Minimal residual disease that persists after chemotherapy is the most valuable prognostic marker for haematological malignancies and solid cancers. Unfortunately, our understanding of the resistance elicited in minimal residual disease is limited due to the rarity and heterogeneity of the residual cells. Here we generated 161,986 single-cell transcriptomes to analyse the dynamic changes of B-cell acute lymphoblastic leukaemia (B-ALL) at diagnosis, residual and relapse by combining single-cell RNA sequencing and B-cell-receptor sequencing. In contrast to those at diagnosis, the leukaemic cells at relapse tended to shift to poorly differentiated states, whereas the changes in the residual cells were more complicated. Differential analyses highlighted the activation of the hypoxia pathway in residual cells, resistant clones and B-ALL with MLL rearrangement. Both in vitro and in vivo models demonstrated that inhibition of the hypoxia pathway sensitized leukaemic cells to chemotherapy. This single-cell analysis of minimal residual disease opens up an avenue for the identification of potent treatment opportunities for B-ALL.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Lymphocytes from B-acute lymphoblastic leukemia patients present differential regulation of the adenosinergic axis depending on risk stratification
Discover Oncology Open Access 30 December 2022
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 per month
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Get just this article for as long as you need it
Prices may be subject to local taxes which are calculated during checkout
The datasets (raw data) generated in this study are available through the Genome Sequence Archive (GSA), BioProject ID: PRJCA003794, accession ID: HRA000489. Bone marrow scRNA-seq data from Human Cell Atlas were downloaded from https://data.humancellatlas.org/explore/projects/cc95ff89-2e68-4a08-a234-480eca21ce79 (ref. 73). Bulk microarray expression data from the ICGC ALL-US project were downloaded from https://dcc.icgc.org/releases/current/Projects/ALL-US (ref. 69). Reference genome data for scRNA-seq and scBCR-seq analyses were downloaded from https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/3.1/. For the databases, we used TRRUST2 (https://www.grnpedia.org/trrust/)64 and MSigDB (http://www.gsea-msigdb.org/gsea/index.jsp)68. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding authors on reasonable request.
Codes are available at https://github.com/Li-Xinqi/Single_Cell_BALL_MRD.git.
Pui, C. H. et al. Childhood acute lymphoblastic leukemia: progress through collaboration. J. Clin. Oncol. 33, 2938–2948 (2015).
Iacobucci, I. & Mullighan, C. G. Genetic basis of acute lymphoblastic leukemia. J. Clin. Oncol. 35, 975–983 (2017).
Hunger, S. P. & Mullighan, C. G. Acute lymphoblastic leukemia in children. N. Engl. J. Med. 373, 1541–1552 (2015).
Zhang, Y. et al. PDGFRB mutation and tyrosine kinase inhibitor resistance in Ph-like acute lymphoblastic leukemia. Blood 131, 2256–2261 (2018).
Schroeder, M. P. et al. Integrated analysis of relapsed B-cell precursor Acute Lymphoblastic Leukemia identifies subtype-specific cytokine and metabolic signatures. Sci. Rep. 9, 4188 (2019).
Saint Fleur-Lominy, S. et al. Evolution of the epigenetic landscape in childhood B acute lymphoblastic leukemia and its role in drug resistance. Cancer Res. 80, 5189–5202 (2020).
Roberts, K. G. et al. Genetic alterations activating kinase and cytokine receptor signaling in high-risk acute lymphoblastic leukemia. Cancer Cell 22, 153–166 (2012).
Ma, X. et al. Rise and fall of subclones from diagnosis to relapse in pediatric B-acute lymphoblastic leukaemia. Nat. Commun. 6, 6604 (2015).
Liu, Y. F. et al. Genomic profiling of adult and pediatric B-cell acute lymphoblastic leukemia. EBioMedicine 8, 173–183 (2016).
Li, B. et al. Therapy-induced mutations drive the genomic landscape of relapsed acute lymphoblastic leukemia. Blood 135, 41–55 (2020).
Dobson, S. M. et al. Relapse-fated latent diagnosis subclones in acute B lineage leukemia are drug tolerant and possess distinct metabolic programs. Cancer Discov. 10, 568–587 (2020).
Zhang, H. et al. Association of GATA3 polymorphisms with minimal residual disease and relapse risk in childhood acute lymphoblastic leukemia. J. Natl Cancer Inst. 113, 408–417 (2020).
Sproll, C., Fluegen, G. & Stoecklein, N. H. Minimal residual disease in head and neck cancer and esophageal cancer. Adv. Exp. Med. Biol. 1100, 55–82 (2018).
Short, N. J. & Jabbour, E. Minimal residual disease in acute lymphoblastic leukemia: how to recognize and treat it. Curr. Oncol. Rep. 19, 6 (2017).
Rambow, F. et al. Toward minimal residual disease-directed therapy in melanoma. Cell 174, 843–855 (2018).
Luskin, M. R., Murakami, M. A., Manalis, S. R. & Weinstock, D. M. Targeting minimal residual disease: a path to cure? Nat. Rev. Cancer 18, 255–263 (2018).
Della Starza, I. et al. Minimal residual disease in acute lymphoblastic leukemia: technical and clinical advances. Front. Oncol. 9, 726 (2019).
Cackowski, F. C. & Taichman, R. S. Minimal residual disease in prostate cancer. Adv. Exp. Med. Biol. 1100, 47–53 (2018).
Leung, W. et al. Detectable minimal residual disease before hematopoietic cell transplantation is prognostic but does not preclude cure for children with very-high-risk leukemia. Blood 120, 468–472 (2012).
Pui, C. H. et al. Clinical utility of sequential minimal residual disease measurements in the context of risk-based therapy in childhood acute lymphoblastic leukaemia: a prospective study. Lancet Oncol. 16, 465–474 (2015).
Berry, D. A. et al. Association of minimal residual disease with clinical outcome in pediatric and adult acute lymphoblastic leukemia: a meta-analysis. JAMA Oncol. 3, e170580 (2017).
Sekiya, Y. et al. Clinical utility of next-generation sequencing-based minimal residual disease in paediatric B-cell acute lymphoblastic leukaemia. Br. J. Haematol. 176, 248–257 (2017).
Li, Z. et al. Identifying IGH disease clones for MRD monitoring in childhood B-cell acute lymphoblastic leukemia using RNA-Seq. Leukemia 34, 2418–2429 (2020).
Faham, M. et al. Deep-sequencing approach for minimal residual disease detection in acute lymphoblastic leukemia. Blood 120, 5173–5180 (2012).
Motwani, J. et al. Predictive value of flow cytometric minimal residual disease analysis in childhood acute lymphoblastic leukaemia at the end of remission induction therapy: results from a single UK centre. Br. J. Haematol. 144, 133–135 (2009).
Lutz, C. et al. Quiescent leukaemic cells account for minimal residual disease in childhood lymphoblastic leukaemia. Leukemia 27, 1204–1207 (2013).
Kim, C. et al. Chemoresistance evolution in triple-negative breast cancer delineated by single-cell sequencing. Cell 173, 879–893 (2018).
Ebinger, S. et al. Characterization of rare, dormant, and therapy-resistant cells in acute lymphoblastic leukemia. Cancer Cell 30, 849–862 (2016).
van Galen, P. et al. Single-cell RNA-seq reveals AMl hierarchies relevant to disease progression and immunity. Cell 176, 1265–1281 (2019).
Witkowski, M. T. et al. Extensive remodeling of the immune microenvironment in B cell acute lymphoblastic leukemia. Cancer Cell 37, 867–882 (2020).
Granja, J. M. et al. Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nat. Biotechnol. 37, 1458–1465 (2019).
Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).
Hay, S. B., Ferchen, K., Chetal, K., Grimes, H. L. & Salomonis, N. The Human Cell Atlas bone marrow single-cell interactive web portal. Exp. Hematol. 68, 51–61 (2018).
Giladi, A. et al. Single-cell characterization of haematopoietic progenitors and their trajectories in homeostasis and perturbed haematopoiesis. Nat. Cell Biol. 20, 836–846 (2018).
Mehtonen, J. et al. Single cell characterization of B-lymphoid differentiation and leukemic cell states during chemotherapy in ETV6–RUNX1-positive pediatric leukemia identifies drug-targetable transcription factor activities. Genome Med. 12, 99 (2020).
Caron, M. et al. Single-cell analysis of childhood leukemia reveals a link between developmental states and ribosomal protein expression as a source of intra-individual heterogeneity. Sci. Rep. 10, 8079 (2020).
Shen, S. et al. Effect of dasatinib vs imatinib in the treatment of pediatric Philadelphia chromosome-positive acute lymphoblastic leukemia: a randomized clinical trial. JAMA Oncol. 6, 358–366 (2020).
Kreis, N.N., Louwen, F. & Yuan, J. The multifaceted p21 (Cip1/Waf1/CDKN1A) in cell differentiation, migration and cancer therapy. Cancers 11, 1220 (2019).
El-Deiry, W. S. p21(WAF1) mediates cell-cycle inhibition, relevant to cancer suppression and therapy. Cancer Res. 76, 5189–5191 (2016).
Legge, D. N. et al. The role of B-cell lymphoma-3 (BCL-3) in enabling the hallmarks of cancer: implications for the treatment of colorectal carcinogenesis. Carcinogenesis 41, 249–256 (2020).
Maldonado, V. & Melendez-Zajgla, J. Role of Bcl-3 in solid tumors. Mol. Cancer 10, 152 (2011).
Wilson, W. R. & Hay, M. P. Targeting hypoxia in cancer therapy. Nat. Rev. Cancer 11, 393–410 (2011).
Minassian, L. M., Cotechini, T., Huitema, E. & Graham, C. H. Hypoxia-induced resistance to chemotherapy in cancer. Adv. Exp. Med. Biol. 1136, 123–139 (2019).
Manoochehri Khoshinani, H., Afshar, S. & Najafi, R. Hypoxia: a double-edged sword in cancer therapy. Cancer Invest. 34, 536–545 (2016).
Giambra, V. et al. Leukemia stem cells in T-ALL require active Hif1α and Wnt signaling. Blood 125, 3917–3927 (2015).
Wang, Y., Liu, Y., Malek, S. N. & Zheng, P. Targeting HIF1α eliminates cancer stem cells in hematological malignancies. Cell Stem Cell 8, 399–411 (2011).
Silveira, V. S. et al. Hypoxia-related gene expression profile in childhood acute lymphoblastic leukemia: prognostic implications. Leuk. Lymphoma 55, 1751–1757 (2014).
Wellmann, S. et al. Activation of the HIF pathway in childhood ALL, prognostic implications of VEGF. Leukemia 18, 926–933 (2004).
Tang, J. et al. Prognostic factors for CNS control in children with acute lymphoblastic leukemia treated without cranial irradiation. Blood 138, 331–343 (2021).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
Faust, G. G. & Hall, I. M. SAMBLASTER: fast duplicate marking and structural variant read extraction. Bioinformatics 30, 2503–2505 (2014).
Tarasov, A. et al. Sambamba: fast processing of NGS alignment formats. Bioinformatics 31, 2032–2034 (2015).
McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
Bergmann, E. A. et al. Conpair: concordance and contamination estimator for matched tumor-normal pairs. Bioinformatics 32, 3196–3198 (2016).
Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).
Saunders, C. T. et al. Strelka: accurate somatic small-variant calling from sequenced tumor–normal sample pairs. Bioinformatics 28, 1811–1817 (2012).
The 1000 Genomes Project Consortium An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).
Sherry, S. T. et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29, 308–311 (2001).
Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).
Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).
Guo, W. et al. scCancer: a package for automated processing of single-cell RNA-seq data in cancer. Brief. Bioinform. 22, bbaa127 (2021).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).
Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381 (2014).
Han, H. et al. TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res. 46, D380–D386 (2018).
Malta, T. M. et al. Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell 173, 338–354 (2018).
Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).
Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).
Zhang, J. et al. The International Cancer Genome Consortium Data Portal. Nat. Biotechnol. 37, 367–369 (2019).
Kang, H. et al. Gene expression classifiers for relapse-free survival and minimal residual disease improve risk classification and outcome prediction in pediatric B-precursor acute lymphoblastic leukemia. Blood 115, 1394–1405 (2010).
Zhang, Y. et al. Rictor is required for early B cell development in bone marrow. PLoS ONE 9, e103970 (2014).
Ianevski, A., Giri, A. K. & Aittokallio, T. SynergyFinder 2.0: visual analytics of multi-drug combination synergies. Nucleic Acids Res. 48, 488–493 (2020).
Hay, S. B., Ferchen, K., Chetal, K., Grimes, H. L. & Salomonis, N. The Human Cell Atlas bone 515 marrow single-cell interactive web portal. Exp. Hematol. 68, 51–61 (2018).
This work was supported by National Key R&D Program of China (grant nos 2021YFA1100900 (T.C.) and 2021YFA1101603 (Y. Zhang)), CAMS Initiative for Innovative Medicine (grant no. 2021-I2M-1-040 (T.C.)), National Natural Science Foundation of China (grant nos 81870131 (X.Z.), 81770175 (Y. Zhang), 61922047 (J.G.), 81890992 (Y. Zhang), 81890993 (J.G.), 62133006 (J.G.) and 61721003(J.G.)), Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (grant no. 2021-PT310-005 (Y. Zhang)), The Central Guidance on Local Science and Technology Development Fund of Tianjin (grant no. 21ZYJDSY00120 (Y. Zhang)) and Beijing National Research Center for Information Science and Technology Young Innovation Fund (grant no. BNR2020RC01009 (J.G.)). The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript.
The authors declare no competing interests.
Peer review information
Nature Cell Biology thanks Iannis Aifantis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 Cell selection and B cell differentiation stage classifier validation.
(a) UMAP plots showing 16 distinct hematopoietic differentiation clusters (demarcated by colour) based on the gene expression profile of CD34+CD19− and CD19+ BM cells from donor H1. (b) UMAP plot with cell points coloured by sample identities. (c) UMAP plot showing the cells selected for downstream analysis (green). (d) UMAP plot showing nine distinct B-lineage clusters (demarcated by colour) based on the gene expression profile of healthy bone marrow samples from donor H2. (e) UMAP plots showing the BM dataset coloured by labels provided by HCA (left), manual annotations (middle) and the classifier’s labels (right). (f) UMAP plots showing the CD19+ cells with heavy chains and/or light chains detected in the two healthy donors H1 and H2.
Extended Data Fig. 2 Schematic showing the treatment course of the index B-ALL patients in CCCG-ALL-2015.
CD19+ cells from BM samples at diagnosis, on day 19 of induction therapy, and at relapse of paediatric B-ALL patients, CD19+ cells and CD19−CD34+ cells from paediatric healthy donors were chosen for single-cell sequencing.
Extended Data Fig. 3 BCR expression patterns of two B-ALL patients.
UMAP plot of the BCR expression pattern and cell identities in samples obtained from two B-ALL patients (B887 and B265) at diagnosis (Dx) (left) and day 19 after induction therapy (D19) (right).
Extended Data Fig. 4 In vitro pharmacotyping of overexpression and inhibition of CDKN1A (P21) in B-ALL cell lines.
(A) Western blot assay validation of P21 overexpression in REH and RS4;11. Experiments were performed in triplicate. Representative chemiluminescence images are shown. (B) Cell growth and viability of parental and P21 overexpressed B-ALL cell lines (REH, RS4;11) were assessed in vitro by proliferation assay after treatment with AraC and DNR for 72 h. Experiments were performed in triplicate. Data are presented as mean values +/− s.e.m. (C) Apoptosis comparison between parental and P21 overexpressed B-ALL cell lines, REH, and RS4;11, after treatment of AraC / DNR with or without UC2288 for 48h, by Annexin V and DAPI staining. (D) Western blot assay validation of P21 knockdown in REH and RS4;11. Experiments were performed in triplicate. Representative chemiluminescence images are shown. (E) Cell growth and viability of parental and P21 knockdown B-ALL cell lines (REH, RS4;11) were assessed in vitro by proliferation assay after treatment with AraC and DNR for 72 h. Experiments were performed in triplicate. Data are presented as mean values +/− s.e.m. (F) Apoptosis assessment by Annexin V and DAPI staining in two B-ALL cell lines, RS4;11 and REH, after treatment of AraC / DNR with or without UC2288 for 48h.
Extended Data Fig. 5 UMAP visualization of the B cell differentiation stages in each patient (A-D) for B887, B069, B265 and B590, respectively.
The stages are annotated by the B cell differentiation stage classifier. Each sub-figure shows results at mix, Dx, D19 and Rel stages respectively.
Extended Data Fig. 6 Gene expressions for the selected genes enriched in hypoxia pathway in leukaemic cells.
Genes enriched in hypoxia pathway were listed as (A-F) for HIF1A, BTG1, CXCR4, DDIT4, PNRC1 and SDC2, respectively. The P-values were determined using two-sided unpaired Student’s t-test and the red lines represent the medians.
Extended Data Fig. 7 Hypoxia inhibitors enhance the response of B-ALL cells to chemotherapy drugs in vitro.
(A-B) Changes in expression levels of genes associated with hypoxia signalling in B-ALL cell lines (A) NALM-6 and (B) RS4;11 were analysed by RT-PCR before and after treatment with AraC, DNR or VCR for 6 h. Experiments were performed in triplicate. P values are calculated by two-tailored Student’s t-test. *P < 0.05; **P < 0.01; ***P < 0.001. Exact P values are provided in Extended Source Data. (C) Cell growth and viability of B-ALL cell lines were assessed in vitro by proliferation assay after treatment with PX478 for 72 h. Experiments were performed in triplicate. Data are presented as mean values +/− s.e.m. (D-F) Cell growth and viability of B-ALL cell lines were assessed in vitro by proliferation assay after treatment with the indicated concentrations of (D) AraC, (E) DNR, (F) VCR with or without PX478 (100nM) for 72 h. Experiments were performed in triplicate. Data are presented as mean values +/− s.e.m. (G) Apoptosis analysis of B-ALL cell lines by flow cytometric measurement of annexin V/DAPI staining after in vitro exposure to PX478, AraC, PX478+AraC, DNR, PX478+DNR, VCR or VCR+PX478 for 48 h. Experiments were performed in triplicate. Data are presented as mean values +/− s.e.m. Representative flow cytometric images are shown.
Extended Data Fig. 8 Hypoxia inhibitor enhanced the response of B-ALL cells to chemotherapy drugs in vivo.
(A) Schema of establishment of in vivo MRD model by injection with 2×106 cells/NSG mouse from two diagnostic B-ALL samples. Leukaemic burden in peripheral blood were tracked every other week after the 2nd week of injection. Right after blast% reaching 40-60%, three arms of treatment were assigned as vehicle, AraC for 24h, and AraC for 48h, respectively. CD45+ sorted NSG BMMCs were sent for immunofluorescence analysis and intracellular flow cytometry, as described in Online Methods. To avoid biases, each arm enrolled 2 mice and was tested in duplicate. (B-C) Changes in expression levels of genes associated with hypoxia signalling in B-ALL PDX B-714 (B), and PDX-B999 (C). Experiments were performed in triplicate. Data are presented as mean values +/− s.e.m. P values are calculated by two-tailored Student’s t-test. *P < 0.05; **P < 0.01; ***P < 0.001. The exact P values are provided as extended source data. (D) In vivo efficacy estimation of HIF1A inhibition in combined with AraC. In B-ALL PDX models by injection with 2×106 cells/NSG mouse from two diagnostic B-ALL samples, 4 arms of treatment were applied as vehicle (Group 1), PX478 10mg/kg bi-weekly (Group 2), AraC 100mg/kg three times per week (Group 3), PX478 and AraC combination (Group 4). Leukaemic burden in peripheral blood at each observational timepoint and leukaemia-free survival were observed and compared among arms respectively, as described in Online Methods.
Extended Data Fig. 9 Comparisons of the hypoxia enrichment scores between the MRD+ and MRD- groups for 200 patients (including 68 MRD+ patients and 132 MRD- patients) at diagnosis using bulk RNA-seq data.
P-values were evaluated by two-sided unpaired Student’s t-test. Boxplot shows the median (midline), the first and third quartiles (lower and upper hinges), whiskers extend to 1.5 * IQR (interquartile range). Outliers are plotted if they extend beyond this range.
Extended Data Fig. 10 Flow cytometry plots of 8 B-ALL patients’ and 2 Children healthy donors’ BMMCs for scRNA-seq.
(a) CD19−CD34+ and CD19+ populations in alive BMMCs were sorted in the two healthy donors. (b) CD19+ population in alive BMMCs were sorted from multi-stages (Dx/D19/Rel) of the eight B-ALL patients.
Supplementary Table 1. Sample information after quality control. Supplementary Table 2. Training data confusion matrix of healthy donor H1. Supplementary Table 3. Testing data confusion matrix of healthy donor H1. Supplementary Table 4. Genes used for calculating the cell-cycle score. Supplementary Table 5. Clinical features and prognosis of the eight patients. Supplementary Table 6. Prediction results of the non-leukaemic/leukaemic cell classifier on healthy donors. Supplementary Table 7. Performance of the non-leukaemic/leukaemic cell classifier on the testing dataset, four diagnostic and four relapsed samples. Supplementary Table 8. Consistent differentially expressed genes in the single-cell analysis and bulk expression data of MLL-r samples. Supplementary Table 9. Filtering upper thresholds for scRNA-seq data by scCancer.
Source Data Fig. 1
Statistical source data for Fig. 1e.
Source Data Fig. 2
Statistical source data for Fig. 2e,i,j.
Source Data Fig. 3
Statistical source data for Fig. 3b,c,e,g,h.
Source Data Fig. 4
Statistical source data for Fig. 4b–d,f–h,k,l.
Source Data Fig. 5
Statistical source data for Fig. 5a–e.
Source Data Fig. 6
Statistical source data for Fig. 6c.
Source Data Extended Data Fig. 4
Statistical source data for Extended Data Fig. 4b,c,e,f.
Source Data Extended Data Fig. 4
Unprocessed Western Blots of Extended Data Fig. 4a,d.
Source Data Extended Data Fig. 7
Statistical source data for Extended Data Fig. 7a–g.
Source Data Extended Data Fig. 8
Statistical source data for Extended Data Fig. 8b,c.
Rights and permissions
About this article
Cite this article
Zhang, Y., Wang, S., Zhang, J. et al. Elucidating minimal residual disease of paediatric B-cell acute lymphoblastic leukaemia by single-cell analysis. Nat Cell Biol 24, 242–252 (2022). https://doi.org/10.1038/s41556-021-00814-7
This article is cited by
Single-cell technologies uncover intra-tumor heterogeneity in childhood cancers
Seminars in Immunopathology (2023)
Lymphocytes from B-acute lymphoblastic leukemia patients present differential regulation of the adenosinergic axis depending on risk stratification
Discover Oncology (2022)