Fang Xie

Check out our latest Postdoc Spotlight of the Month, Dr. Fangfang Wang from the department of Pharmacology in the Case Western Reserve University School of Medicine working in Dr. Youwei Zhang's lab! #CWRUPostdocs #CWRU #Postdoc #Pharmacology

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Happy to share a preprint of our recent work led by Diego Figueroa demonstrating a key role for host CD8+ T cell immunity and opposing roles of lymphodepleting preconditioning in the antitumor efficacy of #AdoptiveCellTherapy (ACT). We show that host CD8+ T cells contribute to ACT-mediated elimination of primary tumors and rejected ACT-resistant melanoma cells lacking the targeted antigen in mouse models. We also show that lymphodepleting preconditioning enhances expansion of transferred cells and ACT-mediated tumor elimination, but abrogated long-term host antitumor immunity and protection against ACT-resistant melanoma cells. This work highlights the interplay between transferred and endogenous CD8+ T cells for the long-term efficacy of ACT, which is undermined by lymphodepleting preconditioning, and may ultimately explain a major mechanism of ACT resistance. Thanks to all the co-authors and collaborators who made this work possible Vincenzo Borgna MD PhD Sofía Hidalgo Triviños Andrés Hernández Oliveras Ximena López Lagos Felipe Galvez-Cancino Fabiola Osorio, PhD Fundación Ciencia & Vida Universidad San Sebastián Vicerrectoría de Investigación y Doctorados USS VRID

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All-optical phase conjugation using diffractive wavefront processing https://lnkd.in/gcC8KQF2 UCLA Henry Samueli School of Engineering and Applied Science California NanoSystems Institute at UCLA ECE Department UCLA Bioengineering #diffractiveprocessors

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Since the early stages of my career, I have been truly interested in understanding how PEGylation of nanocarriers can influence their diffusion across mucosal barriers, access to the lymphatics, and diffusion across tissues. Ruxandra Gref, Justin Hanes, and many others have made incredible contributions in this field. I hope this note published in DDTR serves as inspiration for you. A journey through the history of PEGylated drug delivery nanocarriers: https://lnkd.in/d6PwUB84                 

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Protein design struggles with imperfect alignments. ❌ EPFL scientists introduced #ProtMamba, an alignment-free protein language model that captures evolutionary info from homologous sequences for drug discovery & more. A game-changer for protein modeling! Quick Read: https://lnkd.in/gg9CV66F #bioinformatics #proteindesign #proteomics #mambaarchitecture #deeplearning #ai #drugdiscovery #personalizedmedicine #sciencenews

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🧬🔍 SCOTCH: Single-Cell Omics for Transcriptome CHaracterization 🧫📊 Presented in a recent bioRxiv article by Zhuoran Xu, Hui-Qi Qu, Joe Chan, Charlly Kao, Hakon Hakonarson, and Kai Wang. SCOTCH uses long-read single-cell RNA for isoform-level characterization and novel isoforms discovery. ��� Key Highlights: 🚀 SCOTCH overcomes the limitations of short-read sequencing by employing long-read technologies, enabling thorough analyses of complex genomic regions and precise isoform identification. 🧑‍🔬 Compatible with library preparation platforms from 10X Genomics and Parse Biosciences, and is versatile across diverse cell types such as neurons, hepatocytes, and developing cardiomyocytes. 📈 Differential isoform usage across cell populations, shedding light on cellular functions and disease mechanisms that go beyond traditional gene-level expression analysis. 📚 bioRxiv paper: https://buff.ly/44FsyVx 👨‍💻 GitHub: https://buff.ly/44CEes2 📢 Join the Conversation 📢 Share your ideas, methods, and tools in the comments! 👇 💬 #SingleCellOmics #Transcriptomics #Genomics #Bioinformatics #SCOTCH #BiotechInnovation #PharmaLeaders

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🧬 scDILT: a New Framework for Single-Cell Integration! 🧬 Published in the bioRxiv paper "scDILT: a model-based and constrained deep learning framework for single-cell Data Integration, Label Transferring, and clustering," authored by Xiang Lin, Jianlan Ren, Le Gao, Zhi Wei, and Junwen Wang. 🔍 Highlights of scDILT 🔍 📈 Integrates diverse single-cell data batches while effectively transferring labels from reference datasets. 🛠️ Utilizes a conditional autoencoder (CAE) and deep embedding clustering (DEC) to manage batch effects and enhance data integration & incorporates a zero-inflated negative binomial (ZINB) loss to deal with zero-inflated and over-dispersed data. 📊 Employs pairwise constraints for guided clustering based on labeled reference data. 🎯 Defines clustering loss through Kullback-Leibler (KL) divergence, with iterative updates to cluster centers. 🚀 Serves as a robust tool for integrating single-cell datasets across varied conditions, preserving clustering patterns and mapping cells accurately. 📚 bioRxiv paper: https://buff.ly/4atikZu 👨‍💻 github: https://buff.ly/3UQKyHQ 📢 Join the Conversation 📢 Share your ideas, methods, and tools in the comments! 👇 💬 #SingleCell #Bioinformatics #DataScience #DeepLearning #Biotechnology #Pharmaceuticals #scDILT #Innovation #Research

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The FDA Modernization Act 2.0 is transforming nonclinical drug testing by encouraging the use of advanced models like 3D organoids and #AI, moving away from traditional animal models. Assistant Professor Lanlan Zhou underscores the significance of considering the tumor microenvironment to enhance the predictive accuracy of these models. Discover how these innovative approaches are paving the way for more effective #cancer treatments and personalized medicine.

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𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗺𝗼𝗱𝗲𝗹 𝗶𝗱𝗲𝗻𝘁𝗶𝗳𝗶𝗲𝘀 𝗽𝗼𝘁𝗲𝗻𝘁 𝗰𝗮𝗻𝗰𝗲𝗿-𝗸𝗶𝗹𝗹𝗶𝗻𝗴 𝗶𝗺𝗺𝘂𝗻𝗲 𝗰𝗲𝗹𝗹𝘀 𝗳𝗼𝗿 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱 𝗶𝗺𝗺𝘂𝗻𝗼𝘁𝗵𝗲𝗿𝗮𝗽𝘆 🔹 𝗧𝗮𝗹𝗸 𝘄𝗶𝘁𝗵 𝗮𝗻 𝗲𝘅𝗽𝗲𝗿𝘁 👉https://lnkd.in/e52sdG3P In a landmark achievement reported in the prestigious journal Nature Biotechnology, scientists at Ludwig Cancer Research have unveiled a transformative predictive model empowered by artificial intelligence (AI). Led by Alexandre Harari and Rémy Pétremand, the team has devised an innovative approach to identify the most potent immune cells for combatting cancer, heralding a new era in personalized immunotherapies. At the core of this pioneering work lies the recognition of T cells, the frontline warriors of the body's immune system. Specifically, the focus is on tumor-infiltrating lymphocytes (TILs), tasked with penetrating solid tumors and eradicating cancerous cells. However, not all TILs possess the requisite prowess to effectively target tumors, underscoring the need for precise identification of those with optimal tumor-killing capabilities. Enter TRTpred, the novel AI-driven predictive model developed by Harari and his team. Leveraging sophisticated machine learning algorithms, TRTpred analyzes T cell receptors (TCRs) to discern their tumor reactivity. Drawing on data from 235 TCRs obtained from patients with metastatic melanoma, the model distinguishes between tumor-reactive and non-reactive T cells with remarkable accuracy, offering invaluable insights into the intricate dynamics of immune responses in cancer. The implications of this breakthrough are profound. By harnessing the power of AI, researchers can now pinpoint the most potent immune cells for inclusion in personalized cancer immunotherapies, offering renewed hope to patients facing this formidable disease. This innovative approach represents a paradigm shift in cancer treatment, marking a significant step towards more effective and targeted therapies tailored to the unique cellular makeup of each patient's tumors. As the scientific community celebrates this milestone, the journey towards transformative cancer care continues. With each discovery, we move closer to a future where AI-driven precision medicine empowers patients and clinicians alike in the fight against cancer. The implications are far-reaching, promising to reshape the landscape of oncology and usher in a new era of hope and healing. 🚀 Contact BIOBOSTON CONSULTING today or visit our website to learn more about how we can support your organization. #pharmaceuticals #biotechnology #medicaldevices #consulting #fda  #quality #compliance #qualityassurance #regulatoryaffairs

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Binding site-driven Molecule Design! #BindGPT uses large language model pretraining + reinforcement learning to enable pocket-aware 3D molecule design, outperforming specialized diffusion/graph neural net models. Quick Read: https://lnkd.in/gXnNag85 #bioinformatics #structuralbiology #moleculardocking #drugdiscovery #ai #llm #reinforcementlearning #sciencenews

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Significant Advancement in CRISPR/Cas9 Gene Editing for T Cells! I’m excited to share a transformative study led by Yusuke Ito, alongside colleagues Satoshi Inoue, Takahiro Nakashima, Haosong Zhang, Yang Li, Hitomi Kasuya, Tetsuya Matsukawa, Zhiwen Wu, Toshiaki Yoshikawa, Mirei Kataoka, Tetsuo Ishikawa, and Yuki Kagoya. This research represents a crucial step forward in genetic engineering, particularly for enhancing T-cell functions in cancer immunotherapy. 𝐊𝐞𝐲 𝐟𝐢𝐧𝐝𝐢𝐧𝐠𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞: 🧬 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐠𝐑𝐍𝐀 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧: Integrating epigenetic profiling (ATAC-seq data) with prediction tools significantly improved the precision of gRNA target selection, leading to higher indel percentages in gene editing. 🧬 𝐃𝐮𝐚𝐥 𝐠𝐑𝐍𝐀 𝐓𝐚𝐫𝐠𝐞𝐭𝐢𝐧𝐠 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲: This approach addresses challenges posed by closed chromatin regions, significantly enhancing gene knockout efficacy by targeting adjacent regions. 🧬 𝐈𝐋-7 𝐏𝐫𝐞𝐭𝐫𝐞𝐚𝐭𝐦𝐞𝐧𝐭: The study demonstrates that IL-7 pretreatment enhances gene-editing efficiency in naïve T cells, maintaining their early memory phenotype and improving overall editing outcomes. These findings provide a robust framework for optimizing CRISPR/Cas9-mediated gene knockout in human T cells, with potential applications in advancing CAR-T cell therapies. 𝐊𝐮𝐝𝐨𝐬 to the team for their significant contributions to the field! #CRISPR #GeneEditing #TCellTherapy #Epigenetics #Biotech #CancerResearch

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🌟 EBMT Roundtable Discussion🌟 Join us as Professor Shaoyan Hu's team delves into the **efficacy and safety of Allo HSCT and CAR T cell therapy in pediatric leukemia patients**. Discover groundbreaking insights and advancements in pediatric oncology that could shape the future of treatment! 🧬✨ 📽️ Watch the full discussion and share your thoughts below! #PediatricLeukemia #CAR-TCellTherapy #AlloHSCT #OncologyResearch #MedicalAdvancements #EBMT #ProfessorShaoyanHu #LeukemiaTreatment #HealthcareInnovation

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New #AI method, #LINGER, cracks the code on gene regulation! 💡 LINGER, developed by Clemson University scientists, infers gene regulatory networks from single-cell multiomics data by integrating atlas-scale bulk data and prior knowledge. It outperforms existing methods and enables interpreting disease variants from expression data alone. Quick Read: https://lnkd.in/gYViayW2 #bioinformatics #genomics #machinelearning #generegulation #bigdata #sciencenews

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🚀 Exciting findings in the realm of ADCs and in the key role of payloads to develop effective treatments against cancer!  The latest study conducted by Chen et al. dives deep into the effectiveness of alternative payload strategies in treating lung cancer, shedding light on a potentially game-changing approach. Traditionally, much emphasis has been placed on novel targeting in the development of antibody-drug conjugates (ADCs). However, the research suggests that simply swapping out payloads could offer a higher probability of success compared to pursuing novel targeting. In this investigation involving 79 lung cancer patients, with a majority being HER2-positive, they found that maintaining the same payload while switching antibody formats yielded marginal benefits, with an overall response rate (ORR) of approximately 5.3%. However, the story took a significant turn when they explored the impact of swapping payloads. The ORR skyrocketed to an impressive 22.6% with this approach. What's more intriguing is the observation that in patients with advanced disease progression, a switch to a different payload led to remarkable outcomes. A staggering 41.2% ORR was noted in these patients, with some experiencing durable responses lasting more than 6 months. A compelling case study further illustrates this point, comparing trastuzumab deruxtecan (T-Dxd) with disitamab vedotin (RC48). The difference in progression-free survival 2 (PFS2) between the two treatments was striking, with T-Dxd demonstrating a PFS2 of 5.37 months compared to 3.30 months for RC48. Furthermore, the overall survival (OS) with T-Dxd extended to an impressive 50.6 months, in stark contrast to 20.2 months with RC48. These findings underscore the potential of alternative payload strategies in enhancing the efficacy of cancer therapies, particularly in patients with advanced disease. By focusing on payloads selection, we could pave the way for more effective treatment options and improved patient outcomes. Elikya Therapeutics, a Landmark BioVentures company, is focused on the development of next Generation of ADCs using its First-in-Class toxic payload, with a unique mode of able to address tumor and its microenvironment in parallel, and with a long-standing memory immune response. Elikya's unique approach focuses also on the development of Precision Conjugates: considering that new ADCs shall be designed on the comprehensive mechanistic properties of the payload (payload centric design). #ADCs #toxicpayloads #Precisionsconjugates. #CancerResearch #Innovation #TherapeuticAdvancements

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