Optimized Method for High-Quality Isolation of Single-Nuclei From Mosquito Fat Body for RNA Sequencing

Single-cell and single-nucleus RNA sequencing are revolutionizing our understanding of cellular biology. The identification of molecular markers, single-cell transcriptomic profiling, and differential gene expression at the cellular level has revealed key functional differences between cells within the same tissue. However, tissue dissociation remains challenging for non-model organisms and for tissues with unique biochemical properties. For example, the mosquito fat body, which serves functions analogous to mammalian adipose and liver tissues, consists of trophocytes-large, adipocyte-like cells whose cytoplasm is filled with lipid droplets. Conventional enzymatic dissociation methods are often too harsh for these fragile cells, and their high lipid content can interfere with reagents required for single-cell transcriptomic analysis. Single-nucleus RNA sequencing (snRNA-seq) offers an alternative strategy when intact cells with high-quality RNA cannot be obtained by enzymatic or mechanical dissociation. Here, we present an optimized reproducible methodology for nuclei isolation from the fat body of Anopheles gambiae mosquitoes, enabling high-quality snRNA-seq. Our approach involves tissue fixation and lipid removal, followed by cell lysis and nuclei purification using a sucrose cushion. We validated this protocol on both sugar-fed and blood-fed samples, established quality metrics to remove potential ambient RNA contamination, and demonstrated that snRNA-seq using this method yields high-quality sequencing results. Key features • Optimized nuclei isolation using methanol fixation and lipid removal enables efficient nuclei extraction from the fragile, lipid-rich fat body tissue of Anopheles gambiae. • We validated this procedure in sugar-fed and blood-fed samples, yielding high-quality single-nucleus RNA sequencing data with high gene counts and low mitochondrial RNA content. • Robust quality metrics allow effective filtering of ambient RNA, enhancing transcriptomic accuracy across different physiological states.