| 11. |
- Sackmann, Christopher, 1988-, et al.
(författare)
-
Oligomeric amyloid-beta induces early and widespread changes to the proteome in human iPSC-derived neurons
- 2020
-
Ingår i: Scientific Reports. - : Nature Publishing Group. - 2045-2322. ; 10:1
-
Tidskriftsartikel (refereegranskat)abstract
- Alzheimer’s disease (AD) is the most common form of dementia globally and is characterized by aberrant accumulations of amyloid-beta (Aβ) and tau proteins. Oligomeric forms of these proteins are believed to be most relevant to disease progression, with oligomeric amyloid-β (oAβ) particularly implicated in AD. oAβ pathology spreads among interconnected brain regions, but how oAβ induces pathology in these previously unaffected neurons requires further study. Here, we use well characterized iPSC-derived human neurons to study the early changes to the proteome and phosphoproteome after 24 h exposure to oAβ 1-42. Using nLC-MS/MS and label-free quantification, we identified several proteins that are differentially regulated in response to acute oAβ challenge. At this early timepoint, oAβ induced the decrease of TDP-43, heterogeneous nuclear ribonucleoproteins (hnRNPs), and coatomer complex I (COPI) proteins. Conversely, increases were observed in 20 S proteasome subunits and vesicle associated proteins VAMP1/2, as well as the differential phosphorylation of tau at serine 208. These changes show that there are widespread alterations to the neuronal proteome within 24 h of oAβ uptake, including proteins previously not shown to be related to neurodegeneration. This study provides new targets for the further study of early mediators of AD pathogenesis.
|
|
| 12. |
- Sackmann, Christopher, 1988-, et al.
(författare)
-
TDP-43 Is Efficiently Transferred Between Neuron-Like Cells in a Manner Enhanced by Preservation of Its N-Terminus but Independent of Extracellular Vesicles
- 2020
-
Ingår i: Frontiers in Neuroscience. - : FRONTIERS MEDIA SA. - 1662-4548 .- 1662-453X. ; 14
-
Tidskriftsartikel (refereegranskat)abstract
- The misfolding of transactive response DNA-binding protein (TDP-43) is a major contributor to the pathogenesis of TDP-43 proteinopathies, including amyotrophic lateral sclerosis and frontotemporal lobar degeneration with TDP-43 inclusions, but also plays a role in other neurodegenerative diseases including Alzheimer disease. It is thought that different truncations at the N- and C-termini of TDP-43 contribute to its misfolding and aggregation in the brain, and that these aberrant TDP-43 fragments contribute to disease. Despite this, little is known about whether different truncation events influence the proteins transmissibility between cells and how this cell-to-cell transfer occurs. In this study, we use a well-established cellular model to study the efficiency by which full-length and truncated TDP-43 fragments are transferred between neuron-like cells. We demonstrate that preservation of the N-terminus of TDP-43 enhances its transmissibility between cells and that this protein transmission occurs in a manner exclusive of extracellular vesicles, instead requiring cellular proximity for efficient propagation. These data indicate that the N-terminus of TDP-43 might be a useful target in the generation of therapeutics to limit the spread of TDP-43 pathology.
|
|
| 13. |
|
|
| 14. |
- Stenzinger, Albrecht, et al.
(författare)
-
Trailblazing precision medicine in Europe : A joint view by Genomic Medicine Sweden and the Centers for Personalized Medicine, ZPM, in Germany
- 2022
-
Ingår i: Seminars in Cancer Biology. - : Elsevier. - 1044-579X .- 1096-3650. ; 84, s. 242-254
-
Tidskriftsartikel (refereegranskat)abstract
- Over the last decades, rapid technological and scientific advances have led to a merge of molecular sciences and clinical medicine, resulting in a better understanding of disease mechanisms and the development of novel therapies that exploit specific molecular lesions or profiles driving disease. Precision oncology is here used as an example, illustrating the potential of precision/personalized medicine that also holds great promise in other medical fields. Real-world implementation can only be achieved by dedicated healthcare connected centers which amass and build up interdisciplinary expertise reflecting the complexity of precision medicine. Networks of such centers are ideally suited for a nation-wide outreach offering access to precision medicine to patients independent of their place of residence. Two of these multicentric initiatives, Genomic Medicine Sweden (GMS) and the Centers for Personalized Medicine (ZPM) initiative in Germany have teamed up to present and share their views on core concepts, potentials, challenges, and future developments in precision medicine. Together with other initiatives worldwide, GMS and ZPM aim at providing a robust and sustainable framework, covering all components from technology development to clinical trials, ethical and legal aspects as well as involvement of all relevant stakeholders, including patients and policymakers in the field.
|
|
| 15. |
- Wahl, Joel, et al.
(författare)
-
Impact of preprocessing methods on the Raman spectra of brain tissue
- 2022
-
Ingår i: Biomedical Optics Express. - : Optica Publishing Group (formerly OSA). - 2156-7085. ; 13:12, s. 6763-6777
-
Tidskriftsartikel (refereegranskat)abstract
- Delineating cancer tissue while leaving functional tissue intact is crucial in brain tumor resection. Despite several available aids, surgeons are limited by preoperative or subjective tools. Raman spectroscopy is a label-free optical technique with promising indications for tumor tissue identification. To allow direct comparisons between measurements preprocessing of the Raman signal is required. There are many recognized methods for preprocessing Raman spectra; however, there is no universal standard. In this paper, six different preprocessing methods were tested on Raman spectra (n > 900) from fresh brain tissue samples (n = 34). The sample cohort included both primary brain tumors, such as adult-type diffuse gliomas and meningiomas, as well as metastases of breast cancer. Each tissue sample was classified according to the CNS WHO 2021 guidelines. The six methods include both direct and iterative polynomial fitting, mathematical morphology, signal derivative, commercial software, and a neural network. Data exploration was performed using principal component analysis, t-distributed stochastic neighbor embedding, and k-means clustering. For each of the six methods, the parameter combination that explained the most variance in the data, i.e., resulting in the highest Gap-statistic, was chosen and compared to the other five methods. Depending on the preprocessing method, the resulting clusters varied in number, size, and associated spectral features. The detected features were associated with hemoglobin, neuroglobin, carotenoid, water, and protoporphyrin, as well as proteins and lipids. However, the spectral features seen in the Raman spectra could not be unambiguously assigned to tissue labels, regardless of preprocessing method. We have illustrated that depending on the chosen preprocessing method, the spectral appearance of Raman features from brain tumor tissue can change. Therefore, we argue both for caution in comparing spectral features from different Raman studies, as well as the importance of transparency of methodology and implementation of the preprocessing. As discussed in this study, Raman spectroscopy for in vivo guidance in neurosurgery requires fast and adaptive preprocessing. On this basis, a pre-trained neural network appears to be a promising approach for the operating room.
|
|
| 16. |
- Wahl, Joel, et al.
(författare)
-
Raman spectroscopic analysis of fresh tissue samples from brain tumors
- 2021
-
Ingår i: Diffuse Optical Spectroscopy and Imaging VIII. - : SPIE - International Society for Optical Engineering. - 9781510647077 - 9781510647060
-
Konferensbidrag (refereegranskat)abstract
- We have applied a CNN to preprocess Raman spectra from fresh tissue samples from brain tumors. The neural network can handle the variations that occur naturally, which enables explorative data analysis methods such as PCA.
|
|
