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Neuroinflammation has been implicated in frontotemporal lobar degeneration (FTLD) pathophysiology, including in genetic forms with microtubule-associated protein tau (MAPT) mutations (FTLD-MAPT) or chromosome 9 open reading frame 72 (C9orf72) repeat expansions (FTLD-C9orf72). Iron accumulation as a marker of neuroinflammation has, however, been understudied in genetic FTLD to date. To investigate the occurrence of cortical iron accumulation in FTLD-MAPT and FTLD-C9orf72, iron histopathology was performed on the frontal and temporal cortex of 22 cases (11 FTLD-MAPT and 11 FTLD-C9orf72). We studied patterns of cortical iron accumulation and its colocalization with the corresponding underlying pathologies (tau and TDP-43), brain cells (microglia and astrocytes), and myelination. Further, with ultrahigh field ex vivo MRI on a subset (four FTLD-MAPT and two FTLD-C9orf72), we examined the sensitivity of T2*-weighted MRI for iron in FTLD. Histopathology showed that cortical iron accumulation occurs in both FTLD-MAPT and FTLD-C9orf72 in frontal and temporal cortices, characterized by a diffuse mid-cortical iron-rich band, and by a superficial cortical iron band in some cases. Cortical iron accumulation was associated with the severity of proteinopathy (tau or TDP-43) and neuronal degeneration, in part with clinical severity, and with the presence of activated microglia, reactive astrocytes and myelin loss. Ultra-high field T2*-weighted MRI showed a good correspondence between hypointense changes on MRI and cortical iron observed on histology. We conclude that iron accumulation is a feature of both FTLD-MAPT and FTLD-C9orf72 and is associated with pathological severity. Therefore, in vivo iron imaging using T2*-weighted MRI or quantitative susceptibility mapping may potentially be used as a noninvasive imaging marker to localize pathology in FTLD.

Brain iron accumulation has been found to accelerate disease progression in amyloid-β(Aβ) positive Alzheimer patients, though the mechanism is still unknown. Microglia have been identified as key players in the disease pathogenesis, and are highly reactive cells responding to aberrations such as increased iron levels. Therefore, using histological methods, multispectral immunofluorescence and an automated in-house developed microglia segmentation and analysis pipeline, we studied the occurrence of iron-accumulating microglia and the effect on its activation state in human Alzheimer brains. We identified a subset of microglia with increased expression of the iron storage protein ferritin light chain (FTL), together with increased Iba1 expression, decreased TMEM119 and P2RY12 expression. This activated microglia subset represented iron-accumulating microglia and appeared morphologically dystrophic. Multispectral immunofluorescence allowed for spatial analysis of FTL+Iba1+-microglia, which were found to be the predominant Aβ-plaque infiltrating microglia. Finally, an increase of FTL+Iba1+-microglia was seen in patients with high Aβ load and Tau load. These findings suggest iron to be taken up by microglia and to influence the functional phenotype of these cells, especially in conjunction with Aβ., Pattern Recognition and Bioinformatics, Comp Graphics & Visualisation

Spatially-resolved omics-data enable researchers to precisely distinguish cell types in tissue and explore their spatial interactions, enabling deep understanding of tissue functionality. To understand what causes or deteriorates a disease and identify related biomarkers, clinical researchers regularly perform large-scale cohort studies, requiring the comparison of such data at cellular level. In such studies, with little a-priori knowledge of what to expect in the data, explorative data analysis is a necessity. Here, we present an interactive visual analysis workflow for the comparison of cohorts of spatially-resolved omics-data. Our workflow allows the comparative analysis of two cohorts based on multiple levels-of-detail, from simple abundance of contained cell types over complex co-localization patterns to individual comparison of complete tissue images. As a result, the workflow enables the identification of cohort-differentiating features, as well as outlier samples at any stage of the workflow. During the development of the workflow, we continuously consulted with domain experts. To show the effectiveness of the workflow, we conducted multiple case studies with domain experts from different application areas and with different data modalities., Accepted Author Manuscript, Comp Graphics & Visualisation