Single cell long-read genotyping of transcripts reveals discrete mechanisms of clonal evolution in post-myeloproliferative neoplasm acute myeloid leukemia

Single cell long read genotyping of transcripts reveals discrete mechanisms of clonal evolution in post-MPN AML | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Single cell long read genotyping of transcripts reveals discrete mechanisms of clonal evolution in post-MPN AML View ORCID Profile Julian Grabek , View ORCID Profile Jasmin Straube , Leanne Cooper , Rohit Haldar , View ORCID Profile Ranran Zhang , Inken Dulige , Matthew Barker , Will Gatehouse , Helen Christensen , Gerlinda Amor , Victoria Y. Ling , Caroline McNamara , View ORCID Profile David M. Ross , Andrew Perkins , View ORCID Profile Megan J. Bywater , View ORCID Profile Steven W. Lane doi: https://doi.org/10.1101/2025.08.18.670417 Julian Grabek 1 Cancer Research Program, QIMR Berghofer Medical Research Institute , Brisbane, QLD, Australia 2 The University of Queensland , St Lucia, Brisbane, QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Julian Grabek Jasmin Straube 1 Cancer Research Program, QIMR Berghofer Medical Research Institute , Brisbane, QLD, Australia 2 The University of Queensland , St Lucia, Brisbane, QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jasmin Straube Leanne Cooper 1 Cancer Research Program, QIMR Berghofer Medical Research Institute , Brisbane, QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rohit Haldar 1 Cancer Research Program, QIMR Berghofer Medical Research Institute , Brisbane, QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ranran Zhang 1 Cancer Research Program, QIMR Berghofer Medical Research Institute , Brisbane, QLD, Australia 2 The University of Queensland , St Lucia, Brisbane, QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ranran Zhang Inken Dulige 1 Cancer Research Program, QIMR Berghofer Medical Research Institute , Brisbane, QLD, Australia 3 University of Lübeck , Lübeck, Schleswig-Holstein, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Matthew Barker 1 Cancer Research Program, QIMR Berghofer Medical Research Institute , Brisbane, QLD, Australia 2 The University of Queensland , St Lucia, Brisbane, QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Will Gatehouse 1 Cancer Research Program, QIMR Berghofer Medical Research Institute , Brisbane, QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Helen Christensen 1 Cancer Research Program, QIMR Berghofer Medical Research Institute , Brisbane, QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gerlinda Amor 1 Cancer Research Program, QIMR Berghofer Medical Research Institute , Brisbane, QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Victoria Y. Ling 1 Cancer Research Program, QIMR Berghofer Medical Research Institute , Brisbane, QLD, Australia 2 The University of Queensland , St Lucia, Brisbane, QLD, Australia 4 Department of Haematology, Princess Alexandra Hospital , Brisbane, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Caroline McNamara 5 Cancer Care Services, Royal Brisbane and Women’s Hospital , Brisbane, QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site David M. Ross 6 Department of Haematology, Royal Adelaide Hospital , Adelaide, SA, Australia 7 Acute Leukaemia Laboratory, Centre for Cancer Biology, SA Pathology and University of South Australia , Adelaide, SA, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for David M. Ross Andrew Perkins 8 Alfred Hospital, Melbourne , Victoria, Australia 9 Australian Centre for Blood Diseases, Monash University , Melbourne, Victoria, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Megan J. Bywater 1 Cancer Research Program, QIMR Berghofer Medical Research Institute , Brisbane, QLD, Australia 2 The University of Queensland , St Lucia, Brisbane, QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Megan J. Bywater For correspondence: megan.bywater{at}qimrb.edu.au steven.lane{at}qimrb.edu.au Steven W. Lane 1 Cancer Research Program, QIMR Berghofer Medical Research Institute , Brisbane, QLD, Australia 2 The University of Queensland , St Lucia, Brisbane, QLD, Australia 5 Cancer Care Services, Royal Brisbane and Women’s Hospital , Brisbane, QLD, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Steven W. Lane For correspondence: megan.bywater{at}qimrb.edu.au steven.lane{at}qimrb.edu.au Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Myeloproliferative neoplasms (MPNs) are caused by acquired mutations in hematopoietic stem and progenitor cells (HSPCs). The acquisition of additional mutations like TP53 and the overall mutational burden influence a patient’s risk of disease progression toward lethal post-MPN acute myeloid leukemia (AML). Recent technological advancements in linking single-cell gene expression with genotype have improved our understanding of tumor heterogeneity. However, current methodologies have limitations in simultaneously genotyping low-expression genes (such as JAK2 ) alongside other pathogenic loci. To address this, we developed a novel lo ng read genotyping pipeline of cDNA tr anscripts called LOTR-Seq, which can genotype the full length of expressed transcripts of 30 genes at once. Using LOTR-Seq, we genotyped HSPCs at the JAK2 V617 locus in 9,075 single cells from eight patients with chronic phase MPN (CP-MPN) and in 5,016 cells from four patients with post-MPN AML. We then linked the mutations to the single cell transcriptome of 29,712 JAK2 V617F-driven CP-MPN cells and 16,895 post-MPN AML cells. In our analysis of post-MPN AMLs, we identified nine mutated loci across six genes ( JAK2, IDH1/2, TP53, SRSF2, U2AF1 ) and linked these mutations to specific transcriptional phenotypes. Overall, LOTR-Seq provides novel insights into the evolution of post-MPN AML. Introduction Myeloproliferative Neoplasms (MPN) are clonal blood cancers characterized by the overproduction of mature, functional myeloid elements. MPNs are caused by driver mutations within the hematopoietic stem and progenitor (HSPC) compartment 1 , with the most common being JAK2 V617F 2 . JAK2 V617F gives rise to a clonal stem cell population 3 and results in pathology mediated by mature myeloid cells. Both healthy and MPN HSPCs exhibit both transcriptional evidence of myeloid and lymphoid lineage priming 4 and self-renewal capacity 5 . Exactly how this clonal outgrowth affects the transcriptional diversity of the HSPC compartment is unclear. In contrast to MPN, acute myeloid leukemias (AML) are characterized by the overproduction of immature myeloid cells. Genetic complexity is a common feature of JAK2 V617F post-MPN AML, with patients having multiple additional mutations identified at AML transformation 6 . Disease progression from chronic phase MPN (CP-MPN) to AML is variable and presumed to be dictated by the acquisition of mutations in HSPCs in addition to the MPN driver and subsequent clonal expansion 7 , 8 . Single cell technologies have provided unique insights behind this evolution, however current methods are constrained by the cell number, and the number of genetic loci that can be concurrently analyzed (Supplemental Figure 1) 7 – 10 . We have developed an innovative pipeline called LOTR-Seq, which allows for concurrent single-cell transcription plus genotyping of a comprehensive panel of full-length transcripts from 30 genes that are recurrently mutated in post-MPN AML. This full-length approach facilitates the detection of rare variants, the phasing of mutations, and the identification of different isoforms. Here we used LOTR-Seq to identify clonal evolution during transformation from MPN to AML and to determine the impact of these additional mutations on JAK2 -mutant HSPCs in driving disease progression. Materials and methods Ethics approval and Primary Human Sample processing Patient bone marrow (BM) samples were obtained from the Royal Brisbane Women’s Hospital and from the South Australian Cancer Research Biobank (SACRB), and analyzed with approvals from QIMR Berghofer HREC (p1382). Patients provided written informed consent for the collection, storage, and use of their samples in research. Detailed patient information can be found in Supplemental Table 1. Samples were stained with CD3-PE/Cy7, CD34-PE, CD14-FITC and CD15-APC (Supplemental Table 2). CD34+ HSPCs were FACS sorted, and single cell 3’ RNA sequencing was performed with the 10x Chromium v3.1 on the Illumina NextSeq 500/550 Platform. CD34+ BM bulk genomic DNA capture panel sequencing and mutation calling were performed as described in Magor et al, 2016 11 on 7 of the 12 samples. Long read sequencing of transcripts: LOTR-Seq We first designed a capture panel on the Roche HyperDesign platform targeting all exonic regions of 30 recurrently mutated MPN/AML genes: ASXL1 , BCOR , CALR , CBL , CSF3R , DNMT3A , EZH2 , FLT3 , GATA2 , IDH1 , IDH2 , JAK2 , KIT , KRAS , MPL , MYC , NPM1 , NRAS , PPM1D , PTPN11 , RUNX1 , SF3B1 , SH2B3 , SRSF2 , STAG2 , TET2 , TP53 , TERT , U2AF1, and ZRSR2 . 10x barcoded cDNA underwent pre-capture PCR and read extension using custom primers (Supplemental Table 3) 12 . Amplified and extended cDNA was used to capture genes of interest using the default KAPA HyperCap Workflow v3, followed by post-capture PCR amplification. Further detailed information can be found in the Supplemental methods. Oxford Nanopore Technology (ONT) ligation kits, LSK110 or LSK114, were used in accordance with the manufacturer’s protocols, with modifications to the incubation time from 10 minutes to 20 minutes 13 to improve adapter ligation and ONT Flow Cell loading amount (50-100 fmol; Supplemental Table 4). Single cell short read sequencing processing MPN and post-MPN AML sequenced reads were adapter trimmed and processed through cellranger (v6.0.1, or MPN10 v3.0.2) with GRCh38 genome build to obtain a counts per single cell and gene matrix. 10x single cell RNA-seq data from healthy human CD34+ BM HSPCs from Ainciburu et al. 2023 14 were downloaded from GEO (Supplemental Table 5). Healthy, CP-MPN, and post-MPN AML samples were preprocessed in R (v3.6.3) following the Seurat v4.3 pipline 15 . Clusters were annotated according to key lineage marker gene expression. For analyzing lineage skewing, clusters were broadly grouped by adding up clusters into HSC/LMPP (HSC+LMPP), Mega/Ery-Primed (MEP+MkP+e-Ery+l-Ery+Ery), Gran/Mono-Primed (GMP+Mono+Baso+DC), and Lymphoid-Primed (CLP+B-primed). Pseudo time prediction and cell fate scoring were performed using Palantir (v1.3.0) 16 within Python (v3.11.4). Differential gene expression and gene set enrichment analysis are detailed in Supplemental methods. ONT sequencing, read processing and mutational profiling analysis Base calling and chimeric read separation are detailed in Supplemental methods. Reads were assigned a 10x barcode using FLAMES 17 , allowing for two mismatches. Fastqs were split into individual barcode fastqs using zgrep to identify FLAMES-assigned barcodes in the fastq header line. Deconvoluted reads were mapped against the GRCh38 genome build using minimap2 (v2.27) 18 with option ‘-L -ax splicè. Reads filtering and mutation calling are detailed in the Supplemental methods. Expressed variant allele frequency (eVAF) was calculated in R as the ratio of the number of reads with bases matching the mutant allele to the total number of reads at the locus. Finally, eVAFs were assigned to a single cell transcriptome by matching barcodes recovered by long read and short read sequencing. Post-MPN AML differentiation stage assessment ScRNASeq raw counts from 11 post-MPN AML JAK2 V617F /TP53 bi-allelic loss 8 and our post-MPN AML samples were projected on the Bone marrow map as described in https://github.com/andygxzeng/BoneMarrowMap 21 (accessed 27/04/2025). Raw counts of bulk RNA-Seq data from 45 CP MPN, 34 post-MPN AML, and 11 healthy controls were downloaded from GEO with GSE283710 20 . Single cell RNA-Seq (scRNASeq) data of 11 JAK2 V617F /TP53 bi-allelic loss post-MPN AML and 5 healthy controls (GSE226340) 8 were extracted and raw counts pseudo-bulked. Data were normalized (edgeR v4.2.2) and log2 transformed before differentiation stage scoring was performed as described 21 . AML differentiation scores from bulked and pseudo-bulked RNASeq data were centered, and patient-specific scores Euclidean distance underwent hierarchical clustering (hclust; stats R base package) with default parameters. Data were visualized with heatmap3.R https://github.com/obigriffith/biostar-tutorials/blob/master/Heatmaps/heatmap.3.R . Results HSPC heterogeneity is preserved in chronic phase MPN but with lineage skewing To determine whether the expression of JAK2 V617F alters the transcriptional heterogeneity of the CD34+ HSPC compartment, we compared the single cell transcriptional landscape of CD34+ HSPC-enriched populations ( Figure 1A ) from normal human bone marrow samples 14 (Supplemental Figure 2A; n=6) to that of CD34+ HSPCs from eight patients with JAK2 V617F-driven CP-MPN ( Figure 1B ). Transcriptional heterogeneity was largely preserved in CP-MPN CD34+ cell populations, comprising HSC/LMPPs, megakaryocyte-erythroid-, lymphoid- or granulocyte-monocyte-primed populations, and consistent with that seen in the healthy CD34+ populations ( Figure 1B-C ; Supplemental Figure 2B). However, CP-MPN CD34+ populations demonstrated a higher percentage of cells exhibiting megakaryocyte/erythroid-primed gene expression profiles ( Figure 1D-E : Mega/Ery-Primed mean±SD: 22.5±6.9% healthy vs 34.1±10.3% CP-MPN, p=0.028; Supplemental Figure 2C). Download figure Open in new tab Figure 1. Single cell RNA sequencing identifies expanded megakaryocyte-erythroid primed cells in chronic phase MPN. (A) Schematic of MPN bone marrow hematopoietic stem and progenitor cell (HSPC) enrichment followed by 10x Chromium single cell separation and 3’ cDNA barcoding. (B) Chronic phase MPN UMAPs (n=4, polycythemia vera, PV; n=1, essential thrombocythemia, ET; n=1 primary myelofibrosis, PMF; n=2 secondary myelofibrosis, sMF) of short read sequencing derived whole transcriptomics of HSPCs. Clusters annotated by gene expression associated with HSC, hematopoietic stem cells; LMPP, lympho-myeloid multipotent progenitor; CLP, common lymphoid progenitors; GMP, granulocyte-monocyte progenitors; DC; Dendritic Cell progenitors; Baso, eosinophil/basophil/mast cells progenitors; MEP, megakaryocyte-erythroid progenitor; MkP, megakaryocyte progenitor, e-Ery; early erythroid and l-Ery, late erythroid priming. (C) Pooled healthy sample UMAPs (n=6) showing the expression of key lineage-defining transcriptional markers including HSC-CRHBP, LMPP-HOPX, B-primed-VPREB1, myeloid-MPO, monocyte-LYZ, DC-SPIB, basophil-LMO4, erythroid-KLF1 and megakaryocyte-ITGA2B. (D) Schematic of summarized clusters analysed in (E). (E) Bargraph with mean (black dot) and standard deviation (black line) comparing the percentage (%) of clustered cells in healthy (n=6) vs chronic phase (CP) MPN (n=8). Each grey or blue dot represents data from an individual sample. P-value derived from pairwise, two-sided Welch t-test. (A, D) Created in BioRender. Lane, S. (2025) (A) https://BioRender.com/vygkh71 , (D) https://BioRender.com/9t72apn ). These findings demonstrate that, consistent with healthy CD34+ HSPCs, transcriptional lineage-priming is present in primitive hematopoietic cell populations in CP-MPN; however, CP-MPN exhibits an HSPC compartment with a higher abundance of cells with a megakaryocyte/erythroid transcriptional phenotype, consistent with the pathology of CP-MPN, characterized by the overproduction of mature megakaryocytes and erythrocytes. MEP lineage priming in HSPCs is linked to the expansion of JAK2 V617F mutant cells We were interested to determine how clonal heterogeneity within CP-MPN altered this lineage bias within the CD34+ HSPC population. To identify JAK2 V617F-mutated cells within CP-MPN CD34+ HSPCs, we developed a technique for long read genotyping of transcripts (LOTR-Seq). LOTR-Seq uses the single cell barcoded cDNA derived by the 10x Chromium platform to capture the cDNA of target genes, like JAK2 , followed by ONT long read sequencing ( Figure 2A ) to correlate expressed mutational information to the transcriptome. Download figure Open in new tab Figure 2. Target enrichment and long read sequencing demonstrates the impact of mutational status on the gene transcription in chronic phase MPN. (A) Schematic showing the long read genotyping of transcripts (LOTR-Seq) pipeline that enriches single cell cDNA of 30 MPN/AML associated genes followed by long read sequencing and genotyping of transcripts, with an example of one MPN patient’s UMAP colored by JAK2 V617F expressed variant allele frequency (eVAF). (A) Created in BioRender. Lane, S. (2025) https://BioRender.com/vygkh71 . (B) UMAPs of the CD34+ HSPC single cells (light grey) of n=8 MPNs with the density distribution of JAK2 V617F mutation (red), JAK2 -wild type (blue) and non-genotyped cells (NA; grey). (C) Scatterplots of HSC/LMPP and key lineage-primed compartment percentage (%; x-axis) and the % of JAK2 V617F genotyped cells (y-axis) within the compartment (n=8 chronic phase MPN). Pearson’s correlation coefficient R and test of association p-value. The blue line represents the linear regression line with a 95%-confidence interval (grey band). (D) Smoothed regression lines of the Palantir predicted erythroid cell fate score (y-axis) in dependency of the pseudotime (x-axis) for n=5 healthy, n=4 Polycythemia vera patients (PV) JAK2 V617F and JAK2 wild type cells. (E) Bargraph of the mean erythroid cell fate for each sample comparing n=5 healthy to n=4 PV JAK2 V617F and JAK2 -wild type ( JAK2 WT ) summarized in (D). Black dot and line represent the group mean and standard deviation, respectively. P-values derived from pairwise, two-sided Welch t-tests. (F) Bargraph of the percentage (%) of cell cycle phase (G1, S, G2M) for the Mega/Ery-Primed compartment comparing n=6 healthy to n=8 CP-MPN JAK2 V617F and JAK2 wild type ( JAK2 WT ) . Black dot and line represent the group mean and standard deviation, respectively. P-values derived from pairwise, two-sided Welch t-tests. (G) Bargraph of the KEGG JAK-STAT signaling pathway average single cell gene set enrichment score comparing n=6 healthy to n=8 MPN JAK2 V617F and JAK2 wild type ( JAK2 WT). Black dot and line represent the group mean and standard deviation, respectively. P-values derived from pairwise, two-sided Welch t-tests. When LOTR-Seq was performed on the CP-MPN CD34+ HSPCs, we were able to demonstrate that JAK2 V617F pseudo-bulked eVAF strongly correlated with both genomic VAF (r=0.9; p=0.005; Supplemental Figure 3A) and short read-derived eVAF (r=0.96; p<000.1; Supplemental Figure 3B) of the CD34+ compartment, validating the reproducibility of mutation calling from long read sequencing. LOTR-Seq enabled us to genotype up to 51% of cells at the JAK2 V617 locus, improving upon previous techniques (Supplemental Figure 1) 10 , 22 . Notably, the percentage of genotyped cells at the JAK2 V617 locus correlated with sequencing depth (r=0.8, p=0.0006; Supplemental Figure 3C). We then assessed the contribution of JAK2 V617F-mutated cells to the CD34+ HSPC compartment using the density distribution of JAK2 V617F-mutated and JAK2 -wild type cells within the clusters identified by uniform manifold approximation and projection (UMAP) ( Figure 2B ). The percentage of JAK2 V617F mutated HSPCs within the Mega/Ery-primed CD34+ compartment correlated with the relative size of the Mega/Ery-primed compartment (R=0.75; p=0.031; Figure 2C ) and inversely correlated with that of the Gran/Mono-primed compartment (R=-0.79; p=0.02). PV patients displayed higher JAK2 V617F clone percentages in the Mega/Ery-Primed compartments 10 (Supplemental Figure 3D), conversely, JAK2 V617F lineage quantification was stably distributed in patients with ET or MF. These findings suggest that JAK2 V617F either skews lineage bias towards the Mega/Ery fate or confers a proliferative advantage exclusively to Mega/Ery-primed HSPCs. To resolve this, we used Palantir analysis of differentiation in pseudotime to examine the effect of JAK2 V617F on HSPC cell fate potential 16 . Here, HSPCs of PV patients containing the JAK2 V617F mutation show a higher probability of converging towards an erythroid cell fate, evidenced by a higher average erythroid cell fate score compared to both healthy CD34+ and JAK2 -wild type cells ( Figure 2D-E , Supplemental Figure 3E), while sMF patients exhibit a higher probability of converging towards a megakaryocytic progenitor fate (Supplemental Figure 3F-G). This supports the finding that JAK2 V617F favors erythroid/megakaryocyte differentiation. Furthermore, cell cycle analysis revealed that JAK2 V617F mutant cells contained a greater proportion of cells in G2M phase ( Figure 2F , Supplemental Figure 4A). We next sought to determine the impact of the JAK2 V617F mutation on canonical signaling pathway activation. Compared to healthy CD34+, CP-MPN CD34+ demonstrated increased JAK-STAT signaling across all compartments ( Figure 2G , Supplemental Figure 4B) in both JAK2 V617F, JAK2 -wild type cells, suggesting that the activation of JAK-STAT signaling within HSPC is also regulated by cell-extrinsic factors, such as increased activation of cytokine pathways 23 . The impact of this JAK2 V617F mutation appears most pronounced in the Mega/Ery-Primed compartment with Myc targets and the heme metabolism pathway activated in CP-MPN. Interestingly, inflammatory pathways are suppressed in PV but activated in MF patients, particularly at the HSC/LMPP level (Supplemental Figure 4C). These data validate the LOTR-Seq pipeline to identify JAK2 V617F mutant vs JAK2 -wild type HSPCs within individual CP-MPN samples. JAK2 V617F mutated cells demonstrate increased proliferation and preferential expansion towards the erythroid and megakaryocyte lineages and context-specific transcriptional effects of JAK2 V617F by lineage-primed compartment and MPN subtype, reflecting the heterogeneity observed in the clinical presentation of MPNs. Transformation to post-MPN AML is characterized by the loss of stem cell transcriptional heterogeneity and is driven by genetic complexity We next sought to determine whether the LOTR-Seq pipeline could be used to investigate the relationship between genetic and transcriptional heterogeneity within the HSPC compartment during progression from CP-MPN to post-MPN AML. In dramatic contrast to the transcriptional heterogeneity observed in CP-MPN, the CD34+ HSPC compartment in post-MPN AML is largely dominated by a single transcriptional profile ( Figure 3A-B ). For example, MPN10 AML exhibits a dominant HSC/LMPP-like transcriptional signature, representing 79.4% of all cells ( Figure 3A ), while MPN20 AML shows a dominant Mega/Ery primed-like transcriptional signature (84.8%; Figure 3B ). Download figure Open in new tab Figure 3. Loss of heterogeneity occurs in leukemic transformation and is driven by additional mutations in the JAK2 V617F mutant clone. (A-B) UMAPs of post-MPN AML samples (A) MPN10 and (B) MPN20 showing transcriptionally defined cells clusters and a bargraph depicting the relative percentage of cells per cluster, (C-E) UMAPs of post-MPN AML sample MPN10 showing expressed variant allele frequencies (eVAF) of mutations (C) JAK2 V617F (D) IDH2 R140W and (E) SRSF2 P95_R103del. (F-G) UMAPs of post-MPN AML sample MPN20 showing expressed variant allele frequencies (eVAF) of mutations (F) JAK2 V617F and (G) TP53 C238F. Grey colored dots indicate non-genotyped cells. A limitation of existing protocols integrating single cell RNA-Seq and genotyping is the inability to simultaneously genotype across numerous loci with sufficient coverage and representation to draw inferences on cells with multiple mutations within the CD34+HSPC compartment (Supplemental Figure 1) 22 . The LOTR-Seq pipeline overcomes this limitation by using enrichment for a panel of 30 genes, enabling the genotyping of expressed genes across a range from 0% to 100% (Supplemental Figure 5A). We therefore sought to apply this technology to determine the relationship between the genomic architecture of post-MPN AML and the transcriptional consequences linked with disease progression. MPN10 contained a dominant, HSC/LMPP-like expanded population in post-MPN AML. LOTR-Seq identified the MPN-driver JAK2 V617F mutation together with an IDH2 R140W mutation and SRSF2 P95_R103 in-frame deletion ( Figure 3A , C-E, Supplemental Figure 5B) in 56.7%, 92.4 %, and 83.4% of genotyped cells, respectively, and expressed at high VAF in the HSC/LMPP-like CD34+ cells. Additionally, in JAK2 V617F, IDH2 R140W and SRSF2 P95R co-mutated cells we observed the predominant inclusion of a poison exon cassette 24 , in comparison to cells from a post-MPN AML containing JAK2 V617F and IDH1 R132S without a mutation in SRSF2 (Supplemental Figure 5C). In a second post-MPN AML, MPN20, we identified a dominant Mega/Ery primed-like HSPC population containing JAK2 V617F (89.6% of genotyped cells) with biallelic TP53 C238F mutation (93.9% of genotyped cells) ( Figure 3B , F-G, Supplemental Figure 5D). This identification of a Mega/Ery-signature in JAK2 V617F multi-hit- TP53 post-MPN AML is consistent with other published human data 8 and aligns with data from murine post-MPN AML with this same combination of mutations 25 , 26 . Altogether, these data validate the use of LOTR-Seq in clonal analysis of human post-MPN AML and demonstrate that post-MPN AMLs exhibit loss of HSPC heterogeneity with the expansion of a dominant transcriptional cluster that appears to be linked to the identity of co-existing secondary mutations. Post-MPN AML demonstrates lineage restricted, recurrent differentiation priming Recent findings suggest that molecular subtypes of de novo AML are defined by unique transcriptional profiles that reflect lineage bias 21 , 27 , 28 . Our initial analysis of the HSPC compartment in post-MPN AML suggests lineage priming analogous to that seen in de novo AML 21 , 28 . To determine if these findings can be applied more generally to post-MPN AML, we analyzed bulk RNA-Seq of 34 CD34+ post-MPN AML samples 20 together with single cell RNA-Seq CD34+ HSPCs of a further 11 post-MPN AML cases 8 . Here, we adopted methods developed by Zeng et al 2025 21 to assess differentiation stages in post-MPN AML. Both methods of projecting AML on a detailed, healthy bone marrow hierarchical hematopoiesis map 21 ( Figure 4A-B ) and AML differentiation stage scoring ( Figure 4C ) suggest that even genetically homogenous subgroups, such as JAK2V617F with multi-hit TP53 , can exhibit expansion in different lineage-primed compartments. Compared to adult de novo AML, as reported by Zeng et al 2025 21 (n=28 of 139), post-MPN AML exhibited a significantly higher overrepresentation of HSC/LMPP states (n=23 of 45, Chi-Square p=0.0001). In the 45 post-MPN AML samples, 51% showed an immature differentiation stage (HSC/LMPP/MEP), with HSC/LMPP/early lymphoid stage or MEP/MKP/erythroid stage observed in 31% and 18%, respectively. Strikingly, there was an absence of a promonocytic/monocytic biased subgroup in JAK2 V617F mutated post-MPN AML, a dominant subgroup in de novo AML 21 ( Figure 4C ). These data demonstrate that post-MPN AML is not only genetically distinct from de novo AML, but also phenotypically and transcriptionally distinct with a more restricted differentiation repertoire. Download figure Open in new tab Figure 4. Post-MPN AML exhibit lineage-biased hematopoiesis (A) Bone marrow hematopoietic stem cell atlas UMAP with cell type annotations as generated by Zeng et al. 2025 21 . (B) UMAPs highlighting key lineage gene expression markers HSC- CRHBP , LMPP- HOPX , erythroid- KLF1 , megakaryocyte- ITGA2B , Baso- LMO4 , and B-primed- VPREB1 . (C) Projection of our generated n=3 JAK2 V617F post-MPN AML single cell samples (SA2LT, MPN20, MPN10)and n=11 JAK2 V617F TP53-multi-hit post-MPN AMLs from Rodriguez-Meira et al. 2023 8 on to the healthy bone marrow atlas UMAP. (D) Hierarchical clustering of lineage-defining gene expression scores (y-axis) of n= 11 JAK2 V617F post-MPN AML and n=6 healthy samples from Rodriguez-Meira et al. 2023 8 , n=45 chronic phase MPN, n=34 post-MPN AML and n=11 healthy from Kong et al 2025 20 (x-axis). High-risk mutations in post-MPN AML are already present in chronic phase disease Post-MPN AML is intrinsically chemotherapy resistant, and novel strategies are urgently required to prevent the progression of high-risk CP-MPN to AML. We therefore sought to determine whether LOTR-Seq could identify pre-leukemic high-risk populations within CD34+ cells from patients with CP-MPN. To do this, we analyzed a patient with matched CP-MPN and post-MPN AML samples ( Figure 5 ). The JAK2 V617F mutation was stable between CP-MPN and post-MPN AML ( Figure 5B , E), however deep sequencing analysis of the CP-MPN CD34+ cells identified a rare subpopulation of co-mutated JAK2 V617F and IDH1 R132S (8.2%) in CP-MPN, which expanded to 64.5% in post-MPN AML ( Figure 5C , F). In contrast to what was observed in the analysis of the previous post-MPN AML samples ( Figure 4 ), the expansion of the double-mutated population resulted in the relative expansion of the HSC/LMPP clusters with partially maintained representation of the more committed lineage-primed cells. Download figure Open in new tab Figure 5. Leukemic transformation from chronic phase MPN showing a linear pattern of clonal expansion of a high risk JAK2 V617F and IDH1 R132S mutant clone. UMAPs of the same patient at diagnosis of (A-C) chronic phase MPN (SA2CP) and (D-F) post-MPN AML (SA2LT), showing (A,D) transcriptionally defined clusters with bargraphs showing the percentage of cells in the transcriptionally defined clusters and (B-C,E-F) expressed variant allele frequencies (eVAF) of mutations (B,E) JAK2 V617F and (C,F) IDH1 R132S. Grey colored dots indicate non-genotyped cells. (G) Fish plot illustrating the linear clonal evolution of the JAK2 V617F and IDH1 R132S mutant clone. The co-occurrence of JAK2 V617F and IDH1 R132S clones in chronic phase, with expansion of the double-mutated clone during AML progression, suggests that this leukemia was driven by the linear clonal evolution of the double-mutant cells ( Figure 5G ). Thus, the LOTR-Seq protocol can identify genetic complexity at the single cell level, enabling the identification of pre-leukemic clones that later give rise to post-MPN AML. Additional mutations in JAK2 V617F cells exhibit mutation and context specific gene expression signatures To determine how the presence of additional mutations can impact MPN stem cells and if this changes with leukemic transformation, we first compared IDH1 R132-mutant cells (n=122) with JAK2 V617F/ IDH1 WT cells (n=1562), in the matched chronic phase sample, for differential gene expression and gene set enrichment across the major transcriptional compartments in CD34⁺ bone marrow ( Figure 6A-C , Supplemental Table 9-10). IDH1 R132S cells within the Mega/Ery-Primed compartment displayed heightened activation of inflammatory pathways, notably TNFα and IL2–STAT5A, alongside an increase in STAT5A gene expression, known to have potential roles in MPN pathophysiology 20 , 29 . Concurrently, we observed an upregulation of hypoxia-related genes (e.g., BCL2 ) and a downregulation of oxidative phosphorylation (OXPHOS)-related genes, suggesting a metabolic shift in the IDH1 -mutant cellular environment. Download figure Open in new tab Figure 6. IDH1 mutations in JAK2 V617F cells exhibit mutation and context specific gene expression signatures (A) Schema of analysis performed in B-E, Created in BioRender. Lane, S. (2026) https://BioRender.com/1kuev8s (B) Volcanoplot displaying the log 2 fold change (x-axis) and log 10 P-value (y-axis) from comparing gene expression of JAK2 V617F IDH1 R132S (higher expression marked in darkblue) vs JAK2 V617F IDH1 wildtype (WT) (higher expression marked in darkred) cells of a patient with chronic phase MPN (SA2CP). Colored are significant different genes (p<0.05) associated with msigdb hallmark signatures and signatures of interest: TNFA signaling via NFKB (TNF; green), Hypoxia (purple), epithelial to mesenchymal transition (EMT; blue), heme metabolism (Heme; red), E2F targets (yellow) and Leukemia stem cell and progenitor-primed (LSPC-primed; black). Genes up regulated in IDH-mutant de novo AML are indicated as *. (C) Heatmap of gene set normalized enrichment scores (NES) from the comparison of the average fold changes JAK2 V617F IDH1 R132S vs JAK2 V617F IDH1 WT cells of a patient with chronic phase MPN (SA2CP) in key transcriptional compartments. Darkblue indicates higher NES in JAK2 V617F IDH1 R132S while dark red indicates higher NES in JAK2 V617F WT. Columns indicate key transcriptional compartments in CD34+ namely hematopoietic stem cells and lympho-myeloid multipotent progenitor (HSC/LMPP), megakaryocyte/erythroid-primed (Mega/Ery-Primed) and granulocytic/monocytic-primed (Gran/Mono-Primed). False discovery rate (FDR) significance levels are indicated as follows *<0.05; **<0.01; ***<0.001; ****<0.0001. (D) Volcanoplot displaying the log 2 fold change (x-axis) and log 10 FDR (y-axis) from comparing gene expression of a patients IDH1 R132S mutant cells in chronic phase MPN (SA2CP, higher expression marked in darkgreen) vs leukemia transformation (SA2LT; higher expression marked in yellow). Colored and marked are top 70 significant different genes (FDR<0.05) as described in (B). (E) Heatmap of gene set NES from the comparison of the average fold changes of IDH1 R132S cells in a patient at chronic phase MPN (SA2CP) and after leukemia transformation (SA2LT) in key transcriptional compartments. Darkgreen indicates higher NES in SA2CP while yellow indicates higher NES in SA2LT. Columns indicate key transcriptional compartments in CD34+ namely HSC/LMPP, Mega/Ery-Primed and Gran/Mono-Primed. FDR-significance levels are indicated as follows *<0.05; **<0.01; ***<0.001; ****<0.0001. Interestingly, even within chronic phase disease, the gene expression changes in IDH1 -mutant cells showed similarities to gene expression changes in IDH -mutant de novo AML 30 , also revealing characteristics such as leukemia stem cell priming and quiescence 27 , 31 . This occurred alongside the suppression of genes involved in heme metabolism, E2F-targets, DNA metabolic processes, and replication pathways. However, when comparing matched JAK2 V617F and IDH1 R132S co-mutated cells between chronic phase disease and post-MPN AML ( Figure 6A ), we identified more pronounced alterations across all HSPC compartments ( Figure 6D-E ), including that TNFα signaling and de novo IDH -mutant core pathways were upregulated in the leukemic phase, while E2F-targets and DNA replication processes were suppressed. Of note, we observed significant upregulation of TP53 pathway in the Mega/Ery-Primed compartment and other malignancy associated gene expression 20 , 29 , 32 , 33 . Through linking mutations to transcriptomic profiles via LOTR-Seq, we provide novel insights into the pathways that contribute to IDH1 -mediated progression to post-MPN AML, highlighting the striking similarities with de novo IDH -mutant AML. These findings argue strongly for further investigations into the therapeutic potential of IDH inhibitors during the chronic phase and post-MPN AML settings 34 , 35 . Hyperactivation of JAK-STAT signaling is maintained in JAK2 V617F-negative post-MPN AML It has been observed that in 16-50% of post-MPN AML cases, the MPN driver is absent at the time of leukemia transformation, even when the original disease contained a mutation in a canonical MPN driver gene 36 , 37 , 6 . The origin of this MPN driver-negative post-MPN AML is incompletely understood. We reasoned that this category of post-MPN AML must have a different mechanism of transformation compared to the classical linear clonal evolution model. To examine this further, we used LOTR-Seq to analyze paired, sequential samples from a patient who presented with JAK2 V617F CP-MPN and JAK2 V617F-negative post-MPN AML ( Figure 7A-H , Supplemental Figure 6A-D). Here, the CP-MPN contained a dominant clone with TP53 Y236C mutations in addition to a JAK2 L393V mutation, previously described to result in gain of function 38 ( Figure 6C , Supplemental Figure 7B,D). Within the TP53 Y236C, JAK2 L393V population, there was further heterogeneity with two identifiable sub-clonal populations containing either TP53 R282W ( Figure 7D ) or U2AF1 S34Y and JAK2 V617F (Supplemental Figure 6C; Figure 7B ). With evolution to AML, the TP53 Y236C, JAK2 L393V TP53 R282W containing cells demonstrated selective clonal advantage and are dominant in the post-MPN AML stage ( Figure 7H ). Both TP53 mutations identified are located within the DNA-binding domain (Supplemental Figure 7A), and thus are predicted to confer a loss-of-function phenotype. Importantly, we were also able to demonstrate that the TP53 mutations occurred on independent alleles within the same cell and exhibited random monoallelic expression (i.e., somatically acquired in trans; Supplemental Figure 7B-C). These findings identify leukemic transformation that arose from a genetically complex, high-risk ancestral clone and demonstrate parallel clonal evolution resulting in JAK2 V617F-negative AML ( Figure 5I ; Supplemental Figure 5E). Here, the hyperactivation of JAK-STAT signaling evident in the chronic phase sample was maintained post leukemic transformation, despite the loss of the JAK2 V617F clone (Supplemental Figure 7E-F). Of note, maintained hyperactivation of JAK-STAT signaling post leukemic transformation was also observed in the case of linear evolution, patient SA2 (Supplemental Figure 7E-F). Download figure Open in new tab Figure 7. Leukemic transformation from chronic phase MPN showing parallel clonal evolution of a JAK2 V617F -negative TP53-multi-hit clone. UMAPs of the same patient at diagnosis of (A-D) chronic phase MPN (SA1CP) and (E-H) post-MPN AML (SA1LT), showing (A,E) transcriptionally defined clusters and bargraphs with the percentage of cells in the clusters and (B-D,F-H) expressed variant allele frequencies (eVAF) of mutations (B,F) JAK2 V617F, (C,G) TP53 Y236C and (D,H) TP53 R282W. Grey colored dots indicate non-genotyped cells. (I) Fish plot illustrating the parallel clonal evolution of the JAK2 V617F and TP53 R282W mutant clones. In summary, using LOTR-Seq to combine single cell transcriptional analysis and panel-enriched genotyping of transcripts from loci commonly associated with myeloid malignancies, we have been able to demonstrate that the transformation of CP-MPN to post-MPN AML is associated with the acquisition of recurrent secondary mutations that drive loss of transcriptional heterogeneity and the immortalization of a genotypically defined, transformed progenitor cell population. Discussion Mutational complexity in MPN is common, with more than 40% of CP-MPN patients harboring a mutation relevant to myeloid malignancies in addition to an MPN driver. Importantly, the acquisition of additional mutations in CP-MPN is associated with reduced event-free survival. Consistent with this, AML arising from antecedent MPN is associated with 2 or more genetic mutations in the vast majority of patients 6 , strongly supporting the hypothesis that the acquisition of these additional mutations drives leukemic transformation of CP-MPN. However, our capacity to determine the functional consequences of mutational complexity in MPN stem cells has been limited by our ability to multiplex single cell phenotyping technology with mutational status. Specifically, single cell RNA-sequencing approaches have provided a high-resolution map of the substantial amount of transcriptional heterogeneity within the HSPC compartment in the BM of patients with CP-MPN but have had limited capacity to associate these differences with an individual cell’s genotype across the spectrum of loci recurrently mutated in post-MPN AML 9 , 10 , 22 . Here, we describe a novel pipeline, LOTR-Seq, combining 10x single cell RNA-sequencing of CP-MPN and post-MPN AML, together with broad, panel-based enrichment and long read genotyping of the vast majority of genetic loci frequently mutated in MPN. We demonstrate that cells expressing the MPN-driver JAK2 V617F can exhibit transcriptional lineage priming diversity similar to that of their healthy counterparts, but are heavily biased towards a megakaryocyte/erythroid program. Strikingly, this transcriptional diversity is lost upon leukemic transformation, with the HSPC compartment of post-MPN AML exhibiting a homogenous transcriptional profile that appears to be dictated by the identity of the co-occurring mutations. In the case of the JAK2 V617F-positive, TP53 -mutant AML, compound mutant cells were found almost exclusively within the megakaryocyte/erythroid-primed clusters, corroborating recent findings in additional human samples and mouse models with this same genetic combination. Notably, however, the JAK2 V617F and IDH -mutated AMLs exhibited transcriptional features of more primitive HSCs, suggesting that post-MPN is transcriptionally diverse. Testing this hypothesis using a bulk RNA sequencing dataset with a larger number of patients, we demonstrated that post-MPN AML comprises a group of leukemias with three transcriptionally distinct profiles. Importantly, the identity of these transcriptional profiles both diverges and overlaps with what is observed in de novo AML. Patients with post-MPN AML are excluded from AML clinical trials for targeted agents, attributed to the comparatively dismal outcomes in post-MPN AML. The finding that IDH -mutated post-MPN AMLs exhibit transcriptional similarity with the corresponding de novo AMLs should warrant reconsideration of this approach. This is particularly relevant for IDH inhibitors, as the transcriptional consequences of mutant IDH1 appear to be amplified as either a cause or consequence of leukemic transformation to post-MPN AML. The progression of CP-MPN to post-MPN AML is rare, difficult to predict, and can occur over decades. Consequently, clinical studies evaluating new MPN therapies are often underpowered to use leukemic transformation as an endpoint. Given the association between mutational complexity and reduced event-free survival, monitoring the emergence of compound mutational clones could prove valuable and should be investigated as surrogate endpoint for CP-MPN disease progression. LOTR-Seq improves upon conventional amplicon-based genomic mutational profiling here by demonstrating conclusively whether TP53 mutations are monoallelic or biallelic, which is highly predictive of prognosis in CP-MPN. LOTR-Seq has also enabled the identification of additional mutations in the genes of interest that are not routinely assessed for in MPN. The identification of JAK2 L393V has obvious implications for our understanding of, and the evolution of, MPN driver-negative post-MPN AML and CP-MPN. Furthermore, the additional dimension of transcriptional profiling to the clonal analysis of MPN disease progression allows for both the monitoring of the emergence of high-risk mutations and the assessment of their functional consequence. This information could be used to both inform treatment decisions and allow updated prognostication. It also enables the identification of unique features of high-risk clones that can hopefully be leveraged into new treatments that can prevent the development of post-MPN AML. In aggregate, the development of LOTR-Seq has both advanced our understanding of the functional consequences of mutational complexity associated with CP-MPN disease progression and provides a new tool to be exploited in CP-MPN clinical trial design to progress the field towards surrogate endpoints of disease progression and ultimately improve long-term outcomes for these patients. Authorship SWL, MJB, JS conceptualized, designed, and supervised wet lab experiments and bioinformatics analysis. JG, LC, RH, RZ performed wet lab experiments. JS, JG, ID, MB performed bioinformatic analysis. CM, DR, SWL coordinated primary patient sample collection. GA, HC, WG coordinated the collection, consent, and storage of patient sample information. VYL helped with gene panel design. AP performed genomic DNA NGS analysis of primary patient samples. JG, JS, MJB, SWL wrote the manuscript. All authors contributed to and edited the manuscript. Conflict of Interest Disclosure SWL, MJB, and JS have received research funding from Bristol Myers Squibb for unrelated projects. We acknowledge receipt of reagents from PharmaEssentia for unrelated projects. MJB has received research funding from Cylene Pharmaceuticals for unrelated projects. DMR has consulted for or received honoraria from GSK, Jubilant, Keros, Merck, Menarini, Novartis, Prelude, and Takeda, for unrelated projects. SWL has consulted for AbbVie, Novartis, Astellas, and GSK, also for unrelated projects. Data Availability Statement The 10x sequencing raw and processed data generated in this study is deposited in ArrayExpress with accession E-MTAB-15981. Processed data will be made available via figshare/zendo. Code is available via GitHub https://github.com/JStrau/LOTRSeq . ONT, raw data, due to its size, will be made available upon request. Download figure Open in new tab Supplemental Figure 1 Download figure Open in new tab Supplemental Figure 2 Download figure Open in new tab Supplemental Figure 3 Download figure Open in new tab Supplemental Figure 4 Download figure Open in new tab Supplemental Figure 5 Download figure Open in new tab Supplemental Figure 6 Download figure Open in new tab Supplemental Figure 7 Acknowledgement We are grateful for the assistance of the QIMR Berghofer facilities, including flow cytometry and sequencing, and for the helpful comments from members of the Lane Lab, particularly Stacey Anderson. We thank Rachel Thjissen for the fruitful discussion on ONT barcode recovery, primer sequences, and ONT read extension protocols. We gratefully acknowledge the support of Neil Herron and the Herron Family Trust, and the Gordon and Jessie Gilmour Family Trust as dedicated supporters of leukemia research in Queensland. We gratefully acknowledge the contribution of the MPN Alliance Australia for the valuable patient perspective they provided for this study. SWL was funded by an NHMRC Investigator Grant (1195987 2021-2025) and by a philanthropic donation from the MPN Alliance of Australia. ID was supported by a PROMOS, DAAD stipend. JS was funded by a Cancer Council Queensland fellowship (2025829). JG was supported by an HSANZ/Leukaemia Foundation PhD scholarship. This research was supported by the Australian Cancer Research Foundation Centre for Optimised Cancer Therapy. Funder Information Declared Cancer Council Queensland, https://ror.org/03g5d6c96 , 2025829 NHMRC Investigator , 1195987 Footnotes We have added new analysis, sections and added 1 new Figure and numerous supplementary Figures and Tables after first revision. References 1. ↵ Jamieson CH , Gotlib J , Durocher JA , et al. The JAK2 V617F mutation occurs in hematopoietic stem cells in polycythemia vera and predisposes toward erythroid differentiation . Proceedings of the National Academy of Sciences of the United States of America. Apr 18 2006; 103 ( 16 ): 6224 – 9 . doi: 10.1073/pnas.0601462103 OpenUrl CrossRef 2. ↵ Prins D , González Arias C , Klampfl T , Grinfeld J , Green AR . Mutant Calreticulin in the Myeloproliferative Neoplasms . HemaSphere . Feb 2020 ; 4 ( 1 ): e333 . doi: 10.1097/hs9.0000000000000333 OpenUrl CrossRef 3. ↵ Beer PA , Delhommeau F , LeCouédic JP , et al. Two routes to leukemic transformation after a JAK2 mutation-positive myeloproliferative neoplasm . Blood. Apr 8 2010 ; 115 ( 14 ): 2891 – 900 . doi: 10.1182/blood-2009-08-236596 OpenUrl Abstract / FREE Full Text 4. ↵ Challen GA , Boles NC , Chambers SM , Goodell MA . Distinct hematopoietic stem cell subtypes are differentially regulated by TGF-beta1 . Cell stem cell. Mar 5 2010 ; 6 ( 3 ): 265 – 78 . doi: 10.1016/j.stem.2010.02.002 OpenUrl CrossRef PubMed Web of Science 5. ↵ Yamamoto R , Morita Y , Ooehara J , et al. Clonal analysis unveils self-renewing lineage-restricted progenitors generated directly from hematopoietic stem cells . Cell. Aug 29 2013 ; 154 ( 5 ): 1112 – 1126 . doi: 10.1016/j.cell.2013.08.007 OpenUrl CrossRef PubMed 6. ↵ McNamara CJ , Panzarella T , Kennedy JA , et al. The mutational landscape of accelerated- and blast-phase myeloproliferative neoplasms impacts patient outcomes . Blood Adv. Oct 23 2018 ; 2 ( 20 ): 2658 – 2671 . doi: 10.1182/bloodadvances.2018021469 OpenUrl Abstract / FREE Full Text 7. ↵ Calabresi L , Carretta C , Romagnoli S , et al. Clonal dynamics and copy number variants by single-cell analysis in leukemic evolution of myeloproliferative neoplasms . American journal of hematology . Oct 2023 ; 98 ( 10 ): 1520 – 1531 . doi: 10.1002/ajh.27013 OpenUrl CrossRef PubMed 8. ↵ Rodriguez-Meira A , Norfo R , Wen S , et al. Single-cell multi-omics identifies chronic inflammation as a driver of TP53-mutant leukemic evolution . Nature genetics . Sep 2023 ; 55 ( 9 ): 1531 – 1541 . doi: 10.1038/s41588-023-01480-1 OpenUrl CrossRef PubMed 9. ↵ Rodriguez-Meira A , Buck G , Clark SA , et al. Unravelling Intratumoral Heterogeneity through High-Sensitivity Single-Cell Mutational Analysis and Parallel RNA Sequencing . Mol Cell. Mar 21 2019 ; 73 ( 6 ): 1292 – 1305 e8. doi: 10.1016/j.molcel.2019.01.009 OpenUrl CrossRef PubMed 10. ↵ Van Egeren D , Escabi J , Nguyen M , et al. Reconstructing the Lineage Histories and Differentiation Trajectories of Individual Cancer Cells in Myeloproliferative Neoplasms . Cell stem cell. Mar 4 2021 ; 28 ( 3 ): 514 – 523 .e9. doi: 10.1016/j.stem.2021.02.001 OpenUrl CrossRef PubMed 11. ↵ Magor GW , Tallack MR , Klose NM , et al. Rapid Molecular Profiling of Myeloproliferative Neoplasms Using Targeted Exon Resequencing of 86 Genes Involved in JAK-STAT Signaling and Epigenetic Regulation . J Mol Diagn . Sep 2016 ; 18 ( 5 ): 707 – 718 . doi: 10.1016/j.jmoldx.2016.05.006 OpenUrl CrossRef PubMed 12. ↵ Thijssen R , Tian L , Anderson MA , et al. Single-cell multiomics reveal the scale of multilayered adaptations enabling CLL relapse during venetoclax therapy . Blood. Nov 17 2022 ; 140 ( 20 ): 2127 – 2141 . doi: 10.1182/blood.2022016040 OpenUrl CrossRef PubMed 13. ↵ Wei S , Williams Z . Rapid Short-Read Sequencing and Aneuploidy Detection Using MinION Nanopore Technology . Genetics . Jan 2016 ; 202 ( 1 ): 37 – 44 . doi: 10.1534/genetics.115.182311 OpenUrl Abstract / FREE Full Text 14. ↵ Ainciburu M , Ezponda T , Berastegui N , et al. Uncovering perturbations in human hematopoiesis associated with healthy aging and myeloid malignancies at single-cell resolution . Elife. Jan 11 2023 ; 12 doi: 10.7554/eLife.79363 OpenUrl CrossRef 15. ↵ Hao Y , Hao S , Andersen-Nissen E , et al. Integrated analysis of multimodal single-cell data . Cell . 2021 /06/24/ 2021;184(13):3573-3587. e29 . doi: 10.1016/j.cell.2021.04.048 OpenUrl CrossRef PubMed 16. ↵ Setty M , Kiseliovas V , Levine J , Gayoso A , Mazutis L , Pe’er D . Characterization of cell fate probabilities in single-cell data with Palantir . Nature biotechnology . Apr 2019 ; 37 ( 4 ): 451 – 460 . doi: 10.1038/s41587-019-0068-4 OpenUrl CrossRef PubMed 17. ↵ Tian L , Jabbari JS , Thijssen R , et al. Comprehensive characterization of single-cell full-length isoforms in human and mouse with long-read sequencing . Genome biology. Nov 11 2021 ; 22 ( 1 ): 310 . doi: 10.1186/s13059-021-02525-6 OpenUrl CrossRef PubMed 18. ↵ Li H . Minimap2: pairwise alignment for nucleotide sequences . Bioinformatics . 2018 ; 34 ( 18 ): 3094 – 3100 . doi: 10.1093/bioinformatics/bty191 OpenUrl CrossRef PubMed 19. Koboldt DC , Zhang Q , Larson DE , et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing . Genome Res . Mar 2012 ; 22 ( 3 ): 568 – 76 . doi: 10.1101/gr.129684.111 OpenUrl Abstract / FREE Full Text 20. ↵ Kong T , Laranjeira ABA , Letson CT , et al. RSK1 is an exploitable dependency in myeloproliferative neoplasms and secondary acute myeloid leukemia . Nat Commun. Jan 16 2025 ; 16 ( 1 ): 492 . doi: 10.1038/s41467-024-55643-7 OpenUrl CrossRef PubMed 21. ↵ Zeng AGX , Iacobucci I , Shah S , et al. Single-cell Transcriptional Atlas of Human Hematopoiesis Reveals Genetic and Hierarchy-Based Determinants of Aberrant AML Differentiation . Blood Cancer Discov. Jul 1 2025 ; 6 ( 4 ): 307 – 324 . doi: 10.1158/2643-3230.BCD-24-0342 OpenUrl CrossRef PubMed 22. ↵ Nam AS , Kim KT , Chaligne R , et al. Somatic mutations and cell identity linked by Genotyping of Transcriptomes . Nature . Jul 2019 ; 571 ( 7765 ): 355 – 360 . doi: 10.1038/s41586-019-1367-0 OpenUrl CrossRef PubMed 23. ↵ Austin RJ , Straube J , Bruedigam C , et al. Distinct effects of ruxolitinib and interferon-alpha on murine JAK2V617F myeloproliferative neoplasm hematopoietic stem cell populations . Leukemia . Apr 2020 ; 34 ( 4 ): 1075 – 1089 . doi: 10.1038/s41375-019-0638-y OpenUrl CrossRef PubMed 24. ↵ Kim E , Ilagan JO , Liang Y , et al. SRSF2 Mutations Contribute to Myelodysplasia by Mutant-Specific Effects on Exon Recognition . Cancer Cell. May 11 2015 ; 27 ( 5 ): 617 – 30 . doi: 10.1016/j.ccell.2015.04.006 OpenUrl CrossRef PubMed 25. ↵ Li B , An W , Wang H , et al. BMP2/SMAD pathway activation in JAK2/p53-mutant megakaryocyte/erythroid progenitors promotes leukemic transformation . Blood. Jun 23 2022 ; 139 ( 25 ): 3630 – 3646 . doi: 10.1182/blood.2021014465 OpenUrl CrossRef PubMed 26. ↵ Rampal R , Ahn J , Abdel-Wahab O , et al. Genomic and functional analysis of leukemic transformation of myeloproliferative neoplasms . Proceedings of the National Academy of Sciences of the United States of America. Dec 16 2014 ; 111 ( 50 ): E5401 – 10 . doi: 10.1073/pnas.1407792111 OpenUrl Abstract / FREE Full Text 27. ↵ Zeng AGX , Bansal S , Jin L , et al. A cellular hierarchy framework for understanding heterogeneity and predicting drug response in acute myeloid leukemia . Nat Med . Jun 2022 ; 28 ( 6 ): 1212 – 1223 . doi: 10.1038/s41591-022-01819-x OpenUrl CrossRef PubMed 28. ↵ van Galen P , Hovestadt V , Wadsworth Ii MH , et al. Single-Cell RNA-Seq Reveals AML Hierarchies Relevant to Disease Progression and Immunity . Cell. Mar 7 2019 ; 176 ( 6 ): 1265 – 1281 e24. doi: 10.1016/j.cell.2019.01.031 OpenUrl CrossRef PubMed 29. ↵ Tefferi A , Vaidya R , Caramazza D , Finke C , Lasho T , Pardanani A . Circulating interleukin (IL)-8, IL-2R, IL-12, and IL-15 levels are independently prognostic in primary myelofibrosis: a comprehensive cytokine profiling study . J Clin Oncol. Apr 1 2011 ; 29 ( 10 ): 1356 – 63 . doi: 10.1200/JCO.2010.32.9490 OpenUrl Abstract / FREE Full Text 30. ↵ Sirenko M , Lee S , Sun Z , et al. Deconvoluting clonal and cellular architecture in IDH-mutant acute myeloid leukemia . Cell stem cell. Jul 3 2025 ; 32 ( 7 ): 1102 – 1121 e5. doi: 10.1016/j.stem.2025.04.012 OpenUrl CrossRef PubMed 31. ↵ Ng SW , Mitchell A , Kennedy JA , et al. A 17-gene stemness score for rapid determination of risk in acute leukaemia . Nature. Dec 15 2016 ; 540 ( 7633 ): 433 – 437 . doi: 10.1038/nature20598 OpenUrl CrossRef PubMed 32. ↵ Li R , Colombo M , Wang G , et al. A proinflammatory stem cell niche drives myelofibrosis through a targetable galectin-1 axis . Sci Transl Med. Oct 9 2024 ; 16 ( 768 ): eadj7552 . doi: 10.1126/scitranslmed.adj7552 OpenUrl CrossRef PubMed 33. ↵ Dunbar AJ , Kim D , Lu M , et al. CXCL8/CXCR2 signaling mediates bone marrow fibrosis and is a therapeutic target in myelofibrosis . Blood. May 18 2023 ; 141 ( 20 ): 2508 – 2519 . doi: 10.1182/blood.2022015418 OpenUrl CrossRef PubMed 34. ↵ Goldberg LA , Yoon JJ , Johnston H , et al. Response and outcomes of patients with IDH1/2-mutated accelerated/blast-phase myeloproliferative neoplasms . Blood Neoplasia . May 2025 ; 2 ( 2 ): 100089 . doi: 10.1016/j.bneo.2025.100089 OpenUrl CrossRef PubMed 35. ↵ Chifotides HT , Masarova L , Alfayez M , et al. Outcome of patients with IDH1/2-mutated post-myeloproliferative neoplasm AML in the era of IDH inhibitors . Blood Adv. Nov 10 2020 ; 4 ( 21 ): 5336 – 5342 . doi: 10.1182/bloodadvances.2020001528 OpenUrl CrossRef PubMed 36. ↵ Venton G , Courtier F , Charbonnier A , et al. Impact of gene mutations on treatment response and prognosis of acute myeloid leukemia secondary to myeloproliferative neoplasms . American journal of hematology . Mar 2018 ; 93 ( 3 ): 330 – 338 . doi: 10.1002/ajh.24973 OpenUrl CrossRef PubMed 37. ↵ Theocharides A , Boissinot M , Girodon F , et al. Leukemic blasts in transformed JAK2-V617F-positive myeloproliferative disorders are frequently negative for the JAK2-V617F mutation . Blood. Jul 1 2007 ; 110 ( 1 ): 375 – 9 . doi: 10.1182/blood-2006-12-062125 OpenUrl Abstract / FREE Full Text 38. ↵ Lanikova L , Babosova O , Swierczek S , et al. Coexistence of gain-of-function JAK2 germ line mutations with JAK2V617F in polycythemia vera . Blood. Nov 3 2016 ; 128 ( 18 ): 2266 – 2270 . doi: 10.1182/blood-2016-04-711283 OpenUrl FREE Full Text View the discussion thread. 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Share Single cell long read genotyping of transcripts reveals discrete mechanisms of clonal evolution in post-MPN AML Julian Grabek , Jasmin Straube , Leanne Cooper , Rohit Haldar , Ranran Zhang , Inken Dulige , Matthew Barker , Will Gatehouse , Helen Christensen , Gerlinda Amor , Victoria Y. Ling , Caroline McNamara , David M. Ross , Andrew Perkins , Megan J. Bywater , Steven W. Lane bioRxiv 2025.08.18.670417; doi: https://doi.org/10.1101/2025.08.18.670417 Share This Article: Copy Citation Tools Single cell long read genotyping of transcripts reveals discrete mechanisms of clonal evolution in post-MPN AML Julian Grabek , Jasmin Straube , Leanne Cooper , Rohit Haldar , Ranran Zhang , Inken Dulige , Matthew Barker , Will Gatehouse , Helen Christensen , Gerlinda Amor , Victoria Y. Ling , Caroline McNamara , David M. Ross , Andrew Perkins , Megan J. Bywater , Steven W. 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