Biology subjects

Otto, T. D.

Publications and source records attributed to Otto, T. D..

At least 19 recordsLinked to original sources

bioRxiv2026

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↗

bioRxiv2026

The genome of the avian malaria parasite Haemoproteus majoris (lineage WW2) and its relationship to other Plasmodium species

Avian malaria parasites form a highly prevalent and genetically diverse group within the haemosporidians, yet they have long been overlooked relative to their human- and rodent-infecting counterparts. Among these, parasites of the genus Haemoproteus (Haemosporida, Haemoproteidae) are widespread and prevalent blood parasites of birds, transmitted by louse flies (Hippoboscidae) and biting midges (Ceratopogonidae). Recent phylogenomic analyses place Haemoproteus parasites at the root of the haemosporidian tree, making genomic data from these taxa essential for understanding the evolutionary origins of malaria parasites. To date, only two avian Plasmodium and one avian Haemoproteus genomes have been sequenced. We present the first assembled genome of Haemoproteus majoris (lineage WW2), a common blood parasite of passerine birds. As avian erythrocytes are nucleated, parasite DNA was enriched by FACS-based sorting to discriminate and isolate the parasite from host cell nuclei prior to whole-genome amplification. The genome was assembled using Nanopore long-read sequencing and polished with Illumina short-reads, yielding 145 contigs with a total assembly size of 23.9Mb and a G+C content of 27.85%. Genome annotation identified 5501 protein-coding genes, 69 non-coding RNA genes, and 57 long terminal repeat retrotransposons (LRT-RTs), including one full-length element. This genomic resource represents a critical step towards elucidating the evolutionary history and genomic architecture of avian malaria parasites. SIGNIFICANCE STATEMENTWe present the first assembled genome of Haemoproteus majoris (lineage WW2), a prevalent and generalist avian malaria parasite. Taxonomic resolution of this genus is difficult as there are few distinct morphological differences among closely-related species. This genome provides a valuable resource for studying the evolution within the Haemoproteus genus and to elucidate the evolutionary history of malaria parasites.

genomics↗

bioRxiv2026

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↗

bioRxiv2026

Synovial lining expressed mechanosensor PIEZO1 drives inflammation-permissive macrophage phenotypes and joint inflammation

Synovial tissue-resident macrophages regulate immune homeostasis within the joint, but can adopt an inflammation-permissive phenotype that promotes immune cell infiltration in rheumatoid arthritis (RA). Understanding the factors that drive this phenotypic switch may help prevent the localisation of inflammation in the joints of individuals at-risk of RA. We identified the mechanosensitive ion channel PIEZO1 as a potential regulator of lining-layer synovial tissue macrophage (STM) function. PIEZO1 was highly expressed in homeostatic, tissue resident TREM2pos lining-layer STMs and in its pathogenic chemokine producing TREM2low phenotype that characterises the hyperplastic lining-layer in active RA. Intra-articular injection of a PIEZO1 agonist in mice induced neutrophil and monocyte infiltration, whereas inhibition of PIEZO1 signalling restored the protective macrophage phenotype. Thus, mechanosensing via PIEZO1 is a defining feature of the joint lining-layer, and its aberrant activation by mechanical stress may lead to the localisation of inflammation within the joint, facilitating a transition from asymptomatic autoimmunity of at-risk RA to clinical disease.

immunology↗

bioRxiv2025

BLASE: Bulk Linkage Analysis for Single Cell Experiments - Teasing Out the Secrets of Bulk Transcriptomics with Trajectory Analysis

1MotivationscRNA-seq experiments can capture cell process trajectories. Bulk RNA-seq is more practical, however does not have the granularity to elucidate cell-type specific trajectories. Deconvolution methods can estimate cell-types in RNA-seq data, but there is a need for methods characterising their pseudotime. ResultsWe show that our method, BLASE, can identify the progress of an RNA-seq sample through a trajectory in a scRNA-seq reference. ConclusionBLASE can be used to a) annotate scRNA-seq data from existing RNA-seq, b) identify progress of RNA-seq data through a process based on scRNA-seq data, and c) be used to correct developmental differences in RNA-seq differential expression analysis.

bioinformatics↗

bioRxiv2025

upsAI: A high-accuracy machine learning classifier for predicting Plasmodium falciparum var gene upstream groups

Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1), encoded by the hypervariable var gene family, is central to malaria pathogenesis, influencing both disease severity and immune evasion. Classifying var genes into upstream groups (upsA, upsB, upsC, upsE) is important for understanding parasite biology and clinical outcomes, but remains challenging, especially with partial sequences, such as the DBL tag or RNA-Seq assemblies. We developed upsAI, a machine learning-based classifier trained on 2,530 curated var genes, to accurately assign upstream groups using sequence features from different partial gene regions. We compared seven different methods, including support vector machines, random forest, XGB boost and HMMer models. The best model of upsAI for DBL-tags sequences achieves an overall accuracy of 83%, 92% and for full-length var genes, therefore significantly outperforming existing tools. Further, we propose a new model to distinguish between internal and subtelomeric var genes with high accuracy and scalability. upsAI is available at https://github.com/sii-scRNA-Seq/upsAI, providing a robust and efficient resource for large-scale var gene analysis. It can classify var genes from 20 genomes in under one second.

bioinformatics↗

bioRxiv2025

The dynamic transcriptome of Plasmodium malariae in the mosquito

To survive and multiply in different host niches, malaria parasites require sets of proteins that are expressed during the life cycle in a timely manner. Plasmodium malariae is a human malaria parasite that is present in most malaria-endemic regions. However, this species remains enigmatic because it is very challenging to culture, its low parasitaemia in human infections, and frequent co-infection with other malarias. We investigate the transcriptome of P. malariae during transmission in the vector by analyzing RNA-seq data from midgut and salivary gland parasite stages obtained in two experimental mosquito infections with field P. malariae isolates. Our analysis resulted in 3,699 expressed genes, of which 263 are developmentally regulated and 1,338 are P. malariae specific, including genes with unknown functions and without orthologs in other Plasmodium. We detected unique expression patterns of the ApiAP2 family of transcription factors, many of which appear to function as master regulators during sporogony. We found expressed several members of multigene families, like PIR, PHIST, fam-l, and described new families potentially showing transcriptional heterogeneity. Our findings point to the uniqueness of the P. malariae transcriptome, probably related to different transmission traits in the vector, and the lower pathogenicity and virulence in the human.

microbiology↗

bioRxiv2025

Seasonal transcriptional profiling of malaria parasites using a new single-cell atlas of a Malian Plasmodium falciparum isolate

Asymptomatic persistence is crucial for the survival of malaria parasites in seasonal transmission settings, where transmission halts during dry months, while mosquitos are scarce and new infections rare. How Plasmodium falciparum avoids causing host symptoms or being cleared is not fully understood. Parasites that persist through the dry season circulate longer within the [~]48h replication cycle compared to parasites isolated from clinical cases, where only early stages circulate. The mechanisms promoting increased circulation and avirulent infections bridging two wet seasons remain unclear. We generated a single-cell RNA-seq reference atlas with P. falciparum erythrocytic stages of a recently adapted isolate, and with it defined the developmental stage of over 25000 parasites from six asymptomatic children at the dry-wet season transition, and nine children with clinical malaria in the wet season. Our data confirms that older parasites circulate in the dry season, and reveals transcriptional modulation of exported proteins that affect cytoadhesion of infected erythrocytes, which we validated by immuno-fluorescence microscopy.

molecular biology↗

bioRxiv2025

RNAcare: Integrating Clinical Data with Transcriptomic Evidence using Rheumatoid Arthritis as a Case Study

BackgroundGene expression analysis is a crucial tool for uncovering the biological mechanisms that underlie differences between patient subgroups, offering insights that can inform clinical decisions. However, despite its potential, gene expression analysis remains challenging for clinicians due to the specialised skills required to access, integrate, and analyse large datasets. Existing tools primarily focus on RNA-Seq data analysis, providing user-friendly interfaces but often falling short in several critical areas: they typically do not integrate clinical data, lack support for patient-specific analyses, and offer limited flexibility in exploring relationships between gene expression and clinical outcomes in disease cohorts. Users, including clinicians with a general knowledge of transcriptomics, however, who may have limited programming experience, are increasingly seeking tools that go beyond traditional analysis. To overcome these issues, computational tools must incorporate advanced techniques, such as machine learning, to better understand how gene expression correlates with patient symptoms of interest. ResultsOur RNAcare platform, addresses these limitations by offering an interactive and reproducible solution specifically designed for analysing bulk RNA-Seq data from patient samples in a clinical context. This enables researchers to directly integrate gene expression data with clinical features, perform exploratory data analysis, and identify patterns among patients with similar diseases. By enabling users to integrate transcriptomic and clinical data, and customise the target label, the platform facilitates the analysis of the relationships between gene expression and clinical symptoms, like pain and fatigue. This allows users to generate hypotheses and illustrative visualisations/reports to support their research. As proof of concept, we use RNAcare to link inflammation-related genes to pain and fatigue in rheumatoid arthritis (RA) and detect signatures in the drug response group, confirming previous findings and generating new hypotheses. ConclusionWe present a novel computational platform allowing the interpretation of clinical and transcriptomics data in real-time. The platform can be used for data generated by the user, such as the patient data presented here or using published datasets. The platform is available at https://rna-care.mvls.gla.ac.uk/, with its source code at https://github.com/sii-scRNA-Seq/RNAcare/.

bioinformatics↗

bioRxiv2024

The Trypanosoma cruzi cell atlas; a single-cell resource for understanding parasite population heterogeneity and differentiation.

Trypanosoma cruzi, the causative agent of Chagas disease, exhibits a complex life cycle with multiple hosts, stages and differentiation steps. We present a complete cell atlas for the T. cruzi life cycle, based on single cell transcriptomes for over 31,000 cells and population-based transcriptomics. The atlas reveals many life cycle associated genes and can be utilised to accurately annotate life cycle stages. It provides detailed insights into cell heterogeneity, including cell-specific repertoires of surface antigens in trypomastigotes, with key implications for immune responses. Enabled by single-cell resolution, we define the transcriptomic changes that occur across the epimastigote to metacyclic trypomastigote differentiation axis. Furthermore, we provide comprehensive UTR annotation, identifying previously unannotated transcripts as well as revealing alternative poly-adenylation and an unanticipated complexity of reverse strand and antisense transcripts. This T. cruzi atlas provides a comprehensive resource and unlocks a range of new avenues for research on this important human pathogen.

microbiology↗

bioRxiv2024

PROS1 released by human lung basal cells upon SARS-CoV-2 infection facilitates epithelial cell repair and limits inflammation.

Factors governing the coagulopathy and pneumonitis associated with severe viral infections remain unresolved. We previously found that the expression of protein S (PROS1) is increased in lung epithelium of patients with mild COVID-19 as compared to severe COVID-19. We hypothesised that PROS1 may exert a local effect that protects the upper airway against severe inflammation by modulating epithelial and myeloid cell responses. To test this, in vitro air-interface cultures, seeded from primary healthy human lung epithelial cells, were infected with different SARS-CoV-2 clades. This model, validated by single-cell RNAseq analysis, recapitulated the dynamic cell-profile and pathogenic changes of COVID-19. We showed that PROS1 was located in the basal cells of healthy pseudostratified epithelium. During SARS-Cov-2 infection, PROS1 was released by basal cells, which was partially mediated by interferon. Transcriptome analysis showed that SARS-CoV-2 infection induced proinflammatory phenotypes (CXCL10/11high, PTGS2posF3high, S100A8/A9high) of basal and transitional cells. PROS1 strongly downregulated these cells and transformed the proinflammatory CXCL10/11high basal cells into the regenerative S100A2posKRThigh basal cell phenotype. In addition, SARS-CoV-2 infection elevated M-CSF secretion from epithelium, which induced MERTK, a receptor for PROS1, on monocytes added into 3D lung epithelial culture. We demonstrated that SARS-CoV-2 drives monocyte phenotypes expressing coagulation (F13A1) and complement (C1O) genes. PROS1 significantly downregulated these phenotypes and induced higher expression of MHC class II. Overall, this study demonstrated that the epithelium-derived PROS1 during SARS-CoV-2 infection inhibits the proinflammatory epithelial phenotypes, favours basal cell regeneration, and inhibits myeloid inflammation while enhancing antigen presentation. These findings highlight the importance of basal epithelial cells and PROS1 protection from viral infection induced severe lung pathology. O_FIG O_LINKSMALLFIG WIDTH=193 HEIGHT=200 SRC="FIGDIR/small/612489v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@324deaorg.highwire.dtl.DTLVardef@994f30org.highwire.dtl.DTLVardef@11de2e0org.highwire.dtl.DTLVardef@11a0fe2_HPS_FORMAT_FIGEXP M_FIG 1) SARS-CoV2 infection of the epithelium results in release of IFN. 2) IFN secretion has an autocrine effect on epithelial cells 3) Infection and IFN cause release of PROS1 from the basal cells, as well as M-CSF from the epithelium 4) PROS1 acts on basal cells which express MERTK, a PROS1 receptor 5) PROS1 downregulated the proinflammatory phenotypes expanded by viral infection, while upregulating KRThigh basal cells with repair phenotypes 6) The secreted M-CSF drives MERTK expression on monocytes in cocultures with epithelium. 7) PROS1 induces downregulation of monocyte clusters characteristic of viral infection that express pro-coagulation and complement genes, while upregulating clusters with higher MHC class II. 8) In summary, PROS1 mediates phenotypic switch of SARS-Cov2 induced pathogenic myeloid clusters with complement and coagulation phenotypes into phenotype with efficient antigen presentation, reduces proinflammatory activation of epithelium and induces epithelial barrier repair, resulting in mild COVID-19. C_FIG

cell biology↗

bioRxiv2024

paraCell: A novel software tool for the interactive analysis and visualization of standard and dual host-parasite single cell RNA-Seq data

Advances in sequencing technology have led to a dramatic increase in the number of single-cell transcriptomic datasets available. In the field of parasitology these datasets typically describe the gene expression patterns of a given parasite species under specific experimental conditions, in specific hosts or tissues, or at different life-cycle stages. However, while this wealth of available data represents a significant resource for further research, the analysis of these datasets often requires significant computational skills, preventing a considerable proportion of the parasitology community from meaningfully incorporating existing single-cell data into their work. Here, we present paraCell, a novel software tool that automates the advanced analysis of published single-cell data without requiring any programming ability. On our free web-server, we demonstrated how to visualise data, re-analyse published Plasmodium and Trypanosoma datasets, and present novel Toxoplasma-mouse and Theileira-cow atlases to study the impact of IFN-{gamma} and host genetic susceptibility.

bioinformatics↗

bioRxiv2024

Distinct tissue-niche localization and function of synovial tissue myeloid DC subsets in health, and in active and remission Rheumatoid Arthritis

Current rheumatoid arthritis (RA) treatments do not restore immune tolerance. Investigating dendritic cell (DC) populations in human synovial tissue (ST) may reveal pathways to re-instate tolerance in RA. With single-cell and spatial-transcriptomics of synovial tissue biopsies, validated by micro co-culture systems, we identified condition and niche-specific myeloid DC clusters with distinct differentiation trajectories and functions. Healthy synovium contains a unique tolerogenic AXLpos DC2 cluster in the superficial sublining layer. In active RA, a macrophage-rich lining-layer niche becomes populated with inflammatory DC3 clusters that specifically activate memory CCL5pos TEM and CCL5posCXCL13pos TPH, promoting synovitis. In the sublining lymphoid niche, CCR7pos DC2 mReg specifically interact with naive-T-cells, potentially driving the local expansion of new effector T-cells. Sustained remission sees the resolution of these niches but lacks the recovery of tolerogenic AXLpos DC2, indicating latent potential for disease flare. A human RA disease-flare model showed that the activation of blood predecessor of ST-DC3 clusters precedes the onset of inflammation in joints. Therapeutic strategies targeting pathogenic ST-DC3 clusters, or reinstating tolerogenic AXLpos DC2, may restore immune homeostasis in RA. In briefDeconstruction of human RA synovium, using single-cell spatial transcriptomics and micro-culture systems, reveals distinct neighbourhoods within the synovial architecture across health, and RA patients with active disease or sustained remission. Discrete niches are identified that contain distinct myeloid DC clusters that differ in frequency, differentiation trajectories, and effector functions. HighlightsO_LIHuman RA synovium exhibits condition and niche specific myeloid DC clusters that vary in their tissue differentiation trajectories and functions. C_LIO_LIST-CD14pos DC3 (iDC3) support inflammatory CCL5pos TEM and CCL5pos TPH cell activation in the hyperplastic lining layer. C_LIO_LIST-CCR7pos DC2 (mReg), driven by MIR155, interact with naive-T-cells in sublining lymphoid niches. C_LIO_LIA specific inflammatory signature of blood predecessors of ST-DC3s predict flare in RA. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/600758v1_ufig1.gif" ALT="Figure 1"> View larger version (74K): org.highwire.dtl.DTLVardef@27ab01org.highwire.dtl.DTLVardef@4be238org.highwire.dtl.DTLVardef@1f10734org.highwire.dtl.DTLVardef@1102056_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2024

Annotation and visualisation of parasite, fungi and arthropod genomes with Companion

Although sequencing genomes has become increasingly popular, there is still a bottleneck for the annotation of the resulting assemblies. Structural and functional annotation is still challenging as it includes finding the correct gene sequences, annotating other elements such as RNA and being able to submit those data to databases to share it with the community. We developed the Companion web server to allow non-experts to annotate their genome using a reference-based method, enabling them to analyse their results before submitting to public databases. In this update paper, we describe how we included novel methods for gene finding and made the server more efficient to annotate genomes of up to 1 GB in size. The reference set was increased to genomes from the fungi and arthropod kingdoms. We show that Companion outperforms existing comparable tools. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=145 SRC="FIGDIR/small/580948v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@113b4a8org.highwire.dtl.DTLVardef@b98a5aorg.highwire.dtl.DTLVardef@12a2d8corg.highwire.dtl.DTLVardef@144b21_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

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↗

bioRxiv2023

A novel computational pipeline for var gene expression augments the discovery of changes in the Plasmodium falciparum transcriptome during transition from in vivo to short-term in vitro culture

The pathogenesis of severe Plasmodium falciparum malaria involves cytoadhesive microvascular sequestration of infected erythrocytes, mediated by P. falciparum erythrocyte membrane protein 1 (PfEMP1). PfEMP1 variants are encoded by the highly polymorphic family of var genes, the sequences of which are largely unknown in clinical samples. Previously, we published new approaches for var gene profiling and classification of predicted binding phenotypes in clinical P. falciparum isolates (Wichers et al., 2021), which represented a major technical advance. Building on this, we report here a novel method for var gene assembly and multidimensional quantification from RNA-sequencing that outperforms the earlier approach of Wichers et al., 2021 on both laboratory and clinical isolates across a combination of metrics. Importantly, the tool can interrogate the var transcriptome in context with the rest of the transcriptome and can be applied to enhance our understanding of the role of var genes in malaria pathogenesis. We applied this new method to investigate changes in var gene expression through early transition of parasite isolates to in vitro culture, using paired sets of ex vivo samples from our previous study, cultured for up to three generations. In parallel, changes in non-polymorphic core gene expression were investigated. Modest but unpredictable var gene switching and convergence towards var2csa were observed in culture, along with differential expression of 19% of the core transcriptome between paired ex vivo and generation 1 samples. Our results cast doubt on the validity of the common practice of using short-term cultured parasites to make inferences about in vivo phenotype and behaviour.

bioinformatics↗

bioRxiv2023

A comprehensive analysis of rheumatoid arthritis B cells reveals the importance of CD11c+ve double-negative-2 B cells as the major synovial plasma cell precursor.

B cells are key pathogenic drivers of chronic inflammation in rheumatoid arthritis (RA). There is limited understanding of the relationship between synovial B cell subsets and pathogenic antibody secreting cells (ASCs). This knowledge is crucial for the development of targeted therapies. Here, we combine flow cytometry of circulating B cells with single-cell RNA and paired repertoire sequencing of over 27,000 synovial B cells from patients with established RA. Twelve B cell clusters were identified including previously recognised subsets, and a novel cluster that strongly expressed heat shock proteins. All lineages identified by trajectory analysis contribute to the DN2 B cell population, which is the major precursor to synovial ASCs. This was further supported by B cell receptor (BCR) lineage analysis, which revealed clonal relationships between DN2 cells and ASCs. This study advances our understanding of B cells in RA and reveals the origin of pathogenic ASCs in the RA synovium.

immunology↗

bioRxiv2023

Profiling the bloodstream form and procyclic form Trypanosoma brucei cell cycle using single cell transcriptomics

African trypanosomes proliferate as bloodstream forms and procyclic forms in the mammal and tsetse fly midgut, respectively. This allows them to colonise the host environment upon infection and ensure life cycle progression. Yet, understanding of the mechanisms that regulate and drive the cell replication cycle of these forms is limited. Using single cell transcriptomics on unsynchronised cell populations, we have obtained high resolution cell cycle regulated transcriptomes of both procyclic and slender bloodstream form Trypanosoma brucei without prior cell sorting or synchronisation. Additionally, we describe an efficient freeze-thawing protocol that allows single cell transcriptomic analysis of cryopreserved T. brucei. Computational reconstruction of the cell cycle using periodic pseudotime inference allowed the dynamic expression patterns of cycling genes to be profiled for both life cycle forms. Comparative analyses identify a core cycling transcriptome highly conserved between forms, as well as several genes where transcript levels dynamics are form-specific. Comparing transcript expression patterns with protein abundance revealed that the majority of genes with periodic cycling transcript and protein levels exhibit a relative delay between peak transcript and protein expression. This work reveals novel detail of the cell cycle regulated transcriptomes of both forms, which are available for further interrogation via an interactive webtool.

microbiology↗