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1.
Nat Rev Genet ; 24(8): 535-549, 2023 08.
Artículo en Inglés | MEDLINE | ID: mdl-37085594

RESUMEN

Single-cell genomic technologies are revealing the cellular composition, identities and states in tissues at unprecedented resolution. They have now scaled to the point that it is possible to query samples at the population level, across thousands of individuals. Combining single-cell information with genotype data at this scale provides opportunities to link genetic variation to the cellular processes underpinning key aspects of human biology and disease. This strategy has potential implications for disease diagnosis, risk prediction and development of therapeutic solutions. But, effectively integrating large-scale single-cell genomic data, genetic variation and additional phenotypic data will require advances in data generation and analysis methods. As single-cell genetics begins to emerge as a field in its own right, we review its current state and the challenges and opportunities ahead.


Asunto(s)
Genoma , Genómica , Humanos , Genómica/métodos , Genotipo , Genética Humana
2.
Mol Syst Biol ; 18(8): e10663, 2022 08.
Artículo en Inglés | MEDLINE | ID: mdl-35972065

RESUMEN

Single-cell RNA sequencing (scRNA-seq) enables characterizing the cellular heterogeneity in human tissues. Recent technological advances have enabled the first population-scale scRNA-seq studies in hundreds of individuals, allowing to assay genetic effects with single-cell resolution. However, existing strategies to analyze these data remain based on principles established for the genetic analysis of bulk RNA-seq. In particular, current methods depend on a priori definitions of discrete cell types, and hence cannot assess allelic effects across subtle cell types and cell states. To address this, we propose the Cell Regulatory Map (CellRegMap), a statistical framework to test for and quantify genetic effects on gene expression in individual cells. CellRegMap provides a principled approach to identify and characterize genotype-context interactions of known eQTL variants using scRNA-seq data. This model-based approach resolves allelic effects across cellular contexts of different granularity, including genetic effects specific to cell subtypes and continuous cell transitions. We validate CellRegMap using simulated data and apply it to previously identified eQTL from two recent studies of differentiating iPSCs, where we uncover hundreds of eQTL displaying heterogeneity of genetic effects across cellular contexts. Finally, we identify fine-grained genetic regulation in neuronal subtypes for eQTL that are colocalized with human disease variants.


Asunto(s)
Regulación de la Expresión Génica , Análisis de la Célula Individual , Perfilación de la Expresión Génica/métodos , Humanos , RNA-Seq , Análisis de Secuencia de ARN/métodos , Análisis de la Célula Individual/métodos
3.
Nat Genet ; 56(5): 758-766, 2024 May.
Artículo en Inglés | MEDLINE | ID: mdl-38741017

RESUMEN

Human pluripotent stem (hPS) cells can, in theory, be differentiated into any cell type, making them a powerful in vitro model for human biology. Recent technological advances have facilitated large-scale hPS cell studies that allow investigation of the genetic regulation of molecular phenotypes and their contribution to high-order phenotypes such as human disease. Integrating hPS cells with single-cell sequencing makes identifying context-dependent genetic effects during cell development or upon experimental manipulation possible. Here we discuss how the intersection of stem cell biology, population genetics and cellular genomics can help resolve the functional consequences of human genetic variation. We examine the critical challenges of integrating these fields and approaches to scaling them cost-effectively and practically. We highlight two areas of human biology that can particularly benefit from population-scale hPS cell studies, elucidating mechanisms underlying complex disease risk loci and evaluating relationships between common genetic variation and pharmacotherapeutic phenotypes.


Asunto(s)
Genética de Población , Genómica , Humanos , Enfermedad/genética , Variación Genética , Genómica/métodos , Fenotipo , Células Madre Pluripotentes , Análisis de la Célula Individual/métodos
4.
medRxiv ; 2024 May 16.
Artículo en Inglés | MEDLINE | ID: mdl-38798318

RESUMEN

Understanding the genetic basis of gene expression can help us understand the molecular underpinnings of human traits and disease. Expression quantitative trait locus (eQTL) mapping can help in studying this relationship but have been shown to be very cell-type specific, motivating the use of single-cell RNA sequencing and single-cell eQTLs to obtain a more granular view of genetic regulation. Current methods for single-cell eQTL mapping either rely on the "pseudobulk" approach and traditional pipelines for bulk transcriptomics or do not scale well to large datasets. Here, we propose SAIGE-QTL, a robust and scalable tool that can directly map eQTLs using single-cell profiles without needing aggregation at the pseudobulk level. Additionally, SAIGE-QTL allows for testing the effects of less frequent/rare genetic variation through set-based tests, which is traditionally excluded from eQTL mapping studies. We evaluate the performance of SAIGE-QTL on both real and simulated data and demonstrate the improved power for eQTL mapping over existing pipelines.

5.
Nat Biotechnol ; 39(10): 1202-1215, 2021 10.
Artículo en Inglés | MEDLINE | ID: mdl-33941931

RESUMEN

The development of single-cell multimodal assays provides a powerful tool for investigating multiple dimensions of cellular heterogeneity, enabling new insights into development, tissue homeostasis and disease. A key challenge in the analysis of single-cell multimodal data is to devise appropriate strategies for tying together data across different modalities. The term 'data integration' has been used to describe this task, encompassing a broad collection of approaches ranging from batch correction of individual omics datasets to association of chromatin accessibility and genetic variation with transcription. Although existing integration strategies exploit similar mathematical ideas, they typically have distinct goals and rely on different principles and assumptions. Consequently, new definitions and concepts are needed to contextualize existing methods and to enable development of new methods.


Asunto(s)
Biología Computacional , Interpretación Estadística de Datos , Análisis de la Célula Individual , Animales , Cromatina/genética , Cromatina/metabolismo , Variación Genética , Genómica , Humanos , Medicina de Precisión
6.
Genome Biol ; 22(1): 188, 2021 06 24.
Artículo en Inglés | MEDLINE | ID: mdl-34167583

RESUMEN

BACKGROUND: 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. RESULTS: 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 optimize sc-eQTL mapping. Here, we evaluate the role of different normalization 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. CONCLUSION: We 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.


Asunto(s)
Mapeo Cromosómico/estadística & datos numéricos , Genoma Humano , Células Madre Pluripotentes Inducidas/metabolismo , Sitios de Carácter Cuantitativo , Análisis de la Célula Individual/métodos , Alelos , Línea Celular , Perfilación de la Expresión Génica , Regulación de la Expresión Génica , Humanos , Células Madre Pluripotentes Inducidas/citología , Análisis de Secuencia de ARN , Programas Informáticos , Secuenciación del Exoma
7.
Nat Genet ; 53(3): 304-312, 2021 03.
Artículo en Inglés | MEDLINE | ID: mdl-33664506

RESUMEN

Studying the function of common genetic variants in primary human tissues and during development is challenging. To address this, we use an efficient multiplexing strategy to differentiate 215 human induced pluripotent stem cell (iPSC) lines toward a midbrain neural fate, including dopaminergic neurons, and use single-cell RNA sequencing (scRNA-seq) to profile over 1 million cells across three differentiation time points. The proportion of neurons produced by each cell line is highly reproducible and is predictable by robust molecular markers expressed in pluripotent cells. Expression quantitative trait loci (eQTL) were characterized at different stages of neuronal development and in response to rotenone-induced oxidative stress. Of these, 1,284 eQTL colocalize with known neurological trait risk loci, and 46% are not found in the Genotype-Tissue Expression (GTEx) catalog. Our study illustrates how coupling scRNA-seq with long-term iPSC differentiation enables mechanistic studies of human trait-associated genetic variants in otherwise inaccessible cell states.


Asunto(s)
Neuronas Dopaminérgicas/citología , Neuronas Dopaminérgicas/fisiología , Células Madre Pluripotentes Inducidas/citología , Sitios de Carácter Cuantitativo , Transcriptoma , Diferenciación Celular/genética , Predisposición Genética a la Enfermedad , Humanos , Células Madre Pluripotentes Inducidas/fisiología , Neurogénesis/genética , Estrés Oxidativo/efectos de los fármacos , Receptor Tipo 1 de Factor de Crecimiento de Fibroblastos/genética , Rotenona/toxicidad , Análisis de Secuencia de ARN , Análisis de la Célula Individual
8.
Nat Med ; 27(3): 546-559, 2021 03.
Artículo en Inglés | MEDLINE | ID: mdl-33654293

RESUMEN

Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2, TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2, TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2, TMPRSS2 and CTSL. Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2+TMPRSS2+ cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial-macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention.


Asunto(s)
COVID-19/epidemiología , COVID-19/genética , Interacciones Huésped-Patógeno/genética , SARS-CoV-2/fisiología , Análisis de Secuencia de ARN/estadística & datos numéricos , Análisis de la Célula Individual/estadística & datos numéricos , Internalización del Virus , Adulto , Anciano , Anciano de 80 o más Años , Células Epiteliales Alveolares/metabolismo , Células Epiteliales Alveolares/virología , Enzima Convertidora de Angiotensina 2/genética , Enzima Convertidora de Angiotensina 2/metabolismo , COVID-19/patología , COVID-19/virología , Catepsina L/genética , Catepsina L/metabolismo , Conjuntos de Datos como Asunto/estadística & datos numéricos , Demografía , Femenino , Perfilación de la Expresión Génica/estadística & datos numéricos , Humanos , Pulmón/metabolismo , Pulmón/virología , Masculino , Persona de Mediana Edad , Especificidad de Órganos/genética , Sistema Respiratorio/metabolismo , Sistema Respiratorio/virología , Análisis de Secuencia de ARN/métodos , Serina Endopeptidasas/genética , Serina Endopeptidasas/metabolismo , Análisis de la Célula Individual/métodos
10.
Nat Commun ; 11(1): 810, 2020 02 10.
Artículo en Inglés | MEDLINE | ID: mdl-32041960

RESUMEN

Recent developments in stem cell biology have enabled the study of cell fate decisions in early human development that are impossible to study in vivo. However, understanding how development varies across individuals and, in particular, the influence of common genetic variants during this process has not been characterised. Here, we exploit human iPS cell lines from 125 donors, a pooled experimental design, and single-cell RNA-sequencing to study population variation of endoderm differentiation. We identify molecular markers that are predictive of differentiation efficiency of individual lines, and utilise heterogeneity in the genetic background across individuals to map hundreds of expression quantitative trait loci that influence expression dynamically during differentiation and across cellular contexts.


Asunto(s)
Diferenciación Celular/genética , Expresión Génica/genética , Células Madre Pluripotentes Inducidas/citología , Línea Celular , Endodermo/citología , Femenino , Perfilación de la Expresión Génica , Interacción Gen-Ambiente , Estudios de Asociación Genética , Heterogeneidad Genética , Humanos , Masculino , Sitios de Carácter Cuantitativo , Análisis de la Célula Individual
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