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Open accessJournal ArticleDOI: 10.1016/J.STEM.2020.11.015

Integrative Single-Cell RNA-Seq and ATAC-Seq Analysis of Human Developmental Hematopoiesis

04 Mar 2021-Cell Stem Cell (Elsevier)-Vol. 28, Iss: 3, pp 472-487
Abstract: Regulation of hematopoiesis during human development remains poorly defined. Here we applied single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) to over 8,000 human immunophenotypic blood cells from fetal liver and bone marrow. We inferred their differentiation trajectory and identified three highly proliferative oligopotent progenitor populations downstream of hematopoietic stem cells (HSCs)/multipotent progenitors (MPPs). Along this trajectory, we observed opposing patterns of chromatin accessibility and differentiation that coincided with dynamic changes in the activity of distinct lineage-specific transcription factors. Integrative analysis of chromatin accessibility and gene expression revealed extensive epigenetic but not transcriptional priming of HSCs/MPPs prior to their lineage commitment. Finally, we refined and functionally validated the sorting strategy for the HSCs/MPPs and achieved around 90% enrichment. Our study provides a useful framework for future investigation of human developmental hematopoiesis in the context of blood pathologies and regenerative medicine.

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Topics: ATAC-seq (56%), Chromatin (56%), Epigenetics (51%) ... show more
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31 results found


Journal ArticleDOI: 10.1016/J.IT.2021.04.004
Gökhan Cildir1, Kwok Ho Yip1, Harshita Pant1, Harshita Pant2  +6 moreInstitutions (4)
Abstract: Mast cells (MC)s are evolutionarily conserved, tissue-resident immune cells with diverse roles in allergy, cancer, and protection from infection by helminths and microorganisms. The significant diversity in MC development and tissue-specific functional characteristics has recently begun to be understood. Exciting developments in single-cell-based RNA, protein, and chromatin profiling technologies offer new opportunities to characterize MC heterogeneity and to uncover novel MC functions and subtypes; these developments might lead to new and clinically effective therapies for certain pathologies. In this review, we provide an overview of the current understanding of MC development and heterogeneity and discuss new insights gained from single-cell-based studies that may lead to future research directions and therapeutic opportunities.

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3 Citations


Open accessJournal ArticleDOI: 10.1242/DMM.047340
Abstract: Human lifespan is now longer than ever and, as a result, modern society is getting older. Despite that, the detailed mechanisms behind the ageing process and its impact on various tissues and organs remain obscure. In general, changes in DNA, RNA and protein structure throughout life impair their function. Haematopoietic ageing refers to the age-related changes affecting a haematopoietic system. Aged blood cells display different functional aberrations depending on their cell type, which might lead to the development of haematologic disorders, including leukaemias, anaemia or declining immunity. In contrast to traditional bulk assays, which are not suitable to dissect cell-to-cell variation, single-cell-level analysis provides unprecedented insight into the dynamics of age-associated changes in blood. In this Review, we summarise recent studies that dissect haematopoietic ageing at the single-cell level. We discuss what cellular changes occur during haematopoietic ageing at the genomic, transcriptomic, epigenomic and metabolomic level, and provide an overview of the benefits of investigating those changes with single-cell precision. We conclude by considering the potential clinical applications of single-cell techniques in geriatric haematology, focusing on the impact on haematopoietic stem cell transplantation in the elderly and infection studies, including recent COVID-19 research.

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Topics: Transplantation (51%)

2 Citations


Proceedings ArticleDOI: 10.1109/CEC45853.2021.9504808
28 Jun 2021-
Abstract: The increasing number of single-cell transcriptomic and single-cell RNA sequencing studies are allowing for a deeper understanding of the molecular processes underlying the normal development of an organism as well as the onset of pathologies. These studies continuously refine the functional roles of known cell populations, and provide their characterization as soon as putatively novel cell populations are detected. In order to isolate the cell populations for further tailored analysis, succinct marker panels—composed of a few cell surface proteins and clusters of differentiation molecules—must be identified. The identification of these marker panels is a challenging computational problem due to its intrinsic combinatorial nature, which makes it an NP-hard problem. Genetic Algorithms (GAs) have been successfully used in Bioinformatics and other biomedical applications to tackle combinatorial problems. We present here a GA-based approach to solve the problem of the identification of succinct marker panels. Since the performance of a GA is strictly related to the representation of the candidate solutions, we propose and compare three alternative representations, able to implicitly introduce different constraints on the search space. For each representation, we perform a fine-tuning of the parameter settings to calibrate the GA, and we show that different representations yield different performance, where the most relaxed representations— in which the GA can also evolve the number of genes in the panel—turn out to be the more effective, especially in the case of 0-knowledge problems. Our results also show that the marker panels identified by GAs can outperform manually curated solutions.

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Topics: Computational problem (51%)

1 Citations


Journal ArticleDOI: 10.1038/S41596-021-00583-5
Wei Xu1, Yi Wen1, Yingying Liang1, Qiushi Xu1  +3 moreInstitutions (1)
19 Jul 2021-Nature Protocols
Abstract: Profiling chromatin accessibility at the single-cell level provides critical information about cell type composition and cell-to-cell variation within a complex tissue. Emerging techniques for the interrogation of chromatin accessibility in individual cells allow investigation of the fundamental mechanisms that lead to the variability of different cells. This protocol describes a fast and robust method for single-cell chromatin accessibility profiling based on the assay for transposase-accessible chromatin using sequencing (ATAC-seq). The method combines up-front bulk Tn5 tagging of chromatin with flow cytometry to isolate single nuclei or cells. Reagents required to generate sequencing libraries are added to the same well in the plate where cells are sorted. The protocol described here generates data of high complexity and excellent signal-to-noise ratio and can be combined with index sorting for in-depth characterization of cell types. The whole experimental procedure can be finished within 1 or 2 d with a throughput of hundreds to thousands of nuclei, and the data can be processed by the provided computational pipeline. The execution of the protocol only requires basic techniques and equipment in a molecular biology laboratory with flow cytometry support. This protocol describes a plate-based ATAC-seq assay that combines up-front bulk tagging of accessible DNA by the Tn5 transposase with FACS sorting for robust and cost-efficient profiling of chromatin accessibility in single cells.

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Topics: ATAC-seq (60%), Chromatin (55%)

1 Citations


Open accessJournal ArticleDOI: 10.3389/FCELL.2021.660350
Abstract: Developmental hematopoiesis differs from adult and is far less described. In the developing embryo, waves of lineage-restricted blood precede the ultimate emergence of definitive hematopoietic stem cells (dHSCs) capable of maintaining hematopoiesis throughout life. During the last two decades, the advent of single-cell genomics has provided tools to circumvent previously impeding characteristics of embryonic hematopoiesis, such as cell heterogeneity and rare cell states, allowing for definition of lineage trajectories, cellular hierarchies, and cell-type specification. The field has rapidly advanced from microfluidic platforms and targeted gene expression analysis, to high throughput unbiased single-cell transcriptomic profiling, single-cell chromatin analysis, and cell tracing-offering a plethora of tools to resolve important questions within hematopoietic development. Here, we describe how these technologies have been implemented to address a wide range of aspects of embryonic hematopoiesis ranging from the gene regulatory network of dHSC formation via endothelial to hematopoietic transition (EHT) and how EHT can be recapitulated in vitro, to hematopoietic trajectories and cell fate decisions. Together, these studies have important relevance for regenerative medicine and for our understanding of genetic blood disorders and childhood leukemias.

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1 Citations


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75 results found


Open accessJournal ArticleDOI: 10.1093/BIOINFORMATICS/BTP352
Heng Li1, Bob Handsaker2, Alec Wysoker2, T. J. Fennell2  +5 moreInstitutions (4)
01 Aug 2009-Bioinformatics
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]

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Topics: Variant Call Format (62%), Stockholm format (61%), FASTQ format (56%) ... show more

35,747 Citations


Open accessJournal ArticleDOI: 10.1093/BIOINFORMATICS/BTP324
Heng Li1, Richard Durbin1Institutions (1)
01 Jul 2009-Bioinformatics
Abstract: Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ~10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: [email protected]

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Topics: Hybrid genome assembly (54%), Sequence assembly (53%), 2 base encoding (52%) ... show more

35,234 Citations


Journal ArticleDOI: 10.1038/NATURE14539
Yann LeCun1, Yann LeCun2, Yoshua Bengio3, Geoffrey E. Hinton4  +1 moreInstitutions (5)
28 May 2015-Nature
Abstract: Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

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33,931 Citations


Open accessJournal Article
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.

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33,540 Citations


Open accessPosted Content
Diederik P. Kingma1, Jimmy Ba2Institutions (2)
22 Dec 2014-arXiv: Learning
Abstract: We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.

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23,369 Citations


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