Toggle navigation. Pericak-Vance, Margaret A. Scott, William K. Abstract Funding Institution Related projects Comments. Recent in Grantomics:. Recently viewed grants:. Recently added grants:. Funding Agency. We first tried to create a transcriptome-wide reference panel by selecting the genes that are differentially expressed among cell types [ 26 , 60 , 62 ].
For this reason, we curated a list of marker genes that have been described to tag these distinct cell types [ 31 , 55 , 56 ] Additional file 1 : Table S3.
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A visual inspection of the expression of these marker genes in the samples we compiled suggested a divergent transcriptomic profile among the cell types Additional file 1 : Figure S2a. The PCA showed that their expression was sufficient to cluster samples of neurons, astrocytes, oligodendrocytes, and microglia with their respective cell types, regardless of the species of the reference samples Additional file 1 : Figure S1b; Additional file 1 : Table S2.
We observed that some samples did not cluster with their expected cell types and coincidently the LOOCV indicated that these samples had an expression signatures that differed from the other samples of the same cell type. However, we found that all of these outliers correspond to samples not correctly purified or that were sequenced in early stages of differentiation Additional file 1 : Supplementary Results. Once we identified the optimal approach to perform digital deconvolution from brain RNA-seq, we benchmarked it by using three sets of independent pure cell populations and simulated chimeric libraries.
To evaluate the accuracy of digital deconvolution for measuring cell-type proportion from cell-type admixtures, we simulated RNA-seq libraries by pooling reads from individual cell types into well-defined proportions. We combined randomly sampled reads from neurons, astrocytes, oligodendrocytes, and microglia to create chimeric libraries that mimic bulk RNA-seq from brain, but with a range of pre-defined cell-type distributions Additional file 1 : Figure S3.
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We then quantified the gene expression for the chimeric libraries and inferred the cell-type distribution employing for the reference panel samples that did not contribute reads to the chimeric libraries. Finally, we evaluated whether any gene included in the reference panel was dominating the inference of cell proportions. We re-calculated the cell-type distributions of the chimeric libraries but dropping each of the genes from the reference panel one at a time.
In this way, we verified that the proportions inferred using the reference panel are not driven by the expression of a single gene.
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This reassured us the inference should be robust to any bias introduced by the potential association of a single gene included in the reference panel with a particular trait. Pathologically, AD is associated with neuronal death and gliosis specifically in the cerebral cortex. We evaluated whether we could exploit deconvolution methods using our reference panel to detect altered cellular population structure from the bulk RNA-seq and whether this corresponded to known pathological alterations.
Effects of AD and associations with additional clinical and pathological phenotypes in cell-type distributions were estimated using linear regression model. Mean neuronal blue and astrocytic proportion red for a AD affected brains and controls bars indicate standard deviations. The numbers of participants for each group are shown below the x-axis. Standard errors were depicted in shaded area with LOESS smooth curve fitted to cell-type proportions derived from deconvolution. The proportion of microglia was lower than any other cell types. Therefore, we speculated that the lack of changes in the AD microglial population was neither due to low statistical power nor the inability of our method to estimate the microglial proportions but reflected unaltered neuropathological observations in AD brains.
Replicating our findings from the Mayo dataset, we observed a significant lower relative proportion in neurons and increase in astrocytes in all four areas Table 2 ; Fig. Neuropathological studies have described that the PHG is one of the first brain areas in which AD pathology occurs [ 64 — 66 ]. While the loss of neurons is a common feature of AD, it is not clear whether the mechanism holds true across different forms of AD or AD cases carrying different genetic risk variants. Therefore, we investigated whether AD with distinct etiologies showed different cellular compositions.
The cellular population structure was inferred using the ssNMF method. Effects and p -values for the association with disease status, clinical dementia rating and Braak staging using generalized mixed models. We identified similar trends with approximately the same significance levels. Neuronal and astrocytic proportions plotted against b Braak staging and c by CDR. In summary, our results indicate that ADAD individuals present a higher neuronal loss even in the same stage of the disease, suggesting that in ADAD neuronal death plays a more important role in pathogenesis compared to sporadic AD, in which other factors such as inflammation or immune response may be involved.
A variety of genetic variants increase risk of LOAD; however, it is unclear if the cellular mechanisms are the same across these distinct risk factors. Therefore, we tested the hypothesis that distinct genetic causes of LOAD have characteristic cellular population signatures. Finally, we performed similar analyses with TREM2 carriers.
We analyzed whether the TREM2 carriers provided sufficient power to detect a significant association. In fact, our analyses indicate that TREM2 carriers have a unique cellular brain composition distinct than the other AD cases. The distribution of CDR, mean number of amyloid plaques, and Braak staging do not differ between strata.
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Nonetheless, we verified that the cellular proportions were still significantly different after correcting for each of those variables Table 4. These results suggested that the mechanism that lead to disease in TREM2 carriers is less neuron-centric than in the general AD population. We acknowledge that the accuracy of this platform might be affected by the phenotypic diversity of the reference panel or the disease-induced dysregulation of genes it includes.
However, the deconvolution approach proved to be robust to the genes included in the reference panel, as we demonstrated that the proportions it inferred are not driven by the expression of any single gene. This platform produced reliable cell proportion estimates, as was shown by the evaluation of independent datasets of iPSC-derived neurons and microglia, mice cortical neurons Additional file 1 : Figure S4 , and simulated chimeric libraries. We used this approach to deconvolve studies that include large numbers of neuropathologically defined AD and control brains with their transcriptome ascertained in distinct brain regions.
We observed consistently significant lower relative neuronal proportion and increased relative astrocyte proportions in the cerebral cortex suggesting neuronal loss and astrocytosis. Compatible with other studies, we also identified that the altered cellular proportion is also significantly associated with decline in cognition and Braak staging [ 68 ]. In contrast, we did not identify a significant difference in the cellular population structure in the cerebellum, a region not affected in AD Table 2 ; Fig.
We replicated this finding in a multi-area analysis from the MSBB dataset. These results may implicate that TREM2 risk variants lead to a cascade of pathological events that differ from those occurring in sporadic AD cases, which is also consistent with the known biology of TREM2. Further longitudinal neuroimaging analysis is required to validate our findings. TREM2 is involved in AD pathology through microglia mediated pathways, implicated on altered immune response and inflammation [ 71 ].
Furthermore, TREM2 deficiency was reported to attenuate tauopathy against brain atrophy [ 73 ].
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We found no significant difference in the proportion of microglia between AD cases and controls. However, we found significantly decreased microglia in brains exhibiting PA Additional file 1 : Table S7; Additional file 1 : Figure S6 , proving that these studies are sufficiently powered to identify significant differences. In any case, we cannot rule out the possibility of a change in the activation stage of microglia in these individuals. Therefore, other pathogenic mechanisms should contribute to disease.
We believe that a detailed modeling of immune response cells, reflecting the alternative microglia activation states, will generate more accurate profiles to elucidate the immune cell distribution in AD. There is a large interest in the scientific community to use brain expression studies to try to identity novel pathogenic mechanisms in AD and to identify novel therapeutic targets.
Single-cell sorting needs to be performed with fresh tissue [ 74 ], which restrains the analysis of highly characterized fresh-frozen brains collected by AD research centers. Our results indicate that digital deconvolution methods can accurately infer relative cell distributions from brain bulk RNA-seq data, but we recognize the importance of obtaining traditional neuropathological measures to validate the results we observed.
Having this approach validated for AD can have an important impact in the community, because digital deconvolution analyses can: 1 reveal distinct cellular composition patterns underlying different disease etiologies; 2 provide additional insights about the overall pathologic mechanisms underlying different mutations carriers for variants as in genes such as TREM2 , APOE , APP , PSEN1 , and PSEN2 ; 3 correct the effect that altered cell composition and genetic statuses have in addition to downstream transcriptomic analyses and lead to novel and informative results; and 4 help the analysis of highly informative frozen brains collected over the years.
In conclusion, our study provides a reliable approach to enhance our understanding of the fundamental cellular mechanisms involved in AD and enable the analysis of large bulk RNA-seq data that may lead to novel discoveries and insights into neurodegeneration.
Anterior prefrontal cortex. Clinical Dementia Rating. Dominantly Inherited Alzheimer Network. Digital sorting algorithm. Inferior frontal gyrus. Induced pluripotent stem cell. Charles F. Implementation of method Population-Specific Expression Analysis. Mount Sinai Brain Bank.
Pathological aging. Principal component analyses. Parahippocampal gyrus. Population-Specific Expression Analysis. RNA integrity number. Root-mean-square error.
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