A targeted solution for estimating the cell-type composition of bulk samples

Fine-grained cell-type specific association studies with human bulk brain data using a large single-nucleus RNA sequencing based reference panel

Brain disorders are leading causes of disability worldwide. Gene expression studies provide promising opportunities to better understand their etiology but it is critical that expression is studied on a cell-type level. Cell-type specific association studies can be performed with bulk expression data using statistical methods that capitalize on cell-type proportions estimated with the help of a reference panel. To create a fine-grained reference panel for the human prefrontal cortex, we performed an integrated analysis of the seven largest single nucleus RNA-seq studies. Our panel included 17 cell-types that were robustly detected across all studies, subregions of the prefrontal cortex, and sex and age groups. To estimate the cell-type proportions, we used an empirical Bayes estimator that substantially outperformed three estimators recommended previously after a comprehensive evaluation of methods to estimate cell-type proportions from brain transcriptome data. This is important as being able to precisely estimate the cell-type proportions may avoid unreliable results in downstream analyses particularly for the multiple cell-types that had low abundances. Transcriptome-wide association studies performed with permuted bulk expression data showed that it is possible to perform transcriptome-wide association studies for even the rarest cell-types without an increased risk of false positives.

Not every estimate counts - evaluation of cell composition estimation approaches in brain bulk tissue data

BACKGROUND

Variation in cell composition can dramatically impact analyses in bulk tissue samples. A commonly employed approach to mitigate this issue is to adjust statistical models using estimates of cell abundance derived directly from omics data. While an arsenal of estimation methods exists, the applicability of these methods to brain tissue data and whether or not cell estimates can sufficiently account for confounding cellular composition has not been adequately assessed.

METHODS

We assessed the correspondence between different estimation methods based on transcriptomic (RNA sequencing, RNA-seq) and epigenomic (DNA methylation and histone acetylation) data from brain tissue samples of 49 individuals. We further evaluated the impact of different estimation approaches on the analysis of H3K27 acetylation chromatin immunoprecipitation sequencing (ChIP-seq) data from entorhinal cortex of individuals with Alzheimer's disease and controls.

RESULTS

We show that even closely adjacent tissue samples from the same Brodmann area vary greatly in their cell composition. Comparison across different estimation methods indicates that while different estimation methods applied to the same data produce highly similar outcomes, there is a surprisingly low concordance between estimates based on different omics data modalities. Alarmingly, we show that cell type estimates may not always sufficiently account for confounding variation in cell composition.

CONCLUSIONS

Our work indicates that cell composition estimation or direct quantification in one tissue sample should not be used as a proxy to the cellular composition of another tissue sample from the same brain region of an individual-even if the samples are directly adjacent. The highly similar outcomes observed among vastly different estimation methods, highlight the need for brain benchmark datasets and better validation approaches. Finally, unless validated through complementary experiments, the interpretation of analyses outcomes based on data confounded by cell composition should be done with great caution, and ideally avoided all together.

DNA methylation signatures of childhood trauma predict psychiatric disorders and other adverse outcomes 17 years after exposure

Childhood trauma is robustly linked to a broad range of adverse outcomes with consequences persisting far into adulthood. We conducted a prospective longitudinal study to predict psychiatric disorders and other adverse outcomes from trauma-related methylation changes 16.9 years after trauma exposure in childhood. Methylation was assayed using a sequencing-based approach that provides near-complete coverage of all 28 million sites in the blood methylome. Methylation data involved 673 assays from 489 participants aged 13.6 years (SD = 1.9) with outcomes measures collected at age 30.4 (SD = 2.26). For a subset of 303 participants we also generated methylation data in adulthood. Trauma-related methylation risk scores (MRSs) significantly predicted adult depression, externalizing problems, nicotine dependence, alcohol use disorder, serious medical problems, social problems and poverty. The predictive power of the MRSs was higher than that of reported trauma and could not be explained by the reported trauma, correlations with demographic variables, or a continuity of the predicted health problems from childhood to adulthood. Rather than measuring the occurrence of traumatic events, the MRSs seemed to capture the subject-specific impact of trauma. The majority of predictive sites did not remain associated with the outcomes suggesting the signatures of trauma do not become biologically embedded in the blood methylome. Instead, the long-term effects of trauma therefore seemed more consistent with a developmental mechanism where the initial subject-specific impacts of trauma are magnified over time. The MRSs have the potential to be a novel clinical biomarker for the assessment of trauma-related health risks.