PhD student at @schirmerlab. Dry and wet lab, interested in spatial transcriptomics. BSc @UABBarcelona & MSc @UPFBarcelona
Heidelberg, Germany
Joined January 2017
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📢 It’s out!! 🎉 Huge thanks to everyone involved, and especially to my partner-in-crime @PauBadiaM 🥸🧡 Swipe for ‘Before and After’ pics. 10 years together, and maybe just a little bit wiser 😉 Després de no fer projectes junts a la carrera, aquest ens ha sortit prou bé😋
Our work with @schirmerlab in multiple sclerosis (MS) is out today in @NatureNeuro. We investigated lesion progression and cell-cell communication events in MS using snRNA-seq and spatial transcriptomics 🧠 See below ⬇️
nature.com/articles/s41593-0…
Celia Lerma Martin retweeted
After nearly two years of hard work, the paper I coauthored with my lifelong partner, and now fiancé, is out in @NatureNeuro 🎉 No podria estar més feliç d’haver-ho fet junts @Cels121 😊
Our work with @schirmerlab in multiple sclerosis (MS) is out today in @NatureNeuro. We investigated lesion progression and cell-cell communication events in MS using snRNA-seq and spatial transcriptomics 🧠 See below ⬇️
nature.com/articles/s41593-0…
Celia Lerma Martin retweeted
Our work with @schirmerlab in multiple sclerosis (MS) is out today in @NatureNeuro. We investigated lesion progression and cell-cell communication events in MS using snRNA-seq and spatial transcriptomics 🧠 See below ⬇️
nature.com/articles/s41593-0…
Celia Lerma Martin retweeted
Our pre-print "Comparative evaluation of methods for the prediction of protein-ligand binding sites" is out on Research Square. Check it out!
🔗 tinyurl.com/Utges-LBS-comp
On this paper we benchmark 11 ligand binding site prediction tools on our curated reference dataset: LIGYSIS!
Celia Lerma Martin retweeted
Very happy to see that our review on gene regulatory networks is being featured by @NatureRevGenet and that it inspired its cover! 🥳
Our Featured article: Gene regulatory network inference in the era of single-cell multi-omics go.nature.com/3r8TugN #Review by
@PauBadiaM, @LornaWessels, @sophia_mullerd, @TrimbourR, @roramirezf94, @RArgelaguet & @saezlab @UniHeidelberg
🧠 Delighted to have shared today our research on MS lesions from white matter at #ECTRIMS2023 in Milan! I've talked about how our single-nuc and spatial atlas sheds light on disease progression. Grateful for the opportunity to present and happy to see that the room was packed 😊
Celia Lerma Martin retweeted
📢Fantastic talk from @Cels121
🎯demonstrating tools for examining spatial transcriptomics ✅delineate which cells, what are they doing and where are they in #MS
#ECTRIMS2023 #MSMilan2023 @ECTRIMS
Celia Lerma Martin retweeted
Gene regulatory networks (GRNs) are useful models to understand cellular identity and disease. We have reviewed the GRN inference field in the current era of single-cell multi-omics 📖 out in @NatureRevGenet
🔗doi.org/10.1038/s41576-023-0…
Full-text access: rdcu.be/dfpa2
Celia Lerma Martin retweeted
Transcription factor (TF) activity estimation can help to interpret transcriptomics data 🧬💻. In collaboration with the @NTNU and the @BSC_CNS, we present a high-confidence collection of signed TF regulons that covers >1,100 TFs tinyurl.com/24ks3uud
Celia Lerma Martin retweeted
Cross-condition single cell data are essential for biomedicine💊🏥 and tissue-centric descriptions are needed. We propose a 💻 framework for its sample-level unsupervised analysis and the estimation of multicellular programs. 📄You can read the preprint at tinyurl.com/mvptmsem
Celia Lerma Martin retweeted
New preprint is out! 🚨 We analyzed the spatial map of multiple sclerosis (MS) lesions using snRNA-seq and spatial transcriptomics 🧠. This was a close collab with @schirmerlab, led by @Cels121 and our @PauBadiaM. See below 👇
Celia Lerma Martin retweeted
It is not everyday when one can celebrate submitting a manuscript with their significant other 😄🥳 So happy to had the chance to work together on this @Cels121 ☺️
New preprint is out! 🚨 We analyzed the spatial map of multiple sclerosis (MS) lesions using snRNA-seq and spatial transcriptomics 🧠. This was a close collab with @schirmerlab, led by @Cels121 and our @PauBadiaM. See below 👇
Celia Lerma Martin retweeted
Our very much extended and revised multiomics single cell (RNA/ATAC seq) and spatial atlas of human myocardial infarction 🫀✨🚀 is out now @Nature! 🥳
Collaborative effort w/ @rkramann, @vanvanka123, & H Milting’s labs
nature.com/articles/s41586-0…
Celia Lerma Martin retweeted
Happy to share our latest work @Nature. We studied the remodeling and inflammatory events after human myocardial infarction with single cell and spatial transcriptomics data. 🥳
This is a complete make-over of the first pre-print back in winter 2020: tinyurl.com/5n7sfhd7
Our very much extended and revised multiomics single cell (RNA/ATAC seq) and spatial atlas of human myocardial infarction 🫀✨🚀 is out now @Nature! 🥳
Collaborative effort w/ @rkramann, @vanvanka123, & H Milting’s labs
nature.com/articles/s41586-0…
Celia Lerma Martin retweeted
Happy to see this one out today in @NatMetabolism! 😄 If you are interested in extracting transcription factor or pathway signatures like we did in this work, check out our tool ⚙️ decoupler decoupler-py.readthedocs.io/
Great work! We are happy we could help analyze the data with our resources, PROGENy tinyurl.com/2p8razkk and DoRothEA tinyurl.com/3kwn9b7n, and in particular to extract a transcription factor signature present in COVID-19 & other hepatic infections.
Celia Lerma Martin retweeted
It is finally out! 🎉
Our updated manuscript describing methods for bioactivity inference (pathways, TFs, etc.) is now published in @BioinfoAdv doi.org/10.1093/bioadv/vbac0… 🥳
We have been working on a new #Python 🐍 implementation and its integration with the meta-resource @omnipathdb 👇
Celia Lerma Martin retweeted
There are many computational methods to infer biological activities. Many lab members led by @PauBadiaM have developed decoupleR 💻, a #Bioconductor #rstats package that collects different methods to infer mechanistic signatures from omics 🧬
Paper: tinyurl.com/ad8nb765 1/10
Celia Lerma Martin retweeted
Finally my first co-author paper is out! 🥳
nature.com/articles/s41467-0…
We trained a collection of deep learning models to infer bioactivity signatures for small compounds, even when no experimental information is available.💊
Now you can feed biological information to your models!💻
