Figure 2. Unsupervised clustering at different resolutions identifying spatial domains (SpDs) and defining molecular anatomy of DLPFC.

(A) BayesSpace clustering at k=9, 16, and 28 (broad, fine, and super-fine resolution, respectively, which we refer to as Sp_kD_d for domain d from SpDs at k resolution) for three representative DLPFC tissue sections (Br8667_mid, Br6522_ant, Br6432_ant). (B) Heatmap of spatial registration with manually annotated histological layers from (12). BayesSpace identifies laminar SpDs at increasing k with the majority of Sp_kDs correlating with one or more histological layer(s). SpDs were assigned layer annotations following spatial registration to histological layers. Annotations with high confidence (cor > 0.25, merge ratio = 0.1, see Methods: Spatial registration of Spatial Domains) are marked with an “X”, and this histological layer association is denoted for a given Sp_kD_d by adding “~L,” where L is the most strongly correlated histological layer (or WM). See also Fig S11–Fig S18. (C) Spotplots depicting expression of CLDN5 in vasculature domain 1 at k=9 resolution (Sp₉D₁). (D) Boxplot confirming enrichment of CLDN5 in Sp₉D₁ compared to other Sp₉Ds across 30 tissue sections. (E) Spotplots of representative section Br6522_ant showing identification of molecularly-defined sublayers for histological L1 at k=16 (Sp₁₆D₂ and Sp₁₆D₁₄) and enrichment of HTRA1 and SPARC, respectively. (F) Boxplots quantifying enrichment of SPARC and HTRA1 in Sp₁₆D₁₄ and Sp₁₆D₂, respectively, across 30 tissue sections. (G) PCA plot showing separation of Sp₁₆D₂ and Sp₁₆D₁₄ supporting identification of molecularly distinct SpDs.