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ObjectivesBacterial resistance to antibiotics driven by the increasing use of broad-spectrum agents has become a global concern. Inappropriate antibiotic utilisation can result in decreased susceptibility, emergence of multidrug-resistance pathogens, and increased healthcare costs.1 Ceftriaxone is a commonly prescribed antibiotic in the paediatric age group and existing literature had reported a wide variation of justified prescription in drug use evaluations of ceftriaxone, ranging from 12.1% – 78%.2 3 This study is a retrospective audit aimed at assessing the appropriateness of ceftriaxone usage in the paediatric department at a district general hospital in East of England.MethodsWe retrospectively reviewed the use of ceftriaxone in the paediatric department between 17th to 30th April 2023 (inclusive) by collecting data on patients who were started on ceftriaxone within this period. We reviewed patients‘ medical notes and collected data on demographics (age, gender), indication for ceftriaxone prescription, presence of red flags for sepsis,4 whether ceftriaxone was changed to oral antibiotic subsequently, discharge diagnosis, and culture results.ResultsCeftriaxone was initiated in the department for 33 patients for a range of indications and was determined to be inappropriately prescribed in 18 patients (55%). Appropriate indications include fever in less than 3 months as per NICE guidance, sickle cell crisis, suspected intracranial infection and for those presenting with red flags of sepsis.4 Unjustified use (n=18) comprised of 10 with clear localised infection (tonsillitis, infected eczema, cellulitis, bronchiolitis, urinary tract infection and otitis externa), viral gastroenteritis, unexplained tachycardia with no other symptoms and fever of unclear origin. Of these 18, none had positive bacterial growth in blood cultures and 1 had picornavirus detected on respiratory swab. Ceftriaxone was stopped in 11 patients and was converted to an oral, narrower spectrum antibiotic in 7 patients on day 2 after blood culture was reported to be negative at 36 hours to treat for a ocalized infection.ConclusionA significant proportion of ceftriaxone utilization in the department lacks a valid indication. It was commenced in cases of localized infection, even though established guidelines for first-line, usually a more narrow-spectrum antibiotic in such scenarios are readily available. This indicates an overuse of broad-spectrum antibiotics which could lead to the development of multi-drug resistant organisms and increased healthcare expenses. Addressing this issue requires an intensification of educational initiatives in the department and the implementation of antibiotic control systems specifically focused on ceftriaxone as part of antibiotic stewardship. Our audit proforma can also be adapted to be used for other hospitals.ReferencesBarlam TF, et al. Implementing an antibiotic stewardship program: guidelines by the infectious diseases society of america and the society for healthcare epidemiology of America. Clin Infect Dis. 2016;62:e51-e77.Bantie L. Drug use evaluation (DUE) of ceftriaxone injection in the in-patient wards of Felege Hiwot Referral Hospital (FHRH), Bahir Dar, North Ethiopia. Int J Pharm Sci. 2014;4:671–676.Abebe FA, et al. Drug use evaluation of ceftriaxone: the case of Ayder Referral Hospital, Mekelle, Ethiopia. Int J Pharm Sci Res. 2012;3:2191–2195.Fever in under 5s: assessment and initial management (NICE guideline 2019). Available: https://www.nice.org.uk/guidance/ng143.

Assessment of single-cell gene expression (single-cell RNA sequencing) and adaptive immune receptor (AIR) sequencing (scVDJ-seq) has been invaluable in studying lymphocyte biology. Here we introduce Dandelion, a computational pipeline for scVDJ-seq analysis. It enables the application of standard V(D)J analysis workflows to single-cell datasets, delivering improved V(D)J contig annotation and the identification of nonproductive and partially spliced contigs. We devised a strategy to create an AIR feature space that can be used for both differential V(D)J usage analysis and pseudotime trajectory inference. The application of Dandelion improved the alignment of human thymic development trajectories of double-positive T cells to mature single-positive CD4/CD8 T cells, generating predictions of factors regulating lineage commitment. Dandelion analysis of other cell compartments provided insights into the origins of human B1 cells and ILC/NK cell development, illustrating the power of our approach. Dandelion is available at https://www.github.com/zktuong/dandelion . A computational pipeline enables differential V(D)J usage analysis and pseudotime trajectory inference from single-cell AIR sequencing.

T cells develop from circulating precursor cells, which enter the thymus and migrate through specialized subcompartments that support their maturation and selection 1 . In humans, this process starts in early fetal development and is highly active until thymic involution in adolescence. To map the microanatomical underpinnings of this process in pre- and early postnatal stages, we established a quantitative morphological framework for the thymus—the Cortico-Medullary Axis—and used it to perform a spatially resolved analysis. Here, by applying this framework to a curated multimodal single-cell atlas, spatial transcriptomics and high-resolution multiplex imaging data, we demonstrate establishment of the lobular cytokine network, canonical thymocyte trajectories and thymic epithelial cell distributions by the beginning of the the second trimester of fetal development. We pinpoint tissue niches of thymic epithelial cell progenitors and distinct subtypes associated with Hassall’s corpuscles and identify divergence in the timing of medullary entry between CD4 and CD8 T cell lineages. These findings provide a basis for a detailed understanding of T lymphocyte development and are complemented with a holistic toolkit for cross-platform imaging data analysis, annotation and OrganAxis construction (TissueTag), which can be applied to any tissue. A quantitative morphological framework for the human thymus reveals the establishment of the lobular cytokine network, canonical thymocyte trajectories and thymic epithelial cell distributions in fetal and paediatric thymic development.

Single-cell data analysis can infer dynamic changes in cell populations, for example across time, space or in response to perturbation, thus deriving pseudotime trajectories. Current approaches comparing trajectories often use dynamic programming but are limited by assumptions such as the existence of a definitive match. Here we describe Genes2Genes, a Bayesian information-theoretic dynamic programming framework for aligning single-cell trajectories. It is able to capture sequential matches and mismatches of individual genes between a reference and query trajectory, highlighting distinct clusters of alignment patterns. Across both real world and simulated datasets, it accurately inferred alignments and demonstrated its utility in disease cell-state trajectory analysis. In a proof-of-concept application, Genes2Genes revealed that T cells differentiated in vitro match an immature in vivo state while lacking expression of genes associated with TNF signaling. This demonstrates that precise trajectory alignment can pinpoint divergence from the in vivo system, thus guiding the optimization of in vitro culture conditions. Genes2Genes is a dynamic programming framework that enables precise alignment for single-cell trajectories at the per-gene level.

Abstract Introduction/Background Chronic Recurrent Multifocal Osteomyelitis (CRMO), also known as chronic nonbacterial osteomyelitis (CNO), is a rare autoinflammatory condition affecting the bones. It occurs primarily in children and teenagers and is characterised by bone pain and swelling in the absence of infection or tumour. The incidence of CRMO remains uncertain, with estimates ranging from 0.4-1 per 100,000 person years. Description/Method The primary aim of the study was to identify the incidence of CRMO in patients under the age of 16 in the United Kingdom (UK) and Republic of Ireland (ROI). Additional aims include describing the demographics, clinical features, treatment, and healthcare needs of patients with CRMO. A prospective surveillance study was undertaken via the British Paediatric Surveillance Unit. A monthly e-reporting card was sent to all registered paediatric consultants in the UK and ROI. A parallel surveillance study was sent via the British Society for Children’s Orthopaedics to identify patients managed solely by orthopaedics. A standardised questionnaire was sent to the reporting clinicians to collect further information. Discussion/Results During initial 13 months of surveillance, 168 cases were reported. 23 questionnaires were not returned (13.7% of reported cases). After de-duplication, and removal of cases outside the reporting time period and age-group, 82 confirmed and 8 probable cases were included in these interim results. The estimated incidence of CRMO is 0.605 cases/100,000 children per year. Median age at time of diagnosis was 10 years (range 3-16). 53 (58.9%) of cases were female. Median delay from symptom onset to diagnosis was 5 months and 16 patients (17.78%) had a delay of greater than 12 months. Most (48.9%) of the cases were diagnosed by paediatric rheumatology specialists. Other cases were diagnosed by orthopaedics (16.7%), general paediatricians (15.6%) or by a multidisciplinary team. 34 cases (37.8%) reported requiring hospital admission related to CRMO. The most common presenting feature was bone pain (96.67%). 34 patients (37.8%) presented with clavicular pain, and thirty-one (34.4%) had unifocal bone pain. Patients also presented with bone swelling (52.2%), joint swelling (20.0%), fever (12.2%) and general malaise (13.3%). A median of 3 radiological investigations were reported for each case, of which 61 (67.7%) cases had whole body MRI performed. Additionally, 33 cases (36.67%) had bone biopsy. At initial reporting, the most common treatment was NSAIDs (90.0%) and bisphosphonates (33.3%). Key learning points/Conclusion Our results estimate the incidence of CRMO as 0.605 cases per 100,000 person years. The study will continue to capture new CRMO cases for a further 12 months. Reported cases will be followed up for 24 months. This prospective study of all incident cases of CRMO within the UK and ROI will provide insight into the medium-term outcomes and treatment strategies used by clinicians. These results will provide a valuable baseline for further research and improvement in care for patients with CRMO.

T cells develop from circulating precursors, which enter the thymus and migrate throughout specialised sub-compartments to support maturation and selection. This process starts already in early fetal development and is highly active until the involution of the thymus in adolescence. To map the micro-anatomical underpinnings of this process in pre- vs. post-natal states, we undertook a spatially resolved analysis and established a new quantitative morphological framework for the thymus, the Cortico-Medullary Axis. Using this axis in conjunction with the curation of a multimodal single-cell, spatial transcriptomics and high-resolution multiplex imaging atlas, we show that canonical thymocyte trajectories and thymic epithelial cells are highly organised and fully established by post-conception week 12, pinpoint TEC progenitor states, find that TEC subsets and peripheral tissue genes are associated with Hassall's Corpuscles and uncover divergence in the pace and drivers of medullary entry between CD4 vs. CD8 T cell lineages. These findings are complemented with a holistic toolkit for spatial analysis and annotation, providing a basis for a detailed understanding of T lymphocyte development.

Single cell data analysis can infer dynamic changes in cell populations, for example across time, space or in response to perturbation. To compare these dynamics between two conditions, trajectory alignment via dynamic programming (DP) optimization is frequently used, but is limited by assumptions such as a definite existence of a match. Here we describe Genes2Genes, a Bayesian information-theoretic DP framework for aligning single-cell trajectories. Genes2Genes overcomes current limitations and is able to capture sequential matches and mismatches between a reference and a query at single gene resolution, highlighting distinct clusters of genes with varying patterns of gene expression dynamics. Across both real life and simulated datasets, Genes2Genes accurately captured different alignment patterns, and revealed that T cells differentiated in vitro matched to an immature in vivo state while lacking the final TNFɑ signaling. This use case demonstrates that precise trajectory alignment can pinpoint divergence from the in vivo system, thus providing an opportunity to optimize in vitro culture conditions.Competing Interest StatementIn the past three years, S.A.T. has received remuneration for Scientific Advisory Board Membership from Sanofi, GlaxoSmithKline, Foresite Labs and Qiagen. S.A.T. is a co-founder and holds equity in Transition Bio.

Assessment of single-cell gene expression (scRNA-seq) and antigen receptor sequencing (scVDJ-seq) has been invaluable in studying lymphocyte biology, but current tools are limited. Here, we introduce Dandelion, a computational pipeline for scVDJ-seq analysis. It enables the application of standard V(D)J analysis workflows to single-cell datasets, delivering improved V(D)J contig annotation and the identification of non-productive and partially spliced contigs. We devised a novel strategy to create an antigen receptor feature space that can be used for both differential V(D)J usage analysis and pseudotime trajectory inference. The application of Dandelion improved the alignment of human thymic development trajectories of double positive T cells to mature single-positive CD4/CD8 T cells, with important new predictions of factors regulating lineage commitment. Dandelion analysis of other cell compartments provided novel insights into the origins of human B1 cells and ILC/NK cell development, illustrating the power of our approach. Dandelion is an open access resource (https://www.github.com/zktuong/dandelion) that will enable future discoveries.Competing Interest StatementIn the past three years, S.A.T. has received remuneration for Scientific Advisory Board Membership from Sanofi, GlaxoSmithKline, Foresite Labs and Qiagen. S.A.T. is a co-founder and holds equity in Transition Bio. Z.K.T. has received consulting fees from Synteny Biotechnologies Ltd on activities unrelated to this manuscript.

Single-cell data analysis can infer dynamic changes in cell populations, for example across time, space or in response to perturbation. To compare these dynamics between two conditions, trajectory alignment via dynamic programming (DP) optimization is frequently used, but is limited by assumptions such as a definite existence of a match. Here we describe Genes2Genes, a Bayesian information-theoretic DP framework for aligning single-cell trajectories. Genes2Genes overcomes current limitations and is able to capture sequential matches and mismatches between a reference and a query at single gene resolution, highlighting distinct clusters of genes with varying patterns of expression dynamics. Across both real world and simulated datasets, Genes2Genes accurately captured different alignment patterns, demonstrated its utility in disease cell state trajectory analysis, and revealed that T cells differentiated in vitro matched to an immature in vivo state while lacking expression of genes associated with TNFɑ signaling. This use case demonstrates that precise trajectory alignment can pinpoint divergence from the in vivo system, thus guiding the optimization of in vitro culture conditions.

Combinations of transcription factors (TFs) regulate gene expression and determine cell fate. Much effort has been devoted to understanding TF activity in different tissues and how tissue-specificity is achieved. However, ultimately gene regulation occurs at the single cell level and the recent explosion in the availability of single cell gene expression data now makes it possible to understand TF activity at this granular level of resolution. Here, we leverage a large collection of Human Cell Atlas (HCA) single cell data to explore TF activity by examining cell-type and tissue-specific sets of target genes, or regulons. We compile a regulon atlas, CellRegulon, and map the activity of TFs in an extensive set of healthy adult and foetal tissues spanning hundreds of cell types. Using CellRegulon, we describe dynamic patterns of co-regulation, associate TF-modules with different cellular functions and characterise the distribution of active TFs and TF families across cell types. We show that CellRegulon can link disease gene expression signatures to cell types and TFs relevant to the disease. Finally, using a newly generated multiome dataset of the adult lung, we show how CellRegulon can be extended into an enhancer-gene regulatory network (eGRN) to improve cell-type associations with genetic risk loci for diseases, such as childhood onset asthma, COPD and IPF, and to identify high risk gene modules. Our database for easy download and interactive exploration allows researchers to understand key gene modules activated at cell type transitions and will therefore be valuable for tasks such as cell type engineering (https://www.cellregulondb.org).