Analysis of low-level somatic mosaicism reveals stage and tissue-specific mutational features in human development.

Most somatic mutations that arise during normal development are present at low levels in single or multiple tissues depending on the developmental stage and affected organs. However, the effect of human developmental stages or mutations of different organs on the features of somatic mutations is still unclear. Here, we performed a systemic and comprehensive analysis of low-level somatic mutations using deep whole-exome sequencing (average read depth ~500×) of 498 multiple organ tissues with matched controls from 190 individuals. Our results showed that early clone-forming mutations shared between multiple organs were lower in number but showed higher allele frequencies than late clone-forming mutations [0.54 vs. 5.83 variants per individual; 6.17% vs. 1.5% variant allele frequency (VAF)] along with less nonsynonymous mutations and lower functional impacts. Additionally, early and late clone-forming mutations had unique mutational signatures that were distinct from mutations that originated from tumors. Compared with early clone-forming mutations that showed a clock-like signature across all organs or tissues studied, late clone-forming mutations showed organ, tissue, and cell-type specificity in the mutation counts, VAFs, and mutational signatures. In particular, analysis of brain somatic mutations showed a bimodal occurrence and temporal-lobe-specific signature. These findings provide new insights into the features of somatic mosaicism that are dependent on developmental stage and brain regions.

Introduction

Somatic mutations persistently occur in normal cells during the entire human lifetime [1]. Although unaccompanied with unregulated proliferation as occurs in cancer, the degree of clonality of somatic mutations often depends on their time of occurrence and origin. For example, variants in the early stages of development tend to affect multiple organs in different germ layers and show high variant allele frequencies (VAFs), whereas those in later stages have low VAFs [2, 3]. Somatic variants that occur after birth are potentially transient and restricted to the cellular level. However, mutations in stem or progenitor cells [4], or variants that confer clonal expansion [5], are persistent and accumulate during the lifetime of an individual. Mutations in stem cells show a sufficient level of VAFs that can be detected via bulk-genome sequencing of tissues. Tissue-level somatic mutations are crucial for the pathogenicity of non-cancerous or benign diseases, and the aberration magnitude is associated with the allele frequency of the mutations [6, 7]. For example, mTOR-pathway-activating somatic mutations cause two types of intractable epilepsy (hemimegalencephaly and focal cortical dysplasia) depending on the time of occurrence of the mutation and VAFs (10%–30% VAFs in hemimegalencephaly, and 1%–10% of VAFs in focal cortical dysplasia) [8-10]. Advances in genetic technologies and increased sequencing efficiencies have helped identify characteristics of somatic mutations at the single-cell or mono-clonal levels [11-15]. However, the detectable variants identified using these methods may not be fully representative of the tissue types and mutational processes involved because of the technical challenges associated with single-cell sequencing (e.g., whole gene amplification), restricted cell types (e.g., stem/progenitor cell or reprogrammed cells), or culturing procedure used. The mutational profiles of clone-forming cells during development or aging can help identify tissue types, mutation timing, disease-specific patterns, and distributions in benign disease or normal individuals, despite limited explanations for cellular heterogeneity [16]. Despite several studies on somatic variants in the literature, it is unclear how low-level but clone-forming somatic mosaicisms are characterized by the time and location of occurrence due to the lack of multi-organ sequencing data and its analytical limitations.

To address these issues, we performed a comprehensive analysis of low-level somatic mutations identified using deep whole-exome sequencing (WES) data (average read depth: ~500×) from 498 tissues of 190 individuals. The cohorts analyzed consisted of two or more organs, including the brain and matched-peripheral tissues, and covered a variety of neurological conditions. The pairwise analysis of age, brain locations, and diseases enabled multi-dimensional analysis and direct comparison with cancer mutations identified using the same analysis procedure.

Results

Three somatic mutation analysis categories including early-stage, late-stage, and tumor mutations

The cohorts consisted of multiple organs, including brain (n = 301), blood (n = 100), liver (n = 60), heart (n = 13), and other peripheral tissues (n = 24). The 190 individuals from whom tissues were obtained included patients with ‘non-tumor’ neurological disorders (n = 133), brain tumors (glioblastoma and ganglioglioma, n = 19), and non-diseased controls (n = 38) (Fig 1A and S1 Table).

Fig 1

Detection of early and late-stage somatic variants in the brain and matched peripheral tissues.

(A) Schematic flow showing the bioinformatics pipelines for the analysis of 301 brain tissues and 197 peripheral tissues from 190 individuals. Tumor mutations were identified from primary or recurrent tumor specimens, and confirmed by histologic examination. To identify somatic variants, Mutect2 and RePlow/NeuSomatic were used for reciprocal mutation calling by all-pairs testing. First, to detect somatic mutation in tissue “A”, tissues “A” and “B” were used as a case and control, respectively, and tissue “A” was further used as a control to identify somatic mutations in tissue “B”. For tumor tissues, the same analysis was performed as in other samples. Subsequently, post-call filtering was used. After strict filtration and tests for organ specificity using one-sample proportion tests, the shared and organ-specific mutations were classified. The colored bar indicates read distribution, and the red and blue-colored bars indicate the reference and altered reads, respectively (lower right panel). (B), (C) Early-stage, late-stage, and tumor mutations were classified with a highly accurate precision rate (89.47%, early-stage; and 90.24% in late-stage and tumor mutations). We determined organ specificity using a one-sample proportion test. (D) Correlation of VAFs from two matched tissues and the WES and TASeq data. VAFs were highly concordant between paired tissues (r = 0.84; P < 0.0001), and the WES and TASeq data (r = 0.61; P < 0.0001).

We defined and used three different categories in the analysis: early-stage, late-stage, and tumor mutations (Fig 1B). Early-stage mutations were defined as shared mutations in multiple organs that may occur during early embryonic development before gastrulation. Late-stage mutations are restricted to a single organ and are more likely to be induced by clone-forming cells containing late embryonic (post-gastrulation) or post-natal somatic mutations, although a few mutations may be present during the pre-gastrulation stage. Tumor specimens were acquired from the primary or recurrent tumor, and microscopic tumor analysis via histological evaluation was performed by a neuropathologist using the World Health Organization (WHO) 2016 grading criteria [17].

Based on the above definitions, somatic mutation identification was performed using an ensemble of robust variant calling algorithms such as Mutect2 [18], RePlow [19], and NeuSomatic [20] for the 1034 potential sample pair combinations. After strict filtration and tests for organ specificity using one-sample proportion tests (Fig 1A and Materials and Methods), we detected 103 early and 997 late-stage mutations, as well as 583 tumor mutations. To validate the identified mutations, 114 randomly selected single nucleotide variants (SNVs; ~10% of non-tumor mutations) were sequenced using Sanger sequencing and via targeted amplicon sequencing (TASeq) to obtain an ultra-high depth (average: 507,856× reads). Our call set showed high accuracy for the identification of the early-stage (89.47%, 17/19) and late-stage and tumor mutations (90.24%, 74/82) (Fig 1C and S2 Table). A high concordance in VAFs across tissues (Pearson’s correlation r = 0.84; P = 1.00×10−42) and between the WES and TASeq data (r = 0.61; P = 1.17×10−48) indicated the accuracy of the calls (Fig 1D).

Additionally, we compared the quantitative traits of the mutations including the number and allele frequency at different stages. On average, we identified 0.18 early and 2.8 late-stage somatic mutations per individual (Fig 2A). The number of mutations in normal tissues was substantially lower than that in tumors (8.12 per individual). The overall number of late-stage and tumor mutations was positively correlated with age (Pearson’s r: late-stage, 0.45; and tumor, 0.36) (Fig 2B). However, the VAF of late-stage mutations was inversely correlated with age, probably due to the accumulation of late-occurring somatic mutations in blood cells with a low allelic fraction. Conversely, we found no correlation between early-stage mutations and age, indicating that these mutations are confined to early developmental stages. The VAFs of the mutations were higher in the early (6.17 ± 3.32%) relative to the late stage (1.50 ± 3.29%) of development (Figs 2C and S1A and S1B).

Fig 2

Basic descriptive statistics for somatic mutations.

(A) The number of somatic mutations per individual in the early-stage, late-stage, and tumor mutation groups. (B) Correlation of age with somatic mutation counts in the groups. (C) Average VAFs between the three mutation groups.

Three distinct mutation spectra identified by mutational signature analysis

Next, we performed a mutation profiling analysis to investigate the underlying mutagenic processes in somatic cells (Fig 3A–3D). De novo signature extraction of the 1494 somatic SNVs (94 early, 880 late stage, and 520 tumor SNVs) helped identify three novel signatures (Fig 3A), including signatures A, B1, and B2, all of which exhibited C>T as the major base substitution, and showed an additional T>C enrichment in signature A. Despite the overall similarity in the mutational spectrum, especially between B1 and B2 (cosine similarity: 0.95), a clear distinction was observed in the relative contribution of the mutations to the sample groups, indicating the uniqueness of the signatures (i.e., signatures A, B1, and B2 predominantly contributed to the early, late-stage, and tumor SNVs, respectively) (Fig 3B).

Fig 3

Mutational profiles and functional analysis.

(A) De novo extraction of somatic mutations by non-negative matrix factorization. (B) Each group was classified according to the three signatures (A, B1, and B2). (C) The relative contribution of the common clock-like signatures (SBS1, SBS5, and SBS40 for single-base substitutions, and ID1, ID2, ID5, and ID8 for small insertion-deletions) from PCAWG signatures. Mutations from the three groups were extracted as three signatures by NMF. Mutational contributions were obtained by fitting the extracted signatures to the mutations in each group. (D) The dN/dS score ratios, (E) proportion of trinucleotides with atypical mutability, and (F) pLI score for gnomAD Exome and each group are shown.

Mapping of the three signatures to COSMIC Mutational Signatures (v3.1; June 2020) [21] identified clock-like SNVs (SBS1, SBS5, and SBS40), and small insertion-deletion (indels) signatures (ID1, ID2, ID5, and ID8) as major components of the mutational profiles (Fig 3C). We noted that the relative contributions of the two well-known age-related mutational signatures (SBS1 and SBS5) were altered in early to late-stage SNVs (SBS1: 19% to 29%; SBS5: 78% to 49%).

Differences in functional tolerance based on mutation timing

We identified functional differences between the early and late-stage mutations. In particular, early-stage mutations showed a lower ratio of non-synonymous to synonymous substitutions (quantified as the dN/dS ratio by maximum-likelihood methods) (0.79) than did late-stage mutations (0.94), tumor mutations (0.94), and common germ-line coding variants (0.90; gnomAD Exome) (Fig 3D), indicating a stronger negative selection [22]. Additionally, early-stage mutations were located in trinucleotides less frequently (2.1%) with atypical mutability [23, 24] than late-stage mutations (8.0%), tumor mutations (8.2%), and common germ-line coding variants (9.9%) (Fig 3E). Sites with atypical mutability are more highly mutated in cancer than is expected to occur randomly, indicating their functional significance and drive in cancer [24, 25]. Furthermore, genes that harbor early-stage mutations showed a lower probability of loss-of-function (LoF) intolerance (pLI score) [26] (Fig 3F), indicating that early-stage mutations are more enriched in LoF-tolerant genes.

Organ-specific characteristics of late-stage mutations

Next, we investigated the characteristics of late-stage mutations, with a particular focus on diversity among different organs and cell types. The numbers of mutations varied substantially by organ, with a smaller number in the brain (0.67 per individual) and a higher number in the blood (9.23 per individual) relative to other peripheral organs (average: 1.12 per individual) (Fig 4A). However, the average value of VAFs was inversely proportional in these groups, with the highest number in the brain (7.32%) and the lowest in the blood (0.50%) (Fig 4B). The number of late-stage mutations and the age of individuals showed a significant positive correlation in the blood (r = 0.45; p = 2.99×10−10; Fig 4C), and this phenotype has been observed in post-natal clonal hematopoiesis [27, 28].

Fig 4

Analysis of late-stage mutations based on the mutation source for the various organs (brain, blood, and other organs).

(A), (B) Number of mutations per individual and VAF distribution. (C) Age correlation with mutation counts. (D) Unsupervised hierarchical clustering of late-stage mutations. Late brain somatic mutations were fitted to signatures A and B2, whereas those in the blood were fitted to signatures B1 and B2. (E) The VAFs of three different cell types [neuronal (NeuN+), oligogenic (Olig2+), and others (negative)] for early and late-stage mutations in the brain. (F) Signature distribution of late brain somatic variants that were divided among temporal and non-temporal brain areas, or according to brain-disease status. (G) Mutational-strand asymmetry. Late-onset blood and tumor mutations are noted as having strand bias with T>C.

Unsupervised hierarchical clustering of the three signatures (A, B1, and B2) in late-stage mutations showed that mutations in the brain primarily comprised signatures A (early-stage) and B2 (tumor), whereas blood mutations harbored signatures B1 (late-stage) and B2 (tumor) (Fig 4D).

We assessed the cell-type specificity of the somatic mutations in the brain by evaluating two brain samples. One (NLE-P-0150) contained early-stage mutations (5.47% VAFs) whereas the other (NLE-P-0225) contained five late-stage mutations (average: 8.00% VAF) (Fig 4E), each of which was sorted by fluorescence-activated nuclei sorting (FANS) to isolate three different cell types: neuronal (NeuN+), oligogenic (Olig2+), and others (negative). TASeq of the separated cell populations showed that both early and late-stage mutations were present in multiple cell lineages, but a large asymmetry in mutation frequencies was present among cell types with late-stage mutations (Fig 4E).

We further classified the late-stage brain mutations into temporal and non-temporal areas, and analyzed area-specific mutational signatures (Fig 4F). As reported previously, signatures A and B2 contributed to mutations in both areas; however, signature B2 had a higher contribution in the temporal lobe (70.3%) than non-temporal tissue (25.7%), indicating that the characteristics of somatic mutations in the temporal lobe are more similar to those of tumor mutations.

Furthermore, late-stage mutations in the blood and tumor mutations showed enrichment for T>C mutations on transcribed strands (Fig 4G). Transcription-coupled repair occurs more frequently at higher transcription levels, and this bias is increased in actively replicating templates [29, 30]. We found that clonal expansion-derived somatic mutations were present in the blood, and were similar to those in tumors [31].

Discussion

Based on a large-scale deep whole exome sequencing data analysis using a total of 498 matched sample pairs from 190 individuals, we provided a detailed picture of low-level but clone-forming somatic mutations, the associated mutation counts, and characteristics that are distinguished in time and space. We found that early-stage mutations, which arise before gastrulation and are shared by multiple organs, are lower in number and have a reduced functional impact than late-stage mutations that are restricted within a single organ. Moreover, we showed that late-stage mutations are associated with human mutational processes in the late-embryonic and post-natal developmental stages, but vary by organ, tissue, and cell lineages. In particular, late-stage mutations in the brain showed a bimodal distribution for developmental stages and the asymmetry of mutational features across brain-cell types and regions.

Using pairwise analysis of the brain and other matched organs, we identified mutational counts, variant fractions, and mutational processes with high confidence levels at each developmental or time stage. Our mutational numbers were roughly comparable to those from previous studies, which reported 0.53 and 3.15 shared and non-shared somatic mutations in the brain (numbers were normalized to a genomic size of 50 Mbp from whole-genome sequencing), respectively [16, 32]. We observed higher numbers of VAFs in the early stage (6.17 ± 3.32%) than in the late stages (1.50 ± 3.29%) of development, which is consistent with the hypothesis that somatic mutations that arise earlier have higher numbers of VAFs [3, 16]. VAFs for early-stage somatic mutations have been analyzed in several studies with different criteria for inclusion, and showed a diverse range (0.3%–55%) [3, 11, 32]. However, few studies have directly observed multi-organ-shared mutations using matched tissue sets from the same individuals; thus, our analysis provides a more realistic distribution of VAFs among shared or non-shared mutations in multiple-organs. Notably, VAFs of somatic indels in the early stages were lower than those of somatic SNVs (indels vs. SNVs: 4.00% vs. 6.40%), but higher in the late stages and tumors (2.75% vs. 1.34% in the late-stage; and 18.47% vs. 14.78% in tumors). The lower VAFs of indels, which indicates a lower cellular proportion and later occurrence, may be associated with lower tolerance to damaging mutations in the early-developmental phase.

In terms of mutagenic processes, we extracted three unique signatures that showed overall similarity but contained early stage, late stage, and tumor SNVs. This phenotype indicates that somatic mutations from different stages have different contexts. For mapping to aging-related COSMIC Mutational Signatures, the increased relative proportion of late-stage somatic mutations in SBS1 indicates the presence of active proliferation and clonal expansion during the late-embryonic and post-natal or aging periods [33, 34]. The greater contributions of the ID1 and ID2 signatures in early-stage indels, and ID5 and ID8 in late-stage indel signatures were consistent with a previous study, although the exact etiologies associated with most indel signatures remain unknown [15].

Early stage mutations were less tolerated functionally than late stage mutations. Because the early stages are critical for development, fatal mutations can have an embryonic-lethal phenotype. Therefore, mutations are usually identified in functionally less essential regions. We found that early-stage mutations showed a lower ratio of dN/dS and pLI score, and were less frequently located in trinucleotides with atypical mutability. These functional analyses collectively indicated that strong selective pressure in the early embryonic stage [35, 36] affects overall mutation characteristics that are less damaging because of the rejection of functionally-deleterious mutations. Nevertheless, due to the small total number of early mutations, the signature analysis, and its interpretation require caution, and results from previous studies for early shared mutations have shown comparable results with our data [11].To define the characteristics of late-stage mutations, we assessed mutational profiles in organs and their cell type specificity. The numbers and variant fractions of mutations varied substantially by organ, and late-stage mutations showed fewer mutations in the brain (0.67 per individual), but high VAFs (7.32%). These results indicate that clonal somatic mutations in the brain occur relatively earlier but less frequently than those in the blood and other organs. Additional results for shared multiple lineages, including later-stage brain somatic mutations, showed a relatively early onset with a similar context. Mutational signature clustering with de novo signatures of late-stage mutations showed that mutations in the brain had early-stage and tumor mutation characteristics. The former result reflects the characteristic of low stemness and little or slow progression of division/differentiation in brain cells. Further analysis of late-stage brain mutations in the temporal lobe regions revealed that these were more similar to the signature B2 (tumor signature). These results suggest that late-stage somatic mutations in the brain show a bimodal occurrence during the early embryonic and post-natal periods, with a pattern similar to that in tumor mutations. We hypothesized that tumor-like mutational signatures in the temporal lobe may originate via neurogenesis (e.g., dentate gyrus) that confers clonal proliferation properties, as reported previously [37]. Asymmetric cell divisions resulting from early cellular bottlenecks for stochastic clonal selection contributed to an uneven variant fraction based on developmental timing [3, 38]. These findings suggest that the VAFs of clone-forming somatic mutations reflect the timing of the mutation, and the cell fitness and cell-type specificity for given somatic mutations.

Our study has several limitations. The study was performed using a limited range and number of tissues per individual, and the total number of mutations was small as identified by exome-level sequencing. Thus, finding sufficient SNPs or CNVs for analysis was difficult. We analyzed tissues from different donors with distinct germline or disease-susceptible backgrounds and lifestyles, thus making mutagenesis interpretation challenging. To address these issues, studies have been performed using sequencing for multiple tissues from the same donor [39]. Therefore, the analysis of multiple organs and different individuals can impair the identification of mutational process characteristics in individuals, but highlighting the results for common mutagenesis. Thus, although the common characteristics of each tissue were analyzed, the analysis of external and internal mutagenesis is difficult. An important consideration includes that somatic mutations should vary not only in cell types and tissues, but also in developmental stages, even in healthy individuals [5, 39, 40]. Therefore, it is difficult to have a representative set of normal tissues of other organs. To eliminate this bias, it is necessary to collect several types of tissues from all organs. Our study had a selection bias because age-mismatched “other organs” were a mixture of different organs, whereas brain tissues contained various locations with diverse ages, but diseased brain tissues were obtained from some patients. This can lead to erroneous results, and therefore, molecular properties were compared by subdividing tissues based on other organ compositions and types of brain disease (S1C and S1D Fig). Although there were differences in VAFs for each brain disease, all organs showed high VAFs compared to other organs or blood groups, as described above (S1C Fig). In other organ groups, the VAFs of the heart, liver, and lipoid tissues were 2.88, 1.56, and 0.96, respectively, and there was a statistically significant difference between heart and liver samples (p-value = 0.03715, Wilcoxon rank sum test) (S1D Fig). Nevertheless, the difference in mean VAF values was within 1.32%, and the composition of other organs in this study had little effect on the conclusions. Another issue includes the uneven distribution of organs by age. For the brain, various age groups were included, whereas, for blood samples, there were many samples from patients who were under 20 and over 50 years old. For other organs, samples from patients in age groups under 50 years were included. Therefore, the number and VAFs for late-somatic mutations were not compared using age-matched tissue groups. We extracted data from patients aged 50–75 years, which was directly comparable in all three groups, and the molecular characteristics of late-stage mutations were maintained (e.g., fewer mutations and high VAF in the brain group, moderate mutations and VAF in other organ groups, and high mutation rates with low VAF in the blood group (S1E and S1F Fig)). Despite sampling limitations, our data was representative of late-stage somatic mutations.

Recent studies by Li et al. showed the presence of seven mutational signatures, including two age-related endogenous mutational signatures SBS1 and SBS5 [39]. The relative activities of SBS1 and SBS5 varied across tissues, particularly in the duodenum, colon, and rectum which showed a higher SBS1/SBS5 ratio than the bronchus, pancreas, esophagus, and liver [39]. In addition, exogenous mutational signatures were associated with organs and individuals [39]. We obtained more numbers of mutations in the late stage than those from the early stages. Nevertheless, it is difficult to conclude that such differences in numbers represent the mutation rate in the early and late developmental stages. This is because (i) it is difficult to determine the exact number of mutations due to differences in detectability (e.g., VAF) of clone-forming mutations in the early and late periods. (ii) The number of early and late mutations identified by our method was affected by the number and location of matched tissues within an individual, and it is difficult to conclude that these numbers are representative. (iii) It is difficult to identify an accurate mutation rate because the time difference between each stage and the rate of cell replication/differentiation differs from the time when these categories are established as early and late. Therefore, care is required in concluding that the mutation rate is high in late stages and that they are dominant.

Our body is remodeled by positively selected clones based on the survival of the fittest principle under given conditions, which can set the stage for cancer cell evolution even in normal aging, and the development of non-neoplastic diseases [5, 41]. The degree of remodeling differs substantially depending on the tissue type and the ecosystem, and external mutagens such as chronic inflammation, alcohol consumption, and tobacco smoking can affect the process [5]. In addition, microenvironmental decline, progressive decline of the competitive fitness of the tissue, and relatively low clearing of altered (dysfunctional) cells have been observed in aging tissues, some of which can be oncogenic [42]. In the case of the brain during somatic evolution, cellular division/differentiation is relatively slow or almost quiescent; thus, mutations that occur early in development are less frequent, but show high VAF levels. In the case of blood and other tissues, continuous differentiation proceeds, and if positive selection does not occur due to a special driver mutation, differentiation will result in a relatively low VAF level with a high number of mutations with similar cellular fitness.

The number of variants identified in this study was not large enough to seek read-based phasing or trace the developmental timing of somatic mutations based on WES, although these phenotypes have been investigated in previous large-scale genomic studies [16, 32, 38]. Somatic mutations are increasingly being identified via single-molecule sequencing, but the method is still in the early phases of development and prone to many errors due to culturing or amplification bias [43]. We found somatic mutations in clone-forming cells that would be detectable using WES, and mutational profiles of clone-forming somatic mutations can account for functional effects, including cell fate, predominance, or natural selection. Overall, the well-defined characteristics of each mutational group and target tissue based on their developmental period can provide an accurate representation of currently-observable somatic mutations and an improved understanding of how these were generated.

Materials and methods

Ethics statement

The study was performed with written/verbal consent was obtained from the Participants, Patients or Parents according to the protocols approved by the Institutional Review Boards of Severance Hospital and Korea Advanced Institute of Science and Technology (KAIST), as well as the Committee on Human research. The developmental assessment was performed one month before surgery for child participants, and informed consent according to developmental age was obtained from the patient or patient/parent, then anonymized.

Patient samples

The freshly frozen brain and peripheral samples acquired from 24 autism spectrum disorder (ASD) and five non-ASD cases from the National Institute of Child Health & Human Development (Bethesda, MD, USA) included various brain regions, such as the frontal, temporal, occipital, and cerebellar areas. Paired samples with other organs were derived from 13 ASD and five non-ASD cases, and brain samples were obtained from 11 ASD cases. The Stanley Medical Research Institute (Rockville, MD, USA) provided genomic DNA for brain tissue and other matched organs from 25 non-schizophrenic and 26 schizophrenic cases. Additionally, the institute provided genomic DNA for the brain and matched liver tissues from patients with major depressive disorders. Fresh frozen brain samples from patients with Alzheimer’s disease (AD) were provided from the Netherlands Brain Bank (project number Lee-835) for 96 brain and matched blood samples for AD and non-demented control cases. Additionally, 15 samples from patients with AD and non-demented control cases were obtained from the Human Brain and Spinal Fluid Resource Center (West Los Angeles Healthcare Center, Los Angeles, CA, USA), which is sponsored by NINDS/NIMH (Bethesda, MD, USA), the National Multiple Sclerosis Society (Raleigh, NC, USA), and the US Department of Veterans Affairs (Bethesda, MD, USA). Fresh frozen samples of lumbosacral lipoma were obtained from the Severance Children’s Hospital of Yonsei University College of Medicine (Seoul, Republic of Korea). Bone tissues of patients with non-syndromic craniosynostosis were provided from the Severance Hospital of Yonsei University College of Medicine. Individuals with refractory epilepsy, including focal cortical dysplasia and non-lesional epilepsy, and who had undergone epilepsy surgery, were enrolled through the Severance Children’s Hospital of Yonsei University College of Medicine. Patients with glioblastoma and ganglioglioma were enrolled at the Severance Hospital of Yonsei University College of Medicine, and satisfied diagnostic criteria described in the 2016 World Health Organization Classification of Tumors of the Central Nervous System [17]. Tumor specimens were collected from the primary or recurrent tumor, and we evaluated tumor involvement via histological analysis, with normal brain or blood used as a control sample.

Deep WES

Genomic DNA was extracted with either the QIAamp mini DNA kit (Qiagen, Hilden, Germany) from freshly frozen brain tissues or the Wizard genomic DNA purification kit (Promega, Madison, WI, USA) from blood according to manufacturer instructions. Each sample was prepared according to Agilent library preparation protocols (Human All Exon 50 Mb kit; Agilent Technologies, Santa Clara, CA, USA). Libraries were paired-end sequenced on Illumina Hiseq 2000 and 2500 instruments (Illumina, San Diego, CA, USA) according to the manufacturer’s instructions, and we used a confidence-mapping quality (mapping quality score ≥ 20; base quality score ≥ 20).

Data processing and reciprocal somatic variant calling

We checked the quality of the raw sequencing reads using the FastQC [44] (v.0.11.7) software. The FASTQ-formatted reads from each sample that passed the quality checks were aligned to the human reference genome (build 38; NCBI, Bethesda, MD, USA) using the BWA-MEM [45] algorithm and converted into a BAM file. The initial BAM file was updated with the read groups, and duplicate information was excluded as the analysis progressed using Picard [46] and GATK [47]. Additionally, we performed local realignment and base-quality recalibration with GATK tools for each exome. The processed BAM files were used to analyze contamination between samples, with the probability of swapping assessed using the NGSCheckMate [48] software and cross-contamination tested using GATK tools. The Vecuum [49] software was used to check for vector contamination during library construction, and Depth of Coverage (GATK) was used to analyze sequencing depth. Processes that are not described in detail were performed based on the GATK best-practice pipeline.

Two or more tissue samples from each individual were analyzed using all-pairs testing. First, to detect somatic mutations in tissue “A”, tissue “A” and “B” were used as a case and control, respectively, and further, tissue “A” was used as a control to identify somatic mutations in tissue “B”. We used the somatic mutation-detection pipeline (paired mode) with sample pairs as inputs with a three somatic variant caller Mutect2 [18] and excluding the panel of normal creation (single nucleotide variants and small insertion-deletions), RePlow [19] (single nucleotide variants), and NeuSomatic [20] with the control of the false detection rate control performed by Varlociraptor [50] (small insertion-deletions).

Systematic variant filtering and variant type discrimination

All mutations that met the following criteria were removed from the initial variant-calling results in the VCF format: oxoG-induced errors based on the method described by Costello et al. [51], common single-nucleotide polymorphisms by NCBI dbSNP [52] (build 153), segmental duplication and simple repeat regions according to the UCSC database [53], a mappability score under 0.8 by Umap [54], and the presence of off-target regions [55], whole genome data without exomes, and untranslated regions. After the removal of artifacts, somatic mutations showing “PASS” results for both Mutect2 and the filters of other callers (RePlow/NeuSomatic) were considered organ-specific mutations. We determined allelic frequency concordance using a one-sample proportion test. The one-sample proportion test indicates individual differences based on a binomial proportion analysis, which is used for testing a hypothesized proportion compared to a given theoretical proportion of the population. Thus, we tested whether there is a difference between the individual’s allelic frequency for somatic mutations relative to the population.

To determine whether multiple-organs shared mutations, variants that were present in matched sample pairs alone were included. In the filtering process, paired-mode somatic calling by Mutect2 was used. The probability of the candidates as multiple-organ shared mutations was identified using the combinations of the following identities: no correspondence to the error filter of other callers (RePlow/NeuSomatic), concordance of nucleotide substitution type and genomic location in matched pair samples, and statistical validity using the one-sample proportion test.

Because differences in the number of samples analyzed can lead to biases, we corrected the data by dividing the number of detected mutations by the number of tissues.

Validation sequencing using deep-targeted amplicon or Sanger sequencing

We performed validation sequencing by randomly selecting multi-organ shared and organ-specific mutations. For validation, we used deep-targeted amplicon sequencing or Sanger sequencing of PCR-amplified DNA, with the same genomic DNA used for deep WES. Primers for PCR amplification were designed using the Primer3 software (http://bioinfo.ut.ee/primer3-0.4.0/) [56]. Target regions were amplified by PCR using specific primer sets and high-fidelity PrimeSTAR GXL DNA polymerase (Takara, Shiga, Japan). Sanger sequencing was performed using BigDye Terminator reactions, and samples were loaded onto a 3730xl DNA analyzer (Applied Biosystems, San Francisco, CA, USA).

Bioinformatics analysis

All somatic mutations with false positives excluded by validation sequencing were annotated using VEP [57] (v.99.0) with “-everything -plugin ExACpLI” options. The results were evaluated using an in-house script to analyze the descriptive statistics of the properties of the basic mutations, the effect of each gene, and potential correlations with patient demographics (age, disease, etc.). Non-negative matrix factorization-based novel signature extraction (NMF, 200 iterations) and transcriptional strand-bias analysis were performed using the Mutational Patterns program (R package) [58]. The signature and 96 variant context types were fitted to the clockwise Pan-Cancer Analysis of Whole Genomes (PCAWG) single-base substitution and small insertions and deletions signatures by deconstructSigs [59], Mutalisk [60] (date of use: March 2020), and YAPSA [61]. The maximum-likelihood dN/dS method was implemented using dNdScv (Wellcome Sanger Institute, Cambridge, UK) [22]. Mutability was calculated using NCBI MutaGene [23, 24] (v.0.9.1.0) which is distributed as a Python package.

Nuclei extraction and fluorescence-activated nuclei sorting

Frozen brain samples were minced using pre-chilled razor blades and one or two drops of lysis buffer [0.2% Triton X-100, 1× protease inhibitor, and 1 mM DTT in 2% bovine serum albumin (BSA) in phosphate-buffered saline]. Lysis buffer (1 mL) was added to the homogenate and mixed by pipetting, after which the lysate was fixed in 1% paraformaldehyde at room temperature for 10 min. The fixed lysate was quenched with 0.125 M glycine at room temperature for 5 min. The homogenate was washed with suspension buffer (1 mM EDTA and 2% BSA), and filtered with a 40 μM cell strainer. The sample was incubated with anti-NeuN (mature neuronal marker; 1:1000 dilution, MAB377, Millipore) and anti-Olig2 (oligodendrocyte lineage marker; 1:500 dilution, ab10983, abcam) antibodies overnight at 4°C, followed by washing with suspension buffer and staining with the secondary antibody for 1 h at 4°C. After washing with suspension buffer, nuclei were passed through a 40 μM cell strainer and stained with 1 μg of 4′,6-diamidino-2-phenylindole. Nuclei used to isolate each cell type were analyzed and sorted using a MoFlo Astrios EQ cell sorter (Beckman Coulter, Brea, CA, USA). Nuclei pellets were centrifuged for 10 min at 1500 ×g and processed immediately for gDNA extraction using a QIAamp DNA micro kit (Qiagen) according to manufacturer instructions.

Late mutational analysis by mutagen in different organs (brain, blood, other organs) (A), (B) Age correlation with somatic mutation VAFs in late-stage variants and blood late-stage variants. (C) VAFs of late-stage somatic mutations in brains are classified by the diseases in individuals. (D) VAFs of late-stage somatic mutations in the heart, liver, and lipoid tissues. (E), (F) Number of mutations per individual and VAF distribution in individuals aged 50–75 years.

(TIF)

Click here for additional data file.

Sample information enrolled in this study.

(XLSX)

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List of post-filtered somatic variants.

(XLSX)

Click here for additional data file.

1 Feb 2022

Dear Dr Kim,

Thank you very much for submitting your Research Article entitled 'Multi-organ analysis of low-level somatic mosaicism reveals stage- and tissue-specific mutational features in human development' to PLOS Genetics.

The manuscript was fully evaluated at the editorial level and by independent peer reviewers. The reviewers appreciated the attention to an important problem, but raised some substantial concerns about the current manuscript. Based on the reviews, we will not be able to accept this version of the manuscript, but we would be willing to review a much-revised version. We cannot, of course, promise publication at that time.

In addition to the reviewers' concerns, I would request that in a revision you also carefully evaluate terminology that refers to mutations as pre- or post-gastrulation, since on my read it was not always clear that certain mutations were present in all three germ layers (especially when dealing with limited distribution of tissue sources and the possibility that tissues contain a mixture of germ layer origin.

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Gregory Barsh

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The authors present an interesting study that leverages whole-exome sequencing (WES) data from multiple tissues from a large number of individuals (190) to gain insight into the nature of somatic mutations that occur in early and late embryonic development. The authors classify somatic mutations as early if they are shared by two different tissues and late if they are not. In addition, the authors also sequenced a subset of brain tumors for comparison. The main finding is that early mutations have higher allele frequency and lower functional impact than late mutations as revealed by multiple genetic metrics (lower dN/dS, location in trinucleotides with atypical mutability, and location in LoF-tolerant genes). In addition, novel mutational signatures and their contributions to different brain regions are described.

While the approach is interesting and there is a high need in the field for this sort of comparative analysis, the article has some important caveats, described below.

1. The study is far from being a systemic and comprehensive analysis of low-level somatic mutations, as claimed in the abstract. Only 3 tissue types (brain, blood, and liver) are analyzed in a large number (60+) of individuals. Authors claim multi-organ sequencing but for most individuals only two tissue types are analyzed: either brain and blood, or brain and liver. The authors should tone down the claim of multi-organ analysis since the study is essentially on brain tissue, just using different control tissues.

2. The article is difficult to read because some key points are not clearly explained. For example, in the 19 individuals with brain tumors, the brain tissue analyzed was from tumor or from normal? That is unclear in figure 1 and it is not clearly explained in Material and Methods. The text only mentions: “If the source of the sample was related to a brain tumor, it was separately regarded as tumor mutations.” Please clarify the origin of samples in individuals with cancer and whether brain tumor tissue was used for the case-control comparisons explained in Fig. 1A. If it was used, this could be a source of confounding because in individuals with cancer, shared mutations tumor-blood could be due to ctDNA or cancer cells disseminated in the blood stream.

3. The legend for Fig. 1A needs more explanation, especially regarding what samples are considered cases vs controls and the one-sample proportion test. What are the tests for organ specificity?

4. When quantifying the number of early and late-stage somatic mutations per individual, how do the authors correct for the fact that different number of samples and different tissues were analyzed in different individuals? The article might benefit from removing the ‘other tissues’ and ‘heart’ subgroups since there are not many samples in those subgroups and will make the comparisons more similar across individuals. The different sample types used for different individuals is a concern.

5. It is unclear how the mutational signature analysis might be relevant when the late-stage mutations come from a mixture of different tissues including brain, heart, liver, blood and others. Different tissues harbor different mutational tissues, please see PMID: 34433965 and 34433962. In addition, the number of early-stage mutations is small (94) for robust mutational signature analysis.

6. The comparison of number and VAF of late-stage somatic mutations by tissue type should take into consideration the fact that the 3 groups of tissues are not age-matched. The is no data for older individuals for ‘other organs’.

7. The brain tumors included in the study are glioblastoma and ganglioglioma. Glioblastoma is an adult tumor and ganglioglioma is a child tumor. Is it possible that the unique tumor mutational signature identified is the result of the mixture of mutations derived from these two very different brain tumors? If the ganglioglioma tumors were removed, would the mutations observed resemble to the landscape of mutations (driver genes) and mutational signatures reported for glioblastoma?

8. Very low resolution of figures makes it impossible to read some of the content.

Reviewer #2: Hyeonju Son and associates analyzed mosacism for SNVs and indels by means of deep whole-exome sequencing in 498 tissues from 190 individuals. Their findings were consistent with those of previous studies: somatic mutations with apparent early embryonic origin were fewer in number but had higher variant allele frequencies than variants that appear to have arisen later in life. Mutational signatures among early embryonic and later-arising variants were similar but differed somewhat.

This research is well described, and the paper is generally well written, although there are occassional errors of English usage. There are a few issues that require attention:

Line 233: "Early-stage mutations were not functionally tolerated than those of late-stage mutations." What does this mean? Are some words left out?

Discussion: The authors should discuss the limitations of this study (limited range of tissues studied, limited numbers of tissues per patient, inability to detect some kinds of variants such as CNVs, etc.).

Line 292: "Subjects with refractory epilepsy, including focal cortical dysplasia and non-lesional epilepsy, and who had undergone epilepsy surgery were enrolled through the Severance Children’s Hospital of Yonsei University College of Medicine." If these samples were obtained because of regional brain tissue excision for intractable epilepsy, could the mutations found have been responsible for the seizures? If so, might the frequency of variants and/or the VAF be biased to higher values in these samples? The same question could be raised in any other group of samples obtained by surgery to remove pathological tissue. This is an issue that the authors should discuss.

Reviewer #3: Throughout its lifetime, human body accumulates mutations, which despite affecting multiple body parts rarely cause a pathology. To provide a better grasp of spatiotemporal characteristics of this genetic mosaicism, the Authors of the manuscript performed deep whole exome sequencing of human tissues comparing the pattern of clone-forming mutations in various organs. In its focus on differences between early (pre-gastrulation) and late (post-gastrulation) mutations, this well-designed study addresses an interesting, and largely unexplored problem of developmental shifts in somatic mutagenesis and makes up an important contribution to current studies about somatic mosaicism.

The results include the finding that most somatic mutations are produced after gastrulation and that diversity of late-appearing mutations is higher than of the early ones. However, it is not clear if the observed numerical differences between early and late clone forming mutations reflect distinct mutation rates during these developmental stages. More details should be also provided to elucidate whether differences in mutational signatures across tissues stem from the action of distinct (internal and external) mutagens in these sites. They Authors should refer to recent studies by Li et al., Nature 2021, 597 (7876): 398-403, who also considered the problem of genetic mosaicism and compared the mutational patter across tissues.

I see the value of the present manuscript in its potential to contribute to ongoing studies of clonal evolution and suggest that the Authors elaborate on how their work contributes to recent studies on this subject (Kakiuchi and Ogawa, Nat Rev Cancer 2021, 21(4):239-256; Swiatczak, Cell Mol Life Sci, 2021 78(21-22):6797-6806; Laconi et al. 2020, Br J Cancer 2020, 122(7):943-952). For example, the Authors hint that variant allele frequency (VAF) can be used as a measure of a degree of clonal expansion of mutated cells in various tissues but draw no conclusions pertaining to the question of somatic evolution based on the observed differences between VAF in the brain, blood and other tissues.

All in all, the present manuscript provides an important contribution to cutting edge research on genetic mosaicism, which is getting a lot of attention now, but extending the present research to incorporate the above questions could help to shed light on the mechanisms underlying the observed mutational profiles.

**********

Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Genetics data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

16 Jun 2022

Submitted filename: Response to Reviews_0409_JHK_swkim_0613_final.docx

Click here for additional data file.

13 Jul 2022

Dear Dr Kim,

Thank you for responding to the reviews. All three reviewers feel the manuscript is significantly improved. However, they have continued to identify outstanding concerns that prevent publication at this time, including, in addition to scientific issues identified by Reviewer 1, a need for revision of English usage. We hope that are able to address these concerns and revise the manuscript accordingly.

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Yours sincerely,

Marshall S. Horwitz, M.D., Ph.D.

Consulting Editor - PLoS Genetics

PLOS Genetics

Gregory Barsh

Editor-in-Chief

PLOS Genetics

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The authors have made a substantial effort to address my questions and concerns in the responses to reviewers and have added a very extensive paragraph about the limitations of the study in the Discussion section, which I appreciate. However, I believe the manuscript still lacks some of the necessary amendments.

The first one relates to the problem of different ages for the different sample types, which is very obvious from Fig. 4C. The authors provided an analysis restricted to patients aged 50 to 75 in the responses to reviewers. I believe this data should be discussed in the text and included as a supplementary figure.

The second issue relates to the selection of tissues for the study, which was done based on convenience. This leads to the “other organs” being a random mixture of tissues, which is still not well explained in the article, and to the brain tissue including a large proportion of samples from diseased tissue. The article would greatly benefit from more clarity on the types of samples included and from additional plots (which could be included as supplementary data) in which the authors further analyze the data to demonstrate that the choice of samples is not biasing the results. For example, in response to Reviewer #2, the authors indicate that the VAF of disease tissues is not different from the normal. All those comparisons should be clearly presented with dot plots in the article. Also, the authors have included this sentence in results: “VAF of late-stage mutations was inversely correlated with age, probably due to the accumulation of late-somatic mutations in blood with a low allelic fraction with aging”. This plot should be shown for all late-stage mutations and also for blood mutations only.

Finally, the article still requires attention to the explanation of the figures and methods. What are the numbers in the bars of Fig. 1A? what do red and blue colors indicate? In Fig. 3B, how are the relative contributions calculated? In Methods, there are some sentences that are hard to understand. For example: “In the filtering process of paired-mode somatic calling using Mutect2, “normal artifact” is corresponding to it; variants are non-germline mutation, differ from reference sequence, and contained in matched pair samples. “ Overall, the article would benefit from careful reading to improve some language issues and to make some of the explanations in methods and results clearer.

Reviewer #2: Hyeonju Son and associates have revised and substantially improved their analysis of somatic mosaicism using deep exome sequencing. They have adequately addressed all of my previous major concerns.

There are still numerous errors of English grammar and word usage; this paper would benefit greatly from copy editing for English usage.

Reviewer #3: The manuscript has been much improved in terms of clarity as the Authors have identified the origin of tumor samples and provided essential details about the mutation calling methods. They also brought the importance of their findings into light by relating their research to other somatic mutagenesis and clonal evolution studies.

**********

Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Genetics data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #3: No

12 Aug 2022

Submitted filename: Response to Reviews_0802_KJH.docx

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31 Aug 2022

Dear Dr Kim,

We are pleased to inform you that your manuscript entitled "Analysis of low-level somatic mosaicism reveals stage and tissue-specific mutational features in human development" has been editorially accepted for publication in PLOS Genetics. Congratulations!

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Comments from the reviewers (if applicable):

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The authors have made a substantial effort to address my concerns, including the addition of a supplementary figure that increases confidence on the results. I find a bit unusual, however, that panels C to F of Supplementary Figure 1 are not presented in Results, but in Discussion. That said, I believe that the extra figures have improved the manuscript. Clarity is still an issue and the manuscript would greatly benefit from language editing.

**********

Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Genetics data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

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13 Sep 2022

PGENETICS-D-21-01628R2

Analysis of low-level somatic mosaicism reveals stage and tissue-specific mutational features in human development

Dear Dr Kim,

We are pleased to inform you that your manuscript entitled "Analysis of low-level somatic mosaicism reveals stage and tissue-specific mutational features in human development" has been formally accepted for publication in PLOS Genetics! Your manuscript is now with our production department and you will be notified of the publication date in due course.

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