Klokkaris, A. | bioRxiv Science

bioRxiv2025

Optimised fluorescence-activated nuclei sorting for epigenomic analysis of cortical cell types

Increased understanding of the functional complexity of the genome has led to growing recognition of the role of non-sequence-based regulatory variation in disorders of the human central nervous system. Most genomic analyses of the brain are limited by the use of bulk tissue, which comprises a heterogeneous mix of different neural cell types with distinct epigenetic profiles, thereby limiting the ability to attribute regulatory changes to specific cell populations. Given the limited availability of human post-mortem tissue resources and the importance of integrating multi-omic data from the same samples, there is a critical need for methods that enable parallel, cell-type-resolved genomic profiling. We present optimised protocols using fluorescence-activated nuclei sorting (FANS) to isolate nuclei from different human and mouse brain cell types for downstream multi-omic analysis. Our approach enables the robust purification of neuronal, oligodendrocyte, microglial and other glial-origin nuclei from both adult and fetal brain tissue. We demonstrate that FANS-isolated nuclei are compatible with a wide range of genomic assays, including profiling of DNA modifications, histone modifications, chromatin accessibility, and gene expression. This protocol maximises the utility of limited post-mortem tissue resources and provides a unified workflow for comprehensive, cell-type-specific interrogation of molecular mechanisms involved in the brain.

genomics↗

bioRxiv2024

Guidance for the design and analysis of cell-type specific epigenetic epidemiology studies.

Recent studies on the role of epigenetics in disease have focused on DNA methylation profiled in bulk tissues limiting the detection of the cell-type affected by disease related changes. Advances in isolating homogeneous populations of cells now make it possible to identify DNA methylation differences associated with disease in specific cell-types. Critically, these datasets will require a bespoke analytical framework that can characterise whether the difference affects multiple or is specific to a particular cell-type. We take advantage of a large set of DNA methylation profiles (n = 751) obtained from five different purified cell populations isolated from human prefrontal cortex samples and evaluate the effects on study design, data preprocessing and statistical analysis for cell-specific studies, particularly for scenarios where multiple cell types are included. We describe novel quality control metrics that confirm successful isolation of purified cell populations, which when included in standard preprocessing pipelines provide confidence in the dataset. Our power calculations show substantial gains in detecting differentially methylated positions for some purified cell populations compared to bulk tissue analyses, countering concerns regarding the feasibility of generating large enough sample sizes for informative epidemiological studies. In a simulation study, we evaluated different regression models finding that this choice impacts on the robustness of the results. These findings informed our proposed two-stage framework for association analyses. Overall, our results provide guidance for cell-specific EWAS, establishing standards for study design and analysis, while showcasing the potential of cell-specific DNA methylation analyses to reveal links between epigenetic dysregulation and disease.

bioinformatics↗