Tissue Imaging Research Articles

Accurate 3D single-molecule localization via vectorial in situ point spread function retrieval and aberration assessment

Single-molecule localization microscopy (SMLM) gradually plays an important role in deep tissue imaging. However, current SMLM methods primarily rely on fiducial marks, neglecting aberrations introduced by thick samples, thereby resulting in decreased image quality in deep tissues. Here, we introduce vectorial in situ point spread function (PSF) retrieval (VISPR), a method that retrieves a precise PSF model considering both system- and sample-induced aberrations under SMLM conditions. By employing the theory of vectorial PSF model and maximum likelihood estimation (MLE) phase retrieval, VISPR is capable of reconstructing an accurate in situ 3D PSF model achieving the theoretically minimum uncertainty and accurately reflecting three-dimensional information of single molecules. This capability enables accurate 3D super-resolution reconstruction in deep regions away from the coverslips. Additionally, VISPR demonstrates applicability in low signal-to-noise ratio scenarios and compatibility with various SMLM microscope modalities. From both simulations and experiments, we verified the superiority and effectiveness of VISPR. We anticipate that VISPR will become a pivotal tool for advancing deep tissue SMLM imaging.

SHARD: an improved method for staining and visualizing multiplex immunofluorescence in optically cleared postmortem human brain tissue.

Postmortem human brain tissue is a critical resource for studying neurodegenerative disease, providing critical insights into cellular morphology, pathology, and network connectivity. To improve standard microscopy and enable high-resolution, three-dimensional (3D) images of tissues at the subcellular level, tissue-clearing methods have been developed. These 3D images allow for the analysis of large regions of interest and can be used to study structural and spatial changes that occur during neurodegeneration. Additionally, 3D imaging facilitates the visualization of whole-cell morphology, especially in cells with long processes that would otherwise be truncated in single-plane images. Human brain tissue is especially challenging for tissue clearing due to the abundance of lipids in myelin and the need for optimal fixation and low postmortem intervals. Formaldehyde-based fixatives, commonly used in preserving tissue, hinder antibody binding by crosslinking important antibody epitopes, and fluorescent microscopy requires the incorporation of fluorescent labels through passive diffusion or electrophoresis. Recent studies have focused on optimally fixed human brain tissue with short postmortem intervals, limiting the general applicability of these methods. To address these challenges, we developed SHARD (SHIELD, antigen retrieval, and delipidation), a simple and widely applicable method for clearing and labeling human brain tissue, which can be applied to long-term banked human brain tissue preserved in formaldehyde. SHARD is a novel addition to the SHIELD tissue clarification method, combining antigen retrieval, tissue clearing, and staining of 200-μm sections from long-term banked human brain tissue. The SHARD method is effective for postmortem intervals (PMIs) ranging from 10 to 72 h in multiple neurodegenerative diseases and control samples. In this study, we demonstrate that the SHARD method significantly enhances the immunostaining of glial fibrillary acidic protein (GFAP), an astrocytic cytoskeletal marker. Overall, the combination of antigen retrieval and tissue delipidation holds great potential for achieving detailed 3D immunostaining in long-term formaldehyde-fixed postmortem human brain tissue, opening new avenues for research and discovery.

Strangulation

Detection of manual strangulation, choking and hanging, as well as the intensity of these actions, helps to clarify the course of events, to enforce the state's right to prosecute and ultimately to protect against further attacks. However, this is complicated by the scarcity of externally visible findings. The forensic examination of the head and neck after strangulation is carried out by means of external inspection to detect injuries and congestion above the strangulation level. Lesions below the surface of the skin, in particular of the subcutaneous fat tissue, the muscles or the laryngeal structures, usually escape the external inspection. Imaging techniques allow internal injuries to be recorded and objectified, which can be of considerable added value in the collection of evidence. Since criminal proceedings must meet the highest standards of security, high demands are placed on imaging and diagnosis. Imaging techniques are suitable for detecting and objectifying internal injuries after aperson has survived strangulation. However, their excellent reconstruction and visualization capabilities also make them avaluable addition to postmortem examinations. After reported and survived strangulation, choking or hanging, magnetic resonance imaging (MRI) of the soft tissues of the neck should be performed as soon as possible, and if cerebral damage is suspected, the skull should also be examined. If the event was not survived, whole-body computed tomography (CT) is now standard practice at many forensic medicine institutes.

Vision transformer introduces a new vitality to the classification of renal pathology

Recent advancements in computer vision within the field of artificial intelligence (AI) have made significant inroads into the medical domain. However, the application of AI for classifying renal pathology remains challenging due to the subtle variations in multiple renal pathological classifications. Vision Transformers (ViT), an adaptation of the Transformer model for image recognition, have demonstrated superior capabilities in capturing global features and providing greater explainability. In our study, we developed a ViT model using a diverse set of stained renal histopathology images to evaluate its effectiveness in classifying renal pathology. A total of 1861 whole slide images (WSI) stained with HE, MASSON, PAS, and PASM were collected from 635 patients. Renal tissue images were then extracted, tiled, and categorized into 14 classes on the basis of renal pathology. We employed the classic ViT model from the Timm library, utilizing images sized 384 × 384 pixels with 16 × 16 pixel patches, to train the classification model. A comparative analysis was conducted to evaluate the performance of the ViT model against traditional convolutional neural network (CNN) models. The results indicated that the ViT model demonstrated superior recognition ability (accuracy: 0.96–0.99). Furthermore, we visualized the identification process of the ViT models to investigate potentially significant pathological ultrastructures. Our study demonstrated that ViT models outperformed CNN models in accurately classifying renal pathology. Additionally, ViT models are able to focus on specific, significant structures within renal histopathology, which could be crucial for identifying novel and meaningful pathological features in the diagnosis and treatment of renal disease.

Pathologist-level diagnosis of ulcerative colitis inflammatory activity level using an automated histological grading method

Background and AimsInflammatory bowel disease (IBD) is a global disease that is evolving with increasing incidence. However, there are few works on computationally assisted diagnosis of IBD based on pathological images. Therefore, based on the UK and Chinese IBD diagnostic guidelines, our study established an artificial intelligence-assisted diagnostic system for histologic grading of inflammatory activity in ulcerative colitis (UC). MethodsWe proposed an efficient deep-learning (DL) method for grading inflammatory activity in whole-slide images (WSIs) of UC pathology. Our model was constructed using 603 UC WSIs from Nanjing Drum Tower Hospital for model train set and internal test set. We collected 212 UC WSIs from Zhujiang Hospital as an external test set. Initially, the pre-trained ResNet50 model on the ImageNet dataset was employed to extract image patch features from UC patients. Subsequently, a multi-instance learning (MIL) approach with embedded self-attention was utilized to aggregate tissue image patch features, representing the entire WSI. Finally, the model was trained based on the aggregated features and WSI annotations provided by senior gastrointestinal pathologists to predict the level of inflammatory activity in UC WSIs. ResultsIn the task of distinguishing the presence or absence of inflammatory activity, the Area Under Curve (AUC) value in the internal test set is 0.863 (95% confidence interval [CI] 0.829, 0.898), with a sensitivity of 0.913 (95% [CI] 0.866, 0.961), and specificity of 0.816 (95% [CI] 0.771, 0.861). The AUC in the external test set is 0.947 (95% confidence interval [CI] 0.939, 0.955), with a sensitivity of 0.889 (905% [CI] 0.837, 0.940), and specificity of 0.858 (95% [CI] 0.777, 0.939). For distinguishing different levels of inflammatory activity in UC, the average Macro-AUC in the internal test set and the external test set are 0.827 (95% [CI] 0.803, 0.850) and 0.908 (95% [CI] 0.882, 0.935). the average Micro-AUC in the internal test set and the external test set are 0.816 (95% [CI] 0.792, 0.840) and 0.898 (95% [CI] 0.869, 0.926). ConclusionsComparative analysis with diagnoses made by pathologists at different expertise levels revealed that the algorithm reached a proficiency comparable to the pathologist with 5 years of experience. Furthermore, our algorithm performed superior to other MIL algorithms.

Supramolecular Guest Exchange in Cucurbit[7]uril for Bioorthogonal Fluorogenic Imaging across the Visible Spectrum.

Fluorogenic probes that unmask fluorescence signals in response to bioorthogonal reactions are a powerful new addition to biological imaging. They can significantly reduce background fluorescence and minimize nonspecific signals, potentially enabling real-time, high-contrast imaging without the need to wash out excess fluorophores. While diverse classes of highly refined synthetic fluorophores are now readily available, integrating them into a bioorthogonal fluorogenic scheme still requires extensive design efforts and customized structural alterations to optimize quenching mechanisms for each specific fluorophore scaffold. Herein, we present a highly generalizable strategy that can produce an efficient bioorthogonal fluorogenic response from essentially any readily available fluorophore without further structural alterations. We designed this strategy based on the macrocyclic cucurbit[7]uril (CB7) host, where a fluorogenic response is achieved by programming a guest exchange reaction within the macrocyclic cavity. We employed this strategy to rapidly create fluorogenic probes across the visible spectrum from diverse fluorophore scaffolds, which enabled no-wash imaging in live cells and tissues with minimal background signal. Finally, we demonstrated that this strategy can be combined with metabolic labeling for fluorogenic detection of metabolically tagged mycobacteria under no-wash conditions and paired with covalently clickable probes for high-contrast super-resolution and multiplexed imaging in cells and tissues.

Whole-Tumor Clearing and Imaging of Intratumor Microbiota in Three Dimensions with miCDaL Strategy.

Acquiring detailed spatial information about intratumor microbiota in situ is challenging, which leaves 3D distributions of microbiota within entire tumors largely unexplored. Here, a modified iDISCO-CUBIC tissue clearing and D-amino acid microbiome labeling-based (miCDaL) strategy are proposed, that integrates microbiota in situ labeling, tissue clearing, and whole-mount tissue imaging to enable 3D visualization of indigenous intratumor microbiota. Leveraging whole-mount spatial resolution and centimeter-scale imaging depth, the 3D biogeography of microbiota is successfully charted across various tumors at different developmental stages, providing quantitative spatial insights in relation to host tumors. By incorporating an immunostaining protocol, 3D imaging of the immunologic microenvironment is achieved in both murine and human mammary tumors that is previously assumed to be bacteria-free. Notably, immune infiltrates, including T cells and NK cells, and tertiary lymphoid structures are conspicuously absent in bacteria-colonized regions. This 3D imaging strategy for mapping Indigenous intratumor microbiota offers valuable insights into host-microbiota interactions.

Novel imaging and biophysical approaches to study tissue hydraulics in mammalian folliculogenesis

AbstractA key developmental stage in mammalian folliculogenesis is the formation of a fluid-filled lumen (antrum) prior to ovulation. While it has long been speculated that the follicular fluid is essential for oocyte maturation and ovulation, little is known about the morphogenesis and the mechanisms driving the antrum formation and ovulation, potentially due to challenges in imaging tissue dynamics in large tissues. Misregulation of such processes leads to anovulation, a hallmark of infertility in ageing and diseases such as the polycystic ovary syndrome (PCOS). In this review, we discuss recent advances in deep tissue imaging techniques, machine learning and theoretical approaches that have been applied to study development and diseases. We propose that an integrative approach combining these techniques is essential for understanding the physics of hydraulics in follicle development and ovarian functions.

3D X-ray microscope acts as an accurate and effective equipment of pathological diagnosis in craniofacial imaging

Craniofacial structure and dental hard tissue used to be researched on by traditional imaging tools such as light microscope, electron microscope and micro-CT. Due to the limitations of imaging principle, resolution and 3D rendering reconstruction technique, traditional imaging tools are constrained for presenting fine structure and precise measurements. Here a brand-new imaging equipment–3D X-ray microscope is introduced to realize a more efficient scanning by demonstrating the comparison of the craniofacial structures and dental hard tissue of diabetes and normal DBA mouse. To explore a higher resolution, more efficient imaging measurement and 3D reconstruction method on craniofacial structure and dental hard tissue. The study included 12 DBA mice which were divided into two groups (control group and diabetes group). The heads were separated and scanned by 3D X-ray microscope, after which regions of interest were selected, followed by measurement and 3D reconstruction based on microscope attached software Dragonfly pro©. Hemi-mandibles were collected for enamel mineral density assessment supported by QRM-MicroCT-HA phantom. Data was submitted to paired t-tests at a 95% confidence level. The automatic assessed enamel thickness of diabetes mice decreased on average, whereas the rest of manual measurements and automatic assessed density showed no statistical difference. We constructed HA phantom assisted enamel density procedure in Dragonfly software. Craniofacial structure and dental hard tissue were well-presented both in 2D slide and 3D reconstruction viewport by 3D X-ray microscope which can be routinely used as craniofacial structure and dental hard tissue imaging tool.

Superluminescent variants of Gaussia luciferase in living animal deep-tissue imaging

Gaussia luciferase (Gluc) is widely recognized as a powerful bioluminescent tool for biosensing and molecular imaging. However, its utility in mammalian deep tissue imaging and high-throughput applications is hampered by two primary limitations: the emission of blue light and the transient nature of its bioluminescence reaction. In this study, we have engineered two novel luciferases, BeM and SuM, by incorporating known amino acid mutations that have demonstrated the ability to stabilize, enhance, or shift the wavelength of Gluc-catalyzed luminescence towards the red spectrum. The BeM and SuM-coelenterazine systems exhibited a more than 100-fold increase in bioluminescence intensity in vitro, and over a 10-fold enhancement in vivo, compared to the conventional firefly luciferase-D-luciferin system. This significant improvement enables the noninvasive deep tissue imaging of a small number of cells within living mice. BeM and SuM emerge as the most efficient Gaussia luciferase variants to date, showcasing their immense potential as bioengineered light sources. Their applications could span various fields, ranging from fundamental scientific research to clinical diagnostics and therapeutic interventions, marking a significant leap forward in bioluminescence technology.

8515 Role of Insulin Receptor in Trpv1-positive Adipocyte Progenitor Cellsin Adipocyte Differentiation and Metabolic Function

Abstract Disclosure: C. Sahin: None. H. Camara: None. F. Shamsi: None. M. Lynes: None. Y. Tseng: None. Brown adipose tissue (BAT), a specialized fat that dissipates energy to produce heat, is an important endocrine organ in the regulation of energy balance. Stimulating BAT activation and beiging of white adipose tissue (WAT) are new strategies to combat metabolic diseases, such as obesity and diabetes. Traditionally, thermogenic adipocytes are known to originate from mesenchymal Pdgfrα-expressing adipocyte progenitor cells (APCs). A previous study conducted by our lab identified vascular smooth muscle cells as a novel cellular origin of APCs. These cells are characterized by the expression of transient receptor potential cation channel subfamily V member 1 (Trpv1+). With cold exposure, the Trpv1+ APC-derived adipocytes are increased in BAT and WAT and differentiated into highly thermogenic adipocytes. These findings indicate that the Trpv1+-APCs may play an important role in the development of brown and beige adipocytes in response to cold. This study was designed to explore the function of the Trpv1+ APC-derived adipocytes in thermogenic adipocyte differentiation and metabolic regulation. We generated a mouse model in which insulin receptor (IR), a well-known player in adipocyte differentiation, was deleted in the Trpv1+ APCs to impair their adipogenic potential. Trpv1-Cre::mTmG::IRflox transgenic and IRwt control mice were housed at cold temperature (5°C) for 7 days. Because the lineage tracing mTmG system was included, we were able to trace the fate of Trpv1+ APCs in the presence or absence of IR. We first examined the adipogenic capacity of the IR-deficient Trpv1+ cells in BAT and WAT tissues using whole-mounted adipose tissue imaging. In control mice, approximately 30% of all mature adipocytes were derived from Trpv1+ APCs in adipose tissue in response to cold. With ablation of IR in Trpv1+ APCs, this contribution was significantly reduced in BAT (by 11.7 % in females; 6.1% in males), inguinal WAT (by 16.8% in females; 19.3% males), and perigonadal WAT (by 11.4% in females; 14.8% in males) in transgenic mice compared to control mice. These results showed that IR-deficiency in the Trpv1-expressing cells impaired their adipogenesis in both male and female mice. In contrast, thermogenic genes (e.g., Ucp1, Pparγc1a, Cox7a1) were not significantly altered in BAT and inguinal WAT of the IR-deficient Trpv1+ APCs mice. Despite the changes in the adipocyte populations, we observed that deletion of IR in Trpv1+ APCs has no impact on body weight, food intake, fat mass, glucose tolerance, and insulin sensitivity relative to control mice. Taken together, blocking the contribution of Trpv1+ APCs to the mature adipocyte population results in no metabolic changes in mice, suggesting that the other APCs, such as PdgfRα+ APCs, may potentially compensate for impaired adipogenesis in different adipose tissues. Presentation: 6/1/2024

Fourier Ptychographic Coherent Anti-Stokes Raman Scattering Microscopy with Point-Scanning for Super-Resolution Imaging.

Fourier ptychography (FP) is a high resolution wide-field imaging method based on the extended aperture in the Fourier space, which is synthesized from raw images with varying illumination angles. If FP is extended to coherent nonlinear optical imaging, the resolution could be further improved due to the increase of the cutoff frequency of the synthesized coherent optical transfer function (C-OTF) with respect to the order of nonlinear optical processes. However, there is a fundamental conflict between wide-field FP and nonlinear optical imaging, whereby the nonlinear optical imaging typically requires a focused excitation laser beam with high power density. To tackle the problem, in this work, a unique point-scanning FP (PS-FP) method is presented for super-resolution nonlinear optical imaging, in which the nonlinear optical signal is obtained by using focused laser beam, while the conventional FP algorithm can still be used to retrieve the super-resolution image. PS-FP coherent anti-Stokes Raman scattering (PS-FP-CARS) imaging on a variety of samples, where a 1.8-fold expansion of the OTF is achieved experimentally for enhancing vibrational imaging. Further theoretical calculation shows that the C-OTF of PS-FP higher-order CARS (PS-FP-HO-CARS) can be expanded up to ≈4.9-fold, thereby improving the spatial resolution by ≈3-fold in comparison with conventional point-scanning CARS with under tightly focused beams. The generality of PS-FP method developed in this work can be adapted to other coherent nonlinear optical imaging modalities for super-resolution imaging in tissue and cells.

Integrative analysis of H&E and IHC identifies prognostic immune subtypes in HPV related oropharyngeal cancer

BackgroundDeep learning techniques excel at identifying tumor-infiltrating lymphocytes (TILs) and immune phenotypes in hematoxylin and eosin (H&E)-stained slides. However, their ability to elucidate detailed functional characteristics of diverse cellular phenotypes within tumor immune microenvironment (TME) is limited. We aimed to enhance our understanding of cellular composition and functional characteristics across TME regions and improve patient stratification by integrating H&E with adjacent immunohistochemistry (IHC) images.MethodsA retrospective study was conducted on patients with Human Papillomavirus-positive oropharyngeal squamous cell carcinoma (OPSCC). Using paired H&E and IHC slides for 11 proteins, a deep learning pipeline was used to quantify tumor, stroma, and TILs in the TME. Patients were classified into immune inflamed (IN), immune excluded (IE), or immune desert (ID) phenotypes. By registering the IHC and H&E slides, we integrated IHC data to capture protein expression in the corresponding tumor regions. We further stratified patients into specific immune subtypes, such as IN, with increased or reduced CD8+ cells, based on the abundance of these proteins. This characterization provided functional insight into the H&E-based subtypes.ResultsAnalysis of 88 primary tumors and 70 involved lymph node tissue images reveals an improved prognosis in patients classified as IN in primary tumors with high CD8 and low CD163 expression (p = 0.007). Multivariate Cox regression analysis confirms a significantly better prognosis for these subtypes.ConclusionsIntegrating H&E and IHC data enhances the functional characterization of immune phenotypes of the TME with biological interpretability, and improves patient stratification in HPV( + ) OPSCC.

WSSS-CRAM: precise segmentation of histopathological images via class region activation mapping.

Fast, accurate, and automatic analysis of histopathological images using digital image processing and deep learning technology is a necessary task. Conventional histopathological image analysis algorithms require the manual design of features, while deep learning methods can achieve fast prediction and accurate analysis, but rely on the drive of a large amount of labeled data. In this work, we introduce WSSS-CRAM a weakly-supervised semantic segmentation that can obtain detailed pixel-level labels from image-level annotated data. Specifically, we use a discriminative activation strategy to generate category-specific image activation maps via class labels. The category-specific activation maps are then post-processed using conditional random fields to obtain reliable regions that are directly used as ground-truth labels for the segmentation branch. Critically, the two steps of the pseudo-label acquisition and training segmentation model are integrated into an end-to-end model for joint training in this method. Through quantitative evaluation and visualization results, we demonstrate that the framework can predict pixel-level labels from image-level labels, and also perform well when testing images without image-level annotations. Future, we consider extending the algorithm to different pathological datasets and types of tissue images to validate its generalization capability.

EZB-type diffuse large B-cell lymphoma cell lines have superior migration capabilities compared to MCD-type.

Diffuse large B-cell lymphoma (DLBCL) represents the most prevalent aggressive B-cell lymphoma. The group is heterogeneous and the outcome is variable. A variety of approaches have been employed with the objective of improving the stratification of DLBCL patients according to their prognosis, based on the cell of origin. Recently, distinct genetic subtypes of DLBCL have been identified. Given the importance of cell migration in immune cells, the objective of this study was to ascertain whether different genetic subtypes of DLBCL exhibit disparate migration abilities. MCD- and EZB-type DLBCL cell lines were subjected to testing to ascertain their basal velocity in straight microchannels and their ability to overcome tight constrictions of 2 μm. The EZB-type cell lines showed superior basal migration velocity and constriction passage time, and a similar trend was observed in live cell imaging of native human DLBCL tissue. In addition, MCD-type DLBCL exhibited significantly elevated levels of nuclear lamin A/C, which is responsible for the stiffness of the nuclear envelope and could thus explain the disparate migration behaviours observed among these subtypes. Our study suggests that different genetic subtypes of DLBCL may not only influence the outcome after therapy but also the motility of the tumour cells.

Comparative Evaluation of Laser System to Conventional Surgical Approaches in Periodontal Healing Using Optical Coherence Tomography

Background: Optical coherence tomography (OCT) is an emerging, radiation-free diagnostic tool in dentistry, providing high-resolution, real-time imaging of both hard and soft tissues, including periodontal areas, for more accurate postoperative evaluations. This study aims to investigate the efficacy of OCT on periodontal tissues in animals by comparing the healing effects of laser therapy with those of conventional surgical instruments. Methods: Six rabbits underwent periodontal surgery using a laser, scalpel, and punch to perform an apically positioned flap on the mandibular anterior incisors and to create a tongue ulcer on the dorsal surface of the tongue. Visual and OCT evaluations were conducted on days 1, 2, 3, 7, and 14. Results: In periodontal surgery, the laser exhibited slightly faster healing compared to other methods. In tongue ulcer formation, the scalpel and punch groups demonstrated slightly faster healing than that of the laser. However, both methods ultimately showed similar healing outcomes. Conclusions: In the dental field, OCT is emerging as a valuable tool for assessing healing, including early stages of healing, in periodontal therapy.

Enhancing eMSOT with nonlinear mixed model approaches for precise blood oxygenation imaging in tissues

Multispectral optoacoustic tomography (MSOT) utilizes multiple wavelengths to illuminate tissue, producing a series of optoacoustic images rich in spectral information. This approach offers a more comprehensive spectral profile compared to conventional optoacoustic techniques. Contrasted with single-wavelength optoacoustic images, the spectral information can be amalgamated with endogenous chromophores or exogenous dyes within biological organisms, thereby unveiling physiological, cellular, and subcellular functions. The development of eigenspectral optoacoustic tomography (eMSOT), grounded in the linear mixture model (LMM), along with its various derivative methods, facilitates label-free imaging of tissue oxygen saturation in deep-seated structures. However, the effectiveness of the LMM may diminish in the presence of multiple scattering effects or inter-substance interactions, thereby impairing the performance of the eMSOT method in heterogeneous tissues. To address this issue, we propose incorporating a nonlinear model to enhance the eMSOT technique, which we refer to as NL-eMSOT (non-linear eMSOT). This model employs the Hadamard product as a nonlinear component of the LMM, effectively characterizing the interactions between photons and both oxygenated and deoxygenated hemoglobin within the near-infrared spectral window. This innovation resolves the nonlinear unmixing problem inherent in optoacoustic imaging. Our approach, validated through numerical simulations, phantom experiments, and in vivo studies, improves the accuracy of quantitative oxygen saturation estimation in heterogeneous tissues by accounting for inter-substance interactions. Consequently, it necessitates the consideration of more complex mixing models to adequately address nonlinear interactions.

Pathomics signatures and cuproptosis-related genes signatures for prediction of prognosis in patients with hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a common malignant tumor with high heterogeneity and poor prognosis, so early prediction and treatment are still difficult. Cuproptosis is a newly discovered type of programmed cell death that has been shown to be closely related to the occurrence and progression of HCC. Cancer morphology is influenced by genetic drivers, and computational pathology methods typically use tissue images such as entire slide images as input to predict clinical or genetic features. Therefore, the comprehensive analysis of pathological features and genomic data provides a feasible way to explore the potential mechanism of the tumor. The objective of this study was to develop a prediction model for HCC prognosis based on the pathomics signatures (PS) and the genomics signatures (GS). A dataset comprising 315 HCC patients was randomly divided into a training set (n=200) and a validation set (n=115). Prognostic models related to PS and GS were constructed by univariate and multivariate Cox regression analyses and least absolute shrinkage and selection operator (LASSO) regression analysis. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve, univariate and multivariate Cox analyses, and nomogram were used to evaluate the predictive performance of the prognostic model. The prognostic value of the model was internally validated. A prognostic model incorporating clinical features, PS, and GS was developed using Cox regression analysis and LASSO regression analyses. Kaplan-Meier survival analysis revealed statistically significant differences in survival time between high-risk and low-risk subgroups in both the training and validation datasets (PS: P=0.003 and <0.001, respectively; GS: P=0.008 and 0.004, respectively). The time-dependent ROC curve showed favorable predictive value for survival in both the training and validation sets. The area under the ROC curves at 1, 3, and 5 years was 0.750, 0.830, and 0.870 in the training set, and 0.780, 0.810, and 0.760 in the validation set, respectively. A nomogram model based on the risk model score could effectively predict the survival probability of HCC patients. The calibration curves further demonstrated the good predictive capability of the nomogram model. The prognostic model incorporating PS and GS could effectively predict the prognosis of HCC patients.

Cover Picture: Optical imaging for brown or beige adipose tissue (View 5/2024)

Cover Picture: Optical imaging for brown or beige adipose tissue (View 5/2024)

Mid-infrared hyperspectral microscopy with broadband 1-GHz dual frequency combs

Mid-infrared microscopy is an important tool for biological analyses, allowing a direct probe of molecular bonds in their low energy landscape. In addition to the label-free extraction of spectroscopic information, the application of broadband sources can provide a third dimension of chemical specificity. However, to enable widespread deployment, mid-infrared microscopy platforms need to be compact and robust while offering high speed, broad bandwidth, and high signal-to-noise ratio. In this study, we experimentally showcase the integration of a broadband, high-repetition-rate dual-comb spectrometer (DCS) in the mid-infrared range with a scanning microscope. We employ a set of 1-GHz mid-infrared frequency combs, demonstrating their capability for high-speed and broadband hyperspectral imaging of polymers and ovarian tissue. The system covers 1000 cm−1 at νc = 2941 cm−1 with 12.86 kHz spectra acquisition rate and 5 µm spatial resolution. Taken together, our experiments and analysis elucidate the trade-off between bandwidth and speed in DCS as it relates to microscopy. This provides a roadmap for the future advancement and application of high-repetition-rate DCS hyperspectral imaging.