bioRxiv : the preprint server for biology最新文献

{"title":"Effects of age on the response to spinal cord injury: optimizing the larval zebrafish model.","authors":"Whitney J Walker, Kirsten L Underwood, Patrick I Garrett, Kathryn B Lorbacher, Shannon M Linch, Thomas P Rynes, Chloe Sloop, Karen Mruk","doi":"10.1101/2023.05.18.541337","DOIUrl":"10.1101/2023.05.18.541337","url":null,"abstract":"<p><p>Zebrafish are an increasingly popular model to study regeneration after spinal cord injury (SCI). The transparency of larval zebrafish makes them ideal to study cellular processes in real time. Standardized approaches, including age at the time of injury, are not readily available making comparisons of the results with other models challenging. In this study, we systematically examined the response to spinal cord transection of larval zebrafish at three different larval ages (3-, 5-, or 7-days post fertilization (dpf)) to determine whether the developmental complexity of the larvae affects the overall response to SCI. We then used imaging and behavioral analysis to evaluate whether differences existed based on the age of injury. Injury led to increased expression of cytokines associated with the immune response; however, we found that the timing of specific inflammatory markers changed with the age of the injury. We also observed changes in glial and axonal bridging with age. Young larvae (3 dpf) were better able to regenerate axons independent of the glial bridge, unlike older larvae (7 dpf), consistent with results seen in adult zebrafish. Finally, locomotor experiments demonstrated that some swimming behavior occurs independent of glial bridge formation, further highlighting the need for standardization of this model and functional recovery assays. Overall, we found differences based on the age of transection in larval zebrafish, underlining the importance of considering age when designing experiments aimed at understanding regeneration.</p>","PeriodicalId":72407,"journal":{"name":"bioRxiv : the preprint server for biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10245662/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9607202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Benchmarking large language models for genomic knowledge with GeneTuring.","authors":"Wenpin Hou, Xinyi Shang, Zhicheng Ji","doi":"10.1101/2023.03.11.532238","DOIUrl":"10.1101/2023.03.11.532238","url":null,"abstract":"<p><p>Large language models have demonstrated great potential in biomedical research. However, their ability to serve as a knowledge base for genomic research remains unknown. We developed GeneTuring, a comprehensive Q&A database containing 1,200 questions in genomics, and manually scored 25,200 answers provided by six GPT models, including GPT-4o, Claude 3.5, and Gemini Advanced. GPT-4o with web access showed the best overall performance and excelled in most tasks. However, it still failed to correctly answer all questions and may not be fully reliable for providing answers to genomic inquiries.</p>","PeriodicalId":72407,"journal":{"name":"bioRxiv : the preprint server for biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/79/09/nihpp-2023.03.11.532238v1.PMC10054955.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9335674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Omics Atlas-Assisted Discovery of Transcription Factors for Selective T Cell State Programming.","authors":"H Kay Chung, Cong Liu, Alexander N Jambor, Brian P Riesenberg, Ming Sun, Eduardo Casillas, Brent Chick, Audrey Wang, Jun Wang, Shixin Ma, Bryan Mcdonald, Peixiang He, Qiyuan Yang, Timothy Chen, Siva Karthik Varanasi, Michael LaPorte, Thomas H Mann, Dan Chen, Filipe Hoffmann, Victoria Tripple, Josephine Ho, Jennifer Modliszewski, April Williams, Ukrae H Cho, Longwei Liu, Yingxiao Wang, Diana C Hargreaves, Jessica E Thaxton, Susan M Kaech, Wei Wang","doi":"10.1101/2023.01.03.522354","DOIUrl":"10.1101/2023.01.03.522354","url":null,"abstract":"<p><p>Transcription factors (TFs) regulate the differentiation of T cells into diverse states with distinct functionalities. To precisely program desired T cell states in viral infections and cancers, we generated a comprehensive transcriptional and epigenetic atlas of nine CD8 ⁺ T cell differentiation states for TF activity prediction. Our analysis catalogued TF activity fingerprints of each state, uncovering new regulatory mechanisms that govern selective cell state differentiation. Leveraging this platform, we focused on two critical T cell states in tumor and virus control: terminally exhausted T cells (TEX _term ), which are dysfunctional, and tissue-resident memory T cells (T _RM ), which are protective. Despite their functional differences, these states share significant transcriptional and anatomical similarities, making it both challenging and essential to engineer T cells that avoid TEX _term differentiation while preserving beneficial T _RM characteristics. Through <i>in vivo</i> CRISPR screening combined with single-cell RNA sequencing (Perturb-seq), we validated the specific TFs driving the TEX _term state and confirmed the accuracy of TF specificity predictions. Importantly, we discovered novel TEX _term -specific TFs such as ZSCAN20, JDP2, and ZFP324. The deletion of these TEX _term -specific TFs in T cells enhanced tumor control and synergized with immune checkpoint blockade. Additionally, this study identified multi-state TFs like HIC1 and GFI1, which are vital for both TEX _term and T _RM states. Furthermore, our global TF community analysis and Perturb-seq experiments revealed how TFs differentially regulate key processes in T _RM and TEX _term cells, uncovering new biological pathways like protein catabolism that are specifically linked to TEX _term differentiation. In summary, our platform systematically identifies TF programs across diverse T cell states, facilitating the engineering of specific T cell states to improve tumor control and providing insights into the cellular mechanisms underlying their functional disparities.</p>","PeriodicalId":72407,"journal":{"name":"bioRxiv : the preprint server for biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9881845/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10294329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating multiple single-cell multi-omics samples with Smmit.","authors":"Changxin Wan, Zhicheng Ji","doi":"10.1101/2023.04.06.535857","DOIUrl":"10.1101/2023.04.06.535857","url":null,"abstract":"<p><p>Multi-sample single-cell multi-omics datasets, which simultaneously measure multiple data modalities in the same cells across multiple samples, facilitate the study of gene expression, gene regulatory activities, and protein abundances on a population scale. We developed Smmit, a computational method for integrating data both across samples and modalities. Compared to existing methods, Smmit more effectively removes batch effects while preserving relevant biological information, resulting in superior integration outcomes. Additionally, Smmit is more computationally efficient and builds upon existing computational pipelines, requiring minimal effort for implementation. Smmit is an R software package that is freely available on Github: https://github.com/zji90/Smmit.</p>","PeriodicalId":72407,"journal":{"name":"bioRxiv : the preprint server for biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/94/13/nihpp-2023.04.06.535857v1.PMC10104121.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9331102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatially resolved DNP-assisted NMR illuminates the conformational ensemble of α-synuclein in intact viable cells.","authors":"Jaka Kragelj, Rupam Ghosh, Yiling Xiao, Rania Dumarieh, Dominique Lagasca, Sakshi Krishna, Kendra K Frederick","doi":"10.1101/2023.10.24.563877","DOIUrl":"10.1101/2023.10.24.563877","url":null,"abstract":"<p><p>The protein α-syn adopts a wide variety of conformations including an intrinsically disordered monomeric form and an α-helical rich membrane-associated form that is thought to play an important role in cellular membrane processes. However, despite the high affinity of α-syn for membranes, evidence that the α-helical form is adopted inside cells has been indirect. DNP-assisted solid state NMR on frozen cellular samples can report on protein conformations inside cells. Moreover, by controlling the distribution of the DNP polarization agent throughout the cellular biomass, such experiments can provide quantitative information upon the entire structural ensemble or provide information about spatially resolved sub-populations. Using DNP-assisted magic angle spinning (MAS) NMR we establish that purified α-syn in the membrane-associated and intrinsically disordered forms have distinguishable spectra. We then introduced isotopically labeled monomeric α-syn into cells. When the DNP polarization agent is dispersed homogenously throughout the cell, we found that a minority of the α-syn inside cells adopted a highly α-helical rich conformation. When the DNP polarization agent is peripherally localized, we found that the α-helical rich conformation predominates. Thus, we provide direct evidence that α-helix rich conformations of α-syn are adopted near the cellular periphery inside cells under physiological conditions. Moreover, we demonstrate how selectively altering the spatial distribution of the DNP polarization agent can be a powerful tool to observe spatially distinct structural ensembles. This approach paves the way for more nuanced investigations into the conformations that proteins adopt in different areas of the cell.</p>","PeriodicalId":72407,"journal":{"name":"bioRxiv : the preprint server for biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10634803/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92157952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetics and Environment Distinctively Shape the Human Immune Cell Epigenome.","authors":"Wenliang Wang, Manoj Hariharan, Wubin Ding, Anna Bartlett, Cesar Barragan, Rosa Castanon, Vince Rothenberg, Haili Song, Joseph Nery, Andrew Aldridge, Jordan Altshul, Mia Kenworthy, Hanqing Liu, Wei Tian, Jingtian Zhou, Qiurui Zeng, Huaming Chen, Bei Wei, Irem B Gündüz, Todd Norell, Timothy J Broderick, Micah T McClain, Lisa L Satterwhite, Thomas W Burke, Elizabeth A Petzold, Xiling Shen, Christopher W Woods, Vance G Fowler, Felicia Ruffin, Parinya Panuwet, Dana B Barr, Jennifer L Beare, Anthony K Smith, Rachel R Spurbeck, Sindhu Vangeti, Irene Ramos, German Nudelman, Stuart C Sealfon, Flora Castellino, Anna Maria Walley, Thomas Evans, Fabian Müller, William J Greenleaf, Joseph R Ecker","doi":"10.1101/2023.06.29.546792","DOIUrl":"10.1101/2023.06.29.546792","url":null,"abstract":"<p><p>The epigenomic landscape of human immune cells is dynamically shaped by both genetic factors and environmental exposures. However, the relative contributions of these elements are still not fully understood. In this study, we employed single-nucleus methylation sequencing and ATAC-seq to systematically explore how pathogen and chemical exposures, along with genetic variation, influence the immune cell epigenome. We identified distinct exposure-associated differentially methylated regions (eDMRs) corresponding to each exposure, revealing how environmental factors remodel the methylome, alter immune cell states, and affect transcription factor binding. Furthermore, we observed a significant correlation between changes in DNA methylation and chromatin accessibility, underscoring the coordinated response of the epigenome. We also uncovered genotype-associated DMRs (gDMRs), demonstrating that while eDMRs are enriched in regulatory regions, gDMRs are preferentially located in gene body marks, suggesting that exposures and genetic factors exert differential regulatory control. Notably, disease-associated SNPs were frequently colocalized with meQTLs, providing new cell-type-specific insights into the genetic basis of disease. Our findings underscore the intricate interplay between genetic and environmental factors in sculpting the immune cell epigenome, offering a deeper understanding of how immune cell function is regulated in health and disease.</p>","PeriodicalId":72407,"journal":{"name":"bioRxiv : the preprint server for biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10327221/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9865759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CaBLAM! A high-contrast bioluminescent Ca²⁺ indicator derived from an engineered <i>Oplophorus gracilirostris</i> luciferase.","authors":"Gerard G Lambert, Emmanuel L Crespo, Jeremy Murphy, Daniela Boassa, Selena Luong, Dmitrijs Celinskis, Stephanie Venn, Daniel K Nguyen, Junru Hu, Brittany Sprecher, Maya O Tree, Richard Orcutt, Daniel Heydari, Aidan B Bell, Albertina Torreblanca-Zanca, Ali Hakimi, Diane Lipscombe, Christopher I Moore, Ute Hochgeschwender, Nathan C Shaner","doi":"10.1101/2023.06.25.546478","DOIUrl":"10.1101/2023.06.25.546478","url":null,"abstract":"<p><p>Ca²⁺ plays many critical roles in cell physiology and biochemistry, leading researchers to develop a number of fluorescent small molecule dyes and genetically encodable probes that optically report changes in Ca²⁺ concentrations in living cells. Though such fluorescence-based genetically encoded Ca²⁺ indicators (GECIs) have become a mainstay of modern Ca²⁺ sensing and imaging, bioluminescence-based GECIs-probes that generate light through oxidation of a small-molecule by a luciferase or photoprotein-have several distinct advantages over their fluorescent counterparts. Bioluminescent tags do not photobleach, do not suffer from nonspecific autofluorescent background, and do not lead to phototoxicity since they do not require the extremely bright extrinsic excitation light typically required for fluorescence imaging, especially with 2-photon microscopy. Current BL GECIs perform poorly relative to fluorescent GECIs, producing small changes in bioluminescence intensity due to high baseline signal at resting Ca²⁺ concentrations and suboptimal Ca²⁺ affinities. Here, we describe the development of a new bioluminescent GECI, \"CaBLAM,\" which displays much higher contrast (dynamic range) than previously described bioluminescent GECIs and has a Ca²⁺ affinity suitable for capturing physiological changes in cytosolic Ca²⁺ concentration. Derived from a new variant of <i>Oplophorus gracilirostris</i> luciferase with superior in vitro properties and a highly favorable scaffold for insertion of sensor domains, CaBLAM allows for single-cell and subcellular resolution imaging of Ca²⁺ dynamics at high frame rates in cultured neurons and <i>in vivo</i>. CaBLAM marks a significant milestone in the GECI timeline, enabling Ca²⁺ recordings with high spatial and temporal resolution without perturbing cells with intense excitation light.</p>","PeriodicalId":72407,"journal":{"name":"bioRxiv : the preprint server for biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/a2/ab/nihpp-2023.06.25.546478v2.PMC10327125.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10186944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Passaging Human Tauopathy Patient Samples in Cells Generates Heterogeneous Fibrils with a Subpopulation Adopting Disease Folds.","authors":"Zhikai Zeng, Karen Tsay, Vishnu Vijayan, Matthew P Frost, Shikhar Prakash, Athena Quddus, Alexa Albert, Michael Vigers, Madhur Srivastava, Amanda L Woerman, Songi Han","doi":"10.1101/2023.07.19.549721","DOIUrl":"10.1101/2023.07.19.549721","url":null,"abstract":"<p><p>The discovery by cryo-electron microscopy (cryo-EM) that the neu-ropathological hallmarks of different tauopathies, including Alzheimer's disease, corticobasal degeneration (CBD), and progressive supranuclear palsy (PSP), are caused by unique misfolded conformations of the protein tau is among the most profound developments in neurodegenerative disease research. To capitalize on these discoveries for therapeutic development, one must achieve <i>in vitro</i> replication of tau fibrils that adopt the representative tauopathy disease folds, which represents a grand challenge for the field. A widely used approach has been seeded propagation using pathological tau fibrils derived from post-mortem patient samples in biosensor cells that expresses a fragment of the tau protein known as K18, or Tau4RD, containing the microtubule-binding repeat domain of tau as the substrate. The new insights from cryo-EM raised the question of whether the Tau4RD fragment is capable of adopting characteristic tau folds found in CBD and PSP patient fibrils, and whether cell-passaged and amplified tau fibrils can be used as seeds to achieve cell-free assembly of recombinant 4R tau into fibrils without the addition of cofactors. Using Double Electron Electron Resonance (DEER) spectroscopy, we discovered that cell-passaged pathological seeds generate heterogeneous fibrils that are, however, distinct between the CBD and PSP lysate-seeded fibrils, and vastly different from heparin-induced tau fibril structures. Moreover, the lysate-seeded fibrils contain a characteristic sub-population that resembles the disease fold corresponding to the respective starting patient sample. These findings indicate that templated propagation using CBD and PSP patient-derived fibrils is possible with a tau fragment that does not contain the entire pathological fibril core, but also that additional mechanisms must be tuned to converge on a homogeneous fibril population.</p>","PeriodicalId":72407,"journal":{"name":"bioRxiv : the preprint server for biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10370138/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9874229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliable protein-protein docking with AlphaFold, Rosetta, and replica-exchange.","authors":"Ameya Harmalkar, Sergey Lyskov, Jeffrey J Gray","doi":"10.1101/2023.07.28.551063","DOIUrl":"10.1101/2023.07.28.551063","url":null,"abstract":"<p><p>Despite the recent breakthrough of AlphaFold (AF) in the field of protein sequence-to-structure prediction, modeling protein interfaces and predicting protein complex structures remains challenging, especially when there is a significant conformational change in one or both binding partners. Prior studies have demonstrated that AF-multimer (AFm) can predict accurate protein complexes in only up to 43% of cases.¹ In this work, we combine AlphaFold as a structural template generator with a physics-based replica exchange docking algorithm to better sample conformational changes. Using a curated collection of 254 available protein targets with both unbound and bound structures, we first demonstrate that AlphaFold confidence measures (pLDDT) can be repurposed for estimating protein flexibility and docking accuracy for multimers. We incorporate these metrics within our ReplicaDock 2.0 protocol²to complete a robust in-silico pipeline for accurate protein complex structure prediction. AlphaRED (AlphaFold-initiated Replica Exchange Docking) successfully docks failed AF predictions including 97 failure cases in Docking Benchmark Set 5.5. AlphaRED generates CAPRI acceptable-quality or better predictions for 63% of benchmark targets. Further, on a subset of antigen-antibody targets, which is challenging for AFm (20% success rate), AlphaRED demonstrates a success rate of 43%. This new strategy demonstrates the success possible by integrating deep-learning based architectures trained on evolutionary information with physics-based enhanced sampling. The pipeline is available at github.com/Graylab/AlphaRED.</p>","PeriodicalId":72407,"journal":{"name":"bioRxiv : the preprint server for biology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10402144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10330657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combined statistical-biophysical modeling links ion channel genes to physiology of cortical neuron types.","authors":"Yves Bernaerts, Michael Deistler, Pedro J Gonçalves, Jonas Beck, Marcel Stimberg, Federico Scala, Andreas S Tolias, Jakob Macke, Dmitry Kobak, Philipp Berens","doi":"10.1101/2023.03.02.530774","DOIUrl":"10.1101/2023.03.02.530774","url":null,"abstract":"<p><p>Neural cell types have classically been characterized by their anatomy and electrophysiology. More recently, single-cell transcriptomics has enabled an increasingly fine genetically defined taxonomy of cortical cell types, but the link between the gene expression of individual cell types and their physiological and anatomical properties remains poorly understood. Here, we develop a hybrid modeling approach to bridge this gap. Our approach combines statistical and mechanistic models to predict cells' electrophysiological activity from their gene expression pattern. To this end, we fit biophysical Hodgkin-Huxley-based models for a wide variety of cortical cell types using simulation-based inference, while overcoming the challenge posed by the mismatch between the mathematical model and the data. Using multimodal Patch-seq data, we link the estimated model parameters to gene expression using an interpretable sparse linear regression model. Our approach recovers specific ion channel gene expressions as predictive of biophysical model parameters including ion channel densities, directly implicating their mechanistic role in determining neural firing.</p>","PeriodicalId":72407,"journal":{"name":"bioRxiv : the preprint server for biology","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11722265/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88763100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}