Skip to main content
Top
Published in: Seminars in Immunopathology 1/2023

10-01-2023 | Review

Single-cell technologies uncover intra-tumor heterogeneity in childhood cancers

Authors: Yu-Chen Lo, Yuxuan Liu, Marte Kammersgaard, Abhishek Koladiya, Timothy J. Keyes, Kara L. Davis

Published in: Seminars in Immunopathology | Issue 1/2023

Login to get access

Abstract

Childhood cancer is the second leading cause of death in children aged 1 to 14. Although survival rates have vastly improved over the past 40 years, cancer resistance and relapse remain a significant challenge. Advances in single-cell technologies enable dissection of tumors to unprecedented resolution. This facilitates unraveling the heterogeneity of childhood cancers to identify cell subtypes that are prone to treatment resistance. The rapid accumulation of single-cell data from different modalities necessitates the development of novel computational approaches for processing, visualizing, and analyzing single-cell data. Here, we review single-cell approaches utilized or under development in the context of childhood cancers. We review computational methods for analyzing single-cell data and discuss best practices for their application. Finally, we review the impact of several studies of childhood tumors analyzed with these approaches and future directions to implement single-cell studies into translational cancer research in pediatric oncology.
Literature
1.
2.
go back to reference Neaga A et al (2021) Why do children with acute lymphoblastic leukemia fare better than adults?. Cancers (Basel) 13 Neaga A et al (2021) Why do children with acute lymphoblastic leukemia fare better than adults?. Cancers (Basel) 13
3.
go back to reference 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
4.
go back to reference 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 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
5.
6.
go back to reference Aynaud MM et al (2020) Transcriptional programs define intratumoral heterogeneity of Ewing sarcoma at single-cell resolution. Cell Rep 30:1767-1779.e1766PubMedCrossRef Aynaud MM et al (2020) Transcriptional programs define intratumoral heterogeneity of Ewing sarcoma at single-cell resolution. Cell Rep 30:1767-1779.e1766PubMedCrossRef
7.
go back to reference 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 81:6813–6822PubMedCrossRef 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 81:6813–6822PubMedCrossRef
8.
go back to reference Ornatsky O et al (2010) Highly multiparametric analysis by mass cytometry. J Immunol Methods 361:1–20PubMedCrossRef Ornatsky O et al (2010) Highly multiparametric analysis by mass cytometry. J Immunol Methods 361:1–20PubMedCrossRef
9.
go back to reference Leelatian N et al (2017) Single cell analysis of human tissues and solid tumors with mass cytometry. Cytometry B Clin Cytom 92:68–78PubMedCrossRef Leelatian N et al (2017) Single cell analysis of human tissues and solid tumors with mass cytometry. Cytometry B Clin Cytom 92:68–78PubMedCrossRef
10.
go back to reference Jaimes MC et al (2021) Full spectrum flow cytometry and mass cytometry: a 32-marker panel comparison. Cytometry A Jaimes MC et al (2021) Full spectrum flow cytometry and mass cytometry: a 32-marker panel comparison. Cytometry A
11.
go back to reference Jager A, Sarno J, Davis KL (2021) Mass cytometry of hematopoietic cells. Methods Mol Biol 2185:65–76PubMedCrossRef Jager A, Sarno J, Davis KL (2021) Mass cytometry of hematopoietic cells. Methods Mol Biol 2185:65–76PubMedCrossRef
13.
go back to reference Good Z et al (2018) Single-cell developmental classification of B cell precursor acute lymphoblastic leukemia at diagnosis reveals predictors of relapse. Nat Med 24:474–483PubMedPubMedCentralCrossRef Good Z et al (2018) Single-cell developmental classification of B cell precursor acute lymphoblastic leukemia at diagnosis reveals predictors of relapse. Nat Med 24:474–483PubMedPubMedCentralCrossRef
15.
go back to reference Giesen C et al (2014) Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat Methods 11:417–422PubMedCrossRef Giesen C et al (2014) Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat Methods 11:417–422PubMedCrossRef
18.
go back to reference 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.e1319PubMedPubMedCentralCrossRef 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.e1319PubMedPubMedCentralCrossRef
19.
go back to reference Keren L et al (2019) MIBI-TOF: a multiplexed imaging platform relates cellular phenotypes and tissue structure. Sci Adv 5:eaax5851 Keren L et al (2019) MIBI-TOF: a multiplexed imaging platform relates cellular phenotypes and tissue structure. Sci Adv 5:eaax5851
20.
go back to reference 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, 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).
21.
go back to reference Liu CC et al (2022) Multiplexed ion beam imaging: insights into pathobiology. Annu Rev Pathol 17:403–423PubMedCrossRef Liu CC et al (2022) Multiplexed ion beam imaging: insights into pathobiology. Annu Rev Pathol 17:403–423PubMedCrossRef
22.
go back to reference 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 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
23.
go back to reference Batth IS et al (2020) Rare osteosarcoma cell subpopulation protein array and profiling using imaging mass cytometry and bioinformatics analysis. BMC Cancer 20:715PubMedPubMedCentralCrossRef Batth IS et al (2020) Rare osteosarcoma cell subpopulation protein array and profiling using imaging mass cytometry and bioinformatics analysis. BMC Cancer 20:715PubMedPubMedCentralCrossRef
24.
go back to reference Gerdtsson E et al (2018) Multiplex protein detection on circulating tumor cells from liquid biopsies using imaging mass cytometry. Converg Sci Phys Oncol 4 Gerdtsson E et al (2018) Multiplex protein detection on circulating tumor cells from liquid biopsies using imaging mass cytometry. Converg Sci Phys Oncol 4
26.
go back to reference Bosisio FM et al (2022) Next-generation pathology using multiplexed immunohistochemistry: mapping tissue architecture at single-cell level. Front Oncol 12:918900PubMedPubMedCentralCrossRef Bosisio FM et al (2022) Next-generation pathology using multiplexed immunohistochemistry: mapping tissue architecture at single-cell level. Front Oncol 12:918900PubMedPubMedCentralCrossRef
27.
go back to reference Cesano A, Marincola FM, Thurin M (2020) Status of immune oncology: challenges and opportunities. Methods Mol Biol 2055:3–21PubMedCrossRef Cesano A, Marincola FM, Thurin M (2020) Status of immune oncology: challenges and opportunities. Methods Mol Biol 2055:3–21PubMedCrossRef
28.
go back to reference Gawad C, Koh W, Quake SR (2014) Dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell genomics. Proc Natl Acad Sci 111:17947–17952PubMedPubMedCentralCrossRef Gawad C, Koh W, Quake SR (2014) Dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell genomics. Proc Natl Acad Sci 111:17947–17952PubMedPubMedCentralCrossRef
29.
go back to reference 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 12:99PubMedPubMedCentralCrossRef 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 12:99PubMedPubMedCentralCrossRef
30.
go back to reference 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 10:8079PubMedPubMedCentralCrossRef 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 10:8079PubMedPubMedCentralCrossRef
31.
go back to reference Louka E et al (2021) Heterogeneous disease-propagating stem cells in juvenile myelomonocytic leukemia. J Exp Med 218 Louka E et al (2021) Heterogeneous disease-propagating stem cells in juvenile myelomonocytic leukemia. J Exp Med 218
34.
go back to reference Gillen AE et al (2020) Single-cell RNA sequencing of childhood ependymoma reveals neoplastic cell subpopulations that impact molecular classification and etiology. Cell Rep 32:108023PubMedPubMedCentralCrossRef Gillen AE et al (2020) Single-cell RNA sequencing of childhood ependymoma reveals neoplastic cell subpopulations that impact molecular classification and etiology. Cell Rep 32:108023PubMedPubMedCentralCrossRef
36.
go back to reference Jansky S et al (2021) Single-cell transcriptomic analyses provide insights into the developmental origins of neuroblastoma. Nat Genet 53:683–693PubMedCrossRef Jansky S et al (2021) Single-cell transcriptomic analyses provide insights into the developmental origins of neuroblastoma. Nat Genet 53:683–693PubMedCrossRef
37.
38.
go back to reference Shahi P, Kim SC, Haliburton JR, Gartner ZJ, Abate AR (2017) Abseq: ultrahigh-throughput single cell protein profiling with droplet microfluidic barcoding. Sci Rep 7:44447PubMedPubMedCentralCrossRef Shahi P, Kim SC, Haliburton JR, Gartner ZJ, Abate AR (2017) Abseq: ultrahigh-throughput single cell protein profiling with droplet microfluidic barcoding. Sci Rep 7:44447PubMedPubMedCentralCrossRef
40.
go back to reference Mimitou EP et al (2019) Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells. Nat Methods 16:409–412PubMedPubMedCentralCrossRef Mimitou EP et al (2019) Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells. Nat Methods 16:409–412PubMedPubMedCentralCrossRef
42.
go back to reference 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 8:2820CrossRef 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 8:2820CrossRef
44.
go back to reference 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 24:1525–1527PubMedCrossRef 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 24:1525–1527PubMedCrossRef
45.
go back to reference Pan N, Rao W, Yang Z (2020) Single-probe mass spectrometry analysis of metabolites in single cells. Methods Mol Biol 2064:61–71PubMedCrossRef Pan N, Rao W, Yang Z (2020) Single-probe mass spectrometry analysis of metabolites in single cells. Methods Mol Biol 2064:61–71PubMedCrossRef
46.
47.
go back to reference 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 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
49.
go back to reference 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 33:1179–1186PubMedPubMedCentralCrossRef 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 33:1179–1186PubMedPubMedCentralCrossRef
50.
53.
go back to reference Gayoso A et al (2022) A Python library for probabilistic analysis of single-cell omics data. Nat Biotechnol 40:163–166PubMedCrossRef Gayoso A et al (2022) A Python library for probabilistic analysis of single-cell omics data. Nat Biotechnol 40:163–166PubMedCrossRef
54.
go back to reference Nowicka M et al (2017) CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets. Res 6:748 Nowicka M et al (2017) CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets. Res 6:748
56.
go back to reference 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 40:555–565PubMedCrossRef 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 40:555–565PubMedCrossRef
58.
go back to reference Kotecha N, Krutzik PO, Irish JM (2021) Web-based analysis and publication of flow cytometry experiments. Current protocols in cytometry Chapter 10, Unit10.17-Unit10.17 Kotecha N, Krutzik PO, Irish JM (2021) Web-based analysis and publication of flow cytometry experiments. Current protocols in cytometry Chapter 10, Unit10.17-Unit10.17
59.
60.
go back to reference Belkina AC et al (2019) Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets. Nat Commun 10:5415PubMedPubMedCentralCrossRef Belkina AC et al (2019) Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets. Nat Commun 10:5415PubMedPubMedCentralCrossRef
62.
go back to reference Butler A, Hoffman P, Smibert P, Papalexi E, Satija R (2018) Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 36:411–420PubMedPubMedCentralCrossRef Butler A, Hoffman P, Smibert P, Papalexi E, Satija R (2018) Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 36:411–420PubMedPubMedCentralCrossRef
63.
64.
go back to reference Kopp W, Akalin A, Ohler U (2022) Simultaneous dimensionality reduction and integration for single-cell ATAC-seq data using deep learning. Nature Machine Intelligence 4:162–168CrossRef Kopp W, Akalin A, Ohler U (2022) Simultaneous dimensionality reduction and integration for single-cell ATAC-seq data using deep learning. Nature Machine Intelligence 4:162–168CrossRef
66.
go back to reference Hardoon DR, Shawe-Taylor J (2011) Sparse canonical correlation analysis. Mach Learn 83:331–353CrossRef Hardoon DR, Shawe-Taylor J (2011) Sparse canonical correlation analysis. Mach Learn 83:331–353CrossRef
67.
69.
70.
go back to reference Avila Cobos F, Alquicira-Hernandez J, Powell JE, Mestdagh P, De Preter K (2020) Benchmarking of cell type deconvolution pipelines for transcriptomics data. Nat Commun 11:5650PubMedPubMedCentralCrossRef Avila Cobos F, Alquicira-Hernandez J, Powell JE, Mestdagh P, De Preter K (2020) Benchmarking of cell type deconvolution pipelines for transcriptomics data. Nat Commun 11:5650PubMedPubMedCentralCrossRef
71.
72.
go back to reference Campana D (2010) Minimal residual disease in acute lymphoblastic leukemia. Hematology Am Soc Hematol Educ Program 2010:7–12PubMedCrossRef Campana D (2010) Minimal residual disease in acute lymphoblastic leukemia. Hematology Am Soc Hematol Educ Program 2010:7–12PubMedCrossRef
73.
go back to reference van der Velden VH, Boeckx N, van Wering ER, van Dongen JJ (2004) Detection of minimal residual disease in acute leukemia. J Biol Regul Homeost Agents 18:146–154PubMed van der Velden VH, Boeckx N, van Wering ER, van Dongen JJ (2004) Detection of minimal residual disease in acute leukemia. J Biol Regul Homeost Agents 18:146–154PubMed
74.
go back to reference Zhang Y et al (2022) Elucidating minimal residual disease of paediatric B-cell acute lymphoblastic leukaemia by single-cell analysis. Nat Cell Biol 24:242–252PubMedCrossRef Zhang Y et al (2022) Elucidating minimal residual disease of paediatric B-cell acute lymphoblastic leukaemia by single-cell analysis. Nat Cell Biol 24:242–252PubMedCrossRef
Metadata
Title
Single-cell technologies uncover intra-tumor heterogeneity in childhood cancers
Authors
Yu-Chen Lo
Yuxuan Liu
Marte Kammersgaard
Abhishek Koladiya
Timothy J. Keyes
Kara L. Davis
Publication date
10-01-2023
Publisher
Springer Berlin Heidelberg
Published in
Seminars in Immunopathology / Issue 1/2023
Print ISSN: 1863-2297
Electronic ISSN: 1863-2300
DOI
https://doi.org/10.1007/s00281-022-00981-1

Other articles of this Issue 1/2023

Seminars in Immunopathology 1/2023 Go to the issue
Live Webinar | 27-06-2024 | 18:00 (CEST)

Keynote webinar | Spotlight on medication adherence

Live: Thursday 27th June 2024, 18:00-19:30 (CEST)

WHO estimates that half of all patients worldwide are non-adherent to their prescribed medication. The consequences of poor adherence can be catastrophic, on both the individual and population level.

Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.

Prof. Kevin Dolgin
Prof. Florian Limbourg
Prof. Anoop Chauhan
Developed by: Springer Medicine
Obesity Clinical Trial Summary

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.

Developed by: Springer Medicine