Protocol for microbiota analysis of a murine stroke model

Abstract

Investigations on the microbiota in neurological diseases such as stroke are increasingly common; however, stroke researchers may have limited experience with designing such studies. Here, we describe a protocol to conduct a stroke microbiota study in mice, from experimental stroke surgery and sample collection to data analysis. We provide details on sample processing and sequencing and provide a reproducible data analysis pipeline. In doing so, we hope to enable researchers to conduct robust studies and facilitate identification of stroke-associated microbial signatures. For complete details on the use and execution of this protocol, please refer to Sorbie et al. (2022).¹.

Keywords: Bioinformatics; Microbiology; Model Organisms; Neuroscience; Sequence Analysis; Sequencing.

Graphical abstract

Figure 1

Schematic representation of the surgical procedure of transient filament occlusion of the left middle cerebral artery (A) The filament is inserted between the first tight suture (1) and the second loose knot (2). The filament is redirected into the ICA and advanced until reaching the MCA origin. (B) Laser Doppler flowmetry graph of blood flow before, during and after occlusion. An acute reduction in the blood flow is observed when the filament occludes the MCA origin. There is a restoration of the blood flow to steady-state level after filament removal. (C) Infarct volume analysis and representative cresyl-violet staining of brain sections.

Figure 2

FASTQC read quality, for forward (left) and reverse (right) reads, generated as part of the R-based pipeline

Figure 3

Example of DADA2 QC report depicting aggregated quality score across all reads, both forward (left) and reverse (right) after trimming of primers and adapter sequences Chosen trimming locations are highlighted by a red dotted line for each set of reads.

Figure 4

Representative figures depicting alpha, beta-diversity and differential abundance results from the R-based pipeline (A) Boxplots of Richness, Shannon effective and Faith’s phylogenetic diversity comparing sham and stroke. Differences between means are tested using a Mann-Whitney U test. Error bars represent the median +/− 1.5 multiplied by the IQR. Outliers are highlighted by an empty square. (B) Non-metric multidimensional scaling plot of Generalized UniFrac distance comparing sham and stroke samples. Differences between groups are tested using PERMANOVA: Adonis R₂ = 0.12, p value = 0.013. (C) Log₂ fold-change of differentially abundant taxa between sham (blue) and stroke (red) as identified by ANCOM-BC.

Figure 5

Screenshots of the interactive quality plots generated by QIIME2 for forward (left) and reverse (right) reads Chosen trimming locations are highlighted by a red dotted line for each set of reads.

Figure 6

Representative figures depicting alpha, beta-diversity and differential abundance results from the QIIME2/python-based pipeline (A) Boxplots of Richness, Shannon effective and Faith’s phylogenetic diversity comparing sham and stroke. Differences between means are tested using a Mann-Whitney U test. Error bars represent the median +/− 1.5 multiplied by the IQR. Outliers are highlighted by a filled diamond. (B) Non-metric multidimensional scaling plot of Generalized UniFrac distance comparing sham and stroke samples. Differences between groups are tested using PERMANOVA: Adonis R₂ = 0.12, p value = 0.013. (C) Log₂ fold-change of differentially abundant taxa between sham (blue) and stroke (red) as identified by ANCOM-BC.