Abstract
Juvenile Myelomonocytic Leukemia (JMML) is a poor prognosis childhood leukemia usually caused by germline or somatic RAS-activating mutations. The cellular hierarchy in JMML is poorly characterized, including the identity of leukemia stem cells (LSCs). FACS and single-cell RNA-sequencing reveal marked heterogeneity of JMML hematopoietic stem/progenitor cells (HSPCs), including an aberrant Lin-CD34+CD38-CD90+CD45RA+ population. Single-cell HSPC index-sorting and clonogenic assays show that (1) all somatic mutations can be backtracked to the phenotypic HSC compartment with RAS-activating mutations as a “first hit”, (2) mutations are acquired with both linear and branching patterns of clonal evolution and (3) mutant HSPCs are present after allogeneic HSC transplant before molecular/clinical evidence of relapse. Stem cell assays reveal inter-patient heterogeneity of JMML-LSCs which are present in, but not confined to, the phenotypic HSC compartment. RNA-sequencing of JMML-LSCs reveals upregulation of stem cell and fetal genes (HLF, MEIS1, CNN3, VNN2, HMGA2) and candidate therapeutic targets/biomarkers (MTOR, SLC2A1, CD96) paving the way for LSC-directed disease monitoring and therapy in this disease.
Introduction
Juvenile myelomonocytic leukemia (JMML) is an aggressive subtype of childhood myelodysplastic syndrome (MDS), usually presenting in the first 5 years of life, that is characterized by abnormal proliferation of dysplastic cells of the monocytic and granulocytic lineages (1). Although JMML is a rare childhood cancer, it has several key features which make it an important paradigm. In particular, it is definitively caused by RAS-activating mutations, typically in PTPN11, KRAS, NRAS, CBL or NF1 in >90% of cases and the molecular landscape appears otherwise relatively simple with a low number of somatic mutations in comparison with other malignancies (2, 3). Some patients show evidence of clonal evolution, with acquisition of monosomy 7 (1) or secondary somatic mutations of SETBP1, ASXL1, EZH2 and other genes which are associated with a worse prognosis (2, 3). The only curative therapy for JMML is allogeneic hematopoietic stem cell transplantation (HSCT), however, relapse rates are high (1) indicating a failure to eradicate the disease-propagating cells in this condition.
The presence of distinct populations of rare disease-propagating cancer stem cells (CSC) has been demonstrated in some cancers, and this is a crucial step towards understanding cellular pathways of disease relapse (4). In adults with chronic myeloid neoplasms, including myeloproliferative neoplasms (MPN) (5), chronic myeloid leukemia (6) and MDS (7), rare and distinct CSCs have been identified which share phenotypic features with normal HSCs. However, in acute myeloid leukemia (AML), leukemia stem cells (LSCs) are more heterogeneous and, although the disease may originate in HSCs, various different progenitor cell populations are transformed and are responsible for disease propagation (8). Furthermore, in childhood acute lymphoblastic leukemia (ALL), blast cells across all stages of differentiation have LSC properties (9). In view of this marked heterogeneity of CSCs/LSCs between different liquid tumors, it is crucial to properly characterize the CSCs in specific disease entities, particularly where the prognosis is poor and targeted therapy remains elusive. Indeed, for JMML, while the genetic basis is now well described, very little is known about the cellular hierarchy, including the identity of cells that propagate the disease and cause relapse.
Results
Single cell phenotypic, functional and molecular analysis reveals heterogeneity of hematopoietic stem/progenitor cells (HSPCs) in JMML
In order to characterize LSCs in JMML, we established a national prospective study and collected serial bone marrow (BM) and peripheral blood (PB) samples from a cohort of 16 patients with JMML for phenotypic, functional and molecular analysis of HSPC pre- and post-HSCT (Table S1; Figure 1A). At diagnosis, the phenotype of early (CD38 negative) HSPC was markedly disrupted in JMML (Figure 1B and 1C). Although numbers of phenotypic HSCs (Lin-CD34+CD38-CD90+CD45RA-) were normal, multipotent progenitors (MPP; Lin-CD34+CD38-CD90-CD45RA-), including lymphoid primed MPPs (LMPP; Lin-CD34+CD38-CD90-CD45RA+) were reduced in JMML in comparison with normal pediatric BM (Figure 1B and 1C). A number of patients (8/14 analyzed) also showed the presence of an aberrant Lin-CD34+CD38-CD90+CD45RA+ (+/+) population of cells. In contrast, the CD38+ myeloid progenitor compartment showed an apparently normal frequency of phenotypically defined common myeloid progenitors (CMP; Lin-CD34+CD38+CD123+CD45RA-), granulocyte monocyte progenitors (GMP; Lin-CD34+CD38+CD123+CD45RA+) and megakaryocyte erythroid progenitors (MEP; Lin-CD34+CD38+CD123-CD45RA-) (Figure 1B and 1C).
At a functional level, clonogenic assays of bulk BM (Figure S1A) or purified single HSPCs (Figure 1D and S1B) showed preserved myeloid output but aberrant erythroid potential of CMP and MEPs from JMML patients in comparison with cord blood (Figure 1D) and pediatric BM (Figure S1B). Erythroid colonies derived from JMML patients were frequently dysplastic (Figure S1C), in keeping with anemia seen in all patients (Table S1). The aberrant +/+ population showed exclusively myeloid output (Figures 1D and S1B). Lymphoid potential of JMML HSCs was reduced (Figure 1E) and megakaryocytic potential severely reduced (Figure S1D) in keeping with patients’ thrombocytopenia (Table S1).
Although FACS analysis supported relative preservation of a number of HSPC subpopulations in JMML, bulk phenotypic analysis may mask underlying heterogeneity of HSPCs in JMML in comparison with normal hematopoiesis. We therefore next assessed the global cellular architecture of JMML Lin-CD34+ HSPC in an unbiased manner by high throughput single-cell RNA-sequencing of 17,547 single HSPCs from JMML (n=2) and cord blood (n=2). Using 1,127 selected genes showing high level of dispersion (Figure S2A), t-distributed stochastic neighbor embedding showed highly distinct clustering of JMML HSPCs (Figure 2A) which were not equally distributed between the identified HSPC subpopulations in comparison with cord blood HSPCs (Figures 2B and S2B). JMML-specific clusters of HSPCs showed upregulation of myeloid genes (MS4A3 and MPO), stem cell and fetal genes (THY1, ZFP36L1, HMGA2), proliferation markers (MKI67) and aberrant expression of leukemia (HOPX, FOS) and erythroid differentiation associated (GATA1) genes (Figure 2C, 2D). Taken together, these findings support pnenotypic evidence of HSPC compartment disruption in JMML, with aberrant myeloid bias and distinct molecular signatures.
Somatic mutations originate exclusively in the phenotypic HSC compartment in JMML
As driver mutations must be present in cells with self-renewal capability in order to propagate the disease, we next set out to track the cellular origin of somatic mutations within the HSPC cellular hierarchy in JMML in order to gain insights into the identity of JMML LSCs. In adult MDS, this approach has been used to identify phenotypic HSCs as the population with stem cell properties (7). In contrast, in AML, some mutations can be tracked to progenitor cell populations, but are absent in HSCs, supporting presence of aberrant stem cell properties in progenitor cells in AML (10). We first carried out a targeted mutation analysis of JMML-associated mutations in our cohort of 16 JMML cases. RAS pathway activating mutations were present in all patients while secondary spliceosome and epigenetic mutations were identified in 7/16 (44%) of patients, including all those with NF1 mutations as previously reported (2) (Figure 3A).
To track these disease-causing mutations to the HSPC hierarchy, single Lin-CD34+ cells from three patients were index-sorted into methylcellulose colony-forming assays and individual colonies picked for targeted genotyping (n=498); the index-sorting data allowed us to derive the FACS phenotype, and hence HSPC population of origin, of each colony (Figure S3A). Genotyping of SNPs demonstrated the low allelic dropout of this assay, with no false positive mutations seen in control samples (Figure S3B-H). Patient ID1 showed evidence of linear evolution with acquisition of an NF1 followed by an ASXL1 mutation, both of which could be tracked to all HSPC subpopulations, including HSCs (Figure 3B). The few residual wild-type cells were present in the HSC compartment and enriched in MPPs whereas mutation-positive cells were infrequent in MPPs, in keeping with phenotypic data (Fig 1C). ID5 was characterized by PTPN11 mutation alone that was again traceable to HSCs as well as CMP and GMPs (Figure 3C). Patient ID15 showed evidence of branching clonal evolution within the HSC compartment, with a first hit NRAS mutation followed by acquisition of monosomy 7 and SETBP1 mutation in separate subclones (Figure 3D). Finally, we analyzed a sample from patient ID5 taken post-HSCT at a time when the patient was in clinical remission but subsequently suffered overt evidence of relapse of JMML (Figure 3E). Parallel genotyping of the PTPN11 mutation together with donor and recipient specific SNPs demonstrated presence of PTPN11 mutation-positive HSPCs (including a single HSC) which predate (and potentially might have helped to predict) subsequent relapse. Notably, mutant positive progenitors (CD38+) were more prominent than HSCs post-HSCT, raising the possibility that these cells may predominantly drive relapse. Taken together, these data support the conclusion that all somatic mutations driving JMML disease initiation and evolution originate in the phenotypic HSC compartment with RAS-activating mutations as a “first hit”.
Heterogeneity of LSCs in JMML
To further characterize JMML-LSCs, we next carried out in vitro and in vivo stem cell assays to assess self-renewal potential of JMML-HSPC subpopulations. Long-term culture-initiating cell (LTC-IC) assays showed LTC-IC potential was present in both HSCs and +/+ populations in JMML, but was absent in GMPs while in cord blood controls LTC-IC potential was restricted to HSCs, as expected (Figure 4A). We then performed xenotransplantation of purified HSCs, +/+ and GMPs, or total CD34+ cells from 4 JMML patients as shown in Figure 4B, with results of serial readout of engraftment shown in Figures 4C and D. JMML CD34+ HSPCs were highly efficient at supporting reconstitution in this NSG xenograft model, unlike adult MDS (7). Lineage analysis (Figure S4A) showed that transplantation from JMML donors resulted in highly myeloid-biased reconstitution in comparison with cord blood. In all cases, mice showed reconstitution following transplantation of purified HSCs from cord blood or JMML. Engraftment potential of specific JMML-HSPC subpopulations (+/+ and GMP), however, showed marked inter-patient heterogeneity of JMML-LSCs (Figure 4C and D). In patient ID1, JMML-LSC potential was restricted to the phenotypic HSC compartment. Patient ID3, who died from disease relapse following HSCT, showed similar repopulating potential in all populations (HSCs, +/+ and GMP). Patient ID5, who also relapsed after HSCT, showed most robust reconstitution with HSCs, but late reconstitution was also observed with +/+, and total CD34+CD38+ cells. It is also noteworthy that at relapse post-HSCT, the mutant positive JMML HSPC compartment in patient ID5 was dominated by mature progenitor cells (Figure 3E) unlike at diagnosis when HSCs were frequent (Figure 3C). This is consistent with relapse being primarily propagated by progenitors rather than HSCs in this patient. Finally, patient ID15 showed reconstitution following transplantation of HSC and +/+, but not GMP.
Terminal analysis of reconstituted mice showed relative enrichment of HSCs and +/+ cells relative to cord blood engrafted animals in 3 of 4 cases analyzed (Figure 4E and Figure S4B), with higher engraftment in BM than PB (Figure 4F). JMML-HSCs also strongly supported robust reconstitution in secondary transplantations (Figure 4G). Mice transplanted with JMML-HSCs developed a JMML-like disease with cytopenias (Figure S4C), characteristic histology (Figure 4H), marked splenomegaly (Figure 4I) and reduced leukemia-free survival (Figure 4J). Taken together, these findings demonstrate that JMML-LSC activity is present in, but not confined to, the phenotypic HSC compartment. JMML-HSCs nevertheless not only showed the most robust reconstituting potential, but were also the only population able to induce JMML-like disease in vivo within 24 weeks and were the cell of origin of all JMML-associated driver mutations and clonal evolution events.
Conservation of Molecular Hierarchy of JMML-HSPCs
To characterize transcriptomic signatures of JMML-LSCs we next carried out RNA-sequencing analysis of three different JMML-LSC populations (HSC, +/+ and GMP) from six JMML patients. JMML HSC showed a higher number of differentially expressed genes in comparison with cord blood HSC (n=5) than in comparison with different JMML HSPC subpopulations (Figure 5A) and, notably, +/+ cells from JMML shared molecular signatures with both HSCs and GMP (Figure S5A). Expression of known HSC genes was higher in HSCs than other populations in both JMML and cord blood, with expression in JMML +/+ cells intermediate between HSC and GMP, suggesting that a hierarchy may be preserved in JMML (Figure 5B). In keeping with this, myeloid-associated genes were more highly expressed in GMPs in both cord blood and JMML, with +/+ cells again showing intermediate expression (Figure 5C). Analysis of cell cycle genes supported that quiescence-associated gene expression was higher in cord blood HSCs with proliferation-associated transcriptional changes in all JMML HSPC populations, including HSCs, but more so in JMML GMPs (Figure 5D). Interestingly, the fetal HSC genes HMGA2, CNN3, and VNN2 were highly expressed in JMML HSCs and to a lesser extent in JMML +/+ and GMP cells (Figure 5E). Gene set enrichment analysis (GSEA) further supported that JMML-HSCs showed more HSC- and quiescence-associated gene expression than JMML-GMPs (Figure 5F). In contrast, JMML-GMPs showed upregulation of proliferation and pediatric cancer signatures in comparison with JMML-HSCs (Figure 5F). Direct comparison of JMML versus cord blood HSCs showed a number of clusters of aberrantly expressed genes, clearly distinguishing JMML HSCs (Figure 5G). Hallmark GSEA revealed enrichment of a number of proliferation-associated (G2M checkpoint, MYC and E2F) and DNA repair gene sets in JMML-HSCs versus cord blood HSCs (Figure 5H). We identified a core set of 24 genes which distinguished JMML-HSCs from JMML-GMPs and cord blood HSCs (Figure 5I) and were overexpressed in JMML-specific HSPC subpopulations identified by single cell RNA-sequencing (Figure S5B). This set of genes included the non-DNA-binding homeodomain protein HOPX, a regulator of primitive hematopoiesis (11) and the serine/threonine kinase STK24, a target of the kinase inhibitor bosutinib (12) (Figure 5J).
Novel Therapeutic Targets for JMML-HSCs
A number of putative therapeutic targets were upregulated in JMML-HSCs, including overexpression of SLC2A1 (GLUT1) and RAS-associated transcription (Figures 6A and B). Specific targeting of GLUT1 with Fasentin or of RAS-associated signaling with the MEK inhibitor PD901 both significantly and differentially (in comparison with cord blood) reduced clonogenicity of JMML HSCs (Figure 6C). Bromodomain inhibitors have been shown to reverse RAS-associated transcriptional changes (13, 14), and we also demonstrated significant differential inhibition of JMML-HSC clonogenicity with JQ1 (Figure 6C).
One of the top differentially expressed genes (overexpressed in JMML-HSCs) was the cell surface receptor CD96 (Figure 6D), also confirmed to be more frequently expressed in JMML-specific subpopulations of HSPCs in our single cell RNA-seq dataset (Figures 6E and F). CD96 has previously been proposed as a biomarker and therapeutic target in AML (15). We confirmed aberrant surface expression of CD96 protein on JMML-LSCs (Figure 6G) and show that this aberrant population of CD96-expressing cells could be detected in a patient post-HSCT who subsequently relapsed (Figure 6H), raising the possibility that CD96 could be a useful biomarker for JMML-LSCs.
Taken together, these data support that JMML-LSCs are heterogeneous, with multiple different HSPC populations showing capacity for self-renewal. JMML driver mutations originate in HSC-like cells which reside at the apex of the JMML-LSC hierarchy, and this population shows increased reconstitution potential and HSC-associated gene expression in comparison with other JMML-HSPC populations.
Discussion
Disease relapse after achievement of clinical/morphological remission is a major cause of treatment failure across many different human cancers. Characterization of distinct CSCs, specific to each type of malignancy, is a crucial step towards improved approaches for disease monitoring in order to predict disease relapse and the development of CSC-directed therapy. This task is difficult in rare pediatric diseases such as JMML, and yet the need for new disease biomarkers in this disease is particularly acute because HSCT, the only curative therapy, carries a high rate of relapse that is often difficult to diagnose promptly. In hematopoietic cancers such as MDS, MPN, ALL and AML (4, 5, 7-9), LSCs are diverse and vary considerably in their frequency and phenotype, with each providing important insights into LSC biology. However, in the rare and poor prognosis childhood leukemia JMML, very little is known about the cellular hierarchy and identity of LSCs, including whether progenitor cells are transformed in this condition. We have carried out a comprehensive phenotypic, functional and molecular analysis of a cohort of JMML patients and describe significant disruption of the HSPC hierarchy in JMML, including presence of an aberrant early progenitor cell (Lin-CD34+CD38-CD90+CD45RA+) which may reflect an underlying aberrant HSC myeloid differentiation pathway in JMML. Interestingly, this population has also been observed in adult patients with myeloid malignancies associated with monosomy 7 (16). We also observed a reduction of LMPPs, a key population of early lymphoid progenitors, as well as reduced lymphoid potential of HSCs, supporting that the myeloid phenotype associated with JMML may be driven by a myeloid-bias already present in the HSC. Consistent with this, we also observed impaired megakaryocyte and erythroid output from JMML HSC.
Backtracking of somatic genetic lesions to distinct HSPC subsets is a powerful method to help identify CSCs, as mutations acquired by short-lived progenitors which lack self renewal ability are not able to propagate the disease (7). Using a similar approach, combining single-cell index sorting and colony genotyping, we demonstrate that all JMML driver mutations could be backtracked to the phenotypic HSC compartment with RAS-activating mutations as a “first hit”. Despite being relatively genetically simple in comparison with other tumors, we observed JMML patients with both linear and branching patterns of clonal evolution originating in JMML-HSCs. Importantly, we were able to detect mutant HSPCs in a post-HSCT patient a month before molecular/clinical evidence of relapse. At the same time point, a population of CD96+ cells within the Lin-CD34+CD90+ gate could be detected by FACS on patient cells but not normal controls. Taken together, this strongly suggests that the presence of JMML-HSPCs with aberrant phenotype could be used to predict impending relapse in JMML patients, thus providing a much needed biomarker of residual disease post-HSCT, although this will need to be validated in larger clinical cohorts before this could be applied clinically.
Although our colony genotyping strongly supported that HSC are the cell of origin in JMML, functional analysis (xenotransplantation and LTC-IC assays) also revealed marked inter-patient heterogeneity of JMML-LSCs. Thus, while JMML HSCs were consistently able to support LTC-IC potential and engraftment in xenograft models, some patients also showed evidence of self-renewal of progenitor cells, including GMPs and the novel +/+ cell population that we describe. Interestingly, this suggests that JMML displays a distinct biology, sharing some features with MDS/MPN, which are propagated by the counterparts of HSCs (5, 7), and other features with acute leukemias, which show transformation of progenitor populations, but cannot usually be propagated by HSCs lacking the late driver mutations acquired by transformed progenitor/precursors (8-10). Furthermore, JMML-HSPC engrafted robustly in NSG mice, rather than exhibiting the poor in vitro and in vivo proliferative capacity, characteristic of adult MDS stem cells (7). These biological features of pediatric versus adult MDS may reflect the impact of RAS driver mutations on JMML-HSC and/or co-expression of a proliferative fetal gene program, such as we observed here in the JMML-HSC but not reported in adult MDS (7). Of specific note, the fetal HSC specific gene VNN2, encoding the glycophosphatidylinositol-anchored surface protein GPI-80, was markedly overexpressed by JMML-HSCs (17). This is of particular interest as disease-associated mutations in JMML can often be tracked back to neonatal bloodspots and fetal hemoglobin is often increased in JMML cases (1), together suggesting a fetal origin of JMML. However, a limitation of this current analysis is that earlier neonatal samples taken at birth (cord blood or neonatal blood spots) were not available. Consequently, whilst our data support a fetal origin of the JMML cases studied, this can not be definitively established.
Although phenotypic analysis of HPSCs showed considerable overlap with normal pediatric BM HSPCs, such analyses, based on a small number of canonical surface markers, often fail to reveal underlying cellular heterogeneity. Single cell RNA-sequencing analysis is a powerful method to resolve such heterogeneity and has been widely used to analyze normal HSPCs, but less so malignant hematopoiesis (18). We have recently shown that in chronic myeloid leukemia, such an approach can help to resolve normal and leukemic stem cells (19). We therefore used a similar approach to analyze over 17,000 HSPCs from JMML patients and cord blood, revealing that JMML-HSPC are molecularly highly distinct. Bulk RNA-sequencing analysis of JMML-LSCs revealed evidence of a hierarchical organization of HSPCs in JMML, with HSCs residing at the apex of this hierarchy and +/+ cells sharing features of both HSCs and GMPs. Importantly, this also allowed us to identify a number of upregulated or aberrantly expressed putative therapeutic targets in the JMML-HSC, including SLC2A1 (GLUT1) and RAS-associated pathways. Furthermore, the clonogenicity of JMML-HSC was specifically reduced by targeting either GLUT1 (with Fasentin) or RAS-associated pathways (with the MEK inhibitor PD901 or the bromodomain inhibitor JQ1). As eradication of CSC/LSC is not only necessary but also potentially sufficient to achieve disease eradication (4), further preclinical evaluation of these targets is warranted.
In summary, we describe an integrated phenotypic, functional and molecular analysis of JMML-LSCs, illustrating marked intra- and inter-patient heterogeneity of LSCs. We identified a number of candidate biomarkers and therapeutic targets, paving the way for LSC-directed disease monitoring and therapy in JMML.
Methods
Patient Sample Collection
Patient samples and normal controls were prospectively collected in accordance with the Declaration of Helsinki for sample collection and use in research under the UK NIHR Paediatric MDS/JMML study (NHS REC reference number 15/LO/0961) or normal pediatric bone marrow (NHS REC reference number 12/LO/0426). An additional JMML patient sample was provided by the Bloodwise Childhood Leukemia Cell Bank (NHS REC reference number 16/SW/0219). Cord blood samples were commercially sourced (Zenbio US, Cat# SER-CD34-F). Detailed phenotypic and clinical characteristics (Table S1) were captured on a secure online platform, using the Human Ontology Database tool. Clinical details for the sample provided by the Leukemia Cell bank were not available. Mononuclear cells (MNC) from peripheral blood and BM samples were isolated on Ficoll density gradients and cryopreserved in 90% fetal bovine serum (FBS) and 10% dimethylsulfoxide (DMSO). Cells were thawed and processed for downstream analysis as previously described (Woll et al., 2014). Additional information on FACS sorting of samples are detailed in Supplementary Methods.
In Vitro Assays
Materials and methods for in vitro assays including cell clonogenic assays, Erythroid / megakaryocytic culture assay, B-cell differentiation MS5 co-cultures, Long-Term Culture-Initiating Cell assay, and Mutational analysis of single cell colonies are detailed in the Supplementary Methods.
NSG Mouse Studies
All procedures involving animals were approved by the UK Home Office Project license (PPL 30 – 3103). NSG mice were obtained through Jackson Laboratory and maintained in individually ventilated cages in a specific pathogen free facility. Male and female mice were randomly assigned to groups. NSG xenografts were carried out in 10-14 week old mice. Additional methods on analysis of NSG experiments are detailed in Supplementary Methods.
Single Cell RNA-sequencing
18,333 – 24,000 single cells from the Lin-CD34+ population from two JMML patient samples (Patient ID1 and ID5) and 2 normal cord blood controls, were sorted and processed for RNA seq using the 10X chromium platform as per the manufacturer’s instructions (10X Genomics, Cat# 120237). In short, cells were sorted into a total volume of 40 µL and 33.8 µL of the sample was loaded onto the Single Cell 3’ Chromium 10X Chip with 66.2 µL of the 10X master mix. cDNA was generated on the Chromium 10X controller. Post RT clean up was performed as per the manufacturer’s instructions and the product was amplified with 8 PCR cycles. Post amplification clean up and library construction was performed as per the manufacturer’s instructions and samples were sequenced on the Illumina HiSeq4000, with read1 being 26bp and read2 98bp. Additional methods for analysis are detailed in Supplementary Methods.
Bulk RNA-sequencing
FACS purified populations were processed for RNA-seq using the Smart Seq2 protocol as previously described (20). Briefly, fifty purified cells were sorted directly into 4 µl of lysis buffer containing 0.4% Triton X-100 (Sigma-Aldrich), RNase inhibitor (Clontech), 2.5 mM dNTPs (Thermo Fisher) and 2.5 µM oligo-dT30VN primer (Biomers.net). cDNA was generated using SuperScript II (Invitrogen), pre-amplified using KAPA HiFi HotStart ReadyMix (KAPA Biosystems) using 19 cycles of amplification. After PCR amplification, the cDNA libraries were purified with AMPure XP beads (Beckman Coulter) using a ratio of 0.8:1 beads to cDNA, according to the manufacturer’s instructions. Post purification libraries were resuspended in EB buffer (Qiagen). The quality of cDNA traces was assessed by using a High Sensitivity DNA Kit in a Bioanalyzer instrument (Agilent Technologies). Tagmentation and library preparation was performed using the Nextera XT DNA Library Preparation Kit (Illumina, Cat# FC-131) according to the manufacturer’s instructions. Samples were sequenced using the Illumina NextSeq 500 platform, generating 75 bp single-end reads. Additional information on analysis of bulk RNA-seq are detailed in the Supplemantary Methods.
Author Contributions
EL and BP performed experiments, analyzed the data and helped to write the manuscript. ARM performed experiments and analyzed data. AH and AR analyzed and interpreted data. GB, NA, CAGB, NE, DI, NS, NF and SOB conducted experiments and analyzed data. JDLF provided patient samples. AR conducted the clinical study, helped advise on experimental design and provided patient samples. IR and AJM conceived, designed and supervised the research, analyzed the data and wrote the manuscript.
Declaration of Interests
The authors declare no competing interests.
Acknowledgments
The authors thank all patients and their families for contributing samples for this research and the BMS, Oxford University, for technical assistance. This work was funded by a Medical Research Council Senior Clinical Fellowship (MR/L006340/1) to AJM, a National Institute for Health Research Fellowship to EL, a MRC-funded Oxford Consortium for Single-cell Biology (MR/M00919X/1) and the Oxford NIHR Biomedical Centre based at Oxford University Hospitals NHS Trust (IR) and University of Oxford. AR is supported by a Bloodwise Clinician Scientist Fellowship (17001), NF by a Kay Kendall Leukaemia Fund Fellowship (KKL1124) and DI by a Bloodwise Clinical Research Fellowship. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. The authors acknowledge the contributions of the WIMM Flow Cytometry Facility, supported by the MRC HIU; MRC MHU (MC_UU_12009); NIHR Oxford BRC and John Fell Fund (131/030 and 101/517), the EPA fund (CF182 and CF170) and by the WIMM Strategic Alliance awards G0902418 and MC_UU_12025.