Bulk Tissue Cell Type Deconvolution with Multi-Subject Single-Cell Expression Reference

Wang, Xuran; Park, Jihwan; Suszták, Katalin; Zhang, Nancy R.; Li, Mingyao

doi:10.1101/354944

Cited by 75 publications

(137 citation statements)

References 12 publications

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“…The tumor/cell line differences we identified (MNN correction vectors) also varied across disease type, emphasizing the importance of using a non-linear method that allows for disease-type-specific differences. As single-cell data from normal tissues become more readily available, methods that use these data to estimate and remove the effect of different contaminating cells 62,63 could be incorporated to further improve comparisons between tumors and cell lines.…”

Section: Discussionmentioning

confidence: 99%

Global computational alignment of tumor and cell line transcriptional profiles

Warren

Jones

Shibue

et al. 2020

Preprint

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Cell lines are key tools for preclinical cancer research, but it remains unclear how well they represent patient tumor samples. Identifying cell line models that best represent the features of particular tumor samples, as well as tumor types that lack in vitro model representation, remain important challenges. Gene expression has been shown to provide rich information that can be used to identify tumor subtypes, as well as predict the genetic dependencies and chemical vulnerabilities of cell lines. However, direct comparisons of tumor and cell line transcriptional profiles are complicated by systematic differences, such as the presence of immune and stromal cells in tumor samples and differences in the cancer-type composition of cell line and tumor expression datasets. To address these challenges, we developed an unsupervised alignment method (Celligner) and applied it to integrate several large-scale cell line and tumor RNA-Seq datasets. While our method aligns the majority of cell lines with tumor samples of the same cancer type, it also reveals large differences in tumor/cell line similarity across disease types. Furthermore, Celligner identifies a distinct group of several hundred cell lines from diverse lineages that present a more mesenchymal and undifferentiated transcriptional state and which exhibit distinct chemical and genetic dependencies. This method could thus be used to guide the selection of cell lines that more closely resemble patient tumors and improve the clinical translation of insights gained from cell line models.

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Section: Discussionmentioning

confidence: 99%

Global computational alignment of tumor and cell line transcriptional profiles

Warren

Jones

Shibue

et al. 2020

Preprint

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“…The existence of the neuronal evolutionary signature shared among brain regions could be used to deconvolute bulk RNA-seq data, analogous to the cell type marker-based deconvolution procedure [Wang et al, 2019]. Supporting this notion, human-specificity ratios of genes preferentially expressed in neuronal subtypes correlated positively and significantly between single nuclei and bulk RNA-seq datasets (Fig.…”

Section: Deconvolution Of Bulk Human-specific Expression Differencesmentioning

confidence: 65%

Single-cell-resolution transcriptome map of human, chimpanzee, bonobo, and macaque brains

Khrameeva

Kurochkin

Han

et al. 2019

Preprint

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Identification of gene expression traits unique to the human brain sheds light on the mechanisms of human cognition. Here we searched for gene expression traits separating humans from other primates by analyzing 88,047 cell nuclei and 422 tissue samples representing 33 brain regions of humans, chimpanzees, bonobos, and macaques. We show that gene expression evolves rapidly within cell types, with more than two-thirds of cell type-specific differences not detected using conventional RNA sequencing of tissue samples. Neurons tend to evolve faster in all hominids, but non-neuronal cell types, such as astrocytes and oligodendrocyte progenitors, show more differences on the human lineage, including alterations of spatial distribution across neocortical layers.

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“…For example, analysis using genes highly expressed in prostate BCC reveals the existence of basal cell features in some bulk samples of prostate cancer, which is consistent with recent classification of prostate cancer samples into basal-like and luminal-like subtypes using the PAM50 classifier. 1,48 The single-cell resolution profiles of constitutional cell types from prostate BCC provide further advantage for future deconvolution of bulk samples, 49 which will maximize the values of current genomic data of prostate cancer. With the increasing single-cell data from large programs, a comparison between human normal basal cells and our prostate BCC tumor cells can provide more information about the malignancy markers for this disease.…”

Section: Discussionmentioning

confidence: 99%

Mutational and transcriptomic landscapes of a rare human prostate basal cell carcinoma

Long

Jiang

et al. 2020

The Prostate

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Background As a rare subtype of prostate carcinoma, basal cell carcinoma (BCC) has not been studied extensively and thus lacks systematic molecular characterization. Methods Here, we applied single‐cell genomic amplification and RNA‐Seq to a specimen of human prostate BCC (CK34βE12+/P63+/PAP−/PSA−). The mutational landscape was obtained via whole exome sequencing of the amplification mixture of 49 single cells, and the transcriptomes of 69 single cells were also obtained. Results The five putative driver genes mutated in BCC are CASC5, NUTM1, PTPRC, KMT2C, and TBX3, and the top three nucleotide substitutions are C>T, T>C, and C>A, similar to common prostate cancer. The distribution of the variant allele frequency values indicated that these single cells are from the same tumor clone. The 69 single cells were clustered into tumor, stromal, and immune cells based on their global transcriptomic profiles. The tumor cells specifically express basal cell markers like KRT5, KRT14, and KRT23 and epithelial markers EPCAM, CDH1, and CD24. The transcription factor covariance network analysis showed that the BCC tumor cells have distinct regulatory networks. By comparison with current prostate cancer datasets, we found that some of the bulk samples exhibit basal cell signatures. Interestingly, at single‐cell resolution the gene expression patterns of prostate BCC tumor cells show uniqueness compared with that of common prostate cancer‐derived circulating tumor cells. Conclusions This study, for the first time, discloses the comprehensive mutational and transcriptomic landscapes of prostate BCC, which lays a foundation for the understanding of its tumorigenesis mechanism and provides new insights into prostate cancers in general.

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Bulk Tissue Cell Type Deconvolution with Multi-Subject Single-Cell Expression Reference

Cited by 75 publications

References 12 publications

Global computational alignment of tumor and cell line transcriptional profiles

Global computational alignment of tumor and cell line transcriptional profiles

Single-cell-resolution transcriptome map of human, chimpanzee, bonobo, and macaque brains

Mutational and transcriptomic landscapes of a rare human prostate basal cell carcinoma

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