featuring the Echo Acoustic Technology
TITLES and AUTHORS
The High Throughput Biomedicine (HTB) unit at the Institute for Molecular Medicine Finland FIMM was established in 2010 to serve as a national and international academic screening unit providing access to state of the art instrumentation for chemical and RNAi-based high throughput screening. The initial focus of the unit was multiwell plate based chemical screening and high content microarray-based siRNA screening. However, over the first four years of operation, the unit has moved to a more flexible service platform where both chemical and siRNA screening is performed at different scales primarily in multiwell plate-based assays with a wide range of readout possibilities with a focus on ultraminiaturization to allow for affordable screening for the academic users. In addition to high throughput screening, the equipment of the unit is also used to support miniaturized, multiplexed and high throughput applications for other types of research such as genomics, sequencing and biobanking operations. Importantly, with the translational research goals at FIMM, an increasing part of the operations at the HTB unit is being focused on high throughput systems biological platforms for functional profiling of patient cells in personalized and precision medicine projects.
Single-cell genomics is a powerful tool for exploring the genetic makeup of environmental microorganisms, the vast majority of which are difficult, if not impossible, to cultivate with current approaches. Here we present a comprehensive protocol for obtaining genomes from uncultivated environmental microbes via high-throughput single-cell isolation by FACS. The protocol encompasses the preservation and pretreatment of differing environmental samples, followed by the physical separation, lysis, whole-genome amplification and 16S rRNA–based identification of individual bacterial and archaeal cells. The described procedure can be performed with standard molecular biology equipment and a FACS machine. It takes <12 h of bench time over a 4-d time period, and it generates up to 1 μg of genomic DNA from an individual microbial cell, which is suitable for downstream applications such as PCR amplification and shotgun sequencing. The completeness of the recovered genomes varies, with an average of ∼50%.
Inositol-requiring enzyme 1 alpha (IRE1α) is a transmembrane sensor protein with both kinase and ribonuclease activity, which plays a crucial role in the unfolded protein response (UPR). Protein misfolding in the endoplasmic reticulum (ER) lumen triggers dimerization and subsequent trans-autophosphorylation of IRE1α. This leads to the activation of its endoribonuclease (RNase) domain and splicing of the mRNA of the transcriptional activator XBP1, ultimately generating an active XBP1 (XBP1s) implicated in multiple myeloma survival. Previously, we have identified human IRE1α as a target for the development of kinase inhibitors that could modulate the UPR in human cells, which has particular relevance for multiple myeloma and other secretory malignancies. Here we describe the development and validation of a 384-well high-throughput screening assay using DELFIA technology that is specific for IRE1α autophosphorylation. Using this format, a focused library of 2312 potential kinase inhibitors was screened, and several novel IRE1α kinase inhibitor scaffolds were identified that could potentially be developed toward new therapies to treat multiple myeloma.
With the aim of fuelling open-source, translational, early-stage drug discovery activities, the results of the recently completed antimycobacterial phenotypic screening campaign against Mycobacterium bovis BCG with hit confirmation in M. tuberculosis H37Rv were made publicly accessible. A set of 177 potent noncytotoxic H37Rv hits was identified and will be made available to maximize the potential impact of the compounds toward a chemical genetics/proteomics exercise, while at the same time providing a plethora of potential starting points for new synthetic lead-generation activities. Two additional drug-discovery- relevant datasets are included: a) a drug-like property analysis reflecting the latest lead-like guidelines and b) an early lead-generation package of the most promising hits within the clusters identified.
We present an individualized systems medicine (ISM) approach to optimize cancer drug therapies one-patient-at-a-time. ISM is based on i) molecular profiling and ex vivo drug sensitivity and resistance testing (DSRT) of patients' cancer cells to 187 oncology drugs, ii) clinical implementation of therapies predicted to be effective and iii) studying consecutive samples from the treated patients to understand the basis of resistance. Here, application of ISM to 28 samples from patients with acute myeloid leukemia (AML) uncovered five major taxonomic drug response subtypes based on DSRT profiles, some with distinct genomic features (e.g. MLL gene fusions in subgroup IV and FLT3-ITD mutations in subgroup V). Therapy based on DSRT resulted in several clinical responses. After progression under DSRT-guided therapies, AML cells displayed significant clonal evolution, novel genomic changes potentially explaining resistance, while ex vivo DSRT data showed resistance to the clinically applied drugs and new vulnerabilities to previously ineffective drugs.
A recent trend in drug development is to identify drug combinations or multi-target agents that effectively modify multiple nodes of disease-associated networks. Such polypharmacological effects may reduce the risk of emerging drug resistance by means of attacking the disease networks through synergistic and synthetic lethal interactions. However, due to the exponentially increasing number of potential drug and target combinations, systematic approaches are needed for prioritizing the most potent multi-target alternatives on a global network level. We took a functional systems pharmacology approach toward the identification of selective target combinations for specific cancer cells by combining large-scale screening data on drug treatment efficacies and drug-target binding affinities. Our model-based prediction approach, named TIMMA, takes advantage of the polypharmacological effects of drugs and infers combinatorial drug efficacies through system-level target inhibition networks. Case studies in MCF-7 and MDA-MB-231 breast cancer and BxPC-3 pancreatic cancer cells demonstrated how the target inhibition modeling allows systematic exploration of functional interactions between drugs and their targets to maximally inhibit multiple survival pathways in a given cancer type. The TIMMA prediction results were experimentally validated by means of systematic siRNA-mediated silencing of the selected targets and their pairwise combinations, showing increased ability to identify not only such druggable kinase targets that are essential for cancer survival either individually or in combination, but also synergistic interactions indicative of nonadditive drug efficacies. These system-level analyses were enabled by a novel model construction method utilizing maximization and minimization rules, as well as a model selection algorithm based on sequential forward floating search. Compared with an existing computational solution, TIMMA showed both enhanced prediction accuracies in cross validation as well as significant reduction in computation times. Such cost-effective computational-experimental design strategies have the potential to greatly speed-up the drug testing efforts by prioritizing those interventions and interactions warranting further study in individual cancer cases.
There has been increasing interest in the development of cellular behavior models that take advantage of three-dimensional (3D) cell culture. To enable assessment of differential perturbagen impacts on cell growth in 2D and 3D, we have miniaturized and adapted for high-throughput screening (HTS) the soft agar colony formation assay, employing a laserscanning cytometer to image and quantify multiple cell types simultaneously. The assay is HTS compatible, providing high-quality, image-based, replicable data for multiple, co-cultured cell types. As proof of concept, we subjected colorectal carcinoma colonies in 3D soft agar to a mini screen of 1528 natural product compounds. Hit compounds from the primary screen were rescreened in an HTS 3D co-culture matrix containing colon stromal cells and cancer cells. By combining tumor cells and normal, nontransformed colon epithelial cells in one primary screening assay, we were able to obtain differential IC50 data, thereby distinguishing tumor-specific compounds from general cytotoxic compounds. Moreover, we were able to identify compounds that antagonized tumor colony formation in 3D only, highlighting the importance of this assay in identifying agents that interfere with 3D tumor structural growth. This screening platform provides a fast, simple, and robust method for identification of tumor-specific agents in a biologically relevant microenvironment.
Selecting the most suitable liquid handling method can have a huge influence over your final results, particularly with miniaturised volumes. Acoustic liquid handling using the relatively new direct dilution technique may offer scientists greater accuracy.
Dispensing and dilution processes may profoundly influence estimates of biological activity of compounds. Published data show Ephrin type-B receptor 4 IC50 values obtained via tip-based serial dilution and dispensing versus acoustic dispensing with direct dilution differ by orders of magnitude with no correlation or ranking of datasets. We generated computational 3D pharmacophores based on data derived by both acoustic and tip-based transfer. The computed pharmacophores differ significantly depending upon dispensing and dilution methods. The acoustic dispensing-derived pharmacophore correctly identified active compounds in a subsequent test set where the tip-based method failed. Data from acoustic dispensing generates a pharmacophore containing two hydrophobic features, one hydrogen bond donor and one hydrogen bond acceptor. This is consistent with X-ray crystallography studies of ligand-protein interactions and automatically generated pharmacophores derived from this structural data. In contrast, the tip-based data suggest a pharmacophore with two hydrogen bond acceptors, one hydrogen bond donor and no hydrophobic features. This pharmacophore is inconsistent with the X-ray crystallographic studies and automatically generated pharmacophores. In short, traditional dispensing processes are another important source of error in high-throughput screening that impacts computational and statistical analyses. These findings have far-reaching implications in biological research.
The process of validating an assay for high-throughput screening (HTS) involves identifying sources of variability and developing procedures that minimize the variability at each step in the protocol. The goal is to produce a robust and reproducible assay with good metrics. In all good cell-based assays, this means coefficient of variation (CV) values of less than 10% and a signal window of fivefold or greater. HTS assays are usually evaluated using Z′ factor, which incorporates both standard deviation and signal window. A Z′ factor value of 0.5 or higher is acceptable for HTS. We used a standard HTS validation procedure in developing small interfering RNA (siRNA) screening technology at the HTS center at Southern Research. Initially, our assay performance was similar to published screens, with CV values greater than 10% and Z′ factor values of 0.51 ± 0.16 (average ± standard deviation). After optimizing the siRNA assay, we got CV values averaging 7.2% and a robust Z′ factor value of 0.78 ± 0.06 (average ± standard deviation). We present an overview of the problems encountered in developing this whole-genome siRNA screening program at Southern Research and how equipment optimization led to improved data quality.
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