Holotomography Research Articles

Label-free long-term measurements of adipocyte differentiation from patient-driven fibroblasts and quantitative analyses of in situ lipid droplet generation

The visualization and tracking of adipocytes and their lipid droplets (LDs) during differentiation are pivotal in developmental biology and regenerative medicine studies. Traditional staining or labeling methods, however, pose significant challenges due to their labor-intensive sample preparation, potential disruption of intrinsic cellular physiology, and limited observation timeframe. This study introduces a novel method for long-term visualization and quantification of biophysical parameters of LDs in unlabeled adipocytes, utilizing the refractive index (RI) distributions of LDs and cells. We employ low-coherence holotomography (HT) to systematically investigate and quantitatively analyze the 42-day redifferentiation process of fat cells into adipocytes. This technique yields three-dimensional, high-resolution refractive tomograms of adipocytes, enabling precise segmentation of LDs based on their elevated RI values. Subsequent automated analysis quantifies the mean concentration, volume, projected area, and dry mass of individual LDs, revealing a gradual increase corresponding with adipocyte maturation. Our findings demonstrate that HT is a potent tool for non-invasively monitoring live adipocyte differentiation and analyzing LD accumulation. This study, therefore, offers valuable insights into adipogenesis and lipid research, establishing HT and image-based analysis as a promising approach in these fields.

Integrating holotomography and deep learning for rapid detection of NPM1 mutations in AML

Rapid and accurate diagnosis of acute myeloid leukemia (AML) remains a significant challenge, particularly in the context of myelodysplastic syndrome (MDS) or MDS/myeloproliferative neoplasm with NPM1 mutations. This study introduces an innovative approach using holotomography (HT), a 3D label-free quantitative phase imaging technique, to detect NPM1 mutations. We analyzed a dataset of 2073 HT myeloblast images from 48 individuals, including both NPM1 wild-type and mutated samples, to distinguish subcellular morphological changes associated with NPM1 mutations. Employing a convolutional neural network, we analyzed 3D cell morphology, focusing on refractive index distributions. The machine learning model showed high accuracy, with an area under the receiver operating characteristic curve of 0.9375 and a validation accuracy of 76.0%. Our findings reveal distinct morphological differences between the NPM1 wild-type and mutation at the subcellular level. This study demonstrates the potential of HT combined with deep learning for early, efficient, and cost-effective diagnosis of AML, offering a promising alternative to traditional stepwise genetic testing methods and providing additional assistance in morphological myeloblast discrimination. This approach may revolutionize the diagnostic process in leukemia, facilitating early detection and potentially reducing the reliance on extensive genetic testing.

Label-free three-dimensional imaging and quantitative analysis of living fibroblasts and myofibroblasts by holotomographic microscopy.

Holotomography (HT) is a cutting-edge fast live-cell quantitative label-free imaging technique. Based on the principle of quantitative phase imaging, it combines holography and tomography to record a three-dimensional map of the refractive index, used as intrinsic optical and quantitative imaging contrast parameter of biological samples, at a sub-micrometer spatial resolution. In this study HT has been employed for the first time to analyze the changes of fibroblasts differentiating towards myofibroblasts - recognized as the main cell player of fibrosis - when cultured in vitro with the pro-fibrotic factor, namely transforming growth factor-β1. In parallel, F-actin, vinculin, α-smooth muscle actin, phospho-myosin light chain 2, type-1 collagen, peroxisome proliferator-activated receptor-gamma coactivator-1α expression and mitochondria were evaluated by confocal laser scanning microscopy. Plasmamembrane passive properties and transient receptor potential canonical channels' currents were also recorded by whole-cell patch-clamp. The fluorescence images and electrophysiological results have been compared to the data obtained by HT and their congruence has been discussed. HT turned out to be a valid approach to morphologically distinguish fibroblasts from well differentiated myofibroblasts while obtaining objective measures concerning volume, surface area, projection area, surface index and dry mass (i.e., the mass of the non-aqueous content inside the cell including proteins and subcellular organelles) of the entire cell, nuclei and nucleoli with the major advantage to monitor outer and inner features in living cells in a non-invasive, rapid and label-free approach. HT might open up new research opportunities in the field of fibrotic diseases. RESEARCH HIGHLIGHTS: Holotomography (HT) is a label-free laser interferometric imaging technology exploiting the intrinsic optical property of cells namely refractive index (RI) to enable a direct imaging and analysis of whole cells or intracellular organelles. HT turned out a valid approach to distinguish morphological features of living unlabeled fibroblasts from differentiated myofibroblasts. HT provided quantitative information concerning volume, surface area, projection area, surface index and dry mass of the entire fibroblasts/myofibroblasts, nuclei and nucleoli.

Abstract 7401: High-level multiplexed virtual immunofluorescence staining using holotomography and deep learning

Abstract Multiplexed immunofluorescence (IF) staining plays a crucial role in cancer research, allowing simultaneous visualization of multiple biomarkers to elucidate complex cellular interactions within the tumor microenvironment. However, the conventional multiplexed IF approach has inherent limitations, characterized by labor-intensiveness and a constrained capacity for high-level multiplexing. These challenges hinder its scalability and comprehensive analysis of intricate molecular landscapes.To overcome the limitations of conventional multiplexed IF, we propose a novel approach that integrates holotomography (HT) and deep learning. HT is a label-free imaging technique that quantitatively measures the refractive index distribution of the specimen without any staining. To provide subcellular specificity to the label-free HT images, HT is integrated with virtual staining. Virtual staining is a deep learning-based framework that converts label-free images to the desired stained images, in this case, from HT images to multiplexed IF images. This synergistic combination of HT and deep learning may address the current limitations of multiplexed IF protocols.Our proposed methodology aims at high-level multiplexed virtual IF staining from label-free cancer tissue slides using HT and deep learning. This approach not only eliminates the need for chemical staining but also enhances the level of multiplexing through computational staining. Specifically, we acquire HT and fluorescence images of stained tissue slides for training. Utilizing this paired dataset, we then employ supervised learning to create a neural network that can map between label-free HT images and corresponding fluorescence images. Subsequently, the trained neural network is applied to label-free tissue slides, inferring fluorescence images without the need for chemical staining.We believe the proposed virtual staining framework will contribute to a more efficient and scalable paradigm for cancer research, facilitating a deeper understanding of tumor biology, and advancing the field of precision medicine. Citation Format: Juyeon Park, Su-Jin Shin, Geon Kim, Kwang Suk Lee, Ji Eun Heo, YongKeun Park. High-level multiplexed virtual immunofluorescence staining using holotomography and deep learning [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 7401.

Abstract 7400: AI-driven multimodal 3D tumor modeling with spatial molecular insights for predicting ICI response in gastric cancer

Abstract Traditional cancer research methodologies predominantly focus on 2D tumor modeling, including spatial profiling and AI-driven analysis at the 2D level. To advance beyond these limitations, we implemented Holotomography (HT), a 3D, label-free optical imaging method. This technique, leveraging refractive index contrasts, allows for enhanced visualization of tumor tissues, organoids, and cells in three dimensions. Using serial H&E sections and unstained slides, we created detailed 3D tumor models in gastric cancer, providing insights into tumor, immune, and stromal composition, as well as cell-cell interactions and extracellular matrix characteristics. AI algorithms further enhanced our 3D tumor modeling, offering novel perspectives on the initiation and progression of tumor, immune, and stromal components at the subcellular level. Analyzing gastric tumors treated with immune checkpoint inhibitors before, during, and after treatment revealed dynamic changes in spatial composition over time, offering unique insights into the evolution of the 3D tumor ecosystem. Additionally, by integrating spatial transcriptomic and in situ molecular imaging data, we developed a novel Multimodal 3D Tumor Model. This model not only uncovers subcellular-level molecular characteristics but also elucidates cellular interactions during treatment, suggesting potential biomarkers and therapeutic approaches. Lastly, by constructing 3D cellular interaction networks incorporating true 3D interactions, we provided an in-depth view of cellular communication within the tumor microenvironment. This pioneering approach, representing the first Multimodal 3D Tumor Model at the subcellular level, offers novel insights into gastric cancer's spatial evolution, potentially revolutionizing our understanding and treatment of gastric cancer. Citation Format: Yoon Ho Choi, Minji Kim, Katya Redekop, Inyeop Jang, Jean R. Clemenceau, Isabel Barnfather, Sumanth Reddy Nakkireddy, Changjin Hong, Sunho Park, Ji-Youn Sung, Jeong Hwan Park, Shitara Kohei, Aoki Yu, Sung Hak Lee, Sam Wang, Jaeho Cheong, Tae Hyun Hwang. AI-driven multimodal 3D tumor modeling with spatial molecular insights for predicting ICI response in gastric cancer [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 7400.

Unlocking the Potential: A Deep Learning Approach for Objective Screening of AML with NPM1 Mutation Via Holotomography

Introduction: As the clinical significance of genetic mutations becomes clearer, diagnostic classifications are continuously evolving, making judicious decisions about implementing costly genetic testing for diagnosis crucial. Intriguing observations suggest that Myelodysplastic Syndrome (MDS) or MDS/Myeloproliferative Neoplasm with NPM1 mutation can rapidly progress to Acute Myeloid Leukemia (AML), leading to ambiguity in acute leukemia diagnosis. Consequently, this finding has led to ambiguity surrounding the diagnostic criteria for acute leukemia with low blast counts. Holotomography (HT) is a cutting-edge microscope that utilizes refractive index (RI) as a contrast medium. It not only provides 2D or 3D images but also offers numerical information about every pixel of cells, promising a more objective diagnosis. Previous studies have successfully distinguished mature cells and blasts using HT. In this study, our aim is to construct a sophisticated deep learning model using data from HT to predict the presence of NPM1 mutations in blasts. This research endeavor holds the potential to enable early detection of these mutations in hematologic malignancies and facilitate the effective utilization of valuable medical resources. Methods: We utilized HT to analyze blasts of AML with mutated NPM1 (experimental group) and AML not otherwise specified (control group) but exhibiting similar morphological characteristics from May 2021 to May 2022. We analyzed a total of 648 blasts from 15 patients in the experimental group and 762 blasts from 24 patients in the control group. Cell segmentation for Rule-based features was performed using ilastik version 1.3.3 and MATLAB 2022a. After the initial segmentation using ilastik's machine learning pixel classification feature, further refinements were made in MATLAB, based on prior knowledge of the size and topology of each structure. Subsequently, volumetric and morphological information was measured within these segments. The cell data of each patient were divided into a training set and a test set. The deep learning algorithm, built using the training set, was then validated with data from the test set. For the deep learning and prediction tasks, we employed a Convolutional Neural Network algorithm (Figure). The classification process involved utilizing both the Rule-based features and the entire feature set, where a linear kernel Support Vector Machine was used. Additionally, we employed the Ensemble average approach, where multiple cells' scores (numerical values associated with the probabilities of each classification) were averaged, and the classification with the highest score was selected as the final prediction. Finally, the deep learning model was validated using previously unused test set data through the Receiver Operating Characteristic (ROC) curve. Results: The dry mass of the nucleolus was significantly increased in AML, NOS; Exp, 0.29341 ± 0.014499 pg; Control, 0.71184 ± 0.033521 pg ( P<0.05). The dry mass of the cytoplasm was higher in AML with mutated NPM1, showing a higher value than that of the nucleolus; Exp, 45.7334 ± 0.50003 pg; Control, 42.9807 ± 0.63882 pg (P<0.05). When validating the deep learning model constructed based on individual cells, the area under the ROC curve (AUC) was found to be 0.8836. Even within the same individual, the cell morphology can be heterogeneous. Therefore, when the data from 5 cells were treated as a single entity for validating the deep learning model, the AUC was 0.9944. Conclusions: It is widely acknowledged that the NPM1 protein, typically localized in the nucleolus, undergoes relocation to the cytoplasm as a result of NPM1 mutation. In this investigation, we devised an advanced deep learning model leveraging numerical data derived from HT, enabling us to prognosticate variations in protein expression based on NPM1 mutations. The HT numerical data exhibited distinctive variations in both nuclear and cytoplasmic analyses contingent on NPM1 mutations. Given the current uncertainty in leukemia diagnosis, partly attributable to the costly selection of genetic testing, we are optimistic that the integration of HT with our sophisticated deep learning model will pave the way for early diagnosis prediction before conducting additional genetic tests.

Ecotoxicity of Polyvinylidene Difluoride (PVDF) and Polylactic Acid (PLA) Microplastics in Marine Zooplankton.

The aim of this study was to investigate the ecotoxicity of polyvinylidene difluoride (PVDF) and polylactic acid (PLA) microplastics (MPs) in two marine zooplankton: the crustacean Artemia franciscana and the cnidarian Aurelia sp. (common jellyfish). To achieve this goal, (i) MP uptake, (ii) immobility, and (iii) behavior (swimming speed, pulsation mode) of crustacean larval stages and jellyfish ephyrae exposed to MPs concentrations (1, 10, 100 mg/L) were assessed for 24 h. Using traditional and novel techniques, i.e., epifluorescence microscopy and 3D holotomography (HT), PVDF and PLA MPs were found in the digestive systems of the crustaceans and in the gelatinous tissue of jellyfish. Immobility was not affected in either organism, while a significant behavioral alteration in terms of pulsation mode was found in jellyfish after exposure to both PVDF and PLA MPs. Moreover, PLA MPs exposure in jellyfish induced a toxic effect (EC50: 77.43 mg/L) on the behavioral response. This study provides new insights into PLA and PVDF toxicity with the potential for a large impact on the marine ecosystem, since jellyfish play a key role in the marine food chain. However, further investigations incorporating additional species belonging to other trophic levels are paramount to better understand and clarify the impact of such polymers at micro scale in the marine environment. These findings suggest that although PVDF and PLA have been recently proposed as innovative and, in the case of PLA, biodegradable polymers, their effects on marine biota should not be underestimated.

Experimental comparison of grating- and propagation-based hard X-ray phase tomography of soft tissue

When imaging soft tissues with hard X-rays, phase contrast is often preferred over conventional attenuation contrast due its superior sensitivity. However, it is unclear which of the numerous phase tomography methods yields the optimized results at given experimental conditions. Therefore, we quantitatively compared the three phase tomography methods implemented at the beamline ID19 of the European Synchrotron Radiation Facility: X-ray grating interferometry (XGI), and propagation-based phase tomography, i.e., single-distance phase retrieval (SDPR) and holotomography (HT), using cancerous tissue from a mouse model and an entire heart of a rat. We show that for both specimens, the spatial resolution derived from the characteristic morphological features is about a factor of two better for HT and SDPR compared to XGI, whereas the XGI data generally exhibit much better contrast-to-noise ratios for the anatomical features. Moreover, XGI excels in fidelity of the density measurements, and is also more robust against low-frequency artifacts than HT, but it might suffer from phase-wrapping artifacts. Thus, we can regard the three phase tomography methods discussed as complementary. The application will decide which spatial and density resolutions are desired, for the imaging task and dose requirements, and, in addition, the applicant must choose between the complexity of the experimental setup and the one of data processing.

Holotomography versus X-ray grating interferometry: A comparative study

X-ray phase-contrast imaging techniques are used at synchrotron facilities to visualize tiny density variations in bulk samples. They overcome the limitations of other non-destructive methods, which often provide insufficient spatial and/or density resolution. Holotomography (HT) and X-ray grating interferometry (XGI) are among the most powerful phase-contrast techniques. Here, we show a direct comparison of HT versus XGI. We find that XGI excels in fidelity of the density measurements and is more robust against low-frequency artifacts, while HT is superior in spatial resolution. This study gives indications for applications and developments of phase-contrast imaging.