Whole-organism atlasing using Evercode Penta produces comprehensive and consistent scRNA-seq transcript detection across multiple tissues

Single cell RNA sequencing (scRNA-seq) has become a ubiquitous and powerful tool for studying a large variety of biological systems across different research applications. The research community has quickly adopted scRNA-seq as an effective method to comprehensively profile their systems of interest. Due to technical limitations, early efforts were limited to thousands of cells, often resulting in atlases that captured only a subset of the full cellular diversity within a system. In order to gain insight, attempts have been made to aggregate these small experiments; However, this presents an obvious problem of combining disparate datasets from different experimental contexts. Batch effects from these combined experiments can be normalized computationally to some degree, but not completely resolved. As a result, the research community has been limited to relying on sub-optimal reference data. The Parse Evercode Penta kit addresses this issue by scaling up combinatorial barcoding technology to accommodate up to 5 million cells or nuclei in a single run. As proof-of-concept, nuclei preparations from 7 different tissues isolated from two mice (male and female), representing a total of 14 samples, were barcoded and sequenced at appropriate proportions based on sample complexity. The resulting dataset is an incredibly comprehensive and high-quality atlas comprising brain, colon, eye, heart, kidney, liver, and muscle nuclei. Analyses of these tissues yielded robust cell types with corresponding markers consistent with literature. More importantly, the quality of gene detection can distinguish subtle features such as diverse subtypes within tissue, sexual dimorphism of the tissues and rare cell types. Overall, over 200 unique cell types were detected across the 5 million cells profiled. This proves to be the most comprehensive and high-quality murine atlas generated in a single barcoding experiment. The need to provide accurate and reliable cell atlases for the research community is critical. The species-agnostic design of the Evercode kit delivers the scale, sensitivity, and flexibility necessary for generating large multi-tissue datasets across organisms beyond mouse and human. Building on the proof-of-concept presented here, researchers can expand this approach to larger sample sizes and comprehensive whole-organism profiling, thereby offering deeper insights into disease mechanisms.