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. 2024 May 24;10(21):eadh2588.
doi: 10.1126/sciadv.adh2588. Epub 2024 May 23.

Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data

Rujia Dai  1 , Tianyao Chu  2 , Ming Zhang  2 , Xuan Wang  2 , Alexandre Jourdon  3 , Feinan Wu  3 , Jessica Mariani  3 , Flora M Vaccarino  3   4 , Donghoon Lee  5 , John F Fullard  5 , Gabriel E Hoffman  5 , Panos Roussos  5 , Yue Wang  6 , Xusheng Wang  7 , Dalila Pinto  8 , Sidney H Wang  9 , Chunling Zhang  10 ; PsychENCODE consortium; Chao Chen  2 , Chunyu Liu  1   2   10
Affiliations

Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data

Rujia Dai et al. Sci Adv. .

Abstract

Sample-wise deconvolution methods estimate cell-type proportions and gene expressions in bulk tissue samples, yet their performance and biological applications remain unexplored, particularly in human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk tissue RNA sequencing (RNA-seq), single-cell/nuclei (sc/sn) RNA-seq, and immunohistochemistry. A total of 1,130,767 nuclei per cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expressions. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk tissue or single-cell eQTLs did alone. Differential gene expressions associated with Alzheimer's disease, schizophrenia, and brain development were also examined using the deconvoluted data. Our findings, which were replicated in bulk tissue and single-cell data, provided insights into the biological applications of deconvoluted data in multiple brain disorders.

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Figures

Fig. 1.
Fig. 1.. Study overview.
This figure was created with BioRender software.
Fig. 2.
Fig. 2.. Assessment of cell proportions estimated by examined deconvolution methods.
(A) Sample-level RMSE values between estimated cell proportions and ground truth. IHC, immunohistochemistry; scprop, cell proportions calculated from sc/snRNA-seq data; scprop, the number of cells of specific cell type/number of total cells. (B) Cell-type level RMSE values between estimated cell proportions and ground truth data. RMSE values were normalized by the value of cell proportions to make them comparable across cell types. Cell types were ordered by cell proportions in a decreasing way. Ex, excitatory neurons; In, inhibitory neurons; Ast, astrocytes; Opc, oligodendrocyte precursor cells; Mic, microglia; Per, pericytes; End, endothelial cells; RG, radial glia; EN.PP, early born excitatory neurons of the preplate/subplate; CP.mixed, cortical plate mixed neurons; MCP, medial cortical plate; EN-DCP, dorsal cortical plate excitatory neurons; IPC-nN, intermediate progenitor cell or newborn neuron; RG.tRG, truncated radial glia; RG.oRG, outer radial glia; RG.hem, radial glia in cortical hem; IN, inhibitory neurons; RG-LGE, progenitors corresponding to a putative ventrolateral ganglionic eminence fate.
Fig. 3.
Fig. 3.. Assessment of sample-wise cell-type expressions deconvoluted from bulk tissue data.
(A) Overall assessment of methods for estimating cell-type expressions. Spearman’s correlations between deconvoluted data and sc/snRNA-seq data from matched samples. The averaged expression by cell types was used as ground truth. (B) Cell-type level assessment of methods for estimating cell-type expressions. Correlations between deconvoluted data by bMIND and sc/snRNA-seq data were calculated for each cell type. Cell proportions estimated by dtangle were used for input. Cell types on the y axis were ordered by cell proportions computed from sc/snRNA-seq. (C) Assessment of cell-type specificity in estimated expressions. The figure shows the expression of marker genes in deconvoluted data by bMIND.
Fig. 4.
Fig. 4.. Cell-type eQTL mapping based on deconvoluted sample-wise expression data.
(A) Illustration of decon-eQTL mapping. (B) Number of decon-eQTLs identified in different cell types at FDR < 0.05 in the permutation test. (C) Pi1 statistics of decon-eQTLs in BrainGVEX decon-eQTLs and (D) eQTLs from snRNA-seq study of Bryois et al. (31). For calculating Pi1 statistics, decon-eQTLs from ROSMAP were used as testing data, and eQTLs from BrainGVEX and Bryois et al. (31) were used as references. (E) Comparison of decon-eQTLs and bulk tissue eQTLs. The top barplot shows the Pi1 values of decon-eQTLs (testing data) in bulk tissue eQTLs (reference). The bottom plot shows the intersections between decon-eQTLs and bulk tissue eQTLs, as well as intersections of decon-eQTLs across various cell types.
Fig. 5.
Fig. 5.. SCZ GWAS heritability explained by cell-type eQTLs and bulk tissue eQTLs.
(A) Total SCZ GWAS heritability (h2) explained by eQTLs. (B) SCZ GWAS heritability enrichment in eQTLs. Enrichment = h2/number of SNPs in each eQTL category.
Fig. 6.
Fig. 6.. Replication of PAGs.
The proportions of PAGs replicated in bulk tissue data (left) and sc/snRNA-seq data (right) were displayed. The light blue bar shows the proportions of replicated PAGs with FDR < 0.05. The dark blue bar shows the proportions of replicated PAGs with FDR < 0.05 and having the same direction of changes in replication data. expr denotes expression. The maturity of organoids was measured by the days of cell culture.
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