Mechanobiology requires precise quantitative information on processes taking place in specific 3D microenvironments. Connecting the abundance of microscopical, molecular, biochemical, and cell mechanical data with defined topologies has turned out to be extremely difficult. Establishing such structural and functional 3D maps needed for biophysical modeling is a particular challenge for the cytoskeleton, which consists of long and interwoven filamentous polymers coordinating subcellular processes and interactions of cells with their environment. To date, useful tools are available for the segmentation and modeling of actin filaments and microtubules but comprehensive tools for the mapping of intermediate filament organization are still lacking. In this work, we describe a workflow to model and examine the complete 3D arrangement of the keratin intermediate filament cytoskeleton in canine, murine, and human epithelial cells both, in vitro and in vivo. Numerical models are derived from confocal airyscan high-resolution 3D imaging of fluorescence-tagged keratin filaments. They are interrogated and annotated at different length scales using different modes of visualization including immersive virtual reality. In this way, information is provided on network organization at the subcellular level including mesh arrangement, density and isotropic configuration as well as details on filament morphology such as bundling, curvature, and orientation. We show that the comparison of these parameters helps to identify, in quantitative terms, similarities and differences of keratin network organization in epithelial cell types defining subcellular domains, notably basal, apical, lateral, and perinuclear systems. The described approach and the presented data are pivotal for generating mechanobiological models that can be experimentally tested. Quantification of cellular properties is a central task of cell biology . It is nThe intermediate filament cytoskeleton consists of polypeptides, which are expressed in a tissue- and context-specific manner. The largest group of intermediate filaments, the keratin intermediate filaments, are the main cytoplasmic networks in epithelial tissues. They confer, together with their associated desmosomal cell-cell and hemidesmosomal cell-extracellular matrix adhesions, mechanical resilience . The humSegmentation of cellular elements is a key task in biological image analysis. Its application to specific structures, however, can be quite difficult and requires appropriate adjustment. Networks of curvilinear objects such as keratin networks are ubiquitous ranging from cytoskeletal ﬁlaments at the micro- to nanoscale to blood vessels or neuronal networks at the tissue and organ level . FilamenA wide range of specialized tools are available for segmentation of cytoskeletal actin , which aTo segment keratin filament networks and generate data structures with defined nodes and internodal regions ( = segments), we used the open-source program TSOAX. TSOAX has been applied for 3D segmentation of actin networks using fluorescence images of yeast cells . It is aThe developed image analysis tools were applied to confocal airyscan high-resolution fluorescence image stacks of keratin networks in single cells either in cultured epithelial monolayers or within their native tissue context. We show that the image analysis tools provide cell-type-specific information on filament mesh characteristics as well as information on filament thickness, directionality, and connectivity in 3D. Cinematic rendering and immersive visualization make analysis of network heterogeneity accessible at a hitherto unknown quality and precision of 3D reconstruction.i) Canine kidney epithelial MDCK cells producing YFP-tagged keratin 8 (subclone H9) were chosen as representatives of polarized simple epithelial cells. MDCK cells form a one-layered coherent epithelium with the cuboidal to cylindrical cells attached to each other by highly organized junctional complexes. Intracellularly, the keratin filaments have a complex arrangement consisting of a dense subapical network, perinuclear and radial keratin filaments, and interdesmosomal filaments below the plasma membrane (ii) Spontaneously immortalized human epidermal keratinocytes expressing YFP-tagged keratin 5 (HaCaT B9) were chosen as representatives of squamous epithelial cells (iii) To examine the 3D organization of the keratin intermediate filament network in its native tissue context, we made use of the recently described knock-in mouse line expressing only YFP-tagged keratin 8 but no wild-type keratin 8. Retinal pigment epithelium cells (RPE) were selected for the analysis because of their well-delineated cytoplasmic keratin filament network (see below). High-resolution airyscan confocal image stacks were prepared from each cell type.In a first step, we wanted to generate high-resolution fluorescence data that would be suitable for image analysis of single filaments and filament bundles and would represent keratin filament networks in different epithelial cell types. We selected three non-carcinogenic epithelial cell types: (membrane . Immunobmembrane . (ii) Spal cells . They coal cells . (iii) TThe recorded image stacks were transformed into Euclidean 3D maps using TSOAX and the KerNet software  and 2. ITo obtain more detailed comparisons of the calculated vertices with the fluorescence data, vertices were first plotted in 3D and their brightness was encoded as filament diameter. The 3D plots were then convolved by Gaussian blurring to simulate the microscope’s point spread function. For close inspection of the keratin filament network in its subcellular complexity, we used cinematic rendering. The rendering displays the network as a true 3D model including the brightness of filaments which are shown as tubes with different degrees of thickness thus providing information on the segments in a 3D-viewer. To distinguish individual segments, each segment was randomly color-coded .kernet.rwth-aachen.de. Furthermore, immersive visualization was used to provide unrivaled impressions of the spatial arrangements of the keratin network in stereoscopic 3D , classification of network domains becomes feasable.The numerical network representations were used to quantify keratin filament properties. A prominent property, which was already graphically encoded in the 3D-models, is filament brightness as a measure of filament thickness. The assumption is that filament thickness is a reliable indicator of filament bundling which can now be easily quantified throughout entire cells. We will therefore use the terms brightness and thickness interchangeably. The histograms in Maps of filament brightness are shown in 12 epidermal cells  in HaCaT cells is ~4.4 mm. By using the following formula ((MPL * Ltot * Bf) / Na) [Na = Avogadro constant], the amount of keratin per cell is 3.47 pg.It is now possible to estimate the amount of keratin from our observations and to compare it to the previously estimated 8.07 ± 1.88 pg keratin in single basal keratinocytes of the murine epidermis . To thisAn alternative estimate can be obtained by considering the dimensions and mass of the tetrameric subunit. A keratin tetramer has a length of 44 nm  and consPerforming the same type of calculation we deduced a keratin content of 1.3 pg or 1.4 pg per MDCK cell and 1.2 pg or 1.4 pg for single RPE cells.Next, we analyzed branching and intersection of keratin filament bundles. Different types of branching could be envisioned depending on the number of segments per node. Three segments per node would define a Y-shaped branch, whereas four segments would define an X-shaped intersection of two filaments . More th–1) than in MDCK cells (2.54 ± 0.11 µm–1) and HaCaT cells (2.97 ± 0.72 µm–1). The curvature distribution in HaCaT cells was more widely spread than in the other two cell types. Maps of curvature distribution are shown from two different perspectives in Bending of keratin intermediate filaments is of interest as it may provide information on material properties of filaments and may indicate whether filaments are under longitudinal compression or tension. First, we calculated the distance/length ratio of segments . The higTo calculate how evenly the segments are orientated in space, the azimuth  and the elevation  of segments were calculated for each cell  and 2. TTo obtain insight about the relationship between segment directionality and cell shape, the orientation of the segments was analyzed with respect to the cell center. To this end, all segments were translocated to the cell center and their spatial distribution was plotted in radial 2D histograms. As expected, MDCK and RPE cells showed a homogenous distribution of horizontal segment orientation in the x/y plane while HaCaT cells were more polarized . In the To compare the 3D-isotropy of entire keratin filament networks, a sum vector of all segments was constructed for each cell. To this end, the orientation vectors of segments were translocated to the cell center as described above and mapped with the length of each segment. With this work we provide, for the first time, experimentally derived numerical 3D representations of entire keratin networks in eukaryotic cells. 3D fluorescence data were used to define segments that are connected by nodes. This enabled us to compare local properties of keratin intermediate filament networks such as filament thickness, curvature and orientation in different cell types in vitro and in vivo. It further facilitated comprehensive comparisons of entire networks by quantifying parameters such as entire filament length, degree of filament connectivity, and spatial network orientation.A critical aspect of the work was evaluation of the quantification process. Since synthetic 3D data of cellular keratin networks were lacking, the quality of segmentation was evaluated by visual inspection of superimposed imaging and segmentation data demonstrating that the location and brightness of filaments were mapped with high precision and reliability. The subsequent allocation of nodes and segments was visually assessed in rendered 3D models (cinematic rendering) again revealing agreement of the assignments. Nevertheless, the numerical representation has its limitations in accuracy. Proper segment and node detection was particularly challenging in HaCaT cells because of their complex network geometry in combination with low filament bundling. Extreme bending at the single filament level with bending radii of 100 nm  are not A major advance of the current report is the fabrication of numerical models of the keratin filament network in 3D. The rendered 3D models can be thoroughly inspected with the help of 3D display devices such as 3D screens, virtual reality-headsets (HMDs) or Cave Automatic Virtual Environments (CAVEs). The benefits of such immersive environments have been demonstrated . Three fThe derived digital network models furthermore allow comparisons between cells with similar or dissimilar differentiation status . As a prBrightness levels correspond to filaments thickness and are therefore a measure of keratin filament bundling. The observed variation in filament bundling in the different cell types may therefore be a consequence of differences in the local force balance. This force balance is the result of intrinsic cell type-specific properties such as cell shape and arrangement of the actomyosin system and is impacted by forces imposed by neighboring cells and the extracellular matrix. This is supported by the observed differences in bundling of the apical, basal and perinuclear keratin filament networks. However, the variation in filament thickness may also reflect other properties such as the propensity of the network to reorganize, for example, at different times of the cell cycle. Correlating the derived image data with information on mechanical keratin filament properties obtained by laser cutting experiments, force-sensor measurements or traction force analyses may help to better understand the relationship between filament bundling and mechanics.Keratin filament segment length is an indirect measure of mesh size and may therefore describe another crucial mechanical property of the network. We were surprised to find that the segment length varied only by 10% between the different cell types and thus showed less variation than filament brightness or curvature. This means that the mesh size is fixed within a relatively small range, although the number of meshes per volume differed significantly between the different cell types. A conclusion of this observation is that mesh size may be determined by intrinsic keratin filament properties such as elastic modulus or branching capacity. However time-lapse fluorescence analyses e.g.  have shoExperimental and mechanistic evidence has been provided for keratin filament branching . The facBending of keratin filaments provides important information on whether keratins are stretched, relaxed or compressed. Keratin intermediate filaments are highly elastic . They caTo understand the response of the keratin network to internal and external forces, it is important to have measures of filament orientation. We suggest that uniformly radial orientation of keratin filament segments indicates that cells experience mechanical forces that are homogenous in all directions. In contrast, a preferential orientation in one direction indicates that keratin filaments align in response to mechanical cues that may be induced by unevenly distributed cell-cell contacts, polarized cell-extracellular matrix adhesions or contractile activity. Our data therefore indicate that HaCaT cells are subjected to mechanical strain (uneven azimuth distribution and large sum vector), whereas MDCK and RPE are subjected to little mechanical strain . Fine mapping of the distribution patterns of hemidesmosomes and desmosomes may help to understand the differences and to initiate mechanical modelling, improving previous models of keratin network, morphogenesis, dynamics and mechanics .The observed high degree of segment property variability must be taken as an indication of the amazing plasticity of the keratin filament system. The node/segment arrangement revealed a unique 3D structure that can be modified in multiple ways affecting bundling, flexibility, and local cross-talk. Keratin isotypes, posttranslational modifications, external and internal forces, and interactions with other cytoskeletal and cellular components may determine these properties and thereby act as modulators of network structure at different length and time scales.Madin-Darby Canine Kidney (MDCK) cells were stably transfected with a plasmid encoding human full-length keratin eight with an EYFP tag at its carboxyterminus  has been described . HaCaT cRetinas were prepared from homozygous keratin 8-YFP knock-in mice, which synthesize fluorescence-tagged keratin eight from the modified endogenous gene locus . Mice weTo obtain statistically relevant data, ten image data sets were recorded of each cell type with a Zeiss LSM710 using airyscan mode . An oil immersion Plan-Apochromat 63 x/1.4-NA, DIC M27 objective (Carl Zeiss) was used. Fluorescence was detected with an 488 nm argon laser (module LGK 7872 ML8) and filter set BP 495–550 using 2.5% power. Optimal factory settings for z-stacks were used resulting in a voxel size of xy = 0.044–0.065 µm and z = 0.17–0.2 µm. No further image optimization was used. To assure that only single cells were analyzed, the fluorescence in individual cells was cropped from confluent MDCK monolayers and retinal pigment epithelium (RPE) using Fiji standard tools. Cell borders were manually defined for each z-slice and the cell exterior was set to a grey value of 0. Wild-type HaCaT and HK5-EYFP producing HaCaT clone B10 cells were mixed prior to imaging to prevent overlap of fluorescence signals in neighbouring cells.https://www.itc.rwth-aachen.de/go/id/eucm).TSOAX software  was usedn vertices ( = snakes). Each vertex is defined in x, y, z and has an associated density ( = fluorescence intensity). However, the snakes are not associated with each other via nodes and may cross each other.  and their associated densities. The segments therefore inform about the trajectory of the snake between its two endpoints and the density progression along the segment. To summarize, nodes represent the first and last vertex of a segment and mark the connection between segments whose endpoints are close  that do not cross each other, and can be connected at their ends by nodes. Segmentations using TSOAX defines polygonal chains with h other. . To obtare close . All segFor validation of the segmentation, the original fluorescence image stacks were compared with the segmented 3D data. All vertices retrieved from KerNet were plotted into the original image stacks. The z-values of the vertices were rounded to the nearest z-slice and all segment vertices were drawn as discs with a diameter proportional to their brightness. To simulate the blurriness of the raw images, the vertex stacks were 3D-Gauss filtered using Fiji .http://www.martinreddy.net/gfx/3d/OBJ.spec), which, in turn, can be imported into 3D rendering programs. For the representation of segment brightness as renderable tubes, density properties of segments were compiled into 3ds Max (Autodesk) script files that translate segment brightness into tube thickness, resulting in renderable splines for each segment. In some instances, random colors were assigned to each segment . It displays the datasets of cells as 3D models. The individual filaments are visualized as tubes. Their brightness is encoded in the tubes’ thickness. The resulting structure of interconnected tubes can then be visually inspected in VR, allowing a detailed view on the digitalized subcellular network in any magnification. Individual filaments can be interactively assigned to one of several distinct classes, pre-defined by the domain expert. Color-coding is then used to visually differentiate the classes in the immersive visualization.For further visual inspection of the keratin networks, a basic Virtual Reality (VR) application was designed. The VR application was implemented using Unreal Engine 4 . The manuscript is well-written, well-illustrated, and the authors are thorough in their recognition of previous studies of relevance to their own. We believe that this article will be foundational in the specialized field of intermediate filament biology and will have a significant impact in the broad field of cell biology. public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors and the evaluation has been overseen by Anna Akhmanova as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: John E Eriksson/John (Reviewer #2).Thank you for submitting your article "Quantitative Mapping of Keratin Networks in 3D" 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) Congratulations to the authors on a beautiful and important body of work.2) The authors are expected to address all of the comments that were deemed "required" by the three reviewers, each of whom is an expert in the field and provided a balanced and informed opinion about the work.3) The issue of whether to retain Figure 3 in the main space of the paper (cf. reviewer #3) or relocate it to supplementary material is one worth thinking about. As the reviewing editor I support reviewer 3's opinion that Figure 3 does not belong to the main space of the paper, though I would like to see it accessible to the public. The editor is expected to weigh in on this specific issue.4) There are a number of additional comments and suggestions that were provided by the three reviewers. The authors should make a deliberate effort to address them. This contribution has the clear potential of becoming a landmark paper in the field and addressing (many of) these comments would help make the contribution clearer, stronger, and potentially more impactful.Reviewer #1 (Recommendations for the authors):Issues that should be addressed by the authors include:Resolution of the microscopic imaging. Can the authors detect single 10 nm filaments in the cells they studied? If not, is there a sense of how many filaments are comprised in the segments they can detect, microscopically?Can the author convert their whole cell imaging data into a concentration of keratin proteins, and number of filaments, inside cells? Some assumptions would need to be made, but it would be interesting to compare the outcome of this analysis to biochemical measurements that have been published, e.g., for basal keratinocytes in the epidermis in situ . How does the imaging-derived information compare to actual  keratin concentrations in the cell lines analyzed ? This comparative analysis would provide a sense of "how much of the keratin pool" is being captured through the imaging approach developed by the authors.Section 3.6. Is there any trend regarding subcellular distribution for the Y-shape, X-shape, star-shaped and more complex arrangements of intersections between filaments?Section 3.7. One would expect that thicker segments show a lower degree of bending, as they are likely to represent bundles of filaments. Comments? A key determinant, as pointed out by the authors in their Discussion, is whether fiber segments are subjected to forces .Bundling properties. Given the known arrangement of subunits in the keratin filament lattice, there likely is a fair amount of GFP moieties in the filaments analyzed. Does the presence of a GFP fusion, which presumably is exposed at the surface of filaments, impact their ability to undergo bundling? (of relevance: what is the stoichiometry of fusion protein relative to endogenous K8 and other type II keratins?).Line 136 – Imaging of HaCaT cells. "When" were the analyses performed relative to time in culture and degree of confluence?Line 294. What do the authors mean by "good correlation"?Segment 3.8. Orientation. The authors may wish to illustrate the terminology they use, e.g., in a schematic presented in the setting of a suppl. figure.Discussion – line 446 – one wonders why the authors did not routinely mix fluorescently labeled and non-labeled cells to avoid the edge effects they describe.Line 456. Please define "motion parallax".Reviewer #2 (Recommendations for the authors):– Can you give an assumption why more segments can be detected in HaCaT cells? Why the average length was shortest in MDCK cells and longest in RPE cells? How does it link to the biological feature or function of the cells? if we have a random cell given, can you infer the biological property of this cell?– It is stated in Abstract and Introduction that super-resolution imaging was used for image capture, but the Materials and methods section states that Airyscan was used, which is not a real super-resolution technique. Later on "high resolution images from Airyscan" is used, which would be more accurate– In the "2.10 Directional vectors" section, the authors describe how the directionality of the segments was calculated. This section does not come across as detailed enough as it seemed challenging to decipher how this works out. Grateful for some added details.– In some of the renderings in the images , it possible to see that it has not formed lines, but dots in a line. Any reason for this? Only partially successful segmentation or something else?– Interesting to know if one could observe any differences between keratin in physiological state and diseased state using this type of modelling, for example, non-cancerous cells vs. cancer cells, or cells in confluent state vs. a state mimicking wound healing?Reviewer #3 (Recommendations for the authors):1. After a careful inspection of Figure 1A, it seems that the position of the vertices are not always at the center of the fluorescence signal, shouldn't it be at the very center of the original signal?2. The fluorescent signal is translated into a tube, however:a. In your images, similar intensities have sometimes different dimensions (thicker thinner), this is not always reflected in the thickness of the rendered structure.b. In Y-shape junction, shouldn't one bundle should be thicker?c. Why is it correct to estimate bundles as tubes and not with oval shapes?d. Since the signal of an individual keratin filament can be detected, why the thickness of the rendered tube is proportional to an (at least) estimated number of filaments? even a naïve integer multiplication of the signal would give a more realistic view of the network.3. This reviewer does not see any scientific incentive to including figure 3.4. The curvatures of the segments are slightly different between the cell types studied. It is not clear what would that means or how would it influence the keratin network?5. The authors indicate the total length of the keratin bundle network in cells, but a reference number is missing. How is it in comparison to actin and microtubules?6. Are there any significant variations between the keratin network, e.g. density or segment directionality, at the vicinity of the nucleus in comparison to other cytoplasmic regions? Essential revisions:1) Congratulations to the authors on a beautiful and important body of work.2) The authors are expected to address all of the comments that were deemed "required" by the three reviewers, each of whom is an expert in the field and provided a balanced and informed opinion about the work.3) The issue of whether to retain Figure 3 in the main space of the paper (cf. reviewer #3) or relocate it to supplementary material is one worth thinking about. As the reviewing editor I support reviewer 3's opinion that Figure 3 does not belong to the main space of the paper, though I would like to see it accessible to the public. The editor is expected to weigh in on this specific issue.As suggested, we moved Figure 3 to the Supplementary Material section.4) There are a number of additional comments and suggestions that were provided by the three reviewers. The authors should make a deliberate effort to address them. This contribution has the clear potential of becoming a landmark paper in the field and addressing (many of) these comments would help make the contribution clearer, stronger, and potentially more impactful.Reviewer #1 (Recommendations for the authors):Issues that should be addressed by the authors include:Resolution of the microscopic imaging. Can the authors detect single 10 nm filaments in the cells they studied? If not, is there a sense of how many filaments are comprised in the segments they can detect, microscopically?We can detect single 10 nm filaments by fluorescence microscopy but are unable to determine the number of filaments within a given keratin bundle. Quantification of filament bundle brightness did not reveal a stepwise but a continuous distribution.Can the author convert their whole cell imaging data into a concentration of keratin proteins, and number of filaments, inside cells? Some assumptions would need to be made, but it would be interesting to compare the outcome of this analysis to biochemical measurements that have been published, e.g., for basal keratinocytes in the epidermis in situ .We thank the Reviewer for helping us to connect our results with previous observations. We added the following section:“3.6 Estimation of cellular keratin contentIt is now possible to estimate the amount of keratin from our observations and to compare it to the previously estimated 8.07 ± 1.88 pg keratin in single basal keratinocytes of the murine epidermis . […] Performing the same type of calculation we deduced a keratin content of 1.3 pg or 5.0 pg per MDCK cell and 1.2 or 5.5 pg for single RPE cells.”How does the imaging-derived information compare to actual  keratin concentrations in the cell lines analyzed ? This comparative analysis would provide a sense of "how much of the keratin pool" is being captured through the imaging approach developed by the authors.We appended an immunoblot in new SupplementaryFigure 1—figure supplement 1 showing the relative amount of endogenous keratin 8 in the newly prepared MDCK cell line and added the following statement in section 2.1:“Immunoblot analysis of total cell lysates revealed comparable amounts of YFP-tagged and endogenous keratin 8 .”We furthermore emphasize in section 2.1 that the entire keratin 8 is YFP-tagged in RPE cells:“To examine the 3D organization of the keratin intermediate filament network in its native tissue context, we made use of the recently described knock-in mouse line expressing only YFP-tagged but no wild-type keratin 8.”Section 3.6. Is there any trend regarding subcellular distribution for the Y-shape, X-shape, star-shaped and more complex arrangements of intersections between filaments?We could not find a preference of subcellular distribution for the different shapes.Section 3.7. One would expect that thicker segments show a lower degree of bending, as they are likely to represent bundles of filaments. Comments? A key determinant, as pointed out by the authors in their Discussion, is whether fiber segments are subjected to forces .We did not find a correlation between keratin bundle thickness and bending and therefore added the following statements:- Previous section 2.7/new section 2.8:“We did not see a correlation between filament bundle thickness and curvature .”- Result section:“The observation that filament bundle thickness and curvature are not correlated suggests that the 10 nm filaments are only loosely connected in keratin bundles.”Bundling properties. Given the known arrangement of subunits in the keratin filament lattice, there likely is a fair amount of GFP moieties in the filaments analyzed. Does the presence of a GFP fusion, which presumably is exposed at the surface of filaments, impact their ability to undergo bundling? (of relevance: what is the stoichiometry of fusion protein relative to endogenous K8 and other type II keratins?).As specified in our answer above, the YFP-tagged keratin 8 makes up to 100% of keratin 8. While we cannot formally exclude a mild effect on bundling, the current manuscript together with many publications not only from our own lab but also many other labs world-wide indicate that fluorescent protein-tagged keratins are highly reliable, precise and fully functional reporters of the wild-type polypeptides. The healthy and inconspicuous homozygous Krt8-YFP mouse colony further attests this conclusion.For the Reviewers’ perusal we provide a comparison of an anti-keratin immunostaining of MDCK cells (left) with one of the MDCK cells analysed in the current manuscript (right). While the quality of the immunodetection is inferior to that of the fluorescence in the viable cell because of the necessary fixation and permeabilisation, overall network morphology and filament bundling appear to be comparable.Line 136 – Imaging of HaCaT cells. "When" were the analyses performed relative to time in culture and degree of confluence?HaCaT B10 cells were imaged 1-2 days after reaching confluence corresponding to 5-7 after seeding. This information is now included in section 4.1.Line 294. What do the authors mean by "good correlation"?We now write “…that the recorded and derived numerical 3D representations of the keratin filament network are in agreement.”.Segment 3.8. Orientation. The authors may wish to illustrate the terminology they use, e.g., in a schematic presented in the setting of a suppl. figure.We provide a scheme in new Supplementary Figure 7 suppl 1 illustrating the terminology used in the dissection of the complex 3D keratin network orientation.Discussion – line 446 – one wonders why the authors did not routinely mix fluorescently labeled and non-labeled cells to avoid the edge effects they describe.While it is obviously not possible to do mixing in the native RPE tissue context, we failed to mix cells in our initial analyses of MDCK cells. Obviously, mixing would have been advantageous.Line 456. Please define "motion parallax".We now write:“… (iii) head tracking provides enhanced motion parallax. Motion parallax is a monocular depth cue which allows observers to perceive the depth of the visualized cell from their own motion. The filaments close to the observers are perceived to be moving faster than filaments in the distance.”Reviewer #2 (Recommendations for the authors):– Can you give an assumption why more segments can be detected in HaCaT cells? Why the average length was shortest in MDCK cells and longest in RPE cells? How does it link to the biological feature or function of the cells? if we have a random cell given, can you infer the biological property of this cell?These are clearly the most important but also most difficult questions. Correlation of network architecture with functional readouts such as cellular mechanics, signalling, proliferation, differentiation, stress and others are needed to address these questions. Our tools allow to investigate such aspects and serve as a strong motivation for our ongoing research.– It is stated in Abstract and Introduction that super-resolution imaging was used for image capture, but the Materials and methods section states that Airyscan was used, which is not a real super-resolution technique. Later on "high resolution images from Airyscan" is used, which would be more accurateIn spite of the advertisement by Zeiss , we substituted “super-resolution” by “high resolution” in the context of Airyscan technology.– In the "2.10 Directional vectors" section, the authors describe how the directionality of the segments was calculated. This section does not come across as detailed enough as it seemed challenging to decipher how this works out. Grateful for some added details.As specified in our response to Reviewer 1, we added another scheme to illustrate aspects of the vector orientation in new Supplementary Figure .– In some of the renderings in the images , it possible to see that it has not formed lines, but dots in a line. Any reason for this? Only partially successful segmentation or something else?In the 3D renderings vertices are connected as tubes , whereas the heatmaps in Figures 3, 4, 6 and 7 depict properties of individual vertices that are not evenly spaced and are therefore represented as puncta in certain regions.– Interesting to know if one could observe any differences between keratin in physiological state and diseased state using this type of modelling, for example, non-cancerous cells vs. cancer cells, or cells in confluent state vs. a state mimicking wound healing?We wholeheartedly agree with the Reviewer that these are pressing issues to be answered but would like to emphasize that reliable evaluation requires careful and detailed analyses.Reviewer #3 (Recommendations for the authors):1. After a careful inspection of Figure 1A, it seems that the position of the vertices are not always at the center of the fluorescence signal, shouldn't it be at the very center of the original signal?The Reviewer correctly points out that there is local imprecision in the co-localization between the segmentation data and original fluorescence data. This is due to the transformation of the slice-based fluorescence z-positions into continuous positions in the segmentation data. Thus, ‘new’ z-positions are created in between the slices of the original z-stack positions in the segmentation data leading to slight incongruences in the overlays of the projection images.2. The fluorescent signal is translated into a tube, however:a. In your images, similar intensities have sometimes different dimensions (thicker thinner), this is not always reflected in the thickness of the rendered structure.We do not quite understand the point of criticism. Fluorescence brightness was taken as a measure of filament thickness. This was done consistently without any manual interference. To further clarify this we added the following statement in section 2.4:“We will therefore use the terms brightness and thickness interchangeably.”b. In Y-shape junction, shouldn't one bundle should be thicker?Unfortunately, the data do not support this obvious concept.c. Why is it correct to estimate bundles as tubes and not with oval shapes?ACS 2016, Nano 10,: 3553–61. https://doi.org/10.1021/acsnano.5b07871) and appears to be the most simple way to represent filament bundles. It is fully consistent with the lack of asymmetry in our data sets. We are also not aware that other geometries such as an oval or square arrangement have been discussed in the literature.The choice for a circular geometry was based on limited data  estimated number of filaments? even a naïve integer multiplication of the signal would give a more realistic view of the network.We would like to point out that the microscopic resolution was insufficient to resolve filament thickness. Furthermore, as detailed in our answer to Reviewer 1, we did not find a stepwise distribution of filament brightness.3. This reviewer does not see any scientific incentive to including figure 3.We moved Figure 3 into the supplements.4. The curvatures of the segments are slightly different between the cell types studied. It is not clear what would that means or how would it influence the keratin network?As stated in the Discussion, we interpret differences in curvature as indicators of positive and negative tension on keratin filaments.5. The authors indicate the total length of the keratin bundle network in cells, but a reference number is missing. How is it in comparison to actin and microtubules?To our knowledge there are no solid data on the total length of actin filaments and microtubules in single cells.6. Are there any significant variations between the keratin network, e.g. density or segment directionality, at the vicinity of the nucleus in comparison to other cytoplasmic regions?We hereby repeat our response to Reviewer 1:Exploration of subdomains is afforded by the interactive 3D renderings provided at KerNet.rwth-aachen.de. However, systematic and quantitative analyses of segment properties in subcellular domains is an obvious but quite challenging issue. The main difficulty is a precise spatial definition of subdomains in 3D, which would require substantial effort.


Patients with chronic obstructive pulmonary disease (COPD) are often hospitalised with acute exacerbations (AECOPD) and many patients get readmitted. Intervening with hospitalised patients may be optimal timing to provide support. Our previous work demonstrated use of a digital monitoring and self-management support tool in the community. However, we wanted to explore the feasibility of recruiting patients whilst hospitalised for an AECOPD, and to identify the rate of dropout attrition around admission for AECOPD.Patients were recruited to the EDGE2 study between May 2019 and March 2020. Patients were identified by the clinical teams and patients were recruited by members of the clinical research team. Participants were aged 40 years or older, had a diagnosis of COPD and were attending or admitted to hospital for an AECOPD. Participants were given a tablet computer, Bluetooth-linked pulse oximeter and wrist-worn physical activity monitor to use until 6 months post-discharge. Use of the system aimed to support COPD self-management by enabling self-monitoring of vital signs, COPD symptoms, mood and physical activity, and access to multi-media educational resources.281 patients were identified and 126 approached. The main referral source was the specialist respiratory nursing and physiotherapist team (49.8% of patients identified). Twenty-six (37.1%) patients were recruited. As of 21 April 2020, 14 (53.8%) participants withdrew and 11  participants withdrew within four weeks of discharge. The remaining participants withdrew between one and three months follow-up  and between three and six months follow-up .A large number of patients were screened to recruit a relatively small sample and a high rate of dropout was observed. It does not appear feasible to recruit patients with COPD to digital interventional studies from the hospital setting when they have the burden of coping with acute illness. Prior to discharge, patients with COPD are administered a discharge care bundle as part of standard care . Integrating a digital intervention into this existing care pathway could be an effective approach to prevent readmission with implementation as part of standard clinical pathways.Patients with chronic obstructive pulmonary disease (COPD) are often hospitalised with acute exacerbations (AECOPD), presenting a mean age of 67 ± 8 years.7 Our evaluation reported low attrition (12.7%) during the 12-month study and 80% using the system daily.8 We have subsequently moved to identifying patients admitted with AECOPD to hospital to evaluate linkage of data from hospital and the support tool. This study summarises the feasibility of recruiting patients whilst hospitalised for an AECOPD (primary aim) and identifies the extent to which study participants withdraw (secondary aim).We have previously successfully evaluated a digital monitoring and self-management support tool with COPD patients in the community.The sElf-management anD support proGrammE (EDGE2) for COPD research study was approved by the London-Surrey Research Ethics Committee (ref 18/LO/1939) in December 2018 and prospectively registered (ISRCTN82570166). Data reported here relates to the 11-month recruitment period between May 2019 and March 2020. Participants provided written informed consent.Adults visiting or admitted to the John Radcliffe Hospital site of the Oxford University Hospitals NHS Foundation Trust (OUHT), were eligible if they were willing and able to give informed consent, aged 40 years or older, had a clinical diagnosis of COPD in their medical records, were hospitalised because of an exacerbation, were a current or ex-smoker, have a post-discharge destination that was not a medical facility or prison, lived in Oxfordshire or surrounding counties, were able to adequately understand verbal and written English, and were able to complete questionnaires and use a tablet computer. Patients were excluded if they had another substantial lung condition, had severe heart failure or had a life expectancy of less than six months. Clinical teams at the hospital were asked to identify potentially eligible patients whilst doing routine ward rounds. The clinical teams shared patients’ details with the clinical research team in the event the patient agreed they were happy to be approached about participating in research. The clinical research team then screened and enrolled patients.Participant details were recorded in a screening and enrolment log, detailing the date, referral source , whether they were subsequently approached and recruited, and reasons for not being approached, ineligibility and non-participation. Using the ICD-10 codes of J44.0, J44.1 and J44.9, the total number of patients at the hospital for an exacerbation during this period was extracted from routine electronic data recorded by the OUHT.Self-monitor vital signs COPD symptoms Mood Physical activity .Access multi-media educational resources via the tablet computer to support their COPD self-management, such as videos to demonstrate correct inhaler technique, pulmonary rehabilitation exercises and self-management techniques for breathlessness.Participants were given a tablet computer containing the EDGE mHealth application, a Bluetooth-linked pulse oximeter and wrist-worn physical activity monitor. The devices were given to the participants whilst in hospital and participants were encouraged to use them up until six months post-discharge. Participants were given a verbal description of the system and a brief demonstration. Participants were encouraged to use the system to:9The occurrence, timing and reasoning for participant withdrawals were recorded using REDCap electronic data capture tools hosted at the University of Oxford.In total, 281 patients were identified by the clinical teams and 126 patients were approached . The cliAs of 21 April 2020, eight participants completed the study , four participants are still taking part and 14 (53.8%) participants have withdrawn. Six participants withdrew because they did not want to use the EDGE2 support tool and six participants withdrew because they did not feel well enough or found that they had “too much else going on” and the support tool was not manageable in parallel. Two participants died whilst in hospital (during the index admission and a re-admission).The 14 participants who withdrew did so at varying points in the study. Eleven  participants withdrew before the one-month follow up, whilst one participant withdrew between one and three months follow-up  and two participants withdrew between three and six months after discharge .Recruiting patients hospitalised for an AECOPD to the EDGE2 study evaluating use of a digital health intervention does not appear to be feasible. A large number of patients had to be screened to recruit a relatively small number of participants. The main concerns regarding the recruitment of hospitalised patients related to the burden of illness and capacity to engage with self-care after discharge, with a high rate of attrition also recorded.10 Ineligibility reasons largely related to feeling too unwell (40%) and too severe comorbidities (36.4%). Common reasons for withdrawal related to feeling unwell and overwhelmed after experiencing an exacerbation . A trial of the MyCOPD app in patients recently hospitalised with COPD found that of the 124 potentially eligible patients, more than half declined study inclusion without giving a reason and recorded a 15% withdrawal rate.11 Another study enrolled patients with COPD whilst hospitalised but this time to a non-digital supported self-management programme (“SPACE For COPD).12 They revealed that 35.3% of approached patients declined participation in the study without reason, with others deciding not to participate due to comorbidities, mental health problems and being unable to read/see. In comparison to the present study, their withdrawal rate was much lower at 10.3%. A web-based version of that study (instead offering information on a tablet or PC) reported more than two thousand patients were screened to reach 100 participants (4.8% of patients screened). Predominate reasons for exclusion reported were not being web literate (69%), unwilling (15%) or had comorbidities precluding involvement (12%).13 Our study findings that recruiting patients with COPD during hospitalisation can be difficult appear to be supported by the literature.A comparable digital intervention study recruiting patients hospitalised with AECOPD in the UK asked participants to wear a lower-back belt sitting monitor for two weeks after discharge. They also experienced low patient uptake (15.6% versus 37.1%) and a high proportion of participant withdrawals (48.5% versus 53.8%).8 The intervention development process focused on the provision of a simple and intuitive application that could be used - and which was used - regardless of participants’ previous computer experience or self-reported digital literacy. No changes have been made to the intervention components or delivery between that proof-of-concept study and the current work. It is therefore reasonable to assume that attrition in the current study can be explained more by implementation processes than by limitations in the intervention itself. The distinction between the introduction of an intervention in the community versus introduction during a hospital admission is likely to be a critical factor in the implementation process. Timing of introducing interventions around hospitalisation is important in this vulnerable patient group. It is possible that interventions that focus on non-intrusive monitoring by health care staff may be acceptable. But where patients need to make measurements or engage in self-management, the acute hospital may be a suboptimal setting to start a digital intervention.Previous work in which the original EDGE system was evaluated with patients in the community demonstrated that patients were able to use the application, interpret clinical data, and use these within their self-management approach regardless of previous knowledge.Consideration needs to be given to the timing of introducing an intervention and balancing this against the burden of coping with acute illness. There is a risk that, if digital tools to support self-management were to be introduced into acute care settings, vulnerable patients could potentially be overburdened at a time when their illness was imposing great physical and psychosocial stress.


Understanding of climate “tipping points” is extremely limited. Their representation within Earth System models (ESMs) is completely unconstrained because of a lack of any actual occurrences of these events in recent times. As an example, most ESMs fail to simulate the rapid desertification of the Sahara during the Holocene. Here, we overcome this by tuning uncertain aspects of one ESM using the climate of 6,000 y ago. This approach reveals a hitherto-absent tipping point within this ESM that produces excellent agreement between the modeled and observed timing of abrupt change in North Africa. This demonstrates how paleoclimate information can be used to radically improve the representation of abrupt changes in the ESMs that are employed for future projections. While paleoclimate records show that the Earth System is characterized by several different tipping points, their representation within Earth System models (ESMs) remains poorly constrained. This is because historical observations do not encompass variations large enough to provoke such regime changes, and paleoclimate conditions are rarely used to help develop and tune ESMs, which potentially ignores a rich source of information on abrupt climate change. A critical example is the early to mid-Holocene “greening” and subsequent rapid desertification of the Sahara, which most ESMs fail to reproduce, casting doubt on the representation of land–atmosphere coupling and monsoon dynamics. Here, we show that this greening and abrupt termination can be successfully simulated with one ESM after optimizing uncertain model components using both present-day observations and crucially mid-Holocene  reconstructions. The optimized model displays abrupt threshold behavior, which shows excellent agreement with long paleoclimate records that were not used in the original optimization. These results suggest that in order to realistically capture climate-system thresholds, ESMs first need to be conditioned with appropriate paleoclimate information. The stability of Earth’s climate is critical for understanding past and future environments –5. The l4A prominent example of an abrupt climate change occurred in North Africa during the mid-Holocene. Multiple independent lines of evidence support a significant greening of the Sahara during the early to mid-Holocene. Fossil pollen records show a widespread northward expansion of shrub and savannah biomes –12. SediThe Holocene African humid period (AHP) came to an end around 4,000 y (4 ka) to 6 ka before present (BP) , 18. EarCharney et al.  first suA relatively abrupt reduction in vegetation cover and precipitation at around 5,500 y BP was simulated with the intermediate-complexity model CLIMBER-2  or only The reasons for the failure of ESMs to simulate the AHP remain unclear, and it is possible that new processes need to be considered –33. SomeHere, we analyze a suite of four transient simulations of the Holocene from 10 ka BP to present day using versions of HadCM3-M2.1 (the Hadley Centre Coupled Model Version 3 coupled to Version 2.1 of the Met Office Surface Exchange Scheme [MOSES]) –40. ThesSI Appendix, Figs. S1 and S2). This is driven by reductions in ice volume and increasing greenhouse-gas forcing, mostly due to rising CO2, as shown in SI Appendix, Fig. S2. Total radiative forcing increases by around 1.2 Wm– 2 over the course of the simulation. The gradual reduction in Northern Hemisphere summer insolation forces a reduction in the strength of the Northern Hemisphere monsoons .The four simulations all show a global mean warming of ∼1.2 K over the course of the Holocene  Phase II and is similar to other PMIP simulations  shows an out-of-phase response. It is unclear whether this could be due to chronological uncertainty, which is around 75 y for this part of record, with 14 C dates every 1 to 5 ka, or because of potential site-specific factors.The average precipitation reconstruction from the four cores agrees remarkably well with the +CONV+VMS simulation, both in terms of the amplitude and the timing of reduction around 6.1 ka BP. Three of the individual records shows excellent agreement with the +CONV+VMS model in the timing of the abrupt collapse at around 6 ka BP. The remaining core  increases between 9.5 and 6.5 ka BP in the +CONV+VMS simulation. The variance increase in +CONV+VMS is consistent with the abrupt behavior at around 7.5 and 6.5 ka BP and appears to signify bistability in the system. Conversely, the remaining three model configurations show constant or even decreasing variance . These diagnostic simulations show a strong dependence of the precipitation over North Africa  on the fraction of vegetation . The rate of precipitation increase per fractional vegetation cover is 276 mm/y for the 8 ka simulation and 217 mm/y at 6 ka after the vegetation has contracted. These values are consistent with a strong land–atmosphere coupling in this region   argued tHere, we show that a realistic and spontaneous collapse of the greening of the Sahara occurs only in the model version, which has been optimized with both present-day observations and mid-Holocene reconstructions. The successful replication of this event by the model demonstrates that coupled GCMs, like the one used here, probably resolve the required spectrum of biophysical processes. These include initiation and environmental mixing of convection, biophysical coupling to land surface, and coupling with the large-scale circulation. However, in the standard model version, these processes have potentially been misspecified, probably because present-day observations provide limited constraints for how these coupled processes operate during abrupt events .This example should motivate further work with different paleoclimate events covering a wider range of systems and feedbacks. It would be beneficial to apply these developments in other ESMs to test the universality of parameterizations and to see whether this could reduce intermodel uncertainty in future projections. Different statistical approaches to model tuning can also be beneficial. Future work may consider an explicit focus on transient climate changes and on model-emulation techniques. A key development would be to robustly integrate across a hierarchy of models, from idealized atmospheric dynamical schemes  to lowerThe absence of appreciable abrupt climate events during the instrumental era and probably several preceding centuries could be taken as evidence that such events were unlikely in the late Holocene preindustrial climate state. Future climate change will almost certainly increase the probability of encountering threshold and tipping points. Our results show the value of paleoclimate information and that present-day climate is a necessary, but insufficient, constraint on the behavior of ESMs, particularly for tipping events, where there are no parallels in the historical record. We have demonstrated that “paleoclimate tuning” of ESMs can radically improve their ability to simulate past abrupt transitions, potentially giving us more confidence in their performance for future projections.∘ (longitude–latitude) with 19 unequally spaced vertical levels. It uses a Eulerian hydrostatic dynamical core with schemes for convection, turbulence, gravity waves, large-scale clouds and precipitation, and cloud microphysics  conditions. The fourth version combines both the convection and moisture stress changes. These model versions are labeled STD, +CONV, +VMS, and +CONV+VMS, respectively, and are summarized in HadCM3-M2.1aD is used here in four configurations. In addition to the standard version (STD) evaluated before , we intr2, CH4, and N2 O from ice-cores adjusted to the Antarctic Ice Core Chronology 2012  for around 100 y. The impact on precipitation over Africa is short-lived, with an ∼60-y reduction in precipitation by around 1 mm×dWe reran 150-y segments every 1,000 y of the transient simulation (+CONV+VMS) to diagnose the role of vegetation change in the precipitation response. We deactivated dynamic vegetation in three sets of simulations. The first was initialized with the simulated vegetation coverage and the second and third with the observed present-day distribution. In the third, the soil albedo over the Sahara was set equal to the clear-sky surface albedo produced by the vegetation field in the first set. The difference between the first and second configuration quantifies the vegetation feedback to first order. The difference between the second and third configurations allows a separation of the vegetation feedback into components due to vegetation-induced changes in albedo and in land-surface moisture recycling.


Supplemental Digital Content is Available in the Text.The present results support the coexistence of altered body image (subjective without objective swelling) and pain, disability, and maladaptive beliefs in people with knee osteoarthritis. There are complex interactions between pain and perceptions of the painful body part in musculoskeletal disorders, and disruption of various body representations in people with chronic pain.The purpose of this study was to investigate how frequently people with knee osteoarthritis (OA) complain of swelling without objective evidence of swelling, and describe the clinical characteristics of this population.Forty-six people with knee OA (68.1 ± 8.8 years) participated in this cross-sectional study. Subjective and objective swelling was evaluated by knee-specific body perception questionnaire and ultrasonography, respectively. Pain intensity, disability, pain-related beliefs, 2-point discrimination threshold, and quadriceps muscle strength were also evaluated.Approximately 1/3 of participants (n = 15) had subjective feelings of knee swelling in the absence of objective swelling (S only). Fifteen participants had both subjective and objective knee swelling (S + O group) and 16 had neither subjective nor objective knee swelling (No S/O group). Participants in the S only group had similar pain or disability as those in the S + O group but had more severe pain or disability than those with in the No S/O group. Those in the S only group also had larger 2-point discrimination distance threshold at the medial knee (impaired tactile acuity) than those in the S + O group and had more dysfunctional pain catastrophizing and pain-related self-efficacy than both other groups.Our results suggest that about 30% of people with knee OA perceive swelling of the knee in the absence of any objective swelling and that this is accompanied by severe pain and functional disability. Considering altered body image of the knee may reveal relevant treatment-based subgroups in people with knee OA. We found that this item was significantly easier to endorse for people with knee OA than the similarly worded item was for people with low back pain,35 neck pain,63 or shoulder pain,38 which suggests this issue might be particularly relevant in knee OA.Preliminary evidence indicates that disrupted body perception might be a feature of painful knee osteoarthritis (OA). The Fremantle Knee Awareness Questionnaire (FreKAQ) was developed to measure knee-specific body perception in people with knee pain12 Currently it is unclear if the endorsement of this item reflects a truly enlarged (objectively swollen) knee or a knee that is simply perceived as enlarged. We were interested in exploring this concept, particularly the interaction between the perception of an enlarged knee and objective markers of swelling within the knee and its influence on clinical status. The aims of this study, therefore, were to investigate how common it is in people with knee OA to perceive their knee as enlarged without objective swelling being present. Furthermore, we aimed to describe the clinical characteristics of people who did and did not have perceived or objective knee swelling.One potential reason why this item is more readily endorsed in knee OA is that knee swelling is relatively common in this condition.Ethical approval was obtained from the Institutional Ethics Committee of Nishikawa Orthopaedic Hospital . Written informed consent was obtained from all participants before the study. The study was conducted in compliance with the Declaration of Helsinki.2 and aged between 40 and 85 years were considered for inclusion in this study. People were excluded if they had mechanical derangement of the knee , any neurological disorder affecting lower-limb function, significant cognitive impairment, neurological or orthopedic injury that might affect touch discrimination at the knee, significant uncorrectable visual impairment, concomitant diagnosis of fibromyalgia, a psychiatric disorder, or had undergone previous knee surgery such as arthroplasty, arthroscopy, or osteotomy. All participants underwent an x-ray examination and were examined and screened for eligibility by an orthopedist (K.N.), who also confirmed the presence of current knee joint pain.Participants were consecutively sampled from an orthopaedic outpatient clinic between February 2019 and April 2020. People with symptomatic knee OA, diagnosed according to the American College of Rheumatology clinical or radiographic classification criteria,20 All participants in the current study had a Kellgren–Lawrence (KL) score of II or more.The affected knee and unaffected knee were determined for each participant. In those with bilateral knee pain, the most painful knee was deemed the affected side. Where appropriate, we report data from both knees, but only data from the affected knee were used to compare between groups. Demographic data , severity of degenerative changes, and the presence or absence of regular nonsteroidal anti-inflammatory drug use were assessed in all participants. Severity of osteoarthritic changes in the knee was evaluated using the Kellgren and Lawrence (K/L) grade, which is a method of classifying the severity of knee OA using a 5 point scale, with 0 representing no features of knee OA and IV severe sclerosis and bone deformity.35 The FreKAQ has 5 response categories: “never,” “rarely,” “occasionally,” “often,” and “always.” We defined the patient as having subjective swelling if the patient answered “often” or “always” to this item.The degree of subjective swelling was assessed using item 7 of the FreKAQ (back translated from the Japanese “I feel like my knee is bigger [swollen]”).6 All ultrasound scans were evaluated by 2 independent examiners . The ultrasound scan assessed the degree of effusion in the suprapatellar bursa. The subjects were examined lying in the supine position with both knees semiflexed to 20° and their feet in the neutral position. The ultrasound scan image of the suprapatellar bursa was acquired by placing a linear probe longitudinally on the suprapatellar pouch. During examination, the transducers were placed as gently as possible, as pressure on the skin through the transducers can affect the acquired effusion area. The effusion area was calculated by tracing the margin of the echo-free space that corresponds with the suprapatellar pouch. Although the margins of these echo-free spaces were traced, the area (mm2) of suprapatellar effusion was calculated automatically  based on a previously published protocol.On the basis of the results of these 2 swelling measures, people were categorized into 3 groups: the subjective swelling only group (S only), the subjective and objective swelling group (S + O), and the no subjective or objective swelling group (No S/O). No participant who had objective evidence of knee swelling failed to perceive the knee as enlarged.35 The FreKAQ is composed of 9 items that relate to neglect-like symptoms, reduced proprioceptive acuity, and perceived body part shape and size. Higher scores on the FreKAQ indicate more disturbed body perception.An overall measure of self-reported body perception of the affected knee was obtained using the total score derived from all items of the FreKAQ questionnaire.Pain intensity at rest and during movement was measured for both knees using a 0 to 10 Numeric Rating Scale anchored at the left with “0 = no pain” and at the right with “10 = unbearable pain.” Pain with movement was evaluated in reference to the following question, “What is the intensity of your knee pain with movement?”53 The scale is scored from 0 to 48, with higher scores indicating better function.Disability was measured using the Japanese-validated version of the Oxford Knee Score for people with knee OA.43 Two-point discrimination was assessed using a digital Vernier caliper  and was defined as the smallest distance between caliper points at which the participant could clearly detect 2 points instead of one. Following the Moberg protocol, this measure was tested in the vertical direction on the medial  and lateral  aspect of both knees, using the tibiofemoral joint line as a reference point.50 Data from one descending run and one ascending run at each location were averaged to obtain the final value.27Two-point discrimination (TPD) thresholds were measured on both the medial and lateral sides of the knee. Tactile acuity, as measured by the TPD threshold, is considered a possible clinical signature of the primary sensory cortex representation of the tested area.3 Knee extension ROM was the angle of passive straightening of the knee while the patient was lying supine.59The same investigator (S.T.) measured the range of motion (ROM) of both knee joints with a standard goniometer. Knee flexion ROM was the value of active bending of the knee while the patient was lying supine.39 using a calibrated dynamometer . The thigh was fixed to the seat at the distal femur. The moment arm was attached to the tibia just above the malleoli. The knee and hip angles were fixed at 90°. Both legs were tested separately, and the trial order was randomized. The subjects performed as many maximal actions until the peak value no longer increased. The results were divided by body weight and expressed as N·m/kg. The examiner assessing TPD thresholds, ROM, and strength was blind to swelling classification.Maximal voluntary isometric knee extension strength was measured with the participant sitting,26 The scale comprises 13 items related to magnification, rumination, and helplessness about pain with higher scores indicating greater levels of pain-related catastrophization.51Pain-related catastrophizing was measured using the Japanese version of the Pain Catastrophizing Scale.1 Higher scores of the PSEQ indicate higher levels of confidence to use the knee despite pain.33The Japanese version of the Pain Self-Efficacy Questionnaire (PSEQ) was used to assess the confidence people with knee pain have in performing activities while in pain.19This study was considered a preliminary investigation, and no formal power calculation was performed. We planned to recruit between 12 and 15 participants per group based on the recommendation that preliminary studies for which little data exist to inform a formal sample size calculation should seek to recruit around 12 participants per group.2 .7 All P values were adjusted using the Benjamini–Hochberg procedure for multiple tests. False discovery rate–adjusted P values are reported.Statistical analysis was performed using SPSS statistics ver.26 . A one-way analysis of variance and Fisher exact test were performed to test the differences in the sample characteristic between the groups. Analysis of covariance was conducted to assess group differences in clinical symptoms, adjusting for age, sex, pain duration, body mass index, K-L grade, and nonsteroidal anti-inflammatory drug use or nonuse. The Bonferroni method was performed for post hoc tests. To help with interpretation of the results from this preliminary investigation, effect sizes were calculated based on ηSample characteristics are summarized in Table http://links.lww.com/PR9/A135) (unaffected side).Clinical symptoms are summarized in Table P < 0.05).As expected, the extent of the effusion area was significantly greater in the S + O group than in the S only group and the No S/O group .There was no significant difference in FreKAQ scores between the S only group and the S + O group, whereas scores in both these groups were significantly higher than the No S/O group .Both pain intensity at rest and pain intensity with movement were significantly higher in the S only group than in the No S/O group (P < 0.05).The Oxford Knee Score scores in the S only group and the S + O group were significantly lower (more disability) than those in the No S/O group . Two-point discrimination threshold at the medial aspect of the knee was significantly larger in the S only group than in the S + O group (P < 0.05), indicative of reduced tactile acuity in the S only group.No significant differences were found between the 3 groups for the TPD threshold at the lateral aspect of the knee (P = 0.06) or knee extension ROM (P = 0.76).No significant differences between the 3 groups were found for knee flexion (P < 0.05). There was significant difference between the S + O group and No S/O group (P < 0.05).Quadriceps muscle strength was lowest in the S only group and was significantly less than both the S + O group and the No S/O group . There was significant difference between the S + O group and No S/O group (P < 0.05).The Pain Catastrophizing Scale scores were highest  in the S only group and was significantly higher than both the S + O group and the No S/O group . There was no significant difference between the S + O group and No S/O group (P = 1.00).The PSEQ scores were lowest  in the S only group and were significantly lower than both the S + O group and the No S/O group  and have less confidence in using the knee and more maladaptive beliefs about the knee in pain.9 This is in close agreement with this study in which 33% of people had joint effusion. Some previous investigations have found a relationship between the presence of joint effusion and both pain32 and disability.46 This is consistent with the results reported here as we also found that people with objective signs of swelling (S + O group) reported higher levels of pain and disability than those with no effusion (No S/O group). However, there are data supporting a less clear relationship between joint effusion and pain,64 and it may be that the presence of a phenotype with high levels of pain, who only perceive the knee as swollen, contributes to this lack of a clear relationship. It is possible that a more in-depth evaluation of the relationship between objective and subjective swelling will help resolve these discrepancies in the literature.A multicenter prospective study investigated the presence of effusion in the suprapatellar area in 600 people with chronic, painful, and primary knee OA and showed that joint effusion was present in 30% of this population.41 and low back pain34 was associated with increased TPD values. Some authors have suggested that the TPD threshold might serve as a simple clinical signature of reorganization within the somatosensory cortex specific to the body part tested.30 We know of no direct evidence of cortical changes in people with knee OA, but it is plausible that disruption of the brain grounded representation of the knee might be present in those who report disrupted conscious representation of the knee. Previous data support that increased TPD thresholds seem to be specific to the painful area,62 and the failure to detect any differences on the lateral side of the knee might reflect the greater prevalence of medial knee pain in knee OA,61 or be a reflection of the small sample size. Indeed, past work in knee OA has shown TPD impairments both medial and lateral to the patella.50 The small sample size, along with an analysis that corrected for multiple comparisons, might also contribute to the lack of observed difference in TPD threshold between the subjective swelling only group and the no subjective/objective swelling group. Clearly, further research is required.The TPD threshold on the medial side of the knee was significantly larger for the subjective swelling only group than the group with both subjective and objective swelling. This finding is consistent with previous studies that showed that an expanded pictorial representation of the painful area in people with complex regional pain syndrome47 and knee joint effusion can influence knee mechanics and muscle activity during gait in knee OA.44 Hence, although there are clear mechanisms that might explain muscle weakness in the presence of an actual joint effusion, we found quadriceps weakness to be greatest in the subjective swelling only group. The reasons for this result can only be speculated based on the data available, but may be a protective response to a body part that does not feel right, a manifestation of sensory dysfunction and potential cortical reorganization or related to the more dysfunctional cognitive appraisal of the painful knee. A self-reinforcing interaction of these factors is also plausible.57Experimental studies have found that acute knee effusions induce quadriceps alpha motor neuron inhibition at the spinal cordOur results also showed that pain catastrophization and pain-related self-efficacy in the subjective swelling only group were significantly more impaired than both other groups. It is interesting that the confidence in the knee and appraisal of the threat to the knee in pain is greatest in those whose self-perception of the knee is most disrupted, ie, it feels swollen although it is not swollen. Cross-sectional studies such as this preclude suggesting any but the most speculative causal relationships; however, it is plausible that a knee that does not feel right is more likely to be appraised as untrustworthy and under threat. Longitudinal data that sequentially evaluates these constructs are required to further explore these issues.48 This may impact on the generalizability of these findings. Fourth, x-ray was used to estimate OA severity by KL grades; thus, our results our limited to pathology seen on x-ray. It is possible that more sensitive imaging methods (such as computed tomography or MRI) might detect further detail, although this comes with similar limitations given recent work showing that the prevalence of knee OA features on MRI is high even in uninjured, asymptomatic people.8 Fifth, the study was cross-sectional; so, any causal relationship between pain, disability, tactile acuity, muscle strength, maladaptive belief, and expanded body image is unknown and beyond the scope of this article. Hence, a longitudinal study is needed to explore causality and the temporal sequencing of these findings.There are a few limitations in this study. First, there are multiple methods available to evaluate perceived body size, such as questionnaires, template matching tasks in which people select from a range of images the one that best reflects their perception of their own body form, tasks in which participants are asked to complete drawings of how they perceive their body, or digital equivalents in which on-screen representations of the body are manipulated to match the perceived body size. However, there is still no golden standard method; thus, the criterion-related validity of the question used in this study is currently unknown. Second, the sample size is relatively small, which may have affected the results, as discussed above. Third, only 5 of the 46 participants in this study were male (11%), which does not reflect the typical gender balance in the knee OA population.17 or Örebro Musculoskeletal Pain Questionnaire.22 Here, we have provided preliminary evidence that a portion of people with knee OA experience perceptions of knee swelling in the absence of objective markers of swelling. Past work in therapeutic targeting of body perception has shown that visuotactile illusions applied using mediated reality can alter the perceived size of the knee in people with knee OA and that such perceptual shifts are analgesic.49 Intriguingly, these illusions can also alter objective knee swelling in some cases,24 making them an interesting method to explore whether the phenotypes identified here might have differing clinical responses to brain-targeted treatment. Furthermore, the group reporting only subjective feelings of swelling also had impaired tactile acuity, raising the possibility that treatment targeting impaired sensorimotor representation (such as touch discrimination training and implicit motor imagery training) may be most relevant in this group. Indeed, failure of such treatments to improve pain relief or knee function in people with knee OA in past work15 may well represent a mismatch between the patient  and the treatment. Further work to explore whether this group of people with knee OA who have perceptual impairments regarding the painful body part respond differently to treatment than those without perceptual dysfunction is warranted.There are potentially relevant clinical implications from our findings. In recent years, it has been suggested that improved clinical outcomes may occur if individualized treatment, based on relevant clinical phenotypes, is undertaken. For example, this has underscored recommendations in back pain care, including the use of the STarT Back Screening ToolOur results show that some people with knee pain experience subjective feelings of knee swelling without any evidence of objective swelling detected by ultrasonography and that this group has severe pain and functional disability. Furthermore, these people seem to have poorer tactile acuity, decreased muscle strength, and more dysfunctional beliefs about the knee in pain. Longitudinal data are needed to further understand how these factors interact. Specific exploration of altered perception of the knee might be useful in people with knee OA and targeting any maladaptive size perception may be a potential treatment target for this group.T. R. Stanton is supported by a National Health & Medical Research Council Career Development Fellowship (ID1141735). The remaining authors have no conflicts of interest to declare. The authors alone are responsible for the contents and writing up of our study.http://links.lww.com/PR9/A135.Supplemental digital content associated with this article can be found online at


Drosophila, Davis et al. identify novel factors that render dopaminergic neurons vulnerable to degeneration. As dopaminergic neurons are selectively vulnerable in Parkinson’s Disease, these results provide insight into the development of potential therapeutic targets to protect these vulnerable neuronal populations.Using  Drosophila melanogaster as part of the Drosophila Genetic Reference Panel. Here, we identify the top-associated genes containing single-nucleotide polymorphisms that render DA neurons vulnerable. These genes were further analyzed by using mutant analysis and tissue-specific knockdown for functional validation. We found that this loss of DA neurons caused progressive locomotor dysfunction in mutants and gene knockdown analysis. The identification of genes associated with the progressive loss of DA neurons should help to uncover factors that render these neurons vulnerable in PD, and possibly develop strategies to make these neurons more resilient.Parkinson’s disease (PD) is primarily characterized by the loss of dopaminergic (DA) neurons in the brain. However, little is known about why DA neurons are selectively vulnerable to PD. To identify genes that are associated with DA neuron loss, we screened through 201 wild-caught populations of  Parkinson’s disease (PD) is the second most prevalent neurodegenerative disease and the most prominent movement disorder globally . The mosDrosophila is a well-established model system to study neurodegenerative diseases such as PD  (Control vector for KK collection (ID: 60100) . Each group was allowed to acclimate to the glass vials for a total of 5 minutes. The climbing index for each group was defined as the percentage of flies that climbed to a 8 cm mark in the glass vials within 20 seconds of tapping the glass vial onto a mouse pad. Three trials were carried out for each group of flies. Between each trial the flies were allowed to recover for at least 1 minute. Average values for each measurement are listed in Supplementary Table S3.http://dgrp2.gnets.ncsu.edu/) . SNPs associated with the loss of DA neurons were identified using the Drosophila Genetics Reference Panel Freeze 2 GWAS webtool (t) values. We used Actin 5c as our internal control ((−ΔΔCT) method. Primer sequences are listed in Supplementary Table S4 along with the references/sources for each.RNA was isolated and qPCR was carried out as previously described  cluster . It is possible that the level of knockdown was insufficient to produce a measurable phenotype. To evaluate the strength of the knockdown, we performed qPCR to analyze the transcript levels for each gene upon expression of the RNAi transgenes. While transcript levels of both CG42339 and Megalin are noticeably reduced using RNAi, we did not observe a clear knockdown of Lim3 . We also determined that the UAS-RNAi transgenes themselves did not have an impact on neuronal loss . Thus, the lack of neuronal loss using RNAi for Lim3 could be due to the inefficiency of the transgene. For CG42339 and Megalin, it is possible that the drastic reduction in expression using RNAi is still insufficient to cause neuronal loss. Another possibility is that neither of these genes are required directly within DA neurons to maintain these cells.To functionally validate the genes identified in our screen, we knocked these genes down in DA neurons using RNAi. We found that when six of these genes are knocked down in DA neurons using a Tyrosine hydroxylase driver S-Dicer2 , there aS-Dicer2 , demonstS-Dicer2 . The tisTrf2. As combinations of fluorescent markers with this RNAi transgene were very unhealthy, we were unable to quantify PPL1 neurons in this manner. Thus, we cannot rule out the possibility that Trf2 knockdown leads to a specific decrease in TH staining without neuronal loss.To further test whether the reduced number of TH+ neurons is indeed a loss of neurons, we knocked down our candidate genes using RNAi while co-expressing the fluorescent marker mCherry. We observed a significant decrease in the number of mCherry+ PPL1 neurons under the same conditions using TH staining . These results suggest that knocking down candidate genes in DA neurons results in the loss of neurons rather than a specific reduction in TH staining. The one exception for this experiment was Although our screen was focused on the PPL1 cluster of DA neurons, we also examined whether other clusters of DA neurons were similarly vulnerable under the same conditions. To determine whether the variability of PPL1 neurons within the DGRP stocks would show a similar pattern in other DA neuron clusters, we reexamined brain samples from DGRP lines showing the highest as well the lowest average number of PPL1 neurons and measured the number of neurons within the protocerebral posterior medial 1 and 2 (PPM1/2) and protocerebral posterior medial 3 (PPM3) clusters at day 21. Interestingly, we found no significant difference in the amount of DA neurons between any of these DGRP lines for the PPM1/2 and PPM3 clusters . Thus, the variation in the number of PPL1 neurons from our screen does not appear to correlate with natural variation in other clusters. It is currently unclear whether examination of all DGRP stocks would reveal natural variation in the maintenance of DA neurons within these clusters.Sestrin . To further verify that the loss of PPM1/2 neurons is not due to a loss of TH staining, we used expression of UAS-mCherry with TH to drive each RNAi line and UAS-Dicer2. As with the PPL1 cluster, the loss of mCherry signal was consistent with the loss of TH staining in nearly every condition . The two exceptions were again Sestrin and Lim3. This could be explained by differences in the exact number of DA neurons labeled using a TH antibody in comparison to transgenic expression using TH-Gal4, as previously reported . Images show no loss of neurons on day 21 when compared to day 3 for all groups . We also found that when using mcherry with TH-Gal4 for all knockdown lines coupled with UAS-dicer2 still showed no loss of DA neurons in the PPM3 cluster at day 21 . These results suggest that PPM3 DA neurons are more resilient to the loss of these candidate genes when compared to the PPL1 and PPM1/2 clusters.Finally, we investigated DA neuronal maintenance in the PPM3 cluster in a similar manner to that of the other clusters. Interestingly, knockdown of the candidate genes did not result in any significant loss of DA neurons in the PPM3 cluster from day 3 and day 21 . RepreseTrf2, Tweek, and Tow were not homozygous viable in our experiments. For the remaining mutant stocks, we quantified DA neurons in each mutant compared to the genetic background. . Thus, these genes may play a role in varying biological mechanisms and may cause dysfunction upon other processes. A more comprehensive analysis of mutations in each of these genes may provide more detailed information regarding the role of these genes in DA neuron maintenance.In addition to the tissue-specific knockdown of our candidate genes using RNAi, we also examined DA neuron maintenance using publicly available mutant stocks of these genes. Mutants of the genes kground. . Among t did not , B–L. CGand Lim3 , M, R, Uand Lim3 , X–H’. TDrosophila melanogaster as a behavioral assay for neurodegeneration , has primarily been studied in the developing wing disc of Drosophila, where it acts as a repressor of the hedgehog signaling pathway (tow was shown to act downstream of both frizzled and disheveled in the planar cell polarity (PCP) signaling pathway , which encodes a core promoter recognition factor  . The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.None declared.