DOI: 10.5061/dryad.547d7wmhw
title: Aberrant basal cell clonal dynamics shape early lung carcinogenesis

description: We have used single-cell RNA sequencing (scRNA-seq) and low-input
 whole-genome sequencing (WGS) to investigate epithelial changes in the
 carcinogen-exposed airways and to track clonal trajectories. This dataset
 includes: Seurat object containing the processed scRNA-seq data for mouse
 tracheal cells from NTCU-treated and Control individuals. Seurat object
 containing data from epithelial subclustered cells isolated from mouse
 NTCU-treated and Control tracheal samples. Seurat object containing
 integrated data from human tracheal epithelial cells, sampled from both
 current-smoker and non-smoker individuals. The data spans multiple
 datasets and has been harmonised for downstream analysis. List of
 annotated substitutions, double base substitutions and indels detected
 across murine clones identified in WGS analyses.
description: # Aberrant basal cell clonal dynamics shape early lung carcinogenesis We
 have submitted our processed data from **single-cell RNA sequencing
 (scRNA-seq)** and **whole-genome sequencing (WGS)**, including Seurat
 objects, annotated variant calls, and detailed metadata for further
 analysis in the study: [Aberrant Basal Cell Clonal Dynamics Shape Early
 Lung Carcinogenesis](https://doi.org/10.5061/dryad.547d7wmhw). ##
 Descriptions ### **NTCU\_GEX\_Trachea\_processed\_Celltype\_seurat.rds**
 This Seurat object contains processed single-cell RNA sequencing data for
 mouse tracheal cells from **NTCU-treated** and **Control** groups. The
 data includes annotations for various cell types, allowing for the
 analysis of gene expression profiles and cellular heterogeneity. *
 **Metadata columns**: * `orig.ident`: Original identifier for the sample *
 `nCount_originalexp`: Total RNA count per cell * `nFeature_originalexp`:
 Number of features detected per cell * `sample`: Sample identifier *
 `patientID`: Identifier for the mouse * `tissue`: Tissue type (trachea) *
 `phenotype`: Phenotype group (NTCU-treated, control) * `age`: Age of the
 animal in weeks * `scrublet__predicted_multiplet`: Predicted multiplet
 status (Y/N) * `scrublet__multiplet_scores`: Scrublet scores for multiplet
 detection * `scrublet__multiplet_zscores`: Z-scores from Scrublet *
 `batch`: Batch number for sample processing * `n_genes`: Number of genes
 detected per cell * `n_genes_by_counts`: Number of genes expressed,
 calculated by counts * `log1p_n_genes_by_counts`: Log-transformed count of
 genes by expression * `total_counts`: Total number of counts for the
 sample * `log1p_total_counts`: Log-transformed total counts per sample *
 `total_counts_mt`: Total mitochondrial counts per sample *
 `log1p_total_counts_mt`: Log-transformed mitochondrial counts per sample *
 `pct_counts_mt`: Percentage of mitochondrial counts relative to total
 counts * `total_counts_ribo`: Total ribosomal RNA counts per sample *
 `log1p_total_counts_ribo`: Log-transformed ribosomal RNA counts *
 `pct_counts_ribo`: Percentage of ribosomal RNA counts relative to total
 counts * `Cell_types`: Annotated cell types based on clustering ###
 **NTCU\_Trachea\_Epithelial\_integrated\_seurat.rds** This Seurat object
 contains data from epithelial subclustered cells isolated from **mouse
 NTCU-treated** and **Control trachea** samples. The cells are integrated
 for comparative analysis, facilitating the identification of cell subtypes
 and gene expression signatures. * **Metadata columns**: * `orig.ident`:
 Sample identifier * `nCount_originalexp`: Total RNA count per cell *
 `nFeature_originalexp`: Number of features detected per cell * `sample`:
 Sample identifier * `patientID`: Identifier for the mouse * `tissue`:
 Tissue type (trachea) * `phenotype`: Phenotype group (NTCU-treated,
 control) * `age`: Age of the animal * `scrublet__predicted_multiplet`:
 Predicted multiplet status (Y/N) * `scrublet__multiplet_scores`: Scrublet
 scores for multiplet detection * `scrublet__multiplet_zscores`: Z-scores
 from Scrublet * `batch`: Batch number for sample processing * `n_genes`:
 Number of genes detected per cell * `n_genes_by_counts`: Number of genes
 expressed, calculated by counts * `log1p_n_genes_by_counts`:
 Log-transformed count of genes by expression * `total_counts`: Total
 number of counts for the sample * `log1p_total_counts`: Log-transformed
 total counts per sample * `total_counts_mt`: Total mitochondrial counts
 per sample * `log1p_total_counts_mt`: Log-transformed mitochondrial counts
 per sample * `pct_counts_mt`: Percentage of mitochondrial counts relative
 to total counts * `total_counts_ribo`: Total ribosomal RNA counts per
 sample * `log1p_total_counts_ribo`: Log-transformed ribosomal RNA counts *
 `pct_counts_ribo`: Percentage of ribosomal RNA counts relative to total
 counts * `Montoro_Basal_Score`: Basal cell score based on Montoro et al.
 scoring * `Montoro_Club_Score`: Club cell score based on Montoro et al. *
 `Montoro_Ciliated_Score`: Ciliated cell score based on Montoro et al. *
 `Montoro_Tuft_Score`: Tuft cell score based on Montoro et al. *
 `Montoro_Neuroendocrine_Score`: Neuroendocrine cell score from Montoro et
 al. * `Montoro_Ionocyte_Score`: Ionocyte cell score based on Montoro et
 al. * `Hurskainen_AT2_Score`: AT2 cell score based on Hurskainen et al. *
 `Hurskainen_AT1_Score`: AT1 cell score from Hurskainen et al. *
 `Hurskainen_Ciliated_Score`: Ciliated cell score based on Hurskainen et
 al. * `Hurskainen_Club_Score`: Club cell score based on Hurskainen et al.
 * `Chen_AT2_Score`: AT2 cell score from Chen et al. * `Chen_Club_Score`:
 Club cell score from Chen et al. * `Chen_AT1_Score`: AT1 cell score from
 Chen et al. * `Chen_Basal_Score`: Basal cell score based on Chen et al. *
 `Chen_Goblet_Score`: Goblet cell score from Chen et al. *
 `Chen_Krt8_Score`: KRT8-expressing cell score from Chen et al. *
 `Chen_Proliferation_Score`: Proliferation score based on Chen et al. *
 `Chen_Club_Progenitor_Score`: Club progenitor score from Chen et al. *
 `Chen_AT1_AT2_Score`: Combined AT1 and AT2 score from Chen et al. *
 `Chen_Activated_Club_Score`: Activated club cell score from Chen et al. *
 `Chen_Tumour_Score`: Tumour cell score based on Chen et al. *
 `Chen_DATP_Score`: DATP score from Chen et al. * `Chen_PATS_Score`: PATS
 score from Chen et al. * `Goldfarbmuren_SMG_Basal_Score`: SMG basal score
 from Goldfarbmuren et al. * `Goldfarbmuren_Ciliated_Score`: Ciliated cell
 score from Goldfarbmuren et al. * `Goldfarbmuren_Diff_Basal_Score`:
 Differentiated basal cell score from Goldfarbmuren et al. *
 `Goldfarbmuren_Ionocytes_Tuft_Score`: Ionocyte and tuft cell score from
 Goldfarbmuren et al. * `Goldfarbmuren_Krt8_High_Score`: High KRT8
 expression score from Goldfarbmuren et al. *
 `Goldfarbmuren_Mucus_Secretory_Score`: Mucus secretory cell score from
 Goldfarbmuren et al. * `Goldfarbmuren_PNEC_Score`: PNEC cell score from
 Goldfarbmuren et al. * `Goldfarbmuren_Proliferating_Basal_Score`:
 Proliferating basal score from Goldfarbmuren et al. *
 `Goldfarbmuren_Proteasomal_Basal_Score`: Proteasomal basal cell score from
 Goldfarbmuren et al. * `Goldfarbmuren_SMG_Secretory_Score`: SMG secretory
 cell score from Goldfarbmuren et al. *
 `Goldfarbmuren_Core_Response_Down_Score`: Core response down-regulation
 score from Goldfarbmuren et al. * `Goldfarbmuren_Core_Response_Up_Score`:
 Core response up-regulation score from Goldfarbmuren et al. *
 `integrated_snn_res.0.6`: Seurat cluster resolution * `seurat_clusters`:
 Cluster assignments from Seurat analysis * `integrated_snn_res.0.4`:
 Seurat cluster resolution (alternative) * `celltypes`: Cell type
 annotations (e.g., basal, club, etc.) ###
 **Human\_tracheal\_epithelial\_harmony\_seurat.rds** This Seurat object
 contains harmonised data from human tracheal epithelial cells sampled from
 **current-smoker** and **non-smoker** individuals. The dataset spans
 multiple studies and has been integrated for downstream analysis. *
 **Metadata columns**: * `orig.ident`: Original identifier for the sample *
 `nCount_RNA`: Total RNA count per cell * `nFeature_RNA`: Number of
 features detected per cell * `Donor`: Pseudonymized identifier for the
 individual from whom the sample was obtained * `Sample`: Sample identifier
 * `Method`: Method of RNA extraction or processing * `Sex`: Biological sex
 of the donor * `age_range`: Broad age category of the donor *
 `Smoking_status`: Smoking status of the donor (current-smoker, non-smoker)
 * `dataset`: Dataset identifier included * `nUMI`: Total UMI counts per
 cell * `nGene`: Total gene count per cell * `log10GenesPerUMI`:
 Log-transformed number of genes per UMI * `Batch`: Batch number for sample
 processing * `percent.mito`: Percentage of mitochondrial genes detected *
 `percent.ribo`: Percentage of ribosomal genes detected *
 `RNA_snn_res.0.6`: Seurat cluster resolution * `seurat_clusters`: Cluster
 assignments from Seurat analysis * `Cluster_Annotations`: Cell type or
 cluster annotation ### **Murine\_Variant\_Calls.xlsx** This file contains
 a list of annotated substitutions and indels detected across murine clones
 from **NTCU-treated** mice. The variants were identified using **low-input
 whole-genome sequencing (WGS)**. * **Columns**: * `mouseID`: Identifier
 for each mouse in the study * `VariantID`: Unique identifier for the
 detected variant * `Chrom`: Chromosome where the variant was located *
 `Pos`: Position of the variant on the chromosome * `Ref`: Reference allele
 * `Alt`: Alternate allele * `Qual`: Quality score for the variant *
 `Filter`: Filter status (passed/failed) * `Gene`: Gene affected by the
 variant * `Transcript`: Transcript associated with the variant * `RNA`:
 RNA sequence affected by the variant * `CDS`: Coding sequence affected by
 the variant * `Protein`: Protein sequence affected by the variant *
 `Type`: Type of variant (SNV, insertion, deletion) * `Effect`: Predicted
 functional effect (missense, silent, etc.) * `sampleID`: Identifier for
 the sample * `Mut_Frags`: Number of mutant fragments detected * `mut_id`:
 Mutation identifier * `mutID_sampleID`: Combination of mutation ID and
 sample ID * `coverage`: Sequencing coverage at the variant site * `vaf`:
 Variant allele frequency (VAF) * `cluster_id`: ID of the genomic cluster
 to which the variant belongs * `lung_id`: Identifier for the anotomical
 site of lung * `mouseID_cluster`: Combination of mouse ID and cluster ID
 --- ## Key Information Sources 1. **Single-cell RNA sequencing
 (scRNA-seq)** data from **mouse** and **human** tracheal epithelial cells
 was used in the analysis. 2. **Whole-genome sequencing (WGS)** for
 annotated substitutions and indels was used to identify variants in murine
 clones, based on samples obtained through **laser capture
 microdissection**. ## Code/Software R and Seurat are required to run the
 analysis. The Seurat version used for the analysis is 5.0.1. The script
 used for data processing and integration is provided on
 [https://zenodo.org/records/15168190](https://zenodo.org/records/15168190). Annotations are included throughout the script to explain the major steps, from data loading and cleaning to clustering and visualization.