DOI: 10.5281/zenodo.8417887
title: Spatiotemporal Profiling Unveiling the Cellular Organization Patterns and Local Protumoral Immune Microenvironment Remodeling in Early Lung Adenocarcinoma Progression

description: Spatial cellular organization patterns (COPs) in tumor microenvironment influence the tumor progression and therapeutic response, however, little is known about the cellular composition and functional potential of these multicellular structures during lung adenocarcinoma progression. Here, we integrate spatial transcriptomics with single cell RNA sequencing to characterize the local tumor and immunological landscape of samples from 8 patients with lung adenocarcinoma at different pathological stages. We identified ten COPs that show distinct associations with local immune states and clinical outcomes, including survival and therapy response. The local infiltration levels of regulatory and dysfunctional immune cells are increased with pathological progression. Cell-to-cell interactions between malignant cells and tumor microenvironment (TME) cells were involved in protumor immune state remodeling. Finally, we detected a group of malignant cells that were specifically located at the tumor boundary, representing a more aggressive state, were involved in the invasion of invasive adenocarcinoma (IAC). Altogether, these results can improve our understanding of the local microenvironment characteristics that underlie LUAD progression and may facilitate the identification of drug targets to prevent invasive progression and biomarkers for diagnosis.
If you're going to use this dataset, please cite 'Haojie Chen et al. 2023. Protumoral Immune Microenvironment Remodeling in Early Lung Adenocarcinoma Progression. bioRxiv. https://doi.org/10.1101/2023.10.29.564580'
description: Single-cell suspensions for scRNA-seq
1 mm3 tumor pieces were enzymatically digested with 20ml digestion medium containing 250 U/ml collagenase I, 100 U/ml collagenase IV (Worthington) and 30 U/ml DNase I (Worthington) for 45 min at 37°C in a shaking water bath with agitation. Then, the suspension was filtered through a 70-μm cell strainer, and centrifuged at 300g for 8 min. After removing the supernatant, the cell pellet was suspended in red blood cell lysis buffer (Miltenyi Biotec) for 5 min and centrifuged at 300g for 8 min. the pellet was washed twice and re-suspended with PBS with 0.04% BSA. After re-filtered through a 35μm cell strainer, the dissociated single cells were stained with AO/PI for cell concentration and viability assessment through Countstar Fluorescence Cell Analyzer (ALIT). The cell concentration was adjusted to 1000 cells/μl approximately.
scRNA-seq
Library construction was performed according to the protocol of 10X Genomics Chromium Controller Instrument and Chromium Single Cell 3' V3 Reagent Kits (10X Genomics). After quantified by the Qubit High Sensitivity DNA assay (Thermo Fisher Scientific) and Bioanalyzer 2200 (Agilent), the constructed libraries were sequenced on NovaSeq 6000 (Illumina) platform. 
Tissue section preparation, fixation, staining and imaging
Cryosections were cut in the cryostat (Leica) with a thickness of 10 μm. Firstly, RNA integrity number was assessed with 10 slides through the RNeasy Mini Kit (QIAGEN), of which the acceptable quality level is ≥7. Secondly, the permeabilization time was optimized with Visium Spatial Tissue Optimization Reagent Kits (10X Genomics). Sections on Visium Spatial Tissue Optimization Slides (10X Genomics) were fixed, stained, and then permeabilized for different times. The degree of mRNA release is transferred to a fluorescence signal and captured by the fluorescent microscope (ECLIPSE Ti, Nikon). The permeabilization time that produces the highest fluorescence signal with minimum signal diffusion was considered optimal. In this study, 6 min is optimal permeabilization time. Then, tissue sections on Visium Spatial Gene Expression Slides (10X Genomics) were incubated at 37℃ 1 min, fixed in methanol at -20℃ for 30 min. The slides were stained after being incubated in isopropanol for 1 min, Hematoxylin for 7 min, Bluing Buffer for 2 min, and Eosin for 1 min. After each staining step, slides were washed with DNase and RNase free water. Stained tissue sections are imaged by the microscope (ECLIPSE Ti, Nikon) and carefully reviewed by pathologist to annotate as tumor tissue, tumor adjacent tissue, bronchiole, coal dust, vascular, lymphoid aggregates and tertiary lymphoid structures. 
ST library construction and sequencing
Tissue permeabilization cDNA Synthesis, Library Construction were performed with Visium Spatial Gene Expression Reagent Kits (10X Genomics) and Library Construction Kit (10x Genomics) according to the Visium Spatial Gene Expression User Guide (CG000239, Rev B, 10x Genomics). the constructed libraries were sequenced on NovaSeq 6000 (Illumina) platform.

Spatial transcriptomics data processing and basic analysis
Raw Visium Spatial RNA-seq output and bright field of images of hematoxylin and eosin (H&E) stained biopsy were processed by spaceranger (V1.2.0) to detect tissue, align reads and generate feature-spot matrix. Basic QC was applied on these data. In detail, spots with low transcript content (UMIs count less than 1,000) were removed and HE stained images were labeled by a pathologist to indicate which spots contained cancerous and noncancerous tissues. Then we defined tumor signature genes and TAT signature genes by performing differential expression genes analysis between bulk RNA-seq profiles of bulk tumor and bulk normal lung tissues (Supplementary Figure2). To verify whether the spatial transcriptomic features are consistent with the histological information, we designed a model to distinguish cancerous spots and non-cancerous spots using tumor signature genes score and normal lung signature genes score. We used the Area Under the Curve (AUC) to measure the ability of this model to distinguish between two classes. A randomly selected genes set was used as a negative control.
Raw single-cell sequencing data processing and basic analysis
Raw scRNA-seq data were preprocessed, demultiplexed, align to human reference genome (hg38) by using cellranger (v.4.0.0)11. Cellular debris and doublets were considered as outliers and outliers were detected using the median absolute deviation (MAD) from the median. For a given metric (e.g., library size or the number of detected genes), outlier was one that lies over two number of MAD away from median. Low quality cells where greater than 10% of transcripts were derived from the mitochondrial genome were excluded. Finally, 45,053 cells were retained for downstream analysis.
Raw UMI counts were normalized and scaled and used for PCA using Seurat (v.3.2.3)52. Unsupervised clustering analysis were performed. UMAP was used for visualization for clusters. First, we identified DEGs for each cluster using FindAllMarkers function in Seurat and inferred the major cell types of each cluster based on the top-ranked DEGs. Totally, we defined 8 major cell types, including T/NK cells (CD3D/CD3E), B lineage cells (MZB1/MS4A1), mast cells (TPSB2/TPSAB1), myeloid cells (CD68/S100A8/S100A9), endothelial cells (VWF/PECAM1/CD34), fibroblasts (DCN/COL1A2/COL3A1), ciliated epithelial cells (TPPP3/FOXJ1) and epithelial cells (EPCAM/SFTPB).