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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”
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MethodSingle-cell genomics to understand disease pathogenesis
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“developed genotyping of transcriptomes, a method to integrate genotyping with droplet-based single-cell transcriptomes, and used it to profile ~40,000 CD34+ cells from patients with CALRmutated myeloproliferative neoplasms, identifying an association between the activation of the unfolded protein response and NF-κB pa…”
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Single‐Cell Multi‐Omics and Its Prospective Application in Cancer Biology
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Somatic Variants: New Kids on the Block in Human Immunogenetics
Horebeek, L. V., Dubois, B., & Goris, A. (2019) · Trends in Genetics · doi:10.1016/j.tig.2019.09.005 · Cited by 46
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“As the NfkB pathway is also upregulated in CALR-mutated HSPCs [44], it is also likely that BET inhibition would be beneficial in CALR-mediated MPNs. Early data using the BET inhibitor CPI-0610 in PMF patients, either as a monotherapy or with Ruxolitinib, reduced bone marrow fibrosis, spleen size and ameliorated cytope…”
BackgroundHigh-throughput microfluidic droplets in biomolecular analytical system: A review
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Integrative genotyping of cancer and immune phenotypes by long-read sequencing
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“However, as the coverage of scRNA-seq data is generally insuf fi - cient for consistently calling nucleotide variants, targeted ampli fi cation of loci containing somatic and mtDNA mutations is usually required 4,5 .”
BackgroundMethodJun, S., Lim, H., Chun, H., Lee, J. H., & Bang, D. (2020) · Communications Biology · doi:10.1038/s42003-020-0888-2 · Cited by 39
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Mobilized peripheral blood: an updated perspective
Karpova, D., Rettig, M. P., & DiPersio, J. F. (2019) · F1000Research · doi:10.12688/f1000research.21129.1 · Cited by 39
Nanopore Sequencing in Blood Diseases: A Wide Range of Opportunities
Minervini, C. F., Cumbo, C., Orsini, P., Anelli, L., Zagaria, A., Specchia, G., & Albano, F. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00076 · Cited by 40
“Changes in the promoter methylation of TAL1, ERHV-3, CDKN2A, CALCA, p15, IDH1/2, and TP53 genes have been associated with relapse in T-cell acute lymphoblastic leukemia (T-ALL) (Hogan et al., 2011; Nordlund et al., 2013).”
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Temporal recording of mammalian development and precancer
Islam, M., Yang, Y., Simmons, A. J., Shah, V. M., Pavan, M. K., Xu, Y., Tasneem, N., Chen, Z., Trinh, L. T., Molina, P., et al. (2024) · Nature · doi:10.1038/s41586-024-07954-4 · Cited by 41
Dissecting microbial communities with single-cell transcriptome analysis
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Functional analysis of structural variants in single cells using Strand-seq
Jeong, H., Grimes, K., Rauwolf, K. K., Bruch, P., Rausch, T., Hasenfeld, P., Benito, E., Roider, T., Sabarinathan, R., Porubskỳ, D., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01551-4 · Cited by 45
“3 | Haplotype-specific NO analysis shows local effects of a copy-neutral driver SV in AML. a , Balanced t(8;21) translocation in AML_1, discovered based on strand cosegregation ( P value = 0.00003 for translocation discovery using strand cosegregation 24 , FDR-adjusted Fisher’s exact test;…”
InfluentialThe Role of Megakaryocytes in Myelofibrosis
Melo‐Cardenas, J., Migliaccio, A. R., & Crispino, J. D. (2021) · Hematology/Oncology Clinics of North America · doi:10.1016/j.hoc.2020.11.004 · Cited by 38
Quantitative Single-Cell Proteomics as a Tool to Characterize Cellular HierarchiesPreprint
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LACE: Inference of cancer evolution models from longitudinal single-cell sequencing data
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Genetic and Transcriptional Contributions to Relapse in Normal Karyotype Acute Myeloid Leukemia
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The Microenvironment in Myeloproliferative Neoplasms
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Genetic heterogeneity and clonal evolution in acute myeloid leukemia
Romer-Seibert, J. S., & Meyer, S. E. (2020) · Current Opinion in Hematology · doi:10.1097/moh.0000000000000626 · Cited by 33
“Recent studies have improved the detection of low-level transcripts, longer read lengths to detect mutations distantly located from transcript ends, as well as increased throughput to sequence thousands of cells such as genotyping of transcriptomes and other single-cell method adaptations [13,14,21].”
BackgroundHuman embryonic genetic mosaicism and its effects on development and disease
Waldvogel, S. M., Posey, J. E., & Goodell, M. A. (2024) · Nature Reviews Genetics · doi:10.1038/s41576-024-00715-z · Cited by 36
“Some groups have attempted to overcome the challenges of genotyping with scRNA-seq by first amplifying specific regions of interest in the scRNA-seq library 39 or by pairing it with bulk deep genome sequencing 40 .”
Calreticulin mutations affect its chaperone function and perturb the glycoproteome
Schürch, P. M., Malinovska, L., Hleihil, M., Losa, M., Hofstetter, M., Wildschut, M. H. E., Lysenko, V., Lakkaraju, A. K., Maat, C. A., Benke, D., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111689 · Cited by 31
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“Landau’s team performed the genotyping of transcriptomes of CALR-mutated cells, but also of cells carrying JAK2, SF3B1 and NFE2 mutations [52].”
BackgroundPrins, D., Park, H. J., Watcham, S., Li, J., Vacca, M., Bastos, H. P., Gerbaulet, A., Vidal‐Puig, A., Göttgens, B., & Green, A. R. (2020) · Science Advances · doi:10.1126/sciadv.abd3139 · Cited by 30
“In addition, mutant CALR cells showed an increase in cell cycle genes, again consistent with observations from human patient cells (34) and in agreement with our in vitro data, which showed that mutant CALR HSCs and MkPs were more proliferative.”
InfluentialApplication of omics in the diagnosis, prognosis, and treatment of acute myeloid leukemia
Zhang, Z., Huang, J., Zhang, Z., Shen, H., Tang, X., Wu, D., Bao, X., Xu, G., & Chen, S. (2024) · Biomarker Research · doi:10.1186/s40364-024-00600-1 · Cited by 30
“Metabolic information can also be integrated through measuring mitochondrial marker mutations (genotyping of transcriptomes, TARGET-seq, and MutaSeq) [283–285].”
BackgroundDeconvoluting clonal and cellular architecture in IDH-mutant acute myeloid leukemia
Sirenko, M., Lee, S., Sun, Z., Chaligne, R., Loghavi, S., Asimomitis, G., Brierley, C. K., Bernard, E., Cai, S. F., Myers, R. M., et al. (2025) · Cell Stem Cell · doi:10.1016/j.stem.2025.04.012 · Cited by 28
Self-citationStrickland, M., Quek, L. S., & Psaila, B. (2021) · British Journal of Haematology · doi:10.1111/bjh.17850 · Cited by 26
Jutzi, J. S., Marneth, A. E., Jiménez‐Santos, M. J., Hem, J. W., Guerra-Moreno, Á., Rolles, B., Bhatt, S., Myers, S. A., Carr, S. A., Hong, Y., et al. (2022) · Leukemia · doi:10.1038/s41375-022-01781-0 · Cited by 27
“Here, using mutant Calr knockin mice and primary CALR-mutant MPN samples, we find differential upregulation of the IRE1a-XBP1 axis of the UPR as well as the proteasome in CALR-mutated hematopoietic stem cells (HSCs) and megakaryocytes.”
BackgroundInfluentialSelf-citation
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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