Morphological Characteristics Of Cells Research Articles

Exploring the Therapeutic Potential of TROP2 Gene Silencing in Hepatocellular Carcinoma.

Trophoblast Cell Surface Antigen 2 (Trop2) is a transmembrane glycoprotein that has been implicated in the progression and metastasis of various cancers, including hepatocellular carcinoma (HCC). Targeting Trop2 expression may represent a promising approach for the development of novel therapeutic strategies. This study aimed to investigate the effects of Trop2 knockdown using small interfering RNA (siRNA) on the phenotypic and molecular characteristics of the HepG2 liver cancer cell line. HepG2 cells were transfected with different concentrations of Trop2-targeting siRNA (3 nM, 5 nM, and 7 nM) at various time intervals (6, 24, and 48 hrs). The expression of Trop2 was assessed by real-time PCR before and after transfection. The impact of Trop2 knockdown on cell apoptosis, migration, morphology, histopathological features, wound-healing assays, and microscopic analysis was examined. Additionally, the expression of the TPM1 gene was evaluated using immunohistochemical analysis. Trop2 mRNA level was significantly decreased in HepG2 cells in a time- and concentration-dependent manner following siRNA transfection. The downregulation of Trop2 resulted in a marked increase in apoptosis, a reduction in cell migration, and alterations in cell morphology and histopathological characteristics. Furthermore, the expression of the TPM1 gene was found to be upregulated in Trop2-knockdown HepG2 cells. These results highlight the potential of Trop2 as a therapeutic target for the management of hepatocellular carcinoma.

Azotobacter biodiversity in Egypt using microbiological, biochemical, and molecular-biology multidisciplinary approach.

The presence of Azotobacter bacteria in the soil plays an important role in increasing its fertility and enhancing plant health. Azotobacter diversity depends on several environmental factors, particularly soil texture, pH, and nutrient content. The current study investigated the diversity of Azotobacter in various soil samples collected from 10 different governorates along the river Nile valley and its delta, Northern Mediterranean shore, Sinai, and Upper Egypt regions. The sampling sites spanned different environmental and ecological conditions of the Egyptian land either cultivated (agricultural land) or uncultivated (desert land). Fifty Azotobacter isolates were isolated and characterized based on cell morphology, culture properties, physiological, biochemical, and molecular characteristics. In addition, the alginate production capacity of the isolates was investigated. The results indicated that Egyptian soils are rich in Azotobacter diversity. The isolates were Gram-negative short rods, appearing either as single cells or in diploid structures. The isolates showed high variability in alginate production where two isolates (BH3 and AST4) were the highest alginate producers (3.12 and 4.22g alginate L- 1), respectively. 16S-rDNA sequencing and 16S-rDNA RFLP analyses indicated that despite the presence of Azotobacter salinestris and Azotobacter vinelandii in the Egyptian soil, Azotobacter chroococcum was the predominant species. In addition, sequence analysis of the gene coding for the transcription factor AlgU confirmed the results of 16S-rRNA gene sequence analysis. RAPD-REP and BOX-PCR were used to study the polymorphism among the isolates. High levels of microbial diversity were found using these DNA primers as 6-9 fingerprinting profiles were retrieved.

Biophysical and Morphological Cell Features Retrieved by Digital Holographic Microscopy Correlate with Drug-Induced Changes in Cell Migration Behavior.

Quantitative analysis of cancer cell migration is critical for developing effective therapies to curb cancer metastasis. However, traditional methods are time-consuming and labor-intensive and lack quantitative capabilities. Cell volume change, a key physiological indicator of cell migration, is directly linked to phase change. In this work, we have developed a model that connects phase features from digital holographic microscopy (DHM) with cell healrate values from the wound healing assay. This approach aims to provide a rapid and quantitative evaluation of the breast cancer cell migration capability. Using DHM, six phase features of 231 cells treated with varying drug concentrations were extracted. It was observed that the rate of change of these phase features, termed characteristic parameters, showed a high linear correlation with cell healrate values from wound healing assays. Based on these linear correlations, a composite coefficient was derived by linearly combining the characteristic parameters of the six phase features. This composite coefficient was then linearly correlated with the cell healrate values to create a correlation model. This model establishes a strong connection between DHM-extracted morphological/biophysical features and cell migration metrics from a complementary assay. It provides a new, rapid, and quantitative method for assessing cancer cell migration in vitro and delivering valuable insights for cancer research.

Gastric metastasis of small cell lung carcinoma: A rare but noteworthy entity to consider.

Small cell lung carcinoma (SCLC) is an aggressive malignancy known for its propensity for early and extensive metastatic spread. Gastric metastasis, where cancer cells disseminate from the lung to the stomach, is a rare but increasingly recognized complication of SCLC. This review provides a comprehensive overview of gastric metastasis in SCLC, addressing its clinical significance, diagnostic challenges, management strategies, and prognosis. Additionally, it examines the broader metastatic patterns of SCLC and compares them with other malignancies known for gastric metastasis. Gastric metastasis in SCLC, though infrequent, is clinically significant and often indicates advanced disease with a poor prognosis. SCLC typically metastasizes to the liver, brain, bones, and adrenal glands, with the stomach being an unusual site. The incidence of gastric metastasis ranges from 1% to 5% in autopsy studies, although this may be underestimated due to diagnostic difficulties and asymptomatic early lesions. Diagnosing gastric metastasis presents several challenges, including the asymptomatic nature of many cases, limitations of conventional imaging techniques, and difficulties in distinguishing metastatic lesions from primary gastric cancer via endoscopy. Histopathological diagnosis requires careful examination to identify SCLC cells through their characteristic small cell morphology and neuroendocrine markers. Management of gastric metastasis in SCLC typically involves a multidisciplinary approach. Systemic therapy, primarily chemotherapy, remains the cornerstone of treatment, with palliative care addressing symptoms and complications. Surgical intervention is usually reserved for specific cases requiring symptomatic relief. The prognosis for patients with gastric metastasis from SCLC is generally poor, reflecting the advanced stage of the disease. Median survival is significantly reduced compared to patients without gastric metastasis. This review emphasizes the need for enhanced awareness and early detection to improve patient outcomes and highlights the importance of ongoing research into better diagnostic and therapeutic strategies.

Protective Role of Eicosapentaenoic and Docosahexaenoic and Their N-Ethanolamide Derivatives in Olfactory Glial Cells Affected by Lipopolysaccharide-Induced Neuroinflammation

Neuroinflammation is a symptom of different neurodegenerative diseases, and growing interest is directed towards active drug development for the reduction of its negative effects. The anti-inflammatory activity of polyunsaturated fatty acids, eicosapentaenoic (EPA), docosahexaenoic (DHA), and their amide derivatives was largely investigated on some neural cells. Herein, we aimed to elucidate the protective role of both EPA and DHA and the corresponding N-ethanolamides EPA-EA and DHA-EA on neonatal mouse Olfactory Ensheathing Cells (OECs) after exposition to lipopolysaccharide (LPS)-induced neuroinflammation. To verify their anti-inflammatory effect and cell morphological features on OECs, the expression of IL-10 cytokine, and cytoskeletal proteins (vimentin and GFAP) was evaluated by immunocytochemical procedures. In addition, MTT assays, TUNEL, and mitochondrial health tests were carried out to assess their protective effects on OEC viability. Our results highlight a reduction in GFAP and vimentin expression in OECs exposed to LPS and treated with EPA or DHA or EPA-EA or DHA-EA in comparison with OECs exposed to LPS alone. We observed a protective role of EPA and DHA on cell morphology, while the amides EPA-EA and DHA-EA mainly exerted a superior anti-inflammatory effect compared to free acids.

Analysis of Morphologic Classification System for Acute Promyelocytic Leukemia and Its Correlation with Laboratory Tests and FLT3-ITD Mutation

To establish a morphologic classification system for characterizing blast cells in patients with acute promyelocytic leukemia (APL) and analyze the correlation of different APL morphologic characteristics with conventional tests and genetic variants. Based on the morphological characteristics of APL blast cells, a classification system of 14 categories was established to characterize the inter- and intra-individual cellular morphological heterogeneity of patients. The classification system was used for the morphological analysis of 40 APL patients, and the classification results were statistically analyzed with the patients' conventional test indexes and gene variant characteristics to analyze the correlation of different APL blast cell morphological features with conventional test indexes and gene variants. In the FLT3-ITD mutation-positive group, there were significantly fewer cells with regular nuclear shape, hyper granularity, and missing Auer rods (category 1) than in the FLT3 mutation-negative group (P < 0.05). The activated partial thromboplastin (APTT) was significantly longer in the group with regular nucleus compared to the group with irregular nucleus (P < 0.05). In the hypo-granular group, the APTT was also significantly longer compared to the hyper-granular group (P < 0.01), and the proportion of myeloid blast cells was relatively lower (P < 0.05). The peripheral blood white blood cell counts, D-dimer, lactate dehydrogenase and proportion of bone marrow blast cells were significantly higher in the Auer rods (-) group than Auer rods increasing group (all P < 0.05). The newly established morphologic classification system in this study can objectively characterize different types of APL blast cells, which helps to better assess the intra- and inter-individual heterogeneity of APL blast cells, and further use in accurately analyzing the correlation of morphological phenotypes with biological properties of APL.

A novel computational pathology approach for identifying gene signatures prognostic of disease-free survival for papillary thyroid carcinomas

IntroductionPapillary thyroid carcinoma (PTC) is the most prevalent form of thyroid cancer, with the classical and follicular variants representing most cases. Despite generally favorable prognoses, approximately 10% of patients experience recurrence post-surgery and radioactive iodine therapy. Attempts to stratify risk of recurrence have relied on gene expression-based prognostic and predictive signatures with a focus on mutations of well-known driver genes, while hallmarks of tumor morphology have been ignored. ObjectivesWe introduce a new computational pathology approach to develop prognostic gene signatures for PTC that is informed by quantitative features of tumor and immune cell morphology. MethodsWe quantified nuclear and immune-related features of tumor morphology to develop a pathomic signature, which was then used to inform an RNA-expression signature model provides a notable advancement in risk stratification compared to both standalone and pathology-informed gene-expression signatures. ResultsThere was a 17.8% improvement in the C-index (from 0.605 to 0.783) for 123 cPTCs and 15% (from 0.576 to 0.726) for 38 fvPTCs compared to the standalone gene-expression signature. Hazard ratios also improved for cPTCs from 0.89 (0.67,0.99) to 4.43 (3.65,6.68) and fvPTC from 0.98 (0.76,1.32) to 2.28 (1.87,3.64). We validated the image-based risk model on an independent cohort of 32 cPTCs with hazard ratio 1.8 (1.534,2.167).

Molecular connectomics: Placing cells into morphological tissue context.

Here we propose "molecular connectomics" to link molecular and morphological cell features in three dimensions across scales, using machine learning and artificial intelligence to reveal emergent properties of complex biological systems.

DermoGAN: multi-task cycle generative adversarial networks for unsupervised automatic cell identification on in-vivo reflectance confocal microscopy images of the human epidermis.

Accurate identification of epidermal cells on reflectance confocal microscopy (RCM) images is important in the study of epidermal architecture and topology of both healthy and diseased skin. However, analysis of these images is currently done manually and therefore time-consuming and subject to human error and inter-expert interpretation. It is also hindered by low image quality due to noise and heterogeneity. We aimed to design an automated pipeline for the analysis of the epidermal structure from RCM images. Two attempts have been made at automatically localizing epidermal cells, called keratinocytes, on RCM images: the first is based on a rotationally symmetric error function mask, and the second on cell morphological features. Here, we propose a dual-task network to automatically identify keratinocytes on RCM images. Each task consists of a cycle generative adversarial network. The first task aims to translate real RCM images into binary images, thus learning the noise and texture model of RCM images, whereas the second task maps Gabor-filtered RCM images into binary images, learning the epidermal structure visible on RCM images. The combination of the two tasks allows one task to constrict the solution space of the other, thus improving overall results. We refine our cell identification by applying the pre-trained StarDist algorithm to detect star-convex shapes, thus closing any incomplete membranes and separating neighboring cells. The results are evaluated both on simulated data and manually annotated real RCM data. Accuracy is measured using recall and precision metrics, which is summarized as the -score. We demonstrate that the proposed fully unsupervised method successfully identifies keratinocytes on RCM images of the epidermis, with an accuracy on par with experts' cell identification, is not constrained by limited available annotated data, and can be extended to images acquired using various imaging techniques without retraining.

TRPS1 Expression Is Frequently Seen in a Subset of Cutaneous Mesenchymal Neoplasms and Tumors of Uncertain Differentiation: A Potential Diagnostic Pitfall.

Although extensively studied in cutaneous epithelial neoplasms, the TRPS1 immunoreactivity in cutaneous mesenchymal neoplasms and tumors of uncertain differentiation (CMNTUDs), such as atypical fibroxanthoma (AFX), remains largely unexplored. We assessed TRPS1 immunoreactivity in 135 CMNTUDs, comprising 46 fibrohistiocytic/fibroblastic tumors, 28 vascular tumors, 24 peripheral nerve sheath tumors (PNSTs), 21 tumors of uncertain differentiation, and 16 smooth muscle tumors. Additionally, we included selected cases of melanoma with spindled cell morphology or desmoplastic features (n = 9) and sarcomatoid squamous cell carcinoma (SSCC) (n = 5) to compare TRPS1 expression patterns with those of AFX. TRPS1 expression was prevalent in dermatofibromas (24/24), leiomyomas (8/8), AFXs/pleomorphic dermal sarcoma (PDS) (20/21), dermatofibrosarcomas protuberans (14/22), and leiomyosarcomas (6/8). It was uncommon in angiosarcomas (3/20), Kaposi sarcomas (2/8), and neurofibromas (5/17) and absent in perineuriomas (0/2). AFXs/PDS exhibited the highest median H-score of 240, contrasting with minimal TRPS1 immunoreactivity in vascular neoplasms and PNSTs, with median H-scores consistently below 10. Significant differences in H-score were observed between AFXs/PDS and angiosarcomas (p < 0.001), melanomas (p < 0.001), and leiomyosarcomas (p = 0.029). However, no significant difference was found compared to SSCCs, suggesting limited discriminatory power of TRPS1 in this context. This study sheds light on TRPS1 expression patterns in a subset of CMNTUDs, extending beyond prior studies primarily focused on epithelial tumors, while underscoring potential pitfalls associated with TRPS1 immunohistochemistry.

Correlative Raman Imaging: Development and Cancer Applications.

Despite extensive research efforts, cancer continues to stand as one of the leading causes of death on a global scale. To gain profound insights into the intricate mechanisms underlying cancer onset and progression, it is imperative to possess methodologies that allow the study of cancer cells at the single-cell level, focusing on critical parameters such as cell morphology, metabolism, and molecular characteristics. These insights are essential for effectively discerning between healthy and cancerous cells and comprehending tumoral progression. Recent advancements in microscopy techniques have significantly advanced the study of cancer cells, with Raman microspectroscopy (RM) emerging as a particularly powerful tool. Indeed, RM can provide both biochemical and spatial details at the single-cell level without the need for labels or causing disruptions to cell integrity. Moreover, RM can be correlated with other microscopy techniques, creating a synergy that offers a spectrum of complementary insights into cancer cell morphology and biology. This review aims to explore the correlation between RM and other microscopy techniques such as confocal fluoresce microscopy (CFM), atomic force microscopy (AFM), digital holography microscopy (DHM), and mass spectrometry imaging (MSI). Each of these techniques has their own strengths, providing different perspectives and parameters about cancer cell features. The correlation between information from these various analysis methods is a valuable tool for physicians and researchers, aiding in the comprehension of cancer cell morphology and biology, unraveling mechanisms underlying cancer progression, and facilitating the development of early diagnosis and/or monitoring cancer progression.

High-resolution adaptive optics-trans-scleral flood illumination (AO-TFI) imaging of retinal pigment epithelium (RPE) in central serous chorioretinopathy (CSCR)

This study aims to correlate adaptive optics-transscleral flood illumination (AO-TFI) images of the retinal pigment epithelium (RPE) in central serous chorioretinopathy (CSCR) with standard clinical images and compare cell morphological features with those of healthy eyes. After stitching 125 AO-TFI images acquired in CSCR eyes (including 6 active CSCR, 15 resolved CSCR, and 3 from healthy contralateral), 24 montages were correlated with blue-autofluorescence, infrared and optical coherence tomography images. All 68 AO-TFI images acquired in pathological areas exhibited significant RPE contrast changes. Among the 52 healthy areas in clinical images, AO-TFI revealed a normal RPE mosaic in 62% of the images and an altered RPE pattern in 38% of the images. Morphological features of the RPE cells were quantified in 54 AO-TFI images depicting clinically normal areas (from 12 CSCR eyes). Comparison with data from 149 AO-TFI images acquired in 33 healthy eyes revealed significantly increased morphological heterogeneity. In CSCR, AO-TFI not only enabled high-resolution imaging of outer retinal alterations, but also revealed RPE abnormalities undetectable by all other imaging modalities. Further studies are required to estimate the prognosis value of these abnormalities. Imaging of the RPE using AO-TFI holds great promise for improving our understanding of the CSCR pathogenesis.

CellPLATO - an unsupervised method for identifying cell behaviour in heterogeneous cell trajectory data.

Advances in imaging, segmentation and tracking have led to the routine generation of large and complex microscopy datasets. New tools are required to process this 'phenomics' type data. Here, we present 'Cell PLasticity Analysis Tool' (cellPLATO), a Python-based analysis software designed for measurement and classification of cell behaviours based on clustering features of cell morphology and motility. Used after segmentation and tracking, the tool extracts features from each cell per timepoint, using them to segregate cells into dimensionally reduced behavioural subtypes. Resultant cell tracks describe a 'behavioural ID' at each timepoint, and similarity analysis allows the grouping of behavioural sequences into discrete trajectories with assigned IDs. Here, we use cellPLATO to investigate the role of IL-15 in modulating human natural killer (NK) cell migration on ICAM-1 or VCAM-1. We find eight behavioural subsets of NK cells based on their shape and migration dynamics between single timepoints, and four trajectories based on sequences of these behaviours over time. Therefore, by using cellPLATO, we show that IL-15 increases plasticity between cell migration behaviours and that different integrin ligands induce different forms of NK cell migration.

A transfer learning-based graph neural network model using mIF images to predict neoadjuvant immunochemotherapy response in patients with gastrointestinal cancer.

e13592 Background: Neoadjuvant immunochemotherapy has great potential to revolutionize the treatment of gastrointestinal cancer. However, effective biomarkers to classify responders from non-responders are still lacking. We aimed to build a response prediction model using multiplex immunofluorescence (mIF) images from patients with gastrointestinal cancer. Methods: We proposed a graph neural network-based (GNN) approach based on the pre-treatment mIF samples collected from 77 patients, including 30 gastric cancer (GC) patients, 34 colorectal cancer (CRC) patients, and 13 esophageal cancer (ESCA) patients. All the tissue samples were stained by two panels of mIF agents, namely Panel-A (CD3, CD8, PD1, PD-L1) and Panel-B (CD56, CD68, CD163). Cell coordinates, agent densities, and cell morphology features were used to generate cell graphs for model training. Results: Initially, we trained a pan-cancer GNN model for each panel and assessed its performance on the patch- and patient-level using five-fold cross-validation. The Panel-A model achieved higher areas under the receiver operating characteristic curve (AUROC) (patch-level: 0.71 ± 0.036, patient-level: 0.65 ± 0.010) than the Panel-B model (patch-level: 0.59 ±0.017, patient-level: 0.62 ± 0.021) and conventional mIF-based markers (i.e., positive cell ratios/counts of fluorescence agents, AUROC: 0.34 ~ 0.54). Specifically, the performance of CRC was better in the Panel-A model (patch-level: 0.75± 0.053, patient-level: 0.76 ± 0.053) than that in Panel-B (patch-level: 0.52 ± 0.027, patient-level: 0.60 ± 0.028), while the performance of GC was better in Panel-B (patch-level: 0.64 ± 0.030, patient-level: 0.71 ± 0.034) than that in Panel-A (patch-level: 0.53 ± 0.019, patient-level: 0.51 ± 0.018). The AUROCs of both panels of ESCA were less than 0.5, reflecting feature differences associated with treatment response between ESCA and GC/CRC. Furthermore, discrepancies of GC and CRC between Panel-A and -B motivated us to further optimize the performance for individual cancer types using transfer learning (TL). The GC TL-model performance (patch-level: 0.691±0.025, patient-level: 0.751 ± 0.046) was improved compared with the pan-cancer model, but not for the CRC TL-model (patch-level: 0.636 ± 0.066, patient-level: 0.697 ± 0.039). Moreover, the best patient-level specificity and sensitivity under the optimal Youden-Index were 70% and 88% for CRC, and 93% and 62% for GC. Conclusions: The GNN-based approach using mIF images demonstrated promising potential in predicting the immunochemotherapy response of gastrointestinal cancer patients, despite the limited number of samples were curated. The performance variation among cancer types indicated the cancer heterogeneity and the necessity of building single-cancer models when more data becomes available.

Morphological profiling for drug discovery in the era of deep learning.

Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high throughput. These efforts have facilitated understanding of compound mechanism of action, drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering- and deep learning-based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.

Glucose-Induced Cellular Morphological Changes Across Mammalian Species

All organisms encounter environmental fluctuations and must contend with these changes to maintain homeostasis. Some mammals tolerate a wide range of intracellular conditions while others do not. Altered glucose levels are an example of a physiological challenge that can be encountered by cells. We studied responses of dermal fibroblasts obtained from skin biopsies to altered glucose treatments in culture using a comparative approach. Specifically, we investigated responses of mammals known to tolerate a range of blood glucose levels (fruit-eating geladas and Egyptian rousettes) as well as a hibernator (ground squirrel), which exhibits a marked winter decrease in glucose metabolism. As a comparison, we investigated species that maintain fairly stable blood glucose (human and rat). Fibroblasts were cultured at baseline (8mM glucose), then subjected to hypoglycemic (2.5mM) and hyperglycemic (30mM) treatment over 24h. We assayed glycolytic rate in human and rat fibroblasts using a Seahorse XFp analyzer, and found no significance between oxygen consumption or glycolysis across glucose treatments (all p&gt;0.05). To investigate underlying cell phenotypes, we used Cell Painting, employing fluorescent stains to mark organelles. Hoechst 34580 stained DNA, to quantify number and size of nuclei, offering insights into proliferation. MitoTracker Deep Red visualized mitochondria, providing information about energy generation. Alexa Fluor 488 Concanavalin A conjugate marked endoplasmic reticulum. F-actin was stained with Alexa Fluor 555 Phalloidin conjugate, detecting cytoskeletal organization and overall cell structure. Images were analyzed with CellProfiler software, which extracts ~1400 morphological cell features, including area, compactness, fluorescence intensity, and cell connectivity to quantify subtle changes in phenotype. Each extracted feature will be compared across treatments and within a comparative context. We predict that fibroblasts exposed to hyperglycemia exhibit mitochondrial fragmentation and redistribution (MitoTracker) and increased production of reactive oxygen species (assessed with MitoSOX), which is linked to the opening of mitochondrial permeability transition pores. Cell Profiler analysis will identify structural changes that could indicate negative outcomes, such as mitophagy. Hyperglycemia may also induce endoplasmic stress, increasing lipid droplets and altering shape and size of the endoplasmic reticulum. In contrast, hypoglycemia will cause mitochondrial degradation. In both hyper- and hypoglycemia, proliferation rates are predicted to decrease, resulting in fewer nuclei and reduced cell connectivity. Phenotypic differences across species, evaluated in the context of their organismal phenotype, will help explain the mechanisms of cell function in mammals under environmental stress. Funding by NSF #2022046. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Application of CytoPath®Easy Vials in Cervical Cancer Screening: Self-Sampling Approach.

CytoPath®Easy kit (DiaPath S.p.A.) offers a major advantage compared to other commercially available kits available for the screening of cervical cancer, as it does not require additional equipment for sample processing. Using this methodology, collected epithelial cells are immersed in a preservative liquid before setting as a thin layer on a slide via gravity sedimentation. To evaluate the suitability of the CytoPath®Easy kit for the processing of cervical samples, detection of pre-neoplastic lesions, and nucleic preservation and extraction for HR-HPV diagnosis. A total of 242 self-sampled cervical specimens were utilized, with 192 collected in CytoPath®Easy vials and 50 collected and processed using the ThinPrepTM for comparative analysis. The samples underwent processing, Papanicolaou staining, and microscopic evaluation for morphological parameters. The extracted nucleic acids were assessed for purity and integrity, and the detection of high-risk human papillomavirus (HR-HPV) was carried out using the Alinitym HR HPV system kit (Abbott Laboratórios Lda). Both methods demonstrated effective performance, enabling the morphological assessment of the cervical epithelium. Statistical analysis indicated that ThinPrepTM yielded significantly better results in terms of cellularity. Conversely, CytoPath®Easy exhibited superior performance in terms of the quantity of extracted DNA and its degree of purification. Concerning the time consumed during processing, both methods were comparable, with the CytoPath®Easy methodology standing out for its cost-effectiveness, as it does not necessitate additional instruments and consumables. The novel CytoPath®Easy methodology proves effective in preserving both nucleic acids and cell morphology characteristics, two crucial features for cervical cancer screening.

Low UV radiation influenced DNA methylation, gene regulation, cell proliferation, viability, and biochemical differentiation in the cell suspension cultures of Cannabis indica

The effect of low artificial Ultraviolet (UV) on the DNA methylation remains controversial. This study addresses how differential photoperiods of UV radiation affect the biochemical and molecular behaviors of Cannabis indica cell suspension cultures. The cell suspensions were illuminated with the compact fluorescent lamps (CFL), emitting a combination of 10% UVB, 30% UVA, and the rest visible wavelengths for 0, 4, 8, and 16 h. The applied photoperiods influenced cell morphological characteristics. The 4 h photoperiod was the most effective treatment for improving biomass, growth index and cell viability percentage while these indices remained non-significant in the 16 h treatment. The methylation-sensitive amplified polymorphism (MASP) assay revealed that the UV radiation was epigenetically accompanied by DNA hypermethylation. The light-treated cells significantly displayed higher relative expression of the cannabidiolic‌ acid synthase (CBDAS) and delta9-tetrahydrocannabinolic acid synthase (THCAS) genes about 4-fold. The expression of the olivetolic acid cyclase (OAC) and olivetol synthase (OLS) genes exhibited an upward trend in response to the UV radiation. The light treatments also enhanced the proline content and protein concentration. The 4 h illumination was significantly capable of improving the cannabidiol (CBD) and delta-9-tetrahydrocannabinol (THC) concentrations, in contrast with 16 h. By increasing the illumination exposure time, the activity of the phenylalanine ammonia-lyase (PAL) enzyme linearly upregulated. The highest amounts of the phenylpropanoid derivatives were observed in the cells cultured under the radiation for 4 h. Taken collective, artificial UV radiation can induce DNA methylation modifications and impact biochemical and molecular differentiation in the cell suspensions in a photoperiod-dependent manner.

Abstract 2589: Novel workflow to generate natural killer cell sensitivity prediction model based on the epithelial mesenchymal transition related reprogramming at a single cell resolution

Abstract Single cell technology allows us to examine cell heterogeneity and identify rare cell populations; however, the high cost of single cell RNAseq limits its adoption into clinical and research environments. As an inexpensive alternative, immunofluorescent assays analyzing cell morphologies have been used in high content screening to examine cell phenotype and response to external stresses. Natural Killer cells (NK) are the bodies first line of defense and gained traction as a potential target in treating cancer. A study showed that cancer cells undergoing the process epithelial mesenchymal transition (EMT) increase their vulnerability to NK mediated cytotoxicity. We hypothesize that the EM morphology change can be correlated with NK sensitivity. Our goal is to develop an image-based, cost-effective, and efficient workflow capable of extracting cancer cell morphological features to predict its NK sensitivity at the single-cell level. To optimize this workflow, we use A549 cell line cells treated with transforming growth factor- β (TGF-β) as a representative models of cells with various EM phenotypes as NK sensitivities. We utilized a Cell Painting inspired novel image based live cell cytotoxicity assay. To capture cell morphological features, we optimized an organelle specific and live cell compatible staining panel that targets the nucleus, nucleic acid, cell membrane, F-actin, and mitochondria. The cell viability is assessed by Propidium Iodide, which stains dead cell nuclei. Our image analysis pipeline can extract the morphological features and track the cells of interest during the experiment and assess their viability. Subsequently, the morphology and viability data will be processed by a classification algorithm that could discern morphological features of cancer cells that indicate high NK vulnerability. We started with treating A549 cells with 5ng/mL TGF-β for 0, 24, 48, and 72 hours, and successfully applied the organelle staining panel. We observed distinct cell morphologies at each timepoint of TGF-β treatment, with cells shifting from a cobblestone (no TGF-β) to a spindle-like (72 hours TGF-β) morphology. Notably, the F-actin signal was significantly overexpressed in the 72 hours TGF-β treated A549 cells. A CellProfiler based preliminary image analysis pipeline was established for cell segmentation, cell features extraction, and cell viability tracking with quality control filters. Further optimization for cell segmentation, viability tracking, and classification algorithms will be needed to enhance the robustness of the workflow. Our established workflow demonstrates the feasibility of live cell functional assay with organelle staining. This workflow could be adopted to correlate and predict single tumor cell susceptibility to cytotoxic immune cells, with the goal of developing a therapeutic index. Citation Format: Yin Zou, Harrison Ball, Cade Harris, Nithya Ramnath, Venkateshwar Keshamouni, Sunitha Nagrath. Novel workflow to generate natural killer cell sensitivity prediction model based on the epithelial mesenchymal transition related reprogramming at a single cell resolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2589.

Abstract 4227: Human-derived hydrogels to support phenotypic changes in ER+ breast cancer cell line

Abstract Adipose tissue plays a critical role in breast cancer incidence, progression, and response to therapy. The development of human derived 3D culture systems can accelerate the understanding of the role of environmental factors in the progression of Breast cancer as well as a model for preclinical studies during drug development. MCF-7 spheroids were cultured in Obatala Sciences’ Obavate and human-derived hydrogels ObaGel® and ObaGel®-ECM and control media. Organoid structure and phenotypic changes were analyzed via live cell imaging on the Incucyte S3. Migration studies were performed and gene expression analysis of EMT markers. Finally, adipocyte-breast cancer cell crosstalk was evaluated using pooled adipocytes co-cultured with MCF-7 spheroids. The results demonstrated that ObaGel® and ObaGel®-ECM 3D cultures support the growth and proliferation of MCF-7 tumorspheres during an extended culture period. In addition, MCF-7 cells exhibit the characteristic morphology of tumorsphere-forming cells when cultured in Obavate, with morphological changes in the ObaGel® and ObaGel®-ECM 3D. ObaGel® and ObaGel®-ECM differentially supports MCF-7 migratory behavior and phenotypic changes characteristic of EMT. Conclusions: Human derived hydrogels are developed to support 3D culture of breast cancer cells and to recapitulate the phenotypic changes associated with their metastatic potential. Furthermore, the use of a human derived 3D coculture system provides a platform for the understanding of adipose tissue-breast cancer cell interactions as it relates to breast cancer initiation, progression, and treatment response. Defining these interactions will support the development of a tool for future use in patient stratification and precision medicine in identifying underlying causes of breast cancer progression and response to treatments. Citation Format: Cecilia G. Sanchez, Katie Hamel, Emma Rogers, Jordan Robinson, Haley Lassiter, Trivia Frazier, Christopher Williams. Human-derived hydrogels to support phenotypic changes in ER+ breast cancer cell line [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4227.