Resident in Neurology | physician-scientist at @schirmerlab with a special focus on #SingleCell, heterogeneity of #NeuroGlia and #MultipleSclerosis

Joined July 2019
Tim Trobisch retweeted
Our free Single Cell Genomics Day workshop starts tomorrow at 10AM EST! You can ask watch the Youtube livestream at satijalab.org/scgd. See you soon!
Interested in single cell genomics but need help getting started? Check out the full agenda for our Single Cell Genomics Day next Friday (4/7). All talks will be live-streamed (no registration required) at satijalab.org/scgd23
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Let’s talk about proteins whose abundance generally does not correlate the abundance of the transcripts that templates them. Give examples.
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People always ask how the protein is expressed if I show the RNA data. Here are the 6 resources for protein data 👇🧵
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Tim Trobisch retweeted
Interested in single cell genomics but need help getting started? Check out my lab's Single Cell Genomics Day on April 7. Talks will feature recent exciting computational and experimental advances and will be livestreamed at satijalab.org/scgd. Please RT/spread the word!
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Save the date! Single Cell Genomics 2023 #SCG2023 takes place Oct. 9-11 2023 in the beautiful mountain town Engelberg in Switzerland! 10th year anniversary of this unique experience. @IdoAmitLab, @slinnarsson, @AlexandervanOu1, @MarioniLab, @sandberglab, Aviv Regev, Amos Tanay
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Check out our latest work on targeting neuron-oligodendrocyte potassium shuttling at nodes of Ranvier as a neuroprotective approach in inflammatory demyelination @jclinicalinvest @schirmerlab doi.org/10.1172/JCI164223
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Extremely happy that this piece is out! A wonderful multi-lab endeavor spearheaded by ⁦@hannah_kapell⁩ about neuron-oligodendrocyte potassium shuttling at nodes of Ranvier that protects against inflammatory demyelination ⁦⁦@jclinicalinvest⁩ jci.org/articles/view/164223
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Tim Trobisch retweeted
Excited to share our preprint on multimodal single-cell sequencing to characterize antigen-specific CD8+ T cells across COVID vaccination and disease. We also release datasets profiling ATAC, RNA, protein, TCR and MHC dextramers at single-cell resolution: biorxiv.org/content/early/20…
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10 courses/books to learn bioinformatics 🧵👇
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It was a great pleasure to present our latest data on cross-regional transcriptomic reactivity in MS @ECTRIMS - the first study to integrate three CNS regions on single cell resolution in human disease 🥳 many thanks for this opportunity @schirmerlab #ECTRIMS2022 #MS #SingleCell
⁦@TimTrobisch⁩ rocked the poster session with a brilliant journey profiling #neuroglia cell types across the #CNS reporting novel #oligodendrocyte encoded #biomarkers in #MS - a fantastic endeavor that recently made it to paper, check it out: link.springer.com/article/10…
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Do you want to get into scRNA-seq but are lost in the sea of never-ending papers? No worries, I got you covered. Here's a list of the best review that have help me understand the core concepts of the technique, analysis and interpretation:🧵
Replying to @rmassonix
do any of you have similar ones for single-cell RNAseq? We want to establish both techniques in my lab!
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Tim Trobisch retweeted
If you don’t feel stupid doing science you’re not trying hard enough.
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Fantastic opportunity to dive deep into multiplexed spatial omics and apply novel spatially resolved transcriptomics and proteomics to inflamed tissues of muscle and brain! Drop us a line for more details and apply @DenisSchapiro and @schirmerlab @UniHeidelberg @ResearchLifesci
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I am pleased to share with you my review about cell types, published in @CellCellPress yesterday. 1/ What is a cell type and how to define it? cell.com/cell/fulltext/S0092…
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Tim Trobisch retweeted
We've released an Azimuth API that lets you process, map, and annotate your scRNA-seq dataset with a single R command. Just needs the raw counts matrix as input, and accepts Seurat objects, AnnData (h5ad), or hdf5 files. Check out the vignette at: satijalab.github.io/azimuth/…
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We are happy to see a significantly improved version of our MISTy paper "Explainable multiview framework for dissecting spatial relationships from highly multiplexed data", published in @GenomeBiology tinyurl.com/572naut2 We would like to highlight some of the contributions 🧵
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