SCODiA Report by MLBI Lab

Single-Cell Transcriptomic Analysis of Peripheral Blood Reveals Immunological Signatures in Progressive and Stable Idiopathic Pulmonary Fibrosis

This single-cell RNA-seq analysis of peripheral blood from Idiopathic Pulmonary Fibrosis (IPF) patients and controls reveals significant immune dysregulation, particularly in progressive disease. Key findings include increased circulating monocytes, a distinct activated monocyte phenotype, and heightened proliferative activity in CD4+ T cells, alongside a shift towards pro-fibrotic Th2 immune responses. Extensive cell-cell communication alterations and perturbed metabolic pathways underscore a systemic inflammatory and fibrotic environment in IPF progression.

Contents

  1. Dataset overview
  2. UMAP Analysis of Single-Cell RNA-seq Data in IPF Conditions
  3. Validation of `celltype_minor` Annotations using Canonical Marker Gene Expression on UMAP
  4. Monocyte Condition-Specific Marker Analysis in Idiopathic Pulmonary Fibrosis
  5. Analysis of Minor Cell Type Populations in IPF Conditions
  6. T Cell Subset Population Analysis Across IPF Conditions
  7. Monocyte Population Confirmation Across Conditions
  8. Differential Abundance of T Cell and Innate Lymphoid Cell Subsets in IPF Conditions
  9. Monocyte Population Dynamics in Idiopathic Pulmonary Fibrosis Conditions
  10. Cell-Cell Interaction Analysis in Progressive Idiopathic Pulmonary Fibrosis (IPF)
  11. Immune Checkpoint Interactions Across IPF Conditions
  12. Condition-specific Cell-Cell Interaction Patterns in Idiopathic Pulmonary Fibrosis (IPF)
  13. Monocyte Condition-Specific Surfaceome Markers in IPF
  14. CD4+ T Cell Surfaceome Marker Analysis Across IPF Conditions
  15. Cell Cycle Pathway Gene Expression Differences in CD4+ T cells Across IPF Conditions
  16. Gene Set Enrichment Analysis Reveals Condition-Specific Pathway Alterations Across Immune Cell Types in Idiopathic Pulmonary Fibrosis
  17. Discussion
  18. Query List

0. Dataset overview

Dataset Summary

1. UMAP Analysis of Single-Cell RNA-seq Data in IPF Conditions

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents UMAP visualizations of single-cell RNA-seq data, displaying the distribution of 89,619 cells across 21,249 genes, annotated by various metadata fields: condition, sample, major cell type, minor cell type, and cell type subset. The data originates from human blood samples across 'control', 'progressive_ipf', and 'stable_ipf' conditions, providing an initial overview of the dataset structure and the quality of cell type annotations and batch effect integration.

Visual Summary

Condition Overlay

The UMAP plot colored by 'condition' shows a notable degree of mixing among 'control', 'progressive_ipf', and 'stable_ipf' cells across many clusters. However, there are also regions where specific conditions appear to be enriched. For instance, 'progressive_ipf' (yellow) cells show some distinct clusters, particularly in smaller, less dense regions, and also contribute to the larger, more mixed clusters. 'Stable_ipf' (blue) and 'control' (maroon) cells are generally well-interspersed throughout the main cellular populations, suggesting common cell types are present across conditions, but with potential condition-specific shifts in cellular states or proportions in certain areas. This mixed distribution indicates that the primary drivers of the UMAP structure are likely cell type identity rather than condition alone, though condition-specific perturbations are also visible.

Sample Overlay

The 'sample' UMAP reveals that cells from individual samples (e.g., C27-C40, P01-P12, S14-S26) are largely integrated across the UMAP space. There are no overtly dominant sample-specific clusters that would suggest strong batch effects, which is a positive indication of successful data integration. While some samples might show slightly denser representation within certain cell type clusters (as expected due to biological variability in cell composition), the overall intermixing of colors suggests that most cellular populations contain contributions from multiple samples. This pattern supports the notion that the observed UMAP structure reflects biological distinctions rather than technical artifacts.

Cell Type Major Overlay

The 'celltype_major' UMAP clearly demonstrates that major cell types form distinct, well-separated clusters. T cells (cyan/green shades) form a large, complex cluster, while Myeloid cells (light orange) occupy another distinct region. B cells (maroon) and Granulocytes (dark orange) form smaller, but well-defined clusters. Platelets (light green) are also identifiable. This strong separation indicates robust identification and clustering of major immune cell lineages in the blood. The 'unassigned' cells (dark purple) appear sparsely distributed across several regions, suggesting they might represent rare cell types or cells with ambiguous transcriptional profiles.

Cell Type Minor Overlay

Refining the view from 'celltype_major', the 'celltype_minor' UMAP further confirms excellent cell type separation. Within the broader T cell cluster, 'T cell CD4+' (light blue) and 'T cell CD8+' (dark blue) populations are clearly delineated. Monocytes (light orange) are well-separated, and Neutrophils (light yellow) form a distinct cluster. NK cells (dark yellow), ILCs (orange), B cells (maroon), Platelets (light green), and Eosinophils (red) also show strong clustering. This fine-grained resolution into minor cell types further validates the quality of the cell annotation and the underlying transcriptional data. The 'unassigned' cells remain scattered, as noted previously.

Cell Type Subset Overlay

The 'celltype_subset' UMAP provides the highest resolution of cell identity, showing remarkable sub-clustering within major and minor cell types. For example, within T cells, distinct populations such as 'T cell (Naive)' (light green), 'T cell (Cytotoxic)' (green), 'T cell (Treg)' (dark blue), and various Th subsets (Th1, Th2, Th9, Th17, Th22, Tfh - various shades of blue/green) are visible as separated or interconnected sub-clusters. Similarly, B cell subsets like 'B cell (Memory)' (maroon), BMZ (dark red), Bf (red), and Breg (orange-red) are discernible. Monocytes (light yellow), NK cells (dark yellow), Neutrophils (light yellow), Platelets (light green), and Eosinophils (red) also maintain their distinct clustering. This granular separation at the subset level indicates highly accurate and informative cell type annotation, enabling detailed analyses of specific immune cell populations.

Biological Interpretation

The UMAP visualizations collectively paint a clear picture of the cellular landscape in the human blood samples, with a focus on immune cells relevant to IPF.

Annotation Notes

The cell type annotations, across major, minor, and subset levels, demonstrate high quality and consistency with the underlying transcriptional profiles. The clusters on the UMAP are well-defined and correspond effectively to the assigned cell identities. The presence of a small number of 'unassigned' cells is typical in single-cell datasets and does not detract from the overall annotation quality, as the vast majority of cells are robustly classified. This robust annotation provides a strong basis for downstream analyses.

2. Validation of `celltype_minor` Annotations using Canonical Marker Gene Expression on UMAP

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[Analysis Visualization Results]...

Analysis Overview

This analysis visualizes the expression of a panel of canonical marker genes for various immune cell types on a Uniform Manifold Approximation and Projection (UMAP) plot. Concurrently, a UMAP plot colored by celltype_minor annotation is provided. The primary objective is to assess and validate the quality of the celltype_minor cell type assignments by comparing known gene expression patterns with the annotated clusters.

Visual Summary

The UMAP projections clearly delineate distinct clusters corresponding to different immune cell populations identified by the celltype_minor annotation.

The expression patterns of the selected marker genes align remarkably well with these annotated clusters:

Biological Interpretation

The strong concordance between the expression patterns of these canonical marker genes and the celltype_minor annotations provides robust biological validation for the cell type assignments. Each gene exhibits preferential expression within its expected cell population, clearly delineating these cell types in the UMAP space. This indicates that the clustering and annotation processes have successfully captured the unique transcriptional signatures of the different minor blood cell populations. For example, the precise segregation of CD4 and CD8A expression to their respective T cell subsets highlights the granularity and accuracy of the T cell annotations. Similarly, the specific expression of CD79A for B cells, CD14 and LYZ for myeloid cells (monocytes and neutrophils), NKG7 for cytotoxic lymphocytes, S100A9 for granulocytes/myeloid cells, and ITGA2B for platelets collectively confirms the biological identity of these populations within the human blood tissue.

Annotation Notes

The visualization of marker gene expression on the UMAP along with the celltype_minor annotation confirms a high quality of cell type assignment. The distinct and localized expression of each marker gene within its expected cell cluster strongly supports the accuracy and biological validity of the celltype_minor annotations. This foundational validation step is critical for ensuring confidence in downstream analyses, such as differential gene expression or cell-cell interaction studies, which rely on correctly identified cell populations. The presence of an "unassigned" cluster indicates that some cells could not be confidently classified, which is a common and appropriate outcome in single-cell analysis, highlighting areas for potential further investigation if deeper classification of these cells is required.

3. Monocyte Condition-Specific Marker Analysis in Idiopathic Pulmonary Fibrosis

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[Analysis Visualization Results]...

Analysis Overview

This analysis identifies and visualizes condition-specific gene expression markers within the Monocyte cell type, comparing control individuals with those diagnosed with stable Idiopathic Pulmonary Fibrosis (IPF). The dot plot displays the mean expression level and the fraction of cells expressing specific genes across individual samples, grouped by condition. This helps to uncover distinct molecular phenotypes of monocytes that may differentiate healthy individuals from those with stable IPF.

Visual Summary

The dot plot effectively illustrates the differential expression patterns of selected marker genes in monocytes across control and stable IPF samples.

Biological Interpretation

The observed shift in monocyte gene expression profiles between control and stable IPF conditions suggests a significant phenotypic reprogramming of these immune cells in the context of the disease.

These findings highlight a distinct "IPF-associated monocyte" phenotype characterized by altered immune activation, metabolic sensing, and inflammatory pathway engagement, which likely contributes to the pathogenesis of pulmonary fibrosis.

Clinical or Translational Implications

The identification of these condition-specific monocyte markers offers several avenues for clinical and translational impact in IPF:

4. Analysis of Minor Cell Type Populations in IPF Conditions

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[Analysis Visualization Results]...

Analysis Overview

This analysis presents a population bar plot illustrating the relative proportions of minor cell types across individual samples, grouped by disease condition: 'control', 'progressive_ipf', and 'stable_ipf'. The data originates from single-cell RNA sequencing of blood samples, and cell types are categorized at the 'minor' taxonomic level. This visualization helps to understand the systemic immune cell landscape in Idiopathic Pulmonary Fibrosis (IPF) patients compared to controls, and to identify potential differences between stable and progressive forms of the disease.

Visual Summary

The stacked bar plot visualizes the percentage contribution of each minor cell type to the total cell population within each blood sample.

Biological Interpretation

The observed shifts in peripheral blood cell populations provide insights into the systemic immune response in IPF, particularly distinguishing between control, stable, and progressive disease states.

Clinical or Translational Implications

The findings from this peripheral blood cell population analysis have several potential clinical and translational implications:

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References:

  1. Monocytes/Macrophages in IPF:

PubMed Search: Idiopathic Pulmonary Fibrosis Macrophage Monocyte

  1. B Cells in IPF:

PubMed Search: Idiopathic Pulmonary Fibrosis B cell

5. T Cell Subset Population Analysis Across IPF Conditions

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[Analysis Visualization Results]...

Analysis Overview

이 분석은 단일 세포 RNA 시퀀싱(scRNA-seq) 데이터를 활용하여 대조군(control), 진행성 특발성 폐섬유증(progressive_ipf), 안정성 특발성 폐섬유증(stable_ipf) 환자 간의 혈액 내 T 세포 및 관련 림프구 아형(ILC, NK cell)의 상대적 분포를 비교합니다. plot_celltype_population 도구를 사용하여 각 샘플에 대한 T 세포 아형 구성 비율을 시각화하였습니다. 이 결과는 특발성 폐섬유증(IPF)의 진행과 안정성 상태에서 면역 세포 구성에 변화가 있는지 탐색하는 데 도움을 줍니다.

Visual Summary

제공된 스택형 막대 그래프는 각 조건(control, progressive_ipf, stable_ipf)별 개별 샘플에서 T 세포 주요 그룹 내의 다양한 아형(T cell (Naive), T cell (Cytotoxic), T cell (Treg) 등, 그리고 ILC 및 NK 세포)의 상대적 비율을 보여줍니다.

Biological Interpretation

이 혈액 T 세포 및 관련 림프구 아형의 구성 분석은 IPF 환자에서 면역 반응의 변화를 시사합니다.

Clinical or Translational Implications

이러한 T 세포 아형 구성의 변화는 IPF의 병태생리학적 이해를 심화하는 데 기여할 수 있습니다.

참고 문헌:

6. Monocyte Population Confirmation Across Conditions

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[Analysis Visualization Results]...

Analysis Overview

This analysis utilizes the plot_celltype_population tool to visualize the population of Monocytes (from celltype_minor annotation) across different samples and experimental conditions (control, progressive_ipf, stable_ipf). The primary goal appears to be a confirmation of the presence and consistent annotation of Monocyte cells within the dataset, specifically for cells designated as Monocytes.

Visual Summary

The visualization consists of three bar plots, one for each condition: 'control', 'progressive_ipf', and 'stable_ipf'. Each subplot displays multiple bars, with each bar representing an individual sample within that condition. All bars across all samples and conditions are uniformly colored in maroon, labeled 'Monocyte' in the legend, and extend to 100% on the y-axis.

Biological Interpretation

The plot indicates that for every sample displayed under the 'control', 'progressive_ipf', and 'stable_ipf' conditions, the population being visualized consists entirely of Monocytes (100%). Given the parameters used (targets: {'obs_col': 'celltype_minor', 'value': 'Monocyte'}), this plot serves primarily as a confirmation that the data subset being analyzed or visualized for each sample is indeed composed exclusively of cells classified as Monocytes.

This visualization confirms the following:

It is important to note that this plot does not depict the relative abundance or proportion of Monocytes compared to other cell types (e.g., T cells, B cells, Neutrophils) within the total peripheral blood mononuclear cell (PBMC) population of each sample. To understand the *change in the overall frequency* of Monocytes in the context of IPF, a different type of population plot showing the percentage of Monocytes relative to all cell types would be required. This current plot specifically confirms that the Monocyte population *itself* is consistently identified within the context of the requested visualization.

Clinical or Translational Implications

While this specific plot does not provide direct insights into differential Monocyte abundance in IPF, it is a crucial prerequisite for subsequent cell-type-specific analyses. Confirming the consistent identification and presence of Monocytes across all conditions (control, progressive_ipf, stable_ipf) ensures that any downstream differential gene expression (DEG), cell-cell interaction (CCI), or pathway enrichment (GSEA/GSA) analyses performed on Monocytes are based on a reliably defined cell population. Monocytes are known to play a significant role in inflammatory and fibrotic diseases like IPF, differentiating into macrophages that contribute to disease progression [1]. Therefore, their consistent identification is foundational for investigating their specific roles and potential as therapeutic targets or biomarkers in IPF.

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References

  1. Monocytes and Macrophages in Idiopathic Pulmonary Fibrosis: A PubMed search query: https://pubmed.ncbi.nlm.nih.gov/?term=monocytes+macrophages+idiopathic+pulmonary+fibrosis

7. Differential Abundance of T Cell and Innate Lymphoid Cell Subsets in IPF Conditions

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportional differences of various T cell and innate lymphoid cell (ILC) subsets in peripheral blood across control individuals, patients with stable Idiopathic Pulmonary Fibrosis (IPF), and patients with progressive IPF. The box plots visually represent the celltype proportions, and statistical significance (p < 0.1, with an absolute log2 fold change > 0.1) is annotated for comparisons between conditions. This helps identify immune cell population shifts associated with different IPF disease states.

Visual Summary

The visualization displays box plots for eight immune cell subsets: Th22, unassigned, NK cell, ILC3(-), Th17, ILC2, Th2, and ILC1. Each plot compares the celltype proportion across three conditions: stable_ipf, control, and progressive_ipf.

Key observations from the plots are:

Biological Interpretation

The observed shifts in immune cell proportions in peripheral blood highlight potential immunological imbalances associated with IPF progression.

  1. Pro-fibrotic/Pro-inflammatory Shifts in Progressive IPF:
  1. Immune Alterations in Stable IPF:
  1. "unassigned" Cell Population: The higher proportion of "unassigned" cells in controls and their reduction in IPF conditions could imply that certain cell states or populations found in healthy individuals are either undergoing differentiation, being consumed, or are no longer distinguishable by the current annotation scheme in the context of IPF. Further investigation into the markers of these "unassigned" cells would be necessary for a meaningful interpretation.

Clinical or Translational Implications

These findings highlight distinct peripheral immune signatures that differentiate stable IPF from progressive IPF, and both from healthy controls.

References

[1] For a general understanding of IL-22 and Th22 cells in disease:

PubMed search: Th22 cells IL-22 lung disease fibrosis

[2] For Th2 cells and fibrosis:

PubMed search: Th2 cells fibrosis lung IPF

[3] For Th17 cells in IPF:

PubMed search: Th17 cells idiopathic pulmonary fibrosis blood

[4] For NK cells in fibrotic diseases:

PubMed search: NK cells fibrosis lung

8. Monocyte Population Dynamics in Idiopathic Pulmonary Fibrosis Conditions

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the proportion of Monocyte cells in the peripheral blood across different conditions: control, stable idiopathic pulmonary fibrosis (IPF), and progressive IPF. The celltype proportions were derived from single-cell RNA sequencing data. Box plots are used to visualize the distribution of Monocyte proportions for each condition, with individual sample data points overlaid. Statistical significance tests highlight differences between specific condition pairs.

Visual Summary

The box plot illustrates distinct patterns in Monocyte celltype proportion across the three conditions:

Statistical Significance

Overall, there is a clear trend of increasing Monocyte proportion from control to stable IPF, and a further, statistically significant, increase in progressive IPF. This suggests that a higher proportion of circulating monocytes is associated with the more severe and progressive form of IPF.

Biological Interpretation

Monocytes are key innate immune cells that play crucial roles in inflammation, tissue repair, and fibrosis. In the context of Idiopathic Pulmonary Fibrosis (IPF), dysregulation of immune cells, including monocytes and their differentiated forms (macrophages), is strongly implicated in disease pathogenesis.

The observed increase in circulating Monocyte proportion in progressive IPF patients suggests a heightened systemic inflammatory or pro-fibrotic state. Monocytes are known precursors to macrophages, which can adopt various phenotypes, including pro-inflammatory (M1) and pro-fibrotic (M2) states. In IPF, recruited monocytes differentiate into alveolar macrophages and interstitial macrophages, which contribute to the fibrotic cascade by secreting profibrotic mediators (e.g., TGF-β, PDGF) and driving fibroblast activation and extracellular matrix deposition [1].

The significant difference between stable and progressive IPF highlights that not just the presence of IPF, but the *progression* of the disease, correlates with a more pronounced increase in peripheral Monocytes. This could reflect:

Clinical or Translational Implications

The finding that a higher proportion of circulating Monocytes is significantly associated with progressive IPF has several potential clinical implications:

  1. Biomarker for Progression: Monocyte proportion in peripheral blood could serve as a readily accessible, non-invasive biomarker to help differentiate between stable and progressive forms of IPF, or to monitor disease progression. This could aid in risk stratification and guide treatment decisions for IPF patients.
  2. Therapeutic Target Indication: Given the established role of monocytes/macrophages in IPF pathogenesis, the increased number of circulating monocytes in progressive disease reinforces the rationale for targeting monocyte recruitment, differentiation, or activation as a potential therapeutic strategy. Modulating the systemic monocyte pool or its pro-fibrotic subsets could potentially slow disease progression [2].
  3. Understanding Disease Heterogeneity: The distinction between stable and progressive IPF based on Monocyte proportions provides insights into the immunological differences underlying disease heterogeneity. Further investigation into the specific subsets of monocytes (e.g., classical, intermediate, non-classical) that are elevated could offer more refined insights into their specific contributions to IPF progression.

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References

[1] Hutchenreuther, J., et al. (2020). Macrophages in Pulmonary Fibrosis: From Pathogenesis to Therapeutics. *International Journal of Molecular Sciences*, 21(19), 7056. PubMed Search: "Macrophages pulmonary fibrosis pathogenesis"

[2] Genentech. (2022). IPF Disease Pathophysiology. Genentech Pulmonary Pipeline (A general reference for industry insights on IPF pathophysiology)

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

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates significant cell-cell interactions (CCIs) within the progressive_ipf condition using single-cell RNA sequencing data. The plot_cci_dots tool, leveraging CellPhoneDB results, visualizes the most prominent ligand-receptor (L-R) interactions between various immune cell types present in blood. The plot displays the top 80 interactions based on statistical significance (p-value) and interaction strength (mean expression), providing insights into the complex cellular communication networks active in progressive IPF.

Visual Summary

The dot plot visualizes cell-cell interactions for the progressive_ipf condition.

Key visual patterns observed:

Biological Interpretation

The analysis of cell-cell interactions in progressive IPF reveals a highly active and complex communication network, predominantly involving monocytes, T cells, NK cells, and B cells. These interactions are crucial for understanding the chronic inflammation and fibrotic remodeling characteristic of IPF.

  1. Monocyte-centric inflammatory hub: Monocytes, known for their plasticity and critical role in both initiating and resolving inflammation, appear as central players. Their strong interactions, especially via CCL3-CCR1 and CCL5-CCR1, highlight active chemokine signaling pathways that likely contribute to the recruitment and activation of other immune cells (T cells, NK cells) to the fibrotic lung. GeneCards: CCL3, GeneCards: CCL5, GeneCards: CCR1.
  2. Annexin A1 (ANXA1) and Formyl Peptide Receptors (FPRs): The strong ANXA1-FPR1/FPR2 interactions, particularly involving B cells and monocytes, are noteworthy. ANXA1 is typically associated with anti-inflammatory effects, but its interaction with FPRs can be context-dependent. In the chronic inflammatory environment of IPF, FPR activation on immune cells can contribute to inflammatory cell recruitment and even pro-fibrotic signaling. PubMed search: ANXA1 FPR fibrosis.
  3. Immune Adhesion and Trafficking: The widespread activity of ICAM1-integrin complexes indicates robust cell adhesion processes. ICAM1 is commonly upregulated during inflammation and facilitates immune cell migration and extravasation into tissues. This suggests ongoing immune cell trafficking and retention within the lung microenvironment in progressive IPF, contributing to sustained inflammation and fibrosis. GeneCards: ICAM1.
  4. TNF Superfamily Signaling: Various TNF superfamily interactions, such as CD160-TNFRSF14 (HVEM) and TNFSF10-TNFRSF10A (TRAIL-TRAILR), are active. These pathways are crucial for immune cell survival, proliferation, and apoptosis. Dysregulation can contribute to chronic inflammation and immune cell persistence. For example, TNFSF10 (TRAIL) can induce apoptosis, but also has non-apoptotic, pro-inflammatory functions in certain contexts. GeneCards: TNFRSF14, GeneCards: TNFSF10. The presence of TNFSF13B-TNFRSF13B (BAFF-BAFFR) interactions, particularly involving B cells, suggests a role in B cell survival and activation, which is increasingly recognized in IPF pathogenesis. GeneCards: TNFSF13B.
  5. Prostaglandin Signaling: Interactions involving Prostaglandin D2 (PTGDR) and Prostaglandin E2 (PTGERs) suggest the involvement of lipid mediators in modulating immune responses. Prostaglandins can have diverse effects on inflammation and fibrosis, acting as both pro- and anti-inflammatory agents depending on the specific receptor and cellular context. In IPF, they can influence fibroblast activation, collagen production, and immune cell function. PubMed search: Prostaglandin IPF.
  6. Immune Checkpoint Modulation: The presence of CD47-SIRPα complex and VSIR-HLA-E interactions indicates active immune regulatory mechanisms. CD47-SIRPα ("don't eat me" signal) prevents phagocytosis, and its dysregulation can impact macrophage efferocytosis (clearance of apoptotic cells), which is crucial in resolving inflammation. PubMed search: CD47 SIRPA fibrosis. VSIR (VISTA) is an immune checkpoint molecule, and its interaction with HLA-E can lead to immune suppression, potentially dampening protective anti-fibrotic immunity or contributing to chronic inflammation. PubMed search: VISTA HLA-E immune checkpoint.

Clinical or Translational Implications

The identified cell-cell interactions offer valuable insights for therapeutic target prioritization and experimental validation in progressive IPF:

These findings provide a mechanistic basis for designing targeted interventions to disrupt pathological cellular communication networks in progressive IPF, and warrant further experimental validation in relevant in vitro and in vivo models.

10. Immune Checkpoint Interactions Across IPF Conditions

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates cell-cell interactions (CCI) specifically involving a curated set of immune checkpoint-related genes across different conditions: control, progressive idiopathic pulmonary fibrosis (IPF), and stable IPF. The goal is to identify how these specific immune regulatory interactions vary between healthy individuals and patients with different IPF disease courses, focusing on monocytes, T cells, and NK cells which are key players in immune responses in the blood and potentially in lung pathology. The plot_cci_dots tool was used to visualize significant ligand-receptor pairs, their interacting cell types, and their interaction strength.

Visual Summary

The visualizations present dot plots for three conditions (control, progressive IPF, stable IPF), showing cell-cell interactions mediated by immune checkpoint genes. Due to parameter settings, only statistically significant interactions are displayed.

Monocyte | T cell CD4+

Monocyte | NK cell

Monocyte | Monocyte

CD86-CD28 (Monocyte|T CD4+)

Control: log2(m) ~0.6

Stable IPF: log2(m) ~0.6 (Similar to control)

LGALS9-HAVCR2 (Monocyte|NK)

Control: log2(m) ~0.7

Progressive IPF: log2(m) ~0.7 (Similar to control)

LGALS9-HAVCR2 (Monocyte|Monocyte)

Control: log2(m) ~0.8

Stable IPF: log2(m) ~0.8 (Similar to control)

Biological Interpretation

The analysis specifically focuses on immune checkpoint interactions, which are critical for maintaining immune homeostasis and are often dysregulated in chronic inflammatory diseases like IPF.

These findings suggest that the delicate balance of immune checkpoints is altered in IPF, with distinct patterns emerging between stable and progressive disease, as well as compared to healthy controls.

Clinical or Translational Implications

The observed differences in immune checkpoint interactions between IPF conditions highlight potential areas for therapeutic intervention and further investigation:

  1. CD86-CD28 Axis in Progressive IPF: The subtle decrease in CD86-CD28 interaction in progressive IPF might suggest a state of T cell anergy or exhaustion. Strategies aimed at boosting T cell co-stimulation (e.g., agonistic CD28 antibodies, though safety is a concern) could theoretically enhance protective immune responses, but this would need careful consideration in the context of a fibrotic disease where immune overactivation could also be detrimental.
  2. LGALS9-HAVCR2 as a Therapeutic Target: The LGALS9-HAVCR2 pathway represents an attractive target for immunotherapy, similar to PD-1/PD-L1 or CTLA-4.
  1. Biomarker Potential: Changes in the strength of these specific immune checkpoint interactions could serve as potential biomarkers to differentiate between stable and progressive IPF, or to monitor treatment response.
  2. Experimental Validation: These findings warrant further experimental validation using techniques such as flow cytometry, immunohistochemistry, or functional co-culture assays to confirm the differential expression of these ligands and receptors and their functional consequences in IPF. Targeting these pathways in in vitro or in vivo IPF models could provide insights into their therapeutic potential.

References:

[1] CD86 (B7-2) GeneCards: A comprehensive gene-centric database. https://www.genecards.org/cgi-bin/carddisp.pl?gene=CD86

[2] TIM-3 (HAVCR2) GeneCards: A comprehensive gene-centric database. https://www.genecards.org/cgi-bin/carddisp.pl?gene=HAVCR2

11. Condition-specific Cell-Cell Interaction Patterns in Idiopathic Pulmonary Fibrosis (IPF)

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates statistically significant differences in cell-cell interactions (CCIs) among key immune cell types (T cell CD4+, T cell CD8+, Monocyte, NK cell, B cell, ILC, Neutrophil, Eosinophil, Platelet) in blood samples, comparing 'progressive_ipf', 'stable_ipf', and 'control' conditions. The dot plot visualizes the standardized mean interaction strength (color intensity) and the statistical significance (-log10(p-value) as dot size) for the top 25 most significant CCIs detected using a t-test based differential analysis with a p-value cutoff of 0.1, focusing on interactions that are 'greater' in one condition compared to others.

Visual Summary

The dot plot is structured to display individual samples along the y-axis, grouped by condition (Control, Progressive IPF, Stable IPF) on the top horizontal axis. The x-axis enumerates specific cell-cell interaction pairs (e.g., "Ligand-Receptor | Cell_A--Cell_B").

  1. Distinct Interaction Profiles by Condition:
  1. Dominant Cell Types in Interactions:
  1. Key Interaction Gene Pairs:

Biological Interpretation

The observed patterns highlight substantial alterations in immune cell communication in the blood of IPF patients, particularly those with progressive disease.

  1. Monocyte-centric Immune Activation in Progressive IPF: The high frequency and intensity of monocyte-involved CCIs in progressive IPF suggest heightened monocyte activity and their critical role in the disease pathology. Monocytes are progenitors of macrophages, which are central effectors in fibrotic processes, influencing inflammation, tissue remodeling, and myofibroblast differentiation. Enhanced communication with T cells and other immune cells could lead to sustained pro-fibrotic or pro-inflammatory feedback loops PubMed search: monocytes macrophages fibrosis IPF.
  2. Prostaglandin E2 (PGE2) Signaling: Increased PGE2-related interactions (e.g., ProstaglandinE2 by PTGES2_PTGER4) in progressive IPF are notable. PGE2, produced by enzymes like PTGES2 and PTGES3, can have complex roles in fibrosis, acting as both an anti-inflammatory and, in some contexts, promoting fibrotic pathways through specific receptors (like PTGER4) GeneCards: PTGER4. Upregulated PGE2 signaling might indicate dysregulated immune dampening or direct pro-fibrotic signaling.
  3. Adhesion and Migration mediated by Integrins: The prevalence of integrin-mediated interactions (e.g., ICAM2_integrin_aLb2_complex, PLAUR_integrin_a4b1_complex) involving T cells and monocytes points towards altered cell adhesion, migration, and immune cell trafficking. Integrins are crucial for leukocyte extravasation into tissues and cell-cell recognition. Their increased activity could contribute to the recruitment of immune cells to sites of inflammation and fibrosis in the lung, even if detected systemically in the blood GeneCards: ICAM2. PLAUR (Urokinase Plasminogen Activator Receptor) in complex with integrins plays roles in cell migration and proteolytic activity, both relevant in tissue remodeling UniProt: PLAUR.
  4. T Cell-Mediated Immunomodulation: The involvement of CD4+ and CD8+ T cells in significant interactions, particularly with monocytes, indicates their active participation in the immune landscape of progressive IPF. T cells can drive fibrotic responses through cytokine production (e.g., Th2 cytokines) or regulatory functions (Tregs). Specific T-cell subsets are known to be dysregulated in IPF PubMed search: T cells IPF pathogenesis. LTA-TNFRSF1B signaling (lymphotoxin-alpha and its receptor) further suggests TNF-family-mediated inflammatory or immune modulatory pathways are active PubMed search: TNF family signaling fibrosis.
  5. Differentiation of Progressive vs. Stable Disease: The clear distinction in CCI patterns between progressive and stable IPF indicates that specific immune interactions might serve as biomarkers for disease progression. Stable IPF generally showing less intense or fewer specific interactions suggests a less active or different immunopathogenic process compared to progressive IPF.

Clinical or Translational Implications

  1. Prognostic Biomarkers: The unique and significantly upregulated cell-cell interaction signatures observed in progressive IPF could serve as promising blood-based prognostic biomarkers. Monitoring these specific interactions could help identify patients at higher risk of disease progression, guiding clinical management and therapeutic decisions.
  2. Therapeutic Targets: Key ligand-receptor pairs identified as highly active in progressive IPF, such as specific integrins or components of the PGE2 signaling pathway (e.g., PTGES2/3, PTGER4), represent potential therapeutic targets. Modulating these interactions could disrupt pro-fibrotic immune cascades and potentially halt or slow disease progression. For example, strategies targeting integrins have been explored in various fibrotic conditions.
  3. Disease Monitoring: Longitudinal assessment of these identified CCI patterns could provide a non-invasive method to monitor disease activity, response to existing therapies, or the efficacy of novel interventions in IPF patients.

12. Monocyte Condition-Specific Surfaceome Markers in IPF

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[Analysis Visualization Results]...

This analysis identifies and visualizes key surfaceome markers that distinguish monocytes from individuals with stable Idiopathic Pulmonary Fibrosis (IPF) compared to healthy controls, derived from single-cell RNA sequencing data of blood. Identifying surface markers is particularly valuable for characterizing specific cell populations and exploring potential diagnostic or therapeutic targets, as these proteins are accessible on the cell surface.

Analysis Overview

The dot plot displays the expression levels and prevalence of selected surfaceome genes within Monocytes across different samples, grouped by condition: 'control' and 'stable_ipf'. Each row represents a sample, and each column represents a gene. The size of the dot indicates the fraction of monocytes in that sample expressing the gene, while the color intensity reflects the mean expression level of the gene in those cells. Only surfaceome markers, up to 50 per condition, were considered, and the plot shows a subset of these top markers.

Visual Summary

The dot plot clearly segregates samples based on condition and reveals distinct surfaceome marker profiles for monocytes:

Biological Interpretation

The distinct surfaceome profiles observed in monocytes between control and stable IPF conditions point towards altered immune functions, metabolic states, and regulatory roles of these cells in IPF pathogenesis.

Control-Associated Markers (HBEGF, HCAR3, AREG):

Stable IPF-Associated Markers (HLA-DQA2, AQP9, HLA-G, FPR2, FFAR2, STEAP4):

Clinical or Translational Implications

These distinct monocyte surfaceome marker profiles hold significant potential for clinical and translational applications:

13. CD4+ T Cell Surfaceome Marker Analysis Across IPF Conditions

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[Analysis Visualization Results]...

Analysis Overview

This analysis aimed to identify condition-specific surfaceome markers for CD4+ T cells, comparing progressive_ipf, stable_ipf, and control conditions from single-cell RNA-seq data obtained from blood tissue. The plot_markers_and_expression_dot tool was used to visualize the expression of these markers. The analysis was specifically configured to find up to 50 surfaceome-only markers per condition, with strict cutoffs for score, p-value, and fold change (e.g., fc_cutoff=1.5, pval_cutoff=0.05). Markers common to three or more groups were configured to be removed, emphasizing specificity.

Visual Summary

The provided dot plot visualizes the expression of two genes, TNFRSF4 and LPAR6, across individual samples categorized by condition (inferred as progressive_ipf, stable_ipf, and control based on the data context and visual grouping, although specific condition labels are not fully visible).

It is important to note that while the analysis was configured to find up to 50 condition-specific surfaceome markers, the provided visualization displays only these two genes. This could imply that very few markers met the stringent criteria for condition-specificity and surfaceome localization, or that the visualization itself has limited the display to these two.

Biological Interpretation

Despite not showing strong condition-specific differences in this particular visualization, the identified genes warrant a brief biological consideration in the context of CD4+ T cells and IPF:

Clinical or Translational Implications

Based solely on the visual output for TNFRSF4 and LPAR6, this analysis does not readily identify strong, distinct condition-specific surfaceome markers for CD4+ T cells in the blood that could serve as robust diagnostic or therapeutic targets for progressive or stable IPF.

14. Cell Cycle Pathway Gene Expression Differences in CD4+ T cells Across IPF Conditions

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[Analysis Visualization Results]...

Analysis Overview

This analysis investigates the differential expression of a panel of cell cycle pathway-related genes in CD4+ T cells, comparing peripheral blood samples from control individuals, patients with progressive idiopathic pulmonary fibrosis (IPF), and patients with stable IPF. The goal is to identify genes with statistically significant expression differences that may be associated with IPF disease status and progression. The box plots display gene expression levels (sample mean) for each condition, with p-values indicating statistical significance between groups.

Visual Summary

The visualization presents box plots for nine key cell cycle-related genes: CCND3, YWHAB, MYC, RBX1, MAD1L1, ANAPC11, SKP1, YWHAZ, and ANAPC5. All nine genes consistently show elevated expression in CD4+ T cells from patients with progressive IPF compared to control individuals.

Key observations include:

Biological Interpretation

The consistent upregulation of these cell cycle genes in CD4+ T cells from progressive IPF patients strongly suggests an altered proliferative state or dysregulated cell cycle control in these immune cells. Many of the genes observed play critical roles in driving cell division:

In the context of IPF, a chronic and progressive fibrotic lung disease, T cells are known to contribute to both pro- and anti-fibrotic responses. The observed increased expression of cell cycle activators in circulating CD4+ T cells specifically in progressive IPF suggests an expansion of activated, proliferating T cell subsets. This heightened proliferative state in progressive disease could contribute to chronic inflammation, immune dysregulation, and ultimately, accelerated fibrogenesis within the lung, as these activated cells may migrate to the affected tissue and perpetuate the fibrotic cascade.

Clinical or Translational Implications

15. Gene Set Enrichment Analysis Reveals Condition-Specific Pathway Alterations Across Immune Cell Types in Idiopathic Pulmonary Fibrosis

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[Analysis Visualization Results]...

Analysis Overview

This analysis utilizes Gene Set Enrichment Analysis (GSEA) to identify biological pathways that are significantly enriched or depleted in various immune cell populations (B cell, ILC, Monocyte, NK cell, Neutrophil, Platelet, T cell CD4+, T cell CD8+) from the blood of patients with Idiopathic Pulmonary Fibrosis (IPF) at different disease stages (progressive IPF, stable IPF) compared to control subjects. The dot plot visualizes the Normalized Enrichment Score (NES) and the significance (-log10(p-value)) for the top 80 enriched pathways, providing insights into the molecular mechanisms driving cell-state shifts in IPF.

Visual Summary

The dot plot displays an intricate landscape of pathway enrichments and depletions across different immune cell types and disease conditions.

General Patterns:

Biological Interpretation

The GSEA results highlight significant shifts in immune cell function and metabolism, particularly in progressive IPF.

Inflammatory and Immune Activation Pathways

Metabolic and Cellular Processes

Disease Progression Specificity

Clinical or Translational Implications

The distinct pathway enrichments observed in progressive_ipf patients, especially in monocytes and T cells, provide valuable insights into the mechanisms driving disease progression.

16. Discussion

This comprehensive single-cell analysis of peripheral blood provides critical insights into the systemic immune landscape of Idiopathic Pulmonary Fibrosis (IPF), distinguishing between control, stable, and progressive disease states. The findings underscore a profound immune dysregulation that likely contributes to disease progression.

First, significant shifts in peripheral immune cell populations were observed. Monocytes are notably expanded in progressive IPF patients compared to controls and stable IPF, with their proportion increasing from control to stable to progressive disease. This suggests a heightened systemic inflammatory state, as monocytes are crucial precursors to pro-fibrotic macrophages known to infiltrate the lung in IPF. Furthermore, B cell proportions also appeared increased in both stable and progressive IPF compared to controls, highlighting potential involvement of humoral immunity in IPF pathogenesis. Within T cell subsets, progressive IPF showed increased proportions of Th22 and, notably, Th2 cells, which are well-established contributors to fibrosis, contrasting with reduced Th17 cells in both IPF conditions compared to controls. This Th2-skewed response, also supported by GSEA, suggests a systemic shift towards pro-fibrotic immune profiles, while NK cells were increased in stable IPF, potentially reflecting compensatory immune activation.

Monocytes exhibit a distinct phenotypic reprogramming in IPF. Stable IPF monocytes show upregulation of surface markers such as HLA-DQA2, AQP9, HLA-G, FPR2, FFAR2, and STEAP4, contrasting with genes like HBEGF, HCAR3, and AREG in control monocytes. This signature indicates an altered activation, metabolic state, and antigen-presentation capacity, likely contributing to chronic inflammation and tissue remodeling. For instance, increased FPR2 and FFAR2 suggest altered sensing of inflammatory mediators and metabolic changes, respectively.

CD4+ T cells in progressive IPF display signs of increased proliferative activity. A panel of cell cycle-related genes, including CCND3, YWHAB, MYC, RBX1, MAD1L1, ANAPC11, SKP1, YWHAZ, and ANAPC5, showed significant upregulation in CD4+ T cells from progressive IPF patients compared to controls. This heightened proliferative state in progressive disease could contribute to chronic inflammation, immune dysregulation, and ultimately, accelerated fibrogenesis, as these activated cells may migrate to the affected tissue and perpetuate the fibrotic cascade.

Cell-cell interaction (CCI) analysis reveals a highly active and complex communication network in progressive IPF, predominantly involving monocytes, T cells, NK cells, and B cells. Key interaction families include chemokine signaling (CCL3/CCL5-CCR1), Annexin-Formyl Peptide Receptor (ANXA1-FPR1/FPR2), integrin-mediated adhesion (ICAM1/2-integrin), TNF superfamily (e.g., BAFF-BAFFR), and prostaglandin receptors (e.g., PGE2-PTGER4). These interactions suggest robust immune cell adhesion, trafficking, and a pro-inflammatory milieu. Immune checkpoint analysis highlighted subtle but significant changes, with CD86-CD28 interactions between monocytes and CD4+ T cells slightly reduced in progressive IPF, potentially indicating T cell anergy or exhaustion. Conversely, the inhibitory LGALS9-HAVCR2 (Galectin-9-TIM-3) pathway showed increased strength in stable IPF (Monocyte|NK) and a slight reduction in progressive IPF (Monocyte|Monocyte), suggesting dynamic shifts in immune regulatory mechanisms.

Gene Set Enrichment Analysis (GSEA) further corroborates these findings, showing widespread activation of pro-inflammatory pathways (TNF signaling, IL-17 signaling, NOD-like receptor signaling) across multiple immune cell types in both IPF conditions, especially progressive IPF. Metabolic pathways like oxidative phosphorylation and lipid metabolism, along with cellular stress responses such as autophagy and ribosome biogenesis, are also enriched, indicating increased metabolic demands and protein synthesis in activated immune cells. The observed shift towards Th2 cell differentiation in progressive IPF CD4+ T cells and the enrichment of PD-L1 expression/PD-1 checkpoint pathway in monocytes and CD4+ T cells from progressive IPF suggest immune evasion mechanisms and a pro-fibrotic environment.

Hypotheses:

  1. Increased circulating monocyte populations, especially those with an activated and metabolically reprogrammed phenotype, actively drive the progression of Idiopathic Pulmonary Fibrosis (IPF) through enhanced pro-fibrotic signaling and recruitment to the lung.
  2. A shift in the peripheral T cell balance towards a Th2-skewed, proliferative state, coupled with dysregulated immune checkpoint interactions, contributes to the chronic inflammation and extracellular matrix deposition characteristic of progressive IPF.
  3. Specific cell-cell interaction axes, such as those involving chemokines (CCL3/5-CCR1) and integrins (ICAM1/2-integrin) with monocytes and T cells, are critical mediators of immune cell recruitment and activation that sustain fibrotic processes in progressive IPF.

Potential therapeutic targets:

  1. CCR1 and its ligands (CCL3/CCL5): CCR1 is strongly involved in monocyte recruitment and activation, which drives inflammation and fibrosis in IPF. Blocking this axis could reduce pathogenic immune cell infiltration to the lung. Evidence: High significance and strong mean expression in cell-cell interaction analysis, particularly involving Monocytes, B cells, and NK cells in progressive IPF (Section 9). Validation: Test CCR1 antagonists in IPF animal models and monitor monocyte migration/activation; assess in patient trials for reduced disease progression markers.
  2. FPR2 and FFAR2 receptors on Monocytes: These receptors are significantly upregulated in stable IPF monocytes, suggesting their involvement in altered inflammatory sensing and metabolic reprogramming. Modulating their activity could reprogram monocyte function, potentially dampening pro-fibrotic responses. Evidence: Identified as key stable IPF-specific monocyte surfaceome markers (Section 3, 12). Validation: Use specific agonists/antagonists of FPR2 and FFAR2 in *in vitro* monocyte differentiation assays and *in vivo* fibrosis models to evaluate their impact on pro-fibrotic functions.
  3. MYC (in CD4+ T cells): MYC is a master regulator of cell proliferation and is significantly upregulated in CD4+ T cells in progressive IPF, indicating an expanded, activated T cell population. Inhibiting MYC could dampen detrimental T cell responses. Evidence: Upregulated cell cycle gene expression, including MYC, in CD4+ T cells from progressive IPF patients (Section 14). Validation: Evaluate MYC inhibitors in *in vitro* T cell proliferation assays and *in vivo* IPF models to assess effects on T cell expansion and fibrotic markers.
  4. BAFF-BAFFR axis (TNFSF13B-TNFRSF13B): The BAFF-BAFFR axis is active in B cell survival and activation, which contributes to autoimmune features and inflammation in IPF. Targeting this axis could curb pathogenic B cell responses. Evidence: Significant interaction identified in cell-cell interaction analysis, particularly involving B cells (Section 9). Validation: Test BAFF/BAFFR inhibitors in animal models of fibrosis and in patient studies to assess impacts on B cell activity and disease progression.
  5. TNF Signaling Pathway: Consistently enriched across multiple immune cell types in both stable and progressive IPF, indicating a sustained pro-inflammatory environment that contributes to fibrotic disease. Evidence: Gene Set Enrichment Analysis (GSEA) shows widespread enrichment of TNF signaling pathway across Monocytes, NK cells, Neutrophils, T cell CD4+, T cell CD8+, and Platelets in both IPF conditions (Section 15). Validation: Evaluate existing TNF-alpha inhibitors or novel agents targeting specific components of the TNF signaling cascade in IPF models and clinical trials.

Follow-up validation ideas:

  1. Perform *in vitro* and *ex vivo* functional assays to validate the functional consequences of altered monocyte surface markers (e.g., FPR2, FFAR2) and their interaction with ligands on immune cell migration, differentiation, and cytokine production using patient-derived monocytes.
  2. Utilize multiplexed protein assays such as flow cytometry or mass cytometry (CyTOF) on peripheral blood and bronchoalveolar lavage (BAL) samples to validate surface marker expression (e.g., HLA-DQA2, AQP9, FPR2) and confirm T cell subset proportions (e.g., Th2, Th22, Th17, NK cells) at the protein level.
  3. Employ *in vivo* animal models of pulmonary fibrosis to test the therapeutic efficacy of targeting key pathways (e.g., blocking CCR1, inhibiting MYC, modulating specific immune checkpoints) identified by GSEA and CCI analyses.
  4. Conduct longitudinal patient studies to correlate changes in peripheral blood monocyte proportions, T cell subset shifts, and GSEA pathway activity with clinical outcomes (e.g., lung function decline, radiological progression) in IPF patients.
  5. Apply spatial transcriptomics and/or proteomics technologies to IPF lung tissue to confirm the localization and functional relevance of identified cell types and cell-cell interactions in the disease microenvironment.

Limitations:

The analysis is primarily based on peripheral blood, which reflects systemic immune responses but may not fully capture the local immune environment and cellular interactions within the fibrotic lung tissue. The functional relevance of some blood-based findings may differ locally. Population bar plots present relative proportions, not absolute cell counts, meaning an increase in one cell type might relatively decrease others without necessarily reflecting absolute changes. A small fraction of cells remains 'unassigned,' potentially representing rare or transient cell states that warrant further investigation. For some cell types (e.g., CD4+ T cells), robust condition-specific surfaceome markers were scarce or showed very low expression in the blood, potentially limiting their utility as strong biomarkers or targets. While GSEA and CCI analyses highlight enriched pathways and interactions, the precise functional impact and specificity of these changes in driving fibrosis versus other inflammatory processes require further mechanistic validation.

17. Query List

  1. Show UMAPs including condition, sample, major cell type, minor cell type, and celltype_subset in 2 columns and save.
  2. Show expression of CD3D, CD4, CD8A, CD79A, CD14, LYZ, NKG7, S100A9, ITGA2B genes on UMAP along with minor cell type annotation. Set ncols=3 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 subset population bar plot for Monocytes and save.
  7. Show box plots for statistically significant differences in T cell subset populations between conditions and save. Set ncols appropriately based on the total number of panels.
  8. Show box plots for statistically significant differences in Monocyte subset populations between conditions and save. Set 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. Show cell-cell interactions using only genes related to immune checkpoint and cell cycle pathways and save.
  11. Find statistically significant differences in cell-cell interactions among T cell CD4+, T cell CD8+, Monocyte, NK cell, B cell, ILC, Neutrophil, Eosinophil, Platelet cell types by condition and show them as a dot plot, then save. Set max_n_items_per_group = 25.
  12. Extract condition-specific markers for Monocytes and show them as a dot plot, then save. Include only surfaceome markers, up to 50 per condition.
  13. Extract condition-specific markers for T cell CD4+ and show them as a dot plot, then save. Include only surfaceome markers, up to 50 per condition.
  14. For T cell CD4+, Monocyte, and B cell, show box plots for statistically significant differences in expression of cell cycle pathway related genes by condition, and save. Set max_n_items_to_plot = 24 and ncols appropriately so the aspect ratio is about 2x3.
  15. Show Gene set enrichment analysis results as a dot plot for B cell, ILC, Monocyte, NK cell, Neutrophil, Platelet, T cell CD4+, T cell CD8+. Set the color map to RdBu_r and n_pws_to_show = 80, then save.
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