Reactive astrocytes evolve after brain injury, inflammatory and degenerative diseases, whereby they undergo transcriptomic re-programming. In malignant brain tumors, their function and crosstalk to ...other components of the environment is poorly understood. Here we report a distinct transcriptional phenotype of reactive astrocytes from glioblastoma linked to JAK/STAT pathway activation. Subsequently, we investigate the origin of astrocytic transformation by a microglia loss-of-function model in a human organotypic slice model with injected tumor cells. RNA-seq based gene expression analysis of astrocytes reveals a distinct astrocytic phenotype caused by the coexistence of microglia and astrocytes in the tumor environment, which leads to a large release of anti-inflammatory cytokines such as TGFβ, IL10 and G-CSF. Inhibition of the JAK/STAT pathway shifts the balance of pro- and anti-inflammatory cytokines towards a pro-inflammatory environment. The complex interaction of astrocytes and microglia cells promotes an immunosuppressive environment, suggesting that tumor-associated astrocytes contribute to anti-inflammatory responses.
Objectives
Computed tomography (CT) is employed to evaluate surgical outcome after spinal interventions. Here, we investigate the potential of multispectral photon-counting computed tomography ...(PC-CT) on image quality, diagnostic confidence, and radiation dose compared to an energy-integrating CT (EID-CT).
Methods
In this prospective study, 32 patients underwent PC-CT of the spine. Data was reconstructed in two ways: (1) standard bone kernel with 65-keV (PC-CT
std
) and (2) 130-keV monoenergetic images (PC-CT
130 keV
). Prior EID-CT was available for 17 patients; for the remaining 15, an age–, sex–, and body mass index–matched EID-CT cohort was identified. Image quality (5-point Likert scales on overall, sharpness, artifacts, noise, diagnostic confidence) of PC-CT
std
and EID-CT was assessed by four radiologists independently. If metallic implants were present (
n
= 10), PC-CT
std
and PC-CT
130 keV
images were again assessed by 5-point Likert scales by the same radiologists. Hounsfield units (HU) were measured within metallic artifact and compared between PC-CT
std
and PC-CT
130 keV
. Finally, the radiation dose (CTDI
vol
) was evaluated.
Results
Sharpness was rated significantly higher (
p
= 0.009) and noise significantly lower (
p
< 0.001) in PC-CTstd vs. EID-CT. In the subset of patients with metallic implants, reading scores for PC-CT
130 keV
revealed superior ratings vs. PC-CT
std
for image quality, artifacts, noise, and diagnostic confidence (all
p
< 0.001) accompanied by a significant increase of HU values within the artifact (
p
< 0.001). Radiation dose was significantly lower for PC-CT vs. EID-CT (mean CTDI
vol
: 8.83 vs. 15.7 mGy;
p
< 0.001).
Conclusions
PC-CT of the spine with high-kiloelectronvolt reconstructions provides sharper images, higher diagnostic confidence, and lower radiation dose in patients with metallic implants.
Key Points
•
Compared to energy-integrating CT, photon-counting CT of the spine had significantly higher sharpness and lower image noise while radiation dose was reduced by 45%.
•
In patients with metallic implants, virtual monochromatic photon-counting images at 130 keV were superior to standard reconstruction at 65 keV in terms of image quality, artifacts, noise, and diagnostic confidence.
Abstract
Background
In glioblastoma (GBM), the effects of altered glycocalyx are largely unexplored. The terminal moiety of cell coating glycans, sialic acid, is of paramount importance for cell-cell ...contacts. However, sialic acid turnover in gliomas and its impact on tumor networks remain unknown.
Methods
We streamlined an experimental setup using organotypic human brain slice cultures as a framework for exploring brain glycobiology, including metabolic labeling of sialic acid moieties and quantification of glycocalyx changes. By live, 2-photon and high-resolution microscopy we have examined morphological and functional effects of altered sialic acid metabolism in GBM. By calcium imaging we investigated the effects of the altered glycocalyx on a functional level of GBM networks.
Results
The visualization and quantitative analysis of newly synthesized sialic acids revealed a high rate of de novo sialylation in GBM cells. Sialyltrasferases and sialidases were highly expressed in GBM, indicating that significant turnover of sialic acids is involved in GBM pathology. Inhibition of either sialic acid biosynthesis or desialylation affected the pattern of tumor growth and lead to the alterations in the connectivity of glioblastoma cells network.
Conclusions
Our results indicate that sialic acid is essential for the establishment of GBM tumor and its cellular network. They highlight the importance of sialic acid for glioblastoma pathology and suggest that dynamics of sialylation have the potential to be targeted therapeutically.
Intraoperative histopathological examinations are routinely performed to provide neurosurgeons with information about the entity of tumor tissue. Here, we quantified the neuropathological ...interpretability of stimulated Raman histology (SRH) acquired using a Raman laser imaging system in a routine clinical setting without any specialized training or prior experience. Stimulated Raman scattering microscopy was performed on 117 samples of pathological tissue from 73 cases of brain and spine tumor surgeries. A board-certified neuropathologist — novice in the interpretation of SRH — assessed image quality by scoring subjective tumor infiltration and stated a diagnosis based on the SRH images. The diagnostic accuracy was determined by comparison to frozen hematoxylin–eosin (H&E)-stained sections and the ground truth defined as the definitive neuropathological diagnosis. The overall SRH imaging quality was rated high with the detection of tumor cells classified as inconclusive in only 4.2% of all images. The accuracy of neuropathological diagnosis based on SRH images was 87.7% and was non-inferior to the current standard of fast frozen H&E-stained sections (87.3 vs. 88.9%,
p
= 0.783). We found a substantial diagnostic correlation between SRH-based neuropathological diagnosis and H&E-stained frozen sections (κ = 0.8). The interpretability of intraoperative SRH imaging was demonstrated to be equivalent to the current standard method of H&E-stained frozen sections. Further research using this label-free innovative alternative vs. conventional staining is required to determine to which extent SRH-based intraoperative decision-making can be streamlined in order to facilitate the advancement of surgical neurooncology.
Objectives
To investigate whether in patients undergoing surgery for oral squamous cell carcinoma, stimulated Raman histology (SRH), in comparison with H&E-stained frozen sections, can provide ...accurate diagnoses regarding neoplastic tissue and sub-classification of non-neoplastic tissues.
Materials and methods
SRH, a technology based on Raman scattering, was applied to generate digital histopathologic images of 80 tissue samples obtained from 8 oral squamous cell carcinoma (OSCC) patients. Conventional H&E-stained frozen sections were then obtained from all 80 samples. All images/sections (SRH and H&E) were analyzed for squamous cell carcinoma, normal mucosa, connective tissue, muscle tissue, adipose tissue, salivary gland tissue, lymphatic tissue, and inflammatory cells. Agreement between SRH and H&E was evaluated by calculating Cohen’s kappa. Accuracy of SRH compared to H&E was quantified by calculating sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) as well as area under the receiver operating characteristic curve (AUC).
Results
Thirty-six of 80 samples were classified as OSCC by H&E-based diagnosis. Regarding the differentiation between neoplastic and non-neoplastic tissue, high agreement between H&E and SRH (kappa: 0.880) and high accuracy of SRH (sensitivity: 100%; specificity: 90.91%; PPV: 90.00%, NPV: 100%; AUC: 0.954) were demonstrated. For sub-classification of non-neoplastic tissues, SRH performance was dependent on the type of tissue, with high agreement and accuracy for normal mucosa, muscle tissue, and salivary glands.
Conclusion
SRH provides high accuracy in discriminating neoplastic and non-neoplastic tissues. Regarding sub-classification of non-neoplastic tissues in OSCC patients, accuracy varies depending on the type of tissue examined.
Clinical relevance
This study demonstrates the potential of SRH for intraoperative imaging of fresh, unprocessed tissue specimens from OSCC patients without the need for sectioning or staining.
Histopathological diagnosis is the current standard for the classification of brain and spine tumors. Raman spectroscopy has been reported to allow fast and easy intraoperative tissue analysis. Here, ...we report data on the intraoperative implementation of a stimulated Raman histology (SRH) as an innovative strategy offering intraoperative near real-time histopathological analysis. A total of 429 SRH images from 108 patients were generated and analyzed by using a Raman imaging system (Invenio Imaging Inc.). We aimed at establishing a dedicated workflow for SRH serving as an intraoperative diagnostic, research, and quality control tool in the neurosurgical operating room (OR). First experiences with this novel imaging modality were reported and analyzed suggesting process optimization regarding tissue collection, preparation, and imaging. The Raman imaging system was rapidly integrated into the surgical workflow of a large neurosurgical center. Within a few minutes of connecting the device, the first high-quality images could be acquired in a “plug-and-play” manner. We did not encounter relevant obstacles and the learning curve was steep. However, certain prerequisites regarding quality and acquisition of tissue samples, data processing and interpretation, and high throughput adaptions must be considered. Intraoperative SRH can easily be integrated into the workflow of neurosurgical tumor resection. Considering few process optimizations that can be implemented rapidly, high-quality images can be obtained near real time. Hence, we propose SRH as a complementary tool for the diagnosis of tumor entity, analysis of tumor infiltration zones, online quality and safety control and as a research tool in the neurosurgical OR.
Despite their life-saving capabilities, cerebrospinal fluid (CSF) shunts exhibit high failure rates, with a large fraction of failures attributed to the regulating valve. Due to a lack of methods for ...the detailed analysis of valve malfunctions, failure mechanisms are not well understood, and valves often have to be surgically explanted on the mere suspicion of malfunction. The presented pilot study aims to demonstrate radiological methods for comprehensive analysis of CSF shunt valves, considering both the potential for failure analysis in design optimization, and for future clinical in-vivo application to reduce the number of required shunt revision surgeries. The proposed method could also be utilized to develop and support in situ repair methods (e.g. by lysis or ultrasound) of malfunctioning CSF shunt valves.
The primary methods described are contrast-enhanced radiographic time series of CSF shunt valves, taken in a favorable projection geometry at low radiation dose, and the machine-learning-based diagnosis of CSF shunt valve obstructions. Complimentarily, we investigate CT-based methods capable of providing accurate ground truth for the training of such diagnostic tools. Using simulated test and training data, the performance of the machine-learning diagnostics in identifying and localizing obstructions within a shunt valve is evaluated regarding per-pixel sensitivity and specificity, the Dice similarity coefficient, and the false positive rate in the case of obstruction free test samples.
Contrast enhanced subtraction radiography allows high-resolution, time-resolved, low-dose analysis of fluid transport in CSF shunt valves. Complementarily, photon-counting micro-CT allows to investigate valve obstruction mechanisms in detail, and to generate valid ground truth for machine learning-based diagnostics. Machine-learning-based detection of valve obstructions in simulated radiographies shows promising results, with a per-pixel sensitivity >70%, per-pixel specificity >90%, a median Dice coefficient >0.8 and <10% false positives at a detection threshold of 0.5.
This ex-vivo study demonstrates obstruction detection in cerebro-spinal fluid shunt valves, combining radiological methods with machine learning under conditions compatible to future in-vivo application. Results indicate that high-resolution contrast-enhanced subtraction radiography, possibly including time-series data, combined with machine-learning image analysis, has the potential to strongly improve the diagnostics of CSF shunt valve failures. The presented method is in principle suitable for in-vivo application, considering both measurement geometry and radiological dose. Further research is needed to validate these results on real-world data and to refine the employed methods. In combination, the presented methods enable comprehensive analysis of valve failure mechanisms, paving the way for improved product development and clinical diagnostics of CSF shunt valves.
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