Biology subjects

Lerner, B.

Publications and source records attributed to Lerner, B..

3 recordsLinked to original sources

bioRxiv2020

Metronomic doses and drug schematic combination response tested within microfluidic models for the treatment of breast cancer cells (JIMT-1).

Low-dose metronomic (LDM) chemotherapy is an alternative to conventional chemotherapy and is the most common use of low-dose levels of traditional chemotherapeutics in patients. The selection of patients, drug dosages and dosing intervals in LDM is empirical. In this study we systematically examined the schedule-dependent interaction of drugs on a breast cancer cell line (BCC) cultured in Lab on a Chip (LOC) microdevices. The LDM studies were combined with cell staining in order to better characterize different cell stages and modes of cell death, including caspase-dependent apoptosis, caspase-independent cell death and autophagy-dependent cell death. Microscope images were examined using the Fiji plugin Trainable Weka Segmentation to analyze cell area in 7500 images showing different types of cell death modes. Paclitaxel combined with LDM chemotherapy demonstrated a reduction in the area covered by live cells. In contrast, there was an induction of high levels of cell death due to caspase-dependent apoptosis. Furthermore, the microdevice used in this study is also an attractive alternative for staining cells in order to characterize and study BCC growth and development in situ.

cancer biology↗

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↗