Writ large: Genomic dissection of the effect of cellular environment on immune response

Yosef, Nir; Regev, Aviv

doi:10.1126/science.aaf5453

Cited by 46 publications

(42 citation statements)

References 60 publications

(127 reference statements)

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“…225 ). Unfortunately, most high-throughput single-cell approaches require the dissociation of tissues into single-cell suspensions and therefore lose this spatial information.…”

Section: Revealing the Vectors Of Cellular Identitymentioning

confidence: 99%

Revealing the vectors of cellular identity with single-cell genomics

2016

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Single-cell genomics has now made it possible to create a comprehensive atlas of human cells. At the same time, it has reopened definitions of a cell’s identity and type and of the ways in which they are regulated by the cell’s molecular circuitry. Emerging computational analysis methods, especially in single-cell RNA sequencing (scRNA-seq), have already begun to reveal, in a data-driven way, the diverse simultaneous facets of a cell’s identity, from a taxonomy of discrete cell types to continuous dynamic transitions and spatial locations. These developments will eventually allow a cell to be represented as a superposition of ‘basis vectors’, each determining a different (but possibly dependent) aspect of cellular organization and function. However, computational methods must also overcome considerable challenges—from handling technical noise and data scale to forming new abstractions of biology. As the scale of single-cell experiments continues to increase, new computational approaches will be essential for constructing and characterizing a reference map of cell identities.

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“…225 ). Unfortunately, most high-throughput single-cell approaches require the dissociation of tissues into single-cell suspensions and therefore lose this spatial information.…”

Section: Revealing the Vectors Of Cellular Identitymentioning

confidence: 99%

Revealing the vectors of cellular identity with single-cell genomics

2016

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“…For Th1 cells to maintain cellular fitness and proliferative capacity while producing their signature cytokine, Th1 cells need to adapt to the local cellular environment they create, i.e. high level of IFN-γ (Yosef and Regev, 2016). As opposed to an SDTF, like STAT1 that is directly activated by IFN-γ to induce transactivation of targets, the role of an LDTF like T-bet in IFN-γ signaling has not been fully addressed.…”

Section: Introductionmentioning

confidence: 99%

The Transcription Factor T-bet Limits Amplification of Type I IFN Transcriptome and Circuitry in T Helper 1 Cells

et al. 2017

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Summary Host defense requires the specification of CD4+ helper T (Th) cells into distinct fates including Th1 cells that preferentially produce interferon-γ(IFN-γ) IFN-γ, a member of a large family of anti-pathogenic and anti-tumor IFNs, induces T-bet, a lineage defining transcription factor for Th1 cells, which in turn supports IFN-γ production in a feed-forward manner. Herein, we showed a cell intrinsic role of T-bet to influence how T cells perceive their secreted product in the environment. In the absence of T-bet, IFN-γ aberrantly induced a type I IFN transcriptomic program. T-bet preferentially repressed genes and pathways ordinarily activated by type I IFNs to ensure that its transcriptional response does not evoke an aberrant amplification of type I IFN signaling circuitry otherwise triggered by its own product. Thus, in addition to promoting Th1 effector commitment, T-bet acts as a repressor in differentiated Th1 cells to prevent abberant autocrine type I IFN and downstream signaling.

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“…However, we currently have a limited understanding of the molecular components governing this process; in particular, the regulatory elements orchestrating it. Changes in enhancer activity are thought to play a pivotal role in cell fate specification (3). For example, multiple temporal enhancers are thought to sequentially function in orthodenticle homeobox 2 (Otx2) gene expression in epiblast, anterior neuroectoderm and fore-and midbrain during development (4)(5)(6).…”

Section: Main Textmentioning

confidence: 99%

Massively parallel characterization of regulatory dynamics during neural induction

Inoue

Kreimer²,

Ashuach³

et al. 2018

Preprint

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The molecular components governing neural induction remain largely unknown. Here, we applied a suite of genomic and computational tools to comprehensively identify these components. We performed RNA-seq, ChIP-seq (H3K27ac, H3K27me3) and ATAC-seq on human embryonic stem cells (hESCs) at seven early neural differentiation time points (0-72 hours) and identified thousands of induced genes and regulatory regions. We analyzed the function of ~2,500 selected regions using massively parallel reporter assays at all time points. We found numerous temporal enhancers that correlated with similarly timed epigenetic marks and gene expression. Development of a prioritization method that incorporated all genomic data identified key transcription factors (TFs) involved in neural induction. Individual overexpression of eleven TFs and several combinations in hESCs found novel neural induction regulators. Combined, our results provide a comprehensive map of genes and functional regulatory elements involved in neural induction and identify master regulator TFs that are instrumental for this process.One Sentence SummaryUsing numerous genomic assays and computational tools we characterized the dynamic changes that take place during neural induction.

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Writ large: Genomic dissection of the effect of cellular environment on immune response

Cited by 46 publications

References 60 publications

Revealing the vectors of cellular identity with single-cell genomics

Revealing the vectors of cellular identity with single-cell genomics

The Transcription Factor T-bet Limits Amplification of Type I IFN Transcriptome and Circuitry in T Helper 1 Cells

Massively parallel characterization of regulatory dynamics during neural induction

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