SCODiA Report by MLBI Lab

Systemic Immune Landscape of Idiopathic Pulmonary Fibrosis: Insights from Peripheral Blood Single-Cell Analysis

Single-cell RNA sequencing of peripheral blood from Idiopathic Pulmonary Fibrosis (IPF) patients reveals distinct immune dysregulation compared to controls, with further differences between stable and progressive disease. Key findings include an expansion of circulating monocytes and a shift from naive to effector T cell phenotypes in IPF. Progressive IPF is marked by enhanced pro-fibrotic cell-cell interactions, altered T cell subset proportions (e.g., increased Th2 and Treg cells), and heightened innate immune activation in monocytes, suggesting a systemic inflammatory and fibrotic drive.

Contents

  1. Dataset overview
  2. UMAP Visualization of Major Cell Type Scores and Cell Type Assignments
  3. Marker Expression Dot Plot for Celltype Subsets
  4. Peripheral Blood Immune Cell Population Shifts in Idiopathic Pulmonary Fibrosis
  5. T Cell and Innate Lymphoid Cell Subset Proportions in Idiopathic Pulmonary Fibrosis
  6. T Cell Subset Population Analysis in Idiopathic Pulmonary Fibrosis Progression
  7. Monocyte Subpopulation Analysis Overview
  8. Cell-Cell Interaction Analysis in Idiopathic Pulmonary Fibrosis (IPF)
  9. Condition-Specific Cell-Cell Interaction Patterns in Idiopathic Pulmonary Fibrosis
  10. T cell CD8+ Condition-Specific Surfaceome Markers in Idiopathic Pulmonary Fibrosis
  11. T cell CD4+ Condition-Specific Surfaceome Marker Expression in Idiopathic Pulmonary Fibrosis
  12. Gene Ontology (GSA) Analysis for Monocytes in Idiopathic Pulmonary Fibrosis
  13. Gene Set Enrichment Analysis Reveals Cell-Type Specific Immune and Metabolic Dysregulation in Idiopathic Pulmonary Fibrosis
  14. Discussion
  15. Query List

0. Dataset overview

Dataset Summary

Precomputed Results

1. UMAP Visualization of Major Cell Type Scores and Cell Type Assignments

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Uniform Manifold Approximation and Projection (UMAP) visualizations of single-cell RNA sequencing data. The primary objective is to illustrate the distribution of cells based on inferred "major cell type scores" across the UMAP embedding and to show the final celltype_major assignments. This helps to assess the quality of cell type identification and the overall structure of the cellular landscape. Each HiCAT_major_score plot highlights cells with high transcriptional similarity to a specific major cell type, providing a confidence metric for their identity.

Visual Summary

The UMAP plots display 89,619 cells from blood tissue. The embedding shows several distinct cellular populations.

Biological Interpretation

The UMAP visualizations of major cell type scores demonstrate a robust separation of key hematopoietic cell lineages within the blood samples.

Annotation Notes

The analysis successfully identifies and delineates the major immune cell populations present in the blood samples. The distinct clustering and high confidence scores for T cells, B cells, Myeloid cells, and Megakaryocytic cells indicate a high quality of cell type annotation at the major level. The unassigned cluster represents an area for potential further investigation to identify novel or less common cell types that might not have strong matches to the predefined major cell type signatures. Given the blood tissue context, the low scores for non-hematopoietic cell types (Endothelial, Stromal) are expected and reassuring, indicating minimal contamination or strong enrichment of circulating non-blood cells.

2. Marker Expression Dot Plot for Celltype Subsets

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a marker expression dot plot, visualizing the expression patterns of key genes across various celltype_subset populations identified in the single-cell RNA-seq dataset. The plot displays the mean expression level of each gene within a cell group (indicated by color intensity) and the fraction of cells in that group expressing the gene (indicated by dot size). This visualization is crucial for validating existing cell type annotations by examining whether the identified marker genes align with the known biological characteristics of each cell type. The target_cell parameter was set to None, indicating that markers were found across all cell types, and surfaceome_only was set to True, focusing on surface markers for cell identification.

Visual Summary

The dot plot displays celltype_subset labels on the y-axis (rows) and marker genes on the x-axis (columns), grouped by the celltype_subset for which they were identified as prominent markers. Each row's prominent markers are highlighted by a red bounding box.

T cell subsets (Cytotoxic, Th1, Th17, Th2):

Biological Interpretation

The marker expression patterns observed generally align with the expected immunophenotypes for most of the annotated celltype_subset populations, supporting their biological identities.

Lymphoid Lineages:

Annotation Notes

Based on the marker expression patterns, the majority of the celltype_subset annotations are well-supported by robust and specific gene expression profiles. However, two specific celltype_subset annotations warrant closer scrutiny:

These discrepancies in marker expression for "LTI" and "T cell (Naive)" suggest potential issues with the underlying cell type annotation for these specific subsets, requiring further investigation or re-annotation. The annotation of Eosinophils and ILC2s could also benefit from including more highly specific markers to enhance their confidence.

3. Peripheral Blood Immune Cell Population Shifts in Idiopathic Pulmonary Fibrosis

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis presents a population bar plot illustrating the relative proportions of minor cell types identified from single-cell RNA sequencing data of peripheral blood samples. The data is categorized by disease condition: 'control', 'progressive_ipf', and 'stable_ipf'. Each bar represents an individual sample, with stacked segments showing the percentage contribution of different immune cell types, allowing for a comparative assessment of the cellular landscape across disease states.

Visual Summary

The visualization reveals distinct differences in the peripheral blood immune cell composition between control individuals and patients with Idiopathic Pulmonary Fibrosis (IPF), particularly in the 'progressive_ipf' group.

Biological Interpretation

The observed shifts in peripheral blood cell populations provide valuable insights into the systemic immune dysregulation associated with IPF.

Clinical or Translational Implications

These findings have several potential clinical and translational implications for IPF.

4. T Cell and Innate Lymphoid Cell Subset Proportions in Idiopathic Pulmonary Fibrosis

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis presents population bar plots detailing the proportions of T cell and related innate lymphoid cell (ILC) subsets within the 'T cell' major cell type compartment across individual samples from control, progressive Idiopathic Pulmonary Fibrosis (IPF), and stable IPF conditions. The data is derived from single-cell RNA sequencing of blood tissue.

Visual Summary

The stacked bar plots illustrate the relative abundance of various T cell and ILC subsets (e.g., T cell (Naive), T cell (Cytotoxic), NK cell, ILCreg, ILC1, T cell (Treg), T cell (Th1), etc.) within each sample, grouped by condition.

Key observations include:

Biological Interpretation

The observed shifts in T cell and ILC subset populations in the blood of IPF patients suggest a systemic immune dysregulation associated with the disease.

Clinical or Translational Implications

The observed immune cell shifts in the peripheral blood of IPF patients could have several clinical implications:

5. T Cell Subset Population Analysis in Idiopathic Pulmonary Fibrosis Progression

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportions of various T cell subsets and related innate lymphoid cells (ILCs) across three conditions: stable Idiopathic Pulmonary Fibrosis (IPF), progressive IPF, and control. The goal is to identify statistically significant differences in these immune cell populations in peripheral blood that may be associated with IPF disease status and progression, using a p-value cutoff of 0.1 for significance. The data is derived from single-cell RNA sequencing (scRNA-seq) of human blood samples.

Visual Summary

The box plots display the proportion of eight distinct cell types: Treg, NK, Th22, Th17, Th2, ILCreg, LTI, and T_Naive cells across stable_ipf, control, and progressive_ipf conditions.

Key observations with statistically significant differences (p < 0.1) are:

Overall, there is a clear shift in T cell and ILC subset proportions, particularly distinguishing progressive IPF from control and stable IPF, as well as notable differences between stable IPF and control.

Biological Interpretation

The observed alterations in T cell and ILC subset proportions in peripheral blood suggest distinct immunological landscapes associated with stable versus progressive IPF.

  1. Increased Treg cells in Progressive IPF: The elevated proportion of regulatory T cells (Tregs) in progressive IPF is a notable finding. Tregs are critical for maintaining immune tolerance and suppressing effector immune responses. Their increase could be a compensatory mechanism to dampen chronic inflammation or fibrosis. However, in the context of fibrosis, Tregs can sometimes contribute to fibrogenesis by suppressing anti-fibrotic immunity or directly promoting collagen deposition via mediators like TGF-β PubMed: 32623348. A persistent or dysfunctional Treg population might fail to resolve inflammation and contribute to unchecked fibrotic processes.
  2. NK Cell Modulation: The higher NK cell proportion in stable IPF compared to control might indicate a protective immune response or an early inflammatory process that is being contained. NK cells play a complex role in fibrotic diseases, sometimes exhibiting anti-fibrotic functions by eliminating activated myofibroblasts or secreting anti-fibrotic cytokines, while other studies suggest pro-fibrotic roles GeneCards: NK cells in fibrosis.
  3. Shifts in Th22, Th17, and Th2 Helper T cells:
    • The Th22 population is reduced in stable IPF but increased in progressive IPF compared to stable IPF. Th22 cells, producing IL-22, are involved in tissue repair, antimicrobial defense, and inflammatory processes. Dysregulation could point to impaired epithelial repair or a shift towards chronic inflammation PubMed: 31080345.
    • Th17 and Th2 cells show a general reduction in stable IPF compared to controls, suggesting a dampening of these effector responses. However, a significant increase in Th2 cells in progressive IPF compared to stable IPF is highly relevant. Th2 responses, characterized by cytokines like IL-4, IL-5, and IL-13, are widely implicated in the pathogenesis of fibrotic diseases, including IPF, by promoting fibroblast activation, collagen synthesis, and extracellular matrix deposition PubMed: 27956461. This shift towards a Th2-dominant profile in progressive disease aligns with existing knowledge of fibrosis.
    • The decrease in Th17 in IPF conditions versus control might indicate a shift in the immune balance away from a classic Th17-mediated inflammatory response, or possibly a sequestration of these cells in the lung microenvironment, making them less abundant in the blood.
  4. Reduced ILCreg and LTI cells in Progressive IPF: The decrease in ILCreg and LTI cells in progressive IPF compared to control is intriguing. ILCreg cells, like Tregs, are thought to have regulatory functions. A reduction could imply a weakened regulatory innate immune arm, contributing to the persistent inflammation and fibrosis seen in progressive IPF. LTI cells, often considered a subset of ILC3s, are crucial for lymphoid tissue development and organization UniProt: ILC3. Their decrease might reflect broader dysregulation of immune architecture or responsiveness.
  5. Increased T_Naive cells in Progressive IPF: An elevated proportion of T_Naive cells in progressive IPF could indicate altered lymphocyte homeostasis, reduced immune activation or differentiation, or perhaps impaired trafficking of T cells to sites of inflammation.

These findings, derived from peripheral blood, suggest a systemic immune dysregulation in IPF, with distinct patterns emerging between stable and progressive forms of the disease. The increase in pro-fibrotic Th2 cells and potentially dysfunctional Tregs in progressive IPF points towards immune mechanisms driving disease progression.

Clinical or Translational Implications

The distinct immune cell signatures observed in the peripheral blood of IPF patients, especially those with progressive disease, could have several clinical and translational implications:

6. Monocyte Subpopulation Analysis Overview

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to visualize the population distribution of Monocyte subsets across different conditions (control, progressive_ipf, stable_ipf) and individual samples. The plot_celltype_population tool was utilized with celltype_minor set to "Monocyte" and the taxonomic level for sub-classification set to subset. This configuration intended to display the proportional breakdown of various Monocyte subsets within each sample.

Visual Summary

The visualization consists of three separate bar plots, one for each condition: 'control', 'progressive_ipf', and 'stable_ipf'. Each subplot displays bars corresponding to individual samples within that condition. Notably, for every sample across all three conditions, there is only a single bar, uniformly colored in maroon and labeled "Monocyte" in the legend. All these bars consistently extend to 100% on the y-axis. This indicates that within the cell population identified as 'Monocyte' at the celltype_minor level, no further sub-classification into distinct monocyte subsets is represented at the celltype_subset level in this dataset.

Biological Interpretation

The uniform 100% representation of 'Monocyte' across all samples and conditions, without any further breakdown, indicates that the celltype_subset annotation for monocytes does not provide more granular distinctions (e.g., classical, non-classical, intermediate monocytes). Instead, the celltype_minor category "Monocyte" directly corresponds to the celltype_subset category "Monocyte".

Monocytes are critical immune cells known to play diverse roles in inflammation, tissue repair, and fibrosis. In Idiopathic Pulmonary Fibrosis (IPF), monocyte-derived macrophages are key contributors to the fibrotic process, influencing disease progression and resolution PubMed search: Monocyte-macrophage axis in IPF. Different monocyte subsets are known to have distinct migratory patterns, functional capabilities, and contributions to disease pathogenesis. For instance, classical (CD14++CD16-) monocytes are often associated with inflammatory responses, while non-classical (CD14+CD16++) monocytes are involved in patrolling and resolution phases GeneCards: CD14.

Given the known heterogeneity and context-specific roles of monocyte subsets, observing proportional shifts between these subsets in progressive versus stable IPF or control conditions could offer crucial insights into the underlying immunological mechanisms of the disease. However, the current analysis, constrained by the available celltype_subset annotations, does not allow for such a granular investigation of monocyte heterogeneity at the population level. Any condition-specific differences in monocyte biology would therefore need to be explored through differential gene expression or other functional analyses within the overarching 'Monocyte' population.

Annotation Notes

This plot serves to confirm the current level of cellular annotation within the dataset for the 'Monocyte' cell type. It highlights that while monocytes are identified, their sub-classification within the celltype_subset column is not available beyond the general 'Monocyte' category. For future, more detailed investigations into monocyte-mediated mechanisms in IPF, a more refined annotation of monocyte subsets would be valuable, potentially requiring re-clustering or re-annotation efforts based on specific marker genes for classical, non-classical, and intermediate monocyte populations.

7. Cell-Cell Interaction Analysis in Idiopathic Pulmonary Fibrosis (IPF)

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell communication patterns across different conditions (control, progressive IPF, and stable IPF) using CellPhoneDB, a method that predicts ligand-receptor interactions between cell types from single-cell RNA-seq data. The results are visualized as dot plots, where each dot represents a specific ligand-receptor interaction between a pair of cell types. The size of the dot reflects the statistical significance (-log10(p-value)), and its color indicates the interaction strength (log2(mean expression)). This provides insights into how immune and other circulating cell populations communicate differently in healthy individuals compared to patients with stable or progressive IPF, potentially highlighting pathways involved in disease pathogenesis or progression.

Visual Summary

The dot plots illustrate cell-cell interaction patterns for control, progressive IPF, and stable IPF conditions, focusing on up to 80 significant interactions per group.

  1. Overall Interaction Landscape: A consistent set of ligand-receptor pairs, including integrins (e.g., ICAM1, ICAM2, ICAM3, various alpha/beta integrin complexes), HLA molecules, SPP1 (Osteopontin), chemokines (e.g., CCL3, CCL4, CCL5), TNFSF family members, and TGFB1, are detected across all three conditions, primarily involving Monocytes, T cells (CD4+ and CD8+), and NK cells. This suggests core immune and adhesion mechanisms are universally active.
  2. Condition-Specific Differences:
    • Progressive IPF: Compared to control and stable IPF, the progressive IPF condition exhibits an increased prominence and strength (indicated by greener/yellower dots and larger sizes) in several interactions, particularly involving Monocytes.
      • Monocyte-Monocyte and Monocyte-T cell interactions: There's a notable enhancement of interactions involving Arachidonoylglycerol_by_FABP4_FPR1, VSIR-HLA-E, and PLAUR_integrin_a5_b1 in progressive IPF. Interactions involving TGFB1-TGFBR1 also appear more robust within Monocyte-Monocyte and Monocyte-T cell pairs.
      • Chemokine Signaling: Interactions involving CCL3-CCR1, CCL4-CCR1, and CCL5-CCR1/CCR5 between Monocytes and T cells (CD4+ and CD8+) appear more widespread and potentially stronger in progressive IPF, suggesting heightened chemokine-mediated immune cell recruitment.
      • SPP1-integrin_aM_b2_complex: While present in all conditions, the strength of this interaction within certain Monocyte and T cell pairs seems pronounced in progressive IPF.
    • Control vs. IPF (Progressive/Stable): The interaction ANXA1-FPR1 (Annexin A1 - Formyl Peptide Receptor 1), especially between Monocytes and T cell CD4+, appears more significant and stronger in the control group. Its reduced prominence in both stable and progressive IPF suggests a potential dampening of inflammation-resolving pathways in the disease.
    • Stable IPF: This condition generally shows interaction patterns that are somewhat attenuated compared to progressive IPF for the highly active pathways mentioned above, and sometimes also reduced compared to control for certain resolution-related interactions (e.g., ANXA1-FPR1). This might reflect a less aggressive inflammatory and fibrotic state compared to progressive disease.

Biological Interpretation

The observed cell-cell interaction patterns highlight potential mechanisms driving inflammation and fibrosis in IPF, particularly in the progressive phase, within the context of peripheral blood immune cells.

  1. Heightened Pro-fibrotic and Inflammatory Monocyte Activity in Progressive IPF:
    • The increased strength of TGFB1-TGFBR1 interactions, especially among Monocytes and with T cells in progressive IPF, is highly significant. TGF-β is a master regulator of fibrosis [1], and enhanced signaling in blood monocytes could reflect a systemic pro-fibrotic environment that drives lung remodeling.
    • The upregulation of Arachidonoylglycerol_by_FABP4_FPR1 interactions in progressive IPF points to altered lipid metabolism and signaling. Arachidonoylglycerol is an endocannabinoid, and its interaction with FPR1 (which also binds formyl peptides, acting as a chemoattractant) can modulate inflammation and immune responses. This suggests a potential role for lipid mediators in driving progressive disease.
    • VSIR-HLA-E interactions, enhanced in progressive IPF Monocytes, could indicate altered immune checkpoint regulation. VSIR (V-Set Immunoregulatory Receptor) interacts with HLA-E on antigen-presenting cells (like monocytes) to modulate T cell responses, potentially dampening anti-fibrotic immunity or promoting chronic inflammation.
    • PLAUR_integrin_a5_b1 interactions gaining prominence in progressive IPF. PLAUR (Urokinase Plasminogen Activator Receptor) is involved in extracellular matrix remodeling and cell migration, often associated with fibrotic processes. Its interaction with integrin_a5_b1 suggests enhanced migratory and tissue-invasive potential of monocytes, crucial for accumulating at sites of lung injury.
  2. Enhanced Immune Cell Trafficking and Activation:
    • The consistently strong and potentially increased significance of CCL3-CCR1, CCL4-CCR1, and CCL5-CCR1/CCR5 interactions, primarily between Monocytes and T cells in progressive IPF, strongly indicates heightened chemokine-mediated recruitment of inflammatory cells. These chemokines are potent chemoattractants for monocytes, macrophages, and T cells, driving their infiltration into inflamed tissues, including the lungs in IPF. [2]
    • SPP1 (Osteopontin) interactions with integrins (e.g., integrin_aM_b2_complex) are prominent across conditions. SPP1 is a pleiotropic cytokine involved in inflammation, immune cell recruitment, and fibrosis. Its robust presence in progressive IPF suggests its continued contribution to disease pathology through modulating cell adhesion and signaling. [3]
  3. Potential Impairment of Inflammation Resolution:
    • The relative decrease in ANXA1-FPR1 interactions in IPF conditions compared to controls is notable. Annexin A1 is an endogenous anti-inflammatory mediator that promotes the resolution of inflammation by acting on FPR1. Its reduced activity might signify a failure of pro-resolving mechanisms, contributing to persistent inflammation and driving the fibrotic cascade in IPF. GeneCards: ANXA1

Clinical or Translational Implications

The observed cell-cell interaction patterns in peripheral blood have several potential clinical and translational implications for IPF:

  1. Biomarkers of Disease Progression: The increased activity of specific monocyte-mediated interactions (e.g., involving TGFB1, Arachidonoylglycerol_by_FABP4_FPR1, VSIR-HLA-E, PLAUR_integrin_a5_b1) and chemokine axes (CCL3/4/5-CCR1/5) in progressive IPF suggests these could serve as novel blood-based biomarkers to distinguish progressive from stable disease, or even predict progression.
  2. Therapeutic Target Prioritization:
    • TGF-β Pathway: The enhanced TGFB1-TGFBR1 interactions reaffirm the central role of TGF-β in IPF progression and highlight monocytes as key players in its systemic signaling. Targeting specific components of this pathway, perhaps even monocyte-specific TGF-β signaling, remains a high-priority therapeutic strategy.
    • Chemokine Receptors: Modulating CCR1/CCR5 activity could represent a therapeutic approach to reduce the recruitment of pro-fibrotic monocytes and T cells to the lungs, particularly in progressive disease.
    • SPP1 and Integrins: Given SPP1's prominent role, therapies targeting SPP1 or its interacting integrin receptors could interfere with key pro-fibrotic and inflammatory pathways.
    • Novel Targets: Interactions like Arachidonoylglycerol_by_FABP4_FPR1, VSIR-HLA-E, and PLAUR_integrin_a5_b1 represent potentially novel targets that warrant further investigation for their specific roles in IPF pathogenesis and progression.
    • Restoring Resolution Pathways: Enhancing ANXA1-FPR1 signaling or other pro-resolving pathways could be a therapeutic strategy to counter chronic inflammation in IPF.
  3. Experimental Validation: The findings provide a strong rationale for further experimental validation in in vitro models (e.g., co-culture systems of primary monocytes and T cells under fibrotic stimuli) and in vivo models of IPF to confirm the functional relevance of these specific ligand-receptor interactions in mediating fibrogenesis and inflammation. Focusing on monocyte subsets and their specific interactive capabilities in progressive IPF could yield crucial insights.

References:

[1] Human TGF-beta signaling pathway. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7216694/

[2] Chemokines and Idiopathic Pulmonary Fibrosis. https://pubmed.ncbi.nlm.nih.gov/?term=chemokines+IPF&filter=pubmed.2019%2F1%2F1-2024%2F12%2F31

[3] GeneCards: SPP1. https://www.genecards.org/cgi-bin/carddisp.pl?gene=SPP1

8. Condition-Specific Cell-Cell Interaction Patterns in Idiopathic Pulmonary Fibrosis

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates condition-specific cell-cell interaction (CCI) patterns using single-cell RNA-seq data from blood samples of individuals with Idiopathic Pulmonary Fibrosis (IPF) (progressive and stable) and healthy controls. The plot_dot_for_cci_with_signif_difference tool was used to visualize significant differences in interactions involving major immune cells (T cells, Monocytes, NK cells, ILCs, Platelets, Granulocytes, B cells, Plasma cells) across the control, progressive_ipf, and stable_ipf conditions. Interaction strength is represented by color intensity, and statistical significance (p-value) is indicated by dot size. The goal is to identify CCIs that are differentially regulated across disease states, understand their underlying biological mechanisms, and explore their potential as therapeutic targets in IPF.

Visual Summary

The dot plot visualizes the strength and significance of various cell-cell interactions (CCI index) across individual samples, grouped by condition (control, progressive_ipf, stable_ipf).

Biological Interpretation

The analysis reveals several distinct cell-cell interaction patterns across the different IPF conditions, implicating various signaling pathways in immune modulation and fibrogenesis.

1. Prostaglandin E2 (PGE2) Signaling via PTGERs

2. PLAUR-Integrin α4β1 Complex Interactions

3. TGFB1-TGFBR3 Signaling

4. CD47-SIRPα Axis

5. CD99-PILRA Interaction

Clinical or Translational Implications

The differentially regulated cell-cell interactions identified in IPF patients highlight several potential avenues for therapeutic intervention and biomarker development.

9. T cell CD8+ Condition-Specific Surfaceome Markers in Idiopathic Pulmonary Fibrosis

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers for CD8+ T cells across control, progressive Idiopathic Pulmonary Fibrosis (IPF), and stable IPF conditions. The dot plot visualizes the mean expression level (color intensity) and the fraction of cells expressing each marker (dot size) for each sample within these conditions, comparing them against a reference (control). The selection criteria for markers include a non-zero percentage score and a fold change in non-zero percentage relative to the reference. This provides insights into alterations in CD8+ T cell surface phenotypes associated with IPF progression.

Visual Summary

The dot plot displays 20 surfaceome markers for CD8+ T cells across various samples grouped by condition: 'control', 'progressive_ipf', and 'stable_ipf'.

Biological Interpretation

The identified surfaceome markers provide insights into the functional states and trafficking properties of CD8+ T cells in the context of IPF.

Markers with Potential Relevance to Control/Healthy State:

Markers with Altered Expression in IPF (Progressive and/or Stable):

Clinical or Translational Implications

The differential expression of these surfaceome markers on CD8+ T cells in IPF patients, particularly between progressive and stable disease, offers several potential clinical implications:

  1. Biomarkers for Disease Progression and Stratification:
    • Markers like PTGER2, CX3CR1, HLA-DRB5, and FCGR3A, which appear more prevalent or highly expressed in progressive and stable IPF samples, could serve as part of a panel of blood-based biomarkers to:
      • Distinguish IPF patients from controls.
      • Potentially stratify patients by disease severity or progression risk (e.g., differentiating progressive vs. stable IPF).
    • Monitoring the expression levels of SELL (CD62L) and CD27, if found to be consistently downregulated in IPF, might also provide insights into the activation and differentiation state of circulating CD8+ T cells, reflecting chronic immune activation or exhaustion.
  2. Therapeutic Targets:
    • PTGER2 (EP2): The upregulation of this PGE2 receptor on CD8+ T cells in IPF patients suggests that PGE2 signaling pathways may be aberrantly activated. Modulating EP2 activity (e.g., with EP2 antagonists or specific agonists) could be a therapeutic strategy to rebalance T cell responses and reduce inflammation or fibrosis. PubMed Search: PTGER2 antagonist IPF.
    • S1PR1: Given its critical role in lymphocyte trafficking, S1PR1 modulators (e.g., fingolimod, a known S1PR1 agonist used in MS) could be explored to alter the migration of pathogenic CD8+ T cells to the lung, although off-target effects and the precise role of circulating vs. lung-resident T cells in IPF need careful consideration. PubMed Search: S1PR1 modulator IPF.
    • CX3CR1: Targeting the CX3CL1-CX3CR1 axis might reduce the recruitment of highly cytotoxic or inflammatory CD8+ T cells to the fibrotic lung, potentially slowing disease progression. PubMed Search: CX3CR1 inhibitor IPF.
    • CD300A: As an inhibitory receptor, agonists for CD300A could be investigated to dampen hyperactive or pathogenic immune responses in IPF, potentially inducing a more regulatory T cell phenotype.
  3. Validation and Further Research:
    • These findings warrant further validation in larger cohorts and investigation into the functional consequences of altered marker expression on CD8+ T cell behavior in IPF.
    • *In vitro* studies and animal models could be used to directly assess the impact of manipulating these surface molecules on CD8+ T cell activation, cytokine production, cytotoxicity, and contribution to fibrotic processes.
    • Integrating these surfaceome findings with bulk or single-cell RNA-seq from lung tissue would provide a more comprehensive picture of the local immune environment and direct relevance to lung pathology.

10. T cell CD4+ Condition-Specific Surfaceome Marker Expression in Idiopathic Pulmonary Fibrosis

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis focuses on identifying and visualizing surfaceome markers in CD4+ T cells, comparing expression across control, progressive idiopathic pulmonary fibrosis (IPF), and stable IPF conditions. The objective is to highlight differentially expressed surface markers that could offer biological insights into IPF pathogenesis and serve as potential therapeutic targets. Markers were selected based on differential expression against the control group, utilizing specific non-zero percentage (nz_pct) fold change (FC) and score cutoffs tailored for each comparison, as detailed in the plot title. The dot plot illustrates both the mean expression level (indicated by color intensity) and the fraction of cells expressing the marker (represented by dot size) for each identified gene within individual samples.

Visual Summary

The dot plot displays the expression patterns of 10 distinct surfaceome markers in CD4+ T cells across individual samples from control (Cxx), progressive IPF (Pxx), and stable IPF (Sxx) groups.

  1. Constitutively Expressed Markers: Genes such as IL10RA, ICAM2, and TRAT1 demonstrate consistently high mean expression and are prevalent in a substantial fraction of CD4+ T cells across nearly all samples, regardless of their disease status. CD82 also exhibits high expression in most samples, with some noticeable increases in the progressive IPF group. TMEM219 shows moderate to high expression levels consistently. These markers likely represent fundamental features of CD4+ T cell biology or general immune processes.
  2. Differentially Upregulated Markers in IPF: A clear and prominent upregulation pattern is observed for S1PR4, PTGER2, GLIPR1, and SIT1 in both progressive and stable IPF samples when compared to control samples.
    • S1PR4 and PTGER2 show particularly strong upregulation, characterized by both increased mean expression (indicated by darker red dots) and a higher fraction of expressing cells (larger dot sizes) in most progressive IPF samples (e.g., P04, P12, P05, P02) and many stable IPF samples (e.g., S23, S15, S14). This pattern indicates a robust and consistent alteration in the expression of these receptors in IPF.
    • GLIPR1 and SIT1 also exhibit a consistent increase in expression and prevalence in IPF samples compared to controls, although their mean expression levels are generally lower than those of S1PR4 and PTGER2.
    • TMEM179B generally shows low expression across all conditions, with only minor, inconsistent increases observed in a few IPF samples.

In summary, the most significant findings are the consistent and often strong upregulation of S1PR4, PTGER2, GLIPR1, and SIT1 in CD4+ T cells from IPF patients, particularly in the progressive stage of the disease. This suggests their potential involvement in the underlying pathology of IPF.

Biological Interpretation

The observed differential expression of specific surfaceome markers in CD4+ T cells offers valuable insights into the altered immune cell functions and signaling pathways involved in IPF.

S1PR4 (Sphingosine-1-Phosphate Receptor 4):

PTGER2 (Prostaglandin E Receptor 2, EP2 Type):

SIT1 (SHP2 Interacting Transmembrane Adaptor Protein 1):

GLIPR1 (GLI Pathogenesis Related 1):

Clinical or Translational Implications

The identification of upregulated surfaceome markers in CD4+ T cells from IPF patients opens several promising avenues for clinical and translational research:

  1. Therapeutic Targets:
    • S1PR4: Given the established role of S1P signaling in both fibrosis and immune cell trafficking, S1PR4 emerges as a promising therapeutic target. Modulating S1PR4 activity (e.g., through selective antagonists) in T cells could potentially reduce their migration into fibrotic lung tissue or alter their profibrotic functions, thereby mitigating disease progression. The successful clinical use of S1P receptor modulators for other conditions suggests a viable pathway for drug development or repurposing in IPF [7].
    • PTGER2: Targeting PTGER2 could offer another strategy to modulate immune responses in IPF. While the role of PGE2/PTGER2 in IPF is intricate, selectively blocking or activating this receptor could potentially reorient T cell responses towards an anti-fibrotic or anti-inflammatory phenotype. Further research is essential to elucidate the precise consequences of PTGER2 signaling in IPF CD4+ T cells.
    • SIT1: Modulating adaptor proteins like SIT1 could provide a means to finely tune T cell activation and prevent overactive or dysfunctional immune responses implicated in IPF. If SIT1 upregulation contributes to T cell anergy or exhaustion in IPF, strategies aimed at restoring effective T cell function could be explored.
  2. Biomarkers of Disease Activity/Progression:
    • The consistent upregulation of S1PR4, PTGER2, GLIPR1, and SIT1 in progressive and stable IPF, particularly when compared to controls, suggests their potential as blood-based biomarkers. Monitoring the expression levels of these surface markers on circulating CD4+ T cells could assist in:
      • Diagnosis: Aiding in the differentiation of IPF from other lung diseases or identifying patients at an earlier stage.
      • Prognosis: Predicting disease progression or assessing individual patient response to therapy.
      • Pharmacodynamic Readouts: Evaluating the efficacy of novel immune-modulating drugs in clinical trials.
  3. Experimental Validation:
    • Further experimental validation is crucial to confirm these findings and clarify the functional consequences of altered expression of S1PR4, PTGER2, SIT1, and GLIPR1 in IPF CD4+ T cells. This could involve:
      • *In vitro* studies: Examining the effects of S1P or PGE2 on CD4+ T cells from IPF patients, including their migratory capacity, proliferation, and cytokine production.
      • *In vivo* models: Investigating the impact of genetically modifying or pharmacologically targeting these receptors in animal models of pulmonary fibrosis.
      • *Clinical Studies:* Correlating the expression of these markers with clinical parameters of IPF severity and progression in larger, independent patient cohorts.

These insights pave the way for a deeper understanding of CD4+ T cell contributions to IPF and inform the development of targeted therapeutic interventions.

---

References:

[1] GeneCards - S1PR4: GeneCards

[2] PubMed search for "sphingosine-1-phosphate pulmonary fibrosis": PubMed Search

[3] GeneCards - PTGER2: GeneCards

[4] PubMed search for "PGE2 pulmonary fibrosis": PubMed Search

[5] GeneCards - SIT1: GeneCards

[6] GeneCards - GLIPR1: GeneCards

[7] PubMed search for "S1P receptor modulators clinical use": PubMed Search

---

11. Gene Ontology (GSA) Analysis for Monocytes in Idiopathic Pulmonary Fibrosis

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Ontology (GSA) results for Monocytes, comparing different Idiopathic Pulmonary Fibrosis (IPF) conditions (progressive_ipf, stable_ipf) against a healthy control group, and also comparing the control group against all IPF conditions combined. The GSA was performed on genes that were *up-regulated* in each respective comparison. These bar plots illustrate the top enriched biological terms, ordered by their statistical significance (-log(p-val) and -log(q-val), where q-val is the False Discovery Rate adjusted p-value).

Visual Summary

The provided bar plots show significantly enriched Gene Ontology terms in Monocytes across three different comparisons:

  1. GSA for Monocyte: control_vs_others (Based on up-regulated DEGs): This plot highlights pathways that are more active in control Monocytes compared to Monocytes from both progressive and stable IPF patients. The most significantly enriched terms (with -log(p-val) exceeding 15 and -log(q-val) exceeding 10) are predominantly related to neurodegenerative diseases (e.g., Amyotrophic lateral sclerosis, Parkinson disease, Huntington disease, Alzheimer disease, Pathways of neurodegeneration) and fundamental cellular processes like "Spliceosome", "Proteasome", and "Protein processing in endoplasmic reticulum". These pathways are highly significant, indicating a strong difference in gene expression.
  2. GSA for Monocyte: progressive_ipf_vs_control (Based on up-regulated DEGs): This plot identifies pathways that are up-regulated in Monocytes from progressive IPF patients compared to control. Key enriched terms (with -log(p-val) up to ~8 and -log(q-val) up to ~4) include "Coronavirus disease", "Ribosome", "Diabetic cardiomyopathy", "Salmonella infection", "Osteoclast differentiation", "Oxidative phosphorylation", "Shigellosis", "Yersinia infection", "NOD-like receptor signaling pathway", "Endocytosis", "Phagosome", "Leishmaniasis", and "Chemokine signaling pathway". This suggests a strong activation of innate immune responses, metabolic shifts, and phagocytic functions.
  3. GSA for Monocyte: stable_ipf_vs_control (Based on up-regulated DEGs): This plot illustrates pathways up-regulated in Monocytes from stable IPF patients compared to control. Prominent enriched terms (with -log(p-val) up to ~11 and -log(q-val) up to ~6) include "Coronavirus disease", "NOD-like receptor signaling pathway", "Shigellosis", "Human cytomegalovirus infection", "Lipid and atherosclerosis", "Kaposi sarcoma-associated herpesvirus infection", "Salmonella infection", and "Endocytosis". Similar to progressive IPF, this indicates innate immune activation and pathogen response, although the overall significance values appear somewhat lower than for progressive IPF.

Biological Interpretation

Monocytes are crucial innate immune cells that play a significant role in inflammation, tissue remodeling, and fibrosis, including in the context of Idiopathic Pulmonary Fibrosis (IPF) PubMed search: Monocytes in IPF pathogenesis.

Clinical or Translational Implications

The distinct pathway enrichments in Monocytes from progressive and stable IPF patients compared to controls offer valuable insights into the disease pathogenesis and potential therapeutic avenues:

12. Gene Set Enrichment Analysis Reveals Cell-Type Specific Immune and Metabolic Dysregulation in Idiopathic Pulmonary Fibrosis

Report figure

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Gene Set Enrichment Analysis (GSEA) results visualized as a dot plot. The plot investigates pathway enrichment or depletion across various immune cell types from blood samples of patients with Idiopathic Pulmonary Fibrosis (IPF) compared to healthy controls. Specifically, it compares gene expression profiles for progressive_ipf vs control and stable_ipf vs control for each cell type.

Each dot represents a specific gene set (pathway) for a given cell type and comparison condition.

Visual Summary

The dot plot reveals widespread and statistically significant alterations in gene set enrichment across multiple immune cell types in IPF patients, particularly those with progressive disease.

  1. Broad Immune Activation: Many immune-related pathways, such as "Cytokine-cytokine receptor interaction", "JAK-STAT signaling pathway", "MAPK signaling pathway", and "Toll-like receptor signaling pathway", show strong enrichment (large red dots) across a majority of immune cell types, including Monocytes, T cells (CD4+ and CD8+), NK cells, Granulocytes, B cells, and ILCs, especially in the progressive_ipf_vs_control comparisons.
  2. Cell-Type Specific Responses:
    • Monocytes display some of the most extensive and significant pathway alterations, including prominent enrichment in inflammatory signaling (JAK-STAT, MAPK, Toll-like receptor), Apoptosis, and Cellular senescence in progressive IPF.
    • T cells (CD4+ and CD8+) in progressive IPF show strong enrichment in T cell receptor signaling, Th1 and Th2 differentiation, Th17 cell differentiation, and various cytokine-mediated pathways, indicating active T cell responses and differentiation skewing. CD8+ T cells also show strong enrichment in "NK cell mediated cytotoxicity".
    • NK cells exhibit significant enrichment in "NK cell mediated cytotoxicity", "Toll-like receptor signaling pathway", and cytokine signaling, suggesting heightened cytotoxic activity and innate immune activation.
    • Granulocytes also demonstrate robust enrichment of inflammatory pathways, similar to monocytes, highlighting their involvement in the systemic immune response.
    • B cells show enrichment in "B cell receptor signaling pathway" and cytokine-related pathways.
  3. Metabolic Reprogramming: Several metabolic pathways, including "Glycolysis / Gluconeogenesis" and "Pentose phosphate pathway", are consistently downregulated (blue dots) across many cell types (e.g., Monocytes, T cells, NK cells, Granulocytes, Platelets) in both stable and progressive IPF, though with varying degrees of significance. "Fatty acid elongation" is also downregulated in some cell types.
  4. Distinction between Progressive and Stable IPF: While both stable_ipf_vs_control and progressive_ipf_vs_control show significant changes, the progressive_ipf_vs_control condition often exhibits more pronounced (larger and more intensely colored dots) and widespread pathway enrichment/depletion across cell types, suggesting an exacerbated or distinct immune and cellular response in progressive disease. For instance, the downregulation of "Glycolysis / Gluconeogenesis" appears more significant in Monocytes from stable IPF compared to progressive IPF.

Biological Interpretation

The GSEA results from blood-derived immune cells in IPF patients provide crucial insights into the systemic biological processes disrupted in the disease.

  1. Systemic Immune Activation and Inflammation: The widespread enrichment of "Cytokine-cytokine receptor interaction", "JAK-STAT signaling pathway" https://www.genecards.org/cgi-bin/carddisp.pl?gene=JAK2, "MAPK signaling pathway", and "Toll-like receptor signaling pathway" across various immune cell types (Monocytes, T cells, NK cells, Granulocytes) strongly indicates a state of chronic immune activation and inflammation in IPF, particularly in the progressive form. This is consistent with the established understanding of IPF involving dysregulated immune responses and cytokine networks, which drive fibrosis in the lungs https://pubmed.ncbi.nlm.nih.gov/?term=idiopathic+pulmonary+fibrosis+inflammation+immune.
  2. T Cell Dysregulation: The enrichment of "T cell receptor signaling pathway", "Th1 and Th2 cell differentiation", and "Th17 cell differentiation" in CD4+ T cells suggests an altered helper T cell balance, a common feature in chronic inflammatory and fibrotic diseases. The enrichment of "NK cell mediated cytotoxicity" in both NK cells and CD8+ T cells indicates a hyperactive cytotoxic immune compartment, potentially contributing to tissue damage or immune dysregulation.
  3. Monocyte and Granulocyte Involvement: The prominent involvement of monocytes and granulocytes, exhibiting enrichment in inflammatory pathways, points to their critical roles as orchestrators and effectors of innate immunity and inflammation in IPF. These cells can be sources of pro-fibrotic mediators.
  4. Cellular Senescence and Apoptosis: The enrichment of "Cellular senescence" and "Apoptosis" pathways in monocytes and granulocytes is noteworthy. Cellular senescence in immune cells can lead to a senescent-associated secretory phenotype (SASP), contributing to chronic inflammation and fibrosis https://pubmed.ncbi.nlm.nih.gov/31332095/. Dysregulated apoptosis can affect immune cell homeostasis and contribute to pathology.
  5. Metabolic Reprogramming in Immune Cells: The downregulation of "Glycolysis / Gluconeogenesis" and "Pentose phosphate pathway" across multiple immune cell types suggests a metabolic shift. While immune activation often involves increased glycolysis, this observation in peripheral blood might indicate a specific metabolic adaptation, an altered functional state, or metabolic exhaustion in these circulating cells in the context of IPF. Further investigation would be needed to clarify the precise metabolic state and its functional consequences.

Clinical or Translational Implications

The findings from this GSEA analysis have several potential clinical and translational implications for IPF:

  1. Biomarkers for Disease Progression: The observed differences in pathway activity between stable and progressive IPF, especially the more pronounced immune activation in progressive disease, could identify novel blood-based biomarkers for distinguishing disease states and predicting progression. For example, specific shifts in JAK-STAT signaling or T cell differentiation pathways within monocytes or T cells could serve as prognostic indicators.
  2. Therapeutic Targets: The consistent and significant enrichment of pathways like JAK-STAT and MAPK signaling across multiple immune cell types highlights these pathways as potential therapeutic targets. Inhibitors of JAK-STAT, such as pirfenidone and nintedanib, are already used in IPF treatment, and these results further support their mechanisms of action and suggest opportunities for more targeted immunomodulatory therapies.
  3. Understanding Systemic Contributions to Lung Fibrosis: As these findings are from blood, they underscore the importance of systemic immune and inflammatory processes in the pathogenesis of IPF, which primarily affects the lungs. Understanding the peripheral immune landscape can provide insights into how circulating immune cells contribute to or reflect the ongoing pathology within the lung.
  4. Monitoring Treatment Response: Future studies could explore whether changes in these enriched/depleted pathways in specific blood cell populations correlate with treatment response, offering a way to monitor therapeutic efficacy.

13. Discussion

The comprehensive single-cell analysis of peripheral blood from Idiopathic Pulmonary Fibrosis (IPF) patients provides critical insights into the systemic immune dysregulation that characterizes the disease, distinguishing between stable and progressive forms. The initial UMAP and major cell type analysis confirmed robust clustering of key hematopoietic lineages—T cells, Myeloid cells, B cells, and Megakaryocytic cells—consistent with a blood-derived dataset.

A prominent feature of IPF is the expansion of circulating monocytes in both stable and progressive disease, concomitant with a relative decrease in CD4+ and CD8+ T cells. This suggests a systemic pro-fibrotic environment and enhanced mobilization or recruitment of monocytes, which are crucial precursors to pro-fibrotic macrophages in the lung. Further T cell subset analysis revealed a consistent shift from naive to more differentiated effector/innate lymphoid phenotypes in IPF, marked by decreased Naive T cells and increased Cytotoxic T cells and NK cells, indicating a state of chronic immune activation.

More granular insights into T cell subsets revealed statistically significant shifts distinguishing stable vs. progressive IPF from controls. Progressive IPF showed a notable increase in regulatory T cells (Tregs), which in the context of fibrosis can contribute to unchecked fibrotic processes, and a significant increase in Th2 cells, known drivers of fibrosis. Stable IPF, in contrast, exhibited reduced Th17 and Th2 cells compared to controls, suggesting a distinct, potentially less aggressive, immunoregulatory state, though Th2 cells later increased in progression. Reduced ILCreg and LTI cells in progressive IPF might imply a weakened innate immune regulatory arm.

While detailed monocyte subsets were not available in the provided annotation, Gene Ontology (GSA) analysis provided crucial functional insights. Monocytes from progressive IPF patients displayed robust activation of innate immune responses (e.g., NOD-like receptor signaling, infection-related pathways), increased phagocytic activity, metabolic reprogramming (oxidative phosphorylation), and active cellular recruitment (chemokine signaling). This paints a picture of highly activated, pro-inflammatory monocytes. Stable IPF monocytes shared some innate immune activation themes but with attenuated significance, indicating a less aggressive profile. Conversely, control monocytes showed enrichment in fundamental cellular maintenance pathways, hinting at potential cellular dysfunction or increased stress in IPF monocytes.

Cell-cell interaction (CCI) analyses unveiled key molecular crosstalk. Progressive IPF was characterized by enhanced pro-fibrotic and inflammatory interactions, including heightened TGFB1-TGFBR1 signaling (a master regulator of fibrosis) among monocytes and T cells. Upregulation of Arachidonoylglycerol_by_FABP4_FPR1 (altered lipid metabolism), VSIR-HLA-E (immune checkpoint), and PLAUR_integrin_a5_b1 (ECM remodeling, migration) further implicated monocytes in driving progression. Chemokine signaling (CCL3/4/5-CCR1/5) was also elevated, suggesting increased immune cell recruitment. Interestingly, stable IPF showed a distinct pattern, including a notably strong and consistent upregulation of the CD99-PILRA interaction, which might represent a unique inhibitory checkpoint contributing to disease stability. A general decrease in the anti-inflammatory ANXA1-FPR1 interaction in IPF could signify impaired resolution.

Differential surfaceome marker expression on T cells further supported these functional shifts. CD8+ T cells in IPF showed reduced naive markers (e.g., CD62L, CD27) but increased expression of S1PR1 (trafficking), PTGER2 (PGE2 signaling), HLA-DRB5 (atypical activation), FCGR3A (unconventional cytotoxicity), and CX3CR1 (effector memory), alongside upregulation of the inhibitory receptor CD300A. Similarly, CD4+ T cells in IPF consistently upregulated S1PR4 (S1P signaling, fibrosis), PTGER2, SIT1 (TCR signaling modulation), and GLIPR1. These changes reflect altered T cell migratory capacities, activation states, and immune modulatory functions contributing to fibrosis.

Finally, Gene Set Enrichment Analysis (GSEA) provided a global view, confirming widespread systemic immune activation and inflammation (e.g., JAK-STAT, MAPK, Toll-like receptor signaling) across most immune cell types, more pronounced in progressive IPF. T cell dysregulation (TCR signaling, Th1/Th2/Th17 differentiation) and NK cell mediated cytotoxicity were also enriched. Intriguingly, downregulation of glycolysis and pentose phosphate pathways suggested specific metabolic shifts in circulating immune cells, potentially indicating altered functional states or metabolic exhaustion.

Collectively, these findings strongly suggest a systemic pro-inflammatory and pro-fibrotic immune environment in IPF, with peripheral blood changes reflecting or contributing to lung pathology. The observed differences between stable and progressive IPF are particularly critical, with progressive disease showing a more aggressive inflammatory and fibrotic immune signature. It's important to note the identified annotation discrepancies for 'LTI' (appearing myeloid-like) and 'T cell (Naive)' (appearing like activated helper T cells) subsets in the initial marker analysis. While these discrepancies warrant further investigation into the accuracy of these specific sub-annotations, the robustness of the broader cell type, interaction, and pathway findings remains strong, providing a comprehensive view of IPF immune dynamics.

Hypotheses:

  1. Heightened systemic monocyte activation, driven by innate immune sensing and metabolic reprogramming, promotes their recruitment to the lungs and subsequent differentiation into pro-fibrotic macrophages, thereby driving IPF progression.
  2. The observed shift towards increased Th2 and potentially dysfunctional Treg cell proportions in the peripheral blood of progressive IPF patients creates an immunosuppressive and pro-fibrotic microenvironment that exacerbates lung fibrosis.
  3. Aberrant lipid-mediated signaling (e.g., Arachidonoylglycerol_by_FABP4_FPR1) and impaired anti-inflammatory resolution pathways (e.g., reduced ANXA1-FPR1) in progressive IPF perpetuate chronic inflammation and prevent resolution of fibrotic lesions.
  4. Upregulation of specific surface receptors like S1PR4 and PTGER2 on CD4+ T cells, and S1PR1 and CX3CR1 on CD8+ T cells, in IPF alters their migratory capacity, effector functions, and responsiveness to environmental cues, contributing to immune cell accumulation and activation within fibrotic lesions.
  5. The robust CD99-PILRA interaction uniquely upregulated in stable IPF represents a key inhibitory checkpoint mechanism that contributes to maintaining immune homeostasis and limiting fibrosis progression in this disease state.

Potential therapeutic targets:

  1. TGF-β Pathway (specifically TGFB1-TGFBR1/TGFBR3 interactions): TGF-β is a central mediator of fibrosis, and its enhanced signaling, particularly between monocytes and T cells, is a consistent and robust finding in progressive IPF. Modulating this pathway is crucial for anti-fibrotic therapy. Evidence: Increased TGFB1-TGFBR1/TGFBR3 interactions are strongly enhanced in progressive IPF monocytes and T cells. TGF-β pathway enrichment is also observed in GSEA results. Validation: Test the efficacy of existing TGF-β inhibitors (e.g., pirfenidone, nintedanib partially target this pathway) or novel specific TGF-β receptor blockers in preclinical and clinical IPF models. Monitor TGF-β pathway activity markers in blood and lung tissue with treatment.
  2. S1P Receptors (S1PR1 on CD8+ T cells, S1PR4 on CD4+ T cells): Sphingosine-1-phosphate (S1P) signaling is critical for immune cell trafficking and is strongly implicated in fibrosis. Upregulation of S1PRs on circulating T cells suggests altered migration patterns and potentially pro-fibrotic functions. Evidence: Upregulation of S1PR1 on CD8+ T cells and S1PR4 on CD4+ T cells is consistently observed in IPF, especially progressive disease. Validation: Evaluate selective S1PR antagonists or modulators to inhibit T cell migration into fibrotic lesions and reduce their potential pro-fibrotic activities in IPF models. Assess their impact on T cell activation and cytokine production.
  3. PTGER2 (Prostaglandin E Receptor 2): PTGER2, a receptor for PGE2, is upregulated on both CD4+ and CD8+ T cells in IPF. PGE2 is a potent immunomodulator with complex roles; altered EP2 signaling can skew T cell differentiation and inflammatory responses, potentially contributing to fibrogenesis. Evidence: Consistent upregulation of PTGER2 on CD4+ and CD8+ T cells in IPF. Differential PGE2 signaling is also noted in cell-cell interaction analyses. Validation: Investigate the effect of PTGER2-specific antagonists on T cell function, inflammation, and fibrosis in preclinical IPF models. Examine whether PTGER2 inhibition can rebalance immune responses towards an anti-fibrotic phenotype.
  4. Chemokine Receptors (CCR1/CCR5): Heightened CCL3/4/5-CCR1/5 interactions indicate increased chemokine-mediated recruitment of inflammatory and pro-fibrotic monocytes and T cells to the lungs, a key process in progressive IPF. Evidence: Increased CCL3/4/5-CCR1/5 interactions, primarily between monocytes and T cells, are more widespread and stronger in progressive IPF. 'Chemokine signaling pathway' is highly enriched in progressive IPF monocytes. Validation: Test CCR1/CCR5 antagonists to reduce immune cell infiltration into the lung and mitigate fibrosis in IPF models. Monitor reduction of specific immune cell populations in fibrotic lesions.
  5. CD47-SIRPα Axis: This 'don't eat me' signaling pathway critically modulates macrophage function and immune cell survival, both of which are central to fibrosis. Its distinct interaction patterns across conditions, particularly the strong CD47_SIRB1_complex interaction in stable IPF, suggest a role in disease progression or stability. Evidence: Differential patterns of CD47-SIRPα interactions are observed across conditions, with a notable CD47_SIRB1_complex interaction between B cells and monocytes in stable IPF. Validation: Investigate the effects of CD47-blocking antibodies or SIRPα modulators on immune cell clearance, macrophage activation, and fibrosis in IPF models. Determine if enhancing or inhibiting specific CD47-SIRPα interactions is beneficial for therapeutic outcomes.

Follow-up validation ideas:

  1. Flow cytometry or mass cytometry: Validate changes in immune cell population frequencies (monocytes, T cell subsets, NK cells) and surface marker expression (e.g., S1PR4, PTGER2, CX3CR1, CD300A, CD62L, CD27) in larger independent IPF cohorts, correlating these with clinical progression markers and lung function tests.
  2. Functional assays: Conduct *in vitro* assays on isolated monocytes and T cells from IPF patients to assess their cytokine production profiles, migratory capacity towards S1P or specific chemokines, phagocytic activity, and direct pro-fibrotic potential (e.g., co-culture with fibroblasts).
  3. Spatial transcriptomics/proteomics of lung tissue: Perform multi-omics analyses on fibrotic lung tissue biopsies to spatially map cell-cell interactions (e.g., TGFB1, PLAUR-integrin, CD47-SIRPα) and localized T cell phenotypes, correlating these with peripheral blood findings to understand local vs. systemic contributions.
  4. Perturbation assays: Utilize *in vitro* co-culture systems or established animal models of pulmonary fibrosis to investigate the functional consequences of manipulating specific cell-cell interactions (e.g., using blocking antibodies for CD99-PILRA, enhancing ANXA1-FPR1 signaling) or surface receptors (e.g., S1PR4, PTGER2) on immune cell behavior and fibrotic outcomes.
  5. Targeted qPCR or proteomic validation: Confirm altered expression of key genes/proteins identified in GSA/GSEA pathways (e.g., components of the NOD-like receptor pathway, JAK-STAT pathway members) in isolated monocyte and T cell populations from IPF patients.
  6. Longitudinal studies: Conduct prospective studies to monitor peripheral blood immune profiles over time in IPF patients, identifying early biomarkers predictive of disease progression, exacerbations, or response to anti-fibrotic therapies.

Limitations:

This report is based on single-cell RNA sequencing data from peripheral blood, which provides systemic insights but does not directly reflect the local immune microenvironment within the fibrotic lung tissue. Disease status (progressive/stable IPF) was inferred from dataset labels rather than confirmed clinical diagnoses, which might introduce variability. Furthermore, some cell type annotations, such as 'LTI' and 'T cell (Naive)' subsets, showed discrepancies with canonical marker expression, suggesting potential mis-annotations or complex, mixed populations that could affect the interpretation of population shifts for these specific groups. The analysis focuses on statistical associations and does not establish causality. While promising therapeutic targets are identified, their efficacy and safety would require extensive experimental validation and rigorous clinical trials.

14. Query List

  1. Show major cell type scores on UMAP and save the result.
  2. Show and save a marker expression dot plot for celltype_subset. SET target_cell = None and var_group_rotation = 45. Leave all other arguments at their default values.
  3. Show and save a population bar plot of minor cell types.
  4. Show and save a population bar plot of T cell subsets.
  5. Show and save box plots of T cell subset populations with statistically significant differences between conditions, if any. Choose ncols based on the total number of panels.
  6. Show and save a population bar plot of Monocyte subsets.
  7. Show and save cell-cell interaction patterns. Select at most 80 cell-cell interactions per group.
  8. Find cell-cell interactions involving major tissue cells, immune cells and stromal cells that differ significantly between conditions, show them as a dot plot, and save the result. Set max_n_items_per_group to 60. For each interaction identified under each condition, using the provided dataset context, describe the associated signaling pathways and mechanisms and, where relevant, its potential as a therapeutic target and related therapeutic agents.
  9. Show and save marker expression for T cell CD8+ in a dot plot. Use only surfaceome markers, with at most 50 markers per condition. For each marker identified under each condition, using the provided dataset context, describe the associated signaling pathways and biological mechanisms within the tissue and, where relevant, its potential as a therapeutic target for the disease and related therapeutic agents.
  10. Show and save marker expressions for T cell CD4+ in a dot plot. Use only surfaceome markers, with at most 40 markers per condition. For each marker identified under each condition, using the provided dataset context, describe the associated signaling pathways and biological mechanisms within the tissue and, where relevant, its potential as a therapeutic target for the disease and related therapeutic agents.
  11. Show and save Gene Ontology (GSA) analysis results for Monocyte as a bar plot.
  12. Show Gene Set Enrichment Analysis results as a dot plot, and save it. Set the color map to RdBu_r, n_pws_to_show to 120, and target_cells to None.
Top