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1.
Nat Methods ; 19(9): 1076-1087, 2022 09.
Artículo en Inglés | MEDLINE | ID: mdl-36050488

RESUMEN

A central problem in spatial transcriptomics is detecting differentially expressed (DE) genes within cell types across tissue context. Challenges to learning DE include changing cell type composition across space and measurement pixels detecting transcripts from multiple cell types. Here, we introduce a statistical method, cell type-specific inference of differential expression (C-SIDE), that identifies cell type-specific DE in spatial transcriptomics, accounting for localization of other cell types. We model gene expression as an additive mixture across cell types of log-linear cell type-specific expression functions. C-SIDE's framework applies to many contexts: DE due to pathology, anatomical regions, cell-to-cell interactions and cellular microenvironment. Furthermore, C-SIDE enables statistical inference across multiple/replicates. Simulations and validation experiments on Slide-seq, MERFISH and Visium datasets demonstrate that C-SIDE accurately identifies DE with valid uncertainty quantification. Last, we apply C-SIDE to identify plaque-dependent immune activity in Alzheimer's disease and cellular interactions between tumor and immune cells. We distribute C-SIDE within the R package https://github.com/dmcable/spacexr .


Asunto(s)
Perfilación de la Expresión Génica , Transcriptoma , Perfilación de la Expresión Génica/métodos
3.
Nat Biotechnol ; 40(4): 517-526, 2022 04.
Artículo en Inglés | MEDLINE | ID: mdl-33603203

RESUMEN

A limitation of spatial transcriptomics technologies is that individual measurements may contain contributions from multiple cells, hindering the discovery of cell-type-specific spatial patterns of localization and expression. Here, we develop robust cell type decomposition (RCTD), a computational method that leverages cell type profiles learned from single-cell RNA-seq to decompose cell type mixtures while correcting for differences across sequencing technologies. We demonstrate the ability of RCTD to detect mixtures and identify cell types on simulated datasets. Furthermore, RCTD accurately reproduces known cell type and subtype localization patterns in Slide-seq and Visium datasets of the mouse brain. Finally, we show how RCTD's recovery of cell type localization enables the discovery of genes within a cell type whose expression depends on spatial environment. Spatial mapping of cell types with RCTD enables the spatial components of cellular identity to be defined, uncovering new principles of cellular organization in biological tissue. RCTD is publicly available as an open-source R package at https://github.com/dmcable/RCTD .


Asunto(s)
Análisis de la Célula Individual , Transcriptoma , Animales , Ratones , Análisis de Secuencia de ARN , Programas Informáticos , Transcriptoma/genética , Secuenciación del Exoma
4.
Nat Methods ; 18(9): 1075-1081, 2021 09.
Artículo en Inglés | MEDLINE | ID: mdl-34354266

RESUMEN

Epigenetic editing is an emerging technology that uses artificial transcription factors (aTFs) to regulate expression of a target gene. Although human genes can be robustly upregulated by targeting aTFs to promoters, the activation induced by directing aTFs to distal transcriptional enhancers is substantially less robust and consistent. Here we show that long-range activation using CRISPR-based aTFs in human cells can be made more efficient and reliable by concurrently targeting an aTF to the target gene promoter. We used this strategy to direct target gene choice for enhancers capable of regulating more than one promoter and to achieve allele-selective activation of human genes by targeting aTFs to single-nucleotide polymorphisms embedded in distally located sequences. Our results broaden the potential applications of the epigenetic editing toolbox for research and therapeutics.


Asunto(s)
Repeticiones Palindrómicas Cortas Agrupadas y Regularmente Espaciadas , Marcación de Gen/métodos , Regiones Promotoras Genéticas , Factores de Transcripción/genética , Alelos , Apolipoproteína C-III/genética , Apolipoproteínas A/genética , Línea Celular , Elementos de Facilitación Genéticos , Humanos , Subunidad alfa del Receptor de Interleucina-2/genética , Proteína MioD/genética , Polimorfismo de Nucleótido Simple , Activación Transcripcional , Globinas beta/genética
5.
Cell ; 182(6): 1474-1489.e23, 2020 09 17.
Artículo en Inglés | MEDLINE | ID: mdl-32841603

RESUMEN

Widespread changes to DNA methylation and chromatin are well documented in cancer, but the fate of higher-order chromosomal structure remains obscure. Here we integrated topological maps for colon tumors and normal colons with epigenetic, transcriptional, and imaging data to characterize alterations to chromatin loops, topologically associated domains, and large-scale compartments. We found that spatial partitioning of the open and closed genome compartments is profoundly compromised in tumors. This reorganization is accompanied by compartment-specific hypomethylation and chromatin changes. Additionally, we identify a compartment at the interface between the canonical A and B compartments that is reorganized in tumors. Remarkably, similar shifts were evident in non-malignant cells that have accumulated excess divisions. Our analyses suggest that these topological changes repress stemness and invasion programs while inducing anti-tumor immunity genes and may therefore restrain malignant progression. Our findings call into question the conventional view that tumor-associated epigenomic alterations are primarily oncogenic.


Asunto(s)
Cromatina/metabolismo , Cromosomas/metabolismo , Neoplasias Colorrectales/genética , Neoplasias Colorrectales/metabolismo , Metilación de ADN , Epigénesis Genética , Regulación Neoplásica de la Expresión Génica/genética , División Celular , Senescencia Celular/genética , Secuenciación de Inmunoprecipitación de Cromatina , Cromosomas/genética , Estudios de Cohortes , Neoplasias Colorrectales/mortalidad , Neoplasias Colorrectales/patología , Biología Computacional , Metilación de ADN/genética , Epigenómica , Células HCT116 , Humanos , Hibridación Fluorescente in Situ , Microscopía Electrónica de Transmisión , Simulación de Dinámica Molecular , RNA-Seq , Análisis Espacial , Proteínas Supresoras de Tumor/genética , Proteínas Supresoras de Tumor/metabolismo
6.
Mol Metab ; 32: 109-121, 2020 02.
Artículo en Inglés | MEDLINE | ID: mdl-32029221

RESUMEN

OBJECTIVE: Type 2 diabetes (T2D) is a complex disease characterized by pancreatic islet dysfunction, insulin resistance, and disruption of blood glucose levels. Genome-wide association studies (GWAS) have identified > 400 independent signals that encode genetic predisposition. More than 90% of associated single-nucleotide polymorphisms (SNPs) localize to non-coding regions and are enriched in chromatin-defined islet enhancer elements, indicating a strong transcriptional regulatory component to disease susceptibility. Pancreatic islets are a mixture of cell types that express distinct hormonal programs, so each cell type may contribute differentially to the underlying regulatory processes that modulate T2D-associated transcriptional circuits. Existing chromatin profiling methods such as ATAC-seq and DNase-seq, applied to islets in bulk, produce aggregate profiles that mask important cellular and regulatory heterogeneity. METHODS: We present genome-wide single-cell chromatin accessibility profiles in >1,600 cells derived from a human pancreatic islet sample using single-cell combinatorial indexing ATAC-seq (sci-ATAC-seq). We also developed a deep learning model based on U-Net architecture to accurately predict open chromatin peak calls in rare cell populations. RESULTS: We show that sci-ATAC-seq profiles allow us to deconvolve alpha, beta, and delta cell populations and identify cell-type-specific regulatory signatures underlying T2D. Particularly, T2D GWAS SNPs are significantly enriched in beta cell-specific and across cell-type shared islet open chromatin, but not in alpha or delta cell-specific open chromatin. We also demonstrate, using less abundant delta cells, that deep learning models can improve signal recovery and feature reconstruction of rarer cell populations. Finally, we use co-accessibility measures to nominate the cell-specific target genes at 104 non-coding T2D GWAS signals. CONCLUSIONS: Collectively, we identify the islet cell type of action across genetic signals of T2D predisposition and provide higher-resolution mechanistic insights into genetically encoded risk pathways.


Asunto(s)
Secuenciación de Inmunoprecipitación de Cromatina , Aprendizaje Profundo , Diabetes Mellitus Tipo 2/genética , Islotes Pancreáticos/patología , Análisis de la Célula Individual , Diabetes Mellitus Tipo 2/metabolismo , Diabetes Mellitus Tipo 2/patología , Perfilación de la Expresión Génica , Humanos , Islotes Pancreáticos/metabolismo , Polimorfismo de Nucleótido Simple/genética
7.
BMC Genomics ; 19(1): 390, 2018 May 23.
Artículo en Inglés | MEDLINE | ID: mdl-29792182

RESUMEN

BACKGROUND: Bisulfite sequencing is widely employed to study the role of DNA methylation in disease; however, the data suffer from biases due to coverage depth variability. Imputation of methylation values at low-coverage sites may mitigate these biases while also identifying important genomic features associated with predictive power. RESULTS: Here we describe BoostMe, a method for imputing low-quality DNA methylation estimates within whole-genome bisulfite sequencing (WGBS) data. BoostMe uses a gradient boosting algorithm, XGBoost, and leverages information from multiple samples for prediction. We find that BoostMe outperforms existing algorithms in speed and accuracy when applied to WGBS of human tissues. Furthermore, we show that imputation improves concordance between WGBS and the MethylationEPIC array at low WGBS depth, suggesting improved WGBS accuracy after imputation. CONCLUSIONS: Our findings support the use of BoostMe as a preprocessing step for WGBS analysis.


Asunto(s)
Biología Computacional/métodos , Metilación de ADN/efectos de los fármacos , Sulfitos/farmacología , Secuenciación Completa del Genoma , Algoritmos , Secuenciación de Nucleótidos de Alto Rendimiento , Humanos
8.
Gut ; 66(12): 2132-2140, 2017 12.
Artículo en Inglés | MEDLINE | ID: mdl-27609830

RESUMEN

OBJECTIVE AND DESIGN: The goal of the study was to determine whether the mutational profile of early colorectal polyps correlated with growth behaviour. The growth of small polyps (6-9 mm) that were first identified during routine screening of patients was monitored over time by interval imaging with CT colonography. Mutations in these lesions with known growth rates were identified by targeted next-generation sequencing. The timing of mutational events was estimated using computer modelling and statistical inference considering several parameters including allele frequency and fitness. RESULTS: The mutational landscape of small polyps is varied both within individual polyps and among the group as a whole but no single alteration was correlated with growth behaviour. Polyps carried 0-3 pathogenic mutations with the most frequent being in APC, KRAS/NRAS, BRAF, FBXW7 and TP53. In polyps with two or more pathogenic mutations, allele frequencies were often variable, indicating the presence of multiple populations within a single tumour. Based on computer modelling, detectable mutations occurred at a mean polyp size of 30±35 crypts, well before the tumour is of a clinically detectable size. CONCLUSIONS: These data indicate that small colon polyps can have multiple pathogenic mutations in crucial driver genes that arise early in the existence of a tumour. Understanding the molecular pathway of tumourigenesis and clonal evolution in polyps that are at risk for progressing to invasive cancers will allow us to begin to better predict which polyps are more likely to progress into adenocarcinomas and which patients are at greater risk of developing advanced disease.


Asunto(s)
Pólipos del Colon/genética , Neoplasias Colorrectales/genética , Mutación , Alelos , Transformación Celular Neoplásica , Pólipos del Colon/diagnóstico por imagen , Pólipos del Colon/patología , Colonografía Tomográfica Computarizada , Neoplasias Colorrectales/diagnóstico por imagen , Neoplasias Colorrectales/patología , Progresión de la Enfermedad , Femenino , Regulación Neoplásica de la Expresión Génica , Secuenciación de Nucleótidos de Alto Rendimiento , Humanos , Masculino , Inestabilidad de Microsatélites , Persona de Mediana Edad , Modelos Genéticos , Modelos Estadísticos , Estadificación de Neoplasias , Fenotipo
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