Neuronal innervation regulates the secretion of neurotrophic myokines and exosomes from skeletal muscle

Myokines and exosomes, originating from skeletal muscle, are shown to play a significant role in maintaining brain homeostasis. While exercise has been reported to promote muscle secretion, little is known about the effects of neuronal innervation and activity on the yield and molecular composition of biologically active molecules from muscle. As neuromuscular diseases and disabilities associated with denervation impact muscle metabolism, we hypothesize that neuronal innervation and firing may play a pivotal role in regulating secretion activities of skeletal muscles. We examined this hypothesis using an engineered neuromuscular tissue model consisting of skeletal muscles innervated by motor neurons. The innervated muscles displayed elevated expression of mRNAs encoding neurotrophic myokines, such as interleukin-6, brain-derived neurotrophic factor, and FDNC5, as well as the mRNA of peroxisome-proliferator-activated receptor γ coactivator 1α, a key regulator of muscle metabolism. Upon glutamate stimulation, the innervated muscles secreted higher levels of irisin and exosomes containing more diverse neurotrophic microRNAs than neuron-free muscles. Consequently, biological factors secreted by innervated muscles enhanced branching, axonal transport, and, ultimately, spontaneous network activities of primary hippocampal neurons in vitro. Overall, these results reveal the importance of neuronal innervation in modulating muscle-derived factors that promote neuronal function and suggest that the engineered neuromuscular tissue model holds significant promise as a platform for producing neurotrophic molecules.

EVATOM: an optical, label-free, machine learning assisted embryo health assessment tool

The combination of a good quality embryo and proper maternal health factors promise higher chances of a successful in vitro fertilization (IVF) procedure leading to clinical pregnancy and live birth. Of these two factors, selection of a good embryo is a controllable aspect. The current gold standard in clinical practice is visual assessment of an embryo based on its morphological appearance by trained embryologists. More recently, machine learning has been incorporated into embryo selection "packages". Here, we report EVATOM: a machine-learning assisted embryo health assessment tool utilizing an optical quantitative phase imaging technique called artificial confocal microscopy (ACM). We present a label-free nucleus detection method with, to the best of our knowledge, novel quantitative embryo health biomarkers. Two viability assessment models are presented for grading embryos into two classes: healthy/intermediate (H/I) or sick (S) class. The models achieve a weighted F1 score of 1.0 and 0.99 respectively on the in-distribution test set of 72 fixed embryos and a weighted F1 score of 0.9 and 0.95 respectively on the out-of-distribution test dataset of 19 time-instances from 8 live embryos.

Late Complications after Laparoscopic Longitudinal Gastrectomy - Case Report

INTRODUCTION

Bariatric surgery is now widely regarded as the most effective treatment for morbid obesity. It aims to enhance patients' health by achieving long-lasting weight loss, reducing associated comorbidities, and improving their quality of life.

CASE REPORT

The case involves a 51-year-old female patient who underwent sleeve gastrectomy eight years ago to address morbid obesity (Body Mass Index (BMI) = 43). Subsequently, the patient developed gastric obstructive syndrome, leading to diagnostic investigations including repeated upper digestive endoscopies and native computed tomography. These examinations revealed the presence of a hiatal hernia. In 2021, the patient underwent fundoplication type Dor with anterior hemivalve. However, post-surgery, the patient's condition did not improve, with persistent symptoms including regurgitation, heartburn, difficulty ingesting food, sensation of gastric fullness, and epigastralgia. Further exploratory procedures, including upper digestive endoscopy, abdominal computed tomography with contrast substance, and barium transit with contrast substance, led to the diagnosis of mediogastric stenosis postgastrectomy longitudinal, necessitating surgical intervention. This finding prompted a surgical approach involving distal gastric resection and restoration of digestive tract continuity through Hoffmeister-Finsterer gastro-jejunal anastomosis. Following the surgery, the patient's postoperative symptoms showed improvement.

DISCUSSION

Several other studies have demonstrated that the incisura angularis is the most common site of obstruction, as was observed in our study. This particular location appears to be more prone to narrowing, likely attributable to its angular shape. The linear staple line in this area can create a locus minoris resistentiae for kinking, as well as increase the risk of true stenosis if stapling is performed too close to the incisura angularis.

CONCLUSIONS

Bariatric surgery should not be considered as the initial treatment option. However, in cases where it becomes necessary, postoperative monitoring is essential to prevent complications or address them promptly.

Machine learning assisted health viability assay for mouse embryos with artificial confocal microscopy (ACM)

The combination of a good quality embryo and proper maternal health factors promise higher chances of a successful in vitro fertilization (IVF) procedure leading to clinical pregnancy and live birth. Of these two factors, selection of a good embryo is a controllable aspect. The current gold standard in clinical practice is visual assessment of an embryo based on its morphological appearance by trained embryologists. More recently, machine learning has been incorporated into embryo selection "packages". Here, we report a machine-learning assisted embryo health assessment tool utilizing a quantitative phase imaging technique called artificial confocal microscopy (ACM). We present a label-free nucleus detection method with novel quantitative embryo health biomarkers. Two viability assessment models are presented for grading embryos into two classes: healthy/intermediate (H/I) or sick (S) class. The models achieve a weighted F1 score of 1.0 and 0.99 respectively on the in-distribution test set of 72 fixed embryos and a weighted F1 score of 0.9 and 0.95 respectively on the out-of-distribution test dataset of 19 time-instances from 8 live embryos.

Artificial confocal microscopy for deep label-free imaging

Widefield microscopy of optically thick specimens typically features reduced contrast due to "spatial crosstalk", in which the signal at each point in the field of view is the result of a superposition from neighbouring points that are simultaneously illuminated. In 1955, Marvin Minsky proposed confocal microscopy as a solution to this problem. Today, laser scanning confocal fluorescence microscopy is broadly used due to its high depth resolution and sensitivity, but comes at the price of photobleaching, chemical, and photo-toxicity. Here, we present artificial confocal microscopy (ACM) to achieve confocal-level depth sectioning, sensitivity, and chemical specificity, on unlabeled specimens, nondestructively. We equipped a commercial laser scanning confocal instrument with a quantitative phase imaging module, which provides optical path-length maps of the specimen in the same field of view as the fluorescence channel. Using pairs of phase and fluorescence images, we trained a convolution neural network to translate the former into the latter. The training to infer a new tag is very practical as the input and ground truth data are intrinsically registered, and the data acquisition is automated. The ACM images present significantly stronger depth sectioning than the input (phase) images, enabling us to recover confocal-like tomographic volumes of microspheres, hippocampal neurons in culture, and 3D liver cancer spheroids. By training on nucleus-specific tags, ACM allows for segmenting individual nuclei within dense spheroids for both cell counting and volume measurements. In summary, ACM can provide quantitative, dynamic data, nondestructively from thick samples, while chemical specificity is recovered computationally.

WHEN KIDNEY BIOPSY GUIDES THE DIAGNOSIS OF HANTAVIRUS INFECTION

Hantavirus infection is increasingly widespread in the last years, but diagnosing this infection could be difficult sometimes, given the nonspecific symptoms and the lack of experience by physicians into this direction. Hantavirus infection is a viral infection causing two major syndromes: Hemorrhagic fever with renal syndrome (HFRS) and Cardiopulmonary syndrome (CPS). We present the case of a young man, admitted to the hospital with fever, nausea, vomiting, diarrhea, abdominal pain and oligoanuria, in whom the diagnosis of hantavirus infection was established following the kidney biopsy, in addition to the ELISA assay. According to the literature, all histological findings concluded to hantavirus nephritis. Interstitial hemorrhage in the medulla is a main characteristic of hantavirus infection. Despite the fact that the identified serotype (Hantaan virus) is responsible for a severe form of this disease, the patient's evolution was favorable, with recovery of renal function after 1 month.

Interferometric imaging with ring-shaped apertures

We use a scattering model for image formation to demonstrate how to simulate images formed by ring-shaped illumination. The description assumes weakly scattering samples, such as phase objects of broad interest in live cell imaging, which also makes no angular approximations and covers forward and backward scattering, single-angle and angle-averaged illumination, as well as monochromatic and broadband light. The numerical experiments reveal the image formation behavior that is consistent with recent experiments reported in the literature, which shows this model can be applied to different imaging systems that are based on ring-shaped illumination with good performance, by considering the incident as a plane wave incident originating at the ring aperture.

White blood cell detection, classification and analysis using phase imaging with computational specificity (PICS)

Treatment of blood smears with Wright's stain is one of the most helpful tools in detecting white blood cell abnormalities. However, to diagnose leukocyte disorders, a clinical pathologist must perform a tedious, manual process of locating and identifying individual cells. Furthermore, the staining procedure requires considerable preparation time and clinical infrastructure, which is incompatible with point-of-care diagnosis. Thus, rapid and automated evaluations of unlabeled blood smears are highly desirable. In this study, we used color spatial light interference microcopy (cSLIM), a highly sensitive quantitative phase imaging (QPI) technique, coupled with deep learning tools, to localize, classify and segment white blood cells (WBCs) in blood smears. The concept of combining QPI label-free data with AI for the purpose of extracting cellular specificity has recently been introduced in the context of fluorescence imaging as phase imaging with computational specificity (PICS). We employed AI models to first translate SLIM images into brightfield micrographs, then ran parallel tasks of locating and labelling cells using EfficientNet, which is an object detection model. Next, WBC binary masks were created using U-net, a convolutional neural network that performs precise segmentation. After training on digitally stained brightfield images of blood smears with WBCs, we achieved a mean average precision of 75% for localizing and classifying neutrophils, eosinophils, lymphocytes, and monocytes, and an average pixel-wise majority-voting F1 score of 80% for determining the cell class from semantic segmentation maps. Therefore, PICS renders and analyzes synthetically stained blood smears rapidly, at a reduced cost of sample preparation, providing quantitative clinical information.

GANscan: continuous scanning microscopy using deep learning deblurring

Most whole slide imaging (WSI) systems today rely on the "stop-and-stare" approach, where, at each field of view, the scanning stage is brought to a complete stop before the camera snaps a picture. This procedure ensures that each image is free of motion blur, which comes at the expense of long acquisition times. In order to speed up the acquisition process, especially for large scanning areas, such as pathology slides, we developed an acquisition method in which the data is acquired continuously while the stage is moving at high speeds. Using generative adversarial networks (GANs), we demonstrate this ultra-fast imaging approach, referred to as GANscan, which restores sharp images from motion blurred videos. GANscan allows us to complete image acquisitions at 30x the throughput of stop-and-stare systems. This method is implemented on a Zeiss Axio Observer Z1 microscope, requires no specialized hardware, and accomplishes successful reconstructions at stage speeds of up to 5000 μm/s. We validate the proposed method by imaging H&E stained tissue sections. Our method not only retrieves crisp images from fast, continuous scans, but also adjusts for defocusing that occurs during scanning within +/- 5 μm. Using a consumer GPU, the inference runs at <20 ms/ image.

Circadian Volume Changes in Hippocampal Glia Studied by Label-Free Interferometric Imaging

Complex brain functions, including learning and memory, arise in part from the modulatory role of astrocytes on neuronal circuits. Functionally, the dentate gyrus (DG) exhibits differences in the acquisition of long-term potentiation (LTP) between day and night. We hypothesize that the dynamic nature of astrocyte morphology plays an important role in the functional circuitry of hippocampal learning and memory, specifically in the DG. Standard microscopy techniques, such as differential interference contrast (DIC), present insufficient contrast for detecting changes in astrocyte structure and function and are unable to inform on the intrinsic structure of the sample in a quantitative manner. Recently, gradient light interference microscopy (GLIM) has been developed to upgrade a DIC microscope with quantitative capabilities such as single-cell dry mass and volume characterization. Here, we present a methodology for combining GLIM and electrophysiology to quantify the astrocyte morphological behavior over the day-night cycle. Colocalized measurements of GLIM and fluorescence allowed us to quantify the dry masses and volumes of hundreds of astrocytes. Our results indicate that, on average, there is a 25% cell volume reduction during the nocturnal cycle. Remarkably, this cell volume change takes place at constant dry mass, which suggests that the volume regulation occurs primarily through aqueous medium exchange with the environment.

Cell Cycle Stage Classification Using Phase Imaging with Computational Specificity

Traditional methods for cell cycle stage classification rely heavily on fluorescence microscopy to monitor nuclear dynamics. These methods inevitably face the typical phototoxicity and photobleaching limitations of fluorescence imaging. Here, we present a cell cycle detection workflow using the principle of phase imaging with computational specificity (PICS). The proposed method uses neural networks to extract cell cycle-dependent features from quantitative phase imaging (QPI) measurements directly. Our results indicate that this approach attains very good accuracy in classifying live cells into G1, S, and G2/M stages, respectively. We also demonstrate that the proposed method can be applied to study single-cell dynamics within the cell cycle as well as cell population distribution across different stages of the cell cycle. We envision that the proposed method can become a nondestructive tool to analyze cell cycle progression in fields ranging from cell biology to biopharma applications.

SWOT-AHP hybrid method for ranking the relaunching strategies of an industrial company

Our paper aims to assess the current situation of an industrial company, which is in a difficult situation caused by two factors: the Covid-19 pandemic and the situation created by reducing the production of cars with diesel engines in favour of electric/hybrid, and defining and prioritizing the most efficient strategies for relaunching the company's activity and for reorienting it towards other components instead of those in current production. The company's current situation was determined by identifying the environmental factors (SWOT) and using the TOWS matrix, and the most appropriate strategies were defined. The research is based on a case study using information collected from the company managers and existing annual reports. To quantitatively determine the importance of each factor in the SWOT matrix, classify the defined strategies, and verify the views of the study participants, the AHP (Analytical Hierarchy Process) method has been used. The calculations were performed with Excel software. The results obtained showed that the top three strategies for developing the company are: investment programs in high-performance equipment, increasing the degree of the processes integration, and attracting new strategic suppliers to develop essential projects.

Clinicopathological features, imaging characteristics and surgical management in a novel tumour entity - aggressive meningiomas

Meningiomas are common neoplasms of the central nervous system, comprising between 24 and 30% of primary intracranial tumours, most commonly affecting females in their middle age or later adult life [1] [2]. Meningiomas are classified as benign, atypical or anaplastic meningiomas depending mostly on histopathological criteria known to be associated with worse prognosis in terms of tumour progression, recurrence risk after surgery and overall survival. The 2016 edition of the World Health Organization (WHO) classification of Central Nervous System (CNS) tumours recognizes brain invasion as an independent criterion for diagnosing an atypical grade II meningioma [3]. Meningioma grading based on the WHO classification of CNS tumours thoroughly impacts therapeutic management and tumour prognosis. Aggressive meningiomas, a different phenotype of tumours, characterized by rapid growth and involvement of adjacent brain tissue, blood vessels and bone, was not previously listed as an independent entity in the WHO classification of meningothelial-cell tumours.  Regarding the increasing importance of tumour grading in meningioma treatment strategies, the authors here provide an overview of clinicopathological and radiographic features, surgical management and long-term prognosis of this novel meningothelial tumour entity, the aggressive meningioma. In particular, we aimed to describe pre-, intra- and postoperative methods for recognizing aggressive meningiomas and explore the implications of this diagnosis on both surgical strategies and adjuvant therapy.

Imaging characteristics, histopathological features and surgical considerations regarding aggressive meningiomas

Meningiomas are tumours of the meninges that arise primarily from arachnoidal cap cells, but they can also occur rarely as primary tumours in other localizations, such as within the ventricles. They stand for 24-30% of primary intracranial tumours and affect mostly women in their middle age or later adult life [1, 2]. Meningiomas can be classified, according to the World Health Organization (WHO) classification of Central Nervous System (CNS) tumours, as benign (grade I, most frequently encountered type), atypical (grade II) or anaplastic (grade III), based mostly on histopathological criteria known to be associated with tumour progression, recurrence risk and survival. Since meningioma grading based on the WHO classification is the most important factor determining therapeutic management and tumour prognosis, there has been an increasing interest in adding new criteria for better characterization of these tumours. Thus, the 2016 edition of the WHO classification recognized brain invasion as an independent criterion for atypical (grade II) meningioma diagnosis [3]. However, meningiomas that display aggressive features such as rapid growth and higher recurrence rate, can also involve blood vessels and bone. Hence, the authors aim to describe a different entity, aggressive meningiomas, not previously listed as a tumour phenotype in the WHO classification of meningothelial-cell tumours, with regard to pre-, intra- and postoperative methods for diagnosis and explore the implications on surgical strategies and adjuvant therapy.

Synthetic aperture interference light (SAIL) microscopy for high-throughput label-free imaging

Quantitative phase imaging (QPI) is a valuable label-free modality that has gained significant interest due to its wide potentials, from basic biology to clinical applications. Most existing QPI systems measure microscopic objects via interferometry or nonlinear iterative phase reconstructions from intensity measurements. However, all imaging systems compromise spatial resolution for the field of view and vice versa, i.e., suffer from a limited space bandwidth product. Current solutions to this problem involve computational phase retrieval algorithms, which are time-consuming and often suffer from convergence problems. In this article, we presented synthetic aperture interference light (SAIL) microscopy as a solution for high-resolution, wide field of view QPI. The proposed approach employs low-coherence interferometry to directly measure the optical phase delay under different illumination angles and produces large space-bandwidth product label-free imaging. We validate the performance of SAIL on standard samples and illustrate the biomedical applications on various specimens: pathology slides, entire insects, and dynamic live cells in large cultures. The reconstructed images have a synthetic numeric aperture of 0.45 and a field of view of 2.6 × 2.6 mm2. Due to its direct measurement of the phase information, SAIL microscopy does not require long computational time, eliminates data redundancy, and always converges.

Diffraction as scattering under the Born approximation

Light diffraction at an aperture is a basic problem that has generated a tremendous amount of interest in optics. Some of the most significant diffraction results are the Fresnel-Kirchhoff and Rayleigh-Sommerfeld formulas. These theories are based on solving the wave equation using Green's theorem and result in slightly different expressions depending on the particular boundary conditions employed. In this paper, we show that the diffraction by a thin screen, which includes apertures, gratings, transparencies etc, can be treated more generally as a particular case of scattering. Furthermore, applying the first order Born approximation to 2D objects, we obtain a general diffraction formula, without angular approximations. Finally, our result, which contains no obliquity factor, is consistent with the 3D theory of scattering. We discuss several common approximations and place our results in the context of existing theories.

Label-free SARS-CoV-2 detection and classification using phase imaging with computational specificity

Efforts to mitigate the COVID-19 crisis revealed that fast, accurate, and scalable testing is crucial for curbing the current impact and that of future pandemics. We propose an optical method for directly imaging unlabeled viral particles and using deep learning for detection and classification. An ultrasensitive interferometric method was used to image four virus types with nanoscale optical path-length sensitivity. Pairing these data with fluorescence images for ground truth, we trained semantic segmentation models based on U-Net, a particular type of convolutional neural network. The trained network was applied to classify the viruses from the interferometric images only, containing simultaneously SARS-CoV-2, H1N1 (influenza-A virus), HAdV (adenovirus), and ZIKV (Zika virus). Remarkably, due to the nanoscale sensitivity in the input data, the neural network was able to identify SARS-CoV-2 vs. the other viruses with 96% accuracy. The inference time for each image is 60 ms, on a common graphic-processing unit. This approach of directly imaging unlabeled viral particles may provide an extremely fast test, of less than a minute per patient. As the imaging instrument operates on regular glass slides, we envision this method as potentially testing on patient breath condensates. The necessary high throughput can be achieved by translating concepts from digital pathology, where a microscope can scan hundreds of slides automatically.

Label-free screening of brain tissue myelin content using phase imaging with computational specificity (PICS)

Inadequate myelination in the central nervous system is associated with neurodevelopmental complications. Thus, quantitative, high spatial resolution measurements of myelin levels are highly desirable. We used spatial light interference microcopy (SLIM), a highly sensitive quantitative phase imaging (QPI) technique, to correlate the dry mass content of myelin in piglet brain tissue with dietary changes and gestational size. We combined SLIM micrographs with an artificial intelligence (AI) classifying model that allows us to discern subtle disparities in myelin distributions with high accuracy. This concept of combining QPI label-free data with AI for the purpose of extracting molecular specificity has recently been introduced by our laboratory as phase imaging with computational specificity. Training on 8000 SLIM images of piglet brain tissue with the 71-layer transfer learning model Xception, we created a two-parameter classification to differentiate gestational size and diet type with an accuracy of 82% and 80%, respectively. To our knowledge, this type of evaluation is impossible to perform by an expert pathologist or other techniques.

Spatial light interference microscopy: principle and applications to biomedicine

In this paper, we review spatial light interference microscopy (SLIM), a common-path, phase-shifting interferometer, built onto a phase-contrast microscope, with white-light illumination. As one of the most sensitive quantitative phase imaging (QPI) methods, SLIM allows for speckle-free phase reconstruction with sub-nanometer path-length stability. We first review image formation in QPI, scattering, and full-field methods. Then, we outline SLIM imaging from theory and instrumentation to diffraction tomography. Zernike's phase-contrast microscopy, phase retrieval in SLIM, and halo removal algorithms are discussed. Next, we discuss the requirements for operation, with a focus on software developed in-house for SLIM that enables high-throughput acquisition, whole slide scanning, mosaic tile registration, and imaging with a color camera. We introduce two methods for solving the inverse problem using SLIM, white-light tomography, and Wolf phase tomography. Lastly, we review the applications of SLIM in basic science and clinical studies. SLIM can study cell dynamics, cell growth and proliferation, cell migration, mass transport, etc. In clinical settings, SLIM can assist with cancer studies, reproductive technology, blood testing, etc. Finally, we review an emerging trend, where SLIM imaging in conjunction with artificial intelligence brings computational specificity and, in turn, offers new solutions to outstanding challenges in cell biology and pathology.

Multiscale Assay of Unlabeled Neurite Dynamics Using Phase Imaging with Computational Specificity

Primary neuronal cultures have been widely used to study neuronal morphology, neurophysiology, neurodegenerative processes, and molecular mechanism of synaptic plasticity underlying learning and memory. However, the unique behavioral properties of neurons make them challenging to study, with phenotypic differences expressed as subtle changes in neuronal arborization rather than easy-to-assay features such as cell count. The need to analyze morphology, growth, and intracellular transport has motivated the development of increasingly sophisticated microscopes and image analysis techniques. Due to its high-contrast, high-specificity output, many assays rely on confocal fluorescence microscopy, genetic methods, or antibody staining techniques. These approaches often limit the ability to measure quantitatively dynamic activity such as intracellular transport and growth. In this work, we describe a method for label-free live-cell cell imaging with antibody staining specificity by estimating the associated fluorescence signals via quantitative phase imaging and deep convolutional neural networks. This computationally inferred fluorescence image is then used to generate a semantic segmentation map, annotating subcellular compartments of live unlabeled neural cultures. These synthetic fluorescence maps were further applied to study the time-lapse development of hippocampal neurons, highlighting the relationships between the cellular dry mass production and the dynamic transport activity within the nucleus and neurites. Our implementation provides a high-throughput strategy to analyze neural network arborization dynamically, with high specificity and without the typical phototoxicity and photobleaching limitations associated with fluorescent markers.

Computational interference microscopy enabled by deep learning

Quantitative phase imaging (QPI) has been widely applied in characterizing cells and tissues. Spatial light interference microscopy (SLIM) is a highly sensitive QPI method due to its partially coherent illumination and common path interferometry geometry. However, SLIM's acquisition rate is limited because of the four-frame phase-shifting scheme. On the other hand, off-axis methods such as diffraction phase microscopy (DPM) allow for single-shot QPI. However, the laser-based DPM system is plagued by spatial noise due to speckles and multiple reflections. In a parallel development, deep learning was proven valuable in the field of bioimaging, especially due to its ability to translate one form of contrast into another. Here, we propose using deep learning to produce synthetic, SLIM-quality, and high-sensitivity phase maps from DPM using single-shot images as the input. We used an inverted microscope with its two ports connected to the DPM and SLIM modules such that we have access to the two types of images on the same field of view. We constructed a deep learning model based on U-net and trained on over 1000 pairs of DPM and SLIM images. The model learned to remove the speckles in laser DPM and overcame the background phase noise in both the test set and new data. The average peak signal-to-noise ratio, Pearson correlation coefficient, and structural similarity index measure were 29.97, 0.79, and 0.82 for the test dataset. Furthermore, we implemented the neural network inference into the live acquisition software, which now allows a DPM user to observe in real-time an extremely low-noise phase image. We demonstrated this principle of computational interference microscopy imaging using blood smears, as they contain both erythrocytes and leukocytes, under static and dynamic conditions.

Osteoporotic Hip Fracture Incidence Rates in the Main Urban Area of Romania

CONTEXT

Estimation of osteoporotic hip fracture incidence and Romanian FRAX model were based on nationally reported hospital ICD 10 coding admissions of all hip fractures (without a validation process).

OBJECTIVE

We aimed to calculate, based on individual hospital charts analysis, the incidence of osteoporotic hip fracture in the main urban area of Romania and compare it with data reported to the National Institute of Public Health (NIPH).

DESIGN

We retrospectively analyzed the charts of all patients (>40 years old) admitted for hip fracture in a 12-month period in hospitals with an Orthopedic Department in Bucharest and surrounding Ilfov County (11.8% of Romania population).

SUBJECTS AND METHODS

All ICD 10 fracture and event/fall codes were validated against the charts. We calculated the age and sex adjusted incidence of osteoporotic hip fracture and used the national reported hip fracture data base for comparison.

RESULTS

There were 2203 hip fractures of which 1997 (90.65%) were fragility fractures. The crude incidence of low-energy hip fractures was 171/100,000 (225/100,000 in women, 103/100,000 in men). The incidence rose with age to a maximum rate of 1902/100,000 in women >85 years. The NIPH-reported incidence of hip fracture was 181/100,000 for the region of interest and 176/100,000 at the national level.

CONCLUSION

The incidence of osteoporotic hip fracture was lower than the incidence based on hip fractures reported codes in the national database, but the incidence of fragility fractures calculated by our group was higher than the incidence reported in previous national studies. Nationwide studies are warranted.

High-resolution impedance mapping using electrically activated quantitative phase imaging

Retrieving electrical impedance maps at the nanoscale rapidly via nondestructive inspection with a high signal-to-noise ratio is an unmet need, likely to impact various applications from biomedicine to energy conversion. In this study, we develop a multimodal functional imaging instrument that is characterized by the dual capability of impedance mapping and phase quantitation, high spatial resolution, and low temporal noise. To achieve this, we advance a quantitative phase imaging system, referred to as epi-magnified image spatial spectrum microscopy combined with electrical actuation, to provide complementary maps of the optical path and electrical impedance. We demonstrate our system with high-resolution maps of optical path differences and electrical impedance variations that can distinguish nanosized, semi-transparent, structured coatings involving two materials with relatively similar electrical properties. We map heterogeneous interfaces corresponding to an indium tin oxide layer exposed by holes with diameters as small as ~550 nm in a titanium (dioxide) over-layer deposited on a glass support. We show that electrical modulation during the phase imaging of a macro-electrode is decisive for retrieving electrical impedance distributions with submicron spatial resolution and beyond the limitations of electrode-based technologies (surface or scanning technologies). The findings, which are substantiated by a theoretical model that fits the experimental data very well enable achieving electro-optical maps with high spatial and temporal resolutions. The virtues and limitations of the novel optoelectrochemical method that provides grounds for a wider range of electrically modulated optical methods for measuring the electric field locally are critically discussed.

Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments

Due to its specificity, fluorescence microscopy has become a quintessential imaging tool in cell biology. However, photobleaching, phototoxicity, and related artifacts continue to limit fluorescence microscopy's utility. Recently, it has been shown that artificial intelligence (AI) can transform one form of contrast into another. We present phase imaging with computational specificity (PICS), a combination of quantitative phase imaging and AI, which provides information about unlabeled live cells with high specificity. Our imaging system allows for automatic training, while inference is built into the acquisition software and runs in real-time. Applying the computed fluorescence maps back to the quantitative phase imaging (QPI) data, we measured the growth of both nuclei and cytoplasm independently, over many days, without loss of viability. Using a QPI method that suppresses multiple scattering, we measured the dry mass content of individual cell nuclei within spheroids. In its current implementation, PICS offers a versatile quantitative technique for continuous simultaneous monitoring of individual cellular components in biological applications where long-term label-free imaging is desirable.

Quantifying myelin content in brain tissue using color Spatial Light Interference Microscopy (cSLIM)

Deficient myelination of the brain is associated with neurodevelopmental delays, particularly in high-risk infants, such as those born small in relation to their gestational age (SGA). New methods are needed to further study this condition. Here, we employ Color Spatial Light Interference Microscopy (cSLIM), which uses a brightfield objective and RGB camera to generate pathlength-maps with nanoscale sensitivity in conjunction with a regular brightfield image. Using tissue sections stained with Luxol Fast Blue, the myelin structures were segmented from a brightfield image. Using a binary mask, those portions were quantitatively analyzed in the corresponding phase maps. We first used the CLARITY method to remove tissue lipids and validate the sensitivity of cSLIM to lipid content. We then applied cSLIM to brain histology slices. These specimens are from a previous MRI study, which demonstrated that appropriate for gestational age (AGA) piglets have increased internal capsule myelination (ICM) compared to small for gestational age (SGA) piglets and that a hydrolyzed fat diet improved ICM in both. The identity of samples was blinded until after statistical analyses.

Real-time Jones phase microscopy for studying transparent and birefringent specimens

Tissue birefringence is an intrinsic marker of potential value for cancer diagnosis. Traditionally, birefringence properties have been studied by using intensity-based formalisms, through the Mueller matrix algebra. On the other hand, the Jones matrix description allows for a direct assessment of the sample's anisotropic response. However, because Jones algebra is based on complex fields, requiring measurements of both phase and amplitude, it is less commonly used. Here we propose a real-time imaging method for measuring Jones matrices by quantitative phase imaging. We combine a broadband phase imaging system with a polarization-sensitive detector to obtain Jones matrices at each point in a megapixel scale image, with near video rate capture speeds. To validate the utility of our approach, we measured standard targets, partially birefringent samples, dynamic specimens, and thinly sliced histopathological tissue.

Matrix Softness-Mediated 3D Zebrafish Hepatocyte Modulates Response to Endocrine Disrupting Chemicals

Endocrine disrupting chemicals (EDC) include synthetic compounds that mimic the structure or function of natural hormones. While most studies utilize live embryos or primary cells from adult fish, these cells rapidly lose functionality when cultured on plastic or glass substrates coated with extracellular matrix proteins. This study hypothesizes that the softness of a matrix with adhered fish cells can regulate the intercellular organization and physiological function of engineered hepatoids during EDC exposure. We scrutinized this hypothesis by culturing zebrafish hepatocytes (ZF-L) on collagen-based hydrogels with controlled elastic moduli by examining morphology, urea production, and intracellular oxidative stress of hepatoids exposed to 17β-estradiol. Interestingly, the softer gel drove cells to form a cell sheet with a canaliculi-like structure compared to its stiffer gel counterpart. The hepatoids cultured on the softer gel exhibited more active urea production upon exposure to 17β-estradiol and displayed faster recovery of intracellular reactive oxygen species level confirmed by gradient light interference microscopy (GLIM), a live-cell imaging technique. These results are broadly useful to improve screening and understanding of potential EDC impacts on aquatic organisms and human health.

Are body mass index and age independent risk factors for new-onset atrial fibrillation after cardiac surgery regardless of left atrial size and left ventricular ejection fraction value?

Background

Obesity, advanced age and left atrium dimensions have been linked to atrial fibrillation (AF).

Purpose

This study aimed at evidencing if the above mentioned risk factors have a role among the others in conditioning the onset of post operative AF (PoAF) in patients undergoing cardiac surgery without previous AF.

Methods

This study evaluated 249 consecutive patients undergoing coronary artery by-pass surgery either isolated or in combination with aortic or mitral valve repair/replacement, or isolated valve repair/replacement. Prior to surgery, in all patients the following data were collected: age (yrs), body mass index (BMI, kg/m2), left atrium (LA) diameter (cm), LA area (cm2), left ventricular ejection fraction (LVEF, %), the presence/absence of arterial hypertension (AH) and diabetes, creatinine (mg/dL). To detect the presence of PoAF, cardiac rhythm was continouosly recorded during the first seven postoperative days.

Results

PoAF occurred in 127 patients (51%). We compared patients with and without PoAF. Mean values (±1 SD) of continuous variables and the frequency of dicothomic ones are reported in the table. No difference was observed for sex, LA diameter, LA area, LVEF and diabetes. Instead, patients with PoAF had higher values of age, BMI, creatinine and a greater prevalence of AH. According to multivariable binary logistic analysis the independent predictors of PoAF were: age (OR = 1.05, CI 95%: 1.026–1.074, p=0.018) and BMI (OR = 1.09, CI 95%: 1.015–1.171, p=0.0001).

Conclusions

Results suggest that advanced age and a higher value of BMI could be strong risk factors for PoAF in patients who undergo cardiac surgery without previous AF. This considering that in the present population the values of LA diameter, LA area and LVEF showed no statistically significant difference between patients with and without PoAF.

Funding Acknowledgement

Type of funding source: None

Electrothermal soft manipulator enabling safe transport and handling of thin cell/tissue sheets and bioelectronic devices

"Living" cell sheets or bioelectronic chips have great potentials to improve the quality of diagnostics and therapies. However, handling these thin and delicate materials remains a grand challenge because the external force applied for gripping and releasing can easily deform or damage the materials. This study presents a soft manipulator that can manipulate and transport cell/tissue sheets and ultrathin wearable biosensing devices seamlessly by recapitulating how a cephalopod's suction cup works. The soft manipulator consists of an ultrafast thermo-responsive, microchanneled hydrogel layer with tissue-like softness and an electric heater layer. The electric current to the manipulator drives microchannels of the gel to shrink/expand and results in a pressure change through the microchannels. The manipulator can lift/detach an object within 10 s and can be used repeatedly over 50 times. This soft manipulator would be highly useful for safe and reliable assembly and implantation of therapeutic cell/tissue sheets and biosensing devices.

Network science characteristics of brain-derived neuronal cultures deciphered from quantitative phase imaging data

Understanding the mechanisms by which neurons create or suppress connections to enable communication in brain-derived neuronal cultures can inform how learning, cognition and creative behavior emerge. While prior studies have shown that neuronal cultures possess self-organizing criticality properties, we further demonstrate that in vitro brain-derived neuronal cultures exhibit a self-optimization phenomenon. More precisely, we analyze the multiscale neural growth data obtained from label-free quantitative microscopic imaging experiments and reconstruct the in vitro neuronal culture networks (microscale) and neuronal culture cluster networks (mesoscale). We investigate the structure and evolution of neuronal culture networks and neuronal culture cluster networks by estimating the importance of each network node and their information flow. By analyzing the degree-, closeness-, and betweenness-centrality, the node-to-node degree distribution (informing on neuronal interconnection phenomena), the clustering coefficient/transitivity (assessing the “small-world” properties), and the multifractal spectrum, we demonstrate that murine neurons exhibit self-optimizing behavior over time with topological characteristics distinct from existing complex network models. The time-evolving interconnection among murine neurons optimizes the network information flow, network robustness, and self-organization degree. These findings have complex implications for modeling neuronal cultures and potentially on how to design biological inspired artificial intelligence.

Harmonic optical tomography of nonlinear structures

Second-harmonic generation microscopy is a valuable label-free modality for imaging non-centrosymmetric structures and has important biomedical applications from live-cell imaging to cancer diagnosis. Conventional second-harmonic generation microscopy measures intensity signals that originate from tightly focused laser beams, preventing researchers from solving the scattering inverse problem for second-order nonlinear materials. Here, we present harmonic optical tomography (HOT) as a novel modality for imaging microscopic, nonlinear and inhomogeneous objects. The HOT principle of operation relies on inter-ferometrically measuring the complex harmonic field and using a scattering inverse model to reconstruct the three-dimensional distribution of harmonophores. HOT enables strong axial sectioning via the momentum conservation of spatially and temporally broadband fields. We illustrate the HOT operation with experiments and reconstructions on a beta-barium borate crystal and various biological specimens. Although our results involve second-order nonlinear materials, we show that this approach applies to any coherent nonlinear process.

Wolf phase tomography (WPT) of transparent structures using partially coherent illumination

In 1969, Emil Wolf proposed diffraction tomography using coherent holographic imaging to extract 3D information from transparent, inhomogeneous objects. In the same era, the Wolf equations were first used to describe the propagation correlations associated with partially coherent fields. Combining these two concepts, we present Wolf phase tomography (WPT), which is a method for performing diffraction tomography using partially coherent fields. WPT reconstruction works directly in the space-time domain, without the need for Fourier transformation, and decouples the refractive index (RI) distribution from the thickness of the sample. We demonstrate the WPT principle using the data acquired by a quantitative-phase-imaging method that upgrades an existing phase-contrast microscope by introducing controlled phase shifts between the incident and scattered fields. The illumination field in WPT is partially spatially coherent (emerging from a ring-shaped pupil function) and of low temporal coherence (white light), and as such, it is well suited for the Wolf equations. From three intensity measurements corresponding to different phase-contrast frames, the 3D RI distribution is obtained immediately by computing the Laplacian and second time derivative of the measured complex correlation function. We validate WPT with measurements of standard samples (microbeads), spermatozoa, and live neural cultures. The high throughput and simplicity of this method enables the study of 3D, dynamic events in living cells across the entire multiwell plate, with an RI sensitivity on the order of 10^-5.

Wolf phase tomography (WPT) of transparent structures using partially coherent illumination

In 1969, Emil Wolf proposed diffraction tomography using coherent holographic imaging to extract 3D information from transparent, inhomogeneous objects. In the same era, the Wolf equations were first used to describe the propagation correlations associated with partially coherent fields. Combining these two concepts, we present Wolf phase tomography (WPT), which is a method for performing diffraction tomography using partially coherent fields. WPT reconstruction works directly in the space-time domain, without the need for Fourier transformation, and decouples the refractive index (RI) distribution from the thickness of the sample. We demonstrate the WPT principle using the data acquired by a quantitative-phase-imaging method that upgrades an existing phase-contrast microscope by introducing controlled phase shifts between the incident and scattered fields. The illumination field in WPT is partially spatially coherent (emerging from a ring-shaped pupil function) and of low temporal coherence (white light), and as such, it is well suited for the Wolf equations. From three intensity measurements corresponding to different phase-contrast frames, the 3D RI distribution is obtained immediately by computing the Laplacian and second time derivative of the measured complex correlation function. We validate WPT with measurements of standard samples (microbeads), spermatozoa, and live neural cultures. The high throughput and simplicity of this method enables the study of 3D, dynamic events in living cells across the entire multiwell plate, with an RI sensitivity on the order of 10-5.

High-throughput sperm assay using label-free microscopy: morphometric comparison between different sperm structures of boar and stallion spermatozoa

The capacity for microscopic evaluation of sperm is useful for assisted reproductive technologies (ART), because this can allow for specific selection of sperm cells for in vitro fertilization (IVF). The objective of this study was to analyze the same sperm samples using two high-resolution methods: spatial light interference microscopy (SLIM) and atomic force microscopy (AFM) to determine if with one method there was more timely and different information obtained than the other. To address this objective, there was evaluation of sperm populations from boars and stallions. To the best of our knowledge, this is the first reported comparison when using AFM and high-sensitivity interferometric microscopy (such as SLIM) to evaluate spermatozoa. Results indicate that with the use of SLIM microscopy there is similar nanoscale sensitivity as with use of AFM while there is approximately 1,000 times greater throughput with use of SLIM. With SLIM, there is also allowace for the measurement of the dry mass (non-aqueous content) of spermatozoa, which may be a new label-free marker for sperm viability. In the second part of this study, there was analysis of two sperm populations. There were interesting correlations between the different compartments of the sperm and the dry mass in both boars and stallions. Furthermore, there was a correlation between the dry mass of the sperm head and the length and width of the acrosome in both boars and stallions. This correlation is positive in boars while it is negative in stallions.

Label-free colorectal cancer screening using deep learning and spatial light interference microscopy (SLIM)

Current pathology workflow involves staining of thin tissue slices, which otherwise would be transparent, followed by manual investigation under the microscope by a trained pathologist. While the hematoxylin and eosin (H&E) stain is well-established and a cost-effective method for visualizing histology slides, its color variability across preparations and subjectivity across clinicians remain unaddressed challenges. To mitigate these challenges, recently we have demonstrated that spatial light interference microscopy (SLIM) can provide a path to intrinsic, objective markers, that are independent of preparation and human bias. Additionally, the sensitivity of SLIM to collagen fibers yields information relevant to patient outcome, which is not available in H&E. Here, we show that deep learning and SLIM can form a powerful combination for screening applications: training on 1,660 SLIM images of colon glands and validating on 144 glands, we obtained a benign vs. cancer classification accuracy of 99%. We envision that the SLIM whole slide scanner presented here paired with artificial intelligence algorithms may prove valuable as a pre-screening method, economizing the clinician's time and effort.

Harmonically decoupled gradient light interference microscopy (HD-GLIM)

Differential phase sensitive methods, such as Nomarski microscopy, play an important role in quantitative phase imaging due to their compatibility with partially coherent illumination and excellent optical sectioning ability. In this Letter, we propose a new system, to the best of our knowledge, to retrieve differential phase information from transparent samples. It is based on a 4f optical system with an amplitude-type spatial light modulator (SLM), which removes the need for traditional differential interference contrast (DIC) optics and specialized phase-only SLMs. We demonstrate the principle of harmonically decoupled gradient light interference microscopy using standard samples, as well as static and dynamic biospecimens.

Quantitative phase imaging of stromal prognostic markers in pancreatic ductal adenocarcinoma

New quantitative prognostic markers are needed for improved pancreatic ductal adenocarcinoma (PDAC) prognosis. Second harmonic generation microscopy has been used to show that collagen fiber alignment in PDAC is a negative prognostic factor. In this work, a series of PDAC and normal adjacent tissue (NAT) biopsies were imaged with spatial light interference microscopy (SLIM). Quantitative analysis performed on the biopsy SLIM images show that PDAC fiber structures have lower alignment per unit length, narrower width, and are longer than NAT controls. Importantly, fibrillar collagen in PDAC shows an inverse relationship between survival data and fiber width and length (p < 0.05).

Engineering geometrical 3-dimensional untethered in vitro neural tissue mimic

Formation of tissue models in 3 dimensions is more effective in recapitulating structure and function compared to their 2-dimensional (2D) counterparts. Formation of 3D engineered tissue to control shape and size can have important implications in biomedical research and in engineering applications such as biological soft robotics. While neural spheroids routinely are created during differentiation processes, further geometric control of in vitro neural models has not been demonstrated. Here, we present an approach to form functional in vitro neural tissue mimic (NTM) of different shapes using stem cells, a fibrin matrix, and 3D printed molds. We used murine-derived embryonic stem cells for optimizing cell-seeding protocols, characterization of the resulting internal structure of the construct, and remodeling of the extracellular matrix, as well as validation of electrophysiological activity. Then, we used these findings to biofabricate these constructs using neurons derived from human embryonic stem cells. This method can provide a large degree of design flexibility for development of in vitro functional neural tissue models of varying forms for therapeutic biomedical research, drug discovery, and disease modeling, and engineering applications.

Effects of substrate patterning on cellular spheroid growth and dynamics measured by gradient light interference microscopy (GLIM)

The development of three-dimensional (3D) cellular architectures during development and pathological processes involves intricate migratory patterns that are modulated by genetics and the surrounding microenvironment. The substrate composition of cell cultures has been demonstrated to influence growth, proliferation and migration in 2D. Here, we study the growth and dynamics of mouse embryonic fibroblast cultures patterned in a tissue sheet which then exhibits 3D growth. Using gradient light interference microscopy (GLIM), a label-free quantitative phase imaging approach, we explored the influence of geometry on cell growth patterns and rotational dynamics. We apply, for the first time to our knowledge, dispersion-relation phase spectroscopy (DPS) in polar coordinates to generate the radial and rotational cell mass-transport. Our data show that cells cultured on engineered substrates undergo rotational transport in a radially independent manner and exhibit faster vertical growth than the control, unpatterned cells. The use of GLIM and polar DPS provides a novel quantitative approach to studying the effects of spatially patterned substrates on cell motility and growth.

Epi-illumination gradient light interference microscopy for imaging opaque structures

Multiple scattering and absorption limit the depth at which biological tissues can be imaged with light. In thick unlabeled specimens, multiple scattering randomizes the phase of the field and absorption attenuates light that travels long optical paths. These obstacles limit the performance of transmission imaging. To mitigate these challenges, we developed an epi-illumination gradient light interference microscope (epi-GLIM) as a label-free phase imaging modality applicable to bulk or opaque samples. Epi-GLIM enables studying turbid structures that are hundreds of microns thick and otherwise opaque to transmitted light. We demonstrate this approach with a variety of man-made and biological samples that are incompatible with imaging in a transmission geometry: semiconductors wafers, specimens on opaque and birefringent substrates, cells in microplates, and bulk tissues. We demonstrate that the epi-GLIM data can be used to solve the inverse scattering problem and reconstruct the tomography of single cells and model organisms.

Cell-to-cell influence on growth in large populations

Recent studies have revealed the importance of outlier cells in complex cellular systems. Quantifying heterogeneity in such systems may lead to a better understanding of organ engineering, microtumor growth, and disease models, as well as more precise drug design. We used the ability of quantitative phase imaging to perform long-term imaging of cell growth to estimate the "influence" of cellular clusters on their neighbors. We validated our approach by analyzing epithelial and fibroblast cultures imaged over the course of several days. Interestingly, we found that there is a significant number of cells characterized by a medium correlation between their growth rate and distance (modulus of the Pearson coefficient between 0.25-.5). Furthermore, we found a small percentage of cells exhibiting strong such correlations, which we label as "influencer" cellular clusters. Our approach might find important applications in studying dynamic phenomena, such as organogenesis and metastasis.

Optical inspection of nanoscale structures using a novel machine learning based synthetic image generation algorithm

In this paper, we present a novel interpretable machine learning technique that uses unique physical insights about noisy optical images and a few training samples to classify nanoscale defects in noisy optical images of a semiconductor wafer. Using this technique, we not only detected both parallel bridge defects and previously undetectable perpendicular bridge defects in a 9-nm node wafer using visible light microscopy [Proc. SPIE9424, 942416 (2015)], but we also accurately classified their shapes and estimated their sizes. Detection and classification of nanoscale defects in optical images is a challenging task. The quality of images is affected by diffraction and noise. Machine learning techniques can reduce noise and recognize patterns using a large training set. However, for detecting a rare "killer" defect, acquisition of a sufficient training set of high quality experimental images can be prohibitively expensive. In addition, there are technical challenges involved in using electromagnetic simulations and optimization of the machine learning algorithm. This paper proposes solutions to address each of the aforementioned challenges.

Graphene oxide substrates with N-cadherin stimulates neuronal growth and intracellular transport

Intracellular transport is fundamental for neuronal function and development and is dependent on the formation of stable actin filaments. N-cadherin, a cell-cell adhesion protein, is actively involved in neuronal growth and actin cytoskeleton organization. Various groups have explored how neurons behaved on substrates engineered to present N-cadherin; however, few efforts have been made to examine how these surfaces modulate neuronal intracellular transport. To address this issue, we assembled a substrate to which recombinant N-cadherin molecules are physiosorbed using graphene oxide (GO) or reduced graphene oxide (rGO). N-cadherin physisorbed on GO and rGO led to a substantial enhancement of intracellular mass transport along neurites relative to N-cadherin on glass, due to increased neuronal adhesion, neurite extensions, dendritic arborization and glial cell adhesion. This study will be broadly useful for recreating active neural tissues in vitro and for improving our understanding of the development, homeostasis, and physiology of neurons. STATEMENT OF SIGNIFICANCE: Intracellular transport of proteins and chemical cues is extremely important for culturing neurons in vitro, as they replenish materials within and facilitate communication between neurons. Various studies have shown that intracellular transport is dependent on the formation of stable actin filaments. However, the extent to which cadherin-mediated cell-cell adhesion modulates intracellular transport is not heavily explored. In this study, N-cadherin was adsorbed onto graphene oxide-based substrates to understand the role of cadherin at a molecular level and the intracellular transport within cells was examined using spatial light interference microscopy. As such, the results of this study will serve to better understand and harness the role of cell-cell adhesion in neuron development and regeneration.

Optical excitation and detection of neuronal activity

Optogenetics has emerged as an exciting tool for manipulating neural activity, which in turn, can modulate behavior in live organisms. However, detecting the response to the optical stimulation requires electrophysiology with physical contact or fluorescent imaging at target locations, which is often limited by photobleaching and phototoxicity. In this paper, we show that phase imaging can report the intracellular transport induced by optogenetic stimulation. We developed a multimodal instrument that can both stimulate cells with subcellular spatial resolution and detect optical pathlength (OPL) changes with nanometer scale sensitivity. We found that OPL fluctuations following stimulation are consistent with active organelle transport. Furthermore, the results indicate a broadening in the transport velocity distribution, which is significantly higher in stimulated cells compared to optogenetically inactive cells. It is likely that this label-free, contactless measurement of optogenetic response will provide an enabling approach to neuroscience.

Simultaneous cell traction and growth measurements using light

Characterizing the effects of force fields generated by cells on proliferation, migration and differentiation processes is challenging due to limited availability of nondestructive imaging modalities. Here, we integrate a new real-time traction stress imaging modality, Hilbert phase dynamometry (HPD), with spatial light interference microscopy (SLIM) for simultaneous monitoring of cell growth during differentiation processes. HPD uses holographic principles to extract displacement fields from chemically patterned fluorescent grid on deformable substrates. This is converted into forces by solving an elasticity inverse problem. Since HPD uses the epi-fluorescence channel of an inverted microscope, cellular behavior can be concurrently studied in transmission with SLIM. We studied the differentiation of mesenchymal stem cells (MSCs) and found that cells undergoing osteogenesis and adipogenesis exerted larger and more dynamic stresses than their precursors, with MSCs developing the smallest forces and growth rates. Thus, we develop a powerful means to study mechanotransduction during dynamic processes where the matrix provides context to guide cells toward a physiological or pathological outcome.

Quantitative phase imaging reveals matrix stiffness-dependent growth and migration of cancer cells

Cancer progression involves complex signals within the tumor microenvironment that orchestrate proliferation and invasive processes. The mechanical properties of the extracellular matrix (ECM) within this microenvironment has been demonstrated to influence growth and the migratory phenotype that precedes invasion. Here we present the integration of a label-free quantitative phase imaging technique, spatial light interference microscopy (SLIM)-with protein-conjugated hydrogel substrates-to explore how the stiffness of the ECM influences melanoma cells of varying metastatic potential. Melanoma cells of high metastatic potential demonstrate increased growth and velocity characteristics relative to cells of low metastatic potential. Cell velocity in the highly metastatic population shows a relative insensitivity to matrix stiffness suggesting adoption of migratory routines that are independent of mechanics to facilitate invasion. The use of SLIM and engineered substrates provides a new approach to characterize the invasive properties of live cells as a function of microenvironment parameters. This work provides fundamental insight into the relationship between growth, migration and metastatic potential, and provides a new tool for profiling cancer cells for clinical grading and development of patient-specific therapeutic regimens.