2 research outputs found
Label-free detection and dynamic monitoring of drug-induced intracellular vesicle formation enabled using a 2-dimensional matched filter
<p>Analysis of vesicle formation and degradation is a central issue in autophagy research and microscopy imaging is revolutionizing the study of such dynamic events inside living cells. A limiting factor is the need for labeling techniques that are labor intensive, expensive, and not always completely reliable. To enable label-free analyses we introduced a generic computational algorithm, the label-free vesicle detector (LFVD), which relies on a matched filter designed to identify circular vesicles within cells using only phase-contrast microscopy images. First, the usefulness of the LFVD is illustrated by presenting successful detections of autophagy modulating drugs found by analyzing the human colorectal carcinoma cell line HCT116 exposed to each substance among 1266 pharmacologically active compounds. Some top hits were characterized with respect to their activity as autophagy modulators using independent in vitro labeling of acidic organelles, detection of LC3-II protein, and analysis of the autophagic flux. Selected detection results for 2 additional cell lines (DLD1 and RKO) demonstrate the generality of the method. In a second experiment, label-free monitoring of dose-dependent vesicle formation kinetics is demonstrated by recorded detection of vesicles over time at different drug concentrations. In conclusion, label-free detection and dynamic monitoring of vesicle formation during autophagy is enabled using the LFVD approach introduced.</p
NMR Spectroscopy-Based Metabolic Profiling of Drug-Induced Changes In Vitro Can Discriminate between Pharmacological Classes
Drug-induced
changes in mammalian cell line models have already
been extensively profiled at the systemic mRNA level and subsequently
used to suggest mechanisms of action for new substances, as well as
to support drug repurposing, i.e., identifying new potential indications
for drugs already licensed for other pharmacotherapy settings. The
seminal work in this field, which includes a large database and computational
algorithms for pattern matching, is known as the “Connectivity
Map” (CMap). However, the potential of similar exercises at
the metabolite level is still largely unexplored. Only recently, the
first high-throughput metabolomic assay pilot study was published,
which involved screening the metabolic response to a set of 56 kinase
inhibitors in a 96-well format. Here, we report results from a separately
developed metabolic profiling assay, which leverages <sup>1</sup>H
NMR spectroscopy to the quantification of metabolic changes in the
HCT116 colorectal cancer cell line, in response to each of 26 compounds.
These agents are distributed across 12 different pharmacological classes
covering a broad spectrum of bioactivity. Differential metabolic profiles,
inferred from multivariate spectral analysis of 18 spectral bins,
allowed clustering of the most-tested drugs, according to their respective
pharmacological class. A more-advanced supervised analysis, involving
one multivariate scattering matrix per pharmacological class and using
only 3 spectral bins (3 metabolites), showed even more distinct pharmacology-related
cluster formations. In conclusion, this type of relatively fast and
inexpensive profiling seems to provide a promising alternative to
that afforded by mRNA expression analysis, which is relatively slow
and costly. As also indicated by the present pilot study, the resulting
metabolic profiles do not seem to provide as information-rich signatures
as those obtained using systemic mRNA profiling, but the methodology
holds strong promise for significant refinement