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Orthogonal high-throughput thermal scanning method for rank ordering protein formulations.

ABSTRACT: A high-throughput thermal-scanning method to rank-order formulation conditions for therapeutic proteins is described. Apparent transition temperatures for unfolding and aggregation of four different proteins are determined using the dyes SYPRO Orange and thioflavin T (ThT) under a variety of buffer conditions. The results indicate that the ThT-based thermal scanning method offers several advantages over the previously described SYPRO Orange-based thermal scanning and allows rapid rank ordering of solution conditions relevant toward long-term storage of therapeutic molecules. The method is also amenable to high protein concentration and does not require sample dilution or extensive preparation. Additionally, this parallel use of SYPRO Orange and ThT can be readily applied to the screening of mutants for their unfolding and aggregation propensities.

SUBMITTER: Nashine VC

PROVIDER: S-EPMC3840783 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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Orthogonal high-throughput thermal scanning method for rank ordering protein formulations.

Nashine Vishal C VC Kroetsch Andrew M AM Sahin Erinc E Zhou Rong R Adams Monica L ML

AAPS PharmSciTech 20130904 4

A high-throughput thermal-scanning method to rank-order formulation conditions for therapeutic proteins is described. Apparent transition temperatures for unfolding and aggregation of four different proteins are determined using the dyes SYPRO Orange and thioflavin T (ThT) under a variety of buffer conditions. The results indicate that the ThT-based thermal scanning method offers several advantages over the previously described SYPRO Orange-based thermal scanning and allows rapid rank ordering o ...[more]

PMID: 24002823

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Project description:BackgroundHigh throughput microfluidic protocols in single cell RNA sequencing (scRNA-seq) collect mRNA counts from up to one million individual cells in a single experiment; this enables high resolution studies of rare cell types and cell development pathways. Determining small sets of genetic markers that can identify specific cell populations is thus one of the major objectives of computational analysis of mRNA counts data. Many tools have been developed for marker selection on single cell data; most of them, however, are based on complex statistical models and handle the multi-class case in an ad-hoc manner.ResultsWe introduce RANKCORR, a fast method with strong mathematical underpinnings that performs multi-class marker selection in an informed manner. RANKCORR proceeds by ranking the mRNA counts data before linearly separating the ranked data using a small number of genes. The step of ranking is intuitively natural for scRNA-seq data and provides a non-parametric method for analyzing count data. In addition, we present several performance measures for evaluating the quality of a set of markers when there is no known ground truth. Using these metrics, we compare the performance of RANKCORR to a variety of other marker selection methods on an assortment of experimental and synthetic data sets that range in size from several thousand to one million cells.ConclusionsAccording to the metrics introduced in this work, RANKCORR is consistently one of most optimal marker selection methods on scRNA-seq data. Most methods show similar overall performance, however; thus, the speed of the algorithm is the most important consideration for large data sets (and comparing the markers selected by several methods can be fruitful). RANKCORR is fast enough to easily handle the largest data sets and, as such, it is a useful tool to add into computational pipelines when dealing with high throughput scRNA-seq data. RANKCORR software is available for download at https://github.com/ahsv/RankCorr with extensive documentation.

Dataset Information

Orthogonal high-throughput thermal scanning method for rank ordering protein formulations.

Publications

Orthogonal high-throughput thermal scanning method for rank ordering protein formulations.

Similar Datasets

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