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Somatic mutations and cell identity linked by Genotyping of Transcriptomes
Open accessPublished version of a preprint
Abstract
Defining the transcriptomic identity of malignant cells is challenging in the absence of surface markers that distinguish cancer clones from one another, or from admixed non-neoplastic cells. To address this challenge, here we developed Genotyping of Transcriptomes (GoT), a method to integrate genotyping with high-throughput droplet-based single-cell RNA sequencing. We apply GoT to profile 38,290 CD34+ cells from patients with CALR-mutated myeloproliferative neoplasms to study how somatic mutations corrupt the complex process of human haematopoiesis. High-resolution mapping of malignant versus normal haematopoietic progenitors revealed an increasing fitness advantage with myeloid differentiation of cells with mutated CALR. We identified the unfolded protein response as a predominant outcome of CALR mutations, with a considerable dependency on cell identity, as well as upregulation of the NF-κB pathway specifically in uncommitted stem cells. We further extended the GoT toolkit to genotype multiple targets and loci that are distant from transcript ends. Together, these findings reveal that the transcriptional output of somatic mutations in myeloproliferative neoplasms is dependent on the native cell identity.
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“We further incorporated cell hashing 24 that enabled multiplexing baseline and IFNa-treated samples into the same scRNA-seq reactions via time-point specifying barcoded antibodies, to obviate technical batch effects between serial samples ( Fig. 1b ).”
From Type 1 interferon perturbates clonal competition by reshaping human blood development (2026) · cites this paper as [24]
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“we incorporated time-point specifying barcoded antibodies 24 to combine serial samples from the same individuals into a single experiment to remove technical batch effects”
From Type 1 interferon perturbates clonal competition by reshaping human blood development (2026) · cites this paper as [24]
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“also linked genotyping of expressed genes to their transcriptional profile [Genotyping of Transcriptomes (GoT)] (24).”
BackgroundInfluentialClonal evolution and hierarchy in myeloid malignancies
Takahashi, K., & Tanaka, T. N. (2023) · Trends in cancer · doi:10.1016/j.trecan.2023.05.004 · Cited by 28
Mizumaki, H., Gao, S., Wu, Z., Gutierrez‐Rodrigues, F., Bissa, M., Feng, X., Groarke, E. M., Li, H., Alemu, L., Raffo, D. Q., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59890-0 · Cited by 26
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“In the absence of surface markers, researchers have also developed genotyping of transcriptome (GoT), which has been used to study how somatic mutations disrupt the complex process of hematopoiesis in humans (50).”
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“to the hematopoietic stem compartment compared to JAK2V617 F in ET.(43,47) (Table 2) Preclinical experiments suggest that mutations leading to the release of CALR in MPNs would interfere with therapeutic measure designed to (re)establish an anticancer immune response, a conjecture that must be validated at the clinica…”
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MethodSingle-cell methods in myeloproliferative neoplasms: old questions, new technologies
O’Sullivan, J., Mead, A. J., & Psaila, B. (2022) · Blood · doi:10.1182/blood.2021014668 · Cited by 24
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“RNA-based approaches are less efficient but can establish links between altered cell states and somatic mutations, for example through deconvolution of differential gene expression between mutated and non-mutated cells within the same cluster (8, 95).”
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Transcripts of repetitive DNA elements signal to block phagocytosis of hematopoietic stem cells
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“targeted RNA qPCR, Genotyping of Transcriptomes (GoT)] (53, 54).”
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“Recent studies further dissected the MF SC compartment at the single-cell level.(30,43) These studies revealed significant heterogeneity at the clonal level.”
ResultIntegrated protein and transcriptome high-throughput spatial profilingPreprint
Ben‐Chetrit, N., Niu, X., Swett, A. D., Sotelo, J., Jiao, M. S., Roelli, P., Stoeckius, M., & Landau, D. A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.15.484516 · Cited by 19
Self-citationProteogenetic drug response profiling elucidates targetable vulnerabilities of myelofibrosis
Wildschut, M. H. E., Mena, J., Doerdelmann, C., van Oostrum, M., Hale, B. D., Settelmeier, J., Festl, Y., Lysenko, V., Schürch, P. M., Ring, A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-42101-z · Cited by 18
“Finally, at a single-cell level, transcriptional UPR activation was found in CALR - mutated compared to WT HSPCs within individual ET and MF patients 39 and in mouse models of MF 55 .”
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“This is reflected in their early procurement in a primitive hematopoietic cell with subsequent progression from ET, PV, or pre-PMF into overt PMF or acute leukemia after the acquisition of additional somatic or non-driver mutations [20,23,37,38].”
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Michelozzi, I. M., Kirtsios, E., & Giustacchini, A. (2021) · Cancers · doi:10.3390/cancers13112816 · Cited by 17
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“…comes from several recently developed assays on droplet-based microfluidics platforms, where frequently mutated loci 60 can be amplified and sequenced either by themselves or side by side with scRNA-seq 61 or scATAC-seq 62 on the 10x Genomics Chromium platform (https:// www.10xgenomics.com).”
The human brain through the lens of somatic mosaicism
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“Although identification of somatic mutations in specific cell types is now possible thanks to approaches such as cell-type sorting, G&T-seq (Genome and Transcriptome sequencing), PRDD-seq (Parallel RNA and DNA analysis after Deepsequencing), GoT (Genotyping of Transcriptomes), and single-cell transcriptomics coupled w…”
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“Upregulation of TGF β in mutant megakaryocytic progenitors, correlating with the degree of myelofibrosis, has been confirmed by a study at the single cell level [53].”
BackgroundResultThe stress response regulator HSF1 modulates natural killer cell anti-tumour immunity
Hockemeyer, K., Sakellaropoulos, T., Chen, X., Ivashkiv, O., Sirenko, M., Zhou, H., Gambi, G., Battistello, E., Avrampou, K., Sun, Z., et al. (2024) · Nature Cell Biology · doi:10.1038/s41556-024-01490-z · Cited by 17
“Cells were scored for the expression of Stress and Effec-tor modules using log 2 of the ratio of the unique molecular identifiers (UMIs) to the total UMIs per cell as previously described 58 .”
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Multi-Omics Profiling of the Tumor Microenvironment
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“For example, the application of singlecell genotyping methods in future work would help to classify and characterize the transcriptomes of malignant tumor cells (2, 16).”
MethodSingle-cell genotype-phenotype mapping identifies therapeutic vulnerabilities in VEXAS syndromePreprint
Ganesan, S., Murray, R. M., Sotélo, J. A. L., Eton, E. O., Takashima, K., Botella, T., Beattie, K., Indart, A. C., Chraiki, N., Croizier, C., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.19.594376 · Cited by 18
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Self-citationSingle-cell technologies and analyses in hematopoiesis and hematological malignancies
Campillo‐Marcos, I., Álvarez‐Errico, D., Alandes, R. A., Mereu, E., & Esteller, M. (2021) · Experimental Hematology · doi:10.1016/j.exphem.2021.05.001 · Cited by 14
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Hematopoietic fitness of JAK2V617F myeloproliferative neoplasms is linked to clinical outcome
Abu-Zeinah, G. F., Giandomenico, S. D., Choi, D. S., Cruz, T., Erdos, K., Taylor, E., Ritchie, E. K., Silver, R. T., & Scandura, J. M. (2022) · Blood Advances · doi:10.1182/bloodadvances.2022007128 · Cited by 14
Self-citationIsobe, T., Kuciński, I., Barile, M., Wang, X., Hannah, R., Bastos, H. P., Chabra, S., Vijayabaskar, M. S., Sturgess, K., Williams, M., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100426 · Cited by 15
“…protein response genes, reported to be upregulated in CALR -mutated myeloproliferative neoplasms, 37 were significantly upregulated in our Calr mutant model with pseudotemporal expression patterns similar to those in patients with CALR -mutated essential thrombocythemia 37 (Figures S5B–S5F).”
MethodResultInfluentialCooper, S., Coelho, M. A., Strauß, M. E., Gontarczyk, A. M., Wu, Q., Garnett, M. J., Marioni, J. C., & Bassett, A. R. (2024) · Genome biology · doi:10.1186/s13059-024-03169-y · Cited by 12
Cell of origin epigenetic priming determines susceptibility to Tet2 mutation
Schiroli, G., Kartha, V. K., Duarte, F. M., Kristiansen, T. A., Mayerhofer, C., Shrestha, R., Earl, A., Hu, Y., Tay, T., Rhee, C., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48508-6 · Cited by 15
“Recent studies exploiting combined single-cell allele genotyping with transcriptomics/epigenomics analyses 84,92,93 have characterized the landscape of human CH mutant cells.”
Integrating model systems and genomic insights to decipher mechanisms of cancer metastasis
Leung, M. M., Swanton, C., & McGranahan, N. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00825-2 · Cited by 15
“…progression — the metastatic potential of tumour cells, epithelial and mesenchymal transitions, the impact of the tumour microenvironment (TME) and tumour cell dissemination — to be better understood, with the aim of identifying targets for better disease prevention, detection and treatment.”
Studying clonal evolution of myeloid malignancies using induced pluripotent stem cells
Doulatov, S. R., & Papapetrou, E. P. (2020) · Current Opinion in Hematology · doi:10.1097/moh.0000000000000620 · Cited by 15
De novo identification of expressed cancer somatic mutations from single-cell RNA sequencing data
Zhang, T., Jia, H., Song, T., Lv, L., Gulhan, D., Wang, H., Guo, W., Xi, R., Guo, H., & Shen, N. (2023) · Genome Medicine · doi:10.1186/s13073-023-01269-1 · Cited by 14
“Experimental technologies, such as targeted genotyping coupled with scRNA-seq, have been developed to enable the study of expressional and somatic variations together at single-cell level [3–5].”
BackgroundMethodAnalyzing the functional effects of DNA variants with gene editing
Cooper, S., Obolenski, S., Waters, A. J., Bassett, A. R., & Coelho, M. A. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100776 · Cited by 14
“…higher-throughput techniques have even higher allele dropout rates Reading out edited variants directly from the RNA from single-cell approaches 76,77 high-throughput and easy to adapt from current droplet-based workflows using short or long reads may suffer template switching between SNV and…”
BackgroundMethodIdentifying cancer cells from calling single-nucleotide variants in scRNA-seq data
Marot-Lassauzaie, V., Beneyto‐Calabuig, S., Obermayer, B., Velten, L., Beule, D., & Haghverdi, L. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae512 · Cited by 15
Paz, D. L., Bader, M. S., Nienhold, R., Rai, S., Fonseca, T. A., Stetka, J., Hao-Shen, H., Mild-Schneider, G., Passweg, J. R., & Skoda, R. C. (2023) · HemaSphere · doi:10.1097/hs9.0000000000000885 · Cited by 13
Cell type-specific role of CBX2 and its disordered region in spermatogenesisPreprint
Kim, J. J., Steinson, E. R., Lau, M. S., de Rooij, D. G., Page, D. C., & Kingston, R. E. (2023) · Genes & Development · doi:10.1101/gad.350393.122 · Cited by 14
Artifacts in single-cell mitochondrial DNA mutation analyses misinform phylogenetic inferencePreprint
Lareau, C. A., Chapman, M. S., Penter, L., Nawy, T., Pe’er, D., & Ludwig, L. S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.28.605517 · Cited by 13
The Crossroads of Clonal Evolution, Differentiation Hierarchy, and Ontogeny in Leukemia Development
Sturgeon, C. M., Wagenblast, E., Izzo, F., & Papapetrou, E. P. (2024) · Blood Cancer Discovery · doi:10.1158/2643-3230.bcd-24-0235 · Cited by 12
Self-citationDecision level integration of unimodal and multimodal single cell data with scTriangulate
Li, G., Song, B., Singh, H., Prasath, V. B. S., Grimes, H. L., & Salomonis, N. (2023) · Nature Communications · doi:10.1038/s41467-023-36016-y · Cited by 10
“addressed by existing approaches, in particular: (1) overcoming arbitrary user-defined cluster resolutions that can result in biologically incoherent populations, (2) quantitative assessment of cluster reliability (stability, doublets) across the different modalities, (3) integration across algorithm or modality-speci…”
BackgroundMethodResultInfluentialMansier, O., Prouzet‐Mauleon, V., Jégou, G., Barroso, K., Raymundo, D. P., Chauveau, A., Dumas, P., Lagarde, V., Turcq, B., Pasquet, J., et al. (2019) · Cancers · doi:10.3390/cancers11121921 · Cited by 13
Single-Cell RNA Sequencing to Disentangle the Blood System
Acosta, J., Ssozi, D., & van Galen, P. (2021) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.120.314654 · Cited by 13
Single-nuclei transcriptomics enable detection of somatic variants in patient brain tissue
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Modelling the ageing dependence of cancer evolutionary trajectories
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“showed that malignant hematopoietic progenitors (CD34+ cells) with mutated CALR have a fitness advantage with respect to myeloid differentiation and upregulation of the nuclear factor kappa B (NF-κB) pathway, highlighting the cell-identity dependency of somatic mutations in human hematopoiesis [22].”
BackgroundA practical guide to targeted single-cell RNA sequencing technologies
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Charting single-cell lineages with synthetic and natural barcodes
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The molecular map of CLL and Richter's syndrome
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RNA splicing as a therapeutic target in myelodysplastic syndromes
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Precisely defining disease variant effects in CRISPR-edited single cells
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“Alternatively, CRISPR-induced mutations in coding regions can be identified using methods such as GoT 17,18 and GoT–ChA 18 , which use 10x chemistry to capture regions of interest in the transcriptome or open chromatin.”
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Haematopoietic ageing through the lens of single-cell technologies
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“The Landau group developed a novel Genotyping of Transcriptomes (GoT) method, which allows the distinguishing of mutation-positive from mutation-negative cells within the same cell type (Nam et al., 2019).”
BackgroundMethodInfluentialPearson, H. C. L., Hunt, K. V., Trahair, T. N., Lock, R. B., Lee, H. J., & de Bock, C. E. (2022) · HemaSphere · doi:10.1097/hs9.0000000000000734 · Cited by 9
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“Among the methods designed to perform integrative analysis of genomic and transcriptomic data are TARGETseq [80,81], genome and transcriptome sequencing (G&T-seq) [82] and Genotyping of Transcriptomes (GoT) [83].”
MethodNam, A. S., Dusaj, N. N., Izzo, F., Murali, R., Myers, R. M., Mouhieddine, T. H., Sotelo, J., Benbarche, S., Waarts, M. R., Gaiti, F., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.14.476225 · Cited by 10
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Myers, R. M., Izzo, F., Kottapalli, S., Prieto, T., Dunbar, A. J., Bowman, R. L., Mimitou, E. P., Stahl, M. F., Ghaity-Beckley, S. E., Arandela, J., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.11.491515 · Cited by 13
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“…RNA sequencing (scRNA-seq) is a powerful tool to analyze transcriptional signatures in individual cells, but current technology, by design, produces short-read sequencing biased towards the 3 ʹ end of transcripts, which has limited value for genotyping variants distal from the polyA tail 28–30 .”
MethodCaprioli, C., Nazari, I., Milovanovic, S., & Pelicci, P. G. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2021.796477 · Cited by 9
The end of the beginning: application of single-cell sequencing to chronic lymphocytic leukemia
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A Bayesian method to infer copy number clones from single-cell RNA and ATAC sequencing
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Hematopoietic Stem Cells in Health and Disease—Insights from Single-Cell Multi-omic Approaches
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Mutant Calreticulin in MPN: Mechanistic Insights and Therapeutic Implications
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Cell type–specific purifying selection of synonymous mitochondrial DNA variation
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Resolving leukemic stem cell heterogeneity and plasticity with single-cell multiomics
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Exploring genome-transcriptome correlations in cancer
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“Although this review focuses primarily on the relationship between CNVs and gene expression, single-nucleotide variations [66,67] also exhibit complex relationships with cancer phenotypes and represent another interesting area of study.”
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Computational modelling in single-cell cancer genomics: methods and future directions
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“However, recent work performing simultaneous single-cell sequencing of DNA and RNA from MPN patients suggests a potential avenue for post-hoc separation and phenotyping of multi-gene edited cells versus cells with unintended genetic outcomes [31, 60].”
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Megakaryocyte maturation involves activation of the adaptive unfolded protein response
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“…a feature of both normal megakaryopoiesis and thrombopoiesis (Kovuru et al., 2020; Lopez et al., 2013; Morishima & Nakanishi, 2016), and abnormal megakaryopoiesis in CALR - mutated myeloproliferative neoplasms (El-Khoury et al., 2020; Fosselteder et al., 2023; Jutzi et al., 2023; Nam et al., 2019).”
BackgroundCalreticulin mutations in myeloproliferative neoplasms
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Application of Single-Cell Sequencing to Immunotherapy
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Application of Single-Cell Approaches to Study Myeloproliferative Neoplasm Biology
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Metadata
- Authors
- Nam, A. S., Kim, K.-T., Chaligne, R., Izzo, F., Ang, C., Taylor, J., Myers, R. M., Abu-Zeinah, G., Brand, R., Omans, N. D., Alonso, A., Sheridan, C., Mariani, M., Dai, X., Harrington, E., Pastore, A., Cubillos-Ruiz, J. R., Tam, W., Hoffman, R., Rabadan, R., Scandura, J. M., Abdel-Wahab, O., Smibert, P., & Landau, D. A.
- Affiliations
- New York Genome Center (US)
- Memorial Sloan Kettering Cancer Center (US)
- Weill Cornell Medicine (US)
- Oxford Nanopore Technologies (United Kingdom) (GB)
- Tisch Cancer Institute (US)
- Corresponding author
- Landau, Dan A.
- ORCID
- Venue
- Nature
- Published
- 2019-07-03
- Volume
- 571
- Issue
- 7765
- Pages
- 355-360
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC6782071
- OpenAlex
- W2954384525
- Cited by
- 333
- References
- 92
- Citations by year
- 2026: 35 · 2025: 54 · 2024: 59 · 2023: 41 · 2022: 44 · 2021: 49 · 2020: 36 · 2019: 13
- FWCI
- 11.74
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Burroughs Wellcome Fund
- Brigham and Women's Hospital
- National Institutes of Health (1dp2ca239065-01, 5r01ca229902-05, UL1TR002384)
- Stand Up To Cancer
- National Heart, Lung, and Blood Institute (R01HL145283, P01HL053762)
- National Cancer Institute (DP2-CA239065, U54CA193313, R01-CA229902, P30CA008748, P01CA108671)
- National Center for Advancing Translational Sciences
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Health Sciences › Medicine › Hematology › Myeloproliferative Neoplasms: Diagnosis and Treatment
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Keywords
- somatic mutations, cell identity, CALR mutation, myeloproliferative neoplasms, hematopoiesis, unfolded protein response, NF-κB signaling, myeloid differentiation, clonal fitness, single-cell RNA sequencing, droplet-based sequencing
- MeSH terms
- Animals, Cell Line, Clone Cells, Endoribonucleases, Genotype, Hematopoiesis, Hematopoietic Stem Cells, Humans, Models, Molecular, Mutation, Myeloproliferative Disorders, Neoplasms, Neoplastic Stem Cells, NF-kappa B, Protein Serine-Threonine Kinases, Sequence Analysis, RNA, Antigens, CD34, Calreticulin, Cell Proliferation, Mice, Primary Myelofibrosis, Unfolded Protein Response, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, Transcriptome
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