The Orsburn Lab
at the Johns Hopkins University School of Medicine
Proteomics News (www.proteomics.rocks)
Hard work is paying off. Recent proof!
Colten Eberhard was awarded an ASPET travel award to present at WCP2023 conference in Scotland!
We just dropped the largest single cell proteomics study ever performed.
Ben has been invited to speak about it at SCP 2023 in Boston, ESCP in Vienna, and Harvard in May.
Ahmed presented on how to drastically reduce the cost of large cohort proteomics studies at US HUPO 2023.
Abigail's work identifying ultralow copy number proteins that shouldn't be in the brain....in specific brain cell types...just published! as well as a review she led on applying single cell technologies.
Multiple papers demonstrating our focus on making single cell proteomics a tool for medical research have been published in the last few months!
You can read about our methods in Nature Comms here
And (new preprint!)
And some logic for our approach in JPR here.
The sFloMPro method was the Proteomes most read paper of 2022!
5954.01 is the first high resolution LCMS assay to meet ISO 17025 chemical measurement guidelines. The 2022 audit is complete and the Department of Health has provided approval for use through 2024! We can protect patients with rigorous mass spectrometry and 5954.01 is 24-7 365 operational evidence of this fact.
The Orsburn Lab specializes in the application of mass spectrometry to solve unconventional challenges in human health and pharmacology. Our research ranges from the analysis of post-translationally modified proteins through products of drug metabolism, with many stops in between.
The effective application of modern mass spectrometry requires collaborative interfaces with clinicians and biologists. These collaborations are essential so we can focus on method development at the bench, in the vacuum chambers and developing the new software to pull it all together. Whenever possible we make all of our data publicly available to the global community in the most accessible ways we can.
Here are some of our current projects.
Applying single cell proteomics to understand resistance to chemotherapies
Just accepted in Nature Communications!
Cancer recurrence can begin with a single cell that has survived a chemotherapy regimen.
We specialize in the application of single cell proteomics (SCP) by mass spectrometry to better understand the response of individual cells to drugs.
We first study the phenotypic responses of cells treated with drugs with proteomics of thousands of homogenized cells. Once responses are identified, we use single cell proteomics to understand the level of heterogeneity that exists in this response at a cellular level. Our first study was just accepted in Nature Communications.
This isn't all, you can see why we're using Time of Flight Technology here.
AND a new proof of concept preprint demonstrating that we can scale this technology to thousands of single cells.
Leveraging scalable computing to obtain unprecedented coverage of the human proteome
LCMS based proteomics typically relies on desktop computing architecture. While these workflows allow us to identify most human protein groups, they do not allow us to understand the actual proteoforms of interest. By leveraging scalable Cloud based platforms we can study proteomics in a true biological context. Using this approach we can identify human mutations without the need for expensive genomics input. Our recent work has simplified cancer neo-antigen discovery and resulted in the most comprehensive proteome of human tumors ever assembled.
Modernizing clinical assays with mass spectrometry
Many assays used for patient diagnostics have not changed in any meaningful way in decades. We have identified both metabolic and proteomic assays of low sensitivity or specificity. We were recently awarded our first collaborative grant to replace a historic neonatal assay requiring multiple blood draws with something better, faster, and less invasive for children.
Want to collaborate?
725 N. Wolfe Street Baltimore, MD
email@example.com (remove the dashes)