Characterization of the neurogenic niche in the aging dentate gyrus using iterative immunofluorescence imaging

Article activity feed

1. 

   Version published to 10.7554/elife.68000 on eLife

   Jan 28, 2022
2. 

   eLife

   Sep 9, 2021

   Author Response:

   Reviewer #2 (Public Review):

   Methods to characterize cell types in intact tissue using large scale analysis of molecular expression profiles are now readily available, with the best example being in situ RNA sequencing (spatial transcriptomics). However, these methods depend on separate immunohistochemical investigations to define the precise cellular and subcellular distribution of the protein products. Cole et al use iterative indirect immunofluorescence imaging (4i, Gut et al Science 2018) to compare the immunoreactivity of an impressive 18 different molecules within the same brain sections containing the dentate gyrus from young and old mice. First, they demonstrate that the method can be applied to not only adult mouse brain tissue, but also to human embryonic stem cell derived organoids and mouse embryonic …

   Author Response:

   Reviewer #2 (Public Review):

   Methods to characterize cell types in intact tissue using large scale analysis of molecular expression profiles are now readily available, with the best example being in situ RNA sequencing (spatial transcriptomics). However, these methods depend on separate immunohistochemical investigations to define the precise cellular and subcellular distribution of the protein products. Cole et al use iterative indirect immunofluorescence imaging (4i, Gut et al Science 2018) to compare the immunoreactivity of an impressive 18 different molecules within the same brain sections containing the dentate gyrus from young and old mice. First, they demonstrate that the method can be applied to not only adult mouse brain tissue, but also to human embryonic stem cell derived organoids and mouse embryonic tissue, which is an advance on the original report (Gut et al 2018). This demonstration is particularly important as it shows the potential for applying 4i to different biological disciplines. The rest of the manuscript focuses on the mouse dentate gyrus (DG) at 2, 6 and 12 months of age in order to map the complex changes and associations in the tissue across age. Various combinations of the 18 molecules are used to define different cell types and it incredibly informative to be able to view so many molecules in exactly the same area and will advance the field. This is the greatest strength of the manuscript. They find that neurogenic, radial glia-like stem cells (R cells) and proliferating cells are reduced in aged animals, as are immature (DCX+) cells, but claim that fluorescence intensity increases for the remaining R cells in 12 month old mice. They report that the density of vasculature also decreased with age, as did the associated pericytes, but astrocytes associated with the blood vessels increased. The last part of the manuscript defines 'microniches' (random or targeted regions of interest within the DG) and attempts to show how cell types, especially Nestin+ R cells, change in their associations with vasculature within these sub-regions at 2, 6 and 12 months of age. It is a commendable approach and the authors use a variety of statistical tests to compare the different cell types. However, there are several parts of the methods, along with insufficient details of the results that prevent full interpretation of the data, meaning that it is difficult to determine whether all conclusions are supported.

   1. There are many factors that can affect the measurements of immunoreactive structures (Fritschy, Eur J Neurosci, 2008 vol 28, p. 2365-70). The main limitation is not providing sufficient detail for the immunolabelling design and imaging parameters but providing some unclear details for the imaging analysis (below).

   We understand the reviewer’s concerns (outlined below) and tried to carefully address all raised points.

   a. In terms of immunohistochemistry, with the impressive number of tested antibodies, there is potential for variation due to antibody antibody penetration, unreported combinations of secondary antibodies, tissue quality (variations in fixation), etc. It is difficult to have confidence in the conclusions based on a total of 3 mice per age group for a single 40 um section per mouse. Ideally, to increase confidence in individual section variability, it is recommended that measurements should be taken from at least 3 sections per mouse then averaged, before averaging for the age group.

   We have now added additional experiments testing the elution properties of used antibodies (please refer also to point 4 of Rev#1). We have also tested the properties of secondary antibodies in terms of elution properties (now included in revised extended data Figure 1). Indeed, all analyses were done in 6 dentate gyri per mouse with the exception of quantifications shown in Figure 3B, C. Following the reviewer’s advice we have now expanded the analyses and include data from 3 sections of the DG per mouse per age group (please refer to revised Figure 3 and modified Supplemental Table 1).

   b. Assuming there were 3 primary antibodies with 3 secondary antibodies per cycle before elution, were the combinations used consistent for all brain sections and mice? Was the testing and elution order the same (i.e. systematic)? There is a risk of cross-excitation and mis-interpretation of true immunoreactivity if spectrally close fluorophores for the secondary antibodies were selected for primary antibodies that recognize spatially overlapping structures. Can the authors show the cycle number and fluorophore for the examples in figures 1 and 2 to determine which markers were imaged together in the same cycle? This would give confidence to the methods for colocalisation and cell type descriptions. For example, can cross-excitation be ruled out for some of the signals in the images used in Fig 2 (duplicated in Fig 4) such as intensely immunopositive Laminin-B1 cells in the MT3 and Sox2 channels (2A) and Ki167, SOX2 and phospho-histone 3 channels (2C)?

   We understand the reviewer’s point and have now added cycle combinations on page 20 of the manuscript (as we had done previously for Figure 1D). Given the fluorophores used and the setting of the laser scanning microscope (the description of which we have now expanded) there is basically no or extremely little chance of cross-excitation/detection. For the individual cells pointed out, cross-excitation is not possible because LaminB1, MT3, SOX2, KI67, and phospho-histone 3 were stained in separate cycles and therefore had no fluorescent labeling at the time each were imaged respectively. Figure 2C: indeed, that is a biological overlap as this SOX2-labled cell is in mitosis (Ki67 and phospho-H3 positive). The cycle order is now also provided in the revised Figure 2 supplement 1.

   c. For image acquisition, details are required on the resolution (numerical aperture of the lenses) in order to interpret colocalisation measurements in the later figures. Which beamsplitters/filters were used, and was the same laser power used for the same markers over different specimens (important for interpreting figure 4 data)?

   We have included that information in the revised manuscript. Please refer to page 20-21 of the revised manuscript. For Figure 4 data: we have added new analyses of proteins where expression levels do not change with advancing age (please also refer to point 2 of Rev#1).

   d. For the analysis of ROIs (figures 3-6), were the 20x or 40x images used?

   We used 20x images for analysis shown in Figure 2. This has been clarified. Please refer to page 20 of the revised manuscript.

   e. Details of the antibody specificity controls should be provided.

   All antibodies used are standard in the field and have been used in dozens of studies. None of the presented stainings is “novel” per se. The iterative approach is novel. This has been clarified. All antibody information is also available via the RRID that we provided.

   1. Numerous markers have been used to define different cells, but the proportions are not reported. For example, R cells are defined differently in figures 3 and 4. How many types of R cells (based on combinations of markers) were observed? High resolution examples of each defined cell type (neuronal and glial) would assist the reader in the confidence of the measurements (ideally as single channels side by side, with arrows indicating areas of detectable immunoreactivity that the authors would use to define each cell).

   All R cells were identified using the criteria outlined on pages 6/7 in the main text. The regions of interest created during the quantification of cell density in Figure 3 were used to measure the fluorescent intensities of HOPX, MT3, and LaminB1 in R cells. (see page 22 of the manuscript). We have added further clarification of this in the main text on page 8.

   “We next used 4i to analyze expression levels of selected proteins in the same R cells identified in the quantification of cell density.”

   1. The authors use HOPX and GFAP immunoreactivity and a lack of detectable S100beta immunoreactivity to distinguish R cells from triple immunopositive mature astrocytes. In Figure 3, the images are too low power to be able to confirm this. This part would benefit from some single cell examples showing the separate channels.

   We have added now high-magnification images in the revised extended Figure 3 to show the S100beta-negativity of R cells.

   a. Furthermore, the results (paragraph 2, page 7) report changes in cell number, but rather density is reported. Please either state the numbers or refer to density.

   This has been corrected.

   b. Related to Fig 3, there are no details of the number of R cells counted in supplementary table 1. How were the density measurements obtained? How thick were the image stacks and how many R cells per section? Similarly, as stated in methods, for glial cells, 100 cells were randomly counted in each section (presumably the same count for each age), so how was it reported that specifically the numbers of astrocytes were reduced and no significant differences in other glial cell types? (bottom of p.7)

   We have clarified how cellular densities were calculated on page 23. For density measurements, all immune-positive cells in each section were counted. The subset of 100 cells were only used for analysis of LaminB1 fluorescence intensity. All cells were counted throughout the entire images using the Cell Counter plugin in Fiji using localization identifiers for the ML, hilus/CA3, and the supra and infrapyramidal blades of the GCL. The areas of the hippocampal subregions were measured. Cell density was calculated by dividing the number of cells by the regional volume expressed as mm3 (region area[mm2 ] x tissue thickness [0.04mm]).

   1. An increase in fluorescence intensity for HOPX and MT3 (also marks R cells) was observed with age (Fig 4), with methods stating that the 5 ROIs used to calculate the background intensity were measured at each [optical?] slice for where the cells were measured, to account for unequal antibody penetrance. Several clarifications are required in order to interpret these results: For the example HOPX images in Fig 4A, for the 2 month old mouse, the background is low, whereas for 12 months, the background is far higher, meaning different background ROI values. Can this difference be explained by differences in laser power, contrast adjustments, optical slice thickness, or whether these are maximum intensity projections of different z thickness? These values must be reported, and for each image presented in the manuscript, details must be included as to what type of image (z-projection or single optical slice, z thickness). Was the optical section(s) of the 12 month mouse imaged closer to the surface of the section for this example in Fig 4A? Were cells sampled at all depths of the imaged volume? Did the antibody show better penetration in the 12 month old mice than the 2 month old mice? How many optical slices would a cell soma cover? In these cases, how was the fluorescence intensity measured? If a soma covered several optical slices, which one was selected for the ROI measurement?

   It is common to have higher background in immunofluorescence in tissues from older mice. All images for each individual stain were acquired in a single continuous imaging session using identical microscope settings as we have now clarified on page 20.

   “Within each cycle, all samples were labelled with the same antibodies, and imaged with identical microscopy settings for laser power, gain, digital offset, pinhole diameter, and z-step.”

   The example images in Figure 4A are maximum intensity projections including all frames containing positive immunoreactivity spanning the entire thickness of the tissue (62 frames for 64 frames for 12 months). There was no obvious difference in antibody penetration between ages and cells were sampled throughout the entire thickness of the tissue. We have now included clarification on where we acquired measures for fluorescent intensity on page 23

   “Fluorescent intensity was measured in the z-position in which it was brightest for each cell.”

   1. The described methods for studying cellular interactions are not clear, making it difficult to interpret the associations between vasculature, cell types, and age. How was colocalisation defined, and at what resolution? For example, it is expected that GFAP would be associated with but not directly colocalized with collagen IV (Fig 5). In these cases, the manuscript would benefit from high resolution examples of this colocalization/interaction. How many ROIs were taken, how exactly were the ROIs for cell types associated with collagen IV selected, was this in 2D or 3D?

   We understand that concern and have toned down the interpretation of our findings regarding “interaction” and now rather refer to “proximity” which is indeed much more correct (true interaction would require methods going beyond light microscopy). Please also refer to point 7 of Rev#1.

   1. The methods for random microniches are difficult to follow, as are the methods for investigating the associations of other markers to radial processes of R cells. Please provide a definition of a 'spot'. Again, details of the micron per pixel resolution and optical slice thickness would help in the interpretation of results. Additionally, if possible, illustrated examples of the full procedure for niche mapping should be provided in order to follow how the measurements were collected.

   We have tried to clarify the data acquisition and analyses of the microniches and modified explanation (see page 10 in the main text)

   “We speculated that within the aging DG neurogenic niche, micro-environments may exist possessing distinct capacities for preservation of neurogenic processes. Spots were randomly distributed across the GCL spaced 50µm apart. Utilizing the multidimensionality of the dataset acquired with tissue 4i, volumes of 11 cell markers were measured within a 50µm radius of each spot to achieve contiguous sampling of “microniches” in the GCL and bordering areas of the hilus and molecular layer (Figure 6A).”

   We have also added the required information to the revised Methods section regarding pixel resolution and optical slice thickness (please refer to page 20).

   Read the original source
3. 

   eLife

   May 14, 2021

   Reviewer #3 (Public Review):

   Cole and co-authors report the development of a novel immunofluorescence technique, where targets of interest are analysed over iterative cycles of staining-imaging-elution(stripping). This method allows for the multiplexed analysis of protein targets, well beyond the usual constraints of such technique (limited by availability of filters and non-overlapping wavelengths of fluorophores). The authors also present several applications of such technique, highlighting how the advantage of being able to record additional parameters (such as cell morphology) can be an advantage over more high-throughput methods such as spatial-resolved transcriptomics.

   The technique has been carefully tested. Staining for the same markers after several rounds of stripping/reprobing shows high concordance, indicating that the …

   Reviewer #3 (Public Review):

   Cole and co-authors report the development of a novel immunofluorescence technique, where targets of interest are analysed over iterative cycles of staining-imaging-elution(stripping). This method allows for the multiplexed analysis of protein targets, well beyond the usual constraints of such technique (limited by availability of filters and non-overlapping wavelengths of fluorophores). The authors also present several applications of such technique, highlighting how the advantage of being able to record additional parameters (such as cell morphology) can be an advantage over more high-throughput methods such as spatial-resolved transcriptomics.

   The technique has been carefully tested. Staining for the same markers after several rounds of stripping/reprobing shows high concordance, indicating that the iterative treatment and staining of the same tissue section is not altering the detection of protein markers.

   The authors tested staining with a total of 18 antibodies, and suggest that this number can be increased arbitrarily, as the number of iterations is not limited. Further, they suggest that this technique can be applied to virtually any tissue. It is quite possible that this technique can be readily applied to any other tissue, as the only constraint seem to be the robustness of antibodies. The authors may include the suggestion that previous success of immunofluorescence on a particular tissue type could be a good indication for the success of the iterative staining.

   The proposed 4i method is quite interesting, has great potential and is likely to be of very wide interest.

   Read the original source
4. 

   eLife

   May 14, 2021

   Reviewer #2 (Public Review):

   Methods to characterize cell types in intact tissue using large scale analysis of molecular expression profiles are now readily available, with the best example being in situ RNA sequencing (spatial transcriptomics). However, these methods depend on separate immunohistochemical investigations to define the precise cellular and subcellular distribution of the protein products. Cole et al use iterative indirect immunofluorescence imaging (4i, Gut et al Science 2018) to compare the immunoreactivity of an impressive 18 different molecules within the same brain sections containing the dentate gyrus from young and old mice. First, they demonstrate that the method can be applied to not only adult mouse brain tissue, but also to human embryonic stem cell derived organoids and mouse embryonic tissue, which is an …

   Reviewer #2 (Public Review):

   Methods to characterize cell types in intact tissue using large scale analysis of molecular expression profiles are now readily available, with the best example being in situ RNA sequencing (spatial transcriptomics). However, these methods depend on separate immunohistochemical investigations to define the precise cellular and subcellular distribution of the protein products. Cole et al use iterative indirect immunofluorescence imaging (4i, Gut et al Science 2018) to compare the immunoreactivity of an impressive 18 different molecules within the same brain sections containing the dentate gyrus from young and old mice. First, they demonstrate that the method can be applied to not only adult mouse brain tissue, but also to human embryonic stem cell derived organoids and mouse embryonic tissue, which is an advance on the original report (Gut et al 2018). This demonstration is particularly important as it shows the potential for applying 4i to different biological disciplines. The rest of the manuscript focuses on the mouse dentate gyrus (DG) at 2, 6 and 12 months of age in order to map the complex changes and associations in the tissue across age. Various combinations of the 18 molecules are used to define different cell types and it incredibly informative to be able to view so many molecules in exactly the same area and will advance the field. This is the greatest strength of the manuscript. They find that neurogenic, radial glia-like stem cells (R cells) and proliferating cells are reduced in aged animals, as are immature (DCX+) cells, but claim that fluorescence intensity increases for the remaining R cells in 12 month old mice. They report that the density of vasculature also decreased with age, as did the associated pericytes, but astrocytes associated with the blood vessels increased. The last part of the manuscript defines 'microniches' (random or targeted regions of interest within the DG) and attempts to show how cell types, especially Nestin+ R cells, change in their associations with vasculature within these sub-regions at 2, 6 and 12 months of age. It is a commendable approach and the authors use a variety of statistical tests to compare the different cell types. However, there are several parts of the methods, along with insufficient details of the results that prevent full interpretation of the data, meaning that it is difficult to determine whether all conclusions are supported.

   1. There are many factors that can affect the measurements of immunoreactive structures (Fritschy, Eur J Neurosci, 2008 vol 28, p. 2365-70). The main limitation is not providing sufficient detail for the immunolabelling design and imaging parameters but providing some unclear details for the imaging analysis (below).

   a. In terms of immunohistochemistry, with the impressive number of tested antibodies, there is potential for variation due to antibody antibody penetration, unreported combinations of secondary antibodies, tissue quality (variations in fixation), etc. It is difficult to have confidence in the conclusions based on a total of 3 mice per age group for a single 40 um section per mouse. Ideally, to increase confidence in individual section variability, it is recommended that measurements should be taken from at least 3 sections per mouse then averaged, before averaging for the age group.

   b. Assuming there were 3 primary antibodies with 3 secondary antibodies per cycle before elution, were the combinations used consistent for all brain sections and mice? Was the testing and elution order the same (i.e. systematic)? There is a risk of cross-excitation and mis-interpretation of true immunoreactivity if spectrally close fluorophores for the secondary antibodies were selected for primary antibodies that recognize spatially overlapping structures. Can the authors show the cycle number and fluorophore for the examples in figures 1 and 2 to determine which markers were imaged together in the same cycle? This would give confidence to the methods for colocalisation and cell type descriptions. For example, can cross-excitation be ruled out for some of the signals in the images used in Fig 2 (duplicated in Fig 4) such as intensely immunopositive Laminin-B1 cells in the MT3 and Sox2 channels (2A) and Ki167, SOX2 and phospho-histone 3 channels (2C)?

   c. For image acquisition, details are required on the resolution (numerical aperture of the lenses) in order to interpret colocalisation measurements in the later figures. Which beamsplitters/filters were used, and was the same laser power used for the same markers over different specimens (important for interpreting figure 4 data)?

   d. For the analysis of ROIs (figures 3-6), were the 20x or 40x images used?

   e. Details of the antibody specificity controls should be provided.

   1. Numerous markers have been used to define different cells, but the proportions are not reported. For example, R cells are defined differently in figures 3 and 4. How many types of R cells (based on combinations of markers) were observed? High resolution examples of each defined cell type (neuronal and glial) would assist the reader in the confidence of the measurements (ideally as single channels side by side, with arrows indicating areas of detectable immunoreactivity that the authors would use to define each cell).

   2. The authors use HOPX and GFAP immunoreactivity and a lack of detectable S100beta immunoreactivity to distinguish R cells from triple immunopositive mature astrocytes. In Figure 3, the images are too low power to be able to confirm this. This part would benefit from some single cell examples showing the separate channels.

   a. Furthermore, the results (paragraph 2, page 7) report changes in cell number, but rather density is reported. Please either state the numbers or refer to density.

   b. Related to Fig 3, there are no details of the number of R cells counted in supplementary table 1. How were the density measurements obtained? How thick were the image stacks and how many R cells per section? Similarly, as stated in methods, for glial cells, 100 cells were randomly counted in each section (presumably the same count for each age), so how was it reported that specifically the numbers of astrocytes were reduced and no significant differences in other glial cell types? (bottom of p.7)

   1. An increase in fluorescence intensity for HOPX and MT3 (also marks R cells) was observed with age (Fig 4), with methods stating that the 5 ROIs used to calculate the background intensity were measured at each [optical?] slice for where the cells were measured, to account for unequal antibody penetrance. Several clarifications are required in order to interpret these results: For the example HOPX images in Fig 4A, for the 2 month old mouse, the background is low, whereas for 12 months, the background is far higher, meaning different background ROI values. Can this difference be explained by differences in laser power, contrast adjustments, optical slice thickness, or whether these are maximum intensity projections of different z thickness? These values must be reported, and for each image presented in the manuscript, details must be included as to what type of image (z-projection or single optical slice, z thickness). Was the optical section(s) of the 12 month mouse imaged closer to the surface of the section for this example in Fig 4A? Were cells sampled at all depths of the imaged volume? Did the antibody show better penetration in the 12 month old mice than the 2 month old mice? How many optical slices would a cell soma cover? In these cases, how was the fluorescence intensity measured? If a soma covered several optical slices, which one was selected for the ROI measurement?

   2. The described methods for studying cellular interactions are not clear, making it difficult to interpret the associations between vasculature, cell types, and age. How was colocalisation defined, and at what resolution? For example, it is expected that GFAP would be associated with but not directly colocalized with collagen IV (Fig 5). In these cases, the manuscript would benefit from high resolution examples of this colocalization/interaction. How many ROIs were taken, how exactly were the ROIs for cell types associated with collagen IV selected, was this in 2D or 3D?

   3. The methods for random microniches are difficult to follow, as are the methods for investigating the associations of other markers to radial processes of R cells. Please provide a definition of a 'spot'. Again, details of the micron per pixel resolution and optical slice thickness would help in the interpretation of results. Additionally, if possible, illustrated examples of the full procedure for niche mapping should be provided in order to follow how the measurements were collected.

   Read the original source
5. 

   eLife

   May 14, 2021

   Reviewer #1 (Public Review):

   Overall the analysis is conducted well and is convincing. The characterisation of neural stem cells using 7 markers as well as their morphology and position, is particularly thorough.

   My main criticism is that the study purports to address the effect of aging but the ages analysed only range from 2 months to 12-months. As 12 month-old mice are still middle aged, it is difficult to conclude anything about the process of ageing, which is usually studied in much older mice (18-24 months). Indeed, some of the changes that the authors associate with an "ageing phenotype" appear in microniches already in 2 month-old mice and are predominant at 6 months. This suggest that the authors are documenting the transition from an immature/juvenile state, which is predominant in 2 month-old mice, to a mature/adult state, …

   Reviewer #1 (Public Review):

   Overall the analysis is conducted well and is convincing. The characterisation of neural stem cells using 7 markers as well as their morphology and position, is particularly thorough.

   My main criticism is that the study purports to address the effect of aging but the ages analysed only range from 2 months to 12-months. As 12 month-old mice are still middle aged, it is difficult to conclude anything about the process of ageing, which is usually studied in much older mice (18-24 months). Indeed, some of the changes that the authors associate with an "ageing phenotype" appear in microniches already in 2 month-old mice and are predominant at 6 months. This suggest that the authors are documenting the transition from an immature/juvenile state, which is predominant in 2 month-old mice, to a mature/adult state, which already appears at 2 months but becomes predominant at 6 and 12 months. Importantly, this adult state, including the reduced number of neural stem cells, might not be dysfunctional but on the contrary, may perform very well its role of producing small numbers of new neurons as required during adult neurogenesis.

   Another, lesser concern is that, based on antibody staining performed in tissues from 2-month and 12-month-old mice, conclusions are made on the different expression levels of HOPX, MT3 and LaminB1 analysed at different ages. This assumes that the efficiency of antibody staining is the same in different samples analysed in parallel but this is not shown.

   Read the original source
6. 

   eLife

   May 14, 2021

   Evaluation Summary:

   The objective of this study is to develop a novel immunofluorescence technique allowing for the multiplexed analysis of protein targets. This 4i method is an important technical advance will be of great interest for the scientific community.

   (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.)

   Read the original source
7. 

   Version published to 10.1101/2021.03.03.433747v2 on bioRxiv

   Mar 9, 2021
8. 

   Version published to 10.1101/2021.03.03.433747v1 on bioRxiv

   Mar 4, 2021