful of these models are the diffusion model (DDM) . Diffusion Decision Model: Current Issues and History. Trends in Cognitive Sciences 20, 260-281) and the linear ballistic accumulator (LBA) . The simplest complete model of choice response time: linear ballistic accumulation. Cognitive Psychology 57, 153-178.2008).Even though the DDM and LBA adopt different architectures to each other , they are intended to explain the same data. Of relevance, the same model (a DDM or an LBA) can explain differences in both the response distribution and the mean response time via changes in the starting point of evidence accumulation, rate of evidence accumulation, and/or the boundary or threshold at which evidence is translated into choice behavior. So, for either a difference accumulator model (DDM) or a race model (LBA), the difference between sampling and choice performance could reflect changes in how the model is operating between these two phases, including a change in the starting point of the decision [bias], a change in rate of accumulation [evidence], a change in threshold [caution] or collapsing boundary scenario, rather than reflecting operation of a completely different decision-making mechanism.Another option could be to acknowledge this possibility and discuss it. e.g., does it really matter if it is a qualitatively different decision-making process or different operation of the same decision-making mechanism? The action-habit distinction doesn't live or die by reaction/response time data, this distinction is almost certainly far less absolute than often portrayed in the addiction literature, and it is generally intended as an account of what is learned rather than an account of how that learning is translated into behaviour (even if an S-R mechanism provides an account of both). Response time data likely reflect something different- about how what has been learned is translated into behaviour.It would indeed be very interesting to determine whether the differences in response latencies during sampling versus choice trials reflect operation of different decision-making mechanisms or different operations of the same mechanism. We agree with the reviewer that our analysis falls short of addressing this question, but we feel that it is beyond the scope of our study. In fact, the sequential choice model is oblivious to the decision-making mechanisms underlying the selection of a particular option. However, as suggested by the reviewer, we now raise this question in the Discussion and discuss in some details how the diffusion and linear ballistic accumulator models could help to tackle it (p11 lines 12-30). We also acknowledge the limitations of the action-habit dichotomy and, thus, discuss the possibility of a gradient between action and habit that may involve a single decision-making mechanism with a change in underlying operations .The third, marginally more difficult but more interesting option, would be to explore these issues formally and to move beyond simple descriptive or LDA analyses of response time distributions. The LBA has a full analytical solution and there are reasonable approximations for the DDM. Formal modelling of choice response times  could indicate whether a single decision-making mechanism (LBA or DDM or something else) can explain response times under both sample and choice conditions or not. This is a standard approach in cognitive modelling. This would be compelling if it showed the dissociation the authors argue – i.e. one model cannot be fit to both sample and choice datasets for all animals. However, if one model can expain both, then formal modelling would show which decision making parameters change between the sample and choice conditions for cocaine v sacc v ind animals to putatively cause the differences in response times observed. Either way, more formal modelling would provide a platform towards identification of those specific features of the decision-making mechanisms that are being affected.This is an interesting perspective for future research that requires collaboration with expert modelers. Formal modelling on our dataset could provide compelling supplemental information on the decision-making mechanisms or parameters differing between sampling versus choice, limited versus extended training or as a function of individual preference. We now describe this perspective of our study in the Discussion .3. The use of response times between sampling and choice behavior as a proxy for assessing habitual vs. goal-directed behaviors is quite clever and the predictions are straightforward. However, the authors do not provide any data to validate this metric. For example, are there data in these animals using more traditional tests of habit  that could be used to support their argument that behavior is habitual when sampling latencies are equivalent to choice latencies? Even using devaluation procedures there is variation across subjects, so perhaps the authors could correlate the difference score with responses during devaluation? These additional data would better support the utility of this task and analytic approach.As suggested by the reviewer, we analyzed choice and sampling latencies in two independent experiments in which the nondrug reward was devalued . When preference was insensitive to devaluation of the nondrug reward, choice and sampling latencies were similar, whereas a lengthening of choice latencies compared to sampling latencies was observed when preference became sensitive to devaluation after repeated devaluation training. These results confirm our model and have now been included in the manuscript with an additional main figure  and a supplemental figure .Analysis of correlation between sensitivity to devaluation and the difference score between sampling and choice latencies did not provide consistent results in the different stages and experiments. This negative result could be explained by the low inter-individual variability in the sensitivity to devaluation when choice behavior is habitual.4. Most animals prefer saccharin and perform more saccharin-seeking responses; thus, the outcomes are not matched. Would cocaine RT become faster with more equivalent practice? This model should be applied perhaps in the first instance to responding for two natural rewards, where devaluation could confirm the nature of control and directly test the ideas from the model.Although most animals prefer saccharin over cocaine, they received similar initial operant training with these two options before choice testing . Thus, it is unlikely that long response latencies for cocaine results from less operant practice with this reward. In fact, response latencies for cocaine remain longer than response latencies for saccharin even in rats that receive largely more operant practice with cocaine than with saccharin before choice testing . We agree, however, with the reviewer that our conclusions would benefit from an independent confirmation of our model in a choice situation between two nondrug rewards. This perspective for future research is now acknowledged in the Discussion .5. The stated aim of the study was to determine the decision-making processes underlying choice, but the data presented do not directly address this aim. It may predict choice, but not how that choice is arrived at? To address this, the model should be applied perhaps in the first instance to responding for two natural rewards, where devaluation could confirm the nature of control and directly test the ideas from the model.Our study does show that the goal-directed versus habitual nature of preference can be inferred from the comparison of choice and sampling latencies. Thus, the stated aim of our study, consisting in determining the decision-making mechanisms  underlying choice between a drug and a nondrug reward is addressed. We now report additional analysis of two independent experiments assessing preference sensitivity to devaluation that further supports our main conclusions (see response to comment 3). We also acknowledge the value of additional research in a choice situation involving two nondrug rewards (see our response to comment 4). However, our main goal here was to investigate decision-making mechanisms underlying choice between drug and nondrug rewards. Indeed, it remains difficult to determine whether preference for the drug is also under habitual control, mainly because there is no effective method of drug reward devaluation in animals, particularly for drugs administered intravenously. Thus, our study provides an alternative approach to drug devaluation to infer the goal-directed versus habitual nature of choice.6. The authors state that they can't rule out a floor effect, but this is an important possibility that if true would completely change the interpretation of the findings. More discussion of the implications is warranted.As suggested by the reviewer, we are now more thoroughly discussing this issue in the Discussion and its potential implications for our conclusions (p10 lines 1-7). Overall, we conclude that a floor effect is real, but this does not compromise the interpretation of our findings.Essential revisions:1. While the authors were generally responsive to prior concerns, additional revisions are needed to temper the claims surrounding goal-directed versus habitual control of reward-seeking behavior. As noted by all of the reviewers previously, the reported procedures do not allow the unequivocal identification of the psychological bases of action vs. habit. Thus, in lieu of additional data or analyses, the authors need to re-frame the manuscript and moderate their conclusions. The reviewers believe that the importance of these data is independent of the action vs. habit distinction, and feel that the impact of the manuscript will be greater if alternative interpretations are further considered.We re-framed the manuscript under a broader perspective and tempered our conclusions about the distinction between goal-directed and habitual decision-making mechanisms based on the analysis of response latencies.Reviewer #1:I remain of the view that this is a fascinating and potentially important manuscript. However, I am remain as unsure as to what it really means. There are differences in RTs, but as noted by all reviewers previously, the procedures simply do not allow unequivocal identification of the psychological bases for these differences . This is somewhat unfortunate as the manuscript is framed in terms of these differences. As indicated previously, I think the importance of these data is independent of the kinds of action/habit distinctions the authors are trying to draw. Likewise, the modelling is novel but does not really solve the problem of whether different decision mechanisms for choice are involved or whether the same mechanism is operating in different ways. For these reasons, I find myself in much the same place after reading the revision as I did the original manuscript: intrigued, seeking to be supportive, but quite unsure as to what it means.We understand the reviewer’s concern and appreciate his/her willingness to be supportive. In response to this last comment, we tried to re-frame the manuscript to test the validity of the deliberative choice model (DCM) and the sequential choice model (SCM) as a function of prior training. In this new revision, we are not pretending to tease apart habit from action on the basis of response latencies. Instead, we tempered our conclusions and highlighted in the Discussion the similitudes between habit and the SCM, on one hand, and between goal-directed behavior and the DCM, on the other hand (p10 lines 17-29). Overall, we now suggest that the comparison of sampling and choice latencies could be used to infer the involvement of deliberative processes during drug versus nondrug choice.


Despite the abundant literature on vasopressor therapy, few studies have focused on vasopressor-sparing strategies in patients with shock. We performed a scoping-review of the published studies evaluating vasopressor-sparing strategies by analyzing the results from randomized controlled trials conducted in patients with shock, with a focus on vasopressor doses and/or duration reduction. We analyzed 143 studies, mainly performed in septic shock. Our analysis demonstrated that several pharmacological and non-pharmacological strategies are associated with a decrease in the duration of vasopressor therapy. These strategies are as follows: implementing a weaning strategy, vasopressin use, systemic glucocorticoid administration, beta-blockers, and normothermia. On the contrary, early goal directed therapies, including fluid therapy, oral vasopressors, vitamin C, and renal replacement therapy, are not associated with an increase in vasopressor-free days. Based on these results, we proposed an evidence-based vasopressor management strategy. Because fluid and vasopressors are the main treatments for shock, they are used on a day-to-day basis as symptomatic treatment for arterial hypotension. Vasoplegia is associated with vasodilation and vascular hypo-responsiveness, and involves multiple mechanisms . The aimOur objective was to perform a scoping-review of the published studies evaluating the vasopressor-sparing strategies. We therefore analyzed the results from randomized controlled trials (RCTs) conducted in patients with shock, focusing on vasopressor doses and/or duration reduction.We aimed to study vasopressor-sparing strategies in adult patients with shock. We conducted a scoping-review by following PRISMA-scoping review guidelines . BecauseThe inclusion criteria were RCT studies , with at least one of the reported outcomes including vasopressors. Outcomes were classified as follows: (1) shock reversal, (2) duration of vasopressor use, (3) day free of vasopressors, (4) cumulative dose of vasopressors, and (5) dose of vasopressors.The exclusion criteria were as follows: articles not available in English, as well as those on traditional Chinese medicine, anaphylactic shock, and reporting COVID-19.Studies with non-significant outcomes in terms of vasopressor use were reported only if the studies reporting significant outcomes for the same intervention were found.All of the studies were reviewed by two independent reviewers (A.M. and V.B.). When most studies agreed with the vasopressor-sparing effect, the authors concluded a positive effect. When the results did not meet a consensus, the authors concluded uncertainty, and when most studies were negative, the authors concluded a negative effect.We included all of the relevant studies found in the Medline database (Pubmed) and Cochrane Library from searches conducted in May 2020 and updated in March 2021, using the following algorithm: (“Cardiovascular Agents/therapeutic use” [Mesh Terms] OR “Cardiovascular Agents/administration and dosage” [Mesh Terms] OR “vasoconstrictor agents/administration and dosage” [Mesh Terms] OR “norepinephrine/administration and dosage” [Mesh Terms] OR “Catecholamines/administration and dosage” [Mesh Terms] OR “Vasoconstrictor Agents/therapeutic use” OR “Catecholamines/therapeutic use” [Mesh Terms] OR “norepinephrine/therapeutic use” [MeSH Terms] OR “Shock/therapy” [MeSH Terms] OR “Algorithms” [MeSH Terms]) AND “humans” [MeSH Terms] AND (“adult” [MeSH Terms] OR “aged” [MeSH Terms] OR “middle aged” [MeSH Terms]) AND (“Shock” [MeSH Terms] OR “Multiple Organ Failure” [MeSH Terms] OR (“Lactic Acid/blood” [MeSH Terms] AND (“vasoconstrictor agents/administration and dosage” [Mesh Terms] OR “norepinephrine/administration and dosage” [Mesh Terms] OR “Catecholamines/administration and dosage” [Mesh Terms] OR “Vasoconstrictor Agents/therapeutic use” OR “Catecholamines/therapeutic use” [Mesh Terms] OR “norepinephrine/therapeutic use” [MeSH Terms] “Cardiovascular Agents/therapeutic use” [Mesh Terms] OR “Cardiovascular Agents/administration and dosage” [Mesh Terms])) AND Randomized Controlled Trial [Publication Type]). The search included all publications since 1995.Among the 830 studies screened, 143 were included . Forty sThe main results are summarized in The SOAP (Sepsis Occurrence in Acutely Ill Patients) II study, which compared norepinephrine to dopamine, demonstrated a higher number of vasopressor-free days with norepinephrine . In a smThe most studied adjuvant therapy was glucocorticoids ,50,51,52Vitamin C has also been the object of several negative RCTs ,77,78. TIn patients with septic shock and tachycardia, esmolol decreased the norepinephrine requirements . In 70 pN-acetyl cysteine has been reported to decrease a composite vaso-inotropic score in burn patients [Two small randomized trials evaluating methylene blue demonstrated a decrease in vasopressor use in both septic and post cardiopulmonary bypass vasoplegia ,71. N-acpatients . Howeverpatients ,74.Drotrecogin alfa (activated) , pyridoxOne RCT study demonstrated a decrease in norepinephrine and dopamine administration with heptaminol . MidodriDuring early goal-directed therapy (EGDT), the relationship between the volume of fluid administered and the norepinephrine doses were inconstant ,60,62,63In septic patients, external fever control aiming for 36.5 to 37 °C decreased the vasopressor requirement . HypotheIn patients with septic shock, the early application of continuous veno-venous filtration resulted in a longer time to shock reversal . Three sThis review suggests that several pharmacological and non-pharmacological strategies are associated with a decrease in the duration of vasopressor therapy. These strategies are as follows. (1) The implementation of a blood pressure objective affects vasopressor duration, but without evidence in terms of mortality. (2) Implementing a weaning strategy decreases the duration of vasopressor treatment and ICU stays. (3) Vasopressin may decrease norepinephrine doses—weaning norepinephrine after vasopressin in case of co-administration seemed to decrease hypotensive episodes. (4) Systemic glucocorticoid administration increased the number of vasopressor-free days. (5) Beta-blockers might decrease norepinephrine doses and duration in selected patients. (6) Targeting normothermia might make it feasible to decrease vasopressor administration. (7) On the contrary, an analysis of the literature demonstrated that EGDT, including fluid therapy, oral vasopressors, vitamin C, and renal replacement therapy, are not associated with an increase in vasopressor-free days.Adding a non-catecholaminergic vasopressor to norepinephrine makes is possible to lower its dosage, and the administration of a non-catecholaminergic drug is recommended for patients in refractory shock . VasopreGlucocorticoids are known to restore vascular responsiveness , and canCardio-selective beta-blockers were demonstrated to lower vasopressor doses in patients with sepsis and tachycardia . This phDuring hemodynamic resuscitation, fluid therapy does not result in shorter vasopressor treatment, and could even increase vasopressor duration . Thus, iAdyn = respiratory pulse pressure variation/respiratory stroke volume variation), which may reflect ventriculo-arterial coupling and vasomotor tone, can predict the pressure response to norepinephrine weaning [Adyn-based algorithm decreased norepinephrine administration [In practice, norepinephrine weaning is implemented empirically: when the arterial pressure is consistently above the given objective, the dose of norepinephrine is decreased until discontinuation. Because vasopressors act on several pathological mechanisms involved in blood pressure  is an index of arterial load [A can be estimated by using the equation EA = mean arterial pressure/stroke volume (mmHg/mL) [V) is an indicator of cardiac function [A to EV, and it is an indicator of the balance between cardiac effort and arterial load. When the left ventricle and the vascular system are coupled, this ratio is around 1 [Several approaches can be followed depending on the type of monitoring . Basic msuitable ,138,139.pressure ,140. Artial load  that intmmHg/mL) . Left vefunction , and migfunction . Ventricaround 1 . Such aparound 1 ,138,139.around 1 . A recenaround 1 .Several limitations can be discussed. We can question the clinical benefit of a vasopressor sparing strategy. There is no direct evidence that reducing pressor “stress” decreases mortality. However, studies have demonstrated that decreasing the duration of vasopressor use is associated with a decrease of ICU length of stays and morbidity . MoreoveThere are a few published RCTs focused specifically on sparing strategies. In patients with shock, several pharmacological strategies, such as hydrocortisone, can be safely used to optimize vasopressor treatment. In addition, it appears that optimized patient management using hemodynamic guidelines could be associated with more vasopressor-free days and shorter ICU stays.


Saccharomyces cerevisiae is regulated in relation to cell volume. Total mRNA concentration is constant with cell volume since global RNApol II-dependent nascent transcription rate (nTR) also keeps constant but mRNA stability increases with cell size. In this paper, we focus on the case of rRNA and RNA polymerase I. Contrarily to that found for RNA pol II, we detected that RNA polymerase I nTR increases proportionally to genome copies and cell size in polyploid cells. In haploid mutant cells with larger cell sizes, the rDNA repeat copy number rises. By combining mathematical modeling and experimental work with the large-size cln3 strain, we observed that the increasing repeat copy number is based on a feedback mechanism in which Sir2 histone deacetylase homeostatically controls the amplification of rDNA repeats in a volume-dependent manner. This amplification is paralleled with an increase in rRNA nTR, which indicates a control of the RNA pol I synthesis rate by cell volume.The adjustment of transcription and translation rates to the changing needs of cells is of utmost importance for their fitness and survival. We have previously shown that the global transcription rate for RNA polymerase II in budding yeast  Synthesis rates of biological macromolecules should be strictly regulated and adjusted to the changing conditions of cells. The change in volume is one of the commonest variables along individual cell life and also when comparing different cell types. We previously found that cells with asymmetric division, such as budding yeasts, use a compensatory change in the global RNA polymerase II synthesis rate and mRNA decay rate to maintain mRNA homeostasis. In the present study, we address the same issue for the RNA polymerase that makes rRNAs, which are essential components of ribosomes and the most abundant RNAs in the cell. We found that the copy number of the gene encoding 35S rRNA, transcribed by RNA polymerase I, changes proportionally to the cell volume in budding yeast via a feedback mechanism based on the Sir2 histone deacetylase, which guarantees that yeast cells have the appropriate RNA polymerase I synthesis rate required for rRNA homeostasis. Saccharomyces cerevisiae, RNA pol I uses 60% of all ribonucleotides for its single gene target [Eukaryotic cells have distributed the transcription work in its nuclei among three different RNA polymerases (RNA pol). RNA pol II transcribes most genes, including those that encode proteins. RNA pol III transcribes some several hundreds of genes (mostly tRNAs and 5S rRNA genes). RNA pol I, specialized in transcribing only one gene, the large rRNA 35-47S gene (depending on the particular eukaryote) is, however, the biggest consumer of ribonucleotides given the need for a vast amount of this precursor of ribosomes ,2. Ribose target ,4. This e target ,9. rDNA e target –13. Apare target  or elonge target  for a ree target . This lae target . This mee target .In order to maintain its identity and physiological features, a cell should keep the concentrations of its molecules constant, or at least within a certain limited range. This homeostatic control involves maintaining the total amount of RNAs and proteins, which are performers of genetic information. Total protein content is homeostatically controlled due to the high protein content of all cells and the high cost of protein synthesis .We have previously shown that the nTR for RNA pol II is regulated differently in cells presenting symmetrical or asymmetrical cell division . In thatS. pombe . For budS. pombe . FinallyIn this study, we extend the previous study by focusing on RNA pol I regulation in budding yeast with regards to changes in cell volume. We considered changes in cell volume when comparing the average cell volume in asynchronous actively growing yeast populations. By running different types of experiments to determine the RNA pol I nTR, we found that budding yeast adapts the number of chromosomal rDNA repeats to the population’s average cell volume. We propose a model of regulation, based on that previously proposed by D. Shore  and T. Kcln3 and whi5) respectively with large and small volumes, and three polyploid  strains created by D. Pellman’s group [In a previous study, which aimed to elucidate the influence of cell size on transcription rates , we used’s group  (with ce’s group . We foun’s group .whi5 and cln3 by filter run-on (see below) and confirmed it by chromatin immunoprecipitation. Transcriptional run-on is based on capturing elongating RNA pol when rNTPs are depleted by the detergent permeabilization of cells. Then these paused RNA pols are allowed to resume elongation after the addition of external rNTPs, including 33P-UTP. It detects all types of nuclear active elongating RNA pols  [cln3 and slightly lower in whi5 than in the wild-type and diploid strains  . The run strains . Thus, aS. cerevisiae forms tandem repeats between 100–200 copies [cln3 mutant and decreased in whi5 to adjust its number to the average cell size. To test this, we measured the rDNA repeat number in the series of yeast strains by qPCR. Figs ACT1) is more than double in the cln3 mutant and slightly lower in the whi5 mutant compared to BY4741. In fact the relative rDNA copy number change in both mutants with regards to BY4741 (2.2 and 0.9) comes very close to the relative changes in the observed RNA pol I nTR . This mcln3 mutants, this would appear to be modulated by an amplification of the rDNA repeats occurring along many genome replications. T. Kobayashi’s group has shown that the rDNA locus is very plastic and undergoes expansions and contractions. Expansions are driven by unequal sister-chromatid recombination, which produces two cells with different repeat copy numbers, followed by a gradual evolution . Thi. Thicln3nt paper  postulatur model  assumes for rDNA . Therefoy number . When thΔcln3 mutant evolutionary experiment  to obtain the right synthesis rate depends on cell division symmetry . Symmetrromoters ,39. In tromoters . It has on rates . Due to on rates . Insteadtability .cln3 and whi5), also adapted their total nTR to their cell size. We suggested that it could be related to the plasticity of rDNA repeats, which are known to be variable in number [SIR2 gene . We proposed a new scenario (#2), in which the rise in the nTR in large-sized polyploid cells would be caused by their increase in ploidy, which would proportionally augment the total number of rDNA repeats by keeping the SR constant. However, we found that other yeast strains with smaller or bigger sizes than the wild type, but with haploid genomes , the increase in cell volume involves a parallel increase in the total UAF molecules per cell but, because of the invariant rDNA target number, the concentration of free UAF will increase and lead to 3 mutant , with a 3 mutant , provokee values  along wie values . This che values . Interesgulation . The incgulation . In thatPrevious studies have shown that rRNA synthesis is determined by the RNA pol I loading rate and not by the number of active genes, and that the number of elongating RNA pol I molecules remains constant in spite of the change in rDNA repeats . This rewhi5 and cln3, but the parallel increase in rDNA repeats  or YPGal  media. Experimental assays were performed with the cells exponentially grown for at least seven generations until OD600 ~0.4–0.5 at 30°C.The 600 were measured. GRs (h-1) were calculated from growth curves in YPD and/or YPGal and are shown in The growth rate (GR) was calculated by growing 50 mL of yeast cultures in 250 mL flasks with shaking (190 rpm) at 30°C. Aliquots were taken every 60 min in the exponential phase and their ODΔcln3 mutants were made from strain BY4741 by substituting the natural allele for the KanMX4 PCR-amplified cassette with the oligos described in + was discarded by qPCR test (see below) in the obtained clones. Small colonies (1 mm diameter) were assumed to contain ~2×105 cells and corresponded to progenies after about 18 generations, starting from single ancestor cells [600. Repeated subculturing was done by inoculating up to 104 cells/mL into 20 mL of fresh medium and allowing them to grow for 24 h to 5–6 OD600 . An aliquot of the culture was spun down daily, the supernatant was discarded and the cell pellet was frozen onto liquid nitrogen. Four different small colonies Δcln3  were used to make four replicates of the experiment.For culture evolution purposes, the or cells . The subThe median values of the cell volumes of the population were obtained by a Coulter-Counter Z series device , as previously described . The abs260 estimation in a Nanodrop device (ThermoFisher Scientific) or using the Qubit RNA BR Assay kit (Thermo Fisher Scientific) measured in a Qubit fluorimeter for accurate quantification purposes.To determine the amount of RNA, cells were grown in rich media until the exponential phase (see above). Then total RNA was purified by phenol:chloroform extraction, as described in  in threeSIR2 and UAF30 was measured by RT-qPCR in the DNase I-treated RNA samples, extracted as previously described before being normalized against the ACT1 mRNA levels. Specific primers were designed for this aim and are listed in 15VN with Maxima Reverse Transcriptase (Thermo Fisher Scientific). cDNA was labeled with SYBR Pre-mix Ex Taq  and the Cq values were obtained from the CFX96 Touch Real-Time PCR Detection System (BioRad).The expression of S. cerevisiae cells were grown to OD600 0.4–0.6. Three biological replicates were performed for each yeast strain. For each sample, two aliquots of 2.5x107 cells were collected by centrifugation at 4,000 rpm for 3 min in a 1.5 mL Eppendorf tube. Pellets were resuspended in 0.5 mL of 0.5% sarkosyl and spun down again by centrifugation under the same conditions as above. Supernatants were discarded to completely eliminate sarkosyl. Then the pellet was resuspended in 7.2 μL of distilled water. The run-on pulse was performed by adding to the cell pellet, per sample, 9.87 μL of a master transcription mix, composed, per sample, of 7.5 μL of 2.5× transcription buffer , 1 μL of the rNTP mix , 0.375 μL of 0.1 M DTT, 1 μL of UTP  at a final, per sample, volume of 18.75 μL. To allow transcription elongation, the mix was incubated with agitation (650 rpm) for 5 min at 30°C. The reaction was stopped by adding 82 μL of cold distilled water to the mix and leaving it on ice. To measure the total amount of radioactivity present in the mix , 15 μL of the reaction were directly spotted onto glass fiber paper discs and dried in an aerated heater at 65°C. Another 15 μL aliquot of the mix was spotted onto glass fiber discs and precipitated by soaking it in 4 mL of 10% (v/v) of trichloroacetic acid (TCA) at 4°C for 20 min. TCA was removed and a new wash of 4 mL of cold TCA (10% v/v) was added for 10 min. TCA was removed and discs were washed with 3 mL of cold 70% (v/v) EtOH. Discs were dried again in a heater at 65°C. Once dried, 3.5 mL of a scintillation cocktail  were added to each disc for radioactive counting purposes. All three biological replicates were performed in three technical replicates for TCA and two for the total and were averaged. For each individual sample, incorporation was calculated as “TCA/Total” being the total nTR. Synthesis rates (SR) were estimated by dividing the nTR values by cell volume (see above). The nTR values per genome copy were obtained by dividing the nTR by ploidy.For the total (RNA pol I + II + III) nascent transcription rates (nTR), a run-on experiment was used, followed by TCA precipitation onto a glass fiber filter. For these experiments, the ACT1 gene signal to give the nTR values per genome copy.As an alternative way to measure the nTR, the recruitment of RNA polymerase I to rDNA chromatin was assayed by a chromatin immunoprecipitation (ChIP) analysis with a specific antibody for RNA pol I. Cells were grown in rich medium until the exponential phase. Crosslinking between proteins and the associated DNA was performed by adding formaldehyde to 0.75% for 15 minutes. Samples were sonicated 12 times  at high intensity in a Bioruptor (Diagenode) device. Chromatin fragments were immunoprecipitated using a rabbit polyclonal antibody (diluted 1:50) against RNA polymerase I subunit 135 (A135), coupled to protein A-conjugated magnetic beads (Thermo Fisher Scientific). DNA fragments were then amplified by qPCR and the Cq values were analyzed. SYBR Pre-mix Ex Taq (Takara) was used for qPCR following the manufacturer’s instructions. The reaction was performed in the CFX96 Touch Real-Time PCR Detection System (BioRad). The primers designed to detect ribosomal genes  are listed in ACT1 gene signal. For this purpose, genomic DNA was purified by a standard phenol:chloroform extraction [The estimation of the number of ribosomal RNA gene (rDNA) repeats was measured by qPCR and normalized against the traction  and prec8 yeast cells was done by suspending the cell pellet in 200 μL of NaOH 0.2M. After 5 minutes, samples were centrifuged and then mixed with 100 μL 2X SDS-PAGE loading buffer. They were incubated at 95°C for 5 minutes to completely extract proteins from cells. For the Western blot analysis [Protein extraction from 1-2x10analysis , similar33P-labelled ACT1 and rDNA probes. Probes were generated from a template that was PCR-amplified from genomic DNA and random-primed labeled with the High Prime kit (Sigma). After hybridization , blots were washed with washing solution I  and washing solution II  at 65°C. ERCs and ACT1 levels were defined with a phosphorimaging system and quantification was done using the ImageJ software. The ERC and genomic rDNA values were normalized against the ACT1 values.Total cellular DNA was obtained from saturated YPD cultures as previously described. Approximately 10 μg of undigested genomic DNA were resolved by gel electrophoresis at a low voltage  in TAE buffer . Gel was soaked in 0.25 M HCl for 10 min and DNA was transferred to a positively charged nylon membrane under alkaline conditions  for at least 8 h. Membranes were then neutralized  for 15 min and successively hybridized with S1 FigA) The number of rDNA repeats per genome in different strains is constant, except for cell size haploid mutants. A t-test was used for statistical significance: ***: p-value < 0.0005; the other samples were not statistically different. B) The average cell volume (in arbitrary units) increases with the rDNA repeat copy number in a list of mutant strains from . This gr(PDF)Click here for additional data file.S2 FigBamHI and was used as a size marker (left lane). DNA was transferred by alkaline Southern blot and successively hybridized with ACT1 and rDNA (18S) probes. The signal from ACT1 hybridization was used for normalization between samples to obtain the relative genomic rDNA repeats number (see sir2 strain was used as a control because it was described to have a high proportion of ERCs [BamHI ACT1 band (16.5 kb) is shown. As the rDNA repeat has no BamHI cutting site, it shows a band for it in the genomic DNA position.The DNA from different strains was isolated and quantified. About 10 μg of DNA were electrophoresed in 0.7% agarose gel in TAE buffer at 1.5 v/cm for 40 h. A sample of BY4741 (wt) DNA was digested with mber see . The sum(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S1 Appendix(PDF)Click here for additional data file.


The “writers” of four types of adenosine (A)-related RNA modifications  are closely related to the tumorigenesis and progression of many cancer types, including skin cutaneous melanoma (SKCM). However, the potential roles of the crosstalk between these RNA modification “writers” in the tumor microenvironment (TME) remain unclear. The RNA modification patterns were identified using an unsupervised clustering method. Subsequently, based on differentially expressed genes responsible for the aforementioned RNA modification patterns, an RNA modification “writer” scoring model (W_Score) was constructed to quantify the RNA modification-associated subtypes in individual patients. Moreover, a correlation analysis for W_Score and the TME characteristics, clinical features, molecular subtypes, drug sensitivities, immune responses, and prognosis was performed. We identified three RNA modification patterns, corresponding to distinct tumor immune microenvironment characteristics and survival outcomes. Based on the W_Score score, which was extracted from the RNA modification-related signature genes, patients with SKCM were divided into high- and low-W_Score groups. The low-W_Score group was characterized by better survival outcomes and strengthened immunocyte infiltration. Further analysis showed that the low-W_Score group was positively associated with higher tumor mutation burden and PD-L1 expression. Of note, two immunotherapy cohorts demonstrated that patients with low W_Score exhibited long-term clinical benefits and an enhanced immune response. This study is the first to systematically analyze four types of A-related RNA modifications in SKCM, revealing that these “writers” essentially contribute to TME complexity and diversity. We quantitatively evaluated the RNA modification patterns in individual tumors, which could aid in developing personalized immunotherapy strategies for patients. Worldwide, there were 324,635 and 57,043 estimated new skin cutaneous melanoma (SKCM) cases and deaths, respectively, in 2020 . SKCM acOver 170 types of RNA modifications have been identified . Some ofm6A is the most common RNA modification. Till date, this modification has been found in mRNAs, lncRNAs, miRNAs, circRNAs, rRNAs, tRNAs, and snRNAs . MethyltThe crosstalk among m1A, m6A, A-to-I, and APA can help reveal the mechanism underlying RNA modifications and the significance of post-transcriptional modifications. In SKCM, a complex regulatory network may be formed by the “writers” of the m1A, m6A, A-to-I, and APA modifications. Therefore, systematic analysis of the interaction between these “writers” could provide novel insights into the pathogenesis of SKCM and has potential clinical significance for tumor therapeutic target identification.+ T cells appears to be a more effective immunotherapeutic response (Current immunotherapies based on immune-checkpoint inhibitors (ICIs) have achieved astounding clinical efficacy in some patients. Unfortunately, immunotherapy is not effective in approximately 40–50% of patients with SKCM . Tumor cresponse . Base onresponse .Recent studies have showed that the RNA modification “writers” are closely related to TME immune cell infiltration. Gao et al. revealed that m1A regulators, including “writers”, modulated the infiltration of immune cells in TME . Wang etIn this study, we comprehensively analyzed the relationship between four types of A-related RNA modification patterns and the characteristics of infiltrating immune cells by integrating transcriptomic data from 1526 SKCM samples from the Genotype-Tissue Expression (GTEx), Gene Expression Omnibus (GEO), and The Cancer Genome Atlas (TCGA) databases. Three distinct RNA modification patterns were identified, and the characteristics of the TME in these three patterns corresponded to immune-desert phenotype, immune-excluded phenotype, and immune-inflamed phenotype, respectively. We further constructed an RNA modification “writers” scoring model to quantify RNA modifications patterns in individual patients and to evaluate the patient response to targeted therapy and immunotherapy.https://tcga-data.nci.nih.gov/tcga/), GTEx , and GEO (http://www.ncbi.nlm.nih.gov/geo) databases. Clinical information  and somatic mutation and copy number variation (CNV) data from corresponding patients were retrieved from the University of California Santa Cruz genome browser (http://genome.ucsc.edu/). Patients with overall survival times <90 days and without survival information were excluded. Two GEO-SKCM cohorts (GSE78220 and GSE65904), one TCGA-SKCM cohort, and one GTEx-SK cohort were obtained for further analysis. The “limma with normalizeBetweenArrays” package was used to correct for batch effects of non-biotechnology deviation, and the basic information of these datasets is shown in The workflow of this study is presented in via the unsupervised clustering analysis , four m1A regulators , three A-I regulators , and 12 APA regulators . Based on the expression of 26 RNA modification “writers”, the tumor samples were classified (defined as “writer cluster”) analysis . For guap < 0.05. The functional annotation for 26 RNA modification “writers” was performed using the “clusterProfiler” R package  was conducted . Gene se package .+ T cells, activated CD8+ T cells, gamma delta T cells, immature B cells, regulatory T cells, T follicular helper cells, type 1 T helper cells, type 17 T helper cells, and type 2 T helper cells) and innate immune cells   was employed to evaluate the relative proportion of each infiltrating immune cell type in the SKCM TME , includioid DCs) . The enr+ T effectors, antigen processing machinery, and immune-checkpoint genes , angiogenesis, DNA replication, nucleotide excision repair, mismatch repair, WNT targets, cell cycle, CD8nt genes .p < 0.05 was adopted  among the different RNA modification phenotypes , an empirical Bayesian approach in the “limma” R package with an adjusted  adopted . FurtherA univariate Cox regression analysis was performed to obtain prognosis-related DEGs, and unsupervised clustering analysis was then employed to classify these genes. In the PAM algorithm (using the “ConsensuClusterPlus” package) , 1,000 ii denotes the expression of prognosis-related DEGs. Finally, according to the cutoff value (−0.646) determined using the “survminer” package, the patients with SKCM were divided into two categories (high W_Score and low W_Score).Principal component analysis was used to construct the W_Score scoring model. First, we established a matrix consisting of expression levels of the prognosis-related DEGs of each patient. Then, principal components 1 and 2 were both selected act as signature scores. Finally, we calculated the W_Score for each cancer patient (similar to GGI) : W–Scor+ T effectors, antigen processing machinery, immune-checkpoint, apoptosis regulation, ABL signaling, cytoskeleton, mitosis, ERK/MAPK signaling, RTK signaling, PI3K/MTOR_signaling, p53 pathway, protein stability and degradation, IGF1R signaling, genome integrity, JNK and p38 signaling, chromatin histone acetylation, EGFR signaling, metabolism, and WNT signaling pathways  , transcriptomic classifications , and mutations subtypes  , and thewnloads)  (Yang etwnloads) , and thehttp://tcia.at/home) (http://research-pub.gene.com/IMvigor210CoreBiologies and GEO (GSE78220), respectively.Gene expression profiles of the immune-checkpoint in patients with SKCM were retrieved from The Cancer Immunome Database (TCIA) (at/home) , includiat/home) , and metat/home) , were dovia univariate Cox regression model. We used the Kaplan-Meier method with log-rank tests to obtain survival curves, and the reliability of the models was analyzed by the receiver operating characteristic (ROC) curve. A genome-wide CNV map with the 26 RNA modification “writers” was drawn using the R package “RCircos”  of RNA modification “writers” and DEGs were separately calculated RCircos” . All stavia GSVA revealed that tumor hallmarks associated gene sets, including the KRAS signaling pathway, DNA repair pathway, and the PI3K/AKT/MTOR signaling pathway, were mainly enriched in “writers” mutation group  . Among ton group . It has on group . These fWe also investigated the CNVs of these “writers” and found that somatic copy number alterations were widespread in the 26 RNA modification regulator . CPSF1 eThe comprehensive landscape of the interactions of 26 RNA modification “writers” and their prognostic values in patients with SKCM was described in the regulator network . We founp = 0.007). To further explore the biological significance underlying these distinct RNA modification patterns, GSVA enrichment analysis was conducted. The writer cluster A was significantly related to carcinogenic activation pathways   . The writophagy) , while wtophagy) . These rtophagy) .Because patients with writer cluster B presented a remarkable survival advantage and showed enrichment of immune-related pathways, we attempted to examine the functional roles of distinct RNA modification patterns in TME. The results showed that in the TCGA cohort , the wrivia RNA modification, participating in the antitumor immune response.Next, Spearman’s correlation analysis was conducted to investigate the specific associations between infiltrating immune cells and RNA modification “writers” . We obseDespite patients with SKCM could be divided into three RNA modification phenotypes based on the expression of the RNA modification “writers” , the gen+ T cells, activated CD4+ T cells, and regulatory T cells)   to quantify the m6A modification pattern of individual patients with SKCM. We discovered that patients with writer cluster C exhibited a high W_Score, whereas those in writer cluster B di not. A similar association also observed between the W_Score and gene clusters . Figure T cells)  and a sipathway) . Therefop < 0.001), and the 5-years survival rate was twice than patients in the high-W_Score group . Interestingly, multivariate Cox regression analysis  confirmed that W_Score was an independent prognostic biomarker (HR = 1.022 (95% CI = 1.013–1.032), p < 0.001; p < 0.001), and W_Score could act as an independent prognostic indicator (HR = 1.082 (95% CI = 1.045–1.120), p < 0.001; To further evaluate the clinical relevance of the W_Score, patients with SKCM were divided into high- and low-W_Score groups based on the cutoff value (0.0291) determined by the “survminer” package. Patients with a low W_Score had a survival advantage , the WNT/β-catenin pathway inhibitor FH535 , and the AKT and ERK inhibitors Sorafenib , whereas 23 drugs showed the resistance related to the W_Score, including the DNA double-strand break and apoptosis activator Cisplatin , the AKT inhibitor VIII , and the cyclin-dependent kinase inhibitor CGP-60474 . Then, the signaling pathway enrichment analysis based on drug-targeted genes revealed that the W_Score was linked to drug-associated signaling pathways, including the PI3K/MTOR signaling pathway, RTK signaling pathway, ERK/MAPK signaling pathway, p53 signaling pathway, and cell cycle signaling pathway . The better therapeutic outcomes and improved clinical response to anti-PD-1/L1 immunotherapy in W_Score-low patients were confirmed (Immunotherapies based on CTLA-4/PD-1/PD-L1 inhibitors have achieved astounding clinical efficacy in malignant tumor therapy. Significant efforts have been made to identify biomarkers for predicting immunotherapy response, such as the expression of PD-L1 and CTLA-4 . As showonfirmed . Moreoveonfirmed . Taken tMounting evidence has demonstrated that RNA modifications have an essential role in innate immunity, inflammation, and antitumor activity by interacting with multiple “writers” . While cIn this work, three distinct RNA modification patterns were identified based on 26 RNA modification “writers”. These three modification patterns showed remarkably different tumor immune microenvironment characteristics. The writer cluster B was characterized by the immune activation and abundant lymphocyte-infiltration, which correspond to the immune-inflamed phenotype. The writer cluster C was characterized by the immune-desert phenotype. The writer cluster A was characterized by the infiltration of immune cells together with the activation of TGF-β signaling pathway, JAK/STAT signaling pathway, and WNT signaling pathway, which correspond to the immune-excluded phenotype. The activated TGF-β pathways may suppress immune function by preventing lymphocytes from entering the tumor parenchyma, while specific molecular inhibitors that target TGF-β can restore the antitumor immunity by remodeling the tumor immune microenvironment . StudiesFurthermore, GO analysis and univariate Cox regression analysis suggested that prognosis-related DEGs could act as the signature genes of A-associated RNA modification. Of note, similar to the classification results of the RNA modification “writers”, the three gene categories based on A-associated RNA modification signature genes were constructed and were found to be remarkably related with different clinical outcomes and immunophenotypes. This confirmed that the significance of RNA modification “writers” in shaping the different landscape of TME. Because of the heterogeneity in RNA modification, we built a W_Score model to quantify the RNA modification patterns of individual patients. The RNA modification pattern characterized as the immune-inflamed phenotype displayed a lower W_Score, whereas those the modification patterns characterized as immune-desert and immune-excluded phenotypes presented a higher W_Score. We also found that the W_Score was significantly associated with clinical features of patients with SKCM and tumor subtypes. Noticeably, the W_Score was shown to be an independent prognostic biomarker for patients with SKCM, and this finding was verified in the GEO-SKCM cohort.+ T effectors and resulted in faster clearance of infection (Because the benefits of survival and therapeutic responses of patients subjected to tumor immunotherapy remain limited to a small population, classifying patients to obtain better insight into the optimal use of tumor immunotherapy is an effective strategy . Patientnfection . Furthernfection . Of noteIn this study, we comprehensively analyzed RNA modification patterns based on four types of A-associated RNA modification “writers”, and revealed the association between these RNA modification patterns and tumor immune microenvironment characteristics. We also constructed a W_Score model and identified their clinical utility in targeted therapy and tumor immunotherapy. This paper has some limitations that further experimental verification are needed to validate these findings. This study thus highlighted the clinical significance of the crosstalk of multiple A-associated RNA modification. We believe that the findings of this study will contribute to developing personalized immunotherapy strategies for patients with SKCM.


Although RNA sequencing (RNA-seq) has become the most advanced technology for transcriptome analysis, it also confronts various challenges. As we all know, the workflow of RNA-seq is extremely complicated and it is easy to produce bias. This may damage the quality of RNA-seq dataset and lead to an incorrect interpretation for sequencing result. Thus, our detailed understanding of the source and nature of these biases is essential for the interpretation of RNA-seq data, finding methods to improve the quality of RNA-seq experimental, or development bioinformatics tools to compensate for these biases. Here, we discuss the sources of experimental bias in RNA-seq. And for each type of bias, we discussed the method for improvement, in order to provide some useful suggestions for researcher in RNA-seq experimental. With the development of massive parallel sequencing, high-throughput sequencing (NGS) of RNA (RNA-seq) has become a very common tool in molecular biology. It almost affects our understanding for the function of genomic  and provGenerally, the representative workflow of RNA-seq analysis includes the extraction and purification of RNA from cell or tissue, the preparation of sequencing library, including fragmentation, linear or PCR amplification, RNA sequencing, and the processing and analysis of sequencing data . CommonlIn this perspective article, we summarize the current situation and solutions on biases and discuss the source of bias in RNA-seq. The key point will be on solutions to reduce bias and improve the quality of library sequencing platform. Furthermore, we highlight the bias sources of different methods of amplification and how can amplification bias be reduced.Despite many studies have shown that RNA-seq has many advantages, it is still a rapidly developing biotechnology and faces several challenges. Among them, one often overlooked aspect is the sample preparation process, which may also bring potential variations and deviations on RNA-seq experiment, including RNA isolation, sample processing, library storage time, RNA input level (such as the difference in the number of start-up RNA), and sample cryopreservation (such as fresh or frozen preservation). Generally, good preservation of sample that may be used for transcriptome studies is more important, because many transcriptome protocols require high-quantity and high-quality nucleic acids . TherefoStudies have been demonstrated that RNA degradation is closely related to sample preservation or fixation method. At the present, as far as we know, the standard storage of tissues for RNA-seq has been in liquid nitrogen or freeze stored at -80°C. Unfortunately, frozen specimens are not widely available because they are costly to collect and maintain. Therefore, in diagnostic pathology archives, most tissue samples rely on the formalin-fixed and paraffin-embedded (FFPE) method for preservation . NeverthHigh-quality RNA purification is the premise of RNA-seq. However, due to the widespread existence of RNA degrading enzymes (RNases) –12, succAfter RNA isolation and extraction, the next step is library construction of transcriptome sequencing. Library construction usually begins with the depletion of ribosomal RNA (rRNA) or the enrichment of mRNA enrichment, because most of the total RNA of cellular or tissue is rRNA. For eukaryotic transcriptome, polyadenylated mRNAs are usually extracted by oligo-dT beads, or rRNAs are selectively depleted. Unlikely, prokaryote mRNAs are not stably polyadenylated. Hence, oligo d(T)-mediated messenger enrichment is not suitable; there is only the second option. Then, RNA is usually fragmented to a certain size range by physical or chemical method. The subsequent steps differ among experimental design and NGS platforms. However, studies indicated that most of the protocols currently used for library construction may introduce serious deviations. For example, RNA fragmentation can introduce length biases or errors, subsequently propagating to later cycles. Furthermore, library amplification may also be affected by primer bias, such as primer bias in multiple displacement amplification (MDA) , primer Notwithstanding, RNA-seq can be used to measure transcripts of any sample in principle; it has been a challenge to apply standard protocols to samples with either very low quantity or low quality  input RNA. It is due to the fact that the bias associated with low amounts of input RNA has strong and harmful effects on downstream analysis. If not noticed, this may have significant impact on the subsequent biological interpretation. Recently, the researcher has proposed several different methods to overcome the challenges of low-quality or low-quantity RNA sample, including RNase H  , Ribo-ZerRNAs are very abundant, often constituting 80% to 90% of total RNA. Due to the fact that rRNA sequence rarely arouse people's interest in RNA-seq experiments, it is necessary to remove rRNA from sample before library construction. The aim is in order to prevent most of the library and the majority of sequencing reads from being rRNA. A standard solution is to enrich for the polyadenylated (poly (A)) RNA transcripts with oligo (dT) primers. For eukaryotes, studies have been shown that oligo (dT) provides technical convenience for enriching mRNA from sample; it is due to the fact that most mRNA and many long noncoding RNAs (lncRNAs) have poly (A) tail . NeverthThe second rRNA depletion approach takes the opposite method, targeting rRNA molecules and removing them. This rRNA depletion method can be used for subsequent sequencing of all non-rRNA molecules and is not limited to complete mRNA molecules. Unlike the oligo (dT), rRNA depletion relies on the exact sequence content of the ribosomal RNA, so commercially available or each given kits will only be effective for a specific group of species whose rRNA sequences complement the probes in the kit . HoweverCurrently, RNA is usually fragmented due to read length restriction (<600 bp) of sequencing technologies and the sensitivity of amplification to long cDNA molecules. There are two major approaches of RNA fragmentation: chemical  and enzymatic (using RNase III) . CommonlCommonly, after mRNA is fragmented, which can be reverse transcribed into cDNA by random hexamers. However, studies have been indicated that random hexamer primer can lead to the deviation of nucleotide content of RNA sequencing reads, which also affects the consistency of the locations of reads along expressed transcripts. This may result in low complexity of RNA sequencing data. Given this bias, Mamanova et al.  proposedGenerally, as for the deep sequencing of RNA library preparation, a critical step is the ligation of adapter sequences. The selection of T4 RNA ligase (Rnl1 or Rnl2) or other RNA ligase is very important. Subsequently, the ligation products were amplified by PCR. Or, nucleotide homopolymer sequences were added by poly (A) polymerase  or termiCurrently, the strategies of transcriptome analysis are still to convert RNA to cDNA before sequencing. A known feature of reverse transcriptases is that they tend to produce false second strand cDNA through DNA-dependent DNA polymerase. This may not be able to distinguish the sense and antisense transcript and create difficulties for the data analysis. The researcher proposed several modifications. Among them, the deoxyuridine triphosphate (dUTP) method, one of the leading cDNA-based strategies, can be specifically removed by enzymatic digestion . It can PCR is a basic tool widely in molecular biology laboratories. In particular, the combination of PCR and NGS sequencing promoted the explosive development of RNA sequence acquisition. However, PCR amplification has been proved to be the main source of artifacts and base composition bias in the process of library construction, which may lead to misleading or inaccurate conclusions in data analysis. Therefore, it is essential to avoid PCR bias, and great efforts have been expended on trying to control and mitigate bias in current. In the next section, we will discuss the sources of bias in PCR amplification and suggestions for improvement.Studies have been indicated that fragments of GC-neutral can be amplified more than GC-rich or AT-rich fragments. Therefore, the fragments with high AT or very high GC content often have little or no amplification at all , 48. TheBesides, the Phusion polymerase method is commonly used in PCR amplification at present, compared with other polymerase methods, which have processivity and fidelity . HoweverTm between AT and GC base pairs and has been effectively used to improve the coverage of GC-rich templates [β-mercaptoethanol or dithiothreitol (DTT) [In addition, in order to overcome the amplification bias of AT/GC-rich, it is necessary to the addition of substances for enhancement PCR specificity and/or yield. The most effective PCR enhancing additives currently used are betaine . It is aemplates . Furtheremplates  tested vol (DTT) .As we all know, PCR can exponentially amplify DNA/cDNA templates, thus leading to a significant increase of amplification bias with the number of PCR cycles . TherefoAlthough PCR amplification has many advantages, it also has some disadvantages. For example, time-consuming thermal cycling is needed to obtain the target sequence amplification at different temperatures, which led to the development of alternative amplification methods , 57. Amoφ29 DNA polymerase and random exonuclease-resistant primers [At present, the preparation of genomic from clinical samples is still a bottleneck in sequencing analysis and frequently limits by the amount of specimen available. Therefore, the amplification of samples is indispensable to get sufficient sample yield in sequencing. Researchers proposed a whole-genome amplification (WGA) method, which can generate a large amount of DNA/cDNA directly from small cell samples. Subsequently, the entire genome fragments were amplified by multiple displacement amplification (MDA) in 30°C condition, which uses  primers . In addi primers  study shAdditionally, what is worth mentioning here is that an amplification-free RNA-seq protocol has been reported . In the It is very important for the selection of sequencing platform in RNA-seq experiment. Currently, commercially available NGS platforms include Illumina/Solexa Genome Analyser, Life Technologies/ABI SOLiD System, and Roche/454 Genome Sequencer FLX . These pCurrently, the Illumina HiSeq platform is the most widely used next-generation RNA sequencing technology and has become the standard of NGS sequencing. The platform has two flowcells, each of which provides eight separate channels for sequencing reaction. The sequencing reaction takes 1.5 to 12 days to complete, depending on the total read length of the library. Minoche et al.'s  study diFurthermore, the emergence of single-molecule sequencing platforms such as PacBio makes single-molecule real-time (SMRT) sequencing possible . In thisAt the present, RNA-seq has been widely used in biological, medical, clinical, and pharmaceutical research. However, all these sequencing studies are limited by the accuracy of underlying sequencing experiments, because RNA-seq technology may introduce various errors and biases in sample preparation, library construction, sequencing and imaging, etc.It is well known that RNA is extremely labile and degradable. Therefore, if the sample cannot be separated immediately after collection, which can be kept in intermediary solution. At the present, RNAlater (Thermo Fisher Scientific and Qiagen) and RNAstable (Sigma-Aldrich) are commonly used stabilizers, which can prevent RNA degradation and maintain RNA integrity. Additionally, the extraction and isolation of RNA have been proved as one of the most bias sources. Currently, TRIzol is a frequently used method. Furthermore, some protocols of RNA extraction and isolation may carry over some gDNA into total RNA samples, which can be removed by DNA enzyme treatment to prevention gDNA contamination .Additionally, library construction methods are frequently biased, which is a main concern for RNA-seq data quality. Among them, PCR amplification is the major source of bias. A previous study showed that GC content has a virtual influence on PCR amplification efficiency. Therefore, we suggest that Kapa HiFi  or AccuPrime Taq DNA Polymerase High Fidelity (Life Technologies) can be selected to PCR amplification. It has been shown that these enzymes can minimize the amplification bias caused by the extreme GC content. Furthermore, Aird et al.  found thIn summary, the major goal of constructing the sequencing library is to minimize the bias. The bias was frequently defined as the systematic distortion of data due to the experimental protocols. Therefore, it is impossible to eliminate all sources of experimental bias. The best strategies are as follows: (i) to understand how the bias is generated and to take measures to minimize it; (ii) to pay attention to the experimental design and minimize the influence of irreducible bias on the final analysis.