Technology Prime Features Limitations Ease of execution
Proteomics
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. 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. 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.
Transcriptomics
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. 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. 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.
Metabolomics
Metabolic profiling in quantitative manner. Ability to assess metabolic flux in response to perturbation from homeostasis. Real time metabolic profiling is difficult. Lipids can be particularly hard to ionize using MS based approaches. MS based approaches for metabolite quantification are well defined in the literature. Difficult to execute without access to a high-resolution Mass spectrometer.
Single Cell omics
Deeper analysis of rare cell types. Recent advances have increased throughput and reproducibility. Phenotypical classification by assessing intrapopulation heterogeneity at each omics layer. 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. 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.
High Throughput Imaging
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. The availability of antibodies specific to the markers of interest. High resolution imaging instruments are expensive.
Computational Modeling
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. 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. 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.