In #JPM2023 news, $AKYA Akoya Biosciences presented with their #SpatialBiology solutions. Here the 3 instruments lined up from Discovery to Translational to Clinical segments.
Instrument numbers: 229 PhenoCyclers, 83 PhenoImager Fusions, and 551 PhenoImager HTs, Mantras & Vectras, as of September 30, 2022.
They see the 3 segment TAMs in the $3B, $4B and $7B range. I wonder if there are others leaning more on the clinical for their TAM predictions, rather than having earlier segments already accounting for so much.
Some numbers of the current Spatial Biology Market Trends and where they see CAGR opportunities. DeciBio Projects for Akoya highlighted.
Announcement of the RNAscope by ACD (Bio-Techne) on the PhenoCycler-Fusion: a direct RNA detection technology, automated 12-plex RNA configuration, 24 hours TAT.
On the Panels side, PhenoCode Panels deliver a high-plex RNA and protein suite of panels.
An example of one of these PhenoCode Signature panels here is Immuno-Oncology (IO), here first 5 panels for 1Q '23.
Then continuing in 2023, the PhenoCycler-Fusion 2.0 with 100+ plex / 20+ samples per week both RNA and protein enabled.
For the PhenoImager HT, it's all about adoption, with biopharma partnerships and workflow ecosystem tuning.
A little bit of data analysis: being used to #NextGenerationSequencing, and read data, of the like of FASTQ and BAM files, it's a bit refreshing to see companies in #InSituImaging talking about Data Analysis in terms of turning terabytes of image data in GBs: the QPTIFF format.
A partnership example, here with Acrivon Therapeutics, for one of the OncoSignature tests.
And a partnership between $A Agilent and $AKYA Akoya for End-to-End workflows.
Final slide for Financial Overview: $20-21M Q4 2022 estimated revenue range, and almost $75M for 2022.
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.