iScienceCancer BiologyPeer-reviewed journal article
Spatial Transcriptomics Reveals Inflammation and Trans-differentiation States of Acute Myeloid Leukemia in Extramedullary and Medullary Tissues
Enes DasdemiriD, Ivo VeletićiD, Christopher P. LyiD, Andres Ernesto QuesadaiD, Christopher D. PachecoiD, Fatima Zahra JellouliD, Pâmella BorgesiD, Sreyashi BasuiD, Sonali JindaliD, Zhiqiang WangiD, Alexander J. LazariD, Khalida WaniiD, Dinler A. AntunesiD, Patrick Kevin RevilleiD, Preethi H. GunaratneiD, Robert J. ToweriD, Padmanee SharmaiD, Hussein A. AbbasiD
Abstract
Acute myeloid leukemia (AML) is a heterogeneous disease of the bone marrow (medullary) but can also involve extramedullary tissues. While single cell dynamics of AML in suspension are previously explored, a comprehensive spatial transcriptomic assessment in AML remain underexplored. Here, we used Visium spatial transcriptomics to resolve medullary and extramedullary AML environments. We reveal spatial co-localization of monocytes and granulocyte-monocyte progenitors with leukemic populations in the bone marrow, sharing molecular signatures with extramedullary sites. Cell-cell communication via the CXCL12- CXCR4 axis correlated with PI3K/AKT/mTOR signaling in high inflammatory niches. Trans- differentiation states were concentrated in AML-infiltrated regions, with committed-like AML populations present in inflammatory niches and away from the trabeculae, while primitive-like AML cells localized near the endosteal niche. We validated these findings in GeoMx-based Digital Spatial profiling (DSP). Our study applied multimodal spatial transcriptomic approaches to characterize the spatial hierarchy and microenvironmental dynamics of AML differentiation states. We also demonstrated the feasibility of applying Visium-based spatial transcriptomics in decalcified bone tissues.
- organism: human
- assay: spatial transcriptomics
- research focus: cell-cell communication
- research focus: clustering
- research focus: differential expression
- research focus: spatial domain detection
- approach: deep learning
- approach: statistical model
- Citations
- 8
- FWCI
- 5.91
- Field percentile
- top 3%
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