Biology Reference
In-Depth Information
transfected cells in a mixture of non-transfected cells
[87,88]
. Transfected cell microarrays facilitate large screens
with many replicates, as they offer the advantage of using
minimal amounts of antibody compared to traditional plate-
screening formats. For example, Lindquist et al. performed
a genome-scale RNAi screen on microarrays of Drosophila
cells to identify novel regulators of mTOR (mammalian
Target of Rapamycin) complex 1 (TORC1) signaling by
immunofluorescence
[89]
. A total of 70 novel genes were
identified as significant regulators of RPS6, a TORC1
effector. Cell microarrays facilitate large screens with many
replicates, as they offer the advantage of using minimal
amounts of antibody compared to the traditional plate-
screening format. The amount of immunofluorescence from
the phospho-specific antibody can be normalized to cyto-
plasmic area using standard image analysis software pack-
ages such as those developed for HCS image analysis
[90]
.
Although plate reader assays have the advantage of
speed and ease of performance, HCS is undeniably one of
the most powerful HTS assays because it allows multiple
cellular features/parameters, such as protein abundance as
well as localization, to be measured simultaneously. Image-
based screens typically use either fluorescently conjugated
primary or secondary antibodies to visualize proteins or
cellular structures of interest (for example anti-Fibrillarin
antibody to visualize the nucleolus) or fluorescently labeled
dyes and GFPs tagged with the appropriate localization
signal (for example nuclei, mitochondria, Golgi, and actin
filaments). HCS has been performed to identify targets of
small molecules/drugs
[91
mitochondrial dynamics
[123]
, circadian clock
[124]
,
hypoxia
[125]
, phagocytosis
[113,126]
, innate immunity
[127
129]
, cell susceptibility to infection by viruses or
other intracellular pathogens
[104
e
107]
as well as most of
the major signaling pathways
[7,47,66,69,70,75,77,85,86,
130,131]
. Results from these screens have not only identi-
fied new components of the process under consideration but
have also provided insights into the complexity of signaling
networks. RNAi screens in mammalian cells
[47]
have led
to the identification of novel oncogenes and putative drug
targets for the development of therapeutics
[64,132
e
136]
.
Although HTS based on RNAi has transformed the field
of systems biology in the identification of gene functions, it
is important to keep in mind that inhibition of gene
expression by RNAi is not the same as gene inactivation by
mutation. RNAi acts at the level of the messenger RNA
(mRNA), either by reducing mRNA levels or by blocking
mRNA translation
[137
e
139]
. Thus, RNAi-based assays
can suffer from high rates of false negatives due to incom-
plete knockdown of mRNA levels (or knockdown of only
specific splice forms). Another significant issue associated
with RNAi reagents is that they can lack specificity due to
suppression of unintended genes, leading to false positives.
False positives due to sequence-dependent off-target effects
(OTEs) have been shown for RNAi reagents with
e
19 nt
regions of homology with unintended targets
[140]
.Ithas
also been demonstrated that sequence-dependent OTEs are
particularly problematic when the RNAi reagents target gene
regions containing CAN repeats (where N can be any
nucleotide)
[75]
that are found in many fly genes. In addi-
tion, siRNAs can also interfere with mRNA stability and/or
translation through the microRNA pathway
[137
95]
and also in a number of
RNAi screens to identify genes that affect diverse cellular
functions, including cell morphology
[96
e
98]
, cell cycle
progression
[99]
, mitosis
[100,101]
, endocytosis
[102, 103]
and host
139]
.
MicroRNAs (miRNAs) are non-coding RNAs that are
encoded by the organism's genome and help regulate gene
expression. Mature miRNAs are 22 nt RNAs and are similar
in structure to siRNAs that are produced from exogenously
introduced long dsRNAs. miRNAs bind to complementary
sites that are 7
e
e
107]
. Quantitative
image analysis has also been used to identify genes
required for growth and morphology of fluorescently
labelled primary neurons/glia and muscle cells in response
to RNAi-mediated gene knockdown
[108,109]
. Although
multi-parametric, quantitative image analysis applied to
large-scale functional genomic screens promises to
generate systems-wide insights into many fundamental
cellular processes, automated image acquisition and anal-
ysis, feature extraction, and data storage can be challenging
and are still undergoing rapid development
[110,111]
.
Other cell-based assays include the use of flow cytometry to
measure response to RNAi treatments
[112
pathogen interactions
[104
e
e
8 nt long within 3
0
UTRs (untranslated
region) of target genes, leading to cleavage or translational
repression
[141]
. The siRNA and microRNA pathways
converge downstream of initial processing steps and share
some of the same silencing machinery
[118]
.The5
0
region
of the siRNA can act like the seed region of a microRNA,
which extends from position 2 to position 8 of the guide
strand and is complementary to sequences in the 3
0
untranslated region (3
0
UTR) of target genes. Since a perfect
match of only 7
e
114]
.
RNAi HTS in various cell lines using the different cell-
based assays discussed above, have been conducted for
a diverse array of biological processes, including cell
viability
[68]
, cell morphology
[96,98]
, cell cycle
[112]
,
cytokinesis
[91]
, susceptibility to DNA-damaging agents
[115,116]
, RNA processing
[117,118]
, general
e
8ntisrequiredbetweentheseedregion
and the target mRNA for repression, it is difficult to identify
all of the many putative targets in a cell
[142,143]
. Thus,
sequence-dependent OTEs of siRNAs seem to result at least
in part via microRNA-like off-target activity, which may
result from siRNAs entering the microRNA pathway and
functioning as microRNAs on targets with matches to the
seed region in their 3
0
UTRs.
e
and
specialized secretion
[67]
, calcium stores
[119
121]
,
factors influencing polyQ aggregation and toxicity
[122]
,
e
