Single Cell Transcriptomics
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Single Cell Transcriptomics
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Author : Raffaele A. Calogero
language : en
Publisher: Springer Nature
Release Date : 2022-12-10
Single Cell Transcriptomics written by Raffaele A. Calogero and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-10 with Science categories.
This volume provides up-to-date methods on single cell wet and bioinformatics protocols based on the researcher experiment requirements. Chapters detail basic analytical procedures, single-cell data QC, dimensionality reduction, clustering, cluster-specific features selection, RNA velocity, multi-modal data integration, and single cell RNA editing. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and comprehensive, Single Cell Transcriptomics: Methods and Protocols aims to be a valuable resource for all researchers interested in learning more about this important and developing field.
Single Cell Omics
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Author : Debmalya Barh
language : en
Publisher: Academic Press
Release Date : 2019-07-30
Single Cell Omics written by Debmalya Barh and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-30 with Medical categories.
Single-cell Omics, Volume 2: Advances in Applications provides the latest single-cell omics applications in the field of biomedicine. The advent of omics technologies have enabled us to identify the differences between cell types and subpopulations at the level of the genome, proteome, transcriptome, epigenome, and in several other fields of omics. The book is divided into two sections: the first is dedicated to biomedical applications, such as cell diagnostics, non-invasive prenatal testing (NIPT), circulating tumor cells, breast cancer, gliomas, nervous systems and autoimmune disorders, and more. The second focuses on cell omics in plants, discussing micro algal and single cell omics, and more. This book is a valuable source for bioinformaticians, molecular diagnostic researchers, clinicians and several members of biomedical field interested in understanding more about single-cell omics and its potential for research and diagnosis. - Covers the diverse single cell omics applications in the biomedical field - Summarizes the latest progress in single cell omics and discusses potential future developments for research and diagnosis - Written by experts across the world, it brings different points-of-view and study cases to fully give a comprehensive overview of the topic
Machine Learning In Single Cell Rna Seq Data Analysis
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Author : Khalid Raza
language : en
Publisher: Springer Nature
Release Date : 2024-09-02
Machine Learning In Single Cell Rna Seq Data Analysis written by Khalid Raza and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-02 with Computers categories.
This book provides a concise guide tailored for researchers, bioinformaticians, and enthusiasts eager to unravel the mysteries hidden within single-cell RNA sequencing (scRNA-seq) data using cutting-edge machine learning techniques. The advent of scRNA-seq technology has revolutionized our understanding of cellular diversity and function, offering unprecedented insights into the intricate tapestry of gene expression at the single-cell level. However, the deluge of data generated by these experiments presents a formidable challenge, demanding advanced analytical tools, methodologies, and skills for meaningful interpretation. This book bridges the gap between traditional bioinformatics and the evolving landscape of machine learning. Authored by seasoned experts at the intersection of genomics and artificial intelligence, this book serves as a roadmap for leveraging machine learning algorithms to extract meaningful patterns and uncover hidden biological insights within scRNA-seq datasets.
Single Cell Omics
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Author : Debmalya Barh
language : en
Publisher: Academic Press
Release Date : 2019-06-06
Single Cell Omics written by Debmalya Barh and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-06 with Medical categories.
Single-Cell Omics: Volume 1: Technological Advances and Applications provides the latest technological developments and applications of single-cell technologies in the field of biomedicine. In the current era of precision medicine, the single-cell omics technology is highly promising due to its potential in diagnosis, prognosis and therapeutics. Sections in the book cover single-cell omics research and applications, diverse technologies applied in the topic, such as pangenomics, metabolomics, and multi-omics of single cells, data analysis, and several applications of single-cell omics within the biomedical field, for example in cancer, metabolic and neuro diseases, immunology, pharmacogenomics, personalized medicine and reproductive health. This book is a valuable source for bioinformaticians, molecular diagnostic researchers, clinicians and members of the biomedical field who are interested in understanding more about single-cell omics and its potential for research and diagnosis. - Covers not only the technological aspects, but also the diverse applications of single cell omics in the biomedical field - Summarizes the latest progress in single cell omics and discusses potential future developments for research and diagnosis - Written by experts across the world, bringing different points-of-view and case studies to give a comprehensive overview on the topic
Introduction To Single Cell Omics
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Author : Xinghua Pan
language : en
Publisher: Frontiers Media SA
Release Date : 2019-09-19
Introduction To Single Cell Omics written by Xinghua Pan and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-19 with categories.
Single-cell omics is a progressing frontier that stems from the sequencing of the human genome and the development of omics technologies, particularly genomics, transcriptomics, epigenomics and proteomics, but the sensitivity is now improved to single-cell level. The new generation of methodologies, especially the next generation sequencing (NGS) technology, plays a leading role in genomics related fields; however, the conventional techniques of omics require number of cells to be large, usually on the order of millions of cells, which is hardly accessible in some cases. More importantly, harnessing the power of omics technologies and applying those at the single-cell level are crucial since every cell is specific and unique, and almost every cell population in every systems, derived in either vivo or in vitro, is heterogeneous. Deciphering the heterogeneity of the cell population hence becomes critical for recognizing the mechanism and significance of the system. However, without an extensive examination of individual cells, a massive analysis of cell population would only give an average output of the cells, but neglect the differences among cells. Single-cell omics seeks to study a number of individual cells in parallel for their different dimensions of molecular profile on genome-wide scale, providing unprecedented resolution for the interpretation of both the structure and function of an organ, tissue or other system, as well as the interaction (and communication) and dynamics of single cells or subpopulations of cells and their lineages. Importantly single-cell omics enables the identification of a minor subpopulation of cells that may play a critical role in biological process over a dominant subpolulation such as a cancer and a developing organ. It provides an ultra-sensitive tool for us to clarify specific molecular mechanisms and pathways and reveal the nature of cell heterogeneity. Besides, it also empowers the clinical investigation of patients when facing a very low quantity of cell available for analysis, such as noninvasive cancer screening with circulating tumor cells (CTC), noninvasive prenatal diagnostics (NIPD) and preimplantation genetic test (PGT) for in vitro fertilization. Single-cell omics greatly promotes the understanding of life at a more fundamental level, bring vast applications in medicine. Accordingly, single-cell omics is also called as single-cell analysis or single-cell biology. Within only a couple of years, single-cell omics, especially transcriptomic sequencing (scRNA-seq), whole genome and exome sequencing (scWGS, scWES), has become robust and broadly accessible. Besides the existing technologies, recently, multiplexing barcode design and combinatorial indexing technology, in combination with microfluidic platform exampled by Drop-seq, or even being independent of microfluidic platform but using a regular PCR-plate, enable us a greater capacity of single cell analysis, switching from one single cell to thousands of single cells in a single test. The unique molecular identifiers (UMIs) allow the amplification bias among the original molecules to be corrected faithfully, resulting in a reliable quantitative measurement of omics in single cells. Of late, a variety of single-cell epigenomics analyses are becoming sophisticated, particularly single cell chromatin accessibility (scATAC-seq) and CpG methylation profiling (scBS-seq, scRRBS-seq). High resolution single molecular Fluorescence in situ hybridization (smFISH) and its revolutionary versions (ex. seqFISH, MERFISH, and so on), in addition to the spatial transcriptome sequencing, make the native relationship of the individual cells of a tissue to be in 3D or 4D format visually and quantitatively clarified. On the other hand, CRISPR/cas9 editing-based In vivo lineage tracing methods enable dynamic profile of a whole developmental process to be accurately displayed. Multi-omics analysis facilitates the study of multi-dimensional regulation and relationship of different elements of the central dogma in a single cell, as well as permitting a clear dissection of the complicated omics heterogeneity of a system. Last but not the least, the technology, biological noise, sequence dropout, and batch effect bring a huge challenge to the bioinformatics of single cell omics. While significant progress in the data analysis has been made since then, revolutionary theory and algorithm logics for single cell omics are expected. Indeed, single-cell analysis exert considerable impacts on the fields of biological studies, particularly cancers, neuron and neural system, stem cells, embryo development and immune system; other than that, it also tremendously motivates pharmaceutic RD, clinical diagnosis and monitoring, as well as precision medicine. This book hereby summarizes the recent developments and general considerations of single-cell analysis, with a detailed presentation on selected technologies and applications. Starting with the experimental design on single-cell omics, the book then emphasizes the consideration on heterogeneity of cancer and other systems. It also gives an introduction of the basic methods and key facts for bioinformatics analysis. Secondary, this book provides a summary of two types of popular technologies, the fundamental tools on single-cell isolation, and the developments of single cell multi-omics, followed by descriptions of FISH technologies, though other popular technologies are not covered here due to the fact that they are intensively described here and there recently. Finally, the book illustrates an elastomer-based integrated fluidic circuit that allows a connection between single cell functional studies combining stimulation, response, imaging and measurement, and corresponding single cell sequencing. This is a model system for single cell functional genomics. In addition, it reports a pipeline for single-cell proteomics with an analysis of the early development of Xenopus embryo, a single-cell qRT-PCR application that defined the subpopulations related to cell cycling, and a new method for synergistic assembly of single cell genome with sequencing of amplification product by phi29 DNA polymerase. Due to the tremendous progresses of single-cell omics in recent years, the topics covered here are incomplete, but each individual topic is excellently addressed, significantly interesting and beneficial to scientists working in or affiliated with this field.
The Individual Microbe Single Cell Analysis And Agent Based Modelling
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Author : Johan H. J. Leveau
language : en
Publisher: Frontiers Media SA
Release Date : 2019-02-19
The Individual Microbe Single Cell Analysis And Agent Based Modelling written by Johan H. J. Leveau and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-19 with categories.
Recent technological advances in single-cell microbiology, using flow cytometry, microfluidics, x-ray fluorescence microprobes, and single-cell -omics, allow for the observation of individuals within populations. Simultaneously, individual-based models (or more generally agent-based models) allow for individual microbes to be simulated. Bridging these techniques forms the foundation of individual-based ecology of microbes (µIBE). µIBE has elucidated genetic and phenotypic heterogeneity that has important consequences for a number of human interests, including antibiotic or biocide resistance, the productivity and stability of industrial fermentations, the efficacy of food preservatives, and the potential of pathogens to cause disease. Individual-based models can help us to understand how these sets of traits of individual microbes influence the above. This eBook compiles all publications from a recent Research Topic in Frontiers in Microbiology. It features recent research where individual observational and/or modelling techniques are applied to gain unique insights into the ecology of microorganisms. The Research Topic “The Individual Microbe: Single-Cell Analysis and Agent-Based Modelling” arose from the 2016 @ASM conference of the same name hosted by the American Society for Microbiology at its headquarters in Washington, D.C. We are grateful to ASM for funding and hosting this conference.
Guide To Plant Single Cell Technology
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Author : Jen-Tsung Chen
language : en
Publisher: Elsevier
Release Date : 2024-11-25
Guide To Plant Single Cell Technology written by Jen-Tsung Chen and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-25 with Technology & Engineering categories.
Guide to Plant Single-Cell Technology: Functional Genomics and Crop Improvement summarizes the current status of single-cell technology in plants involving food and energy crops. Presenting methods and applications of emerging high-throughput technologies performed using the single-cell platform it includes an emphasis on single-cell RNA sequencing and eventually towards single-cell omics, which are highly complementary and effective for profiling the plant cell subject to either environmental factors or pathogenic threats. These technologies can advance the exploration of plant physiology as well as precision crop breeding for future anti-stress and high-yield plants and achieve sustainable agriculture.The book covers crop improvement and breeding strategies involving single-cell technology to produce future stress-tolerant and high-yield plants, which have better performances on growth, and development to achieve enhanced production of foods and biomass.Guide to Plant Single-Cell Technology: Functional Genomics and Crop Improvement will be a valuable reference resource for academics and researchers in plant and crop sciences. - Focuses on plant molecular profiling using single-cell technology and the integration with functional genomics - Discusses the current methods and challenges of single-cell RNA sequencing in plants - Summarizes the emerging findings of plant single-cell technology - Presents advanced high-throughput technologies for plant omics
Understanding Cell Identity With Single Cell Transcriptomics
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Author : Geoffrey Stanley
language : en
Publisher:
Release Date : 2019
Understanding Cell Identity With Single Cell Transcriptomics written by Geoffrey Stanley and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
In my thesis work, I use single-cell whole-transcriptome sequencing to reveal new insights into cell identity: when cell types arise in development, how cell types are patterned in the adult, how splicing and transcription factors are modulated by cell identity, and the molecules that may be responsible for generating these patterns. In the first study, I sequenced neurons from the mouse striatum, a large brain region involved in Parkinsons and Huntingtons, in collaboration with Ozgun Gokce and Thomas Sudhof. I created a well-resolved classification of striatal cell type of the mouse striatum; transcriptome analysis revealed 10 differentiated distinct cell types, including neurons, astrocytes, oligodendrocytes, ependymal, immune, and vascular cells, and enabled the discovery of numerous novel marker genes. I further explored neuronal heterogeneity in the adult murine striatum by combining single-cell RNA-seq of SPNs combined with quantitative RNA in situ hybridization (ISH) using the RNAscope platform. I developed a novel computational algorithm that distinguishes discrete versus continuous cell identities in scRNA-seq data, and used it to show that SPNs in the striatum can be classified into four major discrete types with little overlap and no implied spatial relationship. I found that these discrete classes that continuously vary along multiple spatial gradients axes of expression; these gradients define anatomical location by a combinatorial mechanism. I used this information to support the description of a novel region of the striatum. Broadly, our results suggest that neuronal circuitry has a substructure at far higher resolution than is typically interrogated which is defined by the precise identity and location of a neuron. In a collaboration with Rahul Sinha and Irving Weissman, I discovered and investigated an artifact in Illumina sequencing data. Illumina-based next generation sequencing (NGS) has accelerated biomedical discovery through its ability to generate thousands of gigabases of sequencing output at low cost. In 2015, a new chemistry of cluster generation was introduced in the newer Illumina machines called exclusion amplification (ExAmp). This advance has been widely adopted for genome sequencing because greater sequencing depth can be achieved for lower cost without compromising the quality of longer reads. We show that this promising chemistry is problematic, however, when multiplexing samples. We discovered that up to 0.4-10% of sequencing reads (or signals) are incorrectly assigned from a given sample to other samples in a multiplexed pool. We provide evidence that this "spreading-of-signals" arises from low levels of free index primers present in the pool. The rate of signal spreading depending on the level of free index primers present in a library pool, and therefore, variable among experiments. In a collaboration with Tianying Su, Rahul Sinha, and Kristy Red-Horse, I investigated the development of mouse coronary arteries using scRNA-Seq and mouse genetics. I developed a statistical test that categorizes subpopulations within scRNA-Seq datasets as continuous or discrete to identify candidate developmental transitions. I analyzed the transitions between coronary progenitors and artery cells computationally and in vivo, which revealed that the progenitor cells of the mouse heart undergo a gradual conversion from vein to artery before a subset crosses a threshold to differentiate into pre-artery cells. I showed that pre-artery cells in scRNA-Seq data appear prior to blood flow, contrary to previous assumptions about how the heart develops. We showed that a venous transcription factor, COUP-TFII, blocked progression to the pre-artery state through activation of cell cycle genes. I was also interested in how transcription factors maintained cell identity. I therefore analyzed a dataset composed of more than 100,000 cells from 20 organs and tissues, produced by the Tabula Muris Consortium, to understand the transcription factor codes specifying cell identity in the mouse. One of the challenges of scRNA-Seq data is that nearly all studies are specific to a single organ, and it is challenging to compare data collected from different animals by independent labs with varying experimental techniques. To understand which TFs were most informative for specifying cell types, we used random forest machine learning to show that 136 TFs are needed to simultaneously define all cell types across all organs. I collected a compendium of transcription factor reprogramming protocols and showed that for nearly all reprogramming protocols, the TFs used also specified the targeted cell type in our data, suggesting that whole-organism scRNA-Seq data can inform novel reprogramming schemes.
Transcriptome And Single Cell Sequencing Analyses To Classify Immune Subtypes Uncover Novel Biomarkers And Assess Immunotherapeutic Responses In Cancer
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Author : Hongda Liu
language : en
Publisher: Frontiers Media SA
Release Date : 2024-07-24
Transcriptome And Single Cell Sequencing Analyses To Classify Immune Subtypes Uncover Novel Biomarkers And Assess Immunotherapeutic Responses In Cancer written by Hongda Liu and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-24 with Medical categories.
According to the most recent projections of the International Agency for Research on Cancer (IARC), there would be around 19.3 million new cases of cancer and 10 million cancer-related deaths globally in 2022. Cancer research has never halted. In particular, research into the cancer immunological microenvironment is gaining popularity.
Machine Learning And Mathematical Models For Single Cell Data Analysis
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Author : Le Ou-Yang
language : en
Publisher: Frontiers Media SA
Release Date : 2022-11-29
Machine Learning And Mathematical Models For Single Cell Data Analysis written by Le Ou-Yang and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-29 with Science categories.