Towards Understanding RNA Biology with Cryo-Electron Tomography: Moving from Breathtaking Renderings to Quantitative, Context-Aware RNA Cartography
Date:
Upcoming!!
Cryo-electron tomography (Cryo-ET) provides a unique window into the native architecture of cells, capturing macromolecular complexes in their native context. In our large-scale cryo-ET study of Chlamydomonas reinhardtii [1], we generated open-access dataset of ~1800 tomograms, yielding subnanometer resolution averages of canonical complexes such as ribosomes, rubisco, clathrin, microtubules, and nucleosomes. Additional investigations allowed us to visualize mitochondrial respiratory chain in its native context [2]. However, biologically critical RNP assemblies, for example, splicing complexes, mRNPs, or decay machinery remained underrepresented or ambiguous, despite the scale of the dataset.
This gap highlights a fundamental challenge: the complexity, flexibility, and heterogeneity of complexes involved in the RNA life cycle demand a shift in how we extract meaning from cryo-ET data. In recent works, I have focused on building computational frameworks for quantitative classification [3], structural heterogeneity [4], and spatial context [5]. These efforts aim to move beyond resolution-driven paradigms toward a context-aware systems-level approach to RNA biology, henceforth defined as RNA Cartography.
This emerging vision of RNA Cartography proposes that only by integrating ab initio detection and flexible modeling, contextual and statistical analysis, and post hoc correlation with imaging-based Omics can we begin to map the comprehensive organizational landscape of the RNA life cycle in situ. I will discuss methodological advances and conceptual drivers that underpin this shift, and how we might collectively define a roadmap toward quantitative structural systems biology of the RNA world.