Single Cell RNA Sequencing of Rare Immune Cell Populations

Nguyen, Akira; Khoo, Weng Hua; Moran, Imogen; Croucher, Peter I.; Phan, Tri Giang

doi:10.3389/fimmu.2018.01553

Cited by 98 publications

(73 citation statements)

References 74 publications

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“…This by no means implies that CD4 + T cells are not crucially involved. However, with the introduction of scRNA-Seq, the borders between traditional cell lineages are beginning to blur, and an unexpected complexity and dynamic nature of the cellular immune system are beginning to emerge (38,39).…”

Section: Methodsmentioning

confidence: 99%

Early adaptive immune activation detected in monozygotic twins with prodromal multiple sclerosis

Beltrán

Gerdes

Hansen

et al. 2019

Journal of Clinical Investigation

106

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Section: Methodsmentioning

confidence: 99%

Early adaptive immune activation detected in monozygotic twins with prodromal multiple sclerosis

Beltrán

Gerdes

Hansen

et al. 2019

Journal of Clinical Investigation

106

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“…Data derived from single-cell assays have enabled researchers to unravel tissue heterogeneity at unprecedented levels of detail, enabling the identification of novel cell types, as well as rare cell populations that were previously unidentifiable using bulk assays [92][93][94]. Unsupervised clustering -the process of grouping cells based on a similarity metric without a known reference -is a fundamental step in deconvoluting heterogeneous single-cell data into clusters that relate to biological concepts, such as discrete cell types or cell states.…”

Section: Clusteringmentioning

confidence: 99%

Orchestrating Single-Cell Analysis with Bioconductor

Carey

Carpp

Lun

et al. 2019

Preprint

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Recent developments in experimental technologies such as single-cell RNA sequencing have enabled the profiling a high-dimensional number of genome-wide features in individual cells, inspiring the formation of large-scale data generation projects quantifying unprecedented levels of biological variation at the single-cell level. The data generated in such projects exhibits unique characteristics, including increased sparsity and scale, in terms of both the number of features and the number of samples. Due to these unique characteristics, specialized statistical methods are required along with fast and efficient software implementations in order to successfully derive biological insights. Bioconductor -an open-source, open-development software project based on the R programming language -has pioneered the analysis of such high-throughput, high-dimensional biological data, leveraging a rich history of software and methods development that has spanned the era of sequencing. Featuring state-of-the-art computational methods, standardized data infrastructure, and interactive data visualization tools that are all easily accessible as software packages, Bioconductor has made it possible for a diverse audience to analyze data derived from cutting-edge single-cell assays. Here, we present an overview of single-cell RNA sequencing analysis for prospective users and contributors, highlighting the contributions towards this effort made by Bioconductor. Figure 1: 10 years of Bioconductor in the high-throughput sequencing era. Bioconductor software packages associated with the analysis of sequencing technology were tracked by the total number of packages (left) and the number of distinct IPs (data recorded monthly) visiting their online documentation (right) over the course of ten years. Software packages were uniquely defined by their primary sequencing technology association, with examples of specific terms used for annotation below in parentheses. * Co-second authors. These authors (VJC, LNC, LG, ATLL, FM, KR, DR, CS, LW) contributed equally and are listed alphabetically. † Co-senior authors. These authors (RG, SCH) contributed equally.sparsity, due to biological fluctuations in the measured traits or limited sensitivity in quantifying small numbers of molecules [17][18][19]. In addition, data derived from single-cell assays have revealed more heterogeneity than previously seen [20][21][22][23][24][25][26][27]. This has led to the rapid development of statistical methods to address the increased sparsity and heterogeneity seen in this data [28][29][30][31]. The profound increase in the complexity of data measured at the single-cell level, along with the continued increases in the number of samples measured, have precipitated the need for fundamental changes in data access, management, and infrastructure to make data analyses scalable to empower scientific progress. Specifically, specialized statistical methods along with fast and memory-efficient software implementation are required to reap the full scientific potential of hig...

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“…Not all the known cells of the innate and adaptive immunity of hematopoietic origins were touched on, as our concern was to attempt to summarize the required components for immune microenvironment recapitulation. Besides, with the advent of single‐cell genomics, rare immune cell populations and new functions of known immune cells are emerging constantly . The immune niche is perhaps the personification of complexity, as the stromal cellular components are not passive in immunity, but indeed have active roles in recruiting immune cells, modulating immune response, and secreting stroma proteins; all this is achieved through the secretion of ECM components, chemokines, cytokines, and growth factors.…”

Section: Conclusion and Perspectives For The Futurementioning

confidence: 99%

Deconstructing Immune Microenvironments of Lymphoid Tissues for Reverse Engineering

Witzel¹,

Nasser

Garcia-Sabaté

et al. 2018

Adv Healthcare Materials

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The immune microenvironment presents a diverse panel of cues that impacts immune cell migration, organization, differentiation, and the immune response. Uniquely, both the liquid and solid phases of every specific immune niche within the body play an important role in defining cellular functions in immunity at that particular location. The in vivo immune microenvironment consists of biomechanical and biochemical signals including their gradients, surface topography, dimensionality, modes of ligand presentation, and cell–cell interactions, and the ability to recreate these immune biointerfaces in vitro can provide valuable insights into the immune system. This manuscript reviews the critical roles played by different immune cells and surveys the current progress of model systems for reverse engineering of immune microenvironments with a focus on lymphoid tissues.

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Single Cell RNA Sequencing of Rare Immune Cell Populations

Cited by 98 publications

References 74 publications

Early adaptive immune activation detected in monozygotic twins with prodromal multiple sclerosis

Early adaptive immune activation detected in monozygotic twins with prodromal multiple sclerosis

Orchestrating Single-Cell Analysis with Bioconductor

Deconstructing Immune Microenvironments of Lymphoid Tissues for Reverse Engineering

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