Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis
Open accessAbstract
Acute myeloid leukemia (AML) manifests as phenotypically and functionally diverse cells, often within the same patient. Intratumor phenotypic and functional heterogeneity have been linked primarily by physical sorting experiments, which assume that functionally distinct subpopulations can be prospectively isolated by surface phenotypes. This assumption has proven problematic, and we therefore developed a data-driven approach. Using mass cytometry, we profiled surface and intracellular signaling proteins simultaneously in millions of healthy and leukemic cells. We developed PhenoGraph, which algorithmically defines phenotypes in high-dimensional single-cell data. PhenoGraph revealed that the surface phenotypes of leukemic blasts do not necessarily reflect their intracellular state. Using hematopoietic progenitors, we defined a signaling-based measure of cellular phenotype, which led to isolation of a gene expression signature that was predictive of survival in independent cohorts. This study presents new methods for large-scale analysis of single-cell heterogeneity and demonstrates their utility, yielding insights into AML pathophysiology.
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PhenoGraph clustering identified 18 distinct populations within 48,822 wildtype cells, including emergent mesenchymal, myocardial, and endothelial populations.
“We used PhenoGraph39 clustering to identify diversity captured within the 48,822 wildtype cells and annotated 18 distinct populations, including emergent mesenchymal, myocardial, and endothelial populations”
From Cell diversity and plasticity during atrioventricular heart valve EMTs (2023) · cites this paper as [39]
The cellular transcriptomic state space can be modeled as a smooth, low-dimensional manifold or a set of manifolds.
“We begin by modeling the cellular transcriptomic state space as a smooth, low-dimensional manifold or set of manifolds.”
From Quantifying the effect of experimental perturbations at single-cell resolution (2021) · cites this paper as [6]
The scale and precision of single-cell studies has continued to increase, reaching tens of thousands of cells for massively parallel scRNA-seq and millions of cells for mass cytometry protein measurements.
“The scale and precision of such studies has continued to increase, reaching tens of thousands of cells for massively parallel scRNA-seq30,31 and millions of cells for mass cytometry signature protein measurements32–34.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [34]
Using the latest protocols for massively parallel scRNA-seq and mass cytometry, a single laboratory can now readily collect tens of thousands to hundreds of thousands of single-cell RNA-seq profiles and tens of millions of single-cell protein profiles.
“Using the latest protocols for massively parallel scRNA-seq30,31 and mass cytometry32–34, a single laboratory can now readily collect tens of thousands to hundreds of thousands of single-cell RNA-seq profiles and tens of millions of single-cell protein profiles.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [34]
Some microbiotypes were clearly separate, while others overlapped with neighboring clusters.
“Some were distinct (for example, clusters 9 and 19), while others (for example, clusters 13 and 27) had overlapping features with neighboring clusters (Supplementary Fig. 14a)54.”
From Intestinal microbiota signatures of clinical response and immune-related adverse events in melanoma patients treated with anti-PD-1 (2022) · cites this paper as [54]
Phenograph is a modularity-based clustering algorithm that produces robust clusterings on scRNA-seq data.
“Clustering was performed on the resulting k-nearest neighbors graph using Phenograph (Levine et al., 2015), a modularity-based clustering algorithm that produces robust clusterings on scRNA-seq data (Shekhar et al., 2016).”
From Single-Cell Analysis of Regional Differences in Adult V-SVZ Neural Stem Cell Lineages (2019) · cites this paper as Levine et al., 2015
After PhenoGraph, the standard approach to clustering single-cell datasets has become multi-resolution modularity optimization as implemented in the Louvain algorithm on single-cell KNN graphs.
“After the pioneering PhenoGraph method (Levine et al, 2015), the standard approach to clustering single‐cell datasets has become multi‐resolution modularity optimization (Newman & Girvan, 2004; Reichardt & Bornholdt, 2006) as implemented in the Louvain algorithm (Blondel et al, 2008) on single‐cell KNN graphs.”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Levine et al, 2015
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“…distance_ratio = 0.5, euclidean_distance_ratio=1)), we designated cycling cells at the opposite end of UMAP project from the senescent cells as the start point manually ( Trapnell et al., 2014 ; Qiu et al., 2017 ; Cao et al., 2019 ; Traag et al., 2019 ; Levine et al., 2015 ; McInnes et al., 2018 ).”
InfluentialEvolution of cellular diversity in primary motor cortex of human, marmoset monkey, and mouse
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- Authors
- Levine, J. H., Simonds, E. F., Bendall, S. C., Davis, K. L., Amir, E.-A. D., Tadmor, M. D., Litvin, O., Fienberg, H. G., Jager, A., Zunder, E. R., Finck, R., Gedman, A. L., Radtke, I., Downing, J. R., Pe’er, D., & Nolan, G. P.
- Affiliations
- Columbia University (US)
- Stanford University (US)
- Stanford Medicine (US)
- St. Jude Children's Research Hospital (US)
- Corresponding author
- Pe’er, Dana
- Nolan, Garry P.
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2015-07
- Volume
- 162
- Issue
- 1
- Pages
- 184-197
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC4508757
- OpenAlex
- W1631320694
- Cited by
- 2,303
- References
- 32
- Citations by year
- 2026: 151 · 2025: 251 · 2024: 318 · 2023: 291 · 2022: 305 · 2021: 277 · 2020: 200 · 2019: 202 · 2018: 135 · 2017: 99 · 2016: 60 · 2015: 11 · 2014: 1
- FWCI
- 80.05
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (HHSN272200700038C, U54CA149145, U19 AI057229, DP1-HD084071, SU2C-AACR-PS04, 1R01CA130826, 1R00-GM104148, 5U54CA143907, P01 CA034233, R01-CA164729 U54-CA121852, N01-HV-00242, DP2-OD002414)
- Entertainment Industry Foundation
- Damon Runyon Cancer Research Foundation (DRG-2017-09)
- California Institute for Regenerative Medicine (DR1-01477, RB2-01592)
- U.S. Food and Drug Administration (HHSF223201210194C)
- European Commission (HEALTH.2010.1.2-1)
- Topics
- Health Sciences › Medicine › Hematology › Acute Myeloid Leukemia Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Retinoids in leukemia and cellular processes
- Keywords
- acute myeloid leukemia, intratumoral heterogeneity, mass cytometry, single-cell heterogeneity, intracellular signaling, prognostic gene signature, survival prediction
- MeSH terms
- Bone Marrow, Child, Humans, Neoplastic Stem Cells, Cohort Studies, Leukemia, Myeloid, Acute, Genetic Heterogeneity, Computational Biology, Single-Cell Analysis, Transcriptome
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