Shaozhen Xie
Cambridge, Massachusetts, United States
355 followers
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Hi all, I am very happy to announce that I have started a new position as full professor (tenured) at the University of Augsburg in Germany. You can…
Hi all, I am very happy to announce that I have started a new position as full professor (tenured) at the University of Augsburg in Germany. You can…
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I am thrilled to share that I have joined Alentis Therapeutics as the Head of Translational Medicine. I am truly impressed with the depth and quality…
I am thrilled to share that I have joined Alentis Therapeutics as the Head of Translational Medicine. I am truly impressed with the depth and quality…
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Mariana Oncology today announced the company will be acquired by Novartis for up to $1.75B. Together, we will accelerate the development and delivery…
Mariana Oncology today announced the company will be acquired by Novartis for up to $1.75B. Together, we will accelerate the development and delivery…
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In 2023, there were over 20 deals involving ADCs, surpassing the number of deals in other emerging cancer drug categories. In stark contrast to the…
In 2023, there were over 20 deals involving ADCs, surpassing the number of deals in other emerging cancer drug categories. In stark contrast to the…
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As we celebrate Thanksgiving and reflecting how thankful we are for the progress we have been making in fighting cancer, we cannot forget the…
As we celebrate Thanksgiving and reflecting how thankful we are for the progress we have been making in fighting cancer, we cannot forget the…
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Mikael Kubista
Very important discussion Parashar Dhapola! We need tools to classify cells and we need them now, but we must live with uncertainty, because there is an underlying problem: we lack a definition for a Cell Type. As we discussed in our editorial on single cell profiling in MAM. Cells respond to changes in environment and some changes may be transient, while other may be more permanent. Some transient changes may require the environment conditions to be maintained, while other respond to a stimuli. Some changes may require cell division. Also, turnover of biomarkers (mRNA, microRNA, proteins, metabolites, methylation, chromatin modifications etc.) is very different. which biomarker is the determinant? We have fundamental questions to answer, before we can set the perfect bioinformatics criteria. But until then, we do our best :-) Thanks for initiating this! https://lnkd.in/dsNf9TrU
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Prabakaran (Praba) Ponraj
Fascinating work! Transforming public antibody clones into therapeutics to save human lives!! Six years ago, I was inspired to write this correspondence in Nature (https://lnkd.in/dmY7t3k) when I noticed a convergence between some therapeutic antibodies and antibody sequences from published Next-Generation Sequencing (NGS) data. I realized there was a lack of proper sequence annotations and analysis pipelines for the NGS data deposited at the Sequence Read Archive (SRA) and considered the patent issues in the era of NGS. #mabs #mab #antibody #antibodies #biologics #vaccines #vaccine #therapeutics
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🎯 Ming "Tommy" Tang
Spatial transcriptomics is developing fast, my take on challenges in data analysis and its translational potential: I spoke at the Festival of Genomics (FOG) in Boston last week. Many exciting spatial technologies are under development. 10x visium HD is pretty exciting. You can capture the whole transcriptome by sequencing at single-cell resolution. We can generate high-quality spatial data, but the analysis methodology is still being developed very fast. Seurat, squidpy, SpatialExperiment are different ecosystems to analyze the data. The size of the data is challenging. a 1cm x1cm tissue slide can contain >1M cells how to efficiently extract biological information from those many of cells is key. many of the methods focus on a single sample (plug in of our monkeybread python package for analyzing spatial data https://lnkd.in/evb29A_e). But to have statistical power, we need to analyze many samples ( many millions of cells!). I am now thinking more about the translational side as I am supporting our biomarker data analysis for our clinical trials. although spatial data can be very powerful for understanding the MOA, it is still early to integrate it into the trials. Asaf Rotem showed how AZ is integrating regular single cell RNAseq data into trials. There are a lot of challenges. You have to think about patient compliance, how easy it is for the trial sites to collect the samples and perform the assay. The whole-blood scRNAseq is the way to go. How about tumor tissues? It is much more challenging to get them, especially if you want to have pre- and post pairs of samples. Even for regular bulk nanostring, we can loose samples because the sample quality is bad or you just never get the biopsy. Performing scRNAseq requires expertise in-house or in CRO. Even for routine assays, IHC staining, multiplexIF (small panels), it takes a lot of time to optimize. you can think of how many challenges if you want to use spatial data in trials given how hard is it for regular bulk RNAseq and scRNAseq. Conclusion: spatial data is a great tool for research in understanding MOA but we still have a long way to implement it in translation.
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