Siegel RL, Miller KD, Fuchs HE, Jemal A (2021) Cancer statistics, 2021. CA Cancer J Clin 71:7–33

Article  Google Scholar 

Neaga A et al (2021) Why do children with acute lymphoblastic leukemia fare better than adults?. Cancers (Basel) 13

Lee SHR, Li Z, Tai ST, Oh BLZ, Yeoh AEJ (2021) Genetic alterations in childhood acute lymphoblastic leukemia: interactions with clinical features and treatment response. Cancers (Basel) 13

Lee SHR, Li Z, Tai ST, Oh BLZ, Yeoh AEJ (2021) Genetic alterations in childhood acute lymphoblastic leukemia: interactions with clinical features and treatment response. Cancers (Basel) 13:4068

Radtke I et al (2009) Genomic analysis reveals few genetic alterations in pediatric acute myeloid leukemia. Proc Natl Acad Sci U S A 106:12944–12949

Article  CAS  Google Scholar 

Aynaud MM et al (2020) Transcriptional programs define intratumoral heterogeneity of Ewing sarcoma at single-cell resolution. Cell Rep 30:1767-1779.e1766

Article  CAS  Google Scholar 

Bandura DR et al (2009) Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal Chem 81:6813–6822

Article  CAS  Google Scholar 

Ornatsky O et al (2010) Highly multiparametric analysis by mass cytometry. J Immunol Methods 361:1–20

Article  CAS  Google Scholar 

Leelatian N et al (2017) Single cell analysis of human tissues and solid tumors with mass cytometry. Cytometry B Clin Cytom 92:68–78

Article  CAS  Google Scholar 

Jaimes MC et al (2021) Full spectrum flow cytometry and mass cytometry: a 32-marker panel comparison. Cytometry A

Jager A, Sarno J, Davis KL (2021) Mass cytometry of hematopoietic cells. Methods Mol Biol 2185:65–76

Article  CAS  Google Scholar 

Spitzer MH, Nolan GP (2016) Mass cytometry: single cells, many features. Cell 165:780–791

Article  CAS  Google Scholar 

Good Z et al (2018) Single-cell developmental classification of B cell precursor acute lymphoblastic leukemia at diagnosis reveals predictors of relapse. Nat Med 24:474–483

Article  CAS  Google Scholar 

Angelo M et al (2014) Multiplexed ion beam imaging of human breast tumors. Nat Med 20:436–442

Article  CAS  Google Scholar 

Giesen C et al (2014) Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat Methods 11:417–422

Article  CAS  Google Scholar 

Black S et al (2021) CODEX multiplexed tissue imaging with DNA-conjugated antibodies. Nat Protoc 16:3802–3835

Article  CAS  Google Scholar 

Goltsev Y et al (2018) Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174:968-981.e915

Article  CAS  Google Scholar 

Keren L et al (2018) A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 174:1373-1387.e1319

Article  CAS  Google Scholar 

Keren L et al (2019) MIBI-TOF: a multiplexed imaging platform relates cellular phenotypes and tissue structure. Sci Adv 5:eaax5851

Liu, C.C. et al. Reproducible, high-dimensional imaging in archival human tissue by multiplexed ion beam imaging by time-of-flight (MIBI-TOF). Laboratory Investigation (2022).

Liu CC et al (2022) Multiplexed ion beam imaging: insights into pathobiology. Annu Rev Pathol 17:403–423

Article  Google Scholar 

Kammersgaard MB et al (2020) Abstract PO-041: Multiplexed ion beam imaging to describe tumor-immune microenvironment and tumor heterogeneity in neuroblastoma. Cancer Res 80:PO-041-PO-041

Batth IS et al (2020) Rare osteosarcoma cell subpopulation protein array and profiling using imaging mass cytometry and bioinformatics analysis. BMC Cancer 20:715

Article  CAS  Google Scholar 

Gerdtsson E et al (2018) Multiplex protein detection on circulating tumor cells from liquid biopsies using imaging mass cytometry. Converg Sci Phys Oncol 4

Black S et al (2021) CODEX multiplexed tissue imaging with DNA-conjugated antibodies. Nat Protoc 16:3802–3835

Article  CAS  Google Scholar 

Bosisio FM et al (2022) Next-generation pathology using multiplexed immunohistochemistry: mapping tissue architecture at single-cell level. Front Oncol 12:918900

Article  Google Scholar 

Cesano A, Marincola FM, Thurin M (2020) Status of immune oncology: challenges and opportunities. Methods Mol Biol 2055:3–21

Article  CAS  Google Scholar 

Gawad C, Koh W, Quake SR (2014) Dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell genomics. Proc Natl Acad Sci 111:17947–17952

Article  CAS  Google Scholar 

Mehtonen J et al (2020) 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

Article  CAS  Google Scholar 

Caron M et al (2020) 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

Article  CAS  Google Scholar 

Louka E et al (2021) Heterogeneous disease-propagating stem cells in juvenile myelomonocytic leukemia. J Exp Med 218

Hovestadt V et al (2019) Resolving medulloblastoma cellular architecture by single-cell genomics. Nature 572:74–79

Article  CAS  Google Scholar 

Vladoiu MC et al (2019) Childhood cerebellar tumours mirror conserved fetal transcriptional programs. Nature 572:67–73

Article  CAS  Google Scholar 

Gillen AE et al (2020) Single-cell RNA sequencing of childhood ependymoma reveals neoplastic cell subpopulations that impact molecular classification and etiology. Cell Rep 32:108023

Article  CAS  Google Scholar 

Hovestadt V et al (2019) Resolving medulloblastoma cellular architecture by single-cell genomics. Nature 572:74–79

Article  CAS  Google Scholar 

Jansky S et al (2021) Single-cell transcriptomic analyses provide insights into the developmental origins of neuroblastoma. Nat Genet 53:683–693

Article  CAS  Google Scholar 

Corces MR et al (2017) An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat Methods 14:959–962

Article  CAS  Google Scholar 

Shahi P, Kim SC, Haliburton JR, Gartner ZJ, Abate AR (2017) Abseq: ultrahigh-throughput single cell protein profiling with droplet microfluidic barcoding. Sci Rep 7:44447

Article  CAS  Google Scholar 

Stoeckius M et al (2017) Simultaneous epitope and transcriptome measurement in single cells. Nat Methods 14:865–868

Article  CAS  Google Scholar 

Mimitou EP et al (2019) Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells. Nat Methods 16:409–412

Article  CAS  Google Scholar 

Hao Y et al (2021) Integrated analysis of multimodal single-cell data. Cell 184:3573-3587.e3529

Article  CAS  Google Scholar 

Bai Z et al (2022) Single-cell antigen-specific landscape of CAR T infusion product identifies determinants of CD19-positive relapse in patients with ALL. Sci Adv 8:2820

Article  Google Scholar 

Lee J, Hyeon DY, Hwang D (2020) Single-cell multiomics: technologies and data analysis methods. Exp Mol Med 52:1428–1442

Article  CAS  Google Scholar 

Mizuno H, Tsuyama N, Date S, Harada T, Masujima T (2008) Live single-cell metabolomics of tryptophan and histidine metabolites in a rat basophil leukemia cell. Anal Sci 24:1525–1527

Article  CAS  Google Scholar 

Pan N, Rao W, Yang Z (2020) Single-probe mass spectrometry analysis of metabolites in single cells. Methods Mol Biol 2064:61–71

Article  CAS  Google Scholar 

Ahl PJ et al (2020) Met-Flow, a strategy for single-cell metabolic analysis highlights dynamic changes in immune subpopulations. Commun Biol 3:305

Article  CAS  Google Scholar 

Arguello RJ et al (2020) SCENITH: a flow cytometry-based method to functionally profile energy metabolism with single-cell resolution. Cell Metab 1063–1075:e1067

Google Scholar 

Sharick JT et al (2020) Metabolic heterogeneity in patient tumor-derived organoids by primary site and drug treatment. Front Oncol 10:553

Article  Google Scholar 

McCarthy DJ, Campbell KR, Lun AT, Wills QF (2017) Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 33:1179–1186

CAS  Google Scholar 

Satija R, Farrell JA, Gennert D, Schier AF, Regev A (2015) Spatial reconstruction of single-cell gene expression data. Nat Biotechnol 33:495–502

Article  CAS  Google Scholar 

Wolf FA, Angerer P, Theis FJ (2018) SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19:15

Article  Google Scholar 

Palla G et al (2022) Squidpy: a scalable framework for spatial omics analysis. Nat Methods 19:171–178

Article  CAS  Google Scholar 

Gayoso A et al (2022) A Python library for probabilistic analysis of single-cell omics data. Nat Biotechnol 40:163–166

Article  CAS  Google Scholar 

Nowicka M et al (2017) CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets. Res 6:748

Google Scholar 

Chen H et al (2016) Cytofkit: a bioconductor package for an integrated mass cytometry data analysis pipeline. PLoS Comput Biol 12:e1005112

Article  Google Scholar 

Greenwald NF et al (2022) Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nat Biotechnol 40:555–565