{"id":166,"date":"2025-06-04T04:48:45","date_gmt":"2025-06-04T08:48:45","guid":{"rendered":"https:\/\/sites.bu.edu\/bmsip\/?p=166"},"modified":"2025-06-08T03:09:12","modified_gmt":"2025-06-08T07:09:12","slug":"bmsip-projects-2025","status":"publish","type":"post","link":"https:\/\/sites.bu.edu\/bmsip\/2025\/06\/04\/bmsip-projects-2025\/","title":{"rendered":"BMSIP Projects 2025"},"content":{"rendered":"<style type=\"text\/css\"><span data-mce-type=\"bookmark\" style=\"display: inline-block; width: 0px; overflow: hidden; line-height: 0;\" class=\"mce_SELRES_start\">\ufeff<\/span><span data-mce-type=\"bookmark\" style=\"display: inline-block; width: 0px; overflow: hidden; line-height: 0;\" class=\"mce_SELRES_start\">\ufeff<\/span><!--td {border: 1px solid #cccccc;}br {mso-data-placement:same-cell;}--><\/style>\n<table xmlns=\"http:\/\/www.w3.org\/1999\/xhtml\" cellspacing=\"0\" cellpadding=\"0\" dir=\"ltr\" border=\"1\" data-sheets-root=\"1\" data-sheets-baot=\"1\" width=\"747\" height=\"700\">\n<colgroup>\n<col width=\"440\" \/>\n<col width=\"100\" \/>\n<col width=\"131\" \/><\/colgroup>\n<tbody>\n<tr>\n<td><strong>Project title<\/strong><\/td>\n<td><strong>PI<\/strong><\/td>\n<td><strong>Intern<\/strong><\/td>\n<\/tr>\n<tr>\n<td><a href=\"#Beffert_Bozadjian2025\">Alt splicing in AD, PacBio<\/a><\/td>\n<td>Uwe Beffert<\/td>\n<td>Rachel Bozadjian<\/td>\n<\/tr>\n<tr>\n<td><a href=\"#Beffert_Jadhav2025\">Ligand-Receptor in AD, AlphaFold<\/a><\/td>\n<td>Uwe Beffert<\/td>\n<td>Riya Jadhav<\/td>\n<\/tr>\n<tr>\n<td><a href=\"#Flynn_Sorbello2025\">TCAB1 Mutations in Pediatric Osteosarcoma<\/a><\/td>\n<td>Rachel Flynn<\/td>\n<td>Sydney Sorbello<\/td>\n<\/tr>\n<tr>\n<td><a href=\"#Mitra_Kwok2025\">GPS2-mediated signaling and mitonuclear contact sites<\/a><\/td>\n<td>Sahana Mitra<\/td>\n<td>Tyler Kwok<\/td>\n<\/tr>\n<tr>\n<td><a href=\"#Strickland_Kitrick2025\">Diff expression in beetle<\/a><\/td>\n<td>Lynette Strickland<\/td>\n<td>Katherine Kitrick<\/td>\n<\/tr>\n<tr>\n<td><a href=\"#Khalil_White2025\">Measuring kinetic rates of synthetic transcription factors via proSEQ<\/a><\/td>\n<td>Mo Khalil<\/td>\n<td>Nicholas White<\/td>\n<\/tr>\n<tr>\n<td><a href=\"#Hou_He2025\">Epigenomics underlying microglia cellular states transitions<\/a><\/td>\n<td>Lei Hou<\/td>\n<td>Wenshou He<\/td>\n<\/tr>\n<tr>\n<td><a href=\"#Osborne_Pfeiffer2025\">Multi-omic Approaches to metagenomics<\/a><\/td>\n<td>Melisa Osborne<\/td>\n<td>Benjamin Pfeiffer<\/td>\n<\/tr>\n<tr>\n<td><a href=\"#DRIES_Basyal2025\">Multi-modal omics integration to study HSC engraftment potential.<\/a><\/td>\n<td>RUBEN DRIES<\/td>\n<td>Anuradha Basyal<\/td>\n<\/tr>\n<tr>\n<td><a href=\"#Zhang_Han2025\">Cross-species data Integration for Single-Cell Neural Datasets<\/a><\/td>\n<td>Chao Zhang<\/td>\n<td>Jinglin Han<\/td>\n<\/tr>\n<tr>\n<td><a href=\"#Zhang_Patra2025\">Single-Cell Db for Brain Aging and Neurodegeneration<\/a><\/td>\n<td>Chao Zhang<\/td>\n<td>Sofiya Patra<\/td>\n<\/tr>\n<tr>\n<td><a href=\"#Zhang_Huang2025\">Building a multimodal model for treatment outcome prediction by integrating histology images and gene expression data<\/a><\/td>\n<td>Chao Zhang<\/td>\n<td>Elaine Huang<\/td>\n<\/tr>\n<tr>\n<td><a href=\"#Chen_Dhruvi2025\">Alzheimer\u2019s Disease Risk Modeling<\/a><\/td>\n<td>Jinying Chen<\/td>\n<td>Joshi, Dhruvi<\/td>\n<\/tr>\n<tr>\n<td><a href=\"#Leshchiner_Bhamidipat2025\">Deep learning to detect base modifications<\/a><\/td>\n<td>Ignaty Leshchiner<\/td>\n<td>Sandilya Bhamidipat<\/td>\n<\/tr>\n<tr>\n<td><a href=\"#Leshchiner_Bergamo2025\">Phylogenetic reconstruction models in tumor biology<\/a><\/td>\n<td>Ignaty Leshchiner<\/td>\n<td>Beatriz Bergamo<\/td>\n<\/tr>\n<tr>\n<td><a href=\"#Leshchiner_Bergamo2025\">GPS2-mediated signaling and mtUPR pathway<\/a><\/td>\n<td>Valentina Perissi<\/td>\n<td>Xinyu Li<\/td>\n<\/tr>\n<tr>\n<td><a href=\"#Zeldich_Pimparkar2025\">scRNA-seq generated from human iPSC-derived organoids<\/a><\/td>\n<td>Ella Zeldich<\/td>\n<td>Shivani Pimparkar<\/td>\n<\/tr>\n<tr>\n<td><a href=\"#Lau_Manseeb2025\">Genomics and transcriptomics of the Betta splendens skin<\/a><\/td>\n<td>Nelson Lau<\/td>\n<td>Hossain, Manseeb<\/td>\n<\/tr>\n<tr>\n<td><a href=\"#Lang_Dirabou2025\">Stem cells in cancer and aging<\/a><\/td>\n<td>Deborah Lang<\/td>\n<td>Jacques Dirabou<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><!--more--><\/p>\n<p><a id=\"Beffert_Bozadjian2025\"><\/a><br \/>\n<strong>Alternative splicing in Alzheimer&#8217;s Disease, PacBio <a href=\"#\" style=\"font-size: small;\">top<\/a><\/strong><\/p>\n<p><strong>PI<\/strong>: Uwe Beffert<br \/>\n<strong>Intern<\/strong>: Rachel Bozadjian<\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN\" lang=\"EN\" class=\"TextRun SCXW185994896 BCX0\"><span class=\"NormalTextRun SCXW185994896 BCX0\">Neurons rely on precise alternative splicing to generate diverse protein isoforms essential for function and survival. In Alzheimer\u2019s disease (AD), dysregulation of splicing can lead to aberrant protein expression, affecting neurodegeneration pathways. Long-read sequencing technologies, such as PacBio Iso-Seq, allow for full-length transcript identification, making them well-suited for studying alternative isoform expression in AD. This project will analyze PacBio long-read RNA sequencing data from postmortem AD and control brains, providing insights into transcriptional and splicing differences that may contribute to disease pathology.<\/span><\/span><span class=\"EOP SCXW185994896 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240,&quot;335572071&quot;:0,&quot;335572072&quot;:0,&quot;335572073&quot;:0,&quot;335572075&quot;:0,&quot;335572076&quot;:0,&quot;335572077&quot;:0,&quot;335572079&quot;:0,&quot;335572080&quot;:0,&quot;335572081&quot;:0,&quot;335572083&quot;:0,&quot;335572084&quot;:0,&quot;335572085&quot;:0,&quot;335572087&quot;:0,&quot;335572088&quot;:0,&quot;335572089&quot;:0,&quot;469789798&quot;:&quot;nil&quot;,&quot;469789802&quot;:&quot;nil&quot;,&quot;469789806&quot;:&quot;nil&quot;,&quot;469789810&quot;:&quot;nil&quot;,&quot;469789814&quot;:&quot;nil&quot;}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW249137359 BCX0\"><span class=\"NormalTextRun SCXW249137359 BCX0\">Despite extensive research on AD, the role of alternative splicing in neurodegeneration <\/span><span class=\"NormalTextRun SCXW249137359 BCX0\">remains<\/span><span class=\"NormalTextRun SCXW249137359 BCX0\"> unclear. Most transcriptomics studies use short-read sequencing, which struggles to resolve full-length isoforms accurately. This project will leverage <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW249137359 BCX0\">PacBio<\/span><span class=\"NormalTextRun SCXW249137359 BCX0\"> long-read sequencing to <\/span><span class=\"NormalTextRun SCXW249137359 BCX0\">identify<\/span><span class=\"NormalTextRun SCXW249137359 BCX0\"> AD-specific splicing patterns, uncovering novel transcript variants that may contribute to disease mechanisms. By applying bioinformatics approaches to detect isoform-level expression differences, we aim to generate hypotheses about disease-relevant transcript isoforms that could serve as biomarkers or therapeutic targets.<\/span><\/span><span class=\"EOP SCXW249137359 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><strong><a id=\"Beffert_Jadhav2025\"><\/a><\/strong><br \/>\n<strong>Ligand-Receptor interactions in Alzheimer&#8217;s Disease, AlphaFold<\/strong> <a href=\"#\" style=\"font-size: small;\">top<\/a><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW18064421 BCX0\"><span class=\"NormalTextRun SCXW18064421 BCX0\"><strong>PI<\/strong>: Uwe Beffert<br \/>\n<strong>Intern<\/strong>: Riya Jadhav<\/span><\/span><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW18064421 BCX0\"><span class=\"NormalTextRun SCXW18064421 BCX0\">Alzheimer\u2019s disease (AD) is influenced by key extracellular proteins that regulate synaptic plasticity and neuronal function. Two such proteins, Reelin and apolipoprotein E (<\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW18064421 BCX0\">apoE<\/span><span class=\"NormalTextRun SCXW18064421 BCX0\">), interact with members of the LDL receptor family, including ApoER2, VLDLR, and LDLR. These interactions influence synaptic signaling and <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW18064421 BCX0\">tau<\/span><span class=\"NormalTextRun SCXW18064421 BCX0\"> phosphorylation, both of which are implicated in AD pathogenesis. Understanding how Reelin and <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW18064421 BCX0\">apoE<\/span><span class=\"NormalTextRun SCXW18064421 BCX0\"> interact with their receptors\u2014and whether they synergize or compete for binding sites<\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW18064421 BCX0\">\u2014could<\/span><span class=\"NormalTextRun SCXW18064421 BCX0\"> provide insights into disease mechanisms and therapeutic targets.<\/span><\/span><span class=\"EOP SCXW18064421 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240,&quot;335572071&quot;:0,&quot;335572072&quot;:0,&quot;335572073&quot;:0,&quot;335572075&quot;:0,&quot;335572076&quot;:0,&quot;335572077&quot;:0,&quot;335572079&quot;:0,&quot;335572080&quot;:0,&quot;335572081&quot;:0,&quot;335572083&quot;:0,&quot;335572084&quot;:0,&quot;335572085&quot;:0,&quot;335572087&quot;:0,&quot;335572088&quot;:0,&quot;335572089&quot;:0,&quot;469789798&quot;:&quot;nil&quot;,&quot;469789802&quot;:&quot;nil&quot;,&quot;469789806&quot;:&quot;nil&quot;,&quot;469789810&quot;:&quot;nil&quot;,&quot;469789814&quot;:&quot;nil&quot;}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW77432781 BCX0\"><span class=\"NormalTextRun SCXW77432781 BCX0\">This project aims to use AlphaFold-Multimer (AF-M) to computationally predict ligand-receptor interactions between Reelin, <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW77432781 BCX0\">apoE<\/span><span class=\"NormalTextRun SCXW77432781 BCX0\">, and their receptors. Experimental data suggest that Reelin binds ApoER2 and VLDLR strongly, but poorly to LDLR. However, the accuracy of AF-M in predicting these affinities <\/span><span class=\"NormalTextRun SCXW77432781 BCX0\">remains<\/span><span class=\"NormalTextRun SCXW77432781 BCX0\"> unclear. By comparing AF-M predictions to known experimental binding affinities, we will assess its utility for modeling biologically relevant interactions and explore whether <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW77432781 BCX0\">apoE<\/span><span class=\"NormalTextRun SCXW77432781 BCX0\"> and Reelin compete or cooperate at receptor binding sites.<\/span><\/span><span class=\"EOP SCXW77432781 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><a id=\"Flynn_Sorbello2025\"><\/a><\/p>\n<p><strong>TCAB1 Mutations in Pediatric Osteosarcoma <\/strong><a href=\"#\" style=\"font-size: small;\">top<\/a><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW18064421 BCX0\"><span class=\"NormalTextRun SCXW18064421 BCX0\"><strong>PI<\/strong>: Rachel Flynn<br \/>\n<strong>Intern<\/strong>: <span data-sheets-root=\"1\">Sydney Sorbello<\/span><\/span><\/span><\/p>\n<p class=\"p2\">Telomeres protect chromosome ends and shorten with each cell division, leading to cellular aging or death when critically short. Cancer cells bypass this limit using one of two telomere-elongation mechanisms: telomerase reactivation (common in ~90% of cancers) or the alternative lengthening of telomeres (ALT) pathway, which relies on homologous recombination and is prevalent in certain tumor types like osteosarcoma.<\/p>\n<p class=\"p2\">ALT tumors often harbor mutations in chromatin remodeling and DNA repair genes. However, these mutations alone do not fully explain ALT activation. Recent evidence suggests that simultaneous disruption of the telomerase RNA component (hTR) and other key regulators may be necessary to trigger ALT.<\/p>\n<p class=\"p2\">The telomerase holoenzyme includes hTR and a scaffold of additional proteins necessary for processing and trafficking. Loss of one such component, TCAB1, compromises telomerase activity and is implicated in telomere disorders. Notably, TCAB1 overlaps with TP53, a tumor suppressor gene frequently mutated in cancers, especially osteosarcoma. Preliminary analysis of ALT-positive osteosarcoma tumors revealed frequent deletions or reduced expression of both TCAB1 and TP53, suggesting that alterations in this genomic region may promote ALT activation.<\/p>\n<p class=\"p3\"><em>Hypothesis:<\/em><\/p>\n<p class=\"p2\">Functional inactivation of TCAB1 is an early genetic event in ALT pathway activation.<\/p>\n<p class=\"p3\"><em>Specific Aim:<\/em><\/p>\n<p class=\"p2\">Define TCAB1 complex loss of function in osteosarcoma.<br \/>\nUsing whole-genome sequencing of five osteosarcoma tumors, the project aims to investigate whether genetic alterations at the TCAB1\/TP53 locus lead to the loss of hTR function and contribute to ALT activation.<\/p>\n<p><a id=\"Mitra_Kwok2025\"><\/a><\/p>\n<p><strong>GPS2-mediated signaling and mitonuclear contact sites<\/strong><a href=\"#\" style=\"font-size: small;\">\u00a0top<\/a><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW18064421 BCX0\"><span class=\"NormalTextRun SCXW18064421 BCX0\"><strong>PI<\/strong>: Sahana Mitra<br \/>\n<strong>Intern<\/strong>: Tyler Kwok<\/span><\/span><\/p>\n<p data-start=\"66\" data-end=\"613\">Recent studies suggest that mitochondria communicate with the nucleus through mitonuclear contact sites, which are essential for retrograde signaling (MRR). Disruption of these sites may contribute to cancer and neurodegeneration. While TSPO has been identified as a component in cancer cells, other tethering molecules in humans remain largely unknown. Evidence from <em data-start=\"434\" data-end=\"453\">Toxoplasma gondii<\/em> and our own unpublished data suggests that the nuclear pore complex (NPC) may mediate contact sites and GPS2-driven retrograde signaling independently of TSPO.<\/p>\n<p><em>Project Objective:<\/em><\/p>\n<p data-start=\"642\" data-end=\"739\">Identify components of mitonuclear contact sites required for GPS2-mediated retrograde signaling.<\/p>\n<p data-start=\"741\" data-end=\"1014\" data-is-last-node=\"\" data-is-only-node=\"\">We use a GFP-based reporter assay to screen for candidate tethering proteins (e.g., TOMMs, NUPs). While effective, the method is time-consuming. This project aims to develop an automated image analysis pipeline to quantify contact site formation across large-scale screens.<\/p>\n<p><a id=\"Strickland_Kitrick2025\"><\/a><\/p>\n<p><strong><span data-contrast=\"auto\" xml:lang=\"EN\" lang=\"EN\" class=\"TextRun MacChromeBold SCXW15688707 BCX0\"><span class=\"NormalTextRun SCXW15688707 BCX0\">A differential gene expression approach to investigating the maintenance of phenotypic variation in a color polymorphic beetle<\/span><\/span><span class=\"EOP SCXW15688707 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240,&quot;335572071&quot;:0,&quot;335572072&quot;:0,&quot;335572073&quot;:0,&quot;335572075&quot;:0,&quot;335572076&quot;:0,&quot;335572077&quot;:0,&quot;335572079&quot;:0,&quot;335572080&quot;:0,&quot;335572081&quot;:0,&quot;335572083&quot;:0,&quot;335572084&quot;:0,&quot;335572085&quot;:0,&quot;335572087&quot;:0,&quot;335572088&quot;:0,&quot;335572089&quot;:0,&quot;469789798&quot;:&quot;nil&quot;,&quot;469789802&quot;:&quot;nil&quot;,&quot;469789806&quot;:&quot;nil&quot;,&quot;469789810&quot;:&quot;nil&quot;,&quot;469789814&quot;:&quot;nil&quot;}\">\u00a0<\/span> <\/strong><a href=\"#\" style=\"font-size: small;\">top<\/a><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW18064421 BCX0\"><span class=\"NormalTextRun SCXW18064421 BCX0\"><strong>PI<\/strong>: Lynette Strickland<br \/>\n<strong>Intern<\/strong>: Katherine Kitrick<\/span><\/span><\/p>\n<p data-start=\"94\" data-end=\"507\">This project focuses on <em data-start=\"118\" data-end=\"141\">Chelymorpha alternans<\/em>, a Neotropical beetle with five genetically determined color morphs. Typically, two to three phenotypes coexist within a population, maintained by ecological and evolutionary pressures across life stages. Larval survival may depend on environmental or host plant factors, while adult predation, influenced by predator learning, may also shape phenotype frequencies.<\/p>\n<p data-start=\"509\" data-end=\"766\">Although aposematic species are expected to converge on a single warning signal, many &#8212; like <em data-start=\"600\" data-end=\"614\">C. alternans &#8212; <\/em>retain phenotypic variation. One possible explanation is variation in chemical defense: some morphs may be better at acquiring or storing plant toxins.<\/p>\n<p data-start=\"768\" data-end=\"871\">Genomic resources available (RAD-seq, reference genome, RNA-seq) enable exploration of this question.<\/p>\n<p data-start=\"768\" data-end=\"871\"><em>Project Objective:<\/em><\/p>\n<p data-start=\"900\" data-end=\"1077\" data-is-last-node=\"\" data-is-only-node=\"\">Use differential gene expression analysis to identify genes involved in toxin sequestration and assess whether their expression varies across color phenotypes of <em data-start=\"1062\" data-end=\"1076\">C. alternans<\/em>.<\/p>\n<p><a id=\"Khalil_White2025\"><\/a><\/p>\n<p><strong>Measuring kinetic rates of synthetic transcription factors via proSEQ data <\/strong><a href=\"#\" style=\"font-size: small;\">top<\/a><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW18064421 BCX0\"><span class=\"NormalTextRun SCXW18064421 BCX0\"><strong>PI<\/strong>: Mo Khalil<br \/>\n<strong>Intern<\/strong>: Nicholas White<\/span><\/span><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW156746462 BCX0\"><span class=\"NormalTextRun SCXW156746462 BCX0\" data-ccp-parastyle=\"Normal (Web)\">Previously, a framework for constructing synthetic transcription factors (<\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW156746462 BCX0\" data-ccp-parastyle=\"Normal (Web)\">sTF<\/span><span class=\"NormalTextRun SCXW156746462 BCX0\" data-ccp-parastyle=\"Normal (Web)\">) in <\/span><\/span><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW156746462 BCX0\"><span class=\"NormalTextRun SCXW156746462 BCX0\" data-ccp-parastyle=\"Normal (Web)\">S. cerevisiae<\/span><\/span><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW156746462 BCX0\"><span class=\"NormalTextRun SCXW156746462 BCX0\" data-ccp-parastyle=\"Normal (Web)\"> <\/span><\/span><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW156746462 BCX0\"><span class=\"NormalTextRun SCXW156746462 BCX0\" data-ccp-parastyle=\"Normal (Web)\">was developed in the Khalil lab. In this <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW156746462 BCX0\" data-ccp-parastyle=\"Normal (Web)\">follow up<\/span><span class=\"NormalTextRun SCXW156746462 BCX0\" data-ccp-parastyle=\"Normal (Web)\"> work, <\/span><span class=\"NormalTextRun SCXW156746462 BCX0\" data-ccp-parastyle=\"Normal (Web)\">second generation <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW156746462 BCX0\" data-ccp-parastyle=\"Normal (Web)\">sTFs<\/span><span class=\"NormalTextRun SCXW156746462 BCX0\" data-ccp-parastyle=\"Normal (Web)\"> will be examined for potential kinetic differences in mRNA transcription. <\/span><span class=\"NormalTextRun SCXW156746462 BCX0\" data-ccp-parastyle=\"Normal (Web)\">To<\/span><span class=\"NormalTextRun SCXW156746462 BCX0\" data-ccp-parastyle=\"Normal (Web)\"> capture mRNA transcription with granular temporal resolution, precision run-on sequencing (<\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW156746462 BCX0\" data-ccp-parastyle=\"Normal (Web)\">PROseq<\/span><span class=\"NormalTextRun SCXW156746462 BCX0\" data-ccp-parastyle=\"Normal (Web)\">) <\/span><span class=\"NormalTextRun SCXW156746462 BCX0\" data-ccp-parastyle=\"Normal (Web)\">will be<\/span><span class=\"NormalTextRun SCXW156746462 BCX0\" data-ccp-parastyle=\"Normal (Web)\"> used,<\/span><span class=\"NormalTextRun SCXW156746462 BCX0\" data-ccp-parastyle=\"Normal (Web)\"> which <\/span><span class=\"NormalTextRun SCXW156746462 BCX0\" data-ccp-parastyle=\"Normal (Web)\">offers <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW156746462 BCX0\" data-ccp-parastyle=\"Normal (Web)\">single<\/span><span class=\"NormalTextRun SCXW156746462 BCX0\" data-ccp-parastyle=\"Normal (Web)\"> nucleotide resolution of nascent RNA 3&#8242; ends.<\/span><span class=\"NormalTextRun SCXW156746462 BCX0\" data-ccp-parastyle=\"Normal (Web)\">\u00a0<\/span><\/span><span class=\"EOP SCXW156746462 BCX0\" data-ccp-props=\"{&quot;134233117&quot;:true,&quot;134233118&quot;:true,&quot;201341983&quot;:0,&quot;335557856&quot;:16777215,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">In \u201cA Synthetic Biology Framework for Programming Eukaryotic Transcription Functions\u201d by Khalil et al, a framework for engineering Zinc Finger to be modular and tunable was demonstrated. These synthetic transcription factors (sTFs) have been decomposed into DNA binding domain which allows for promoter specificity, an activation domain for recruiting transcription machinery and an additional protein-protein interaction domain which allows for cooperative interaction with other transcription factors.\u00a0<\/span><span data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Dose dependent curves were demonstrated previously but to get a more accurate look at the kinetic data for these sTFs, precision run-on sequencing will be used, a new technique which effectively stalls mRNA mid transcription with biotin labeled nucleotide. This dataset will be analyzed to determine more accurate kinetics.\u00a0<\/span><span data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><a id=\"Hou_He2025\"><\/a><\/p>\n<p><strong><span data-contrast=\"auto\" xml:lang=\"EN\" lang=\"EN\" class=\"TextRun MacChromeBold SCXW56467276 BCX0\"><span class=\"NormalTextRun SCXW56467276 BCX0\">Understanding epigenomic landscape underlying microglia cellular states transitions<\/span><\/span><span class=\"EOP SCXW56467276 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240,&quot;335572071&quot;:0,&quot;335572072&quot;:0,&quot;335572073&quot;:0,&quot;335572075&quot;:0,&quot;335572076&quot;:0,&quot;335572077&quot;:0,&quot;335572079&quot;:0,&quot;335572080&quot;:0,&quot;335572081&quot;:0,&quot;335572083&quot;:0,&quot;335572084&quot;:0,&quot;335572085&quot;:0,&quot;335572087&quot;:0,&quot;335572088&quot;:0,&quot;335572089&quot;:0,&quot;469789798&quot;:&quot;nil&quot;,&quot;469789802&quot;:&quot;nil&quot;,&quot;469789806&quot;:&quot;nil&quot;,&quot;469789810&quot;:&quot;nil&quot;,&quot;469789814&quot;:&quot;nil&quot;}\">\u00a0<\/span> <\/strong><a href=\"#\" style=\"font-size: small;\">top<\/a><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW18064421 BCX0\"><span class=\"NormalTextRun SCXW18064421 BCX0\"><strong>PI<\/strong>: Lei Hou<br \/>\n<strong>Intern<\/strong>: <span data-sheets-root=\"1\">Wenshou He<\/span><\/span><\/span><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW134973899 BCX0\"><span class=\"NormalTextRun SCXW134973899 BCX0\">Microglia<\/span><span class=\"NormalTextRun SCXW134973899 BCX0\"> (MG) is highly involved in <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW134973899 BCX0\">pathology<\/span><span class=\"NormalTextRun SCXW134973899 BCX0\"> of Alzheimer\u2019s diseases<\/span><span class=\"NormalTextRun SCXW134973899 BCX0\"> (AD)<\/span><span class=\"NormalTextRun SCXW134973899 BCX0\"> and other brain disorders. <\/span><span class=\"NormalTextRun SCXW134973899 BCX0\">Previous<\/span><span class=\"NormalTextRun SCXW134973899 BCX0\"> single-nuclei-<\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW134973899 BCX0\">RNAseq<\/span><span class=\"NormalTextRun SCXW134973899 BCX0\"> studies have <\/span><span class=\"NormalTextRun SCXW134973899 BCX0\">identified<\/span><span class=\"NormalTextRun SCXW134973899 BCX0\"> multiple MG cellular states. Some of these states, including inflammatory MG<\/span><span class=\"NormalTextRun SCXW134973899 BCX0\"> and <\/span><span class=\"NormalTextRun SCXW134973899 BCX0\">lipid loaded MG<\/span><span class=\"NormalTextRun SCXW134973899 BCX0\">, are known to be associated with AD. The regulatory mechanisms, especially epigenomic regulation underlying the transitions among these cellular <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW134973899 BCX0\">states<\/span><span class=\"NormalTextRun SCXW134973899 BCX0\"> <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW134973899 BCX0\">is<\/span><span class=\"NormalTextRun SCXW134973899 BCX0\"> an important topic in the field.<\/span><\/span><span class=\"EOP SCXW134973899 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240,&quot;335572071&quot;:0,&quot;335572072&quot;:0,&quot;335572073&quot;:0,&quot;335572075&quot;:0,&quot;335572076&quot;:0,&quot;335572077&quot;:0,&quot;335572079&quot;:0,&quot;335572080&quot;:0,&quot;335572081&quot;:0,&quot;335572083&quot;:0,&quot;335572084&quot;:0,&quot;335572085&quot;:0,&quot;335572087&quot;:0,&quot;335572088&quot;:0,&quot;335572089&quot;:0,&quot;469789798&quot;:&quot;nil&quot;,&quot;469789802&quot;:&quot;nil&quot;,&quot;469789806&quot;:&quot;nil&quot;,&quot;469789810&quot;:&quot;nil&quot;,&quot;469789814&quot;:&quot;nil&quot;}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW20938384 BCX0\"><span class=\"NormalTextRun SCXW20938384 BCX0\">Our hypothesis is that a plastic epigenomic regulatory network <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW20938384 BCX0\">govern<\/span><span class=\"NormalTextRun SCXW20938384 BCX0\"> the stability of MG cellular states and their transitions. We would like to test the hypothesis by <\/span><span class=\"NormalTextRun SCXW20938384 BCX0\">testing<\/span><span class=\"NormalTextRun SCXW20938384 BCX0\"> a series of quantitative models to predict differential expressed genes between two cellular states and predict the key <\/span><span class=\"NormalTextRun SCXW20938384 BCX0\">regulators<\/span><span class=\"NormalTextRun SCXW20938384 BCX0\"> that potentially drive the transitions.<\/span><span class=\"NormalTextRun SCXW20938384 BCX0\"> Investigation on such epigenomic regulatory network will enable us to capture <\/span><span class=\"NormalTextRun SCXW20938384 BCX0\">key <\/span><span class=\"NormalTextRun SCXW20938384 BCX0\">enhancers which will <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW20938384 BCX0\">missed<\/span><span class=\"NormalTextRun SCXW20938384 BCX0\"> by <\/span><span class=\"NormalTextRun SCXW20938384 BCX0\">TF-gene regulatory <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW20938384 BCX0\">network, and<\/span><span class=\"NormalTextRun SCXW20938384 BCX0\"> also bridge<\/span><span class=\"NormalTextRun SCXW20938384 BCX0\"> <\/span><span class=\"NormalTextRun SCXW20938384 BCX0\">the non-coding variants associated with AD to the potential regulatory mechanisms.<\/span><\/span><span class=\"EOP SCXW20938384 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><a id=\"Osborne_Pfeiffer2025\"><\/a><strong><\/strong><\/p>\n<p><strong><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun MacChromeBold SCXW79003011 BCX0\"><span class=\"NormalTextRun SCXW79003011 BCX0\">Multi-<\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW79003011 BCX0\">omic<\/span><span class=\"NormalTextRun SCXW79003011 BCX0\"> Approaches to Building Genome Scale Models of Bacteria<\/span><span class=\"NormalTextRun SCXW79003011 BCX0\"> from Soil, Marine, and Human Microbiomes<\/span><\/span><span class=\"EOP SCXW79003011 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240,&quot;335572071&quot;:0,&quot;335572072&quot;:0,&quot;335572073&quot;:0,&quot;335572075&quot;:0,&quot;335572076&quot;:0,&quot;335572077&quot;:0,&quot;335572079&quot;:0,&quot;335572080&quot;:0,&quot;335572081&quot;:0,&quot;335572083&quot;:0,&quot;335572084&quot;:0,&quot;335572085&quot;:0,&quot;335572087&quot;:0,&quot;335572088&quot;:0,&quot;335572089&quot;:0,&quot;469789798&quot;:&quot;nil&quot;,&quot;469789802&quot;:&quot;nil&quot;,&quot;469789806&quot;:&quot;nil&quot;,&quot;469789810&quot;:&quot;nil&quot;,&quot;469789814&quot;:&quot;nil&quot;}\">\u00a0<\/span> <\/strong><a href=\"#\" style=\"font-size: small;\">top<\/a><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW18064421 BCX0\"><span class=\"NormalTextRun SCXW18064421 BCX0\"><strong>PI<\/strong>: Melisa Osborne<br \/>\n<strong>Intern<\/strong>: Benjamin Pfeiffer<\/span><\/span><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun MacChromeBold SCXW138564039 BCX0\"><span class=\"NormalTextRun SCXW138564039 BCX0\">Broadly, it will address the lack of diversity in model organisms with genome scale models that are available for computational investigation of communities of microbes in silico<\/span><span class=\"NormalTextRun SCXW138564039 BCX0\">.\u00a0 <\/span><span class=\"NormalTextRun SCXW138564039 BCX0\">More specifically, we will be developing pipelines for building genome scale models of specific organisms using <\/span><span class=\"NormalTextRun SCXW138564039 BCX0\">various types<\/span><span class=\"NormalTextRun SCXW138564039 BCX0\"> of <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW138564039 BCX0\">wetlab<\/span><span class=\"NormalTextRun SCXW138564039 BCX0\"> data.<\/span><\/span><span class=\"EOP SCXW138564039 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun MacChromeBold SCXW19209851 BCX0\"><span class=\"NormalTextRun SCXW19209851 BCX0\">Whole genome sequencing data \u2013 analyzed using <\/span><span class=\"NormalTextRun SCXW19209851 BCX0\">software tools in KBASE (DOE organized genomic database) and independent software packages such as <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW19209851 BCX0\">EggNog<\/span><span class=\"NormalTextRun SCXW19209851 BCX0\">, <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW19209851 BCX0\">CarveMe<\/span><span class=\"NormalTextRun SCXW19209851 BCX0\">, RAST, <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW19209851 BCX0\">Prokka<\/span><span class=\"NormalTextRun SCXW19209851 BCX0\">, <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW19209851 BCX0\">etc<\/span><span class=\"NormalTextRun SCXW19209851 BCX0\"> will be used to generate draft Genome Scale Models\/Metabolic Networks<\/span><span class=\"NormalTextRun SCXW19209851 BCX0\">.\u00a0 <\/span><span class=\"NormalTextRun SCXW19209851 BCX0\">The COMETS software package will be used to evaluate the performance of these models<\/span><span class=\"NormalTextRun SCXW19209851 BCX0\">.\u00a0 <\/span><span class=\"NormalTextRun SCXW19209851 BCX0\">Part of the project will include assessing and <\/span><span class=\"NormalTextRun SCXW19209851 BCX0\">utilizing<\/span><span class=\"NormalTextRun SCXW19209851 BCX0\"> <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW19209851 BCX0\">best<\/span><span class=\"NormalTextRun SCXW19209851 BCX0\"> software tools for the <\/span><span class=\"NormalTextRun SCXW19209851 BCX0\">additional<\/span><span class=\"NormalTextRun SCXW19209851 BCX0\"> incorporation of other data types \u2013 metabolomic data, proteomic data, and <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW19209851 BCX0\">TNSeq<\/span><span class=\"NormalTextRun SCXW19209851 BCX0\"> data \u2013 into model building.<\/span><\/span><span class=\"EOP SCXW19209851 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><a id=\"DRIES_Basyal2025\"><\/a><strong><\/strong><\/p>\n<p><strong>Multi-modal omics integration to study HSC engraftment potential <\/strong><a href=\"#\" style=\"font-size: small;\">top<\/a><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW18064421 BCX0\"><span class=\"NormalTextRun SCXW18064421 BCX0\"><strong>PI<\/strong>: Ruben Dries<br \/>\n<strong>Intern<\/strong>: Anuradha Basyal<\/span><\/span><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN\" lang=\"EN\" class=\"TextRun SCXW247744009 BCX0\"><span class=\"NormalTextRun SCXW247744009 BCX0\">Hematopoietic stem cells (HSCs) are <\/span><span class=\"NormalTextRun SCXW247744009 BCX0\">a cornerstone in cellular therapy to treat diseases such as <\/span><\/span><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW247744009 BCX0\"><span class=\"NormalTextRun SCXW247744009 BCX0\">leukemia, inherited metabolic and auto-immune disorders and hemoglobinopathies<\/span><span class=\"NormalTextRun SCXW247744009 BCX0\">. However, ex vivo expansion and genetic editing of HSC have been shown to lead to loss of engraftment <\/span><span class=\"NormalTextRun SCXW247744009 BCX0\">capacity<\/span><span class=\"NormalTextRun SCXW247744009 BCX0\">, thereby limiting treatment efficiencies. Understanding how to <\/span><span class=\"NormalTextRun SCXW247744009 BCX0\">better <\/span><span class=\"NormalTextRun SCXW247744009 BCX0\">maintain<\/span><span class=\"NormalTextRun SCXW247744009 BCX0\"> <\/span><span class=\"NormalTextRun SCXW247744009 BCX0\">or enhance <\/span><span class=\"NormalTextRun SCXW247744009 BCX0\">engraftment potential <\/span><span class=\"NormalTextRun SCXW247744009 BCX0\">is critical and will result in <\/span><\/span><span data-contrast=\"none\" xml:lang=\"EN\" lang=\"EN\" class=\"TextRun SCXW247744009 BCX0\"><span class=\"NormalTextRun SCXW247744009 BCX0\">safer and more effective treatments<\/span><span class=\"NormalTextRun SCXW247744009 BCX0\">. <\/span><span class=\"NormalTextRun SCXW247744009 BCX0\">Previous<\/span><span class=\"NormalTextRun SCXW247744009 BCX0\"> analysis on human fetal liver (FL)-derived HSCs <\/span><span class=\"NormalTextRun SCXW247744009 BCX0\">demonstrated<\/span><span class=\"NormalTextRun SCXW247744009 BCX0\"> that these cells <\/span><span class=\"NormalTextRun SCXW247744009 BCX0\">possess<\/span><span class=\"NormalTextRun SCXW247744009 BCX0\"> superior <\/span><span class=\"NormalTextRun SCXW247744009 BCX0\">engraftment <\/span><span class=\"NormalTextRun SCXW247744009 BCX0\">capacity<\/span><span class=\"NormalTextRun SCXW247744009 BCX0\"> as compared to postnatal sources<\/span><span class=\"NormalTextRun SCXW247744009 BCX0\">.<\/span><\/span><span class=\"EOP SCXW247744009 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240,&quot;335572071&quot;:0,&quot;335572072&quot;:0,&quot;335572073&quot;:0,&quot;335572075&quot;:0,&quot;335572076&quot;:0,&quot;335572077&quot;:0,&quot;335572079&quot;:0,&quot;335572080&quot;:0,&quot;335572081&quot;:0,&quot;335572083&quot;:0,&quot;335572084&quot;:0,&quot;335572085&quot;:0,&quot;335572087&quot;:0,&quot;335572088&quot;:0,&quot;335572089&quot;:0,&quot;469789798&quot;:&quot;nil&quot;,&quot;469789802&quot;:&quot;nil&quot;,&quot;469789806&quot;:&quot;nil&quot;,&quot;469789810&quot;:&quot;nil&quot;,&quot;469789814&quot;:&quot;nil&quot;}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN\" lang=\"EN\" class=\"TextRun SCXW81593678 BCX0\"><span class=\"NormalTextRun SCXW81593678 BCX0\">Our h<\/span><span class=\"NormalTextRun SCXW81593678 BCX0\">ypothesis states that the human fetal liver niche harbors a unique set of regulatory instructions to control the repopulating potential of HSC. We will use various omics datasets to decode the signals orchestrating this unique biological process during human development. <\/span><span class=\"NormalTextRun SCXW81593678 BCX0\">We will consider spatial niche composition, signaling pathways, and processes related to 3\u2019 alternative <\/span><span class=\"NormalTextRun SCXW81593678 BCX0\">polyadenylation.<\/span><\/span><span class=\"EOP SCXW81593678 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><a id=\"Zhang_Patra2025\"><\/a><strong><\/strong><\/p>\n<p><strong><span data-contrast=\"auto\" xml:lang=\"EN\" lang=\"EN\" class=\"TextRun MacChromeBold SCXW178063042 BCX0\"><span class=\"NormalTextRun SCXW178063042 BCX0\">Cross-Species Single-Cell Database for Brain Aging and Neurodegenerative Diseases<\/span><\/span><span class=\"EOP SCXW178063042 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240,&quot;335572071&quot;:0,&quot;335572072&quot;:0,&quot;335572073&quot;:0,&quot;335572075&quot;:0,&quot;335572076&quot;:0,&quot;335572077&quot;:0,&quot;335572079&quot;:0,&quot;335572080&quot;:0,&quot;335572081&quot;:0,&quot;335572083&quot;:0,&quot;335572084&quot;:0,&quot;335572085&quot;:0,&quot;335572087&quot;:0,&quot;335572088&quot;:0,&quot;335572089&quot;:0,&quot;469789798&quot;:&quot;nil&quot;,&quot;469789802&quot;:&quot;nil&quot;,&quot;469789806&quot;:&quot;nil&quot;,&quot;469789810&quot;:&quot;nil&quot;,&quot;469789814&quot;:&quot;nil&quot;}\">\u00a0<\/span> <\/strong><a href=\"#\" style=\"font-size: small;\">top<\/a><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW18064421 BCX0\"><span class=\"NormalTextRun SCXW18064421 BCX0\"><strong>PI<\/strong>: <span data-contrast=\"auto\" xml:lang=\"EN\" lang=\"EN\" class=\"TextRun SCXW120835162 BCX0\"><span class=\"NormalTextRun SCXW120835162 BCX0\">Chao Zhang<\/span><\/span><span class=\"EOP SCXW120835162 BCX0\" data-ccp-props=\"{}\">\u00a0<\/span><br \/>\n<strong>Intern<\/strong>: Sofiya Patra<br \/>\n<\/span><\/span><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW217305800 BCX0\"><span class=\"NormalTextRun SCXW217305800 BCX0\">Alzheimer\u2019s disease (AD) is a progressive neurodegenerative disorder and a growing public health crisis. In the U.S., the number of AD dementia cases is expected to rise dramatically, from 4 million to 14 million by 2050. While aging is the most significant risk factor for AD, it is not synonymous with disease. Healthy aging does not inherently lead to neurodegeneration, yet distinguishing between normal aging and pathological processes <\/span><span class=\"NormalTextRun SCXW217305800 BCX0\">remains<\/span><span class=\"NormalTextRun SCXW217305800 BCX0\"> a major challenge. Understanding this distinction is crucial for developing effective treatments. Animal models, such as monkeys and mice, are widely used to study AD and aging. However, these species do not naturally develop the full spectrum of AD pathology seen in humans. This limitation hinders our ability to accurately model disease progression and <\/span><span class=\"NormalTextRun SCXW217305800 BCX0\">identify<\/span><span class=\"NormalTextRun SCXW217305800 BCX0\"> therapeutic targets. Advancements in single-cell technologies <\/span><span class=\"NormalTextRun SCXW217305800 BCX0\">provide<\/span><span class=\"NormalTextRun SCXW217305800 BCX0\"> an opportunity to analyze aging and AD at a cellular level, offering insights into shared and species-specific mechanisms<\/span><span class=\"NormalTextRun SCXW217305800 BCX0\">.<\/span><\/span><span class=\"EOP SCXW217305800 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW203882782 BCX0\"><span class=\"NormalTextRun SCXW203882782 BCX0\">Despite extensive research, the biological mechanisms that distinguish healthy aging from AD remain poorly understood. A major challenge in AD research is the lack of an ideal animal model that fully recapitulates human disease progression. Current studies rely on mice and monkeys, but their genetic and pathological differences from humans limit translational insights. To overcome these limitations, integrating single-cell data across species could provide a more comprehensive understanding of aging and AD. However, existing data integration tools may not be <\/span><span class=\"NormalTextRun SCXW203882782 BCX0\">optimized<\/span><span class=\"NormalTextRun SCXW203882782 BCX0\"> for cross-species comparisons. We propose to develop a novel algorithm to integrate single-cell datasets from humans, monkeys, and mice at different age stages. By comparing our method with existing integration tools, we aim to improve cross-species analysis, uncover conserved and divergent aging processes, and enhance our understanding of AD pathogenesis.<\/span><\/span><span class=\"EOP SCXW203882782 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><a id=\"Zhang_Huang2025\"><\/a><strong><\/strong><\/p>\n<p><strong><span data-contrast=\"auto\" xml:lang=\"EN\" lang=\"EN\" class=\"TextRun MacChromeBold SCXW251076697 BCX0\"><span class=\"NormalTextRun SCXW251076697 BCX0\">Building a multimodal model for treatment outcome prediction by integrating histology images and gene expression data<\/span><span class=\"NormalTextRun SCXW251076697 BCX0\">.<\/span><\/span><\/strong><span class=\"EOP SCXW251076697 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240,&quot;335572071&quot;:0,&quot;335572072&quot;:0,&quot;335572073&quot;:0,&quot;335572075&quot;:0,&quot;335572076&quot;:0,&quot;335572077&quot;:0,&quot;335572079&quot;:0,&quot;335572080&quot;:0,&quot;335572081&quot;:0,&quot;335572083&quot;:0,&quot;335572084&quot;:0,&quot;335572085&quot;:0,&quot;335572087&quot;:0,&quot;335572088&quot;:0,&quot;335572089&quot;:0,&quot;469789798&quot;:&quot;nil&quot;,&quot;469789802&quot;:&quot;nil&quot;,&quot;469789806&quot;:&quot;nil&quot;,&quot;469789810&quot;:&quot;nil&quot;,&quot;469789814&quot;:&quot;nil&quot;}\">\u00a0<\/span> <a href=\"#\" style=\"font-size: small;\">top<\/a><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW18064421 BCX0\"><span class=\"NormalTextRun SCXW18064421 BCX0\"><strong>PI<\/strong>: Chao Zhang<br \/>\n<strong>Intern<\/strong>: Elaine Huang<\/span><\/span><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW26293756 BCX0\"><span class=\"NormalTextRun SCXW26293756 BCX0\">Ovarian and breast cancers are among the leading causes of cancer-related deaths in women worldwide. The pathological analysis of Whole Slide Images (WSIs) of ovarian and breast tumors plays a crucial role in understanding the progression and classification of these diseases. <\/span><span class=\"NormalTextRun SCXW26293756 BCX0\">Advanced medical imaging techniques, combined with computational analysis, have the potential to unlock insights into the morphological characteristics of these cancers, leading to better diagnostic accuracy and personalized treatment strategies.<\/span><span class=\"NormalTextRun SCXW26293756 BCX0\"> However, the analysis of WSIs is challenged by the sheer size of the images and the complexity of tumor biology, <\/span><span class=\"NormalTextRun SCXW26293756 BCX0\">necessitating<\/span><span class=\"NormalTextRun SCXW26293756 BCX0\"> the development of sophisticated image processing and machine learning algorithms<\/span><span class=\"NormalTextRun SCXW26293756 BCX0\">.<\/span><\/span><span class=\"EOP SCXW26293756 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW44720550 BCX0\"><span class=\"NormalTextRun SCXW44720550 BCX0\">This internship aims to tackle the critical challenge of processing and analyzing pathological WSIs of ovarian and breast cancers. The project&#8217;s goal is to develop and implement algorithms that can efficiently and accurately <\/span><span class=\"NormalTextRun SCXW44720550 BCX0\">identify<\/span><span class=\"NormalTextRun SCXW44720550 BCX0\"> cancerous tissues, quantify tumor heterogeneity, and predict clinical outcomes. This involves overcoming obstacles such as image segmentation, feature extraction, and <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW44720550 BCX0\">the classification<\/span><span class=\"NormalTextRun SCXW44720550 BCX0\"> of cancer subtypes. By enhancing our capability to analyze WSIs, we aim to contribute to the early detection of ovarian and breast cancers, improve the accuracy of prognosis predictions, and <\/span><span class=\"NormalTextRun SCXW44720550 BCX0\">assist<\/span><span class=\"NormalTextRun SCXW44720550 BCX0\"> in the formulation of personalized treatment plans<\/span><span class=\"NormalTextRun SCXW44720550 BCX0\">.<\/span><\/span><span class=\"EOP SCXW44720550 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><a id=\"Chen_Dhruvi2025\"><\/a><strong><\/strong><\/p>\n<p>&nbsp;<\/p>\n<p><strong>Enhancing Interpretability and Generalizability in Alzheimer\u2019s Disease Risk Modeling<\/strong> <a href=\"#\" style=\"font-size: small;\">top<\/a><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW18064421 BCX0\"><span class=\"NormalTextRun SCXW18064421 BCX0\"><strong>PI<\/strong>: Jinying Chen<br \/>\n<strong>Intern<\/strong>: Joshi, Dhruvi<\/span><\/span><\/p>\n<p>The development of Alzheimer\u2019s disease (AD) can span many years before symptom manifestation and clinical diagnosis. Effective treatments for AD are considered controversial and only minimally slow down progression. Assessing and identifying people with risk in cognitive decline and progression along the AD disease continuum may enable earlier intervention and better prognosis via modifiable risk factors and therefore reduce the disease burden.<\/p>\n<p>Many factors (sociodemographic, genetic, environmental\/lifestyle, clinical, and the presentation of AD hallmarks) contribute to the risk of developing AD or AD-related cognitive decline. Advanced machine learning (ML) models can incorporate many features and model complex, non-linear relationships between these features and the outcome. However, they often face criticisms on lack of interpretability (i.e., lacking obvious connections between some features important for the model\u2019s prediction accuracy and the outcome to predict) which limits their application, especially in clinical settings. In addition, external evaluations of ML models trained\/optimized using one dataset on another dataset can be challenging if the two datasets contain related but different variables. This internship seeks a summer intern student to assist in developing an analysis pipeline that supports the development of interpretable ML models for AD risk prediction and the evaluation of these models across datasets.<\/p>\n<p><a id=\"Leshchiner_Bhamidipat2025\"><\/a><strong><\/strong><\/p>\n<p><strong>Deep Learning Approaches for Detecting Epigenetic and Epitranscriptomic Modifications in Cancer<\/strong>\u00a0<a href=\"#\" style=\"font-size: small;\">top<\/a><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW18064421 BCX0\"><span class=\"NormalTextRun SCXW18064421 BCX0\"><strong>PI<\/strong>: Ignaty Leshchiner<br \/>\n<strong>Intern<\/strong>: Sandilya Bhamidipati<\/span><\/span><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun MacChromeBold SCXW25534715 BCX0\"><span class=\"NormalTextRun SCXW25534715 BCX0\">There is an ever-growing body of literature which <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW25534715 BCX0\">show<\/span><span class=\"NormalTextRun SCXW25534715 BCX0\"> how modifications to <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW25534715 BCX0\">the DNA<\/span><span class=\"NormalTextRun SCXW25534715 BCX0\">, both genetically and epigenetically, as well as modifications to proteins are distinct in the setting of cancer<\/span><span class=\"NormalTextRun SCXW25534715 BCX0\"> and neurological disease<\/span><span class=\"NormalTextRun SCXW25534715 BCX0\">. Changes to RNA, especially those <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW25534715 BCX0\">epitranscriptomic<\/span><span class=\"NormalTextRun SCXW25534715 BCX0\"> in nature, have not yet <\/span><span class=\"NormalTextRun SCXW25534715 BCX0\">extensively been<\/span><span class=\"NormalTextRun SCXW25534715 BCX0\"> studied. This is because while the technologies to accurately assess th<\/span><span class=\"NormalTextRun SCXW25534715 BCX0\">e<\/span><span class=\"NormalTextRun SCXW25534715 BCX0\"> b<\/span><span class=\"NormalTextRun SCXW25534715 BCX0\">ase-<\/span><span class=\"NormalTextRun SCXW25534715 BCX0\">p<\/span><span class=\"NormalTextRun SCXW25534715 BCX0\">air<\/span><span class=\"NormalTextRun SCXW25534715 BCX0\"> changes to DNA are well established, the ability to detect native <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW25534715 BCX0\">epitranscriptomic<\/span><span class=\"NormalTextRun SCXW25534715 BCX0\"> changes in DNA and RNA is not yet <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW25534715 BCX0\">a robust<\/span><span class=\"NormalTextRun SCXW25534715 BCX0\"> technology. We are developing deep learning methods that enable calling of modifications from both RNA and DNA with higher accuracy from Native Nanopore based sequencing and <\/span><span class=\"NormalTextRun SCXW25534715 BCX0\">identify<\/span><span class=\"NormalTextRun SCXW25534715 BCX0\"> tumor type specific DNA\/RNA modifications. The project will involve analyzing and training the models to improve call accuracy and detect biological changes within samples.<\/span><\/span><span class=\"EOP SCXW25534715 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><a id=\"Leshchiner_Bergamo2025\"><\/a><strong><\/strong><\/p>\n<p><strong>Phylogenetic reconstruction models in tumor biology<\/strong> <a href=\"#\" style=\"font-size: small;\">top<\/a><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW18064421 BCX0\"><span class=\"NormalTextRun SCXW18064421 BCX0\"><strong>PI<\/strong>: Ignaty Leshchiner<br \/>\n<strong>Intern<\/strong>: Beatriz Bergamo<\/span><\/span><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun MacChromeBold SCXW63420131 BCX0\"><span class=\"NormalTextRun SCXW63420131 BCX0\">Computational biology and cancer bioinformatics. Research in the lab is focused on applying new genomic technologies, computational <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW63420131 BCX0\">analysis<\/span><span class=\"NormalTextRun SCXW63420131 BCX0\"> and AI methods on data from patients\u2019 tumors to understand the biology behind tumor development, treatment evasion, and progression to metastasis. We are developing and applying tools for simultaneous analysis of multiple samples from the same patient, clonal structure, integration of single cell genomics and transcriptomics, reconstruction of cell subpopulations, their growth kinetics and expression, tumor micro-environment effects, estimation of order of events (\u201ctiming\u201d) during tumor development and progression. We work with pre- and post- treatment samples, <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW63420131 BCX0\">autopsies<\/span><span class=\"NormalTextRun SCXW63420131 BCX0\"> and longitudinal blood biopsies in solid and blood malignancies.<\/span><\/span><span class=\"EOP SCXW63420131 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240,&quot;335572071&quot;:0,&quot;335572072&quot;:0,&quot;335572073&quot;:0,&quot;335572075&quot;:0,&quot;335572076&quot;:0,&quot;335572077&quot;:0,&quot;335572079&quot;:0,&quot;335572080&quot;:0,&quot;335572081&quot;:0,&quot;335572083&quot;:0,&quot;335572084&quot;:0,&quot;335572085&quot;:0,&quot;335572087&quot;:0,&quot;335572088&quot;:0,&quot;335572089&quot;:0,&quot;469789798&quot;:&quot;nil&quot;,&quot;469789802&quot;:&quot;nil&quot;,&quot;469789806&quot;:&quot;nil&quot;,&quot;469789810&quot;:&quot;nil&quot;,&quot;469789814&quot;:&quot;nil&quot;}\">\u00a0<\/span><\/p>\n<p>Our methodologies allow us to study the sequence of events that occur in the developing cells all the way from normal to malignant state, both on single patient and cohort level.<\/p>\n<p>In the proposed project, we aim to address the premalignant progression knowledge gap by developing and applying computational methodologies to infer progression models and enhance our understanding of the genetic events occurring during the premalignant phase of various cancer subtypes and subclonal progression. By utilizing publicly available and lab generated whole exome and whole genome sequencing data from primary or advanced tumors, we are looking to establish a map of cancer progression for cancers that currently lack detailed information on the premalignant state.<\/p>\n<p><a id=\"Perissi_Li2025\"><\/a><strong><\/strong><\/p>\n<p><strong>GPS2-mediated signaling and mtUPR pathway<\/strong> <a href=\"#\" style=\"font-size: small;\">top<\/a><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW18064421 BCX0\"><span class=\"NormalTextRun SCXW18064421 BCX0\"><strong>PI<\/strong>: Valentina Perissi<br \/>\n<strong>Intern<\/strong>: Xinyu Li<\/span><\/span><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW22731225 BCX0\"><span class=\"NormalTextRun SCXW22731225 BCX0\">Mitochondria are unique among intracellular organelles in that they <\/span><span class=\"NormalTextRun SCXW22731225 BCX0\">contain<\/span><span class=\"NormalTextRun SCXW22731225 BCX0\"> multiple copies of their own <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW22731225 BCX0\">DNA,<\/span><span class=\"NormalTextRun SCXW22731225 BCX0\"> <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW22731225 BCX0\">however<\/span><span class=\"NormalTextRun SCXW22731225 BCX0\"> most of the mitochondrial proteome is encoded in the nuclear genome. Coordinated regulation of gene expression across the two interdependent genomes is therefore essential <\/span><span class=\"NormalTextRun SCXW22731225 BCX0\">to <\/span><span class=\"NormalTextRun SCXW22731225 BCX0\">maintain<\/span><span class=\"NormalTextRun SCXW22731225 BCX0\"> cellular homeostasis <\/span><span class=\"NormalTextRun SCXW22731225 BCX0\">and guaranteed by bidirectional communication pathways, referred to as anterograde (nucleus to mitochondria) and retrograde (mitochondria to nucleus) signaling. We identified G-protein Pathway Suppressor 2 (GPS2) as a key mediator of mitochondria retrograde signaling in mammals and characterized GPS2 chromatin occupancy by <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW22731225 BCX0\">ChIPseq<\/span><span class=\"NormalTextRun SCXW22731225 BCX0\"> in different cell lines<\/span><span class=\"NormalTextRun SCXW22731225 BCX0\">, including murine 3T3-L1 <\/span><span class=\"NormalTextRun SCXW22731225 BCX0\">pre<\/span><span class=\"NormalTextRun SCXW22731225 BCX0\">adipocytes <\/span><span class=\"NormalTextRun SCXW22731225 BCX0\">treated with mitochondrial stressors<\/span><span class=\"NormalTextRun SCXW22731225 BCX0\">, however it remains unclear how it <\/span><span class=\"NormalTextRun SCXW22731225 BCX0\">gets<\/span><span class=\"NormalTextRun SCXW22731225 BCX0\"> recruited to <\/span><span class=\"NormalTextRun SCXW22731225 BCX0\">specific target<\/span><span class=\"NormalTextRun SCXW22731225 BCX0\"> genes in response to retrograde translocation<\/span><span class=\"NormalTextRun SCXW22731225 BCX0\"> from stressed mitochondria<\/span><span class=\"NormalTextRun SCXW22731225 BCX0\"> and whether it acts in synergy with the classic mitochondrial unfolded protein response (<\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW22731225 BCX0\">mtUPR<\/span><span class=\"NormalTextRun SCXW22731225 BCX0\">) pathway<\/span><span class=\"NormalTextRun SCXW22731225 BCX0\">. <\/span><span class=\"NormalTextRun SCXW22731225 BCX0\">A better understanding of the molecular mechanisms regulating the mitochondrial stress response is critical to understand the basis of adaptive responses towards the maintenance of metabolic homeostasis in mammalian cells, with important implication for <\/span><span class=\"NormalTextRun SCXW22731225 BCX0\">studies on diseases with a metabolic <\/span><span class=\"NormalTextRun SCXW22731225 BCX0\">component<\/span><span class=\"NormalTextRun SCXW22731225 BCX0\">, including not only mitochondrial diseases and metabolic syndromes, but also aging and cancer.\u00a0<\/span><\/span><span class=\"EOP SCXW22731225 BCX0\" data-ccp-props=\"{&quot;335551550&quot;:6,&quot;335551620&quot;:6}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW131083904 BCX0\"><span class=\"NormalTextRun SCXW131083904 BCX0\">The overall <\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">goal<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\"> of this project<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\"> is<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\"> to explore the crosstalk between GPS2-mediated retrograde signaling and the <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW131083904 BCX0\">mtUPR<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\"> pathway. <\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">Our hypothesis is that <\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">different <\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">retrograde signaling pathways converge to regulate common target genes and <\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">GPS2 is recruited to <\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">chromatin<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\"> through its interaction with <\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">stress-response TFs like <\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">ATF4\/ATF5. This hypothesis <\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">wa<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">s based on the following preliminary observations: <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW131083904 BCX0\">i<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">) Numerous classic ATF4\/5 target genes were found among the DEGs regulated by GPS2 upon mitochondrial-to-nucleus translocation in 3T3-L1 cells<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\"> (Cardamone et al., 2018)<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">; ii) Direct interaction between ATF4 and GPS2 is reported in <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW131083904 BCX0\">BioGRID<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\"> and was validated by co-immunoprecipitation in Hela cells<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">.<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\"> <\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">Preliminary <\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">analysis of two sets of published ATF4 <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW131083904 BCX0\">ChIPseq<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\"> datasets (which was performed by a BMSIP student <\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">as part of their<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\"> Summer 2024<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\"> internship<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">)<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\"> confirmed this hypothesis by <\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">identifying<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\"> <\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">several<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\"> genes<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\"> <\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">occupied by both <\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">GPS2 and ATF4.<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\"> <\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">T<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">hese analyses <\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">also <\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">revealed that clustering of GPS2\/ATF4 target genes <\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">based on promoter topology and relative position of <\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">GPS2 and ATF4 binding sites correlate<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">d<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\"> to <\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">defined<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\"> stress response and metabolic pathways<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">, thus suggesting an unexpected specificity in the mechanisms of action. We would like to confirm these findings <\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">using<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\"> other published <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW131083904 BCX0\">ChIPseq<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\"> datasets for ATF4 and extend our studies to other stress response factors (such as FOXO and NRF2) which may contribute to GPS2 recruitment to <\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">subsets of targets<\/span><span class=\"NormalTextRun SCXW131083904 BCX0\">.\u00a0<\/span><\/span><span class=\"EOP SCXW131083904 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><a id=\"Zeldich_Pimparkar2025\"><\/a><strong><\/strong><\/p>\n<p>&nbsp;<\/p>\n<p><strong><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun MacChromeBold SCXW133352533 BCX0\"><span class=\"NormalTextRun SCXW133352533 BCX0\">Preserving neuronal activity in human cortical organoids<\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW133352533 BCX0\">.<\/span><\/span><\/strong><span class=\"EOP SCXW133352533 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240,&quot;335572071&quot;:0,&quot;335572072&quot;:0,&quot;335572073&quot;:0,&quot;335572075&quot;:0,&quot;335572076&quot;:0,&quot;335572077&quot;:0,&quot;335572079&quot;:0,&quot;335572080&quot;:0,&quot;335572081&quot;:0,&quot;335572083&quot;:0,&quot;335572084&quot;:0,&quot;335572085&quot;:0,&quot;335572087&quot;:0,&quot;335572088&quot;:0,&quot;335572089&quot;:0,&quot;469789798&quot;:&quot;nil&quot;,&quot;469789802&quot;:&quot;nil&quot;,&quot;469789806&quot;:&quot;nil&quot;,&quot;469789810&quot;:&quot;nil&quot;,&quot;469789814&quot;:&quot;nil&quot;}\">\u00a0<\/span> <a href=\"#\" style=\"font-size: small;\">top<\/a><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW18064421 BCX0\"><span class=\"NormalTextRun SCXW18064421 BCX0\"><strong>PI<\/strong>: Ella Zeldich<br \/>\n<strong>Intern<\/strong>: Shivani Pimparkar<\/span><\/span><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW138200163 BCX0\"><span class=\"NormalTextRun SCXW138200163 BCX0\">Cortical organoids (COs) <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">can <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW138200163 BCX0\">be generated<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW138200163 BCX0\">generated<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> from induced pluripotent stem cells (iPSCs). COs <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW138200163 BCX0\">contain<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> mature neurons and astrocytes <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">and <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW138200163 BCX0\">exhibit<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> spontaneous neuronal activity<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> and<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW138200163 BCX0\">provid<\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW138200163 BCX0\">e<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> an invaluable platform for <\/span><\/span><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW138200163 BCX0\"><span class=\"NormalTextRun SCXW138200163 BCX0\">in vitro<\/span><\/span><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW138200163 BCX0\"><span class=\"NormalTextRun SCXW138200163 BCX0\"> study of <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW138200163 BCX0\">the <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW138200163 BCX0\">cell<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">-to-cell interactions mimicking <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW138200163 BCX0\">human<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> brain. <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">Recent <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW138200163 BCX0\">study<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">demonstrated<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> that expanding <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">native oligodendrocyte progenitor cell population present in COs <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">results in the<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> generat<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">ion<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">of <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">oligodendrocyte <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">containing<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> organoids (OCOs)<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">. Within the OCOs, oligodendrocyte can undergo functional maturation and actively myelinate neuronal axons. While this approach offers a promising avenue to u<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">nderstanding oligodendrocyte biology <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">in a 3D human cellular system, our p<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">reliminary <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">observations<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> suggest that expansion of the native OPC population <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">in <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">OCOs<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">results in diminished neuronal <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">maturation and <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">activity. Thus, <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">a functional <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">characterization <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">of mature oligodendrocytes and the impact of neuronal activity on their biology <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">is <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">limited<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> in the current OCO model. <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">In our ongoing study, we utilized <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW138200163 BCX0\">BrainPhys<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> medium,<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">optimized<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> for electrophysiological activity<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> and neuronal <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">maturation;<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> to rescue neuronal maturation in OCOs. <\/span><\/span><span data-contrast=\"none\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW138200163 BCX0\"><span class=\"NormalTextRun SCXW138200163 BCX0\">By matching the physiological conditions of the CNS, <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SpellingErrorHighlight SCXW138200163 BCX0\">BrainPhys<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> creates a cellular environment that increases synaptic activity during development and <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">consequently<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> enhances neuronal maturation<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">. <\/span><\/span><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW138200163 BCX0\"><span class=\"NormalTextRun SCXW138200163 BCX0\">OCOs were divided into three groups: one receiving <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW138200163 BCX0\">BrainPhys<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> prior to oligodendrocyte expansion, the second receiving <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW138200163 BCX0\">BrainPhys<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> after expansion, and the final remaining in standard basal medium. <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">Using these three groups, our goal <\/span><\/span><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun Underlined SCXW138200163 BCX0\"><span class=\"NormalTextRun SCXW138200163 BCX0\">was to <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">determine<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> the conditions that will result in the presence of oligodendrocyte population and preserved neuronal <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW138200163 BCX0\">activity, and<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> assess the impact on enhanced neuronal activity of the maturation of oligodendrocytes.<\/span><\/span><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW138200163 BCX0\"><span class=\"NormalTextRun SCXW138200163 BCX0\"> Using i<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">mmunohistochemical staining <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">and f<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">unctional neuronal assessment with calcium imaging<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">, we found that <\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">transitioning<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> OCOs<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> to <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW138200163 BCX0\">BrainPhys<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\"> prior to oligodendrocyte expansion generates OCOs with functional neurons<\/span><span class=\"NormalTextRun SCXW138200163 BCX0\">, while preserving oligodendrocyte maturation.\u00a0<\/span><\/span><span class=\"EOP SCXW138200163 BCX0\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">We also submitted samples from the three experimental groups to sc-RNA-seq. <\/span><span data-contrast=\"auto\">The purpose of the analysis would be to compare cell populations within the OCOs exposed to different media and assess the markers of neuronal maturation and oligodendrocyte development on a single-cell resolution. <\/span><span data-contrast=\"auto\">We expect that our scRNA-seq data analysis will uncover the cellular processes underlying the ability of the<\/span><span data-contrast=\"none\"> BrainPhys media to promote neuronal functions as well as oligodendrocyte development and maturation, leading to a more advanced organoid model that can be widely used in the biomedical field.\u00a0\u00a0<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Upon completion, our study has the potential to utilize this model to recapitulate known network abnormalities and cellular pathologies in neurodevelopmental disorders. Taken together, our study presents an increasingly comprehensive organoid system capable of modeling neuronal activity, oligodendrocyte lineage, and neuron- oligodendrocyte crosstalk.\u00a0<\/span><span data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><a id=\"Lau_Manseeb2025\"><\/a><strong><\/strong><\/p>\n<p><strong>Genomics and transcriptomics of the Betta splendens skin<\/strong> <a href=\"#\" style=\"font-size: small;\">top<\/a><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW18064421 BCX0\"><span class=\"NormalTextRun SCXW18064421 BCX0\"><strong>PI<\/strong>: Nelson Lau<br \/>\n<strong>Intern<\/strong>: Manseeb Hossain<\/span><\/span><\/p>\n<p><span class=\"NormalTextRun SCXW72617005 BCX0\">The Betta splendens <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW72617005 BCX0\">siamese<\/span><span class=\"NormalTextRun SCXW72617005 BCX0\"> fighting fish has a very compact ~450mb genome and <\/span><span class=\"NormalTextRun SCXW72617005 BCX0\">a very diverse<\/span><span class=\"NormalTextRun SCXW72617005 BCX0\"> and amazing skin coloration pattern<\/span><span class=\"NormalTextRun SCXW72617005 BCX0\">.\u00a0 <\/span><span class=\"NormalTextRun SCXW72617005 BCX0\">We hypothesize that transposons and unique transcriptome signatures may underlie the koi\/marble skin coloration pattern in the Betta fish.<\/span><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun MacChromeBold SCXW212949287 BCX0\"><span class=\"NormalTextRun SCXW212949287 BCX0\">The project will entail analysis of the gene expression network and genome structural variants in the skin tissues of the model fish Betta splendens, also known as the Siamese Fighting Fish. This common pet has an amazing display of skin coloration variations that we hypothesize <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW212949287 BCX0\">is<\/span><span class=\"NormalTextRun SCXW212949287 BCX0\"> affected by transposons either mobilizing or expressing transcripts that modulate the skin transcriptome.\u202f<\/span><\/span><span class=\"EOP SCXW212949287 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240}\">\u00a0<\/span><\/p>\n<p><a id=\"Lang_Dirabou2025\"><\/a><strong><\/strong><\/p>\n<p>&nbsp;<\/p>\n<p data-start=\"132\" data-end=\"208\"><strong data-start=\"132\" data-end=\"208\">Developmental Pathways in Stem Cell Maintenance, Cancer Progression <\/strong><strong>and Aging<\/strong> <a href=\"#\" style=\"font-size: small;\">top<\/a><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW18064421 BCX0\"><span class=\"NormalTextRun SCXW18064421 BCX0\"><strong>PI<\/strong>: Deborah Lang<br \/>\n<strong>Intern<\/strong>: Jacques Dirabou<\/span><\/span><\/p>\n<p data-start=\"201\" data-end=\"654\">This project investigates how developmental pathways that support stem cell maintenance are repurposed in adult melanocyte stem cells and subverted during melanoma progression. Our work centers on melanocytes, melanocyte stem cells, and melanoma, using a multidisciplinary approach that integrates cellular, molecular, and genomic tools. We work with protein, RNA, and DNA analyses, along with mouse models and datasets derived from patient populations.<\/p>\n<p data-start=\"656\" data-end=\"1073\">We are addressing two central questions. First, we aim to define the cistromic and transcriptomic targets of transcription factors that play key roles in development, and to understand how these factors are reused in adult stem cells and corrupted in cancer. Second, we seek to identify distinct populations of melanocyte stem cells and examine how these populations differentiate and change over the course of aging.<\/p>\n<p data-start=\"1075\" data-end=\"1485\" data-is-last-node=\"\" data-is-only-node=\"\">Our approach combines functional genomics, in vivo and in vitro models, and patient-derived data to dissect the mechanisms underlying stem cell behavior and tumor progression. By exploring how developmental programs are recycled or hijacked, we aim to uncover fundamental insights into both melanoma biology and stem cell aging. This work has potential applications in regenerative medicine and cancer therapy.<\/p>\n<p><a id=\"Zhang_Han2025\"><\/a><strong><\/strong><\/p>\n<p><strong><span data-contrast=\"auto\" xml:lang=\"EN\" lang=\"EN\" class=\"TextRun MacChromeBold SCXW178063042 BCX0\"><span class=\"NormalTextRun SCXW178063042 BCX0\">Cross-species data Integration for Single-Cell Neural Datasets<\/span><\/span><span class=\"EOP SCXW178063042 BCX0\" data-ccp-props=\"{&quot;134245417&quot;:false,&quot;201341983&quot;:0,&quot;335559740&quot;:240,&quot;335572071&quot;:0,&quot;335572072&quot;:0,&quot;335572073&quot;:0,&quot;335572075&quot;:0,&quot;335572076&quot;:0,&quot;335572077&quot;:0,&quot;335572079&quot;:0,&quot;335572080&quot;:0,&quot;335572081&quot;:0,&quot;335572083&quot;:0,&quot;335572084&quot;:0,&quot;335572085&quot;:0,&quot;335572087&quot;:0,&quot;335572088&quot;:0,&quot;335572089&quot;:0,&quot;469789798&quot;:&quot;nil&quot;,&quot;469789802&quot;:&quot;nil&quot;,&quot;469789806&quot;:&quot;nil&quot;,&quot;469789810&quot;:&quot;nil&quot;,&quot;469789814&quot;:&quot;nil&quot;}\">\u00a0<\/span> <\/strong><a href=\"#\" style=\"font-size: small;\">top<\/a><\/p>\n<p><span data-contrast=\"auto\" xml:lang=\"EN-US\" lang=\"EN-US\" class=\"TextRun SCXW18064421 BCX0\"><span class=\"NormalTextRun SCXW18064421 BCX0\"><strong>PI<\/strong>: <span data-contrast=\"auto\" xml:lang=\"EN\" lang=\"EN\" class=\"TextRun SCXW120835162 BCX0\"><span class=\"NormalTextRun SCXW120835162 BCX0\">Chao Zhang<\/span><\/span><span class=\"EOP SCXW120835162 BCX0\" data-ccp-props=\"{}\">\u00a0<\/span><br \/>\n<strong>Intern<\/strong>: <span>Jinglin Han<\/span><\/span><\/span><\/p>\n<p>Alzheimer\u2019s disease (AD) is one of the most pressing public health challenges of our time, with the number of affected individuals in the U.S. expected to rise from 4 million to over 14 million by 2050. Although aging is the greatest risk factor, AD is not an inevitable outcome of aging, and distinguishing between normal aging and neurodegeneration remains a fundamental challenge. One key limitation in current research is the lack of accurate models that fully recapitulate human AD pathology, as commonly used model organisms like mice and monkeys do not naturally develop the full spectrum of AD observed in humans. To address this translational gap, this project proposes a computational strategy to align single-cell data across species and developmental stages.<\/p>\n<p>We aim to develop a graph neural diffusion algorithm tailored to integrate brain-derived single-cell and single-nucleus datasets from humans, mice, and monkeys. These include scRNA-seq, snRNA-seq, and snATAC-seq data, spanning healthy, aged, and AD-affected conditions. The goal is to enable cross-species knowledge transfer at the cellular and molecular level, deepening our understanding of neurodegeneration and aging in humans through better-aligned animal models.<\/p>\n<p>The project will focus on several key tasks. First, we will collect and preprocess publicly available single-cell and single-nucleus datasets, ensuring consistency across samples. Next, we will perform orthologous gene mapping using a combination of strategies, including gene symbol matching, sequence-based alignment, database lookups, and functional similarity approaches. These methods will be evaluated for accuracy and biological relevance.<\/p>\n<p>Following gene mapping, we will design and implement a graph neural diffusion algorithm that integrates data across species while preserving tissue- and modality-specific features. This model will be fine-tuned for neural tissue to optimize biological interpretability. To evaluate performance, we will benchmark our method against leading integration tools such as Seurat, Harmony, fastMNN, SAMap, and more recent large language model\u2013based tools like scGPT, CAMEX, and SATURN.<\/p>\n<p>Finally, we will apply the model to real-world neural datasets to assess its effectiveness in transferring biological knowledge across species. This downstream analysis will focus on validating the model in practical scenarios related to aging and Alzheimer\u2019s disease.<\/p>\n<p>The focus of the project will be on data collection, preprocessing, orthologous gene mapping, and initial benchmarking of the integration algorithm. The long-term objective is to establish a robust, generalizable framework for cross-species single-cell integration in the context of brain aging and neurodegeneration.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Project title PI Intern Alt splicing in AD, PacBio Uwe Beffert Rachel Bozadjian Ligand-Receptor in AD, AlphaFold Uwe Beffert Riya Jadhav TCAB1 Mutations in Pediatric Osteosarcoma Rachel Flynn Sydney Sorbello GPS2-mediated signaling and mitonuclear contact sites Sahana Mitra Tyler Kwok Diff expression in beetle Lynette Strickland Katherine Kitrick Measuring kinetic rates of synthetic transcription factors [&hellip;]<\/p>\n","protected":false},"author":24347,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"_links":{"self":[{"href":"https:\/\/sites.bu.edu\/bmsip\/wp-json\/wp\/v2\/posts\/166"}],"collection":[{"href":"https:\/\/sites.bu.edu\/bmsip\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sites.bu.edu\/bmsip\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/bmsip\/wp-json\/wp\/v2\/users\/24347"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/bmsip\/wp-json\/wp\/v2\/comments?post=166"}],"version-history":[{"count":25,"href":"https:\/\/sites.bu.edu\/bmsip\/wp-json\/wp\/v2\/posts\/166\/revisions"}],"predecessor-version":[{"id":195,"href":"https:\/\/sites.bu.edu\/bmsip\/wp-json\/wp\/v2\/posts\/166\/revisions\/195"}],"wp:attachment":[{"href":"https:\/\/sites.bu.edu\/bmsip\/wp-json\/wp\/v2\/media?parent=166"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.bu.edu\/bmsip\/wp-json\/wp\/v2\/categories?post=166"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.bu.edu\/bmsip\/wp-json\/wp\/v2\/tags?post=166"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}