There is an increasing interest in real-time and in situ monitoring of living cell activities in life science and medicine. This paper reports a whole cell sensing protocol over the interface of Au film coupled in a wavelength-modulated surface plasmon resonance (WMSPR) biosensor. With dual parabolic mirrors integrated in the sensor, the compact and miniaturized instrument shows satisfactory refractive index sensitivity (2220 nm/RIU) and a high resolution of resonance wavelength shift of 0.3 nm to liquid samples. The affinity interactions between the biomarker of human tumor endothelial marker 8 (TEM8) and antibody (Ab) or specific polypeptide (PEP) were firstly introduced to WMSPR biosensor analysis. Both the interaction events of Ab-cell and PEP-cell over the Au film interface can be recognized by the sensor and the balance time of interactions is about 20 min. The concentration range of Ab for quantitative monitoring of the TEM8 expression on human colon carcinoma SW620 cells was investigated. The present low-cost and time-saving method provides a time resolution of binding specificity between Ab/PEP and TEM8 for real-time analysis of antigen on living tumor cell surface.

Identification of induced pluripotent stem (iPS) progenitor cells, the iPS forming cells in early stage of reprogramming, could provide valuable information for studying the origin and underlying mechanism of iPS cells. However, it is very difficult to identify experimentally since there are no biomarkers known for early progenitor cells, and only about 6 days after reprogramming initiation, iPS cells can be experimentally determined via fluorescent probes. What is more, the ratio of progenitor cells during early reprograming period is below 5%, which is too low to capture experimentally in the early stage. In this paper, we propose a novel computational approach for the identification of iPS progenitor cells based on machine learning and microscopic image analysis. Firstly, we record the reprogramming process using a live cell imaging system after 48 hours of infection with retroviruses expressing Oct4, Sox2 and Klf4, later iPS progenitor cells and normal murine embryonic fibroblasts (MEFs) within 3 to 5 days after infection are labeled by retrospectively tracing the time-lapse microscopic image. We then calculate 11 types of cell morphological and motion features such as area, speed, etc., and select best time windows for modeling and perform feature selection. Finally, a prediction model using XGBoost is built based on the selected six types of features and best time windows. Our model allows several missing values/frames in the sample datasets, thus it is applicable to a wide range of scenarios. Cross-validation, holdout validation and independent test experiments show that the minimum precision is above 52%, that is, the ratio of predicted progenitor cells within 3 to 5 days after viral infection is above 52%. The results also confirm that the morphology and motion pattern of iPS progenitor cells is different from that of normal MEFs, which helps with the machine learning methods for iPS progenitor cell identification. Identification of induced pluripotent stem (iPS) progenitor cells could provide valuable information for studying the origin and underlying mechanism of iPS cells. However, it is very difficult to identify experimentally since there are no biomarkers known for early progenitor cells, and only after about 6 days of induction, iPS cells can be experimentally determined via fluorescent probes. What is more, the percentage of the progenitor cells during the early induction period is below 5%, too low to capture experimentally in early stage. In this work, we proposed an approach for the identification of iPS progenitor cells, the iPS forming cells, based on machine learning and microscopic image analysis. The aim is to help biologists to enrich iPS progenitor cells during the early stage of induction, which allows experimentalists to select iPS progenitor cells with much higher probability, and furthermore to study the biomarkers which trigger the reprogramming process.