Article activity feed

  1. Author Response:

    Reviewer #2 (Public Review):

    This paper presents an intriguing pipeline that can be applied to understand and predict the mechanical and biophysical properties of intermediate filaments in a given cell type. The work is very well documented as a continuum from obtaining the imaging data to the analyses in Matlab and Fiji to the translation into virtual reality. The descriptions are concisely written so anyone can understand the essence of different parameters. The strategy forms a rather pioneering multi-dimensional visualization approach that revealed hallmark features of different keratin filaments networks in various cell types.

    • There is a good selection of cell lines to accommodate the varied presentations of keratin filaments in cell lines with different properties. The morphological representations of the figures in 3D very well illustrate the nature and organization of the cells in vitro and in vivo which is further examined in the measurements of different parameters such as curvature and orientation. • This pipeline introduces a fresh strategy to analyze, compare and interpret network organization of cellular filaments. The comparison of the filament orientation between MDCK and HaCaT B9 cells was intriguing, as it highlights the nature of their arrangement in in vitro monolayers and draws a parallel between how cell shape influences network arrangement aside from the assumed polarity. • It would be interesting to compare how these parameters differ in MDCK cells with cuboid or cylindrical geometries.

    We agree with the Reviewer that this would be an interesting property to analyse in the future, since we are especially interested in the biomechanical function of the cortical keratin cytoskeleton and its contribution to cell shape changes.

    • With regards to segmentation of images, there seems to be a difficulty in segmentation of denser areas and some dim segments in light to medium intensity areas as noticeable in Fig .1. Any remedy for this?

    We concur with the Reviewer that segmentation is limited by the microscopic resolution in xy and especially in z. Improvement is expected by increasing the microscopic resolution and by further improvement of segmentation algorithms using, e.g. machine learning.

    • It would be informative if an expert panel would manually segment some images to compare with automatically segmented ones so that a false positive/negative ratio could be established.

    We are aware that manual segmentation has the reputation of being the gold standard but question that even experts would fully agree on a ground truth. The technical difficulties of segmentation and annotation of 3D data as well as the human bias make this approach quite challenging. By making the data sets publicly available, specific questions, however, may now be addressed by interested individuals.

    • In the transformation of 3D fluorescence recordings of keratin filaments into digital networks, other than whole-cell networks, it will be interesting to show a few examples of keratin structures at representative subcellular domains, such as the nucleus.

    • The authors pointed out that in MDCK cells, the basal domain has thicker bundles compared to the apical domain, while the lateral keratin network is more heterogeneous. Is it possible to statistically present this feature of keratin filaments? And what would be the case in HaCaT and REP cells?

    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.

    Was this evaluation helpful?
  2. Evaluation Summary:

    Windoffer et al. developed an image processing platform to quantify the 3D network of keratin filaments. The concept of this approach is based on 3D visualization of fluorescently labeled proteins using confocal scanning microscopy. The major advantage of this approach is that after initial segmentation of the network, filaments are divided into pieces for detailed analyses in silico. This approach allows for quantification of the segmented polymer and compute some of the network properties of the keratin filaments in cells, in cultured cells ex vivo and specific cell types in situ. Additionally, this approach allows nice visualization of the keratin network in 3D. The resulting contribution is original, provides insight at both a methodological and biological levels, and extends emerging information about the high resolution structure of intermediate filaments in situ using cryoelectron tomography.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors. Reviewer #1 and Reviewer #3 opted to remain anonymous to the authors.)

    Was this evaluation helpful?
  3. Reviewer #1 (Public Review):

    The authors adapted and developed tools for the three-dimensional visualization and systematic analysis of the entire keratin filament network in three different types of cells. The resulting contribution is highly original, provides insight at both a methodological and biological levels, and nicely complements and extends emerging information about the high resolution structure of intermediate filaments in situ (by cryoelectron tomography). The manuscript is well-written, well-illustrated, and the authors are thorough in their recognition of previous studies of relevance to their own. 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.

    Was this evaluation helpful?
  4. Reviewer #2 (Public Review):

    This paper presents an intriguing pipeline that can be applied to understand and predict the mechanical and biophysical properties of intermediate filaments in a given cell type. The work is very well documented as a continuum from obtaining the imaging data to the analyses in Matlab and Fiji to the translation into virtual reality. The descriptions are concisely written so anyone can understand the essence of different parameters. The strategy forms a rather pioneering multi-dimensional visualization approach that revealed hallmark features of different keratin filaments networks in various cell types.

    • There is a good selection of cell lines to accommodate the varied presentations of keratin filaments in cell lines with different properties. The morphological representations of the figures in 3D very well illustrate the nature and organization of the cells in vitro and in vivo which is further examined in the measurements of different parameters such as curvature and orientation.
    • This pipeline introduces a fresh strategy to analyze, compare and interpret network organization of cellular filaments. The comparison of the filament orientation between MDCK and HaCaT B9 cells was intriguing, as it highlights the nature of their arrangement in in vitro monolayers and draws a parallel between how cell shape influences network arrangement aside from the assumed polarity.
    • It would be interesting to compare how these parameters differ in MDCK cells with cuboid or cylindrical geometries.
    • With regards to segmentation of images, there seems to be a difficulty in segmentation of denser areas and some dim segments in light to medium intensity areas as noticeable in Fig .1. Any remedy for this?
    • It would be informative if an expert panel would manually segment some images to compare with automatically segmented ones so that a false positive/negative ratio could be established.
    • In the transformation of 3D fluorescence recordings of keratin filaments into digital networks, other than whole-cell networks, it will be interesting to show a few examples of keratin structures at representative subcellular domains, such as the nucleus.
    • The authors pointed out that in MDCK cells, the basal domain has thicker bundles compared to the apical domain, while the lateral keratin network is more heterogeneous. Is it possible to statistically present this feature of keratin filaments? And what would be the case in HaCaT and REP cells?

    Was this evaluation helpful?
  5. Reviewer #3 (Public Review):

    The authors developed an image processing platform to quantify the 3D network of keratin filaments. The concept of this approach is based on 3D visualization of fluorescent labeled proteins using confocal scanning microscopy. The major advantage of this approach is that after the initial segmentation of the network, the filaments are divided into pieces (in silico). This approach allows for quantify the segmented and compute some of the network properties of the keratin networks in cells. Additionally, this approach allows nice visualization of the keratin network in 3D.

    I find this development interesting and believe that substantial characterization of the keratin network would be conducted, in particular during physiologically important cellular processes.

    Was this evaluation helpful?