Single-cell transcriptomics are scalable and widely used, but proteins remain closer to cellular function and phenotype. This project aims to bridge that gap by inferring protein abundance directly from RNA data, approximating protein-level insights whilst significantly reducing the associated experimental costs.

We present the second generation of our previously published method, named scLinear2, that translates transcriptomic profiles into predicted protein signatures and evaluate its ability to capture biologically meaningful variation. ScLinear2 performs at state-of-the-art accuracy levels across different datasets, while employing an efficient and interpretable linear regression approach. We demonstrate that predicted protein profiles can support downstream analysis tasks such as automated cell-type annotation by training tissue specific models. Moreover, we utilize the interpretable nature of scLinear2 to characterize the most informative genes for each protein across datasets, revealing interesting mechanistic insights. Of note, ScLinear2 is developed as both R and Python package and is directly compatible with the most widely used single-cell analysis workflows, allowing for its easier adaptation.

Taken together, we present scLinear2: an efficient and interpretable cross-modality prediction method. Our results highlight the potential downstream applications of conversion between different omics modalities whilst conserving much of the functional differences between cells. Future work includes the application of scLinear2 in clinical data, which will allow the translational exploration of inferred protein levels.

Co-authors: Panagiotis Chouvardas, Albert Widjaja, Kristin Olsen, Beat Roth, Marianna Kruithof-de Julio