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Lubin, J.

Publications and source records attributed to Lubin, J..

2 recordsLinked to original sources

Transcriptional regulation of disease-relevant microglial activation programs

Microglia, the brains innate immune cells, can adopt a wide variety of activation states relevant to health and disease. Dysregulation of microglial activation occurs in numerous brain disorders, and driving or inhibiting specific states could be therapeutic. To discover regulators of microglia activation states, we conducted CRISPR interference screens in iPSC-derived microglia for inhibitors and activators of six microglial states. We characterized 31 regulators at the single-cell transcriptomic and cell-surface proteome level in two distinct iPSC-derived microglia models, uncovering new protein markers of relevant states. We functionally characterized several multi- state regulators. ZNF532 and PRDM1 knockdown drive disease-associated, lipid-rich signatures and enhance phagocytosis while showing opposing effects on antigen-presentation signatures. DNMT1 knockdown results in widespread loss of methylation, activating negative regulators of interferon signaling. These findings provide a framework to direct microglial activation to selectively enrich microglial activation states, define their functional outputs, and inform future therapies. HighlightsO_LICRISPRi screening reveals novel regulators of six microglia activation states C_LIO_LIMulti-modal single-cell screens highlight new markers of microglial states C_LIO_LIDifferent iPSC-microglia models show different landscapes of activation states at baseline C_LIO_LILoss of DNMT1 leads to widespread DNA demethylation, promoting some states but limiting the interferon-response state C_LIO_LILoss of PRDM1 or ZNF532 drives the microglial disease-associated state C_LI

neuroscience↗

Outcome of Crash Course Training on Protein Structure Prediction with Artificial Intelligence

Protein structure predictions have broad impact on several science disciplines such as biology, bioengineering, and medical science. AlphaFold2[1] and RoseTTAFold[2] are the current state-of-the-art AI methods to predict the structures of proteins with an accuracy comparable to lower-resolution experimental methods. In its 2021 year review, both these methods were recognized as "breakthrough of the year" by Science magazine[3] and "method of the year" by Nature magazine [4]. It is timely and important to provide training and support on these emerging methods. Our crash course "Enabling Protein Structure Prediction with Artificial Intelligence "was conducted in collaboration with domain experts and research computing professionals. The crash course was well received by the community as there were 750 registrants from all over the world. Here we provide the summary of the crash course, describe our findings in organizing the crash course, and explain what preparation steps helped us with the hands-on training. CCS CONCEPTSComputing methodologies a Machine learning a Machine learning approaches a Bio-inspired approaches

scientific communication and education↗