Periodic Reporting for period 1 - switchDecoding (Decoding the path to cellular variation within pathogen populations)
Reporting period: 2023-04-01 to 2025-09-30
Cellular variation is a key strategy used by parasites to adapt to different environments, evade the host immune response, and establish lasting infections. However, little is known about how the necessary level of cellular heterogeneity is achieved. This lack of knowledge poses a significant challenge to efforts to control and eradicate parasites. With switchDecoding, our goal is to generate a much improved understanding of the mechanisms underlying cellular heterogeneity.
The lack of knowledge regarding the regulation of cell-to-cell heterogeneity can be partly explained by the technical challenges associated with studying unicellular organisms at the single-cell level and studying the functional consequences of genome architecture on gene expression. However, advances in several technologies are now making this possible.
Thus, switchDecoding aims to fill this knowledge gap by developing technologies that go beyond the state of the art to: 1) determine temporal transcriptome trajectories, 2) generate high-resolution maps of genome architecture, and 3) perform large-scale screens. We will use the unicellular parasite Trypanosoma brucei as a model.
The lack of knowledge regarding the regulation of cell-to-cell heterogeneity can be partly explained by the technical challenges associated with studying unicellular organisms at the single-cell level and studying the functional consequences of genome architecture on gene expression. However, advances in several technologies are now making this possible.
Thus, switchDecoding aims to fill this knowledge gap by developing technologies that go beyond the state of the art to: 1) determine temporal transcriptome trajectories, 2) generate high-resolution maps of genome architecture, and 3) perform large-scale screens. We will use the unicellular parasite Trypanosoma brucei as a model.
Since the project began two years ago, we have made significant progress toward all three of these goals.
1) We implemented a robust strategy for metabolic labeling of RNA using 4-TU and the SLAM-seq pipeline to differentiate old RNA from new RNA. This was a critical first step in generating temporal transcriptome trajectories. Additionally, our SLAM-seq analysis revealed that changes in both RNA maturation and stability are important, independent regulators of gene expression in T. brucei (Luzak et al, Nucleic Acids Research, 2025).
2) To generate high-resolution 3D maps of T. brucei genome architecture, we improved genome contiguity using ultra-long nanopore sequencing reads. We also implemented Micro-C, a high-resolution derivative of Hi-C, which revealed intriguing T. brucei organization around polymerase-class-specific transcriptional hubs. (Rabuffo et al. Nature Communications, 2024).
3) To improve our ability to conduct large-scale perturbation screens to identify regulators of cell-to-cell heterogeneity, we concentrated on three areas.
a) To obtain reliable transcriptome readouts, we developed a new, highly sensitive single-cell RNA-seq approach (Keneskhanova et al., Nature, 2025).
b) switching, we began implementing Cas9-based approaches for gene deletion and overexpression (unpublished).
c) To reliably measure VSG switching frequency at high resolution, we developed a strategy to grow T. brucei into micro populations on a large scale by encapsulating individual cells in semipermeable membranes (unpublished).
1) We implemented a robust strategy for metabolic labeling of RNA using 4-TU and the SLAM-seq pipeline to differentiate old RNA from new RNA. This was a critical first step in generating temporal transcriptome trajectories. Additionally, our SLAM-seq analysis revealed that changes in both RNA maturation and stability are important, independent regulators of gene expression in T. brucei (Luzak et al, Nucleic Acids Research, 2025).
2) To generate high-resolution 3D maps of T. brucei genome architecture, we improved genome contiguity using ultra-long nanopore sequencing reads. We also implemented Micro-C, a high-resolution derivative of Hi-C, which revealed intriguing T. brucei organization around polymerase-class-specific transcriptional hubs. (Rabuffo et al. Nature Communications, 2024).
3) To improve our ability to conduct large-scale perturbation screens to identify regulators of cell-to-cell heterogeneity, we concentrated on three areas.
a) To obtain reliable transcriptome readouts, we developed a new, highly sensitive single-cell RNA-seq approach (Keneskhanova et al., Nature, 2025).
b) switching, we began implementing Cas9-based approaches for gene deletion and overexpression (unpublished).
c) To reliably measure VSG switching frequency at high resolution, we developed a strategy to grow T. brucei into micro populations on a large scale by encapsulating individual cells in semipermeable membranes (unpublished).
I believe that the development of high-resolution single-cell RNA sequencing (scRNA-seq) and the findings made with this technology — namely, that the presence or absence of repair template availability following a double-strand break in the actively expressed surface antigen controls the antigen selection mechanism — represents a major breakthrough in our understanding of antigen variation.
Keneskhanova Z, McWilliam KR, Cosentino RO, Barcons-Simon A, Dobrynin A, Smith JE et al. (2025). Genomic determinants of antigen expression hierarchy in African trypanosomes. Nature, DOI: 10.1038/s41586-025-08720-w
Keneskhanova Z, McWilliam KR, Cosentino RO, Barcons-Simon A, Dobrynin A, Smith JE et al. (2025). Genomic determinants of antigen expression hierarchy in African trypanosomes. Nature, DOI: 10.1038/s41586-025-08720-w