DOI: 10.5281/zenodo.16945965
title: Complex interaction of tumor-derived factors instructs the niche specific phenotypes of tumor-associated macrophages

description: Dataset information

Abstract



Despite the pivotal role of tumor-associated macrophages (TAMs) in modulating anti-tumor immunity, the conserved patterns and molecular determinates for the formation of diverse TAM subsets remain largely elusive. In this study, we uncovered the exclusive distribution of pro-angiogenic and MHC-II programs of TAMs are well-conserved, and identified key genes and pathways required for macrophage polarization by tumor cells by CRISPR screen. Notably, we demonstrated that multiple TAM phenotypes were mainly shaped by the synergistic and antagonistic interactions between GM-CSF, PGE2, and lactic acid. We further found the inactivation of Adar, an RNA-editing enzyme, reprograms TAMs into an ISG+ phenotype characterized by the high expression of immune stimulatory genes, thereby enhancing the anti-tumor immune response. Thus, our study illuminates the molecular principles underlying the generation and rewiring of TAM functional phenotypes.


Methods



1) 10X Visium spatial gene expression: Freshly collected human lung cancer tissue was divided into appropriate size and embedded in OCT and quickly frozen on dry ice. Tissue sections were subjected to methanol fixation, HE staining, imaging and destaining following the 10x Genomics recommended experimental procedure (CG000614). All the instructions for Tissue Optimization and Library preparation were followed according to manufacturer’s protocol. Data were analyzed and quality controlled by the spatial ranger pipeline provided by 10X. For further analysis we developed a framework for spatial data analysis. The spatial ranger output can be imported into Scanpy or Squidpy by either a direct import function or manually imported using count matrix and barcode-coordinate matrix as well the H&E staining.

 

2) AFADESI-MSI spatial metabolomics: MS-grade acetonitrile was purchased from Thermo Fisher (Thermo Fisher, U.S.A). Purified water was obtained from Watsons (Hongkong, China). Formic acid was provided by Merck (Merck, Germany), the tissue freezing medium was obtained from Leica (Leica Microsystem, Germany), Eosin Y-solution 0.5% aqueous and Hematoxylin was purchased from Sigma-Aldrich (St. Louis, MO, USA). The embedded samples were stored at -80 °C before being sectioned. The samples were cut into consecutive sagittal slices 10 μm about 10 slices by a cryostat microtome (Leica CM 1950, Leica Microsystem, Germany) and were thaw-mounted on positive charge desorption plate (Thermo Scientific, U.S.A). Sections were stored at -80 °C before further analysis. They were desiccated at -20 °C for 1 h and then at room temperature for 2 h before mass spectrometry imaging (MSI) analysis. Meanwhile, an adjacent slice was left for hematoxylin-eosin (H&E) staining. The analyses was performed as previously reported (Luo et al., Anal Chem., 2013, PMID: 23384246). In brief, this experiment was carried out with an AFADESI-MSI platform (Beijing Victor Technology Co., LTD, Beijing, China) in tandem with a Q-Orbitrap mass spectrometer (Q Exactive, Thermo Scientific, U.S.A.). Here, the solvent formula was acetonitrile (ACN) /H2O (8:2) at negative mode and ACN/H2O (8:2, 0.1% FA(formylic acid (HCOOH))) at positive mode and the solvent flow rate was 5 μL/min, the transporting gas flow rate was 45 L/min, the spray voltage was set at 7 kV, and the distance between the sample surface and the sprayer was 3 mm as was the distance from the sprayer to the ion transporting tube. The MS resolution was set at 70,000, the mass range was 70-1000 Da, the automated gain control (AGC) target was 2E6, the maximum injection time was set to 200 ms, the S-lens voltage was 55 V, and the capillary temperature was 350 °C. The MSI experiment was carried out with a constant rate of 0.2 mm/s continuously scanning the surface of the sample section in the x direction and a 100 μm vertical step in the y direction. The ions detected by AFADESI were annotated by the pySM pipeline and an in-house SmetDB database (Lumingbio, Shanghai, China).

 

The collected .raw files were converted into .imzML format using imzMLConverter and then imported into MSiReader (an open-source interface to view and analyze high resolving power MS imaging files on Matlab platform) for ion image reconstructions after background subtraction using the Cardinal software package. All MS images were normalized using total ion count normalization (TIC) in each pixel (Wang et al., Nat Metab., 2022, PMID:36008550). Region-specific MS profiles were precisely extracted by matching high-spatial resolution H&E images. The intensity matrix and the corresponding spatial coordinates were imported into an AnnData object for further spatial data analysis using the Scanpy and Squidpy.

 

3) Point-to-point matching of spatial transcriptomics and spatial metabolomics: The method involves aligning images containing spatial transcriptomics information (e.g., HE-stained images) with spatial metabolomics images of adjacent sections to obtain spatial coordinate information of specific marker points. It then converts the spatial information from both datasets into a unified spatial coordinate system, calculates the final scaling factor and rotation angle, and uses these parameters to transform the spatial coordinates of the metabolomics data to match those of the transcriptomics data. Finally, it fits the spatial metabolomics data and associates it with the corresponding spots in the spatial transcriptomics data, summing the underlying pixel data to obtain the spatial metabolomics data corresponding to each spot in the spatial transcriptomics data. The aligned spatial metabolomics data were then imported into an AnnData object for integrated analysis with the corresponding spatial transcriptomics data using Scanpy and Squidpy.


Subjects

Biological sciences, Tumor immunology, Macrophages, Metabolomics, Transcriptome analysis

Funding



National Natural Science Foundation of China, 82341026

Center for Life Sciences


Data files

Alignment.zip 101.23 MBSpatialMetabolomics.zip 768.87 KBSpatialTranscriptome.zip 413.21 MB
description: README

Date of data collection:

2023-2024

Geographic location of data collection:

Wuhan, China

Funding sources that supported the collection of the data:

National Science Foundation of China grant (82341026) (DP)

Tsinghua-Peking University Center of Life Science (DP and ZZ)

Description of the data and file structure

In this dataset, the spatial architecture of lung adenocarcinoma was charted using 10X Visium Spatial Transcriptomics (n=2) and spatially resolved metabolomics (n=2) measured on adjacent sections.

Spatial Transcriptomics:

The datasets can be found in SpatialTranscriptome.zip file. Space ranger pipeline from 10X genomics was used to create the output files as shown in the “LUAD1” and “LUAD2” folders:



filtered_feature_bc_matrix folder: This folder contains gzipped TSV files with feature and barcodes sequences corresponding to row and column indices respectively.

spatial folder: This folder contains all images and spatial coordinate-related parameters used for the spatial analysis.

filtered_feature_bc_matrix.h5: Feature barcode matrices that contains only tissue-associated barcodes and are used in secondary analysis in R or Python.


We used Scanpy and Squidpy for downstream analysis, for detailed standard pipeline, please refer to the package documentation page: https://scanpy-tutorials.readthedocs.io/en/latest/spatial/basic-analysis.html and https://squidpy.readthedocs.io/en/latest/notebooks/tutorials/tutorial_visium_hne.html.

 

AFADESI-MSI Spatial metabolomics:

The spatial metabolomics datasets can be found in SpatialMetabolomics.zip. This zip file contains the the “LUAD1” and “LUAD2” folders. Each folder includes (1) raw data with .imzML extension and (2) intensity images.

The raw files can be imported into MSiReader (an open-source interface to view and analyze high resolving power MS imaging files on Matlab platform) for ion image reconstructions after background subtraction using the Cardinal software package. Cardinal is an R package that implements statistical & computational tools for analysing mass spectrometry imaging datasets, including methods for efficient pre-processing, spatial segmentation and classification.

Point-to-point matched spatial transcriptomics and spatial metabolomics:

The aligned datasets can be found in the Alignment.zip file. This zip file contains the the “LUAD1” and “LUAD2” folders. Each folder includes (1) point-to-point matched spatial metabolomics data in spatial transcriptomics coordinates (metabolites by spots) in the metadata-both.txt file and (2) the spatial transcriptomics data matrix (genes by spots) in the transdata.txt file.

The aligned spatial metabolomics data were imported into an AnnData object for integrated analysis with the corresponding spatial transcriptomics data using Scanpy and Squidpy.