Nuclear Morphology and the Biology of Cancer Cells

A study to evaluate association of nuclear grooving in benign thyroid lesions with RET/PTC1 and RET/PTC3 gene translocation

INTRODUCTION

Papillary thyroid carcinoma (PTC) is the most common malignant lesion of the thyroid characterized by unique histological features like nuclear grooving, nuclear clearing, and intra-nuclear inclusions. However, nuclear grooves are observed even in benign thyroid lesions (BTL) like nodular goiter (NG), Hashimoto's thyroiditis (HT), and follicular adenoma (FA) resulting in diagnostic dilemma of the presence of PTC in such BTL. RET/PTC gene translocation is one of the most common oncogenic rearrangements seen in PTC, known to be associated with nuclear grooving. Among different types of RET/PTC translocations, RET/PTC1 and RET/PTC3 gene translocations are the most common types. These translocations have also been identified in many BTL like hyperplastic nodules and HT. Our study aimed to determine the frequency of nuclear grooving in BTL and evaluate their association with RET/PTC1 and RET/PTC3 gene translocation.

METHODS

Formalin-fixed, paraffin-embedded (FFPE) tissue blocks of NG, HT, and FA were included in the study. The hematoxylin and eosin (H&E) stained sections were evaluated for the presence of nuclear grooving/high power field (hpf) and a scoring of 0 to 3 was used for the number of grooves. Sections of 10 μ thickness were cut and the cells containing the nuclear grooves were picked using Laser-Capture microdissection. About 20 to 50 such cells were microdissected in each of the cases followed by RNA extraction, cDNA conversion, realtime-polymerase chain reaction (RQ-PCR) for RET/PTC1 and RET/PTC3 gene translocation, and the findings were analyzed for statistical significance.

RESULTS

Out of 87 BTL included in the study, 67 (77.0%) were NG, 12 (13.7%) were HT, and 8 (9.2%) were FA. Thirty-two cases (36.8%) had nuclear grooving with 18 out of 67 NG, 6 out of 12 HT, and all 8 cases of FA showing a varying number of nuclear grooves. A significant association between the number of nuclear grooves with RET/PTC gene translocation (p-value of 0.001) was obtained. A significant association of HT with RET/PTC gene translocation (p-value of 0.038) was observed. RET/PTC1 and RET/PTC3 translocation were seen in 5 out of 87 cases, with HT showing positivity in 2 and FA in 1 case for RET/PTC1 and HT in 1 and FA in 2 cases for RET/PTC3 gene translocation with 1 case of FA being positive for both RET/PTC1 and RET/PTC3 gene translocation.

CONCLUSIONS

The frequency of nuclear grooving among BTLs in our study was 36.8%. Our study shows, that when BTLs, show nuclear grooves, with an increase in the nuclear size, oval and elongated shape, favors the possibility of an underlying genetic aberration like RET/PTC gene translocation, which in turn supports the reporting pathologist to suggest a close follow up of the patients on seeing such nuclear features on cytology or histopathology sample, particularly in HT.

The Structural Profile of HPV 18 in Terms of Chromosomal and Nuclear Degenerative Changes and the Ratio of Nucleus/Cytoplasm on Liquid based Cervical Cells

Background

HPV 18 is one of the important oncogenic types. HPV 18 is generally evaluated together with HPV 16 and/or high-risk HPV types in light microscopic studies. The purpose of this study was to evaluate the impact of only HPV 18 on the nucleus/cytoplasm ratio, and chromosomal and nuclear degenerative changes in liquid-based samples.

Materials and Methods

Eighty liquid-based cervical samples were used in this retrospective study. These smears were prepared by HPV Deoxyribonucleic Acid (DNA) detection and genotyping with the Cobas 4800 HPV system. Forty HPV 18 infected and forty smears with no infection agent were evaluated for chromosomal (nuclear budding, micronuclei), nuclear degenerative changes (membrane irregularity, nuclear enlargement, hyperchromasia, abnormal chromatin distribution, binucleation (BN), karyorrhexis (KR), karyolysis (KL), karyopyknosis (KP)), and cytologic findings (koilocyte (KC), cells with perinuclear PR) using light microscopy. Cellular diameters were evaluated using image analysis software. Statistical analysis was performed with Statistical Package for Social Sciences (SPSS) 19.0. p values < .05 were considered significant.

Results

The statistically significant difference between the presence of HPV 18 and karyorrectic cell, KC, nuclear membrane irregularity, enlargement, the mean nuclear width and height (p < 0.05). No cellular changes other than those mentioned were observed.

Conclusions

The present study is significant in that, it reveals the relationship between only and particularly HPV 18 and nucleus/cytoplasm ratio, and chromosomal and nuclear degenerative changes in liquid-based cytology. HPV 18 affects KR, koilocytosis, nuclear membrane irregularity, enlargement, and nuclear diameters. Light microscopic analysis of these abnormalities increases the sensitivity and specificity of cytology in the evaluation of cellular pictures due to HPV 18.

The presence of circulating genetically abnormal cells in blood predicts risk of lung cancer in individuals with indeterminate pulmonary nodules

PURPOSE

Computed tomography is the standard method by which pulmonary nodules are detected. Greater than 40% of pulmonary biopsies are not lung cancer and therefore not necessary, suggesting that improved diagnostic tools are needed. The LungLB™ blood test was developed to aid the clinical assessment of indeterminate nodules suspicious for lung cancer. LungLB™ identifies circulating genetically abnormal cells (CGACs) that are present early in lung cancer pathogenesis.

METHODS

LungLB™ is a 4-color fluorescence in-situ hybridization assay for detecting CGACs from peripheral blood. A prospective correlational study was performed on 151 participants scheduled for a pulmonary nodule biopsy. Mann-Whitney, Fisher's Exact and Chi-Square tests were used to assess participant demographics and correlation of LungLB™ with biopsy results, and sensitivity and specificity were also evaluated.

RESULTS

Participants from Mount Sinai Hospital (n = 83) and MD Anderson (n = 68), scheduled for a pulmonary biopsy were enrolled to have a LungLB™ test. Additional clinical variables including smoking history, previous cancer, lesion size, and nodule appearance were also collected. LungLB™ achieved 77% sensitivity and 72% specificity with an AUC of 0.78 for predicting lung cancer in the associated needle biopsy. Multivariate analysis found that clinical and radiological factors commonly used in malignancy prediction models did not impact the test performance. High test performance was observed across all participant characteristics, including clinical categories where other tests perform poorly (Mayo Clinic Model, AUC = 0.52).

CONCLUSION

Early clinical performance of the LungLB™ test supports a role in the discrimination of benign from malignant pulmonary nodules. Extended studies are underway.

Nuclear morphology predicts cell survival to cisplatin chemotherapy

The emergence of chemotherapy resistance drives cancer lethality in cancer patients, with treatment initially reducing overall tumor burden followed by resistant recurrent disease. While molecular mechanisms underlying resistance phenotypes have been explored, less is known about the cell biological characteristics of cancer cells that survive to eventually seed the recurrence. To identify the unique phenotypic characteristics associated with survival upon chemotherapy exposure, we characterized nuclear morphology and function as prostate cancer cells recovered following cisplatin treatment. Cells that survived in the days and weeks after treatment and resisted therapy-induced cell death showed increasing cell size and nuclear size, enabled by continuous endocycling resulting in repeated whole genome doubling. We further found that cells that survive after therapy release were predominantly mononucleated and likely employ more efficient DNA damage repair. Finally, we show that surviving cancer cells exhibit a distinct nucleolar phenotype and increased rRNA levels. These data support a paradigm where soon after therapy release, the treated population mostly contains cells with a high level of widespread and catastrophic DNA damage that leads to apoptosis, while the minority of cells that have successful DDR are more likely to access a pro-survival state. These findings are consistent with accession of the polyaneuploid cancer cell (PACC) state, a recently described mechanism of therapy resistance and tumor recurrence. Our findings demonstrate the fate of cancer cells following cisplatin treatment and define key cell phenotypic characteristics of the PACC state. This work is essential for understanding and, ultimately, targeting cancer resistance and recurrence.

Motionless volumetric structured light sheet microscopy

To meet the increasing need for low-cost, compact imaging technology with cellular resolution, we have developed a microLED-based structured light sheet microscope for three-dimensional ex vivo and in vivo imaging of biological tissue in multiple modalities. All the illumination structure is generated directly at the microLED panel-which serves as the source-so light sheet scanning and modulation is completely digital, yielding a system that is simpler and less prone to error than previously reported methods. Volumetric images with optical sectioning are thus achieved in an inexpensive, compact form factor without any moving parts. We demonstrate the unique properties and general applicability of our technique by ex vivo imaging of porcine and murine tissue from the gastrointestinal tract, kidney, and brain.

Microendoscopy in vivo for the pathological diagnosis of cervical precancerous lesions and early cervical cancer

BACKGROUND

Cervical cancer is an important public health problem. Conventional colposcopy is inefficient in the diagnosis of cervical lesions and massive biopsies result in trauma. There is an urgent need for a new clinical strategy to triage women with abnormal cervical screening results immediately and effectively. In this study, the high-resolution microendoscopy combined with methylene blue cell staining technology was used to perform real-time in vivo imaging of the cervix for the first time.

METHODS

A total of 41 patients were enrolled in the study. All patients underwent routine colposcopy and cervical biopsy, and high-resolution images of methylene blue-stained cervical lesions were obtained in vivo using microendoscopy. The cell morphological features of benign and neoplastic cervical lesions stained with methylene blue under microendoscopy were analyzed and summarized. The microendoscopy and histopathology findings of the high-grade squamous intraepithelial lesion (HSIL) and more severe lesions were compared.

RESULTS

The overall consistency of microendoscopy diagnosis with pathological diagnosis was 95.12% (39/41). Diagnostic cell morphological features of cervicitis, low-grade squamous intraepithelial lesion (LSIL), HSIL, adenocarcinoma in situ, and invasive cancer were clearly demonstrated in methylene blue stained microendoscopic images. In HSIL and more severe lesions, microendoscopic methylene blue cell staining technology can show the microscopic diagnostic features consistent with histopathology.

CONCLUSIONS

This study was an initial exercise in the application of the microendoscopy imaging system combined with methylene blue cell staining technology to cervical precancerous lesions and cervical cancer screening. The results provided the basis for a novel clinical strategy for triage of women with abnormal cervical screening results using in vivo non-invasive optical diagnosis technology.

Optical Manipulation Heats up: Present and Future of Optothermal Manipulation

Optothermal manipulation is a versatile technique that combines optical and thermal forces to control synthetic micro-/nanoparticles and biological entities. This emerging technique overcomes the limitations of traditional optical tweezers, including high laser power, photon and thermal damage to fragile objects, and the requirement of refractive-index contrast between target objects and the surrounding solvents. In this perspective, we discuss how the rich opto-thermo-fluidic multiphysics leads to a variety of working mechanisms and modes of optothermal manipulation in both liquid and solid media, underpinning a broad range of applications in biology, nanotechnology, and robotics. Moreover, we highlight current experimental and modeling challenges in the pursuit of optothermal manipulation and propose future directions and solutions to the challenges.

Cyclic Stretch-Induced Mechanical Stress Applied at 1 Hz Frequency Can Alter the Metastatic Potential Properties of SAOS-2 Osteosarcoma Cells

Recently, there has been an increasing focus on cellular morphology and mechanical behavior in order to gain a better understanding of the modulation of cell malignancy. This study used uniaxial-stretching technology to select a mechanical regimen able to elevate SAOS-2 cell migration, which is crucial in osteosarcoma cell pathology. Using confocal and atomic force microscopy, we demonstrated that a 24 h 0.5% cyclic elongation applied at 1 Hz induces morphological changes in cells. Following mechanical stimulation, the cell area enlarged, developing a more elongated shape, which disrupted the initial nuclear-to-cytoplasm ratio. The peripheral cell surface also increased its roughness. Cell-based biochemical assays and real-time PCR quantification showed that these morphologically induced changes are unrelated to the osteoblastic differentiative grade. Interestingly, two essential cell-motility properties in the modulation of the metastatic process changed following the 24 h 1 Hz mechanical stimulation. These were cell adhesion and cell migration, which, in fact, were dampened and enhanced, respectively. Notably, our results showed that the stretch-induced up-regulation of cell motility occurs through a mechanism that does not depend on matrix metalloproteinase (MMP) activity, while the inhibition of ion-stretch channels could counteract it. Overall, our results suggest that further research on mechanobiology could represent an alternative approach for the identification of novel molecular targets of osteosarcoma cell malignancy.

High-Resolution Ultrasound Characterization of Local Scattering in Cancer Tissue

Ultrasound (US) has afforded an approach to tissue characterization for more than a decade. The challenge is to reveal hidden patterns in the US data that describe tissue function and pathology that cannot be seen in conventional US images. Our group has developed a high-resolution analysis technique for tissue characterization termed H-scan US, an imaging method used to interpret the relative size of acoustic scatterers. In the present study, the objective was to compare local H-scan US image intensity with registered histologic measurements made directly at the cellular level. Human breast cancer cells (MDA-MB 231, American Type Culture Collection, Manassas, VA, USA) were orthotopically implanted into female mice (N = 5). Tumors were allowed to grow for approximately 4 wk before the study started. In vivo imaging of tumor tissue was performed using a US system (Vantage 256, Verasonics Inc., Kirkland, WA, USA) equipped with a broadband capacitive micromachined ultrasonic linear array transducer (Kolo Medical, San Jose, CA, USA). A 15-MHz center frequency was used for plane wave imaging with five angles for spatial compounding. H-scan US image reconstruction involved use of parallel convolution filters to measure the relative strength of backscattered US signals. Color codes were applied to filter outputs to form the final H-scan US image display. For histologic processing, US imaging cross-sections were carefully marked on the tumor surface, and tumors were excised and sliced along the same plane. By use of optical microscopy, whole tumor tissue sections were scanned and digitized after nuclear staining. US images were interpolated to have the same number of pixels as the histology images and then spatially aligned. Each nucleus from the histologic sections was automatically segmented using custom MATLAB software (The MathWorks Inc., Natick, MA, USA). Nuclear size and spacing from the histology images were then compared with local H-scan US image features. Overall, local H-scan US image intensity exhibited a significant correlation with both cancer cell nuclear size (R2 > 0.27, p < 0.001) and the inverse relationship with nuclear spacing (R2 > 0.17, p < 0.001).

Lossless Immunocytochemistry Based on Large-Scale Porous Hydrogel Pellicle for Accurate Rare Cell Analysis

Rare cells, such as circulating tumor cells or circulating fetal cells, provide important information for the diagnosis and prognosis of cancer and prenatal diagnosis. Since undercounting only a few cells can lead to significant misdiagnosis and incorrect decisions in subsequent treatment, it is crucial to minimize cell loss, particularly for rare cells. Moreover, the morphological and genetic information on cells should be preserved as intact as possible for downstream analysis. The conventional immunocytochemistry (ICC), however, fails to meet these requirements, causing unexpected cell loss and deformation of the cell organelles which may mislead the classification of benign and malignant cells. In this study, a novel ICC technique for preparing lossless cellular specimens was developed to improve the diagnostic accuracy of rare cell analysis and analyze intact cellular morphology. To this end, a robust and reproducible porous hydrogel pellicle was developed. This hydrogel encapsulates cells to minimize cell loss from the repeated exchange of reagents and prevent cell deformation. The soft hydrogel pellicle allows stable and intact cell picking for further downstream analysis, which is difficult with conventional ICC methods that permanently immobilize cells. The lossless ICC platform will pave the way for robust and precise rare cell analysis toward clinical practice.

Tumor Cellularity Assessment of Breast Histopathological Slides via Instance Segmentation and Pathomic Features Explainability

The segmentation and classification of cell nuclei are pivotal steps in the pipelines for the analysis of bioimages. Deep learning (DL) approaches are leading the digital pathology field in the context of nuclei detection and classification. Nevertheless, the features that are exploited by DL models to make their predictions are difficult to interpret, hindering the deployment of such methods in clinical practice. On the other hand, pathomic features can be linked to an easier description of the characteristics exploited by the classifiers for making the final predictions. Thus, in this work, we developed an explainable computer-aided diagnosis (CAD) system that can be used to support pathologists in the evaluation of tumor cellularity in breast histopathological slides. In particular, we compared an end-to-end DL approach that exploits the Mask R-CNN instance segmentation architecture with a two steps pipeline, where the features are extracted while considering the morphological and textural characteristics of the cell nuclei. Classifiers that are based on support vector machines and artificial neural networks are trained on top of these features in order to discriminate between tumor and non-tumor nuclei. Afterwards, the SHAP (Shapley additive explanations) explainable artificial intelligence technique was employed to perform a feature importance analysis, which led to an understanding of the features processed by the machine learning models for making their decisions. An expert pathologist validated the employed feature set, corroborating the clinical usability of the model. Even though the models resulting from the two-stage pipeline are slightly less accurate than those of the end-to-end approach, the interpretability of their features is clearer and may help build trust for pathologists to adopt artificial intelligence-based CAD systems in their clinical workflow. To further show the validity of the proposed approach, it has been tested on an external validation dataset, which was collected from IRCCS Istituto Tumori "Giovanni Paolo II" and made publicly available to ease research concerning the quantification of tumor cellularity.

Translocation of nuclear chromatin distribution to the periphery reflects dephosphorylated threonine-821/826 of the retinoblastoma protein (pRb) in T24 cells treated with Bacillus Calmette-Guérin

The standard treatment for non-muscle-invasive bladder cancer is intravesical Bacillus Calmette-Guérin (BCG) therapy, which is considered the only intravesical therapy that reduces the risk of progression to muscle-invasive cancer. BCG unresponsiveness, in which intravesical BCG therapy is ineffective, has become a problem. It is thus important to evaluate the effectiveness of BCG treatment for patients as soon as possible in order to identify the optimal therapy. Urine cytology is a noninvasive, easy, and cost-effective method that has been used during BCG treatment, but primarily only to determine benign or malignant status; findings concerning the efficacy of BCG treatment based on urine cytology have not been reported. We investigated the relationship between BCG exposure and nuclear an important criterion in urine cytology, i.e., nuclear chromatin patterns. We used three types of cultured cells to evaluate nuclear chromatin patterns and the cell cycle, and we used T24 cells to evaluate the phosphorylation of retinoblastoma protein (pRb) in six-times of BCG exposures. The results revealed that after the second BCG exposure, (i) nuclear chromatin is distributed predominantly at the nuclear periphery and (ii) the dephosphorylation of threonine-821/826 in pRb occurs. This is the first report of a dynamic change in the nuclear chromatin pattern induced by exposure to BCG. Molecular findings also suggested a relationship between this phenomenon and cell-cycle proteins. Although these results are preliminary, they contribute to our understanding of the cytomorphological changes that occur with BCG exposure.

Nuclear envelope, chromatin organizers, histones, and DNA: The many achilles heels exploited across cancers

In eukaryotic cells, the genome is organized in the form of chromatin composed of DNA and histones that organize and regulate gene expression. The dysregulation of chromatin remodeling, including the aberrant incorporation of histone variants and their consequent post-translational modifications, is prevalent across cancers. Additionally, nuclear envelope proteins are often deregulated in cancers, which impacts the 3D organization of the genome. Altered nuclear morphology, genome organization, and gene expression are defining features of cancers. With advances in single-cell sequencing, imaging technologies, and high-end data mining approaches, we are now at the forefront of designing appropriate small molecules to selectively inhibit the growth and proliferation of cancer cells in a genome- and epigenome-specific manner. Here, we review recent advances and the emerging significance of aberrations in nuclear envelope proteins, histone variants, and oncohistones in deregulating chromatin organization and gene expression in oncogenesis.

Three-Dimensional Collagen Topology Shapes Cell Morphology, beyond Stiffness

Cellular heterogeneity is associated with many physiological processes, including pathological ones, such as morphogenesis and tumorigenesis. The extracellular matrix (ECM) is a key player in the generation of cellular heterogeneity. Advances in our understanding rely on our ability to provide relevant in vitro models. This requires obtainment of the characteristics of the tissues that are essential for controlling cell fate. To do this, we must consider the diversity of tissues, the diversity of physiological contexts, and the constant remodeling of the ECM along these processes. To this aim, we have fabricated a library of ECM models for reproducing the scaffold of connective tissues and the basement membrane by using different biofabrication routes based on the electrospinning and drop casting of biopolymers from the ECM. Using a combination of electron microscopy, multiphoton imaging, and AFM nanoindentation, we show that we can vary independently protein composition, topology, and stiffness of ECM models. This in turns allows one to generate the in vivo complexity of the phenotypic landscape of ovarian cancer cells. We show that, while this phenotypic shift cannot be directly correlated with a unique ECM feature, the three-dimensional collagen fibril topology patterns cell shape, beyond protein composition and stiffness of the ECM. On this line, this work is a further step toward the development of ECM models recapitulating the constantly remodeled environment that cells face and thus provides new insights for cancer model engineering and drug testing.

Cells infected with human papilloma pseudovirus display nuclear reorganization and heterogenous infection kinetics

Human papillomaviruses (HPV) are small, non-enveloped DNA viruses, which upon chronic infection can provoke cervical and head-and-neck cancers. Although the infectious life cycle of HPV has been studied and a vaccine is available for the most prevalent cancer-causing HPV types, there are no antiviral agents to treat infected patients. Hence, there is a need for novel therapeutic entry points and a means to identify them. In this work, we have used high-content microscopy to quantitatively investigate the early phase of HPV infection. Human cervical cancer cells and immortalized keratinocytes were exposed to pseudoviruses (PsV) of the widespread HPV type 16, in which the viral genome was replaced by a pseudogenome encoding a fluorescent reporter protein. Using the fluorescent signal as readout, we measured differences in infection between cell lines, which directly correlated with host cell proliferation rate. Parallel multiparametric analysis of nuclear organization revealed that HPV PsV infection alters nuclear organization and inflates promyelocytic leukemia protein body content, positioning these events at the early stage of HPV infection, upstream of viral replication. Time-resolved analysis revealed a marked heterogeneity in infection kinetics even between two daughter cells, which we attribute to differences in viral load. Consistent with the requirement for mitotic nuclear envelope breakdown, pharmacological inhibition of the cell cycle dramatically blunted infection efficiency. Thus, by systematic image-based single cell analysis, we revealed phenotypic alterations that accompany HPV PsV infection in individual cells, and which may be relevant for therapeutic drug screens.

UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues

Upcoming technologies enable routine collection of highly multiplexed (20-60 channel), subcellular resolution images of mammalian tissues for research and diagnosis. Extracting single cell data from such images requires accurate image segmentation, a challenging problem commonly tackled with deep learning. In this paper, we report two findings that substantially improve image segmentation of tissues using a range of machine learning architectures. First, we unexpectedly find that the inclusion of intentionally defocused and saturated images in training data substantially improves subsequent image segmentation. Such real augmentation outperforms computational augmentation (Gaussian blurring). In addition, we find that it is practical to image the nuclear envelope in multiple tissues using an antibody cocktail thereby better identifying nuclear outlines and improving segmentation. The two approaches cumulatively and substantially improve segmentation on a wide range of tissue types. We speculate that the use of real augmentations will have applications in image processing outside of microscopy.

The solvent chosen for the manufacturing of electrospun polycaprolactone scaffolds influences cell behavior of lung cancer cells

The development of a trustworthy in vitro lung cancer model is essential to better understand the illness, find novel biomarkers, and establish new treatments. Polycaprolactone (PCL) electrospun nanofibers are a cost-effective and ECM-like approach for 3D cell culture. However, the solvent used to prepare the polymer solution has a significant impact on the fiber morphology and, consequently, on the cell behavior. Hence, the present study evaluated the effect of the solvent employed in the manufacturing on the physical properties of 15%-PCL electrospun scaffolds and consequently, on cell behavior of NCI-H1975 lung adenocarcinoma cells. Five solvents mixtures (acetic acid, acetic acid-formic acid (3:1, v/v), acetone, chloroform-ethanol (7:3, v/v), and chloroform-dichloromethane (7:3, v/v)) were tested. The highest cell viability ([Formula: see text] = 33.4%) was found for cells cultured on chloroform-ethanol (7:3) PCL scaffolds. Chloroform-dichloromethane (7:3) PCL scaffolds exhibited a roughness that enhanced the quality of electrospun filament, in terms of cell viability. Our findings highlighted the influence of the solvent on fiber morphology and protein adsorption capacity of nanofilaments. Consequently, these features directly affected cell attachment, morphology, and viability.

Skin cancer margin detection using nanosensitive optical coherence tomography and a comparative study with confocal microscopy

Excision biopsy and histology represent the gold standard for morphological investigation of the skin, in particular for cancer diagnostics. Nevertheless, a biopsy may alter the original morphology, usually requires several weeks for results, is non-repeatable on the same site and always requires an iatrogenic trauma. Hence, diagnosis and clinical management of diseases may be substantially improved by new non-invasive imaging techniques. Optical Coherence Tomography (OCT) is a non-invasive depth-resolved optical imaging modality based on low coherence interferometry that enables high-resolution, cross-sectional imaging in biological tissues and it can be used to obtain both structural and functional information. Beyond the resolution limit, it is not possible to detect structural and functional information using conventional OCT. In this paper, we present a recently developed technique, nanosensitive OCT (nsOCT), improved using broadband supercontinuum laser, and demonstrate nanoscale sensitivity to structural changes within ex vivo human skin tissue. The extended spectral bandwidth permitted access to a wider distribution of spatial frequencies and improved the dynamic range of the nsOCT. Firstly, we demonstrate numerical and experimental detection of a few nanometers structural difference using the nsOCT method from single B-scan images of phantoms with sub-micron periodic structures, acting like Bragg gratings, along the depth. Secondly, our study shows that nsOCT can distinguish nanoscale structural changes at the skin cancer margin from the healthy region in en face images at clinically relevant depths. Finally, we compare the nsOCT en face image with a high-resolution confocal microscopy image to confirm the structural differences between the healthy and lesional/cancerous regions, allowing the detection of the skin cancer margin.

Mutually exclusive expression of EZH2 and H3K27me3 in non-small cell lung carcinoma

Enhancer of zeste homolog 2 (EZH2) epigenetically represses gene expression via trimethylation of lysine 27 on histone 3 (H3K27me3). Non-small cell carcinoma (NSCLC) has been reported to show high EZH2 and low H3K27me3 expression compared to normal lung tissues, but there are no studies examining the expression of EZH2 and H3K27me3 simultaneously with immunohistochemistry. In the present study, the expression of EZH2 and H3K27me3 was examined in surgically resected NSCLC. We enrolled 27 cases of squamous cell carcinoma (SCC), 73 cases of Lepidic, 77 of Papillary/Acinar, 51 of Solid, 31 of Micropapillary, and 12 of Mucinous subtypes of adenocarcinoma. First, we examined the expression of EZH2 and H3K27me3 in normal and metaplastic bronchial epithelium adjacent to NSCLC. Normal bronchial epithelium showed EZH2 expression in a limited number of basal cells and H3K27me3 expression in surface differentiated cells with cilia or mucus. In metaplastic bronchial epithelium, the number of EZH2-positive cells increased in multilayered basal cells, and H3K27me3-positive cells were observed in the superficial layer. Then, EZH2 and H3K27me3 expression was analyzed in NSCLC. Abundant EZH2 and rare H3K27me3 expression was detected in SCC, Papillary/Acinar, Solid and Micropapillary subtypes. In Mucinous subtype, EZH2 expression was hardly detected, and H3K27me3 expression was detected in almost all tumor cells. EZH2-expressing and H3K27me3-expressing tumor cells were similarly observed in Lepidic subtype, but double immunofluorescence revealed that EZH2 and H3K27me3 expression pattern was mutually exclusive. No co-expression of EZH2 and H3K27me3 was detected in all examined subtypes. To our knowledge, there have been no reports describing mutually exclusive expression pattern of EZH2 and H3K27me3.

Anaplastik tiroid kanseri hücre hattının morfolojik analizi

Aim: Thyroid follicular cell derived cancers are classified into three groups such as papillary thyroid cancer (85%), follicular thyroid cancer (12%) and anaplastic (undifferentiated) thyroid cancer (ATC) (3%). ATCs have very rapid course, poor treatment outcomes and they are very aggressive. The aim of current study was to assess the analysis of the morphological differences of ATC cell line with the normal thyroid cell line (NTC). Materials and Methods: NTH and ATC cells were examined with haematoxylin and eosin, the nucleus: cytoplasm (N:C) ratios were detected, and cell cycles were investigated. These cell lines were compared according to their N:C ratio and their abundance in cell cycle phases. Results: The N:C ratio was higher in ATC than NTC. Both cell groups were mostly found in G0/G1 phase (68.4; 82.8) and have statistical difference in both G0/G1 and S phases. Conclusion: The rapid course and the rarity of ATC are significant barriers for clinical trials. Cultured cell lines are very important to explore the behaviour in the biology of ATC cells (such as the cell cycle), to understand the course of the disease, and to find an effective target for treatment.

LiveCellMiner: A new tool to analyze mitotic progression

Live-cell imaging has become state of the art to accurately identify the nature of mitotic and cell cycle defects. Low- and high-throughput microscopy setups have yield huge data amounts of cells recorded in different experimental and pathological conditions. Tailored semi-automated and automated image analysis approaches allow the analysis of high-content screening data sets, saving time and avoiding bias. However, they were mostly designed for very specific experimental setups, which restricts their flexibility and usability. The general need for dedicated experiment-specific user-annotated training sets and experiment-specific user-defined segmentation parameters remains a major bottleneck for fully automating the analysis process. In this work we present LiveCellMiner, a highly flexible open-source software tool to automatically extract, analyze and visualize both aggregated and time-resolved image features with potential biological relevance. The software tool allows analysis across high-content data sets obtained in different platforms, in a quantitative and unbiased manner. As proof of principle application, we analyze here the dynamic chromatin and tubulin cytoskeleton features in human cells passing through mitosis highlighting the versatile and flexible potential of this tool set.

An efficient deep equilibrium model for medical image segmentation

In this paper, we propose an effective method that takes the advantages of classical methods and deep learning technology for medical image segmentation through modeling the neural network as a fixed point iteration seeking for system equilibrium by adding a feedback loop. In particular, the nuclear segmentation of medical image is used as an example to demonstrate the proposed method where it can successfully complete the challenge of segmenting nuclei from cells in different histopathological images. Specifically, the nuclei segmentation is formulated as a dynamic process to search for the system equilibrium. Starting from an initial segmentation generated either by a classic algorithm or pre-trained deep learning model, a sequence of segmentation output is created and combined with the original image to dynamically drive the segmentation towards the expected value. This dynamical extension to neural networks requires little extra change on the backbone deep neural network while it significantly increased model accuracy, generalizability, and stability as demonstrated by intensive experimental results from pathological images of different tissue types across different open datasets.

Scaling multi-instance support vector machine to breast cancer detection on the BreaKHis dataset

MOTIVATION

Breast cancer is a type of cancer that develops in breast tissues, and, after skin cancer, it is the most commonly diagnosed cancer in women in the United States. Given that an early diagnosis is imperative to prevent breast cancer progression, many machine learning models have been developed in recent years to automate the histopathological classification of the different types of carcinomas. However, many of them are not scalable to large-scale datasets.

RESULTS

In this study, we propose the novel Primal-Dual Multi-Instance Support Vector Machine to determine which tissue segments in an image exhibit an indication of an abnormality. We derive an efficient optimization algorithm for the proposed objective by bypassing the quadratic programming and least-squares problems, which are commonly employed to optimize Support Vector Machine models. The proposed method is computationally efficient, thereby it is scalable to large-scale datasets. We applied our method to the public BreaKHis dataset and achieved promising prediction performance and scalability for histopathological classification.

AVAILABILITY AND IMPLEMENTATION

Software is publicly available at: https://1drv.ms/u/s!AiFpD21bgf2wgRLbQq08ixD0SgRD?e=OpqEmY.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identifying pathological slices of gastric cancer via deep learning

BACKGROUND

The accuracy of histopathology diagnosis largely depends on the pathologist's experience. It usually takes over 10 years to cultivate a senior pathologist, and small numbers of them lead to a high workload for those available. Meanwhile, inconsistent diagnostic results may arise among different pathologists, especially in complex cases, because diagnosis based on morphology is subjective. Computerized analysis based on deep learning has shown potential benefits as a diagnostic strategy.

METHODS

This research aims to automatically determine the location of gastric cancer (GC) in the images of GC slices through artificial intelligence. We use image data from a regional teaching hospital in Taiwan for training. We collect images of patients diagnosed with GC from January 1, 2019 to December 31, 2020. In this study, scanned images are used to dissect 13,600 images from 50 different patients with GC sections whose GC sections are stained with hematoxylin and eosin (H&E stained) through a whole slide scanner, the scanned images from 50 different GC slice patients are divided into 80% training combinations, 2200 images of 40 patients are trained. The remaining 20%, totaling 10 people, are validated from a test set of 550 images.

RESULTS

The validation results show that 91% of the correct rates are interpreted as GC images through deep learning. The sensitivity, specificity, PPV, and NPV were 84.9%, 94%, 87.7%, and 92.5%, respectively. After creating a 3D model through the grayscale value, the position of the GC is completely marked by the 3D model. The purpose of this research is to use artificial intelligence (AI) to determine the location of the GC in the image of GC slice.

CONCLUSION

In patients undergoing pancreatectomy for pancreatic cancer, intraoperative infusion of lidocaine did not improve overall or disease-free survival. Reduced formation of circulating NETs was absent in pancreatic tumour tissue.

CONCLUSION

For AI to assist pathologists in daily practice, to help a pathologist making a definite diagnosis is not the prime purpose at present time. The benefits could come from cancer screening and double-check quality control in a heavy workload which could distract the attention of pathologist during the time constraint examination process. We propose a two-steps method to identify cancerous areas in endoscopic gastric biopsy slices via deep learning. Then a 3D model is used to further mark all the positions of GC in the picture, and the model overcomes the problem that deep learning can't catch all GC.

Machine Learning to Predict Risk of Relapse Using Cytologic Image Markers in Patients With Acute Myeloid Leukemia Posthematopoietic Cell Transplantation

PURPOSE

Allogenic hematopoietic stem-cell transplant (HCT) is a curative therapy for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). Relapse post-HCT is the most common cause of treatment failure and is associated with a poor prognosis. Pathologist-based visual assessment of aspirate images and the manual myeloblast counting have shown to be predictive of relapse post-HCT. However, this approach is time-intensive and subjective. The premise of this study was to explore whether computer-extracted morphology and texture features from myeloblasts' chromatin patterns could help predict relapse and prognosticate relapse-free survival (RFS) after HCT.

MATERIALS AND METHODS

In this study, Wright-Giemsa-stained post-HCT aspirate images were collected from 92 patients with AML/MDS who were randomly assigned into a training set (St = 52) and a validation set (Sv = 40). First, a deep learning-based model was developed to segment myeloblasts. A total of 214 texture and shape descriptors were then extracted from the segmented myeloblasts on aspirate slide images. A risk score on the basis of texture features of myeloblast chromatin patterns was generated by using the least absolute shrinkage and selection operator with a Cox regression model.

RESULTS

The risk score was associated with RFS in St (hazard ratio = 2.38; 95% CI, 1.4 to 3.95; P = .0008) and Sv (hazard ratio = 1.57; 95% CI, 1.01 to 2.45; P = .044). We also demonstrate that this resulting signature was predictive of AML relapse with an area under the receiver operating characteristic curve of 0.71 within Sv. All the relevant code is available at GitHub.

CONCLUSION

The texture features extracted from chromatin patterns of myeloblasts can predict post-HCT relapse and prognosticate RFS of patients with AML/MDS.

Impact of Nuclear Envelope Stress on Physiological and Pathological Processes in Central Nervous System

The nuclear envelope (NE) separates genomic DNA from the cytoplasm and provides the molecular platforms for nucleocytoplasmic transport, higher-order chromatin organization, and physical links between the nucleus and cytoskeleton. Recent studies have shown that the NE is often damaged by various stresses termed "NE stress", leading to critical cellular dysfunction. Accumulating evidence has revealed the crucial roles of NE stress in the pathology of a broad spectrum of diseases. In the central nervous system (CNS), NE dysfunction impairs neural development and is associated with several neurological disorders, such as Alzheimer's disease and autosomal dominant leukodystrophy. In this review, the structure and functions of the NE are summarized, and the concepts of NE stress and NE stress responses are introduced. Additionally, the significant roles of the NE in the development of CNS and the mechanistic connections between NE stress and neurological disorders are described.

Identification of leader cells by filopodia in collective cell migration using computer vision

BACKGROUND: Imaging of cells and cellular organelles has been of great interest among researchers and medical staff because it can provide useful information on cell physiology and pathology. Many researches related to collective cell migration have been established and leader cells seem to be the ones that regulate the migration, however, the identification of leader cells is very time-consuming. OBJECTIVE: This study utilized computer vision with deep learning to segment cell shape and to identify leader cells through filopodia. METHODS: Healthy Madin–Darby Canine Kidney (MDCK) cells cultured in a Polydimethylsiloxane (PDMS) microchannel device allowed collective cell migration as well as the formation of leader cells. The cells were stained, and cell images were captured to train the computer using UNet++ together with their corresponding masks created using Photoshop for automated cell segmentation. Lastly, cell shape and filopodia were filtered out using Filopodyan and FiloQuant were detected. RESULTS: The segmentation of cell shape and the identification of filopodia were successful and produced accurate results in less than one second per image. CONCLUSIONS: The proposed approach of image analysis would be a great help in the field of cell science, engineering, and diagnosis.

Lamin B1 deletion in myeloid neoplasms causes nuclear anomaly and altered hematopoietic stem cell function

Abnormal nuclear morphology is a hallmark of malignant cells widely used in cancer diagnosis. Pelger-Huët anomaly (PHA) is a common abnormality of neutrophil nuclear morphology of unknown molecular etiology in myeloid neoplasms (MNs). We show that loss of nuclear lamin B1 (LMNB1) encoded on chromosome 5q, which is frequently deleted in MNs, induces defects in nuclear morphology and human hematopoietic stem cell (HSC) function associated with malignancy. LMNB1 deficiency alters genome organization inducing in vitro and in vivo expansion of HSCs, myeloid-biased differentiation with impaired lymphoid commitment, and genome instability due to defective DNA damage repair. Nuclear dysmorphology of neutrophils in patients with MNs is associated with 5q deletions spanning the LMNB1 locus, and lamin B1 loss is both necessary and sufficient to cause PHA in normal and 5q-deleted neutrophils. LMNB1 loss thus causes acquired PHA and links abnormal nuclear morphology with HSCs and progenitor cell fate determination via genome organization.

Small but Mighty-Exosomes, Novel Intercellular Messengers in Neurodegeneration

Simple Summary

Exosomes are biological nanoparticles recently recognized as intercellular messengers. They contain a cargo of lipids, proteins, and RNA. They can transfer their content to not only cells in the vicinity but also to cells at a distance. This unique ability empowers them to modulate the physiology of recipient cells. In brain, exosomes play a role in neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease and amyotrophic lateral sclerosis.

Abstract

Exosomes of endosomal origin are one class of extracellular vesicles that are important in intercellular communication. Exosomes are released by all cells in our body and their cargo consisting of lipids, proteins and nucleic acids has a footprint reflective of their parental origin. The exosomal cargo has the power to modulate the physiology of recipient cells in the vicinity of the releasing cells or cells at a distance. Harnessing the potential of exosomes relies upon the purity of exosome preparation. Hence, many methods for isolation have been developed and we provide a succinct summary of several methods. In spite of the seclusion imposed by the blood–brain barrier, cells in the CNS are not immune from exosomal intrusive influences. Both neurons and glia release exosomes, often in an activity-dependent manner. A brief description of exosomes released by different cells in the brain and their role in maintaining CNS homeostasis is provided. The hallmark of several neurodegenerative diseases is the accumulation of protein aggregates. Recent studies implicate exosomes’ intercellular communicator role in the spread of misfolded proteins aiding the propagation of pathology. In this review, we discuss the potential contributions made by exosomes in progression of Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. Understanding contributions made by exosomes in pathogenesis of neurodegeneration opens the field for employing exosomes as therapeutic agents for drug delivery to brain since exosomes do cross the blood–brain barrier.

Ultraviolet photoacoustic microscopy with tissue clearing for high-contrast histological imaging

Ultraviolet photoacoustic microscopy (UV-PAM) has been investigated to provide label-free and registration-free volumetric histological images for whole organs, offering new insights into complex biological organs. However, because of the high UV absorption of lipids and pigments in tissue, UV-PAM suffers from low image contrast and shallow image depth, hindering its capability for revealing various microstructures in organs. To improve the UV-PAM imaging contrast and imaging depth, here we propose to implement a state-of-the-art optical clearing technique, CUBIC (clear, unobstructed brain/body imaging cocktails and computational analysis), to wash out the lipids and pigments from tissues. Our results show that the UV-PAM imaging contrast and quality can be significantly improved after tissue clearing. With the cleared tissue, multilayers of cell nuclei can also be extracted from time-resolved PA signals. Tissue clearing-enhanced UV-PAM can provide fine details for organ imaging.

Use of Patient-Derived Organoids as a Treatment Selection Model for Colorectal Cancer: A Narrative Review

Simple Summary

Colorectal cancer (CRC) is the third most common type of cancer globally. Despite successful treatment, it has a 40% chance of recurrence within five years after surgery. While neoadjuvant chemotherapy is offered for stage IV cancers, it comes with a risk of resistance and disease progression. CRC tumors vary biologically, recur frequently, and pose a significant risk for cancer-related mortality, making it increasingly relevant to develop methods to study personalized treatment. A tumor organoid is a miniature, multicellular, and 3D replica of a tumor in vitro that retains its characteristics. Here, we discuss the current methods of culturing organoids and the correlation of drug response in organoids with clinical responses in patients. This helps us to determine whether organoids can be used for treatment selection in a clinical setting. Based on the studies included, there was a strong correlation between treatment responses of organoids and clinical treatment responses.

Abstract

Surgical resection is the mainstay in intended curative treatment of colorectal cancer (CRC) and may be accompanied by adjuvant chemotherapy. However, 40% of the patients experience recurrence within five years of treatment, highlighting the importance of improved, personalized treatment options. Monolayer cell cultures and murine models, which are generally used to study the biology of CRC, are associated with certain drawbacks; hence, the use of organoids has been emerging. Organoids obtained from tumors display similar genotypic and phenotypic characteristics, making them ideal for investigating individualized treatment strategies and for integration as a core platform to be used in prediction models. Here, we review studies correlating the clinical response in patients with CRC with the therapeutic response in patient-derived organoids (PDO), as well as the limitations and potentials of this model. The studies outlined in this review reported strong associations between treatment responses in the PDO model and clinical treatment responses. However, as PDOs lack the tumor microenvironment, they do not genuinely account for certain crucial characteristics that influence therapeutic response. To this end, we reviewed studies investigating PDOs co-cultured with tumor-infiltrating lymphocytes. This model is a promising method allowing evaluation of patient-specific tumors and selection of personalized therapies. Standardized methodologies must be implemented to reach a “gold standard” for validating the use of this model in larger cohorts of patients. The introduction of this approach to a clinical scenario directing neoadjuvant treatment and in other curative and palliative treatment strategies holds incredible potential for improving personalized treatment and its outcomes.

Recent advancements in optical biosensors for cancer detection

Optical biosensors are rapid, real-time, and portable, have a low detection limit and a high sensitivity, and have a great potential for diagnosing various types of cancer. Optical biosensors can detect cancer in a few million malignant cells, in comparison to conventional diagnosis techniques that use 1 billion cells in tumor tissue with a diameter of 7 nm-10 nm. Current cancer detection methods are also costly, inconvenient, complex, time consuming, and require technical specialists. This review focuses on recent advances in optical biosensors for early detection of cancer. It is primarily concerned with advancements in the design of various biosensors using resonance, scattering, chemiluminescence, luminescence, interference, fluorescence, absorbance or reflectance, and various fiber types. The development of various two-dimensional materials with optical properties such as biocompatibility, field enhancement, and a higher surface-to-volume ratio, as well as advancements in microfabrication technologies, have accelerated the development of optical sensors for early detection of cancer and other diseases. Surface enhanced Raman spectroscopy technology has the potential to detect a single molecule with high specificity, and terahertz waves are a recently explored technology for cancer detection. Due to the low electromagnetic interference, small size, multiplexing, and remote sensing capabilities of optical fiber-based platforms, they may be a driving force behind the rapid development of biosensors. The advantages and disadvantages of existing and future optical biosensor designs for cancer detection are discussed in detail. Additionally, a prospect for future advancements in the development of optical biosensors for point-of-care and clinical applications is highlighted.

Optical Redox Imaging Is Responsive to TGFβ Receptor Signalling in Triple-Negative Breast Cancer Cells

Co-enzyme nicotinamide adenine dinucleotide NAD(H) regulates hundreds of biochemical reactions within the cell. We previously reported that NAD(H) redox status may have prognostic value for predicting breast cancer metastasis. However, the mechanisms of NAD(H) involvement in metastasis remain elusive. Given the important roles of TGFβ signalling in metastatic processes, such as promoting the epithelial-to-mesenchymal transition, we aimed to investigate the involvement of the mitochondrial NAD(H) redox status in TGFβ receptor signalling. Here we present the initial evidence that NAD(H) redox status is responsive to TGFβ receptor signalling in triple-negative breast cancer cells in culture. The mitochondrial NAD(H) redox status was determined by the optical redox imaging (ORI) technique. Cultured HCC1806 (less aggressive) and MDA-MB-231 (more aggressive) cells were subjected to ORI after treatment with exogenous TGFβ1 or LY2109761, which stimulates or inhibits TGFβ receptor signalling, respectively. Cell migration was determined with the transwell migration assay. Global averaging quantification of the ORI images showed that 1) TGFβ1 stimulation resulted in differential responses between HCC1806 and MDA-MB-231 lines, with HCC1806 cells having a significant change in the mitochondrial redox status, corresponding to a larger increase in cell migration; 2) HCC1806 cells acutely treated with LY2109761 yielded immediate increases in ORI signals. These preliminary data are the first evidence that suggests the existence of a cell line-dependent shift of the mitochondrial NAD(H) redox status in the TGFβ receptor signalling induced migratory process of breast cancer cells. Further research should be conducted to confirm these results as improved understanding of the underlying mechanisms of metastatic process may contribute to the identification of prognostic biomarkers and therapeutic targets.

Nuclear Morphological Abnormalities in Cancer: A Search for Unifying Mechanisms

Irregularities in nuclear shape and/or alterations to nuclear size are a hallmark of malignancy in a broad range of cancer types. Though these abnormalities are commonly used for diagnostic purposes and are often used to assess cancer progression in the clinic, the mechanisms through which they occur are not well understood. Nuclear size alterations in cancer could potentially arise from aneuploidy, changes in osmotic coupling with the cytoplasm, and perturbations to nucleocytoplasmic transport. Nuclear shape changes may occur due to alterations to cell-generated mechanical stresses and/or alterations to nuclear structural components, which balance those stresses, such as the nuclear lamina and chromatin. A better understanding of the mechanisms underlying abnormal nuclear morphology and size may allow the development of new therapeutics to target nuclear aberrations in cancer.

Role of Lamin A and emerin in maintaining nuclear morphology in different subtypes of ovarian epithelial cancer

The nuclear lamina protein, Lamin A and inner nuclear membrane protein, emerin participate in maintaining nuclear morphology. However, their correlations with the nuclear shape in the four representative ovarian epithelial cancer subtypes, high-grade serous carcinoma (HGSCa), clear cell carcinoma (CCCa), endometrioid carcinoma (EMCa) and mucinous carcinoma (MUCa), remains unclear. The present study aimed to investigate the association between nuclear morphology and nuclear membrane protein expression in four histological subtypes of ovarian epithelial cancer. A total of 140 surgically resected ovarian cancer specimens were subjected to Feulgen staining to evaluate nuclear morphology, and immunohistochemistry analysis to assess Lamin A and emerin expression. The histological images were analyzed via computer-assisted image analysis (CAIA). The results demonstrated that the mean nuclear area of EMCa was significantly smaller compared with CCCa (P=0.0009). The standard deviation of the mean nuclear area was used to assess nuclear size variation, and the results indicated that EMCa lesions were significantly smaller than CCCa lesions (P=0.0006). Regarding the correlation between the Lamin A-positive rate and nuclear morphological factors, positive correlations were observed with nuclear area in CCCa and EMCa (R=0.2855 and R=0.2858, respectively) and nuclear perimeter in CCCa, EMCa and MUCa (R=0.2409, R=0.4054 and R=0.2370, respectively); however, a negative correlation with nuclear shape factor was observed in HGSCa and EMCa (R=-0.2079 and R=-0.3707, respectively). With regards to the correlation between emerin positivity and nuclear morphological factors, positive correlations were observed with nuclear shape factor in HGSCa (R=0.2673) and nuclear area in CCCa (R=0.3310). It is well-known that HGSCa and CCCa have conspicuous nuclear size variation, and EMCa has small nuclei without strong atypia. These findings were verified in the present study via CAIA. Taken together, the results of the present study suggest that Lamin A strongly contributes to the maintenance of nuclear morphology in ovarian epithelial cancer compared with emerin, although their contributions differ based on tumor subtype.

Nuclear Dynamics and Chromatin Structure: Implications for Pancreatic Cancer

Changes in nuclear shape have been extensively associated with the dynamics and functionality of cancer cells. In most normal cells, nuclei have a regular ellipsoid shape and minimal variation in nuclear size; however, an irregular nuclear contour and abnormal nuclear size is often observed in cancer, including pancreatic cancer. Furthermore, alterations in nuclear morphology have become the 'gold standard' for tumor staging and grading. Beyond the utility of altered nuclear morphology as a diagnostic tool in cancer, the implications of altered nuclear structure for the biology and behavior of cancer cells are profound as changes in nuclear morphology could impact cellular responses to physical strain, adaptation during migration, chromatin organization, and gene expression. Here, we aim to highlight and discuss the factors that regulate nuclear dynamics and their implications for pancreatic cancer biology.

Modeling Notch-Induced Tumor Cell Survival in the Drosophila Ovary Identifies Cellular and Transcriptional Response to Nuclear NICD Accumulation

Notch is a conserved developmental signaling pathway that is dysregulated in many cancer types, most often through constitutive activation. Tumor cells with nuclear accumulation of the active Notch receptor, NICD, generally exhibit enhanced survival while patients experience poorer outcomes. To understand the impact of NICD accumulation during tumorigenesis, we developed a tumor model using the Drosophila ovarian follicular epithelium. Using this system we demonstrated that NICD accumulation contributed to larger tumor growth, reduced apoptosis, increased nuclear size, and fewer incidents of DNA damage without altering ploidy. Using bulk RNA sequencing we identified key genes involved in both a pre- and post- tumor response to NICD accumulation. Among these are genes involved in regulating double-strand break repair, chromosome organization, metabolism, like raptor, which we experimentally validated contributes to early Notch-induced tumor growth. Finally, using single-cell RNA sequencing we identified follicle cell-specific targets in NICD-overexpressing cells which contribute to DNA repair and negative regulation of apoptosis. This valuable tumor model for nuclear NICD accumulation in adult Drosophila follicle cells has allowed us to better understand the specific contribution of nuclear NICD accumulation to cell survival in tumorigenesis and tumor progression.

Gastrointestinal cancer classification and prognostication from histology using deep learning: Systematic review

BACKGROUND

Gastrointestinal cancers account for approximately 20% of all cancer diagnoses and are responsible for 22.5% of cancer deaths worldwide. Artificial intelligence-based diagnostic support systems, in particular convolutional neural network (CNN)-based image analysis tools, have shown great potential in medical computer vision. In this systematic review, we summarise recent studies reporting CNN-based approaches for digital biomarkers for characterization and prognostication of gastrointestinal cancer pathology.

METHODS

Pubmed and Medline were screened for peer-reviewed papers dealing with CNN-based gastrointestinal cancer analyses from histological slides, published between 2015 and 2020.Seven hundred and ninety titles and abstracts were screened, and 58 full-text articles were assessed for eligibility.

RESULTS

Sixteen publications fulfilled our inclusion criteria dealing with tumor or precursor lesion characterization or prognostic and predictive biomarkers: 14 studies on colorectal or rectal cancer, three studies on gastric cancer and none on esophageal cancer. These studies were categorised according to their end-points: polyp characterization, tumor characterization and patient outcome. Regarding the translation into clinical practice, we identified several studies demonstrating generalization of the classifier with external tests and comparisons with pathologists, but none presenting clinical implementation.

CONCLUSIONS

Results of recent studies on CNN-based image analysis in gastrointestinal cancer pathology are promising, but studies were conducted in observational and retrospective settings. Large-scale trials are needed to assess performance and predict clinical usefulness. Furthermore, large-scale trials are required for approval of CNN-based prediction models as medical devices.

Nuclear Envelope Integrity in Health and Disease: Consequences on Genome Instability and Inflammation

The dynamic nature of the nuclear envelope (NE) is often underestimated. The NE protects, regulates, and organizes the eukaryote genome and adapts to epigenetic changes and to its environment. The NE morphology is characterized by a wide range of diversity and abnormality such as invagination and blebbing, and it is a diagnostic factor for pathologies such as cancer. Recently, the micronuclei, a small nucleus that contains a full chromosome or a fragment thereof, has gained much attention. The NE of micronuclei is prone to collapse, leading to DNA release into the cytoplasm with consequences ranging from the activation of the cGAS/STING pathway, an innate immune response, to the creation of chromosomal instability. The discovery of those mechanisms has revolutionized the understanding of some inflammation-related diseases and the origin of complex chromosomal rearrangements, as observed during the initiation of tumorigenesis. Herein, we will highlight the complexity of the NE biology and discuss the clinical symptoms observed in NE-related diseases. The interplay between innate immunity, genomic instability, and nuclear envelope leakage could be a major focus in future years to explain a wide range of diseases and could lead to new classes of therapeutics.

Computer-assisted image analysis of cytological specimens clarify the correlation between nuclear size and intranuclear cytoplasmic inclusions regardless of BRAFV600E mutation in papillary thyroid carcinoma

OBJECTIVE

The morphological features of nuclei in cytological and histological specimens were compared and examined for the presence of BRAFV600E mutation and the appearance rate of intranuclear cytoplasmic inclusions (NI).

METHODS

BRAFV600E mutation was identified using a mutation-specific antibody (clone; VE1) in 103 thyroid papillary carcinoma cases at Gunma University Hospital. The nuclear area, perimeter, and roundness of the corresponding cytological specimens and haematoxylin and eosin-stained specimens were analysed using image analysis software, and the appearance rate of NI was calculated and compared.

RESULTS

BRAFV600E mutation was detected in 71 (69%) cases. The appearance rate of NI was significantly higher in the BRAFV600E mutation-positive group in cytological and histological specimens (P = .0070 and .0184, respectively). Significant differences were observed between the BRAFV600E mutation-negative and -positive groups in the average nuclear area and average nuclear perimeter in cytological specimens (P = .0137 and .0152, respectively). In addition, nuclear enlargement was correlated with the appearance rate of NI regardless of the presence of BRAFV600E mutation in cytological specimens. In the BRAFV600E mutation-negative group, the nuclear area and perimeter were significantly smaller in the lymph node metastasis-positive cases (P = .0182 and .0260, respectively).

CONCLUSION

This study found that the appearance rate of NI was positively correlated with the nuclear area and perimeter and negatively correlated with nuclear roundness in cytological specimens. Furthermore, these results were observed regardless of the existence of BRAFV600E mutation. These results have never been previously reported and clearly demonstrate the usefulness of cytological specimens in computer-assisted image analysis.

Investigating the Impact of the Bit Depth of Fluorescence-Stained Images on the Performance of Deep Learning-Based Nuclei Instance Segmentation

Nuclei instance segmentation can be considered as a key point in the computer-mediated analysis of histological fluorescence-stained (FS) images. Many computer-assisted approaches have been proposed for this task, and among them, supervised deep learning (DL) methods deliver the best performances. An important criterion that can affect the DL-based nuclei instance segmentation performance of FS images is the utilised image bit depth, but to our knowledge, no study has been conducted so far to investigate this impact. In this work, we released a fully annotated FS histological image dataset of nuclei at different image magnifications and from five different mouse organs. Moreover, by different pre-processing techniques and using one of the state-of-the-art DL-based methods, we investigated the impact of image bit depth (i.e., eight bits vs. sixteen bits) on the nuclei instance segmentation performance. The results obtained from our dataset and another publicly available dataset showed very competitive nuclei instance segmentation performances for the models trained with 8 bit and 16 bit images. This suggested that processing 8 bit images is sufficient for nuclei instance segmentation of FS images in most cases. The dataset including the raw image patches, as well as the corresponding segmentation masks is publicly available in the published GitHub repository.

A Mathematical Radiobiological Model (MRM) to Predict Complex DNA Damage and Cell Survival for Ionizing Particle Radiations of Varying Quality

Predicting radiobiological effects is important in different areas of basic or clinical applications using ionizing radiation (IR); for example, towards optimizing radiation protection or radiation therapy protocols. In this case, we utilized as a basis the ‘MultiScale Approach (MSA)’ model and developed an integrated mathematical radiobiological model (MRM) with several modifications and improvements. Based on this new adaptation of the MSA model, we have predicted cell-specific levels of initial complex DNA damage and cell survival for irradiation with ¹¹Β, ¹²C, ¹⁴Ν, ¹⁶Ο, ²⁰Νe, ⁴⁰Αr, ²⁸Si and ⁵⁶Fe ions by using only three input parameters (particle’s LET and two cell-specific parameters: the cross sectional area of each cell nucleus and its genome size). The model-predicted survival curves are in good agreement with the experimental ones. The particle Relative Biological Effectiveness (RBE) and Oxygen Enhancement Ratio (OER) are also calculated in a very satisfactory way. The proposed integrated MRM model (within current limitations) can be a useful tool for the assessment of radiation biological damage for ions used in hadron-beam radiation therapy or radiation protection purposes.