Proteomics
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Quantitative
Ability to identify 1000s of proteins in single experimental run.
Native (e.g., protein complexes) and non-native (e.g., peptide level)
states can be examined.
Lower sample amounts required (nanograms).
Ability to assess PTMs and their distribution.
Characterization of binding sites that modulate protein effector
function.
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Data analysis is customized based on the experiment and need knowledge
of a variety of software.
Isobaric labeling approaches for targeted proteomics may suffer from
incomplete incorporation in cell culture.
Membrane proteins are hard to isolate and digest due to difficulty of
precipitation because of aggregation.
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High-resolution instruments are cost prohibitive; specialized
personnel needed to perform advanced workflows.
Proteomics based MS workflows are well documented in literature, which
makes execution of these methods easy to follow.
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Transcriptomics
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Accurate quantification of transcript abundance.
Alignment to a reference genome allows for identification of target
genes.
Can be used to assess differential gene expression after perturbation
from homeostasis.
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cDNA library construction can lead to dimerization of primers making
datasets noisy.
During cDNA library construction spurious second strand cDNA artefacts
can be generated, which can confound sense vs antisense transcripts.
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Next generation sequencing instruments are expensive. However, sending
samples to specialized sequencing facilities is cost effective and
often a better alternative.
Protocols for sequencing, alignment, and statistical analysis are well
defined making RNA-Seq practical for the masses.
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Metabolomics
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Metabolic profiling in quantitative manner.
Ability to assess metabolic flux in response to perturbation from
homeostasis.
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Real time metabolic profiling is difficult.
Lipids can be particularly hard to ionize using MS based approaches.
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MS based approaches for metabolite quantification are well defined in
the literature.
Difficult to execute without access to a high-resolution Mass
spectrometer.
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Single Cell omics
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Deeper analysis of rare cell types.
Recent advances have increased throughput and reproducibility.
Phenotypical classification by assessing intrapopulation heterogeneity
at each omics layer.
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Sample preparation, isolation of single cells and throughput are
limitations for all single cell approaches.
Targeted approaches are reliant on antibody availability.
With new improvements coming up rapidly, reproducibility could be an
issue.
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Due to the abundance of methodologies for both targeted and untargeted
approaches, it is now relatively easy to incorporate single cell
workflows into any workflow.
Some approaches rely on expensive instrumentation (MS, NGS), others,
for example, miniaturized immunoassays for scProteomics are more
affordable.
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High Throughput Imaging
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Localization of surface molecules, subcellular localization of
organelles and macromolecules.
Phenotypical characterizations based on expression of specific markers
in response to homeostatic perturbations.
Accurate characterization of cellular morphology.
Assessment of intrapopulation heterogeneity based on expression of
specific markers or overall cellular morphology.
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The availability of antibodies specific to the markers of interest.
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High resolution imaging instruments are expensive.
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Computational Modeling
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Ability to profile the kinetics of molecular interactions.
In silico validation of experimental findings can help predict the
outcome of experiments.
Highly accurate 3-dimensional modeling of protein structures.
Crucial for early stages of drug discovery.
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Specific parameters need to be set from experimental results for the
simulation to mimic endogenous conditions.
Assumptions are required at the software end that may not accurately
describe the environment, for example, assuming the reaction volume to
be homogenous.
Alphafold multimer does not accurately account for PTMs and not all
PPIs are accurately mapped.
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Most software packages that can be used to model molecular dynamics,
pathways, or protein modeling are open source and do not require a
commercial license. This makes in-silico computational models
highly accessible and easy to use.
Some modeling software are computationally costly and require the use
of high-performance clusters.
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