Bourguignon, N. | Search bioRxiv

bioRxiv2020

Single cell transfection of human induced pluripotent stem cells using a droplet-based microfluidic system.

Microfluidic tools have recently made possible many advances in biological and biomedical research. Research fields such as Physics, Engineering, Chemistry and Biology have combined to produce innovation in Microfluidics which has positively impacted on areas as diverse as nucleotide sequence, functional genomics, single-cell studies, single molecules assays, and biomedical diagnostics. Among these areas regenerative medicine and stem cells have benefited from Microfluidics due to these tools have had a profound impact on their applications. In the study, we present a high-performance droplet-based system for transfecting individual human-induced pluripotent stem cells. We show that this system has great efficiency in single cells and captured droplets, similar to other microfluidic methods and lower cost. We demonstrate that this microfluidic approach can be associated with the PiggyBac transposase-based system to increase its transfection efficiency. Our results provide a starting point for subsequent applications in more complex transfection systems, single-cell differentiation interactions, cell subpopulations, cell therapy, among other potential applications.

bioengineering↗

bioRxiv2019

Dynamic Bayesian networks for integrating multi-omics time-series microbiome data

A key challenge in the analysis of longitudinal microbiome data is the inference of temporal interactions between microbial taxa, their genes, the metabolites they consume and produce, and host genes. To address these challenges we developed a computational pipeline, PALM, that first aligns multi-omics data and then uses dynamic Bayesian networks (DBNs) to reconstruct a unified model. Our approach overcomes differences in sampling and progression rates, utilizes a biologically-inspired multi-omic framework, reduces the large number of entities and parameters in the DBNs, and validates the learned network. Applying PALM to data collected from inflammatory bowel disease patients, we show that it accurately identifies known and novel interactions. Targeted experimental validations further support a number of the predicted novel metabolite-taxa interactions. Source code and data will be freely available after publication under the MIT Open Source license agreement on our GitHub page. IMPORTANCEWhile a number of large consortia are collecting and profiling several different types of microbiome and genomic time series data, very few methods exist for joint modeling of multi-omics data sets. We developed a new computational pipeline, PALM, which uses Dynamic Bayesian Networks (DBNs) and is designed to integrate multi-omics data from longitudinal microbiome studies. When used to integrate sequence, expression, and metabolomics data from microbiome samples along with host expression data, the resulting models identify interactions between taxa, their genes and the metabolites they produce and consume, and their impact on host expression. We tested the models both by using them to predict future changes in microbiome levels, and by comparing the learned interactions to known interactions in the literature. Finally, we performed experimental validations for a few of the predicted interactions to demonstrate the ability of the method to identify novel relationships and their impact.

bioinformatics↗