Current methods for cancer detection rely on tissue biopsy, chemical labeling/staining, and examination of the tissue by a pathologist. Though these methods continue to remain the gold standard, they are non-quantitative and susceptible to human error. Fourier transform infrared (FTIR) spectroscopic imaging has shown potential as a quantitative alternative to traditional histology. However, identification of histological components requires reliable classification based on molecular spectra, which are susceptible to artifacts introduced by noise and scattering. Several tissue types, particularly in heterogeneous tissue regions, tend to confound traditional classification methods. Convolutional neural networks (CNNs) are the current state-of-the-art in image classification, providing the ability to learn spatial characteristics of images. In this paper, we demonstrate that CNNs with architectures designed to process both spectral and spatial information can significantly improve classifier performance over per-pixel spectral classification. We report classification results after applying CNNs to data from tissue microarrays (TMAs) to identify six major cellular and acellular constituents of tissue, namely adipocytes, blood, collagen, epithelium, necrosis, and myofibroblasts. Experimental results show that the use of spatial information in addition to the spectral information brings significant improvements in the classifier performance and allows classification of cellular subtypes, such as adipocytes, that exhibit minimal chemical information but have distinct spatial characteristics. This work demonstrates the application and efficiency of deep learning algorithms in improving the diagnostic techniques in clinical and research activities related to cancer.
Background: Sporadic Alzheimer’s disease (sAD) lacks a unifying hypothesis that can account for the lipid peroxidation observed early in the disease, enrichment of ApoE in the core of neuritic plaques, hallmark plaques and tangles, and selective vulnerability of entorhinal-hippocampal structures. Objective: We hypothesized that 1) high expression of ApoER2 (receptor for ApoE and Reelin) helps explain this anatomical vulnerability; 2) lipid peroxidation of ApoE and ApoER2 contributes to sAD pathogenesis, by disrupting neuronal ApoE delivery and Reelin-ApoER2-Dab1 signaling cascades. Methods: In vitro biochemical experiments; Single-marker and multiplex fluorescence-immunohistochemistry (IHC) in postmortem specimens from 26 individuals who died cognitively normal, with mild cognitive impairment or with sAD. Results: ApoE and ApoER2 peptides and proteins were susceptible to attack by reactive lipid aldehydes, generating lipid-protein adducts and crosslinked ApoE-ApoER2 complexes. Using in situ hybridization alongside IHC, we observed that: 1) ApoER2 is strongly expressed in terminal zones of the entorhinal-hippocampal ‘perforant path’ projections that underlie memory; 2) ApoE, lipid aldehyde-modified ApoE, Reelin, ApoER2, and the downstream Reelin-ApoER2 cascade components Dab1 and Thr19-phosphorylated PSD95 accumulated in the vicinity of neuritic plaques in perforant path terminal zones in sAD cases; 3) several ApoE/Reelin-ApoER2-Dab1 pathway markers were higher in sAD cases and positively correlated with histological progression and cognitive deficits. Conclusion: Results demonstrate derangements in multiple ApoE/Reelin-ApoER2-Dab1 axis components in perforant path terminal zones in sAD and provide proof-of-concept that ApoE and ApoER2 are vulnerable to aldehyde-induced adduction and crosslinking. Findings provide the foundation for a unifying hypothesis implicating lipid peroxidation of ApoE and ApoE receptors in sAD.
Mapping biological processes in brain tissues requires piecing together numerous histological observations of multiple tissue samples. We present a direct method that generates readouts for a comprehensive panel of biomarkers from serial whole-brain slices, characterizing all major brain cell types, at scales ranging from subcellular compartments, individual cells, local multi-cellular niches, to whole-brain regions from each slice. We use iterative cycles of optimized 10-plex immunostaining with 10-color epifluorescence imaging to accumulate highly enriched image datasets from individual whole-brain slices, from which seamless signal-corrected mosaics are reconstructed. Specific fluorescent signals of interest are isolated computationally, rejecting autofluorescence, imaging noise, cross-channel bleed-through, and cross-labeling. Reliable large-scale cell detection and segmentation are achieved using deep neural networks. Cell phenotyping is performed by analyzing unique biomarker combinations over appropriate subcellular compartments. This approach can accelerate pre-clinical drug evaluation and system-level brain histology studies by simultaneously profiling multiple biological processes in their native anatomical context.
Background Uncontrolled seizures in patients with gliomas have a significant impact on quality of life and morbidity, yet the mechanisms through which these tumors cause seizures remain unknown. Here, we hypothesize that the active metabolite D-2-hydroxyglutarate (D-2-HG) produced by the IDH-mutant enzyme leads to metabolic disruptions in surrounding cortical neurons that consequently promote seizures. Methods We use a complementary study of in vitro neuron-glial cultures and electrographically sorted human cortical tissue from patients with IDH-mutant gliomas to test this hypothesis. We utilize micro-electrode arrays for in vitro electrophysiological studies in combination with pharmacological manipulations and biochemical studies in order to better elucidate the impact of D-2-HG on cortical metabolism and neuronal spiking activity. Results We demonstrate that D-2-HG leads to increased neuronal spiking activity and promotes a distinct metabolic profile in surrounding neurons, evidenced by distinct metabolomic shifts and increased LDHA expression, as well as upregulation of mTOR signaling. The increases in neuronal activity are induced by mTOR activation and reversed with mTOR inhibition. Conclusion Together, our data suggest that metabolic disruptions in the surrounding cortex due to D-2-HG may be a driving event for epileptogenesis in patients with IDH-mutant gliomas.
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