Label-free Live Cell Imaging Research Articles

Determination of adipogenesis stages of human umbilical cord-derived mesenchymal stem cells using three-dimensional label-free holotomography

Adipogenesis involves complex changes in gene expression, morphology, and cytoskeletal organization. However, the quantitative analysis of live cell images to identify their stages through morphological markers is limited. Distinct adipogenesis markers on human umbilical cord-derived mesenchymal stem cells (UC-MSCs) were identified through holotomography, a label-free live cell imaging technique. In the MSC-to-preadipocyte transition, the nucleus-to-cytoplasm ratio (0.080 vs. 0.052) and lipid droplet (LD) refractive index variation decreased (0.149 % vs. 0.061 %), whereas the LD number (20 vs. 65) increased. This event was also accompanied by the downregulation and upregulation of THY1 and Preadipocyte Factor-1 (PREF-1), respectively. In the preadipocyte to immature adipocyte shift, cell sphericity (0.20 vs. 0.43) and LD number (65 vs. 200) surged, large LDs (>10 μm3) appeared, and the major axis of the cell was reduced (143.7 μm vs. 83.12 μm). These findings indicate features of preadipocyte and immature adipocyte stages, alongside the downregulation of PREF-1 and upregulation of Peroxisome Proliferator-Activated Receptor gamma (PPARγ). In adipocyte maturation, along with PPARγ and Fatty Acid-Binding Protein 4 upregulation, cell compactness (0.15 vs. 0.29) and sphericity (0.43 vs. 0.59) increased, and larger LDs (>30 μm3) formed, marking immature and mature adipocyte stages. The study highlights the distinct adipogenic morphological biomarkers of adipogenesis stages in UC-MSCs, providing potential applications in biomedical and clinical settings, such as fostering innovative medical strategies for treating metabolic disease.

Abstract 492: Investigating solute carrier SLC46A3 to enhance nanotherapeutic delivery for cancer

Abstract BACKGROUND: Nanoparticle therapeutics have multifaceted potential to improve efficacy and decrease toxicity of cancer treatments. Nanotherapeutics can encapsulate a range of cargos and have tunable surface chemistry that can enhance drug delivery to tumors. Cellular features also play a role in successful drug delivery, and predictive biomarkers may improve patient selection for clinical trials. In previous work, we identified SLC46A3, a poorly characterized lysosomal solute carrier, as a strong negative regulator of lipid-based nanoparticle uptake across many cancer models. We hypothesize that the expression of SLC46A3 will act as a predictive biomarker for therapeutic nanoparticles. METHODS: To test this hypothesis, we used two engineered model systems to probe the relationship between SLC46A3 expression and therapeutic response: lentiviral overexpression in a natively low-expressing melanoma model and genetic deactivation in a natively high-expressing breast cancer model. A library of FDA-approved and experimental therapeutic nanoparticles were screened in both models under a range of conditions. Liposomal formulations of chemotherapeutics were included with high and low surface functionalization of polyethylene glycol to investigate the impact of this ubiquitous ‘stealth’ polymer on our biomarker of interest. Solid lipid nanoparticles with nucleic acid cargo were also included. Orthogonal methods were used to probe these interactions and the therapeutic efficacy of nanotherapeutics, including pulsed dosing viability assays and label-free live-cell imaging. RESULTS: We found that SLC46A3 expression is a class-specific biomarker of lipid-based nanoparticle uptake and is predictive of distinctive patterns of response to therapy-loaded nanoparticles in both formulation and time-dependent manners. Additionally, we present a new technology for long-term, live cell imaging of nanoparticle uptake and trafficking that can discriminate mechanisms of cytotoxicity, accelerating the study of nano-bio interactions. These findings highlight a promising approach for the functional validation of nanoparticle-specific biomarkers using clinically relevant formulations that have the potential for rapid repurposing. Citation Format: Julianna Kenny-Serrano, Elizabeth B. Nelson, Gil Covarrubias, Namita Nabar, Joelle Straehla, Aanchal Gupta, Natalie Boehnke, Paula Hammond. Investigating solute carrier SLC46A3 to enhance nanotherapeutic delivery for 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 492.

Enhanced sensitivity in label-free live-cell imaging using multi-pass stimulated Raman scattering microscopy

Enhanced sensitivity in label-free live-cell imaging using multi-pass stimulated Raman scattering microscopy

Label-free third harmonic generation imaging and quantification of lipid droplets in live filamentous fungi

We report the utilization of Third-Harmonic Generation microscopy for label-free live cell imaging of lipid droplets in the hypha of filamentous fungus Phycomyces blakesleeanus. THG microscopy images showed bright spherical features dispersed throughout the hypha cytoplasm in control conditions and a transient increase in the number of bright features after complete nitrogen starvation. Colocalization analysis of THG and lipid-counterstained images disclosed that the cytoplasmic particles were lipid droplets. Particle Size Analysis and Image Correlation Spectroscopy were used to quantify the number density and size of lipid droplets. The two analysis methods both revealed an increase from 16 × 10−3 to 23 × 10−3 lipid droplets/µm2 after nitrogen starvation and a decrease in the average size of the droplets (range: 0.5–0.8 µm diameter). In conclusion, THG imaging, followed by PSA and ICS, can be reliably used for filamentous fungi for the in vivo quantification of lipid droplets without the need for labeling and/or fixation. In addition, it has been demonstrated that ICS is suitable for THG microscopy.

Pixel-level multimodal fusion deep networks for predicting subcellular organelle localization from label-free live-cell imaging.

Complex intracellular organizations are commonly represented by dividing the metabolic process of cells into different organelles. Therefore, identifying sub-cellular organelle architecture is significant for understanding intracellular structural properties, specific functions, and biological processes in cells. However, the discrimination of these structures in the natural organizational environment and their functional consequences are not clear. In this article, we propose a new pixel-level multimodal fusion (PLMF) deep network which can be used to predict the location of cellular organelle using label-free cell optical microscopy images followed by deep-learning-based automated image denoising. It provides valuable insights that can be of tremendous help in improving the specificity of label-free cell optical microscopy by using the Transformer-Unet network to predict the ground truth imaging which corresponds to different sub-cellular organelle architectures. The new prediction method proposed in this article combines the advantages of a transformer's global prediction and CNN's local detail analytic ability of background features for label-free cell optical microscopy images, so as to improve the prediction accuracy. Our experimental results showed that the PLMF network can achieve over 0.91 Pearson's correlation coefficient (PCC) correlation between estimated and true fractions on lung cancer cell-imaging datasets. In addition, we applied the PLMF network method on the cell images for label-free prediction of several different subcellular components simultaneously, rather than using several fluorescent labels. These results open up a new way for the time-resolved study of subcellular components in different cells, especially for cancer cells.

Label-Free Imaging to Track Reprogramming of Human Somatic Cells.

The process of reprogramming patient samples to human-induced pluripotent stem cells (iPSCs) is stochastic, asynchronous, and inefficient, leading to a heterogeneous population of cells. In this study, we track the reprogramming status of patient-derived erythroid progenitor cells (EPCs) at the single-cell level during reprogramming with label-free live-cell imaging of cellular metabolism and nuclear morphometry to identify high-quality iPSCs. EPCs isolated from human peripheral blood of three donors were used for our proof-of-principle study. We found distinct patterns of autofluorescence lifetime for the reduced form of nicotinamide adenine dinucleotide (phosphate) and flavin adenine dinucleotide during reprogramming. Random forest models classified iPSCs with ∼95% accuracy, which enabled the successful isolation of iPSC lines from reprogramming cultures. Reprogramming trajectories resolved at the single-cell level indicated significant reprogramming heterogeneity along different branches of cell states. This combination of micropatterning, autofluorescence imaging, and machine learning provides a unique, real-time, and nondestructive method to assess the quality of iPSCs in a biomanufacturing process, which could have downstream impacts in regenerative medicine, cell/gene therapy, and disease modeling.

Roadmap on digital holography [Invited

This Roadmap article on digital holography provides an overview of a vast array of research activities in the field of digital holography. The paper consists of a series of 25 sections from the prominent experts in digital holography presenting various aspects of the field on sensing, 3D imaging and displays, virtual and augmented reality, microscopy, cell identification, tomography, label-free live cell imaging, and other applications. Each section represents the vision of its author to describe the significant progress, potential impact, important developments, and challenging issues in the field of digital holography.

Label-free imaging for quality control of cardiomyocyte differentiation

Human pluripotent stem cell (hPSC)-derived cardiomyocytes provide a promising regenerative cell therapy for cardiovascular patients and an important model system to accelerate drug discovery. However, cost-effective and time-efficient platforms must be developed to evaluate the quality of hPSC-derived cardiomyocytes during biomanufacturing. Here, we develop a non-invasive label-free live cell imaging platform to predict the efficiency of hPSC differentiation into cardiomyocytes. Autofluorescence imaging of metabolic co-enzymes is performed under varying differentiation conditions (cell density, concentration of Wnt signaling activator) across five hPSC lines. Live cell autofluorescence imaging and multivariate classification models provide high accuracy to separate low (< 50%) and high (≥ 50%) differentiation efficiency groups (quantified by cTnT expression on day 12) within 1 day after initiating differentiation (area under the receiver operating characteristic curve, 0.91). This non-invasive and label-free method could be used to avoid batch-to-batch and line-to-line variability in cell manufacturing from hPSCs.

Temporal Imaging of Live Cells by High-Speed Confocal Raman Microscopy.

Label-free live cell imaging was performed using a custom-built high-speed confocal Raman microscopy system. For various cell types, cell-intrinsic Raman bands were monitored. The high-resolution temporal Raman images clearly delineated the intracellular distribution of biologically important molecules such as protein, lipid, and DNA. Furthermore, optical phase delay measured using quantitative phase microscopy shows similarity with the image reconstructed from the protein Raman peak. This reported work demonstrates that Raman imaging is a powerful label-free technique for studying various biomedical problems in vitro with minimal sample preparation and external perturbation to the cellular system.

Abstract 1305: Classification of cell morphology using machine learning and label-free live-cell imaging

Abstract Cell morphology is incredibly diverse and provides valuable insight into cellular dynamics, including cell health and differentiation. For ease of analysis, morphology studies often focus on quantifying one or two metrics, e.g. circularity or area. However, this may lead to incorrect conclusions as information about cell size, shape, brightness and texture all capture different nuances of morphology. By using multivariate data analysis (MVDA), multiple properties can be combined into a single metric that simultaneously describes the many different aspects of cell morphology. Supervised machine learning tools further enable identifying subpopulations of cells by their morphology alone. Here we describe a workflow for label-free classification of heterogeneous cells using phase contrast images. Classification of live and dead cells across range of cancer cell types was evaluated. Cells were seeded into 96-well plates and maintained in a physiologically relevant environment to ensure morphology was unperturbed. After 24h, cells were treated with compounds exerting cytotoxic effects via a range of mechanisms. All plates contained camptothecin (CMP, 10 µM) as a control for cell death and were in the presence of a fluorescent cell health reagent (Incucyte® Annexin V) to verify cell death. Images were acquired using an Incucyte® Live-Cell Analysis System (10x objective, every 2h for 3 days) and were segmented using the integrated Incucyte® Cell-by-Cell Analysis Software Module. For validation, cells were also classified based on fluorescence (Annexin V positive) to yield a dead cell percentage. An MVDA regression model was trained for each cell type using only the label-free morphology metrics extracted from the segmented phase contrast images of live (untreated cells, range of confluence values) and dead (10 µM CMP, 72h only) cells. This model was subsequently applied to all acquired images to classify every cell as live or dead. Time- and concentration-dependent increases in the fraction of dead cells closely matched that of the fluorescence classification for all tested conditions. For example, A549 cells treated with CMP produced EC50 values of 0.53 µM (label-free) and 0.66 µM (fluorescence). The analysis proved robust across multiple cell types and compounds, even in cases where morphological change occurred unrelated to cell death. In conclusion, our data demonstrates the utility of an MVDA approach for measuring cell morphology change using label-free live/dead classification as validation. Similar classifications may be applied to alternative biological paradigms which undergo morphological change, such as cell differentiation. Additionally, as the use of morphology metrics for classification requires accurate delineation of cells, improved cell segmentation tools utilizing convolutional neural network models may further enable application of these methods to more challenging cell types. Citation Format: Gillian F. Lovell, Daniel A. Porto, Timothy R. Jackson, Jasmine Trigg, Nicola Bevan, Christoffer Edlund, Rickard Sjöegren, Nevine Holtz, Daniel M. Appledorn, Timothy Dale. Classification of cell morphology using machine learning and label-free live-cell imaging [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1305.

Holotomography: Refractive Index as an Intrinsic Imaging Contrast for 3-D Label-Free Live Cell Imaging.

Live cell imaging provides essential information in the investigation of cell biology and related pathophysiology. Refractive index (RI) can serve as intrinsic optical imaging contrast for 3-D label-free and quantitative live cell imaging, and provide invaluable information to understand various dynamics of cells and tissues for the study of numerous fields. Recently significant advances have been made in imaging methods and analysis approaches utilizing RI, which are now being transferred to biological and medical research fields, providing novel approaches to investigate the pathophysiology of cells. To provide insight into how RI can be used as an imaging contrast for imaging of biological specimens, here we provide the basic principle of RI-based imaging techniques and summarize recent progress on applications, ranging from microbiology, hematology, infectious diseases, hematology, and histopathology.

Pushing the limits of deep-ultraviolet scanning near-field optical microscopy

Deep-ultraviolet (DUV) microscopy and microspectroscopy have received much attention in label-free live-cell imaging, selective molecular analysis, and optical characterizations of ultrawide bandgap materials. Far-field optics approaches usually suffer from the diffraction limit of light. Meanwhile, near-field optics technology is immature in the DUV spectral region. Herein, we develop a DUV scanning near-field optical microscope (SNOM) with an excitation wavelength of 210 nm. The fourth harmonic generation of a continuous-wave Ti:sapphire laser is the excitation source. Two negative feedback control systems stabilize the power and pointing, allowing scanning microscopy to be carried out over a long period of time even in the DUV spectral region. A reflective objective couples the excitation beam onto an optical fiber probe. The optical fiber probe is solarization-resistant and is not coated by metallic materials. The reflective-based objective optical design does not introduce chromatic aberrations in the detection signals. Our DUV-SNOM is used to measure the photoluminescence (PL) spectra of the quantum well structure of aluminum gallium nitride, which is an ultrawide bandgap material. The PL mapping images indicate that emissive localization centers can be individually visualized via our DUV-SNOM with a lateral resolution exceeding 150 nm.

Label-Free Live-Cell Imaging with Confocal Raman Microscopy

Confocal Raman spectroscopy is a noninvasive alternative to established cell imaging methods because it does not require chemical fixation, the use of fluorescent markers, or genetic engineering. In particular, single live-cell, high-resolution imaging by confocal Raman microscopy is desirable because it allows further experiments concerning the individually investigated cells. However, to derive meaningful images from the spectroscopic data, one must identify cell components within the dataset. Using immunofluorescence images as a reference, we derive Raman spectral signatures by means of information measures to identify cell components such as the nucleus, the endoplasmic reticulum, the Golgi apparatus, and mitochondria. The extracted signatures allow us to generate representations equivalent to conventional (immuno)fluorescence images with more than three cell components at a time, exploiting the Raman spectral information alone.