Application of large format tissue processing in the histology laboratory

Large field-of-view metabolic profiling of murine brain tissue following morphine incubation using label-free multiphoton microscopy

BACKGROUND

Although the effects on neural activation and glucose consumption caused by opiates such as morphine are known, the metabolic machinery underlying opioid use and misuse is not fully explored. Multiphoton microscopy (MPM) techniques have been developed for optical imaging at high spatial resolution. Despite the increased use of MPM for neural imaging, the use of intrinsic optical contrast has seen minimal use in neuroscience.

NEW METHOD

We present a label-free, multimodal microscopy technique for metabolic profiling of murine brain tissue following incubation with morphine sulfate (MSO4). We evaluate two- and three-photon excited autofluorescence, and second and third harmonic generation to determine meaningful intrinsic contrast mechanisms in brain tissue using simultaneous label-free, autofluorescence multi-harmonic (SLAM) microscopy.

RESULTS

Regional differences quantified in the cortex, caudate, and thalamus of the brain demonstrate region-specific changes to metabolic profiles measured from FAD intensity, along with brain-wide quantification. While the overall intensity of FAD signal significantly decreased after morphine incubation, this metabolic molecule accumulated near the nucleus accumbens.

COMPARISON WITH EXISTING METHODS

Histopathology requires tissue fixation and staining to determine cell type and morphology, lacking information about cellular metabolism. Tools such as fMRI or PET imaging have been widely used, but lack cellular resolution. SLAM microscopy obviates the need for tissue preparation, permitting immediate use and imaging of tissue with subcellular resolution in its native environment.

CONCLUSIONS

This study demonstrates the utility of SLAM microscopy for label-free investigations of neural metabolism, especially the intensity changes in FAD autofluorescence and structural morphology from third-harmonic generation.

Comparison of two pathological processing methods for large endoscopic submucosal dissection (ESD) specimens

AIMS

Accurate histopathological evaluation of the endoscopic submucosal dissection (ESD) specimens is essential for clinicians to guide further triage and management. This study aimed to report a novel processing technique for large ESD (≥4 cm) specimens.

METHODS

92 patients with colorectal neoplasms who had undergone ESD were included. 46 ESD specimens were treated with conventional handling process, while the rest 46 cases were given the optimised method. Macrobiocassettes and L-shaped embedding moulds were applied in the optimised method. We evaluated the efficacy of this improved procedure in terms of the number of paraffin blocks, storage space and time consumption of pathological assessment.

RESULTS

The average diameter of ESD specimens was 4.5±0.4 cm and 4.7±0.5 cm in the control and test group (p=0.023), respectively. In control group, 398 paraffin blocks of 46 cases were obtained. With the same cases number and larger lesion size, only 276 blocks were achieved in test group (p<0.001). As for the storage space, the total volume of paraffin blocks and slides (4554.0 cm3 and 1207.5 cm3) of optimised method was significantly reduced compared with the control group (6208.8 cm3 and 1741.3 cm3) (p=0.001, p<0.001). In addition, the optimised method was superior to the conventional one in shortening time consumption of pathological assessment (164.5 min and 269.0 min, p<0.001).

CONCLUSIONS

The optimised technique not only reduced the workload and storage space, but also facilitated accurate pathological assessment.

Enhanced study of facial soft tissues using a novel large scale histology technique

The safety and effectiveness of facial cosmetic surgery procedures are dependent on detailed 3D understanding of the complex surgical anatomy of the face. Traditional, small sample size anatomical dissection studies have limitations in providing definitive clarification of the fascial layers of the face, and especially in their relationship with the facial nerve, and their reaction to surgical manipulation. The objective study of large tissue areas is required to effectively demonstrate the broader architecture. Conventional histology techniques were modified to handle extraordinarily large tissue samples to fulfill this requirement. Full-thickness soft tissue samples (skin to bone) of maximum length 18 cm, width 4 cm, and tissue thickness 1 cm, were harvested from 20 hemifaces of 15 fresh human cadavers (mean age at death = 81 years). After fixation, the samples were processed with an automated processor using paraffin wax for 156 h, sectioned at 30 μm, collected on gelatin-chromium-coated glass slides, stained with a Masson's Trichrome technique and photographed. Using this technique, excellent visualization was obtained of the fascial connective tissue and its relationship with the facial mimetic muscles, muscles of mastication and salivary glands in 73 large histological slides. The resulting slides improved the study of the platysma and superficial musculo-aponeurotic system (SMAS), the spaces and ligaments, the malar fat pad, and the facial nerve in relations to the deep fascia. Additionally, surgically induced changes in the soft-tissue organization were successfully visualized. This technique enables improved insight into the broad structural architecture and histomorphology of large-scale facial tissues.

Progress on deep learning in digital pathology of breast cancer: a narrative review

BACKGROUND AND OBJECTIVE

Pathology is the gold standard criteria for breast cancer diagnosis and has important guiding value in formulating the clinical treatment plan and predicting the prognosis. However, traditional microscopic examinations of tissue sections are time consuming and labor intensive, with unavoidable subjective variations. Deep learning (DL) can evaluate and extract the most important information from images with less need for human instruction, providing a promising approach to assist in the pathological diagnosis of breast cancer. To provide an informative and up-to-date summary on the topic of DL-based diagnostic systems for breast cancer pathology image analysis and discuss the advantages and challenges to the routine clinical application of digital pathology.

METHODS

A PubMed search with keywords ("breast neoplasm" or "breast cancer") and ("pathology" or "histopathology") and ("artificial intelligence" or "deep learning") was conducted. Relevant publications in English published from January 2000 to October 2021 were screened manually for their title, abstract, and even full text to determine their true relevance. References from the searched articles and other supplementary articles were also studied.

KEY CONTENT AND FINDINGS

DL-based computerized image analysis has obtained impressive achievements in breast cancer pathology diagnosis, classification, grading, staging, and prognostic prediction, providing powerful methods for faster, more reproducible, and more precise diagnoses. However, all artificial intelligence (AI)-assisted pathology diagnostic models are still in the experimental stage. Improving their economic efficiency and clinical adaptability are still required to be developed as the focus of further researches.

CONCLUSIONS

Having searched PubMed and other databases and summarized the application of DL-based AI models in breast cancer pathology, we conclude that DL is undoubtedly a promising tool for assisting pathologists in routines, but further studies are needed to realize the digitization and automation of clinical pathology.

Updates in staging and pathologic evaluation of esophageal carcinoma following neoadjuvant therapy

Esophageal carcinoma comprises two major subtypes-squamous cell carcinoma and adenocarcinoma, the incidences of which vary widely across the world and also depend on the location within the esophagus. The staging of esophageal cancer (EC) also remains unique among various gastrointestinal carcinomas, as it takes into account the location, histologic type, and grade. Its management has been evolving over the years and the recent American Joint Committee on Cancer staging system has been updated to reflect the changing practice and new data. It is clear that preoperative neoadjuvant therapy is increasingly being used for the treatment of locally advanced esophageal carcinomas, followed by surgical resection that improves survival. A variety of histologic changes can be seen after neoadjuvant therapy, which can be challenging for the pathologists. The presence of residual tumor in the surgically resected specimen and lymph node following neoadjuvant therapy is associated with poor prognosis. Hence, a thorough pathologic assessment of tumor regression grade and accurate tumor staging is required by pathologists to provide valuable prognostic information to guide further management. Tumor regression grading in ECs needs to be improved and standardized.