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Our review on mass cytometry methods is out! Plenty of tips/tricks and insight into applying MC to your research. Check it out on early access at authors.elsevier.com/a/1ebUL3QxxSnI… and a BIG thank you to my co-first author Sam Kimmey with support from Bendall Lab!

Today I and @kausaliavbosse are thrilled to announce our Bendall Lab preprint profiling spatial proteomic imaging of single-cells and proteopathy in human neuropathology! #MIBI #NeuroTwitter #systemsbio biorxiv.org/content/10.110… (1/13)

Thrilled to share that our HSS-LDA manuscript is out in Cell Patterns! Supervised dimensionality reduction for exploration of single-cell data by Hybrid Subset Selection - Linear Discriminant Analysis (HSS-LDA) cell.com/patterns/fullt…

Cell Patterns interviewed David and I for the "People of Data" article in their August issue! We talk about our journey as (data) scientists, how the HSS-LDA project started, and more. Check it out to learn a bit more about us beyond the research! cell.com/patterns/fullt…

Excited to share the latest preprint with Michael Angelo! While imaging studies often focus on cell objects, images capture significant info outside of cells. Enter Pixie, a pipeline for the quantitative annotation of both pixel and cell level features🧵biorxiv.org/content/10.110…

Excited to share my preprint with Bendall Lab and William J. Greenleaf on novel human lymphoid progenitors! Turns out they had been misidentified as myeloid progenitors thus far. Check out how we revealed their true identity in the tweetorial below 🧵biorxiv.org/content/10.110…

Better late than never! Happy to share this work alongside my co-authors including Steven Tang Graham Erwin out now Joaquin C. Initially, we were interested in studying the gp41 pocket of HIV-1, a highly conserved drug/vaccine target..🧵(1/5) doi.org/10.1016/j.jbc.…

Excited to share that our work on regional T cell clonal responses to immune checkpoint blockade is out now! Very grateful to Andrew Chow, Matthew Hellmann, Ansu Satpathy, and all other co-authors for this wonderful collaboration. Check it out here! authors.elsevier.com/c/1gqic_Ywcvmm…

We're excited to announce that the 2nd annual Spatial Biology Workshop will be taking place August 28th-30th at Stanford. The workshop will cover new techniques and tools for generating and analyzing spatial data, as well as new biology discovered using spatial methods.

Thrilled to share the preprint from my postdoc work in the Bendall Lab with Michael Angelo and Thomas Montine at Stanford Medicine looking at human microglia in situ in #aging and #Alzheimers disease by multiplexed proteomic imaging! shorturl.at/agCN0

Come check out #_hubmap’s new body of knowledge and discoveries in the newly released nature paper package – Da Vinci himself would give us a Mona Lisa smile for this renaissance of technology and imaging! nature.com/articles/s4155…

This week in Nature: Body image - HuBMAP initiative maps human organs at single-cell resolution Read the full issue: nature.com/nature/volumes…

Our new Human BioMolecular Atlas Program (HuBMAP) study examining how the maternal fetal interface is established in human pregnancy is out! Big effort led by Inna Averbukh, Erin Soon , Shirley_Greenbaum. So proud of the beautiful study they completed! nature.com/articles/s4158…

Nature research paper: A spatially resolved timeline of the human maternal–fetal interface go.nature.com/44O7TgO

A paper published in nature presents a reference cell map for the human maternal–fetal interface (where placenta and maternal cells coexist), which is part of the Human BioMolecular Atlas Program (HuBMAP) published across the Nature Portfolio journals. go.nature.com/3Dm0RUY

Excited to see this published! Check out Pixie, a pipeline for quantifying non-cellular features (as well as improving cell classifications) in multiplexed images. nature.com/articles/s4146…

The second annual Spatial Biology Workshop is just two weeks away! This free event will showcase the power of spatial techniques to answer biological questions. We'll be showcasing new methods for data generation and analysis, as well as the novel findings they enable (1/x)