Space ranger

Human hg38 reference genome analysis set was downloaded from the University of California Santa Cruz (UCSC) ftp site (Kuhn et al., 2013 (link)). Human hg38 reference genome Ensembl gene annotations were obtained using the UCSC Table Browser Tool (Karolchik et al., 2004 (link)).
For each sequenced scRNA-Seq pool, Cellranger software (version 3.1.0) from 10 × Genomics (https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest) was used to process, align and summarize unique molecular identifier (UMI) counts against hg38 human reference genome.
Similarly, Spaceranger (version 1.0.0) software from 10X Genomics was used to process, align and summarize UMI counts against hg38 human reference genome for each spot on the Visium spatial transcriptomics array.
Corresponding antibody libraries were processed separately using CITE-Seq Count (version 1.4.3) to obtain antibody tag UMI counts for each cell barcode using the following parameters: -cbf 1 -cbl 16 -umif 17 -umil 28 -T 8 -cells 200000. Read UMI counts were summarized using 16-base barcodes for TotalSeq antibody libraries and 12-base barcodes for the in-house conjugated antibody libraries (see Fawkner-Corbett et al., 2020 (link)] for barcode sequences). Antibody UMI count matrices were then further filtered against the 10X cellular barcode whitelist for the corresponding 10x version 3.0 chemistry.

Sequencing was performed with a Novaseq PE150 platform according to the manufacturer’s instructions (Illumina, US) at an average depth of 300 million read-pairs per sample. For raw data processing, we used an in-house script to perform basic statistics, and evaluate the data quality and GC content along the sequencing cycles. Raw FASTQ files and histology images were processed by sample with the Space Ranger (version spaceranger-1.2.0, 10X Genomics, US). The filtered gene-spots matrix and the fiducial-aligned low-resolution image were used for down-stream analysis.

10 μm sections were generated from frozen inflated left lungs using a cryostat and placed on barcoded slides (10x Genomics). The Visium Spatial Gene Expression Slide & Reagent Kit (10x Genomics) was used to generate barcoded cDNA libraries. H&E stained tissues on Visium slides were imaged using a Nikon Eclipse Ti2 inverted microscope. Lung tissue on Visium slides was permeabilized for 14 min to extract mRNA. Barcoded cDNA libraries were sequenced on an Illumina NextSeq 500 instrument using a NextSeq 500/550 High Output Kit v2 (150 cycles) (20024907, Illumina) with the following cycle counts: 28 (read 1), 10 (index 1), 10 (index 2), 90 (read 2). Loupe Browser (v5.0, 10x Genomics) was used to identify which spatial sequencing capture area spots were in contact with tissue. Demultiplexing and alignment was performed with Space Ranger (v2.1, 10x Genomics) and the mm10 2020-A reference transcriptome (10x Genomics). Analysis was mainly performed in R using Seurat (v4.0). Visium datasets were integrated using the SCTransform pipeline, and PCA and UMAP were performed using the top 30 principal components. SPOTlight^{33 (link)} was used to demultiplex Visium data with our integrated scRNA-seq data used as a reference.

Cellranger and Spaceranger software (10x Genomics) were used to preprocess single-nuclei and spatial transcriptomic sequencing libraries, respectively. A formatted reference genome was generated using Cellranger or Spaceranger’s ‘mkref’ function using the Medicago truncatula MedtrA17_4.0 (ref. ^{37 (link)}) whole genome sequence and annotation and the Rhizophagus irregularis Rir_HGAP_ii_V2 (DAOM 181602, DAOM 197198)^{38 (link)} whole genome sequence and annotation using default parameters. Single-nuclei and spatial reads were aligned to the genome references using the ‘count’ function in Cellranger 7.0 and Spaceranger 1.3 software packages (10x Genomics), respectively. Brightfield tissue images were aligned to the spatial capture area fiducial frame and voxels corresponding to overlaying tissue were manually selected for all capture areas in Loupe Browser (10x Genomics). Data analysis for both the single-nuclei and spatial data was performed using the Seurat^{22 (link)} 4.3.0 package in R 4.2.1 available at https://www.R-project.org.

Droplet-based sequencing data were aligned and quantified with the Cell Ranger Software Suite (version 3.1.0, 10x Genomics) using the GRCh38 human reference genome (official Cell Ranger reference, version 3.0.0). To obtain high-quality cells, every sample underwent filtering as follows: (1) cells were filtered if the number of detected genes (log10 scale) was below the medians of all cells minus 3 × the median absolute deviation; (2) cells were filtered if the proportion of mitochondrial genes was higher than the median of all cells plus 3 × the median absolute deviation; and (3) cells were filtered if their unique molecular identifier (UMI) counts were lower than 300.
For spatial transcriptomic sequencing, FASTQ files and histology images were processed by Space Ranger (version spaceranger-1.2.0, 10x Genomics) software with default parameters. The filtered gene-spots matrix and the fiducial-aligned low-resolution image were used for down-streaming data analyses (Seurat).