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
Huuki-Myers, LA; Maynard, KM; Hicks, SC; Zandi, P; Kleinman, JE; Hyde, TM; Goes, FS; Collado-Torres, L
BAPTA
Introduction
Marker Finding
Conclusions
Acknowledgements
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
markers
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
MuSiC
Wang et al, Nature
Communications, 2019
W-NNLS regression
(Weighted - Non-negative
least squares)
None
Tree guided deconvolution, good
for closely related cell types
Bisque
Jew et al, Nature
NNLS regression
Gene specific
transformation of bulk
data
Leverage overlapping bulk & sc
data
SCDC
Dong et al, Briefings in
Bioinformatics, 2020
W-NNLS framework
proposed by MuSiC
Option for Gene
specific transformation
of bulk data (from
Bisque)
Multiple reference datasets can be
used, results combined with
ENSEMBL weights
DWLS
Tsoucas, Nature
Dampened Weighted least
squares
None
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
AMY DLPFC HPC NAc sACC
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
Methods
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:
github.com/LieberInstitute/DeconvoBuddies
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
evaluation
Presenter & Poster requests:
[email protected]
@lahuuki
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
sACC
qSV Cell Proportion Correlations
Amygdala
sACC
-log10(p-value Bonf)

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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

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