Label-Free Detection of Microvesicles and Proteins by the Bundling of Gliding Microtubules.

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.

Tetrazine-trans-cyclooctene Mediated Conjugation of Antibodies to Microtubules Facilitates Subpicomolar Protein Detection.

Engineering cargo-loading strategies is crucial to developing nanotechnological applications of microtubule-based biomolecular transport systems. Here, we report a highly efficient and robust bioconjugation scheme to load antibodies to microtubules. Our method takes advantage of the inverse-electron-demand Diels-Alder addition reaction between tetrazine and trans-cyclooctene: the fastest known bioorthogonal reaction, characterized by its excellent selectivity and biocompatibility. As proof of concept, we performed kinesin-1 gliding motility assays with antibody-conjugated microtubules and demonstrated the highly sensitive detection of fluorescent protein analyte down to 0.1 pM in microliter sample volumes. Importantly, the detection selectivity was retained in the presence of other fluorescent background proteins. We envision the applicability of our fast, simple, and robust conjugation method to a wide range of biosensing platforms based on biomolecular transport systems.

FRET-Based Probe for High-Throughput DNA Intercalator Drug Discovery and In Vivo Imaging.

Molecules that bind DNA by intercalating its bases remain among the most potent cancer therapies and antimicrobials due to their interference with DNA-processing proteins. To accelerate the discovery of novel intercalating drugs, we designed a fluorescence resonance energy transfer (FRET)-based probe that reports on DNA intercalation, allowing rapid and sensitive screening of chemical libraries in a high-throughput format. We demonstrate that the method correctly identifies known DNA intercalators in approved drug libraries and discover previously unreported intercalating compounds. When introduced in cells, the oligonucleotide-based probe rapidly distributes in the nucleus, allowing direct imaging of the dynamics of drug entry and its interaction with DNA in its native environment. This enabled us to directly correlate the potency of intercalators in killing cultured cancer cells with the ability of the drug to penetrate the cell membrane. The combined capability of the single probe to identify intercalators in vitro and follow their function in vivo can play a valuable role in accelerating the discovery of novel DNA-intercalating drugs or repurposing approved ones.

Machine learning based classification of cells into chronological stages using single-cell transcriptomics.

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.

Kinesin-1 Expressed in Insect Cells Improves Microtubule in Vitro Gliding Performance, Long-Term Stability and Guiding Efficiency in Nanostructures.

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.