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NEBULIX's Minjae & COSMX's Yuna Spark Dating Controversy!
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🚨 MINJAE + YUNA DATING SCANDAL AGAIN?! 👀☕
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From Grant M. Barthel:
Quote his LinkedIn post (https://t.co/G48JTfikLR)
“The future of spatial biology isn't single cells—it's spatial neighborhoods.
A new Cancer Cell study is a great example of why.
Using the #CosMx SMI 6K assay, the authors leveraged high-plex, single-cell spatial transcriptomics to map the tumor microenvironment with remarkable molecular depth. Their work reinforces an important shift in how we think about biology, not just individual cell types, but how cells are organized and interact within tissues.
A few takeaways:
• Spatial neighborhoods reveal biology that cell identity alone cannot fully explain.
• Single-cell spatial context links tissue architecture to tumor and immune function.
• High-plex spatial profiling enables deeper interrogation of complex multicellular programs.
Plex isn't just about measuring more genes, it's about asking more sophisticated biological questions.
As spatial biology continues to evolve, studies like this reinforce that spatial neighborhoods are becoming the new functional unit of biological discovery.
📖 Citation:
Liang, Z. et al. Tumor cells metabolically resist immune-checkpoint therapy by macrophage efferocytosis-mediated fatty acid recycling. Cancer Cell44, 1235–1254.e11 (2026). https://t.co/xK3u8Jd5eL”
Unquote
Now, the concept of spatial neighborhoods is everywhere now - Goltsev et al. (2018, Cell) introduced windowed neighborhood analysis with CODEX, and Schürch et al. (2020, Cell) popularized the “cellular neighborhoods” framing, clustering each cell’s local window of neighbors into recurring niches.
But the more I use it (or read about it) the more I catch myself thinking: wait… is this a real thing, or did we just make it up? Why doesn’t it quite click?
The honest answer is both, and I find the mix kind of satisfying.
Part of it is real. Space actually has structure. Distances are what they are, some things touch and some don’t, and there’s a genuine pattern underneath: near things tend to be more related than far ones. Nobody invented that. It’s just how the world tends to behave.
But part of it is us. Where does “near” end and “far” begin? Shared edge, or shared corner? A distance cutoff, or the closest few? Every one of those choices spits out a different answer to the same question…and there’s no rule handed down from nature telling you which to pick. That part is on us.
For a while I thought that made the whole concept a little suspect. It doesn’t. “We built this” and “this is sound” aren’t opposites. A made-up concept can still be a good one, as long as it’s tracking something real and actually helping you explain or predict.
That’s the sweet spot, I think. The nearness is real. The boundary is a choice. And the good work happens when you’re honest about which is which.
Excited to highlight our comparison of #spatial #transcriptomics technologies #Xenium #Visium @10xGenomics and #CosMx @brukerspatial in #GenomeBiology @BioMedCentral led by @eportacangen
https://t.co/a6B2dRayHn
Timothy Glidewell on CosMx – A Promise for Severe Aplastic Anemia
@NIH
https://t.co/kCP2Pocrdx
#AplasticAnemia #CosMx #Health #Hematology #Hemostasis #HemostasisToday #Medicine #NCI #MedEd #MedX
@Xiaojie_Qiu @CellCellPress Thank you for your very kind words, Xiaojie! We think this should be compatible with most, if not all, image-based spatial transcriptomic instruments out there, so this should be quite adaptable. Looking forward to collaborating with you and your team too!
👀 I spy two CosMx Spatial Molecular Imagers!👏 Applause for Anna Pascual Reguant, Principal Investigator at CNAG, whose pioneering work in spatial genomics is shaping the future of biomedicine. Learn more: https://t.co/EWgez2RPqj
#SpatialGenomics #CosMx #SingleCell
Who wants to see over 18,000 genes? Using the CosMx Spatial Molecular Imager (SMI), we've characterized human colon FFPE tissue with the new CosMx Human Whole Transcriptome Assay. Download the new whole transcriptome dataset today! 👇
https://t.co/wmNAMMiDSX
#SingleCell #CosMx
Are you at AACR?
15 minutes left! The countdown begins!
Get ready for “Expanding the Limits of Biology: Comprehensive Integrated Technologies for Multiomics in Oncology” at Exhibitor Spotlight Theater C.
#AACR25 #SpatialBiology #CosMx #nCounter #Paintscape #Cellscape
This is a MUST-SEE! A true crowd-puller.
Get ur Qs answered directly by none other than Joe Beechem as he presents whole transcriptome single-cell spatial imaging of tumor vs. normal tissue microarrays using #CosMx
Section 32, Poster 2080
#AACR2025 #SpatialBiology
What hidden patterns lie within colon carcinoma?
Margaret Hoang reveals tumor heterogeneity and immune microenvironment shifts with single-cell spatial transcriptomics.
Section 32, Poster 2078.
#AACR2025 #SpatialBiology #CosMx #ColonCancer
How does gene expression shift from normal to tumor in renal clear cell carcinoma?
Shanshan He maps the spatial full transcriptome and Proteome to reveal the answers.
Section 32, Poster 2068.
#AACR2025 #SpatialBiology #CosMx #RenalCancer
@JiwoonPark_ more space samples from @masen_lab at #AGBT2025 on #AstroSTAMP: enabling #singlecell profiling of PBMCs at a fraction of the cost. Rare cell types identified with #spatialomics. #CosMx #BrukerSpatial @AGBT #AGBT
@JiwoonPark_ Space Biology & SOMA @EliahOverbey from @masen_lab at #AGBT2025 on #spatialomics: Mapping 10M+ cells across 100 patients, 20B+ transcripts, adding spatial context to high-res data. #CosMx #CellScape @AGBT #AGBT
“@JoeBeechem at #AGBT2025 #AGBT25 with #BrukerSpatial on next-gen #spatialbiology: highest-plex #multiomics, AI/ML-ready #singlecell data (1.82B cells, 600B transcripts), and breaking the 4-digit #protein plex barrier. #CosMx #AtoMx #GeoMx @AGBT
HIV latency remains a key barrier to a cure, with limited biomarkers to identify dormant, infected cells. This #FridayRead spotlights how Merck researchers, led by Guoxin Wu, are tackling this with CosMx SMI 👉 https://t.co/5qb0K3xxoI
#Virology #SingleCellSpatial #HIV
Comparing Performance of Imaging-based #SpatialTranscriptimics methods
#CosMx vs #MERFISH vs #Xenium
1k vs 500 vs 339 plex
93-gene overlap
FFPE Human Lung/Mesothelium Tumor Tissue Microarray
CosMX▶️highest transcript counts/cell
Xenium▶️lowest FDR & closest biological cell shape
Cell type detection
CosMx 9
MERFISH 6
Xenium 14 (UM) 18 (MM)
scRNAseq Label transfer helps
Xenium MM▶️the highest F1 score (cell segmentation precision)
@kchenken bioRxiv 2024
https://t.co/iWbfAWsSWv@
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