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HD Image

Characterization of immune cell populations in the tumor microenvironment of colorectal cancer using high definition spatial profiling

Michelli F. Oliveira†, Juan P. Romero†, Meii Chung†, Stephen Williams, Andrew D. Gottscho, Anushka Gupta, Sue Pilipauskas, Syrus Mohabbat, Nandhini Raman, David Sukovich, David Patterson, Visium HD Development Team, Sarah E. B. Taylor‡

† These authors contributed equally to this work

‡ Corresponding author

LINK TO PAPER

Abstract

Colorectal cancer (CRC) is the second-deadliest cancer in the world, yet a deeper understanding of spatial patterns of gene expression in the tumor microenvironment (TME) remains elusive. Here, we introduce the Visium HD platform (10x Genomics) and use it to investigate human CRC and normal adjacent mucosal tissues from formalin fixed paraffin embedded (FFPE) samples. The first assay available on Visium HD is a probe-based spatial transcriptomics workflow that was developed to enable whole transcriptome single cell scale analysis. We demonstrate highly refined unsupervised spatial clustering in Visium HD data that aligns with the hallmarks of colon tissue morphology and is notably improved over earlier Visium assays. Using serial sections from the same FFPE blocks we generate a single cell atlas of our samples, then we integrate the data to comprehensively characterize the immune cell types present in the TME, specifically at the tumor periphery. We observed enrichment of two pro-tumor macrophage subpopulations with differential gene expression profiles that were localized within distinct tumor regions. Further characterization of the T cells present in one of the samples revealed a clonal expansion that we were able to localize in the tissue using in situ gene expression analysis. In situ analysis also allowed us to perform in-depth characterization of the microenvironment of the clonally expanded T cell population and we identified a third macrophage subpopulation with gene expression profiles consistent with an anti-tumor response. Our study provides a comprehensive map of the cellular composition of the CRC TME and identifies phenotypically and spatially distinct immune cell populations within it. We show that the single cell-scale resolution afforded by Visium HD and the whole transcriptome nature of the assay allows investigations into cellular function and interaction at the tumor periphery in FFPE tissues, which has not been previously possible.

Data

The full dataset used in this repository and in the manuscript can be downloaded from the following link Dataset

Repository

This repository contains the scripts to replicate the findings displayed in the manuscript. It is organized into two folders Figures and Methods. The Figures folder has the scripts to replicate the figures in the manuscript and the files are named accordingly. The methods folder contains the different custom methods developed for the manuscript.

The Figures files require specific outputs generated with the Methods scripts.

Methods

In this section with provide a description and a start guide for the different methods used and developed for the manuscript.

AuxFunctions.R

R script with multiple custom R functions used in the manuscript. To load all the functions, we use the source function:

  source("~/HumanColonCancer_VisiumHD/Methods/AuxFunctions.R")

FlexSingleCell.R

R script used to process the FLEX single cell data. It takes the outputs from cellranger aggr.

Given the dataset's large size, we adopted the sketch-based analysis approach in Seurat1 v5 sketched-based analysis, sampling 15% of the entire dataset (~37,000 cells) for downstream analysis. After completing the analysis on the subsampled data, we extended it to the entire single cell dataset.

The script saves the full processed Seurat object and the Metadata for plotting purposes in the Figure scripts.

saveRDS(ColonCancer_Flex,file='~/Outputs/Flex/FlexSeuratV5.rds') # Full Seurat Object
saveRDS(ColonCancer_Flex@meta.data,file='~/Outputs/Flex/FlexSeuratV5_MetaData.rds') #Meta Data

Deconvolution.R

R script used to run spaceXR2 for deconvolution. It requires the UMI count matrix from cellranger aggr and the MetaData generated with the FlexSingleCell.R script to generate the reference. For Visium HD the Space Ranger outs are also required.

Due to the number of barcodes in Visium HD, we modified the source code of spaceXR to improve runtime. The modified version can be found in the following Pull Request. However, the original version can also be used to deconvolve the Visium HD data.

In the script we use sample P1CRC as a template to run the algorithm, but it can also be used for any other sample.

NucleiSegmentation.py

Python script used to run nuclei segmentation on H&E images used for the tissue sections processed with Visium HD. To create the the conda environemnt please see yml section

The script takes an HE image as an imput and performs nuclei segmentation on the full section using the stardist3 package. The user can provide a set of coordinates to generate a crop of the image along with the corresponding masks located within that region. The user also provides the path to the outputs directory for a given bin size (i.e. 2µm) and will output a .csv file that assigns all the barcodes located within the all segmentation masks.

The script can be called as follows:

python ./HumanColonCancer_VisiumHD/Methods/NucleiSegmentation.py -i ./PATH_TO_HE_image -r1 rowmin -r2 rowmax -c1 colmin -c2 colmax -s ./PATH_TO_SR_outs/binned_outputs/square_002um/ -o Output_directory

More details on the required inputs:

    parser.add_argument('-i','--image', type=str, help='Path to HE image')
    parser.add_argument('-r1','--rmin', type=int, help='row min for zoom in')
    parser.add_argument('-r2','--rmax', type=int, help='row max for zoom in')
    parser.add_argument('-c1','--cmin', type=int, help='column min for zoom in')
    parser.add_argument('-c2','--cmax', type=int, help='column max for zoom in')
    parser.add_argument('-s','--srdir', type=str, help='Path to spaceranger outs at a given bin size')
    parser.add_argument('-o',"--out", type=str,help="Directory where to save outputs")

Outputs:

  • Nuclei_Barcode_Map.csv csv file with the Nuclei and barcode relationship for the full section
  • labels_FullSection.pckl Labels of the identified nuclei for the full section
  • polys_FullSection.pckl Coordinates of the identified polygons for the full section.
  • img_rois_Stardist_Subset.zip if coordinates are given, segmented nuclei for the selected region. Can be visualized with QuPath4
  • img_Stardist.tif if coordinates are given, tif file with the selected zoom in region. Can be visualized with QuPath4

environment_nucleisegmentation.yml

yml file to create a conda environment with all the required dependencies for the NucleiSegmentation.py script. To create the evironment using the provided file:

conda env create --name NucleiSeg --file=./HumanColonCancer_VisiumHD/Methods/environment_nucleisegmentation.yml

To activate the environment:

conda activate NucleiSeg

Figures

The Figures folder contains all the scripts to create the figures used in the manuscript. Most of the scripts within this folder require outputs generated from the Methods section.

The required R packages are common across the files:

library(Seurat)
library(scattermore)
library(tidyverse)
library(data.table)
library(wesanderson)
library(patchwork)
library(RColorBrewer)
library(furrr)
library(paletteer)
library(arrow)
library(pheatmap)
library(RColorBrewer)
library(distances)
library(rhdf5)
library(glue)
library(Matrix)
library(ggpubr)
library(ggeasy)
library(arrow)

The beginning of each file starts with a data.frame that can be used as a template to generate the output for the different sections. We use P1CRC as a template, but can be replaced with any other section.

SampleData<-data.frame(Patient = "PatientCRC1", # Name of the Sample
                       PathSR="~/VisiumHD/PatientCRC1/outs/", # Path to space ranger outs folder
                       PathDeconvolution="~/Outputs/Deconvolution/PatientCRC1_Deconvolution_HD.rds") #spaceXR Deconvolution results

References

  1. Hao, Yuhan, et al. "Dictionary learning for integrative, multimodal and scalable single-cell analysis." Nature biotechnology 42.2 (2024): 293-304. Paper

  2. Cable, Dylan M., et al. "Robust decomposition of cell type mixtures in spatial transcriptomics." Nature biotechnology 40.4 (2022): 517-526. Paper

  3. Weigert, Martin, and Uwe Schmidt. "Nuclei instance segmentation and classification in histopathology images with stardist." 2022 IEEE International Symposium on Biomedical Imaging Challenges (ISBIC). IEEE, 2022. Paper

  4. Bankhead, Peter, et al. "QuPath: Open source software for digital pathology image analysis." Scientific reports 7.1 (2017): 1-7. Paper