Characterising live cell behaviour: Traditional label-free and quantitative phase imaging approaches

Made to measure: An introduction to quantifying microscopy data in the life sciences

Images are at the core of most modern biological experiments and are used as a major source of quantitative information. Numerous algorithms are available to process images and make them more amenable to be measured. Yet the nature of the quantitative output that is useful for a given biological experiment is uniquely dependent upon the question being investigated. Here, we discuss the 3 main types of information that can be extracted from microscopy data: intensity, morphology, and object counts or categorical labels. For each, we describe where they come from, how they can be measured, and what may affect the relevance of these measurements in downstream data analysis. Acknowledging that what makes a measurement 'good' is ultimately down to the biological question being investigated, this review aims at providing readers with a toolkit to challenge how they quantify their own data and be critical of conclusions drawn from quantitative bioimage analysis experiments.

Label-free morpho-molecular phenotyping of living cancer cells by combined Raman spectroscopy and phase tomography

Accurate, rapid and non-invasive cancer cell phenotyping is a pressing concern across the life sciences, as standard immuno-chemical imaging and omics require extended sample manipulation. Here we combine Raman micro-spectroscopy and phase tomography to achieve label-free morpho-molecular profiling of human colon cancer cells, following the adenoma, carcinoma, and metastasis disease progression, in living and unperturbed conditions. We describe how to decode and interpret quantitative chemical and co-registered morphological cell traits from Raman fingerprint spectra and refractive index tomograms. Our multimodal imaging strategy rapidly distinguishes cancer phenotypes, limiting observations to a low number of pristine cells in culture. This synergistic dataset allows us to study independent or correlated information in spectral and tomographic maps, and how it benefits cell type inference. This method is a valuable asset in biomedical research, particularly when biological material is in short supply, and it holds the potential for non-invasive monitoring of cancer progression in living organisms.

Label-Free, Noninvasive Bone Cell Classification by Hyperspectral Confocal Raman Microscopy

Characterizing and identifying cells in multicellular in vitro models remain a substantial challenge. Here, we utilize hyperspectral confocal Raman microscopy and principal component analysis coupled with linear discriminant analysis to form a label-free, noninvasive approach for classifying bone cells and osteosarcoma cells. Through the development of a library of hyperspectral Raman images of the K7M2-wt osteosarcoma cell lines, 7F2 osteoblast cell lines, RAW 264.7 macrophage cell line, and osteoclasts induced from RAW 264.7 macrophages, we built a linear discriminant model capable of correctly identifying each of these cell types. The model was cross-validated using a k-fold cross validation scheme. The results show a minimum of 72% accuracy in predicting cell type. We also utilize the model to reconstruct the spectra of K7M2 and 7F2 to determine whether osteosarcoma cancer cells and normal osteoblasts have any prominent differences that can be captured by Raman. We find that the main differences between these two cell types are the prominence of the β-sheet protein secondary structure in K7M2 versus the α-helix protein secondary structure in 7F2. Additionally, differences in the CH2 deformation Raman feature highlight that the membrane lipid structure is different between these cells, which may affect the overall signaling and functional contrasts. Overall, we show that hyperspectral confocal Raman microscopy can serve as an effective tool for label-free, nondestructive cellular classification and that the spectral reconstructions can be used to gain deeper insight into the differences that drive different functional outcomes of different cells.

Stochastic epithelial-mesenchymal transitions diversify non-cancerous lung cell behaviours

Epithelial-mesenchymal plasticity (EMP) is a hallmark of cancer. By enabling cells to shift between different morphological and functional states, EMP promotes invasion, metastasis and therapy resistance. We report that near-diploid non-cancerous human epithelial lung cells spontaneously shift along the EMP spectrum without genetic changes. Strikingly, more than half of single cell-derived clones adopt a mesenchymal morphology. We independently characterise epithelial-like and mesenchymal-like clones. Mesenchymal clones lose epithelial markers, display larger cell aspect ratios and lower motility, with mostly unaltered proliferation rates. Stemness marker expression and metabolic rewiring diverge independently of phenotypes. In 3D culture, more epithelial clones become mesenchymal-like. Thus, non-cancerous epithelial cells may acquire cancer metastasis-associated features prior to genetic alterations and cancerous transformation.

High throughput microscopy and single cell phenotypic image-based analysis in toxicology and drug discovery

Measuring single cell responses to the universe of chemicals (drugs, natural products, environmental toxicants etc.) is of paramount importance to human health as phenotypic variability in sensing stimuli is a hallmark of biology that is considered during high throughput screening. One of the ways to approach this problem is via high throughput, microscopy-based assays coupled with multi-dimensional single cell analysis methods. Here, we will summarize some of the efforts in this vast and growing field, focusing on phenotypic screens (e.g., Cell Painting), single cell analytics and quality control, with particular attention to environmental toxicology and drug screening. We will discuss advantages and limitations of high throughput assays with various end points and levels of complexity.

Spectroscopic techniques for monitoring stem cell and organoid proliferation in 3D environments for therapeutic development

Spectroscopic techniques for monitoring stem cell and organoid proliferation have gained significant attention in therapeutic development. Spectroscopic techniques such as fluorescence, Raman spectroscopy, and infrared spectroscopy offer noninvasive and real-time monitoring of biochemical and biophysical changes that occur during stem cell and organoid proliferation. These techniques provide valuable insight into the underlying mechanisms of action of potential therapeutic agents, allowing for improved drug discovery and screening. This review highlights the importance of spectroscopic monitoring of stem cell and organoid proliferation and its potential impact on therapeutic development. Furthermore, this review discusses recent advances in spectroscopic techniques and their applications in stem cell and organoid research. Overall, this review emphasizes the importance of spectroscopic techniques as valuable tools for studying stem cell and organoid proliferation and their potential to revolutionize therapeutic development in the future.

The CellPhe toolkit for cell phenotyping using time-lapse imaging and pattern recognition

With phenotypic heterogeneity in whole cell populations widely recognised, the demand for quantitative and temporal analysis approaches to characterise single cell morphology and dynamics has increased. We present CellPhe, a pattern recognition toolkit for the unbiased characterisation of cellular phenotypes within time-lapse videos. CellPhe imports tracking information from multiple segmentation and tracking algorithms to provide automated cell phenotyping from different imaging modalities, including fluorescence. To maximise data quality for downstream analysis, our toolkit includes automated recognition and removal of erroneous cell boundaries induced by inaccurate tracking and segmentation. We provide an extensive list of features extracted from individual cell time series, with custom feature selection to identify variables that provide greatest discrimination for the analysis in question. Using ensemble classification for accurate prediction of cellular phenotype and clustering algorithms for the characterisation of heterogeneous subsets, we validate and prove adaptability using different cell types and experimental conditions.

Non-Invasive and Minute-Frequency 3D Tomographic Imaging Enabling Long-Term Spatiotemporal Observation of Single Cell Fate

Non-invasive and rapid imaging technique at subcellular resolution is significantly important for multiple biological applications such as cell fate study. Label-free refractive-index (RI)-based 3D tomographic imaging constitutes an excellent candidate for 3D imaging of cellular structures, but its full potential in long-term spatiotemporal cell fate observation is locked due to the lack of an efficient integrated system. Here, a long-term 3D RI imaging system incorporating a cutting-edge white light diffraction phase microscopy module with spatiotemporal stability, and an acoustofluidic device to roll and culture single cells in a customized live cell culture chamber is reported. Using this system, 3D RI imaging experiments are conducted for 250 cells and demonstrate efficient cell identification with high accuracy. Importantly, long-term and frequency-on-demand 3D RI imaging of K562 and MCF-7 cancer cells reveal different characteristics during normal cell growth, drug-induced cell apoptosis, and necrosis of drug-treated cells. Overall, it is believed that the proposed 3D tomographic imaging technique opens up a new avenue for visualizing intracellular structures and will find many applications such as disease diagnosis and nanomedicine.

Naxitamab Activity in Neuroblastoma Cells Is Enhanced by Nanofenretinide and Nanospermidine

Neuroblastoma cells highly express the disialoganglioside GD2, a tumor-associated carbohydrate antigen, which is also expressed in neurons, skin melanocytes, and peripheral nerve fibers. Immunotherapy with monoclonal anti-GD2 antibodies has a proven efficacy in clinical trials and is included in the standard treatment for children with high-risk neuroblastoma. However, the strong neuro-toxicity associated with anti-GD2 antibodies administration has hindered, until now, the possibility for dose-escalation and protracted use, thus restraining their therapeutic potential. Strategies to increase the efficacy of anti-GD2 antibodies are actively sought, with the aim to enable chronic treatments that could eradicate minimal residual disease and subsequent relapses, often occurring after treatment. Here, we report that Nanofenretinide and Nanospermidine improved the expression of GD2 in neuroblastoma cells (CHP-134) and provided different effects in combination with the anti-GD2 antibody naxitamab. In particular, Nanofenretinide significantly increased the cytotoxic effect of naxitamab while Nanospermidine inhibited cell motility at extents proportional to naxitamab concentration. In neuroblastoma cells characterized by a low and heterogeneous basal expression of GD2, such as SH-SY5Y, which may represent the cell heterogeneity in tumors after chemotherapy, both Nanofenretinide and Nanospermidine increased GD2 expression in approximately 50% of cells, thus shifting the tumor population towards improved sensitivity to anti-GD2 antibodies.

Beyond conventional microscopy: Observing kidney tissues by means of fourier ptychography

Kidney microscopy is a mainstay in studying the morphological structure, physiology and pathology of kidney tissues, as histology provides important results for a reliable diagnosis. A microscopy modality providing at same time high-resolution images and a wide field of view could be very useful for analyzing the whole architecture and the functioning of the renal tissue. Recently, Fourier Ptychography (FP) has been proofed to yield images of biology samples such as tissues and in vitro cells while providing high resolution and large field of view, thus making it a unique and attractive opportunity for histopathology. Moreover, FP offers tissue imaging with high contrast assuring visualization of small desirable features, although with a stain-free mode that avoids any chemical process in histopathology. Here we report an experimental measuring campaign for creating the first comprehensive and extensive collection of images of kidney tissues captured by this FP microscope. We show that FP microscopy unlocks a new opportunity for the physicians to observe and judge renal tissue slides through the novel FP quantitative phase-contrast microscopy. Phase-contrast images of kidney tissue are analyzed by comparing them with the corresponding renal images taken under a conventional bright-field microscope both for stained and unstained tissue samples of different thicknesses. In depth discussion on the advantages and limitations of this new stain-free microscopy modality is reported, showing its usefulness over the classical light microscopy and opening a potential route for using FP in clinical practice for histopathology of kidney.

Long-term label-free assessments of individual bacteria using three-dimensional quantitative phase imaging and hydrogel-based immobilization

Three-dimensional (3D) quantitative phase imaging (QPI) enables long-term label-free tomographic imaging and quantitative analysis of live individual bacteria. However, the Brownian motion or motility of bacteria in a liquid medium produces motion artifacts during 3D measurements and hinders precise cell imaging and analysis. Meanwhile, existing cell immobilization methods produce noisy backgrounds and even alter cellular physiology. Here, we introduce a protocol that utilizes hydrogels for high-quality 3D QPI of live bacteria maintaining bacterial physiology. We demonstrate long-term high-resolution quantitative imaging and analysis of individual bacteria, including measuring the biophysical parameters of bacteria and responses to antibiotic treatments.

Multi-Photon Microscopy

In this series of papers on light microscopy imaging, we have covered the fundamentals of microscopy, super-resolution microscopy, and lightsheet microscopy. This last review covers multi-photon microscopy with a brief reference to intravital imaging and Brainbow labeling. Multi-photon microscopy is often referred to as two-photon microscopy. Indeed, using two-photon microscopy is by far the most common way of imaging thick tissues; however, it is theoretically possible to use a higher number of photons, and three-photon microscopy is possible. Therefore, this review is titled "multi-photon microscopy." Another term for describing multi-photon microscopy is "non-linear" microscopy because fluorescence intensity at the focal spot depends upon the average squared intensity rather than the squared average intensity; hence, non-linear optics (NLO) is an alternative name for multi-photon microscopy. It is this non-linear relationship (or third exponential power in the case of three-photon excitation) that determines the axial optical sectioning capability of multi-photon imaging. In this paper, the necessity for two-photon or multi-photon imaging is explained, and the method of optical sectioning by multi-photon microscopy is described. Advice is also given on what fluorescent markers to use and other practical aspects of imaging thick tissues. The technique of Brainbow imaging is discussed. The review concludes with a description of intravital imaging of the mouse. © 2023 Wiley Periodicals LLC.

Phase Contrast Image-Based Rapid Antimicrobial Susceptibility Testing of Bacteria in Liquid Culture Media

Currently, the world is facing the problem of bacterial resistance, which threatens public health, and bacterial antimicrobial susceptibility testing (AST) plays an important role in biomedicine, dietary safety and aquaculture. Traditional AST methods take a long time, usually 16-24 h, and cannot meet the demand for rapid diagnosis in the clinic, so rapid AST methods are needed to shorten the detection time. In this study, by using an in-house built centrifuge to centrifuge bacteria in a liquid medium onto the inner wall of the bottom surface of a counting plate, and using a phase contrast microscope to track bacterial growth under the effect of different antibiotic concentrations, the results of the minimum inhibitory concentration (MIC) of bacteria under the effect of antibiotics can be obtained in as early as 4 h. We used a combination of E. coli and tigecycline and obtained MIC results that were consistent with those obtained using the gold standard broth micro-dilution method, demonstrating the validity of our method; due to the time advantage, the complete set can be used in the future for point of care and clinical applications, helping physicians to quickly obtain the MIC used to inhibit bacterial growth.

High-fidelity optical diffraction tomography of live organisms using iodixanol refractive index matching

Optical diffraction tomography (ODT) enables the three-dimensional (3D) refractive index (RI) reconstruction. However, when the RI difference between a sample and a medium increases, the effects of light scattering become significant, preventing the acquisition of high-quality and accurate RI reconstructions. Herein, we present a method for high-fidelity ODT by introducing non-toxic RI matching media. Optimally reducing the RI contrast enhances the fidelity and accuracy of 3D RI reconstruction, enabling visualization of the morphology and intra-organization of live biological samples without producing toxic effects. We validate our method using various biological organisms, including C. albicans and C. elegans.

Epigallocatechin-3-gallate Delivered in Nanoparticles Increases Cytotoxicity in Three Breast Carcinoma Cell Lines

The anticancer activity of epigallocatechin-3-gallate (EGCG), orally administrated, is limited by poor bioavailability, absorption, and unpredictable distribution in human tissues. EGCG charged nanoparticles may represent an opportunity to overcome these limitations. We assayed two different kinds of lipid nanoparticles (LNPs and LNPs functionalized with folic acid) charged with EGCG on three breast carcinoma cell lines (MCF-7, MDA-MB-231, and MCF-7TAM) and the human normal MCF10A mammary epithelial cells. Both LNPs loaded with EGCG, at low concentrations, induced a significant cytotoxicity in the three breast carcinoma cells but not in MCF10A cells. In view of a future application, both LNPs and LNPs-FA were found to be very suitable for in vitro studies and useful to improve EGCG administration in vivo. Since they are produced by inexpensive procedures using bioavailable, biocompatible, and biodegradable molecules, they represent an applicable tool for a more rationale use of EGCG as an anti-cancer agent.

Developing a Reliable Holographic Flow Cyto-Tomography Apparatus by Optimizing the Experimental Layout and Computational Processing

Digital Holographic Tomography (DHT) has recently been established as a means of retrieving the 3D refractive index mapping of single cells. To make DHT a viable system, it is necessary to develop a reliable and robust holographic apparatus in order that such technology can be utilized outside of specialized optics laboratories and operated in the in-flow modality. In this paper, we propose a quasi-common-path lateral-shearing holographic optical set-up to be used, for the first time, for DHT in a flow-cytometer modality. The proposed solution is able to withstand environmental vibrations that can severely affect the interference process. Furthermore, we have scaled down the system while ensuring that a full 360° rotation of the cells occurs in the field-of-view, in order to retrieve 3D phase-contrast tomograms of single cells flowing along a microfluidic channel. This was achieved by setting the camera sensor at 45° with respect to the microfluidic direction. Additional optimizations were made to the computational elements to ensure the reliable retrieval of 3D refractive index distributions by demonstrating an effective method of tomographic reconstruction, based on high-order total variation. The results were first demonstrated using realistic 3D numerical phantom cells to assess the performance of the proposed high-order total variation method in comparison with the gold-standard algorithm for tomographic reconstructions: namely, filtered back projection. Then, the proposed DHT system and the processing pipeline were experimentally validated for monocytes and mouse embryonic fibroblast NIH-3T3 cells lines. Moreover, the repeatability of these tomographic measurements was also investigated by recording the same cell multiple times and quantifying the ability to provide reliable and comparable tomographic reconstructions, as confirmed by a correlation coefficient greater than 95%. The reported results represent various steps forward in several key aspects of in-flow DHT, thus paving the way for its use in real-world applications.

Natural-like Chalcones with Antitumor Activity on Human MG63 Osteosarcoma Cells

Osteosarcoma (OS) is a malignant disease characterized by poor prognosis due to a high incidence of metastasis and chemoresistance. Recently, Licochalcone A (Lic-A) has been reported as a promising agent against OS. Starting from chalcones selected from a wide in-house library, a new series was designed and synthetized. The antitumor activity of the compounds was tested on the MG63 OS cell line through the innovative Quantitative Phase Imaging technique and MTT assay. To further investigate the biological profile of active derivatives, cell cycle progression and apoptosis induction were evaluated. An earlier and more consistent arrest in the G2-M phase with respect to Lic-A was observed. Moreover, apoptosis was assessed by Annexin V staining as well as by the detection of typical morphological features of apoptotic cells. Among the selected compounds, 1e, 1q, and 1r proved to be the most promising antitumor molecules. This study pointed out that an integrated methodological approach may constitute a valuable platform for the rapid screening of large series of compounds.

Nanospermidine in Combination with Nanofenretinide Induces Cell Death in Neuroblastoma Cell Lines

A new strategy to cause cell death in tumors might be the increase of intracellular polyamines at concentrations above their physiological values to trigger the production of oxidation metabolites at levels exceeding cell tolerance. To test this hypothesis, we prepared nanospermidine as a carrier for spermidine penetration into the cells, able to escape the polyamine transport system that strictly regulates intracellular polyamine levels. Nanospermidine was prepared by spermidine encapsulation in nanomicelles and was characterized by size, zeta potential, loading, dimensional stability to dilution, and stability to spermidine leakage. Antitumor activity, ROS production, and cell penetration ability were evaluated in vitro in two neuroblastoma cell lines (NLF and BR6). Nanospermidine was tested as a single agent and in combination with nanofenretinide. Free spermidine was also tested as a comparison. The results indicated that the nanomicelles successfully transported spermidine into the cells inducing cell death in a concentration range (150–200 μM) tenfold lower than that required to provide similar cytotoxicity with free spermidine (1500–2000 μM). Nanofenretinide provided a cytostatic effect in combination with the lowest nanospermidine concentrations evaluated and slightly improved nanospermidine cytotoxicity at the highest concentrations. These data suggest that nanospermidine has the potential to become a new approach in cancer treatment. At the cellular level, in fact, it exploits polyamine catabolism by means of biocompatible doses of spermidine and, in vivo settings, it can exploit the selective accumulation of nanomedicines at the tumor site. Nanofenretinide combination further improves its efficacy. Furthermore, the proven ability of spermidine to activate macrophages and lymphocytes suggests that nanospermidine could inhibit immunosuppression in the tumor environment.

Functional imaging with dynamic quantitative oblique back-illumination microscopy

SIGNIFICANCE

Quantitative oblique back-illumination microscopy (qOBM) is a recently developed label-free imaging technique that enables 3D quantitative phase imaging of thick scattering samples with epi-illumination. Here, we propose dynamic qOBM to achieve functional imaging based on subcellular dynamics, potentially indicative of metabolic activity. We show the potential utility of this novel technique by imaging adherent mesenchymal stromal cells (MSCs) grown in bioreactors, which can help address important unmet needs in cell manufacturing for therapeutics.

AIM

We aim to develop dynamic qOBM and demonstrate its potential for functional imaging based on cellular and subcellular dynamics.

APPROACH

To obtain functional images with dynamic qOBM, a sample is imaged over a period of time and its temporal signals are analyzed. The dynamic signals display an exponential frequency response that can be analyzed with phasor analysis. Functional images of the dynamic signatures are obtained by mapping the frequency dynamic response to phasor space and color-coding clustered signals.

RESULTS

Functional imaging with dynamic qOBM provides unique information related to subcellular activity. The functional qOBM images of MSCs not only improve conspicuity of cells in complex environments (e.g., porous micro-carriers) but also reveal two distinct cell populations with different dynamic behavior.

CONCLUSIONS

In this work we present a label-free, fast, and scalable functional imaging approach to study and intuitively display cellular and subcellular dynamics. We further show the potential utility of this novel technique to help monitor adherent MSCs grown in bioreactors, which can help achieve quality-by-design of cell products, a significant unmet need in the field of cell therapeutics. This approach also has great potential for dynamic studies of other thick samples, such as organoids.

AI-Driven Cell Tracking to Enable High-Throughput Drug Screening Targeting Airway Epithelial Repair for Children with Asthma

The airway epithelium of children with asthma is characterized by aberrant repair that may be therapeutically modifiable. The development of epithelial-targeting therapeutics that enhance airway repair could provide a novel treatment avenue for childhood asthma. Drug discovery efforts utilizing high-throughput live cell imaging of patient-derived airway epithelial culture-based wound repair assays can be used to identify compounds that modulate airway repair in childhood asthma. Manual cell tracking has been used to determine cell trajectories and wound closure rates, but is time consuming, subject to bias, and infeasible for high-throughput experiments. We therefore developed software, EPIC, that automatically tracks low-resolution low-framerate cells using artificial intelligence, analyzes high-throughput drug screening experiments and produces multiple wound repair metrics and publication-ready figures. Additionally, unlike available cell trackers that perform cell segmentation, EPIC tracks cells using bounding boxes and thus has simpler and faster training data generation requirements for researchers working with other cell types. EPIC outperformed publicly available software in our wound repair datasets by achieving human-level cell tracking accuracy in a fraction of the time. We also showed that EPIC is not limited to airway epithelial repair for children with asthma but can be applied in other cellular contexts by outperforming the same software in the Cell Tracking with Mitosis Detection Challenge (CTMC) dataset. The CTMC is the only established cell tracking benchmark dataset that is designed for cell trackers utilizing bounding boxes. We expect our open-source and easy-to-use software to enable high-throughput drug screening targeting airway epithelial repair for children with asthma.

Effects of Regioisomerism on the Antiproliferative Activity of Hydroxystearic Acids on Human Cancer Cell Lines

A series of regioisomers of the hydroxystearic acid (HSA) was prepared, and the effect of the position of the hydroxyl group along the chain on a panel of human cancer cell lines was investigated. Among the various regioisomers, those carrying the hydroxyl at positions 5, 7, and 9 had growth inhibitor activity against various human tumor cell lines, including CaCo-2, HT29, HeLa, MCF7, PC3, and NLF cells. 10-HSA and 11-HSA showed a very weak effect. 8-HSA did not show inhibitory activity in all cell lines. The biological role of 7-HSA and 9-HSA is widely recognized, while little is known about the effects of 5-HSA. Therefore, the biological effects of 5-HSA in HeLa, HT29, MCF7, and NLF cell lines were investigated using the Livecyte's ptychography technology, which allows correlating changes in proliferation, motility, and morphology as a function of treatment at the same time. 5-HSA not only reduces cell proliferation but also induces changes in cell displacement, directionality, and speed. It is important to characterize the biological effects of 5-HSA, this molecule being an important component of fatty acyl esters of hydroxy fatty acids (FAHFA), a class of endogenous mammalian lipids with noticeable anti-diabetic and anti-inflammatory effects.

Cellular analysis using label-free parallel array microscopy with Fourier ptychography

Quantitative phase imaging (QPI) is an ideal method to non-invasively monitor cell populations and provide label-free imaging and analysis. QPI offers enhanced sample characterization and cell counting compared to conventional label-free techniques. We demonstrate this in the current study through a comparison of cell counting data from digital phase contrast (DPC) imaging and from QPI using a system based on Fourier ptychographic microscopy (FPM). Our FPM system offers multi-well, parallel imaging and a QPI-specific cell segmentation method to establish automated and reliable cell counting. Three cell types were studied and FPM showed improvement in the ability to resolve fine details and thin cells, despite limitations of the FPM system incurred by imaging artifacts. Relative to manually counted fluorescence ground-truth, cell counting results after automated segmentation showed improved accuracy with QPI over DPC.

Computational Methods for Single-Cell Imaging and Omics Data Integration

Integrating single cell omics and single cell imaging allows for a more effective characterisation of the underlying mechanisms that drive a phenotype at the tissue level, creating a comprehensive profile at the cellular level. Although the use of imaging data is well established in biomedical research, its primary application has been to observe phenotypes at the tissue or organ level, often using medical imaging techniques such as MRI, CT, and PET. These imaging technologies complement omics-based data in biomedical research because they are helpful for identifying associations between genotype and phenotype, along with functional changes occurring at the tissue level. Single cell imaging can act as an intermediary between these levels. Meanwhile new technologies continue to arrive that can be used to interrogate the genome of single cells and its related omics datasets. As these two areas, single cell imaging and single cell omics, each advance independently with the development of novel techniques, the opportunity to integrate these data types becomes more and more attractive. This review outlines some of the technologies and methods currently available for generating, processing, and analysing single-cell omics- and imaging data, and how they could be integrated to further our understanding of complex biological phenomena like ageing. We include an emphasis on machine learning algorithms because of their ability to identify complex patterns in large multidimensional data.

Epithelial arginase-1 is a key mediator of age-associated delayed healing in vaginal injury

Pelvic organ prolapse is a disorder that substantially affects the quality of life of millions of women worldwide. The greatest risk factors for prolapse are increased parity and older age, with the largest group requiring surgical intervention being post-menopausal women over 65. Due to ineffective healing in the elderly, prolapse recurrence rates following surgery remain high. Therefore, there is an urgent need to elucidate the cellular and molecular drivers of poor healing in pelvic floor dysfunction to allow effective management and even prevention. Recent studies have uncovered the importance of Arginase 1 for modulating effective healing in the skin. We thus employed novel in vitro and in vivo vaginal injury models to determine the specific role of Arginase 1 in age-related vaginal repair. Here we show, for the first time, that aged rat vaginal wounds have reduced Arginase 1 expression and delayed healing. Moreover, direct inhibition of Arginase 1 in human vaginal epithelial cells also led to delayed scratch-wound closure. By contrast, activation of Arginase 1 significantly accelerated healing in aged vaginal wounds in vivo, to rates comparable to those in young animals. Collectively, these findings reveal a new and important role for Arginase 1 in mediating effective vaginal repair. Targeting age-related Arginase 1 deficiency is a potential viable therapeutic strategy to promote vaginal healing and reduce recurrence rate after surgical repair of pelvic organ prolapse.

Mitotic Index Determination on Live Cells From Label-Free Acquired Quantitative Phase Images Using a Supervised Autoencoder

This interdisciplinary work focuses on the interest of a new auto-encoder for supervised classification of live cell populations growing in a thermostated imaging station and acquired by a Quantitative Phase Imaging (QPI) camera. This type of camera produces interferograms that have to be processed to extract features derived from quantitative linear retardance and birefringence measurements. QPI is performed on living populations without any manipulation or treatment of the cells. We use the efficient new autoencoder classification method instead of the classical Douglas-Rachford method. Using this new supervised autoencoder, we show that the accuracy of the classification of the cells present in the mitotic phase of the cell cycle is very high using QPI features. This is a very important finding since we demonstrate that it is now possible to very precisely follow cell growth in a non-invasive manner, without any bias. No dye or any kind of markers are necessary for this live monitoring. Any studies requiring analysis of cell growth or cellular response to any treatment could benefit from this new approach by simply monitoring the proportion of cells entering mitosis in the studied cell population.

Raman and quantitative phase imaging allow morpho-molecular recognition of malignancy and stages of B-cell acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is one of the most common malignancies that account for nearly one-third of all pediatric cancers. The current diagnostic assays are time-consuming, labor-intensive, and require expensive reagents. Here, we report a label-free approach featuring diffraction phase imaging and Raman microscopy that can retrieve both morphological and molecular attributes for label-free optical phenotyping of individual B cells. By investigating leukemia cell lines of early and late stages along with the healthy B cells, we show that phase images can capture subtle morphological differences among the healthy, early, and late stages of leukemic cells. By exploiting its biomolecular specificity, we demonstrate that Raman microscopy is capable of accurately identifying not only different stages of leukemia cells but also individual cell lines at each stage. Overall, our study provides a rationale for employing this hybrid modality to screen leukemia cells using the widefield QPI and using Raman microscopy for accurate differentiation of early and late-stage phenotypes. This contrast-free and rapid diagnostic tool exhibits great promise for clinical diagnosis and staging of leukemia in the near future.

LIVECell-A large-scale dataset for label-free live cell segmentation

Light microscopy combined with well-established protocols of two-dimensional cell culture facilitates high-throughput quantitative imaging to study biological phenomena. Accurate segmentation of individual cells in images enables exploration of complex biological questions, but can require sophisticated imaging processing pipelines in cases of low contrast and high object density. Deep learning-based methods are considered state-of-the-art for image segmentation but typically require vast amounts of annotated data, for which there is no suitable resource available in the field of label-free cellular imaging. Here, we present LIVECell, a large, high-quality, manually annotated and expert-validated dataset of phase-contrast images, consisting of over 1.6 million cells from a diverse set of cell morphologies and culture densities. To further demonstrate its use, we train convolutional neural network-based models using LIVECell and evaluate model segmentation accuracy with a proposed a suite of benchmarks.

Single-shot quantitative phase imaging as an extension of differential interference contrast microscopy

Various studies have been conducted to obtain quantitative phase information based on differential interference contrast (DIC) microscopy. As one such attempt, we propose in this study a single-shot quantitative phase imaging (QPI) method by combining two developments. First, an add-on optical system to a commercialized DIC microscope was developed to perform quantitative phase gradient imaging (QPGI) with single image acquisition using a polarization camera. Second, an algorithm was formulated to reconstitute QPI from the obtained QPGI by reducing linear artifacts, which arise in simply integrated QPGI images. To demonstrate the applicability of the developed system in cell biology, the system was used to measure various cell lines and compared with fluorescence microscopy images of the same field of view. Consistent with previous studies, nucleoli and lipid droplets can be imaged by the system with greater optical path lengths (OPL). The results also implied that combining fluorescence microscopy and the developed system might be more informative for cell biology research than using these methods individually. Exploiting the single-shot performance of the developed system, time-lapse imaging was also conducted to visualize the dynamics of intracellular granules in monocyte-/macrophage-like cells. Our proposed approach may accelerate the implementation of QPI in standard biomedical laboratories.

NeuriTES. Monitoring neurite changes through transfer entropy and semantic segmentation in bright-field time-lapse microscopy

One of the most challenging frontiers in biological systems understanding is fluorescent label-free imaging. We present here the NeuriTES platform that revisits the standard paradigms of video analysis to detect unlabeled objects and adapt to the dynamic evolution of the phenomenon under observation. Object segmentation is reformulated using robust algorithms to assure regular cell detection and transfer entropy measures are used to study the inter-relationship among the parameters related to the evolving system. We applied the NeuriTES platform to the automatic analysis of neurites degeneration in presence of amyotrophic lateral sclerosis (ALS) and to the study of the effects of a chemotherapy drug on living prostate cancer cells (PC3) cultures. Control cells have been considered in both the two cases study. Accuracy values of 93% and of 92% are achieved, respectively. NeuriTES not only represents a tool for investigation in fluorescent label-free images but demonstrates to be adaptable to individual needs.

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Monitoring of cell phenotype changes by fluorescence label-free time-lapse microscopy

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Adaptive semantic segmentation for the robust detection of cell shape

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TE to correlate morphological and textural soma descriptors along time

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Directed TE graph for the representation of mutual relationship among descriptors

One of the most challenging frontiers for the automatic understanding of biological systems is fluorescent label-free imaging in which the behavior changes of living being are characterized without cell staining. To this aim, we present here the NeuriTES platform that revisits standard paradigms of video analysis to detect unlabeled objects and correlate the analysis to phenotype evolution of the mechanisms under observation. Through the exploitation of adaptive algorithms and of transfer entropy measures, the platform assures regular cell detection and the possibility to extract reliable parameters related to the evolving cell system. As a proof-of-concept, NeuriTES is applied to two fascinating phenotype investigation scenarios, amyotrophic lateral sclerosis (ALS) disease mechanism and the study of the effects of a chemotherapy drug on living prostate cancer cells (PC3) cultures. Directed graphs assist the biologists with a visual understanding of the mechanisms identified.

One-Shot Phase Image Distinction of Plasmonic and Dielectric Nanoparticles

Nanoscale phase control is one of the most powerful approaches to specifically tailor electrical fields in modern nanophotonics. Especially the precise subwavelength assembly of many individual nanobuilding blocks has given rise to exciting new materials as diverse as metamaterials, for miniaturizing optics, or 3D assembled plasmonic structures for biosensing applications. Despite its fundamental importance, the phase response of individual nanostructures is experimentally extremely challenging to visualize. Here, we address this shortcoming and measure the quantitative scattering phase of different nanomaterials such as gold nanorods and spheres as well as dielectric nanoparticles. Beyond reporting spectrally resolved responses, with phase changes close to π when passing the particles' plasmon resonance, we devise a simple method for distinguishing different plasmonic and dielectric particles purely based on their phase behavior. Finally, we integrate this novel approach in a single-shot two-color scheme, capable of directly identifying different types of nanoparticles on one sample, from a single widefield image.

Quantitative phase microscopy for non-invasive live cell population monitoring

We present here a label-free development based on preexisting Quantitative Phase Imaging (QPI) that allows non-invasive live monitoring of both individual cells and cell populations. Growth, death, effect of toxic compounds are quantified under visible light with a standard inverted microscope. We show that considering the global biomass of a cell population is a more robust and accurate method to assess its growth parameters in comparison to compiling individually segmented cells. This is especially true for confluent conditions. This method expands the use of light microscopy in answering biological questions concerning live cell populations even at high density. In contrast to labeling or lysis of cells this method does not alter the cells and could be useful in high-throughput screening and toxicity studies.

Simultaneous measurement of neurite and neural body mass accumulation via quantitative phase imaging

Measurement of neuron behavior is crucial for studying neural development and evaluating the impact of potential therapies on neural regeneration. Conventional approaches to imaging neuronal behavior require labeling and do not separately quantify the growth processes that underlie neural regeneration. In this paper we demonstrate the use of quantitative phase imaging (QPI) as a label-free, quantitative measurement of neuron behavior in vitro. By combining QPI with image processing, our method separately measures the mass accumulation rates of soma and neurites. Additionally, the data provided by QPI can be used to separately measure the processes of maturation and formation of neurites. Overall, our approach has the potential to greatly simplify conventional neurite outgrowth measurements, while providing key data on the resources used to produce neurites during neural development.

Application of M1 macrophage as a live vector in delivering nanoparticles for in vivo photothermal treatment

Introduction

To enhance photothermal treatment (PTT) efficiency, a delivery method that uses cell vector for nanoparticles (NPs) delivery has drawn attention and studied widely in recent years.

Objectives

In this study, we demonstrated the feasibility of M1 activated macrophage as a live vector for delivering NPs and investigated the effect of NPs loaded M1 stimulated by Lipopolysaccharide on PTT efficiency in vivo.

Methods

M1 was used as a live vector for delivering NPs and further to investigate the effect of NPs loaded M1 on PTT efficiency. Non-activated macrophage (MФ) was stimulated by lipopolysaccharide (LPS) into M1 and assessed for tumor cell phagocytic capacity towards NPs

Results

We found M1 exhibited a 20-fold higher uptake capacity of NPs per cell volume and 2.9-fold more active infiltration into the tumor site, compared with non-activated macrophage MФ. We injected M1 cells peritumorally and observed that these cells penetrated into the tumor mass within 12 h. Then, we conducted PTT using irradiation of a near-infrared laser for 1 min at 1 W/cm². As a result, we confirmed that using M1 as an active live vector led to a more rapid reduction in tumor size within 1 day indicating that the efficacy of PTT with NPs-loaded M1 is higher than that with NPs-loaded MФ.

Conclusion

Our study demonstrated the potential role of M1 as a live vector for enhancing the feasibility of PTT in cancer treatment.

Root Extracts of Two Cultivars of Paeonia Species: Lipid Composition and Biological Effects on Different Cell Lines: Preliminary Results

The roots of two cultivars of Paeonia, namely Paeonia officinalis “Rubra Plena” and Paeonia “Pink Hawaiian Coral”, have been extracted with chloroform. The composition of the lipid fraction, analyzed by GC–MS technique, revealed the absence of paeonol and the presence of phenol, benzoic acid, fatty acid—and some sterol—derivatives. The chloroformic extracts have been tested on normal and several cancer cell lines but showed antiproliferative activity only on the ovarian carcinoma and the osteosarcoma. The biological activity of extracts was investigated mainly by confocal microscopy, flow cytometry and quantum phase imaging. The results indicated that the root extracts induced a hyperpolarization of mitochondria and an increase in reactive oxygen species levels, without inducing cell death. These effects are associated to an increased doubling time and a retarded confluence.

High-Content Imaging to Phenotype Human Primary and iPSC-Derived Cells

Increasingly powerful microscopy, liquid handling, and computational techniques have enabled cell imaging in high throughput. Microscopy images are quantified using high-content analysis platforms linking object features to cell behavior. This can be attempted on physiologically relevant cell models, including stem cells and primary cells, in complex environments, and conceivably in the presence of perturbations. Recently, substantial focus has been devoted to cell profiling for cell therapy, assays for drug discovery or biomarker identification for clinical decision-making protocols, bringing this wealth of information into translational applications. In this chapter, we focus on two protocols enabling to (1) benchmark human cells, in particular human endothelial cells as a case study and (2) extract cells from blood for follow-up experiments including image-based drug testing. We also present concepts of high-content imaging and discuss the benefits and challenges, with the aim of enabling readers to tailor existing pipelines and bring such approaches closer to translational research and the clinic.

Single-cell cytometry via multiplexed fluorescence prediction by label-free reflectance microscopy

Traditional imaging cytometry uses fluorescence markers to identify specific structures but is limited in throughput by the labeling process. We develop a label-free technique that alleviates the physical staining and provides multiplexed readouts via a deep learning-augmented digital labeling method. We leverage the rich structural information and superior sensitivity in reflectance microscopy and show that digital labeling predicts accurate subcellular features after training on immunofluorescence images. We demonstrate up to three times improvement in the prediction accuracy over the state of the art. Beyond fluorescence prediction, we demonstrate that single cell-level structural phenotypes of cell cycles are correctly reproduced by the digital multiplexed images, including Golgi twins, Golgi haze during mitosis, and DNA synthesis. We further show that the multiplexed readouts enable accurate multiparametric single-cell profiling across a large cell population. Our method can markedly improve the throughput for imaging cytometry toward applications for phenotyping, pathology, and high-content screening.

Cytotoxic (cis,cis-1,3,5-triaminocyclohexane)ruthenium(II)-diphosphine complexes; evidence for covalent binding and intercalation with DNA

We report cytotoxic ruthenium(ii) complexes of the general formula [RuCl(cis-tach)(diphosphine)]+ (cis-tach = cis-cis-1,3,5-triaminocyclohexane) that have been characterised by 1H, 13C and 31P{1H} NMR spectroscopy, mass spectrometry, X-ray crystallography and elemental analysis. The kinetics of aquation and stability of the active species have been studied, showing that the chlorido ligand is substituted by water at 298 K with first order rate constants of 10-2-10-3 s-1, ideal for potential clinical use as anti-tumour agents. Strong interactions with biologically relevant duplex and quadruplex DNA models correlate with the activity observed with A549, A2780 and 293T cell lines, and the degree of activity was found to be sensitive to the chelating diphosphine ligand. A label-free ptychographic cell imaging technique recorded cell death processes over 4 days. The Ru(ii) cis-tach diphosphine complexes exhibit anti-proliferative effects, in some cases outperforming cisplatin and other cytotoxic ruthenium complexes.

Viscoelastically active sutures - A stitch in time?

We present results to show that a commercially available polypropylene suture filament (Ethicon Prolene), following annealing and tensile creep can, after creep load removal, release viscoelastically stored energy over a period of several weeks. Specifically, over 0.1-1000 h, the suture undergoes a time-dependent contraction of ~4% and, following a short recovery time (~3 min) to a fixed strain, produces a progressively increasing recovery force of ~0.1-1 N. We suggest that this time-dependent energy release may facilitate wound healing by the action of viscoelastically induced mechanotransduction (VIM). Moreover, our recent (published) findings have led to evidence of reduced hydrophobicity from viscoelastically recovering polymeric filaments and speculation that this may emanate from the long-term release of electric charges. Thus, we propose that the latter may enhance the VIM mechanism. In this paper, we report on the direct detection of these charges and the first findings from an investigation involving the presence of cell cultures on Prolene samples that are (i) viscoelastically recovering, (ii) annealed only and (iii) in as-received condition. From (i), the results demonstrate a significant increase in cell motility, with migration towards the suture, compared to (ii) and (iii). This suggests greater stimulation of the wound healing process, an effect which is expected to continue for the duration of the viscoelastic recovery period.

Probing pluripotency gene regulatory networks with quantitative live cell imaging

Live cell imaging uniquely enables the measurement of dynamic events in single cells, but it has not been used often in the study of gene regulatory networks. Network components can be examined in relation to one another by quantitative live cell imaging of fluorescent protein reporter cell lines that simultaneously report on more than one network component. A series of dual-reporter cell lines would allow different combinations of network components to be examined in individual cells. Dynamical information about interacting network components in individual cells is critical to predictive modeling of gene regulatory networks, and such information is not accessible through omics and other end point techniques. Achieving this requires that gene-edited cell lines are appropriately designed and adequately characterized to assure the validity of the biological conclusions derived from the expression of the reporters. In this brief review we discuss what is known about the importance of dynamics to network modeling and review some recent advances in optical microscopy methods and image analysis approaches that are making the use of quantitative live cell imaging for network analysis possible. We also discuss how strategies for genetic engineering of reporter cell lines can influence the biological relevance of the data.

Neuroplastinβ-mediated upregulation of solute carrier family 22 member 18 antisense (SLC22A18AS) plays a crucial role in the epithelial-mesenchymal transition, leading to lung cancer cells' enhanced motility

Our recent study revealed an important role of the neuroplastin (NPTN)β downstream signal in lung cancer dissemination in the lung. The molecular mechanism of the signal pathway downstream of NPTNβ is a serial activation of the key molecules we identified: tumor necrosis factor (TNF) receptor-associated factor 2 (TRAF2) adaptor, nuclear factor (NF)IA/NFIB heterodimer transcription factor, and SAM pointed-domain containing ETS transcription factor (SPDEF). The question of how dissemination is controlled by SPDEF under the activated NPTNβ has not been answered. Here, we show that the NPTNβ-SPDEF-mediated induction of solute carrier family 22 member 18 antisense (SLC22A18AS) is definitely required for the epithelial-mesenchymal transition (EMT) through the NPTNβ pathway in lung cancer cells. In vitro, the induced EMT is linked to the acquisition of active cellular motility but not growth, and this is correlated with highly disseminative tumor progression in vivo. The publicly available data also show the poor survival of SLC22A18AS-overexpressing lung cancer patients. Taken together, these data highlight a crucial role of SLC22A18AS in lung cancer dissemination, which provides novel input of this molecule to the signal cascade of NPTNβ. Our findings contribute to a better understanding of NPTNβ-mediated lung cancer metastasis.

Digital Holographic Imaging as a Method for Quantitative, Live Cell Imaging of Drug Response to Novel Targeted Cancer Therapies

Digital holographic imaging (DHI) is a noninvasive, live cell imaging technique that enables long-term quantitative visualization of cells in culture. DHI uses phase-shift imaging to monitor and quantify cellular events such as cell division, cell death, cell migration, and drug responses. In recent years, the application of DHI has expanded from its use in the laboratory to the clinical setting, and currently it is being developed for use in theranostics. Here, we describe the use of the DHI platform HoloMonitorM4 to evaluate the effects of novel, targeted cancer therapies on cell viability and proliferation using the HeLa cancer cell line as a model. We present single cell tracking and population-wide analysis of multiple cell morphology parameters.

PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells

MOTIVATION

New single-cell technologies continue to fuel the explosive growth in the scale of heterogeneous single-cell data. However, existing computational methods are inadequately scalable to large datasets and therefore cannot uncover the complex cellular heterogeneity.

RESULTS

We introduce a highly scalable graph-based clustering algorithm PARC-Phenotyping by Accelerated Refined Community-partitioning-for large-scale, high-dimensional single-cell data (>1 million cells). Using large single-cell flow and mass cytometry, RNA-seq and imaging-based biophysical data, we demonstrate that PARC consistently outperforms state-of-the-art clustering algorithms without subsampling of cells, including Phenograph, FlowSOM and Flock, in terms of both speed and ability to robustly detect rare cell populations. For example, PARC can cluster a single-cell dataset of 1.1 million cells within 13 min, compared with >2 h for the next fastest graph-clustering algorithm. Our work presents a scalable algorithm to cope with increasingly large-scale single-cell analysis.

AVAILABILITY AND IMPLEMENTATION

https://github.com/ShobiStassen/PARC.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Holographic Imaging of Insect Cell Cultures: Online Non-Invasive Monitoring of Adeno-Associated Virus Production and Cell Concentration

The insect cell-baculovirus vector system has become one of the favorite platforms for the expression of viral vectors for vaccination and gene therapy purposes. As it is a lytic system, it is essential to balance maximum recombinant product expression with harvest time, minimizing product exposure to detrimental proteases. With this purpose, new bioprocess monitoring solutions are needed to accurately estimate culture progression. Herein, we used online digital holographic microscopy (DHM) to monitor bioreactor cultures of Sf9 insect cells. Batches of baculovirus-infected Sf9 cells producing recombinant adeno-associated virus (AAV) and non-infected cells were used to evaluate DHM prediction capabilities for viable cell concentration, culture viability and AAV titer. Over 30 cell-related optical attributes were quantified using DHM, followed by a forward stepwise regression to select the most significant (p < 0.05) parameters for each variable. We then applied multiple linear regression to obtain models which were able to predict culture variables with root mean squared errors (RMSE) of 7 × 105 cells/mL, 3% for cell viability and 2 × 103 AAV/cell for 3-fold cross-validation. Overall, this work shows that DHM can be implemented for online monitoring of Sf9 concentration and viability, also permitting to monitor product titer, namely AAV, or culture progression in lytic systems, making it a valuable tool to support the time of harvest decision and for the establishment of controlled feeding strategies.

Correlation of Capacitance and Microscopy Measurements Using Image Processing for a Lab-on-CMOS Microsystem

We present a capacitance sensor chip developed in a 0.35-μm complementary metal-oxide-semiconductor process for monitoring biological cell viability and proliferation. The chip measures the cell-to-substrate binding through capacitance-to-frequency conversion with a sensitivity of 590 kHz/fF. In vitro experiments with two human ovarian cancer cell lines (CP70 and A2780) were performed and showed the ability to track cell viability in realtime over three days. An imaging platform was developed to provide time-lapse images of the sensor surface, which allowed for concurrent visual and capacitance observation of the cells. The results showed the ability to detect single-cell binding events and changes in cell morphology. Image processing was performed to estimate the cell coverage of sensor electrodes, showing good linear correlation and providing a sensor gain of 1.28 ± 0.29 aF/μm², which agrees with values reported in the literature. The device is designed for unsupervised operation with minimal packaging requirements. Only a microcontroller is required for readout, making it suitable for applications outside the traditional laboratory setting.

Noninvasive measurement of cell/colony motion using image analysis methods to evaluate the proliferative capacity of oral keratinocytes as a tool for quality control in regenerative medicine

Image-based cell/colony analyses offer promising solutions to compensate for the lack of quality control (QC) tools for noninvasive monitoring of cultured cells, a regulatory challenge in regenerative medicine. Here, the feasibility of two image analysis algorithms, optical flow and normalised cross-correlation, to noninvasively measure cell/colony motion in human primary oral keratinocytes for screening the proliferative capacity of cells in the early phases of cell culture were examined. We applied our software to movies converted from 96 consecutive time-lapse phase-contrast images of an oral keratinocyte culture. After segmenting the growing colonies, two indices were calculated based on each algorithm. The correlation between each index of the colonies and their proliferative capacity was evaluated. The software was able to assess cell/colony motion noninvasively, and each index reflected the observed cell kinetics. A positive linear correlation was found between cell/colony motion and proliferative capacity, indicating that both algorithms are potential tools for QC.

An artificial-vision- and statistical-learning-based method for studying the biodegradation of type I collagen scaffolds in bone regeneration systems

This work proposes a method based on image analysis and machine and statistical learning to model and estimate osteocyte growth (in type I collagen scaffolds for bone regeneration systems) and the collagen degradation degree due to cellular growth. To achieve these aims, the mass of collagen -subjected to the action of osteocyte growth and differentiation from stem cells- was measured on 3 days during each of 2 months, under conditions simulating a tissue in the human body. In addition, optical microscopy was applied to obtain information about cellular growth, cellular differentiation, and collagen degradation. Our first contribution consists of the application of a supervised classification random forest algorithm to image texture features (the structure tensor and entropy) for estimating the different regions of interest in an image obtained by optical microscopy: the extracellular matrix, collagen, and image background, and nuclei. Then, extracellular-matrix and collagen regions of interest were determined by the extraction of features related to the progression of the cellular growth and collagen degradation (e.g., mean area of objects and the mode of an intensity histogram). Finally, these critical features were statistically modeled depending on time via nonparametric and parametric linear and nonlinear models such as those based on logistic functions. Namely, the parametric logistic mixture models provided a way to identify and model the degradation due to biological activity by estimating the corresponding proportion of mass loss. The relation between osteocyte growth and differentiation from stem cells, on the one hand, and collagen degradation, on the other hand, was determined too and modeled through analysis of image objects' circularity and area, in addition to collagen mass loss. This set of imaging techniques, machine learning procedures, and statistical tools allowed us to characterize and parameterize type I collagen biodegradation when collagen acts as a scaffold in bone regeneration tasks. Namely, the parametric logistic mixture models provided a way to identify and model the degradation due to biological activity and thus to estimate the corresponding proportion of mass loss. Moreover, the proposed methodology can help to estimate the degradation degree of scaffolds from the information obtained by optical microscopy.

Cell segmentation methods for label-free contrast microscopy: review and comprehensive comparison

BACKGROUND

Because of its non-destructive nature, label-free imaging is an important strategy for studying biological processes. However, routine microscopic techniques like phase contrast or DIC suffer from shadow-cast artifacts making automatic segmentation challenging. The aim of this study was to compare the segmentation efficacy of published steps of segmentation work-flow (image reconstruction, foreground segmentation, cell detection (seed-point extraction) and cell (instance) segmentation) on a dataset of the same cells from multiple contrast microscopic modalities.

RESULTS

We built a collection of routines aimed at image segmentation of viable adherent cells grown on the culture dish acquired by phase contrast, differential interference contrast, Hoffman modulation contrast and quantitative phase imaging, and we performed a comprehensive comparison of available segmentation methods applicable for label-free data. We demonstrated that it is crucial to perform the image reconstruction step, enabling the use of segmentation methods originally not applicable on label-free images. Further we compared foreground segmentation methods (thresholding, feature-extraction, level-set, graph-cut, learning-based), seed-point extraction methods (Laplacian of Gaussians, radial symmetry and distance transform, iterative radial voting, maximally stable extremal region and learning-based) and single cell segmentation methods. We validated suitable set of methods for each microscopy modality and published them online.

CONCLUSIONS

We demonstrate that image reconstruction step allows the use of segmentation methods not originally intended for label-free imaging. In addition to the comprehensive comparison of methods, raw and reconstructed annotated data and Matlab codes are provided.

Methamphetamine-induced apoptosis in glial cells examined under marker-free imaging modalities

We used phase microscopy and Raman spectroscopic measurements to assess the response of in vitro rat C6 glial cells following methamphetamine treatment in real time. Digital holographic microscopy (DHM) and three-dimensional (3-D) tomographic nanoscopy allow measurements of live cell cultures, which yield information about cell volume changes. Tomographic phase imaging provides 3-D information about the refractive index distribution associated with the morphology of biological samples. DHM provides similar information, but for a larger population of cells. Morphological changes in cells are associated with alterations in cell cycle and initiation of cell death mechanisms. Raman spectroscopy measurements provide information about chemical changes within the cells. Our Raman data indicate that the chemical changes in proteins preceded morphological changes, which were seen with DHM. Our study also emphasizes that tomographic phase imaging, DHM, and Raman spectroscopy are imaging tools that can be utilized for noninvasive simultaneous monitoring of morphological and chemical changes in cells during apoptosis and can also be used to monitor other dynamic cell processes.

A fully-automated, robust, and versatile algorithm for long-term budding yeast segmentation and tracking

Live cell time-lapse microscopy, a widely-used technique to study gene expression and protein dynamics in single cells, relies on segmentation and tracking of individual cells for data generation. The potential of the data that can be extracted from this technique is limited by the inability to accurately segment a large number of cells from such microscopy images and track them over long periods of time. Existing segmentation and tracking algorithms either require additional dyes or markers specific to segmentation or they are highly specific to one imaging condition and cell morphology and/or necessitate manual correction. Here we introduce a fully automated, fast and robust segmentation and tracking algorithm for budding yeast that overcomes these limitations. Full automatization is achieved through a novel automated seeding method, which first generates coarse seeds, then automatically fine-tunes cell boundaries using these seeds and automatically corrects segmentation mistakes. Our algorithm can accurately segment and track individual yeast cells without any specific dye or biomarker. Moreover, we show how existing channels devoted to a biological process of interest can be used to improve the segmentation. The algorithm is versatile in that it accurately segments not only cycling cells with smooth elliptical shapes, but also cells with arbitrary morphologies (e.g. sporulating and pheromone treated cells). In addition, the algorithm is independent of the specific imaging method (bright-field/phase) and objective used (40X/63X/100X). We validate our algorithm's performance on 9 cases each entailing a different imaging condition, objective magnification and/or cell morphology. Taken together, our algorithm presents a powerful segmentation and tracking tool that can be adapted to numerous budding yeast single-cell studies.