@GenomicsCow, in a recent post you say you’re not sure the next wave 🌊 after
#genomics is
#proteomics. I think a new wave of proteomics and
#multiomics technologies are upon us, just that not everyone is aware of what’s now available.
If, as you say, a major problem for current proteomics is that data from different platforms just doesn’t ‘match’, then the tech is just too noisy (like with
#RTqPCR data, where about half of everything published is just technical noise—true—see publications by Stephen Bustin or Tania Nolan for starters!).
Harmonising protein quantification and gene expression data (multiomics) is easy if each method counts individual molecules (for quantification) while also mapping the spatial location of each molecule (to align biological context).
That’s what NanoString’s
#GeoMx and
#CosMx platforms do, and why, despite being separate and orthogonal technologies, the data from each (for either multiomic analysis or cross-platform validation) is concordant. The common data type for both the CosMx and GeoMx is just individual molecule counts. This we can all understand, even if you start with gene expression- or protein-only expertise.
Don’t believe me? Please digest the example CosMx and GeoMx proteomic dataset images I submit here. Similar validation data for gene expression (out to whole transcriptome) on each platform is equally impressive, but that’s a separate story.
@nanostringtech
@Proteomics @ProteomicsNews
@SpatialOmics @ucdmrt @jcb_31416 @NLKProteomics @drchrispook @rebecca_poulos @AlbertVilella @BlauerPlums @lee_spraggon @NeBanovich @ArpitaBKulkarni @GenomicsCow @aruthak @GESTALT_sp @HumphreysLab @DavidPCook @DrJasPlummer @Elislab_ @TheGregoryLab @m_mohenska @kirkbjensen @nikhil_seq @Kellieiswise @pinkney_holly
@ioavlachos @sarahannbest @dgallegoortega @ANU_Spatials @Genomics_GU @SharmaaLab @TheStarkLab@singlecellworld
@ozsinglecells @drjosephpowell @hoheyn @anothernicwest #SpatialOmics #singlecell #Neurogenomics #OpenScience #RealScience #prot