Images, both in high resolution TIFF + CytAssist TIFF, were loaded into 10X’s Loupe browser (v7) using the built-in Visium CytAssist Image Alignment tool and each slide was manually aligned. Since multiple tissue sections were placed in each Visium window, following fiducial alignment, individual tissue sections on each slide were selected one at the time using the Loupe browser’s spot selection tool and each tissue section alignment was exported as a separate JSON file.
Space Ranger v2.1.1 was run once for each tissue section with default settings and using the transcriptomic reference GRCh38-2020-A and Visium_Human_Transcriptome_Probe_Set_v2.0_GRCh38-2020-A.csv probe set.
The Space Ranger web summaries were inspected for additional quality control metrics, including high rates of valid barcodes and UMIs, high sequencing saturation, and high mapping rates to the probe set.
Space Ranger filtered matrixes were used for downstream analysis using a combination of Seurat v5.1.0 and Semla v1.1.6 {Hao, et. al. 2024; Larsson, et. al. 2023}. Briefly, for each sample an individual Semla object was created.
Known anatomical annotations were applied to each tissue section using a combination of known Immunohistochemistry markers (from nearby-sections) that roughly delineate each region in the midbrain (SNM, SNL, SND, SNV, VTA, SNR, RN and region), and pons (LC, region).
The FeatureViewer function in Semla was used to manually select spots belonging to each anatomical region. All cases were manually annotated and subsequently merged. In conjunction with Semla, a Seurat object with all the cases was merged separately and metadata from Semla was added utilising the AddMetadata function.