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Generation of multi-omic datasets using high-throughput molecular profiling of DNA methylation human data

ABSTRACT: Tumor heterogeneity significantly affects cancer progression and therapeutic response, yet quantifying it from bulk molecular data remains challenging. Deconvolution algorithms, which estimate cell-type proportions in bulk samples, offer a potential solution. However, there is no consensus on the optimal algorithm for transcriptomic or methylomic data. Here, we present an unbiased evaluation framework for the first comprehensive comparison of deconvolution algorithms across both omic types, including reference-based and -free approaches. Our evaluation covers raw performance, stability, and computational efficiency under varying conditions, such as missing or additional cell types and diverse sample compositions. We design a reproducible workflow using containerization and publicly available code to ensure transparency and re-usability. Our results highlight the strengths and limitations of various algorithms, providing practical guidance for selecting the best method based on data type and context. This benchmark sets a new standard for evaluating deconvolution methods and analyzing tumor heterogeneity.

ORGANISM(S): Homo sapiens

PROVIDER: GSE281305 | GEO | 2025/12/30

REPOSITORIES: GEO

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Project description:To date, most epigenetic datasets have been generated on DNA samples isolated from bulk tissues. As the proportion of individual cell types within a sample can vary across individuals, systematic differences in cellular proportions that correlate with the phenotype of interest may manifest as differences in the overall epigenetic profile. Deconvolution algorithms calculate a series of continuous variables reflecting the underlying cellular heterogeneity of each sample from the bulk tissue profile that can be used to adjust for confounding in epigenome-wide association analyses. Here we provide novel reference profiles for brain cell types for use with reference based deconvolution algorithms. We used a FANS protocol recently described by our group to purify nuclei populations from prefrontal cortex tissue from 43 adult donors. Our initial gating strategy used an antibody against NeuN to isolate neuronal nuclei in combination with an antibody against SOX10 to separate oligodendrocyte nuclei from other glial nuclei. Subsequently, in a second gating strategy we additionally included an antibody against IRF8 to enrich microglia from the NeuNNeg/SOX10Neg fraction. Our third gating strategy used an antibody against SATB2 in place of NeuN to isolate excitatory neurons. We generated DNA methylation profiles using the Illumina EPIC array for NeuNPos (neuron-enriched; n = 28), NeuNNeg/SOX10Pos (oligodendrocyte-enriched; n = 24), NeuNNeg/SOX10Neg (microglia- and astrocyte-enriched; n = 21), NeuNNeg/SOX10Neg/IRF8Pos (microglia-enriched; n = 17), NeuNNeg/SOX10Neg/IRF8Neg, (astrocyte-enriched; n = 7), SATB2Pos (excitatory neuron-enriched; n = 9), and SATB2Neg (inhibitory neuron- and glial- enriched; n = 6) nuclei populations. We have demonstrated that these are applicable for use with established deconvolution algorithms to quantify the cellular heterogeneity of the cortex and other regions of the human brain from bulk DNAm data. These variables will be critical covariates to include in future epigenetic studies of brain disorders to minimise the risk of false positive associations and improve our understanding of the changes in the brain that underpin the development of psychiatric disorders and neurodegenerative diseases.

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Generation of multi-omic datasets using high-throughput molecular profiling of DNA methylation human data

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