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Nenonen, J.

Publications and source records attributed to Nenonen, J..

2 recordsLinked to original sources

Cutaneous T cell lymphoma atlas reveals malignant Th2 cells supported by a B cell-rich microenvironment

Cutaneous T-cell lymphoma (CTCL) is a potentially fatal clonal malignancy of T cells primarily affecting the skin. The most common form of CTCL, mycosis fungoides (MF), can be difficult to diagnose resulting in treatment delay. The pathogenesis of CTCL is not fully understood due to limited data from patient studies. We performed single-cell RNA sequencing and spatial transcriptomics profiling of skin from patients with MF-type CTCL, and an integrated comparative analysis with human skin cell atlas datasets from healthy skin, atopic dermatitis and psoriasis. We reveal the co-optation of Th2-immune gene programmes by malignant CTCL cells and modelling of the tumour microenvironment to support their survival. We identify MHC-II+ fibroblast subsets reminiscent of lymph node T-zone reticular cells and monocyte-derived dendritic cells that can maintain Th2-like tumour cells. CTCL Th2-like tumour cells are spatially associated with B cells, forming aggregates reminiscent of tertiary lymphoid structures which are more prominent with progressive disease. Finally, we validated the enrichment of B cells in CTCL skin infiltrates and its association with disease progression across three independent patient cohorts. Our findings provide diagnostic aids, potential biomarkers for disease staging and therapeutic strategies for CTCL.

genomics↗

Comparison of beamformer implementations for MEG source localization

Beamformers are applied for estimating spatiotemporal characteristics of neuronal sources underlying measured MEG/EEG signals. Several MEG analysis toolboxes include an implementation of a linearly constrained minimum-variance (LCMV) beamformer. However, differences in implementations and in their results complicate the selection and application of beamformers and may hinder their wider adoption in research and clinical use. Additionally, combinations of different MEG sensor types (such as magnetometers and planar gradiometers) and application of preprocessing methods for interference suppression, such as signal space separation (SSS), can affect the results in different ways for different implementations. So far, a systematic evaluation of the different implementations has not been performed. Here, we compared the localization performance of the LCMV beamformer pipelines in four widely used open-source toolboxes (FieldTrip, SPM12, Brainstorm, and MNE-Python) using datasets both with and without SSS interference suppression.\n\nWe analyzed MEG data that were i) simulated, ii) recorded from a static and moving phantom, and iii) recorded from a healthy volunteer receiving auditory, visual, and somatosensory stimulation. We also investigated the effects of SSS and the combination of the magnetometer and gradiometer signals. We quantified how localization error and point-spread volume vary with SNR in all four toolboxes.\n\nWhen applied carefully to MEG data with a typical SNR (3-15 dB), all four toolboxes localized the sources reliably; however, they differed in their sensitivity to preprocessing parameters. As expected, localizations were highly unreliable at very low SNR, but we found high localization error also at very high SNRs. We also found that the SNR improvement offered by SSS led to more accurate localization.

neuroscience↗