In #JPM2023 news, $NSTG NanoString presented on January 11, 2023. Started in single-cell gene expression, now expanding into #SpatialBiology. They see a $3B+$3B market opportunity both on the NGS-based and the Imaging-based tech respectively.
The GeoMx is the NGS-based spatial biology solution, akin to the 10X Genomics Visium (and Visium HD in the future), with 25+ instruments in the pipe. CosMx is the imaging-based equivalent (Xenium in 10x portfolio), with about 80 instruments already in the pipe.
Lots of slides to describe CosMx, which is the newest and fastest growing instrument for $NSTG NanoString. High Plex, up to 1000-plex RNA assay, but also does Protein, FFPE Compatible, Tunable Throughput, Complete Informatics (AtoMx). Now 180 cummulative orders.
Comparison between GeoMx and CosMx, and the idea of zooming into either Areas of Interest (AOIs) or true single cell resolution.
And then a bit about the nCounter, which is the first instrument, now utilization drops as installed base matures.
Some market predictions: they see a total TAM for GeoMx and CosMx of a total of 14,000 instruments, if the penetration was 100%, so it means currently about 5% penetration. I wonder if we can extrapolate this numbers by adding up all the #InSituImaging offerings and how quickly
We will reach 7,000 instruments. The GeoMx number is a bit more difficult, as the 10x Genomics alternative is just a small bit of glass rather than an instrument like the GeoMx. Currently #InSitu is around 1,000 of those 7,000 instruments, with 10x Genomics just starting to ship.
On library prep at #Nanoporeconf, a description for PCR-free methods showing the difference between ligation (max output) and rapid mode (10minutes, minimal lab equipment needed). Ultralong reads (ULR) also enabled, all Kit14.
Rapid ULR. Current record is about 4 megabases.
PCR expansion kits enable the use of samples with low input amount.
I did a deep dive on the different workflow management (WFM) tools for #Bioinformatics Data Analysis a few years ago, and since then there have been a few extra entrants in this segment, still mostly concentrated in serving the Next-Generation Sequencing field.
A few years ago, there were two communities dominating the open-source WFM ecosystem in NextFlow and SnakeMake, and two platforms dominating the the commercial offerings in DNAnexus and Illumina BaseSpace.
Since then, a company out of the founders of Nextflow has started offering enterprise support for Nextflow workflows in the cloud: Seqera Labs. They offer the extra level of support that some organizations require to run Nextflow on their Data Analysis setups.
More interesting Next-Generation Sequencing knowledge in the ASeq Discord channel (by @new299). Illumina patterned flowcells and the etching process to "print" the wells into the flowcell. Could be down to 350nm diameter for some flowcell configurations now.
If I remember correctly, Illumina started with a 600nm diameter for the patterned flowcell, in the HiSeq X and then later on in the evolution of the platform that used these patterned flowcells.
They then said to have gone down to 500nm, and what you are showing seems to indicate that it's at 350nm now, at least for the NextSeq 2000? I am not sure if they claimed that for NovaSeq X?
There have been some acquisitions in #CancerDiagnostics and #CancerScreening recently, some of which signify a trend towards consolidation that is worth describing:
$A Agilent is moving towards some more vertical integration in Cancer Dx and Cancer screening
by recently acquiring both announcing a partnership with Akoya Bio and announcing the acquisition of Avida Biomed.
Some may ask: isn’t $A Agilent too small to go into this field? Would they be able to compete against $ILMN Illumina/GrailBio or $GH Guardant Health or $EXAS Exact Sciences?
It is likely that as Spatial Biology tools become more robust and user-friendly, they will become increasingly popular and widely adopted in the scientific community.
This may lead to a shift in the balance between single-cell and Spatial Biology approaches, with the latter eventually becoming more prevalent.
Additionally, as more and more datasets are generated using Spatial Biology techniques, the field of Machine Learning and Artificial Intelligence will likely play an increasingly important role in analysing and interpreting this data.