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Laidlaw, R. F.

Publications and source records attributed to Laidlaw, R. F..

2 recordsLinked to original sources

Spatial transcriptomics reveals focal induction of molecular responses and cellular interactions in the small intestine during Heligmosomoides polygyrus infection.

The infective larvae of the helminth Heligmosomoides polygyrus bakeri migrate to the small intestine, invade the submucosa, and trigger granuloma formation around each parasite. Here, we employ spatial transcriptomics to elucidate the transcriptional intricacies and cell interactions in H. polygyrus-infected mice. We find a generalised reduction in expression of homeostatic genes such as Epcam, Pls1 (fimbrin) and Zg16, while cell adhesion (eg Cldn3, Cdh17) and immune-protective (Pla2g4c) loci are upregulated. Specific genes and cell types are associated with different spatial niches (lower crypt, upper crypt, villi and granuloma). Within the crypts, pathway analysis indicates activation of the osteopontin (Spp1) and pleiotrophin (Ptn) pathways that are poorly represented in steady-state tissues, whilst Wnt signalling within the crypts is abrogated by day 7 of infection. Granulomas contain concentrations of myeloid cells, NK and dendritic cells, with high expression levels of genes linked to M2 macrophages (Arg1, Retlna, Fcer1g) and wound repair pathways (Reg3b and Mxra7) as well as elevated Tmbx4 that has not previously been noted. Analysis of potential ligand-receptor pairs confirmed a major complementarity between granuloma-localised SPP1 and CD44 receptors in both crypt and granuloma, as well as TGF-{beta}/receptor interactions. Infected tissues also revealed abundant chemokine representation; among the latter category CCL6, CCL8 and MIF (macrophage migration inhibitory factor) dominated potential interactions. These results both enhance our understanding of the murine small intestines transcriptional landscape and also identify a new set of molecular interactions underpinning tissue-specific responses to infection that can be targeted for therapeutic intervention.

immunology↗

TrAGEDy: Trajectory Alignment of Gene Expression Dynamics

1MotivationSingle-cell transcriptomics sequencing is used to compare different biological processes. However, often, those processes are asymmetric which are difficult to integrate. Current approaches often rely on integrating samples from each condition before either cluster-based comparisons or analysis of an inferred shared trajectory. ResultsWe present Trajectory Alignment of Gene Expression Dynamics (TrAGEDy), which allows the alignment of independent trajectories to avoid the need for error-prone integration steps. Across simulated datasets, TrAGEDy returns the correct underlying alignment of the datasets, outperforming current tools which fail to capture the complexity of asymmetric alignments. When applied to real datasets, TrAGEDy captures more biologically relevant genes and processes, which other differential expression methods fail to detect when looking at the developments of T cells and the bloodstream forms of Trypanosoma brucei when affected by genetic knockouts. Availability and ImplementationTrAGEDy is freely available at https://github.com/No2Ross/TrAGEDy, and implemented in R. Contactthomasdan.otto@glasgow.ac.uk

bioinformatics↗