Measures of Clonal Hematopoiesis:Are We Missing Something?

Clonal Hematopoiesis (CH) is a common, age-related phenomenon of growing scientific interest, due to its association with hematologic malignancy, cardiovascular disease and decreased overall survival. CH is commonly attributed to the preferential outgrowth of a mutant hematopoietic stem cell (HSC) with enhanced fitness, resulting in clonal imbalance. In-depth understanding of the relation between HSC clonal dynamics, CH and hematologic malignancy requires integration of fundamental lineage tracing studies with clinical data. However, this is hampered by lack of a uniform definition of CH and by inconsistency in the analytical methods used for its quantification. Here, we propose a conceptual and analytical framework for the definition and measurement of CH. First, we transformed the conceptual definition of CH into the CH index, which provides a quantitative measure of clone numbers and sizes. Next, we generated a set of synthetic data, based on the beta-distribution, to simulate clonal populations with different degrees of imbalance. Using these clonal distributions and the CH index as a reference, we tested several established indices of clonal diversity and (in-)equality for their ability to detect and quantify CH. We found that the CH index was distinct from any of the other tested indices. Nonetheless, the diversity indices (Shannon, Simpson) more closely resembled the CH index than the inequality indices (Gini, Pielou). Notably, whereas the inequality indices mainly responded to changes in clone sizes, the CH index and the tested diversity indices also responded to changes in the number of clones in a sample. Accordingly, these simulations indicate that CH can result not only by skewing clonal abundancies, but also by variation in their overall numbers. Altogether, our model-based approach illustrates how a formalized definition and quantification of CH can provide insights into its pathogenesis. In the future, use of the CH index or Shannon index to quantify clonal diversity in fundamental as well as clinical clone-tracing studies will promote cross-disciplinary discussion and progress in the field.</p

Clonal analysis of patient-derived samples using cellular barcodes

Cellular barcoding is a relatively simple method that allows quantitative assessment of the clonal dynamics of normal, nonmalignant hematopoietic stem cells and of leukemia. Cellular barcodes are (semi-)random synthetic DNA sequences of a fixed length, which are used to uniquely mark and track cells over time. A successful barcoding experiment consists of several essential steps, including library production, transfection, transduction, barcode retrieval, and barcode data analysis. Key challenges are to obtain sufficient number of barcoded cells to conduct experiments and reliable barcode data analysis. This is especially relevant for experiments using primary leukemia cells (which are of limited availability and difficult to transduce), when studying low levels of chimerism, or when the barcoded cell population is sorted in different smaller subpopulations (e.g., lineage contribution of normal hematopoietic stem cells in murine xenografts). In these settings, retrieving accurate barcode data from low input material using standard PCR amplification techniques might be challenging and more sophisticated approaches are required. In this chapter we describe the procedures to transfect and transduce patient-derived leukemia cells, to retrieve barcoded data from both high and low input material, and to filter barcode data from sequencing noise prior to quantitative clonal analysis

Detection of chemotherapy-resistant patient-derived acute lymphoblastic leukemia clones in murine xenografts using cellular barcodes

Clonal heterogeneity fuels leukemia evolution, therapeutic resistance, and relapse. Upfront detection of therapy-res istant leukemia clones at diagnosis may allow adaptation of treatment and prevention of relapse, but this is hampered by a paucity of methods to identify and trace single leukemia-propagating cells and their clonal offspring. Here, we tested methods of cellular barcoding analysis, to trace the in vivo competitive dynamics of hundreds of patient-derived leukemia clones upon chemotherapy-mediated selective pressure. We transplanted Nod/Scid/Il2Rg(-/-) (NSG) mice with barcoded patient-derived or SupB15 acute lymphoblastic leukemia (ALL) cells and assessed clonal responses to dexamethasone, methotrexate, and vincristine, longitudinally and across nine anatomic locations. We illustrate that chemotherapy reduces clonal diversity in a drug-dependent manner. At end-stage disease, methotrexate-treated patientderived xenografts had significantly fewer clones compared with placebo-treated mice (100 +/- 10 vs. 160 +/- 15 clones, p = 0.0005), while clonal complexity in vincristineand dexamethasone-treated xenografts was unaffected (115 +/- 33 and 150 +/- 7 clones, p = NS). Using tools developed to assess differential gene expression, we determined whether these clonal patterns resulted from random clonal drift or selection. We identified 5 clones that were reproducibly enriched in methotrexate-treated patient-derived xenografts, suggestive of pre-existent resistance. Finally, we found that chemotherapymediated selection resulted in a more asymmetric distribution of leukemia clones across anatomic sites. We found that cellular barcoding is a powerful method to trace the clonal dynamics of human patient-derived leukemia cells in response to chemotherapy. In the future, integration of cellular barcoding with single-cell sequencing technology may allow in-depth characterization of therapy-resistant leukemia clones and identify novel targets to prevent relapse. (C) 2020 ISEH - Society for Hematology and Stem Cells. Published by Elsevier Inc

Correction:Quantitative distribution of patient-derived leukemia clones in murine xenografts revealed by cellular barcodes

An amendment to this paper has been published and can be accessed via a link at the top of the paper.</p

For personal use only

Clonal analysis is important for many areas of hematopoietic stem cell research, including in vitro cell expansion, gene therapy, and cancer progression and treatment. A common approach to measure clonality of retrovirally transduced cells is to perform integration site analysis using Southern blotting or polymerase chain reaction-based methods. Although these methods are useful in principle, they generally provide a lowresolution, biased, and incomplete assessment of clonality. To overcome those limitations, we labeled retroviral vectors with random sequence tags or &quot;barcodes.&quot; On integration, each vector introduces a unique, identifiable, and heritable mark into the host cell genome, allowing the clonal progeny of each cell to be tracked over time. By coupling the barcoding method to a sequencing-based detection system, we could identify major and minor clones in 2 distinct cell culture systems in vitro and in a long-term transplantation setting. In addition, we demonstrate how clonal analysis can be complemented with transgene expression and integration site analysis. This cellular barcoding tool permits a simple, sensitive assessment of clonality and holds great promise for future gene therapy protocols in humans, and any other applications when clonal tracking is important. (Blood. 2010;115(13):2610-2618

Expression quantitative trait loci are highly sensitive to cellular differentiation state

Blood cell development from multipotent hematopoietic stem cells to specialized blood cells is accompanied by drastic changes in gene expression for which the triggers remain mostly unknown. Genetical genomics is an approach linking natural genetic variation to gene expression variation, thereby allowing the identification of genomic loci containing gene expression modulators (eQTLs). In this paper, we used a genetical genomics approach to analyze gene expression across four developmentally close blood cell types collected from a large number of genetically different but related mouse strains. We found that, while a significant number of eQTLs (365) had a consistent “static” regulatory effect on gene expression, an even larger number were found to be very sensitive to cell stage. As many as 1,283 eQTLs exhibited a “dynamic” behavior across cell types. By looking more closely at these dynamic eQTLs, we show that the sensitivity of eQTLs to cell stage is largely associated with gene expression changes in target genes. These results stress the importance of studying gene expression variation in well-defined cell populations. Only such studies will be able to reveal the important differences in gene regulation between different ce

Genetical genomics: spotlight on QTL hotspots

No abstract available