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Lev Tsypin, PhD

@ltsyp.in
324 followers 605 following 698 posts

・Postdoc working on weirdo microalgae ・Bigger than any known bacterium ・Views represent trillions of little cells ・🌐 ltsyp.in ・🌈🦠🔬🌱🌏

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Lev Tsypin, PhD @ltsyp.in · 11/06/2025
My condolences for your loss
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Carolyn Bertozzi @carolynbertozzi.bskyverified.social · 10/06/2025
“You can get a other job, but you cannot get another soul”.
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Lev Tsypin, PhD @ltsyp.in · 08/06/2025
Thank you, Megan! 😊
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Lev Tsypin, PhD @ltsyp.in · 07/06/2025
Finally, this work raises the possibility of using evolutionarily conserved co-expression patterns to transfer knowledge between organisms that are not very accessible to experimentation. Please let me know what you think of the paper and our interactive tool!
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Lev Tsypin, PhD @ltsyp.in · 07/06/2025
We also use extensive computational negative controls to validate our analysis. This isn't something that I've seen in the literature, despite articles advocating for it over the past 20 years. In my view, computational negative controls are just as important as experimental ones.
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Lev Tsypin, PhD @ltsyp.in · 07/06/2025
Our paper demonstrates that it is possible to revitalize old data, and we bring together microarray and RNA-seq datasets in our analysis. The old results that are publicly available shouldn't be forgotten just because our methods have changed. If there experiments are sound, we should use them.
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Lev Tsypin, PhD @ltsyp.in · 07/06/2025
I started this work 10 years ago as an undergrad in a "Computing for Biologists" class, and now it's finally out in the world! I'm very proud for it to be my first contributing author paper. This work focuses on predicting and testing gene functions based on their co-expression, but there's more:
doi.org
Inferring gene-pathway associations from consolidated transcriptome datasets: an interactive gene network explorer for Tetrahymena thermophila
Abstract. Although an established model organism, Tetrahymena thermophila remains comparatively inaccessible to high throughput screens, and alternative bi
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Lev Tsypin, PhD @ltsyp.in · 12/03/2025
Accepted with minor revisions! 🎉
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Lev Tsypin, PhD @ltsyp.in · 22/01/2025
All of my old grievances and resentment toward faculty and administrators who asserted that science isn't political is bubbling to the surface.
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Lev Tsypin, PhD @ltsyp.in · 18/01/2025
Timely for grad school admissions season...
onlinelibrary.wiley.com
Should research experience be used for selection into graduate school: A discussion and meta‐analytic synthesis of the available evidence
You have to enable JavaScript in your browser's settings in order to use the eReader.
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Reposted by Lev Tsypin, PhD
Chenxin Li, PhD @chenxinli2.bsky.social · 14/01/2025
Had a great time talking to @atinygreencell.bsky.social abt my PhD work - small RNA transcriptome of rice gametes & zygotes. Please give his channel a follow. This podcast series of Sebastian is a great idea, and I found all previous episodes informative/inspirational. I hope mine is as well.
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Lev Tsypin, PhD @ltsyp.in · 11/01/2025
What is the world coming to?
A bag of JACK LINKS beef jerky "doritos" with "taco flavor". "Limited time offer" and $1 off!
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Sebastian S. Cocioba @atinygreencell.bsky.social · 08/01/2025
Tears In Rain Episode 5 is live!!! I hung out with @daniellebeckman.bsky.social where she told us about her experiences as a student in Brazil that shaped her current interests in viruses inside the human brain. I hope you enjoy! Please like and subscribe for more! youtu.be/27AtqcYNHa8
youtu.be
Tears In Rain Ep5: Danielle Beckman
YouTube video by Sebastian Cocioba
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Lev Tsypin, PhD @ltsyp.in · 08/01/2025
Keeping my fingers crossed and sending hugs
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Lev Tsypin, PhD @ltsyp.in · 08/01/2025
Grad school interview season is starting, and Sebastian's collecting biology PhD stories that I wish I had heard when I was figuring out where I should go. 🧪 It was a pleasure to share my experience--if I can help anyone think through things, please send me a message!
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Lev Tsypin, PhD @ltsyp.in · 08/01/2025
Just watched Moonstruck starring Cher and Nic Cage: 12/10 no notes, perfect film
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Lev Tsypin, PhD @ltsyp.in · 06/01/2025
Thank you for inviting me on, Sebastian! This podcast is a wonderful project 🙂
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Lev Tsypin, PhD @ltsyp.in · 01/01/2025
Happy new year, everyone
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Lev Tsypin, PhD @ltsyp.in · 20/12/2024
It's now formatted more-or-less correctly!
doi.org
Inferring gene-pathway associations from consolidated transcriptome datasets: an interactive gene network explorer for Tetrahymena thermophila
Although an established model organism , Tetrahymena thermophila remains comparatively inaccessible to high throughput screens, and alternative bioinformatic approaches still rely on unconnected datas...
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Lev Tsypin, PhD @ltsyp.in · 20/12/2024
When a journal rejects your manuscript but recommends that you transfer it to a subsidiary, how do you decide whether to do it or send the paper somewhere else?
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Elisabeth Bik @elisabethbik.bsky.social · 17/12/2024
Infamous paper that popularized unproven COVID-19 treatment finally retracted Study on hydroxychloroquine by Didier Raoult and colleagues gets pulled on ethical and scientific grounds @cathleenogrady.bsky.social reports. www.science.org/content/arti...
science.org
Infamous paper that popularized unproven COVID-19 treatment finally retracted
Study on hydroxychloroquine by Didier Raoult and colleagues gets pulled on ethical and scientific grounds
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
Question for the bird people out there: how do hummingbirds avoid going into torpor while incubating their eggs?
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
Thank you for reading, and I'm very happy to discuss any aspect of this work! :)
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
That kindness and generosity led to this paper. I don't think I would still be in science/academia without his support.
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
I can't express how much I appreciate my undergraduate advisor, Aaron Turkewitz, who helped shape everything from my scientific sense to my love of pottery. During the height of the pandemic when I was on a medical leave of absence from my PhD, Aaron paid me part time to revisit my undergrad work.
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
A huge thank you to Stefano Allesina, who taught that computing class and made the final assignment, "make something useful for your lab." stefanoallesina.github.io And an equal thanks to Hannah Weller, who encouraged me to take the class in the first place. :) hiweller.rbind.io
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
It's a wonderful privilege to write a paper that allows me to express my philosophy about the field. And while I've been tinkering at this work for the past decade, it would not have come together in such a satisfying way without the contributions of each co-author.
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
I'm really proud of this work. It's wild to be submitting something as the contributing author (and holy moly I did not expect how many predatory journals would be emailing me).
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
In other words, we can apply this sort of approach to organisms that are chosen for their phylogenetic diversity, rather than for their experimental accessibility, and this could provide opportunities for translating experimental results between evolutionarily distant model systems!
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
This view of gene co-expression programs opens a wider perspective on what we can do with "model" systems. It becomes easier to translate insights from more tractable organisms to more obstinate ones. It also allows us to make stronger claims about lineage-specific innovations.
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
The deeper assertion that we make in this paper is that co-expression patterns may be maintained over evolutionary time. In a previous report, we showed that gene co-expression in T. thermophila can lead to genes of interest in Toxoplasma gondii, an apicomplexan. doi.org/10.15252/emb...
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
You can find those HTML files in the supplement of our preprint. Our code is also available for anyone to explore: doi.org/10.6084/m9.f...
doi.org
Inferring gene-pathway associations from consolidated transcriptome datasets: an interactive gene network explorer for Tetrahymena thermophila
Analysis for and development of the Tetrahymena Gene Network Explorer
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
There is clearly much more to explore in these data, as we generated over 600 co-expression clusters, with over a quarter of them being enriched for known cellular functions. To make our analysis accessible to the community, we built standalone HTML files for interactive data exploration.
A labeled diagram of the TGNE dashboard. (A) The “Conditions Selection Tabs” allow the user to specify which life cycle phases are included within the input data to the clustering pipeline. The “Normalization Selection Tabs” allow the user to select which normalization technique should be used on the input data. (B) The search bars can be used to select genes based on their annotations. The left search bar allows searches for TTHERM_ID, common names, descriptions, and module number. The right search bar allows searches for functional annotation terms or codes. Here, “m179” was used as the search term to select the entire module that is enriched for histone-associated functional terms. (C) The heatmap representation of the normalized expression of all genes across all conditions. The selected module is highlighted, and the unselected genes are grayed out. (D) This plot shows all modules with significantly enriched functional terms, which are the same terms as those that can be searched using the right-hand search bar. Moving the cursor over any of the circles in the plot displays the enriched term, its fold-change relative to the genome background, and the Bonferroni-corrected p-value. (E) An interactive UMAP representation of the gene expression with one tab showing the UMAP embedding of each cluster and the other tab showing the UMAP embedding of each gene. Selected genes and modules are highlighted, while unselected ones are grayed out. Clicking on any circle or selecting them with one of the tools to the right of the plot selects those module(s) or gene(s) for display. (F) The graph for displaying the expression profiles of the selected genes. (G) When genes are selected, their annotation information based on the published T. thermophila genome, eggNOG, and InterProScan is populated into this table. (H) The annotation table and functional enrichment information for the selected genes/modules can be downloaded as tab-separated files using these two buttons.
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
We find strong evidence for histone-, ribosome-, and proteasome-associated genes. The most striking example is of the ribosome-associated genes. There is a 49-gene overlap between the microarray and RNA-seq co-expression patterns, and every one of those genes is known to be ribosomal.
Figure 4 from our preprint. The left column shows normalized co-expression patterns from the microarray dataset, and the right column shows normalized co-expression patterns from the RNA-seq dataset. The top row is for histone-associated genes, the middle row is for ribosome-associated genes, and the bottom row is for proteasome-associated genes.
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
We believe that the answer is yes. While it is outside the scope of this paper to genetically dissect other cellular pathways, we checked to see if we are able to detect co-expression clusters that are clearly enriched for specific functions.
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
So, our method generates experimentally-testable hypotheses, and in the case of mucocyst biogenesis, this led us to discover new genes involved the process. This raises the question, "does this result extend to other cellular functions?"
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
Every knockout had a mucocyst secretion defect! To read this figure, all you need to know is that when wildtype cells are stimulated to secrete and then are centrifuged, they end up with a mucus layer over the cell pellet. In each picture, the left tube is wildtype, and the right tube is a knockout.
Figure 3 from our preprint. In each image, the left tube shows the result of a secretion test with the wildtype strain of T. thermophila, and the right tube shows the result with a knockout strain for each of the genes listed above the photos. In each photo, the right tube has a much smaller or absent mucus layer overlying the cell pellet.
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
We knocked out 10 genes: six had been previously implicated to be part of the Mucocyst Docking and Discharge complex by co-immunoprecipitation; four had never been studied before, but were putatively annotated as proton-pumping ATPases, some of which play a role in ciliate membrane trafficking.
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
This approach recovered over 80% of the previously known mucocyst biogenesis genes, which implied to us that there might be more mucocyst biogenesis genes lurking in our co-expression clusters.
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
Another point for not forgetting about old datasets :)
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
Not only that, but these co-expression patterns also intersect with the list of genes that are specifically upregulated during mucocyst biogenesis! We're publishing this mucocyst biogenesis dataset for the first time, which was collected by Prof. Lydia Bright during her PhD ~15 years ago.
Panels from figure 2 of our preprint. C and D show the normalized expression profiles of genes that are co-expressed with previously identified mucocyst biogenesis genes in both the microarray (C) and RNA-seq (D) datasets. Panel E is a volcano plot showing differential expression during mucocyst biogenesis. The red dashed lines indicate the thresholds we used for fold-change and false discovery rate, and the colors correspond to the posterior Bayesian probability of differential expression, with yellow approaching 0% and purple approaching 100%. Panel F is a Venn Diagram showing the overlap between genes that are upregulated during mucocyst biogenesis, genes that are co-expressed with mucocyst biogenesis genes in the microarray dataset, and genes that are co-expressed with mucocyst biogenesis genes in the RNA-seq dataset.
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
In other words, we found co-expression clusters that had a higher fraction of mucocyst-associated genes than would be expected based on their abundance in the whole genome. The microarray and RNA-seq co-expression clusters have significant agreement!
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
Yes and yes! We compiled a list of 33 genes that had been confirmed to play a role in mucocyst biogenesis in prior literature. We used this list to identify the co-expression clusters that are enriched for mucocyst functions relative to the genome background.
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
Once we converted the data sets into a "lingua franca," we could start asking the biological questions. Do genes that tend to cluster together in the microarray/life cycle dataset also tend to cluster together in RNA-seq/mitotic dataset? Does this tell us anything about functional associations?
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
After these steps, we perform simulations to test the null hypothesis that there is no way to cluster gene co-expression patterns in the data. No matter how we normalized the data, our optimized clustering was much better than random chance.
Comparison of our optimized clustering (dashed green line) against the simulated negative controls (black and purple histograms), as evidenced by a modularity metric. The left column corresponds to the microarray data, the right column corresponds to the RNA-seq data, the top row corresponds to a minimum-maximum normalization framework, and the bottom row corresponds to the z-score normalization framework. In each case, our clustering of the real data has a much higher modularity than the simulations, indicating that we are recovering true co-expression structure from the data.Heat maps of gene expression for all genes. On the left, the heat map corresponding to the microarray dataset. On the right, the heat map corresponding to the RNA-seq dataset. Each row corresponds one of the ~19,000 genes in our dataset. The genes are sorted by cluster and inter-cluster similarity, revealing a whole-genome view of co-expression patterns. The darker the color, the closer the normalized expression is to the minimum for the given gene; the brighter the color, the closer the normalized expression is to the maximum for the given gene.
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
In our new paper, we explore whether we can consolidate the different gene expression datasets, bridging the decades and technologies. In short, we can! Our approach relies on finding a "common language" between the datasets, using careful quality control, normalization, and parameter scans.
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
These discoveries focused on a very niche cellular process, mucocyst biogenesis, which T. thermophila (and other ciliates) use to make little packets of mucus that they expel when stressed, kind of like hagfish. If you are interested, you can find some examples here: doi.org/10.1091/mbc....
media.tenor.com
a picture of a hagfish with the words " sparkle on it 's hagfish wednesday "
Alt: A picture of a hagfish with the words "Sparkle on! It's hagfish Wednesday! Don't forget to appreciate a hagfish!"
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
The bulk culture gene expression data were collected using microarrays in 2006-2008. The synchronized mitotic cell cycle data were collected using RNA-seq in 2020-2023. By pretty much every convention, the microarray data should be obsolete, but we found that we could use them to discover new genes.
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
T. thermophila has a complex life cycle, and the available expression datasets span bulk growth, starvation, and sexual reproduction (conjugation), as well as over a synchronized mitotic cell cycle. But these data were collected in different labs over two decades and came from different methods.
A schematic of the T. thermophila asexual and sexual life cycles from Orias (2012). The things to notice are that the cell has two distinct nuclei, which stands in contrast to more familiar eukaryotic cells. The larger nucleus is called the "macronucleus" or the "somatic" nucleus. The smaller nucleus is called the "micronucleus" or the "germline" nucleus. The macronucleus is responsible for gene expression throughout the cell's activities, but the micronucleus only becomes relevant during sexual reproduction (conjugation). During conjugation, the micronucleus undergoes meiosis, performs reciprocal fertilization of the conjugating cells, and patterns the formation of the new macronucleus.
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Lev Tsypin, PhD @ltsyp.in · 17/12/2024
So, in T. thermophila, co-expression might point you to genes of interest even when you have no other insights. When choosing whether to commit funding/person-hour resources to study a set of genes, this is a real boon.
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