Hierarchical Cluster and Region of Interest Analyses Based on Mass Spectrometry Imaging of Human Brain Tumours

Proteomic profiling of FFPE specimens: Discovery of HNRNPA2/B1 and STT3B as biomarkers for determining formalin fixation durations

Recent advancements in proteomics technologies using formalin-fixed paraffin-embedded (FFPE) samples have significantly advanced biomarker discovery. Yet, the effects of varying sample preparation protocols on proteomic analyses remain poorly understood. We analyzed mouse liver FFPE samples that varied in fixatives, fixation duration, and storage temperature using LC/MS. We found that variations in fixation duration significantly affected the abundance of specific proteins, showing that HNRNPA2/B1 demonstrated a significant decrease in abundance in samples fixed for long periods, whereas STT3B exhibited a significant increase in abundance in samples fixed for long durations. These findings were supported by immunohistochemical analysis across liver, spleen, and lung tissues, demonstrating a significant decrease in the nuclear staining of HNRNPA2/B1 in long-duration acid formalin(AF)-fixed FFPE samples, and an increase in cytoplasmic staining of STT3B in long-duration neutral buffered formalin-fixed liver and lung tissues and granular staining in all long-duration AF-fixed FFPE tissue types. Similar trends were observed in the long-duration fixed HeLa cells. These results demonstrate that fixation duration critically affects the proteomic integrity of FFPE samples, emphasizing the urgent need for standardized fixation protocols to ensure consistent and reliable proteomic data. SIGNIFICANCE: The quality of FFPE samples is primarily influenced by the fixation and storage conditions. However, previous studies have mainly focused on their impact on nucleic acids and the extent to which different fixation conditions affect changes in proteins has not been evaluated. In addition, to our knowledge, proteomic research focusing on differences in formalin fixation conditions has not yet been conducted. Here, we analyzed FFPE samples with different formalin fixation and storage conditions using LC/MS and evaluated the impact of different fixation conditions on protein variations. Our study unequivocally established formalin fixation duration as a critical determinant of protein variation in FFPE specimens and successfully identified HNRNPA2/B1 and STT3B as potential biomarkers for predicting formalin fixation duration for the first time. The study findings open new avenues for quality assessment in biomedical research and diagnostics.

Distributions of CHN compounds in meteorites record organic syntheses in the early solar system

Carbonaceous meteorites contain diverse soluble organic compounds. These compounds formed in the early solar system from volatiles accreted on tiny dust particles. However, the difference in the organic synthesis on respective dust particles in the early solar system remains unclear. We found micrometer-scale heterogeneous distributions of diverse CHN_1-2 and CHN_1-2O compounds in two primitive meteorites: the Murchison and NWA 801, using a surface-assisted laser desorption/ionization system connected to a high mass resolution mass spectrometer. These compounds contained mutual relationships of ± H₂, ± CH₂, ± H₂O, and ± CH₂O and showed highly similar distributions, indicating that they are the products of series reactions. The heterogeneity was caused by the micro-scale difference in the abundance of these compounds and the extent of the series reactions, indicating that these compounds formed on respective dust particles before asteroid accretion. The results of the present study provide evidence of heterogeneous volatile compositions and the extent of organic reactions among the dust particles that formed carbonaceous asteroids. The compositions of diverse small organic compounds associated with respective dust particles in meteorites are useful to understand different histories of volatile evolution in the early solar system.

Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools

BACKGROUND

Brain cancer is a destructive and life-threatening disease that imposes immense negative effects on patients' lives. Therefore, the detection of brain tumors at an early stage improves the impact of treatments and increases the patients survival rates. However, detecting brain tumors in their initial stages is a demanding task and an unmet need.

METHODS

The present study presents a comprehensive review of the recent Artificial Intelligence (AI) methods of diagnosing brain tumors using MRI images. These AI techniques can be divided into Supervised, Unsupervised, and Deep Learning (DL) methods.

RESULTS

Diagnosing and segmenting brain tumors usually begin with Magnetic Resonance Imaging (MRI) on the brain since MRI is a noninvasive imaging technique. Another existing challenge is that the growth of technology is faster than the rate of increase in the number of medical staff who can employ these technologies. It has resulted in an increased risk of diagnostic misinterpretation. Therefore, developing robust automated brain tumor detection techniques has been studied widely over the past years.

CONCLUSION

The current review provides an analysis of the performance of modern methods in this area. Moreover, various image segmentation methods in addition to the recent efforts of researchers are summarized. Finally, the paper discusses open questions and suggests directions for future research.

Emerging Computational Methods in Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.

Neuropsychological Assessments of Patients With Acquired Brain Injury: A Cluster Analysis Approach to Address Heterogeneity in Web-Based Cognitive Rehabilitation

We aimed to (1) apply cluster analysis techniques to mixed-type data (numerical and categorical) from baseline neuropsychological standard and widely used assessments of patients with acquired brain injury (ABI) (2) apply state-of-the-art cluster validity indexes (CVI) to assess their internal validity (3) study their external validity considering relevant aspects of ABI rehabilitation such as functional independence measure (FIM) in activities of daily life assessment (4) characterize the identified profiles by using demographic and clinically relevant variables and (5) extend the external validation of the obtained clusters to all cognitive rehabilitation tasks executed by the participants in a web-based cognitive rehabilitation platform (GNPT). We analyzed 1,107 patients with ABI, 58.1% traumatic brain injury (TBI), 21.8% stroke and 20.1% other ABIs (e.g., brain tumors, anoxia, infections) that have undergone inpatient GNPT cognitive rehabilitation from September 2008 to January 2021. We applied the k-prototypes algorithm from the clustMixType R package. We optimized seven CVIs and applied bootstrap resampling to assess clusters stability (fpc R package). Clusters' post hoc comparisons were performed using the Wilcoxon ranked test, paired t-test or Chi-square test when appropriate. We identified a three-clusters optimal solution, with strong stability (>0.85) and structure (e.g., Silhouette > 0.60, Gamma > 0.83), characterized by distinctive level of performance in all neuropsychological tests, demographics, FIM, response to GNPT tasks and tests normative data (e.g., the 3 min cut-off in Trail Making Test-B). Cluster 1 was characterized by severe cognitive impairment (N = 254, 22.9%) the mean age was 47 years, 68.5% patients with TBI and 22% with stroke. Cluster 2 was characterized by mild cognitive impairment (N = 376, 33.9%) mean age 54 years, 53.5% patients with stroke and 27% other ABI. Cluster 3, moderate cognitive impairment (N = 477, 43.2%) mean age 33 years, 83% patients with TBI and 14% other ABI. Post hoc analysis on cognitive FIM supported a significant higher performance of Cluster 2 vs. Cluster 3 (p < 0.001), Cluster 2 vs. Cluster 1 (p < 0.001) and Cluster 3 vs. Cluster 1 (p < 0.001). All patients executed 286,798 GNPT tasks, with performance significantly higher in Cluster 2 and 3 vs. Cluster 1 (p < 0.001).