Digital cell quantification identifies global immune cell dynamics during influenza infection

Altboum, Zeev; Steuerman, Yael; David, Eyal; Barnett‐Itzhaki, Zohar; Valadarsky, Liran; Keren‐Shaul, Hadas; Meningher, Tal; Mendelson, Ella; Mandelboim, Michal; Gat‐Viks, Irit; Amit, Ido

doi:10.1002/msb.134947

Cited by 108 publications

(144 citation statements)

References 48 publications

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“…This initial exon-intron analysis also suggested that some of the transcript changes observed in the triple KO spleens could be due to changes in the cellular composition of the triple KO tissues. In order to test this, we took two different approaches: first, we immunostained the spleen sections for the presence of various cell types, and second, we utilized the digital cell quantitation (DCQ) method (31) to investigate the relative quantities of various immune cell subpopulations in the triple KO spleens. Consistent with the impression of increased EMH observed in the H&E-stained sections of the triple KO mouse spleens, there were increases in staining for neutrophils and overall myeloid cells in the triple KO spleens, suggesting either extramedullary myelopoiesis or neutrophil infiltration of the tissue or both.…”

Section: Discussionmentioning

confidence: 99%

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Effects of Combined Tristetraprolin/Tumor Necrosis Factor Receptor Deficiency on the Splenic Transcriptome

Patial¹,

Stumpo²,

Young

et al. 2016

Molecular and Cellular Biology

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e Tristetraprolin (TTP) acts by binding to AU-rich elements in certain mRNAs, such as tumor necrosis factor (TNF) mRNA, and increasing their decay rates. TTP knockout mice exhibit a profound inflammatory syndrome that is largely due to increased TNF levels. Although TTP's effects on gene expression have been well studied in cultured cells, little is known about its functions in intact tissues. We performed deep RNA sequencing on spleens from TTP knockout mice that were also deficient in both TNF receptors ("triple knockout" mice) to remove the secondary effects of excess TNF activity. To help identify posttranscriptionally regulated transcripts, we also compared changes in mature mRNA levels to levels of transiently expressed pre-mRNA. In the triple knockout spleens, levels of 3,014 transcripts were significantly affected by 1.5-fold or more, but only a small fraction exhibited differential mRNA/pre-mRNA changes suggestive of increased mRNA stability. Transferrin receptor mRNA, which contains two highly conserved potential TTP binding sites, was significantly upregulated relative to its pre-mRNA. This was reflected in increased transferrin receptor expression and increased splenic iron/hemosiderin deposition. Our results suggest that TTP deficiency has profound effects on the splenic transcriptome, even in the absence of secondary increases in TNF activity. Members of the tristetraprolin (TTP) family of CCCH tandem zinc finger proteins can bind to AU-rich elements (AREs) in the 3= untranslated regions (3=UTR) of certain mRNAs and promote their turnover. The ideal binding site appears to be UUAU UUAUU, although variations of this sequence can be tolerated with relatively minor changes in affinity (1-5). TTP and its other family members can bind with high affinity to these AREs and then promote the removal of the poly(A) tail and the ultimate destruction of the transcript (6).The protein members of the TTP family in mice are as follows: TTP itself, encoded by Zfp36; ZFP36L1, also known as BRF1, ERF1, and CMG1, encoded by Zfp36l1; ZFP36L2, also known as BRF2 and ERF2, encoded by Zfp36l2; and ZFP36L3, encoded by Zfp36l3. The identification of TTP, the best-studied member of this family, as an mRNA-destabilizing protein was first recognized by analyzing the phenotype of TTP knockout (KO) mice, which exhibited a severe systemic inflammatory phenotype with polyarticular erosive arthritis, weight loss, dermatitis, autoimmunity, and myeloid hyperplasia (7). The nature of this syndrome suggested excess tumor necrosis factor alpha (TNF) as a causative agent. This was confirmed by the observations that treatment with anti-TNF antibodies or interbreeding the TTP KO mice with mice lacking both TNF receptors prevented the development of the inflammatory phenotype (7,8). Subsequent studies on bone marrow-derived macrophages from these animals demonstrated that the TNF mRNA is a direct target of TTP and that the binding of TTP to binding sites in the 3=UTR of TNF mRNA could promote its decay (9, 10).Since the early demonstration...

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Section: Discussionmentioning

confidence: 99%

“…Immunohistochemically stained slides were graded as described in Materials and Methods. Although not readily apparent in the examples shown in (31). This method uses the genome-wide transcriptional profiles of a tissue to identify changes in the quantities of at least 213 immune cell subpopulations.…”

Section: ------------Atttatttgttcctttgtacctgat-------------mentioning

confidence: 99%

Effects of Combined Tristetraprolin/Tumor Necrosis Factor Receptor Deficiency on the Splenic Transcriptome

Patial¹,

Stumpo²,

Young

et al. 2016

Molecular and Cellular Biology

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“…More recently Newman et al [15] propose robust linear regression (RLR) and ν-SVR regression instead of L 2 based regression, which is highly susceptible to noise. Digital cell quantification (DCQ) [23] is another approach designed for monitoring the immune system during infection. Compared to prior methods, DCQ forces sparsity by combining R 2 and R 1 regularization into an elastic net.…”

Section: Overview Of Prior In Silico Deconvolution Methodsmentioning

confidence: 99%

A Critical Survey of Deconvolution Methods for Separating Cell Types in Complex Tissues

Mohammadi

Zuckerman²,

Goldsmith

et al. 2017

Proc. IEEE

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Abstract-Identifying properties and concentrations of components from an observed mixture, known as deconvolution, is a fundamental problem in signal processing. It has diverse applications in fields ranging from hyperspectral imaging to denoising readings from biomedical sensors. This paper focuses on in-silico deconvolution of signals associated with complex tissues into their constitutive cell-type specific components, along with a quantitative characterization of the celltypes. Deconvolving mixed tissues/cell-types is useful in the removal of contaminants (e.g., surrounding cells) from tumor biopsies, as well as in monitoring changes in the cell population in response to treatment or infection. In these contexts, the observed signal from the mixture of cell-types is assumed to be a convolution, using a linear instantaneous (LI) mixing process, of the expression levels of genes in constitutive celltypes. The goal is to use known signals corresponding to individual celltypes along with a model of the mixing process to cast the deconvolution problem as a suitable optimization problem.In this paper, we present a survey and in-depth analysis of models, methods, and assumptions underlying deconvolution techniques. We investigate the choice of the different loss functions for evaluating estimation error, constraints on solutions, preprocessing and data filtering, feature selection, and regularization to enhance the quality of solutions, along with the impact of these choices on the performance of commonly used regression-based methods for deconvolution. We assess different combinations of these factors and use detailed statistical measures to evaluate their effectiveness. Some of these combinations have been proposed in the literature, whereas others represent novel algorithmic choices for deconvolution. We identify shortcomings of current methods and avenues for further investigation. For many of the identified shortcomings, such as normalization issues and data filtering, we provide new solutions. We summarize our findings in a prescriptive step-bystep process, which can be applied to a wide range of deconvolution problems.

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“…The data were transformed by dividing each gene expression entry by the standard deviation across test samples. Analysis of relative cell quantity was run with three repeats and a lambda minimum of 0.2 as previously described (39).…”

Section: Cells and Virusesmentioning

confidence: 99%

“…The DCQ is a previously validated algorithm that leverages cell type-specific whole-genome transcriptional expression profiles derived from 207 immune cells (Immgen; reviewed in reference 46) to infer changes in immune cell quantities based on cell-specific surface markers (39). We used the DCQ to derive the relative quantity of predicted cells within the infected lung and infer their activation states.…”

Section: Pb2 Is a Key Driver Of Inflammatory Responses Enhancing Immumentioning

confidence: 99%

The 1918 Influenza Virus PB2 Protein Enhances Virulence through the Disruption of Inflammatory and Wnt-Mediated Signaling in Mice

Forero

Tisoncik-Go

Watanabe

et al. 2016

J Virol

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The 1918 A nnual influenza epidemics cause significant morbidity and mortality. On occasion, novel influenza A strains emerge that are capable of producing severe respiratory disease on a global scale (1). The 1918-1919 pandemic was an unprecedented event, with an estimated 40 to 50 million deaths worldwide (2, 3). Sequence analysis indicates that the 1918 pandemic influenza virus is avian in origin (4-8) and 1918 viral genes are highly homologous to those of circulating avian influenza H1N1 viruses (9). In a recent study, a reconstructed virus comprised of avian influenza viral segments with high homology to the 1918 virus was shown to be pathogenic in ferrets and mice. Specific amino acid changes in PB2 and PA viral polymerase proteins, in combination with amino acid changes in the hemagglutinin (HA) viral glycoprotein, resulted in the "1918-like avian virus" efficiently transmitting between ferrets via the respiratory droplet route (9). Host-adaptive changes in polymerase and NP genes enhancing avian influenza virus replicative fitness are well documented (10, 11). In particular, PB2 627K has become a genomic signature of host adaptation, which was present in the 1918 pandemic influenza virus PB2 gene, though novel host adaptive mutations continue to be characterized in the ribonucleoprotein complex (12-14). These humanadaptive changes may confer efficient replication of avian influ-

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Digital cell quantification identifies global immune cell dynamics during influenza infection

Cited by 108 publications

References 48 publications

Effects of Combined Tristetraprolin/Tumor Necrosis Factor Receptor Deficiency on the Splenic Transcriptome

Effects of Combined Tristetraprolin/Tumor Necrosis Factor Receptor Deficiency on the Splenic Transcriptome

A Critical Survey of Deconvolution Methods for Separating Cell Types in Complex Tissues

The 1918 Influenza Virus PB2 Protein Enhances Virulence through the Disruption of Inflammatory and Wnt-Mediated Signaling in Mice

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