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Single-cell analysis of somatic mutations in human bronchial epithelial cells in relation to aging and smoking

Nature Genetics volume 54, pages 492–498 (2022)Cite this article

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Abstract

Although lung cancer risk among smokers is dependent on smoking dose, it remains unknown if this increased risk reflects an increased rate of somatic mutation accumulation in normal lung cells. Here, we applied single-cell whole-genome sequencing of proximal bronchial basal cells from 33 participants aged between 11 and 86 years with smoking histories varying from never-smoking to 116 pack-years. We found an increase in the frequency of single-nucleotide variants and small insertions and deletions with chronological age in never-smokers, with mutation frequencies significantly elevated among smokers. When plotted against smoking pack-years, mutations followed the linear increase in cancer risk until about 23 pack-years, after which no further increase in mutation frequency was observed, pointing toward individual selection for mutation avoidance. Known lung cancer-defined mutation signatures tracked with both age and smoking. No significant enrichment for somatic mutations in lung cancer driver genes was observed.

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Fig. 1: Mutation accumulation in PBBCs with age in never-smokers.
Fig. 2: Mutation accumulation in PBBCs of smokers.
Fig. 3: Cancer driver mutations in normal PBBC nuclei.
Fig. 4: Mutational signatures and smoking.

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Data availability

WGS data are available at dbGap (accession number: phs002758.v1.p1) and can be accessed at https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs002758.v1.p1. Somatic mutation calls, including single-base substitutions and indels from all 134 samples, have been deposited to SomaMutDB at http://vijglab.einsteinmed.org/static/vcf/lung_Huang.et.al.Naturegenetics.tar.gz.

Code availability

Sequencing reads were filtered to remove adapter and low-quality reads by Trim Galore (version 0.4.1), mapped to the human reference genome (GRCh37, including decoy contigs) using BWA (mem; version 0.7.10), with PCR duplication removed by Samtools (version 0.1.19) Realignment of reads and recalibrations of base quality scores were performed by GATK (version 3.5.0). Somatic mutations were called using SCcaller (version 1.2; https://github.com/biosinodx/SCcaller). MASS (version 7.3-53) and lme4 (1.1-26) was employed for statistical analysis in R (4.0.3 GUI 1,73 Catalina build 7892). Custom codes for statistical analysis, permutation analysis, are available through GitHub (https://github.com/Zhenqiu85/Lung_Smoke_analysis).

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Acknowledgements

This study was supported by National Institutes of Health grants U01 ES029519-01 (J.V. and S.D.S), U01HL145560 (S.D.S. and J.V.) AG017242 (J.V.), AG056278 (J.V.), AG047200 (J.V. and V.G.) and the Glenn Foundation for Medical Research. We thank A. Desai and D. Patel (Pulmonary Medicine) for bronchoscopy sample procurement, S. Khader for cytopathology and X. Hao for assisting with data analysis.

Author information

Author notes

  1. These authors contributed equally: Zhenqiu Huang, Shixiang Sun.

Authors and Affiliations

  1. Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA

    Zhenqiu Huang, Shixiang Sun, Moonsook Lee, Alexander Y. Maslov, Xiao Dong, Simon D. Spivack & Jan Vijg

  2. Laboratory of Applied Genomic Technologies, Voronezh State University of Engineering Technologies, Voronezh, Russia

    Alexander Y. Maslov

  3. Department of Pulmonary Medicine, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, NY, USA

    Miao Shi, Spencer Waldman, Ava Marsh, Taha Siddiqui, Ali Sadoughi, Chirag Shah & Simon D. Spivack

  4. Institute on the Biology of Aging and Metabolism, University of Minnesota, Minneapolis, MN, USA

    Xiao Dong

  5. Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN, USA

    Xiao Dong

  6. Department of Biology, Touro College, Queens, NY, USA

    Yakov Peter

  7. Department of Pulmonary Medicine, Albert Einstein College of Medicine, Bronx, NY, USA

    Yakov Peter

  8. Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA

    Kenny Ye & Simon D. Spivack

  9. Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Jan Vijg

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Contributions

J.V., A.Y.M. and S.D.S. conceived this study and designed the experiments. S.D.S., M.S., T.S., Y.P., C.S. and A.S. provided clinical, procedural and specimen-specific study expertise and logistics. Z.H. performed the experiments. Z.H., J.V., A.Y.M., S.S. and K.Y. analyzed the data. Z.H. and J.V. wrote the manuscript.

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Correspondence to Zhenqiu Huang, Simon D. Spivack or Jan Vijg.

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Competing interests

A.Y.M., X.D. and J.V. are cofounders of SingulOmics. The remaining authors declare no competing interests.

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Nature Genetics thanks Peter Campbell, Benjamin Izar, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Mutation frequency and correction deviation error.

SNV frequency of never-smokers versus age. Each data point indicates the mutation frequency per nucleus from each individual, with color intensity indicating relative standard error value (see Methods). The four cells of highest mutational burden were plotted separately with each data point representing median value with standard deviation errors.

Extended Data Fig. 2 Distribution of shared mutations in subject 1320.

a, Stacked bar plot showing the proportional contribution of shared SNVs between all sequenced 3-8 nuclei per subject. b, Upset plot showing the distribution of shared SNVs in six nuclei from subject 1320 (lower part). The bar chart (upper part) represents the number of SNVs shared by each nucleus combination.

Extended Data Fig. 3 An a prori semi-parametric B-spline model to test the non-linearity between mutation frequency and smoking pack-years.

Each data point indicates the SNV frequency of nuclei of individuals. The spline fit evaluated at the average age and the average of random effects, with the 95% confidence interval are shown by the gray line, with the piece-wise linear model fit as the blue line. P value for the spline model is 0.0043 compared to the linear model, and 0.0034 when compared to the null model (see Methods).

Extended Data Fig. 4 INDEL frequency and smoking dose.

a, INDEL frequency versus smoking pack-years across all individuals (n = 33). Each dot indicates the median value and the minimal and maximal range of INDEL frequency of individuals. b, INDEL frequency of different group of individuals according to the smoking pack-years, with boxes indicating median number and interquartile range of the never (n = 14), light (n = 6), moderate (n = 6), and heavy (n = 7) smoking group, respectively.

Extended Data Fig. 5 Effects of smoking cessation on mutation frequency.

Median number of SNV and INDEL frequency among former smokers (n = 7) and current smokers (n = 12). a, each data point indicates the median value and the minimal and maximal range of SNV frequency of 3-8 nuclei per subject. b, each data point indicates the median value and the minimal and maximal range of INDEL frequency of 3-8 nuclei per subject. P values were obtained by likelihood ratio tests using negative binomial mixed-effect model.

Extended Data Fig. 6 SNV frequency in the lung functional genome using scRNA-seq human lung data instead of GTEX.

Each data point represents the number of mutations per nucleus of in functional genome (x axis) and whole genome (y axis) of all subjects colored by smoking status.

Extended Data Fig. 7 Cancer driver mutations.

a, Distribution of driver gene mutations in single nuclei of subjects, with number of mutations and smoking status indicated by colors. b, Total number of single nuclei with unique mutations found in pan-cancer driver genes and number of unique mutations in pan-cancer driver genes across the sample set (n = 134), 22 of 85 driver genes shown (Supplementary Table 5).

Extended Data Fig. 8 Mutational signatures and smoking .

a, Mutation spectra of four novel signatures identified among never-smokers and smokers. The six substitution types are shown across the top. Within each substitution type, the trinucleotide context is shown as four sets of four bars, grouped by whether an A, C, G or T, respectively, is 5′ or 3′ to the mutated base. b-f, Absolute number of major signatures discovered from never-smokers (n = 14) and smokers (n = 19). Each dot indicates the median number of SNV frequency of each individual. Boxes indicate median values and interquartile ranges among each group. The quoted P values were obtained by likelihood ratio tests using linear mixed-effect models. g, APOBEC signatures relative contribution versus SNV frequency of nuclei of never-smokers. Each data point represents a nucleus.

Extended Data Fig. 9 The INDEL mutation signature analysis.

a, Mutation spectra of INDEL in single nuclei from never-smokers (n = 14) and smokers (n = 19). The contributions of different types of INDELs are shown, grouped by whether variants are deletions or insertions; the size of the event; whether they occur at repeat units; and the sequence content of the INDEL. b, Stacked bar plot showing the proportional contribution of mutational signatures to INDELs across all nuclei (n = 134) measured from never-smokers and smokers, four signatures (N1, ID1, ID3, ID4) were extracted by HDP.

Extended Data Fig. 10 Germline genetic variants associated with solid cancers.

A heatmap showing 6 germline variants associated to solid cancers found in each subject per column, with the presence and absence colored. Variant IDs at the left of each row of the heatmap represent 6 different solid cancer associated single-nucleotide polymorphisms found through Clinvar (Supplementary Table 7).

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Huang, Z., Sun, S., Lee, M. et al. Single-cell analysis of somatic mutations in human bronchial epithelial cells in relation to aging and smoking. Nat Genet 54, 492–498 (2022). https://doi.org/10.1038/s41588-022-01035-w

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