RNA-Seq Signatures Normalized by mRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types

Monaco, Gianni; Lee, Bernett; Xu, Weili; Mustafah, Seri; Hwang, You Yi; Carré, Christophe; Burdin, Nicolas; Visan, Lucian; Ceccarelli, Michele; Poidinger, Michael; Zippelius, Alfred; Magalhães, João Pedro de; Larbi, Anis

doi:10.1016/j.celrep.2019.01.041

Cited by 720 publications

(840 citation statements)

References 70 publications

(118 reference statements)

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“…In addition, we used independent external data to verify the interpretability of the patterns. We matched a publicly available single-cell PBMC dataset with cell-sorted, bulk RNA-seq profiles, that considers a total of 29 cell types/states (Figure 2a) 31 . To compare cell state patterns with bulk profiles, we computed partial Pearson's correlation between ACTIONet expression profiles ( ) with corresponding bulk average profiles, after adjustment for the baseline expression of genes in both profiles ( Figure 2b).…”

Section: Recovered Cell State Patterns Match Cell-sorted Profilesmentioning

confidence: 99%

“…However, it is rather common to only have prior information in the form of gene sets. Certain experimental designs also enable cell labeling, for example, based on presorting experiments 31 . To further facilitate interpretation, ACTIONet includes tools to quantify the association between cell state patterns and annotations based on these two sources of information (gene sets or cell labels).…”

Section: Identification and Annotation Of Nonredundant Multiresolutiomentioning

confidence: 99%

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A multiresolution framework to characterize single-cell state landscapes

Mohammadi

Dávila-Velderrain

Kellis

2019

Preprint

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Dissecting the cellular heterogeneity embedded in single-cell transcriptomic data is challenging. Although a large number of methods and approaches exist, robustly identifying underlying cell states and their associations is still a major challenge; given the nonexclusive and dynamic influence of multiple unknown sources of variability, the existence of state continuum at the time-scale of observation, and the inevitable snapshot nature of experiments. As a way to address some of these challenges, here we introduce ACTIONet, a comprehensive framework that combines archetypal analysis and network theory to provide a ready-to-use analytical approach for multiresolution single-cell state characterization. ACTIONet uses multilevel matrix decomposition and network reconstruction to simultaneously learn cell state patterns, quantify single-cell states, and reconstruct a reproducible structural representation of the transcriptional state space that is geometrically mapped to a color space. A color-enhanced quantitative view of cell states enables novel visualization, prediction, and annotation approaches. Using data from multiple tissues, organisms, and developmental conditions, we illustrate how ACTIONet facilitates the reconstruction and exploration of single-cell state landscapes.

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Section: Recovered Cell State Patterns Match Cell-sorted Profilesmentioning

confidence: 99%

Section: Identification and Annotation Of Nonredundant Multiresolutiomentioning

confidence: 99%

A multiresolution framework to characterize single-cell state landscapes

Mohammadi

Dávila-Velderrain

Kellis

2019

Preprint

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“…While previous applications of this atlas enabled the identification of specific tissue-related genes 39,40 , we developed an original use for this resource to simulate cell cross-talks in diverse microenvironments. In addition, ICELLNET can accommodate other original RNAseq datasets of cell populations 41,42 as reference profiles to infer intercellular communication. Hence, ICELLNET is an extremely flexible tool, which can be easily adapted depending on the biological question, by offering the possibility to select communication molecules families and cell types of interest.…”

Section: Discussionmentioning

confidence: 99%

ICELLNET: a transcriptome-based framework to dissect intercellular communication

Noël

Massenet-Regad

Carmi-Levy

et al. 2020

Preprint

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Cell-to-cell communication can be inferred from ligand-receptor expression in cell transcriptomic datasets. However, important challenges remain: 1) global integration of cell-to-cell communication, 2) biological interpretation, and 3) application to individual cell population transcriptomic profiles.We developed ICELLNET, a transcriptomic-based framework integrating: 1) an original expertcurated database of ligand-receptor interactions accounting for multiple subunits expression, 2) quantification of communication scores, 3) the possibility to connect a cell population of interest with 31 reference human cell types (BioGPS), and 4) three visualization modes to facilitate biological interpretation. We applied ICELLNET to uncover different communication in breast cancer associated fibroblast (CAF) subsets. ICELLNET also revealed autocrine IL-10 as a switch to control human dendritic cell communication with up to 12 other cell types, four of which were experimentally validated. In summary, ICELLNET is a global, versatile, biologically validated, and easy-to-use framework to dissect cell communication from single or multiple cell-based transcriptomic profile(s). IntroductionCell-to-cell communication is at the basis of the higher order organization observed in tissues, organs, and organisms, at steady-state and in response to stress. It involves a "messenger" or "sender" cell, which transmits information signals to a "receiving" or "target" cell. Information is generally coded in the form of a chemical molecule that is sensed by the target cell through a cognate receptor.Multiple cells or cell types communicating with each other form cell communication networks.In mammalian organisms, endocrine communication involves cells that may be at very distant anatomical sites. However, cell communication also takes place locally through cell-to-cell contacts, or through inflammatory molecules. Cytokines and other mediators can be involved in distant as well as local communication 1-3 . Hence, when deciphering cell-to-cell communication, one should account for potential signals coming both from spatially proximal and distal cells.Most studies in the past decades have focused on a limited number of communication molecules in a given anatomical site or physiological process. The availability of large-scale transcriptomic datasets from several cell types, tissue locations, and cell activation states, opened the possibility of reconstructing cell-to-cell interactions based on the expression of specific ligand-receptor pairs on sender and target cells, respectively. Many of them exploit single cell RNAseq datasets to infer communication between groups of cells within the same dataset 4-7 . Despite leading to interesting and often innovative hypotheses 4,6,8 , these methods do not integrate putative signals that may come from more distant cells. Also, they cannot be applied to bulk transcriptomic data derived from a given cell population. Such datasets are numerous in public databases, and can be a source of novel insights into how...

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“…Hence, they are expected to be constitutively expressed in all cell types of the organism in normal physiological condition regardless of specific cell function, cell cycle step or developmental stage (1,2). Due to these characteristics, HK genes are useful as references of gene expression in molecular biology and computational experiments (3)(4)(5)(6)(7)(8)(9), as well as in our understanding of various structural and functional genomics and evolutionary features (10)(11)(12)(13). In biomedical research, the importance of the precise identification of HK genes stems from the fact that these genes are used as internal controls for the calibration of quantitative PCR (qPCR), a workhorse technique in molecular biology and biotechnology laboratories used to quantitatively estimate the expression of any gene of interest under different experimental conditions (9,14).…”

Section: Introductionmentioning

confidence: 99%

HRT Atlas v1.1 database: redefining human and mouse housekeeping genes and candidate reference transcripts by mining massive RNA-seq datasets

Hounkpe

Chenou

Lima

et al. 2019

Preprint

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Housekeeping (HK) genes are constitutively expressed genes that are required for the maintenance of basic cellular functions. Despite their importance in the calibration of gene expression, as well as the understanding of many genomic and evolutionary features, important discrepancies have been observed in studies that previously identified these genes. Here, we present Housekeeping Transcript Atlas (HT Atlas v1.0, www.housekeeping.unicamp.br) a web-based database which addresses some of the previously observed limitations in the identification of these genes, and offers a more accurate database of human and mouse HK genes and transcripts. The database was generated by mining massive human and mouse RNA-seq data sets, including 12,482 and 507 high-quality RNA-seq samples from 82 human non-disease tissues/cells and 15 healthy tissues/cells of C57BL/6 wild type mouse, respectively. User can visualize the expression and download lists of 2,158 human HK transcripts from 2,176 HK genes and 3,024 mouse HK transcripts from 3,277 mouse HK genes. HT Atlas also offers the most stable and suitable tissue selective candidate reference transcripts for normalization of qPCR experiments. Specific primers and predictedmodifiers of gene expression for some of these HK transcripts are also proposed. All of these resources can be accessed and downloaded from any computer or small device web browsers.

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RNA-Seq Signatures Normalized by mRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types

Cited by 720 publications

References 70 publications

A multiresolution framework to characterize single-cell state landscapes

A multiresolution framework to characterize single-cell state landscapes

ICELLNET: a transcriptome-based framework to dissect intercellular communication

HRT Atlas v1.1 database: redefining human and mouse housekeeping genes and candidate reference transcripts by mining massive RNA-seq datasets

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