A rapid and robust method for single cell chromatin accessibility profiling
TL;DR: A simple and robust plate-based single-cell ATAC-seq method that works in fresh and cryopreserved cells and identifies distinct immune cell types and reveal cell type-specific regulatory regions and related transcription factors is developed.
Abstract: The assay for transposase-accessible chromatin using sequencing (ATAC-seq) is widely used to identify regulatory regions throughout the genome. However, very few studies have been performed at the single cell level (scATAC-seq) due to technical challenges. Here we developed a simple and robust plate-based scATAC-seq method, combining upfront bulk Tn5 tagging with single-nuclei sorting. We demonstrate that our method works robustly across various systems, including fresh and cryopreserved cells from primary tissues. By profiling over 3000 splenocytes, we identify distinct immune cell types and reveal cell type-specific regulatory regions and related transcription factors. ATAC-seq is widely used to identify regulatory regions in the genome. Here the authors develop a simple and robust plate-based single-cell ATAC-seq method that works in fresh and cryopreserved cells.
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TL;DR: It is shown that microplastics in ocean sediment can significantly alter microbial community structure and nitrogen cycling, indicating that nitrogen cycling processes in sediments can be significantly affected by different microplastic, which may serve as organic carbon substrates for microbial communities.
Abstract: Microplastics are ubiquitous in estuarine, coastal, and deep sea sediments. The impacts of microplastics on sedimentary microbial ecosystems and biogeochemical carbon and nitrogen cycles, however, have not been well reported. To evaluate if microplastics influence the composition and function of sedimentary microbial communities, we conducted a microcosm experiment using salt marsh sediment amended with polyethylene (PE), polyvinyl chloride (PVC), polyurethane foam (PUF) or polylactic acid (PLA) microplastics. We report that the presence of microplastics alters sediment microbial community composition and nitrogen cycling processes. Compared to control sediments without microplastic, PUF- and PLA-amended sediments promote nitrification and denitrification, while PVC amendment inhibits both processes. These results indicate that nitrogen cycling processes in sediments can be significantly affected by different microplastics, which may serve as organic carbon substrates for microbial communities. Considering this evidence and increasing microplastic pollution, the impact of plastics on global ecosystems and biogeochemical cycling merits critical investigation. Plastic pollution has infiltrated every ecosystem, but few studies have quantified the biogeochemical or ecological effects of plastic. Here the authors show that microplastics in ocean sediment can significantly alter microbial community structure and nitrogen cycling.
445 citations
TL;DR: It is reported that layered intermediate complexes formed with the solvent provide a scaffold that facilitates the nucleation and growth of RDPs during annealing, as observed via in situ X-ray scattering.
Abstract: Reduced-dimensional metal halide perovskites (RDPs) have attracted significant attention in recent years due to their promising light harvesting and emissive properties We sought to increase the systematic understanding of how RDPs are formed Here we report that layered intermediate complexes formed with the solvent provide a scaffold that facilitates the nucleation and growth of RDPs during annealing, as observed via in situ X-ray scattering Transient absorption spectroscopy of RDP single crystals and films enables the identification of the distribution of quantum well thicknesses These insights allow us to develop a kinetic model of RDP formation that accounts for the experimentally observed size distribution of wells RDPs exhibit a thickness distribution (with sizes that extend above n = 5) determined largely by the stoichiometric proportion between the intercalating cation and solvent complexes The results indicate a means to control the distribution, composition and orientation of RDPs via the selection of the intercalating cation, the solvent and the deposition technique A systematic analysis is performed to reveal how deposition conditions and the use of cations and solvents affect the composition and orientation of 2D and quasi-2D metal halide perovskites in thin films
317 citations
TL;DR: A high-quality (105 nuclear fragments per cell) droplet-microfluidics-based method for single-cell profiling of chromatin accessibility that enables rapid mapping of genome accessibility in hundreds of thousands of single cells.
Abstract: Recent technical advancements have facilitated the mapping of epigenomes at single-cell resolution; however, the throughput and quality of these methods have limited their widespread adoption. Here we describe a high-quality (105 nuclear fragments per cell) droplet-microfluidics-based method for single-cell profiling of chromatin accessibility. We use this approach, named 'droplet single-cell assay for transposase-accessible chromatin using sequencing' (dscATAC-seq), to assay 46,653 cells for the unbiased discovery of cell types and regulatory elements in adult mouse brain. We further increase the throughput of this platform by combining it with combinatorial indexing (dsciATAC-seq), enabling single-cell studies at a massive scale. We demonstrate the utility of this approach by measuring chromatin accessibility across 136,463 resting and stimulated human bone marrow-derived cells to reveal changes in the cis- and trans-regulatory landscape across cell types and under stimulatory conditions at single-cell resolution. Altogether, we describe a total of 510,123 single-cell profiles, demonstrating the scalability and flexibility of this droplet-based platform.
281 citations
TL;DR: It is proposed that accounting for polygenic background is likely to increase accuracy of risk estimation for individuals who inherit a monogenic risk variant, and in carriers of monogenic variants, they show that disease risk is a gradient influenced by polygenic Background.
Abstract: Genetic variation can predispose to disease both through (i) monogenic risk variants that disrupt a physiologic pathway with large effect on disease and (ii) polygenic risk that involves many variants of small effect in different pathways. Few studies have explored the interplay between monogenic and polygenic risk. Here, we study 80,928 individuals to examine whether polygenic background can modify penetrance of disease in tier 1 genomic conditions - familial hypercholesterolemia, hereditary breast and ovarian cancer, and Lynch syndrome. Among carriers of a monogenic risk variant, we estimate substantial gradients in disease risk based on polygenic background - the probability of disease by age 75 years ranged from 17% to 78% for coronary artery disease, 13% to 76% for breast cancer, and 11% to 80% for colon cancer. We propose that accounting for polygenic background is likely to increase accuracy of risk estimation for individuals who inherit a monogenic risk variant.
244 citations
TL;DR: While a holistic view of lymphocyte infiltration and dysfunction on a single-cell level is emerging, the search for response and prognostic biomarkers is just beginning.
Abstract: The clinical success of cancer immune checkpoint blockade (ICB) has refocused attention on tumor-infiltrating lymphocytes (TILs) across cancer types. The outcome of immune checkpoint inhibitor therapy in cancer patients has been linked to the quality and magnitude of T cell, NK cell, and more recently, B cell responses within the tumor microenvironment. State-of-the-art single-cell analysis of TIL gene expression profiles and clonality has revealed a remarkable degree of cellular heterogeneity and distinct patterns of immune activation and exhaustion. Many of these states are conserved across tumor types, in line with the broad responses observed clinically. Despite this homology, not all cancer types with similar TIL landscapes respond similarly to immunotherapy, highlighting the complexity of the underlying tumor-immune interactions. This observation is further confounded by the strong prognostic benefit of TILs observed for tumor types that have so far respond poorly to immunotherapy. Thus, while a holistic view of lymphocyte infiltration and dysfunction on a single-cell level is emerging, the search for response and prognostic biomarkers is just beginning. Within this review, we discuss recent advances in the understanding of TIL biology, their prognostic benefit, and their predictive value for therapy.
243 citations
References
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Journal Article•
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.
47,974 citations
TL;DR: SAMtools as discussed by the authors implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments.
Abstract: Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments.
Availability: http://samtools.sourceforge.net
Contact: [email protected]
45,957 citations
Journal Article•
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.
Abstract: We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large datasets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of datasets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the datasets.
30,124 citations
Posted Content•
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from this http URL.
28,898 citations
TL;DR: The command-line tool cutadapt is developed, which supports 454, Illumina and SOLiD (color space) data, offers two adapter trimming algorithms, and has other useful features.
Abstract: When small RNA is sequenced on current sequencing machines, the resulting reads are usually longer than the RNA and therefore contain parts of the 3' adapter. That adapter must be found and removed error-tolerantly from each read before read mapping. Previous solutions are either hard to use or do not offer required features, in particular support for color space data. As an easy to use alternative, we developed the command-line tool cutadapt, which supports 454, Illumina and SOLiD (color space) data, offers two adapter trimming algorithms, and has other useful features. Cutadapt, including its MIT-licensed source code, is available for download at http://code.google.com/p/cutadapt/
20,255 citations