André Forjaz

Not the official Science Advances cover of this month, but you can still find it in the current issue! Check our latest paper where we used 3D-CODA to design assembloids that mimic the complexity of human fallopian tubes. Paper:science.org/doi/10.1126/sc…

Precancers in the human pancreas. Imaged in 3D here using CODA. More here: nature.com/articles/s4158… About CODA: nature.com/articles/s4159…

Super grateful for an amazing PhD journey in the @KKLabJHU! Thankful for the mentorship and amazing labmates who made it possible! ✨

1/4 Excited to share that 3 of my doctoral students have successfully defended their theses! Congratulations to @Anindya_SenJHU, @YQinling and @alice_amitrano on this major milestone. They are outstanding researchers and people. Looking forward to seeing all they accomplish next.

CODA maps of responsive vs. non-responsive regions of PDAC tumors Deep Learning Enabled 3D Multi-Omic Analysis Reveals Molecular Signatures of Heterogeneous Response to Chemotherapy in Pancreatic Cancer More here: biorxiv.org/content/10.648…

I think there is a need for a complete re-think for organoid modeling. As long as the architecture of organoids that you produce is not directly compared to a 3D map of the organ (say, fallopian tube) or disease to be modeled, it will be approximate Here: science.org/doi/full/10.11…

Detection of metastatic burden is a task for CODA, which maps organs in 3D and at single-cell resolution. More here: biorxiv.org/content/10.648…

CODA strikes again! Preprint from @AshleyKiemen et al: 3D, multi-omic imaging reveals molecular biomarkers of the pre-metastatic niche in #lungcancer biorxiv.org/content/10.648… Nice demonstration of how 3D mapping enables directed spatial profiling of pre- and micrometastases.

CODA enables mapping of human organs and tissues in 3D and single-cell resolution and integration with spatial transcriptomics and proteomics. Our goal is to revisit work by Bourgery & Jacob using the most modern AI tools....it's working! More here: nature.com/articles/s4159…

I am giving a talk on 3D mapping of tumors, organs, and organisms at single-cell resolution (CODA) at the Sanford Burnham Prebys Institute, La Jolla, Fishman Auditorium, Feb. 17, 12:00pm

Students from the lab who are now faculty at UCSD, USC. UCLA, and OHSU came to see my talk make for a very happy PhD advisor

@lauradelongwood Congrats!! 🎉🎉

No longer assisting or associating...happy to share that I've been promoted to Professor!

I will be giving talks at UC San Diego (Dept Bioengineering) and Sanford Burnham Prebys Institute this coming Friday and the following Tuesday in La Jolla. I will talk about 3D mapping of solid tumors and new CAR T therapies to resolve them. Hope to see you there.

In spatial omic, using standard 2D sections undersamples 3D tumors and tissues, always. We asked how many slides or cores of a TMA are needed to capture the variations in tissue composition in a single 3D tumor? A lot! Download here: sciencedirect.com/science/articl…

I am looking forward to visiting the Dept of Chemical Engineering at the University of Washington. My seminar on CODA and new CAR T cells for solid tumors will be this Tuesday, 4 – 5 p.m in the PAA Detais here: cheme.washington.edu/calendar?trumb…

Dimensionality of cell migration. Cells can move along 1D tracks, in 2D sheets, confined between a hard surface and fluid (2.5D), and within a 3D tissue. Pick the optimal cell migration assay for your application using our review paper here: nature.com/articles/s4159…

3D assessments are necessary to determine the true, spatially resolved composition of tissues. Standard 2D won't do. Use CODA to switch from 2D to 3D spatial omics. More here: sciencedirect.com/science/articl…

If you want to map the spatial composition of a tissue or organ, 3D mapping is not an option. It is absolutely necessary. Due to its inherent undersampling, 2D omic mapping is almost always misleading. See details here: sciencedirect.com/science/articl…

Aligning different spatial omic data (histology, spatial proteomics, spatial transcriptomics, etc) is challenging. PhD students André Forjaz and Valentina Matos Romero developed PIVOT to do this really well and rapidly. Read about PIVOT here: biorxiv.org/content/10.110…

Color-coded CODA maps of fallopian tubes from premenopausal (left) and postmenopausal women. Magenta: secretory epithelial cells, Green: ciliated epithelial cells. More here: biorxiv.org/content/10.110…