mcDETECT 2.1.5

Uncovering the dark transcriptome in polarized neuronal compartments with mcDETECT

mcDETECT

Uncovering the dark transcriptome in polarized neuronal compartments with mcDETECT

Chenyang Yuan, Krupa Patel, Hongshun Shi, Hsiao-Lin Wang, Feng Wang, Ronghua Li, Yangping Li, Victor Corces, Hailing Shi, Sulagna Das, Jindan Yu, Peng Jin, Bing Yao* and Jian Hu*

mcDETECT is a computational framework designed to study the dark transcriptome related to polarized compartments in brain using in situ spatial transcriptomics (iST) data. It begins by examining the subcellular distribution of mRNAs in an iST sample. Each mRNA molecule is treated as a distinct point with its own 3D spatial coordinates considering the thickness of the sample. Unlike many cell-type marker genes, which are typically found within the nucleus or soma, compartmentalized mRNAs often form small aggregates outside the soma. mcDETECT uses a density-based clustering approach to identify these extrasomatic aggregates. This involves calculating the Euclidean distance between mRNA points and defining the neighborhood of each point within a specified search radius. Points are then categorized as core points, border points, or noise points based on their reachability from neighboring points. mcDETECT recognizes each connected bundle of core and border points as a mRNA aggregate. To minimize false positives, it excludes aggregates that substantially overlap with somata, which are estimated by dilating the nuclear masks derived from DAPI staining. mcDETECT then repeats this process for multiple granule markers, merging aggregates from different markers that exhibit high spatial overlap. After aggregating across all markers, an additional filtering step removes aggregates containing mRNAs from negative control genes, which are known to be enriched exclusively in nuclei and somata. The remaining aggregates are considered individual RNA granules. mcDETECT then computes the minimum enclosing sphere for each aggregate to connect neighboring mRNA molecules from all measured genes and summarizes their counts, thereby defining the spatial transcriptome profile of individual RNA granules.