Bozinovic, N. | Search bioRxiv

bioRxiv2019

Method for identification of heme-binding proteins and quantification of their interactions

The standard assay for characterization of interaction of heme with proteins is absorbance spectroscopy. However, this approach demands relatively large quantities of proteins and it is difficult to perform in high-throughput manner. Here, we describe an immunosorbent assay based on the covalent in situ conjugation of heme to a pre-coated carrier. Advantage of this assay is that it allows both identification of heme-binding proteins and quantification of their binding avidity, using only minimal amounts of protein (1-10 μg). Importantly, the same approach can be used for covalent linkage of other natural or synthetic compounds and analyzing their interactions with proteins.Competing Interest StatementThe authors have declared no competing interest.View Full Text

biochemistry↗

bioRxiv2018

Multiparameter cell characterization using nanofluidic technology facilitates real-time phenotypic and genotypic elucidation of intratumor heterogeneity

Genetic and functional complexity of bulk tumor has become evident through rapid advances in sequencing technologies. As a unique integrated approach to characterizing tumor heterogeneity, we demonstrate the multifaceted capabilities of a novel nanofluidic platform to enable single-cell phenotypic and genetic profiling of ovarian cancer patient-derived tumor cells. This approach has enabled increased resolution of tumor cell phenotypic and genetic heterogeneity, providing a better understanding of underlying biological drivers of the disease. A range of CA-125 expression levels is observed within cells from individuals, demonstrating clonal diversity consistent with other phenotypic data. Further, TP53 mutation analysis demonstrates a sub-population of cells exhibiting high mutation frequency that likely drives downstream growth kinetics and protein expression. Finally, genomic data is orthogonally used to address clonal heterogeneity across ovarian tumors when compared to bulk sequencing, illustrating the potential for single-cell sequencing data integrated with cellular functional and growth data toward future therapeutic intervention.

genomics↗