A toolkit for enhanced reproducibility of RNASeq analysis for synthetic biologists.

Sequencing technologies, in particular RNASeq, have become critical tools in the design, build, test and learn cycle of synthetic biology. They provide a better understanding of synthetic designs, and they help identify ways to improve and select designs. While these data are beneficial to design, their collection and analysis is a complex, multistep process that has implications on both discovery and reproducibility of experiments. Additionally, tool parameters, experimental metadata, normalization of data and standardization of file formats present challenges that are computationally intensive. This calls for high-throughput pipelines expressly designed to handle the combinatorial and longitudinal nature of synthetic biology. In this paper, we present a pipeline to maximize the analytical reproducibility of RNASeq for synthetic biologists. We also explore the impact of reproducibility on the validation of machine learning models. We present the design of a pipeline that combines traditional RNASeq data processing tools with structured metadata tracking to allow for the exploration of the combinatorial design in a high-throughput and reproducible manner. We then demonstrate utility via two different experiments: a control comparison experiment and a machine learning model experiment. The first experiment compares datasets collected from identical biological controls across multiple days for two different organisms. It shows that a reproducible experimental protocol for one organism does not guarantee reproducibility in another. The second experiment quantifies the differences in experimental runs from multiple perspectives. It shows that the lack of reproducibility from these different perspectives can place an upper bound on the validation of machine learning models trained on RNASeq data. Graphical Abstract.

Prediction of whole-cell transcriptional response with machine learning.

MOTIVATION: Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we present the host response model (HRM), a machine learning approach that maps response of single perturbations to transcriptional response of the combination of perturbations. RESULTS: The HRM combines high-throughput sequencing with machine learning to infer links between experimental context, prior knowledge of cell regulatory networks, and RNASeq data to predict a gene's dysregulation. We find that the HRM can predict the directionality of dysregulation to a combination of inducers with an accuracy of >90% using data from single inducers. We further find that the use of prior, known cell regulatory networks doubles the predictive performance of the HRM (an R2 from 0.3 to 0.65). The model was validated in two organisms, Escherichia coli and Bacillus subtilis, using new experiments conducted after training. Finally, while the HRM is trained with gene expression data, the direct prediction of differential expression makes it possible to also conduct enrichment analyses using its predictions. We show that the HRM can accurately classify >95% of the pathway regulations. The HRM reduces the number of RNASeq experiments needed as responses can be tested in silico prior to the experiment. AVAILABILITY AND IMPLEMENTATION: The HRM software and tutorial are available at https://github.com/sd2e/CDM and the configurable differential expression analysis tools and tutorials are available at https://github.com/SD2E/omics_tools. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bioprinting of a Cell-Laden Conductive Hydrogel Composite.

Bioprinting has gained significant attention for creating biomimetic tissue constructs with potential to be used in biomedical applications such as drug screening or regenerative medicine. Ideally, biomaterials used for three-dimensional (3D) bioprinting should match the mechanical, hydrostatic, bioelectric, and physicochemical properties of the native tissues. However, many materials with these tissue-like properties are not compatible with printing techniques without modifying their compositions. In addition, integration of cell-laden biomaterials with bioprinting methodologies that preserve their physicochemical properties remains a challenge. In this work, a biocompatible conductive hydrogel composed of gelatin methacryloyl (GelMA) and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) was synthesized and bioprinted to form complex, 3D cell-laden structures. The biofabricated conductive hydrogels were formed by an initial cross-linking step of the PEDOT:PSS with bivalent calcium ions and a secondary photopolymerization step with visible light to cross-link the GelMA component. These modifications enabled tuning the mechanical properties of the hydrogels, with Young's moduli ranging from ∼40-150 kPa, as well as tunable conductivity by varying the concentration of PEDOT:PSS. In addition, the hydrogels degraded in vivo with no substantial inflammatory responses as demonstrated by haematoxylin and eosin (H&E) and immunofluorescent staining of subcutaneously implanted samples in Wistar rats. The parameters for forming a slurry of microgel particles to support 3D bioprinting of the engineered cell-laden hydrogel were optimized to form constructs with improved resolution. High cytocompatibility and cell spreading were demonstrated in both wet-spinning and 3D bioprinting of cell-laden hydrogels with the new conductive hydrogel-based bioink and printing methodology. The synergy of an advanced fabrication method and conductive hydrogel presented here is promising for engineering complex conductive and cell-laden structures.