Protein and mRNA Quantification in Small Samples of Human Induced Pluripotent Stem-cell-derived Cardiomyocytes in 96-well Microplates

We describe a method for protein quantification and for mRNA quantification in small sample quantities of human induced pluripotent stem-cell-derived-cardiomyocytes (hiPSC-CMs). Demonstrated here is how the capillary-based protein detection system WesTM by ProteinSimple and the Power SYBRTM Green Cells-to-CTTM Kit by Invitrogen can be applied to individual samples in a 96-well microplate format and thus made compatible with high-throughput (HT) cardiomyocyte assays. As an example of the usage, we illustrate that Cx43 protein and GJA1 mRNA levels in hiPSC-CMs are enhanced when the optogenetic actuator, channelrodopsin-2 (ChR2), is genetically expressed in them. Instructions are presented for cell culture and lysate preparations from hiPSC-CMs, along with optimized parameter settings and experimental protocol steps. Strategies to optimize primary antibody concentrations as well as ways for signal normalization are discussed, i.e. antibody multiplexing and total protein assay. The sensitivity of both the Wes and Cells-to-CT kit enables protein and mRNA quantification in a HT format, which is important when dealing with precious small samples. In addition to being able to handle small cardiomyocyte samples, these streamlined and semi-automated processes enable quick mechanistic analysis.

The zinc finger E-box binding homeobox transcriptional repressor ZEB1 is differentially expressed in the metastases of patients with breast cancer.

Metastasis is a major problem in patients with breast cancer (1). We analyzed published microarray (2) and multiplexed mRNA quantification (3) datasets to identify transcription factors whose expression changed most significantly between primary tumors in patients with breast cancer and metastatic tissues. We identified ZEB1 as differentially expressed when comparing the transcriptomes of primary tumors of the breast to the metastases they generate (2). Analysis of a separate multiplexed mRNA quantification dataset uncovered similar differential expression of ZEB1 in metastases compared to primary breast tumors (3). ZEB1 expression has been reported as important for metastases and for the epithelial to mesenchymal transition in cancer (4-9); we found in this study, however, that ZEB1 was expressed at significantly lower levels in metastases across tissue type when compared to primary breast tumors.

Platform-integrated mRNA isoform quantification

Motivation Accurate estimation of transcript isoform abundance is critical for downstream transcriptome analyses and can lead to precise molecular mechanisms for understanding complex human diseases, like cancer. Simplex mRNA Sequencing (RNA-Seq) based isoform quantification approaches are facing the challenges of inherent sampling bias and unidentifiable read origins. A large-scale experiment shows that the consistency between RNA-Seq and other mRNA quantification platforms is relatively low at the isoform level compared to the gene level. In this project, we developed a platform-integrated model for transcript quantification (IntMTQ) to improve the performance of RNA-Seq on isoform expression estimation. IntMTQ, which benefits from the mRNA expressions reported by the other platforms, provides more precise RNA-Seq-based isoform quantification and leads to more accurate molecular signatures for disease phenotype prediction. Results In the experiments to assess the quality of isoform expression estimated by IntMTQ, we designed three tasks for clustering and classification of 46 cancer cell lines with four different mRNA quantification platforms, including newly developed NanoString’s nCounter technology. The results demonstrate that the isoform expressions learned by IntMTQ consistently provide more and better molecular features for downstream analyses compared with five baseline algorithms which consider RNA-Seq data only. An independent RT-qPCR experiment on seven genes in twelve cancer cell lines showed that the IntMTQ improved overall transcript quantification. The platform-integrated algorithms could be applied to large-scale cancer studies, such as The Cancer Genome Atlas (TCGA), with both RNA-Seq and array-based platforms available. Availability and implementation Source code is available at: https://github.com/CompbioLabUcf/IntMTQ. Supplementary information Supplementary data are available at Bioinformatics online.