Cardelino: computational integration of somatic clonal substructure and single-cell transcriptomes

McCarthy, Davis J.; Rostom, Raghd; Huang, Yuanhua; Kunz, Daniel J.; Danecek, Petr; Bonder, Marc Jan; Hagai, Tzachi; Lyu, Ruqian; Wang, Wei Lien; Gaffney, Daniel J.; Simons, Benjamin D.; Stegle, Oliver; Teichmann, Sarah A.

doi:10.1038/s41592-020-0766-3

Cited by 59 publications

(73 citation statements)

References 80 publications

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“…In the case of GM08330 one month organoids, cells were demultiplexed using genotype clustering from cells from a different experiment that were sequenced in the same lane. To demultiplex, SNPs were called from CellRanger BAM files with the cellSNP tool v0.1.5, and then the vireo function was used with default parameters and n_donor = 2, from the cardelino R library v0.4.0(Huang et al, 2019, McCarthy et al, 2020) to assign cells to each genotype.…”

Section: Methodsmentioning

confidence: 99%

Human brain organoids reveal accelerated development of cortical neuron classes as a shared feature of autism risk genes

Paulsen

Velasco

Kedaigle

et al. 2020

Preprint

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Genetic risk for autism spectrum disorder (ASD) has been associated with hundreds of genes spanning a wide range of biological functions. The phenotypic alterations in the human brain resulting from mutations in ASD risk genes remain unclear, and the level at which these alterations converge on shared disease pathology is poorly understood. Here, we leveraged reproducible organoid models of the human cerebral cortex to identify cell type-specific developmental abnormalities associated with haploinsufficiency in three ASD risk genes, SUV420H1 (KMT5B), PTEN, and CHD8. We performed comprehensive single-cell RNA-sequencing (scRNA-seq) of over 400,000 cells, and proteomic analysis on individual organoids sampled at different developmental stages to investigate phenotypic convergence among these genes. We find that within a defined period of early cortical development, each of the three mutations demonstrates accelerated development of cortical neurons. Notably, they do so by affecting different neuronal populations: excitatory deep layer (SUV420H1) and callosal (PTEN) neurons, and inhibitory interneurons (CHD8). This work shows that haploinsufficiency in ASD risk genes converge on early developmental defects in the generation of neurons of the cortical microcircuit.

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Section: Methodsmentioning

confidence: 99%

Human brain organoids reveal accelerated development of cortical neuron classes as a shared feature of autism risk genes

Paulsen

Velasco

Kedaigle

et al. 2020

Preprint

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“…We further applied MQuad to a recent scRNA-seq dataset that characterized the somatic clones in healthy fibroblast cell lines 5 . The fibroblast cell line used, joxm, was from a white female aged 45-49.…”

Section: Resultsmentioning

confidence: 99%

MQuad enables clonal substructure discovery using single cell mitochondrial variants

Kwok

Qiao

Huang

et al. 2021

Preprint

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Mitochondrial mutations are increasingly recognised as informative endogenous genetic markers that can be used to reconstruct cellular clonal structure using single-cell RNA or DNA sequencing data. However, there is a lack of effective computational methods to identify informative mtDNA variants in noisy and sparse single-cell sequencing data. Here we present an open source computational tool MQuad that accurately calls clonally informative mtDNA variants in a population of single cells, and an analysis suite for complete clonality inference, based on single cell RNA or DNA sequencing data. Through a variety of simulated and experimental single cell sequencing data, we showed that MQuad can identify mitochondrial variants with both high sensitivity and specificity, outperforming existing methods by a large extent. Furthermore, we demonstrated its wide applicability in different single cell sequencing protocols, particularly in complementing single-nucleotide and copy-number variations to extract finer clonal resolution. MQuad is a Python package available via https://github.com/single-cell-genetics/MQuad.

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“…Briefly, the iPSC lines were multiplexed into 24 experimental pools (4-6 lines in each pool) and single cells were sequenced using the Smart-Seq2 protocol [33]). Cells were assigned to their donor of origin using Cardelino [7]. Here, we focus on the day0 cells (i.e.…”

Section: Single-cell Rna-seq Data Processingmentioning

confidence: 99%

“…Recent technological advances have allowed molecular phenotypes, including gene expression, to be assayed at the level of single cells. In particular, single-cell RNA sequencing (scRNA-seq) is now an established technique, and can be deployed at population scale, across many individuals, by exploiting multiplexed experimental designs and using appropriate demultiplexing tools [7][8][9]. The ability to identify cell types and cell states in an unbiased manner from scRNAseq data from a single experiment can be used to define homogeneous cell populations, quantify expression levels within them, and then map eQTL in each of them separately.…”

Section: Introductionmentioning

confidence: 99%

Optimising expression quantitative trait locus mapping workflows for single-cell studies

Cuomo

Alvari

Azodi

et al. 2021

Preprint

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Single-cell RNA-sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states, and promises to improve our understanding of genetic regulation across tissues in both health and disease. While previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimise sc-eQTL mapping. Here, we evaluate the role of different normalisation and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches and provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.

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Cardelino: computational integration of somatic clonal substructure and single-cell transcriptomes

Cited by 59 publications

References 80 publications

Human brain organoids reveal accelerated development of cortical neuron classes as a shared feature of autism risk genes

Human brain organoids reveal accelerated development of cortical neuron classes as a shared feature of autism risk genes

MQuad enables clonal substructure discovery using single cell mitochondrial variants

Optimising expression quantitative trait locus mapping workflows for single-cell studies

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