Lipid-associated macrophages for osimertinib resistance and leptomeningeal metastases in NSCLC

Lipid-associated macrophages related to osimertinib resistance and leptomeningeal metastases in NSCLC

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Lipid-associated macrophages for osimertinib resistance and leptomeningeal metastases in NSCLC
NSCLCにおけるオシメルチニブ耐性および髄膜播種に関連する脂質関連マクロファージ

Journal Name & Year
Cell Reports, 2024

First and Last Authors
Yang-Si Li, Yi-Long Wu

First Affiliated Institution
Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China

Abstract
To elucidate the role of lipid-associated macrophages related to osimertinib resistance and leptomeningeal metastases (LM) in non-small cell lung cancer (NSCLC), single-cell RNA sequencing was performed on cerebrospinal fluid (CSF). The study identified heterogeneity in immunosuppressive macrophages in LM, revealing that a specific subtype, RNASE1_M, is involved in the development of osimertinib resistance and LM.

Background
Leptomeningeal metastasis in NSCLC is particularly pronounced after the development of osimertinib resistance, with limited treatment options available. Although immune evasion is considered a contributing factor to osimertinib resistance, the detailed mechanisms remain unclear.

Methods
Single-cell RNA sequencing was conducted on cerebrospinal fluid obtained from NSCLC patients with EGFR mutations. The study included both patients with progressive disease and treatment-naïve patients, with characterization and functional analysis of cell clusters.

Results
The study confirmed an increase in lipid-associated macrophages in LM patients, particularly the RNASE1_M subtype, which is associated with osimertinib resistance. Additionally, it was shown that the MDK (Midkine) protein induces the polarization of these macrophages.

Discussion
This study provides a comprehensive understanding of the immune environment in leptomeningeal metastases in NSCLC patients, particularly highlighting the role of macrophages related to osimertinib resistance. This suggests potential new therapeutic targets.

Novelty Compared to Previous Research
The identification of lipid-associated macrophages in leptomeningeal metastasis and their association with osimertinib resistance is novel. Additionally, the role of the MDK protein was newly identified.

Limitations
Limitations include the small sample size and the restriction to cerebrospinal fluid samples. To address this, integration of existing data and validation in independent cohorts were performed.

Potential Applications
The RNASE1_M subtype and the MDK protein identified in this study could be applied as future therapeutic targets or prognostic markers.

What is Osimertinib?

Osimertinib is a third-generation EGFR tyrosine kinase inhibitor (TKI) primarily used in the treatment of non-small cell lung cancer (NSCLC). It is particularly effective against lung cancers with EGFR gene mutations (such as exon 19 deletion and L858R mutation) and tumors with the T790M mutation that confer resistance to first- and second-generation TKIs.

Osimertinib works by binding to the mutated tyrosine kinase domain of EGFR, inhibiting its activity and thereby suppressing cancer cell proliferation. Additionally, it can cross the blood-brain barrier, making it useful in treating central nervous system metastases, including leptomeningeal metastasis and brain metastasis. Osimertinib is widely used as a standard treatment for patients with advanced or metastatic EGFR mutation-positive NSCLC.

What Are Lipid-Associated Macrophages (RNASE1_M)?

Lipid-associated macrophages (RNASE1_M) are a subtype of macrophages that highly express specific lipid metabolism-related genes and are particularly associated with osimertinib resistance and the progression of leptomeningeal metastases (LM) in non-small cell lung cancer (NSCLC). These macrophages possess immunosuppressive properties and contribute to immune evasion within the tumor microenvironment.

RNASE1_M macrophages exhibit high expression of genes involved in lipid metabolism (such as APOE and PLA2G7) as well as genes related to collagen degradation and hypoxic response. This contributes to tumor growth and is thought to enhance resistance to EGFR tyrosine kinase inhibitors like osimertinib.

Additionally, RNASE1_M macrophages are shown to be polarized by the protein MDK (Midkine) secreted by tumor cells, which contributes to the formation of an immunosuppressive environment. Therefore, RNASE1_M macrophages are considered potential targets for novel therapeutic strategies.

Comprehensive Explanation of Lipid-Associated Macrophages

Lipid-associated macrophages (LAM) are a group of specialized macrophages closely related to lipid metabolism and play crucial roles in various pathological environments, such as tumors, chronic inflammation, and metabolic diseases. These macrophages are primarily found in fat-rich environments where they are involved in the uptake, metabolism, and storage of lipids.

Characteristics

• Purpose: To identify gene expression profiles characteristic of specific macrophage subtypes (e.g., RNASE1_M) and elucidate the regulatory factors controlling them.
• Methods:
  • Genes that show differences between subtypes are identified, and their biological functions and related pathways are analyzed.
  • Transcription factor regulatory networks are constructed to identify key transcription factors in specific subtypes.

This study utilizes a variety of single-cell RNA sequencing-based analysis methods to comprehensively analyze the heterogeneity of macrophages in the cerebrospinal fluid of NSCLC patients and the mechanisms related to therapeutic resistance. This detailed understanding at the single-cell level is expected to lead to the discovery of new therapeutic targets.

What Equipment Is Used?

This study used the following equipment and software for conducting single-cell RNA sequencing (scRNA-seq):

Equipment

• BD Rhapsody Single-Cell Analysis System (BD, USA): Used for single-cell library preparation.
• Illumina NextSeq Platform: Used for sequencing.
• NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific): Used for RNA concentration quantification.
• CFX96 Touch Real-Time PCR Detection System (Bio-RAD): Used for real-time PCR.

Software

• fastp: Used for adapter sequence filtering and low-quality read removal.
• UMI-tools: Used for single-cell transcriptome analysis.
• STAR: Used for mapping to the human genome.
• Seurat: Used for normalization and clustering.
• Monocle 2.0: Used for reconstructing macrophage developmental trajectories.
• SCENIC: Used for transcription factor regulatory network analysis.

These tools and software were used in conjunction to perform detailed gene expression analysis at the single-cell level, leading to the identification of macrophage subtypes related to osimertinib resistance and leptomeningeal metastasis as the research aims.

• Purpose: To clarify the interactions between macrophages and other cells (e.g., tumor cells, T cells).
• Methods:
  • Tools like CellPhoneDB are used to predict ligand-receptor interactions between cells.
  • Specific interactions (e.g., CD47-SIRPA pathway) are analyzed to evaluate their contribution to tumor immune evasion and therapeutic resistance.

5. Identification of Subtype-Specific Gene Expression and Regulatory Factors

• Purpose: To identify gene expression profiles characteristic of specific macrophage subtypes (e.g., RNASE1_M) and elucidate the regulatory factors controlling them.
• Methods:
  • Genes that show differences between subtypes are identified, and their biological functions and related pathways are analyzed.
  • Transcription factor regulatory networks are constructed to identify key transcription factors in specific subtypes.

This study utilizes a variety of single-cell RNA sequencing-based analysis methods to comprehensively analyze the heterogeneity of macrophages in the cerebrospinal fluid of NSCLC patients and the mechanisms related to therapeutic resistance. This detailed understanding at the single-cell level is expected to lead to the discovery of new therapeutic targets.

What Equipment Is Used?

This study used the following equipment and software for conducting single-cell RNA sequencing (scRNA-seq):

Equipment

• BD Rhapsody Single-Cell Analysis System (BD, USA): Used for single-cell library preparation.
• Illumina NextSeq Platform: Used for sequencing.
• NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific): Used for RNA concentration quantification.
• CFX96 Touch Real-Time PCR Detection System (Bio-RAD): Used for real-time PCR.

Software

• fastp: Used for adapter sequence filtering and low-quality read removal.
• UMI-tools: Used for single-cell transcriptome analysis.
• STAR: Used for mapping to the human genome.
• Seurat: Used for normalization and clustering.
• Monocle 2.0: Used for reconstructing macrophage developmental trajectories.
• SCENIC: Used for transcription factor regulatory network analysis.

These tools and software were used in conjunction to perform detailed gene expression analysis at the single-cell level, leading to the identification of macrophage subtypes related to osimertinib resistance and leptomeningeal metastasis as the research aims.

• Purpose: To trace the developmental trajectories of macrophages and understand the process of forming subtypes related to osimertinib resistance (e.g., RNASE1_M).
• Methods:
  • A dendrogram is created to visualize the trajectories of cells and the pathways they follow.
  • Gene expression changes along these trajectories are analyzed to identify genes and pathways related to resistance.

4. Analysis of Cell-Cell Interactions

• Purpose: To clarify the interactions between macrophages and other cells (e.g., tumor cells, T cells).
• Methods:
  • Tools like CellPhoneDB are used to predict ligand-receptor interactions between cells.
  • Specific interactions (e.g., CD47-SIRPA pathway) are analyzed to evaluate their contribution to tumor immune evasion and therapeutic resistance.

5. Identification of Subtype-Specific Gene Expression and Regulatory Factors

• Purpose: To identify gene expression profiles characteristic of specific macrophage subtypes (e.g., RNASE1_M) and elucidate the regulatory factors controlling them.
• Methods:
  • Genes that show differences between subtypes are identified, and their biological functions and related pathways are analyzed.
  • Transcription factor regulatory networks are constructed to identify key transcription factors in specific subtypes.

This study utilizes a variety of single-cell RNA sequencing-based analysis methods to comprehensively analyze the heterogeneity of macrophages in the cerebrospinal fluid of NSCLC patients and the mechanisms related to therapeutic resistance. This detailed understanding at the single-cell level is expected to lead to the discovery of new therapeutic targets.

What Equipment Is Used?

This study used the following equipment and software for conducting single-cell RNA sequencing (scRNA-seq):

Equipment

• BD Rhapsody Single-Cell Analysis System (BD, USA): Used for single-cell library preparation.
• Illumina NextSeq Platform: Used for sequencing.
• NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific): Used for RNA concentration quantification.
• CFX96 Touch Real-Time PCR Detection System (Bio-RAD): Used for real-time PCR.

Software

• fastp: Used for adapter sequence filtering and low-quality read removal.
• UMI-tools: Used for single-cell transcriptome analysis.
• STAR: Used for mapping to the human genome.
• Seurat: Used for normalization and clustering.
• Monocle 2.0: Used for reconstructing macrophage developmental trajectories.
• SCENIC: Used for transcription factor regulatory network analysis.

These tools and software were used in conjunction to perform detailed gene expression analysis at the single-cell level, leading to the identification of macrophage subtypes related to osimertinib resistance and leptomeningeal metastasis as the research aims.

• Identification of Clusters:
  • Identified cell groups are classified into T cells, B cells, macrophages, monocytes, dendritic cells (DCs), epithelial cells, and further subtypes (e.g., RNASE1_M, LYVE1_FOLR2_M) within these cells.
• Functional Scoring:
  • The functional characteristics of each cell cluster (e.g., antigen presentation, phagocytosis, angiogenesis) are evaluated to elucidate the role of each cluster.

3. Trajectory Analysis and Pathway Analysis

• Purpose: To trace the developmental trajectories of macrophages and understand the process of forming subtypes related to osimertinib resistance (e.g., RNASE1_M).
• Methods:
  • A dendrogram is created to visualize the trajectories of cells and the pathways they follow.
  • Gene expression changes along these trajectories are analyzed to identify genes and pathways related to resistance.

4. Analysis of Cell-Cell Interactions

• Purpose: To clarify the interactions between macrophages and other cells (e.g., tumor cells, T cells).
• Methods:
  • Tools like CellPhoneDB are used to predict ligand-receptor interactions between cells.
  • Specific interactions (e.g., CD47-SIRPA pathway) are analyzed to evaluate their contribution to tumor immune evasion and therapeutic resistance.

5. Identification of Subtype-Specific Gene Expression and Regulatory Factors

• Purpose: To identify gene expression profiles characteristic of specific macrophage subtypes (e.g., RNASE1_M) and elucidate the regulatory factors controlling them.
• Methods:
  • Genes that show differences between subtypes are identified, and their biological functions and related pathways are analyzed.
  • Transcription factor regulatory networks are constructed to identify key transcription factors in specific subtypes.

This study utilizes a variety of single-cell RNA sequencing-based analysis methods to comprehensively analyze the heterogeneity of macrophages in the cerebrospinal fluid of NSCLC patients and the mechanisms related to therapeutic resistance. This detailed understanding at the single-cell level is expected to lead to the discovery of new therapeutic targets.

What Equipment Is Used?

This study used the following equipment and software for conducting single-cell RNA sequencing (scRNA-seq):

Equipment

• BD Rhapsody Single-Cell Analysis System (BD, USA): Used for single-cell library preparation.
• Illumina NextSeq Platform: Used for sequencing.
• NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific): Used for RNA concentration quantification.
• CFX96 Touch Real-Time PCR Detection System (Bio-RAD): Used for real-time PCR.

Software

• fastp: Used for adapter sequence filtering and low-quality read removal.
• UMI-tools: Used for single-cell transcriptome analysis.
• STAR: Used for mapping to the human genome.
• Seurat: Used for normalization and clustering.
• Monocle 2.0: Used for reconstructing macrophage developmental trajectories.
• SCENIC: Used for transcription factor regulatory network analysis.

These tools and software were used in conjunction to perform detailed gene expression analysis at the single-cell level, leading to the identification of macrophage subtypes related to osimertinib resistance and leptomeningeal metastasis as the research aims.

• Identification of Clusters:
  • Identified cell groups are classified into T cells, B cells, macrophages, monocytes, dendritic cells (DCs), epithelial cells, and further subtypes (e.g., RNASE1_M, LYVE1_FOLR2_M) within these cells.
• Functional Scoring:
  • The functional characteristics of each cell cluster (e.g., antigen presentation, phagocytosis, angiogenesis) are evaluated to elucidate the role of each cluster.

3. Trajectory Analysis and Pathway Analysis

• Purpose: To trace the developmental trajectories of macrophages and understand the process of forming subtypes related to osimertinib resistance (e.g., RNASE1_M).
• Methods:
  • A dendrogram is created to visualize the trajectories of cells and the pathways they follow.
  • Gene expression changes along these trajectories are analyzed to identify genes and pathways related to resistance.

4. Analysis of Cell-Cell Interactions

• Purpose: To clarify the interactions between macrophages and other cells (e.g., tumor cells, T cells).
• Methods:
  • Tools like CellPhoneDB are used to predict ligand-receptor interactions between cells.
  • Specific interactions (e.g., CD47-SIRPA pathway) are analyzed to evaluate their contribution to tumor immune evasion and therapeutic resistance.

5. Identification of Subtype-Specific Gene Expression and Regulatory Factors

• Purpose: To identify gene expression profiles characteristic of specific macrophage subtypes (e.g., RNASE1_M) and elucidate the regulatory factors controlling them.
• Methods:
  • Genes that show differences between subtypes are identified, and their biological functions and related pathways are analyzed.
  • Transcription factor regulatory networks are constructed to identify key transcription factors in specific subtypes.

This study utilizes a variety of single-cell RNA sequencing-based analysis methods to comprehensively analyze the heterogeneity of macrophages in the cerebrospinal fluid of NSCLC patients and the mechanisms related to therapeutic resistance. This detailed understanding at the single-cell level is expected to lead to the discovery of new therapeutic targets.

What Equipment Is Used?

This study used the following equipment and software for conducting single-cell RNA sequencing (scRNA-seq):

Equipment

• BD Rhapsody Single-Cell Analysis System (BD, USA): Used for single-cell library preparation.
• Illumina NextSeq Platform: Used for sequencing.
• NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific): Used for RNA concentration quantification.
• CFX96 Touch Real-Time PCR Detection System (Bio-RAD): Used for real-time PCR.

Software

• fastp: Used for adapter sequence filtering and low-quality read removal.
• UMI-tools: Used for single-cell transcriptome analysis.
• STAR: Used for mapping to the human genome.
• Seurat: Used for normalization and clustering.
• Monocle 2.0: Used for reconstructing macrophage developmental trajectories.
• SCENIC: Used for transcription factor regulatory network analysis.

These tools and software were used in conjunction to perform detailed gene expression analysis at the single-cell level, leading to the identification of macrophage subtypes related to osimertinib resistance and leptomeningeal metastasis as the research aims.

• Purpose: To analyze gene expression profiles at the single-cell level, evaluate heterogeneity within cell populations, and identify specific cell subtypes and their functional characteristics.
• Subjects: Cells obtained from cerebrospinal fluid (CSF) of NSCLC patients with EGFR mutations.
• Analysis Procedures:
  • Cells are isolated, and single-cell RNA sequencing is performed to obtain gene expression data for each cell.
  • Using dimensionality reduction techniques such as t-SNE (t-distributed stochastic neighbor embedding) or UMAP (Uniform Manifold Approximation and Projection), clustering of cell populations is conducted.
  • Each cluster is identified based on the expression of specific marker genes, allowing the determination of cell types.

2. Characterization of Cell Clusters

• Identification of Clusters:
  • Identified cell groups are classified into T cells, B cells, macrophages, monocytes, dendritic cells (DCs), epithelial cells, and further subtypes (e.g., RNASE1_M, LYVE1_FOLR2_M) within these cells.
• Functional Scoring:
  • The functional characteristics of each cell cluster (e.g., antigen presentation, phagocytosis, angiogenesis) are evaluated to elucidate the role of each cluster.

3. Trajectory Analysis and Pathway Analysis

• Purpose: To trace the developmental trajectories of macrophages and understand the process of forming subtypes related to osimertinib resistance (e.g., RNASE1_M).
• Methods:
  • A dendrogram is created to visualize the trajectories of cells and the pathways they follow.
  • Gene expression changes along these trajectories are analyzed to identify genes and pathways related to resistance.

4. Analysis of Cell-Cell Interactions

• Purpose: To clarify the interactions between macrophages and other cells (e.g., tumor cells, T cells).
• Methods:
  • Tools like CellPhoneDB are used to predict ligand-receptor interactions between cells.
  • Specific interactions (e.g., CD47-SIRPA pathway) are analyzed to evaluate their contribution to tumor immune evasion and therapeutic resistance.

5. Identification of Subtype-Specific Gene Expression and Regulatory Factors

• Purpose: To identify gene expression profiles characteristic of specific macrophage subtypes (e.g., RNASE1_M) and elucidate the regulatory factors controlling them.
• Methods:
  • Genes that show differences between subtypes are identified, and their biological functions and related pathways are analyzed.
  • Transcription factor regulatory networks are constructed to identify key transcription factors in specific subtypes.

This study utilizes a variety of single-cell RNA sequencing-based analysis methods to comprehensively analyze the heterogeneity of macrophages in the cerebrospinal fluid of NSCLC patients and the mechanisms related to therapeutic resistance. This detailed understanding at the single-cell level is expected to lead to the discovery of new therapeutic targets.

What Equipment Is Used?

This study used the following equipment and software for conducting single-cell RNA sequencing (scRNA-seq):

Equipment

• BD Rhapsody Single-Cell Analysis System (BD, USA): Used for single-cell library preparation.
• Illumina NextSeq Platform: Used for sequencing.
• NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific): Used for RNA concentration quantification.
• CFX96 Touch Real-Time PCR Detection System (Bio-RAD): Used for real-time PCR.

Software

• fastp: Used for adapter sequence filtering and low-quality read removal.
• UMI-tools: Used for single-cell transcriptome analysis.
• STAR: Used for mapping to the human genome.
• Seurat: Used for normalization and clustering.
• Monocle 2.0: Used for reconstructing macrophage developmental trajectories.
• SCENIC: Used for transcription factor regulatory network analysis.

These tools and software were used in conjunction to perform detailed gene expression analysis at the single-cell level, leading to the identification of macrophage subtypes related to osimertinib resistance and leptomeningeal metastasis as the research aims.

• M2 Macrophages:
  • Clinical Significance: M2 macrophages promote wound healing and tissue repair while potentially aiding in tumor immune evasion and promoting therapeutic resistance.
• LAMs:
  • Clinical Significance: LAMs play crucial roles in enhancing tumor growth and therapeutic resistance, particularly in the tumor microenvironment. They are significant in diseases related to lipid metabolism, such as atherosclerosis, obesity, and cancer, with potential as therapeutic targets.

While M2 macrophages and LAMs both possess immunosuppressive properties, LAMs are particularly specialized in lipid metabolism. M2 macrophages have broader immune-regulating functions and play roles in various pathological situations, whereas LAMs are more specialized in lipid metabolism and the tumor environment. Understanding these differences is crucial for comprehending disease pathology and developing new therapeutic approaches.

What Single-Cell Analyses Are Performed?

Single-cell analysis is a technique used to analyze the gene expression profiles of individual cells, helping to understand the heterogeneity of cell populations. The following methods were used in the study referenced:

1. Single-Cell RNA Sequencing (scRNA-seq)

• Purpose: To analyze gene expression profiles at the single-cell level, evaluate heterogeneity within cell populations, and identify specific cell subtypes and their functional characteristics.
• Subjects: Cells obtained from cerebrospinal fluid (CSF) of NSCLC patients with EGFR mutations.
• Analysis Procedures:
  • Cells are isolated, and single-cell RNA sequencing is performed to obtain gene expression data for each cell.
  • Using dimensionality reduction techniques such as t-SNE (t-distributed stochastic neighbor embedding) or UMAP (Uniform Manifold Approximation and Projection), clustering of cell populations is conducted.
  • Each cluster is identified based on the expression of specific marker genes, allowing the determination of cell types.

2. Characterization of Cell Clusters

• Identification of Clusters:
  • Identified cell groups are classified into T cells, B cells, macrophages, monocytes, dendritic cells (DCs), epithelial cells, and further subtypes (e.g., RNASE1_M, LYVE1_FOLR2_M) within these cells.
• Functional Scoring:
  • The functional characteristics of each cell cluster (e.g., antigen presentation, phagocytosis, angiogenesis) are evaluated to elucidate the role of each cluster.

3. Trajectory Analysis and Pathway Analysis

• Purpose: To trace the developmental trajectories of macrophages and understand the process of forming subtypes related to osimertinib resistance (e.g., RNASE1_M).
• Methods:
  • A dendrogram is created to visualize the trajectories of cells and the pathways they follow.
  • Gene expression changes along these trajectories are analyzed to identify genes and pathways related to resistance.

4. Analysis of Cell-Cell Interactions

• Purpose: To clarify the interactions between macrophages and other cells (e.g., tumor cells, T cells).
• Methods:
  • Tools like CellPhoneDB are used to predict ligand-receptor interactions between cells.
  • Specific interactions (e.g., CD47-SIRPA pathway) are analyzed to evaluate their contribution to tumor immune evasion and therapeutic resistance.

5. Identification of Subtype-Specific Gene Expression and Regulatory Factors

• Purpose: To identify gene expression profiles characteristic of specific macrophage subtypes (e.g., RNASE1_M) and elucidate the regulatory factors controlling them.
• Methods:
  • Genes that show differences between subtypes are identified, and their biological functions and related pathways are analyzed.
  • Transcription factor regulatory networks are constructed to identify key transcription factors in specific subtypes.

This study utilizes a variety of single-cell RNA sequencing-based analysis methods to comprehensively analyze the heterogeneity of macrophages in the cerebrospinal fluid of NSCLC patients and the mechanisms related to therapeutic resistance. This detailed understanding at the single-cell level is expected to lead to the discovery of new therapeutic targets.

What Equipment Is Used?

This study used the following equipment and software for conducting single-cell RNA sequencing (scRNA-seq):

Equipment

• BD Rhapsody Single-Cell Analysis System (BD, USA): Used for single-cell library preparation.
• Illumina NextSeq Platform: Used for sequencing.
• NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific): Used for RNA concentration quantification.
• CFX96 Touch Real-Time PCR Detection System (Bio-RAD): Used for real-time PCR.

Software

• fastp: Used for adapter sequence filtering and low-quality read removal.
• UMI-tools: Used for single-cell transcriptome analysis.
• STAR: Used for mapping to the human genome.
• Seurat: Used for normalization and clustering.
• Monocle 2.0: Used for reconstructing macrophage developmental trajectories.
• SCENIC: Used for transcription factor regulatory network analysis.

These tools and software were used in conjunction to perform detailed gene expression analysis at the single-cell level, leading to the identification of macrophage subtypes related to osimertinib resistance and leptomeningeal metastasis as the research aims.

• M2 Macrophages:
  • Clinical Significance: M2 macrophages promote wound healing and tissue repair while potentially aiding in tumor immune evasion and promoting therapeutic resistance.
• LAMs:
  • Clinical Significance: LAMs play crucial roles in enhancing tumor growth and therapeutic resistance, particularly in the tumor microenvironment. They are significant in diseases related to lipid metabolism, such as atherosclerosis, obesity, and cancer, with potential as therapeutic targets.

While M2 macrophages and LAMs both possess immunosuppressive properties, LAMs are particularly specialized in lipid metabolism. M2 macrophages have broader immune-regulating functions and play roles in various pathological situations, whereas LAMs are more specialized in lipid metabolism and the tumor environment. Understanding these differences is crucial for comprehending disease pathology and developing new therapeutic approaches.

What Single-Cell Analyses Are Performed?

Single-cell analysis is a technique used to analyze the gene expression profiles of individual cells, helping to understand the heterogeneity of cell populations. The following methods were used in the study referenced:

1. Single-Cell RNA Sequencing (scRNA-seq)

• Purpose: To analyze gene expression profiles at the single-cell level, evaluate heterogeneity within cell populations, and identify specific cell subtypes and their functional characteristics.
• Subjects: Cells obtained from cerebrospinal fluid (CSF) of NSCLC patients with EGFR mutations.
• Analysis Procedures:
  • Cells are isolated, and single-cell RNA sequencing is performed to obtain gene expression data for each cell.
  • Using dimensionality reduction techniques such as t-SNE (t-distributed stochastic neighbor embedding) or UMAP (Uniform Manifold Approximation and Projection), clustering of cell populations is conducted.
  • Each cluster is identified based on the expression of specific marker genes, allowing the determination of cell types.

2. Characterization of Cell Clusters

• Identification of Clusters:
  • Identified cell groups are classified into T cells, B cells, macrophages, monocytes, dendritic cells (DCs), epithelial cells, and further subtypes (e.g., RNASE1_M, LYVE1_FOLR2_M) within these cells.
• Functional Scoring:
  • The functional characteristics of each cell cluster (e.g., antigen presentation, phagocytosis, angiogenesis) are evaluated to elucidate the role of each cluster.

3. Trajectory Analysis and Pathway Analysis

• Purpose: To trace the developmental trajectories of macrophages and understand the process of forming subtypes related to osimertinib resistance (e.g., RNASE1_M).
• Methods:
  • A dendrogram is created to visualize the trajectories of cells and the pathways they follow.
  • Gene expression changes along these trajectories are analyzed to identify genes and pathways related to resistance.

4. Analysis of Cell-Cell Interactions

• Purpose: To clarify the interactions between macrophages and other cells (e.g., tumor cells, T cells).
• Methods:
  • Tools like CellPhoneDB are used to predict ligand-receptor interactions between cells.
  • Specific interactions (e.g., CD47-SIRPA pathway) are analyzed to evaluate their contribution to tumor immune evasion and therapeutic resistance.

5. Identification of Subtype-Specific Gene Expression and Regulatory Factors

• Purpose: To identify gene expression profiles characteristic of specific macrophage subtypes (e.g., RNASE1_M) and elucidate the regulatory factors controlling them.
• Methods:
  • Genes that show differences between subtypes are identified, and their biological functions and related pathways are analyzed.
  • Transcription factor regulatory networks are constructed to identify key transcription factors in specific subtypes.

This study utilizes a variety of single-cell RNA sequencing-based analysis methods to comprehensively analyze the heterogeneity of macrophages in the cerebrospinal fluid of NSCLC patients and the mechanisms related to therapeutic resistance. This detailed understanding at the single-cell level is expected to lead to the discovery of new therapeutic targets.

What Equipment Is Used?

This study used the following equipment and software for conducting single-cell RNA sequencing (scRNA-seq):

Equipment

• BD Rhapsody Single-Cell Analysis System (BD, USA): Used for single-cell library preparation.
• Illumina NextSeq Platform: Used for sequencing.
• NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific): Used for RNA concentration quantification.
• CFX96 Touch Real-Time PCR Detection System (Bio-RAD): Used for real-time PCR.

Software

• fastp: Used for adapter sequence filtering and low-quality read removal.
• UMI-tools: Used for single-cell transcriptome analysis.
• STAR: Used for mapping to the human genome.
• Seurat: Used for normalization and clustering.
• Monocle 2.0: Used for reconstructing macrophage developmental trajectories.
• SCENIC: Used for transcription factor regulatory network analysis.

These tools and software were used in conjunction to perform detailed gene expression analysis at the single-cell level, leading to the identification of macrophage subtypes related to osimertinib resistance and leptomeningeal metastasis as the research aims.

• M2 Macrophages:
  • Tissue Distribution: They are found in various tissues throughout the body, particularly in wound sites, areas of parasite infection, and chronic inflammation sites.
  • Environment: M2 macrophages are often activated in post-inflammatory repair processes or chronic inflammation environments.
• LAMs:
  • Tissue Distribution: LAMs are primarily found in adipose tissue, lipid-rich environments, or the tumor microenvironment.
  • Environment: LAMs are activated in environments where lipid metabolism plays a crucial role, such as the tumor microenvironment, atherosclerotic plaques, and obese tissues.

4. Clinical Significance

• M2 Macrophages:
  • Clinical Significance: M2 macrophages promote wound healing and tissue repair while potentially aiding in tumor immune evasion and promoting therapeutic resistance.
• LAMs:
  • Clinical Significance: LAMs play crucial roles in enhancing tumor growth and therapeutic resistance, particularly in the tumor microenvironment. They are significant in diseases related to lipid metabolism, such as atherosclerosis, obesity, and cancer, with potential as therapeutic targets.

While M2 macrophages and LAMs both possess immunosuppressive properties, LAMs are particularly specialized in lipid metabolism. M2 macrophages have broader immune-regulating functions and play roles in various pathological situations, whereas LAMs are more specialized in lipid metabolism and the tumor environment. Understanding these differences is crucial for comprehending disease pathology and developing new therapeutic approaches.

What Single-Cell Analyses Are Performed?

Single-cell analysis is a technique used to analyze the gene expression profiles of individual cells, helping to understand the heterogeneity of cell populations. The following methods were used in the study referenced:

1. Single-Cell RNA Sequencing (scRNA-seq)

• Purpose: To analyze gene expression profiles at the single-cell level, evaluate heterogeneity within cell populations, and identify specific cell subtypes and their functional characteristics.
• Subjects: Cells obtained from cerebrospinal fluid (CSF) of NSCLC patients with EGFR mutations.
• Analysis Procedures:
  • Cells are isolated, and single-cell RNA sequencing is performed to obtain gene expression data for each cell.
  • Using dimensionality reduction techniques such as t-SNE (t-distributed stochastic neighbor embedding) or UMAP (Uniform Manifold Approximation and Projection), clustering of cell populations is conducted.
  • Each cluster is identified based on the expression of specific marker genes, allowing the determination of cell types.

2. Characterization of Cell Clusters

• Identification of Clusters:
  • Identified cell groups are classified into T cells, B cells, macrophages, monocytes, dendritic cells (DCs), epithelial cells, and further subtypes (e.g., RNASE1_M, LYVE1_FOLR2_M) within these cells.
• Functional Scoring:
  • The functional characteristics of each cell cluster (e.g., antigen presentation, phagocytosis, angiogenesis) are evaluated to elucidate the role of each cluster.

3. Trajectory Analysis and Pathway Analysis

• Purpose: To trace the developmental trajectories of macrophages and understand the process of forming subtypes related to osimertinib resistance (e.g., RNASE1_M).
• Methods:
  • A dendrogram is created to visualize the trajectories of cells and the pathways they follow.
  • Gene expression changes along these trajectories are analyzed to identify genes and pathways related to resistance.

4. Analysis of Cell-Cell Interactions

• Purpose: To clarify the interactions between macrophages and other cells (e.g., tumor cells, T cells).
• Methods:
  • Tools like CellPhoneDB are used to predict ligand-receptor interactions between cells.
  • Specific interactions (e.g., CD47-SIRPA pathway) are analyzed to evaluate their contribution to tumor immune evasion and therapeutic resistance.

5. Identification of Subtype-Specific Gene Expression and Regulatory Factors

• Purpose: To identify gene expression profiles characteristic of specific macrophage subtypes (e.g., RNASE1_M) and elucidate the regulatory factors controlling them.
• Methods:
  • Genes that show differences between subtypes are identified, and their biological functions and related pathways are analyzed.
  • Transcription factor regulatory networks are constructed to identify key transcription factors in specific subtypes.

This study utilizes a variety of single-cell RNA sequencing-based analysis methods to comprehensively analyze the heterogeneity of macrophages in the cerebrospinal fluid of NSCLC patients and the mechanisms related to therapeutic resistance. This detailed understanding at the single-cell level is expected to lead to the discovery of new therapeutic targets.

What Equipment Is Used?

This study used the following equipment and software for conducting single-cell RNA sequencing (scRNA-seq):

Equipment

• BD Rhapsody Single-Cell Analysis System (BD, USA): Used for single-cell library preparation.
• Illumina NextSeq Platform: Used for sequencing.
• NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific): Used for RNA concentration quantification.
• CFX96 Touch Real-Time PCR Detection System (Bio-RAD): Used for real-time PCR.

Software

• fastp: Used for adapter sequence filtering and low-quality read removal.
• UMI-tools: Used for single-cell transcriptome analysis.
• STAR: Used for mapping to the human genome.
• Seurat: Used for normalization and clustering.
• Monocle 2.0: Used for reconstructing macrophage developmental trajectories.
• SCENIC: Used for transcription factor regulatory network analysis.

These tools and software were used in conjunction to perform detailed gene expression analysis at the single-cell level, leading to the identification of macrophage subtypes related to osimertinib resistance and leptomeningeal metastasis as the research aims.

• M2 Macrophages:
  • Tissue Distribution: They are found in various tissues throughout the body, particularly in wound sites, areas of parasite infection, and chronic inflammation sites.
  • Environment: M2 macrophages are often activated in post-inflammatory repair processes or chronic inflammation environments.
• LAMs:
  • Tissue Distribution: LAMs are primarily found in adipose tissue, lipid-rich environments, or the tumor microenvironment.
  • Environment: LAMs are activated in environments where lipid metabolism plays a crucial role, such as the tumor microenvironment, atherosclerotic plaques, and obese tissues.

4. Clinical Significance

• M2 Macrophages:
  • Clinical Significance: M2 macrophages promote wound healing and tissue repair while potentially aiding in tumor immune evasion and promoting therapeutic resistance.
• LAMs:
  • Clinical Significance: LAMs play crucial roles in enhancing tumor growth and therapeutic resistance, particularly in the tumor microenvironment. They are significant in diseases related to lipid metabolism, such as atherosclerosis, obesity, and cancer, with potential as therapeutic targets.

While M2 macrophages and LAMs both possess immunosuppressive properties, LAMs are particularly specialized in lipid metabolism. M2 macrophages have broader immune-regulating functions and play roles in various pathological situations, whereas LAMs are more specialized in lipid metabolism and the tumor environment. Understanding these differences is crucial for comprehending disease pathology and developing new therapeutic approaches.

What Single-Cell Analyses Are Performed?

Single-cell analysis is a technique used to analyze the gene expression profiles of individual cells, helping to understand the heterogeneity of cell populations. The following methods were used in the study referenced:

1. Single-Cell RNA Sequencing (scRNA-seq)

• Purpose: To analyze gene expression profiles at the single-cell level, evaluate heterogeneity within cell populations, and identify specific cell subtypes and their functional characteristics.
• Subjects: Cells obtained from cerebrospinal fluid (CSF) of NSCLC patients with EGFR mutations.
• Analysis Procedures:
  • Cells are isolated, and single-cell RNA sequencing is performed to obtain gene expression data for each cell.
  • Using dimensionality reduction techniques such as t-SNE (t-distributed stochastic neighbor embedding) or UMAP (Uniform Manifold Approximation and Projection), clustering of cell populations is conducted.
  • Each cluster is identified based on the expression of specific marker genes, allowing the determination of cell types.

2. Characterization of Cell Clusters

• Identification of Clusters:
  • Identified cell groups are classified into T cells, B cells, macrophages, monocytes, dendritic cells (DCs), epithelial cells, and further subtypes (e.g., RNASE1_M, LYVE1_FOLR2_M) within these cells.
• Functional Scoring:
  • The functional characteristics of each cell cluster (e.g., antigen presentation, phagocytosis, angiogenesis) are evaluated to elucidate the role of each cluster.

3. Trajectory Analysis and Pathway Analysis

• Purpose: To trace the developmental trajectories of macrophages and understand the process of forming subtypes related to osimertinib resistance (e.g., RNASE1_M).
• Methods:
  • A dendrogram is created to visualize the trajectories of cells and the pathways they follow.
  • Gene expression changes along these trajectories are analyzed to identify genes and pathways related to resistance.

4. Analysis of Cell-Cell Interactions

• Purpose: To clarify the interactions between macrophages and other cells (e.g., tumor cells, T cells).
• Methods:
  • Tools like CellPhoneDB are used to predict ligand-receptor interactions between cells.
  • Specific interactions (e.g., CD47-SIRPA pathway) are analyzed to evaluate their contribution to tumor immune evasion and therapeutic resistance.

5. Identification of Subtype-Specific Gene Expression and Regulatory Factors

• Purpose: To identify gene expression profiles characteristic of specific macrophage subtypes (e.g., RNASE1_M) and elucidate the regulatory factors controlling them.
• Methods:
  • Genes that show differences between subtypes are identified, and their biological functions and related pathways are analyzed.
  • Transcription factor regulatory networks are constructed to identify key transcription factors in specific subtypes.

This study utilizes a variety of single-cell RNA sequencing-based analysis methods to comprehensively analyze the heterogeneity of macrophages in the cerebrospinal fluid of NSCLC patients and the mechanisms related to therapeutic resistance. This detailed understanding at the single-cell level is expected to lead to the discovery of new therapeutic targets.

What Equipment Is Used?

This study used the following equipment and software for conducting single-cell RNA sequencing (scRNA-seq):

Equipment

• BD Rhapsody Single-Cell Analysis System (BD, USA): Used for single-cell library preparation.
• Illumina NextSeq Platform: Used for sequencing.
• NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific): Used for RNA concentration quantification.
• CFX96 Touch Real-Time PCR Detection System (Bio-RAD): Used for real-time PCR.

Software

• fastp: Used for adapter sequence filtering and low-quality read removal.
• UMI-tools: Used for single-cell transcriptome analysis.
• STAR: Used for mapping to the human genome.
• Seurat: Used for normalization and clustering.
• Monocle 2.0: Used for reconstructing macrophage developmental trajectories.
• SCENIC: Used for transcription factor regulatory network analysis.

These tools and software were used in conjunction to perform detailed gene expression analysis at the single-cell level, leading to the identification of macrophage subtypes related to osimertinib resistance and leptomeningeal metastasis as the research aims.

• M2 Macrophages:
  • Gene Expression: M2 macrophages highly express genes such as CD206 (MRC1), CD163, and Arginase-1 (ARG1).
  • Function: They are involved in tissue repair, wound healing, parasite clearance, and promoting tumor immune evasion. Generally, they help resolve inflammation and control chronic inflammation.
• LAMs:
  • Gene Expression: LAMs highly express genes related to lipid metabolism, such as APOE, FABP, LGALS3, and PLA2G7.
  • Function: LAMs specialize in lipid metabolism, being involved in lipid uptake, storage, and metabolism. They play roles in tumor growth and therapeutic resistance.

3. Tissue Distribution and Environment

• M2 Macrophages:
  • Tissue Distribution: They are found in various tissues throughout the body, particularly in wound sites, areas of parasite infection, and chronic inflammation sites.
  • Environment: M2 macrophages are often activated in post-inflammatory repair processes or chronic inflammation environments.
• LAMs:
  • Tissue Distribution: LAMs are primarily found in adipose tissue, lipid-rich environments, or the tumor microenvironment.
  • Environment: LAMs are activated in environments where lipid metabolism plays a crucial role, such as the tumor microenvironment, atherosclerotic plaques, and obese tissues.

4. Clinical Significance

• M2 Macrophages:
  • Clinical Significance: M2 macrophages promote wound healing and tissue repair while potentially aiding in tumor immune evasion and promoting therapeutic resistance.
• LAMs:
  • Clinical Significance: LAMs play crucial roles in enhancing tumor growth and therapeutic resistance, particularly in the tumor microenvironment. They are significant in diseases related to lipid metabolism, such as atherosclerosis, obesity, and cancer, with potential as therapeutic targets.

While M2 macrophages and LAMs both possess immunosuppressive properties, LAMs are particularly specialized in lipid metabolism. M2 macrophages have broader immune-regulating functions and play roles in various pathological situations, whereas LAMs are more specialized in lipid metabolism and the tumor environment. Understanding these differences is crucial for comprehending disease pathology and developing new therapeutic approaches.

What Single-Cell Analyses Are Performed?

Single-cell analysis is a technique used to analyze the gene expression profiles of individual cells, helping to understand the heterogeneity of cell populations. The following methods were used in the study referenced:

1. Single-Cell RNA Sequencing (scRNA-seq)

• Purpose: To analyze gene expression profiles at the single-cell level, evaluate heterogeneity within cell populations, and identify specific cell subtypes and their functional characteristics.
• Subjects: Cells obtained from cerebrospinal fluid (CSF) of NSCLC patients with EGFR mutations.
• Analysis Procedures:
  • Cells are isolated, and single-cell RNA sequencing is performed to obtain gene expression data for each cell.
  • Using dimensionality reduction techniques such as t-SNE (t-distributed stochastic neighbor embedding) or UMAP (Uniform Manifold Approximation and Projection), clustering of cell populations is conducted.
  • Each cluster is identified based on the expression of specific marker genes, allowing the determination of cell types.

2. Characterization of Cell Clusters

• Identification of Clusters:
  • Identified cell groups are classified into T cells, B cells, macrophages, monocytes, dendritic cells (DCs), epithelial cells, and further subtypes (e.g., RNASE1_M, LYVE1_FOLR2_M) within these cells.
• Functional Scoring:
  • The functional characteristics of each cell cluster (e.g., antigen presentation, phagocytosis, angiogenesis) are evaluated to elucidate the role of each cluster.

3. Trajectory Analysis and Pathway Analysis

• Purpose: To trace the developmental trajectories of macrophages and understand the process of forming subtypes related to osimertinib resistance (e.g., RNASE1_M).
• Methods:
  • A dendrogram is created to visualize the trajectories of cells and the pathways they follow.
  • Gene expression changes along these trajectories are analyzed to identify genes and pathways related to resistance.

4. Analysis of Cell-Cell Interactions

• Purpose: To clarify the interactions between macrophages and other cells (e.g., tumor cells, T cells).
• Methods:
  • Tools like CellPhoneDB are used to predict ligand-receptor interactions between cells.
  • Specific interactions (e.g., CD47-SIRPA pathway) are analyzed to evaluate their contribution to tumor immune evasion and therapeutic resistance.

5. Identification of Subtype-Specific Gene Expression and Regulatory Factors

• Purpose: To identify gene expression profiles characteristic of specific macrophage subtypes (e.g., RNASE1_M) and elucidate the regulatory factors controlling them.
• Methods:
  • Genes that show differences between subtypes are identified, and their biological functions and related pathways are analyzed.
  • Transcription factor regulatory networks are constructed to identify key transcription factors in specific subtypes.

This study utilizes a variety of single-cell RNA sequencing-based analysis methods to comprehensively analyze the heterogeneity of macrophages in the cerebrospinal fluid of NSCLC patients and the mechanisms related to therapeutic resistance. This detailed understanding at the single-cell level is expected to lead to the discovery of new therapeutic targets.

What Equipment Is Used?

This study used the following equipment and software for conducting single-cell RNA sequencing (scRNA-seq):

Equipment

• BD Rhapsody Single-Cell Analysis System (BD, USA): Used for single-cell library preparation.
• Illumina NextSeq Platform: Used for sequencing.
• NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific): Used for RNA concentration quantification.
• CFX96 Touch Real-Time PCR Detection System (Bio-RAD): Used for real-time PCR.

Software

• fastp: Used for adapter sequence filtering and low-quality read removal.
• UMI-tools: Used for single-cell transcriptome analysis.
• STAR: Used for mapping to the human genome.
• Seurat: Used for normalization and clustering.
• Monocle 2.0: Used for reconstructing macrophage developmental trajectories.
• SCENIC: Used for transcription factor regulatory network analysis.

These tools and software were used in conjunction to perform detailed gene expression analysis at the single-cell level, leading to the identification of macrophage subtypes related to osimertinib resistance and leptomeningeal metastasis as the research aims.

• M2 Macrophages:
  • Gene Expression: M2 macrophages highly express genes such as CD206 (MRC1), CD163, and Arginase-1 (ARG1).
  • Function: They are involved in tissue repair, wound healing, parasite clearance, and promoting tumor immune evasion. Generally, they help resolve inflammation and control chronic inflammation.
• LAMs:
  • Gene Expression: LAMs highly express genes related to lipid metabolism, such as APOE, FABP, LGALS3, and PLA2G7.
  • Function: LAMs specialize in lipid metabolism, being involved in lipid uptake, storage, and metabolism. They play roles in tumor growth and therapeutic resistance.

3. Tissue Distribution and Environment

• M2 Macrophages:
  • Tissue Distribution: They are found in various tissues throughout the body, particularly in wound sites, areas of parasite infection, and chronic inflammation sites.
  • Environment: M2 macrophages are often activated in post-inflammatory repair processes or chronic inflammation environments.
• LAMs:
  • Tissue Distribution: LAMs are primarily found in adipose tissue, lipid-rich environments, or the tumor microenvironment.
  • Environment: LAMs are activated in environments where lipid metabolism plays a crucial role, such as the tumor microenvironment, atherosclerotic plaques, and obese tissues.

4. Clinical Significance

• M2 Macrophages:
  • Clinical Significance: M2 macrophages promote wound healing and tissue repair while potentially aiding in tumor immune evasion and promoting therapeutic resistance.
• LAMs:
  • Clinical Significance: LAMs play crucial roles in enhancing tumor growth and therapeutic resistance, particularly in the tumor microenvironment. They are significant in diseases related to lipid metabolism, such as atherosclerosis, obesity, and cancer, with potential as therapeutic targets.

While M2 macrophages and LAMs both possess immunosuppressive properties, LAMs are particularly specialized in lipid metabolism. M2 macrophages have broader immune-regulating functions and play roles in various pathological situations, whereas LAMs are more specialized in lipid metabolism and the tumor environment. Understanding these differences is crucial for comprehending disease pathology and developing new therapeutic approaches.

What Single-Cell Analyses Are Performed?

Single-cell analysis is a technique used to analyze the gene expression profiles of individual cells, helping to understand the heterogeneity of cell populations. The following methods were used in the study referenced:

1. Single-Cell RNA Sequencing (scRNA-seq)

• Purpose: To analyze gene expression profiles at the single-cell level, evaluate heterogeneity within cell populations, and identify specific cell subtypes and their functional characteristics.
• Subjects: Cells obtained from cerebrospinal fluid (CSF) of NSCLC patients with EGFR mutations.
• Analysis Procedures:
  • Cells are isolated, and single-cell RNA sequencing is performed to obtain gene expression data for each cell.
  • Using dimensionality reduction techniques such as t-SNE (t-distributed stochastic neighbor embedding) or UMAP (Uniform Manifold Approximation and Projection), clustering of cell populations is conducted.
  • Each cluster is identified based on the expression of specific marker genes, allowing the determination of cell types.

2. Characterization of Cell Clusters

• Identification of Clusters:
  • Identified cell groups are classified into T cells, B cells, macrophages, monocytes, dendritic cells (DCs), epithelial cells, and further subtypes (e.g., RNASE1_M, LYVE1_FOLR2_M) within these cells.
• Functional Scoring:
  • The functional characteristics of each cell cluster (e.g., antigen presentation, phagocytosis, angiogenesis) are evaluated to elucidate the role of each cluster.

3. Trajectory Analysis and Pathway Analysis

• Purpose: To trace the developmental trajectories of macrophages and understand the process of forming subtypes related to osimertinib resistance (e.g., RNASE1_M).
• Methods:
  • A dendrogram is created to visualize the trajectories of cells and the pathways they follow.
  • Gene expression changes along these trajectories are analyzed to identify genes and pathways related to resistance.

4. Analysis of Cell-Cell Interactions

• Purpose: To clarify the interactions between macrophages and other cells (e.g., tumor cells, T cells).
• Methods:
  • Tools like CellPhoneDB are used to predict ligand-receptor interactions between cells.
  • Specific interactions (e.g., CD47-SIRPA pathway) are analyzed to evaluate their contribution to tumor immune evasion and therapeutic resistance.

5. Identification of Subtype-Specific Gene Expression and Regulatory Factors

• Purpose: To identify gene expression profiles characteristic of specific macrophage subtypes (e.g., RNASE1_M) and elucidate the regulatory factors controlling them.
• Methods:
  • Genes that show differences between subtypes are identified, and their biological functions and related pathways are analyzed.
  • Transcription factor regulatory networks are constructed to identify key transcription factors in specific subtypes.

This study utilizes a variety of single-cell RNA sequencing-based analysis methods to comprehensively analyze the heterogeneity of macrophages in the cerebrospinal fluid of NSCLC patients and the mechanisms related to therapeutic resistance. This detailed understanding at the single-cell level is expected to lead to the discovery of new therapeutic targets.

What Equipment Is Used?

This study used the following equipment and software for conducting single-cell RNA sequencing (scRNA-seq):

Equipment

• BD Rhapsody Single-Cell Analysis System (BD, USA): Used for single-cell library preparation.
• Illumina NextSeq Platform: Used for sequencing.
• NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific): Used for RNA concentration quantification.
• CFX96 Touch Real-Time PCR Detection System (Bio-RAD): Used for real-time PCR.

Software

• fastp: Used for adapter sequence filtering and low-quality read removal.
• UMI-tools: Used for single-cell transcriptome analysis.
• STAR: Used for mapping to the human genome.
• Seurat: Used for normalization and clustering.
• Monocle 2.0: Used for reconstructing macrophage developmental trajectories.
• SCENIC: Used for transcription factor regulatory network analysis.

These tools and software were used in conjunction to perform detailed gene expression analysis at the single-cell level, leading to the identification of macrophage subtypes related to osimertinib resistance and leptomeningeal metastasis as the research aims.

• M2 Macrophages:
  • Definition: M2 macrophages are “alternatively activated” macrophages involved in anti-inflammatory responses and tissue repair, creating an immunosuppressive environment.
  • Characteristics: They secrete anti-inflammatory cytokines such as IL-10 and TGF-β, suppress inflammation, and promote tissue repair and regeneration. M2 macrophages are involved in parasite infection, wound healing, and tumor progression.
• Lipid-Associated Macrophages (LAMs):
  • Definition: LAMs are a subtype of macrophages with features related to lipid metabolism, primarily activated in fat-rich environments such as adipose tissue and the tumor microenvironment.
  • Characteristics: LAMs specialize in lipid uptake and storage, with high expression of lipid metabolism-related genes (e.g., APOE, FABP, PLA2G7). They play immunosuppressive roles in the tumor microenvironment, contributing to tumor growth and therapeutic resistance.

2. Gene Expression and Function

• M2 Macrophages:
  • Gene Expression: M2 macrophages highly express genes such as CD206 (MRC1), CD163, and Arginase-1 (ARG1).
  • Function: They are involved in tissue repair, wound healing, parasite clearance, and promoting tumor immune evasion. Generally, they help resolve inflammation and control chronic inflammation.
• LAMs:
  • Gene Expression: LAMs highly express genes related to lipid metabolism, such as APOE, FABP, LGALS3, and PLA2G7.
  • Function: LAMs specialize in lipid metabolism, being involved in lipid uptake, storage, and metabolism. They play roles in tumor growth and therapeutic resistance.

3. Tissue Distribution and Environment

• M2 Macrophages:
  • Tissue Distribution: They are found in various tissues throughout the body, particularly in wound sites, areas of parasite infection, and chronic inflammation sites.
  • Environment: M2 macrophages are often activated in post-inflammatory repair processes or chronic inflammation environments.
• LAMs:
  • Tissue Distribution: LAMs are primarily found in adipose tissue, lipid-rich environments, or the tumor microenvironment.
  • Environment: LAMs are activated in environments where lipid metabolism plays a crucial role, such as the tumor microenvironment, atherosclerotic plaques, and obese tissues.

4. Clinical Significance

• M2 Macrophages:
  • Clinical Significance: M2 macrophages promote wound healing and tissue repair while potentially aiding in tumor immune evasion and promoting therapeutic resistance.
• LAMs:
  • Clinical Significance: LAMs play crucial roles in enhancing tumor growth and therapeutic resistance, particularly in the tumor microenvironment. They are significant in diseases related to lipid metabolism, such as atherosclerosis, obesity, and cancer, with potential as therapeutic targets.

While M2 macrophages and LAMs both possess immunosuppressive properties, LAMs are particularly specialized in lipid metabolism. M2 macrophages have broader immune-regulating functions and play roles in various pathological situations, whereas LAMs are more specialized in lipid metabolism and the tumor environment. Understanding these differences is crucial for comprehending disease pathology and developing new therapeutic approaches.

What Single-Cell Analyses Are Performed?

Single-cell analysis is a technique used to analyze the gene expression profiles of individual cells, helping to understand the heterogeneity of cell populations. The following methods were used in the study referenced:

1. Single-Cell RNA Sequencing (scRNA-seq)

• Purpose: To analyze gene expression profiles at the single-cell level, evaluate heterogeneity within cell populations, and identify specific cell subtypes and their functional characteristics.
• Subjects: Cells obtained from cerebrospinal fluid (CSF) of NSCLC patients with EGFR mutations.
• Analysis Procedures:
  • Cells are isolated, and single-cell RNA sequencing is performed to obtain gene expression data for each cell.
  • Using dimensionality reduction techniques such as t-SNE (t-distributed stochastic neighbor embedding) or UMAP (Uniform Manifold Approximation and Projection), clustering of cell populations is conducted.
  • Each cluster is identified based on the expression of specific marker genes, allowing the determination of cell types.

2. Characterization of Cell Clusters

• Identification of Clusters:
  • Identified cell groups are classified into T cells, B cells, macrophages, monocytes, dendritic cells (DCs), epithelial cells, and further subtypes (e.g., RNASE1_M, LYVE1_FOLR2_M) within these cells.
• Functional Scoring:
  • The functional characteristics of each cell cluster (e.g., antigen presentation, phagocytosis, angiogenesis) are evaluated to elucidate the role of each cluster.

3. Trajectory Analysis and Pathway Analysis

• Purpose: To trace the developmental trajectories of macrophages and understand the process of forming subtypes related to osimertinib resistance (e.g., RNASE1_M).
• Methods:
  • A dendrogram is created to visualize the trajectories of cells and the pathways they follow.
  • Gene expression changes along these trajectories are analyzed to identify genes and pathways related to resistance.

4. Analysis of Cell-Cell Interactions

• Purpose: To clarify the interactions between macrophages and other cells (e.g., tumor cells, T cells).
• Methods:
  • Tools like CellPhoneDB are used to predict ligand-receptor interactions between cells.
  • Specific interactions (e.g., CD47-SIRPA pathway) are analyzed to evaluate their contribution to tumor immune evasion and therapeutic resistance.

5. Identification of Subtype-Specific Gene Expression and Regulatory Factors

• Purpose: To identify gene expression profiles characteristic of specific macrophage subtypes (e.g., RNASE1_M) and elucidate the regulatory factors controlling them.
• Methods:
  • Genes that show differences between subtypes are identified, and their biological functions and related pathways are analyzed.
  • Transcription factor regulatory networks are constructed to identify key transcription factors in specific subtypes.

This study utilizes a variety of single-cell RNA sequencing-based analysis methods to comprehensively analyze the heterogeneity of macrophages in the cerebrospinal fluid of NSCLC patients and the mechanisms related to therapeutic resistance. This detailed understanding at the single-cell level is expected to lead to the discovery of new therapeutic targets.

What Equipment Is Used?

This study used the following equipment and software for conducting single-cell RNA sequencing (scRNA-seq):

Equipment

• BD Rhapsody Single-Cell Analysis System (BD, USA): Used for single-cell library preparation.
• Illumina NextSeq Platform: Used for sequencing.
• NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific): Used for RNA concentration quantification.
• CFX96 Touch Real-Time PCR Detection System (Bio-RAD): Used for real-time PCR.

Software

• fastp: Used for adapter sequence filtering and low-quality read removal.
• UMI-tools: Used for single-cell transcriptome analysis.
• STAR: Used for mapping to the human genome.
• Seurat: Used for normalization and clustering.
• Monocle 2.0: Used for reconstructing macrophage developmental trajectories.
• SCENIC: Used for transcription factor regulatory network analysis.

These tools and software were used in conjunction to perform detailed gene expression analysis at the single-cell level, leading to the identification of macrophage subtypes related to osimertinib resistance and leptomeningeal metastasis as the research aims.

• M2 Macrophages:
  • Definition: M2 macrophages are “alternatively activated” macrophages involved in anti-inflammatory responses and tissue repair, creating an immunosuppressive environment.
  • Characteristics: They secrete anti-inflammatory cytokines such as IL-10 and TGF-β, suppress inflammation, and promote tissue repair and regeneration. M2 macrophages are involved in parasite infection, wound healing, and tumor progression.
• Lipid-Associated Macrophages (LAMs):
  • Definition: LAMs are a subtype of macrophages with features related to lipid metabolism, primarily activated in fat-rich environments such as adipose tissue and the tumor microenvironment.
  • Characteristics: LAMs specialize in lipid uptake and storage, with high expression of lipid metabolism-related genes (e.g., APOE, FABP, PLA2G7). They play immunosuppressive roles in the tumor microenvironment, contributing to tumor growth and therapeutic resistance.

2. Gene Expression and Function

• M2 Macrophages:
  • Gene Expression: M2 macrophages highly express genes such as CD206 (MRC1), CD163, and Arginase-1 (ARG1).
  • Function: They are involved in tissue repair, wound healing, parasite clearance, and promoting tumor immune evasion. Generally, they help resolve inflammation and control chronic inflammation.
• LAMs:
  • Gene Expression: LAMs highly express genes related to lipid metabolism, such as APOE, FABP, LGALS3, and PLA2G7.
  • Function: LAMs specialize in lipid metabolism, being involved in lipid uptake, storage, and metabolism. They play roles in tumor growth and therapeutic resistance.

3. Tissue Distribution and Environment

• M2 Macrophages:
  • Tissue Distribution: They are found in various tissues throughout the body, particularly in wound sites, areas of parasite infection, and chronic inflammation sites.
  • Environment: M2 macrophages are often activated in post-inflammatory repair processes or chronic inflammation environments.
• LAMs:
  • Tissue Distribution: LAMs are primarily found in adipose tissue, lipid-rich environments, or the tumor microenvironment.
  • Environment: LAMs are activated in environments where lipid metabolism plays a crucial role, such as the tumor microenvironment, atherosclerotic plaques, and obese tissues.

4. Clinical Significance

• M2 Macrophages:
  • Clinical Significance: M2 macrophages promote wound healing and tissue repair while potentially aiding in tumor immune evasion and promoting therapeutic resistance.
• LAMs:
  • Clinical Significance: LAMs play crucial roles in enhancing tumor growth and therapeutic resistance, particularly in the tumor microenvironment. They are significant in diseases related to lipid metabolism, such as atherosclerosis, obesity, and cancer, with potential as therapeutic targets.

While M2 macrophages and LAMs both possess immunosuppressive properties, LAMs are particularly specialized in lipid metabolism. M2 macrophages have broader immune-regulating functions and play roles in various pathological situations, whereas LAMs are more specialized in lipid metabolism and the tumor environment. Understanding these differences is crucial for comprehending disease pathology and developing new therapeutic approaches.

What Single-Cell Analyses Are Performed?

Single-cell analysis is a technique used to analyze the gene expression profiles of individual cells, helping to understand the heterogeneity of cell populations. The following methods were used in the study referenced:

1. Single-Cell RNA Sequencing (scRNA-seq)

• Purpose: To analyze gene expression profiles at the single-cell level, evaluate heterogeneity within cell populations, and identify specific cell subtypes and their functional characteristics.
• Subjects: Cells obtained from cerebrospinal fluid (CSF) of NSCLC patients with EGFR mutations.
• Analysis Procedures:
  • Cells are isolated, and single-cell RNA sequencing is performed to obtain gene expression data for each cell.
  • Using dimensionality reduction techniques such as t-SNE (t-distributed stochastic neighbor embedding) or UMAP (Uniform Manifold Approximation and Projection), clustering of cell populations is conducted.
  • Each cluster is identified based on the expression of specific marker genes, allowing the determination of cell types.

2. Characterization of Cell Clusters

• Identification of Clusters:
  • Identified cell groups are classified into T cells, B cells, macrophages, monocytes, dendritic cells (DCs), epithelial cells, and further subtypes (e.g., RNASE1_M, LYVE1_FOLR2_M) within these cells.
• Functional Scoring:
  • The functional characteristics of each cell cluster (e.g., antigen presentation, phagocytosis, angiogenesis) are evaluated to elucidate the role of each cluster.

3. Trajectory Analysis and Pathway Analysis

• Purpose: To trace the developmental trajectories of macrophages and understand the process of forming subtypes related to osimertinib resistance (e.g., RNASE1_M).
• Methods:
  • A dendrogram is created to visualize the trajectories of cells and the pathways they follow.
  • Gene expression changes along these trajectories are analyzed to identify genes and pathways related to resistance.

4. Analysis of Cell-Cell Interactions

• Purpose: To clarify the interactions between macrophages and other cells (e.g., tumor cells, T cells).
• Methods:
  • Tools like CellPhoneDB are used to predict ligand-receptor interactions between cells.
  • Specific interactions (e.g., CD47-SIRPA pathway) are analyzed to evaluate their contribution to tumor immune evasion and therapeutic resistance.

5. Identification of Subtype-Specific Gene Expression and Regulatory Factors

• Purpose: To identify gene expression profiles characteristic of specific macrophage subtypes (e.g., RNASE1_M) and elucidate the regulatory factors controlling them.
• Methods:
  • Genes that show differences between subtypes are identified, and their biological functions and related pathways are analyzed.
  • Transcription factor regulatory networks are constructed to identify key transcription factors in specific subtypes.

This study utilizes a variety of single-cell RNA sequencing-based analysis methods to comprehensively analyze the heterogeneity of macrophages in the cerebrospinal fluid of NSCLC patients and the mechanisms related to therapeutic resistance. This detailed understanding at the single-cell level is expected to lead to the discovery of new therapeutic targets.

What Equipment Is Used?

This study used the following equipment and software for conducting single-cell RNA sequencing (scRNA-seq):

Equipment

• BD Rhapsody Single-Cell Analysis System (BD, USA): Used for single-cell library preparation.
• Illumina NextSeq Platform: Used for sequencing.
• NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific): Used for RNA concentration quantification.
• CFX96 Touch Real-Time PCR Detection System (Bio-RAD): Used for real-time PCR.

Software

• fastp: Used for adapter sequence filtering and low-quality read removal.
• UMI-tools: Used for single-cell transcriptome analysis.
• STAR: Used for mapping to the human genome.
• Seurat: Used for normalization and clustering.
• Monocle 2.0: Used for reconstructing macrophage developmental trajectories.
• SCENIC: Used for transcription factor regulatory network analysis.

These tools and software were used in conjunction to perform detailed gene expression analysis at the single-cell level, leading to the identification of macrophage subtypes related to osimertinib resistance and leptomeningeal metastasis as the research aims.

1. Gene Expression Profile: LAMs highly express genes related to lipid metabolism. These include Apolipoprotein E (APOE), Fatty Acid Binding Protein (FABP), Glycosylated Protein (LGALS3), and Phospholipase (PLA2G7).
2. Functions:
   • Lipid Uptake and Storage: LAMs have the ability to uptake lipids from the serum and store them internally. As a result, they are particularly active in environments rich in lipids, such as adipose tissue, atherosclerotic plaques, and the tumor microenvironment.
   • Immune Regulation: LAMs often possess immunosuppressive properties and play a role in aiding immune evasion in the tumor microenvironment.
3. Pathological Roles:
   • Tumor Microenvironment: Within tumors, LAMs promote tumor growth and progression, particularly by aiding immune evasion of tumor cells. For instance, in EGFR-mutant NSCLC, LAMs contribute to the development of osimertinib resistance.
   • Atherosclerosis: LAMs in atherosclerotic plaques influence plaque formation and stability, impacting cardiovascular disease risk.
   • Obesity and Metabolic Disorders: LAMs in adipose tissue regulate the secretion of inflammatory cytokines, contributing to insulin resistance and the development of diabetes.
4. Regulatory Factors: LAMs are regulated by factors secreted by tumor cells or other surrounding cells. For instance, growth factors like Midkine (MDK) have been shown to induce the polarization of LAMs, promoting immune evasion in tumors.

Importance as Potential Therapeutic Targets

Given their critical roles in regulating the tumor microenvironment and chronic inflammation, LAMs are being studied as potential targets for new therapeutic strategies. Particularly, controlling LAM activity in combination with immune checkpoint inhibitors is expected to enhance immune responses against tumors.

A comprehensive understanding of LAMs may contribute to the development of new treatments for cancer and the management of chronic diseases.

How are LAMs Related to Nerve Regeneration?

The relationship between lipid-associated macrophages (LAMs) and nerve regeneration is an emerging area of interest. While LAMs are known to be involved in lipid metabolism and play significant roles in tumor microenvironments and chronic inflammation, they may also play important roles in the nervous system.

Relationship Between LAMs and Nerve Regeneration

1. Lipid Metabolism in the Nervous System and LAMs: The nervous system, particularly the central nervous system (CNS), is rich in lipids, and lipid metabolism is essential for maintaining the function and health of neurons. LAMs may play a role in regulating this lipid metabolism, potentially influencing the nerve regeneration process. For example, LAMs might uptake and process lipids around neurons, providing the necessary energy and components for nerve regeneration and repair.
2. Inflammation and Nerve Regeneration: The inflammatory response following nerve injury plays a dual role in the regeneration process. While excessive inflammation can hinder nerve regeneration, moderate inflammation promotes clearance at the injury site and enhances the regeneration process. LAMs, acting as anti-inflammatory macrophages, may regulate the inflammatory response and support nerve regeneration.
3. Regulation of Supporting Cells by LAMs: In the CNS, macrophages interact with microglia and astrocytes, helping create an environment conducive to nerve regeneration. LAMs can influence these supporting cells, thereby directly or indirectly promoting the regeneration of neurons.
4. Clinical Significance and Research Progress: Although the relationship between LAMs and nerve regeneration is still a developing field, future research may lead to new treatments for nerve injuries or neurodegenerative diseases by targeting LAMs. Understanding how LAMs promote or inhibit nerve regeneration could provide the foundation for developing more effective therapeutic strategies.

LAMs may play a significant role in the nerve regeneration process, serving as key regulators of the mechanisms involved in regeneration and repair following nerve injury. Ongoing research is expected to further clarify the specific relationship between LAMs and nerve regeneration and explore the therapeutic potential of targeting these macrophages.

What is the Relationship Between M2 Macrophages and LAMs?

Lipid-associated macrophages (LAMs) are closely related to M2 macrophages. M2 macrophages are known for their anti-inflammatory properties and are involved in tissue repair, immunosuppression, and tumor progression. Similarly, LAMs are immunosuppressive and are specialized in lipid metabolism, positioning them as a subtype of M2 macrophages.

Relationship Between M2 Macrophages and LAMs

1. Polarization and Function: M2 macrophages are in an “alternative activation” state that promotes anti-inflammatory responses and tissue repair, secreting anti-inflammatory cytokines such as IL-10 and TGF-β. LAMs also have anti-inflammatory roles, particularly in lipid uptake and metabolism. These macrophages exert immunosuppressive effects in the tumor microenvironment, promoting tumor growth and progression.
2. Gene Expression Similarity: LAMs and M2 macrophages often share a common gene expression profile. For example, markers of M2 macrophages such as CD206 (MRC1) and CD163 are also highly expressed in LAMs, suggesting that LAMs may be a subset of M2 macrophages.
3. Relationship with Lipid Metabolism: M2 macrophages are closely related to lipid metabolism. In environments with active lipid metabolism, M2 macrophages are more likely to differentiate into LAMs, with enhanced expression of lipid metabolism-related genes (e.g., APOE, FABP5). This enables LAMs to contribute to the progression of tumors and inflammatory diseases through lipid metabolism.
4. Role in the Tumor Microenvironment: M2 macrophages facilitate immune evasion and tumor progression within the tumor microenvironment. Similarly, LAMs play an immunosuppressive role in the tumor microenvironment, helping tumor cells evade the immune system. Specifically, LAMs with M2 macrophage characteristics have been shown to promote resistance to therapies like osimertinib in EGFR-mutant NSCLC.

LAMs are often classified as a subtype or subset of M2 macrophages, with both involved in immunosuppression, tissue repair, and lipid metabolism. In the context of tumors and chronic inflammation, M2 macrophages may differentiate into LAMs, forming an immunosuppressive environment through lipid metabolism that contributes to disease progression.

How Do M2 and LAM Differ?

While M2 macrophages and lipid-associated macrophages (LAMs) are both subtypes of macrophages with immunosuppressive functions, they differ in several key aspects. Below are the main differences:

1. Definition and Characteristics

• M2 Macrophages:
  • Definition: M2 macrophages are “alternatively activated” macrophages involved in anti-inflammatory responses and tissue repair, creating an immunosuppressive environment.
  • Characteristics: They secrete anti-inflammatory cytokines such as IL-10 and TGF-β, suppress inflammation, and promote tissue repair and regeneration. M2 macrophages are involved in parasite infection, wound healing, and tumor progression.
• Lipid-Associated Macrophages (LAMs):
  • Definition: LAMs are a subtype of macrophages with features related to lipid metabolism, primarily activated in fat-rich environments such as adipose tissue and the tumor microenvironment.
  • Characteristics: LAMs specialize in lipid uptake and storage, with high expression of lipid metabolism-related genes (e.g., APOE, FABP, PLA2G7). They play immunosuppressive roles in the tumor microenvironment, contributing to tumor growth and therapeutic resistance.

2. Gene Expression and Function

• M2 Macrophages:
  • Gene Expression: M2 macrophages highly express genes such as CD206 (MRC1), CD163, and Arginase-1 (ARG1).
  • Function: They are involved in tissue repair, wound healing, parasite clearance, and promoting tumor immune evasion. Generally, they help resolve inflammation and control chronic inflammation.
• LAMs:
  • Gene Expression: LAMs highly express genes related to lipid metabolism, such as APOE, FABP, LGALS3, and PLA2G7.
  • Function: LAMs specialize in lipid metabolism, being involved in lipid uptake, storage, and metabolism. They play roles in tumor growth and therapeutic resistance.

3. Tissue Distribution and Environment

• M2 Macrophages:
  • Tissue Distribution: They are found in various tissues throughout the body, particularly in wound sites, areas of parasite infection, and chronic inflammation sites.
  • Environment: M2 macrophages are often activated in post-inflammatory repair processes or chronic inflammation environments.
• LAMs:
  • Tissue Distribution: LAMs are primarily found in adipose tissue, lipid-rich environments, or the tumor microenvironment.
  • Environment: LAMs are activated in environments where lipid metabolism plays a crucial role, such as the tumor microenvironment, atherosclerotic plaques, and obese tissues.

4. Clinical Significance

• M2 Macrophages:
  • Clinical Significance: M2 macrophages promote wound healing and tissue repair while potentially aiding in tumor immune evasion and promoting therapeutic resistance.
• LAMs:
  • Clinical Significance: LAMs play crucial roles in enhancing tumor growth and therapeutic resistance, particularly in the tumor microenvironment. They are significant in diseases related to lipid metabolism, such as atherosclerosis, obesity, and cancer, with potential as therapeutic targets.

While M2 macrophages and LAMs both possess immunosuppressive properties, LAMs are particularly specialized in lipid metabolism. M2 macrophages have broader immune-regulating functions and play roles in various pathological situations, whereas LAMs are more specialized in lipid metabolism and the tumor environment. Understanding these differences is crucial for comprehending disease pathology and developing new therapeutic approaches.

What Single-Cell Analyses Are Performed?

Single-cell analysis is a technique used to analyze the gene expression profiles of individual cells, helping to understand the heterogeneity of cell populations. The following methods were used in the study referenced:

1. Single-Cell RNA Sequencing (scRNA-seq)

• Purpose: To analyze gene expression profiles at the single-cell level, evaluate heterogeneity within cell populations, and identify specific cell subtypes and their functional characteristics.
• Subjects: Cells obtained from cerebrospinal fluid (CSF) of NSCLC patients with EGFR mutations.
• Analysis Procedures:
  • Cells are isolated, and single-cell RNA sequencing is performed to obtain gene expression data for each cell.
  • Using dimensionality reduction techniques such as t-SNE (t-distributed stochastic neighbor embedding) or UMAP (Uniform Manifold Approximation and Projection), clustering of cell populations is conducted.
  • Each cluster is identified based on the expression of specific marker genes, allowing the determination of cell types.

2. Characterization of Cell Clusters

• Identification of Clusters:
  • Identified cell groups are classified into T cells, B cells, macrophages, monocytes, dendritic cells (DCs), epithelial cells, and further subtypes (e.g., RNASE1_M, LYVE1_FOLR2_M) within these cells.
• Functional Scoring:
  • The functional characteristics of each cell cluster (e.g., antigen presentation, phagocytosis, angiogenesis) are evaluated to elucidate the role of each cluster.

3. Trajectory Analysis and Pathway Analysis

• Purpose: To trace the developmental trajectories of macrophages and understand the process of forming subtypes related to osimertinib resistance (e.g., RNASE1_M).
• Methods:
  • A dendrogram is created to visualize the trajectories of cells and the pathways they follow.
  • Gene expression changes along these trajectories are analyzed to identify genes and pathways related to resistance.

4. Analysis of Cell-Cell Interactions

• Purpose: To clarify the interactions between macrophages and other cells (e.g., tumor cells, T cells).
• Methods:
  • Tools like CellPhoneDB are used to predict ligand-receptor interactions between cells.
  • Specific interactions (e.g., CD47-SIRPA pathway) are analyzed to evaluate their contribution to tumor immune evasion and therapeutic resistance.

5. Identification of Subtype-Specific Gene Expression and Regulatory Factors

• Purpose: To identify gene expression profiles characteristic of specific macrophage subtypes (e.g., RNASE1_M) and elucidate the regulatory factors controlling them.
• Methods:
  • Genes that show differences between subtypes are identified, and their biological functions and related pathways are analyzed.
  • Transcription factor regulatory networks are constructed to identify key transcription factors in specific subtypes.

This study utilizes a variety of single-cell RNA sequencing-based analysis methods to comprehensively analyze the heterogeneity of macrophages in the cerebrospinal fluid of NSCLC patients and the mechanisms related to therapeutic resistance. This detailed understanding at the single-cell level is expected to lead to the discovery of new therapeutic targets.

What Equipment Is Used?

This study used the following equipment and software for conducting single-cell RNA sequencing (scRNA-seq):

Equipment

• BD Rhapsody Single-Cell Analysis System (BD, USA): Used for single-cell library preparation.
• Illumina NextSeq Platform: Used for sequencing.
• NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific): Used for RNA concentration quantification.
• CFX96 Touch Real-Time PCR Detection System (Bio-RAD): Used for real-time PCR.

Software

• fastp: Used for adapter sequence filtering and low-quality read removal.
• UMI-tools: Used for single-cell transcriptome analysis.
• STAR: Used for mapping to the human genome.
• Seurat: Used for normalization and clustering.
• Monocle 2.0: Used for reconstructing macrophage developmental trajectories.
• SCENIC: Used for transcription factor regulatory network analysis.

These tools and software were used in conjunction to perform detailed gene expression analysis at the single-cell level, leading to the identification of macrophage subtypes related to osimertinib resistance and leptomeningeal metastasis as the research aims.