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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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Self-citationCodon affinity in mitochondrial DNA shapes evolutionary and somatic fitnessPreprint
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“5a, b), which is consistent to the single-cell RNA sequencing data of the hematopoietic stem and progenitor cells from CALR mutated patients [42].”
ResultDual‐layered hydrogels allow complete genome recovery with nucleic acid cytometry
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“We are aware of one similar approach that was published during our development phase (Nam et al, 2019), but this approach shows two important methodological differences with ours.”
Haplotype-aware single-cell multiomics uncovers functional effects of somatic structural variationPreprint
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Type 1 interferon perturbates clonal competition by reshaping human blood development
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“Therefore, to enhance our ability to identify unique subclones in both the tumor and stroma, we are working on smaller genomic changes such as single nucleotide variants (SNVs) [58–61], small insertions and deletions (indels), and microsatellite length variations (MSLVs).”
Reconstructing the lifelong history of cells and tissues via somatic mutation analysis
Faienza, S., Margaria, J. P., & Franco, I. (2025) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-025-05946-9 · Cited by 4
“The method (termed “Genotyping Of Transcriptomes Enhanced with Nanopore sequencing” or GO-TEN), is based on a previously developed technology [53 ] and achieves high confidence genotyping of samples destined to single cell RNAseq.”
Leukemic stem cells as drivers of therapeutic resistance in acute myeloid leukemia
Silva, S. L. R., Barbosa, G. A. D. C., Rios, J. V. B., & Bezerra, D. P. (2026) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2026.119295 · Cited by 6
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Lineage tracing from cellular heritage to disease destiny
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“Nuclear whole-genome sequencing offers the most accurate topological reconstruction 85,86 , but the sparsity of mutations in healthy tissues necessitates ultradeep sequencing or ex vivo expansion, limiting scalability 87,88 .”
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Self-citationSplicing the Difference: Harnessing the Complexity of the Transcriptome in Hematopoiesis
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Lucijanić, M., Krečak, I., Sorić, E., Sabljić, A., Galušić, D., Holik, H., Periša, V., Perić, M. M., Zekanović, I., Budimir, L., et al. (2025) · Cancers · doi:10.3390/cancers17091390 · Cited by 3
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The role of single-cell sequencing in studying tumour evolution
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Transcriptional configurations of myeloproliferative neoplasms
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Inflammation drives pressure on TP53 mutant clones in myeloproliferative neoplasms
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Single-cell genomics-based immune and disease monitoring in blood malignancies
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“Our approach is the first to evaluate the strength of genotype to phenotype links in follicular lymphoma in the evolutionary context of the disease.”
BackgroundSIngle cell level Genotyping Using scRna Data (SIGURD)
Graßhoff, M., Kalmer, M., Chatain, N., Kricheldorf, K., Maurer, A., Weiskirchen, R., Koschmieder, S., & Costa, I. G. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae604 · Cited by 3
“For example, the amplification of the CALR locus in MPN samples increased the detection of mutated cells from 1.4 to 88.7% and indicated the association of CALR mutated cells with unfolded protein response and NF-kB signaling when compared to WT [11].”
InfluentialInsights into Leukemia from Single-Cell Genomics
Sariipek, N. E., & van Galen, P. (2024) · Annual Review of Cancer Biology · doi:10.1146/annurev-cancerbio-062024-060443 · Cited by 3
Genetic Heterogeneity Profiling by Single Cell RNA SequencingPreprint
Zhou, Z., Xu, B., Minn, A. J., & Zhang, N. R. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/457622 · Cited by 3
“Although there are now 61 technologies for deep targeted sequencing of select transcripts matched with same-cell whole 62 transcriptome sequencing [27, 28], these methods are still, in effect, profiling DNA-level variation by 63 sequencing expressed transcripts, and are thus subject to the technical issues, especially…”
BackgroundEditorial: Lineage tracing, hematopoietic stem cell and immune cell dynamics
Lareau, C. A., Romagnani, C., & Ludwig, L. S. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1062415 · Cited by 3
“gene functions that play major roles in tumorigenesis, making their co-detection highly desirable (3).”
BackgroundNilsson, M. S., Komic, H., Gustafsson, J., Sheybani, Z., Paul, S., Rolfson, O., Hellstrand, K., Wennström, L., Martner, A., & Thorén, F. B. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.14.557507 · Cited by 3
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Single-cell mapping of regulatory DNA-protein interactions
Chi, W.-Y., Yoon, S.-H., Goksel, E., Mekerishvili, L., Pelt, J., Lin, Y., Prieto, T., Zinno, J., Ganesan, S., Potenski, C. J., et al. (2026) · Cell · doi:10.1016/j.cell.2026.05.014 · Cited by 3
“42 We previously developed genotyping of transcriptomes (GoT) 43 and genotyping of targeted loci and chromatin accessibility (GoT-ChA) 22 to enable single-cell genotyping together with transcriptome or chromatin accessibility profiling.”
Self-citationcircVDJ-seq for T cell clonotype detection in single-cell and spatial multi-omics
Plumbom, I., Obermayer, B., Raspe, R., Reguant, A. P., Theurillat, I., Pentimalli, T. M., Hsieh, Y.-H., Gil, M., Dietrich, C., Seeger-Zografakis, M., et al. (2026) · Genome Medicine · doi:10.1186/s13073-026-01691-1 · Cited by 2
“…sequences or specialized sequencing equipment. cDNA is first tagged with homologous overhangs to facilitate circularization via Gibson assembly [27] and generate a cDNA library in which the VDJ region is juxtaposed to the unique molecular identifier (UMI) and cell barcode at the 3’ end of cDNA…”
Co-mapping clonal and transcriptional heterogeneity in somatic evolution via GoT-Multi
Pak, M., Saurty-Seerunghen, M. S., Wise, K., Abera, T., Lama, C., Parghi, N., Kang, T., Sun, X., Gao, Q., Bao, L., et al. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.101036 · Cited by 2
Self-citationCosta, A. N., & Breccia, M. (2025) · Cancers · doi:10.3390/cancers17213397 · Cited by 2
“Interestingly, some evidence showed that the proportion of mutated cells is not equally distributed within the progenitors, with CALR -mutated HSCs biased towards myeloid differentiation and CALR -mutated cells heavily enriched in megakaryocyte progenitors [23,34].”
Genes-first and phenotypes-first paths to treatment resistance in hematological malignancies
Tamellini, E., Frusteri, C., & Ferrarini, I. (2025) · Cell Death and Disease · doi:10.1038/s41419-025-08127-5 · Cited by 4
“…(GoT), which couples the detection of gene mutations with the analysis of transcriptome at the single cell level, may help identify which resistance mechanism comes fi rst and how much the cellular transcriptome differs between genetically and phenotypically resistant clones [126, 127].”
FAM20C and FAM20A in normal and ectopic mineralization: A focus on oro-renal syndromes
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Sabotaged Integral HSC Heterogeneity Underlies Essential Thrombocythemia Development
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Nagler, A., Sud, A., Ghannam, J. Y., Pomerance, L., Robles-Oteíza, C., Afeyan, A. B., Weir, J. A., Russell, A. J. C., Lu, W. S., Orden, M. V., et al. (2026) · Nature Biotechnology · doi:10.1038/s41587-026-03194-1 · Cited by 2
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A single-cell lens into the co-evolution of genotypes and phenotypes in cancer
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“For example, in MPN, committed blood progenitors with CALR mutations exhibit JAK–STAT activation, which is currently targeted therapeutically 29 .”
InfluentialSelf-citationMamun, S. M. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.23531861 · Cited by 2
Blood’s life history traced through genomic scars
Sekar, A., & Ebert, B. L. (2022) · Nature · doi:10.1038/d41586-022-01304-y · Cited by 2
In vitro differentiated human CD4+ T cells produce hepatocyte growth factor
Ford, S. L., Buus, T. B., Nastasi, C., Geisler, C., Bonefeld, C. M., Ødum, N., & Woetmann, A. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1210836 · Cited by 2
“HGF transcripts were amplified from 50 ng of cDNA using a twostep nested PCR inspired by the genotyping of transcripts protocol (31).”
MethodPredicting single cell genotypes from single cell expression profiles in AML using deep learning
Asimomitis, G., Sirenko, M., Fotis, C., Landau, D. A., Alexopoulos, L. G., & Papaemmanuil, E. (2023) · International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB) · doi:10.1145/3586139.3586140 · Cited by 2
Self-citationInsights from TARGET-seq: Inflammation drives TP53-mediated clonal evolution
Zhao, Y., Huo, W., Liang, Y., Mo, X., Pei, S., & Zhao, Y. (2024) · The Innovation · doi:10.1016/j.xinn.2024.100572 · Cited by 2
Discovering mechanisms of human genetic variation and controlling cell states at scale
Frenkel, M. P. C., & Raman, S. (2024) · Trends in Genetics · doi:10.1016/j.tig.2024.03.010 · Cited by 2
Unveiling aging dynamics in the hematopoietic system insights from single-cell technologies
Jin, X., Zhang, R., Fu, Y., Zhu, Q., Hong, L., Wu, A., & Wang, H. (2024) · Briefings in Functional Genomics · doi:10.1093/bfgp/elae019 · Cited by 2
Unlocking the Complexity: Exploration of Acute Lymphoblastic Leukemia at the Single Cell Level
Aertgeerts, M., Meyers, S., Demeyer, S., Segers, H., & Cools, J. (2024) · Molecular Diagnosis & Therapy · doi:10.1007/s40291-024-00739-5 · Cited by 2
“A high-throughput solution to correlate transcriptome and genotype information of single cells is genotyping of transcriptomes (GoT), which uses targeted DNA sequencing instead of WGS [53].”
Kalmer, M., Graßhoff, M., Maié, T., Pannen, K., Toledo, M. A. S., Vieri, M., Olschok, K., Lemanzyk, R., Lazarevic, J., Junge, B., et al. (2025) · Blood Advances · doi:10.1182/bloodadvances.2024012600 · Cited by 4
Jakobsen, N. A., Turkalj, S., & Vyas, P. (2025) · STAR Protocols · doi:10.1016/j.xpro.2025.103832 · Cited by 2
Stein, A. F. V., Nguyen, P., & ten Hacken, E. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1592574 · Cited by 2
“Genotype-aware multi-omics (e.g. Genotyping-of-Transcriptomes, Genotyping-of-Targeted loci with single-cell Chromatin Accessibility) (83, 84) will further allow to dissect the function of individual (or multiplexed) driver mutations when in complex admixtures, such as those occurring in CLL…”
Single-Cell Analysis of Hematopoietic Stem Cells
Sturgess, K., Calero‐Nieto, F. J., Göttgens, B., & Wilson, N. K. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1425-9_22 · Cited by 2
A human commons cell atlas reveals cell type specificity for OAS1 isoformsPreprint
Gálvez-Merchán, Á., Booeshaghi, A. S., & Pachter, L. S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.23.586412 · Cited by 4
Data Independent Acquisition Approaches for Single Cell ProteomicsPreprint
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Consequences of training data composition for deep learning models in single-cell biologyPreprint
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Clonal evolution: a landmark of MPN evolution
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“While UPR induction in response to mutant Calr has been implied from RNA ‐ Seq and microarray data (Jutzi et al. 2023; Nam et al. 2019; Salati et al. 2019), there have been few direct investigations.”
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“To investigate single-cell genetic heterogeneity, researchers developed various sequencing protocols [5–9] and associated statistical methods to analyze such data.”
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Ph-chromosome-negative myeloproliferative neoplasms: clonal dynamics are the name of the game
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Molecularly Guided Therapy in Hepatobiliary Cancers
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“3j and Supplementary Table 7), consistent with previous reports 35 .”
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“Integration of genotyping, eg, by using Genotyping of Transcriptomes (GoT) [8], and chromatin accessibility analysis, e.”
MethodssMutPA: single-sample mutation-based pathway analysis approach for cancer precision medicine
He, Y., Lai, J., Wang, Q., Pan, B., Li, S., Zhao, X., Wang, Z., Zhang, Y., Tang, Y., & Han, J. (2024) · GigaScience · doi:10.1093/gigascience/giae105
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“Moreover, CALR mutations were reported to upregulate oxidative stress and unfolded protein response (UPR) [19, 31, 32].”
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Raju, S. S., & Ye, C. (2019) · Nature · doi:10.1038/d41586-019-02028-2 · Cited by 1
Carfilzomib Enhances the Suppressive Effect of Ruxolitinib in Myelofibrosis
Claudiani, S., Mason, C. C., Milojković, D., Bianchi, A. S., Pellegrini, C., Marco, A. D., Fiol, C. R., Robinson, M. E., Ponnusamy, K., Mokretar, K., et al. (2021) · Cancers · doi:10.3390/cancers13194863 · Cited by 1
Harnessing Mitochondrial Mutations to ATAC Clonal Evolution in CLL
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Charting hematopoiesis in the single-cell omics era
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Cell type-specific role of CBX2-cPRC1 at the onset of spermatogonial differentiationPreprint
Kim, J. J., Steinson, E. R., Lau, M. S., Page, D. C., & Kingston, R. E. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.25.505322 · Cited by 1
MuVEH and mitoMuVEH improve discovery of genetic variation from single cellsPreprint
Ransom, M., Engel, K. L., Stevens, B. M., Jordan, C. T., & Gillen, A. E. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.22.517553 · Cited by 1
“Previous approaches to call mutations from scRNA-seq data have focused on the detection of small numbers of variants in one or a few genes per sample, necessitating upfront knowledge of the driver mutations present in these samples [4, 5, 19].”
BackgroundAccurate de novo detection of somatic mutations in high-throughput single-cell profiling data setsPreprint
Muyas, F., Li, R., Rahbari, R., Mitchell, T. J., Hormoz, S., & Cortés‐Ciriano, I. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.22.517567 · Cited by 1
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Cell-stereotyped DNA repair outcomes are widespread during genome editingPreprint
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Interferon-α directs hematopoietic stem cells toward competing fates
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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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