Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular heterogeneity, but analyses remain largely restricted to gene expression. Here, we build on emerging work on cell-level expressed genetic variation and show that scRNA-seq captures a pervasive and informative layer of cell-specific expressed single-nucleotide variants (sceSNVs) that contributes to transcriptomic diversity and enables the investigation of novel mechanisms underlying their occurrence and function. We developed an integrated pipeline combining SCExecute, SCReadCounts, and scSNViz to perform de novo sceSNV calling, allele-specific expression quantification, and visualization at singlecell resolution. Applying this workflow to 28 normal and tumor tissue datasets (~230,000 cells), we identified over 7 million sceSNVs, including 2.5 million novel variants absent from reference databases. Variants were categorized as putative germline, somatic, or RNA-origin based on scRNA-seq calls and allelic expression, and cross-referencing with DbSNP, COSMIC, and related resources. Distinct expression signatures distinguished these classes, with RNA-origin variants emerging as a substantial and previously underappreciated source of heterogeneity. Integration with copy-number inference revealed that aneuploid regions harbor significantly higher variant burdens and altered allele balance compared to diploid regions, linking large-scale chromosomal instability with single-cell variant expression. A case study of the recurrent missense variant H4C3V61F highlights the functional implications of RNA-origin variants for chromatin regulation. Together, this work establishes a scalable framework for sceSNV discovery, provides a resource cataloging transcriptome-wide variation across diverse human tissues, and uncovers RNA sequence diversity as a significant contributor to cellular heterogeneity.