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.

Single-cell adhesion strength and contact density drops in the M phase of cancer cells.

The high throughput, cost effective and sensitive quantification of cell adhesion strength at the single-cell level is still a challenging task. The adhesion force between tissue cells and their environment is crucial in all multicellular organisms. Integrins transmit force between the intracellular cytoskeleton and the extracellular matrix. This force is not only a mechanical interaction but a way of signal transduction as well. For instance, adhesion-dependent cells switch to an apoptotic mode in the lack of adhesion forces. Adhesion of tumor cells is a potential therapeutic target, as it is actively modulated during tissue invasion and cell release to the bloodstream resulting in metastasis. We investigated the integrin-mediated adhesion between cancer cells and their RGD (Arg-Gly-Asp) motif displaying biomimetic substratum using the HeLa cell line transfected by the Fucci fluorescent cell cycle reporter construct. We employed a computer-controlled micropipette and a high spatial resolution label-free resonant waveguide grating-based optical sensor calibrated to adhesion force and energy at the single-cell level. We found that the overall adhesion strength of single cancer cells is approximately constant in all phases except the mitotic (M) phase with a significantly lower adhesion. Single-cell evanescent field based biosensor measurements revealed that at the mitotic phase the cell material mass per unit area inside the cell-substratum contact zone is significantly less, too. Importantly, the weaker mitotic adhesion is not simply a direct consequence of the measured smaller contact area. Our results highlight these differences in the mitotic reticular adhesions and confirm that cell adhesion is a promising target of selective cancer drugs as the vast majority of normal, differentiated tissue cells do not enter the M phase and do not divide.