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Banwell, B.

Publications and source records attributed to Banwell, B..

2 recordsLinked to original sources

Pediatric cerebrospinal fluid immune profiling distinguishes pediatric-onset multiple sclerosis from other pediatric-onset acute neurological disorders

The cerebrospinal fluid (CSF) provides a unique glimpse into the central nervous system (CNS) compartment and offers insights into immune processes associated with both healthy immune surveillance as well as inflammatory disorders of the CNS. The latter include demyelinating disorders, such as multiple sclerosis (MS) and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), that warrant different therapeutic approaches yet are not always straightforward to distinguish on clinical and imaging grounds alone. Here, we establish a comprehensive phenotypic landscape of the pediatric CSF immune compartment across a range of non-inflammatory and inflammatory neurological disorders, with a focus on better elucidating CNS-associated immune mechanisms potentially involved in, and discriminating between, pediatric-onset MS (MS) and other pediatric-onset suspected neuroimmune disorders, including MOGAD. We find that CSF from pediatric patients with non-inflammatory neurological disorders is primarily composed of non-activated CD4+ T cells, with few if any B cells present. CSF from pediatric patients with acquired inflammatory demyelinating disorders is characterized by increased numbers of B cells compared to CSF of both patients with other inflammatory or non-inflammatory conditions. Certain features, including particular increased frequencies of antibody-secreting cells (ASCs) and decreased frequencies of CD14+ myeloid cells, distinguish MS from MOGAD and other acquired inflammatory demyelinating disorders.

immunology↗

Proportionality-based association metrics in count compositional data

MotivationCompositional data comprise vectors that describe the constituent parts of a whole. Data arising from various -omics platforms such as 16S and RNA-sequencing are compositional in nature. However, correlations between features on raw counts have no meaningful interpretation. Metrics of proportionality were formulated to address this problem. However, there is an inherent bias that arises when calculating these metrics empirically on count-based measures due to variability in read depths. ResultsWe quantify the bias introduced by empirically calculating proportionality-based association metrics in count data. Additionally, we propose a means of estimating these metrics within a logit-normal multinomial model in pursuit of more accurate estimates. The model-based estimates are shown to outperform empirical estimates in simulated data, and are additionally applied to a mouse embryonic stem-cell single-cell sequencing dataset as well as a pediatric-onset multiple sclerosis metagenomic dataset. Availability and ImplementationAn R package is available at https://CRAN.R-project.org/package=countprop. Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗