Explorers in the wilderness of cancer biology

Dallas, TX
Joined September 2017
S.Zhang Lab retweeted
Every cell in our body comes from 1 fertilized egg — & now we can trace that dev. #HHMIInvestigators Jonathan Weissman & Jay Shendure & teams have created new tools that generate maps of the origin & outcome of vast numbers of cells in a developing mouse: bit.ly/4dVhnhe
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Paget’s hypothesis suggests that seeds need to find permissive soil. We find that changes in the niche’s own cellular composition profoundly shift which seeds it favors. 🩷🩷
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We can’t help but ask: in metastasis, how much is driven by tumor genome heterogeneity, and how much by the identity of the niche cells? 🤔🫨 Inflammation, aging and cancer therapy all remodel the niche. Will they select a different set of seeds? 🌱
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Two anatomically distinct myeloid populations wrote two different epistasis maps. The cells that make up the soil determine which genomic epistasis translates into a metastatic phenotype. 🧬-😲
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We found: The niche acts twice. It first selects which genotype combinations survive colonization. It then erases the differences, driving survivors onto a common transcriptional state regardless of their genotype.
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Removing a specific host myeloid population rewires the epistasis map: some interactions reverse sign, with synergistic epistasis between genes turning into buffering epistasis!
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We stumbled into the classic genetic concept of epistasis - two mutations produce an outcome neither gene does alone. (Thanks to tumor genome heterogeneity! 🤣 ) We paired multiplexed CRISPR/Cas12a tumor-cell perturbation with in vivo Perturb-seq and niche myeloid depletion.
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In 1889, Stephen Paget asked why tumors seed some organs and not others: the “seed and soil” hypothesis. We have been thinking for quite some time about how to resolve the causal interplay between a heterogeneous tumor genome and the dynamic metastatic niche.
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Stellar Ph.D. student Ian Guldner is launching his independent laboratory Jan 2027, after a super productive journey at Stanford. Super excited for you Ian! 👋🙌👍 salk.edu/scientist/ian-guldn…
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S.Zhang Lab retweeted
Important editorial @Nature on the new "AI-scientist" papers "AI scientists can and should empower human researchers. They cannot and should not replace them." nature.com/articles/d41586-0…
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S.Zhang Lab retweeted
AI has already revolutionized how we write code. Soon, it will revolutionize how we study biology. Biology is not just chemistry; it is code: DNA, RNA, proteins, cells, and evolution interacting across scales. AI will help us read it, reason over it, and eventually write it.
Biology is not just chemistry, it is code
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Happy to share our most recent work as we exploring the new field (for us) of genome engineering. 😅 biorxiv.org/content/10.64898…
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S.Zhang Lab retweeted
Interested in single cell and spatial genomics? Check out the agenda for our 10th Single Cell Genomics Day on Friday 6/12. Speakers: Aviv Regev @anshulkundaje @junyue_cao @xinjin + many more! All talks are free and live-streamed at satijalab.org/scgd
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S.Zhang Lab retweeted
Transitioning to being a PI in the age of AI Computational biology is in a period of upheaval that is both exhilarating and terrifying. Rapidly, we are approaching a moment of “analytic abundance”, where basically idea you can think of (and several you didn’t) magically appear within minutes of you thinking of them. Of course, the central proximal challenge is the evaluation of the sheer volume results—how do we know they are right when we don’t have the time to check over every line of code? I think it’s very telling that when I talk to AI-pilled faculty, they are exhilerated, but many trainees seem more cautious and far more ambivalent. I think that’s because faculty often have been removed from the details for a long time and probably haven’t checked over a line of code in years. They are used to managing (rather than doing) analysis. Over time, they usually develop a sense for whether things seem right or wrong. In this day and age, this is the skill that you, too, must develop. How do faculty do it? I am guessing every faculty member has their own list of internal sanity checks, but here are a few of mine: * Checksums. I look for things that should add up correctly (percentages add to 100, etc.). If it looks even a little bit off, I ask questions. * Never let go. If something doesn’t make sense, I don’t let go until it does make sense. Never relent! * Explain stray datapoints. Always dig into outliers in the data. How did they come to be? Often, they reveal some hidden assumption or something unexpected about the data. * Do not tolerate warnings. If code gives you a warning, resolve it. Do not continue, do not pass go, until you either understand or eliminate the warning. * Track the number of datapoints. Even a single missing row can be a sign of some fencepost bug. And I’m sure many more that I’m forgetting right now. Basically, it’s a transition from a maker to an interrogator. I also feel it worth reiterating that this is a highly unsetting period of time. I have been fortunate (?) to have 16 years of time to make a transition that people are now being asked to make in months. Again, exhilarating and terrifying, all at once!
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S.Zhang Lab retweeted
Over 250 million protein sequences are known, but fewer than 0.1% have confirmed functions. Today, @genophoria, @BoWang87 & team introduce BioReason-Pro, a multimodal reasoning model that predicts protein function and explains its reasoning like an expert would.
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Spring is finally here! 🌱 Looking forward to a new season of fresh ideas, focused writing, and productive days ahead in the lab. Let's make it count. 🔬✨
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I am totally impressed by analemma.ai/fars the best part of this that AI researcher is honest and hardworking 😂
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S.Zhang Lab retweeted
We mapped gene interactions across different environmental conditions (GxGxE) at scale for the first time in human cells. These maps lead to the realization that many genes function in a context dependent manner which provides insight into how humans have relatively few genes but many cell types. Congratulations Ben! Paper: cell.com/molecular-cell/full…
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