Summary Significant phenotypic heterogeneity exists in patients with all subtypes of myeloproliferative neoplasms (MPN), including essential thrombocythaemia (ET). Single‐cell RNA sequencing (scRNA‐Seq) holds the promise of unravelling the biology of MPN at an unprecedented level of resolution. Herein we employed this approach to dissect the transcriptomes in the CD34+ cells from the peripheral blood of seven previously untreated ET patients and one healthy adult. The mutational profiles in these patients were as follows: JAK2 V617F in two, CALR in three (one type I and two type II) and triple‐negative (TN) in two. Our results reveal substantial heterogeneity within this enrolled cohort of patients. Activation of JAK/STAT signalling was recognized in discrepant progenitor lineages among different samples. Significantly disparate molecular profiling was identified in the comparison between ET patients and the control, between patients with different driver mutations (JAK2 V617F and CALR exon 9 indel), and even between patients harbouring the same driver. Intra‐individual clonal diversity was also found in the CD34+ progenitor population of a patient, possibly indicating the presence of multiple clones in this case. Estimation of subpopulation size based on cellular immunophenotyping suggested differentiation bias in all analysed samples. Furthermore, combining the transcriptomic information with data from targeted sequencing enabled us to unravel key somatic mutations that are molecularly relevant. To conclude, we demonstrated that scRNA‐Seq extended our knowledge of clonal diversity and inter‐individual heterogeneity in patients with ET. The obtained information could potentially leapfrog our efforts in the elucidation of the pathogenesis of the disease.

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Background:Diffuse large B‐cell lymphoma (DLBCL), though highly curable with immunochemotherapy, is a heterogenous group of diseases that include subsets of tumors with different cells of origin (COO). Using gene‐expression profiling, DLBCLs can be categorized into 2 clinically distinct subgroups, namely activated B‐cell (ABC) and germinal center B‐cell (GCB). More recently, double‐expressor (DEL) or double‐hit lymphoma (DHL) was described as a new form of aggressive lymphoma based on the expression or re‐arrangement of 3 genes (MYC, BCL2, and BCL6), respectively. It is speculated that divergent signaling pathways are utilized among DLBCLs with different subtypes, and heterogeneity might even exist in the same subcategory.Aims:To improve our understanding in DLBCL biology, we aim to explore the signaling activities of tumor cells at the mRNA transcripts and protein levels in a cohort of patients with DLBCL.Methods:We enrolled 60 DLBCLs and 6 healthy adults in this study. The DLBCL samples were supplied in FFPE sections while the healthy samples were collected with fresh buffy coats followed by positive selection of pan‐B cells. RNA was extracted using the ReliaPrep™ FFPE kit, and digital GEP was performed to assess the expression of 240 genes using a barcoding profiling method (nCounter technology, NanoString). After data processing with nSolver4.0, the geometric mean expression of all house‐keeping genes was used as an inter‐sample normalization factor and the levels of expression in DLBCL cells were presented in log2(fold changes). The barcoded protein profiling was performed with nCounter® Vantage 3D™ Protein Panels which detected 35 proteins in one FFPE slide. The raw values of protein expression were subtracted with isotype background, normalized with house‐keeping proteins, and similarly presented in log2(fold change).Results:The nCounter technology successfully classified samples into ABC and GCB subtypes. Outcome analysis unequivocally demonstrated a worse overall survival in those with ABC. Activation of B‐cell receptor (BCR)‐AKT was a common theme in most GCBs, whereas ABC tumors could be further stratified into two groups, one with NF‐kB activation and the other with BCR‐AKT signaling, a phenomenon similar to that seen in GCBs. Patients with ABC tumors expressing activated BCR‐AKT exhibited a trend toward better progression‐free survival than those with NF‐kB activated ABC DLBCL. Compensated PTEN downregulation was found in samples with NF‐kB activation. We next employed nCounter method to identify DEL‐DLBCL, which constituted 35.1% and 28.1% of the whole cohort DLBCL using protein‐based and mRNA transcript‐based stratification, respectively. Although both groups of patients had significantly worse outcome, the poor prognosis of DEL‐DLBCL patients was better delineated in the stratification strategy determined by mRNA transcript levels. Lastly, we also unearthed prominent crosstalk between various signaling pathways including BCR, cell cycle, apoptosis, JAK‐STAT, NF‐kB, and RAS‐GEF/Gap in our patient samples.Summary/Conclusion:Barcode analysis expands our knowledge in the genetic complexity of DLBCLs. Importantly, this can be achieved even when only limited tumor tissue is available. Our data provide strong evidence that heterogeneity does exist, even among those within the same COO cluster. It also helps better sub‐categorize DEL‐DLBCL prognostically. The information on disparate signaling activities in different subtypes could lead to improved strategies in molecularly stratified targeted therapy.image

Background Myeloproliferative neoplasm (MPN) is a heterogeneous group of clonal disorders. The underlying mechanisms of pathogenesis, especially in subtype specification and stochastic malignant transformation, are still largely unknown. Single-cell RNA sequencing (scRNA-seq) is a novel tool that can be used to identify the transcriptomic signature of individual cells. In the current study, we aimed to employ scRNA-seq to analyze genetic profiling of individual cells at different hematopoietic hierarchy in essential thrombocythemia (ET) patients. Methods We enrolled 7 ET patients and one healthy adult. Individual CD34+ progenitor cells were enriched from peripheral blood (PB). Harvested viable cells were barcoded and sequenced with the Illumina HiSeq 4000. Data was visualized with 10x Genomics Loupe software. We performed t-distributed stochastic neighbor embedding (t-SNE) plotting to dissect the scRNA-seq data and cluster cells with transcriptional similarity. Cellular sub-populations were stratified by the surface markers, including hematopoietic stem cells (HSC, CD34+CD38-Lin-), common myeloid progenitors (CMP, CD34+KIT+FLT3+IL3RAlowLin-), megakaryocyte-erythroid progenitor (MEP, CD34+CD38+PTPRC-) and granulocyte-macrophage progenitor (GMP, CD34+CD38+PTPRC+). Differentially expressed genes were subjected to gene ontology analysis. Gene Set Enrichment Analysis (GSEA) and the Reactome analysis were also employed. To clarify the distinct genetic background of these patients, targeted deep sequencing of PB granulocytes was also performed. Results Among the 7 ET patients, two carry JAK2 mutation [one heterozygous (h-JAK2)and one homozygous (H-JAK2)] and three carry CALR mutation (1 type I, 2 type II). The remaining two cases are triple-negative (TN). Integrative analysis showed significant activation of JAK-STAT signaling in ET patients. Compared with control, the t-SNE analysis revealed disparate expression profiling in ET patients across various hematopoietic lineages. The discrepancy grew wider as the hematopoiesis became more lineage-restricted. Significant heterogeneity existed even among different ET patients, suggesting the high diversity of the disease. In the two JAK2-mutated patients, the t-SNE analysis demonstrated divergent transcriptomic profiling which scarcely overlapped. In the H-JAK2 sample, the HSCs exhibited a distinct profile different from the rest of hematopoietic progenitors. The CMPs were more closely related to MEP, which possibly suggested skewed differentiation and resultant ET phenotype. The phenomenon, however, was not similarly seen in the h-JAK2 sample. Using GSEA, we identified a subset of miR-21-targeted genes that were down-regulated in the h-JAK2 sample. Furthermore, there was apparent aberrant signaling activity of TGF-β, widely considered a regulator of miR-21, in this ET sample as compared with the H-JAK2 sample. Therefore, it was probably not a coincidence that, two months after blood sampling, the h-JAK2 patient suffered from disease transformation to secondary myelofibrosis. Among the three CALR-mutated patients, the expression patterns and the mutational profiles were also significantly discrepant. In the patient with type I CALR mutation, the CD34+ cells exhibited aberrant activity in epigenetic regulators. Coupled with the identified somatic mutations in some epigenetic modifiers from the targeted sequencing results, it is speculated that these mutations occur in a very early hematopoietic stage and contribute to ET pathogenesis through abnormal epigenetic regulation in this patient. Lastly, principal component analysis showed that the pathognomonic molecular events initiated at different hierarchical level of hematopoiesis in the 2 TN ET patients. Reactome analysis also disclosed one patient had altered DNA repair activity, and targeted sequencing confirmed the presence of TP53 mutation. Clinically, this patient exhibited highly aggressive, treatment-refractory disease. Conclusion We demonstrate that scRNA-seq extends our knowledge of clonal diversity and inter-individual heterogeneity in patients with ET. Combined with the results from targeted sequencing, we were able to uncover unique transcriptomic pattern in samples carrying specific somatic mutations. The obtained information could potentially leapfrog our effort in the elucidation of the pathogenesis of ET. Disclosures No relevant conflicts of interest to declare.

We propose a novel method, GABOLA, which utilizes long-range genomic information provided by accurate linked short reads jointly with long reads to improve the integrity and resolution of whole genome assemblies especially in complex genetic regions. We validated GABOLA on human and Japanese eel genomes. On the two human samples, we filled in more bases spanning 23.3 Mbp and 46.2 Mbp than Supernova assembler, covering over 3,200 functional genes which includes 8,500 exons and 15,000 transcripts. Among them, multiple genes related to various types of cancer were identified. Moreover, we discovered additional 11,031,487 base pairs of repeat sequences and 218 exclusive repeat patterns, some of which are known to be linked to several disorders such as neuron degenerative diseases. As for the eel genome, we successfully raised the genetic benchmarking score to 94.6% while adding 24.7 million base pairs. These results manifest the capability of GABOLA in the optimization of whole genome assembly and the potential in precise disease diagnosis and high-quality non-model organism breeding.