SCODiA Report by MLBI Lab

Single-Cell Landscape of Peripheral Immune and Myeloid Cells in Idiopathic Pulmonary Fibrosis: Insights into Disease Progression and Therapeutic Targets

This single-cell RNA sequencing analysis of peripheral blood reveals distinct immune and myeloid cell dynamics in Idiopathic Pulmonary Fibrosis (IPF) patients compared to controls, with further differentiation between stable and progressive disease. Progressive IPF is characterized by an expansion of monocytes and B cells, along with an increase in regulatory T cells and activation of broad inflammatory, metabolic, and fibrotic signaling pathways. Condition-specific cell-cell interactions highlight shifts in prostaglandin E2 signaling and immune checkpoint engagement, suggesting dynamic immunomodulation throughout disease progression.

Contents

0. Dataset overview
1. UMAP Embedding Analysis by Condition, Sample, and Cell Type in Blood Samples
2. Major Cell Type Score Visualization on UMAP Embedding
3. Celltype_subset Marker Gene Expression Dot Plot Analysis
4. Blood Minor Cell Type Population Analysis in IPF Conditions
5. T Cell 및 관련 림프구 아형의 혈액 내 구성 분석
6. Differential Proportions of Lymphocyte Subpopulations in Idiopathic Pulmonary Fibrosis
7. Myeloid Cell Subpopulation Proportions in Peripheral Blood Across IPF Conditions
8. Myeloid Cell Subset Population Analysis: Eosinophil Proportions in IPF
9. Cell-Cell Interaction Analysis Across IPF Conditions
10. Condition-Specific Cell-Cell Interaction Patterns in IPF Progression
11. Monocyte Condition-Specific Surfaceome Markers in Idiopathic Pulmonary Fibrosis
12. B cell, Granulocyte, ILC, Monocyte, NK cell, Platelet, T cell CD4+, T cell CD8+ 세포 유형별 유전자 세트 농축 분석 결과
13. Discussion
14. Query List

0. Dataset overview

Dataset Summary

• This dataset contains single-cell RNA-seq data from human blood, processed by SCODA.
• It comprises 98,992 cells and 21,249 genes.
• Conditions: progressive_ipf, stable_ipf, control.
• Major Cell Types: T cell, Myeloid cell, unassigned, B cell, Megakaryocytic cell, Erythroid cell.
• Minor Cell Types: T cell CD4+, Monocyte, T cell CD8+, NK cell, unassigned, Granulocyte, ILC, B cell, Platelet, Plasma cell, Erythroid-like and erythroid precursor cell.
• Subset Cell Types: T cell (Naive), Monocyte, T cell (Cytotoxic), T cell (Treg), NK cell, unassigned, T cell (Th22), T cell (Th2), T cell (Th1), Neutrophil, T cell (Tfh), T cell (Th9), Eosinophil, ILC1, T cell (Th17), LTI, B cell (Follicular), Platelet, B cell (Memory), B cell (MZ), B cell (Breg), Plasma cell, ILCreg, ILC3 (NCR+), ILC3 (NCR-), ILC2, Erythroid-like and erythroid precursor cell, Basophil.
• Precomputed Results: Cell-cell interaction (CCI) results, Differential Expression Gene (DEG) results, Gene Set Enrichment Analysis (GSEA) results, and Gene Ontology (GSA/GO) results are available, typically precomputed per celltype_minor across conditions.

1. UMAP Embedding Analysis by Condition, Sample, and Cell Type in Blood Samples

[Analysis Visualization Results]...

Analysis Overview

This analysis presents Uniform Manifold Approximation and Projection (UMAP) embeddings of single-cell RNA sequencing data from human blood, visualizing the distribution of cells across different experimental conditions, individual samples, and various hierarchical levels of cell type annotation. The primary goal is to assess the overall data structure, the quality of cell type assignments, and how different conditions and samples are represented within the cellular landscape.

Visual Summary

Condition-Specific Distribution

• The UMAP colored by condition shows a broad intermixing of cells from control (dark red), progressive_ipf (light yellow), and stable_ipf (dark blue) across the overall cellular landscape. This suggests that the major cell type populations are present in all conditions.
• However, certain regions within the UMAP exhibit distinct enrichments for specific conditions. For example, progressive_ipf cells appear more concentrated in a sub-region of the large cluster on the far left, and also show differential density within the large T cell cluster (upper right). Similarly, control and stable_ipf cells also show distinct areas of relative enrichment, indicating potential condition-specific shifts in cell proportions or activation states within specific cell populations.

Sample-Level Distribution

• The sample UMAP reveals a relatively good mixing of cells from different individual samples across the embedding. This is a positive indication, as it suggests that technical variations or "batch effects" between samples are not the primary drivers of the overall cellular clustering.
• While there is general mixing, some samples (especially those from a particular condition group, e.g., 'P' samples associated with IPF conditions) show minor localized enrichments, particularly in regions enriched for specific cell types. This minor pattern could reflect inter-individual biological variability or subtle residual batch effects, but it does not appear to dominate the clustering.

Cell Type Hierarchy Visualization

• celltype_major: This UMAP clearly delineates distinct clusters corresponding to major cell types. T cells form a large, well-separated cluster in the upper-right. Myeloid cells comprise a significant, distinct cluster in the lower-left. B cells form a compact cluster at the very top. Erythroid cells and Megakaryocytic cells form smaller, yet distinct, clusters. unassigned cells (dark blue/purple) are scattered and do not form a coherent cluster, indicating that the vast majority of cells have been successfully assigned to a major cell type.
• celltype_minor: Further refinement is observed at the minor cell type level. Within the T cell major cluster, T cell CD4+, T cell CD8+, and NK cell populations are clearly resolved into distinct yet related sub-clusters. Monocytes are prominent within the myeloid compartment. Plasma cells emerge as a separate population distinct from general B cells. Platelets and Erythroid-like and erythroid precursor cells are well-defined. The unassigned population remains sparse and broadly distributed.
• celltype_subset: At the highest resolution, the celltype_subset UMAP demonstrates fine-grained distinctions within minor cell types. Various T cell subsets (e.g., T cell (Naive), T cell (Cytotoxic), T cell (Treg), Th1, Th2, Th17), B cell subsets (e.g., B cell (Memory), Plasma cell), and specific myeloid populations (e.g., Neutrophil, Eosinophil, Basophil) are identifiable as distinct clusters or sub-regions. The presence of discrete clusters for these subsets underscores the robustness of the clustering and annotation process. The unassigned cells are again minor and diffuse.

Biological Interpretation

The UMAP embeddings demonstrate a robust and well-structured representation of the cellular heterogeneity in human blood. The clear separation of cell types across major, minor, and subset levels confirms the high quality of the single-cell RNA-seq data and the accuracy of the cell type annotations. The hierarchical nesting of minor and subset cell types within their parent major types is biologically consistent and reflects the expected relationships between these populations.

The observation that unassigned cells are rare and scattered across the UMAP, rather than forming a distinct cluster, indicates a comprehensive and effective cell type annotation strategy. This suggests that most cells have been confidently assigned to known lineages or states, minimizing the presence of ambiguous or poorly defined populations.

The distribution of conditions (control, stable_ipf, progressive_ipf) across the UMAP hints at condition-specific biological alterations. While there is overlap, the subtle yet discernible enrichments of certain conditions in specific cell type clusters suggest that IPF, particularly progressive IPF, might be associated with shifts in cell type proportions, the activation state of certain immune cells, or the emergence of novel disease-associated cell states. For instance, the unique distribution of progressive_ipf cells could imply an expansion of certain T cell subsets or myeloid populations, or a distinct transcriptional profile within these cells that distinguishes them from control or stable IPF states.

The minimal batch effect observed at the sample level ensures that subsequent differential expression or cell-cell interaction analyses will likely reflect true biological differences related to the disease conditions rather than technical artifacts.

Clinical or Translational Implications

This foundational UMAP analysis provides critical insights for understanding the cellular pathology of IPF.

• Disease Heterogeneity: The distinct distribution of progressive_ipf cells compared to stable_ipf and control suggests that the immune and myeloid cell compartments in the blood may undergo significant changes during IPF progression. Identifying the specific cell types or states enriched in progressive_ipf could point to key cellular players in disease exacerbation.
• Biomarker Discovery: The well-defined and annotated cell type subsets provide a solid basis for identifying cell-type-specific biomarkers of disease progression or therapeutic response. For example, if a specific T cell subset is significantly altered in progressive IPF, it could be further investigated as a diagnostic marker or a target for therapeutic intervention.
• Patient Stratification: Differences between stable_ipf and progressive_ipf in the UMAP suggest that single-cell analysis of peripheral blood could potentially contribute to stratifying IPF patients based on their disease trajectory, aiding in personalized treatment strategies.
• Annotation Quality: The high quality of cell type annotations observed in this UMAP analysis builds confidence for subsequent in-depth analyses, such as differential gene expression (DEG), gene set enrichment analysis (GSEA), or cell-cell interaction (CCI) studies, ensuring that findings are anchored to well-defined biological cell types.

Further analyses on differential expression and cell-cell interactions within these identified cell types will be crucial to pinpoint specific molecular mechanisms driving the observed cellular shifts in IPF.

2. Major Cell Type Score Visualization on UMAP Embedding

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the distribution of major cell type scores across a UMAP embedding generated from single-cell RNA sequencing data. For each cell, a score representing its similarity to predefined gene signatures of various major cell types (T cell, B cell, Myeloid cell, Mast cell, Endothelial cell, Stromal cell, Erythroid cell, Megakaryocytic cell) is calculated and displayed. Additionally, a final UMAP plot colored by the celltype_major annotation (derived from the AnnData obs metadata) is provided, serving as a reference for the assigned cell identities. This allows for an assessment of how well the cell type scores delineate the final annotations and the overall structure of the cellular landscape.

Visual Summary

The UMAP plots illustrate the embedding of approximately 98,992 cells in a 2D space, where cell proximity generally reflects transcriptional similarity.

• Overall UMAP Structure: The UMAP shows distinct clusters of cells, indicating clear separation of major cellular populations. There are primarily two large, interconnected clusters, with several smaller, more isolated clusters.

HiCAT_major_score Plots

• T cell, B cell, Myeloid cell: These plots show strong, distinct enrichment of scores in specific, well-separated regions of the UMAP. The high scores for T cells populate the large cluster on the right-hand side, Myeloid cells predominantly occupy the large cluster on the left, and B cells form a smaller, distinct cluster towards the top-left.
• Erythroid cell, Megakaryocytic cell: These cell types show highly localized and relatively intense scores in very specific, compact regions of the UMAP, suggesting they represent smaller, well-defined populations. Megakaryocytic cells show particularly high scores in a small central cluster.
• Mast cell, Endothelial cell, Stromal cell: The scores for these cell types are generally much lower in magnitude (note the different color bar scales, e.g., max 3.0 for Mast, 0.6 for Endothelial, 0.5 for Stromal, compared to 8-20 for other major types). While some localized enrichment is visible, particularly for Endothelial and Stromal cells in a small region near the top-center, these scores do not delineate large, distinct clusters as clearly as the primary immune and hematopoietic cell types.
• celltype_major Plot: This final plot, colored by the assigned celltype_major annotations (B cell, Erythroid cell, Megakaryocytic cell, Myeloid, T cell, unassigned), largely confirms the patterns seen in the individual score plots.
• The large clusters correspond to T cells (dark green) and Myeloid cells (light green).
• B cells (red), Erythroid cells (orange), and Megakaryocytic cells (yellow) form smaller, distinct clusters that align well with their respective high-score regions.
• An "unassigned" population (purple/blue) is also present, scattered across various regions, and forming some less defined clusters.

Biological Interpretation

The distinct clustering and strong, localized cell type scores for T cells, B cells, Myeloid cells, Erythroid cells, and Megakaryocytic cells indicate that these major immune and hematopoietic lineages are transcriptionally well-resolved in this single-cell RNA-seq dataset from blood. This reflects the fundamental differences in gene expression programs that define these cell identities within the human blood compartment.

• Immune Cell Lineage Separation: The clear separation of T cells, B cells, and Myeloid cells underscores their distinct immunological functions and developmental origins. T cells are crucial for adaptive immunity, B cells for antibody production, and Myeloid cells (e.g., monocytes, granulocytes) for innate immunity.
• Hematopoietic Precursors/Mature Cells: The distinct clusters for Erythroid cells (red blood cell precursors) and Megakaryocytic cells (platelet precursors) highlight the presence of these lineages in the blood, indicating active hematopoiesis or circulating progenitors/mature forms.
• Minor Populations/Non-canonical Blood Cells: The generally lower and less defined scores for Mast cells, Endothelial cells, and Stromal cells are consistent with blood being the tissue source. While Mast cells can be found in blood, and endothelial/stromal cells are typically tissue-resident, their presence as sparse or low-scoring populations might indicate rare circulating precursors, contamination, or cells with weak blood-specific signatures. The fact that these types are not explicitly listed in the final celltype_major annotations suggests that they either were not confidently assigned as major populations or are encompassed within the 'unassigned' category.

Annotation Notes

• Robust Major Cell Type Identification: The HiCAT_major_score plots for T cell, B cell, Myeloid cell, Erythroid cell, and Megakaryocytic cell show excellent correspondence with the final celltype_major assignments. This suggests that the major cell type annotations are well-supported by their respective gene expression signatures and that the UMAP embedding effectively separates these cell populations.
• Consistency of Scores and Labels: The high scoring regions for each of the main cell types (T, B, Myeloid, Erythroid, Megakaryocytic) almost perfectly overlap with the corresponding colored clusters in the celltype_major plot, validating the current annotations.
• "Unassigned" Population: The presence of an "unassigned" population is notable. These cells could represent:
• Rare cell types not explicitly considered in the major cell type definitions.
• Cells with ambiguous transcriptional profiles (e.g., transitional states, progenitor cells).
• Cells with low-quality transcriptomes.
• The sparse regions with some scores for Mast, Endothelial, and Stromal cells, which are not explicitly included in the celltype_major list, likely contribute to or are part of this "unassigned" population. Their relatively low scores across the UMAP suggest that they are not abundant major populations in this blood dataset or their gene signatures are not strong enough to warrant clear assignment as major cell types. Further investigation into the "unassigned" cluster's gene expression profiles would be valuable to refine these annotations.

3. Celltype_subset Marker Gene Expression Dot Plot Analysis

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the expression of marker genes across different celltype_subset populations identified in the single-cell RNA-seq data from human blood. The plot_markers_and_expression_dot tool was used with default parameters, except surfaceome_only was set to True, meaning that the analysis focused on identifying and plotting cell surface markers. This type of plot is crucial for assessing the quality of cell type annotations and confirming the biological identity of cell populations.

Visual Summary

The dot plot presents a comprehensive overview of marker gene expression, where each row represents a celltype_subset and each column represents a marker gene.

• Dot Size: Corresponds to the fraction of cells within a group that express the specific gene, with larger dots indicating a higher percentage of expressing cells.
• Dot Color Intensity: Represents the mean expression level of the gene within that cell group, with darker red indicating higher mean expression.
• Red Boxes: Highlight clusters of marker genes that are highly expressed and specific to particular celltype_subset populations, typically found along the diagonal, suggesting distinct cellular identities.
• Cell Counts: A bar chart on the right indicates the total number of cells for each celltype_subset, providing context on population sizes.

Overall, the plot demonstrates clear, distinct expression patterns for numerous marker genes, allowing for robust validation of the assigned celltype_subset identities.

Biological Interpretation

The marker gene expression patterns largely support the assigned celltype_subset annotations, reflecting known immunological and hematological cell identities.

B Cell Lineage

• B cell subsets (Breg, Follicular, MZ, Memory): These subsets consistently express core B cell transcription factors like POU2AF1 and EBF1, as well as surface markers like CD22. While these markers confirm their general B cell lineage, more nuanced differences in specific marker expression would typically be needed to definitively distinguish these closely related subsets. For instance, IGH D expression, often associated with naive B cells, is visible.
• Plasma cell: This population displays highly specific and strong expression of canonical plasma cell markers, including XBP1 (X-box binding protein 1), TNFRSF17 (B-cell maturation antigen, BCMA), and PRDM1 (B lymphocyte-induced maturation protein 1, BLIMP1) GeneCards: XBP1, GeneCards: TNFRSF17, GeneCards: PRDM1. This robustly confirms the annotation of plasma cells.

Myeloid Lineage

• Monocyte: This cell type is characterized by strong expression of CD68 (a pan-macrophage/monocyte marker) and LYZ (lysozyme) GeneCards: CD68, GeneCards: LYZ. Additionally, S100A8 and S100A9, which are characteristic of myeloid cells, are highly expressed.
• Neutrophil: Neutrophils show distinct expression of S100A8, S100A9, LYZ, NCF1 (neutrophil cytosolic factor 1), and CSF3R (G-CSFR) GeneCards: CSF3R. This marker profile strongly validates the neutrophil identity.
• Eosinophil: The presence of IL5RA (Interleukin-5 receptor alpha) expression supports the eosinophil annotation, as IL-5 is a key cytokine for eosinophil development and function.

Megakaryocytic Lineage

• Platelet: This population is clearly defined by the high and specific expression of well-known platelet markers such as ITGA2B (integrin alpha-2b/CD41), GP9 (glycoprotein IX), PF4 (platelet factor 4), and PPBP (pro-platelet basic protein/CXCL7) GeneCards: ITGA2B, GeneCards: PF4. This provides strong evidence for their correct annotation.

T Cell and ILC Lineages

• T cell (Cytotoxic): Canonical cytotoxic T cell markers, CD8A, CD8B (CD8 co-receptor), and GZMB (granzyme B), are highly expressed, confirming the identity of this effector population GeneCards: CD8A, GeneCards: GZMB.
• T cell (Naive): Characterized by markers such as LEF1 and CCR7, indicating their naive state and migratory properties for lymphoid homing.
• T cell (Tfh): T follicular helper cells express CD40LG (CD40 ligand), essential for B cell help in germinal centers GeneCards: CD40LG.
• T cell (Th1): Shows expression of STAT1 and IFNGR1 (interferon gamma receptor 1), consistent with their role in Type 1 immunity GeneCards: IFNGR1.
• T cell (Th17): Identified by IL21R (interleukin 21 receptor) and STAT3, markers associated with their differentiation and function in mediating inflammatory responses GeneCards: IL21R.
• T cell (Th2): GATA3, the master transcription factor for Th2 cells, is strongly expressed, confirming their role in allergic and anti-helminth responses GeneCards: GATA3.
• T cell (Treg): Regulatory T cells are robustly identified by the expression of their lineage-defining transcription factor FOXP3 and the inhibitory receptor CTLA4 GeneCards: FOXP3, GeneCards: CTLA4.
• NK cell: Natural Killer cells show expression of NCR1 (NKp46) and KLRF1 (CD161), along with effector molecules like GZMB (shared with cytotoxic T cells), supporting their annotation GeneCards: NCR1.
• ILC2: Innate Lymphoid Cell type 2 displays high expression of IL1RL1 (ST2), a key receptor associated with Type 2 immune responses GeneCards: IL1RL1.

Annotation Notes

The marker expression patterns observed in the dot plot generally provide strong support for the assigned celltype_subset annotations within the human blood single-cell RNA-seq dataset. The distinct, specific, and often canonical marker profiles, particularly for plasma cells, platelets, neutrophils, monocytes, and various T cell subsets (e.g., cytotoxic T cells, Tregs), indicate high confidence in these annotations. The use of surfaceome-only markers is particularly valuable for potential downstream validation using techniques like flow cytometry or imaging. Minor overlaps in marker expression among closely related cell types (e.g., GZMB in NK cells and cytotoxic T cells, GATA3 in Th2 cells and ILC2s) are biologically expected given shared developmental pathways or effector functions. These results confirm the quality and biological consistency of the celltype_subset annotations in this dataset.

4. Blood Minor Cell Type Population Analysis in IPF Conditions

[Analysis Visualization Results]...

Analysis Overview

This analysis presents the relative proportions of minor cell types in peripheral blood mononuclear cells (PBMCs) from control subjects, patients with progressive Idiopathic Pulmonary Fibrosis (IPF), and patients with stable IPF. The stacked bar plots illustrate the cellular composition for individual samples within each condition, providing insights into potential immune dysregulation associated with IPF progression.

Visual Summary

The stacked bar plots display the percentage contribution of various minor cell types (e.g., B cell, Monocyte, NK cell, T cell CD4+, T cell CD8+) to the total cellular population for each individual sample, grouped by condition: 'control', 'progressive_ipf', and 'stable_ipf'.

• Control Group: This group generally exhibits a more balanced distribution of cell types. Monocytes (light orange) and T cells (CD4+ in light green, CD8+ in teal) are consistently prominent, forming the largest fractions. B cells (dark red) and NK cells (light yellow) are present in smaller, yet consistent, proportions.
• Progressive IPF Group: A striking shift in cellular composition is observed.
• Monocytes show a clear and substantial increase in relative proportion across most progressive IPF samples, often accounting for 30-60% of the total cells.
• B cells also show a notable increase, particularly evident in several samples (e.g., P02, P08, P12), where they can constitute over 10% of the population, a higher proportion compared to control.
• Concomitantly, the relative proportions of T cells (CD4+ and CD8+) and NK cells appear to be reduced, likely due to the expansion of monocytes and B cells.
• There is considerable inter-sample variability within this group, indicating potential heterogeneity in immune responses among patients with progressive disease.
• Stable IPF Group: This group shows patterns somewhat intermediate between control and progressive IPF.
• Monocytes remain elevated compared to control, although perhaps slightly less uniformly and often to a lesser extent than in the progressive IPF group.
• B cells show some increase in certain samples, but the magnitude and consistency of this increase are generally less pronounced than in the progressive IPF cohort.
• Similar to progressive IPF, T cells (CD4+ and CD8+) and NK cells show a relative decrease in proportion compared to control.

Biological Interpretation

The observed shifts in peripheral blood cell populations highlight distinct immune profiles associated with different stages of IPF.

• Monocyte Expansion in IPF: The most consistent finding is the elevated proportion of monocytes in both stable and progressive IPF, which is most pronounced in the progressive form. Monocytes are critical innate immune cells that differentiate into macrophages. In the context of IPF, macrophages play a central role in driving inflammation and fibrosis by producing pro-fibrotic mediators, growth factors, and cytokines [PubMed Search: "monocyte macrophage idiopathic pulmonary fibrosis"]. An expanded circulating monocyte pool could indicate a systemic inflammatory state and an increased potential for recruitment to the lungs, where they contribute to the fibrotic cascade.
• B Cell Accumulation in Progressive IPF: The marked increase in B cells, especially in progressive IPF, is highly significant. While traditionally recognized for antibody production, B cells contribute to fibrotic diseases through various mechanisms, including antigen presentation, secretion of pro-fibrotic cytokines (e.g., TGF-β), and formation of tertiary lymphoid structures in affected tissues [PubMed Search: "B cell fibrosis pulmonary"]. Their higher proportion in progressive IPF suggests an exacerbated humoral immune or autoimmune component contributing to disease progression.
• Relative Reduction of T Cells and NK Cells: The relative decrease in T cell (CD4+ and CD8+) and NK cell populations in both IPF groups might suggest a 'dilution' effect due to the expansion of monocyte and B cell compartments, or it could reflect altered trafficking of these lymphocytes to the diseased lung tissue. Dysregulation of T cell subsets is well-documented in IPF, where imbalances (e.g., between Th1 and Th2 responses) can favor fibrosis [GeneCards: CD4 (T cell marker), CD8A (T cell marker)]. NK cells are important for immune surveillance, and their relative decrease could imply compromised anti-fibrotic or anti-inflammatory regulatory functions.

Clinical or Translational Implications

These findings suggest that specific shifts in peripheral blood cell populations could be valuable for clinical management of IPF:

• Biomarkers for Disease Activity and Progression: The elevated proportions of monocytes and B cells, particularly in progressive IPF, could serve as peripheral blood biomarkers to monitor disease activity, distinguish between stable and progressive forms, or potentially predict disease trajectory. Non-invasive monitoring of these cell populations via flow cytometry or other methods might offer prognostic insights.
• Potential Therapeutic Targets: The increased abundance of monocytes and B cells identifies these cell types as promising therapeutic targets for IPF. Strategies aimed at modulating monocyte-to-macrophage differentiation, inhibiting macrophage activation, or selectively depleting/modulating B cell function could be explored to halt or slow disease progression. For example, B cell-targeting therapies are already used in other autoimmune/fibrotic conditions [PubMed Search: "rituximab pulmonary fibrosis"].
• Understanding Disease Heterogeneity: The observed variability within the IPF cohorts underscores the heterogeneous nature of the disease. Profiling individual patients' peripheral immune cell compositions could facilitate personalized medicine approaches, allowing for tailored therapeutic interventions based on their specific immune dysregulation profiles.

5. T Cell 및 관련 림프구 아형의 혈액 내 구성 분석

[Analysis Visualization Results]...

Analysis Overview

제공된 막대 그래프는 단일 세포 RNA 시퀀싱(scRNA-seq) 데이터를 통해 얻은 AnnData 객체에서, 'T cell'로 분류된 주요 세포 유형 내의 하위 집단(즉, celltype_subset 레벨)의 상대적 분포를 보여줍니다. 분석은 혈액 샘플을 대상으로 진행되었으며, 대조군(control), 진행성 특발성 폐섬유증(progressive_ipf), 안정성 특발성 폐섬유증(stable_ipf)의 세 가지 임상 조건에서 각 샘플별 T 세포 및 관련 림프구(NK 세포, ILC 포함)의 아형 구성 비율을 시각화합니다.

Visual Summary

• 주요 아형 분포: 모든 조건의 샘플에서 T cell (Naive) (가장 밝은 노란색)과 T cell (Cytotoxic) (옅은 노란색), 그리고 NK cell (주황색)이 T 세포 및 관련 림프구 집단에서 가장 높은 비율을 차지하고 있습니다. 이는 말초 혈액 내 림프구 구성의 주요 부분을 나타냅니다.
• 보조 아형의 일관된 낮은 비율: Tfh, Th1, Th2, Th9, Th17, Th22, Treg과 같은 다른 T 세포 아형들과 ILC1, ILC2, ILC3 (NCR+), ILC3 (NCR-), ILCreg, LTI와 같은 선천 림프구(ILC) 아형들은 전반적으로 낮은, 그러나 일관된 비율로 존재합니다.

조건별 변화 경향

• T cell (Naive): 시각적으로, 대조군에 비해 진행성 IPF 및 안정성 IPF 환자 그룹에서 T cell (Naive)의 상대적 비율이 다소 감소하는 경향을 보입니다. 그러나 샘플 간의 변동성도 존재합니다.
• T cell (Cytotoxic): 이 세포 집단은 모든 조건에서 상당한 비율을 유지하며, 조건에 따른 뚜렷한 변화는 관찰되지 않습니다.
• NK cell: NK 세포는 IPF 환자군에서 대조군보다 약간 증가하는 경향을 보이는 샘플들이 있으나, 역시 샘플 간 편차가 있습니다.
• 미미한 변화: T cell (Treg)을 포함한 다른 T 세포 아형 및 ILC 아형들은 모든 조건에서 매우 낮은 비율을 차지하며, 시각적으로 명확한 조건별 변화를 파악하기는 어렵습니다.
• Unassigned 세포: 'unassigned' (진한 파란색)로 분류된 세포는 모든 샘플에서 매우 낮은 비율로 나타나며, 전반적인 세포 구성에 미치는 영향은 미미합니다.

Biological Interpretation

• 말초 혈액 림프구 구성의 특징: 말초 혈액의 T 세포 및 관련 림프구는 T cell (Naive) (항원 비경험 T 세포), T cell (Cytotoxic) (세포 독성 T 세포), NK cell이 주를 이룹니다. 이는 면역 감시 및 초기 면역 반응에 중요한 역할을 하는 세포 집단임을 시사합니다.
• IPF에서의 T cell (Naive) 감소 가능성: IPF 환자에서 T cell (Naive) 비율의 잠재적 감소는 만성 염증이나 자가항원 노출 등으로 인해 T 세포가 활성화 및 분화되어 Effector T 세포나 기억 T 세포로 전환되었을 가능성을 시사합니다. 이러한 전환은 질병 진행과 관련된 면역 반응의 변화를 반영할 수 있습니다.
• IPF에서 NK 세포의 역할: NK 세포는 선천 면역계의 중요한 구성원으로, 감염된 세포나 암세포를 직접 제거하는 능력을 가지고 있습니다. IPF에서 NK 세포의 비율이 유지되거나 다소 증가하는 경향은 섬유화 과정에서 NK 세포의 관여 가능성을 나타내며, 이들의 역할은 염증 및 섬유화의 맥락에서 이중적일 수 있습니다 [참고: PubMed search for "NK cells idiopathic pulmonary fibrosis" (PubMed Search)].
• Treg 세포 및 Th 세포의 중요성: Treg 세포는 면역 반응을 억제하고 자가면역을 방지하는 데 필수적입니다. Th1, Th2, Th17과 같은 다양한 Helper T 세포 아형은 섬유화 발생 및 진행에 중요한 사이토카인을 분비하여 복합적인 영향을 미칩니다. 이들 세포의 혈액 내 비율이 낮게 관찰되었지만, 조직 내에서는 훨씬 더 중요한 역할을 할 수 있습니다 [참고: PubMed search for "Treg Th1 Th2 Th17 pulmonary fibrosis" (PubMed Search)].
• ILC의 조직 특이성: ILC는 특히 조직 항상성 및 염증에서 중요한 역할을 하지만, 말초 혈액에서는 일반적으로 낮은 비율로 존재합니다. ILC2와 같은 아형은 폐 섬유화에 기여한다고 알려져 있으나, 본 분석에서 혈액 내 비율이 매우 낮아 뚜렷한 변화를 확인하기는 어렵습니다 [참고: Annu Rev Immunol. 2017;35:251-285. PMCID: PMC5976269 (NCBI)].

Clinical or Translational Implications

• 바이오마커로서의 가능성: 말초 혈액 내 T cell (Naive) 및 NK 세포의 상대적 비율 변화는 IPF의 진행 상태(진행성 vs. 안정성)를 구별하거나 질병 진행 위험을 평가하는 비침습적 바이오마커로서의 잠재력을 가질 수 있습니다. 그러나 이러한 변화가 통계적으로 유의미한지 확인하기 위한 추가적인 정량적 분석과 더 큰 규모의 코호트 연구가 필요합니다.
• 치료 표적 발굴: T 세포 및 NK 세포 아형 구성의 변화를 이해하는 것은 IPF의 면역 병리학적 기전을 밝히고, 특정 면역 세포 집단을 표적으로 하는 새로운 치료 전략을 개발하는 데 기여할 수 있습니다. 예를 들어, 특정 T 세포 아형의 활성화 또는 억제를 조절하는 접근 방식이 고려될 수 있습니다.
• 제한점: 본 분석은 말초 혈액 샘플에 기반하므로, 폐 조직 자체의 면역 환경과는 다를 수 있습니다. 폐 조직 내 면역 세포 조성에 대한 추가 분석은 질병의 국소적 병태생리를 이해하는 데 중요할 것입니다.

6. Differential Proportions of Lymphocyte Subpopulations in Idiopathic Pulmonary Fibrosis

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportions of various T cell subsets and other related lymphocyte populations (Natural Killer (NK) cells, Innate Lymphoid Cells (ILCs)) in peripheral blood across different conditions: stable Idiopathic Pulmonary Fibrosis (IPF), progressive IPF, and healthy controls. The objective was to identify statistically significant shifts in these immune cell populations that may correlate with IPF presence and disease progression.

Visual Summary

The box plots display the proportion of eight distinct lymphocyte subpopulations: Th17 cells, NK cells, Regulatory T (Treg) cells, Th2 cells, ILC2, ILC3(-), Naive T (T_Naive) cells, and ILCreg cells. Statistical significance (p-values) between conditions is indicated.

Key observations include:

• Th17 cells: Significantly lower proportions were observed in both stable IPF (p ≤ 0.01) and progressive IPF (p ≤ 0.01) patients compared to controls. There was no significant difference between stable and progressive IPF groups.
• NK cells: The proportion of NK cells was significantly higher in stable IPF patients compared to controls (p ≤ 0.05). No significant differences were found when comparing progressive IPF to controls or stable IPF.
• Treg cells: Patients with progressive IPF showed a significantly higher proportion of Treg cells compared to controls (p ≤ 0.05). A borderline trend for higher Tregs was also noted in controls compared to stable IPF (p = 0.08).
• Th2 cells: Similar to Th17 cells, Th2 cell proportions were significantly lower in stable IPF patients compared to controls (p ≤ 0.01). No significant differences were observed when comparing progressive IPF to controls or stable IPF.
• ILCreg cells: The proportion of ILCreg cells was significantly lower in progressive IPF patients compared to controls (p = 0.05).
• T_Naive cells: While not statistically significant at p < 0.05, there was a trend for lower T_Naive cells in stable IPF compared to progressive IPF (p = 0.06) and controls (p = 0.10), suggesting potential shifts in the naive T cell compartment.
• ILC2 and ILC3(-) cells: No statistically significant differences were observed in the proportions of ILC2 or ILC3(-) cells across any of the compared conditions.

It is important to note that while the user query focused on "T cell subset populations," the analysis also included NK cells and various ILCs (ILC2, ILC3(-), ILCreg), which are distinct lymphocyte lineages but play crucial roles in immunity.

Biological Interpretation

The observed shifts in lymphocyte subpopulations in the peripheral blood suggest an altered immune landscape in IPF patients, particularly when compared to healthy controls.

1. Th17 and Th2 Cells: The significant decrease in both Th17 and Th2 cell proportions in IPF patients (especially stable IPF for Th2, and both for Th17) compared to controls is notable.

• Th17 cells are typically involved in pro-inflammatory responses and host defense. Their reduction could imply a dysregulated immune response in IPF, potentially contributing to an environment where chronic fibrotic processes can dominate without sufficient immune clearance or regulation.
• Th2 cells are associated with type 2 immunity, allergic responses, and fibrosis through cytokines like IL-4, IL-5, and IL-13. A decrease in circulating Th2 cells in stable IPF might reflect their recruitment to the lung tissue, their conversion to other phenotypes, or a systemic suppression of Th2 responses, which could have complex implications in a disease where Th2-driven fibrosis is often implicated. PubMed search: Th2 cells idiopathic pulmonary fibrosis

2. Regulatory T (Treg) Cells: The significant increase in Treg cells in progressive IPF patients compared to controls is a critical finding. Tregs are immunosuppressive and crucial for maintaining immune tolerance. An expansion of Tregs in progressive disease might contribute to immune evasion, allowing for unchecked fibrosis progression by suppressing beneficial anti-fibrotic or pro-resolving immune responses. This imbalance between pro-inflammatory (e.g., Th17) and regulatory (Treg) T cells is a common feature in many chronic inflammatory and fibrotic diseases. PubMed search: Treg cells idiopathic pulmonary fibrosis
3. NK Cells: The elevated NK cell proportion in stable IPF compared to controls could indicate an activated innate immune response in the earlier or stable phases of the disease. NK cells play roles in immunosurveillance and tissue remodeling, and their altered numbers could reflect an ongoing, potentially protective or maladaptive, immune response. PubMed search: NK cells idiopathic pulmonary fibrosis
4. ILCreg Cells: The significantly lower proportion of ILCreg cells in progressive IPF compared to controls suggests a reduction in regulatory innate lymphoid populations in advanced disease. ILCs are diverse and play roles in tissue homeostasis and inflammation. A decrease in ILCreg could signify impaired innate immune regulation, potentially exacerbating pathogenic processes in progressive IPF.
5. T_Naive Cells: The trend towards lower T_Naive cell proportions in stable IPF, although not strongly significant, could suggest a shift in the T cell compartment towards more differentiated or effector/memory phenotypes due to chronic immune activation, leaving fewer naive cells available for novel immune responses.

These findings, derived from peripheral blood, offer a systemic view of immune dysregulation in IPF. While these proportional changes do not directly indicate cellular function or their precise localization within the lung, they provide valuable insights into potential immune mechanisms contributing to disease pathogenesis and progression.

Clinical or Translational Implications

The observed differences in lymphocyte subpopulations hold potential clinical and translational implications:

• Biomarkers of Disease Status/Progression: The distinct shifts in Treg cells (higher in progressive IPF) and ILCreg cells (lower in progressive IPF) could serve as potential peripheral blood biomarkers to distinguish progressive IPF from healthy individuals and potentially monitor disease progression. Similarly, the changes in Th17, Th2, and NK cells in stable IPF could be indicative of the disease onset or stable phase.
• Therapeutic Targets: Understanding the roles of these altered cell populations could guide the development of novel immunomodulatory therapies. For instance, strategies aimed at modulating Treg function or restoring ILCreg populations might be considered for IPF treatment.
• Disease Monitoring: Longitudinal monitoring of these cell proportions could provide insights into treatment efficacy or disease trajectory, complementing existing clinical measures.

Further studies are warranted to validate these findings in larger cohorts, investigate their functional implications in the context of IPF, and correlate peripheral blood changes with immune cell dynamics within the affected lung tissue.

7. Myeloid Cell Subpopulation Proportions in Peripheral Blood Across IPF Conditions

[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the relative proportions of Granulocytes and Monocytes within the overall Myeloid cell population for individual samples across different conditions: control, progressive_ipf, and stable_ipf. The data is derived from single-cell RNA-seq of peripheral blood samples, aiming to understand compositional shifts in major myeloid cell subsets in idiopathic pulmonary fibrosis (IPF).

Visual Summary

The stacked bar plots display the percentage distribution of Granulocytes (maroon) and Monocytes (light yellow) within the Myeloid cell population for each sample, grouped by condition.

• Across all three conditions (control, progressive_ipf, stable_ipf) and in nearly every individual sample, Monocytes constitute the vast majority of the Myeloid cell population, consistently exceeding 95% of the total myeloid cells shown.
• Granulocytes represent a very minor fraction, generally less than 5%, and often appear as a thin band at the base of the stacked bars.
• While there is some minor sample-to-sample variability in the low granulocyte fraction (e.g., slightly more prominent in control sample C33, progressive IPF samples P10 and P06, and stable IPF sample S17), no substantial or consistent shift in the relative proportions of Monocytes versus Granulocytes is evident when comparing the control, progressive_ipf, and stable_ipf conditions at this celltype_minor level.

Biological Interpretation

This visualization highlights that in the peripheral blood samples analyzed, Monocytes are the predominantly captured Myeloid cell type, with Granulocytes making up a consistently small proportion, irrespective of the IPF disease status.

• Monocyte Dominance: The overwhelming representation of monocytes suggests they are the primary circulating myeloid cells interrogated in this dataset, which aligns with common knowledge regarding myeloid cell composition in peripheral blood mononuclear cell (PBMC) preparations often used in scRNA-seq studies (granulocytes are typically depleted or have a shorter half-life post-isolation).
• Limited Granulocyte Signal: The low proportion of granulocytes, even in IPF conditions, indicates that the overall relative abundance of these cells within the Myeloid cell major population in the peripheral blood may not be a primary driver of observed differences between disease states, or that their contribution is subtle and requires more granular analysis (e.g., specific granulocyte subtypes or their activation status). While Neutrophils, Eosinophils, and Basophils are listed under celltype_subset as granulocyte components, this plot aggregates them into Granulocytes at the celltype_minor level. Their low overall representation here does not preclude their significant roles locally within the lung tissue in IPF pathology.
• No Major Proportional Shifts: The absence of clear, condition-specific changes in the relative proportions of these two myeloid subsets suggests that if myeloid cells are involved in IPF pathogenesis, the alterations might be more subtle, affecting gene expression programs, activation states, or specific functional subsets (e.g., inflammatory monocytes versus reparative monocytes) rather than dramatic shifts in their overall minor cell type proportions in the blood.

Clinical or Translational Implications

• Given the dominant Monocyte population observed, future investigations into IPF pathogenesis, particularly those leveraging the DEG, GSEA, and GSA results available in the AnnData object, should prioritize focusing on Monocytes (and their celltype_subset distinctions or activation states) to uncover condition-associated biology.
• While granulocytes play roles in IPF pathogenesis, their low representation in this peripheral blood myeloid cell fraction suggests that their impact might be more pronounced in the lung tissue microenvironment or involve transient systemic changes not captured as major proportional shifts here. Therefore, conclusions regarding granulocyte involvement in IPF based on this blood scRNA-seq dataset should be made cautiously.
• Understanding the functional changes (e.g., through gene expression analysis) within the dominant monocyte population in progressive versus stable IPF could yield valuable insights into disease mechanisms and potential therapeutic targets.

8. Myeloid Cell Subset Population Analysis: Eosinophil Proportions in IPF

[Analysis Visualization Results]...

Analysis Overview

This analysis presents the proportion of Eosinophil cells, a subset of Myeloid cells, in peripheral blood samples from human donors across three conditions: control, stable idiopathic pulmonary fibrosis (IPF), and progressive IPF. The box plot visualizes cell type proportions, with individual data points overlaid, and highlights statistically significant differences between conditions using a predefined p-value cutoff of 0.1 for significance testing against the 'control' group, and implied significance testing between other groups (e.g., stable vs progressive IPF).

Visual Summary

The box plot displays the Celltype proportion of Eosinophils for each condition.

• Control vs. stable_ipf: The proportion of Eosinophils is significantly lower in the stable_ipf group compared to the control group (p ≤ 0.05). The median Eosinophil proportion in stable_ipf is markedly reduced, close to zero, suggesting a near absence or very low levels in these patients.
• Control vs. progressive_ipf: There is no statistically significant difference in Eosinophil proportion between the control and progressive_ipf groups (p = 0.24), based on the analysis configuration's p-value cutoff of 0.1.
• stable_ipf vs. progressive_ipf: The proportion of Eosinophils is significantly higher in the progressive_ipf group compared to the stable_ipf group (p ≤ 0.01). While the progressive_ipf group shows a wider distribution and somewhat higher median than stable_ipf, its proportion is still generally lower than that of the control group.

Biological Interpretation

Eosinophils are granulocytes, a type of Myeloid cell, well-known for their roles in allergic diseases, parasitic infections, and various inflammatory and fibrotic processes. The observed changes in peripheral blood Eosinophil proportions in IPF patients suggest dynamic immune responses tied to disease state.

• Reduction in stable IPF: The significant decrease in peripheral blood Eosinophils in stable_ipf patients compared to control subjects could indicate several biological phenomena. It might suggest a systemic suppression of eosinophilopoiesis, an increased sequestration or migration of eosinophils from the circulation into the affected lung tissue, or a difference in their survival or clearance in stable disease PubMed search for Eosinophils IPF role. Such a reduction could imply a less active inflammatory or pro-fibrotic environment in the peripheral blood during the stable phase.
• Increase in progressive IPF relative to stable IPF: The subsequent significant increase in peripheral blood Eosinophils in progressive_ipf compared to stable_ipf suggests a shift in the immune landscape as the disease progresses. This increase, even if not statistically significantly different from controls, could reflect a re-mobilization of eosinophils into the circulation, a change in their tissue-specific trafficking, or an activation of pathways that lead to their increased presence in peripheral blood during disease progression GeneCards for Eosinophil. Eosinophils are known to contribute to tissue remodeling and fibrosis in some contexts, and their elevated presence in progressive IPF could hint at their involvement in the ongoing fibrotic process or an exacerbation of inflammation.

These findings, derived from peripheral blood, provide a snapshot of systemic immune alterations and could reflect distinct pathogenic mechanisms operating in stable versus progressive IPF.

Clinical or Translational Implications

The differential Eosinophil proportions observed between stable and progressive IPF have potential clinical implications:

• Biomarker Potential: The significant reduction in Eosinophils in stable IPF and their subsequent increase during progression (relative to stable IPF) suggest that peripheral blood Eosinophil proportion could serve as a valuable biomarker for monitoring IPF disease activity or distinguishing between stable and progressive disease states. Such a biomarker could potentially aid in patient stratification and treatment response assessment.
• Pathogenic Insights: Understanding the mechanisms driving these Eosinophil dynamics could offer new insights into IPF pathogenesis. If Eosinophils are found to be actively involved in driving fibrosis or inflammation in progressive IPF, they might represent a therapeutic target. For instance, therapies modulating eosinophil recruitment or function could be explored.
• Further Validation: These findings warrant further validation in larger cohorts and correlation with other clinical parameters such as lung function decline, radiological progression, and patient outcomes to establish their full clinical utility.

9. Cell-Cell Interaction Analysis Across IPF Conditions

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interaction (CCI) profiles derived from single-cell RNA sequencing data of human blood samples across three conditions: healthy control, progressive idiopathic pulmonary fibrosis (IPF), and stable IPF. Using CellPhoneDB, ligand-receptor interactions between various immune cell types (major and minor cell types such as T cells, B cells, Monocytes, NK cells) were identified and quantified. The goal is to compare the CCI landscapes between these conditions to uncover potential disease-specific immunological alterations and pathways relevant to IPF pathogenesis and progression. The visualizations display the top 80 significant cell-cell interactions for each condition, indicating interaction strength (log2(mean expression product)) and significance (-log10(p-value)).

Visual Summary

The three dot plots visualize cell-cell interactions for 'control', 'progressive_ipf', and 'stable_ipf' conditions, respectively. Each plot shows cell-pair combinations on the y-axis and specific ligand-receptor pairs on the x-axis. The size of the dot corresponds to the statistical significance (-log10(p-value)), with larger dots indicating higher significance (lower p-value). The color intensity of the dot represents the interaction strength (log2(mean expression product)), with brighter colors (yellow/lime green) indicating stronger interactions.

1. Shared Interactions: Across all three conditions, several interactions are consistently prominent, particularly those involving Monocytes. These include interactions mediated by HLA class I and II complexes (e.g., HLA-DR_complex, HLA-A_complex) with T cells and other monocytes, and ICAM1-integrin_aM_b2_complex (CD54-CD11b/CD18) and SPP1-integrin_aM_b2_complex. These interactions suggest fundamental antigen presentation, cell adhesion, and immune recognition processes that are active irrespective of disease status.
2. Progressive IPF Specific Interactions:

• Increased TNFRSF/TNFSF signaling: Interactions involving TNFSF10 (TRAIL)-TNFRSF10A/B/C/D and TNFSF13B (BAFF)-TNFRSF13B appear more widespread and significant, particularly within T cell and B cell populations (e.g., T CD4+|B cell for TNFSF13B_TNFRSF13B). TRAIL can induce apoptosis, while BAFF promotes B cell survival and differentiation.
• Prostaglandin D2 signaling: Interactions involving ProstaglandinD2 with its receptors PTGDR and PTGDR2 are notably more frequent and stronger in progressive IPF, especially in Monocyte and T cell interactions (e.g., Mono|Mono, T CD4+|Mono).
• Enhanced Adhesion: Several ICAMs (ICAM1, ICAM2, ICAM3) interacting with various integrins show increased prominence, often involving Monocytes and T cells, suggesting enhanced cell adhesion and transmigration.

3. Stable IPF Specific Interactions:

• CD40LG-CD40 axis: The interaction between CD40LG (CD154) on T cells and CD40 on B cells is particularly strong and significant in stable IPF (e.g., T CD4+|B cell, T CD8+|B cell). This pathway is crucial for T cell-dependent B cell activation and antibody production.
• BTLA-TNFRSF14: This immune checkpoint interaction appears more active in T cell populations (e.g., T CD4+|T CD4+) in stable IPF, potentially indicating an attempt at immune regulation or T cell exhaustion.
• Many of the Prostaglandin D2 and TNFSF/TNFRSF interactions observed in progressive IPF are also present in stable IPF, albeit with some subtle differences in strength or cellular contexts.

Biological Interpretation

The differential cell-cell interaction profiles observed in the blood across control, progressive IPF, and stable IPF highlight distinct immunological states associated with disease progression.

• Core Immune Functions: The consistent presence of strong HLA and integrin-mediated (ICAM1, SPP1) interactions, particularly among monocytes and T cells, underscores the fundamental roles of antigen presentation, cell adhesion, and immune surveillance in the blood compartment, which are maintained across health and disease. SPP1 (Osteopontin) is a cytokine involved in inflammation, fibrosis, and cell adhesion, and its persistent strong interaction suggests its ongoing role in tissue remodeling and immune cell recruitment. GeneCards: SPP1
• Progressive IPF - Inflammatory and Apoptotic Signaling: The heightened TNFSF10 (TRAIL) and TNFSF13B (BAFF) signaling in progressive IPF suggests an environment of increased immune cell turnover and activation. TRAIL can induce apoptosis in various cell types, including immune cells and potentially lung epithelial cells, contributing to tissue damage. BAFF is a crucial survival factor for B cells, and its upregulation could indicate enhanced B cell activity and autoantibody production, which can perpetuate inflammation in fibrotic diseases. PubMed search: TRAIL IPF apoptosis, PubMed search: BAFF IPF B cells. The increased Prostaglandin D2 signaling points to altered lipid mediator pathways, which are known immunomodulators and can influence inflammatory responses and fibrotic processes. PubMed search: Prostaglandin D2 fibrosis. The enhanced ICAM-integrin interactions suggest a more pro-adhesive and migratory state for leukocytes, potentially facilitating their recruitment to sites of inflammation and fibrosis in the lung.
• Stable IPF - Adaptive Immune Activation and Regulation: The strong CD40LG-CD40 axis in stable IPF indicates robust T cell-B cell collaboration. This can drive potent humoral immune responses and memory formation, potentially contributing to persistent inflammation or specific antibody-mediated processes. GeneCards: CD40LG. The upregulation of BTLA-TNFRSF14 (B and T Lymphocyte Attenuator – Herpesvirus Entry Mediator, HVEM) interactions, an immune checkpoint, suggests an active attempt at immune regulation or a state of T cell exhaustion. This could reflect a dampening of inflammatory responses compared to the progressive phase or a sustained immune activation leading to exhaustion. GeneCards: BTLA

Clinical or Translational Implications

These findings provide valuable insights into the immunological landscape of IPF and its progression, suggesting potential therapeutic targets and biomarkers.

• Biomarker Potential: The distinct CCI signatures, particularly the increased TRAIL/BAFF and Prostaglandin D2 signaling in progressive IPF, could serve as blood-based biomarkers to differentiate progressive from stable disease, or even predict progression. Monitoring the strength and prevalence of these interactions might offer a non-invasive way to assess disease activity.

Therapeutic Target Prioritization:

• Blocking TRAIL or BAFF pathways could be explored in progressive IPF to mitigate immune cell apoptosis, modulate B cell activity, and reduce inflammation.
• Modulating Prostaglandin D2 signaling could offer a strategy to control the inflammatory milieu in progressive IPF.
• In stable IPF, the strong CD40-CD40LG interactions suggest that targeting this axis could reduce chronic B cell activation and subsequent inflammation or autoantibody production, if these are found to be detrimental.
• The increased BTLA-TNFRSF14 interaction in stable IPF warrants further investigation, as modulating immune checkpoints could either enhance protective immunity or dampen pathogenic responses, depending on the context.
• Experimental Validation: These computationally predicted interactions provide a strong basis for further experimental validation. *In vitro* co-culture experiments with patient-derived immune cells could confirm the functional relevance of these specific ligand-receptor pairs. Flow cytometry or mass cytometry could be used to quantify the expression of these receptors and ligands on different cell subsets and correlate them with clinical parameters. Targeted inhibition studies using neutralizing antibodies or small molecules in preclinical models could assess the therapeutic potential of modulating these interactions in IPF.

10. Condition-Specific Cell-Cell Interaction Patterns in IPF Progression

[Analysis Visualization Results]...

Analysis Overview

This analysis investigates statistically significant differences in cell-cell interactions (CCIs) among major immune cells (T cells, Monocytes, NK cells, B cells, Granulocytes, ILCs, Plasma cells) across different clinical conditions: control, progressive_ipf (Idiopathic Pulmonary Fibrosis), and stable_ipf. The results, derived from single-cell RNA-seq data from human blood, highlight condition-specific patterns in ligand-receptor communication. The dot plot visualizes the standardized sample mean of interaction strength (dot color intensity) and the significance of the interaction (dot size, based on -log10(p-value)) for the top 25 most significantly different interactions within each condition group.

Visual Summary

The dot plot effectively illustrates distinct cell-cell interaction landscapes across the three conditions.

• Overall Activity: All three conditions (control, progressive_ipf, stable_ipf) display numerous highly significant (large dots) and strong (dark red color) cell-cell interactions, indicating active intercellular communication within the immune compartment.
• Control Condition: The control samples (C27-C40) exhibit a robust set of interactions, notably strong ProstaglandinE2_byPTGES3_PTGER4 interactions involving Monocytes, NK cells, T cells (CD8+), and B cells, as well as PLAUR_integrin_a4b1_complex interactions from Monocytes to NK and T cells. APP_CD74 interactions are also highly prominent.
• Progressive IPF Condition: Samples from patients with progressive IPF (P01-P13) show a shift in interaction patterns. While some ProstaglandinE2 interactions persist, there's a particular prominence of ProstaglandinE2_byPTGES3_PTGER2 involving T cell CD4+ and NK cells, and B cells and Monocytes. ICAM3_integrin_aL2_complex interactions involving T cells (CD8+, CD4+) and NK cells are also highly active and significant. The CD47_SIRPB1_complex-B cell|Monocyte interaction is notably strong.
• Stable IPF Condition: Patients with stable IPF (S14-S26) display another distinct set of dominant interactions. CD52_SIGLEC10 interactions are widespread, linking Monocytes to T cells (CD4+, CD8+), NK cells, and B cells. A significant interaction observed is HLA_C_KIR2DL1-Mono|NK. Additionally, TNFSF12_TNFRSF25 and FLT3LG_FLT3 interactions involving Monocytes and T cells are notable.

Biological Interpretation

The observed condition-specific CCI patterns provide critical insights into the immunological shifts occurring in IPF, particularly distinguishing progressive from stable disease.

1. Prostaglandin E2 (PGE2) Signaling Remodeling:

• The ubiquitous presence of ProstaglandinE2_byPTGES3 interactions across all conditions underscores the central role of PGE2 in immune regulation and fibrosis. PTGES3 is a key enzyme in PGE2 synthesis.
• In control samples, interactions predominantly involve PTGER4 (PGE2 receptor EP4) on monocytes, NK cells, T cells, and B cells. EP4 activation is known to have diverse effects, often including immunosuppression and contributions to fibrosis in other contexts PubMed search: PGE2 EP4 fibrosis immune.
• In progressive IPF, there's a notable shift towards PTGER2 (EP2) interactions (e.g., PTGER2-T cell CD4+|NK, PTGER2-B cell|Mono). This change in receptor usage suggests a qualitative alteration in PGE2's immunomodulatory effects, potentially favoring pro-fibrotic or pro-inflammatory pathways that contribute to disease progression. EP2 activation has also been implicated in promoting fibrotic responses and immune cell dysfunction PubMed search: PGE2 EP2 fibrosis.

2. Dysregulated Adhesion and Trafficking in Progressive IPF:

• PLAUR_integrin_a4b1_complex interactions (Monocyte to NK/T cells) are strong in both control and progressive IPF. PLAUR (uPAR) complexes with integrins to facilitate cell adhesion, migration, and extracellular matrix remodeling, processes crucial for immune cell trafficking to sites of inflammation in IPF GeneCards: PLAUR.
• The significant increase in ICAM3_integrin_aL2_complex (LFA-1) interactions, particularly involving T cells and NK cells, in progressive IPF suggests heightened immune cell adhesion and activation. These interactions are critical for stable synapse formation between immune cells and their targets, indicating an amplified immune response and potential infiltration into lung tissue, driving fibrotic progression.

3. Immune Checkpoint Dysregulation:

• The CD47_SIRPB1_complex-B cell|Monocyte interaction is notably strong in progressive IPF. CD47, the "don't eat me" signal, interacting with SIRPβ1 on monocytes can prevent phagocytosis and influence immune cell survival. Upregulation of this axis could contribute to the persistence of immune cells in the fibrotic milieu, potentially hindering efferocytosis of apoptotic cells or maintaining B cell-driven inflammatory responses, which are implicated in IPF pathogenesis PubMed search: CD47 SIRP IPF.

4. Immunosuppressive/Regulatory Signatures in Stable IPF:

• The prevalence of CD52_SIGLEC10 interactions (Monocyte to T cells, NK, B cells) in stable IPF indicates active engagement of inhibitory signaling pathways. SIGLEC10 is a sialic acid-binding immunoglobulin-like lectin, often associated with immune suppression. Its increased activity might represent an attempt by the immune system to dampen chronic inflammation or could reflect immune exhaustion, contributing to the "stable" disease phenotype GeneCards: SIGLEC10.
• The specific highlight of HLA_C_KIR2DL1-Mono|NK in stable IPF is significant. This interaction results in inhibition of NK cell cytotoxicity. Its prominence suggests a more immunosuppressive environment or exhausted NK cell state, potentially contributing to disease stability by reducing NK-mediated tissue damage or cytokine release GeneCards: KIR2DL1.

Clinical or Translational Implications

The distinct CCI patterns identified for progressive versus stable IPF could have significant clinical and translational implications:

1. Biomarkers of Disease Progression: The specific CCI signatures, such as the shift in PGE2 receptor engagement (PTGER4 vs. PTGER2), the heightened integrin-mediated adhesion (ICAM3-LFA-1), or the active CD47-SIRPB1 axis in progressive IPF, could serve as blood-based biomarkers to distinguish progressive from stable disease states. This could aid in patient stratification and personalized treatment strategies.
2. Potential Therapeutic Targets:

• PGE2 pathway components: The differential involvement of PTGER2 and PTGER4 in IPF progression suggests these receptors could be targeted to modulate PGE2's pro-fibrotic or inflammatory effects. Specific EP2/EP4 receptor antagonists or agonists might offer new therapeutic avenues.
• Adhesion molecules: Inhibiting critical adhesion interactions like ICAM3-LFA-1 in progressive IPF could reduce immune cell infiltration and subsequent tissue damage, potentially slowing disease progression.
• Immune checkpoints: Targeting the CD47-SIRPB1 axis, which appears upregulated in progressive IPF, could re-engage monocyte phagocytic functions or modulate B cell survival, offering a novel immunotherapeutic strategy for IPF.
• Immunomodulatory pathways: The prominent inhibitory interactions (CD52-SIGLEC10, HLA-C-KIR2DL1) in stable IPF warrant further investigation. Understanding how these pathways maintain stability could lead to strategies to induce or enhance such "stable" immune states in progressive disease.

Overall, this analysis provides a foundation for understanding the intricate cell-cell communication networks driving IPF pathology and progression, paving the way for further research into diagnostic and therapeutic strategies.

11. Monocyte Condition-Specific Surfaceome Markers in Idiopathic Pulmonary Fibrosis

[Analysis Visualization Results]...

Analysis Overview

This analysis identifies condition-specific surfaceome markers in Monocytes from single-cell RNA-seq data, comparing control individuals to patients with stable_ipf. The aim is to highlight cell-surface proteins that are differentially expressed in Monocytes across these conditions, which could serve as potential biomarkers or therapeutic targets. The dot plot visualizes the expression patterns of these markers across individual samples, showing both the fraction of cells expressing each gene and the mean expression level within those cells.

Visual Summary

The dot plot effectively distinguishes Monocyte surface marker profiles between control and stable_ipf samples.

• Clustering by Condition: Samples largely cluster by their condition, with control samples (C27-C40) forming one group and stable_ipf samples (S20-S14) forming another, with some intermediate or variable samples (P06-P12).
• Control-Enriched Markers: Genes such as HBEGF, HCAR3, and AREG show distinctly higher mean expression (darker red color) and are expressed in a larger fraction of Monocytes (larger dot size) in the control samples compared to stable_ipf samples.
• Stable IPF-Enriched Markers: Conversely, a prominent set of genes including HLA-DQA2, AQP9, FPR2, HLA-G, FFAR2, and STEAP4 exhibit elevated mean expression and higher prevalence in Monocytes from stable_ipf patients. These markers are largely absent or expressed at very low levels in control Monocytes.
• Sample-Level Heterogeneity: While clear condition-specific patterns emerge, there is also some variability in marker expression across individual samples within each group, suggesting patient-to-patient heterogeneity.

Biological Interpretation

The differential expression of these surfaceome markers highlights distinct functional states of Monocytes in the context of stable_ipf compared to healthy controls, reflecting changes in immune regulation, metabolism, and cellular communication.

Markers Upregulated in Control Monocytes:

• HBEGF (Heparin-binding EGF-like growth factor) and AREG (Amphiregulin) are both ligands for the EGF receptor (EGFR) family. They are known to promote cell proliferation, migration, and survival, and are involved in tissue repair and inflammation. Their higher expression in control Monocytes might indicate a healthy, homeostatic state where Monocytes are primed for routine tissue maintenance or controlled responses GeneCards: HBEGF, GeneCards: AREG.
• HCAR3 (Hydroxycarboxylic Acid Receptor 3) is a G protein-coupled receptor involved in metabolic sensing and anti-inflammatory signaling. Its higher expression in control Monocytes could reflect a metabolically regulated and less inflammatory monocyte phenotype GeneCards: HCAR3.

Markers Upregulated in Stable IPF Monocytes:

• HLA-DQA2 (MHC Class II DQ alpha 2) and HLA-G (MHC Class I G) are both Major Histocompatibility Complex molecules. Upregulation of HLA-DQA2 suggests enhanced antigen presentation capacity of Monocytes in stable_ipf, potentially driving adaptive immune responses that contribute to disease pathology GeneCards: HLA-DQA2. HLA-G is known for its immunomodulatory and immunosuppressive properties, often found at sites of inflammation or in tumors to dampen immune responses. Its upregulation might represent an attempt to regulate the inflammatory environment in stable_ipf, or it could contribute to immune evasion mechanisms within the fibrotic process GeneCards: HLA-G.
• AQP9 (Aquaporin 9) is a water and glycerol channel. Its upregulation could indicate altered cell volume regulation, metabolic changes, or enhanced migratory capacity of Monocytes in the fibrotic lung environment GeneCards: AQP9.
• FPR2 (Formyl Peptide Receptor 2) is a versatile G protein-coupled receptor involved in mediating both pro- and anti-inflammatory responses depending on its ligand. Its upregulation suggests Monocytes in stable_ipf are actively sensing and responding to various inflammatory signals GeneCards: FPR2.
• FFAR2 (Free Fatty Acid Receptor 2) is a receptor for short-chain fatty acids (SCFAs), often linked to gut microbiota and immune modulation. Its presence on Monocytes in stable_ipf could signify altered metabolic signaling or an attempt to engage SCFA-mediated anti-inflammatory pathways GeneCards: FFAR2.
• STEAP4 (STEAP Family Member 4) is a metalloreductase involved in lipid and glucose metabolism, and inflammation. Upregulation of STEAP4 points towards altered metabolic profiles and potentially oxidative stress pathways within Monocytes of stable_ipf patients GeneCards: STEAP4.

Collectively, these findings suggest that Monocytes in stable_ipf patients exhibit a distinct activated and immunomodulatory phenotype, characterized by enhanced antigen presentation, metabolic reprogramming, and altered responses to inflammatory and homeostatic signals.

Clinical or Translational Implications

The identified condition-specific surfaceome markers in Monocytes hold significant clinical and translational potential:

• Biomarker Discovery: The distinct expression profiles of these surface markers (e.g., high HLA-DQA2, AQP9, FPR2, HLA-G, FFAR2, STEAP4 in stable_ipf vs. high HBEGF, HCAR3, AREG in control) could serve as diagnostic or prognostic biomarkers for IPF, potentially distinguishing stable disease from healthy states. These could be measured using techniques like flow cytometry on peripheral blood monocytes.
• Therapeutic Targets: The upregulated surface markers in stable_ipf Monocytes represent promising therapeutic targets. For example, modulating the activity of FPR2 could fine-tune inflammatory responses, or targeting HLA-G might influence immune tolerance/suppression in the fibrotic lung. Further research would be needed to determine if inhibiting or activating these pathways can modify disease progression or severity.
• Patient Stratification: Understanding these monocyte phenotypes could contribute to patient stratification for clinical trials or personalized medicine approaches, potentially identifying subsets of patients who might respond differently to existing or novel therapies based on their monocyte surfaceome profile.
• Understanding Pathogenesis: These markers provide insights into the underlying cellular mechanisms driving IPF, even in a "stable" phase. By understanding how monocytes contribute to the disease, new mechanistic pathways can be explored for therapeutic intervention.

12. B cell, Granulocyte, ILC, Monocyte, NK cell, Platelet, T cell CD4+, T cell CD8+ 세포 유형별 유전자 세트 농축 분석 결과

[Analysis Visualization Results]...

Analysis Overview

이 분석은 단일 세포 RNA 시퀀싱(scRNA-seq) 데이터를 활용하여 특발성 폐섬유증(IPF) 환자의 혈액에서 B cell, Granulocyte, ILC, Monocyte, NK cell, Platelet, T cell CD4+, T cell CD8+ 등 주요 면역 세포 유형별 유전자 세트 농축 분석(GSEA) 결과를 제시합니다. 각 세포 유형에 대해 'progressive_ipf', 'stable_ipf', 'control' 세 가지 조건 중 하나를 다른 모든 조건과 비교하여 유전자 세트 활성화 및 억제 패턴을 확인했습니다. 시각화는 80개의 가장 유의미한 경로를 대상으로 하며, NES(Normalized Enrichment Score) 값을 색상(빨간색은 상향 조절, 파란색은 하향 조절)으로, p-값의 -log10 값을 점의 크기로 나타냅니다.

Visual Summary

도트 플롯은 다양한 세포 유형과 조건에서 여러 생물학적 경로의 차등 농축 패턴을 명확하게 보여줍니다.

• 전반적인 패턴: 'progressive_ipf_vs_others' 조건에서 많은 경로가 광범위하게 상향 조절(붉은색 점)되는 경향이 두드러집니다. 이는 진행성 IPF에서 전반적인 면역 활성화 및 세포 활동 증가를 시사합니다.
• NES 및 P-값: 붉은색 점은 해당 경로가 비교 조건(여기서는 progressive IPF)에서 상향 조절된 유전자 발현과 관련하여 농축되었음을 나타내고, 푸른색 점은 하향 조절되었음을 나타냅니다. 점의 크기는 농축의 통계적 유의성을 반영하며, 큰 점일수록 더 유의미한 결과를 의미합니다.
• 주요 상향 조절 경로: 특히 JAK-STAT signaling pathway, NF-kappa B signaling pathway, Toll-like receptor signaling pathway, TGF-beta signaling pathway, Oxidative phosphorylation, Glycolysis / Gluconeogenesis, Cell cycle, DNA replication, Ribosome, Ribosome biogenesis in eukaryotes 등이 여러 면역 세포 유형(B cell, ILC, Monocyte, NK cell, Platelet, CD4+ T cell, CD8+ T cell)에서 'progressive_ipf_vs_others' 비교 시 강하게 상향 조절되는 것으로 관찰됩니다.
• 주요 하향 조절 경로: 일부 세포 유형에서 DNA replication, Endocytosis, RNA transport, Tight junction 등이 'progressive_ipf_vs_others' 조건에서 하향 조절(푸른색 점)되는 경향을 보입니다. 그러나 전반적으로 상향 조절되는 경로의 수가 더 많고 신호가 더 강합니다.
• 조건별 차이: 'control_vs_others' 비교에서는 'progressive_ipf_vs_others'에서 관찰된 강한 상향 조절 패턴이 나타나지 않거나, 오히려 반대 방향의 변화를 보이기도 합니다. 이는 진행성 IPF 특유의 병리학적 변화를 반영합니다.

Biological Interpretation

이번 GSEA 결과는 진행성 IPF 환자의 혈액 내 다양한 면역 세포 유형에서 특징적인 생물학적 변화를 명확히 보여줍니다.

1. 광범위한 면역 활성화 및 염증 반응:

• JAK-STAT, NF-kappa B, Toll-like receptor signaling pathway의 동시 상향 조절은 B cell, ILC, Monocyte, NK cell, Platelet, T cell CD4+, T cell CD8+ 등 대부분의 분석 대상 면역 세포 유형에서 나타납니다. 이는 진행성 IPF에서 전신성 염증 및 면역 반응이 광범위하게 활성화되어 있음을 강력히 시사합니다 PubMed search: JAK-STAT signaling immune, PubMed search: NF-kappa B signaling immune.
• 특히, Fc epsilon RI signaling pathway도 여러 세포 유형에서 활성화되어 있으며, 이는 알레르기 또는 비만세포 활성화와 관련된 경로이나, 전반적인 면역 과민 반응의 일환으로 해석될 수 있습니다.

2. 대사 재프로그래밍(Metabolic Reprogramming):

• Oxidative phosphorylation 및 Glycolysis / Gluconeogenesis 경로가 대부분의 면역 세포 유형에서 'progressive_ipf_vs_others' 조건에서 상향 조절됩니다. 이는 활성화된 면역 세포가 증식, 분화 및 기능 수행을 위해 필요한 에너지를 확보하기 위해 대사 경로를 변화시키는 전형적인 특징입니다 PubMed search: immune cell metabolic reprogramming. 이러한 대사 변화는 면역 세포의 염증성 및 섬유화 유발 기능에 영향을 미칠 수 있습니다.

3. 세포 증식 및 단백질 합성 증가:

• Cell cycle, DNA replication, Ribosome, Ribosome biogenesis in eukaryotes 경로들이 ILC, NK cell, T cell CD4+, T cell CD8+를 포함한 여러 세포 유형에서 강하게 상향 조절됩니다. 이는 진행성 IPF에서 특정 면역 세포 아형의 증식 또는 활성화에 따른 단백질 합성 기구의 증강이 일어남을 나타냅니다. Platelet에서도 DNA replication이 관찰되는데, 이는 혈소판의 전구 세포인 거핵구의 활성 또는 혈소판 자체의 mRNA 발현 변화와 관련될 수 있습니다.

4. TGF-beta signaling pathway의 중요성:

• TGF-beta signaling pathway는 B cell, ILC, Monocyte, NK cell, Platelet, T cell CD4+, T cell CD8+ 등 거의 모든 분석 대상 세포 유형에서 'progressive_ipf_vs_others' 조건에서 강하게 상향 조절됩니다. TGF-beta는 IPF 병리학에서 핵심적인 섬유화 촉진 인자이며, 이 경로의 광범위한 활성화는 이들 면역 세포가 직간접적으로 섬유화 과정에 기여하거나 섬유화 환경에 반응하고 있음을 강력히 시사합니다 GeneCards: TGFB1.

5. 세포 부착 및 상호작용 관련 변화:

• Cell adhesion molecules 경로의 상향 조절은 B cell, Monocyte, NK cell, Platelet, T cell CD4+에서 관찰됩니다. 이는 염증 부위로의 면역 세포 유입 증가 및 세포 간, 세포-기질 간 상호작용 증진을 나타내며, 염증 및 섬유화 진행에 중요한 역할을 할 수 있습니다.

6. 과립구(Granulocyte)의 독특한 패턴:

• Granulocyte에서는 Cell cycle, DNA replication, Oxidative phosphorylation 등이 상향 조절되는 반면, Fc epsilon RI signaling pathway, NF-kappa B signaling pathway, Phagosome, Toll-like receptor signaling pathway와 같은 주요 면역 활성 경로는 오히려 하향 조절되는 경향을 보입니다. 이는 진행성 IPF의 과립구가 다른 면역 세포와는 다른 형태의 활성화 또는 기능적 변화를 겪고 있음을 시사하며, 특정 아형의 변화를 반영할 수 있습니다.

Clinical or Translational Implications

이 GSEA 결과는 진행성 IPF 환자의 혈액에서 전신적인 면역 및 염증 반응의 활성화, 그리고 대사 재프로그래밍이 광범위하게 일어남을 보여주는 중요한 증거를 제공합니다.

• 진행성 질환의 바이오마커 발굴: JAK-STAT, NF-kappa B, Toll-like receptor signaling 및 TGF-beta signaling 경로의 활성화는 진행성 IPF를 안정형 IPF 또는 대조군과 구별하는 잠재적인 바이오마커가 될 수 있습니다. 특히, 혈액 내 특정 면역 세포 유형에서 이러한 경로의 변화를 모니터링하는 것은 질병의 진행을 예측하는 데 도움이 될 수 있습니다.
• 잠재적 치료 표적: TGF-beta signaling pathway가 다양한 면역 세포에서 일관되게 상향 조절되는 것은 IPF의 섬유화 과정에 대한 강력한 증거이며, 이 경로를 표적으로 하는 치료제 개발의 중요성을 재확인합니다. 또한, JAK-STAT 및 NF-kappa B와 같은 주요 염증 경로의 억제제는 진행성 IPF의 면역-염증성 측면을 조절하는 데 유용할 수 있습니다.
• 면역 조절 전략: 대사 재프로그래밍(산화적 인산화 및 해당 과정)이 여러 면역 세포에서 관찰되므로, 면역 세포의 대사를 조절하여 염증 및 섬유화 기능을 억제하는 새로운 치료 접근법의 가능성을 탐색할 수 있습니다.
• 세포 유형별 맞춤 치료: 각 면역 세포 유형이 진행성 IPF에서 고유한 GSEA 패턴을 보이는 것은, 특정 세포 유형을 표적으로 하는 치료 전략이 질병 진행을 억제하는 데 더 효과적일 수 있음을 시사합니다. 예를 들어, Granulocyte에서 관찰된 독특한 패턴은 이 세포 유형이 다른 면역 세포와는 차별화된 역할을 할 수 있음을 보여주므로, 이에 대한 추가 연구가 필요합니다.

13. Discussion

The comprehensive single-cell analysis of peripheral blood in Idiopathic Pulmonary Fibrosis (IPF) patients reveals a profoundly altered immune and myeloid cell landscape that distinctly separates control individuals from those with stable and progressive disease. While UMAP embeddings show a general intermixing of cells across conditions, distinct enrichments of progressive IPF cells within T cell and myeloid clusters hint at specific disease-associated shifts in cellular composition or state.

A striking finding is the significant expansion of circulating monocytes, particularly in progressive IPF, where they constitute up to 60% of peripheral immune cells, suggesting a systemic inflammatory state and an increased reservoir for recruitment to fibrotic lesions. Concurrently, B cells are notably increased in progressive IPF, pointing to an exacerbated humoral immune or autoimmune component. These expansions lead to a relative reduction in T cells and NK cells, which may reflect altered trafficking or dilution effects. Further dissecting T cell subsets reveals a critical imbalance: Th17 and Th2 cells are significantly reduced in stable IPF compared to controls, while regulatory T (Treg) cells are significantly elevated in progressive IPF. This Treg expansion likely contributes to an immunosuppressive environment, potentially hindering effective anti-fibrotic responses and allowing unchecked fibrosis. Similarly, Eosinophils, a myeloid subset, show a dynamic pattern, being significantly reduced in stable IPF but increasing in progressive IPF relative to the stable phase, suggesting their re-mobilization or active involvement during disease exacerbation. Concurrently, ILCreg cells are significantly lower in progressive IPF, suggesting impaired innate immune regulation in advanced disease.

Monocytes in stable IPF exhibit a distinct surfaceome profile, with upregulation of HLA-DQA2 (enhanced antigen presentation), AQP9 (altered metabolism/migration), FPR2 (inflammatory sensing), HLA-G (immunomodulation), FFAR2 (metabolic signaling), and STEAP4 (metabolic reprogramming). This suggests that even in a 'stable' phase, monocytes are actively adapting to the disease environment through altered metabolic and immunological functions.

Cell-cell interaction (CCI) analysis further illuminates condition-specific communication networks. While fundamental interactions like HLA and ICAM1-integrin are preserved, progressive IPF is marked by increased TNFRSF/TNFSF signaling (e.g., TRAIL, BAFF), elevated Prostaglandin D2 signaling, and enhanced adhesion molecules like ICAM3-LFA-1. A critical observation is the shift in PGE2 receptor engagement from PTGER4 (dominant in controls) to PTGER2 (prominent in progressive IPF), suggesting a qualitative change in PGE2's immunomodulatory effects that may favor fibrosis. The strong CD47-SIRPB1 interaction in progressive IPF implies a mechanism for immune cell persistence. In contrast, stable IPF features a strong CD40LG-CD40 axis (T-B cell collaboration) alongside an increased presence of immune checkpoint and inhibitory interactions (e.g., BTLA-TNFRSF14, CD52-SIGLEC10, HLA-C-KIR2DL1), which may represent active, albeit potentially insufficient, attempts at immune regulation.

Gene set enrichment analysis (GSEA) in progressive IPF reveals widespread activation across multiple immune cell types. Pathways related to immune activation (JAK-STAT, NF-kappa B, Toll-like receptor), metabolic reprogramming (Oxidative phosphorylation, Glycolysis), cell proliferation (Cell cycle, DNA replication), and critically, the TGF-beta signaling pathway, are consistently upregulated. This broad activation underscores a systemic pro-inflammatory and pro-fibrotic milieu in progressive disease. The consistent upregulation of TGF-beta signaling across nearly all immune cell types is a key finding, strongly implicating these circulating cells in driving or responding to the fibrotic cascade. Granulocytes, however, exhibit a somewhat distinct GSEA pattern, suggesting heterogeneous roles even within myeloid populations.

In summary, progressive IPF is characterized by a hyper-inflammatory and pro-fibrotic immune environment in the peripheral blood, involving specific shifts in cell proportions, functional states, and intercellular communication. These systemic changes likely reflect and contribute to the ongoing pathology in the lung, offering valuable insights into disease mechanisms and potential targets for therapeutic intervention.

Hypotheses:

1. Progressive IPF is characterized by a systemic pro-fibrotic and inflammatory immune environment, driven by expanded monocytes and B cells, alongside an immunosuppressive regulatory T cell response that fails to resolve chronic inflammation.
2. The observed shift in peripheral blood monocyte surfaceome markers in stable IPF (e.g., upregulation of HLA-DQA2, AQP9, STEAP4) represents a unique metabolic and immunoregulatory reprogramming of these cells, priming them for roles distinct from those in healthy individuals or progressive disease.
3. Altered PGE2 receptor engagement, specifically the shift from PTGER4 (predominant in controls) to PTGER2 (prominent in progressive IPF), orchestrates distinct immunomodulatory effects that contribute to persistent inflammation and fibrosis in progressive disease.
4. The strong CD47-SIRPβ1 interaction observed in progressive IPF promotes the survival and persistence of immune cells, including monocytes and B cells, within the fibrotic milieu by inhibiting efferocytosis, thus exacerbating chronic inflammation and fibrosis.
5. Stable IPF involves active immune regulatory mechanisms, evidenced by prominent inhibitory cell-cell interactions (e.g., CD52-SIGLEC10, HLA-C-KIR2DL1, BTLA-TNFRSF14), which become overwhelmed or dysfunctional as the disease progresses.

Potential therapeutic targets:

1. TGF-beta signaling pathway: The TGF-beta signaling pathway is a master regulator of fibrosis, and its widespread activation across multiple circulating immune cell types (B cells, ILC, Monocytes, NK cells, Platelets, T cell CD4+, T cell CD8+) in progressive IPF suggests a systemic contribution to fibrogenesis and inflammation. Evidence: GSEA showed strong upregulation of the 'TGF-beta signaling pathway' in nearly all analyzed immune cell types when comparing progressive IPF to other conditions. Validation: Evaluate the effect of TGF-beta receptor inhibitors or anti-TGF-beta antibodies on pro-fibrotic gene expression, cytokine production, and cellular activation in patient-derived immune cells. Assess anti-fibrotic efficacy in preclinical models of IPF.
2. JAK-STAT signaling pathway: JAK-STAT signaling is central to inflammatory cytokine responses and immune cell activation. Its widespread upregulation in multiple immune subsets in progressive IPF indicates a broad inflammatory drive that contributes to disease progression. Evidence: GSEA showed strong upregulation of the 'JAK-STAT signaling pathway' in B cells, ILC, Monocytes, NK cells, Platelets, T cell CD4+, and T cell CD8+ in progressive IPF. Validation: Test the efficacy of pan-JAK or specific JAK isoform inhibitors (e.g., JAK1/2) in modulating inflammatory cytokine profiles from IPF patient immune cells. Clinical trials with JAK inhibitors (e.g., Nintedanib, a multi-kinase inhibitor with JAK activity) are relevant in IPF, suggesting potential for further targeted JAK inhibition.
3. CD47-SIRPβ1 axis: CD47, the 'don't eat me' signal, interacting with SIRPβ1 on monocytes can prevent phagocytosis and influence immune cell survival. Its upregulation in progressive IPF could lead to persistence of pro-fibrotic immune cells, hindering resolution of inflammation and fibrosis. Evidence: CCI analysis showed a notably strong 'CD47_SIRPB1_complex-B cell|Monocyte' interaction in progressive IPF. Validation: Investigate if blocking CD47 or SIRPβ1 with antibodies enhances phagocytosis of apoptotic cells by monocytes from IPF patients, or reduces B cell survival/activation *in vitro*. Assess anti-fibrotic effects of CD47/SIRPβ1 blockade in preclinical models of fibrosis.
4. Prostaglandin E2 receptors (PTGER2/EP2 and PTGER4/EP4): The shift in PGE2 receptor engagement from PTGER4 (dominant in controls) to PTGER2 (prominent in progressive IPF) suggests a qualitative change in PGE2's immunomodulatory effects, potentially driving pro-fibrotic or pro-inflammatory pathways that contribute to disease progression. Evidence: Condition-specific CCI patterns revealed prominent 'ProstaglandinE2_byPTGES3_PTGER4' interactions in controls and 'ProstaglandinE2_byPTGES3_PTGER2' interactions in progressive IPF, particularly involving T cell CD4+ and NK cells, and B cells and Monocytes. Validation: Conduct *in vitro* studies with immune cells from IPF patients to evaluate the impact of selective EP2 or EP4 modulators on cytokine production, proliferation, and pro-fibrotic gene expression. Test the anti-fibrotic effects of selective EP2/EP4 antagonists/agonists in relevant animal models of fibrosis.
5. BAFF (TNFSF13B) pathway: BAFF is a crucial survival factor for B cells, and its increased signaling in progressive IPF could promote B cell accumulation, activation, and autoantibody production, thereby contributing to chronic inflammation and fibrosis. Evidence: CCI analysis for progressive IPF showed increased 'TNFSF13B (BAFF)-TNFRSF13B' interactions, particularly between T CD4+ cells and B cells. Validation: Evaluate the effects of BAFF neutralizing antibodies or inhibitors of its receptors (e.g., TACI, BCMA) on B cell survival, differentiation, and autoantibody production from progressive IPF patient samples *in vitro*. Assess anti-fibrotic effects in preclinical models of IPF.

Follow-up validation ideas:

1. Validate cell proportion changes (Monocytes, B cells, Tregs, NK cells, Eosinophils) and monocyte surface marker expression (e.g., HLA-DQA2, AQP9, FPR2, HLA-G) using flow cytometry or mass cytometry in larger, independent IPF patient cohorts.
2. Conduct *in vitro* functional assays (e.g., co-culture experiments, cytokine secretion, proliferation, phagocytosis, Treg suppression) with patient-derived immune cells to confirm the functional consequences of specific cell-cell interactions (e.g., PGE2 signaling, CD40-CD40LG, CD47-SIRPβ1) and altered monocyte phenotypes.
3. Perform targeted qPCR or immunohistochemistry on sorted peripheral blood cells and paired lung tissue biopsies from IPF patients to confirm the expression of key genes and proteins implicated in altered pathways (e.g., TGF-beta, JAK-STAT components, specific surfaceome markers) and correlate peripheral findings with local pathology.
4. Utilize spatial transcriptomics or proteomics on IPF lung tissue to map the localization of identified cell types and their activated pathways (e.g., adhesion molecules, inflammatory mediators) within fibrotic lesions, validating the relevance of peripheral blood findings in the disease microenvironment.
5. Investigate the therapeutic potential of modulating identified pathways (e.g., PGE2 receptors, CD47-SIRPβ1, JAK-STAT signaling) using specific inhibitors or agonists in established preclinical models of pulmonary fibrosis, assessing their impact on disease progression and immune cell function.

Limitations:

The findings are predominantly derived from peripheral blood single-cell RNA sequencing, which may not fully reflect the complex immune microenvironment within the fibrotic lung tissue. While strong associations and potential mechanisms are identified, this report does not establish direct causality between observed immune alterations and IPF pathogenesis or progression. The analysis of cell type proportions and gene expression patterns indicates changes in cell states (e.g., activation, differentiation) rather than solely changes in cell abundance. The sample size, while sufficient to reveal distinct patterns, still warrants validation in larger, independent cohorts, and individual patient heterogeneity is observed across several analyses. Functional validation experiments are crucial to confirm the biological impact and therapeutic relevance of the identified cellular and molecular changes.

14. Query List

1. Show UMAPs including condition, sample, major cell type, minor cell type, and celltype_subset in 2 columns and save.
2. Show major celltype scores on UMAP and save.
3. Show the overall Celltype_subset marker expression dot plot and save it. Keep target_cell as None and keep the other arguments at their default values.
4. Show a population bar plot of minor cell types and save.
5. Show a subset population bar plot for T cells and save.
6. Show a box plot for T cell subset populations, highlighting statistically significant differences between conditions, and save. Determine ncols appropriately based on the total number of panels.
7. Show a subset population bar plot for Myeloid cells and save.
8. Show a box plot for Myeloid cell subset populations, highlighting statistically significant differences between conditions, and save. Determine ncols appropriately based on the total number of panels.
9. Show cell-cell interactions by condition and save the result. Select at most 80 cell-cell interactions for each condition.
10. Find statistically significant differences in cell-cell interactions among major immune and stromal cells by condition and show them as a dot plot, then save. Set max_n_items_per_group = 25.
11. Extract condition-specific markers for Monocytes and show them as a dot plot, then save. Include only surfaceome markers, up to 50 per condition.
12. Show Gene set enrichment analysis results for B cell, Granulocyte, ILC, Monocyte, NK cell, Platelet, T cell CD4+, T cell CD8+ as a dot plot and save. Set color map to RdBu_r and n_pws_to_show = 80.