The use of genomics is firmly established in clinical practice, resulting in innovations across a wide range of disciplines such as genetic screening, rare disease diagnosis and molecularly guided therapy choice. This new field of genomic medicine has led to improvements in patient outcomes. However, most clinical applications of genomics rely on information generated from bulk approaches, which do not directly capture the genomic variation that underlies cellular heterogeneity. With the advent of single-cell technologies, research is rapidly uncovering how genomic data at cellular resolution can be used to understand disease pathology and mechanisms. Both DNA-based and RNA-based single-cell technologies have the potential to improve existing clinical applications and open new application spaces for genomics in clinical practice, with oncology, immunology and haematology poised for initial adoption. However, challenges in translating cellular genomics from research to a clinical setting must first be overcome.
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Duan, M. et al. Diverse modes of clonal evolution in HBV-related hepatocellular carcinoma revealed by single-cell genome sequencing. Cell Res. 28, 359–373 (2018).
Morita, K. et al. Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics. Nat. Commun. 11, 5327 (2020).
Azizi, E. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 1293–1308.e1236 (2018).
Singh, M. et al. Lymphoma driver mutations in the pathogenic evolution of an iconic human autoantibody. Cell 180, 878–894.e819 (2020).
Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).
Vandereyken, K. et al. Methods and applications for single-cell and spatial multi-omics. Nat. Rev. Gen. https://doi.org/10.1038/s41576-023-00580-2 (2023).
Jones, W. et al. Deleterious effects of formalin-fixation and delays to fixation on RNA and miRNA-Seq profiles. Sci. Rep. 9, 6980 (2019).
Slyper, M. et al. A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors. Nat. Med. 26, 792–802 (2020).
Wang, W., Penland, L., Gokce, O., Croote, D. & Quake, S. R. High fidelity hypothermic preservation of primary tissues in organ transplant preservative for single cell transcriptome analysis. BMC Genomics 19, 140–140 (2018).
O’Flanagan, C. H. et al. Dissociation of solid tumor tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses. Genome Biol. 20, 210 (2019).
Denisenko, E. et al. Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows. Genome Biol. 21, 130 (2020).
Madissoon, E. et al. scRNA-seq assessment of the human lung, spleen, and esophagus tissue stability after cold preservation. Genome Biol. 21, 1 (2019).
Doan, M. et al. Diagnostic potential of imaging flow cytometry. Trends Biotechnol. 36, 649–652 (2018).
Pösel, C. et al. Density gradient centrifugation compromises bone marrow mononuclear cell yield. PLoS ONE 7, e50293 (2012).
Guillaumet-Adkins, A. et al. Single-cell transcriptome conservation in cryopreserved cells and tissues. Genome Biol. 18, 45 (2017).
Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
Goldstein, L. D. et al. Massively parallel nanowell-based single-cell gene expression profiling. BMC Genomics 18, 519 (2017).
Fan, C. et al. Combitorial labeling of single cells or gene expression cytometry. Science 347, 6222 (2015).
Hagemann-Jensen, M. et al. Single-cell RNA counting at allele and isoform resolution using Smart-seq3. Nat. Biotechnol. 38, 78–714 (2020).
Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).
Nadeu, F. et al. Detection of early seeding of Richter transformation in chronic lymphocytic leukemia. Nat. Med. 28, 1662–1671 (2022).
Xu, L. et al. Clonal evolution and changes in two AML patients detected with a novel single-cell DNA sequencing platform. Sci. Rep. 9, 11119 (2019).
Nam, A. S. et al. Single-cell multi-omics of human clonal hematopoiesis reveals that DNMT3A R882 mutations perturb early progenitor states through selective hypomethylation. Nat. Genet. 54, 1514–1526 (2022).
Redmond, D., Poran, A. & Elemento, O. Single-cell TCRseq: paired recovery of entire T-cell alpha and beta chain transcripts in T-cell receptors from single-cell RNAseq. Genome Med. 8, 80 (2016).
Goldstein, L. D. et al. Massively parallel single-cell B-cell receptor sequencing enables rapid discovery of diverse antigen-reactive antibodies. Commun. Biol. 2, 304 (2019).
Chiffelle, J. et al. T-cell repertoire analysis and metrics of diversity and clonality. Curr. Opin. Biotechnol. 65, 284–295 (2020).
Kiselev, V. Y., Andrews, T. S. & Hemberg, M. Challenges in unsupervised clustering of single-cell RNA-seq data. Nat. Rev. Genet. 20, 273–282 (2019).
Cakir, B. et al. Comparison of visualization tools for single-cell RNAseq data. NAR Genom. Bioinform. 2, lqaa052 (2020).
Qi, R., Ma, A., Ma, Q. & Zou, Q. Clustering and classification methods for single-cell RNA-sequencing data. Brief. Bioinform. 21, 1196–1208 (2019).
Rood, J. E., Maartens, A., Hupalowska, A., Teichmann, S. A. & Regev, A. Impact of the Human Cell Atlas on medicine. Nat. Med. 28, 2486–2496 (2022).
Cortes, J. E. et al. Quizartinib versus salvage chemotherapy in relapsed or refractory FLT3-ITD acute myeloid leukaemia (QuANTUM-R): a multicentre, randomised, controlled, open-label, phase 3 trial. Lancet Oncol. 20, 984–997 (2019).
Daver, N., Schlenk, R. F., Russell, N. H. & Levis, M. J. Targeting FLT3 mutations in AML: review of current knowledge and evidence. Leukemia 33, 299–312 (2019).
Shouval, R. et al. Single cell analysis exposes intratumor heterogeneity and suggests that FLT3-ITD is a late event in leukemogenesis. Exp. Hematol. 42, 457–463 (2014).
Sufliarska, S. et al. Establishing the method of chimerism monitoring after allogeneic stem cell transplantation using multiplex polymerase chain reaction amplification of short tandem repeat markers and amelogenin. Neoplasma 54, 424–430 (2007).
Stahl, T., Böhme, M. U., Kröger, N. & Fehse, B. Digital PCR to assess hematopoietic chimerism after allogeneic stem cell transplantation. Exp. Hematol. 43, 462–468.e461 (2015).
Vives, J., Casademont-Roca, A., Martorell, L. & Nogués, N. Beyond chimerism analysis: methods for tracking a new generation of cell-based medicines. Bone Marrow Transplant. 55, 1229–1239 (2020).
de Almeida, G. P. et al. Human skin-resident host T cells can persist long term after allogeneic stem cell transplantation and maintain recirculation potential. Sci. Immunol. 7, eabe2634 (2022).
Robinson, T. M. et al. Single cell genotypic and phenotypic analysis of measurable residual disease in acute myeloid leukemia. bioRxiv https://doi.org/10.1101/2022.09.20.508786 (2022).
King, D. et al. Detection of structural mosaicism from targeted and whole-genome sequencing data. Genome Res. 27, 1704–1714 (2017).
Reina-Castillón, J. et al. Detectable clonal mosaicism in blood as a biomarker of cancer risk in Fanconi anemia. Blood Adv. 1, 319–329 (2017).
Zhao, X. et al. Single-cell RNA-seq reveals a distinct transcriptome signature of aneuploid hematopoietic cells. Blood 130, 2762–2773 (2017).
Ediriwickrema, A. et al. Single-cell mutational profiling enhances the clinical evaluation of AML MRD. Blood Adv. 4, 943–952 (2020).
Jang, J. S. et al. Molecular signatures of multiple myeloma progression through single cell RNA-Seq. Blood Cancer J. 9, 2 (2019).
Petti, A. A. et al. A general approach for detecting expressed mutations in AML cells using single cell RNA-sequencing. Nat. Commun. 10, 3660 (2019).
Stosch, J. M. et al. Gene mutations and clonal architecture in myelodysplastic syndromes and changes upon progression to acute myeloid leukaemia and under treatment. Br. J. Haematol. 182, 830–842 (2018).
Giustacchini, A. et al. Single-cell transcriptomics uncovers distinct molecular signatures of stem cells in chronic myeloid leukemia. Nat. Med. 23, 692–702 (2017).
Warfvinge, R. et al. Single-cell molecular analysis defines therapy response and immunophenotype of stem cell subpopulations in CML. Blood 129, 2384–2394 (2017).
Zhang, X. & Grimes, H. L. Why single-cell sequencing has promise in MDS. Front. Oncol. 11, 769753 (2021).
Warren, J. T. & Link, D. C. Clonal hematopoiesis and risk for hematologic malignancy. Blood 136, 1599–1605 (2020).
Acha, P. et al. Analysis of intratumoral heterogeneity in myelodysplastic syndromes with isolated del(5q) using a single cell approach. Cancers 13, 841 (2021).
van Galen, P. et al. Single-cell RNA-Seq reveals AML hierarchies relevant to disease progression and immunity. Cell 176, 1265–1281.e1224 (2019).
Pellegrino, M. et al. High-throughput single-cell DNA sequencing of acute myeloid leukemia tumors with droplet microfluidics. Genome Res. 28, 1345–1352 (2018).
Furness, C. L. et al. The subclonal complexity of STIL-TAL1+ T-cell acute lymphoblastic leukaemia. Leukemia 32, 1984–1993 (2018).
Zhang, Y. et al. Elucidating minimal residual disease of paediatric B-cell acute lymphoblastic leukaemia by single-cell analysis. Nat. Cell Biol. 24, 242–252 (2022).
Heuser, M. et al. 2021 Update on MRD in acute myeloid leukemia: a consensus document from the European LeukemiaNet MRD Working Party. Blood 138, 2753–2767 (2021).
Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).
Zheng, H. et al. Single-cell analysis reveals cancer stem cell heterogeneity in hepatocellular carcinoma. Hepatology 68, 127–140 (2018).
Das, R. et al. Early B cell changes predict autoimmunity following combination immune checkpoint blockade. J. Clin. Invest. 128, 715–720 (2018).
Wang, J., Xu, R., Yuan, H., Zhang, Y. & Cheng, S. Single-cell RNA sequencing reveals novel gene expression signatures of trastuzumab treatment in HER2+ breast cancer: a pilot study. Medicine 98, e15872 (2019).
Ross, A. A. et al. Detection and viability of tumor cells in peripheral blood stem cell collections from breast cancer patients using immunocytochemical and clonogenic assay techniques. Blood 82, 2605–2610 (1993).
Sharma, S. Circulating tumor cell isolation, culture, and downstream molecular analysis. Biotechnol. Adv. 36, 1063–1078 (2018).
Rhim, A. D. et al. Detection of circulating pancreas epithelial cells in patients with pancreatic cystic lesions. Gastroenterology 146, 647–651 (2014).
Dago, A. E. et al. Rapid phenotypic and genomic change in response to therapeutic pressure in prostate cancer inferred by high content analysis of single circulating tumor cells. PLoS ONE 9, e101777 (2014).
Paoletti, C. et al. Abstract P1-01-01: Circulating tumor cell number and CTC-endocrine therapy index predict clinical outcomes in ER positive metastatic breast cancer patients: results of the COMETI phase 2 trial. Cancer Res. 77 (Suppl. 4), P1-01-1, https://doi.org/10.1158/1538-7445.Sabcs16-p1-01-01 (2017).
Aceto, N. et al. AR expression in breast cancer CTCs associates with bone metastases. Mol. Cancer Res. 16, 720–727 (2018).
Criscitiello, C., Sotiriou, C. & Ignatiadis, M. Circulating tumor cells and emerging blood biomarkers in breast cancer. Curr. Opin. Oncol. 22, 552–558 (2010).
Gorin, M. A. et al. Circulating tumour cells as biomarkers of prostate, bladder, and kidney cancer. Nat. Rev. Urol. 14, 90–97 (2017).
D’Avola, D. et al. High-density single cell mRNA sequencing to characterize circulating tumor cells in hepatocellular carcinoma. Sci. Rep. 8, 11570 (2018).
Barbazán, J. et al. Molecular characterization of circulating tumor cells in human metastatic colorectal cancer. PLoS ONE 7, e40476 (2012).
Magbanua, M. J. M. et al. Expanded genomic profiling of circulating tumor cells in metastatic breast cancer patients to assess biomarker status and biology over time (CALGB 40502 and CALGB 40503, Alliance). Clin. Cancer Res. 24, 1486–1499 (2018).
Marcuello, M. et al. Circulating biomarkers for early detection and clinical management of colorectal cancer. Mol. Asp. Med. 69, 107–122 (2019).
Ting, D. T. et al. Single-cell RNA sequencing identifies extracellular matrix gene expression by pancreatic circulating tumor cells. Cell Rep. 8, 1905–1918 (2014).
Kvastad, L. et al. Single cell analysis of cancer cells using an improved RT-MLPA method has potential for cancer diagnosis and monitoring. Sci. Rep. 5, 16519 (2015).
Lambros, M. B. et al. Single-cell analyses of prostate cancer liquid biopsies acquired by apheresis. Clin. Cancer Res. 24, 5635–5644 (2018).
Yang, L. et al. Hexokinase 2 discerns a novel circulating tumor cell population associated with poor prognosis in lung cancer patients. Proc. Natl Acad. Sci. USA 118, e2012228118 (2021).
Slamon, D. J. et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N. Engl. J. Med. 344, 783–792 (2001).
Rosell, R. et al. Erlotinib versus standard chemotherapy as first-line treatment for European patients with advanced EGFR mutation-positive non-small-cell lung cancer (EURTAC): a multicentre, open-label, randomised phase 3 trial. Lancet Oncol. 13, 239–246 (2012).
Marabelle, A. et al. Efficacy of pembrolizumab in patients with noncolorectal high microsatellite instability/mismatch repair-deficient cancer: results from the phase II KEYNOTE-158 study. J. Clin. Oncol. 38, 1–10 (2020).
Doebele, R. C. et al. Entrectinib in patients with advanced or metastatic NTRK fusion-positive solid tumours: integrated analysis of three phase 1–2 trials. Lancet Oncol. 21, 271–282 (2020).
Bang, Y.-J. et al. Trastuzumab in combination with chemotherapy versus chemotherapy alone for treatment of HER2-positive advanced gastric or gastro-oesophageal junction cancer (ToGA): a phase 3, open-label, randomised controlled trial. Lancet 376, 687–697 (2010).
Businello, G., Galuppini, F. & Fassan, M. The impact of recent next generation sequencing and the need for a new classification in gastric cancer. Best. Pract. Res. Clin. Gastroenterol. 50–51, 101730 (2021).
Kim, C. et al. Chemoresistance evolution in triple-negative breast cancer delineated by single-cell sequencing. Cell 173, 879–893.e813 (2018).
Gao, Y. et al. V211D mutation in MEK1 causes resistance to MEK inhibitors in colon cancer. Cancer Discov. 9, 1182–1191 (2019).
Zhao, Y. et al. Diverse alterations associated with resistance to KRAS(G12C) inhibition. Nature 599, 679–683 (2021).
Robert, C. et al. Pembrolizumab versus ipilimumab in advanced melanoma (KEYNOTE-006): post-hoc 5-year results from an open-label, multicentre, randomised, controlled, phase 3 study. Lancet Oncol. 20, 1239–1251 (2019).
Reck, M. et al. Pembrolizumab versus chemotherapy for PD-L1–positive non–small-cell lung cancer. N. Engl. J. Med. 375, 1823–1833 (2016).
Motzer, R. J. et al. Nivolumab versus everolimus in advanced renal-cell carcinoma. N. Engl. J. Med. 373, 1803–1813 (2015).
McDermott, D. F. et al. Clinical activity and molecular correlates of response to atezolizumab alone or in combination with bevacizumab versus sunitinib in renal cell carcinoma. Nat. Med. 24, 749–757 (2018).
Guo, X. et al. Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing. Nat. Med. 24, 978–985 (2018).
Fairfax, B. P. et al. Peripheral CD8+ T cell characteristics associated with durable responses to immune checkpoint blockade in patients with metastatic melanoma. Nat. Med. 26, 193–199 (2020).
Krishna, C. et al. Single-cell sequencing links multiregional immune landscapes and tissue-resident T cells in ccRCC to tumor topology and therapy efficacy. Cancer Cell 39, 662–677 (2021).
Liu, B., Zhang, Y., Wang, D., Hu, X. & Zhang, Z. Single-cell meta-analyses reveal responses of tumor-reactive CXCL13+ T cells to immune-checkpoint blockade. Nat. Cancer 3, 1123–1136 (2022).
Paulson, K. G. et al. Acquired cancer resistance to combination immunotherapy from transcriptional loss of class I HLA. Nat. Commun. 9, 3868 (2018).
Morotti, M. et al. Promises and challenges of adoptive T-cell therapies for solid tumours. Br. J. Cancer 124, 1759–1776 (2021).
Lu, Y.-C. et al. An efficient single-cell RNA-Seq approach to identify neoantigen-specific T cell receptors. Mol. Ther. 26, 379–389 (2018).
Sharma, A. et al. Non-genetic intra-tumor heterogeneity is a major predictor of phenotypic heterogeneity and ongoing evolutionary dynamics in lung tumors. Cell Rep. 29, 2164–2174.e2165 (2019).
Bucktrout, S. L. et al. Advancing T cell–based cancer therapy with single-cell technologies. Nat. Med. 28, 1761–1764 (2022).
Nossal, G. One cell, one antibody: prelude and aftermath. Nat. Immunol. 8, 1015–1017 (2007).
Conde, D. et al. Cross-tissue immune cell analysis reveals tissue-specific features in humans. Science 376, eabl5197 (2022).
Zhang, F. et al. Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry. Nat. Immunol. 20, 928–942 (2019).
Rao, D. A. et al. Pathologically expanded peripheral T helper cell subset drives B cells in rheumatoid arthritis. Nature 542, 110–114 (2017).
Der, E. et al. Tubular cell and keratinocyte single-cell transcriptomics applied to lupus nephritis reveal type I IFN and fibrosis relevant pathways. Nat. Immunol. 20, 915–927 (2019).
razi, A. et al. The immune cell landscape in kidneys of patients with lupus nephritis. Nat. Immunol. 20, 902–914 (2019).
Penkava, F. et al. Single-cell sequencing reveals clonal expansions of pro-inflammatory synovial CD8 T cells expressing tissue-homing receptors in psoriatic arthritis. Nat. Commun. 11, 4767 (2020).
Ramesh, A. et al. A pathogenic and clonally expanded B cell transcriptome in active multiple sclerosis. Proc. Natl Acad. Sci. USA 117, 22932–22943 (2020).
Mitsialis, V. et al. Single-cell analyses of colon and blood reveal distinct immune cell signatures of ulcerative colitis and Crohn’s disease. Gastroenterology 159, 591–608.e510 (2020).
Boland, B. S. et al. Heterogeneity and clonal relationships of adaptive immune cells in ulcerative colitis revealed by single-cell analyses. Sci. Immunol. 5, eabb4432 (2020).
Yoshitomi, H. & Ueno, H. Shared and distinct roles of T peripheral helper and T follicular helper cells in human diseases. Cell. Mol. Immunol. 18, 523–527 (2021).
Kim, D. et al. Targeted therapy guided by single-cell transcriptomic analysis in drug-induced hypersensitivity syndrome: a case report. Nat. Med. 26, 236–243 (2020).
Eugster, A. et al. High diversity in the TCR repertoire of GAD65 autoantigen-specific human CD4+ T cells. J. Immunol. 194, 2531–2538 (2015).
Cerosaletti, K. et al. Single-cell RNA sequencing reveals expanded clones of islet antigen-reactive CD4+ T cells in peripheral blood of subjects with type 1 diabetes. J. Immunol. 199, 323–335 (2017).
Gaublomme, J. T. et al. Single-cell genomics unveils critical regulators of Th17 cell pathogenicity. Cell 163, 1400–1412 (2015).
Savola, P. et al. Somatic mutations in clonally expanded cytotoxic T lymphocytes in patients with newly diagnosed rheumatoid arthritis. Nat. Commun. 8, 15869 (2017).
Moorcraft, S. Y. et al. Patients’ willingness to participate in clinical trials and their views on aspects of cancer research: results of a prospective patient survey. Trials 17, 17 (2016).
Alquicira-Hernandez, J., Sathe, A., Ji, H. P., Nguyen, Q. & Powell, J. E. scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data. Genome Biol. 20, 264 (2019).
Deans, Z. C. et al. Recommendations for reporting results of diagnostic genomic testing. Eur. J. Hum. Genet. 30, 1011–1016 (2022).
The authors declare no competing interests.
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Lim, J., Chin, V., Fairfax, K. et al. Transitioning single-cell genomics into the clinic. Nat Rev Genet 24, 573–584 (2023). https://doi.org/10.1038/s41576-023-00613-w
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