Peer review of: Blood circulation of soft nanomaterials is governed by dynamic remodeling of protein (2020)

Reviewers' Comments: 
 
Reviewer #1: 
Remarks to the Author: 
In their manuscript entitled ”The blood circulation of soft nanomaterials in vivo is governed by 
dynamic remodeling of protein opsonins at the nano-biointerface” Abbina et al. investigate protein 
corona formation on a small panel of three differently sized single molecule polymer 
nanomaterials. 
The manuscript is overall well written and figures are nicely formatted. 
Most of the claims of the paper are based on a “quantitative” proteomic analysis of nanoparticle 
bound proteins. 
However, as it stands, the presented data by no means support the conclusions presented. 
Reproducibility of the proteomics data is very low. I cannot see how any interpretation from only 
two biological replicates in combination with high quantitative variability (>5 fold differences even 
for proteins in the Top10 most abundant) can be valid. 
 
 
 
Materials and Methods: 
Nanoparticle isolation: 
The authors must prove that isolation of SMPNs is possible with this protocol. The SMPNs will have 
a density very similar to proteins. It may have escaped the authors that centrifugation at 
100.000xg will pellet all kinds of larger (lipo-)protein complexes from plasma. 
Negative controls (without SMPNs) are missing. No buffer compositions for isolation / wash steps 
are provided. No volumes are provided. 
In my opinion, the authors are pelleting “something large from plasma” but by no means 
specifically nanoparticles or their interacting proteins. 
Why were the particles stored for a week – apparently without protease inhibitors - at 4°C before 
subsequent proteomic analysis? This will likely lead to unwanted sample degradation and 
negatively affect data quality. 
LC-MS Analysis: 
Many parameters are missing. Digestion conditions, desalting conditions, nanoLC column material 
etc. 
 
 
 
Figure 1d: I cannot reproduce the half-life times reported by the authors. 
Time values for 50% doses are: 
SMPN-1 :24h 
SMPN-3: 40h 
SMPN-9: 8h 
Time values for 25% doses are: 
SMPN-1 :90h 
SMPN-3: 120h 
SMPN-9: 48h 
 
Initial half-life times seem to be a lot shorter than the values reported by the authors, and based 
on Figure 1d, half-life time of SMPN-3 must be longer than that of SMPN-1 
 
Figure 2b: 
There are extreme differences in tissue structure. Scalebars are missing. All conditions for each 
tissue must be shown at same magnification 
 
 
 

Figure S1 
The authors claim to see a difference between SMPN-1 and SMPN-3, but I doubt that the slight 
shift in elution time can explain a 2.2-fold size difference? 
 
Figure S3: 
The data for kidney look most strange. How can SMPN-1 accumulate at 8h and 48h, but not 24h? 
For spleen, it looks like SMPN-1 accumulate at 8h, but 24h and 48h look even less intense than the 
negative control? 
 
Figure S4: Negative Controls and 24h timepoints are missing. 
Why does Kidney tissue structure for SMPN-9 look completely different at 8h compared to 48h? 
There seems to be an issue with the spleen (WP) image series – tissue structures seem completely 
different. 
 
Table S3: Calculating a standard deviation from only two measurements does not make a lot of 
sense. 
 
Table S4: data for SMPN-3 and SMPN-9 are missing for 24h and 48h timepoints. 
 
Table S5: these data are misleading. Investigating the Excel-File (Supplementary dataset1, tab 
labeled Dataset S6-S8) reveals that of the 364 proteins (number given by the authors in Table 
S5), only 244 have any non-zero value. It is completely unclear, why the authors count proteins 
having only zero values as identified? 
Furthermore, there is a reproducibility issue. Of the 244 proteins having any non-zero value, only 
106 are identified in more than one condition/replicate. This indicates either a highly 
unreproducible proteomic workflow or a serious problem with FDR control. 
Interestingly, the samples are labeled “1.1”, “2.3” and “6.5” – what do these numbers refer to? It 
looks like molecular weights, but those would be in stark contrast with the MW reported in the 
manuscript. 
 
Table S6: Again, these data indicate a non-reproducible proteomic workflow. File (Supplementary 
dataset2, tab labeled Dataset S9-S11) reveals that of the 364 proteins (number given by the 
authors in Table S5), only 316 have any non-zero value. It is completely unclear, why the authors 
count proteins having a zero value as identified? 
Furthermore, there is a reproducibility issue. Of the 316 proteins having any non-zero value, only 
116 are identified in more than one condition/replicate. This indicates either a highly 
unreproducible proteomic workflow or a serious problem with FDR control. 
The relative amounts between replicates are not at all reproducible, e.g. a2-Macroglobulin varies 
between 43.5% (48h.R2) and 7.0% (48h.R1). Carbonic Anhydrase is rank 6 (4.6%) in replicate 1, 
but <1% (rank 19) in replicate 2. 
Analysis must be repeated, at least three biological replicates must be analyzed. Each biological 
sample must be analyzed in at least three technical replicates. 
 
 
 
Reviewer #2: 
Remarks to the Author: 
This is a nice and informative paper demonstrating the dynamic nature of protein corona during its 
in vivo residency time. I would publish this paper after major revision noted below: 
 
1- The observed variation of protein corona can be the results of analyzing the remaining 
nanoparticles in blood over the time and not the remodeling of protein corona. In other words, the 
particles with high opsonin amounts might have been removed by immune system and over the 
time the captured and analyzed particles were the ones that had not the opsonin participation in 
corona composition. 

 
 
2- The number of presented proteins in Venn Diagrams of Figures 3 and 5 should have SD, if the 
authors performed enough repeats of the mass spect data. 
 
 
3- What had been seen by the authors may be valid for the employed nanoparticles; the authors 
should tone down their conclusions, as their results might not be valid for other types of 
nanoparticles. 
 
4- The authors are supposed to conduct the experiments on both male and female mice to 
consider the effect of sex as well. Recent reports revealed that both the sex have considerable 
effects in both biological identity and fate of nanoparticles. 
 
5- It is now increasingly being understood that any type of disease may change the plasma 
composition and therefore the biological identity of nanoparticles (known as disease-specific or 
personalized protein corona); the author are encouraged to discuss this effect to draw the 
attention of the readers to the fact that reproducing the presented data may be only valid for the 
employed healthy mice.