Missionbio

Acute myeloid leukemia (AML) remains one of the most heterogeneous hematologic malignancies, both at the genetic and phenotypic levels. Despite initial responses to therapy, relapse is frequent and largely driven by a rare population of leukemic stem cells (LSC), which persist in up to 70–80% of AML cases and act as a reservoir for disease propagation and resistance. Traditional bulk approaches have provided key insights into AML biology but inherently obscure the cellular diversity that shapes disease evolution and therapeutic escape.

Here, we leveraged single-cell proteogenomics to dissect the heterogeneity of LSC at unprecedented resolution. By combining high-dimensional genotyping with a custom-designed antibody panel, we were able to simultaneously capture mutational landscapes and phenotypic profiles of stem and progenitor compartments. This approach uncovered unexpected subclonal architectures, revealed co-existing immunophenotypic states within genetically defined clones, and highlighted rare but clinically relevant LSC subsets that would have been invisible using conventional methods.

Our work illustrates how single-cell proteogenomics can bridge the gap between genotype and phenotype in AML, offering a powerful framework to better understand LSC biology, monitor minimal residual disease, and ultimately inform precision therapeutic strategies.


Dissecting leukemic stem cells at single-cell resolution: A proteogenomic approach

See how **single-cell DNA + DNA methylation + surface protein** profiling with **scTAM-seq (on Tapestri)** dissects treatment response in **MDS/AML** using **sequential patient bone-marrow samples** collected during targeted therapy.

*   **Multi-omic resolution, one assay:** scTAM-seq simultaneously profiles **somatic mutations, methylation, and protein** at single-cell resolution.
*   **Sharper cell typing:** Integrating **scTAM-seq + ADT-seq** improves **cell-type identification**, resolving subtle hematopoietic populations.
*   **Track clonal evolution:** Combining **scTAM-seq with single-cell genotyping** maps **clonal architecture** and **evolution under therapy** across **sequential samples**.

**Rethinking response metrics:** Understand **clinical response** that can occur **independent of changes in mutant allelic burden**, and implications for disease monitoring.


Deconvolution of clonal dynamics with scTAM-seq

Recent advances in single-cell profiling and targeted therapy are reshaping acute myeloid leukemia (AML) management. In this webinar, we will present results from a randomized trial of CXCR4 inhibition in AML, integrating single-cell MRD analysis and biomarker stratification. We will explore how molecular heterogeneity and CXCR4 expression inform therapeutic response and relapse risk, highlighting new opportunities for personalized patient care.


Single-Cell MRD Profiling in AML Identifies CXCR4 Expression as a Predictor of Motixafortide Response

Genetic variants,both coding and noncoding,can profoundly influence gene regulation and disease progression. Yet, systematically linking genetic variants to functional consequences with single-cell resolution remains a major challenge.

In this webinar, **Dr. Dominik Lindenhofer (EMBL Heidelberg)** will present his pioneering work on **single-cell DNA–RNA sequencing (SDR-seq)**, a breakthrough method developed to simultaneously detect hundreds of genomic variants and transcripts within the same cell. By applying SDR-seq to induced pluripotent stem cells and B cell lymphoma samples, Dr. Lindenhofer demonstrates how coding and noncoding genetic variants influence gene expression and tumorigenic signaling. SDR-seq has major clinical applications because it can help pinpoint how specific genetic variants alter gene expression to guide more precise diagnostics and targeted therapeutic strategies through single-cell analysis.


Dissecting leukemic stem cells at single-cell resolution: A proteogenomic approach

Deconvolution of clonal dynamics with scTAM-seq

Single-Cell MRD Profiling in AML Identifies CXCR4 Expression as a Predictor of Motixafortide Response

Functional phenotyping of genomic variants using joint multiomic single-cell DNA–RNA sequencing