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Laidlaw, R. F.

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

5 recordsLinked to original sources

Single-cell transcriptomics reveals life cycle and cell cycle gene expression dynamics during in vitro growth of two Leishmania species

Leishmania species are transmitted between mammals by sand flies, within which they undergo a complex developmental programme involving transitions between replicative and non-replicative life cycle forms. Recent work using single-cell transcriptomics has revealed the developmental pathways of Leishmania major in the sand fly, but it is unclear how faithfully such development is recapitulated during Leishmania culture in vitro. In addition, no work has revealed how gene expression changes across the cell cycle of replicative Leishmania forms. Using single-cell transcriptomics on unsynchronised cultures of L. major and L. mexicana, we addressed both questions. We show that in vitro L. mexicana promastigotes display the same two developmental endpoints seen in L. major cells recovered from sand flies, metacyclics and haptomonads. In contrast, L. major promastigote development in vitro does not predict the presence of leptomonads or haptomonads, although non-dividing late metacyclics form through an early metacyclic stage as parasites enter stationary phase. Using computational reconstruction of the cell cycle in procyclic promastigote forms of both Leishmania species identified hundreds of genes whose transcript levels vary as the cell cycle progresses, revealing core conserved processes between the two species and with the closely related Trypanosoma brucei. These genes allowed us to examine connections between life cycle and cell cycle progression, revealing that some developmental transitions are strongly linked to a cell cycle phase, whereas others occur asynchronously. This work provides a comprehensive atlas and marker gene list for the interlinked cell and life cycles of two laboratory-cultured Leishmania species.

microbiology↗

Cell DiffErential Expression by Pooling (CellDEEP) highlights issues in differential gene expression in scRNA-seq

Accurate identification of differentially expressed genes (DEGs) in single-cell RNA sequencing (scRNA-seq) data remains challenging. Single-cell-specific statistical models often report large numbers of candidate genes but can exhibit inflated false positive rates, whereas pseudobulk approaches improve false discovery control at the cost of reduced sensitivity. To overcome the noise and bias that other tools have, and allow the user to have more control of the DEG process, we present CellDEEP, which uses a cell aggregation (metacell) approach. This tool provides a framework for flexible selection of pooling strategies and parameterisation for differential expression analysis (DE). Benchmarking on simulated and real datasets, including COVID-19 and rheumatoid arthritis, shows that CellDEEP often outperforms other methods, consistently reduces false positives compared to single-cell methods and recovers more true positives than pseudobulk methods. Our work shifts the focus from selecting a single "best" method to an approach that reduces cell-level noise while preserving biological signal, together with transparent validation framework, advancing more reliable differential-expression analysis in single-cell transcriptomics. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=189 HEIGHT=200 SRC="FIGDIR/small/710522v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@14692f9org.highwire.dtl.DTLVardef@5b37d6org.highwire.dtl.DTLVardef@aece11org.highwire.dtl.DTLVardef@5ade3d_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

Phage-display immunoprecipitation sequencing reveals distinct antibody repertoire patterns across a rural-urban gradient

Environmental microbial exposures are hypothesized to drive immune profile differences across rural-urban gradients, yet direct evidence linking exposures to immune phenotypes remains limited. Here, we employed phage-displayed immunoprecipitation sequencing (PhIP-seq) to profile antibody repertoires against 357,192 epitopes from >700 organisms in 62 age-matched healthy adults from rural Senegal (n=29), urban Senegal (n=20), and urban Netherlands (n=13). PhIP-Seq analysis identified two major patterns of antibody repertoire variation: geographically restricted signatures distinguishing Senegal from the Netherlands, and a trajectory associated with rural-urban gradients spanning from rural Senegal to urban Netherlands. Integration of PhIP-seq data with high-dimensional immunological profiling revealed immune signatures correlated with the rural-urban antibody repertoire trajectory. Our findings demonstrate that antibody repertoire profiling captures distinct microbial exposure histories across both geographic regions and rural-urban gradients and correlates with variation in immune signatures. This work establishes PhIP-seq as a powerful tool for linking environmental exposure histories to population-level immune landscapes and provides insights into immune adaptation across diverse environments.

immunology↗

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↗