Department of Mathematics, Queen Mary University of London, London, United Kingdom.
Evolutionary Dynamics Group, Centre for Cancer Genomics and Computational Biology, Barts Cancer Centre, Queen Mary University of London, London, United Kingdom.
Elife. 2024 Jan 24;12:RP89780. doi: 10.7554/eLife.89780.
Intra-tissue genetic heterogeneity is universal to both healthy and cancerous tissues. It emerges from the stochastic accumulation of somatic mutations throughout development and homeostasis. By combining population genetics theory and genomic information, genetic heterogeneity can be exploited to infer tissue organization and dynamics in vivo. However, many basic quantities, for example the dynamics of tissue-specific stem cells remain difficult to quantify precisely. Here, we show that single-cell and bulk sequencing data inform on different aspects of the underlying stochastic processes. Bulk-derived variant allele frequency spectra (VAF) show transitions from growing to constant stem cell populations with age in samples of healthy esophagus epithelium. Single-cell mutational burden distributions allow a sample size independent measure of mutation and proliferation rates. Mutation rates in adult hematopietic stem cells are higher compared to inferences during development, suggesting additional proliferation-independent effects. Furthermore, single-cell derived VAF spectra contain information on the number of tissue-specific stem cells. In hematopiesis, we find approximately 2 × 10 HSCs, if all stem cells divide symmetrically. However, the single-cell mutational burden distribution is over-dispersed compared to a model of Poisson distributed random mutations. A time-associated model of mutation accumulation with a constant rate alone cannot generate such a pattern. At least one additional source of stochasticity would be needed. Possible candidates for these processes may be occasional bursts of stem cell divisions, potentially in response to injury, or non-constant mutation rates either through environmental exposures or cell-intrinsic variation.
组织内遗传异质性在健康组织和癌组织中普遍存在。它源于整个发育和稳态过程中体细胞突变的随机积累。通过结合群体遗传学理论和基因组信息,可以利用遗传异质性来推断体内组织的结构和动态。然而,许多基本数量,例如组织特异性干细胞的动态,仍然难以精确地量化。在这里,我们表明单细胞和批量测序数据可以反映潜在随机过程的不同方面。批量衍生的变异等位基因频率谱(VAF)显示,在健康食管上皮样本中,随着年龄的增长,干细胞群体从生长到恒定的转变。单细胞突变负担分布允许对突变和增殖率进行样本大小独立的测量。与发育过程中的推断相比,成体造血干细胞中的突变率更高,这表明存在额外的与增殖无关的影响。此外,单细胞衍生的 VAF 谱包含有关组织特异性干细胞数量的信息。在造血中,如果所有的干细胞都对称分裂,我们发现大约有 2×10 个 HSCs。然而,与泊松分布随机突变的模型相比,单细胞突变负担分布呈现过度分散。仅具有恒定速率的突变积累的时间相关模型不能产生这样的模式。至少需要额外的一个随机性来源。这些过程的可能候选者可能是干细胞分裂的偶尔爆发,可能是对损伤的反应,或者是通过环境暴露或细胞内在变异导致的非恒定突变率。