Dataset Information

Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex.

ABSTRACT:

Background

Cellular deconvolution of bulk RNA-sequencing (RNA-seq) data using single cell or nuclei RNA-seq (sc/snRNA-seq) reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as human brain. Several deconvolution methods have been developed and they have been previously benchmarked against simulated data, pseudobulked sc/snRNA-seq data, or cell type proportions derived from immunohistochemistry reference data. A major limitation preventing the improvement of deconvolution algorithms has been the lack of highly integrated datasets with orthogonal measurements of gene expression and estimates of cell type proportions on the same tissue block. The performance of existing deconvolution algorithms has not yet been explored across different RNA extraction methods (e.g. cytosolic, nuclear, or whole cell RNA), different library preparation types (e.g. mRNA enrichment vs. ribosomal RNA depletion), or with matched single cell reference datasets.

Results

A rich multi-assay dataset was generated in postmortem human dorsolateral prefrontal cortex (DLPFC) from 22 tissue blocks. Assays included spatially-resolved transcriptomics, snRNA-seq, bulk RNA-seq across six RNA extraction and RNA-seq library combinations, and orthogonal cell type measurements via RNAScope/Immunofluorescence (RNAScope/IF). The Mean Ratio method was developed for selecting cell type marker genes for deconvolution and is implemented in the DeconvoBuddies R package. Five extensively benchmarked computational deconvolution algorithms were evaluated in DLPFC across six RNA-seq combinations and predicted cell type proportions were compared to those measured by RNAScope/IF.

Conclusions

We show that Bisque and hspe are the top performing methods with performance dependent on the RNA-seq library preparation conditions. We provide a multi-assay resource for the development and evaluation of deconvolution algorithms.

SUBMITTER: Huuki-Myers LA

PROVIDER: S-EPMC10888823 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Publications

Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex.

Huuki-Myers Louise A LA Montgomery Kelsey D KD Kwon Sang Ho SH Cinquemani Sophia S Eagles Nicholas J NJ Gonzalez-Padilla Daianna D Gonzalez-Padilla Daianna D Maden Sean K SK Kleinman Joel E JE Hyde Thomas M TM Hicks Stephanie C SC Maynard Kristen R KR Collado-Torres Leonardo L

bioRxiv : the preprint server for biology 20240407

<h4>Background</h4>Cellular deconvolution of bulk RNA-sequencing (RNA-seq) data using single cell or nuclei RNA-seq (sc/snRNA-seq) reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as human brain. Computational methods for deconvolution have been developed and benchmarked against simulated data, pseudobulked sc/snRNA-seq data, or immunohistochemistry reference data. A major limitation in developing improved deconvolution algorithms has be ...[more]

PMID: 38405805

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Project description:The pathophysiology of schizophrenia includes altered neurotransmission, dysregulated intracellular signaling pathway activity, and abnormal dendritic morphology that contribute to deficits of synaptic plasticity in the disorder. These processes all require dynamic protein-protein interactions at cell membranes. Lipid modifications target proteins to membranes by increasing substrate hydrophobicity by the addition of a fatty acid or isoprenyl moiety, and recent evidence suggests that dysregulated posttranslational lipid modifications may play a role in multiple neuropsychiatric disorders, including schizophrenia. Consistent with these emerging findings, we have recently reported decreased protein S-palmitoylation in schizophrenia. Protein prenylation is a lipid modification that occurs upstream of S-palmitoylation on many protein substrates, facilitating membrane localization and activity of key intracellular signaling proteins. Accordingly, we hypothesized that, in addition to palmitoylation, protein prenylation may be abnormal in schizophrenia. To test this, we assayed protein expression of the five prenyltransferase subunits (FNTA, FNTB, PGGT1B, RABGGTA, and RABGGTB) in postmortem dorsolateral prefrontal cortex from patients with schizophrenia and paired comparison subjects (n = 13 pairs). We found decreased levels of FNTA (14%), PGGT1B (13%), and RABGGTB (8%) in schizophrenia. To determine whether upstream or downstream factors may be driving these changes, we also assayed protein expression of the isoprenoid synthases FDPS and GGPS1 and prenylation-dependent processing enzymes RCE and ICMT. We found these upstream and downstream enzymes to have normal protein expression. To rule out effects from chronic antipsychotic treatment, we assayed FNTA, PGGT1B, and RABGGTB in the cortex from rats treated long-term with haloperidol decanoate and found no change in the expression of these proteins. Given the role prenylation plays in localization of key signaling proteins found at the synapse, these data offer a potential mechanism underlying abnormal protein-protein interactions and protein localization in schizophrenia.

Dataset Information

Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex.

Background

Results

Conclusions

Publications

Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex.

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