Investigating Alzheimer's Disease-Associated Genes Using Differential Splicing Frequency Analysis

Accurately quantifying the expression of individual transcript isoforms remains a formidable challenge, especially in contexts such as neurodegenerative diseases and cancers, which are characterized by high isoform diversity. The present study introduces a junction-based method, named differential splicing frequency analysis (DSFA), which enables more sensitive detection of differential splicing using RNA-seq data. Unlike the existing exon-, isoform-, and event-based methods, DSFA quantifies splice junction usage. We applied DSFA to Alzheimer's disease (AD)-associated genes through large-scale RNA-seq data mining. The present study is the first to establish that the APP770-, APP751-, APP695-, and APP752-encoding isoforms represent major isoforms of the <i>APP</i> gene. Three important findings are: (1) the APP752-encoding isoform exhibits immune cell specificity; (2) the relative proportion of the APP752-encoding isoform increases during the differentiation of induced pluripotent stem cells (iPSCs) into microglia, akin to the increase in relative proportion of the APP695-encoding isoform during iPSC differentiation into neurons; and (3) the APP751-encoding isoform predominates in both cancer and immune cells. Additionally, we identified APP/58417N and App/52804N as differentially expressed splice junctions in humans and mice, respectively. Through over-expression of U1 snRNA in human embryonic stem cell (hESC)-derived neurons, we found that U1 snRNA over-expression decreases the usage of APP/58417N in neurons, similar to the effects observed in AD samples. Our research highlights that the major isoforms of a gene can differ markedly in their expression across tissue and cell types.