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Optimizing cord blood selection.

ABSTRACT: Nowadays a donor can be found for virtually all patients in need of an allogeneic stem cell transplantation, and the decision whether to use a matched or mismatched unrelated donor, an unrelated donor for umbilical cord blood transplantation (UCBT), or a haploidentical donor depends not only on the availability of the donor but also on patient-, disease-, and center-related factors. This paper summarizes the recent criteria in the selection of cord blood unit, including the cell dose requirement and the HLA typing for the optimal donor choice. The main strategies to optimize the results of UCBT, the conditioning regimens, and the use of antithymocyte globulin and the other platforms of graft-versus-host disease prophylaxis are discussed. The paper describes the results of UCBT in children and adults with malignant and nonmalignant diseases and the comparative analysis with other donor type and stem cell sources. Emerging strategies, focusing on the different platforms of ex vivo expansion and the new applications using cord blood stem cell, are also examined.

SUBMITTER: Ruggeri A

PROVIDER: S-EPMC6913431 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Optimizing cord blood selection.

Ruggeri Annalisa A

Hematology. American Society of Hematology. Education Program 20191201 1

Nowadays a donor can be found for virtually all patients in need of an allogeneic stem cell transplantation, and the decision whether to use a matched or mismatched unrelated donor, an unrelated donor for umbilical cord blood transplantation (UCBT), or a haploidentical donor depends not only on the availability of the donor but also on patient-, disease-, and center-related factors. This paper summarizes the recent criteria in the selection of cord blood unit, including the cell dose requirement ...[more]

PMID: 31808851

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Project description:BACKGROUND:Umbilical cord blood (UCB) is commonly used in epigenome-wide association studies of prenatal exposures. Accounting for cell type composition is critical in such studies as it reduces confounding due to the cell specificity of DNA methylation (DNAm). In the absence of cell sorting information, statistical methods can be applied to deconvolve heterogeneous cell mixtures. Among these methods, reference-based approaches leverage age-appropriate cell-specific DNAm profiles to estimate cellular composition. In UCB, four reference datasets comprising DNAm signatures profiled in purified cell populations have been published using the Illumina 450?K and EPIC arrays. These datasets are biologically and technically different, and currently, there is no consensus on how to best apply them. Here, we systematically evaluate and compare these datasets and provide recommendations for reference-based UCB deconvolution. RESULTS:We first evaluated the four reference datasets to ascertain both the purity of the samples and the potential cell cross-contamination. We filtered samples and combined datasets to obtain a joint UCB reference. We selected deconvolution libraries using two different approaches: automatic selection using the top differentially methylated probes from the function pickCompProbes in minfi and a standardized library selected using the IDOL (Identifying Optimal Libraries) iterative algorithm. We compared the performance of each reference separately and in combination, using the two approaches for reference library selection, and validated the results in an independent cohort (Generation R Study, n = 191) with matched Fluorescence-Activated Cell Sorting measured cell counts. Strict filtering and combination of the references significantly improved the accuracy and efficiency of cell type estimates. Ultimately, the IDOL library outperformed the library from the automatic selection method implemented in pickCompProbes. CONCLUSION:These results have important implications for epigenetic studies in UCB as implementing this method will optimally reduce confounding due to cellular heterogeneity. This work provides guidelines for future reference-based UCB deconvolution and establishes a framework for combining reference datasets in other tissues.

Dataset Information

Optimizing cord blood selection.

Publications

Optimizing cord blood selection.

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