Morphological Characteristics Of Cells Research Articles

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.

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>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.

Abstract 1073: AI-assisted high-dimensional cytological single cell analysis distinguishes normal and malignant urine samples using cell morphology images with high accuracy at the single cell level

Abstract Background: Urothelial cancer (UCC) is the 4th most common malignancy in men. The current clinical problem in UCC management is the high interobserver variability of tissue UCC grading and the low sensitivity of urine cytology for early detection of recurrences and progression. We hypothesize that assessing urine samples and UCC tissues with detailed quantitative image analysis based on artificial intelligence has strong potential in both urine-based early detection of tumor recurrence and progression as well as tissue-based pathological grading. Methods: Here, we utilized the Deepcell® Human Foundation Model (HFM), a self-supervised deep learning model, to assess a cohort of urine samples from 54 normal healthy cases, 19 atypical cases, and 4 UCC cases. High resolution images of label-free single cells were captured and analyzed using the HFM to identify distinct morphological characteristics of cells from different urine origins. The urine samples were loaded onto a single-use microfluidic cartridge as part of the Deepcell® REM-I platform and brightfield images of single cells were captured in real-time. For data analysis, images of all cells run through the REM-I instrument are automatically sent to the Deepcell® Axon Suite, which provides an analysis platform to visualize cell images from specific runs, generate UMAPs of the embedding space, and run additional deep neural network models on samples in silico. Results: High-dimensional analysis of 115 deep learning and morphometric features reveals 12 morphotypes among the pooled urine samples, as represented by morphology UMAP plots. Morphology analysis reveals distinct morphotypes in normal compared to pathological urine samples. Confusion matrix showed deep learning model performance in classifying atypical versus UCC with 89.1% (Atypical (n = 8642), UCC (n = 12637)). This indicates high model performance accuracy for AI prediction of atypical versus UCC samples at the single cell level. Conclusions: The Deepcell® REM-I platform performs well with urine samples and identified numerous cell clusters among normal and pathological urines. AI-assisted high-dimensional morphology analysis distinguishes atypical urine samples from UCC cases with 89.1% accuracy at the single cell level. Future applications of this study include early detection or monitoring of UCC cases using non-invasive patient samples such as urine. Citation Format: Thierry P. van den Bosch, Geert J.L.H. van Leenders, Siamala Sinnadurai, Zhouyang Lian, Ryan Carelli, Matthew Nakaki, Vivian Lu, Manisha Ray, Janifer Cruz, Kelly R. Miller, Mahyar Salek, Maddison Masaeli, Peter J. van der Spek. AI-assisted high-dimensional cytological single cell analysis distinguishes normal and malignant urine samples using cell morphology images with high accuracy at the single cell level [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 1073.

CD73 mitigates ZEB1 expression in papillary thyroid carcinoma

BackgroundZEB1, a core transcription factor involved in epithelial-mesenchymal transition (EMT), is associated with aggressive cancer cell behavior, treatment resistance, and poor prognosis across various tumor types. Similarly, the expression and activity of CD73, an ectonucleotidase implicated in adenosine generation, is an important marker of tumor malignancy. Growing evidence suggests that EMT and the adenosinergic pathway are intricately linked and play a pivotal role in cancer development. Therefore, this study focuses on exploring the correlations between CD73 and ZEB1, considering their impact on tumor progression.MethodsWe employed CRISPR/Cas9 technology to silence CD73 expression in cell lines derived from papillary thyroid carcinoma. These same cells underwent lentiviral transduction of a reporter of ZEB1 non-coding RNA regulation. We conducted studies on cell migration using scratch assays and analyses of cellular speed and polarity. Additionally, we examined ZEB1 reporter expression through flow cytometry and immunocytochemistry, complemented by Western blot analysis for protein quantification. For further insights, we applied gene signatures representing different EMT states in an RNA-seq expression analysis of papillary thyroid carcinoma samples from The Cancer Genome Atlas.ResultsSilencing CD73 expression led to a reduction in ZEB1 non-coding RNA regulation reporter expression in a papillary thyroid carcinoma-derived cell line. Additionally, it also mitigated ZEB1 protein expression. Moreover, the expression of CD73 and ZEB1 was correlated with alterations in cell morphology characteristics crucial for cell migration, promoting an increase in cell polarity index and cell migration speed. RNA-seq analysis revealed higher expression of NT5E (CD73) in samples with BRAF mutations, accompanied by a prevalence of partial-EMT/hybrid state signature expression.ConclusionsCollectively, our findings suggest an association between CD73 expression and/or activity and the post-transcriptional regulation of ZEB1 by non-coding RNA, indicating a reduction in its absence. Further investigations are warranted to elucidate the relationship between CD73 and ZEB1, with the potential for targeting them as therapeutic alternatives for cancer treatment in the near future.

Hypoxia preconditioning of human amniotic mesenchymal stem cells enhances proliferation and migration and promotes their homing via the HGF/C-MET signaling axis to augment the repair of acute liver failure

BackgroundTransplantation of mesenchymal stem cells (MSCs) is a newly developed strategy for treating acute liver failure (ALF). Nonetheless, the low survival rate of MSCs after transplantation and their poor homing to damaged tissues limit the clinical application of MSCs. The research assessed whether hypoxic preconditioning (HPC) can improve the biological activity of human amniotic mesenchymal stem cells (hA-MSCs), promote their homing ability to the liver of mice with ALF, and influence liver tissue repair. MethodsFlow cytometry, CCK8, Transwell, and Western blotting assays were conducted to assess the effects of hypoxic preconditioning on the phenotype, proliferation, and migration of hA-MSCs and the changes in the c-Met and CXCR4 gene expression levels were studied. To evaluate the effects of the transplantation of hypoxic preconditioning of hA-MSCs on the homing and repair of D-galactosamine (D-GalN)/LPS-induced ALF, the mechanism was elucidated by adding c-Met, CXCR4-specific blockers (SU11274 and AMD3100). ResultsAfter hypoxia pretreatment (1% oxygen volume fraction), hA-MSCs maintained the morphological characteristics of adherence and vortex colony growth and showed high CD44, CD90, and CD105 and low CD31, CD34, and CD45 expression levels. Hypoxic preconditioning of hA-MSCs significantly increased their proliferation and migration and highly expressed the c-Met and CXCR4 genes. In vivo and in vitro, this migration-promoting effect was suppressed by the c-Met specific blocker SU11274. In the acute liver failure mouse model, the HGF expression level was considerably elevated in the liver than that in the serum, lungs and kidneys. The transplantation of hypoxic preconditioned hA-MSCs introduced a remarkable improvement in the liver function and survival rate of mice with ALF and enhanced the anti-apoptosis ability of liver cells. The anti-apoptotic enhancing effect of hypoxic preconditioning was suppressed by the c-Met specific blocker SU11274. Hypoxic hA-MSCs administration was observed to have considerably increased the fluorescent cells in the liver than that recorded after administering normal oxygen-hA-MSCs. The number of hepatic fluorescent cells decreased remarkably after adding the c-Met inhibitor SU11274, compared to that recorded after hypoxic pretreatment, whereas the effect of c-Met inhibitor SU11274 on normal oxygen-hA-MSCs was not significant. ConclusionsHypoxic preconditioning depicted no impact on the morphology and phenotype features of the human amniotic mesenchymal stem cells, but it can promote their proliferation, migration, anti-apoptotic effect, and homing rate and improve the repair of acute liver failure, which might be mediated by the HGF/c-Met signaling axis.

Detection of senescence using machine learning algorithms based on nuclear features

Cellular senescence is a stress response with broad pathophysiological implications. Senotherapies can induce senescence to treat cancer or eliminate senescent cells to ameliorate ageing and age-related pathologies. However, the success of senotherapies is limited by the lack of reliable ways to identify senescence. Here, we use nuclear morphology features of senescent cells to devise machine-learning classifiers that accurately predict senescence induced by diverse stressors in different cell types and tissues. As a proof-of-principle, we use these senescence classifiers to characterise senolytics and to screen for drugs that selectively induce senescence in cancer cells but not normal cells. Moreover, a tissue senescence score served to assess the efficacy of senolytic drugs and identified senescence in mouse models of liver cancer initiation, ageing, and fibrosis, and in patients with fatty liver disease. Thus, senescence classifiers can help to detect pathophysiological senescence and to discover and validate potential senotherapies.

Influence of IAA-Producing Bacteria and Phosphate Solubilizers from The Rhizosphere of Serbania grandiflora (L.) Pers on The in vitro Germination of Vigna radiata (L.)

This study aims to determine the effect of IAA-producing bacteria and phosphate solvents from the rhizosphere of Serbania grandiflora (L.) Pers on the germination of Vigna radiata (L.). This research began by isolating the rhizosphere bacterium Serbania grandiflora (L.) Pers, which was then characterized by cell morphology, grammatical and morphological characteristics. The ability of bacteria to dissolve phosphate was evaluated qualitatively by observing the presence of clear zones in Pikovskaya media, and quantitatively with a spectrophotometer. The isolate's ability to produce IAA was tested quantitatively using Salkowski reagent, and quantitatively using a spectrophotometer. For the effect of isolates on the growth of green beans, a completely randomized design (CRD) research design was used, with 4 treatments, namely P0 (sterile distilled water); P1 (IAA-producing bacterial culture); P2 (phosphate solubilizing bacterial culture); P3 (IAA producer and phosphate solubilizer). Vigna radiata L. seedling growth test was carried out in vitro on Murphy's medium. The research data were analyzed using the one-way ANOVA test. The results showed that 5 isolates had the ability to dissolve phosphate, with a phosphate solubility index ranging from 1.70-2.14, where isolate RT7 showed the highest value. The isolate's ability to produce IAA at concentrations ranged from 7.1 - 62.7 ppm, with the highest value shown by isolate RT6. The results showed that treatment with IAA-producing bacterial isolates plus phosphate solubilization properties showed a significantly different effect on Vigna radiata L. plant height compared to other treatments.

From pixels to phenotypes: Integrating image-based profiling with cell health data as BioMorph features improves interpretability.

Cell Painting assays generate morphological profiles that are versatile descriptors of biological systems and have been used to predict in vitro and in vivo drug effects. However, Cell Painting features extracted from classical software such as CellProfiler are based on statistical calculations and often not readily biologically interpretable. In this study, we propose a new feature space, which we call BioMorph, that maps these Cell Painting features with readouts from comprehensive Cell Health assays. We validated that the resulting BioMorph space effectively connected compounds not only with the morphological features associated with their bioactivity but with deeper insights into phenotypic characteristics and cellular processes associated with the given bioactivity. The BioMorph space revealed the mechanism of action for individual compounds, including dual-acting compounds such as emetine, an inhibitor of both protein synthesis and DNA replication. Overall, BioMorph space offers a biologically relevant way to interpret the cell morphological features derived using software such as CellProfiler and to generate hypotheses for experimental validation.

Necropsy in Mouse Research.

Necropsy is the postmortem examination of an animal's body. Experiments on laboratory animals are performed to gather scientific data relevant to basic and, even more often, translational research, mainly in the field of translational medicine. A necropsy conducted on laboratory animals subjected to experimental research provides an opportunity to exhaustively explore pathological processes that took place during life. In order to achieve that goal, procedures of biomaterial collection should be performed timely, bearing in mind the inevitable process of postmortem tissue decay, and precisely, to avoid mechanical tissue damage.In this chapter, procedures of collecting organs and tissue in a way that ensures the preservation of the organ structure, tissue organization, morphological characteristics of cells, and last but not least, intracellular protein and nucleic acid content and chromosome organization are going to be described step by step.

HFM-Tracker: a cell tracking algorithm based on hybrid feature matching.

Cell migration is known to be a fundamental biological process, playing an essential role in development, homeostasis, and diseases. This paper introduces a cell tracking algorithm named HFM-Tracker (Hybrid Feature Matching Tracker) that automatically identifies cell migration behaviours in consecutive images. It combines Contour Attention (CA) and Adaptive Confusion Matrix (ACM) modules to accurately capture cell contours in each image and track the dynamic behaviors of migrating cells in the field of view. Cells are firstly located and identified via the CA module-based cell detection network, and then associated and tracked via a cell tracking algorithm employing a hybrid feature-matching strategy. This proposed HFM-Tracker exhibits superiorities in cell detection and tracking, achieving 75% in MOTA (Multiple Object Tracking Accuracy) and 65% in IDF1 (ID F1 score). It provides quantitative analysis of the cell morphology and migration features, which could further help in understanding the complicated and diverse cell migration processes.

Single-cell Heterogeneity-aware Transformer-guided Multiple Instance Learning for Cancer Aneuploidy Prediction from Whole Slide Histopathology Images.

Aneuploidy is a hallmark of aggressive malignancies associated with therapeutic resistance and poor survival. Measuring aneuploidy requires expensive specialized techniques that are not clinically applicable. Deep learning analysis of routine histopathology slides has revealed associations with genetic mutations. However, existing studies focus on image patches or tiles, and there is no prior work that predicts aneuploidy using single-cell analysis. Here, we present a single-cell heterogeneity-aware and transformer-guided deep learning framework to predict aneuploidy from whole slide histopathology images. First, we perform nuclei segmentation and classification to obtain individual cancer cells, which are clustered into multiple subtypes. The cell subtype distributions are computed to measure cancer cell heterogeneity. Additionally, morphological features of different cell subtypes are extracted. Further, we leverage a multiple instance learning module with Transformer, which encourages the network to focus on the most informative cancer cells. Lastly, a hybrid network is built to unify cell heterogeneity, morphology, and deep features for aneuploidy prediction. We train and validate our method on two public datasets from TCGA: lung adenocarcinoma (LUAD) and head and neck squamous cell carcinoma (HNSC), with 339 and 245 patients. Our model achieves promising performance with AUC of 0.818 (95% CI: 0.718-0.919) and 0.827 (95% CI: 0.704-0.949) on the LUAD and HNSC test sets, respectively. Through extensive ablation and comparison studies, we demonstrate the effectiveness of each component of the model and superior performance over alternative networks. In conclusion, we present a novel deep learning approach to predict aneuploidy from histopathology images, which could inform personalized cancer treatment.

Clinical Characteristics and Diagnosis of Ph-Positive Mixed Phenotype Acute Leukemia.

The goal was to improve the clinical cognition of Ph-positive mixed phenotype acute leukemia and avoid misdiagnosis or delayed diagnosis. The clinical manifestations and laboratory results (bone marrow cell morphology, multiparameter flow cytometry, and cytogenetics) of a case of Ph-positive mixed phenotype acute leukemia were analyzed, and related literature was reviewed. Blood routine: WBC 386.35 x 109/L, HGB 117.00 g/L, PLT 31 x 109/L; 80% of the original cells can be seen by artificial classification. Morphological examination of bone marrow cells showed that the proliferation of nucleated cells was obviously active, and the original cells accounted for 76%. The size of the original cells was somewhat uniform, most of the cells had less mass, were stained light grayish blue, the cytoplasm particles were not obvious, the nuclei were mostly round or quasi-round, some of them showed distortion and nuclear notch, and the chromatin was coarse. Some of the cells were rich in mass, small azurin granules were seen, the nuclei were regular, most of them were round, the chromatin was fine, the myeloperoxidase and esterase staining were negative, the eosinophils accounted for 2.5%, and the basophils accounted for 0.5%. Flow cytometry immunotyping: Two groups of abnormal cells were seen in the bone marrow. 1. A group included 12.32% of nuclear cells and showed abnormal myeloid primitive cell phenotype. Main expression: CD117, CD34, CD38, HLA-DR, CD33, CD64, CD123, weak expression: CD13, CD19. 2. The other group included 45.61% of the nuclear cells and had a B-lymphoblastic phenotype. Main expression: CD34, CD38, HLA-DR, CD123, CD19, CD10, CD9, cCD79a, TDT, weak expression of CD13, CD22. Mixed phenotype acute leukemia (M/B) immunophenotype was considered. Chromosome: 46,XY,t(9; 22)(q34;q11.2) [20]. BCR-ABL (P210) fusion gene was positive. Mixed phenotype acute leukemia (MPAL) is a rare type of malignant hematologic disease. Its diagnosis is based on the comprehensive evaluation of bone marrow cell morphology, immunophenotype, molecular and cytogenetic features.

Prediction of Tuberculosis From Lung Tissue Images of Diversity Outbred Mice Using Jump Knowledge Based Cell Graph Neural Network.

Tuberculosis (TB), primarily affecting the lungs, is caused by the bacterium Mycobacterium tuberculosis and poses a significant health risk. Detecting acid-fast bacilli (AFB) in stained samples is critical for TB diagnosis. Whole Slide (WS) Imaging allows for digitally examining these stained samples. However, current deep-learning approaches to analyzing large-sized whole slide images (WSIs) often employ patch-wise analysis, potentially missing the complex spatial patterns observed in the granuloma essential for accurate TB classification. To address this limitation, we propose an approach that models cell characteristics and interactions as a graph, capturing both cell-level information and the overall tissue micro-architecture. This method differs from the strategies in related cell graph-based works that rely on edge thresholds based on sparsity/density in cell graph construction, emphasizing a biologically informed threshold determination instead. We introduce a cell graph-based jumping knowledge neural network (CG-JKNN) that operates on the cell graphs where the edge thresholds are selected based on the length of the mycobacteria's cords and the activated macrophage nucleus's size to reflect the actual biological interactions observed in the tissue. The primary process involves training a Convolutional Neural Network (CNN) to segment AFBs and macrophage nuclei, followed by converting large (42831*41159 pixels) lung histology images into cell graphs where an activated macrophage nucleus/AFB represents each node within the graph and their interactions are denoted as edges. To enhance the interpretability of our model, we employ Integrated Gradients and Shapely Additive Explanations (SHAP). Our analysis incorporated a combination of 33 graph metrics and 20 cell morphology features. In terms of traditional machine learning models, Extreme Gradient Boosting (XGBoost) was the best performer, achieving an F1 score of 0.9813 and an Area under the Precision-Recall Curve (AUPRC) of 0.9848 on the test set. Among graph-based models, our CG-JKNN was the top performer, attaining an F1 score of 0.9549 and an AUPRC of 0.9846 on the held-out test set. The integration of graph-based and morphological features proved highly effective, with CG-JKNN and XGBoost showing promising results in classifying instances into AFB and activated macrophage nucleus. The features identified as significant by our models closely align with the criteria used by pathologists in practice, highlighting the clinical applicability of our approach. Future work will explore knowledge distillation techniques and graph-level classification into distinct TB progression categories.

Identification Of Tet2 Gene Level Of Expression In Acute Leukemias

Objective: TET2 gene (4q24) is one of the most important regulatory factors of the methylation process in human genome. The homologous protein is directly involved in convertion of 5-methylcytosine into 5-hydroximethylcytosine (1). Loss of function mutations in TET2 gene are considered to be driver mutations (initiating mutations) for leukemogenesis. In some cases they can be found in early clonal cells of Acute Myeloid Leukemia (AML), Acute Lymphoblastic Leukemia (ALL) or myelodisplastic syndromes (MDS) (2). The aim of this study is to determinate the frequency of TET2 mutations in Acute Leukemia (AL) patients. Materials and methods: This retrospective study enrolled 24 patients newly diagnosed with AML (19 patients) and ALL (5 pacients). By using Real-Time PCR we obtained a relative cuantification of TET2 gene level of expression taking ABL gene as reference. Outcomes: Low levels of TET2 gene expression were found in 6 patients (25%), 3 of them being diagnosed with AML and 3 with ALL. No cytogenetic anomalies were noticed. However, all these patients had blastocytosis and higher white blood cells count than pacients with normal levels of expression. There was one case with mutations in both TET2 gene and NPM1 gene and one case that associated a defective TET2 gene and an internal tandem duplication of FLT3-ITD. Conclusions: In our study, TET2 gene loss of function mutations occured on a specific background which involved some characteristic mutations for AL and some particular features of cell morphology. We aim to continue this study by investigating the long term effects of this epigenetic mutation in AL patients.

Using Histologic Image Analysis to Understand Biophysical Regulations of Epithelial Cell Morphology

ABSTRACT Epithelial mechanics and mechanobiology have become 2 important research fields in life sciences and bioengineering. These fields investigate how physical factors induced by cell adhesion and collective behaviors can directly regulate biologic processes, such as organ development and disease progression. Cell mechanics and mechanobiology thus make exciting biophysics education topics to illustrate how fundamental physics principles play a role in regulating cell biology. However, the field currently lacks hands-on activities that engage students in learning science and outreach programs in these topics. One such area is the development of robust hands-on modules that allow students to observe features of cell shape and mechanics and connect them to fundamental physics principles. Here, we demonstrate a workflow that engages students in studying epithelial cell mechanics by using commercial histology slides of frog skin. We show that by using recently developed artificial intelligence–based image-segmentation tools, students can easily quantify different cell morphologic features in a high-throughput manner. Using our workflow, students can reproduce 2 essential findings in cell mechanics: the common gamma distribution of normalized cell aspect ratio in jammed epithelia and the constant ratio between the nuclear and cellular area. Importantly, because the only required instrument for this active learning module is a readily available light microscope and a computer, our module is relatively low cost, as well as portable. These features make the module scalable for students at various education levels and outreach programs. This highly accessible education module provides a fun and engaging way to introduce students to the world of epithelial tissue mechanics.