Cell Imaging Microscopy Research Articles

ArtSeg—Artifact segmentation and removal in brightfield cell microscopy images without manual pixel-level annotations

Brightfield cell microscopy is a foundational tool in life sciences. The acquired images are prone to contain visual artifacts that hinder downstream analysis, and automatically removing them is therefore of great practical interest. Deep convolutional neural networks are state-of-the-art for image segmentation, but require pixel-level annotations, which are time-consuming to produce. Here, we propose ScoreCAM-U-Net, a pipeline to segment artifactual regions in brightfield images with limited user input. The model is trained using only image-level labels, so the process is faster by orders of magnitude compared to pixel-level annotation, but without substantially sacrificing the segmentation performance. We confirm that artifacts indeed exist with different shapes and sizes in three different brightfield microscopy image datasets, and distort downstream analyses such as nuclei segmentation, morphometry and fluorescence intensity quantification. We then demonstrate that our automated artifact removal ameliorates this problem. Such rapid cleaning of acquired images using the power of deep learning models is likely to become a standard step for all large scale microscopy experiments.

An Efficient Deep Neural Framework for Nucleus Semantic Segmentation with Enhanced U-Net

With the advancement of medicine, there is a terrific requirement to process an increasing number of medical imaging where image segmentation comes into play. Single cell division is generally among the first as well as most essential tasks in image-based cell analysis. The identification of the nuclei allows pathologists to determine each cell in the sample, and by measuring how cells respond to various treatments, they can comprehend the fundamental biological processes in work. In this paper we gathered a deep learning network which detects and parts the cell microscopy image. Highly advanced performance is achieved in image segmentation tasks through deep learning-based techniques. These procedures are complex and need the support of compelling computational resources. This paper emphasizes the basic principles of the methods used to segment an image. We have implemented U-Net for the semantic segmentation of nucleus. A successful implementation will aid researchers immensely in their fight to find pharmaceutical solutions to medical crises while saving both valuable research time and funding.

P1732: EVALUATION OF A DIGITAL SYSTEM LEVERAGING MOBILE TECHNOLOGY AND ARTIFICIAL INTELLIGENCE FOR DIGITALIZATION, REMOTE ANALYSIS AND SUPPORTED DIFFERENTIAL CELL COUNT OF BONE MARROW ASPIRATES.

Background: Microscopic examination of bone marrow aspirates (BMA) is still, in almost every organization world-wide, a task that is performed with conventional microscopes relying on an in-person diagnostic process. Being these samples left outside the digital world, it is not possible to leverage tools such as Artificial Intelligence (AI) algorithms to support diagnosis in hematological medical images. Concerning the BMA analysis, the differential cell count (DCC) remains non-automated, being a tedious and time consuming task that is frequently affected by considerable interobserver variability. Aims: To support the work of hematologists in the analysis of BMA samples by implementing a simple digitization system to develop and validate an AI algorithm for the automatic recognition of different bone marrow cell lineages. This system aims at decreasing the interobserver variability associated with bone marrow DCC and improving the efficiency due to the increased speed of cell quantification. Methods: 100 BMA samples from 95 patients were digitized by photographing 20 fields for each sample (1000x magnification) by hematology researchers. The digitization equipment consists of a 3D printed device to align the smartphone to the ocular lens of a conventional microscope; an acquisition app, and a web-based telemedicine platform to remotely visualize and analyze the images. Resulting images were used to train and validate an AI algorithm for the recognition of different bone marrow cell lineages. Subsequently, a total of 32,229 cells were identified and each of them was remotely classified and tagged by a panel of 3 different hematologists (randomly selected from the 6 who participated in the study) according to 6 different lineages: Myeloid, Erythroid, Monocytic, Lymphoid, Blasts and Plasma cells. The AI algorithm was trained only with cells in which all 3 experts agreed on the class (n=28,058). Validation of the algorithm was performed on an independent test set composed by 7,063 cells not included in the training set. Interobserver agreement was calculated for all possible pairs of experts and mean agreement was calculated for each lineage class. 95% confidence intervals (CI) were properly calculated. A two-stage cell detection and lineage classification deep learning-based algorithm was developed to automatically identify and differentiate the aforementioned 6 lineage classes. Ethics approval for the study was obtained from the Clinical Research Ethics Committee. Results: The detection algorithm achieved high overall accuracy for detecting nucleated cells in microscopy images (sensitivity and precision of 91.6% and 91.3% respectively). Identified cells were further classified by the algorithm into 6 different lineage classes with an overall accuracy of 92.6%. Additionally, and for comparative purposes, interobserver agreement was also calculated. Average interobserver agreement was 88.1%. The Figure shows the 95% CI for AI performance and agreement between observers for each lineage class independently. As it can be shown, the AI algorithm demonstrated similar performance to that of a hematologist. Image:Summary/Conclusion: It is possible to digitize BMA samples in a standardized way just relying on smartphones and a simple 3D printed device, transforming BMAs’ DCC in a digital, asynchronous and ubiquitous process. A reliable AI algorithm has been generated to perform an automatic bone marrow DCC with comparable performance to that of a human expert. This AI algorithm can be embedded in any smartphone and has the potential to run in any lab in the world, increasing efficiency and objectivity.

Automated segmentation of Acute Lymphocytic Leukemia (ALL) subtypes by the combination of color space conversion and K-means cluster

Leukemia is blood cancer, and it is one of the most common and deadly causes of death in the world. Morphologically, Leukemia cells are classified into three types of L1, L2, and L3 by the French‑American‑British (FAB) classification. A new method of automatically segmenting blast cells from microscopic blood smear images proposed in this research. This study proposes significant pre-processing to obtain high segmentation performance and presents a new combination of image processing approaches. specially, the five color spaces selected with K-means cluster to segment subtypes of Acute Lymphocytic Leukemia. The majority of the components of the performance color space were chosen based on their similarity with ground truth image through using five evaluation parameters. The proposed codes for Acute Lymphocytic Leukemia subtype accurate segmentation applied on local and public datasets (427 images). The best color space type was YIQ which had 87% performance for the public dataset with segmentation evaluation 96.24% for dice parameter References AL-JABORIY, S. S., SJARIF, N. N. A., CHUPRAT, S. & ABDUALLAH, W. M. 2019. Acute lymphoblastic leukemia segmentation using local pixel information. Pattern Recognition Letters, 125, 85-90. AL HAMAD, H. A. Use an efficient neural network to improve the Arabic handwriting recognition. 2013 IEEE International Conference on Signal and Image Processing Applications, 2013. IEEE, 269-274. AMIN, M. M., KERMANI, S., TALEBI, A. & OGHLI, M. G. 2015. Recognition of acute lymphoblastic leukemia cells in microscopic images using k-means clustering and support vector machine classifier. Journal of medical signals and sensors, 5, 49. ASHOUR, A. S., WAHBA, M. A. & GHANNAM, R. 2021. A Cascaded Classification-Segmentation Reversible System for Computer-aided Detection and Cells Counting in Microscopic Peripheral Blood Smear Basophils and Eosinophils Images. IEEE Access. ASLAN, M. S., MOSTAFA, E., ABDELMUNIM, H., SHALABY, A., FARAG, A. A. & ARNOLD, B. A novel probabilistic simultaneous segmentation and registration using level set. 2011 18th IEEE International Conference on Image Processing, 2011. IEEE, 2161-2164. BHIMANI, J., LEESER, M. & MI, N. Accelerating K-Means clustering with parallel implementations and GPU computing. 2015 IEEE High Performance Extreme Computing Conference (HPEC), 2015. IEEE, 1-6. GHANE, N., VARD, A., TALEBI, A. & NEMATOLLAHY, P. 2017. Segmentation of white blood cells from microscopic images using a novel combination of K-means clustering and modified watershed algorithm. Journal of medical signals and sensors, 7, 92. HEGDE, R. B., PRASAD, K., HEBBAR, H. & SINGH, B. M. K. 2019. Image processing approach for detection of leukocytes in peripheral blood smears. Journal of medical systems, 43, 1-11. KADRY, S., RAJINIKANTH, V., TANIAR, D., DAMAŠEVIČIUS, R. & VALENCIA, X. P. B. 2021. Automated segmentation of leukocyte from hematological images—a study using various CNN schemes. The Journal of Supercomputing, 1-21. KARWAN, M., ABDULLAH, O., AMIN, A., HASAN, B., MOHAMED, Z., SULAIMAN, L., SHEKHA, M., NAJMULDEEN, H., BARZINGI, B. & SALIH, A. 2021. Cancer Statistics in Kurdistan Region of Iraq: A Tale of Two Cities. LABATI, R. D., PIURI, V. & SCOTTI, F. 2011. The Acute Lymphoblastic Leukemia Image Database for Image Processing. Universita Degli Studi Di Milano, 10. MILLER, D. R., LEIKIN, S., ALBO, V., SATHER, H. & HAMMOND, D. 1981. Prognostic importance of morphology (FAB classification) in childhood acute lymphoblastic leukaemia (ALL). British Journal of Haematology, 48, 199-206. PHILIP, A. T., SHIFAANA, S., SUNNY, S. & MANIMEGALAI, P. Detection of Acute Lymphoblastic Leukemia in Microscopic images using Image Processing Techniques. Journal of Physics: Conference Series, 2021. IOP Publishing, 012022. PRABHA, D. S. & KUMAR, J. S. 2016. Performance evaluation of image segmentation using objective methods. Indian J. Sci. Technol, 9, 1-8. SARRAFZADEH, O., DEHNAVI, A. M., RABBANI, H. & TALEBI, A. A simple and accurate method for white blood cells segmentation using K-means algorithm. 2015 IEEE Workshop on Signal Processing Systems (SiPS), 2015. IEEE, 1-6. SHAFIQUE, S. & TEHSIN, S. 2018. Acute lymphoblastic leukemia detection and classification of its subtypes using pretrained deep convolutional neural networks. Technology in cancer research & treatment, 17, 1533033818802789. SIEGEL, R. L., MILLER, K. D., GODING SAUER, A., FEDEWA, S. A., BUTTERLY, L. F., ANDERSON, J. C., CERCEK, A., SMITH, R. A. & JEMAL, A. 2020. Colorectal cancer statistics, 2020. CA: a cancer journal for clinicians, 70, 145-164. TAVAKOLI, E., GHAFFARI, A., KOUZEHKANAN, Z. M. & HOSSEINI, R. 2021. New Segmentation and Feature Extraction Algorithm for Classification of White Blood Cells in Peripheral Smear Images. bioRxiv. TSAI, C.-M. & LEE, H.-J. 2002. Binarization of color document images via luminance and saturation color features. IEEE Transactions on Image Processing, 11, 434-451. ZOU, K. H., WARFIELD, S. K., BHARATHA, A., TEMPANY, C. M., KAUS, M. R., HAKER, S. J., WELLS III, W. M., JOLESZ, F. A. & KIKINIS, R. 2004. Statistical validation of image segmentation quality based on a spatial overlap index1: scientific reports. Academic radiology, 11, 178-189.

An Image Analysis Pipeline for Quantifying the Features of Fluorescently-Labeled Biomolecular Condensates in Cells.

Biomolecular condensates are cellular organelles formed through liquid-liquid phase separation (LLPS) that play critical roles in cellular functions including signaling, transcription, translation, and stress response. Importantly, condensate misregulation is associated with human diseases, including neurodegeneration and cancer among others. When condensate-forming biomolecules are fluorescently-labeled and examined with fluorescence microscopy they appear as illuminated foci, or puncta, in cells. Puncta features such as number, volume, shape, location, and concentration of biomolecular species within them are influenced by the thermodynamics of biomolecular interactions that underlie LLPS. Quantification of puncta features enables evaluation of the thermodynamic driving force for LLPS and facilitates quantitative comparisons of puncta formed under different cellular conditions or by different biomolecules. Our work on nucleoporin 98 (NUP98) fusion oncoproteins (FOs) associated with pediatric leukemia inspired us to develop an objective and reliable computational approach for such analyses. The NUP98-HOXA9 FO forms hundreds of punctate transcriptional condensates in cells, leading to hematopoietic cell transformation and leukemogenesis. To quantify the features of these puncta and derive the associated thermodynamic parameters, we developed a live-cell fluorescence microscopy image processing pipeline based on existing methodologies and open-source tools. The pipeline quantifies the numbers and volumes of puncta and fluorescence intensities of the fluorescently-labeled biomolecule(s) within them and generates reports of their features for hundreds of cells. Using a standard curve of fluorescence intensity versus protein concentration, the pipeline determines the apparent molar concentration of fluorescently-labeled biomolecules within and outside of puncta and calculates the partition coefficient (Kp) and Gibbs free energy of transfer (ΔGTr), which quantify the favorability of a labeled biomolecule partitioning into puncta. In addition, we provide a library of R functions for statistical analysis of the extracted measurements for certain experimental designs. The source code, analysis notebooks, and test data for the Punctatools pipeline are available on GitHub: https://github.com/stjude/punctatools. Here, we provide a protocol for applying our Punctatools pipeline to extract puncta features from fluorescence microscopy images of cells.

Surface Functionalization of Organosilica Nanoparticles With Au Nanoparticles Inhibits Cell Proliferation and Induces Cell Death in 4T1 Mouse Mammary Tumor Cells for DNA and Mitochondrial-Synergized Damage in Radiotherapy.

Radiotherapy is one of the most effective cancer treatments. Au nanoparticles (NPs) are one of the most used X-ray sensitizing materials however the effective small sub-nm size of Au NPs used for X-ray sensitizers is disadvantageous for cellular uptake. Here, we propose the surface functionalization of organosilica NPs (OS) with Au NPs (OS/Au), which combined the 100 nm size of OS and the sub-nm size of Au NPs, and synthesized effective Au materials as an X-ray sensitizer. The X-ray sensitizing potential for 4T1 mouse mammary tumor cells was revealed using a multifaceted evaluation combined with a fluorescence microscopic cell imaging assay. The number of polyethyleneimine (PEI)-modified OS (OS/PEI) and OS/Au (OS/Au/PEI) uptake per 4T1 mouse mammary tumor cell was the same; however, 4T1 cells treated with OS/Au/PEI exhibited significant inhibition of cell proliferation and increases in cell death by X-ray irradiation at 8Gy. The non-apoptotic death of OS/Au/PEI-treated 4T1 cells was increased by DNA and mitochondrial-synergized damage increase and showed potential applications in radiotherapy.

Unique Random-Block Polymer Architecture for Site-Specific Mitochondrial Sequestration-Aided Effective Chemotherapeutic Delivery and Enhanced Fluorocarbon Segmental Mobility-Facilitated 19F Magnetic Resonance Imaging.

The elevation of the chemotherapeutic efficacy and attenuation of its side effects on healthy cells and tissues become one of the prime targets for the treatment of cancer. Toward this direction, a sequential receptor and mitochondria dual-targeting strategy was implemented in the DX TP PG BN 19F theranostic polymer that was anchored with the chemotherapeutic agent doxorubicin, receptor-targeting biotin, and mitochondria-targeting triphenylphosphonium cations. The polymer was flourished with a unique 19F magnetic resonance imaging (MRI) tracer that exhibited high segmental mobility and eventually led to prolonged T2 relaxation time. Furthermore, for the sake of amphiphilicity, the DX TP PG BN 19F polymer spontaneously aggregated into nano-sphere with positive zeta potential, where the MRI tracer and biotin embedded at the exterior and displayed site-specific targeting and remarkable 19F MRI capability simultaneously. The mitochondria-targeting competency of the DX TP PG BN 19F theranostic polymer was investigated by comparing the non-mitochondrial-targeting DX PG BN 19F polymer using fluorescence microscopic cell imaging in human cervical, HeLa, and breast MCF-7 carcinoma cell lines. Moreover, cytotoxicity experiments of the aforementioned theranostic polymers clarified the enhancement of the chemotherapeutic efficacy of DX TP PG BN 19F theranostic polymers through effective and precise mitochondrial doxorubicin delivery that forced to follow the apoptotic path.

Senescence-Associated Molecules and Tumor-Immune-Interactions as Prognostic Biomarkers in Colorectal Cancer

Background and AimsThe initiation of cellular senescence in response to protumorigenic stimuli counteracts malignant progression in (pre)malignant cells. Besides arresting proliferation, cells entering this terminal differentiation state adopt a characteristic senescence-associated secretory phenotype (SASP) which initiates alterations to their microenvironment and effects immunosurveillance of tumorous lesions. However, some effects mediated by senescent cells contribute to disease progression. Currently, the exploration of senescent cells' impact on the tumor microenvironment and the evaluation of senescence as possible target in colorectal cancer (CRC) therapy demand reliable detection of cellular senescence in vivo. Therefore, specific immunohistochemical biomarkers are required. Our aim is to analyze the clinical implications of senescence detection in colorectal carcinoma and to investigate the interactions of senescent tumor cells and their immune microenvironment in vitro and in vivo.MethodsSenescence was induced in CRC cell lines by low-dose-etoposide treatment and confirmed by Senescence-associated β-galactosidase (SA-β-GAL) staining and fluorescence activated cell sorting (FACS) analysis. Co-cultures of senescent cells and immune cells were established. Multiple cell viability assays, electron microscopy and live cell imaging were conducted. Immunohistochemical (IHC) markers of senescence and immune cell subtypes were studied in a cohort of CRC patients by analyzing a tissue micro array (TMA) and performing digital image analysis. Results were compared to disease-specific survival (DSS) and progression-free survival (PFS).ResultsVarying expression of senescence markers in tumor cells was associated with in- or decreased survival of CRC patients. Proximity analysis of p21-positive senescent tumor cells and cytotoxic T cells revealed a significantly better prognosis for patients in which these cell types have the possibility to directly interact. In vitro, NK-92 cells (mimicking natural killer T cells) or TALL-104 cells (mimicking both cytotoxic T cells and natural killer T cells) led to dose-dependent specific cytotoxicity in >75 % of the senescent CRC cells but <20 % of the proliferating control CRC cells. This immune cell-mediated senolysis seems to be facilitated via direct cell-cell contact inducing apoptosis and granule exocytosis.ConclusionCounteracting tumorigenesis, cellular senescence is of significant relevance in CRC. We show the dual role of senescence bearing both beneficial and malignancy-promoting potential in vivo. Absence as well as exceeding expression of senescence markers are associated with bad prognosis in CRC. The antitumorigenic potential of senescence induction is determined by tumor micromilieu and immune cell-mediated elimination of senescent cells.

Morphological profiling of environmental chemicals enables efficient and untargeted exploration of combination effects

Environmental chemicals are commonly studied one at a time, and there is a need to advance our understanding of the effect of exposure to their combinations. Here we apply high-content microscopy imaging of cells stained with multiplexed dyes (Cell Painting) to profile the effects of Cetyltrimethylammonium bromide (CTAB), Bisphenol A (BPA), and Dibutyltin dilaurate (DBTDL) exposure on four human cell lines; both individually and in all combinations. We show that morphological features can be used with multivariate data analysis to discern between exposures from individual compounds, concentrations, and combinations. CTAB and DBTDL induced concentration-dependent morphological changes across the four cell lines, and BPA exacerbated morphological effects when combined with CTAB and DBTDL. Combined exposure to CTAB and BPA induced changes in the ER, Golgi apparatus, nucleoli and cytoplasmic RNA in one of the cell lines. Different responses between cell lines indicate that multiple cell types are needed when assessing combination effects. The rapid and relatively low-cost experiments combined with high information content make Cell Painting an attractive methodology for future studies of combination effects. All data in the study is made publicly available on Figshare.

Butanol Production by a Novel Efficient Method Using Mixed Cultures of Clostridium beijerinckii and Arthrobacter sp. in Stirred-Tank and Gas-Lift Bioreactors

Arthrobacter sp. BCC 72131, an obligate aerobic bacterium, was used to create anaerobic conditions, and Clostridium beijerinckii TISTR 1461 was used as a butanol producer in an acetone-butanol–ethanol (ABE) fermentation. Sweet sorghum juice (SSJ) medium containing 60 g/L of total sugar supplemented with 1.27 g/L of (NH4)2SO4 was used as a butanol production (BP) medium. Arthrobacter sp. was inoculated into the BP medium in 1-L screw-capped bottles. After 2, 4, 6 and 12 h of Arthrobacter sp. cultivation at 30 °C, C. beijerinckii was transferred into the BP medium to start butanol production at 37 °C. The results showed that C. beijerinckii inoculation after 6 h of Arthrobacter sp. cultivation gave the highest butanol titer (PB) at 12.56 g/L, with a butanol yield (YB/S) and volumetric butanol productivity (QB) of 0.34 g/g and 0.23 g/L·h, respectively. These values are approximately 10–27% higher than those of the control experiment using a single culture of C. beijerinckii TISTR 1461 and oxygen-free nitrogen (OFN) gas flushing to create anaerobic conditions. Field emission scanning electron microscopic (FE-SEM) images of Clostridium cells, as well as protein and free amino nitrogen concentrations in the broth during butanol fermentation were also studied to confirm the results. The butanol fermentation was then carried out in a 5.6-L stirred-tank and a 1.2-L low-cost gas-lift bioreactor by the mixed cultures using the optimal time of Clostridium inoculation. The PB, YB/S and QB values obtained were not significantly different from those in the 1-L screw-capped bottles. Hence, Arthrobacter sp. can be used as a novel method to create anaerobic conditions instead of a traditional method employing OFN gas flushing. Using mixed cultures of Arthrobacter sp. BCC 72131 and C. beijerinckii TISTR 1461 is a practical method to produce butanol on a large-scale, both in complex and low-cost bioreactors.

Quantum Light-Enhanced Two-Photon Imaging of Breast Cancer Cells.

Correct biological interpretation from cell imaging can be achieved only if the observed phenomena proceed with negligible perturbation from the imaging system. Herein, we demonstrate microscopic images of breast cancer cells created by the fluorescence selectively excited in the process of entangled two-photon absorption in a scanning microscope at an excitation intensity orders of magnitude lower than that used for classical two-photon microscopy. Quantum enhanced entangled two-photon microscopy has shown cell imaging capabilities at an unprecedented low excitation intensity of ∼3.6 × 107 photons/s, which is a million times lower than the excitation level for the classical two-photon fluorescence image obtained in the same microscope. The extremely low light probe intensity demonstrated in entangled two-photon microscopy is of critical importance to minimize photobleaching during repetitive imaging and damage to cells in live-cell applications. This technology opens new avenues in cell investigations with light microscopy, such as enhanced selectivity and time-frequency resolution.

An Effective Medical Image Classification: Transfer Learning Enhanced by Auto Encoder and Classified with SVM

The count of white blood cells is vital for disease diagnosis, which is exploited to identify many diseases like infections and leukemia. COVID-19 is another critical disease which should be detected and cured immediately. These diseases are better diagnosed using radiological and microscopic imaging. A clinical experience is required by a physician, to identify and classify the Chest X-rays or the microscopic blood cell images. In this study a novel approach is proposed for classifying medical images by using transfer learning method which is ResNet-50 where features are reduced with Auto Encoder (AE) and classified with a Support Vector Machine (SVM) instead of softmax classifier which is tested with different optimizers. The proposed method is compared with VGG-16 and ResNet-50, Inception-V3 which use softmax classifiers. Experimental results indicated that the proposed method possess 97.3% and 99% accuracy on WBC and COVID-19 datasets respectively which are higher than compared methods for each dataset.

Synthesis of Microscopic Cell Images Obtained from Bone Marrow Aspirate Smears through Generative Adversarial Networks.

Simple SummaryThis paper proposes a hybrid generative adversarial networks model—WGAN-GP-AC—to generate synthetic microscopic cell images. We generate the synthetic data for the cell types containing fewer data to obtain a balanced dataset. A balanced dataset would help enhance the classification accuracy of each cell type and help with an easy and quick diagnosis that is critical for leukemia patients. In this work, we combine images from three datasets to form a single concrete dataset with variations of multiple microscopic cell images. We provide experimental results that prove the correlation between the original and our synthetically generated data. We also deliver classification results to showcase that the generated synthetic data can be used for real-life experiments and the advancement of the medical domain.Every year approximately 1.24 million people are diagnosed with blood cancer. While the rate increases each year, the availability of data for each kind of blood cancer remains scarce. It is essential to produce enough data for each blood cell type obtained from bone marrow aspirate smears to diagnose rare types of cancer. Generating data would help easy and quick diagnosis, which are the most critical factors in cancer. Generative adversarial networks (GAN) are the latest emerging framework for generating synthetic images and time-series data. This paper takes microscopic cell images, preprocesses them, and uses a hybrid GAN architecture to generate synthetic images of the cell types containing fewer data. We prepared a single dataset with expert intervention by combining images from three different sources. The final dataset consists of 12 cell types and has 33,177 microscopic cell images. We use the discriminator architecture of auxiliary classifier GAN (AC-GAN) and combine it with the Wasserstein GAN with gradient penalty model (WGAN-GP). We name our model as WGAN-GP-AC. The discriminator in our proposed model works to identify real and generated images and classify every image with a cell type. We provide experimental results demonstrating that our proposed model performs better than existing individual and hybrid GAN models in generating microscopic cell images. We use the generated synthetic data with classification models, and the results prove that the classification rate increases significantly. Classification models achieved 0.95 precision and 0.96 recall value for synthetic data, which is higher than the original, augmented, or combined datasets.

Partial Disassembly of the Nuclear Pore Complex Proteins during Semi-Closed Mitosis in Dictyostelium discoideum.

Dictyostelium cells undergo a semi-closed mitosis, during which the nuclear envelope (NE) persists; however, free diffusion between the cytoplasm and the nucleus takes place. To permit the formation of the mitotic spindle, the nuclear envelope must be permeabilized in order to allow diffusion of tubulin dimers and spindle assembly factors into the nucleus. In Aspergillus, free diffusion of proteins between the cytoplasm and the nucleus is achieved by a partial disassembly of the nuclear pore complexes (NPCs) prior to spindle assembly. In order to determine whether this is also the case in Dictyostelium, we analysed components of the NPC by immunofluorescence microscopy and live cell imaging and studied their behaviour during interphase and mitosis. We observed that the NPCs are absent from the contact area of the nucleoli and that some nucleoporins also localize to the centrosome and the spindle poles. In addition, we could show that, during mitosis, the central FG protein NUP62, two inner ring components and Gle1 depart from the NPCs, while all other tested NUPs remained at the NE. This leads to the conclusion that indeed a partial disassembly of the NPCs takes place, which contributes to permeabilisation of the NE during semi-closed mitosis.

Magic of Architecting Oligo‐DNAs: 3D Structure‐Dependent Stability and Programmable Specificity to Tumor Cells

AbstractConstructing oligo DNAs into assemblies dramatically changes the nature of those biomacromolecules. DNA crossover is one of the classic oligo‐based nanostructures in which two or more DNA double helixes connect to each other by crossover points showing the potential to be used as structural units to develop multifunctional systems. However, research about this motif is scarce, especially in the field of biomedicine. In this study, DNase‐resistant and tumor‐distinguishing DNA nano‐assemblies based on the crossover motifs called double crossover motif (DX) and three‐helix bundle motif (3HB) are constructed. Owning to the peculiarity of secondary structure, the crossover motif shows great resistance to DNases. Such resistance is closely relevant to the 3D structure, which is confirmed by circular dichroism spectra (CD) and kinetic studies. The aptamer, AS1411, is conjugated to DNA motifs on the predetermined sites with the specific number. Depending on computer‐aided design, up to two AS1411 are conjugated to DX (DX‐x), and up to three are conjugated to 3HB (3HB‐x). Surprisingly, 3HB equipped with 3 aptamers show the best tumor discriminating ability. Confirmed by super‐resolution microscopy and live cell imaging, the manipulatable tumor specificity renders the DNA motif a potential agent for biomedical applications, including in vivo application.

ALLD: Acute Lymphoblastic Leukemia Detector.

Acute Lymphoblastic Leukemia (ALL) is a life-threatening type of cancer wherein mortality rate is unquestionably high. Early detection of ALL can reduce both the rate of fatality as well as improve the diagnosis plan for patients. In this study, we developed the ALL Detector (ALLD), which is a deep learning-based network to distinguish ALL patients from healthy individuals based on blast cell microscopic images. We evaluated multiple DL-based models and the ResNet-based model performed the best with 98% accuracy in the classification task. We also compared the performance of ALLD against state-of-the-art tools utilized for the same purpose, and ALLD outperformed them all. We believe that ALLD will support pathologists to explicitly diagnose ALL in the early stages and reduce the burden on clinical practice overall.

Computational Intelligence Method for Detection of White Blood Cells Using Hybrid of Convolutional Deep Learning and SIFT.

Infection diseases are among the top global issues with negative impacts on health, economy, and society as a whole. One of the most effective ways to detect these diseases is done by analysing the microscopic images of blood cells. Artificial intelligence (AI) techniques are now widely used to detect these blood cells and explore their structures. In recent years, deep learning architectures have been utilized as they are powerful tools for big data analysis. In this work, we are presenting a deep neural network for processing of microscopic images of blood cells. Processing these images is particularly important as white blood cells and their structures are being used to diagnose different diseases. In this research, we design and implement a reliable processing system for blood samples and classify five different types of white blood cells in microscopic images. We use the Gram-Schmidt algorithm for segmentation purposes. For the classification of different types of white blood cells, we combine Scale-Invariant Feature Transform (SIFT) feature detection technique with a deep convolutional neural network. To evaluate our work, we tested our method on LISC and WBCis databases. We achieved 95.84% and 97.33% accuracy of segmentation for these data sets, respectively. Our work illustrates that deep learning models can be promising in designing and developing a reliable system for microscopic image processing.

3027 – GENERATION OF A CD43 FLUORESCENT REPORTER IPSC LINE TO TRACK HEMATOPOIETIC DEVELOPMENT

Our group recently reported enhanced erythroid differentiation from human iPSC by closer mimicry of physiological cell contacts in a three dimensional network, termed „haematopoietic cell forming complex“ (HCFC)1. From this HCFC, CD43+ hematopoietic cells (purity >95 %) were continuously released into the culture supernatant. Characterization of the HCFC in more detail is currently limited by accompanying destruction of the complex, hindering further haematopoietic cell fate tracking. To overcome this limitation, we developed a CD43 fluorescence reporter hiPSC line that allows live-cell imaging of haematopoietic cells as they emerge in a spatiotemporal manner from the HCFC. We have successfully performed gene knock-in of a CD43 promoter region tagged with GFP using CRISPR/Cas9-mediated homology-directed repair (HDR) mechanisms in the AAVS1 safe harbor locus. Adeno-associated viral vectors (AAV) were used for efficient DNA donor delivery into hiPSCs. Following HDR-mediated donor integration, clones with on-target seamless DNA integration were isolated, which was confirmed by Sanger sequencing and in-out PCR. To confirm the functionality of the CD43 fluorescent reporter, haematopoietic and erythroid differentiation of the CD43R-iPSC was induced1. GFP-expression of CD43+ hematopoietic cells inside the HCFC was monitored by fluorescence microscopy and live cell imaging. Haematopoietic nature of released GFP+ cells was confirmed by flow cytometry (CD43, CD34, CD45) and colony formation assay. The established CD43 fluorescent reporter system allows to track hematopoietic development from hiPSCs within three-dimensional structures like organoids. Therefore, the system represents an attractive tool to investigate the hematopoietic development from hiPSCs.The established system can be used for different hiPSC lines or other primary cells. Our group recently reported enhanced erythroid differentiation from human iPSC by closer mimicry of physiological cell contacts in a three dimensional network, termed „haematopoietic cell forming complex“ (HCFC)1. From this HCFC, CD43+ hematopoietic cells (purity >95 %) were continuously released into the culture supernatant. Characterization of the HCFC in more detail is currently limited by accompanying destruction of the complex, hindering further haematopoietic cell fate tracking. To overcome this limitation, we developed a CD43 fluorescence reporter hiPSC line that allows live-cell imaging of haematopoietic cells as they emerge in a spatiotemporal manner from the HCFC. We have successfully performed gene knock-in of a CD43 promoter region tagged with GFP using CRISPR/Cas9-mediated homology-directed repair (HDR) mechanisms in the AAVS1 safe harbor locus. Adeno-associated viral vectors (AAV) were used for efficient DNA donor delivery into hiPSCs. Following HDR-mediated donor integration, clones with on-target seamless DNA integration were isolated, which was confirmed by Sanger sequencing and in-out PCR. To confirm the functionality of the CD43 fluorescent reporter, haematopoietic and erythroid differentiation of the CD43R-iPSC was induced1. GFP-expression of CD43+ hematopoietic cells inside the HCFC was monitored by fluorescence microscopy and live cell imaging. Haematopoietic nature of released GFP+ cells was confirmed by flow cytometry (CD43, CD34, CD45) and colony formation assay. The established CD43 fluorescent reporter system allows to track hematopoietic development from hiPSCs within three-dimensional structures like organoids. Therefore, the system represents an attractive tool to investigate the hematopoietic development from hiPSCs.The established system can be used for different hiPSC lines or other primary cells.

Cervical cell extraction network based on optimized yolo.

Early screening for cervical cancer is a common form of cancer prevention. In the microscopic images of cervical cells, the number of abnormal cells is small, and some abnormal cells are heavily stacked. How to solve the segmentation of highly overlapping cells and realize the identification of single cells from overlapping cells is still a heavy task. Therefore, this paper proposes an object detection algorithm of Cell_yolo to effectively and accurately segment overlapping cells. Cell_yolo adopts a simplified network structure and improves the maximum pooling operation, so that the information of the image is preserved to the greatest extent during the model pooling process. Aiming at the characteristics of many overlapping cells in cervical cell images, a non-maximum suppression method of center distance is proposed to prevent the overlapping cell detection frame from being deleted by mistake. At the same time, the loss function is improved and the focus loss function is added to alleviate the imbalance of positive and negative samples in the training process. Experiments are conducted on a private dataset (BJTUCELL). Experiments have verified that the Cell_yolo model has the advantages of low computational complexity and high detection accuracy, and it is superior to common network models such as YOLOv4 and Faster_RCNN.

Imaging of Autonomous Bioluminescence Emission From Single Mammalian Cells.

The bioluminescent visualization of individual mammalian cells usually requires the addition of a luciferin substrate. This chapter describes the microscopic imaging of single cells by their bioluminescence (BL) emission generated without an external luciferin. Imaging is based on the expression of codon-optimized lux (co lux) genes and does not require manipulation of the cells apart from transfection. Due to the high brightness of the co lux system, light emission from single cells can be observed continuously for many hours using a specialized microscope.