Jiahao Huang

Abstract Spatially charting molecular cell types at single-cell resolution across the 3D volume is critical for illustrating the molecular basis of brain anatomy and functions. Single-cell RNA sequencing has profiled molecular cell types in the mouse brain 1,2 , but cannot capture their spatial organization. Here we used an in situ sequencing method, STARmap PLUS 3,4 , to profile 1,022 genes in 3D at a voxel size of 194 × 194 × 345 nm 3 , mapping 1.09 million high-quality cells across the adult mouse brain and spinal cord. We developed computational pipelines to segment, cluster and annotate 230 molecular cell types by single-cell gene expression and 106 molecular tissue regions by spatial niche gene expression. Joint analysis of molecular cell types and molecular tissue regions enabled a systematic molecular spatial cell-type nomenclature and identification of tissue architectures that were undefined in established brain anatomy. To create a transcriptome-wide spatial atlas, we integrated STARmap PLUS measurements with a published single-cell RNA-sequencing atlas 1 , imputing single-cell expression profiles of 11,844 genes. Finally, we delineated viral tropisms of a brain-wide transgene delivery tool, AAV-PHP.eB 5,6 . Together, this annotated dataset provides a single-cell resource that integrates the molecular spatial atlas, brain anatomy and the accessibility to genetic manipulation of the mammalian central nervous system.

The precise control of messenger RNA (mRNA) translation is a crucial step in posttranscriptional gene regulation of cellular physiology. However, it remains a challenge to systematically study mRNA translation at the transcriptomic scale with spatial and single-cell resolution. Here, we report the development of ribosome-bound mRNA mapping (RIBOmap), a highly multiplexed three-dimensional in situ profiling method to detect cellular translatome. RIBOmap profiling of 981 genes in HeLa cells revealed cell cycle-dependent translational control and colocalized translation of functional gene modules. We mapped 5413 genes in mouse brain tissues, yielding spatially resolved single-cell translatomic profiles for 119,173 cells and revealing cell type-specific and brain region-specific translational regulation, including translation remodeling during oligodendrocyte maturation. Our method detected widespread patterns of localized translation in neuronal and glial cells in intact brain tissue networks.

Although messenger RNA (mRNA) has proved effective as a vaccine, its potential as a general therapeutic modality is limited by its instability and low translation capacity. To increase the duration and level of protein expression from mRNA, we designed and synthesized topologically and chemically modified mRNAs with multiple synthetic poly(A) tails. Here we demonstrate that the optimized multitailed mRNA yielded ~4.7-19.5-fold higher luminescence signals than the control mRNA from 24 to 72 h post transfection in cellulo and 14 days detectable signal versus <7 days signal from the control in vivo. We further achieve efficient multiplexed genome editing of the clinically relevant genes Pcsk9 and Angptl3 in mouse liver at a minimal mRNA dosage. Taken together, these results provide a generalizable approach to synthesize capped branched mRNA with markedly enhanced translation capacity.

Quantifying RNAs in their spatial context is crucial to understanding gene expression and regulation in complex tissues. In situ transcriptomic methods generate spatially resolved RNA profiles in intact tissues. However, there is a lack of a unified computational framework for integrative analysis of in situ transcriptomic data. Here, we introduce an unsupervised and annotation-free framework, termed ClusterMap, which incorporates the physical location and gene identity of RNAs, formulates the task as a point pattern analysis problem, and identifies biologically meaningful structures by density peak clustering (DPC). Specifically, ClusterMap precisely clusters RNAs into subcellular structures, cell bodies, and tissue regions in both two- and three-dimensional space, and performs consistently on diverse tissue types, including mouse brain, placenta, gut, and human cardiac organoids. We demonstrate ClusterMap to be broadly applicable to various in situ transcriptomic measurements to uncover gene expression patterns, cell niche, and tissue organization principles from images with high-dimensional transcriptomic profiles.