We anticipate that insights gained from our multi-layered analysis will serve as valuable interactive reference point to guide future studies.Peripheral nerves are organ-like structures containing diverse cell types to optimize function. This interactive assembly includes mostly axon-associated Schwann cells, but also endothelial cells of supporting blood vessels, immune system-associated cells, barrier-forming cells of the perineurium surrounding and protecting nerve fascicles, and connective tissue-resident cells within the intra-fascicular endoneurium and inter-fascicular epineurium. We have established transcriptional profiles of mouse sciatic nerve-inhabitant cells to foster the fundamental understanding of peripheral nerves. To achieve this goal, we have combined bulk RNA sequencing of developing sciatic nerves up to the adult with focused bulk and single-cell RNA sequencing of Schwann cells throughout postnatal development, extended by single-cell transcriptome analysis of the full sciatic nerve both perinatally and in the adult. The results were merged in the transcriptome resource Sciatic Nerve ATlas (SNAT:  Pdgfra (encoding Platelet Derived Growth Factor Receptor Alpha) such as endoneurial, perineurial, and epineural cells , vasculEach of the nerve-resident cell types is expected to express a distinct portfolio of genes at particular levels, depending on the differentiation state during development, degeneration and regeneration after injury, and in aging. However, our knowledge in this context is fragmentary and often limited in cellular resolution. For example, several transcriptome analyses in mice and rats have been carried out that reflect peripheral nerves as an organ , complemTaken together, the rather fragmentary time points employed, together with some technical constrains, limit the universal use of the currently available data in a satisfactory manner. Thus, motivated by the demands of current research on peripheral nerve biology in health and disease, together with appreciating the highly informative nature of analogous work in the CNS , we reasSciatic Nerve ATlas (SNAT) at https://www.snat.ethz.ch/.Thus, we performed a transcriptomic evaluation of mouse peripheral nerves using a layered approach. First, we analyzed the transcriptome of mouse peripheral nerves by bulk RNA sequencing, with a particular focus on the endoneurium compartment, covering a time-line that represents successive stages of nerve development. Second, we developed a strategy that allowed the distinction of two fractions of SCs from postnatal day 5 (P5) sciatic nerves by FACS, one enriched in myelinating SCs and the another enriched in not-myelinating SCs. Both fractions were subjected to bulk RNA sequencing. Third, we carried out single-cell RNA sequencing analysis of SCs specifically, collected by FACS at various stages of postnatal differentiation, following the Smart-Seq2 protocol. This approach enabled the recapitulation of the myelinating and non-myelinating differentiation trajectories from early development to adult stages at the molecular level. Finally, we used 10x Genomics to extend the single-cell RNA sequencing analysis to the different cell types residing in sciatic nerves, both in perinatal development and in the adult. To simplify comparative analyses, interpretations, and data mining, we merged our datasets in an openly accessible web-based resource called We started by resolving the transcriptomes of peripheral nerves at various time points of nerve maturation and SC differentiation using bulk RNA sequencing technology. Our analysis was largely focused on the sciatic nerve due to its wide use in studies of peripheral nerves and in SC biology in health, disease, and regeneration . To accoStandard RNA sequencing was then performed using a commercial oligo-dT-based protocol (read counts per sample are shown in Mki67 (encoding KI-67) and Top2a (encoding DNA Topoisomerase II Alpha) (Ngfr (encoding the Low-Affinity Neurotrophin Receptor P75NTR)  and L1cam . Peak expression was found at E17.5, consistent with the high number of immature SCs present in developing nerves at this stage (Pou3f1 (encoding the Octamer-Binding Transcription Factor 6 (Oct6), also known as SCIP) ) (Mpz (encoding Myelin Protein Zero), Mbp (encoding Myelin Basic Protein), and Ncmap (encoding Non-Compact Myelin-Associated Protein)   are highI Alpha) . As matuI Alpha) . Second, P75NTR) , Ncam1   at P1 foas SCIP) . Express, Kip2))  shows a Protein)  increaseWe conclude that this dataset, obtained with bulk RNA sequencing of endoneurium-enriched nerves, provides an accurate transcriptome timeline on postnatal development of the endoneurium compartment of the mouse sciatic nerve and is suitable for appropriate data mining (see SNAT). Of technical note, it is an advantage of this approach that the SCs, as the major cell population in the nerve, together with the other parts of the endoneurium remain in their native context until immediately before RNA extraction. This feature minimizes potentially disturbing impacts of experimental procedures and optimizes that the obtained cellular RNA-seq signatures closely track genuine in vivo transcriptomes.Mki67 and Top2a are strongly elevated in the nmSC gate cells compared to the mSC gate population . To this end, we generated cell suspensions from mouse sciatic nerves and used the ‘allSC’ FACS gate strategy depicted in nd Top2a . Some SCnd Top2a . Immaturnd L1cam . The samnd L1cam , designand L1cam  SC. As a group, these clusters and markers describe the known differentiation and maturation path of non-myelinating (Remak) SCs   SCs . A seconked mSC) . Togetheked mSC)  (compareked mSC) . SCs derked mSC) . On a diked mSC) . Prolifeked mSC)  (Note thked mSC) . For exat manner . Furtherajectory . In addiTaken together, our Smart-Seq2 single-cell RNA sequencing dataset provides a connected collection of clusters along key stages of SC postnatal differentiation. Furthermore, the dataset enables a direct comparison, with single-cell resolution, of the differential transcriptome changes throughout SC maturation during postnatal development. The dataset as such, together with the application of the analytical tools presented, provides the fundamental groundwork to allow detailed comparative mapping of the expression of genes by SCs within the myelinating and non-myelinating trajectory throughout development, a prerequisite for the functional understanding of a given gene in SC differentiation and maturation. We anticipate that such applications will be fostered and facilitated particularly by the use of the accompanying database SNAT.Pdgfra-expressing cells  have been shown to serve as a cellular reservoir during regeneration after toe tip amputation of adult mice  expression SC area of the cluster , formedpression . Howeverpression . Express cluster .Pdgfra expression labeled clusters of epineurial (marked EpC), perineurial (marked PnC), and endoneurial fibroblast-like (marked EnC) nerve cells, whereas expression of Cd34 (encoding Hematopoietic Progenitor Cell Antigen CD34) labels all of the above , and additionally also endothelial cell clusters (Pcolce2 (encoding Procollagen C-Endopeptidase Enhancer 2) and Ly6c1  label the epineurial cell cluster (Itgb4 (encoding Integrin Subunit Beta 4) and Slc2a1 ) (Col2a1 (encoding Collagen Type II Alpha 1 Chain) and Sox9 (encoding the transcription factor SOX9) ) . Finallyor SOX9) .Ptprc  expression and particularly high abundance of the macrophage marker Cd68  (The immune cell cluster (marked IC) can be recognized by ember 1) .Pecam1  (Pdgfrb (encoding Platelet Derived Growth Factor Receptor Beta) (Cldn5 (encoding the tight junction component Claudin 5) (Slc2a1 (Cldn5 and Slc2a1 (Cd300lg (encoding CD300 Molecule Like Family Member G)  and Pdgfrb was identified as pericytes and vascular smooth muscle cells (marked Per/VSMC)  , but notor Beta) . We clasaudin 5)  and Slc2   (marked ember G) . One miger/VSMC) . Finallyd Pdgfrb , canonicThe markers discussed above and plotted in Sox10 expression  at this age  (Mki67 and Top2a (marked prol. SC) , perineurial cells (marked PnC), epineurial cells (marked EpC), leaving one other cluster which we called tentatively fibroblast-related cells (marked FbRel*). Assignments of these clusters were done as follows: Pronounced expression of Sox9 and Col2a1 identified the putative EnC cluster  , some ofrol. SC) . The exp  were assigned by s at P60  and 5F. al stage . FinallyCar3 (encoding Carbonic Anhydrase 3), Cdo1 (encoding Cysteine Dioxygenase Type 1), Adipoq (encoding Adiponectin), and Plin1 (encoding Perilipin 1) (Prox1 (encoding the transcription factor Prox-1), Flt4 (encoding VEGF Receptor-3), and Lyve1   , can be eptor 1)  can be fWe note that the approach of using dissociation of cells from nerves followed by FACS has the limitation of some potential subsampling of a subset of cells . In partOn an outlook note, our data can now be used for detailed comparative mapping of expression of particular genes, at the cellular nerve level, as the fundamental basis to understand peripheral nerve biology in health and disease. As some applied examples, we recommend to search SNAT for your favorite individual gene, or gene family, or genes that encode components of the various functional structures established by the different nerve-resident cell types, or ‘orphan’ genes with incompletely understood functions.https://www.snat.ethz.ch) with the intention to make a contribution to the filling of a gap in current peripheral nerve research. Using this resource as baseline, together with recently published complementary extensions mainly focusing on immune cells in peripheral nerves  (designated in this manuscript as P0Cre) (IMSR_JAX:006148) (Tg) to specifically label SCs with expression of enhanced yellow fluorescent protein (YFP). Furthermore, these mice also expressed DsRed.MST reporter protein under the control of the chicken beta actin promoter coupled with the cytomegalovirus (CMV) immediate early enhancer (B6.Cg-Tg(CAG-DsRed*MST)1Nagy/J; RRID:IMSR_JAX:006051) (Tg). The combination of these reporter proteins was used to select SCs and other nerve-resident cells as described in the figure legends and text. Note that all mice used in this study were not perfused prior to tissue extraction to minimize handling time and tissue alterations.The peripheral nerves used for the timeline shown in s P0Cre) , which p:006148)  (designa:006051)   as per manufacturer’s instructions. Using RNA extracted from peripheral nerves, the libraries were prepared following the Illumina TruSeq stranded mRNA protocol. The quality of the RNA and final libraries was determined using an Agilent 4200 TapeStation System. The libraries were pooled equimolecularly and sequenced in an Illumina HiSeq4000 sequencer (single-end 125 bp) with a theoretical depth of approximately 20 Mio reads per sample.Using RNA from sorted SCs, the libraries were prepared following the Smart-seq2 protocol . The quaThe transcriptome of the single SCs was analyzed using a miniaturized version of the Smart-seq2 protocol with the help of a SPT Labtech Mosquito HV pipetting robot . BrieflyWhole nerve cells suspensions were sorted and the quality and concentration of the single-cell preparations were evaluated using an haemocytometer in a Leica DM IL LED microscope and adjusted to 1000 cells/µl. A total of 4000 cells per sample were loaded into the 10x Genomics Chromium controller and library preparation was performed according to the manufacturer’s indications (single cell 3’ using v2 chemistry). The resulting libraries were sequenced in an Illumina HiSeq2500 sequencer according to 10x Genomics recommendations  to a depth of approximately 50,000 reads per cell.Adapters and low-quality tails were trimmed from reads before mapping to transcriptome. STAR aligner (v2.6.1.c)  was usedfeatureCounts from Rsubread package (v1.32.4)   and counv1.32.4) . The dowv1.32.4)  for eachv1.32.4)  to inferv1.32.4)  from sinCellRanger (v2.0.2) from 10x Genomics was used to demultiplex, align the reads to Ensembl reference build GRCh38.p10 and collapse UMIs. Starting from the filtered gene-cell count matrix by CellRanger’s default cell calling algorithm, we applied the Seurat workflow, as described in Smart-Seq2 data analysis section. Cells from the three independent runs were pooled together. To exclude low-quality cells or possible doublets, we filtered out cells for which fewer than 500 genes or more than 5000 genes were detected, leaving 11339 cells at P1 and 12428 cells at P60 for further analyses. FindVariableGenes function from Seurat identifies 2145 and 2240 highly variable genes at P1 and P60 respectively, with parameters . In the dimensionality reduction step, 10 principal components were selected. A resolution of 0.5 was chosen in the unsupervised clustering algorithm at P1, while 0.4 was used at P60.results and discussion section. Since the lower island of cluster seven expresses both Pdgfrb and Pecam1, we cannot confidently state these are bona fide pericytes. This island was therefore re-labeled as cluster (Per/EC*). The remaining island in cluster 7 is more enriched in Pdgfrb and likely represents pericytes, and cluster 8 is enriched in Acta2 and likely represents vascular smooth muscle cells. The merged cluster adopts the name of both cell types, as pericyte and vascular smooth muscle cell cluster (Per/VSMC). At P1 . At P1 , we mergFigure panels were assembled with Adobe Illustrator (version CC).https://www.snat.ethz.ch. The Seurat objects used to produce the main figures in this publication are available at https://www.snat.ethz.ch/seurat-objects.html. The code to process, analyze and visualize all the sequencing data is available at https://github.com/suterlab/SNAT-code-used-for-primary-data-analysis copy archived at [swh:1:rev:3a94c4740a9c40ece69ce7a620c15757e61d0628]; RNA-sequencing data have been deposited in the GEO database under accession number GSE137870. The SNAT website can be reached at  In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:Gerber et al. provide a rich data resource of mouse sciatic nerve transcriptional profiles across a range of ages and cellular resolutions. The authors performed both bulk and single cell RNA sequencing, documenting gene expression changes among the diverse cell types found in this peripheral nerve, and include specific controls to ensure the quality of the resource. Importantly, they also developed a web-based interface to provide the community ready access to these data. This resource will be of significant value to the peripheral nerve community, as a platform to understand the molecular basis of sciatic nerve development and function.Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Marianne Bronner as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Loyal A Goff (Reviewer #2); Ahmet Hoke (Reviewer #3).Thank you for submitting your article "Transcriptional Profiling of Mouse Peripheral Nerves to the Single-Cell Level to Build a Sciatic Nerve ATlas (SNAT)" 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". Please let us know if you would like to pursue this option. As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv  along with this decision letter and a formal designation that the manuscript is "in revision at Summary:This study provides a rich resource of transcriptional profiles for the mouse sciatic nerve across a range of temporal dynamics and cellular resolutions. The authors begin with a well-designed bulk RNA-Seq study of the dissected mouse sciatic nerve across developmental and postnatal time points. This is followed by a bulk RNA-Seq study of sorted schwann cells (SC) in a select subset of time points to identify the transcriptional signatures of the enriched SC component(s) of the sciatic nerve, isolating both myelinating and unmyelinating subtypes. In total, this effort provides a more focused transcriptional survey of SC cell subtypes/states. Further analysis is done on the FACS sorted SC cells at single cell resolution and finally, the authors perform a comprehensive single cell analysis of the entire sciatic nerve, including non-SC cells. The authors do a commendable job at presenting the raw data and describing the specific quality controls required to assure the quality of the resource. However, there are a few analysis choices and mis-interpretations of differential gene expression that raise significant concern for acceptance of the paper in its current form. There is little to no novel biology uncovered or described in this resource paper, albeit that the intent behind this manuscript is to present the data, not necessarily 'discover' anything from it. Where the authors do attempt to describe potentially novel findings, these are not directly supported by the data alone and it should be noted that these conclusions are speculative requiring validation. Furthermore, while the comprehensiveness of this manuscript in terms of the data generated is impressive, the lack of integrative analysis across datasets, and the relative 'light-touch' analysis for each component of this dataset, is a significant shortfall, particularly as several similar datasets have recently been published.Essential revisions:1. The authors describe a publicly available searchable web interface (www.snat.ethz.ch). However, access to this resource is currently password-protected. This website needs to be made available to the reviewers in order to evaluate the utility and stability of this public resource.2. Many of the axes are unlabeled . Please add labels to the figures directly.3. The clusters presented in Figure 3 Panel G (P60) are learned on a reduced dimensional embedding that the authors understand to be biased with respect to batch. Indeed, this is a particular focus of Figure 3—figure supplement 1 panel D. It is commendable that the authors note this and correct with batch correction in the supplement, however the clusters used to annotate mSC2 and mSC3 are derived from the original, biased embedding. These clusters disproportionately contain cells from each batch and therefore the differential genes between mSC2 and mSC3 in Figure 3—figure supplement 1 panel A (P60) primarily represent genes that differ between batches. This should be addressed prior to acceptance.Given the concerns about batch:cluster confounding above, the designation of mSC2 and mSC3 in the joint embedding remains concerning as well. Potentially confounding the interpretation of pseudotime in Figure 4 and Figure 4—figure supplement 1. Many of the genes identified along pseudotime for mSC3 overlap with the mSC3-specific genes from Figure 3—figure supplement 1 panel A (P60).4. Figure 4: All axes are unlabelled, including the 'pseudotime values' along X and the gene expression levels along Y for Panel D. It is unclear that the slingshot pseudotime trajectories are resolving the best-fit paths through the data instead of potentially being biased by arbitrary cluster assignments. For example, the designation of the tSC cluster as a discrete and/or transient state is not clear from the embedding. However, the designation of this as a cluster will force slingshot to produce a trajectory through the tSC cluster when this may not be borne out directly from the data. If the authors wish to support these trajectories as appropriate paths towards specification, it is highly recommended that they further perform an RNAVelocity analysis on the joint, embedded single cell analysis in Figure 4. This will provide an 'annotation-agnostic' representation of the change in predicted state over the chosen embedding. The hope is that the velocity field mirrors the paths identified by slingshot. This will provide compelling evidence that the trajectories presented are correct and not 'winding' unnecessarily.There are some important details missing from the slingshot methods and analysis. The authors use a GAM model to identify genes with significant differential expression WRT pseudotime, but the details of the model are not presented, and the choice of significance thresholds is not described. Further, the authors do not present a list of genes identified by the GAM model, instead relying on Z-scores for previous cluster assignment specificity,  instead of the significance values from the pseudotime fitting. This does not use the pseudotime analysis appropriately but rather still relies on the 'aggregate' expression of genes across clusters.In general, the application of pseudotime is not complete, and the authors do not use this analysis approach to appropriately resolve trajectories and gene expression differences along these trajectories. The analysis presented is still restricted to 'cluster-level' aggregate differences.The authors interpret the "appearance of multiple mSC clusters as a peculiar feature in the tSNE plots". This is true and due, in part, to both the nature of tSNE  and the previously noted concerns about batch (in P60) that are not addressed in the joint embedding. The authors however, choose to interpret the different mSC clusters as biologically motivated. (lines 361-368). This statement should be re-evaluated and our couched.A semantic point, the authors claim three 'biological' replicates for the 10x phase of their study per time point. Since each replicate consists of two nerves  these are not technically biological replicates but rather technical replicates.5. Figure 5: It is concerning that for the 10x study, SCs account for only 3.3% of the identified cell types, and yet in the bulk analysis presented in the beginning, only the SC marker genes and how they change across time points are presented. Are the SC gene signatures truly dominating in the bulk samples presented, and as suggested by the authors for the motivation for this study? This is further evidenced by the diagram provided in Figure 5—figure supplement which suggests that the vast majority of cells in this dataset should be Schwann cells. It would be very useful to know, or at least retrospectively estimate, what proportion of the gene expression signatures in the bulk study is derived from SC cells or cells of other cell types. Learning this information from the single cell data and applying this type of analysis to their bulk data would further strengthen the application and utility of this resource and demonstrate a clear need to re-evaluate the use of bulk studies in the PNS.6. Figure 6: The authors 'identify clusters of proliferating fibroblasts labelled by Pdgfra expression' and reason that these clusters represent biologically interesting/unique clusters of endoneurial fibroblast-lik cells, perineuial cells, or epineurial cells, and represent a 'mysterious cluster' It is highly probable that the expression of Pdgfra in these clusters is not biological, but rather a consequence of either A) incomplete dissociation from true Pdgfra-expressing cells, or B) background signal detected in otherwise low-quality cells, (both appropriately indicated by the authors). The former is supported by the higher-than average number of genes detected in these populations , despite the authors use of a 'doublet detection' algorithm. The latter is supported by the higher mitochondrial read ratios observed for these clusters as well. Without supporting in situ hybridizations to support the identification and co-expression of Pdgfra in these populations, this finding should be removed or addressed in significantly greater detail. The authors claim(s) that "we cannot exclude that some cells in this cluster might also be in a hypothetical transitory stage of neural crest-derived cells, potentially expressing markers of multiple lineages on their route to differentiation" is a purely artificial explanation that is not supported by the evidence provided. Suggestion of this as an alternative explanation does not satisfy an Occom's razor explanation and must be substantiated with further experimentation.The authors state that " Clusters representing the different cell types were overall in much closer proximity to each other on the tSNE plots at P1 and P60" and use this to suggest that this may represent "potential cell lineage transitions or cell plasticity at early stages…". This is not an appropriate or valid conclusion to be drawn from tSNE plots. The meaningful proximity of clusters in a tSNE embedding cannot be directly inferred and is a common mis-interpretation of the method used. Please see discussion here for cautions and guidelines for interpretation of tSNE embeddings .7. Given the potential utility of this dataset as a resource for the PNS community, it is strongly recommended that the authors make their analysis (scripts/code/etc) publicly available for review.eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Marianne Bronner as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Roman J Giger (Reviewer #1); Loyal A Goff (Reviewer #2); Ahmet Hoke (Reviewer #3).Thank you for submitting your article "Transcriptional Profiling of Mouse Peripheral Nerves to the Single-Cell Level to Build a Sciatic Nerve ATlas (SNAT)" 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.Essential revisions:1. Please address concerns about the validity of conclusions related to the pseudotime analysis raised by Reviewer #2. If additional analysis is possible, please include, otherwise modify the text to temper conclusions that can be made using this approach.2. Please add additional discussion of potential bias that may arise from subsampling a subset of Schwann cells that could be isolated from the nerve.3. Please provide additional validation of the pdgfra+ fibroblasts or edit the text to note the need for further validation of this cluster.Reviewer #2:The authors have done a commendable job of addressing most of the original reviewer requests. The manuscript is certainly improved and many of the incorrect or misleading statements have been addressed as requested. The figures, labels, and legends are significantly improved in clarity.* The requested RNA Velocity analysis is not included in the revision. The authors claim to have performed it but it did not match expectations . This reviewer would have preferred to examine how RNA Velocity field estimates match the existing slingshot trajectories or at least compare the presented trajectories to those created from another tool to provide confirmatory support. The expectation that the RNAVelocity findings might disagree with the presented trajectories was the motivation behind requesting this analysis. It's lack of inclusion in the final manuscript somewhat reinforces my concern regarding the accuracy/validity of the original pseudotime trajectories.* The authors have indeed couched their statements regarding the Pdgfra-expressing fibroblast cells relative to their original statements. I am aware that their arguments are in a 'Results and Discussion' section, however, I am still less than comfortable with their speculation w/o any validation of the Pdgfra+ fibroblast population. The details of why the doublet detection algorithm (and their source of confidence in these results) is less than compelling, and the suggestion that 'damaged and/or dying cells is a contributing factor to the formation of this cluster' is supported only by circumstantial evidence of 'low quality' cells , not necessarily 'dying' cells. I feel that this speculation is not warranted in a Resource paper and should either be experimentally validated or removed. The manuscript would remain just as strong by merely pointing out the existence of this cluster and suggesting that it's origins require follow up analyses. But the speculation is simply not warranted w/o further supporting validation.Reviewer #3:In this paper, Gerber and colleagues conduct bulk and single cell RNA-Seq of mouse sciatic nerves and sorted Schwann cells to generate a developmental transcriptomic profile of Schwann cells. Overall, it is a very nicely done study and will likely serve as a good resource for the scientific community. Strengths of their approach include i) detailed developmental time course and use of 4 biological replicates for each time point, ii) combination approach of bulk sequencing from intact sciatic nerves and comparison to FAC sorted Schwann cells and addition of sc-RNA-Seq of sorted Schwann cells through the postnatal time points. The only weakness is related to the technical difficulty of obtaining higher number of Schwann cells for the scRNA-Seq component. In an intact nerve, Schwann cells account for over 70% all cells, but in the single cell sorted RNA-Seq, only 3.3% of cells were Schwann cells. This low number likely biased the results of gene expression and interpretation of the results because a "hardy" sub-population may survive the cell isolation process and not be a true representative of the whole Schwann cell population.Authors have addressed all of my concerns. No further comments. Essential revisions:1. The authors describe a publicly available searchable web interface (www.snat.ethz.ch). However, access to this resource is currently password-protected. This website needs to be made available to the reviewers in order to evaluate the utility and stability of this public resource.The username and password were included in the original Cover Letter and in the originally Submitted Manuscript . We have now removed this information from the text of the manuscript since we plan to eliminate the password requirement upon publication of the manuscript.2. Many of the axes are unlabeled . Please add labels to the figures directly.We apologize for these omissions. The labels have now been added.3. The clusters presented in Figure 3 Panel G (P60) are learned on a reduced dimensional embedding that the authors understand to be biased with respect to batch. Indeed, this is a particular focus of Figure 3—figure supplement 1 panel D. It is commendable that the authors note this and correct with batch correction in the supplement, however the clusters used to annotate mSC2 and mSC3 are derived from the original, biased embedding. These clusters disproportionately contain cells from each batch and therefore the differential genes between mSC2 and mSC3 in Figure 3—figure supplement 1 panel A (P60) primarily represent genes that differ between batches. This should be addressed prior to acceptance.Given the concerns about batch:cluster confounding above, the designation of mSC2 and mSC3 in the joint embedding remains concerning as well. Potentially confounding the interpretation of pseudotime in Figure 4 and Figure 4—figure supplement 1. Many of the genes identified along pseudotime for mSC3 overlap with the mSC3-specific genes from Figure 3—figure supplement 1 panel A (P60).We show now the batch-corrected dataset at P60 in the main Figure , and in the revised Figure 3—figure supplement 1 . The P60 dataset without batch correction has been included in the revised Figure 3—figure supplement 1C.We thank the reviewers for the remarks regarding the mSC clusters . Our manuscript includes a common section for Results and Discussion. In this context, some of the discussion remarks were perceived by the reviewers rather as conclusions during the consideration of our manuscript . To avoid such interpretations, we have made suitable ameliorations to exert caution, also regarding informatic clusters representing myelinating Schwann cells. We now refer to these clusters commonly as myelinating Schwann cells (mSC). Accordingly, we have adjusted the cluster labels shown on tSNE projections of Figure 4, and we have included new figures that depict the original output of the clustering analysis  to complete the information. In this revised setting, potential batch influences on the delineation of mSC1, mSC2 or mSC3 are no longer relevant (and these designations have been removed from the manuscript), since all these cluster are considered as mSC.4. Figure 4: All axes are unlabelled, including the 'pseudotime values' along X and the gene expression levels along Y for Panel D. It is unclear that the slingshot pseudotime trajectories are resolving the best-fit paths through the data instead of potentially being biased by arbitrary cluster assignments. For example, the designation of the tSC cluster as a discrete and/or transient state is not clear from the embedding. However, the designation of this as a cluster will force slingshot to produce a trajectory through the tSC cluster when this may not be borne out directly from the data. If the authors wish to support these trajectories as appropriate paths towards specification, it is highly recommended that they further perform an RNAVelocity analysis on the joint, embedded single cell analysis in Figure 4. This will provide an 'annotation-agnostic' representation of the change in predicted state over the chosen embedding. The hope is that the velocity field mirrors the paths identified by slingshot. This will provide compelling evidence that the trajectories presented are correct and not 'winding' unnecessarily.There are some important details missing from the slingshot methods and analysis. The authors use a GAM model to identify genes with significant differential expression WRT pseudotime, but the details of the model are not presented, and the choice of significance thresholds is not described. Further, the authors do not present a list of genes identified by the GAM model, instead relying on Z-scores for previous cluster assignment specificity,  instead of the significance values from the pseudotime fitting. This does not use the pseudotime analysis appropriately but rather still relies on the 'aggregate' expression of genes across clusters.In general, the application of pseudotime is not complete, and the authors do not use this analysis approach to appropriately resolve trajectories and gene expression differences along these trajectories. The analysis presented is still restricted to 'cluster-level' aggregate differences.The authors interpret the "appearance of multiple mSC clusters as a peculiar feature in the tSNE plots". This is true and due, in part, to both the nature of tSNE  and the previously noted concerns about batch (in P60) that are not addressed in the joint embedding. The authors however, choose to interpret the different mSC clusters as biologically motivated. (lines 361-368). This statement should be re-evaluated and our couched.A semantic point, the authors claim three 'biological' replicates for the 10x phase of their study per time point. Since each replicate consists of two nerves  these are not technically biological replicates but rather technical replicates.Ncam1, N1cam and Ngfr), we interpret the tSC to be cells along the non-myelinating fate at P14, which temporally precede the cells of the same fate at P60 . We do not intend to state that this cluster represents a novel cell type, but it may rather represent a temporal snapshot along the non-myelinating differentiation trajectory. Therefore, the pseudotime plot depicts the clusters enriched based on prior knowledge. We have ameliorated the manuscript to reflect these issues better. The text changes include also adjustments to acknowledge the remark by the reviewers that our application of pseudotime is not exhaustive. The intention with a supervised application of pseudotime was not to resolve potentially novel trajectories. We use it to help readers visualizing the transcript abundance dynamics along the expected myelinating and non-myelinating Schwann cell lineages, crossing the center of each cluster (which show a progressive enrichment in cells of increasing mouse ages) along each respective trajectory.We apologize for the omission of the labels. They have been added as requested. We have also performed RNA velocity analysis as requested. The resulting projections do not generate evident trajectories along the dataset. One potential explanation for these findings is that RNA velocity predicts the future state of cells on a timescale of hours to days, based on the dynamics of mRNAs, and the time points in the dataset in Figure 4 might be too long apart to allow a robust analysis with this bioinformatic tool. In any case, our understanding of the tSC cluster was steered by previous knowledge of Schwann cell differentiation . Chronologically, iSC progressively sort large-caliber axons from bundles and become pmSC. The ensuing activation of the myelination program leads to the formation of mSC along the myelinating Schwann cell fate. Small caliber axons do not become myelinated. Instead, they are engaged by Schwann cells of the non-myelinating lineage. Using morphological knowledge as a reference, non-myelinating Schwann cells at P60 (designated nm(R)SC in the revised version of this manuscript) encircle each small caliber axon within a Remak bundle with cytoplasm. At P14, this process is ongoing but not yet complete. Based on this knowledge, we tentatively labelled the non-myelinating Schwann cells at P14 as tSC. Considering the expression patterns of established marker genes of the nm(R)SC lineage .We have removed the terminology of biological or technical replicates. The origin of each of the sequenced independent sample is explained in the text instead.5. Figure 5: It is concerning that for the 10x study, SCs account for only 3.3% of the identified cell types, and yet in the bulk analysis presented in the beginning, only the SC marker genes and how they change across time points are presented. Are the SC gene signatures truly dominating in the bulk samples presented, and as suggested by the authors for the motivation for this study? This is further evidenced by the diagram provided in Figure 5—figure supplement which suggests that the vast majority of cells in this dataset should be Schwann cells. It would be very useful to know, or at least retrospectively estimate, what proportion of the gene expression signatures in the bulk study is derived from SC cells or cells of other cell types. Learning this information from the single cell data and applying this type of analysis to their bulk data would further strengthen the application and utility of this resource and demonstrate a clear need to re-evaluate the use of bulk studies in the PNS.The proportion of Schwann cells among all cells populating the adult sciatic nerve endoneurium has been reported to be 72% of all cells , in line with our own unpublished observations. The low percentage of Schwann cells in the 10x study at P60 is likely mainly due to a low extraction efficiency of viable single Schwann cells from the enzymatic and mechanic dissociation of sciatic nerves and cannot be compared to their abundance in intact sciatic nerves (this notion is now included in the revised manuscript). In bulk sequencing of sciatic nerves, however, we mechanically removed the epineurium and perineurium layers (from E17.5 onwards) and sequenced the enriched endoneurium. In this experimental setting, RNA was extracted from the tissue directly and thus, the dominant cell type that contributed to the sequenced RNA are Schwann cells. Nonetheless, other cell types in the endoneurium also contribute to the RNA levels detected in the tissue, and on its own, this experimental set cannot resolve which cell types express a given gene.6. Figure 6: The authors 'identify clusters of proliferating fibroblasts labelled by Pdgfra expression' and reason that these clusters represent biologically interesting/unique clusters of endoneurial fibroblast-lik cells, perineuial cells, or epineurial cells, and represent a 'mysterious cluster' It is highly probable that the expression of Pdgfra in these clusters is not biological, but rather a consequence of either A) incomplete dissociation from true Pdgfra-expressing cells, or B) background signal detected in otherwise low-quality cells, (both appropriately indicated by the authors). The former is supported by the higher-than average number of genes detected in these populations , despite the authors use of a 'doublet detection' algorithm. The latter is supported by the higher mitochondrial read ratios observed for these clusters as well. Without supporting in situ hybridizations to support the identification and co-expression of Pdgfra in these populations, this finding should be removed or addressed in significantly greater detail. The authors claim(s) that "we cannot exclude that some cells in this cluster might also be in a hypothetical transitory stage of neural crest-derived cells, potentially expressing markers of multiple lineages on their route to differentiation" is a purely artificial explanation that is not supported by the evidence provided. Suggestion of this as an alternative explanation does not satisfy an Occom's razor explanation and must be substantiated with further experimentation.The authors state that " Clusters representing the different cell types were overall in much closer proximity to each other on the tSNE plots at P1 and P60" and use this to suggest that this may represent "potential cell lineage transitions or cell plasticity at early stages…". This is not an appropriate or valid conclusion to be drawn from tSNE plots. The meaningful proximity of clusters in a tSNE embedding cannot be directly inferred and is a common mis-interpretation of the method used. Please see discussion here for cautions and guidelines for interpretation of tSNE embeddings .As requested by the reviewer, we have ameliorated the text about Pdgfra-expressing cell clusters and have removed speculative discussion statements about the biology.We agree that our original statement of “Clusters representing the different cell types were overall in much closer proximity to each other on the tSNE plots at P1 than at P60” could be interpreted as that we were inferring that the proximity between isolated clusters in tSNE projections has a meaning . Since our statement could be misconceived, however, we have removed it from the text.7. Given the potential utility of this dataset as a resource for the PNS community, it is strongly recommended that the authors make their analysis (scripts/code/etc) publicly available for review.https://github.com/suterlab/SNAT-code-used-for-primary-data-analysis. We provide also the Seurat object for each dataset, available on https://snat.ethz.ch/seurat-objects.html (Username / Password: As above). Note that with this information in place, we have removed the heatmaps previously shown in Figure 3—figure supplement 1A and Figure 4—figure supplement 1B since they can now be generated conveniently from the Seurat objects by interested researchers. Links to these resources and to the primary data in GEO have now been included in the website that accompanies this manuscript. There is also a link to the final version of the manuscript that will be activated upon publication.The code is available on Essential revisions:1. Please address concerns about the validity of conclusions related to the pseudotime analysis raised by Reviewer #2. If additional analysis is possible, please include, otherwise modify the text to temper conclusions that can be made using this approach.We have added the following two statements to the manuscript to indicate the limitations of our analysis and to temper the conclusions:Related to the pseudotime analysis: “Note that the analysis was not aimed at identifying novel cell differentiation states nor trajectories, but is limited to the visualization of results obtained by a supervised bioinformatic approach in conjunction with current knowledge in the research field”.Related to a particular point: “Note that the designation “tSC” is not intended to present SCs at P14 as a discrete cell type. In our interpretation, tSC refers rather to Remak SCs which are likely undergoing maturation”.2. Please add additional discussion of potential bias that may arise from subsampling a subset of Schwann cells that could be isolated from the nerve.“We note that the approach of using dissociation of cells from nerves followed by FACS has the limitation of some potential subsampling of a subset of cells . In particular, SCs isolated from extensively myelinated nerves might not reveal the full diversity spectrum. The observed low relative extraction efficiency in this context may reflect different sensitivities of distinct SC populations to the experimental procedures, associated with the potential of subsampling”.We have added the following statement to the manuscript to discuss this issue: 3. Please provide additional validation of the pdgfra+ fibroblasts or edit the text to note the need for further validation of this cluster.We have followed the advices and have replaced the section in question accordingly with the statement: “Determination of the origins and validation of this cluster requires further investigations”.Reviewer #2:The authors have done a commendable job of addressing most of the original reviewer requests. The manuscript is certainly improved and many of the incorrect or misleading statements have been addressed as requested. The figures, labels, and legends are significantly improved in clarity.* The requested RNA Velocity analysis is not included in the revision. The authors claim to have performed it but it did not match expectations . This reviewer would have preferred to examine how RNA Velocity field estimates match the existing slingshot trajectories or at least compare the presented trajectories to those created from another tool to provide confirmatory support. The expectation that the RNAVelocity findings might disagree with the presented trajectories was the motivation behind requesting this analysis. It's lack of inclusion in the final manuscript somewhat reinforces my concern regarding the accuracy/validity of the original pseudotime trajectories.Github and we have now complemented this information further, including output data. See .We had uploaded the code for the RNA velocity analysis on which our statement was based on * The authors have indeed couched their statements regarding the Pdgfra-expressing fibroblast cells relative to their original statements. I am aware that their arguments are in a 'Results and Discussion' section, however, I am still less than comfortable with their speculation w/o any validation of the Pdgfra+ fibroblast population. The details of why the doublet detection algorithm (and their source of confidence in these results) is less than compelling, and the suggestion that 'damaged and/or dying cells is a contributing factor to the formation of this cluster' is supported only by circumstantial evidence of 'low quality' cells , not necessarily 'dying' cells. I feel that this speculation is not warranted in a Resource paper and should either be experimentally validated or removed. The manuscript would remain just as strong by merely pointing out the existence of this cluster and suggesting that it's origins require follow up analyses. But the speculation is simply not warranted w/o further supporting validation.See point 3 of Essential revisions.


Gains in real world use were retained at the 3-month followup (p = 0.005). At both post-training and followup time points, biomechanical testing found significant gains in finger ROM and hand displacement in a reaching task (p < 0.05). Baseline functional connectivity correlated with gains in motor function, while changes in EEG functional connectivity paralleled changes in motor recovery. HandSOME II is a low-cost, home-based intervention that elicits brain plasticity and can improve functional motor outcomes in the chronic stroke population.We have developed a passive and lightweight wearable hand exoskeleton (HandSOME II) that improves range of motion and functional task practice in laboratory testing. For this longitudinal study, we recruited 15 individuals with chronic stroke and asked them to use the device at home for 1.5 h per weekday for 8 weeks. Subjects visited the clinic once per week to report progress and troubleshoot problems. Subjects were then given the HandSOME II for the next 3 months, and asked to continue to use it, but without any scheduled contact with the project team. Clinical evaluations and biomechanical testing was performed before and after the 8 week intervention and at the 3 month followup. EEG measures were taken before and after the 8 weeks of training to examine any recovery associated brain reorganization. Ten subjects completed the study. After 8 weeks of training, functional ability (Action Research Arm Test), flexor tone (Modified Ashworth Test), and real world use of the impaired limb (Motor Activity Log) improved significantly ( Each year, stroke affects over 15 million people worldwide and 800,000 people in the US Hankey, . Stroke Due to the heterogeneous nature of stroke, patients demonstrate variance in response to treatment and long-term outcomes , a passive hand device that provides assistance to finger extension within a single gross grasp pattern  diagnosis of stroke more than 6 months before enrollment; (2) ability to close the hand; (3) at least trace extension (opening) of the fingers; (4) adequate cognitive status . The exclusion criteria were: (1) excessive pain in any joint of the affected extremity that could limit ability to cooperate with the protocols; (2) receiving oral or injected antispasticity medications during study treatment; (3) MCP and IP passive extension limit >30 degrees from full extension; (4) excessive tone in the fingers and thumb as determined by Ashworth scores ≥3; receiving physical or occupational therapy for the upper extremity.The HandSOME II is comprised of rigid mechanical linkages that allow isolated movement at almost all finger and thumb joints . For eacAn activity tracker was fully integrated into the HandSOME II and collects index finger movement and the total time that the device was powered on . When thDuring their initial visit, engineers fitted the device to the subject's hand and finger lengths. Different options for small, medium and large phalange lengths were available. Spring tension was adjusted based on the therapist's assessment and patient feedback. Different stiffness springs were available to accommodate subjects with difference impairment levels. Only the minimum spring assistance was given in order to keep the fingers extended or to functionally open to grip objects. The subjects were instructed on how to don device and turn on the activity tracker before each of their home training sessions. Subjects were asked to perform a 90-min therapy session every weekday for 8 weeks, for a target of 60 h of wear time.We chose a treatment intensity of 90-min sessions, five times a week because this was found to produce functional gains in our prior HandSOME study. We extended the treatment period from 4 weeks to 8 weeks in this study to try and produce more durable gains that were retained at followup. At each weekly visit to the lab, adjustments and repairs were made to the device, and the therapist prescribed several grasp and release tasks with various objects, based on performance and individual goals. Subjects were also encouraged to use the hand in everyday activities when wearing HandSOME II. The amount of movement practice during the weekly visits was kept at a minimum, so that any gains could be attributed to the home training. Engineers would collect data from the activity logger SD card and troubleshoot or repair any broken parts at the therapist's direction. Spring tension was also adjusted based on performance. At the end of 8 weeks, the sensor and logging electronics were removed and the HandSOME II was given to the subject. Subjects were encouraged to continue using the device during the 3-month follow-up, but there was no formal scheduled contact with the project team until the end of the follow-up period, when subjects were called in for evaluations. Subjects were instructed to contact the project team if their unit needed a repair. Additionally, subjects were asked to fill out a portion of a survey at 8 weeks and after the 3-month follow-up.Assessments were performed before and after the 8-week training intervention as well as 3 months after the training ended. Motor impairments at the shoulder, elbow, wrist, and fingers were assessed using the Fugl-Meyer (FM) assessment of the upper extremity : The forearm was supported on the table in a pronated position at midline. The task was to straighten fingers as much as possible from a closed fist position. Task #2–Thumb opposition: With the forearm in the same position as Task #1, the task was to touch the thumb tip to the tip of the 5th digit. Task #3–Water Bottle: Subjects were instructed to reach out and grasp a water bottle located laterally and elevate to the mouth to drink, then replace the bottle at the same location. Task #4–Nut pickup: Subjects picked up a M4 nut and placed it on a shoulder height shelf. The layout of the testing is described in detail in Kinematic data was collected using the MiniBirds® (Ascension Technologies) electromagnetic motion capture system controlled by the Motion Monitor® Software (Innovative Sports Technology). Electromagnetic markers were taped at the nail of thumb, index, middle, and ring fingers. Markers were also placed on the back of hand, forearm proximal to the wrist and on the C7 vertebrae. The position and orientation of each marker had a sampling rate of 120 Hz. Raw data was exported into a custom Matlab program that calculated several metrics. Based on the Euler sequences recommended by the International Society of Biomechanics   and Pyy(f) were obtained via fast Fourier transformation and represent the power-spectrum density of signals x and y respectively; Pxy(f) represents the cross-spectrum density, and Cohxy is the frequency (f) dependent coherence coefficient of signals x and y.where the PData was analyzed separately in the Alpha (8–12 Hz) and Beta (13–30 Hz) bands. Data from subjects with right hemisphere infarcts were flipped across the midline for subsequent analyses. The following four regions of interest overlying motor areas were then selected for analysis  baseline time point and 8 weeks post-training; (2) baseline time point and 3-month follow up. To determine if more intensive use of HandSOME produced better outcomes, for outcomes with significant gains, correlations were performed with the number of movements performed during training and the amount of wear time during training. For EEG analysis, paired sample t-tests were used to examine pre-post changes in COHRest. Correlations were then computed between COHRest and behavioral variables. For this linear regression analysis, outliers were removed using the ROUT method . There was evidence that compliance was affected by impairment level, the correlation between number of movements and impairment level (baseline FM) was significant .Ten subjects completed the pre and post-outcome evaluations. Subject characteristics are shown in p = 0.147) points after intervention and by 3.4 ± 4.6 (p = 0.055) points at the follow-up. ARAT scores increased significantly after intervention by 3.4 ± 4.5 (p = 0.039) but gains at the follow-up were no longer significant , as several subjects were unable to retain the gains achieved immediately after the intervention. Ashworth scores for flexors declined significantly after intervention , but changes were not significant at the follow-up . MAL scores improved significantly after intervention , and also at follow-up . When comparing the significant gains in clinical scores with amount of home training performed, several correlations were significant. Subjects who wore the device more had larger gains in the ARAT immediately after the intervention . The significant gains in the MAL immediately after treatment was strongly correlated with number of movements performed . The significant MAL gains at the follow-up time point was significantly correlated with wear time .p = 0.031) and this gain was retained at the follow up . ROM gains were due to a combination of increased flexion and extension limits. Gains in flexion were 8.3 ± 20.2 degrees immediately after training and 5.2 ± 26.9 degrees at the followup. Finger extension gains were 12.1 ± 30.0 degrees after training and 13.0 ± 27.1 degrees at the followup. While each of these mean gains in flexion and extension limit were not significant individually, they combined to create significantly greater ROM immediately after training and also at the followup timepoint. In the water bottle task (Task 3), thumb abduction/adduction ROM increased significantly at the followup timepoint . In the nut pickup task (Task 4), hand displacement increased immediately after training  and at the followup timepoint . There were no other significant changes in the biomechanical measures. Additionally, there was no significant change to grip force performance.A summary of the biomechanical analysis is presented in Rest after 8 weeks of robotic hand rehabilitation in the eight subjects tested with EEG. However, correlation analysis revealed greater improvements in real life functional use of the upper extremity, as indicated in the MAL assessment, were correlated with greater increases in interhemispheric COHRest post-HandSOME II intervention , were correlated with MAL gains. Changes in ARAT post-intervention did correlate with any changes in COHRest.There were no significant changes in alpha or beta COHrvention . SpecifiRest was a predictor for greater clinical gains in the MAL after 8 weeks of robotic hand therapy , and CF and IF baseline COHRest , correlated with MAL gains. There was no correlation between baseline clinical scores in the ARAT and MAL, and the change in clinical scores post-HandSOME II home therapy.Higher baseline COH therapy . SpecifiAs there was no significant change in FM score pre and post-intervention, correlational analysis was not performed on the FM scores.We developed a survey to get design and device feedback at 8 weeks and at 3-month follow-up . ResponsIn this study, we saw users wearing the device in a range of 33–134 h over 8 weeks, with a wide range of total movements performed. Immediately after training, subjects had significant improvements in function (ARAT), flexor tone (Ashworth) and real-world limb use (MAL). At the 3-month followup, the MAL score changes continued to be significant. Biomechanical analysis was generally consistent with this clinical testing. Finger ROM and hand displacement in a reaching task were both improved immediately after training, with further gains during the followup period. EEG coherence analysis found pre-post changes that correlated with changes in self reported real world use (MAL). On average, subjects did not achieve the minimally clinically important difference (MCID) for the FM of about five points , but because of large variability across subjects, the number of movements were not significantly different. Comparison across studies might be compromised by different sensor technologies and differences between devices; HandSOME I constrains grasping movement to 1 DOF, while HandSOME II allows movement at 11 DOF, with the sensor only measuring movement at the index finger MCP and PIP. The HandSOME I study reported ARAT gains of 3.3 ± 2.6 and FM gains of 4.9 ± 4.1 immediately after training. Our study did not produce greater gains on these scales despite the increased dosage . However, the HandSOME II intervention did produce significant improvements in finger ROM, reaching extent and real-world limb use that were retained at the 3-month followup, while no significant effects were found at followup in the prior study. Therefore, it's possible the combination of a more advanced device and increased dosage were key factors in achieving gains in real-world use and finger control not achieved in the prior study. One possible explanation for the why the MAL gains were significant at followup, while the ARAT and FM were not, is the type of home training provided. Subjects were encouraged to use the device to manipulate objects in their home environment as part of the home training, with the more advanced HandSOME II enabling a large variety of grasp patterns. The MAL measures use of the impaired limb during ADL. It is possible that the home-based practice of ADL tasks using the exoskeleton, provides a more direct influence on real-world use of the limb (MAL) than impairment (FM) and functional gains (ARAT). We note that Constraint-Induced Therapy, which employs a forced home practice protocol using the impaired limb exclusively, produces larger gains on the MAL, and much smaller gains in impairment and function  made the greatest improvements in MAL after 8 weeks of HandSOME home therapy. These findings are consistent with previous animal and human studies that show low inter-hemispheric coherence is linked with more severe impairments in motor control and increases in inter-hemispheric sensorimotor coherence parallel recovery of motor control. One such study, Pellegrino et al.  did not predict functional outcome after the 8 weeks of therapy. Similar results were found by Wu et al.  who founRest, nor baseline COHRest and changes in ARAT score. The opposing results between the MAL and ARAT may result from the functionality of the HandSOME II device and the home therapy program. The high DOF assistance the HandSOME II provides was designed to enable practice of activities of daily living. As the MAL is a self-reported amount of usage, this measure may have been affected more by our intervention than the ARAT, which measures task completion of reach and grasp tasks in the clinic. The smaller treatment related gains in the ARAT compared to the MAL, may have affected the correlation analysis with EEG.In contrast with the MAL results, there was no correlation between changes in ARAT and changes in COHIn a sample of 15 chronic stroke subjects, five withdrew from the study due to unrelated medical issues, pain when using the device, and lack of compliance with home training goals. A dropout rate of 33% is concerning and a larger scale study is needed to better identify the patient population who will tolerate this home-training protocol. Also, potential subjects must have some elbow and shoulder function to be able to use the HandSOME II in reach and grasp tasks. Importantly, the 10 subjects that completed the study were able to don the device without the help of a care giver. Devices were 3D printed and customized to individual's hand and finger sizes. Each week, engineers would repair device and do any necessary alterations during the weekly visit. This weekly interaction was likely an important component of the intervention in maintaining compliance, as self-reported wear time with the device decreased during the followup period, during which there were no scheduled interactions with project staff. The importance of the weekly interactions, suggest HandSOME II would be most effective when integrated into outpatient therapy. Alternatively, HandSOME II could be used independently without the need for weekly therapist supervision, if usability of the device can be improved, eliminating the need for frequent repairs, and training subjects to adjust the assistance levels on their own. We believe the main stimulus for gains was the 60 h of home training, and not the weekly visits to the clinic, which were focused on adjustment of the home training program and movement repetitions were kept to a minimum.Recent studies have reported on the effectiveness of home-based approaches based on telerehabilitation strategies that require a remote therapist to motivate and supervise the home-training. Tele-AutoCITE is a home-based version of Constraint-Induced therapy that incorporates an instrumented workstation and remote supervision of the task practice  under Grant 90REGE0004. PL was also supported by the Professor Robert Meister Distinguished Faculty Fellowship.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Micro/nanomachines (MNMs) correspond to human-made devices with motion in aqueous solutions. There are different routes for powering these devices. Light-driven MNMs are gaining increasing attention as fuel-free devices. On the other hand, Plasmonic nanoparticles (NPs) and their photocatalytic activity have shown great potential for photochemistry reactions. Here we review several photocatalyst nanosystems, with a special emphasis in Plasmon induced photocatalytic reactions, as a novel proposal to be explored by the MNMs community in order to extend the light-driven motion of MNMs harnessing the visible and near-infrared (NIR) light spectrum. Micro/nanomachines (MNMs) are human-made devices (colloids) with dimensions ranging between 10 nm and 10 µm, with motion through a specific direction, and can perform different tasks in aqueous solutions .There are different routes for powering these devices, such as: (i) chemical ,4, (ii) To convert the photons into kinetic energy there is a common approach, which is to use an asymmetrical particle (Janus) and a semiconductor with a well-established band gap ,20. The In recent years, plasmonic photochemistry has gained attention due to the possibility of performing chemical reactions employing visible light and NIR light ,22. Ther2) based photocatalysts as light-driven micro/nanomotors in 2 hybrid systems for light-driven reactions, including some examples of Janus –like hybrid systems breaking the symmetry of the assemblies for potential use in propelled motion, in However, very little attention has been paid to harness these energetic charge carriers as a source of energy for MNMs propulsion. In this review we attempt to go beyond the current trend of photochemistry and present a perspective with the potential use of plasmon induced photocatalytic activity for light-driven micro/nanomotors. We start by describing titanium dioxide  and hydroxyl (•OH−) radicals, highly oxidative species which can decompose a great variety of organic compounds 2 Janus nCu2Se-Pt .Redox reactions on the surface of semiconductors generate more product molecules/ions than those necessary to initiate them, leading to the development of local gradients in asymmetric micro/nanomotors ,32,33,34+ on the semiconductor surface and an accumulation of electrons on the opposite hemisphere. This charge accumulation establishes an electric field pointing towards the electron-acceptor hemisphere, which will move the MNM in a direction dependent of its overall charge  . In. In74]. 2+) as target molecule and AuNPs as photocatalyst observing the reduction from MV2+ to MV•+ [To gain insight into the charge injection, Navalon et al. proposed an experiment using methyl viologen  were synthesized to study the photo oxidation induced by LSPR decay, when the NPs were irradiated with green light (exciting the LSPR) they observed under the open circuit conditions an increase in negative potential (photovoltage) which can only be explained by injection of electrons from the adsorbate to the electrode  TiO2 can only absorb UV light, narrowing its ability to harness the solar spectrum radiation. Therefore, TiO2 can be used in conjunction with Plasmonic NPs, to make use of the excellent visible light absorption properties of the Plasmonic NPs. Moreover, as described in 2–Plasmonic NPs can offer a route for capturing hot charge-carriers from the Plasmonic NPs into the TiO2, since titanium dioxide corresponds to a good electron-accepting metal oxide due to the high density of states in its conduction band. All these characteristics make these hybrid materials good candidates to extend the photocatalytic activity to the visible and near-IR regions for light driven reactions.Plasmonic NPs can be mixed with TiO2 electrode with AuNPs or AgNPs (5–20 nm diameter) when irradiated with visible light. They obtained maximum potential responses at the LSPR mode wavelength . At the time, they proposed as a possible mechanism a plasmon-induced charge separation [2 in response to the LSPR excitation with visible light. They evaluated electron transfer from a donor (hole scavenger) to the AuNPs, proposing that irradiation with visible light generates a photoexcited state in AuNPs or AgNPs (LSPRs) which causes that photoexcited electrons are transferred to the TiO2 bulk, where they will participate in different reactions, while the oxidized metal NPs receive electrons from the electron donor in the electrolytic solution [2 system. Larger NPs exhibited greater quantum efficiencies, revealed by a steeper increase in photocurrent [Pioneering efforts to unveil the underlying mechanism behind the enhanced photocatalytic activity of these hybrid systems were reported by Tatsuma’s group ,90,91. Tparation . Later, 2 powders, using methanol as a sacrificial hole scavenger [2 samples resembled their respective absorption spectra (peak positions at 575–585 nm). They proposed that the LSPR excitation of AuNPs could aid for the injection of electrons into the conduction band of TiO2 and reduce adsorbed O2 on the semiconductor surface; while the electron deficient AuNPs would then oxidize organic compounds, such as 2-propanol, and go back to its metallic state [2 was able to perform other types of reactions under visible light such as splitting of water [2. Photocatalytic hydrogen formation with UV and visible light in the presence of Au-TiO2 was addressed, using methanol or EDTA as sacrificial reducing agents, with a Xe-doped Hg lamp as UV light source. In order to prove the visible light photocatalytic activity of Au (1.5%wt)-TiO2 for hydrogen generation, the second harmonic of a Nd:YAG laser at 532 nm  was used. A comparison between the activity of this material and bare-TiO2 for monochromatic (λ ~ 532 nm) and polychromatic (λ > 400 nm) irradiation showed that bare-TiO2 presents no activity in these conditions, while there is a significant increase in the volume of generated H2 when AuNPs are incorporated in the semiconductor. The same set of samples was used for the study of the oxygen generation reaction. No oxygen was evolved when using only P25 TiO2, while the presence of AuNPs introduced visible light photoactivity [Then, Kowalska et al. reported plasmon-induced photocatalytic chemical reactions using a 2%wt Au photodeposited with UV light onto the surface of fifteen commercial TiOcavenger . The actic state . Furtherof water . Hybrid activity .2 surface, rather than by the direct transfer of charges between the Plasmonic NPs and the metal oxide [2 with and without AuNPs irradiated with UV and visible light was measured [2 with a relatively short exciton diffusion length [2 which collects and conducts hot electrons to an unilluminated Pt counter electrode for the hydrogen evolution. Then, the resultant positive charges in the Au nanorods function as holes and were extracted by an oxidant catalyst which evolves oxygen from water, emphasizing the crucial role of a charge carrier mediator (hole scavenger) at the interface between the AuNPs and the solution [The photocatalytic activity increments in the water splitting reaction under visible illumination, was described by an alternative mechanism by Liu et al. with a model based on the near-field optical enhancement of noble metal NPs near the TiOal oxide . They pemeasured . Under Un length . Indepenn length . The samsolution .2 nanostructures) into strategic architectures have been evolving as well. 2 core-shell particles decorated with AuNPs as an efficient approach to develop LSPR-induced visible light active photocatalyst hybrid systems [2@TiO2-AuNPs) was compared with the activity of SiO2@TiO2 without AuNPs under visible light illumination for the degradation of methylene blue molecules, the efficiency of the hybrid system was evident  redox fuel, (ii) catalyst material, and (iii) engineering design. Here, we will focus on the plasmonic catalysts and how to select the proper fuel for each plasmonic device, for the hint to design new types of nanomachines. As previously described, a light-driven motor requires an asymmetric particle (Janus) to achieve the motion . The cheThe first motion mechanism (bubble recoil) can be performed by plasmonic NPs or plasmonic NP/Semiconductor hybrid systems that can reach the redox potentials for the water splitting reactions and overcoming the solubility limit of either hydrogen and/or oxygen, producing bubbles and therefore gaining momentum due to the detach of the bubbles. Several reports have shown the water splitting reaction by tuning the band gap to the Fermi energy of the plasmonic NPs to inject hot-electrons to the conduction band or hot-holes to the valence band for evolving hydrogen or oxygen, respectively ,103,104.The second motion mechanism (self-diffusiophoresis) is performed due to a reaction that only takes place in one side of the Janus motor, which increase the concentration of the specie in this side of the particle favoring an osmotic flow which finally causes a propulsion of the asymmetrical motor ,84. TradIn this sense, Currently, significant advances have been made in the mechanisms for micro/nano machines. However, there are still substantial challenges that must be overcome to apply these machines. The use of light to generate movement to the devices is strongly linked to the band gap of semiconductors. In general, they require highly energetic light, limiting their use in specific applications of medicine. In this context, we believe that plasmonic photochemistry is an alternative to this challenge since the LSPR of the plasmonic NPs can be tuned by the shape, size, and type of metal used.The photothermic effect of plasmonic NPs has been extensively studied, but in the past years notable research has been developed in the plasmonic photochemistry field, which could be extended into the MNMs field by taking advantage of the highly energetic charge carriers in light-driven machines.