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Elucidating minimal residual disease of paediatric B-cell acute lymphoblastic leukaemia by single-cell analysis

Nature Cell Biology volume 24, pages 242–252 (2022)Cite this article

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Abstract

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.

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Fig. 1: B-lineage cell populations in healthy BM samples.
Fig. 2: Identification and characteristics of leukaemic cells in CD19-sorted B-ALL samples.
Fig. 3: Single-cell analysis of paired diagnostic and relapsed leukaemic cells.
Fig. 4: Dynamic evolution of B-ALL at the diagnosis, D19 and relapse time points.
Fig. 5: Dynamic heterogeneity analysis of B265 based on BCR clonotype.
Fig. 6: Single-cell transcriptome characteristics of MLL-r B-ALL.

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Data availability

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.

Code availability

Codes are available at https://github.com/Li-Xinqi/Single_Cell_BALL_MRD.git.

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Acknowledgements

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.

Author information

Author notes

  1. These authors contributed equally: Yingchi Zhang, Shicheng Wang, Jingliao Zhang, Chao Liu, Xinqi Li.

Authors and Affiliations

  1. State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China

    Yingchi Zhang, Jingliao Zhang, Yongjuan Duan, Xiaoyan Chen, Suyu Zong, Jiarui Zheng, Yixuan Wu, Xiaoli Chen, Xuelian Cheng, Wenyu Yang, Xiaojuan Chen, Ye Guo, Li Zhang, Yumei Chen, Yao Zou, Xiaofan Zhu & Tao Cheng

  2. MOE Key Laboratory of Bioinformatics, BNRIST Bioinformatics Division, Department of Automation, Tsinghua University, Beijing, China

    Shicheng Wang, Xinqi Li, Wenbo Guo & Jin Gu

  3. The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China

    Chao Liu, Yanxia Chang, Yue Wang & Feng Ding

  4. Novogene Co, Ltd, Beijing, China

    Feng Ding

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  1. Yingchi Zhang
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Contributions

T.C., J.G., X.Z. and Y. Zhang conceptualized the study. Y. Zhang, S.W., J. Zhang, C.L. and X.L. designed and optimized the experimental methodologies and bioinformatic workflow. J. Zhang, Y.D., C.L., Xiaoyan Chen and X. Cheng performed experiments. S.W., W.G., X.L., Y. Chang, Y.W. and F.D. performed bioinformatic studies. W.Y., Xiaojuan Chen, Y.G., L.Z., Y. Chen and Y. Zou provided patient-associated resources and/or patient samples for the studies. Y. Zhang, J. Zhang and J.G. wrote the original manuscript. T.C., J.G. and X.Z. assisted with the review and editing of the manuscript. S.Z., J. Zheng, Y.W., Xiaoli Chen, S.W. and X.L. assisted with the experimental and bioinformatic workflow in the revision of the manuscript. T.C., J.G. and X.Z. supervised the study and manuscript preparation.

Corresponding authors

Correspondence to Xiaofan Zhu, Jin Gu or Tao Cheng.

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Nature Cell Biology thanks Iannis Aifantis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

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.

Source data

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.

Source data

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.

Source data

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 information

Reporting Summary

Supplementary Tables

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

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.

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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

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