Tumor-Induced Generation of Splenic Erythroblast-like Ter-Cells Promotes Tumor Progression

Yun, Han; Liu, Q; Hou, Jin; Gu, Yan; Zhang, Y; Chen, Z; Fan, Jia; Zhou, Weiping; Qiu, Siqi; Dong, Tao; Li, N; Jiang, Zhengping; Zhu, Hongyan; Zhang, Q; Ma, Yuanyuan; Zhang, L; Wang, Q; Yu, Yizhi; Cao, Xuetao

doi:10.1016/j.cell.2018.02.061

Cited by 120 publications

(151 citation statements)

References 44 publications

Supporting

Mentioning

126

Contrasting

Order By: Relevance

“…When we performed differential gene expression analysis between subtype 2 and all other cell subtypes, subtype 2 shows significantly higher SDC1 (also known as CD138) expression levels, increased TFRC (CD71) expression, insignificantly decreased PTPRC (CD45) expression, and significantly decreased PTPRCAP (CD45 associated protein) expression. Since all of the IUSM MM cells in our study had already been FACS sorted for CD138+, it is possible we have identified a population of CD45-/CD71+/CD138+/CD38+ cells in MM with a similar phenotype to the erythroblast-like cells found previously in HCC [34].…”

Section: Discussionsupporting

confidence: 57%

See 1 more Smart Citation

Diagnostic Evidence GAuge of Single cells (DEGAS): A flexible deep-transfer learning framework for prioritizing cells in relation to disease

Johnson

Huang³

et al. 2020

Preprint

View full text Add to dashboard Cite

With the rapid advance of single cell sequencing techniques, single cell molecular data are quickly accumulated. However, there lacks a sound approach to properly integrate single cell data with the existing large amount of patient-level disease data. To address such need, we proposed DEGAS (Diagnostic Evidence GAuge of Single cells), a novel deep transfer-learning framework which allows for cellular and clinical information, including cell types, disease risk, and patient subtypes, to be cross-mapped between single cell and patient data, provided they share at least one common type of molecular data. We call such transferrable information "impressions", which are generated by the deep learning models learned in the DEGAS framework. Using eight datasets from a wide range of diseases including Glioblastoma Multiforme (GBM), Alzheimer's Disease (AD), and Multiple Myeloma (MM), we demonstrate the feasibility and broad applications of DEGAS in cross-mapping clinical and cellular information across disparate single cell and patient level transcriptomic datasets. Specifically, we correctly mapped clinically known GBM patient subtypes onto single cell data. We also identified previously known neuron loss from AD brains, then mapped the "impression" of AD risk to single cell data. Furthermore, we discovered novel differences in excitatory and inhibitory neuron loss in AD data. From the exploratory MM data, we identified differences in the malignancy of different CD138+ cellular subtypes based on "impressions" of relapse information transferred from MM patients. Through this work, we demonstrated that DEGAS is a powerful framework to cross-infer cellular and patient-level characteristics, which not only unites single cell and patient level transcriptomic data by identifying their latent links using the deep learning approach, but can also prioritize both patient subtypes and cellular subtypes for precision medicine.

show abstract

Section: Discussionsupporting

confidence: 57%

“…Upon further examination, we found evidence that subtype 2 may represent a population of erythroblast-like cells in MM. Tumor-inducible erythroblast-like cells have already been reported in hepatocellular carcinoma (HCC) [34]. These CD71+ erythroblast-like cells have been shown in mice to be derived from embryonic stem cells.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Evidence GAuge of Single cells (DEGAS): A flexible deep-transfer learning framework for prioritizing cells in relation to disease

Johnson

Huang³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…2B). Instead, the SCP-like tumor cells expressed genes such as the artemin receptor GFRA3, recently shown to promote hepatocellular carcinoma initiation and growth 25 , and the integrin subunit ITGB8, which is implicated in differentiation and radiosensitization of glioblastoma 26 (Fig. 2B).…”

Section: Sox10+ Cells Expressed Erbb3 and The Ligand Nrg1 Was Expressmentioning

confidence: 99%

Malignant Schwann cell precursors mediate intratumoral plasticity in human neuroblastoma

Olsen

Otte

Mei

et al. 2020

Preprint

View full text Add to dashboard Cite

Neuroblastoma is a heterogeneous embryonal malignancy and the most deadly tumor of childhood, although a minor subset may show spontaneous differentiation. It arises from the multipotent neural crest lineage during development. Some of this multipotency is retained in neuroblastoma, which can give rise to both adrenergic and mesenchymal tumor cells. The mechanisms enabling such dual fates are unknown, but likely help neuroblastoma to evade existing therapies. To understand neuroblastoma plasticity, we analyzed patient tumors using single-cell transcriptomics. In addition to the heterogeneous adrenergic and mesenchymal

show abstract

“…CD45 is known as a nonerythroid cell marker. To remove the nonerythroid cells from the suspension, CD45-positive cells were excluded from the cell suspension using magnetic separation [18,21]. The resulting purified CD45-negative cells were CD71 high Ter119 high erythroid cells with a purity of 95.2% AE 0.1% (Fig.…”

Section: Flow Cytometry Analysis Of Mouse Spleen-derived Erythroblastmentioning

confidence: 99%

Transferrin receptor 1 is required for enucleation of mouse erythroblasts during terminal differentiation

et al. 2019

View full text Add to dashboard Cite

Enucleation is the process whereby the nucleus is extruded from the erythroblast during late stage mammalian erythropoiesis. However, the specific signaling pathways involved in this process remain unclear. To better understand the mechanisms underlying erythroblast enucleation, we investigated erythroblast enucleation using both the spleens of adult mice with phenylhydrazine‐induced anemia and mouse fetal livers. Our results indicated that both iron‐bound transferrin (holo‐Tf) and the small‐molecule iron transporter hinokitiol with iron ions (hinokitiol plus iron) promote hemoglobin synthesis and the enucleation of mouse spleen‐derived erythroblasts. Although an antitransferrin receptor 1 (TfR1) monoclonal antibody inhibited both enucleation and hemoglobin synthesis promoted by holo‐Tf, it inhibited only enucleation, but not hemoglobin synthesis, promoted by hinokitiol plus iron. Furthermore, siRNA against mouse TfR1 were found to suppress the enucleation of mouse fetal liver‐derived erythroblasts, and the endocytosis inhibitor MitMAB inhibited enucleation, hemoglobin synthesis, and the internalization of TfR1 promoted by both types of stimuli. Collectively, our results suggest that TfR1, iron ions, and endocytosis play important roles in mouse erythroblast enucleation.

show abstract

Tumor-Induced Generation of Splenic Erythroblast-like Ter-Cells Promotes Tumor Progression

Cited by 120 publications

References 44 publications

Diagnostic Evidence GAuge of Single cells (DEGAS): A flexible deep-transfer learning framework for prioritizing cells in relation to disease

Diagnostic Evidence GAuge of Single cells (DEGAS): A flexible deep-transfer learning framework for prioritizing cells in relation to disease

Malignant Schwann cell precursors mediate intratumoral plasticity in human neuroblastoma

Transferrin receptor 1 is required for enucleation of mouse erythroblasts during terminal differentiation

Contact Info

Product

Resources

About