 Okay. Wait a second. Let me do the announcement. Sorry. I know you're really excited about the session. We are all very excited about the short talk session. So hello, everyone. Welcome back. Thank you so much for staying with us so late. And I also want to thank PCC for preparing such a wonderful meal. It was delicious. So now we will have the lightning talk session. Of course, starting with Ashlyn from Mount Sinai. But before we go on, I just want to tell each speaker you have, if you can finish your talk within six minutes, you will be allowed for one quick question. If you go over, then there won't be questions allowed. And if you go over seven minutes, I think the DCC will cut off your mic. I think that's what they threatened to do. So the risk is you are on your side, right? So, yeah, so our first speaker is again Ashlyn Dinser from Mount Sinai. She's going to talk about decoding brain epigenome with the Broad H3K4 Traumatician. Thanks. Is the mic on? I haven't started my clock, so you're fine. Hello. So my name is Ashlyn Dinser. I just recently finished my PhD in Dr. Sharon McBarion's lab. And right now I'm working as a postdoc in Dr. Eric Schatz' lab. And today I will be talking about my research project entitled Neural Epigenome Mapping in Hatan Disease Profound Tool Cortex. This is the outline for my presentation. First, I would like to give a little introduction. Then I will go over my hypothesis and specifics. Finally, I will discuss my results. As you know, in the last 10 years, GWAS studies have found that the genetic architectures of the most human complex diseases have a predominant influence of the common variation. And many of these common disease risk variants are enriched in the regulatory regions of the human genome. And ENCODE wrote, may GTX projects have found that the active regulatory regions and non-coding transcripts are often cell-pan tissue-specific. And most of these studies were performed using human cancer lines and or normal cells from the non-brain tissues. In my context, I'm working on the human brain. And the brain has several cell types. Many of them are still not understood. And characterizing brain epigenome is challenging not only about the great diversity, the number of histone modification, DNA methylation, and chromatin regulator proteins, but also about brain cellular heterogeneity which may bias the cell-type-specific epigenetic patterns influencing the finding in psychiatric epigenetic studies. And therefore, it's really important to construct and map neural epigenome from the brain which will complement existing mapping approaches on blood tissues, ultimately leading to deeper understanding of neurons' identity and functional differences as well as providing a possible window into brain development and diseases. And the focus of my slides today is the one of the particular histone mark, histone-3K-4-3-metallation, which is a well-established promoter mark. And previously, our lab showed histone-3K-4-3-metallation alteration in autism post-mortem brains. And we observed dynamically-regulated 3-metallation peaks during the lifespan in a collaboration with the shipping bank. And recent whole exome sequencing studies in 4,000 autism individuals find the mutation in the genes-encoiding proteins involving the synaptic function and, most importantly, chromatin regulation. And unexpected, the psychiatric genetics consortium showed that histone-3K-4-metallation is emerging as a central pathway in the brain development and function. And as you see in this slide, many of the mutations in the histone-3K-4-3-metallation regulation are linked to some of the neurodevelopmental disorders like, such as schizophrenia and intellectual disability. And I'm investigating a broad histone-3K-4-3-metallation domains in profrontal cortex by performing genome-wide mapping of histone-3K-4-3-metallation in neural and non-neural nuclei from the control and disease postmortem brain. Our hypothesis is the epigenetic dice regulation at non-coding cis-regulator regions may play a key role in cell identity in health and disease states. And the technique used for the cell type specifically, we used the effect of fluorescence-activated cell sorting on a fresh frozen postmortem tissue. And we dissect for the profrontal cortex and use the neon antibody. And then this sort of neural nuclei was subject to the micro-coconuclearized digestion. And then to get the micro-nucleosomal DNA and the chip-60 histone-3K-4-3-metallation. And I investigated my hypothesis in three specific aims. In the first specific aim, we identified the top 5% broadest peaks of the cortical neurons that were longer than the 95% of all histone-3K-4-metallation. And the second specific aim, we validated these neuron-specific broadest domains computationally using the glial and blood cell types. And then we compared using the same pipeline and the same chip-sick experiments on the chimpanzee, russus macaque, and the mouse. I forgot to put the mouse, right? So, and then we integrated with the other arneistic gene expression profiles from the especially white matter and gray matter of the profrontal cortex. And this is, here's my pipeline developed for the chip-sick arneistic and integrated with the Bayesian network. But unfortunately, I don't have time to go to specifically. I will discuss my results. This is the first quality control and validation of the chip-sick experiments. We compiled the histone-3K-4 epigenetic landscape pattern for the ref-sick annotated genes across all samples and computed the pairwise-spearman correlations. And as you see here, across these different cell types, the interest cell type correlation were systematically higher than the inter-cell type correlation and each cell type clustering together in an unsupervised fashion. And here, today, I will just show our neuron-specific, which not show up in the non-urnal cell types in the human brain and the blood cell types. As you see, all of them, mostly, they are associated with the promoter transcription star site. But you can see the green intergenic region. So we can use this broto-cystone-3K-4 methylation peaks as a memory integrated with the arneistic data. This might be the novel transcripts. So this can be also used as an important signature to identify the novel transcripts. And here in the B, I'm showing that the neural cell type, non-urnal, and blood. As you see, the signature is mostly energy in the neural cell type, not show up in the blood. And the C, we are showing that most of the genes are really permanent role in this neuropsychiatric diseases. And this is a summary of two, when I say broto-cystone-3K-4 methylation peaks. As you see, it's one of the genes. This is the first track, is the arneistic data from the brain. But we are dissociated for the gray matter and white matter. And the orange track is the gray matter, which usually enrich with the neurons. And the white matter is mostly contained in the astrocyte glyols. And this, showing that the correlation with the broto-purple neuron-specific broto-cystone-3K-4 has higher expression in the gray matter. And they are mostly enriched in the synaptic transmission gene categories. And the four orange, another example, is the glial sac-stann. So pink, broadest domains, associated with the sac-stann. And they are mostly expressed in the white matter. And they enrich for the myelination and axon-n-shootment. And this is another example for showing that this might be used as a biomarker because they were enriched in all three cell types. Then we compare the, we look all broadest, 500 broadest, okay. So here we check for the neural broadest peaks and looking for the gray cortical matter. And then we also did for the same analysis for the white matter. Here, the next question we were asking is, this is, broadest domains are really important for the brain development. We should observe these broadest domains in the other acro-spaces, like chimpanzee, resus macaque, and mouse. And we did the same, applied the same pipeline. And we identified 131 regions, the conserved. And they are mostly associated with dopamine signaling. And the, lately, we constructed Bayesian network. And we want to see that the, investigated the topological organization of the broadest domains. And we see that the 161 genes was mostly enriched and peripheral cortic neurons and can reach other genes in the network more efficiently. And this is conclusion. So we can use these broadest domains as the important signatures for the brain development and therapeutic, maybe in the future, for the therapeutic applications. Cool, thanks. So our next speaker is Stephen Floor from UC Berkeley. He's going to talk about the role of transcript-specific translation in human neuronal differentiation.