Peer review of: Pharmacologically reversible zonation-dependent endothelial cell transcriptomic changes (2020)

Reviewers' Comments: 
 
Reviewer #1: 
Remarks to the Author: 
The authors present a report detailing the vascular zone-specific changes in brain endothelial cells 
during aging that correspond with changes that occur in human aging and Alzheimer’s disease. 
They use single cell RNAseq to dissect the contributions that the different endothelial sub-types 
make to aging, and also find that these are reversed when treated with exenatide. This work is 
timely, as there is considerable interest in using single cell technologies to dissect cerebrovascular 
changes in aging and neurodegeneration (10.1038/s41591-019-0440-4; 10.1038/s41586-019-
1362-5). This work effectively uses single cell genomics to characterise changes to the ECs in 
different segments brain vasculature that occur during aging. The finding that aged BECs have an 
inflammatory gene expression signal is interesting, and in line with ‘inflamm-aging’ literature. The 
finding that BECs have potentially reduced metabolic capacity is also interesting, because their 
BBB function is highly ATP-dependent. Furthermore, the authors relate EC subtype changes to 
neurodegeneration risk genes, and find DEGs in capillary ECs are enriched in AD risk genes, 
implying a role for these EC subtype changes in AD. The key contribution of this paper to the 
thinking in the field will be in encouraging researchers to consider endothelial zonation when they 
examine changes to the brain vasculature. 
 
Major comments: 
1) While interesting, there is a lack of immunohistochemistry or in situ hybridisation to validate 
single cell RNAseq changes. If some validation experiments were performed it would go a long way 
to reassuring the reader that these changes were meaningful/functional. 
2) Comparing bulk RNAseq to single cell work is fraught in disease/aging where cell populations 
change, accounting for most of the gene expression changes observed. In neurodegenerative 
diseases, where neurons die off, other cell types will be enriched in relative terms leading to 
apparent increases in gene expression. This will tend to exaggerate gene expression increases and 
mask decreases in non-neuronal cells, and may account for disconcordances. This makes it difficult 
to compare bulk vs sorted transcriptomes. The strongest evidence that age-related changes in 
BECs are related to human AD is in F3, and maybe F4 might be better in the supplementary 
material. 
3) The rationale behind targeting GLP1R seems lacking, and could be explained better, leveraging 
the scRNAseq data. Why would you expect exenatide to affect BEC function? Where is GLP1R 
expressed? Does exenatide directly target BECs? Are there changes in the BECs that would 
indicate a deficiency in GLP1R signalling to justify using exenatide? Addressing these questions 
would help justify/rationalise why exenatide may be affecting the brain vasculature. Alternatively, 
the authors may wish to hold on to this until they have a convincing mechanism, and publish this 
separately. 
4) The authors claim to provide the first evidence of zonation-specific endothelial changes in aging, 
however they could further acknowledge a similar study by Yousef et al. (10.1038/s41591-019-
0440-4). It might be helpful to reference this in the intro as well, and the parallels between the 
studies. Yousef et al. however, highlight the role that venous/arterial ECs play in immune cell 
tethering during aging – rather than the role of capillary bed ECs, which is highlighted here. Do the 
authors also see VCAM1 induction in arterial/venous EC populations? It would be worth 
mentioning, either way. It is interesting to note that neither paper saw leakage of 70 kDa dextran, 
but Yousef et al. did not test lower MW dextrans, which were shown to extravasate here. 
5) Would the authors consider creating a searchable database/website for non-experts to easily 
access these data? 
 
Minor points: 
1) Could the preponderance of capillary EC changes be proportional to the oversampling of this 
population? This would provide an alternative interpretation, as statistical tests would have more 
power to detect changes, as opposed to more severe changes to the capillary bed. 
2) The single-cell RNAseq work is convincing, and provides more thoroughly delineated EC 

zonation than Vanlandevijck et al. (https://doi.org/10.1038/nature25739) or Yousef et al. 
(10.1038/s41591-019-0440-4). It may however be important to consider the paper in the broader 
context of single cell/EC genomic studies finding changes to ECs in the aging/diseased brain: 
Tabula Muris Senis (10.1101/661728), Dulken et al. (10.1038/s41586-019-1362-5), Munji et al. 
(10.1038/s41593-019-0497-x). Also studies that characterise the brain at single cell resolution: 
Tabula Muris (10.1038/s41586-018-0590-4), DropViz (10.1016/j.cell.2018.07.028). 
3) In F4, p8, is FDR adjusted for the number of genes you analysed? Or for the entire 
transcriptome? 
4) In F4, would it not be better to run regressions of age vs GE, rather than artificially dividing into 
two groups? 
5) In F4, would the authors also find the publicly available Mayo Clinic AD brain bulk RNAseq 
dataset (10.1038/sdata.2016.89) useful? 
6) P11: “Among BBB-associated pathways with enrichment of expression changes, altered TGF-β, 
VEGF and immune/cytokine signalling appear to impact all vascular segments, while others exhibit 
subtype-specific changes that notably all impact capEC.” Ref: 10.1186/s12974-018-1167-8. 
7) P12: Paragraph 2, start of third line, typo, Intriguing – should be intriguingly. 
8) P12: Paragraph 2, end of fifth line. GLUT1-deficient or heterozygous GLUT1 deletion mice not 
GLUT1 knockdown. 
9) P15: Minor concern, but saline or another type of vehicle is not used to control the exenatide 
experiments. 
10) P16: Second to last line, typo, FindNerighbors. 
11) S2a: subtype-biased? 
 
 
 
Reviewer #2: 
Remarks to the Author: 
This study essentially comprises 3 comparisons: (i) young vs aged mouse endothelium, (ii) aged 
vs human AD database, and (iii) aged mouse endothelium vs exenatide-treated mouse 
endothelium. The overall direction of this study is interesting but there are major issues that must 
be carefully addressed. 
 
1. The conclusions and focus appear to be on BBB. For example, on page 6 … “Overall, capEC had 
the most prominent BBB-regulatory signaling pathways enrichment”. However, many of the genes 
and signaling pathways identified also subserve any other critical non-BBB functions, e.g. TGF, 
VEGF and immune/cytokine signaling. It is not clear whether the singular focus on the BBB is 
justified. 
 
2. BBB regulation is a complex process, and there is not just a “linear” relationship between so-
called “BBB genes” and BBB function. For example, the authors also found that several genes with 
important functions at the BBB such as Cldn5, Slc2a1, Ifitm3, had increased expression in aged 
human brains but downregulated in aged mouse brain ECs (Fig 4a). 
 
3. Although GLP-1R agonist treatment reverses aging-associated EC transcriptomic changes and 
reduces BBB leakage and the treatment also generalize to the subcategory of DEGs whose human 
orthologs are AD-associated (Fig 5e) in capEC, it is still hard to get the conclusion that BBB 
protection is an important underlying mechanism that explains GLP-1R agonists’s benefits in AD. 
In fact, some studies suggest that there may be limited uptake of therapeutic antibodies in 
multiple mouse models of AD, perhaps owing to intact BBB and limited permeability (Bien-Ly et al, 
Neuron 2015). 
 
4. In Fig 4, the authors identified similar expression changes in normal aged mouse and human AD 
brains. The changes of some EC-enriched genes in aged mouse brain, had similar direction with AD 
human brain data, but not the same with aged human brain data, e.g. Slc2a1. But aging is not the 
same as AD. What is the possible meaning of this? Does this mean the normal aged mouse can 

model human AD? Are there any genes show concordant changes in aged mouse and aged human 
brain, but different in AD human brain? These might be more related with AD pathology. 
 
5. The rather sudden “jump” to exenatide is unclear. Not much rationale is provided for GLP-1R. 
Why choose this target? It is mentioned that GLP-1R agonists have been reported to provide 
neuronal protection, but is there also some justification for selecting this for BBB and endothelium? 
In fact, GLP-1R may be a universally expressed gene. Why this focus in aged mouse brain, or aged 
or AD human brains? 
 
6. The cell number of endothelial cells from 5 aged mice appear to be lower than the one from 5 
young mice. It seems unclear whether this was due to a batch effect, or just because aged brains 
have a fewer number of endothelial cells. Vascular density may be important when calculating 
dextran data? When the authors using dextran to detect permeability of BBB, did they check the 
density of the vessels? It seems the vessels density might be higher in the aged mouse’s cortex 
than the young mouse? 
 
7. The authors may need to clarify whether they prepared single-cell RNAseq libraries from mice 
that were also used for two-photon imaging studies. The surgery for cranial window (and the 
procedures of two-photon imaging) may change the mRNA expression patterns in brain cells. 
 
8. In Fig 5b: they stated "n=9 image stacks from 3 mice for each group". Does this mean that the 
values for mean +/- SEM in Fig 5b were from n=9? If yes, probably, they need to recalculate the 
average (and SEM) from 3 mice (the FC value for each mouse should be from the average FC 
value of 3 image stacks in each mouse?). 
 
9. The authors should give a confirmation on cell purity: high gene expression values for genes 
with EC-specific expression and very low or undetectable expression for other cell- type-specific 
markers of the central nervous system. 
 
10. Some key signals should be confirmed with protein. 
 
11. The authors used whole brain to prepare the libraries, but did not discuss the brain-region-
related heterogeneity of the data. Especially because aging affects specific brain areas differently, 
the authors might wish to address whether region-specific (such as cortices and hippocampi etc) 
display key differences in gene expression patterns upon aging. 
 
12. If space allows, it may be nice to provide some discussion into this point. It may be nice to 
discuss potential sex differences because this study used only male mice. 
 
13. Finally, how “specific” are the endothelial patterns found in this study? There are many other 
papers that have previously mapped aging transcriptomes for brain endothelium, astrocytes, 
microglia etc (e.g. Guo et al, Neurobiol Dis 2019; Boisvert et al, Cell Rep 2018; Olah et al, Nat 
Comm 2018). It is a bit surprising that these previous papers were not referenced or discussed.