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Single-cell sequencing technologies have revolutionized biomedical research by enabling deconvolution of cell type-specific properties in highly heterogeneous tissue. While robust tools have been developed to handle bioinformatic challenges posed by single-cell RNA and ATAC data, options for emergent modalities such as methylation are much more limited, impeding the utility of results. Here we present Amethyst, a comprehensive R package for atlas-scale single-cell methylation sequencing data analysis. Amethyst begins with base-level methylation calls and expedites batch integration, doublet detection, dimensionality reduction, clustering, cell type annotation, differentially methylated region calling, and interpretation of results, facilitating rapid data interaction in a local environment. We introduce the workflow using published single-cell methylation human peripheral blood mononuclear cell (PBMC) and human cortex data. We further leverage Amethyst on an atlas-scale brain dataset to discover a noncanonical methylation pattern in human astrocytes and oligodendrocytes, challenging prior assumptions that this form of methylation is only biologically relevant to neurons in the brain.
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The ENCODE Consortium's efforts to annotate noncoding cis-regulatory elements (CREs) have advanced our understanding of gene regulatory landscapes. Pooled, noncoding CRISPR screens offer a systematic approach to investigate cis-regulatory mechanisms. The ENCODE4 Functional Characterization Centers conducted 108 screens in human cell lines, comprising >540,000 perturbations across 24.85 megabases of the genome. Using 332 functionally confirmed CRE-gene links in K562 cells, we established guidelines for screening endogenous noncoding elements with CRISPR interference (CRISPRi), including accurate detection of CREs that exhibit variable, often low, transcriptional effects. Benchmarking five screen analysis tools, we find that CASA produces the most conservative CRE calls and is robust to artifacts of low-specificity single guide RNAs. We uncover a subtle DNA strand bias for CRISPRi in transcribed regions with implications for screen design and analysis. Together, we provide an accessible data resource, predesigned single guide RNAs for targeting 3,275,697 ENCODE SCREEN candidate CREs with CRISPRi and screening guidelines to accelerate functional characterization of the noncoding genome.
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Sistemas CRISPR-Cas , Repeticiones Palindrómicas Cortas Agrupadas y Regularmente Espaciadas , Humanos , Repeticiones Palindrómicas Cortas Agrupadas y Regularmente Espaciadas/genética , Sistemas CRISPR-Cas/genética , Genoma , Células K562 , ARN Guía de Sistemas CRISPR-CasRESUMEN
Pancreatic cancer remains a formidable challenge due to limited treatment options and its aggressive nature. In recent years, the naturally occurring anticancer compound juglone has emerged as a potential therapeutic candidate, showing promising results in inhibiting tumor growth and inducing cancer cell apoptosis. However, concerns over its toxicity have hampered juglone's clinical application. To address this issue, we have explored the use of polymeric micelles as a delivery system for juglone in pancreatic cancer treatment. These micelles, formulated using Poloxamer 407 and D-α-Tocopherol polyethylene glycol 1000 succinate, offer an innovative solution to enhance juglone's therapeutic potential while minimizing toxicity. In-vitro studies have demonstrated that micelle-formulated juglone (JM) effectively decreases proliferation and migration and increases apoptosis in pancreatic cancer cell lines. Importantly, in-vivo, JM exhibited no toxicity, allowing for increased dosing frequency compared to free drug administration. In mice, JM significantly reduced tumor growth in subcutaneous xenograft and orthotopic pancreatic cancer models. Beyond its direct antitumor effects, JM treatment also influenced the tumor microenvironment. In immunocompetent mice, JM increased immune cell infiltration and decreased stromal deposition and activation markers, suggesting an immunomodulatory role. To understand JM's mechanism of action, we conducted RNA sequencing and subsequent differential expression analysis on tumors that were treated with JM. The administration of JM treatment reduced the expression levels of the oncogenic protein MYC, thereby emphasizing its potential as a focused, therapeutic intervention. In conclusion, the polymeric micelles-mediated delivery of juglone holds excellent promise in pancreatic cancer therapy. This approach offers improved drug delivery, reduced toxicity, and enhanced therapeutic efficacy.
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Single-cell whole-genome sequencing (scWGS) enables the assessment of genome-level molecular differences between individual cells with particular relevance to genetically diverse systems like solid tumors. The application of scWGS was limited due to a dearth of accessible platforms capable of producing high-throughput profiles. We present a technique that leverages nucleosome disruption methodologies with the widely adopted 10× Genomics ATAC-seq workflow to produce scWGS profiles for high-throughput copy-number analysis without new equipment or custom reagents. We further demonstrate the use of commercially available indexed transposase complexes from ScaleBio for sample multiplexing, reducing the per-sample preparation costs. Finally, we demonstrate that sequential indexed tagmentation with an intervening nucleosome disruption step allows for the generation of both ATAC and WGS data from the same cell, producing comparable data to the unimodal assays. By exclusively utilizing accessible commercial reagents, we anticipate that these scWGS and scWGS+ATAC methods can be broadly adopted by the research community.
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Cromatina , Nucleosomas , Cromatina/genética , Nucleosomas/genética , Análisis de Secuencia de ADN/métodos , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , GenomaRESUMEN
The human genome contains millions of candidate cis-regulatory elements (CREs) with cell-type-specific activities that shape both health and myriad disease states. However, we lack a functional understanding of the sequence features that control the activity and cell-type-specific features of these CREs. Here, we used lentivirus-based massively parallel reporter assays (lentiMPRAs) to test the regulatory activity of over 680,000 sequences, representing a nearly comprehensive set of all annotated CREs among three cell types (HepG2, K562, and WTC11), finding 41.7% to be functional. By testing sequences in both orientations, we find promoters to have significant strand orientation effects. We also observe that their 200 nucleotide cores function as non-cell-type-specific 'on switches' providing similar expression levels to their associated gene. In contrast, enhancers have weaker orientation effects, but increased tissue-specific characteristics. Utilizing our lentiMPRA data, we develop sequence-based models to predict CRE function with high accuracy and delineate regulatory motifs. Testing an additional lentiMPRA library encompassing 60,000 CREs in all three cell types, we further identified factors that determine cell-type specificity. Collectively, our work provides an exhaustive catalog of functional CREs in three widely used cell lines, and showcases how large-scale functional measurements can be used to dissect regulatory grammar.
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Genome-wide mapping of histone modifications is critical to understanding transcriptional regulation. CUT&Tag is a new method for profiling histone modifications, offering improved sensitivity and decreased cost compared with ChIP-seq. Here, we present GoPeaks, a peak calling method specifically designed for histone modification CUT&Tag data. We compare the performance of GoPeaks against commonly used peak calling algorithms to detect histone modifications that display a range of peak profiles and are frequently used in epigenetic studies. We find that GoPeaks robustly detects genome-wide histone modifications and, notably, identifies a substantial number of H3K27ac peaks with improved sensitivity compared to other standard algorithms.
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Código de Histonas , Procesamiento Proteico-Postraduccional , Inmunoprecipitación de Cromatina/métodos , Genoma , Análisis de Secuencia de ADN/métodosRESUMEN
Single-cell combinatorial indexing (sci) with transposase-based library construction increases the throughput of single-cell genomics assays but produces sparse coverage in terms of usable reads per cell. We develop symmetrical strand sci ('s3'), a uracil-based adapter switching approach that improves the rate of conversion of source DNA into viable sequencing library fragments following tagmentation. We apply this chemistry to assay chromatin accessibility (s3-assay for transposase-accessible chromatin, s3-ATAC) in human cortical and mouse whole-brain tissues, with mouse datasets demonstrating a six- to 13-fold improvement in usable reads per cell compared with other available methods. Application of s3 to single-cell whole-genome sequencing (s3-WGS) and to whole-genome plus chromatin conformation (s3-GCC) yields 148- and 14.8-fold improvements, respectively, in usable reads per cell compared with sci-DNA-sequencing and sci-HiC. We show that s3-WGS and s3-GCC resolve subclonal genomic alterations in patient-derived pancreatic cancer cell lines. We expect that the s3 platform will be compatible with other transposase-based techniques, including sci-MET or CUT&Tag.
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Cromatina , Transposasas , Animales , Cromatina/genética , ADN/genética , Genoma , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Humanos , Ratones , Análisis de Secuencia de ADN , Análisis de la Célula Individual/métodos , Transposasas/genética , Transposasas/metabolismoRESUMEN
Single-cell Hi-C (scHi-C) interrogates genome-wide chromatin interaction in individual cells, allowing us to gain insights into 3D genome organization. However, the extremely sparse nature of scHi-C data poses a significant barrier to analysis, limiting our ability to tease out hidden biological information. In this work, we approach this problem by applying topic modeling to scHi-C data. Topic modeling is well-suited for discovering latent topics in a collection of discrete data. For our analysis, we generate nine different single-cell combinatorial indexed Hi-C (sci-Hi-C) libraries from five human cell lines (GM12878, H1Esc, HFF, IMR90, and HAP1), consisting over 19,000 cells. We demonstrate that topic modeling is able to successfully capture cell type differences from sci-Hi-C data in the form of "chromatin topics." We further show enrichment of particular compartment structures associated with locus pairs in these topics.
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Cromatina , Biología Computacional/métodos , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Análisis de la Célula Individual/métodos , Línea Celular , Cromatina/química , Cromatina/genética , Análisis por Conglomerados , Biblioteca de Genes , Humanos , Procesamiento de Lenguaje NaturalRESUMEN
BACKGROUND: Hi-C is currently the most widely used assay to investigate the 3D organization of the genome and to study its role in gene regulation, DNA replication, and disease. However, Hi-C experiments are costly to perform and involve multiple complex experimental steps; thus, accurate methods for measuring the quality and reproducibility of Hi-C data are essential to determine whether the output should be used further in a study. RESULTS: Using real and simulated data, we profile the performance of several recently proposed methods for assessing reproducibility of population Hi-C data, including HiCRep, GenomeDISCO, HiC-Spector, and QuASAR-Rep. By explicitly controlling noise and sparsity through simulations, we demonstrate the deficiencies of performing simple correlation analysis on pairs of matrices, and we show that methods developed specifically for Hi-C data produce better measures of reproducibility. We also show how to use established measures, such as the ratio of intra- to interchromosomal interactions, and novel ones, such as QuASAR-QC, to identify low-quality experiments. CONCLUSIONS: In this work, we assess reproducibility and quality measures by varying sequencing depth, resolution and noise levels in Hi-C data from 13 cell lines, with two biological replicates each, as well as 176 simulated matrices. Through this extensive validation and benchmarking of Hi-C data, we describe best practices for reproducibility and quality assessment of Hi-C experiments. We make all software publicly available at http://github.com/kundajelab/3DChromatin_ReplicateQC to facilitate adoption in the community.
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Genómica/normas , Secuenciación de Nucleótidos de Alto Rendimiento/normas , Neoplasias/genética , Control de Calidad , Programas Informáticos , Humanos , Reproducibilidad de los Resultados , Células Tumorales CultivadasRESUMEN
Recent advances in chromosome conformation capture technologies have led to the discovery of previously unappreciated structural features of chromatin. Computational analysis has been critical in detecting these features and thereby helping to uncover the building blocks of genome architecture. Algorithms are being developed to integrate these architectural features to construct better three-dimensional (3D) models of the genome. These computational methods have revealed the importance of 3D genome organization to essential biological processes. In this article, we review the state of the art in analytic and modeling techniques with a focus on their application to answering various biological questions related to chromatin structure. We summarize the limitations of these computational techniques and suggest future directions, including the importance of incorporating multiple sources of experimental data in building a more comprehensive model of the genome. This article is categorized under: Analytical and Computational Methods > Computational Methods Laboratory Methods and Technologies > Genetic/Genomic Methods Models of Systems Properties and Processes > Mechanistic Models.
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Diferenciación Celular/fisiología , Biología Computacional , Embrión de Mamíferos/embriología , Desarrollo Embrionario/fisiología , Células Germinales Embrionarias/metabolismo , Genoma/fisiología , Modelos Biológicos , Animales , Células Germinales Embrionarias/citología , Ratones , Transcripción Genética/fisiologíaRESUMEN
Structural variants (SVs) can contribute to oncogenesis through a variety of mechanisms. Despite their importance, the identification of SVs in cancer genomes remains challenging. Here, we present a framework that integrates optical mapping, high-throughput chromosome conformation capture (Hi-C), and whole-genome sequencing to systematically detect SVs in a variety of normal or cancer samples and cell lines. We identify the unique strengths of each method and demonstrate that only integrative approaches can comprehensively identify SVs in the genome. By combining Hi-C and optical mapping, we resolve complex SVs and phase multiple SV events to a single haplotype. Furthermore, we observe widespread structural variation events affecting the functions of noncoding sequences, including the deletion of distal regulatory sequences, alteration of DNA replication timing, and the creation of novel three-dimensional chromatin structural domains. Our results indicate that noncoding SVs may be underappreciated mutational drivers in cancer genomes.
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Genoma Humano , Variación Estructural del Genoma , Neoplasias/genética , Biología de Sistemas/métodos , Células A549 , Línea Celular Tumoral , Mapeo Cromosómico , ADN de Neoplasias/análisis , ADN de Neoplasias/genética , Genes Relacionados con las Neoplasias , Variación Genética , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Humanos , Células K562 , Desequilibrio de Ligamiento , Análisis de Secuencia de ADN/métodos , Integración de SistemasRESUMEN
Motivation: Single-cell Hi-C (scHi-C) data promises to enable scientists to interrogate the 3D architecture of DNA in the nucleus of the cell, studying how this structure varies stochastically or along developmental or cell-cycle axes. However, Hi-C data analysis requires methods that take into account the unique characteristics of this type of data. In this work, we explore whether methods that have been developed previously for the analysis of bulk Hi-C data can be applied to scHi-C data. We apply methods designed for analysis of bulk Hi-C data to scHi-C data in conjunction with unsupervised embedding. Results: We find that one of these methods, HiCRep, when used in conjunction with multidimensional scaling (MDS), strongly outperforms three other methods, including a technique that has been used previously for scHi-C analysis. We also provide evidence that the HiCRep/MDS method is robust to extremely low per-cell sequencing depth, that this robustness is improved even further when high-coverage and low-coverage cells are projected together, and that the method can be used to jointly embed cells from multiple published datasets.
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Núcleo Celular/ultraestructura , Cromatina/ultraestructura , ADN/metabolismo , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Imagenología Tridimensional/métodos , Análisis de la Célula Individual/métodos , Núcleo Celular/metabolismo , Cromatina/metabolismo , ADN/química , ADN/ultraestructura , Eucariontes/metabolismo , Eucariontes/ultraestructura , Conformación de Ácido Nucleico , Análisis de Secuencia de ADN/métodosRESUMEN
Motivation: The three-dimensional organization of chromatin plays a critical role in gene regulation and disease. High-throughput chromosome conformation capture experiments such as Hi-C are used to obtain genome-wide maps of three-dimensional chromatin contacts. However, robust estimation of data quality and systematic comparison of these contact maps is challenging due to the multi-scale, hierarchical structure of chromatin contacts and the resulting properties of experimental noise in the data. Measuring concordance of contact maps is important for assessing reproducibility of replicate experiments and for modeling variation between different cellular contexts. Results: We introduce a concordance measure called DIfferences between Smoothed COntact maps (GenomeDISCO) for assessing the similarity of a pair of contact maps obtained from chromosome conformation capture experiments. The key idea is to smooth contact maps using random walks on the contact map graph, before estimating concordance. We use simulated datasets to benchmark GenomeDISCO's sensitivity to different types of noise that affect chromatin contact maps. When applied to a large collection of Hi-C datasets, GenomeDISCO accurately distinguishes biological replicates from samples obtained from different cell types. GenomeDISCO also generalizes to other chromosome conformation capture assays, such as HiChIP. Availability and implementation: Software implementing GenomeDISCO is available at https://github.com/kundajelab/genomedisco. Supplementary information: Supplementary data are available at Bioinformatics online.
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Cromatina/metabolismo , Biología Computacional/métodos , Programas Informáticos , Línea Celular , Cromatina/ultraestructura , Humanos , Conformación Molecular , Reproducibilidad de los ResultadosRESUMEN
Hi-C is a powerful technology for studying genome-wide chromatin interactions. However, current methods for assessing Hi-C data reproducibility can produce misleading results because they ignore spatial features in Hi-C data, such as domain structure and distance dependence. We present HiCRep, a framework for assessing the reproducibility of Hi-C data that systematically accounts for these features. In particular, we introduce a novel similarity measure, the stratum adjusted correlation coefficient (SCC), for quantifying the similarity between Hi-C interaction matrices. Not only does it provide a statistically sound and reliable evaluation of reproducibility, SCC can also be used to quantify differences between Hi-C contact matrices and to determine the optimal sequencing depth for a desired resolution. The measure consistently shows higher accuracy than existing approaches in distinguishing subtle differences in reproducibility and depicting interrelationships of cell lineages. The proposed measure is straightforward to interpret and easy to compute, making it well-suited for providing standardized, interpretable, automatable, and scalable quality control. The freely available R package HiCRep implements our approach.
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Inmunoprecipitación de Cromatina/métodos , Biología Computacional/métodos , Línea Celular , Linaje de la Célula , Secuenciación de Nucleótidos de Alto Rendimiento , Humanos , Reproducibilidad de los Resultados , Análisis de Secuencia de ARN , Programas InformáticosRESUMEN
SUMMARY: Genome-wide proximity ligation based assays like Hi-C have opened a window to the 3D organization of the genome. In so doing, they present data structures that are different from conventional 1D signal tracks. To exploit the 2D nature of Hi-C contact maps, matrix techniques like spectral analysis are particularly useful. Here, we present HiC-spector, a collection of matrix-related functions for analyzing Hi-C contact maps. In particular, we introduce a novel reproducibility metric for quantifying the similarity between contact maps based on spectral decomposition. The metric successfully separates contact maps mapped from Hi-C data coming from biological replicates, pseudo-replicates and different cell types. AVAILABILITY AND IMPLEMENTATION: Source code in Julia and Python, and detailed documentation is available at https://github.com/gersteinlab/HiC-spector . CONTACT: koonkiu.yan@gmail.com or mark@gersteinlab.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Cromosomas/química , Técnicas Genéticas , Genoma , Biotinilación , ADN/química , Biblioteca de Genes , Humanos , Reproducibilidad de los ResultadosRESUMEN
High-throughput assays for measuring the three-dimensional (3D) configuration of DNA have provided unprecedented insights into the relationship between DNA 3D configuration and function. Data interpretation from assays such as ChIA-PET and Hi-C is challenging because the data is large and cannot be easily rendered using standard genome browsers. An effective Hi-C visualization tool must provide several visualization modes and be capable of viewing the data in conjunction with existing, complementary data. We review five software tools that do not require programming expertise. We summarize their complementary functionalities, and highlight which tool is best equipped for specific tasks.
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Genoma , Genómica/métodos , Modelos Moleculares , Conformación de Ácido Nucleico , Programas Informáticos , Animales , ADN , Conjuntos de Datos como Asunto , Humanos , Imagenología Tridimensional , Navegador WebRESUMEN
Tissue patterns are dynamically maintained. Continuous formation of plant tissues during postembryonic growth requires asymmetric divisions and the specification of cell lineages. We show that the BIRDs and SCARECROW regulate lineage identity, positional signals, patterning, and formative divisions throughout Arabidopsis root growth. These transcription factors are postembryonic determinants of the ground tissue stem cells and their lineage. Upon further activation by the positional signal SHORT-ROOT (a mobile transcription factor), they direct asymmetric cell divisions and patterning of cell types. The BIRDs and SCARECROW with SHORT-ROOT organize tissue patterns at all formative steps during growth, ensuring developmental plasticity.
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Proteínas de Arabidopsis/metabolismo , Arabidopsis/crecimiento & desarrollo , Arabidopsis/genética , Regulación de la Expresión Génica de las Plantas , Raíces de Plantas/crecimiento & desarrollo , Raíces de Plantas/genética , Factores de Transcripción/metabolismo , Arabidopsis/citología , División Celular/genética , Linaje de la Célula/genética , Raíces de Plantas/citología , Transcripción GenéticaRESUMEN
DNaseI footprinting is an established assay for identifying transcription factor (TF)-DNA interactions with single base pair resolution. High-throughput DNase-seq assays have recently been used to detect in vivo DNase footprints across the genome. Multiple computational approaches have been developed to identify DNase-seq footprints as predictors of TF binding. However, recent studies have pointed to a substantial cleavage bias of DNase and its negative impact on predictive performance of footprinting. To assess the potential for using DNase-seq to identify individual binding sites, we performed DNase-seq on deproteinized genomic DNA and determined sequence cleavage bias. This allowed us to build bias corrected and TF-specific footprint models. The predictive performance of these models demonstrated that predicted footprints corresponded to high-confidence TF-DNA interactions. DNase-seq footprints were absent under a fraction of ChIP-seq peaks, which we show to be indicative of weaker binding, indirect TF-DNA interactions or possible ChIP artifacts. The modeling approach was also able to detect variation in the consensus motifs that TFs bind to. Finally, cell type specific footprints were detected within DNase hypersensitive sites that are present in multiple cell types, further supporting that footprints can identify changes in TF binding that are not detectable using other strategies.
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Huella de ADN/métodos , Desoxirribonucleasa I , Análisis de Secuencia de ADN/métodos , Factores de Transcripción/metabolismo , Sitios de Unión , Cromatina/química , Inmunoprecipitación de Cromatina , Humanos , Modelos Genéticos , Motivos de NucleótidosRESUMEN
We tested the idea that ancestral class I and II aminoacyl-tRNA synthetases arose on opposite strands of the same gene. We assembled excerpted 94-residue Urgenes for class I tryptophanyl-tRNA synthetase (TrpRS) and class II Histidyl-tRNA synthetase (HisRS) from a diverse group of species, by identifying and catenating three blocks coding for secondary structures that position the most highly conserved, active-site residues. The codon middle-base pairing frequency was 0.35 ± 0.0002 in all-by-all sense/antisense alignments for 211 TrpRS and 207 HisRS sequences, compared with frequencies between 0.22 ± 0.0009 and 0.27 ± 0.0005 for eight different representations of the null hypothesis. Clustering algorithms demonstrate further that profiles of middle-base pairing in the synthetase antisense alignments are correlated along the sequences from one species-pair to another, whereas this is not the case for similar operations on sets representing the null hypothesis. Most probable reconstructed sequences for ancestral nodes of maximum likelihood trees show that middle-base pairing frequency increases to approximately 0.42 ± 0.002 as bacterial trees approach their roots; ancestral nodes from trees including archaeal sequences show a less pronounced increase. Thus, contemporary and reconstructed sequences all validate important bioinformatic predictions based on descent from opposite strands of the same ancestral gene. They further provide novel evidence for the hypothesis that bacteria lie closer than archaea to the origin of translation. Moreover, the inverse polarity of genetic coding, together with a priori α-helix propensities suggest that in-frame coding on opposite strands leads to similar secondary structures with opposite polarity, as observed in TrpRS and HisRS crystal structures.