Dillon Corvino

account no longer monitored. find me on Bluesky @Eomesodermin.bsky.social

[Dillon Corvino @Eomesodermin]

🚨 Open #position! The group of Zeinab Abdullah is looking for a Technical Assistant/Research Assistant

Application deadline: October 30 https://t.co/QECakVKCgy

[Dillon Corvino @Eomesodermin]

🚨 Open #position! The group of Zeinab Abdullah is looking for a postdoc

Application deadline: October 30 https://t.co/QECakVKCgy

[Dillon Corvino @Eomesodermin]

🚨 Open #position! The group of Zeinab Abdullah is looking for two PhD students for

a) Characterisation of hepatic NK-like T cells

b) Dissecting the role of STING signalling in anti-viral T cell responses

Application deadline: October 30 https://t.co/QECakVKCgy

[Dillon Corvino @Eomesodermin]

My twitter/X feed has like many others become filled with Elon and Trump/Trump adjacent nonsense. I’m giving BlueSky a go and so far looks like a good portion of scitwitter has begun the migration. Come find me on the other app and let’s talk science.

[Dillon Corvino @Eomesodermin]

As always, many thanks to the patients and many collaborators. @Doc_Bald @IEO_baldlab @IEO_Bonn @theHumanBorch

[Dillon Corvino @Eomesodermin]

I am excited to share our latest paper. We profiled CD8+ TILs from HPV- HNSCC patients using scRNA/TCRseq. In addition, we performed an ex vivo stimulation of cells to capture their transcriptional response. 1/n

https://t.co/cP1cAPkBel

[Dillon Corvino @Eomesodermin]

This paper scratches the surface of understanding how prolonged/sustained IFN impacts CD8+ TILs. We found it interesting that these cells can be observed in many scRNAseq datasets but are rarely mentioned. We hope this manuscript inspires others look further at these cells. 6/n

[Dillon Corvino @Eomesodermin]

@QuirinHammer @Vetenskapsradet @CIM_Sweden @karolinskainst Congratulations Quirin.

[Luciano Martelotto 🛠🧬💻🇦🇺 @LGMartelotto]

Following several kind requests, here is a high-level summary of our pre-print in @biorxivpreprint (doi: https://t.co/m4OHy3BP5N):

STAMP: Single-Cell Transcriptomics Analysis and Multimodal Profiling through Imaging

STAMP is a scalable, cost-efficient method for single-cell transcriptomics and multimodal profiling through imaging. It bypasses sequencing, significantly reducing costs while enhancing throughput.
#SingleCellRNA #SpatialOmics #STAMP

The motivation for this paper comes from the limitations of current scRNA-seq methods, which are costly, inefficient, and struggle to capture low- and high-abundance transcripts. Challenges like droplet instability, cell damage, limited cell capture, cross-contamination, inefficient indexing, and high sequencing costs affect ultra-low and ultra-high cell profiling, hindering accurate analysis of complex cell populations. #Limitations #DropletMicrofluidics

STAMP addresses these issues with a scalable, cost-effective, sequencing(costs)-free approach that works efficiently across ultra-low to ultra-high cell numbers, while preserving cellular morphology and enabling multimodal profiling. #SpatialOmics #UltraLow #UltraHigh

STAMP uses an imaging-based approach to perform high-throughput RNA and protein profiling by stamping cells onto slides compatible with CosMx and Xenium platforms, allowing multimodal profiling in a single run.
#Proteomics

STAMP supports single-modal (RNA/protein) and multimodal (RNA + protein) profiling, enhancing experimental flexibility and scalability. This versatility is essential for large-scale atlases and mixed-sample experiments.
#SingleCellAtlas #MultiSampleProfiling

STAMP scales easily: from less than 100 to millions of cells. It overcomes limitations in droplet-based systems, where throughput is often constrained by sequencing costs.
#HighThroughput

STAMP handles diverse sample types: PBMCs, dissociated tumors, nuclei, and stem cells. It also excels with fragile or archived tissues, where traditional methods often fail to yield high-quality data.
#ArchivedSamples

STAMP eliminates sequencing costs, making large-scale single-cell analysis accessible to more labs. #CostEfficiency

Here’s a cost comparison between GEM-X and STAMP-X for RNA profiling:

GEM-X RNA (20k cells): $0.0764/cell
1M cells = $76,400 (cost/cell = $0.0764)
2M cells = $152.800 (cost/cell = $0.0764)
3M cells = $229,200 (cost/cell = $0.0764)

STAMP-X RNA (1M cells): $0.0075/cell
1M cells = $7,500 (cost/cell = $0.0075)
2M cells = $7,500 (cost/cell = $0.00375)
3M cells = $7,500 (cost/cell = $0.0025)

In STAMP cells remain intact after imaging, enabling further downstream applications like histological validation or additional molecular assays, adding value to each sample.
#NonDestructiveAnalysis

We tested STAMP’s sensitivity by identifying, ultra-rare, circulating tumor cells (CTCs) spiked into PBMCs at 1:100,000. Such sensitivity is essential for clinical diagnostics, particularly in rare cell detection.
#CTCs #CancerDiagnostics

In a high-throughput immuno-phenotyping experiment, STAMP profiled 1.7M PBMCs, capturing a median of 83 transcripts and 49 genes per cell. This resulted in high-resolution immune profiling.
#Immunology #HighThroughputProfiling

31 immune cell states were mapped in PBMCs, capturing rare subpopulations (Th1/Th2/Th17 and NK subtypes). Detailed immune phenotyping is critical for understanding immune diversity in health and disease.
#ImmuneMapping

Whole cells vs. nuclei: STAMP's ability to profile both whole cells and nuclei is crucial for samples with low RNA content (e.g., archived tissues), increasing the platform’s applicability.
#NucleiProfiling #SingleCellOmics

STAMP integrates RNA and protein profiling. In cancer cell line profiling, RNA and protein data showed strong correlations, confirming the platform’s robustness in multimodal data collection.
#MultimodalProfiling

STAMP’s multimodal profiling produced high-resolution immune maps, combining RNA and protein data. This approach is essential for dissecting complex immune states and understanding functional responses.

STAMP excels in low-input samples. We demonstrated its ability to accurately profile as few as 100 cells, which is critical for studying rare populations in small samples.
#LowInput #RareCell

In a BMP4-driven stem cell differentiation model, STAMP captured dynamic changes from pluripotency to mesoderm and endoderm progenitors over multiple timepoints, tracking lineage differentiation.
#StemCellDifferentiation #LineageTracing

Trajectory analysis shows that STAMP’s resolution makes it a powerful tool for developmental biology and perturbation studies.
#DevelopmentalBiology #hESC

STAMP allows multi-sample profiling on a single slide, reducing batch effects and improving comparative analysis, particularly in drug response and perturbation studies.
#MultiSampleAnalysis #PerturbationScreening

STAMP represents a significant advance in single-cell research, offering scalable, multimodal RNA/protein profiling at low cost. Its versatility will support a broad range of research, from basic to clinical.
#SingleCellTechnology

Lots more coming soon...

@DrJasPlummer @hoheyn @pascual_reguant @EmanuelePitino1 @helucro @ximbaozao @irepan_salvador @Kellieiswise @m_mohenska @jc_nietos @cnag_eu @StJudeResearch @ACEpigenetics @M_ayco_N @eliseinsing Bill Flynn, Yutian Liu, Hannah Chasteen

[Immunity @ImmunityCP]

Online now: T lymphocyte recruitment to melanoma brain tumors depends on distinct venous vessels https://t.co/BmqMcQWQGH

[Dillon Corvino @Eomesodermin]

@LabWaggoner @jmkirkwood @tonyrcillo @CellCellPress Authors of the preview are pretty generous with the title. “Cyclone” is not a package, there is no documentation. It’s just a repository holding a custom set of functions. I wish reviewers had requested the authors do more to make this algorithm usable by others in the community

[Laura Surace @surace_laura]

We are looking for a highly motivated PhD Student! 🤩
If you are passionate about science and interested in tumor immunology and metabolism… join us in Bonn! 👩🏻‍🔬👨‍🔬
please share🙏

[Chenlei Hu @HuChenlei]

We developed an imaging-free spatial genomics technology where DNA barcodes diffuse to connect locally. Using UMAP, we reconstructed the physical locations of these barcodes, transforming spatial transcriptomics into purely molecular biology.

https://t.co/BIPlyibPXa

[Dillon Corvino @Eomesodermin]

@Jeff_Mold I think its good to use TCR and velocity in conjunction. Can be hard to tell the directionality of a lineage relationship just from TCR data. Velocity can help inform the direction.

[Dillon Corvino @Eomesodermin]

@Jeff_Mold @immunonoah These are very different uses. Not always possible to do barcoding experiments.

Is it possible that there is a trajectory from activated cells to memory cells? This appears plausible to me. Is there barcode data for this. Most barcode data is earlier in development.

[Dillon Corvino @Eomesodermin]

@Jeff_Mold are they not related though? you have pMel T-cells in fig G. Rest are Tcell functional states. Transitions between these states seems a fair assumption. An observation that should be preserved across mice.