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Quinn, J. F.

Publications and source records attributed to Quinn, J. F..

15 recordsLinked to original sources

CellConsensus: An agent-curated atlas for automatic cell typing

Assigning cell types to single-cell and spatial transcriptomic data remains inconsistent because marker gene knowledge is fragmented across thousands of individual studies. Here we present CellConsensus, a cell typing method built on a consensus corpus of marker genes aggregated from curated atlases (2,607 sources) and de novo mining of 1,174 papers. By reconciling overlapping and conflicting marker evidence into a consensus reference, CellConsensus assigns cell type labels that are more accurate and more reproducible than existing marker- and reference-based approaches, while remaining interpretable and applicable across tissues and platforms. CellConsensus is available as an open-source Python package (https://github.com/tansey-lab/cellconsensus), an interactive database (https://cellconsensus.org), and as an agentic MCP server for conversational querying.

bioinformatics↗

The TREM2 targeting small molecule Sob-AM2 improves cognition independent of Aβ plaque alteration in 5xFAD mice

BackgroundInflammation is an early event that substantially influences Alzheimers disease (AD) pathogenesis, making it a compelling target for therapeutic intervention. Sob-AM2 is a brain-penetrating thyromimetic drug capable of inducing the expression of microglial cell surface receptor TREM2, which mediates the switch from pro-inflammatory to a more restorative microglial state. ObjectiveEvaluate the effects of Sob-AM2 on cognition, AD pathology and microglial activity in the 5xFAD mouse model of amyloid-beta (A{beta}) accumulation. MethodsSeven-month-old 5xFAD mice and their wild-type littermates were administered Sob-AM2 subcutaneously three times per week for 12 weeks. In the last two weeks of treatment mice underwent behavioral tests to assess cognition and monitor for off-target mobility effects. At the end of treatment, brain tissue was harvested for gene and protein expression analyses. ResultsSob-AM2 treatment increased TREM2 expression in the brains of 5xFAD mice. This was accompanied by an improvement in both spatial and associative memory as well as an increase in the expression of synaptic genes synaptophysin and PSD-95. No significant changes were detected in A{beta} plaque burden or the expression of microglial activation marker Iba1 in Sob-AM2 treated animals, however, the expression of the phagocytic marker CD68 was significantly increased in the hippocampus, but not the cortex, in Sob-AM2 treated 5xFAD mice. ConclusionThese results suggest that the cognitive-enhancing effects of Sob-AM2 are not the result of reduced overall plaque burden. Future work is needed to further investigate the neuroprotective mechanism of Sob-AM2 and how it may be affecting microglial phenotypes.

neuroscience↗

Decoding Multicellular Communication Motifs from Spatial Transcriptomics with ALARMIST

Cellular organization is driven by recurrent, coordinated interactions between multiple cell types, each sending and receiving multiple signals. Existing computational methods for spatial profiling data consider only individual ligand-receptor interactions and fail to capture the higher-order interactions governing the tissue microenvironment. To address this gap, we developed ALARMIST (Assessment of Ligand And Receptor Motifs And Impacts in Spatial Transcriptomics), a probabilistic framework that infers interpretable multicellular communication patterns from spatial data. ALARMIST decomposes neighborhood-level signaling patterns into motifs: recurrent communication subnetworks involving multiple cell types and sets of enriched ligand-receptor interactions. For each cell, ALARMIST identifies its active motifs and estimates the downstream phenotypic effects of each motif on active cells. We applied alarmist to spatial datasets of lung adenocarcinoma (LUAD) and glioblastoma (GBM) to identify microenvironmental drivers of tumor progression. In paired LUAD and adenocarcinoma-in-situ (AIS) samples, ALARMIST identified an immune-active vascular motif at the tumor-normal boundary and implicated motif-active plasmacytoid dendritic cells as drivers of inflammation in early carcinogenesis. In matched low- and high-grade glioma samples, ALARMIST identified a hub-and-spoke motif centered on a malignant macrophage subpopulation, implicating a GRN-SORT1 signaling axis with a downstream impact gene set predictive of survival in low-grade glioma patients. Code for ALARMIST is available at https://github.com/tansey-lab/alarmist.

bioinformatics↗

Cycle-consistent deep generative modeling unifies cellular states across unpaired spatial and single-cell modalities

Current spatial and single-cell technologies capture complementary but incomplete views of cellular state, with transcriptomic, proteomic, and spatial information distributed across distinct platforms. Integration is challenged by unpaired measurements, mismatched feature spaces, and modality-specific biases. We present MultiTME, a multimodal framework that integrates heterogeneous spatial and single-cell data using a spatially-regularized, cycle-consistent deep generative model. By enforcing consistency of bidirectional mappings, MultiTME learns a shared latent representation that enables translation between modalities without requiring paired observations or shared features. Across benchmarks, MultiTME outperforms existing methods, produces accurate cross-modal cell typing, improves spatial transcriptomic panel completion, and transfers whole-transcriptome information to generate spatially resolved maps at cellular resolution. Applied to a multimodal colorectal cancer dataset, we demonstrate that MultiTME integration reveals a spatially coherent proliferative-invasive tumor axis not directly observable within single modalities. Across five multimodal spatial datasets, we show MultiTME can correct for platform-specific biases between Xenium and CosMx, thereby facilitating cross-dataset harmonization and enabling pan-cancer spatial studies. Code for MultiTME is available at https://github.com/tansey-lab/multitme.

bioinformatics↗

Harnessing Diacylglycerol-Terminated Cationic Oligomers for Next-Generation Antibacterial Therapeutics

Cationic polymers, which mimic the structure of antimicrobial peptides (AMPs), are increasingly recognized as promising antimicrobial materials. Here, we report the synthesis and evaluation of a new class of cationic lipid-terminated oligomers (CLOs), comprised of 2C18-hydrophobic lipid tails, and short oligomeric cationic chains synthesised via Cu(0)-mediated reversible-deactivation radical polymerization (RDRP). Two 2-vinyl-4,4-dimethyl-5-oxazolone (VDM) oligomers with degrees of polymerization (DP) of 20 or 50 were synthesized using the lipid functional initiator (R)-3-((2-bromo-2-methylpropanoyl) oxy)propane-1,2-diyl dioctadecanoate (2C18-Br). Post-polymerization modification of the pendant oxazolone moieties was carried out using reactive amines, including N-Boc-ethylenediamine (BEDA) and N,N-dimethylethylenediamine (DMEN). Subsequent deprotection of the BEDA groups and quaternization of DMEN groups enabled the synthesis of six functional CLOs exhibiting distinct cationic functionalities. Antimicrobial assays against a panel of WHO bacterial and fungal priority pathogens (methicillin-resistant Staphylococcus aureus [MRSA], Escherichia coli, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Candida albicans, and Cryptococcus neoformans) revealed that these CLOs exhibited potent and selective structure-dependent antibacterial activity, particularly against MRSA, with minimum inhibitory concentrations (MICs) in the clinically relevant range, below 4 {micro}g mL-1, comparable to antibiotics vancomycin and colistin. Among these, BEDA-functionalized CLOs demonstrated the strongest antimicrobial profile, which was significantly increased by increasing DP, as evidenced by a reduction in MIC values from 64 {micro}g mL-1 (for DP20) to [≤] 4 {micro}g mL-1 (for DP50) against A. baumannii. Biocompatibility assays against red blood cells and HEK293 cells indicated negligible toxicity, with haemolytic (HC50) and cytotoxic (CC50) values exceeding 512 {micro}g mL-1 across all CLOs. All CLOs displayed minimal activity against C. albicans (MIC [≥] 512 {micro}g mL-1). In contrast, activity against C. neoformans was influenced by both cationic functionality and DP, with DMEN-based CLOs exhibited superior antifungal activity at higher DP relative to their BEDA-based counterparts. Most CLOs displayed high selectivity (SI) toward MRSA (SI >128), while 2C18-O(BEDA)50 exhibited the broadest spectrum, showing potent antimicrobial activity and high selectivity against E. coli (MIC [≤] 4 {micro}g mL-1, SI [≥] 128), A. baumannii (MIC [≤] 4 {micro}g mL-1, SI [≥] 128), and MRSA (MIC [≤] 4 {micro}g mL-1, SI [≥] 128), along with moderate activity against P. aeruginosa (MIC = 32 {micro}g mL-1, SI > 16). Taken together, these findings elucidate the combined influence of end-group lipidation, cationic functionality, and polymer length in modulating antimicrobial activity, thereby establishing 2C18-terminated CLOs as a rationally tunable and biocompatible platform for antimicrobial material development.

microbiology↗

A Pan-Cancer Ex Vivo Drug Screen Atlas for Functional Precision Oncology

Compared to immortalized cell lines, patient-derived organoids and other ex vivo models have been shown to better recapitulate patient responses to therapy. High cost and technical complexity have prevented the creation of pan-cancer ex vivo datasets, limiting comprehensive analyses and predictive modeling for ex vivo drug response. We present the Pan-PreClinical (PPC) project: a drug screen atlas of 2.1M experiments across 1,982 ex vivo samples and 3,100 drugs spanning 134 cancer indications tested across 26 studies. We develop a contrastive Bayesian model to harmonize across studies, identifying 303 tissue-specific drug sensitivities and demonstrating drug sensitivities are predictive of clinically-relevant molecular profiles. Integrating established cell line databases reveals systematic biases across 55 cancer subtypes, with cell line screens favoring drugs targeting highly proliferative cells and undervaluing cell-cell communication targets. We leverage PPC to establish an ex vivo foundation model and computational platform for scalable ex vivo cancer biology and predictive oncology.

cancer biology↗

Bioactive Cationic Lipidated Oligomers (CLOs) as Antimicrobial Materials: Metabolomic Insights into MRSA Membrane Disruption

The escalating incidence of antimicrobial resistance in Staphylococcus aureus, particularly the methicillin-resistant strain (MRSA), necessitates the development of novel therapeutic strategies. Cationic lipidated oligomers (CLOs) have emerged as promising membrane-active antimicrobial agents; however, their mechanisms of action remain insufficiently understood. In this study, untargeted metabolomics was employed to systematically profile the temporal metabolic perturbations induced by two structurally distinct CLOs, C12-o-DMEN-10 and C12-o-BEDA-10, in MRSA across four defined time points (0.25, 0.5, 1, and 3 hours). These CLOs previously demonstrated differential antibacterial activity, as evidenced by dose-dependent propidium iodide (PI) uptake and growth inhibition assays. Metabolomic analysis revealed pronounced and sustained disruptions in bacterial membrane lipid metabolism, including significant depletion of phosphatidylglycerols ([&ge;] -2.5 log2FC, p < 0.05), alongside elevated levels of phosphatidylethanolamines, lysophospholipids, and fatty acid-derived metabolites indicative of membrane destabilization and lipid remodelling. Although both CLOs affected overlapping metabolic pathways, they differed in the extent and temporal dynamics of their effects. These findings provide mechanistic insights into CLO-mediated antibacterial activity and highlight the value of metabolomics in elucidating both direct and downstream cellular responses, which extend beyond the scope of conventional membrane integrity assays, such as PI fluorescence.

systems biology↗

Asiatic acid improves mitochondrial function, activates antioxidant response in the mouse brain and improves cognitive function in beta-amyloid overexpressing mice.

Extracts of the plant Centella asiatica can enhance mitochondrial function, promote antioxidant activity and improve cognitive deficits. Asiatic acid (AA) is one of the constituent triterpene compounds present in the plant. In this study we explore the effects of increasing concentrations of AA on brain mitochondrial function, antioxidant response and cognition in healthy mice and a single concentration of AA in the beta-amyloid overexpressing 5xFAD mouse line. Associative memory and overall activity were assessed. Hippocampal mitochondrial bioenergetics and the expression of mitochondrial and antioxidant response genes was determined. In the 5xFAD line, total beta-amyloid plaque burden after AA treatment was also evaluated. In healthy mice, we report dose responsive effects of increasing concentrations of AA on enhanced associative memory and a dose dependent increase in basal and maximal mitochondrial respiration, mitochondrial gene expression and antioxidant gene expression. Results from the highest AA dose (1% AA) were similar to what was observed with CAW. The high AA dose was then evaluated in the context of A{beta} accumulation in 5xFAD mice. Improvements in mitochondrial and antioxidant response genes were favored in females over males without significant alleviation of A{beta} plaque burden.

neuroscience↗

Providing insight into the mechanism of action of Cationic Lipidated Oligomers (CLOs) using metabolomics

The increasing resistance of clinically relevant microbes against current commercially available antimicrobials underpins the urgent need for alternative and novel treatment strategies. Cationic lipidated oligomers (CLOs) are innovative alternatives to antimicrobial peptides, and have reported antimicrobial potential. An understanding of their antimicrobial mechanism of action is required to rationally design future treatment strategies for CLOs, either in monotherapy or synergistic combinations. In the present study, metabolomics was used to investigate the potential metabolic pathways involved in the mechanisms of antibacterial activity of one CLO, C12-o-(BG-D)-10, which we have previously shown to be effective against methicillin-resistant Staphylococcus aureus (MRSA) ATCC 43300. The metabolomes of MRSA ATCC 43300 at 1, 3 and 6 h following treatment with C12-o-(BG-D)-10 (48 {micro}g/mL i.e., 3x MIC) were compared to those of the untreated controls. Our findings reveal that the studied CLO, C12-o-(BG-D)-10, disorganized the bacterial membrane as the first step towards its antimicrobial effect, as evidenced by marked perturbations in the bacterial membrane lipids and peptidoglycan biosynthesis observed at early time points i.e., 1, and 3 h. Central carbon metabolism, and biosynthesis of DNA, RNA, and arginine were also vigorously perturbed, mainly at early time points. Moreover, bacterial cells were under osmotic and oxidative stress across all time points, evident by perturbations of trehalose biosynthesis and pentose phosphate shunt. Overall, this metabolomics study has, for the first time, revealed that the antimicrobial action of C12-o-(BG-D)-10 may potentially stem from the dysregulation of multiple metabolic pathways. ImportanceAntimicrobial resistance poses a significant challenge to healthcare systems worldwide. Novel anti-infective therapeutics are urgently needed to combat drug-resistant microorganisms. Cationic lipidated oligomers (CLOs) show promise as new antibacterial agents against Gram-positive pathogens like Staphylococcus aureus (MRSA). Understanding their molecular mechanism(s) of antimicrobial action may help design synergistic CLO treatments along with monotherapy. Here, we describe the first metabolomics study to investigate the killing mechanism(s) of CLOs against MRSA. The results of our study indicate that the CLO, C12-o-(BG-D)-10, had a notable impact on the biosynthesis and organization of the bacterial cell envelope. C12-o-(BG-D)-10 also inhibits arginine, histidine, central carbon metabolism, and trehalose production, adding to its antibacterial characteristics. This work illuminates the unique mechanism of action of C12-o-(BG-D)-10 and opens an avenue to design innovative antibacterial oligomers/polymers for future clinical applications.

microbiology↗

The CD74 inhibitor DRhQ improves cognition and mitochondrial function in 5xFAD mouse model of Aβ accumulation

Neuroinflammation and mitochondrial dysfunction are early events in Alzheimers disease (AD) and contribute to neurodegeneration and cognitive impairment. Evidence suggests that the inflammatory axis mediated by macrophage migration inhibitory factory (MIF) binding to its receptor, CD74, plays an important role in many central nervous system (CNS) disorders like AD. Our group has developed DRhQ, a novel CD74 binding construct that competitively inhibits MIF binding, blocks T-cell and macrophage activation and migration into the CNS, enhances anti-inflammatory microglia cell numbers and reduces pro-inflammatory gene expression. Here we evaluate its effects in {beta}-amyloid (A{beta}) overexpressing mice. 5xFAD mice and their wild type littermates were treated with DRhQ (100 {micro}g) or vehicle for 4 weeks. DRhQ improved cognition and cortical mitochondrial function in both male and female 5xFAD mice. A{beta} plaque burden in 5xFAD animals were not robustly impacted by DRhQ treatment nor was microglial activation, although in the hippocampus there was some evidence of a reduction in female 5xFAD mice. Future studies are needed to confirm this possible sex-dependent response on microglial activation as well as to optimize the dose, and timing of DRhQ treatment and gain a better understanding of its mechanism of action.

neuroscience↗

Amelioration of age-related cognitive decline and anxiety in mice by Centella asiatica extract varies by sex, dose and mode of administration.

We have previously reported that a water extract (CAW) of the Ayurvedic plant Centella asiatica administered in drinking water can improve cognitive deficits in mouse models of aging and neurodegenerative diseases. Here we compared the effects of CAW administered in drinking water or the diet on cognition, measures of anxiety and depression-like behavior in healthy aged mice. Three- and eighteen-month-old male and female C57BL6 mice were administered rodent AIN-93M diet containing CAW (0, 0.2, 0.5 or 1% w/w) to provide 0, 200 mg/kg/d, 500 mg/kg/d or 1000 mg/kg/d for a total of 5 weeks. An additional group of eighteen-month-old mice were treated with CAW (10 mg/mL) in their drinking water for a total of five weeks to deliver the same exposure of CAW as the highest dietary dose (1000 mg/kg/d). CAW doses delivered were calculated based on food and water consumption measured in previous experiments. In the fourth and fifth weeks, mice underwent behavioral testing of cognition, anxiety and depression (n=12 of each sex per treatment group in each test). Aged mice of both sexes showed cognitive deficits relative to young mice while only female aged mice showed increased anxiety compared to the young female mice and no differences in depression were observed between the different ages. CAW (1000 mg/kg/d) in the drinking water improved deficits in aged mice in learning, executive function and recognition memory in both sexes and attenuated the increased measures of anxiety observed in the aged female mice. However, CAW in the diet only improved executive function in aged mice at the highest dose (1000 mg/kg/d) in both sexes and did so less robustly than when given in the water. There were no effects of CAW on depression-like behavior in aged animals regardless of whether it was administered in the diet or the water. These results suggest that CAW can ameliorate age-related changes in measures of anxiety and cognition and that the mode of administration is important for the effects of CAW on resilience to these age-related changes.

animal behavior and cognition↗

A Bayesian active learning platform for scalable combination drug screens

Large-scale combination drug screens are generally considered intractable due to the immense number of possible combinations. Existing approaches use ad hoc fixed experimental designs then train machine learning models to impute novel combinations. Here we propose BATCHIE, an orthogonal approach that conducts experiments dynamically in batches. BATCHIE uses information theory and probabilistic modeling to design each batch to be maximally informative based on the results of previous experiments. On retrospective experiments from previous large-scale screens, BATCHIE designs rapidly discover highly effective and synergistic combinations. To validate BATCHIE prospectively, we conducted a combination screen on a collection of pediatric cancer cell lines using a 206 drug library. After exploring only 4% of the 1.4M possible experiments, the BATCHIE model was highly accurate at predicting novel combinations and detecting synergies. Further, the model identified a panel of top combinations for Ewing sarcomas, all of which were experimentally confirmed to be effective, including the rational and translatable top hit of PARP plus topoisomerase I inhibition. These results demonstrate that adaptive experiments can enable large-scale unbiased combination drug screens with a relatively small number of experiments, thereby powering a new wave of combination drug discoveries. BATCHIE is open source and publicly available (https://github.com/tansey-lab/batchie).

bioinformatics↗

BayesTME: A unified statistical framework for spatial transcriptomics

Spatial variation in cellular phenotypes underlies heterogeneity in immune recognition and response to therapy in cancer and many other diseases. Spatial transcriptomics (ST) holds the potential to quantify such variation, but existing analysis methods address only a small part of the analysis challenge, such as spot deconvolution or spatial differential expression. We present BayesTME, an end-to-end Bayesian method for analyzing spatial transcriptomics data. BayesTME unifies several previously distinct analysis goals under a single, holistic generative model. This unified approach enables BayesTME to (i) be entirely reference-free without any need for paired scRNA-seq, (ii) outperform a large suite of methods in quantitative benchmarks, and (iii) uncover a new type of ST signal: spatial differential expression within individual cell types. To achieve the latter, BayesTME models each phenotype as spatially adaptive and discovers statistically significant spatial patterns amongst coordinated subsets of genes within phenotypes, which we term spatial transcriptional programs. On human and zebrafish melanoma tissues, BayesTME identifies spatial transcriptional programs that capture fundamental biological phenomena like bilateral symmetry, differential expression between interior and surface tumor cells, and tumor-associated fibroblast and macrophage reprogramming. Our results demonstrate BayesTMEs power in unlocking a new level of insight from spatial transcriptomics data and fostering a deeper understanding of the spatial architecture of the tumor microenvironment. BayesTME is open source and publicly available (https://github.com/tansey-lab/bayestme).

bioinformatics↗

An aging clock using metabolomic CSF

Quantifying the physiology of aging is essential for improving our understanding of age-related disease and the heterogeneity of healthy aging. Recent studies have shown that in regression models using "-omic" platforms to predict chronological age, residual variation in predicted age is correlated with health outcomes, and suggest that these "omic clocks" provide measures of biological age. This paper presents predictive models for age using metabolomic profiles of cerebrospinal fluid from healthy human subjects, and finds that metabolite and lipid data are generally able to predict chronological age within 10 years. We use these models to predict the age of a cohort of subjects with Alzheimers and Parkinsons disease and find an increase in prediction error, potentially indicating that the relationship between the metabolome and chronological age differs with these diseases. In our analysis of control subjects, we find the carnitine shuttle, sucrose, biopterin, vitamin E metabolism, tryptophan, and tyrosine to be the most associated with age. We showcase the potential usefulness of age prediction models in a small dataset (n = 85), and discuss techniques for drift correction, missing data imputation, and regularized regression which can be used to help mitigate the statistical challenges that commonly arise in this setting. To our knowledge, this work presents the first multivariate predictive metabolomic and lipidomic models for age using mass spectrometry analysis of cerebrospinal fluid.

systems biology↗

CSF sTREM2 correlates with CSF tau in advancing Parkinson’s disease

Parkinsons disease (PD) is the second most common neurodegenerative disease after Alzheimers disease (AD) and affects 1% of the population above 60 years old. Although PD commonly manifests with motor symptoms, a majority of patients with PD subsequently develop cognitive impairment which often progresses to dementia, a major cause of morbidity and disability. PD is characterized by -synuclein accumulation that frequently associates with amyloid beta (A{beta}) and tau fibrils, the hallmarks of AD neuropathologic changes; this co-occurrence suggests that onset of cognitive decline in PD may be associated with appearance of pathologic A{beta} and/or tau. Recent studies have highlighted the appearance of the soluble form of the Triggering Receptor Expressed on Myeloid cells 2 (sTREM2) receptor in CSF during development of AD. Given the known association of microglial activation with advancing PD, we investigated whether CSF and/or plasma sTREM2 increased with progression to PD dementia. We examined 165 participants consisting of 17 cognitively normal elderly, 45 PD patients with no cognitive impairment, 86 with mild cognitive impairment, and 17 with dementia. Stratification of subjects by CSF A{beta} and tau levels revealed that CSF sTREM2 concentrations were elevated in PD subgroups with abnormal tau, but not A{beta}, CSF concentration. These findings indicate that CSF sTREM2 could serve as a surrogate immune biomarker of neuronal injury in PD that is associated with cognitive decline.\n\nOne sentence summaryCSF sTREM2 correlates with CSF tau in PD

neuroscience↗