Systematic evaluation of transcriptomics-based deconvolution methods and references using thousands of clinical samples.

Estimating cell type composition of blood and tissue samples is a biological challenge relevant in both laboratory studies and clinical care. In recent years, a number of computational tools have been developed to estimate cell type abundance using gene expression data. Although these tools use a variety of approaches, they all leverage expression profiles from purified cell types to evaluate the cell type composition within samples. In this study, we compare 12 cell type quantification tools and evaluate their performance while using each of 10 separate reference profiles. Specifically, we have run each tool on over 4000 samples with known cell type proportions, spanning both immune and stromal cell types. A total of 12 of these represent in vitro synthetic mixtures and 300 represent in silico synthetic mixtures prepared using single-cell data. A final 3728 clinical samples have been collected from the Framingham cohort, for which cell populations have been quantified using electrical impedance cell counting. When tools are applied to the Framingham dataset, the tool Estimating the Proportions of Immune and Cancer cells (EPIC) produces the highest correlation, whereas Gene Expression Deconvolution Interactive Tool (GEDIT) produces the lowest error. The best tool for other datasets is varied, but CIBERSORT and GEDIT most consistently produce accurate results. We find that optimal reference depends on the tool used, and report suggested references to be used with each tool. Most tools return results within minutes, but on large datasets runtimes for CIBERSORT can exceed hours or even days. We conclude that deconvolution methods are capable of returning high-quality results, but that proper reference selection is critical.

Amnion responses to intrauterine inflammation and effects of inhibition of TNF signaling in preterm Rhesus macaque.

Intrauterine infection/inflammation (IUI) is a frequent complication of pregnancy leading to preterm labor and fetal inflammation. How inflammation is modulated at the maternal-fetal interface is unresolved. We compared transcriptomics of amnion (a fetal tissue in contact with amniotic fluid) in a preterm Rhesus macaque model of IUI induced by lipopolysaccharide with human cohorts of chorioamnionitis. Bulk RNA sequencing (RNA-seq) amnion transcriptomic profiles were remarkably similar in both Rhesus and human subjects and revealed that induction of key labor-mediating genes such as IL1 and IL6 was dependent on nuclear factor κB (NF-κB) signaling and reversed by the anti-tumor necrosis factor (TNF) antibody Adalimumab. Inhibition of collagen biosynthesis by IUI was partially restored by Adalimumab. Interestingly, single-cell transcriptomics, flow cytometry, and immunohistology demonstrated that a subset of amnion mesenchymal cells (AMCs) increase CD14 and other myeloid cell markers during IUI both in the human and Rhesus macaque. Our data suggest that CD14+ AMCs represent activated AMCs at the maternal-fetal interface.

Analysis of gene expression from human breastmilk cells: A comparison between low and high producers, and the influence of anxiety and depression on milk production, gene expression and bacterial production.

BACKGROUND: Breastmilk is considered the gold standard of infant nutrition. Many mothers have difficulty with breastfeeding and over 50% of women stop due to perceived low production. AIMS AND METHODS: Our study compared gene expression in 8 samples of low and high producers of milk. All subjects were administered GAD-7 and PHQ-9 questionnaires. Low-producers were all found to have more depression and anxiety compared to high-producers. RESULTS: We did not find significant differences between gene expression between low and high milk producers. Only 5 of 8 samples contained a significant number of human cells. We did find differences in the amount of various bacterial populations. CONCLUSION: Our results indicate that gene expression in breastmilk is complicated by collection methods. We recommend that even though some women produced less than 600 ml of milk over a 24-hour period of time, due to the nature of the bacteria found in milk they try to breastfeed as much as they can for the health benefits of their infants. the rich bacterial diversity in all patients including the low producers strongly suggests that even women producing lesser quantities of milk confer their children numerous benefits by breastfeeding them.

The Gene Expression Deconvolution Interactive Tool (GEDIT): accurate cell type quantification from gene expression data.

BACKGROUND: The cell type composition of heterogeneous tissue samples can be a critical variable in both clinical and laboratory settings. However, current experimental methods of cell type quantification (e.g., cell flow cytometry) are costly, time consuming and have potential to introduce bias. Computational approaches that use expression data to infer cell type abundance offer an alternative solution. While these methods have gained popularity, most fail to produce accurate predictions for the full range of platforms currently used by researchers or for the wide variety of tissue types often studied. RESULTS: We present the Gene Expression Deconvolution Interactive Tool (GEDIT), a flexible tool that utilizes gene expression data to accurately predict cell type abundances. Using both simulated and experimental data, we extensively evaluate the performance of GEDIT and demonstrate that it returns robust results under a wide variety of conditions. These conditions include multiple platforms (microarray and RNA-seq), tissue types (blood and stromal), and species (human and mouse). Finally, we provide reference data from 8 sources spanning a broad range of stromal and hematopoietic types in both human and mouse. GEDIT also accepts user-submitted reference data, thus allowing the estimation of any cell type or subtype, provided that reference data are available. CONCLUSIONS: GEDIT is a powerful method for evaluating the cell type composition of tissue samples and provides excellent accuracy and versatility compared to similar tools. The reference database provided here also allows users to obtain estimates for a wide variety of tissue samples without having to provide their own data.

Cell type-specific genetic regulation of gene expression across human tissues.

The Genotype-Tissue Expression (GTEx) project has identified expression and splicing quantitative trait loci in cis (QTLs) for the majority of genes across a wide range of human tissues. However, the functional characterization of these QTLs has been limited by the heterogeneous cellular composition of GTEx tissue samples. We mapped interactions between computational estimates of cell type abundance and genotype to identify cell type-interaction QTLs for seven cell types and show that cell type-interaction expression QTLs (eQTLs) provide finer resolution to tissue specificity than bulk tissue cis-eQTLs. Analyses of genetic associations with 87 complex traits show a contribution from cell type-interaction QTLs and enables the discovery of hundreds of previously unidentified colocalized loci that are masked in bulk tissue.

Genetic Mechanisms of Immune Evasion in Colorectal Cancer.

To understand the genetic drivers of immune recognition and evasion in colorectal cancer, we analyzed 1,211 colorectal cancer primary tumor samples, including 179 classified as microsatellite instability-high (MSI-high). This set includes The Cancer Genome Atlas colorectal cancer cohort of 592 samples, completed and analyzed here. MSI-high, a hypermutated, immunogenic subtype of colorectal cancer, had a high rate of significantly mutated genes in important immune-modulating pathways and in the antigen presentation machinery, including biallelic losses of B2M and HLA genes due to copy-number alterations and copy-neutral loss of heterozygosity. WNT/ß-catenin signaling genes were significantly mutated in all colorectal cancer subtypes, and activated WNT/ß-catenin signaling was correlated with the absence of T-cell infiltration. This large-scale genomic analysis of colorectal cancer demonstrates that MSI-high cases frequently undergo an immunoediting process that provides them with genetic events allowing immune escape despite high mutational load and frequent lymphocytic infiltration and, furthermore, that colorectal cancer tumors have genetic and methylation events associated with activated WNT signaling and T-cell exclusion.Significance: This multi-omic analysis of 1,211 colorectal cancer primary tumors reveals that it should be possible to better monitor resistance in the 15% of cases that respond to immune blockade therapy and also to use WNT signaling inhibitors to reverse immune exclusion in the 85% of cases that currently do not. Cancer Discov; 8(6); 730-49. ©2018 AACR.This article is highlighted in the In This Issue feature, p. 663.