Peer review of: Engineered immunological niches to monitor disease activity and treatment efficacy in relapsing (2020)

Reviewers' comments: 
 
Reviewer #1 (Remarks to the Author): 
 
The manuscript provides an interesting approach to assess immune regulation in experimental 
autoimmune encephalomyelitis (EAE), a model of multiple sclerosis (MS). There are certain 
limitations of the study that can be possibly overcome by argumentation and additional data 
generation. Firstly, the immune response in EAE is leading to tissue damage in the central nervous 
system (CNS). The niche model does not provide the same compartment as the CNS and does not 
provide a blood-brain-barrier (BBB). Therefore, it can be considered rather as a tool to assess the 
immune events in draining lymph nodes. Of course, such a tool to be implanted in MS patients 
would be of great value for assessment of immunity and relapse-preceding events. Secondly, the 
model that is used in the study is induction of EAE in SJL/J mice that are transferred with PLP178-
191 specific T cells. This is a good model for assessment of T cell immunology in EAE. It is not 
such a good model for assessing additional contributions of the immune system to induction of 
CNS lesions like assessment of the B cell compartment in EAE. Therefore, additional models should 
be searched, established and evaluated that provide the possibility for assessment of the more 
complex immune regulation of EAE and MS. 
Specific points: 
The introduction is too general. It would be of interest to know why the authors used the selected 
model. 
In MS research understanding relapse and chronicity is of major importance. Therefore, in the 
results part the authors should provide better description of the findings in this phase of the 
disease. 
The discussion should be also more focused on the findings and provide some assessment what is 
novel about the findings regarding understanding immunity in EAE and MS. Is there anything novel 
that cannot be found by assessment of lymphoid tissues in EAE? Please compare the findings with 
findings in the literature regarding the relapsing and chronic phases of EAE. Please provide the 
rationale why the approach would be still of great value for use in humans. 
 
 
Reviewer #2 (Remarks to the Author): 
 
In the manuscript “Engineered immunological niches to monitor disease activity and treatment 
efficacy in relapsing multiple sclerosis” authored by Dr. Shea and colleagues, the authors describe 
a novel methodology to investigate the profile of the peripheral immune cells and correlate it with 
disease activity in experimental autoimmune encephalomyelitis (EAE), model of MS. According to 
the authors, this novel technology, which relies on subcutaneous injection of a porous scaffold 
matrix, offers a new approach to investigate the immune system and proactively identify a 
treatment window to suppress relapses in EAE and other autoimmune diseases. Although some of 
the data are not referenced in the manuscript, the overall amount of data is convincing; and the 
use of a machine learning approach to predict relapses in SJL EAE model is novel and very 
interesting. Given that MS is a debilitating disease that affects 2.5 million people worldwide and 
that treatments have had limited efficacy, the use of such a technology would greatly advance the 
field and shed light on novel molecules that can be targeted to treat MS as well as it can be used 
to predict relapses. Therefore, there is enthusiasm for this study. However, lack of a more detailed 
analysis of the cellular composition of leukocytes in niches, an unusual animal model (passive 
instead of active EAE) for these analyses, and lack of rigor in data presentation undermine the 
manuscript quality. A summarized list of deficiencies is found below: 
1. In fig. 1, authors screen the niches for leukocytes involved in EAE pathogenesis and conclude 
that there is a preponderance of innate cells over adaptive cells. However, EAE is known to be a T 
cell-dependent model. Authors should have better characterized the cellular subpopulations (Th17, 
Th1, ThG) of T cells and other cell types to have a better assessment of the inflammation that 
predicts relapses, and then followed the dynamics of such cell populations in different stages of 
actively induced EAE. 

2. It is not clear why authors resourced to passive EAE instead of active EAE, given that active 
immunization would result in RR EAE, which would provide more insight into the dynamics of the 
immune response over the course of the disease. 
3. Throughout the manuscript, figures are not referenced in the order they appear in the text. 
Some of them are not referenced at all (e.g. Fig. 2C, S. Fig 11 and S. Fig. 12). 
4. Did authors compare the cellular composition in niches with those in CNS and dLN? This piece of 
information could be important to strengthen their model. 
5. For the preemptive intervention experiments, active immunization would be a better model than 
passive EAE given the natural course of the disease. 
6. Still on the preemptive experiments: authors utilized an Ag-specific tolerance induction 
treatment to test their hypothesis. However, wouldn’t it be better to test the effect of FDA-
approved drugs for MS instead as it is more relevant from a clinical perspective? And, according to 
their hypothesis that probing the niches at different time-points would provide data that could 
predict a relapse, wouldn’t a better proof-of-concept experiment rely on treating mice at different 
time-points according to the data provided from the niches and check for relapses? 
6. Discussion is too short and lacks depth. 
7. Fix figure legend for S. Fig 3. 
8. Stainings on Fig. S11 and S12 are not convincing: (i) CD11b+F480- does not seem realistic, (ii) 
gates for CD11b+F4/80+, Ly6c+F4/80+, CD11b+Gr1+Ly6c-, CD19+CD49b+ are cutting through 
the negative population, (iii) plot CD11c+F4/80+, staining is not compensated.