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The research paper was about how individuals that had exposure to early life adversity (ELA) had a significant impact on their brain. The study can definitely be considered a breakthrough in this field as it introduced the first complete study of a full set of RNA molecules in the Ventromedial Prefrontal Cortex in ELA patients. The paper overall was concise but there can also be some improvements made.  

PROS:

• Presents the first complete study of RNA set in ELA set patients vs normal

• High preservation of microvessel structural integrity

• The methodology's emphasis on isolating microvessels, which are essential for researching neurovascular malfunction in depression and childhood trauma, is one of its strong points.

• Although multiple hypothesis correction should be made clear, RRHO is a useful technique for comparing gene expression datasets.

SUGGESTIONS/IMPROVEMENTS

Abstract

• Could include Sample size (N = #)

Introduction

• Could add more statistics regarding ACE in developed countries; indicates an external factor in the study that it didn’t address

• Could potentially add other brain regions information that might be affected (i.e. Amygdala) to establish established research on these topics

Methodology

Statistical & Methodological Considerations

•  Further checks on outlier handling and batch effects would strengthen results

• Additional validation on FISH and RNA extraction methods could improve data robustness

Sample base

• Male sample size is greater for both control and experimental given a “Female” oriented study based on Title; future studies could incorporate a greater amount of female participants

Human Post-Mortem Brain Samples

• One drawback is the absence of information about ethical issues, donor permission protocols, and the exclusion standards for subject selection; These elements are essential to comprehending the study's dependability and morality

• The authors do not specify whether age, race, or lifestyle factors—all of which may have an impact on gene expression and neurovascular function—were taken into consideration when matching samples.

• The subjects' medication histories are also missing; many psychiatric patients are taking drugs that affect gene expression and the vasculature of the brain, which may cause confusion.

Tissue Dissections

• One major methodological flaw is the ambiguity of the dissection process. The specificity of tissue collection would be increased if laser capture microdissection (LCM) was employed; nevertheless, if a manual method was chosen, the investigation must address possible sample contamination concerns

• There is no reference to the researchers' blindness during the dissections; To lessen any bias in tissue handling and processing, blinding is required

• The dissection of the ventromedial prefrontal cortex (vmPFC) lacks precise anatomical coordinates. It is important to take into consideration that various subregions within the vmPFC may have unique functional and molecular traits

Microvessel Isolation from Tissue Microdissection

• But the precise isolation method—density gradient centrifugation, filtering, or enzymatic digestion—is not explained; The cell purity produced by each technique varies

• The cleanliness of the microvessel has not been validated. Were endothelial markers (such GLUT1 and CD31) verified by qPCR or Western blot? The study must confirm that the sample was not contaminated by non-vascular cells

Visualization of Microvessels from the Enriched Pellet using Immunofluorescence

• Negative controls, which are necessary to make sure that fluorescence signals are not the result of non-specific binding, are not mentioned

Extraction of Total RNA from Isolated Microvessels

• Whether TRIzol, column-based approaches, or other techniques were used for RNA extraction is not specified in the study; The yield, purity, and quality of downstream sequencing can all be impacted by the RNA extraction process

• RNA integrity (RIN scores) is not mentioned; It may be challenging to understand biological effects if sequencing findings contain technical bias due to poor RNA quality

Library Construction and Bulk RNA-Sequencing

• Whether poly-A selection, ribosomal RNA depletion, or total RNA sequencing were employed is not stated in the paper; This is important because ribosomal depletion provides a more comprehensive picture of regulatory RNAs, while poly-A selection concentrates on coding RNAs

• Technical replicates are not mentioned; Their inclusion would increase the sequencing findings' dependability

UMI Extraction, Alignment, Deduplication, Metrics, and Generation of Count Matrix

• The removal of mitochondrial RNA, which is crucial since too much mitochondrial RNA can signal sample destruction, is not mentioned

Bioinformatic Pipeline and Analyses of RNA Sequencing Data

• The study's data processing pipeline is unclear. Transparency and reproducibility would be enhanced with a detailed plan (raw data processing → quality control → differential expression analysis, for example)

• There is no mention of batch effect correction; were techniques like Harmony or Combat applied to reduce confusing variation between samples?

RNA-Sequencing Deconvolution

• Lack of openness in the reference dataset: were single-cell RNA-seq sources or post-mortem human brain data used to determine cell type proportions

Differential Gene Expression Analysis and Normalization

• The application of multiple testing correction, such as the Benjamini-Hochberg FDR correction, which is necessary to account for false positives in high-throughput investigations, is not specified in the work

• Uncertainty surrounds the filtering criteria for low-expression genes; To prevent noise, several RNA-seq processes filter out genes with extremely low expression

• There is no mention of variance stabilization or log transformation, two methods frequently employed to deal with severely skewed expression data

PsyGeNET Analysis

• It's a smart idea to use PsyGeNET to connect DEGs to neuropsychiatric conditions, however the authors don't explain how they choose which genes to include in their analysis

Functional Enrichment Analysis

• Although the study effectively analyzes biological pathways using Gene Ontology (GO) databases, it does not specify which particular databases were employed (e.g., KEGG, Reactome, Panther)

• Lack of mention of threshold cutoffs, which can influence how results are interpreted (e.g., adjusted p-value < 0.05, fold-change > 2)

Gene Set Enrichment Analysis (GSEA)

• The analysis of leading-edge genes, the subset of genes responsible for enrichment signals, is not mentioned in the article

Rank-Rank Hypergeometric Overlap (RRHO)

• The functional interpretation of overlapping genes—were they connected to certain diseases or biological pathways—is not covered in the study

Weighted Correlation Network Analysis (WGCNA)

• The paper does not specify if connection thresholds were improved, however WGCNA reveals important co-expression modules

• Module preservation analysis, which is crucial for figuring out whether the identified gene modules are reliable across several datasets, is not covered

Validation of DEGs with Fluorescence In Situ Hybridization (FISH)

• Although FISH is a powerful validation tool, the specificity of the fluorescence signals should have been verified with negative controls

• Were several areas of the brain examined? The results might not apply to other areas linked to neurovascular dysfunction if only the vmPFC was examined

Imaging and Analysis of In Situ mRNA Expression of DEG Candidates

• Blinded analysis, which is essential for reducing bias, is not mentioned in the report

• The differences between automated and manual quantification are not made clear; automated analysis minimizes bias and human mistake in interpretation

Statistics

• Normality testing, such as the Shapiro-Wilk test, is crucial when selecting statistical models, however it is not included in the paper

• No details about the handling of outliers, such as if extreme values were changed or eliminated

Grammar

• Change ‘overtime’ to ‘over time’ in the sentence “Overtime, a new homeostatic point is set by the NVU”

Competing interests

The authors declare that they have no competing interests.