High-Resolution Imaging Methods for Identification of Calcium Crystal Types in Osteoarthritis

Round 1

Reviewer 1 Report

This is an important manuscript that warrants publication. It deals with a critical issue in the field of crystal arthritis, which is accurate identification of calcium crystals.  

I have several minor issues with the current version

1) please place some arrows over the crystal deposits in figure 7A and 8A.

2) ANK is not ankryn.  This acronym stands for progressive ankylosis protein in mice and the acronym used for humans should be ANKH which is progressive ankylosis protein human homolog.   Please fix this on line 447.

3) In line 448, there is also a mistake. Recent evidence suggests that ANKH transports  or regulates transport of ATP, not PPi.   Once ATP is in the extracellular space, it is degraded by enzymes which convert it to AMP and PPi. 

Author Response

We would like to thank the reviewer for the comment and will answer on a point to point basis.

1) please place some arrows over the crystal deposits in figure 7A and 8A.

We have indicated with white squares which region of the picture A was amplified in picture B, as well as in picture B for picture C.

2) ANK is not ankryn.  This acronym stands for progressive ankylosis protein in mice and the acronym used for humans should be ANKH which is progressive ankylosis protein human homolog.   Please fix this on line 447.

We thank the reviewer for bringing this mistake to our attention. We have corrected the acronym.

3) In line 448, there is also a mistake. Recent evidence suggests that ANKH transports or regulates transport of ATP, not PPi.   Once ATP is in the extracellular space, it is degraded by enzymes which convert it to AMP and PPi.

Again, we thank the reviewer for this remark. We have corrected the text as follows:….” In addition, transmembrane proteins such as progressive ankylosis protein human homolog (ANK) regulate extracellular trafficking of PPi and ATP, where it is de-graded to AMP and PPi. Therefore, increased ANK expression in chondrocytes increases extracellular PPi, excess PPi eventually leads to CPP cal-cification (Johnson & Terkeltaub, 2004).”

Reviewer 2 Report

Comments for author File: Comments.pdf

Author Response

Materials and methods

Sample preparation

The authors use standard microscope slides as a support for samples. The drawback of these slides is that they generate a huge background on Raman spectra. Would it be possible to use another microscope slides such as CaF2 or Quartz ? why the use of the Quartz or CaF2 is not discussed ?

 

We chose glass because the paper was aimed at people with less microscopic experience and therefore needed to be as methodologically low-threshold as possible. However, it is correct that the legitimate alternatives CaF2 and quartz should also be discussed, which is why we have added the following corresponding paragraph.

 

[page 3] Consecutive sections of 4 µm thickness were cut on a microtome (Hyrax M55, Zeiss) and mounted on glass slides (Epredia™). Microscope slides made from calcium fluoride or quartz are suitable alternatives as they account for significantly less background on Raman spectra due to lower autofluorescence. The comparatively high autofluorescence of glass is rarely relevant in practice if the sample thickness is sufficient. Sections were deparaffinised using xylene (9713.5 Carl Roth, Karlsruhe, Germany) and rehydrated using a degrading ethanol series. Sections were then washed with aqua dest. before staining, Raman spectroscopy or SEM imaging, respectively.

 

 

Raman microscopy

It is written that the lateral resolution is 0.5 µm with a laser at 785 nm and an objective x10. I’m surprised with the lateral resolution value of 0.5 µm with an objective of x10. There is an equation with allow to calculate the theoretical resolution with numerical aperture and the wavelength. Could the author calculate this value and put it in the manuscript ?

Regarding the stated lateral resolution we used the one specified by Bruker, which is < 0,5 µm. The equation that you asked for probably is the Airy formula (d=(0.61*wavelength)/NA ). Putting our numbers (NA=0.25 with 785nm laser) results in an actual resolution of 1.915 µm, which is admittedly significantly larger. We assume that Bruker speaks of a best-case scenario with their specification, leading to this incorrectness. In any case, we have corrected the passage and thank you for the useful critique..

[page 4] Using a red excitation laser with a wavelength of 785 nm and a 10x, 0.25 objective, the focal spot had an approximate lateral dimension of 1.915 µm according to the Airy formula (Everall, 2010). First, a single point within the ROI, which was selected using the corresponding Von Kossa staining of serial sections, was measured 5 times with an exposure time of 2 s each in order to generate a good signal to noise ratio and exclude possible outliers, e.g. cosmic rays from the final spectrum.

 

 

The term Region of Interest is employed but it is not clear what is designating. Do the authors designate a single spot or an area of the sample ? Please clarify the sentence.

We have included the information that we investigated an area, and we also specified how we defined the region of interest (ROI).

 

Results

There is a typo error in the item “Sample Preparation”, first bullet point “Tissue disseection” should be “Tissue dissection”.

We have corrected the typo.

 

The sample preparation protocol should be in the material and method section and not in the result section.

We have moved the sample preparation protocol to the methods section.

 

Figure 6, 7 and 8: the scale bar is missing

Figure 6, 7 and 8 were augmented with corresponding scale bars and

 

On Figure 12, the red spot should be highlighted with an arrow or a circle

Figure 12 was made easier to read using the proposed arrow.

 

Regarding the paragraph Manual correction and the cosmic ray, I would moderate this point (lines 359 – 374). When possible, the user should set the number of accumulations per spectrum to 2. I know that it multiply by a factor 2 the acquisition time, but it allows to remove automatically the cosmic ray and the it smooth the signal. This is done without the use of the software. Even if the software is efficient, it remains a software which can do mistakes by confusing Raman band and cosmic ray.

 

Following your very appreciated recommendations, we have also made the paragraph on cosmic rays and manual correction more open. As an alternative to manual correction, the possibility of taking multiple measurements is now briefly discussed.

         

[page 19] In the given figure, however, they appear at around 960 〖cm〗^(-1) and, therefore, distort the integration algorithm for crystal identification, which is why they need to be removed.

Another option to prevent the distortion of the spectra or mappings by cosmic rays would be to increase the number of measurements per point to at least N = 2. This multiplies the measuring time by the factor N, but also smooths out possibly occurring cosmic ray distortion as the mapping is generated based on the average of all spectra measured on one spot. At the expense of a significantly longer measurement time, it is thus possible to eliminate the distorting influence of cosmic rays without having to accept potential errors on the software or user side. Consequently, this method is recommended for very inexperienced users.

 

Line 378 – 386. The intensity varies because the sample is not flat, but not only. The samples analysed are biological samples which are highly heterogeneous. The variation of intensity are also linked to the heterogeneity of the composition of the sample. I would moderate the sentence for the use of the function normalize. The normalization procedure will give more importance to small bands and less to high bands. I’d rather advise the reader that the normalization procedure could improve the signal in the aim of the characterization of crystal. But the normalization should be used when it is necessary.

 

The section on varying Raman signal intensities, their causes and the possible correction using the normalize function has also been revised based on your feedback. Thank you very much for the helpful suggestions.

 

Another common issue in analyzing these data is the diverging intensity of individual spectra. This can be caused, for example, by specimens that are not completely flat, resulting in varying distances of the tissue sample to the detector i.e. a higher measured intensity. The naturally very heterogeneous composition of the biological samples examined is another reason for varying signal intensities. This problem can be identified by displacement of the graphs in y-direction and may lead to distortion of the mapping. However, this matter can also be solved using a specific software. The software Opus for instance has a “normalize” function, which puts all graphs into the same y-range without changing the course of the curves, solving the described problem. For the presented method, the Min-Max-normalization works very reliably. In general, normalization functions tend to reduce higher bands more than lower bands. However, this is not relevant for the use case presented here, since the relevant bands are in a very similar range. Nevertheless, this could lead to unintended changes in other contexts. Accordingly, the use of the normalization function should be re-evaluated for each case

 

Concerning the normalization procedure, the author should specify which type of normalization. It is the normalization based on the maximum height or is it the normalization of the area under the curve ? I’m surprised that the baseline correction is not presented in this comparison. Why the authors didn’t discuss about the baseline correction, which is a common procedure with Raman spectra.

 

We deliberately decided not to use baseline correction as it’s potential for error and complicity outweigh the positive benefit, which is negligible for mapping anyhow. However, the editors are right that this essential method should at least be discussed in such publication. Therefore, we dedicate one paragraph to the discussion of advantages and disadvantages.

 

[page 20] Baseline correction was not used in the present method for several reasons. First, the quality of individual spectra in a mapping experiment is not critical, as the focus is on generating spatially resolved chemical information rather than on the spectral quality of each pixel. Second, the method was designed to be beginner-friendly and as objective as possible, and the addition of a subjective step like baseline correction could potentially introduce variability and bias into the analysis. Finally, the biological samples used in this study were highly heterogeneous, making it difficult to apply a uniform baseline correction across all spectra. While baseline correction can be a useful tool for improving the quality of Raman spectra, the potential risks of introducing bias or distortion into the data outweighed the potential benefits in the present method. Instead, other preprocessing steps like normalization or smoothing were used to improve the signal-to-noise ratio of the spectra.