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What Makes Chamber Analysis Unique?

The human heart consists of four distinct chambers, each with specialized cellular composition and molecular profiles that reflect their unique physiological roles:
  • RA (Right Atrium): Receives deoxygenated blood from the body
  • RV (Right Ventricle): Pumps blood to the lungs
  • LA (Left Atrium): Receives oxygenated blood from the lungs
  • LV (Left Ventricle): Pumps blood to the entire body
Unlike general single-cell tools, HeartMAP is purpose-built to analyze these chamber-specific differences, enabling precision cardiology approaches and chamber-targeted therapeutic strategies.

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The Four Chambers

Right Atrium (RA)

28.4% of heart cellsBlood collection from systemic circulation, initiates cardiac electrical signals

Right Ventricle (RV)

18.2% of heart cellsLow-pressure pump to pulmonary circulation

Left Atrium (LA)

26.4% of heart cellsReceives oxygenated blood, atrial compliance critical

Left Ventricle (LV)

27.0% of heart cellsHigh-pressure pump to systemic circulation, thickest walls

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Chamber-Specific Markers

Each chamber exhibits unique molecular signatures that can be identified through differential expression analysis.

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Right Atrium (RA)

NPPA - Natriuretic Peptide A
  • Hormone regulating blood pressure and volume
  • Highly expressed in atrial cardiomyocytes
  • Clinical biomarker for heart failure
MIR100HG - MicroRNA 100 Host Gene
  • Non-coding RNA involved in cardiac development
  • Regulatory role in cardiomyocyte function
MYL7 - Myosin Light Chain 7 (Atrial)
  • Atrial-specific contractile protein
  • Distinguishes atrial from ventricular myocytes
MYL4 - Myosin Light Chain 4 (Atrial)
  • Another atrial contractile marker
  • Co-expressed with MYL7
PDE4D - Phosphodiesterase 4D
  • Regulates cAMP signaling
  • Important for atrial rhythm regulation

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Right Ventricle (RV)

NEAT1 - Nuclear Paraspeckle Assembly Transcript 1
  • Long non-coding RNA
  • Stress response and RNA processing
  • Higher in RV due to pulmonary circulation demands
MYH7 - Beta-Myosin Heavy Chain
  • Slow-twitch myosin isoform
  • Higher in RV compared to fetal MYH6
  • Efficiency over speed
FHL2 - Four and a Half LIM Domains 2
  • Mechanosensing and signaling
  • Responds to wall stress
  • Important for RV adaptation
C15orf41 - Chromosome 15 Open Reading Frame 41
  • RV-specific expression
  • Function still under investigation
PCDH7 - Protocadherin 7
  • Cell adhesion molecule
  • Cardiac development and structure

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Left Atrium (LA)

NPPA - Natriuretic Peptide A
  • Shared with RA, but expression patterns differ
  • Response to pressure/volume changes
ELN - Elastin
  • Provides compliance and elasticity
  • Critical for LA distensibility
  • Higher than other chambers
MYL7 - Myosin Light Chain 7
  • Atrial marker (shared with RA)
  • Contractile function
EBF2 - Early B-Cell Factor 2
  • Transcription factor
  • Cardiac development
  • LA-specific expression pattern
RORA - RAR Related Orphan Receptor A
  • Nuclear receptor
  • Circadian rhythm and metabolism
  • Cardiac transcriptional regulation

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Left Ventricle (LV)

CD36 - Fatty Acid Translocase
  • Major fatty acid uptake protein
  • High metabolic demand of LV
  • Energy substrate preference
LINC00486 - Long Intergenic Non-Protein Coding RNA 486
  • LV-specific non-coding RNA
  • Regulatory functions
  • Ventricular identity
FHL2 - Four and a Half LIM Domains 2
  • Shared with RV, but higher in LV
  • Wall stress sensing
  • Hypertrophic response
RP11-532N4.2 - LV-Specific Long Non-Coding RNA
  • Chamber identity marker
  • Transcriptional regulation
MYH7 - Beta-Myosin Heavy Chain
  • Predominant isoform in adult LV
  • Efficient, sustained contractions
  • Shifts in disease states

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Cross-Chamber Correlations

HeartMAP analyzes expression pattern similarities between chambers to reveal functional relationships.

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Correlation Matrix

Based on analysis of 287,269 cells from 7 healthy human hearts (SCP498 dataset)
Chamber PairCorrelation (r)Interpretation
RV vs LV0.985Highest similarity - both are ventricles with similar contractile machinery
RA vs LA0.960High similarity - both are atria with shared structural features
RA vs RV0.920Moderate-high - right-side chambers, shared circulation
LA vs RV0.895Moderate - different sides and functions
RA vs LV0.880Moderate - different sides, atrium vs ventricle
LA vs LV0.870Lowest - same side but greatest functional specialization

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Key Insights

Despite different workloads:
  • Shared contractile protein genes (MYH7, FHL2)
  • Similar metabolic programs
  • Common response to mechanical stress
  • Both use beta-myosin for efficiency
Implication: Ventricular therapies may be broadly applicable, but dosing may differ due to wall thickness differences.
Common atrial features:
  • NPPA/NPPB natriuretic peptide expression
  • Atrial-specific contractile proteins (MYL7, MYL4)
  • Thinner walls, compliance-focused
  • Electrical conduction properties
Implication: Atrial fibrillation therapies may benefit both atria, but LA-specific ablation often needed.
Despite being on the same (left) side:
  • LA: compliance (ELN), atrial contractile proteins
  • LV: power (CD36, metabolic genes), ventricular proteins
  • Different mechanical stress profiles
  • Divergent transcriptional programs
Implication: Left-sided heart failure requires chamber-specific therapeutic approaches.
Right side (RA-RV): r=0.920
  • Shared deoxygenated blood
  • Similar oxygen tension
  • Common congenital defects
Left side (LA-LV): r=0.870 (lowest)
  • Oxygenated blood
  • Higher metabolic demands
  • Greater functional divergence
Implication: Right-sided heart failure progression differs from left-sided.

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Chamber-Specific Cell Populations

Each chamber contains different proportions of cell types:
Common Cell Types:
  • Cardiomyocytes (chamber-specific subtypes)
  • Endothelial cells (vascular lining)
  • Fibroblasts (structural support)
  • Immune cells (macrophages, T cells)
  • Pericytes (vascular support)

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Running Chamber Analysis

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Using MultiChamberPipeline

from heartmap import Config
from heartmap.pipelines import MultiChamberPipeline

# Initialize configuration
config = Config.default()
config.analysis.n_marker_genes = 25  # Top markers per chamber

# Create pipeline
pipeline = MultiChamberPipeline(config)

# Run analysis (requires 'chamber' column in adata.obs)
results = pipeline.run(
    data_path='data/multi_chamber_heart.h5ad',
    output_dir='results/chamber_analysis'
)

# Access chamber-specific markers
chamber_markers = results['results']['chamber_markers']

for chamber in ['RA', 'RV', 'LA', 'LV']:
    markers = chamber_markers[chamber]
    print(f"\n{chamber} top markers:")
    for gene in markers[:5]:
        print(f"  - {gene}")

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Required Data Format

Your AnnData object must contain chamber labels: 
import scanpy as sc

# Load your data
adata = sc.read_h5ad('heart_data.h5ad')

# Must have chamber annotations
print(adata.obs['chamber'].unique())
# Expected output: ['RA', 'RV', 'LA', 'LV']

# Or alternative column names
# 'location', 'heart_chamber', 'cardiac_chamber'

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Output Interpretation

1

Chamber Markers

Differentially expressed genes per chamber:
chamber_markers = {
    'RA': ['NPPA', 'MYL7', 'MYL4', ...],
    'RV': ['NEAT1', 'MYH7', 'FHL2', ...],
    'LA': ['NPPA', 'ELN', 'RORA', ...],
    'LV': ['CD36', 'FHL2', 'MYH7', ...]
}
2

Cross-Chamber Correlations

Expression correlation matrix:
correlations = pd.DataFrame({
    'RA': [1.00, 0.92, 0.96, 0.88],
    'RV': [0.92, 1.00, 0.90, 0.99],
    'LA': [0.96, 0.90, 1.00, 0.87],
    'LV': [0.88, 0.99, 0.87, 1.00]
}, index=['RA', 'RV', 'LA', 'LV'])
3

Visualizations

Automatically generated:
  • chamber_composition.png - Cell type distribution per chamber
  • chamber_markers.png - Heatmap of marker expression
  • chamber_correlations.png - Correlation network diagram

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Clinical Applications

Precision Cardiology

Chamber-Specific Therapies:
  • LA ablation for atrial fibrillation
  • LV-targeted drugs for heart failure
  • RV-specific treatments for pulmonary hypertension

Drug Target Discovery

Chamber-Specific Targets:
  • NPPA pathway modulation (atria)
  • CD36 for LV metabolic support
  • FHL2 for mechanosensing

Disease Mechanisms

Understanding Progression:
  • Why LV failure is most common
  • LA enlargement in AF
  • RV failure in pulmonary disease

Biomarker Development

Chamber-Specific Markers:
  • NPPA for atrial stretch
  • Troponins for ventricular damage
  • Chamber-specific RNA signatures

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Advanced: Custom Chamber Analysis

For custom chamber comparisons: 
import scanpy as sc
import pandas as pd

# Load data
adata = sc.read_h5ad('results/chamber_analysis/heartmap_complete.h5ad')

# Custom comparison: Compare left vs right side
left_chambers = adata[adata.obs['chamber'].isin(['LA', 'LV'])]
right_chambers = adata[adata.obs['chamber'].isin(['RA', 'RV'])]

# Differential expression
sc.tl.rank_genes_groups(
    adata,
    groupby='chamber',
    groups=['LA', 'LV'],
    reference='RA',
    method='wilcoxon'
)

# Extract markers
left_markers = sc.get.rank_genes_groups_df(adata, group='LA')
print(left_markers.head(10))

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Next Steps

Cell Communication

Analyze chamber-specific communication networks

Configuration

Optimize chamber analysis parameters

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References

Primary Study: Kgabeng, T., et al. (2025). HeartMAP: A Multi-Chamber Spatial Framework for Cardiac Cell-Cell Communication. Computational and Structural Biotechnology Journal.Dataset: Single Cell Portal SCP498 - 287,269 cells from 7 healthy human heart donors

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