Work flow – five basic steps were necessary to implement spatial transcriptomics technology. Step 1, placement of FFPE tissue (abdominal segments) on capture areas of a Visium gene expression (GEX) slide. Step 2, H&E staining followed by brightfield microscopic imaging with ZEISS Axioscan 7 high-performance slide scanner (White Plains, NY). Step 3, permeabilization of tissue and construction of barcoded libraries with a final sample index PCR all according to the manufacturer’s instructions. Step 4, NGS short-read sequencing (Illumina NovaSeq) of barcoded libraries by Genewiz (Azenta US, Inc, South Plainfield, NJ). Step 5, data analysis of tissue images and sequencing files in FASTQ format with Space Ranger run on Ubuntu 22.04 LTS –Thelio Mira-b3 by System76, Inc. (Denver, CO). The space ranger aggr pipeline was used to aggregate data from replicate samples and from samples from the different biological conditions (IC, ID). Loupe browser visualization software was accessed in a desktop application via Windows (Dell Optiplex 990).
FFPE sections – abdominal tissue sections (5 µm) from IC and IDβ Hltf KO newborn mice were processed with the RNeasy FFPE kit for DV200 analysis. Replicate sections from IC and IDβ HltfKO newborn mice were placed within fiducial frames of capture areas A,B and C,D respectively, on Visium GEX slide V11D13-089-A1. 10X Genomics best practices guide helped to maintain tissue adhesion and RNA integrity before and after sectioning.
GEX slide – 4 capture areas (6.5 x 6.5 mm each) inside fiducial frames that measures 8 x 8 mm. Each capture area contains 5,000 gene expression spots (55 µm in diameter) spaced with a distance of 100 µm between the centers of each spot and captures gene expression data for 1-10 cells. Visium for FFPE uses RNA-templated ligation (RTL) probes targeting the whole transcriptome. The assay does not capture transcripts directly, but captures probes via a capture sequence, e.g. poly-A for Visium for FFPE probes. Each gene expression spot has primers with a unique spatial barcode Probes are designed against the entire mouse genome, each with primers that include Illumina TruSeq Read 1 (partial read 1 sequencing primer), 16 nt spatial barcode (all primers in a specific spot share the same spatial barcode), 12 nt unique molecular identifier (UMI), and 30 nt poly(dT) sequence (captures ligation product). Spatially barcoded, ligated products were released from the slide, and harvested for qPCR with KAPA SYBR Fast qPCR master mix. The threshold for determining the Cq value for each sample was set along the exponential phase of the amplification plot at ~25% of the peak fluorescence value with QuantStudio 12 K Flex real-time PCR system (ThermoFisher Scientific). Sample index sets were selected to distinguish each of the 4 samples in a multiplexed sequencing run. Samples were amplified using Ilumina-compatible indexing primers, cleaned up with SPRIselect reagent, and bi-directionally sequenced.
Mouse Probe Set – Visium Mouse Transcriptome Probe Set v1.0 contains 20,551 gene ids targeted by 20,873 probes. Gene ids (1,086, 5.3%) targeted by 1,110 probes were excluded by default due to predicted off-target activity to a different gene. As a result, 19,465 gene_ids (targeted by 19,763 probes) were present in the final filtered output. During data analysis, read 2 sequences were mapped against the reference mouse genome C57BL/6J (GRCm38/mm10) and read 1 sequences were used for UMI filtering to obtain spatial information.
Sequencing – Illumina NovaSeq at GenWiz (Azenta Life Sciences, South Plainfield, NJ). Unique dual indexing — unique identifiers on both ends of the sample — allows for an increase in the number of samples sequenced per run and reduce per-sample cost compared to other indexing strategies. Sequencing depth was a minimum of 50k read pairs per spot covered with tissue. This was calculated by estimating the percent of capture area covered by the tissue section based upon the H&E brightfield image. Actual values are provided in Table 5.
Bioinformatics analysis utilizes the Visium Spatial Gene Expression Software Suite that includes Space Ranger and Loupe Browser. Space Ranger has five pipelines relevant to spatial gene expression experiments. Three are available for FFPE data analysis.
Spaceranger mkfastq demultiplexed the Illumina sequencer’s base call files (BCLs) for each flow cell directory into FASTQ files. Spaceranger count combined a brightfield microscope slide image and FASTQ files from spaceranger mkfastq and performed alignment, tissue detection, fiducial detection, barcode/UMI counting., and prepared a full resolution slide image for visualization in Loupe Browser.
The pipeline used the Visium spatial barcodes to generate feature-spot matrices, determine clusters, and perform gene expression analyses. The pipeline uses a probe aligner algorithm for FFPE tissues. Outputs were delivered in BAM, MEX, CSV, HDF5, TIFF, PNG, JPEG and HTML formats. Spaceranger aggr used the output of multiple runs of spaceranger count from related samples and aggregated their input, normalizing those runs to the same sequencing depth, and then recomputed the feature-barcode matrices and the analysis on the combined data. The aggr pipeline combined data from multiple samples into an experiment-wide feature-barcode matrix and analysis. Loupe Browser was used to interrogate significant genes, characterize and refine gene clusters, and perform differential expression analyses.