The cytoskeletal motor protein kinesin-1 has been successfully used for many nanotechnological applications. Most commonly, these applications use a gliding assay geometry where substrate-attached motor proteins propel microtubules along the surface. So far, this assay has only been shown to run undisturbed for up to 8 h. Longer run times cause problems like microtubule shrinkage, microtubules getting stuck and slowing down. This is particularly problematic in nanofabricated structures where the total number of microtubules is limited and detachment at the structure walls causes additional microtubule loss. We found that many of the observed problems are caused by the bacterial expression system, which has so far been used for nanotechnological applications of kinesin-1. We strive to enable the use of this motor system for more challenging nanotechnological applications where long-term stability and/or reliable guiding in nanostructures is required. Therefore, we established the expression and purification of kinesin-1 in insect cells which results in improved purity and--more importantly--long-term stability > 24 h and guiding efficiencies of > 90% in lithographically defined nanostructures.

Development of miniaturized devices for the rapid and sensitive detection of analyte is crucial for various applications across healthcare, pharmaceutical, environmental, and other industries. Here, we report on the detection of unlabeled analyte by using fluorescently labeled, antibody-conjugated microtubules in a kinesin-1 gliding motility assay. The detection principle is based on the formation of fluorescent supramolecular assemblies of microtubule bundles and spools in the presence of multivalent analytes. We demonstrate the rapid, label-free detection of CD45+ microvesicles derived from leukemia cells. Moreover, we employ our platform for the label-free detection of multivalent proteins at subnanomolar concentrations, as well as for profiling the cross-reactivity between commercially available secondary antibodies. As the detection principle is based on the molecular recognition between antigen and antibody, our method can find general application where it identifies any analyte, including clinically relevant microvesicles and proteins.

Age-associated deterioration of cellular physiology leads to pathological conditions. The ability to detect premature aging could provide a window for preventive therapies against age-related diseases. However, the techniques for determining cellular age are limited, as they rely on a limited set of histological markers and lack predictive power. Here, we implement GERAS (GEnetic Reference for Age of Single-cell), a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes. GERAS displays greater than 90% accuracy in classifying the chronological stage of zebrafish and human pancreatic cells. The framework demonstrates robustness against biological and technical noise, as evaluated by its performance on independent samplings of single-cells. Additionally, GERAS determines the impact of differences in calorie intake and BMI on the aging of zebrafish and human pancreatic cells, respectively. We further harness the classification ability of GERAS to identify molecular factors that are potentially associated with the aging of beta-cells. We show that one of these factors, junba, is necessary to maintain the proliferative state of juvenile beta-cells. Our results showcase the applicability of a machine learning framework to classify the chronological stage of heterogeneous cell populations, while enabling detection of candidate genes associated with aging.