Metabolic interactions in the tumor microenvironment¶

Explore metabolic interactions within the tumour microenvironment by analyzing the exchange of metabolites between spatially adjacent spots.

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Interaction Scores

Quantify the strength of metabolite exchange between spatially adjacent spots.
Interaction Types
- Competition — Spatially adjacent spots (cells) compete for the metabolites available in the microenvironment.
- Release — Spatially adjacent cells engage in metabolite exchange that benefits both, typically where a metabolite secreted by one cell is taken up and utilized by another.
- Cooperation — Cells secrete metabolites into the microenvironment without evidence of uptake by neighboring cells, contributing to the shared metabolite pool.
- Amensalism — One cell either secrets or consumes the metabolite, while spatially adjacent cell do not utilize it.
- Neutralism — Spatially adjacent cells coexist without detectable metabolic interaction, showing no significant exchange or competition for metabolites.

[ ]:

%load_ext autoreload
%autoreload
import matplotlib.pyplot as plt
import seaborn as sns
import scanpy as sc
import pandas as pd
import spmetatme.plotting as pl

import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
warnings.filterwarnings("ignore")

BATCH_SIZE = 80
K = 150

c:\Users\suraj\anaconda3\envs\spatialEnv\Lib\site-packages\tqdm\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

Load Spatial metabolic fluxes¶

[ ]:

metabolic_adata = sc.read_h5ad(r"output/metabolic_Breast_cancer_Block_A.h5ad")
interaction_type = pd.read_parquet("output/interaction_type_Breast_cancer_Block_A.parquet")
interaction_scores = pd.read_parquet("output/Interaction_score_Breast_cancer_Block_A.parquet")

Visualise the distribution of Interaction Types¶

[23]:

counts = interaction_type['Interaction type'].value_counts()
plt.figure(figsize=(4, 4))
plt.pie(
    counts,
    labels=counts.index,
    autopct='%1.1f%%',
    counterclock=True
)
plt.title("Distribution of Interaction Types")
plt.tight_layout()
plt.show()

Tissue-level Metabolic Interaction Types for selected exchange reactions¶

[31]:

reactions = ['EX_glc_D[e]','EX_gly[e]_f','EX_asn_L[e]_f','EX_leu_L[e]_f','EX_ser_L[e]_f','EX_met_L[e]_f']
chart = pl.plot_TME_interactions(metabolic_adata, interaction_type, interaction_scores, percentile_threshold=95, reactions = reactions, cols=3, save_path="global_interaction-types_Breast_cancer_Block_A_domain_1.png")
chart

[31]:

Domain-wise Metabolic Interaction Types for selected exchange reactions¶

[ ]:

reactions = ['EX_glc_D[e]','EX_gly[e]_f','EX_asn_L[e]_f','EX_leu_L[e]_f','EX_ser_L[e]_f','EX_met_L[e]_f']
charts = pl.plot_TME_domain_interactions(metabolic_adata, interaction_type, interaction_scores, reactions = reactions, key='domain', show_background=True, use_rxn_names=True, cols=3, save_path="TME_interaction-types_domain_Breast_cancer_Block_A_of_interest.png")
charts.display()

Investigate the metabolic interaction types of `top n (n = 2)` exchange reactions differentially enriched in each domain.¶

[27]:

charts = pl.plot_TME_domain_interactions(metabolic_adata, interaction_type, interaction_scores, key='domain', show_background=True, use_rxn_names=False, cols=4, save_path="TME_interaction-types_domain_Breast_cancer_Block_A_diff_expr.png")
charts.display()

Quantify Metabolic Communication in the Tissue¶

Create a network visualization of metabolic communication between spatial regions:

Edge Weight: Indicates strength of metabolic interaction

[32]:

pl.plot_cell_communication(interaction_scores, metabolic_adata, figsize=(8, 8), save_path="metabolic_communication_Breast_cancer_Block_A.pdf")