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.
2) 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).
3) 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)
4) 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?
5) 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?
6) 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.