MoleculeExperiment enables consistent infrastructure for molecule-resolved spatial omics data in bioconductor.

MOTIVATION: Imaging-based spatial transcriptomics (ST) technologies have achieved subcellular resolution, enabling detection of individual molecules in their native tissue context. Data associated with these technologies promise unprecedented opportunity toward understanding cellular and subcellular biology. However, in R/Bioconductor, there is a scarcity of existing computational infrastructure to represent such data, and particularly to summarize and transform it for existing widely adopted computational tools in single-cell transcriptomics analysis, including SingleCellExperiment and SpatialExperiment (SPE) classes. With the emergence of several commercial offerings of imaging-based ST, there is a pressing need to develop consistent data structure standards for these technologies at the individual molecule-level. RESULTS: To this end, we have developed MoleculeExperiment, an R/Bioconductor package, which (i) stores molecule and cell segmentation boundary information at the molecule-level, (ii) standardizes this molecule-level information across different imaging-based ST technologies, including 10× Genomics' Xenium, and (iii) streamlines transition from a MoleculeExperiment object to a SpatialExperiment object. Overall, MoleculeExperiment is generally applicable as a data infrastructure class for consistent analysis of molecule-resolved spatial omics data. AVAILABILITY AND IMPLEMENTATION: The MoleculeExperiment package is publicly available on Bioconductor at https://bioconductor.org/packages/release/bioc/html/MoleculeExperiment.html. Source code is available on Github at: https://github.com/SydneyBioX/MoleculeExperiment. The vignette for MoleculeExperiment can be found at https://bioconductor.org/packages/release/bioc/html/MoleculeExperiment.html.

Tolerogenic dendritic cells protect against acute kidney injury.

Ischemia reperfusion injury is a common precipitant of acute kidney injury that occurs following disrupted perfusion to the kidney. This includes blood loss and hemodynamic shock, as well as during retrieval for deceased donor kidney transplantation. Acute kidney injury is associated with adverse long-term clinical outcomes and requires effective interventions that can modify the disease process. Immunomodulatory cell therapies such as tolerogenic dendritic cells remain a promising tool, and here we tested the hypothesis that adoptively transferred tolerogenic dendritic cells can limit kidney injury. The phenotypic and genomic signatures of bone marrow-derived syngeneic or allogeneic, Vitamin-D3/IL-10-conditioned tolerogenic dendritic cells were assessed. These cells were characterized by high PD-L1:CD86, elevated IL-10, restricted IL-12p70 secretion and a suppressed transcriptomic inflammatory profile. When infused systemically, these cells successfully abrogated kidney injury without modifying infiltrating inflammatory cell populations. They also provided protection against ischemia reperfusion injury in mice pre-treated with liposomal clodronate, suggesting the process was regulated by live, rather than reprocessed cells. Co-culture experiments and spatial transcriptomic analysis confirmed reduced kidney tubular epithelial cell injury. Thus, our data provide strong evidence that peri-operatively administered tolerogenic dendritic cells have the ability to protect against acute kidney injury and warrants further exploration as a therapeutic option. This technology may provide a clinical advantage for bench-to-bedside translation to affect patient outcomes.

Investigating higher-order interactions in single-cell data with scHOT.

Single-cell genomics has transformed our ability to examine cell fate choice. Examining cells along a computationally ordered 'pseudotime' offers the potential to unpick subtle changes in variability and covariation among key genes. We describe an approach, scHOT-single-cell higher-order testing-which provides a flexible and statistically robust framework for identifying changes in higher-order interactions among genes. scHOT can be applied for cells along a continuous trajectory or across space and accommodates various higher-order measurements including variability or correlation. We demonstrate the use of scHOT by studying coordinated changes in higher-order interactions during embryonic development of the mouse liver. Additionally, scHOT identifies subtle changes in gene-gene correlations across space using spatially resolved transcriptomics data from the mouse olfactory bulb. scHOT meaningfully adds to first-order differential expression testing and provides a framework for interrogating higher-order interactions using single-cell data.

scFeatures: multi-view representations of single-cell and spatial data for disease outcome prediction.

MOTIVATION: With the recent surge of large-cohort scale single cell research, it is of critical importance that analytical methods can fully utilize the comprehensive characterization of cellular systems that single cell technologies produce to provide insights into samples from individuals. Currently, there is little consensus on the best ways to compress information from the complex data structures of these technologies to summary statistics that represent each sample (e.g. individuals). RESULTS: Here, we present scFeatures, an approach that creates interpretable cellular and molecular representations of single-cell and spatial data at the sample level. We demonstrate that summarizing a broad collection of features at the sample level is both important for understanding underlying disease mechanisms in different experimental studies and for accurately classifying disease status of individuals. AVAILABILITY AND IMPLEMENTATION: scFeatures is publicly available as an R package at https://github.com/SydneyBioX/scFeatures. All data used in this study are publicly available with accession ID reported in the Section 2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

spicyR: spatial analysis of in situ cytometry data in R.

MOTIVATION: High parameter histological techniques have allowed for the identification of a variety of distinct cell types within an image, providing a comprehensive overview of the tissue environment. This allows the complex cellular architecture and environment of diseased tissue to be explored. While spatial analysis techniques have revealed how cell-cell interactions are important within the disease pathology, there remains a gap in exploring changes in these interactions within the disease process. Specifically, there are currently few established methods for performing inference on cell-type co-localization changes across images, hindering an understanding of how cellular environments change with a disease pathology. RESULTS: We have developed the spicyR R package to perform inference on changes in the spatial co-localization of types across groups of images. Application to simulated data demonstrates a high sensitivity and specificity. We the utility of spicyR by applying it to a type 1 diabetes imaging mass cytometry dataset, revealing changes in cellular associations that were relevant to the disease progression. Ultimately, spicyR allows changes in cellular environments to be explored under different pathologies or disease states. AVAILABILITY AND IMPLEMENTATION: R package is freely available at http://bioconductor.org/packages/release/bioc/html/spicyR.html and shiny app implementation at http://shiny.maths.usyd.edu.au/spicyR/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cross-platform comparison of immune signatures in immunotherapy-treated patients with advanced melanoma using a rank-based scoring approach.

BACKGROUND: Gene expression profiling is increasingly being utilised as a diagnostic, prognostic and predictive tool for managing cancer patients. Single-sample scoring approach has been developed to alleviate instability of signature scores due to variations from sample composition. However, it is a challenge to achieve comparable signature scores across different expressional platforms. METHODS: The pre-treatment biopsies from a total of 158 patients, who have received single-agent anti-PD-1 (n = 84) or anti-PD-1 + anti-CTLA-4 therapy (n = 74), were performed using NanoString PanCancer IO360 Panel. Multiple immune-related signature scores were measured from a single-sample rank-based scoring approach, singscore. We assessed the reproducibility and the performance in reporting immune profile of singscore based on NanoString assay in advance melanoma. To conduct cross-platform analyses, singscores between the immune profiles of NanoString assay and the previous orthogonal whole transcriptome sequencing (WTS) data were compared through linear regression and cross-platform prediction. RESULTS: singscore-derived signature scores reported significantly high scores in responders in multiple PD-1, MHC-1-, CD8 T-cell-, antigen presentation-, cytokine- and chemokine-related signatures. We found that singscore provided stable and reproducible signature scores among the repeats in different batches and cross-sample normalisations. The cross-platform comparisons confirmed that singscores derived via NanoString and WTS were comparable. When singscore of WTS generated by the overlapping genes to the NanoString gene set, the signatures generated highly correlated cross-platform scores (Spearman correlation interquartile range (IQR) [0.88, 0.92] and r2 IQR [0.77, 0.81]) and better prediction on cross-platform response (AUC = 86.3%). The model suggested that Tumour Inflammation Signature (TIS) and Personalised Immunotherapy Platform (PIP) PD-1 are informative signatures for predicting immunotherapy-response outcomes in advanced melanoma patients treated with anti-PD-1-based therapies. CONCLUSIONS: Overall, the outcome of this study confirms that singscore based on NanoString data is a feasible approach to produce reliable signature scores for determining patients' immune profiles and the potential clinical utility in biomarker implementation, as well as to conduct cross-platform comparisons, such as WTS.

Plasmacytoid dendritic cells have divergent effects on HIV infection of initial target cells and induce a pro-retention phenotype.

Although HIV infection inhibits interferon responses in its target cells in vitro, interferon signatures can be detected in vivo soon after sexual transmission, mainly attributed to plasmacytoid dendritic cells (pDCs). In this study, we examined the physiological contributions of pDCs to early HIV acquisition using coculture models of pDCs with myeloid DCs, macrophages and the resting central, transitional and effector memory CD4 T cell subsets. pDCs impacted infection in a cell-specific manner. In myeloid cells, HIV infection was decreased via antiviral effects, cell maturation and downregulation of CCR5 expression. In contrast, in resting memory CD4 T cells, pDCs induced a subset-specific increase in intracellular HIV p24 protein expression without any activation or increase in CCR5 expression, as measured by flow cytometry. This increase was due to reactivation rather than enhanced viral spread, as blocking HIV entry via CCR5 did not alter the increased intracellular p24 expression. Furthermore, the load and proportion of cells expressing HIV DNA were restricted in the presence of pDCs while reverse transcriptase and p24 ELISA assays showed no increase in particle associated reverse transcriptase or extracellular p24 production. In addition, pDCs also markedly induced the expression of CD69 on infected CD4 T cells and other markers of CD4 T cell tissue retention. These phenotypic changes showed marked parallels with resident memory CD4 T cells isolated from anogenital tissue using enzymatic digestion. Production of IFNα by pDCs was the main driving factor for all these results. Thus, pDCs may reduce HIV spread during initial mucosal acquisition by inhibiting replication in myeloid cells while reactivating latent virus in resting memory CD4 T cells and retaining them for immune clearance.

Herpes Simplex Virus type 1 infects Langerhans cells and the novel epidermal dendritic cell, Epi-cDC2s, via different entry pathways.

Skin mononuclear phagocytes (MNPs) provide the first interactions of invading viruses with the immune system. In addition to Langerhans cells (LCs), we recently described a second epidermal MNP population, Epi-cDC2s, in human anogenital epidermis that is closely related to dermal conventional dendritic cells type 2 (cDC2) and can be preferentially infected by HIV. Here we show that in epidermal explants topically infected with herpes simplex virus (HSV-1), both LCs and Epi-cDC2s interact with HSV-1 particles and infected keratinocytes. Isolated Epi-cDC2s support higher levels of infection than LCs in vitro, inhibited by acyclovir, but both MNP subtypes express similar levels of the HSV entry receptors nectin-1 and HVEM, and show similar levels of initial uptake. Using inhibitors of endosomal acidification, actin and cholesterol, we found that HSV-1 utilises different entry pathways in each cell type. HSV-1 predominantly infects LCs, and monocyte-derived MNPs, via a pH-dependent pathway. In contrast, Epi-cDC2s are mainly infected via a pH-independent pathway which may contribute to the enhanced infection of Epi-cDC2s. Both cells underwent apoptosis suggesting that Epi-cDC2s may follow the same dermal migration and uptake by dermal MNPs that we have previously shown for LCs. Thus, we hypothesize that the uptake of HSV and infection of Epi-cDC2s will stimulate immune responses via a different pathway to LCs, which in future may help guide HSV vaccine development and adjuvant targeting.

Spatial analysis for highly multiplexed imaging data to identify tissue microenvironments.

Highly multiplexed in situ imaging cytometry assays have made it possible to study the spatial organization of numerous cell types simultaneously. We have addressed the challenge of quantifying complex multi-cellular relationships by proposing a statistical method which clusters local indicators of spatial association. Our approach successfully identifies distinct tissue architectures in datasets generated from three state-of-the-art high-parameter assays demonstrating its value in summarizing the information-rich data generated from these technologies.

Non-invasive diagnosis of early cutaneous squamous cell carcinoma.

Early cutaneous squamous cell carcinoma (cSCC) can be challenging to diagnose using clinical criteria as it could present similar to actinic keratosis (AK) or Bowen's disease (BD), precursors of cSCC. Currently, histopathological assessment of an invasive biopsy is the gold standard for diagnosis. A non-invasive diagnostic approach would reduce patient and health system burden. Therefore, this study used non-invasive sampling by tape-stripping coupled with data-independent acquisition mass spectrometry (DIA-MS) proteomics to profile the proteome of histopathologically diagnosed AK, BD and cSCC, as well as matched normal samples. Proteomic data were analysed to identify proteins and biological functions that are significantly different between lesions. Additionally, a support vector machine (SVM) machine learning algorithm was used to assess the usefulness of proteomic data for the early diagnosis of cSCC. A total of 696 proteins were identified across the samples studied. A machine learning model constructed using the proteomic data classified premalignant (AK + BD) and malignant (cSCC) lesions at 77.5% accuracy. Differential abundance analysis identified 144 and 21 protein groups that were significantly changed in the cSCC, and BD samples compared to the normal skin, respectively (adj. p < 0.05). Changes in pivotal carcinogenic pathways such as LXR/RXR activation, production of reactive oxygen species, and Hippo signalling were observed that may explain the progression of cSCC from premalignant lesions. In summary, this study demonstrates that DIA-MS analysis of tape-stripped samples can identify non-invasive protein biomarkers with the potential to be developed into a complementary diagnostic tool for early cSCC.

Proteomic profiling of cutaneous melanoma explains the aggressiveness of distant organ metastasis.

Despite recent developments in managing metastatic melanomas, patients' overall survival remains low. Therefore, the current study aims to understand better the proteome-wide changes associated with melanoma metastasis that will assist with identifying targeted therapies. The latest development in mass spectrometry-based proteomics, together with extensive bioinformatics analysis, was used to investigate the molecular changes in 60 formalin-fixed and paraffin-embedded samples of primary and lymph nodes (LN) and distant organ metastatic melanomas. A total of 4631 proteins were identified, of which 72 and 453 were significantly changed between the LN and distant organ metastatic melanomas compared to the primary lesions (adj. p-value <0.05). An increase in proteins such as SLC9A3R1, CD20 and GRB2 and a decrease in CST6, SERPINB5 and ARG1 were associated with regional LN metastasis. By contrast, increased metastatic activities in distant organ metastatic melanomas were related to higher levels of CEACAM1, MC1R, AKT1 and MMP3-9 and decreased levels of CDKN2A, SDC1 and SDC4 proteins. Furthermore, machine learning analysis classified the lesions with up to 92% accuracy based on their metastatic status. The findings from this study provide up to date proteome-level information about the progression of melanomas to regional LN and distant organs, leading to the identification of protein signatures with potential for clinical translation.

Using Transcriptomic Hidden Variables to Infer Context-Specific Genotype Effects in the Brain.

Deciphering the environmental contexts at which genetic effects are most prominent is central for making full use of GWAS results in follow-up experiment design and treatment development. However, measuring a large number of environmental factors at high granularity might not always be feasible. Instead, here we propose extracting cellular embedding of environmental factors from gene expression data by using latent variable (LV) analysis and taking these LVs as environmental proxies in detecting gene-by-environment (GxE) interaction effects on gene expression, i.e., GxE expression quantitative trait loci (eQTLs). Applying this approach to two largest brain eQTL datasets (n = 1,100), we show that LVs and GxE eQTLs in one dataset replicate well in the other dataset. Combining the two samples via meta-analysis, 895 GxE eQTLs are identified. On average, GxE effect explains an additional ∼4% variation in expression of each gene that displays a GxE effect. Ten of these 52 genes are associated with cell-type-specific eQTLs, and the remaining genes are multi-functional. Furthermore, after substituting LVs with expression of transcription factors (TF), we found 91 TF-specific eQTLs, which demonstrates an important use of our brain GxE eQTLs.

AFid: a tool for automated identification and exclusion of autofluorescent objects from microscopy images.

MOTIVATION: Autofluorescence is a long-standing problem that has hindered the analysis of images of tissues acquired by fluorescence microscopy. Current approaches to mitigate autofluorescence in tissue are lab-based and involve either chemical treatment of sections or specialized instrumentation and software to 'unmix' autofluorescent signals. Importantly, these approaches are pre-emptive and there are currently no methods to deal with autofluorescence in acquired fluorescence microscopy images. RESULTS: To address this, we developed Autofluorescence Identifier (AFid). AFid identifies autofluorescent pixels as discrete objects in multi-channel images post-acquisition. These objects can then be tagged for exclusion from downstream analysis. We validated AFid using images of FFPE human colorectal tissue stained for common immune markers. Further, we demonstrate its utility for image analysis where its implementation allows the accurate measurement of HIV-Dendritic cell interactions in a colorectal explant model of HIV transmission. Therefore, AFid represents a major leap forward in the extraction of useful data from images plagued by autofluorescence by offering an approach that is easily incorporated into existing workflows and that can be used with various samples, staining panels and image acquisition methods. We have implemented AFid in ImageJ, Matlab and R to accommodate the diverse image analysis community. AVAILABILITY AND IMPLEMENTATION: AFid software is available at https://ellispatrick.github.io/AFid. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The acute phase protein lactoferrin is a key feature of Alzheimer's disease and predictor of Aß burden through induction of APP amyloidogenic processing.

Amyloidogenic processing of the amyloid precursor protein (APP) forms the amyloid-ß peptide (Aß) component of pathognomonic extracellular plaques of AD. Additional early cortical changes in AD include neuroinflammation and elevated iron levels. Activation of the innate immune system in the brain is a neuroprotective response to infection; however, persistent neuroinflammation is linked to AD neuropathology by uncertain mechanisms. Non-parametric machine learning analysis on transcriptomic data from a large neuropathologically characterised patient cohort revealed the acute phase protein lactoferrin (Lf) as the key predictor of amyloid pathology. In vitro studies showed that an interaction between APP and the iron-bound form of Lf secreted from activated microglia diverted neuronal APP endocytosis from the canonical clathrin-dependent pathway to one requiring ADP ribosylation factor 6 trafficking. By rerouting APP recycling to the Rab11-positive compartment for amyloidogenic processing, Lf dramatically increased neuronal Aß production. Lf emerges as a novel pharmacological target for AD that not only modulates APP processing but provides a link between Aß production, neuroinflammation and iron dysregulation.

Research trends in Australia and New Zealand Nephrology: basic science and gender equity in peril.

Basic science research remains fundamental to progress in clinical care, understanding of disease pathophysiology and underpinning the evolution of personalised medicine. Exposure to research is pivotal to educating students, but a declining profile of basic science research has the potential to erode research capacity further. The capacity for women to engage in research and remain in academia long term is continually challenged by negative gender-based experiences and institutional barriers. The authors explored themes and authorship of abstracts presented at Australia and New Zealand--based nephrology conferences, as a surrogate marker of trends in research activity and gender engagement. Basic science research and female senior authorship declined during the study period, which has serious implications for the future of nephrology.

Transcriptional network dynamics during the progression of pluripotency revealed by integrative statistical learning.

The developmental potential of cells, termed pluripotency, is highly dynamic and progresses through a continuum of naive, formative and primed states. Pluripotency progression of mouse embryonic stem cells (ESCs) from naive to formative and primed state is governed by transcription factors (TFs) and their target genes. Genomic techniques have uncovered a multitude of TF binding sites in ESCs, yet a major challenge lies in identifying target genes from functional binding sites and reconstructing dynamic transcriptional networks underlying pluripotency progression. Here, we integrated time-resolved 'trans-omic' datasets together with TF binding profiles and chromatin conformation data to identify target genes of a panel of TFs. Our analyses revealed that naive TF target genes are more likely to be TFs themselves than those of formative TFs, suggesting denser hierarchies among naive TFs. We also discovered that formative TF target genes are marked by permissive epigenomic signatures in the naive state, indicating that they are poised for expression prior to the initiation of pluripotency transition to the formative state. Finally, our reconstructed transcriptional networks pinpointed the precise timing from naive to formative pluripotency progression and enabled the spatiotemporal mapping of differentiating ESCs to their in vivo counterparts in developing embryos.

A Wnt-mediated phenotype switch along the epithelial-mesenchymal axis defines resistance and invasion downstream of ionising radiation in oral squamous cell carcinoma.

BACKGROUND: Dynamic transitions of tumour cells along the epithelial-mesenchymal axis are important in tumorigenesis, metastasis and therapy resistance. METHODS: In this study, we have used cell lines, 3D spheroids and tumour samples in a variety of cell biological and transcriptome analyses to highlight the cellular and molecular dynamics of OSCC response to ionising radiation. RESULTS: Our study demonstrates a prominent hybrid epithelial-mesenchymal state in oral squamous cell carcinoma cells and tumour samples. We have further identified a key role for levels of E-cadherin in stratifying the hybrid cells to compartments with varying levels of radiation response and radiation-induced epithelial-mesenchymal transition. The response to radiation further entailed the generation of a new cell population with low expression levels of E-cadherin, and positive for Vimentin (ECADLow/Neg-VIMPos), a phenotypic signature that showed an enhanced capacity for radiation resistance and invasion. At the molecular level, transcriptome analysis of spheroids in response to radiation showed an initial burst of misregulation within the first 30 min that further declined, although still highlighting key alterations in gene signatures. Among others, pathway analysis showed an over-representation for the Wnt signalling pathway that was further confirmed to be functionally involved in the generation of ECADLow/Neg-VIMPos population, acting upstream of radiation resistance and tumour cell invasion. CONCLUSION: This study highlights the functional significance and complexity of tumour cell remodelling in response to ionising radiation with links to resistance and invasive capacity. An area of less focus in conventional radiotherapy, with the potential to improve treatment outcomes and relapse-free survival.

Deconvolving the contributions of cell-type heterogeneity on cortical gene expression.

Complexity of cell-type composition has created much skepticism surrounding the interpretation of bulk tissue transcriptomic studies. Recent studies have shown that deconvolution algorithms can be applied to computationally estimate cell-type proportions from gene expression data of bulk blood samples, but their performance when applied to brain tissue is unclear. Here, we have generated an immunohistochemistry (IHC) dataset for five major cell-types from brain tissue of 70 individuals, who also have bulk cortical gene expression data. With the IHC data as the benchmark, this resource enables quantitative assessment of deconvolution algorithms for brain tissue. We apply existing deconvolution algorithms to brain tissue by using marker sets derived from human brain single cell and cell-sorted RNA-seq data. We show that these algorithms can indeed produce informative estimates of constituent cell-type proportions. In fact, neuronal subpopulations can also be estimated from bulk brain tissue samples. Further, we show that including the cell-type proportion estimates as confounding factors is important for reducing false associations between Alzheimer's disease phenotypes and gene expression. Lastly, we demonstrate that using more accurate marker sets can substantially improve statistical power in detecting cell-type specific expression quantitative trait loci (eQTLs).

DCARS: differential correlation across ranked samples.

MOTIVATION: Genes act as a system and not in isolation. Thus, it is important to consider coordinated changes of gene expression rather than single genes when investigating biological phenomena such as the aetiology of cancer. We have developed an approach for quantifying how changes in the association between pairs of genes may inform the outcome of interest called Differential Correlation across Ranked Samples (DCARS). Modelling gene correlation across a continuous sample ranking does not require the dichotomisation of samples into two distinct classes and can identify differences in gene correlation across early, mid or late stages of the outcome of interest. RESULTS: When we evaluated DCARS against the typical Fisher Z-transformation test for differential correlation, as well as a typical approach testing for interaction within a linear model, on real TCGA data, DCARS significantly ranked gene pairs containing known cancer genes more highly across several cancers. Similar results are found with our simulation study. DCARS was applied to 13 cancers datasets in TCGA, revealing several distinct relationships for which survival ranking was found to be associated with a change in correlation between genes. Furthermore, we demonstrated that DCARS can be used in conjunction with network analysis techniques to extract biological meaning from multi-layered and complex data. AVAILABILITY AND IMPLEMENTATION: DCARS R package and sample data are available at https://github.com/shazanfar/DCARS. Publicly available data from The Cancer Genome Atlas (TCGA) was used using the TCGABiolinks R package. Supplementary Files and DCARS R package is available at https://github.com/shazanfar/DCARS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Blood CRP levels are elevated in children and adolescents with functional neurological symptom disorder.

There is accumulating evidence that patients with functional neurological symptom disorder (FND) show activation of multiple components of the stress system-the hypothalamic-pituitary-adrenal axis, autonomic nervous system, and brain regions involved in arousal- and emotion-processing. This study aims to examine whether the immune-inflammatory component of the stress system is also activated. C-reactive protein (CRP) blood titre levels were measured in 79 children and adolescents with FND. CRP values ≥ 2 mg/L suggest low-grade inflammation. CRP values > 10 mg/L suggest a disease process. Sixty-six percent of subjects (n = 52) had CRP titres ≥ 2 mg/L. The upward shift in the distribution of CRP levels suggested low-grade inflammation (median CRP concentration was 4.60 mg/L, with 75th and 90th percentiles of 6.1 and 10.3 mg/L, respectively). Elevated CRP titres were not explained by sex, pubertal status, BMI, or medical factors. Confounder analyses suggested that history of maltreatment (χ2 = 2.802, df = 1, p = 0.094, φ = 0.190; ß = 2.823, p = 0.04) and a diagnosis of anxiety (χ2 = 2.731, df = 1, p = 0.098, φ = 0.187; ß = 4.520, p = 0.061) contributed to elevated CRP levels. Future research will need to identify the origins and locations of immune cell activation and the pathways and systems contributing to their activation and modulation. Because functional activity in neurons and glial cells-the brain's innate effector immune cells-is tightly coupled, our finding of elevated CRP titres suggests activation of the immune-inflammatory component of the brain's stress system. A more direct examination of inflammation-related molecules in the brain will help clarify the role of immune-inflammatory processes in FND.