Computational deconvolution to estimate cell type-specific gene expression from bulk data

Computational deconvolution is a time and cost-efficient approach to obtain cell type-specific information from bulk gene expression of heterogeneous tissues like blood. Deconvolution can aim to either estimate cell type proportions or abundances in samples, or estimate how strongly each present cell type expresses different genes, or both tasks simultaneously. Among the two separate goals, the estimation of cell type proportions/abundances is widely studied, but less attention has been paid on defining the cell type-specific expression profiles. Here, we address this gap by introducing a novel method Rodeo and empirically evaluating it and the other available tools from multiple perspectives utilizing diverse datasets.</p

Estimating cell type-specific differential expression using deconvolution

When differentially expressed genes are detected from samples containing different types of cells, only a very coarse overview without any cell type-specific information is obtained. Although several computational methods have been published to estimate cell type-specific differentially expressed genes from bulk samples, their performance has not been evaluated outside the original publications. Here, we compare accuracies of nine of these methods, test their sensitivity to various factors often present in real studies and provide practical guidelines for end users about when reliable results can be expected and when not. Our results show that TOAST, CARseq, CellDMC and TCA are accurate methods with their own strengths and weaknesses. Notably, methods designed to detect cell type-specific differential methylation were comparable to those designed for gene expression, and both types outperformed methods originally designed for other tasks. The most important factors affecting the accuracy of the estimated cell type-specific differentially expressed genes are (i) abundance of the cell type (rare cell types are harder to analyze) and (ii) individual heterogeneity in the cell type-specific expression profiles (stable cell types are easier to analyze)</p

Comparison of methods to detect differentially expressed genes between single-cell populations

We compared five statistical methods to detect differentially expressed genes between two distinct single-cell populations. Currently, it remains unclear whether differential expression methods developed originally for conventional bulk RNA-seq data can also be applied to single-cell RNA-seq data analysis. Our results in three diverse comparison settings showed marked differences between the different methods in terms of the number of detections as well as their sensitivity and specificity. They, however, did not reveal systematic benefits of the currently available single-cell-specific methods. Instead, our previously introduced reproducibility-optimization method showed good performance in all comparison settings without any single-cell-specific modifications.</p

PASI: A novel pathway method to identify delicate group effects

Pathway analysis is a common approach in diverse biomedical studies, yet the currently-available pathway tools do not typically support the increasingly popular personalized analyses. Another weakness of the currently-available pathway methods is their inability to handle challenging data with only modest group-based effects compared to natural individual variation. In an effort to address these issues, this study presents a novel pathway method PASI (Pathway Analysis for Sample-level Information) and demonstrates its performance on complex diseases with different levels of group-based differences in gene expression. PASI is freely available as an R package

ROTS: An R package for reproducibility-optimized statistical testing

Differential expression analysis is one of the most common types of analyses performed on various biological data (e.g. RNA-seq or mass spectrometry proteomics). It is the process that detects features, such as genes or proteins, showing statistically significant differences between the sample groups under comparison. A major challenge in the analysis is the choice of an appropriate test statistic, as different statistics have been shown to perform well in different datasets. To this end, the reproducibility-optimized test statistic (ROTS) adjusts a modified t-statistic according to the inherent properties of the data and provides a ranking of the features based on their statistical evidence for differential expression between two groups. ROTS has already been successfully applied in a range of different studies from transcriptomics to proteomics, showing competitive performance against other state-of-the-art methods. To promote its widespread use, we introduce here a Bioconductor R package for performing ROTS analysis conveniently on different types of omics data. To illustrate the benefits of ROTS in various applications, we present three case studies, involving proteomics and RNA-seq data from public repositories, including both bulk and single cell data. The package is freely available from Bioconductor (https://www.bioconductor.org/packages/ROTS)

Enterovirus-associated changes in blood transcriptomic profiles of children with genetic susceptibility to type 1 diabetes

Aims/hypothesis Enterovirus infections have been associated with the development of type 1 diabetes in multiple studies, but little is known about enterovirus-induced responses in children at risk for developing type 1 diabetes. Our aim was to use genome-wide transcriptomics data to characterise enterovirus-associated changes in whole-blood samples from children with genetic susceptibility to type 1 diabetes. Methods Longitudinal whole-blood samples (356 samples in total) collected from 28 pairs of children at increased risk for developing type 1 diabetes were screened for the presence of enterovirus RNA. Seven of these samples were detected as enterovirus-positive, each of them collected from a different child, and transcriptomics data from these children were analysed to understand the individual-level responses associated with enterovirus infections. Transcript clusters with peaking or dropping expression at the time of enterovirus positivity were selected as the enterovirus-associated signals. Results Strong signs of activation of an interferon response were detected in four children at enterovirus positivity, while transcriptomic changes in the other three children indicated activation of adaptive immune responses. Additionally, a large proportion of the enterovirus-associated changes were specific to individuals. An enterovirus-induced signature was built using 339 genes peaking at enterovirus positivity in four of the children, and 77 of these genes were also upregulated in human peripheral blood mononuclear cells infected in vitro with different enteroviruses. These genes separated the four enterovirus-positive samples clearly from the remaining 352 blood samples analysed. Conclusions/interpretation We have, for the first time, identified enterovirus-associated transcriptomic profiles in whole-blood samples from children with genetic susceptibility to type 1 diabetes. Our results provide a starting point for understanding the individual responses to enterovirus infections in blood and their potential connection to the development of type 1 diabetes.Peer reviewe

Enterovirus-associated changes in blood transcriptomic profiles of children with genetic susceptibility to type 1 diabetes

Aims/hypothesis Enterovirus infections have been associated with the development of type 1 diabetes in multiple studies, but little is known about enterovirus-induced responses in children at risk for developing type 1 diabetes. Our aim was to use genome-wide transcriptomics data to characterise enterovirus-associated changes in whole-blood samples from children with genetic susceptibility to type 1 diabetes. Methods Longitudinal whole-blood samples (356 samples in total) collected from 28 pairs of children at increased risk for developing type 1 diabetes were screened for the presence of enterovirus RNA. Seven of these samples were detected as enterovirus-positive, each of them collected from a different child, and transcriptomics data from these children were analysed to understand the individual-level responses associated with enterovirus infections. Transcript clusters with peaking or dropping expression at the time of enterovirus positivity were selected as the enterovirus-associated signals. Results Strong signs of activation of an interferon response were detected in four children at enterovirus positivity, while transcriptomic changes in the other three children indicated activation of adaptive immune responses. Additionally, a large proportion of the enterovirus-associated changes were specific to individuals. An enterovirus-induced signature was built using 339 genes peaking at enterovirus positivity in four of the children, and 77 of these genes were also upregulated in human peripheral blood mononuclear cells infected in vitro with different enteroviruses. These genes separated the four enterovirus-positive samples clearly from the remaining 352 blood samples analysed. Conclusions/interpretation We have, for the first time, identified enterovirus-associated transcriptomic profiles in whole-blood samples from children with genetic susceptibility to type 1 diabetes. Our results provide a starting point for understanding the individual responses to enterovirus infections in blood and their potential connection to the development of type 1 diabetes.Peer reviewe

Bexmarilimab-induced macrophage activation leads to treatment benefit in solid tumors:The phase I/II first-in-human MATINS trial

Macrophage Clever-1 contributes to impaired antigen presentation and suppression of anti-tumor immunity. This first-in-human trial investigates the safety and tolerability of Clever-1 blockade with bexmarilimab in patients with treatment-refractory solid tumors and assesses preliminary anti-tumor efficacy, pharmacodynamics, and immunologic correlates. Bexmarilimab shows no dose-limiting toxicities in part I (n = 30) and no additional safety signals in part II (n = 108). Disease control (DC) rates of 25%–40% are observed in cutaneous melanoma, gastric, hepatocellular, estrogen receptor-positive breast, and biliary tract cancers. DC associates with improved survival in a landmark analysis and correlates with high pre-treatment intratumoral Clever-1 positivity and increasing on-treatment serum interferon γ (IFNγ) levels. Spatial transcriptomics profiling of DC and non-DC tumors demonstrates bexmarilimab-induced macrophage activation and stimulation of IFNγ and T cell receptor signaling selectively in DC patients. These data suggest that bexmarilimab therapy is well tolerated and show that macrophage targeting can promote immune activation and tumor control in late-stage cancer

Enterovirus-associated changes in blood transcriptomic profiles of children with genetic susceptibility to type 1 diabetes

AIMS/HYPOTHESIS: Enterovirus infections have been associated with the development of type 1 diabetes in multiple studies, but little is known about enterovirus-induced responses in children at risk for developing type 1 diabetes. Our aim was to use genome-wide transcriptomics data to characterise enterovirus-associated changes in whole-blood samples from children with genetic susceptibility to type 1 diabetes.METHODS: Longitudinal whole-blood samples (356 samples in total) collected from 28 pairs of children at increased risk for developing type 1 diabetes were screened for the presence of enterovirus RNA. Seven of these samples were detected as enterovirus-positive, each of them collected from a different child, and transcriptomics data from these children were analysed to understand the individual-level responses associated with enterovirus infections. Transcript clusters with peaking or dropping expression at the time of enterovirus positivity were selected as the enterovirus-associated signals.RESULTS: Strong signs of activation of an interferon response were detected in four children at enterovirus positivity, while transcriptomic changes in the other three children indicated activation of adaptive immune responses. Additionally, a large proportion of the enterovirus-associated changes were specific to individuals. An enterovirus-induced signature was built using 339 genes peaking at enterovirus positivity in four of the children, and 77 of these genes were also upregulated in human peripheral blood mononuclear cells infected in vitro with different enteroviruses. These genes separated the four enterovirus-positive samples clearly from the remaining 352 blood samples analysed.CONCLUSIONS/INTERPRETATION: We have, for the first time, identified enterovirus-associated transcriptomic profiles in whole-blood samples from children with genetic susceptibility to type 1 diabetes. Our results provide a starting point for understanding the individual responses to enterovirus infections in blood and their potential connection to the development of type 1 diabetes.DATA AVAILABILITY: The datasets analysed during the current study are included in this published article and its supplementary information files ( www.btk.fi/research/computational-biomedicine/1234-2 ) or are available from the Gene Expression Omnibus (GEO) repository (accession GSE30211).</div

Early exposure to secondhand tobacco smoke and the development of allergic diseases in 4 year old children in Malmö, Sweden

<p>Abstract</p> <p>Background</p> <p>Earlier studies have shown an association between secondhand tobacco smoke and allergy development in children. Furthermore, there is an increased risk of developing an allergy if the parents have an allergy. However, there are only few studies investigating the potential synergistic effect of secondhand tobacco smoke and allergic heredity on the development of an allergy.</p> <p>Methods</p> <p>The study was population-based cross-sectional with retrospective information on presence of secondhand tobacco smoke during early life. The study population consisted of children who visited the Child Health Care (CHC) centres in Malmö for their 4-year health checkup during 2006-2008 and whose parents answered a self-administered questionnaire (n = 4,278 children). The questionnaire was distributed to parents of children registered with the CHC and invited for the 4-year checkup during the study period.</p> <p>Results</p> <p>There was a two to four times increased odds of the child having an allergy or having sought medical care due to allergic symptoms if at least one parent had an allergy, while there were rather small increased odds related to presence of secondhand smoke during the child's first month in life or at the age of 8 months. However, children with heredity for allergies and with presence of secondhand tobacco smoke during their first year in life had highly increased odds of developing an allergy and having sought medical care due to allergic symptoms at 4 years of age. Thus, there was a synergistic effect enhancing the independent effects of heredity and exposure to secondhand tobacco smoke on allergy development.</p> <p>Conclusions</p> <p>Children with a family history of allergies and early exposure to secondhand tobacco smoke is a risk group that prevention and intervention should pay extra attention to. The tobacco smoke effect on children is an essential and urgent question considering it not being self chosen, possibly giving life lasting negative health effects and being possible to reduce.</p