Analysis of differentiation trajectories of lung alveolar and malignant cells was performed using Monocle 2 by inferring the pseudotemporal ordering of cells according to their transcriptome similarity. Monocle 2 analysis of malignant cells from P14 was performed using default parameters with the detectGenes function. Detected genes were further required to be expressed by at least 50 cells. For construction of the differentiation trajectory of lineage-labelled epithelial cells (GFP+), the top 150 DEGs (FDR-adjusted P value < 0.05, log(fold change) > 1.5, expressed in ≥50 cells) ranked by fold-change of each cell population from NNK-treated samples were used for ordering cells with the setOrderingFilter function. Trajectories were generated using the reduceDimension function with the method set to ‘DDRTree’
Trajectory roots were selected based on the following criteria:
Deconvolution showed that KACs were closer to tumour regions relative to alveolar cells. ST analysis of a KAC-enriched region showed that KACs were intermediary in the transition of alveolar parenchyma to tumour cells. Tumour regions had markedly reduced expression of NKX2-1 and the alveolar signature, a result in line with reduced alveolar differentiation in KM-LUADs。
可惜的是这些结果都放在了附图里面,可见作者也知道不能作为主要的分析结果展示
Mapping KRAS codon 12 mutations. To map somatic KRAS mutations at single-cell resolution, alignment records were extracted from the corresponding BAM files using mutation location information. Unique mapping alignments (MAPQ = 255) labelled as either PCR duplication or secondary mapping were filtered out. The resulting somatic variant carrying reads were evaluated using Integrative Genomics Viewer (IGV) and the CB tags were used to identify cell identities of mutation-carrying reads. To estimate the VAF of KRAS<sup>G12D</sup> mutation and cell fraction of KRAS<sup>G12D</sup>-carrying cells within malignant and non-malignant epithelial cell subpopulations (for example, malignant cells from all LUADs, malignant cells from KM-LUADs, KACs from KM-LUADs), reads were first extracted based on their unique cell barcodes and BAM files were generated for each subpopulation using samtools. Mutations were then visualized using IGV, and VAFs were calculated by dividing the number of KRAS<sup>G12D</sup>-carrying reads by the total number of uniquely aligned reads for each subpopulation. A similar approach was used to visualize KRAS<sup>G12D</sup>-carrying reads and to calculate the VAF of KRAS<sup>G12D</sup> in KACs of normal tissues from KM-LUAD cases. To calculate the mutation-carrying cell fraction, extracted reads were mapped to the KRAS<sup>G12D</sup> locus from BAM files using AlignmentFile and fetch functions in pysam package. Extracted reads were further filtered using the ‘Duplicate’ and ‘Quality’ tags to remove PCR duplicates and low-quality mappings. The number of reads with or without KRAS<sup>G12D</sup> mutation in each cell was summarized using the CB tag in read barcodes. Mutation-carrying cell fractions were then calculated as the ratio of the number of cells with at least one KRAS<sup>G12D</sup> read over the number of cells with at least one high-quality read mapped to the locus.
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原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。
如有侵权,请联系 cloudcommunity@tencent.com 删除。