Cell type–specific gene expression differences in complex tissues

Shen-Orr, Shai S.; Tibshirani, Robert; Khatri, Purvesh; Bodian, Dale L.; Staedtler, Frank; Perry, Nicholas; Hastie, Trevor; Sarwal, Minnie M.; Davis, Mark M.; Butte, Atul J.

doi:10.1038/nmeth.1439

Cited by 463 publications

(553 citation statements)

References 12 publications

(10 reference statements)

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“…A fraction of the expression differences detected for the parasite load effect after accounting for total cell counts is likely caused by average differences in the proportions of subtypes of PBMCs (15). To infer these effects in our sample, we used the genomic signature of flow cytometry-sorted immune cell types (16) in which cell type-specific modules are constructed based on transcript abundance of each gene relative to each other cell type in the PBMC mixture.…”

Section: Resultsmentioning

confidence: 99%

Evidence for additive and interaction effects of host genotype and infection in malaria

Idaghdour

Quinlan

Goulet

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

124

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The host mechanisms responsible for protection against malaria remain poorly understood, with only a few protective genetic effects mapped in humans. Here, we characterize a host-specific genome-wide signature in whole-blood transcriptomes of Plasmodium falciparum-infected West African children and report a demonstration of genotype-by-infection interactions in vivo. Several associations involve transcripts sensitive to infection and implicate complement system, antigen processing and presentation, and T-cell activation (i.e., SLC39A8, C3AR1, FCGR3B, RAD21, RETN, LRRC25, SLC3A2, and TAPBP), including one association that validated a genome-wide association candidate gene (SCO1), implicating binding variation within a noncoding regulatory element. Gene expression profiles in mice infected with Plasmodium chabaudi revealed and validated similar responses and highlighted specific pathways and genes that are likely important responders in both hosts. These results suggest that host variation and its interplay with infection affect children's ability to cope with infection and suggest a polygenic model mounted at the transcriptional level for susceptibility.host response | parasite load | eQTL | eSNP | genotype-by-environment interactions

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Section: Resultsmentioning

confidence: 99%

Evidence for additive and interaction effects of host genotype and infection in malaria

Idaghdour

Quinlan

Goulet

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

124

View full text Add to dashboard Cite

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“…Advanced procedures being developed in other areas of research (eg, transcriptomics) that help determine cell content of samples will mitigate this concern in future studies. 29,30 Finally, the generalizability of findings needs to be enhanced by conducting similar experiments in more diverse study populations.…”

Section: Discussionmentioning

confidence: 99%

Early Pregnancy Maternal Blood DNA Methylation in Repeat Pregnancies and Change in Gestational Diabetes Mellitus Status—A Pilot Study

et al. 2015

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Repeat pregnancies with different perinatal outcomes minimize underlying maternal genetic diversity and provide unique opportunities to investigate nongenetic risk factors and epigenetic mechanisms of pregnancy complications. We investigated gestational diabetes mellitus (GDM)-related differential DNA methylation in early pregnancy peripheral blood samples collected from women who had a change in GDM status in repeat pregnancies. Six study participants were randomly selected from among women who had 2 consecutive pregnancies, only 1 of which was complicated by GDM (case pregnancy) and the other was not (control pregnancy). Epigenome-wide DNA methylation was profiled using Illumina HumanMethylation 27 BeadChips. Differential Identification using Mixture Ensemble and false discovery rate (<10%) cutoffs were used to identify differentially methylated targets between the 2 pregnancies of each participant. Overall, 27 target sites, 17 hypomethylated (fold change [FC] range: 0.77-0.99) and 10 hypermethylated (FC range: 1.01-1.09), were differentially methylated between GDM and control pregnancies among 5 or more study participants. Novel genes were related to identified hypomethylated (such as NDUFC1, HAPLN3, HHLA3, and RHOG) or hypermethylated sites (such as SEP11, ZAR1, and DDR). Genes related to identified sites participated in cell morphology, cellular assembly, cellular organization, cellular compromise, and cell cycle. Our findings support early pregnancy peripheral blood DNA methylation differences in repeat pregnancies with change in GDM status. Similar, larger, and repeat pregnancy studies can enhance biomarker discovery and mechanistic studies of GDM.

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“…This challenge imposes severe limitations on researchers' ability to account for the cell-lineage-specific expression and function of most human genes. This problem is distinct from the task of identifying the fractional composition of a heterogeneous sample (e.g., whole blood), and methods to address such problems require whole-genome expression measurements for each underlying cell type, which are unavailable for most solid human cell lineages (Shen-Orr et al 2010). Our iterative machine-learningbased approach leverages heterogeneous expression data from human tissue homogenates.…”

Section: [Supplemental Materials Is Available For This Article]mentioning

confidence: 99%

Defining cell-type specificity at the transcriptional level in human disease

et al. 2013

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Cell-lineage–specific transcripts are essential for differentiated tissue function, implicated in hereditary organ failure, and mediate acquired chronic diseases. However, experimental identification of cell-lineage–specific genes in a genome-scale manner is infeasible for most solid human tissues. We developed the first genome-scale method to identify genes with cell-lineage–specific expression, even in lineages not separable by experimental microdissection. Our machine-learning–based approach leverages high-throughput data from tissue homogenates in a novel iterative statistical framework. We applied this method to chronic kidney disease and identified transcripts specific to podocytes, key cells in the glomerular filter responsible for hereditary and most acquired glomerular kidney disease. In a systematic evaluation of our predictions by immunohistochemistry, our in silico approach was significantly more accurate (65% accuracy in human) than predictions based on direct measurement of in vivo fluorescence-tagged murine podocytes (23%). Our method identified genes implicated as causal in hereditary glomerular disease and involved in molecular pathways of acquired and chronic renal diseases. Furthermore, based on expression analysis of human kidney disease biopsies, we demonstrated that expression of the podocyte genes identified by our approach is significantly related to the degree of renal impairment in patients. Our approach is broadly applicable to define lineage specificity in both cell physiology and human disease contexts. We provide a user-friendly website that enables researchers to apply this method to any cell-lineage or tissue of interest. Identified cell-lineage–specific transcripts are expected to play essential tissue-specific roles in organogenesis and disease and can provide starting points for the development of organ-specific diagnostics and therapies.

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Cell type–specific gene expression differences in complex tissues

Cited by 463 publications

References 12 publications

Evidence for additive and interaction effects of host genotype and infection in malaria

Evidence for additive and interaction effects of host genotype and infection in malaria

Early Pregnancy Maternal Blood DNA Methylation in Repeat Pregnancies and Change in Gestational Diabetes Mellitus Status—A Pilot Study

Defining cell-type specificity at the transcriptional level in human disease

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