Biology subjects

Calabresi, P.

Publications and source records attributed to Calabresi, P..

8 recordsLinked to original sources

bioRxiv2023

Spatial Transcriptomics of Meningeal Inflammation Reveals Variable Penetrance of Inflammatory Gene Signatures into Adjacent Brain Parenchyma

While modern high efficacy disease modifying therapies have revolutionized the treatment of relapsing-remitting multiple sclerosis, they are less effective at controlling progressive forms of the disease. Meningeal inflammation is a recognized risk factor for cortical grey matter pathology which can result in disabling symptoms such as cognitive impairment and depression, but the mechanisms linking meningeal inflammation and grey matter pathology remain unclear. Here, we performed MRI-guided spatial transcriptomics in a mouse model of autoimmune meningeal inflammation to characterize the transcriptional signature in areas of meningeal inflammation and the underlying brain parenchyma. We found broadly increased activity of inflammatory signaling pathways at sites of meningeal inflammation, but only a subset of these pathways active in the adjacent brain parenchyma. Sub-clustering of regions adjacent to meningeal inflammation revealed the subset of immune programs induced in brain parenchyma, notably complement signaling and antigen processing/presentation. Trajectory gene and gene set modeling analysis confirmed variable penetration of immune signatures originating from meningeal inflammation into the adjacent brain tissue. This work contributes a valuable data resource to the field, provides the first detailed spatial transcriptomic characterization in a model of meningeal inflammation, and highlights several candidate pathways in the pathogenesis of grey matter pathology.

neuroscience↗

bioRxiv2023

Deconfounded Dimension Reduction via Partial Embeddings

Dimension reduction tools preserving similarity and graph structure such as t-SNE and UMAP can capture complex biological patterns in high-dimensional data. However, these tools typically are not designed to separate effects of interest from unwanted effects due to confounders. We introduce the partial embedding (PARE) framework, which enables removal of confounders from any distance-based dimension reduction method. We then develop partial t-SNE and partial UMAP and apply these methods to genomic and neuroimaging data. Our results show that the PARE framework can remove batch effects in single-cell sequencing data as well as separate clinical and technical variability in neuroimaging measures. We demonstrate that the PARE framework extends dimension reduction methods to highlight biological patterns of interest while effectively removing confounding effects.

neuroscience↗

bioRxiv2020

Single-Cell Transcriptomic Analysis Reveals Molecular Diversity of Human Oligodendrocyte Progenitor Cells

Injury and loss of oligodendrocytes can cause demyelinating diseases such as multiple sclerosis. To improve our understanding of oligodendrocyte development, which could facilitate development of remyelination-based treatment strategies, we performed single-cell-transcriptomic-analysis of developing human oligodendrocyte-precursor-cells (hOPCs). We engineered knock-in hESC-reporter lines in which an Identification-and-Purification tag is expressed under control of the endogenous, OPC-specific, PDGFR promoter, and performed time-course single-cell-RNA-sequencing of purified hOPCs. Our analysis uncovered marked transcriptional heterogeneity of PDGFR+ hOPCs and identified regulatory genes and networks that control their differentiation and myelination competence. Pseudotime trajectory analysis revealed two distinct trajectories for the development of oligodendrocytes vs astrocytes from hOPCs. We also identified novel transcription factors and other genes that developing hOPCs potentially use to choose between oligodendrocyte vs astrocyte lineages. In addition, pathway enrichment analysis followed by pharmacological intervention of those pathways confirm that mTOR and cholesterol biosynthesis signaling pathways are involved in maturation of oligodendrocytes from hOPCs.

neuroscience↗

bioRxiv2020

Imaging meningeal inflammation in CNS autoimmunity identifies a therapeutic role for BTK inhibition

Leptomeningeal inflammation in multiple sclerosis is associated with worse clinical outcomes and greater cortical pathology. Despite progress in identification of this process in multiple sclerosis patients using post-contrast FLAIR imaging, early trials attempting to target meningeal inflammation have been unsuccessful. There is a lack of appropriate model systems to screen potential therapeutic agents targeting meningeal inflammation. We utilized ultra-high field (11.7 Tesla) MRI to perform post-contrast FLAIR imaging in SJL/J mice with experimental autoimmune encephalomyelitis induced using immunization with proteolipid protein peptide (PLP139-151) and complete Freunds adjuvant. Imaging was performed in both a cross-sectional and longitudinal fashion at time-points ranging from 2 to 14 weeks post-immunization. Following imaging, we euthanized animals and collected tissue for pathological evaluation, which identified dense cellular infiltrates corresponding to areas of contrast-enhancement involving the leptomeninges. These areas of meningeal inflammation contained B cells (B220+), T cells (CD3+) and myeloid cells (Mac2+). We also noted features consistent with tertiary lymphoid tissue within these areas - presence of peripheral node addressin (PNAd) positive structures, CXCL13 producing cells and FDC-M1+ follicular dendritic cells. In the cortex adjacent to areas of meningeal inflammation we identified astrocytosis, microgliosis, demyelination and evidence of axonal stress/damage. Since areas of meningeal contrast enhancement persisted over several weeks in longitudinal experiments, we utilized this model to test the effects of a therapeutic intervention on established meningeal inflammation. We randomized mice with evidence of meningeal contrast enhancement on MRI scans performed at 6 weeks post-immunization, to treatment with either vehicle or evobrutinib (a Brutons tyrosine kinase inhibitor) for a period of 4 weeks. These mice underwent serial imaging and we examined the effect of treatment on the areas of meningeal contrast enhancement and noted a significant reduction in the evobrutinib group compared to vehicle (30% reduction versus 5% increase; P = 0.003). We utilized ultra-high field MRI imaging to identify areas of meningeal inflammation and to track them over time in SJL/J mice with experimental autoimmune encephalomyelitis and then utilized this model to identify Brutons tyrosine kinase inhibition as a novel therapeutic approach to target meningeal inflammation. The results of this study provide support for future studies in multiple sclerosis patients with imaging evidence of meningeal inflammation.

neuroscience↗

bioRxiv2019

Mutations in CalDAG-GEFI Lead to Striatal Signaling Deficits and Psychomotor Symptoms in Multiple Species Including Human

Syndromes caused by mutations in Ras-MAP kinase signaling molecules are known as RASopathies and share features such as developmental delay, autistic traits, and cancer. Syndromic features of Rap-MAP kinase signaling defects remain undefined. CalDAG-GEFI is a calcium-responsive Rap-GTPase activator that is enriched in the matrix of the sensorimotor striatum and down-regulated in Huntingtons disease. We show here that CalDAG-GEFI mutations, including striatum-specific deletions and spontaneous mutations in the enzymatic domain, are associated with psychomotor phenotypes in humans, dogs and mice. The identification of these neural mutants was guided by the overt bleeding phenotype in CalDAG-GEFI knockout mice, and then in humans and other species with conserved platelet signaling deficits. Knockout mice exhibit loss of striatal long-term potentiation and deficits in dopamine, acetylcholine and glutamate signaling, along with delayed motor learning and drug-induced perseverative behaviors. Thus, loss of CalDAG-GEFI signaling produces an evolutionarily conserved syndrome characterized by bleeding and psychomotor dysfunction.

neuroscience↗

bioRxiv2019

Inhibition of neutral sphingomyelinase-2 facilitates remyelination

For reasons that are not completely understood, remyelination is often incomplete, producing thin myelin sheaths with disorganized structure. We investigated the cellular basis for this altered myelin structure, and found that the response of oligodendrocyte progenitor cells (OPCs), and mature oligodendrocytes to TNF and IL-1{beta} is modified by the expression of the sphingomyelin hydrolase nSMase2. OPCs do not express nSMase2, and exhibit a protective response to these cytokines manifest by decreased ceramide, increased sphingosine 1-phosphate, and increased cell motility. Mature oligodendrocytes express nSMase2, and respond to TNF and IL-1{beta} with a stress phenotype, evidenced by increased ceramide, decreased sphingosine, and active caspase 3. Pharmacological inhibition or a targeted genetic deletion of nSMase2 in vivo increased myelin thickness, and enhanced myelin compaction. These results suggest that inhibition of nSMase2 improves the quality of new myelin by protecting maturing/myelinating oligodendrocytes. Pharmacological inhibition of nSMase2 following a demyelinating event could stabilize the structure of these newly formed myelin sheaths and protect them from secondary demyelination.

molecular biology↗

bioRxiv2019

TAPAS: A Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis

Total brain white matter lesion (WML) volume is the most widely established magnetic resonance imaging (MRI) outcome measure in studies of multiple sclerosis (MS). To estimate WML volume, there are a number of automatic segmentation methods, yet, manual delineation remains the gold standard approach. These approaches often yield a probability map to which a threshold is applied to create lesion segmentation masks. Unfortunately, few approaches systematically determine the threshold employed; many methods use a manually selected threshold, thus introducing human error and bias into the automated procedure. In this study, we propose and validate an automatic thresholding algorithm, Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis (TAPAS), to obtain subject-specific threshold estimates for probability map automatic segmentation of T2-weighted (T2) hyperintense WMLs. Using multimodal MRI, the proposed method applies an automatic segmentation algorithm to obtain probability maps. We obtain the true subject-specific threshold that maximizes Sorensen-Dice Similarity Coefficient (DSC). Then the subject-specific thresholds are modeled on a naive estimate of volume using a general additive model. Applying this model, we predict a subject-specific threshold in data not used for training. We ran a Monte Carlo-resampled split-sample cross-validation (100 validation sets) using two data sets: the first obtained from the Johns Hopkins Hospital (JHH) on a Philips 3 Tesla (3T) scanner (n = 94) and a second collected at the Brigham and Womens Hospital (BWH) using a Siemens 3T scanner (n = 40). By means of the proposed automated technique, in the JHH data, we found an average reduction in subject-level absolute error of 0.1 mL per one mL increase in manual volume. Using Bland-Altman analysis, we found that volumetric bias associated with group-level thresholding is mitigated when applying TAPAS. The BWH data showed similar absolute error estimates using group-level thresholding or TAPAS likely since Bland-Altman analyses indicate no systematic biases associated with group or TAPAS volume estimates. The current study presents the first validated fully automated method for subject-specific threshold prediction to segment brain lesions.

neuroscience↗

bioRxiv2017

MIMoSA: A Method for Inter-Modal Segmentation Analysis

Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WML) in multiple sclerosis. While these lesions have been studied for over two decades using MRI technology, automated segmentation remains challenging. Although the majority of statistical techniques for the automated segmentation of WML are based on a single imaging modality, recent advances have used multimodal techniques for identifying WML. Complementary imaging modalities emphasize different tissue properties, which can help identify and characterize interrelated features of lesions. However, prior work has ignored relationships between imaging modalities, which may be informative in this clinical context. To harness the coherent changes in these measurements, we utilized inter-modal coupling regression (IMCo) to estimate the covariance structure across modalities. We then used a local logistic regression, MIMoSA, which leverages new covariance features from IMCo regression as well as the mean structure of each imaging modality in order to model the probability that any voxel is part of a lesion. Finally, we introduced a novel thresholding algorithm to fully automate the estimation of the probability maps to generate fully automated segmentations masks for 94 subjects. To evaluate the performance of the automated segmentations generated using MIMoSA we compared results with gold standard manual segmentations. We demonstrate the superiority of MIMoSA to other automated segmentation techniques by comparing it to the OASIS algorithm as well as LesionTOADS. MIMoSA resulted in statistically significant improvement in lesion segmentation.

neuroscience↗