De novo detection of somatic mutations in high-throughput single-cell profiling data sets

Muyas, Francesc; Sauer, Carolin M; Valle-Inclán, Jose Espejo; Li, Ruoyan; Rahbari, Raheleh; Mitchell, Thomas J.; Hormoz, Sahand; Cortés-Ciriano, Isidro

doi:10.1038/s41587-023-01863-z

Cited by 30 publications

(18 citation statements)

References 71 publications

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“…To address this, we hypothesised that somatic mutations could be used as naturally occurring barcodes: accrued during cell division and inherited by progeny in a lineage-restricted manner. To infer somatic mutations we used SComatic 34 . This approach is similar to DEsceRNAMut 35 , but enables mutation calling in both RNA and ATAC-seq data.…”

Section: Resultsmentioning

confidence: 99%

Multiomic analysis reveals developmental dynamics of the human heart in health and disease

Cranley,

Kanemaru,

Bayraktar

et al. 2024

Preprint

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Developmental dynamics encompass both the specification of cell types and their spatial organisation into multicellular niches. Here we harness the power of single-cell and spatial multiomics to unravel embryonic and foetal cardiac tissue niches, which lead to the development of a new tool, TissueTypist. We infer developmental cell trajectories, including evidence for lineage relationships based on shared somatic mutations, within first- and second-trimester human hearts. We reveal that cardiac-resident macrophages likely originate from the yolk sac, forming heterogeneous subsets. CX3CR1+ macrophages with a microglia-like profile localise in the sinoatrial node, which may contribute to axon guidance for the innervating autonomic neurons. Foetal pacemaker cells exhibit distinct characteristics compared to their adult counterparts, including the expression of genes that are known to promote parasympathetic innervation. By comparing somatic mutation profiles of cardiomyocytes, we identify an early branching point where pacemaker cells diverge from working cardiomyocytes. We highlight the enhancer-mediated gene regulatory networks governing atrial and ventricular cardiomyocyte specification. The maturation of atrial cardiomyocytes into distinct left and right phenotypes, driven by transcription factors linked to atrial septal defect genes, underscores the significance of this process for healthy heart development. In the ventricle, cellular and transcriptional gradients along both pseudotime and the transmural axis provide a new molecular understanding of myocardial compaction. Finally, generating data from Trisomy 21 hearts and comparing this with the euploid atlas, we reveal a reduced abundance of specific cell types including compact cardiomyocytes. Overall, this extensive dataset and our precomputed models will form a valuable resource for the field.

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Section: Resultsmentioning

confidence: 99%

Multiomic analysis reveals developmental dynamics of the human heart in health and disease

Cranley,

Kanemaru,

Bayraktar

et al. 2024

Preprint

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“…In the absence of prior knowledge (such as identified through panel testing of known disease genes or WE data), we propose to try different filtering thresholds for the variants used as input to the wNMF and afterwards determine the best result based on the metric introduced in Equation 4. Another solution would be to include only variants that are very likely of somatic origin, either through prior knowledge or through filtering and statistical testing [29, 30]. However, allowing the model to make use of likely germline variants if they are informative can result in the capture of CNVs, as was the case for patient A1.…”

Section: Discussionmentioning

confidence: 99%

Identifying cancer cells from calling single-nucleotide variants in scRNA-seq data

Marot-Lassauzaie,

Beneyto-Calabuig,

Obermayer

et al. 2024

Preprint

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Single cell RNA sequencing (scRNA-seq) data is widely used to study cancer cell states and their heterogeneity. However, the tumour microenvironment is usually a mixture of healthy and cancerous cells and it can be difficult to fully separate these two populations based on transcriptomics alone. If available, somatic single nucleotide variants (SNVs) observed in the scRNA-seq data could be used to identify the cancer population. However, calling somatic SNVs in scRNA-seq data is a challenging task, as most variants seen in the short read data are not somatic, but can instead be germline variants, RNA edits or transcription, sequencing or processing errors. Additionally, only variants present in actively transcribed regions for each individual cell will be seen in the data. To address these challenges, we develop CCLONE (Cancer Cell Labelling On Noisy Expression), an interpretable tool adapted to handle the uncertainty and sparsity of SNVs called from scRNA-seq data. CCLONE jointly identifies cancer clonal populations, and their associated variants. We apply CCLONE on two acute myeloid leukaemia datasets and one lung adenocarcinoma dataset and show that CCLONE captures both genetic clones and somatic events for multiple patients. These results show how CCLONE can be used to gather insight into the course of the disease and the origin of cancer cells in scRNA-seq data.

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Section: Clonal Analysis Of Colorectal Precancersmentioning

confidence: 99%

“…To provide additional ancestry evidence, we called somatic SNVs from single-cell transcriptomics data of colorectal polyps using two independent pipelines (Extended Data Fig. 12d) [96, 97]. Clonal composition was then assessed using the variant allele frequency (VAF) distribution of somatic SNVs (Supplemental method).…”

Section: Introductionmentioning

confidence: 99%

Temporal recording of mammalian development and precancer

Islam,

Yang,

Simmons

et al. 2023

Preprint

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Key to understanding many biological phenomena is knowing the temporal ordering of cellular events, which often require continuous direct observations [1, 2]. An alternative solution involves the utilization of irreversible genetic changes, such as naturally occurring mutations, to create indelible markers that enables retrospective temporal ordering [3-8]. Using NSC-seq, a newly designed and validated multi-purpose single-cell CRISPR platform, we developed a molecular clock approach to record the timing of cellular events and clonalityin vivo, while incorporating assigned cell state and lineage information. Using this approach, we uncovered precise timing of tissue-specific cell expansion during murine embryonic development and identified new intestinal epithelial progenitor states by their unique genetic histories. NSC-seq analysis of murine adenomas and single-cell multi-omic profiling of human precancers as part of the Human Tumor Atlas Network (HTAN), including 116 scRNA-seq datasets and clonal analysis of 418 human polyps, demonstrated the occurrence of polyancestral initiation in 15-30% of colonic precancers, revealing their origins from multiple normal founders. Thus, our multimodal framework augments existing single-cell analyses and lays the foundation forin vivomultimodal recording, enabling the tracking of lineage and temporal events during development and tumorigenesis.

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De novo detection of somatic mutations in high-throughput single-cell profiling data sets

Cited by 30 publications

References 71 publications

Multiomic analysis reveals developmental dynamics of the human heart in health and disease

Multiomic analysis reveals developmental dynamics of the human heart in health and disease

Identifying cancer cells from calling single-nucleotide variants in scRNA-seq data

Temporal recording of mammalian development and precancer

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