Single-Cell Sequencing and Multi-Omic Technologies in Acute Myeloid Leukemia: A Literature Review
Bibliographic record
Abstract
Acute Myeloid Leukemia (AML) exemplifies leukemogenesis as a multistep process in which healthy hematopoietic stemand progenitor cells (HSPCs) acquire sequential genetic and molecular alterations, leading to malignant transformation.Traditional bulk sequencing approaches, although informative, cannot resolve the extensive cellular heterogeneity withinAML. The application of single-cell RNA sequencing (scRNA-seq)—as shown by Wu et al.—enables the identification ofdistinct differentiation pathways and gene expression profiles at the individual cell level, which are specific to AML subtypesand relate to disease progression. When combined with advanced surface proteomics techniques such as Cellular Indexing ofTranscriptomes and Epitopes by sequencing (CITE-seq), single-cell profiling offers comprehensive insights into AML’scellular architecture, revealing cell surface markers linked to proliferation, migration, and treatment resistance. Integration ofmulti-omic layers—including transcriptomic, epigenomic, and proteomic data—has been further advanced in chromatinaccessibility profiling, collectively aiding in the discovery of innovative therapeutic targets and enhancing diagnosticstrategies. Comprehensive literature searches across PubMed, Cochrane, and Google Scholar were performed to select studieson single-cell and multi-omic analyses in human AML, focusing on work published from 2014 onward. Studies emphasizingleukemic subtypes, differentiation pathways, and novel therapeutic targets were prioritized, with particular attention to recentconceptual and technological advancements in AML research. Recent work by Clark et al. and others has uncoveredpreviously unrecognized AML subpopulations with unique differentiation states and genetic alterations implicated in diseaseprogression and therapy resistance. The integration of transcriptomic and epigenomic data has clarified molecular pathwaysand transcriptional regulators key to leukemic cell survival and proliferation. These advances have substantially deepened ourunderstanding of AML cellular heterogeneity, providing new insight into the mechanisms behind disease evolution andtreatment response. The application of single-cell and multi-omic approaches in AML marks a pivotal advance towardpersonalized medicine. By enabling the identification of AML subtypes, refining risk stratification, and supporting real-timedisease monitoring, these technologies now facilitate the development of targeted therapies tailored to individual molecularprofiles, ultimately improving patient outcomes and guiding clinical decision-making.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".