Protein and RNA quantification of multiple genes in single cells

Increased FOXJ1 protein expression is associated with improved overall survival in high-grade serous ovarian carcinoma: an Ovarian Tumor Tissue Analysis Consortium Study

BACKGROUND

Recently, we showed a >60% difference in 5-year survival for patients with tubo-ovarian high-grade serous carcinoma (HGSC) when stratified by a 101-gene mRNA expression prognostic signature. Given the varied patient outcomes, this study aimed to translate prognostic mRNA markers into protein expression assays by immunohistochemistry and validate their survival association in HGSC.

METHODS

Two prognostic genes, FOXJ1 and GMNN, were selected based on high-quality antibodies, correlation with protein expression and variation in immunohistochemical scores in a preliminary cohort (n = 134 and n = 80, respectively). Six thousand four hundred and thirty-four (FOXJ1) and 5470 (GMNN) formalin-fixed, paraffin-embedded ovarian neoplasms (4634 and 4185 HGSC, respectively) represented on tissue microarrays from the Ovarian Tumor Tissue Analysis consortium underwent immunohistochemical staining and scoring, then univariate and multivariate survival analysis.

RESULTS

Consistent with mRNA, FOXJ1 protein expression exhibited a linear, increasing association with improved overall survival in HGSC patients. Women with >50% expression had the most favourable outcomes (HR = 0.78, 95% CI 0.67-0.91, p < 0.0001). GMNN protein expression was not significantly associated with overall HSGC patient survival. However, HGSCs with >35% GMNN expression showed a trend for better outcomes, though this was not significant.

CONCLUSION

We provide foundational evidence for the prognostic value of FOXJ1 in HGSC, validating the prior mRNA-based prognostic association by immunohistochemistry.

Convolutional Neural Networks as Summary Statistics for Approximate Bayesian Computation

Approximate Bayesian Computation is widely used in systems biology for inferring parameters in stochastic gene regulatory network models. Its performance hinges critically on the ability to summarize high-dimensional system responses such as time series into a few informative, low-dimensional summary statistics. The quality of those statistics acutely impacts the accuracy of the inference task. Existing methods to select the best subset out of a pool of candidate statistics do not scale well with large pools of several tens to hundreds of candidate statistics. Since high quality statistics are imperative for good performance, this becomes a serious bottleneck when performing inference on complex and high-dimensional problems. This paper proposes a convolutional neural network architecture for automatically learning informative summary statistics of temporal responses. We show that the proposed network can effectively circumvent the statistics selection problem of the preprocessing step for ABC inference. The proposed approach is demonstrated on two benchmark problem and one challenging inference problem learning parameters in a high-dimensional stochastic genetic oscillator. We also study the impact of experimental design on network performance by comparing different data richness and data acquisition strategies.

The Intriguing Landscape of Single-Cell Protein Analysis

Profiling protein expression at single-cell resolution is essential for fundamental biological research (such as cell differentiation and tumor microenvironmental examination) and clinical precision medicine where only a limited number of primary cells are permitted. With the recent advances in engineering, chemistry, and biology, single-cell protein analysis methods are developed rapidly, which enable high-throughput and multiplexed protein measurements in thousands of individual cells. In combination with single cell RNA sequencing and mass spectrometry, single-cell multi-omics analysis can simultaneously measure multiple modalities including mRNAs, proteins, and metabolites in single cells, and obtain a more comprehensive exploration of cellular signaling processes, such as DNA modifications, chromatin accessibility, protein abundance, and gene perturbation. Here, the recent progress and applications of single-cell protein analysis technologies in the last decade are summarized. Current limitations, challenges, and possible future directions in this field are also discussed.

Parental allele-specific protein expression in single cells In vivo

Allelic expression from each parent-of-origin is important as a backup and to ensure that enough protein products of a gene are produced. Thus far, it is not known how each cell throughout a tissue differs in parental allele expression at the level of protein synthesis. Here, we measure the expression of the Ribosomal protein L13a (Rpl13a) from both parental alleles simultaneously in single cells in the living animal. We use genome-edited Drosophila that have a quantitative reporter of protein synthesis inserted into the endogenous Rpl13a locus. We find that individual cells can have large (>10-fold) differences in protein expression between the two parental alleles. Cells can produce protein from only one allele oftentimes, and time-lapse imaging of protein production from each parental allele in each cell showed that the imbalance in expression from one parental allele over the other can invert over time. We also identify the histone methyltransferase EHMT to be involved in the protein synthesis dynamics within cells.