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Deconvolving Cell Type Proportions in Human Postmortem Brain Tissue from Bulk RNA-seq Data

Deconvolving Cell Type Proportions in Human Postmortem Brain Tissue from Bulk RNA-seq Data

Louise Huuki-Myers's 2021 ACNP Poster Presentation.

Louise Huuki-Myers

December 07, 2021

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  1. Deconvolving Cell Type Proportions in Human Postmortem Brain Tissue
    from Bulk RNA-seq Data
    Huuki-Myers, LA; Maynard, KM; Hicks, SC; Zandi, P; Kleinman, JE; Hyde, TM; Goes, FS; Collado-Torres, L
    Marker Finding
    Bisque showed robust deconvolution results on human brain bulk RNA-seq
    • We evaluated four relevant Deconvolution methods, MuSiC and Bisque were a
    good fit for our data sets
    • Bisque showed strongest performance in a benchmark in DLPFC data
    • We observed that the set of markers severely influenced MuSiC’s cell proportion
    estimates while Bisque is robust to marker set
    • Bisque estimates on the GTEx dataset are consistent with expected
    compositions between brain regions (Park et al. bioRxiv, 2021)
    We selected for genes that were highly specific for each cell type using “Mean Ratio”
    • !"#$ %#&'( = *+,- ./01+2234- 45 6,17+6 8+99 6:0+
    *+,-(./01+2234- 3- <37<+26 -4-=6,17+6 8+99 6:0+)
    • This method is designed to find genes specifically expressed in one cell type
    • Compared to 1 vs. All Differential Expression, high mean ratio genes have high
    fold change, but high fold change genes don’t always have high mean ratios
    • Selected the top 25 mean ratio genes from each cell type to serve as the marker
    set (250 genes total)
    • Identified eight known marker genes, 96% identified were new data driven
    Results in MDDseq + BipSeq Data
    Software Selection
    Some cell types have significantly different proportions between diagnosis
    • Cell type proportions were estimated using Bisque, Tran Maynard et al. sn-RNAseq
    reference dataset, and 250 data driven marker genes
    • Differences in Diagnosis: 22/60 pairwise t-test were significant
    • 10 cell types, 2 brain regions, 3 Diagnosis (p.bonf < 0.05)
    • Differences in Sex: 0/20 pairwise t-test were significant
    • 10 cell types, 2 brain regions (p.bonf < 0.05)
    • Effect size of these differences is marginal
    • Jew et al, Nature Communications, 2020, 10.1038/s41467-020-15816-6
    • Park et al. bioRxiv, 2021, 10.1101/2021.01.21.426000
    • Tran, Maynard et al., Neuron, 2021, 10.1101/2020.10.07.329839
    • Wang et al, Nature Communications, 2021, 10.1038/s41467-018-08023-x
    • Wilks et al. Genome Biology, 2021, 10.1186/s13059-021-02533-6
    Method Regression
    Correction for
    Technical Variation
    Other Features
    Wang et al, Nature
    Communications, 2019
    W-NNLS regression
    (Weighted - Non-negative
    least squares)
    Tree guided deconvolution, good
    for closely related cell types
    Jew et al, Nature
    Communications, 2020
    NNLS regression
    Gene specific
    transformation of bulk
    Leverage overlapping bulk & sc
    Dong et al, Briefings in
    Bioinformatics, 2020
    W-NNLS framework
    proposed by MuSiC
    Option for Gene
    specific transformation
    of bulk data (from
    Multiple reference datasets can be
    used, results combined with
    ENSEMBL weights
    Tsoucas, Nature
    Communications, 2019
    Dampened Weighted least
    Deconvolution estimates the composition of cell types in bulk RNA-seq samples
    • Some diagnosis can be the result in the change of cell type specific expression
    • Can be introduced through technical variability (i.e.. differences in dissection)
    • Controlling for cell fraction between samples can improve case vs. control
    • Enable detection of cell type specific eQTLs
    We performed deconvolution on post-mortem, human brain bulk RNA-seq samples
    • Utilized 10x protocol single nucleus RNAseq from Tran, Maynard et al., Neuron, 2021
    • 70k nuclei, 5 brain , regions, 8 donors
    • Analyzed RNAseq datasets: GTEx version 8: 2670 samples over 13 regions, and
    psychENCODE MDDseq + BipSeq: 1091 samples over 2 regions
    Most Specific Marker Genes for Abundant Cell Types
    Amygdala sACC
    Control 187 200
    MDD 231 228
    Bipolar 122 123
    Astrocytes 1638 782 1170 1099 907
    Endothelial 31 0 0 0 0
    Macrophages 0 10 0 22 0
    Microglia 1168 388 1126 492 784
    Mural 39 18 43 0 0
    Oligodendrocytes 6080 5455 5912 6134 4584
    OPC 1459 572 838 669 911
    T-Cells 31 9 26 0 0
    Excitatory Neurons 443 2388 623 0 4163
    Inhibitory Neurons 3117 1580 366 11476 3974
    Classic Oligo Marker MBP Shows Noisy Expression
    Estimated Cell Type Proportions Significant Differences Between Diagnosis
    All cell type proportions had some significant associations with qSV values
    • qSVs adjust for RNA degradation (Jaffe et al. PNAS, 2017)
    • Each cell type has significant linear correlation with at least 4 out of the top 10 qSV
    • qSVs may already capture variation in cell type composition
    Deconvolution was performed with MuSiC version 0.2.0 and the
    ReferenceBasedDecomposition function from BisqueRNA version 1.0.4, using the
    use.overlap = FALSE option.
    Mean Ratio marker finding method available in the DeconvoBuddies R Package:
    R version 4.1 and Bioconductor 3.12 & 3.14
    • Mean Ratio selects marker genes that are highly specific between cell types and
    good for deconvolution when considering broad cell types
    • Bisque is the most accurate and reliable deconvolution method we evaluated
    • Some cell types may have different proportions across diagnosis in the
    psychENCODE MDDseq dataset, but may be accounted for by qSV adjustment
    • Planning to incorporate this work in MMDseq and a Deconvolution methods
    Presenter & Poster requests:
    [email protected]
    Bisque Shows Consistent Performance
    snRNA-seq Dataset
    psychENCODE MDDseq
    & BipSeq Dataset
    Composition Over Region
    Examine eQTL interactions with cell type proportions
    • eQTL analysis of 2k MDD risk SNPs revealed 10 significant cell type interactions
    interactions in Amygdala, 46 in sACC (FDR < 0.01)
    Download this Poster:
    eQTL Cell Proportion Interaction
    Residual Expression
    Proportion Excitatory Neuron
    qSV Cell Proportion Correlations
    -log10(p-value Bonf)

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