Discovering hematoma-stimulated circuits for secondary brain injury after intraventricular hemorrhage by spatial transcriptome analysis

Introduction Central nervous system (CNS) diseases, such as neurodegenerative disorders and brain diseases caused by acute injuries, are important, yet challenging to study due to disease lesion locations and other complexities. Methods Utilizing the powerful method of spatial transcriptome analysis together with novel algorithms we developed for the study, we report here for the first time a 3D trajectory map of gene expression changes in the brain following acute neural injury using a mouse model of intraventricular hemorrhage (IVH). IVH is a common and representative complication after various acute brain injuries with severe mortality and mobility implications. Results Our data identified three main 3D global pseudospace-time trajectory bundles that represent the main neural circuits from the lateral ventricle to the hippocampus and primary cortex affected by experimental IVH stimulation. Further analysis indicated a rapid response in the primary cortex, as well as a direct and integrated effect on the hippocampus after IVH stimulation. Discussion These results are informative for understanding the pathophysiological changes, including the spatial and temporal patterns of gene expression changes, in IVH patients after acute brain injury, strategizing more effective clinical management regimens, and developing novel bioinformatics strategies for the study of other CNS diseases. The algorithm strategies used in this study are searchable via a web service (www.combio-lezhang.online/3dstivh/home).


II. Supplementary of Methods (SM)
This Supplementary of Methods section consists of 11 supplementary files, which is listed as below. Figure1 IVH model (Page 32) Table 1 IVH sample grouping, library patching strategy and permeabilization time (min) (Page 33) Table 2 Parameter Definition for 3D global Pseudo-space-time trajectory reconstruction algorithm (Page 34-36) Table 3 3D global Pseudo-space-time trajectory reconstruction algorithm (Page 37-38) Table 4 Parameter Definition for the algorithm to identify a cell subtype and Similarity algorithm for cell subtypes (Page 39-40) Table 5 The algorithm to identify a cell subtype (Page 41) Table 6 Similarity algorithm for cell subtypes (Page 42) Table 7 Parameter Definition for Cell-cell communication strength (Density) algorithm (Page 43-44) Table 8 Cell-cell communication strength (Density) algorithm (Page 45) Table 9 Parameter Definition for Similarity algorithm for mutual pathway sets (Page 46) Table 10 Similarity algorithm for mutual pathway sets (Page 47)

I. Supplementary of Results (SR)
This supplementary of results section consists of 6 supplementary sections, which is listed as below. Figure 1 H&E (Hematoxylin and Eosin stain) slices of mouse brain after IVH 1.1 H&E (Hematoxylin and Eosin stain) slices of mouse brain after IVH.         Table 2 The marker gene sets of cell subtypes corresponding the selected trajectory at different time https://github.com/JiayidaerBadai/Spatial-transcriptome.git Table 3 The similarity between cell subtypes at different time on the same trajectory https://github.com/JiayidaerBadai/Spatial-transcriptome.git Table 4 The cell types that are as same as cell subtypes corresponding the selected trajectory at different time https://github.com/JiayidaerBadai/Spatial-transcriptome.git Table 5 The cell types shared by our identified cell subtypes at different time on the same trajectory. https://github.com/JiayidaerBadai/Spatial-transcriptome.git        ( ∈ , ∈ [1, ]) Nodes on the graph represents brain regions on the slide n, represents brain region I on the slide n. 5.

Supplementary of Result 5 (SR5)
( ∈ , ∈ [1, ], j ∈ [1, ]) Directed edges on the graph represents the set of directed edge from the brain region I to the brain region j on the slide n. 6.

11.
( _ , _ ) = ( Planar Centroid coordinates of node on the slide n with m spots. 12. 1 1 represents the starting node (brain region) of the set of planar pseudo-spacetime trajectories on the slide n. 13.
( , , ) = ( Distance set of node i. For each brain region I in pseudo-space-time trajectories： 4: Calculate the the average diffusion pseudotime by Eq.1 5: For each brain region I in pseudo-space-time trajectories： 6: Calculate the 3D diffusion pseudotime 3 _ by Eq.2 7: Divide the 3 _ by step size (0.0889104 for brain regions in 3 and 0.1462136 for brain regions in 1 & 2 ) to obtain the diffusion pseudotime level for each 3D brain region from 0 to 6 level.
8: Insert 1 into set 9: For each pseudo-space-time trajectories index n in ： 10: Insert 1 into set 11: For length from 1 to max_length： 12: For each pseudo-space-time trajectories index n： 14: For each vector I in ：

15:
For each vector j in e i n ：

16:
Insert vector j into set 17: If = ℎ + ： 18: Insert vector j into set 19: If == "TH" , == "CA1" or == "CA3" 20: Insert vector j into set 21: Calculate the 3D Centroid coordinates of by Eq.3-4, and distance between vector I and vector j by Eq.   describes a cell subtype in the trajectory (a->b->c) at the first day. i Represents the gene, sign describes upregulation or downregulation. 8.
Represents the set of gene names, the element of the set is represented by . 9.
Eq.11-13 calculate the similarity between different cell subtypes Trajectory .
Represents the discrete interaction intensity after conversion of the continuous interaction intensity .
Represents the sum of the interaction intensity of N points in the corresponding position. (SR5 Table 3.1-3.8) 5.
Represents the number of points for each brain region pair. 6.
Represents the number of points for all brain regions. 7.
Represents the density of the interaction intensity of the ligand-receptor ( ) of cell type over the interaction intensity of all brain regions. (SR5 Table 4   The similarity between _ and _ (SR6. Figure 3).