Label-Free Single-Cell Cancer Classification from the Spatial Distribution of Adhesion Contact Kinetics.

There is an increasing need for simple-to-use, noninvasive, and rapid tools to identify and separate various cell types or subtypes at the single-cell level with sufficient throughput. Often, the selection of cells based on their direct biological activity would be advantageous. These steps are critical in immune therapy, regenerative medicine, cancer diagnostics, and effective treatment. Today, live cell selection procedures incorporate some kind of biomolecular labeling or other invasive measures, which may impact cellular functionality or cause damage to the cells. In this study, we first introduce a highly accurate single-cell segmentation methodology by combining the high spatial resolution of a phase-contrast microscope with the adhesion kinetic recording capability of a resonant waveguide grating (RWG) biosensor. We present a classification workflow that incorporates the semiautomatic separation and classification of single cells from the measurement data captured by an RWG-based biosensor for adhesion kinetics data and a phase-contrast microscope for highly accurate spatial resolution. The methodology was tested with one healthy and six cancer cell types recorded with two functionalized coatings. The data set contains over 5000 single-cell samples for each surface and over 12,000 samples in total. We compare and evaluate the classification using these two types of surfaces (fibronectin and noncoated) with different segmentation strategies and measurement timespans applied to our classifiers. The overall classification performance reached nearly 95% with the best models showing that our proof-of-concept methodology could be adapted for real-life automatic diagnostics use cases. The label-free measurement technique has no impact on cellular functionality, directly measures cellular activity, and can be easily tuned to a specific application by varying the sensor coating. These features make it suitable for applications requiring further processing of selected cells.

Single-cell classification based on label-free high-resolution optical data of cell adhesion kinetics.

Selecting and isolating various cell types is a critical procedure in many applications, including immune therapy, regenerative medicine, and cancer research. Usually, these selection processes involve some labeling or another invasive step potentially affecting cellular functionality or damaging the cell. In the current proof of principle study, we first introduce an optical biosensor-based method capable of classification between healthy and numerous cancerous cell types in a label-free setup. We present high classification accuracy based on the monitored single-cell adhesion kinetic signals. We developed a high-throughput data processing pipeline to build a benchmark database of ~ 4500 single-cell adhesion measurements of a normal preosteoblast (MC3T3-E1) and various cancer (HeLa, LCLC-103H, MDA-MB-231, MCF-7) cell types. Several datasets were used with different cell-type selections to test the performance of deep learning-based classification models, reaching above 70-80% depending on the classification task. Beyond testing these models, we aimed to draw interpretable biological insights from their results; thus, we applied a deep neural network visualization method (grad-CAM) to reveal the basis on which these complex models made their decisions. Our proof-of-concept work demonstrated the success of a deep neural network using merely label-free adhesion kinetic data to classify single mammalian cells into different cell types. We propose our method for label-free single-cell profiling and in vitro cancer research involving adhesion. The employed label-free measurement is noninvasive and does not affect cellular functionality. Therefore, it could also be adapted for applications where the selected cells need further processing, such as immune therapy and regenerative medicine.

Optical sensor reveals the hidden influence of cell dissociation on adhesion measurements.

Cell adhesion experiments are important in tissue engineering and for testing new biologically active surfaces, prostheses, and medical devices. Additionally, the initial state of adhesion (referred to as nascent adhesion) plays a key role and is currently being intensively researched. A critical step in handling all adherent cell types is their dissociation from their substrates for further processing. Various cell dissociation methods and reagents are used in most tissue culture laboratories (here, cell dissociation from the culture surface, cell harvesting, and cell detachment are used interchangeably). Typically, the dissociated cells are re-adhered for specific measurements or applications. However, the impact of the choice of dissociation method on cell adhesion in subsequent measurements, especially when comparing the adhesivity of various surfaces, is not well clarified. In this study, we demonstrate that the application of a label-free optical sensor can precisely quantify the effect of cell dissociation methods on cell adhesivity, both at the single-cell and population levels. The optical measurements allow for high-resolution monitoring of cellular adhesion without interfering with the physiological state of the cells. We found that the choice of reagent significantly alters cell adhesion on various surfaces. Our results clearly demonstrate that biological conclusions about cellular adhesion when comparing various surfaces are highly dependent on the employed dissociation method. Neglecting the choice of cellular dissociation can lead to misleading conclusions when evaluating cell adhesion data from various sources and comparing the adhesivity of two different surfaces (i.e., determining which surface is more or less adhesive).

Continuous distribution of cancer cells in the cell cycle unveiled by AI-segmented imaging of 37,000 HeLa FUCCI cells.

Classification of live or fixed cells based on their unlabeled microscopic images would be a powerful tool for cell biology and pathology. For such software, the first step is the generation of a ground truth database that can be used for training and testing AI classification algorithms. The Application of cells expressing fluorescent reporter proteins allows the building of ground truth datasets in a straightforward way. In this study, we present an automated imaging pipeline utilizing the Cellpose algorithm for the precise cell segmentation and measurement of fluorescent cellular intensities across multiple channels. We analyzed the cell cycle of HeLa-FUCCI cells expressing fluorescent red and green reporter proteins at various levels depending on the cell cycle state. To build the dataset, 37,000 fixed cells were automatically scanned using a standard motorized microscope, capturing phase contrast and fluorescent red/green images. The fluorescent pixel intensity of each cell was integrated to calculate the total fluorescence of cells based on cell segmentation in the phase contrast channel. It resulted in a precise intensity value for each cell in both channels. Furthermore, we conducted a comparative analysis of Cellpose 1.0 and Cellpose 2.0 in cell segmentation performance. Cellpose 2.0 demonstrated notable improvements, achieving a significantly reduced false positive rate of 2.7 % and 1.4 % false negative. The cellular fluorescence was visualized in a 2D plot (map) based on the red and green intensities of the FUCCI construct revealing the continuous distribution of cells in the cell cycle. This 2D map enables the selection and potential isolation of single cells in a specific phase. In the corresponding heatmap, two clusters appeared representing cells in the red and green states. Our pipeline allows the high-throughput and accurate measurement of cellular fluorescence providing extensive statistical information on thousands of cells with potential applications in developmental and cancer biology. Furthermore, our method can be used to build ground truth datasets automatically for training and testing AI cell classification. Our automated pipeline can be used to analyze thousands of cells within 2 h after putting the sample onto the microscope.

Nanoinjection of extracellular vesicles to single live cells by robotic fluidic force microscopy.

In the past decade, extracellular vesicles (EVs) have attracted substantial interest in biomedicine. With progress in the field, we have an increasing understanding of cellular responses to EVs. In this Technical Report, we describe the direct nanoinjection of EVs into the cytoplasm of single cells of different cell lines. By using robotic fluidic force microscopy (robotic FluidFM), nanoinjection of GFP positive EVs and EV-like particles into single live HeLa, H9c2, MDA-MB-231 and LCLC-103H cells proved to be feasible. This injection platform offered the advantage of high cell selectivity and efficiency. The nanoinjected EVs were initially localized in concentrated spot-like regions within the cytoplasm. Later, they were transported towards the periphery of the cells. Based on our proof-of-principle data, robotic FluidFM is suitable for targeting single living cells by EVs and may lead to information about intracellular EV cargo delivery at a single-cell level.

Glycocalyx Components Detune the Cellular Uptake of Gold Nanoparticles in a Size- and Charge-Dependent Manner.

Functionalized nanoparticles (NPs) are widely used in targeted drug delivery and biomedical imaging due to their penetration into living cells. The outer coating of most cells is a sugar-rich layer of the cellular glycocalyx, presumably playing an important part in any uptake processes. However, the exact role of the cellular glycocalyx in NP uptake is still uncovered. Here, we in situ monitored the cellular uptake of gold NPs─functionalized with positively charged alkaline thiol (TMA)─into adhered cancer cells with or without preliminary glycocalyx digestion. Proteoglycan (PG) components of the glycocalyx were treated by the chondroitinase ABC enzyme. It acts on chondroitin 4-sulfate, chondroitin 6-sulfate, and dermatan sulfate and slowly on hyaluronate. The uptake measurements of HeLa cells were performed by applying a high-throughput label-free optical biosensor based on resonant waveguide gratings. The positively charged gold NPs were used with different sizes [d = 2.6, 4.2, and 7.0 nm, small (S), medium (M), and large(L), respectively]. Negatively charged citrate-capped tannic acid (CTA, d = 5.5 nm) NPs were also used in control experiments. Real-time biosensor data confirmed the cellular uptake of the functionalized NPs, which was visually proved by transmission electron microscopy. It was found that the enzymatic digestion facilitated the entry of the positively charged S- and M-sized NPs, being more pronounced for the M-sized. Other enzymes digesting different components of the glycocalyx were also employed, and the results were compared. Glycosaminoglycan digesting heparinase III treatment also increased, while glycoprotein and glycolipid modifying neuraminidase decreased the NP uptake by HeLa cells. This suggests that the sialic acid residues increase, while heparan sulfate decreases the uptake of positively charged NPs. Our results raise the hypothesis that cellular uptake of 2-4 nm positively charged NPs is facilitated by glycoprotein and glycolipid components of the glycocalyx but inhibited by PGs.

Development and In-Depth Characterization of Bacteria Repellent and Bacteria Adhesive Antibody-Coated Surfaces Using Optical Waveguide Biosensing.

Bacteria repellent surfaces and antibody-based coatings for bacterial assays have shown a growing demand in the field of biosensors, and have crucial importance in the design of biomedical devices. However, in-depth investigations and comparisons of possible solutions are still missing. The optical waveguide lightmode spectroscopy (OWLS) technique offers label-free, non-invasive, in situ characterization of protein and bacterial adsorption. Moreover, it has excellent flexibility for testing various surface coatings. Here, we describe an OWLS-based method supporting the development of bacteria repellent surfaces and characterize the layer structures and affinities of different antibody-based coatings for bacterial assays. In order to test nonspecific binding blocking agents against bacteria, OWLS chips were coated with bovine serum albumin (BSA), I-block, PAcrAM-g-(PMOXA, NH2, Si), (PAcrAM-P) and PLL-g-PEG (PP) (with different coating temperatures), and subsequent Escherichia coli adhesion was monitored. We found that the best performing blocking agents could inhibit bacterial adhesion from samples with bacteria concentrations of up to 107 cells/mL. Various immobilization methods were applied to graft a wide range of selected antibodies onto the biosensor's surface. Simple physisorption, Mix&Go (AnteoBind) (MG) films, covalently immobilized protein A and avidin-biotin based surface chemistries were all fabricated and tested. The surface adsorbed mass densities of deposited antibodies were determined, and the biosensor;s kinetic data were evaluated to divine the possible orientations of the bacteria-capturing antibodies and determine the rate constants and footprints of the binding events. The development of affinity layers was supported by enzyme-linked immunosorbent assay (ELISA) measurements in order to test the bacteria binding capabilities of the antibodies. The best performance in the biosensor measurements was achieved by employing a polyclonal antibody in combination with protein A-based immobilization and PAcrAM-P blocking of nonspecific binding. Using this setting, a surface sensitivity of 70 cells/mm2 was demonstrated.

Label-free tracking of whole-cell response on RGD functionalized surfaces to varied flow velocities generated by fluidic rotation.

Fluidic flow plays important roles in colloid and interface sciences. Measuring adsorption, aggregation processes and living cell behavior under a fluidic environment with varied flow velocities in a parallel and high-throughput manner remains to be a challenging task. Here a method is introduced to monitor cell response to well-defined flow with varied velocities over an array of label-free resonant waveguide grating (RWG) based optical biosensors. The arrangement consists of a circular well with an array of biosensors at the bottom surface. By rotating the liquid over the biosensor array using a magnetic stirrer bar, flow velocities from zero to a predefined maximum can be easily established over different locations within the biosensor array as characterized in detail by numerical simulations. Cell adhesion and detachment measurements on an Arg-Gly-Asp (RGD) peptide functionalized surface were performed to demonstrate i) measurements at a wide range of simultaneous flow velocities over the same interface; ii) the possibility of parallel measurements at the same flow conditions in one run; and iii) the simple tuning of the employed range of flow velocities. Our setup made it possible to analyze the magnitude and rate of cell detachment at various flow velocities in parallel and determine the critical velocity and force where cells start to detach from the RGD motif displaying biomimetic surface. Furthermore, cellular response to simultaneous mechanical (flow) and chemical stimulation was also investigated using trypsin as a model. This study opens a new possibility to investigate interface phenomena under predefined and conveniently varied flow conditions.

Glycocalyx regulates the strength and kinetics of cancer cell adhesion revealed by biophysical models based on high resolution label-free optical data.

The glycocalyx is thought to perform a potent, but not yet defined function in cellular adhesion and signaling. Since 95% of cancer cells have altered glycocalyx structure, this role can be especially important in cancer development and metastasis. The glycocalyx layer of cancer cells directly influences cancer progression, involving the complicated kinetic process of cellular adhesion at various levels. In the present work, we investigated the effect of enzymatic digestion of specific glycocalyx components on cancer cell adhesion to RGD (arginine-glycine-aspartic acid) peptide motif displaying surfaces. High resolution kinetic data of cell adhesion was recorded by the surface sensitive label-free resonant waveguide grating (RWG) biosensor, supported by fluorescent staining of the cells and cell surface charge measurements. We found that intense removal of chondroitin sulfate (CS) and dermatan sulfate chains by chondroitinase ABC reduced the speed and decreased the strength of adhesion of HeLa cells. In contrast, mild digestion of glycocalyx resulted in faster and stronger adhesion. Control experiments on a healthy and another cancer cell line were also conducted, and the discrepancies were analysed. We developed a biophysical model which was fitted to the kinetic data of HeLa cells. Our analysis suggests that the rate of integrin receptor transport to the adhesion zone and integrin-RGD binding is strongly influenced by the presence of glycocalyx components, but the integrin-RGD dissociation is not. Moreover, based on the kinetic data we calculated the dependence of the dissociation constant of integrin-RGD binding on the enzyme concentration. We also determined the dissociation constant using a 2D receptor binding model based on saturation level static data recorded at surfaces with tuned RGD densities. We analyzed the discrepancies of the kinetic and static dissociation constants, further illuminating the role of cancer cell glycocalyx during the adhesion process. Altogether, our experimental results and modelling demonstrated that the chondroitin sulfate and dermatan sulfate chains of glycocalyx have an important regulatory function during the cellular adhesion process, mainly controlling the kinetics of integrin transport and integrin assembly into mature adhesion sites. Our results potentially open the way for novel type of cancer treatments affecting these regulatory mechanisms of cellular glycocalyx.

In vivo dosimetry of the rectum in image-guided adaptive interstitial-intracavitary brachytherapy of cervix cancer - A feasibility study.

AIM AND BACKGROUND: To investigate the feasibility of in vivo rectal dosimetry in image-guided adaptive brachytherapy of cervical cancer. MATERIALS AND METHODS: Error of measurement of dose rate in a semiconductor diode probe was investigated depending on the distance and angle in water, and on temperature in a polymethyl methacrylate phantom using an Ir-192 source. Furthermore, the difference between the measured and calculated dose was analysed in the interstitial brachytherapy of 30 cervix cancer patients. The relationship between in vivo measured dose, calculated dose in the point of the diode, calculated maximal dose in the point of the diodes and calculated maximal dose of the rectum were examined. RESULTS: The dosimeter measured with 85% accuracy at more than 5 cm from the source, but within a closer distance the accuracy decreased significantly. At 45-90° angle, the device measured with a 15% error. The error increased with the temperature, 22% at 35 °C. In 8 cases (26.7%) the maximal dose was measured in the correct diode. The device measured 73% of the calculated dose in the point of the diode. The maximum of the calculated doses of diodes was 60% of the calculated maximal dose. The in vivo measured dose was 35% of the calculated maximal dose. CONCLUSIONS: Under treatment conditions, the semiconductor diode does not provide reliable measured data. The probe pushes the rectal wall closer to the high dose areas and underestimates the dose of it. Semiconductor probe is not recommended for in vivo dosimetry of the rectum in image-guided brachytherapy of cervical cancer.