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Biology subjects

Henningsson, R.

Publications and source records attributed to Henningsson, R..

2 recordsLinked to original sources

The cellular state space of AML unveils novel NPM1 subtypes with distinct clinical outcomes and immune evasion properties

Acute myeloid leukemia (AML) is a genetically and cellularly heterogeneous disease. We characterized 120 AMLs using genomic and transcriptomic analyses, including single-cell RNA sequencing. Our results reveal an extensive cellular heterogeneity that distorts the bulk transcriptomic profiles. Selective examination of the transcriptional signatures of >90,000 immature AML cells identified four main clusters, thereby extending current genomic classification of AML. Notably, NPM1 mutated AML could be stratified into two novel, clinically relevant classes, with NPM1class I associated with downregulation of MHC class II and excellent survival following hematopoietic stem cell transplantation (HSCT). NPM1class II was instead associated with resistance to allogeneic T cells in an ex vivo co culture assay, and importantly, dismal survival following HSCT. These findings provide new insights into the cellular state space of AML, define new diagnostic entities, and highlight potential therapeutic intervention points. Key PointsO_LIThe bulk transcriptional profiles of AML are mainly driven by a diverse set of cellular signatures. C_LIO_LISingle cell RNA-sequencing of the most common AML subtypes reveals marked heterogeneity extending beyond current genomic classification schemes. C_LIO_LINPM1-mutated AML can be divided into two new classes, with distinct immune evasion mechanisms and survival after transplantation. C_LI

cancer biology↗

DISSEQT - DIStribution based modeling of SEQuence space Time dynamics

Rapidly evolving microbes are a challenge to model because of the volatile, complex and dynamic nature of their populations. We developed the DISSEQT pipeline (DIStribution-based SEQuence space Time dynamics) for analyzing, visualizing and predicting the evolution of heterogeneous biological populations in multidimensional genetic space, suited for population-based modeling of deep sequencing and high-throughput data. DISSEQT is openly available on GitHub (https://github.com/rasmushenningsson/DISSEQT.jl) and Synapse (https://www.synapse.org/#!Synapse:syn11425758), covering the entire workflow from read alignment to visualization of results. DISSEQT is centered around robust dimension and model reduction algorithms for analysis of genotypic data with additional capabilities for including phenotypic features to explore dynamic genotype-phenotype maps. We illustrate its utility and capacity with examples from evolving RNA virus populations, which present on of the highest degrees of population heterogeneity found in nature. Using DISSEQT, we empirically reconstruct the evolutionary trajectories of evolving populations in sequence space and genotype-phenotype fitness landscapes. We show that while sequence space is vastly multidimensional, the relevant genetic space of evolving microbial populations is of intrinsically low dimension. In addition, evolutionary trajectories of these populations can be faithfully monitored to identify the key minority genotypes contributing most to evolution. Finally, we show that empirical fitness landscapes, when reconstructed to include minority variants, can predict phenotype from genotype with high accuracy.

evolutionary biology↗