Heterogeneous Tissue Research Articles

Unveiling sequential layered heterogeneity in multi-spectral transmission images clustering through iterative terrace compression with adaptive neighborhoods analysis

Multi-spectral transmission imaging looks forward to an early breast cancer screening with cost-effectiveness, safety, and ease of use. However, the biological tissue of the breast owns scattering and strong absorption properties, resulting in transmission images with a low signal-to-noise ratio and prior investigation focused on detection heterogeneity within a single layer, but the different depths in heterogeneity due to the spherical shape of the breast causing the challenges. Accurately detecting heterogeneity in different layers is crucial due to low contrast, veiling layers, and unclear boundaries, which can be difficult to identify in clustering and segmentation. The breast heterogeneity identification can be handled by combining image processing techniques; nevertheless, considering labeling limits and data accessibility, the clustering algorithm provides an acceptable substitute. Therefore, the paper proposes a novel methodology integrating the iterative terrace compression method with adaptive neighborhoods analysis to identify sequential multi-layered heterogeneity and enhance the clustering of multi-spectral transmission images. Our experimental setup involved the collection of low grayscale images across six wavelengths, followed by advanced image processing techniques, including frame accumulation to improve image signal-to-noise ratio and then polynomial surface fitting to eliminate trend items from uneven light. After that, iterative terrace compression with adaptive neighborhoods analysis is applied to reduce redundancy and unveil heterogeneity in different layers and window functions to remove unnecessary gray information. Finally, clustering combined with pseudo-color is used for clustering analysis and visual representation for aiding in heterogeneity detection. Results indicate a significant improvement in detection metrics, with our method outpacing existing techniques regarding the Dice coefficient, F1 score, and Jaccard. This innovative approach not only enhances the characterization of breast tissue heterogeneities in different layers but also promises practical clinical applications in breast cancer screening.

Single-cell RNA sequencing reveals the CRTAC1+ population actively contributes to the pathogenesis of spinal ligament degeneration by SPP1+ macrophage.

Degenerative spinal stenosis is a chronic disease that affects the spinal ligaments and associated bones, resulting in back pain and disorders of the limbs among the elderly population. There are few preventive strategies for such ligament degeneration. We here aimed to establish a comprehensive transcriptomic atlas of ligament tissues to identify high-priority targets for pharmaceutical treatment of ligament degeneration. Here, single-cell RNA sequencing was performed on six degenerative ligaments and three traumatic ligaments to understand tissue heterogeneity. After stringent quality control, high-quality data were obtained from 32,014 cells. Distinct cell clusters comprising stromal and immune cells were identified in ligament tissues. Among them, we noted that collagen degradation associated with CTHRC1+ fibroblast-like cells and calcification linked to CRTAC1+ chondrocyte-like cells were key features of ligament degeneration. SCENIC analysis and further experiments identified ATF3 as a key transcription factor regulating the pathogenesis of CRTAC1+ chondrocyte-like cells. Typically, immune cells infiltrate localized organs, causing tissue damage. In our study, myeloid cells were found to be inflammatory-activated, and SPP1+ macrophages were notably enriched in degenerative ligaments. Further exploration via CellChat analysis demonstrated a robust interaction between SPP1+ macrophages and CRTAC1+ chondrocyte-like cells. Activated by SPP1, ATF3 propels the CRTAC1/MGP/CLU axis, fostering ligament calcification. Our unique resource provides novel insights into possible mechanisms underlying ligament degeneration, the target cell types, and molecules that are expected to mitigate degenerative spinal ligament. We also highlight the role of immune regulation in ligament degeneration and calcification, enhancing our understanding of this disease.

In vivo optimization of the experimental conditions for the non-invasive optical assessment of breast density

In this study, time domain diffuse optical spectroscopy is performed in the range 600–1100 nm on 11 healthy volunteers with a portable system for the quantitative characterization of breast tissue in terms of optical properties and optically-derived blood parameters, tissue constituent concentrations, and scattering parameters. A measurement protocol involving different geometries (reflectance and transmittance), subject’s positions (sitting and lying down), probing locations (outer, lower, and inner breast quadrants), and source-detector distances (2 and 3 cm) allowed us to investigate the effect of tissue heterogeneity and different measurement configurations on the results with the aim of identifying the best experimental conditions for the estimate of breast density (i.e., amount of fibro-glandular tissue in the breast) as a strong independent risk factor for breast cancer. Transmittance results, that in previous studies correlated strongly with mammographic density, are used as a reference for the initial test of the simpler and more comfortable reflectance measurement configuration. The higher source-detector distance, which probes deeper tissue, retrieves optical outcomes in agreement with higher average density tissue. Similarly, results on the outer quadrants indicate higher density than internal quadrants. These findings are coherent with breast anatomy since the concentration of dense fibro-glandular stroma is higher in deep tissue and towards the external portion of the breast, where the mammary gland is located. The dataset generated with this laboratory campaign is used to device an optimal measurement protocol for a future clinical trial, where optical results will be correlated with conventional mammographic density, allowing us to identify a subset of wavelengths and measurement configurations for an effective estimate of breast density. The final objective is the design of a simplified, compact and cost-effective optical device for a non-invasive, routine assessment of density-associated breast cancer risk.

Reconstruction of the Permittivity of Ex Vivo Animal Tissues in the Frequency Range 1-20 GHz Using a Water-Based Dielectric Model.

Several medical techniques are based on the application of electromagnetic fields (EMFs) on the human body with therapeutic and/or diagnostic aims. The response of human tissues to the applied EMF is mediated by the tissues' dielectric properties, which must therefore be characterized at the frequencies of the considered technique. Due to the heterogeneity and complexity of biological tissues, it is necessary to know their properties in vivo for the specific condition of interest. Traditional techniques for the dielectric characterization of biological tissues are invasive and, as such, not adoptable for this aim. Accordingly, alternative sensors and/or sensing methods are needed. Recently, a new wideband spectroscopy technique was proposed, based on quantities derived from the Magnetic Resonance (MRI) signal. Among these quantities, the water content was proposed to evaluate the dielectric properties at frequencies around a few GHz. This work verifies the possibility of deriving tissues' dielectric properties in the frequency range of 1-20 GHz based on knowledge of the water content. The water content was retrieved through a dehydration procedure for five different ex vivo tissues. The achieved results were compared with references from the literature.

Spatial transcriptomic revealed intratumor heterogeneity and cancer stem cell enrichment in colorectal cancer metastasis

Metastasis is the main cause of mortality in colorectal cancer (CRC) patients. Exploring the mechanisms of metastasis is of great importance in both clinical and fundamental CRC research. CRC is a highly heterogeneous disease with variable therapeutic outcomes of treatment. In this study, we applied spatial transcriptomics (ST) to generate a tissue-wide transcriptome from two primary colorectal cancer tissues and their matched liver metastatic tissues. Spatial RNA information showed intratumoral heterogeneity (ITH) of both primary and metastatic tissues. The comparison of gene expressions across tissues revealed an apparent enrichment of cancer stem cells (CSCs) in metastatic tissues and identified FOXD1 as a novel metastatic CSC marker. Trajectory and pseudo-time analyses revealed distinct evolutionary trajectories and a dedifferentiation-differentiation process during metastasis. CellphoneDB analysis suggested a dominant interaction of CD74-MIF with tumor cells in metastatic tissues. Further analysis confirmed FOXD1 as a maker of CSCs and the predictor of patient survival, especially in metastatic diseases. Our study found ITH of primary and metastatic tissues and provides novel insights into the cellular mechanisms underlying liver metastasis of CRC and foundations for therapeutic strategies for CRC metastasis.

Fast formation and maturation enhancement of human liver organoids using a liver-organoid-on-a-chip.

Background: Spatial and functional hepatic zonation, established by the heterogeneous tissue along the portal-central axis of the liver, is important for ensuring optimal liver function. Researchers have attempted to develop reliable hepatic models to mimic the liver microenvironment and analyze liver function using hepatocytes cultured in the developed systems. However, mimicking the liver microenvironment in vitro remains a great challenge owing to the lack of perfusable vascular networks in the model systems and the limitation in maintaining hepatocyte function over time. Methods: In this study, we established a microphysiological system that operated under continuous flush medium flow, thereby allowing the supply of nutrients and oxygen to liver organoids and the removal of waste and release of cytokines therefrom, similar to the function of blood vessels. Results: The application of microphysiological system to organoid culture was advantageous for reducing the differentiation time and enhancing the functional maturity of human liver organoid. Conclusion: Hence, our microphysiological culture system might open the possibility of the miniaturized liver model system into a single device to enable more rational in vitro assays of liver response.

Primary Intrahepatic Mesothelioma: Case Series and Systematic Review of Literature.

Primary intrahepatic mesothelioma (PIHMM) has been rarely reported. Its typical clinical presentation, radiological features and pathology have not been defined. Here, we aimed to summarize its diagnosis and treatment. We conducted a retrospective analysis of three cases of PIHMM in the First Affiliated Hospital of Zhejiang University School of Medicine and reviewed the current literature to investigate the clinical and pathological characteristics and prognosis of PIHMM. Based on our case series and the literature, the mean age of PIHMM was 59.7 (41-83) years. Most patients present with nonspecific symptoms such as abdominal pain, fever, weight loss and weakness. On imaging, PIHMM usually presented as a solid, heterogeneous soft tissue mass with irregular margins and significant enhancement of the margins in the arterial phase. Immunohistochemical markers such as calretinin, cytokeratin (CK)5/6, D2-40, WT-1, mesothelin CK and vimentin may be useful for diagnosis. The 3-year relapse-free survival rate (RFS) was 51.85%, the 3-year overall survival (OS) rate was 83.33% and the 3-year postoperative overall survival rate was 100%. PIHMM can only be diagnosed by careful postoperative pathology, because of its nonspecific clinical presentations, serological indicators or imaging features. Immunohistochemical staining is very useful to distinguish this tumor from other liver tumors. Surgery is the mainstay of treatment.

Coupling Microdroplet-Based Sample Preparation, Multiplexed Isobaric Labeling, and Nanoflow Peptide Fractionation for Deep Proteome Profiling of the Tissue Microenvironment.

There is increasing interest in developing in-depth proteomic approaches for mapping tissue heterogeneity in a cell-type-specific manner to better understand and predict the function of complex biological systems such as human organs. Existing spatially resolved proteomics technologies cannot provide deep proteome coverage due to limited sensitivity and poor sample recovery. Herein, we seamlessly combined laser capture microdissection with a low-volume sample processing technology that includes a microfluidic device named microPOTS (microdroplet processing in one pot for trace samples), multiplexed isobaric labeling, and a nanoflow peptide fractionation approach. The integrated workflow allowed us to maximize proteome coverage of laser-isolated tissue samples containing nanogram levels of proteins. We demonstrated that the deep spatial proteomics platform can quantify more than 5000 unique proteins from a small-sized human pancreatic tissue pixel (∼60,000 μm2) and differentiate unique protein abundance patterns in pancreas. Furthermore, the use of the microPOTS chip eliminated the requirement for advanced microfabrication capabilities and specialized nanoliter liquid handling equipment, making it more accessible to proteomic laboratories.

Impact of Breast Tissue Heterogeneity on Microwave Ablation Performance: Insights from Computational Modeling

Impact of Breast Tissue Heterogeneity on Microwave Ablation Performance: Insights from Computational Modeling

Differences in the expression heterogeneity of ADC-related markers between primary tumors and metastatic lymph nodes in advanced urothelial cancers.

111 Background: The booming development of antibody‒drug conjugates (ADCs), which represent a novel, effective and low-toxicity therapeutic approach, has dramatically improved clinical outcomes in urothelial cancers, especially those with advanced disease. However, previous studies focused less on the targeted genes expression pattern and heterogeneity in metastatic tissues, which are more critical to inhibit tumor progression. Here, we aimed to explore the expression of potential ADC-related genes, including HER2, HER3, Nectin4 and Trop2 in advanced urothelial cancers between primary tumors and metastatic lymph nodes (mLN) in pairs. Methods: A large cohort of 306 urothelial cancer patients (292 patients, pN+), including 65 renal pelvis, 38 ureter, 179 bladder and 24 urethral cancer patients (10 patients, pN+), from SYSUCC was enrolled. Paraffin-embedded primary tumors and corresponding mLN sections were subjected to immunohistochemistry (IHC) to detect ADC-related gene expression. HER2 IHC scores of 2/3+ were defined as high expression as previously described. HER3, Nectin4 and Trop2 were evaluated by H-scores (range 0-300, rank 0 to 3+) multiplied by the extent and intensity of the staining, and H-scores >15 were considered positive. Results: A total of 74.8% of advanced urothelial cancer patients were HER2 positive (1-3+), and 49.4% had high HER2 expression. High HER2 expression was detected in 33.8%, 42.2%, 56.5% and 50% of renal pelvis, ureter, bladder and urethral primary tumors, respectively. The rate of high HER2 expression in mLNs slightly decreased to 30.1%, 41.7%, 51.9% and 50%, and the concordance rate of HER2 expression between primary and mLNs was 71.9%. Similarly, 27.7%, 42.1%, 52%, and 66.7% of primary tumors were positive for HER3 expression; 72.3%, 89.5%, 77.7%, and 87.5% were positive for Nectin4; and 89.2%, 94.7%, 83.2%, and 95.8% were positive for Trop2 in renal pelvis, ureter, bladder and urethral cancers, respectively. Overall, 71.9%, 50.7%, and 65.3% of patients had consistent expression of HER3, Nectin4 and Trop2 in mLNs, respectively. Survival analysis indicated that the overexpression of HER2, HER3 and Trop2 in advanced urothelial cancers was associated with poor OS (p=0.001, 0.025 and 0.022, respectively). More importantly, we found that patients with higher HER2 expression in mLNs at primary sites had a worse prognosis (p=0.006). Conclusions: We first investigated the heterogeneity of ADC-related marker expression in primary urothelial tumors and mLNs in a large single cohort, especially in patients who underwent radical lymphadenectomy. Therapy targeting HER2-ADCs might show better homogeneity in advanced disease, but it is more recommended to detect IHC staining both in primary and metastatic tissues if possible.

Three-Dimensional Visualization of Breast Cancer Pathology Evolution in Clinical Patient Tissues with NIR-II Imaging.

Breast cancer (BC) is the most common tumor worldwide and requires crucial molecular typing for treatment and prognosis assessment. Currently, approaches like pathological staining, immunohistochemistry (IHC), and immunofluorescence (IF) face limitations due to the low signal-to-background ratio (SBR) and high tumor heterogeneity, resulting in a high misdiagnosis rate. Fluorescent assay in the second near-infrared region (NIR-II, 1000-1700 nm) exhibits ultrahigh SBR owing to diminished scattering and tissue autofluorescence. Here, we present a NIR-II strategy for accurate BC molecular typing and three-dimensional (3D) visualization based on the atomically precise fluorescent Au24Pr1 clusters. Single-atom Pr doping results in 3.9-fold fluorescence enhancement and long-term photostability. The Au24Pr1 clusters possess high fluorescence centered at ∼1100 nm and the SBR on pathological section diagnosis was 4 times higher than that of NIR-I imaging. This enables high spatial resolution 3D visualization of biopsy specimens, which can surmount tissue heterogeneity for clinical diagnosis of BC.

Active Surface-Enhanced Raman Spectroscopy (SERS): A Novel Concept for Enhancing Signal Contrast in Complex Matrices Using External Perturbation.

Noninvasive detection of surface-enhanced Raman spectroscopy (SERS) signals from deep within tissue represents a common challenge in many biological and clinical applications including disease diagnosis and therapy monitoring. Such signals are typically weak and not readily discernible from often much larger Raman and fluorescence background signals (e.g., from surrounding tissue). Consequently, suboptimal sensitivity in the detection of SERS signals is often achieved in these situations. Similar issues can arise in SERS measurements in other diffusely scattering samples and complex matrices. Here, we propose a novel concept, active SERS, for the efficient retrieval of SERS signals from deep within complex matrices such as biological tissues that mitigates these issues. It relies on applying an external perturbation to the sample to alter the SERS signal from nanoparticles (NPs) deep inside the matrix. A measurement with and without, or before and after, such perturbation then can provide powerful contrasting data enabling an effective elimination of the matrix signals to reveal more clearly the desired SERS signal without the interfering background and associated artifacts. The concept is demonstrated using ultrasound (US) as an external source of perturbation and SERS NPs inserted deep within a heterogeneous tissue phantom mimicking a cluster of NPs accumulated within a small target lesion. The overall SERS signal intensity induced by the applied US perturbation decreased by ∼21% and the SERS signal contrast was considerably improved by eliminating subtraction artifacts present in a conventional measurement performed at a neighboring spatial location in a heterogeneous tissue sample. Although the technique was demonstrated with SERS gold NPs with a standard Raman label, it is envisaged that active SERS NPs (both the nanoscale metal geometry and Raman label) could be specifically designed to deliver an augmented response to the external stimulus to further enhance the achievable SERS signal contrast and yield even greater improvement in detection sensitivity. The method was demonstrated using transmission Raman spectroscopy; however, it is also applicable to other Raman implementations including spatially offset Raman spectroscopy and conventional Raman spectroscopy performed both at depth and at surfaces of complex matrices.

Single-cell multiomics analysis reveals cell/tissue-specific associations in bipolar disorder

This study investigates the cellular origin and tissue heterogeneity in bipolar disorder (BD) by integrating multiomics data. Four distinct datasets were employed, including single-cell RNA sequencing (scRNA-seq) data (embryonic and fetal brain, n = 8, 1,266 cells), BD Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data (adult brain, n = 210), BD bulk RNA-seq data (adult brain, n = 314), and BD genome-wide association study (GWAS) summary data (n = 413,466). The integration of scRNA-seq data with multiomics data relevant to BD was accomplished using the single-cell disease relevance score (scDRS) algorithm. We have identified a novel brain cell cluster named ADCY1, which exhibits distinct genetic characteristics. From a high-resolution genetic perspective, glial cells emerge as the primary cytopathology associated with BD. Specifically, astrocytes were significantly related to BD at the RNA-seq level, while microglia showed a strong association with BD across multiple panels, including the transcriptome-wide association study (TWAS), ATAC-seq, and RNA-seq. Additionally, oligodendrocyte precursor cells displayed a significant association with BD in both ATAC-seq and RNA-seq panel. Notably, our investigation of brain regions affected by BD revealed significant associations between BD and all three types of glial cells in the dorsolateral prefrontal cortex (DLPFC). Through comprehensive analyses, we identified several BD-associated genes, including CRMP1, SYT4, UCHL1, and ZBTB18. In conclusion, our findings suggest that glial cells, particularly in specific brain regions such as the DLPFC, may play a significant role in the pathogenesis of BD. The integration of multiomics data has provided valuable insights into the etiology of BD, shedding light on potential mechanisms underlying this complex psychiatric disorder.

Bioprinting of osteochondral tissues: A perspective on current gaps and future trends.

Osteochondral tissue regeneration has remained a critical challenge in orthopaedic surgery, especially due to complications of arthritic degeneration arising out of mechanical dislocations of joints. The common gold standard of autografting has several limitations in presenting tissue engineering strategies to solve the unmet clinical need. However, due to the complexity of joint anatomy, and tissue heterogeneity at the interface, the conventional tissue engineering strategies have certain limitations. The advent of bioprinting has now provided new opportunities for osteochondral tissue engineering. Bioprinting can uniquely mimic the heterogeneous cellular composition and anisotropic extra-cellular matrix (ECM) organization, while allowing for targeted gene delivery to achieve heterotypic differentiation. In this perspective, we discuss the current advances made towards bioprinting of composite osteochondral tissues and present an account of challenges—in terms of tissue integration, long-term survival, and mechanical strength at the time of implantation—required to be addressed for effective clinical translation of bioprinted tissues. Finally, we highlight some of the future trends related to osteochondral bioprinting with the hope of in-clinical translation.

Integration of single-cell datasets depicts profiles of macrophages and fibro/adipogenic progenitors in dystrophic muscle

Single-cell technologies have recently expanded the possibilities for researchers to gain, at an unprecedented resolution level, knowledge about tissue composition, cell complexity, and heterogeneity. Moreover, the integration of data coming from different technologies and sources also offers, for the first time, the possibility to draw a holistic portrait of how cells behave to sustain tissue physiology during the human lifespan and disease.Here, we interrogated and integrated publicly available single-cell RNAseq data to advance the understanding of how macrophages, fibro/adipogenic progenitors, and other cell types establish gene regulatory networks and communicate with each other in the muscle tissue. We identified altered gene signatures and signaling pathways associated with the dystrophic condition, including an enhanced Spp1-Cd44 signaling in dystrophic macrophages. We shed light on the differences among dystrophic muscle aging, considering wild type, mdx, and more severe conditions as in the case of the mdx-2d model. Contextually, we provided details on existing communication relations between muscle niche cell populations, highlighting increased interactions and distinct signaling events that these cells stablish in the dystrophic microenvironment.We believe our findings can help scientists to formulate and test new hypotheses by moving towards a more complete understanding of muscle regeneration and immune system biology.

Deciphering deep-sea chemosynthetic symbiosis by single-nucleus RNA-sequencing.

Bathymodioline mussels dominate deep-sea methane seep and hydrothermal vent habitats and obtain nutrients and energy primarily through chemosynthetic endosymbiotic bacteria in the bacteriocytes of their gill. However, the molecular mechanisms that orchestrate mussel host-symbiont interactions remain unclear. Here, we constructed a comprehensive cell atlas of the gill in the mussel Gigantidas platifrons from the South China Sea methane seeps (1100 m depth) using single-nucleus RNA-sequencing (snRNA-seq) and whole-mount in situ hybridisation. We identified 13 types of cells, including three previously unknown ones, and uncovered unknown tissue heterogeneity. Every cell type has a designated function in supporting the gill's structure and function, creating an optimal environment for chemosynthesis, and effectively acquiring nutrients from the endosymbiotic bacteria. Analysis of snRNA-seq of in situ transplanted mussels clearly showed the shifts in cell state in response to environmental oscillations. Our findings provide insight into the principles of host-symbiont interaction and the bivalves' environmental adaption mechanisms.

Deciphering deep-sea chemosynthetic symbiosis by single-nucleus RNA-sequencing

Bathymodioline mussels dominate deep-sea methane seep and hydrothermal vent habitats and obtain nutrients and energy primarily through chemosynthetic endosymbiotic bacteria in the bacteriocytes of their gill. However, the molecular mechanisms that orchestrate mussel host–symbiont interactions remain unclear. Here, we constructed a comprehensive cell atlas of the gill in the mussel Gigantidas platifrons from the South China Sea methane seeps (1100 m depth) using single-nucleus RNA-sequencing (snRNA-seq) and whole-mount in situ hybridisation. We identified 13 types of cells, including three previously unknown ones, and uncovered unknown tissue heterogeneity. Every cell type has a designated function in supporting the gill’s structure and function, creating an optimal environment for chemosynthesis, and effectively acquiring nutrients from the endosymbiotic bacteria. Analysis of snRNA-seq of in situ transplanted mussels clearly showed the shifts in cell state in response to environmental oscillations. Our findings provide insight into the principles of host–symbiont interaction and the bivalves' environmental adaption mechanisms.

Fat-suppression T2 relaxation time and water fraction predict response to intravenous glucocorticoid therapy for thyroid-associated ophthalmopathy.

To investigate the value of fat-suppression (FS) T2 relaxation time (T2RT) derived from FS T2 mapping and water fraction (WF) derived from T2 IDEAL to predict the treatment response to intravenous glucocorticoids (IVGC) in patients with thyroid-associated ophthalmopathy (TAO) based on texture analysis. In this study, 89 patients clinically diagnosed with active and moderate-to-severe TAO were enroled (responsive group, 48 patients; unresponsive group, 41 patients). The baseline clinical characteristics and texture features were compared between the two groups. Multivariate analysis was performed to identify the independent predictors of treatment response to IVGC. ROC analysis and the DeLong test were used to assess and compare the predictive performance of different models. The responsive group exhibited significantly shorter disease duration and higher 90th percentile of FS T2RT and kurtosis of WF in the extraocular muscle (EOM) and 95th percentile of WF in the orbital fat (OF) than the unresponsive group. Model 2 (disease duration + WF; AUC, 0.816) and model 3 (disease duration + FS T2RT + WF; AUC, 0.823) demonstrated superior predictive efficacy compared to model 1 (disease duration + FS T2RT; AUC, 0.756), while there was no significant difference between models 2 and 3. The orbital tissues of responders exhibited more oedema and heterogeneity. Furthermore, OF is as valuable as EOM for assessing the therapeutic efficacy of IVGC. Finally, WF derived from T2 IDEAL processed by texture analysis can provide valuable information for predicting the treatment response to IVGC in patients with active and moderate-to-severe TAO. The texture features of FS T2RT and WF are different between responders and non-responders, which can be the predictive tool for treatment response to IVGC. Texture analysis can be used for predicting response to IVGC in TAO patients. TAO patients responsive to IVGC show more oedema and heterogeneity in the orbital tissues. WF from T2 IDEAL is a tool to predict the therapeutic response of TAO.

ELIMS: Ensemble Learning-Based Spatial Segmentation of Mass Spectrometry Imaging to Explore Metabolic Heterogeneity.

Spatial segmentation is an essential processing method for image analysis aiming to identify the characteristic suborgans or microregions from mass spectrometry imaging (MSI) data, which is critical for understanding the spatial heterogeneity of biological information and function and the underlying molecular signatures. Due to the intrinsic characteristics of MSI data including spectral nonlinearity, high-dimensionality, and large data size, the common segmentation methods lack the capability for capturing the accurate microregions associated with biological functions. Here we proposed an ensemble learning-based spatial segmentation strategy, named eLIMS, that combines a randomized unified manifold approximation and projection (r-UMAP) dimensionality reduction module for extracting significant features and an ensemble pixel clustering module for aggregating the clustering maps from r-UMAP. Three MSI datasets are used to evaluate the performance of eLIMS, including mouse fetus, human adenocarcinoma, and mouse brain. Experimental results demonstrate that the proposed method has potential in partitioning the heterogeneous tissues into several subregions associated with anatomical structure, i.e., the suborgans of the brain region in mouse fetus data are identified as dorsal pallium, midbrain, and brainstem. Furthermore, it effectively discovers critical microregions related to physiological and pathological variations offering new insight into metabolic heterogeneity.

Improving diffuse optical tomography imaging quality using APU-Net: an attention-based physical U-Net model.

Traditional diffuse optical tomography (DOT) reconstructions are hampered by image artifacts arising from factors such as DOT sources being closer to shallow lesions, poor optode-tissue coupling, tissue heterogeneity, and large high-contrast lesions lacking information in deeper regions (known as shadowing effect). Addressing these challenges is crucial for improving the quality of DOT images and obtaining robust lesion diagnosis. We address the limitations of current DOT imaging reconstruction by introducing an attention-based U-Net (APU-Net) model to enhance the image quality of DOT reconstruction, ultimately improving lesion diagnostic accuracy. We designed an APU-Net model incorporating a contextual transformer attention module to enhance DOT reconstruction. The model was trained on simulation and phantom data, focusing on challenges such as artifact-induced distortions and lesion-shadowing effects. The model was then evaluated by the clinical data. Transitioning from simulation and phantom data to clinical patients' data, our APU-Net model effectively reduced artifacts with an average artifact contrast decrease of 26.83% and improved image quality. In addition, statistical analyses revealed significant contrast improvements in depth profile with an average contrast increase of 20.28% and 45.31% for the second and third target layers, respectively. These results highlighted the efficacy of our approach in breast cancer diagnosis. The APU-Net model improves the image quality of DOT reconstruction by reducing DOT image artifacts and improving the target depth profile.