The immunometabolic topography of tuberculosis granulomas governs cellular organization and bacterial control Despite being heavily infiltrated by immune cells, tuberculosis (TB) granulomas often subvert the host response to Mycobacterium tuberculosis (Mtb) infection and support bacterial persistence. We prev...

Happy to share a preprint from the Angelo lab many years in the making. Read on for a saga of multiplexed imaging, immunometabolism, and TB granulomas with some fun side quests into the realms of geographical information sciences and transcriptomics… (1/20) doi.org/10.1101/2025...

GitHub - angelolab/toffy: Scripts for interacting with and generating data from the commercial MIBIScope Scripts for interacting with and generating data from the commercial MIBIScope - angelolab/toffy

We developed a dedicated pipeline for mibi data: github.com/angelolab/to..., but for other data modalities I’m not as familiar what people generally do. Right now there isn’t a good cross platform solution for data normalization, at least not that we’ve found

Great point. We spent a lot of effort addressing batch effects earlier in our processing pipeline so that SpaceCat wouldn't have to deal with them. In general, I would say the earlier you can address your batch correction issues, the better, but there aren't as many options for spatial data

Complex rearrangements fuel ER+ and HER2+ breast tumours - Nature A study identifies three dominant genomic archetypes of breast cancer induced by discrete mutational processes, describing a continuum of genomic profiles and detailing the mechanisms underlying the p...

The Curtis Lab’s latest study on the genomic architecture of breast cancer from the pre-invasive to metastatic setting is now out in Nature! www.nature.com/articles/s41...

Thanks Kieran!

If you run into any problems getting the codebase to work, have questions about what we found, or want to chat, please don’t hesitate to reach out (/end) bsky.app/profile/noah...

This wouldn’t have been possible without an amazing team (most of whom have not migrated over to the good place yet!), including Iris, Cami, Seongyeol, Manon, as well as Christina, Marleen and Mike (9/x)

GitHub - angelolab/SpaceCat: Generate a spatial catalogue from multiplexed imaging data Generate a spatial catalogue from multiplexed imaging data - angelolab/SpaceCat

This was just a sampling of what we found; for the full details, please check out the paper, as well as our github, where we’ve made all the underlying code open source and available (8/x)
github.com/angelolab/Sp...

Evaluation of multivariate models trained on different timepoints (x axis) and data types (colors) to predict patient outcome.

Finally, to look at how these features could be combined together, as well as to compare modalities, we built multivariate models to predict outcome from each data type at each timepoint. We found large differences across both assay types and sample timepoints! (7/x)

The same feature (T / Cancer Ratio) has no association with outcome when looking at the primary tumor, but very strong association with outcome when looking at the on treatment (on-nivo) sample

When we looked at the specific features we defined, we found some that were temporally dependent, with good predictive power at one timepoint but poor predictive power at another timepoint (6/x)

Volcano plot on the left showing association with outcome for each of the SpaceCat features. Barplot on the right shows the enrichment in top predictive features for those defined within specific regions (compartments) of the tumor

We then tested which of the 800+ features from SpaceCat could predict response to immunotherapy, finding numerous strong predictors. Interestingly, features defined in specific regions of the tumor did an especially good job at predicting outcome (5/x)

Summary of the types of features that SpaceCat generates, with representative images from four of the categories

To help us make sense of this spatially-resolved data, we built SpaceCat, an algorithm to quantify and summarize the key features from spatial datasets. SpaceCat can be applied to processed imaging data from any multiplexed imaging platform! (4/x)

Heatmap showing the cell types identified in our study. Each row is a cell type, and each column is a different marker on the antibody panel

We then generated highly multiplexed imaging data using an antibody panel of 37 antibodies. This allowed us to identify 22 cell types across the more than 650 TMA cores we imaged from 117 total patients (3/x)

Cartoon overview of the samples collected from patients at each timepoint, as well as the number of different modalities (MIBI, DNA, RNA) collected from each.

Our awesome collaborators at NKI put together a unique cohort spanning primary disease, pre-treatment metastases, and on-treatment metastases from triple negative breast cancer patients enrolled in the TONIC clinical trial (2/x)

Temporal and spatial composition of the tumor microenvironment predicts response to immune checkpoint inhibition Immune checkpoint inhibition (ICI) has fundamentally changed cancer treatment. However, only a minority of patients with metastatic triple negative breast cancer (TNBC) benefit from ICI, and the deter...

I’m super excited to share what I’ve been working on for the last (many) years: a spatial + genomic + transcriptomic characterization of how the breast cancer microenvironment evolves through immunotherapy! (1/x) 🧪🧬 🖥️ #AcademicSky #MLSky #ImmunoSky www.biorxiv.org/content/10.1...

I wanted to write briefly about a very pleasant experience we recently had coordinating and collaborating closely on competing publications with 2 other teams. 1/

Hi Erik, I work on tissue imaging, spatial biology, and cancer research. Could you please add me to the feed? Thanks!scholar.google.com/citations

Reposting our Penn Postdoctoral Fellowship in Genetics!
www.med.upenn.edu/apps/my/bpp_...

Hi all, I just joined! I’m a PhD student at Stanford studying tumor immunology. Excited to try this thing out #HiSciSky #AcademicSky