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Fast, accurate, and versatile data analysis platform for the quantification of molecular spatiotemporal signals

Cell 2025年第10期188卷 2794-2809.e21页

作者： Mi, Xuelong Chen, Alex Bo-Yuan Duarte, Daniela Carey, Erin Taylor, Charlotte R. Braaker, Philipp N. Bright, Mark Almeida, Rafael G. Lim, Jing-Xuan Ruetten, Virginia M.S. Wang, Yizhi Wang, Mengfan Zhang, Weizhan Zheng, Wei Reitman, Michael E. Huang, Yongkang Wang, Xiaoyu Li, Lei Deng, HanFei Shi, Song-Hai Poskanzer, Kira E. Lyons, David A. Nimmerjahn, Axel Ahrens, Misha B. Yu, Guoqiang Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Arlington 22203 VA United States Janelia Research Campus Howard Hughes Medical Institute Ashburn 20147 VA United States Department of Molecular and Cellular Biology Harvard University Cambridge 02138 MA United States Graduate Program in Neuroscience Harvard Medical School Boston 02115 MA United States Waitt Advanced Biophotonics Center The Salk Institute for Biological Studies La Jolla 92037 CA United States Department of Biochemistry & Biophysics University of California San Francisco San Francisco CA United States Neuroscience Graduate Program University of California San Francisco San Francisco CA United States Centre for Discovery Brain Sciences University of Edinburgh Edinburgh BioQuarter Edinburgh EH16 4SB United Kingdom Gatsby Computational Neuroscience Unit UCL London W1T 4JG United Kingdom Kavli Institute for Fundamental Neuroscience San Francisco CA United States Department of Automation Tsinghua University Beijing 100084 China IDG/McGovern Institute for Brain Research Tsinghua University Beijing 100084 China New Cornerstone Science Laboratory Tsinghua-Peking Center for Life Sciences Beijing Frontier Research Center for Biological Structure School of Life Sciences Tsinghua University Beijing 100084 China State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University Shanghai 200032 China Beijing National Research Center for Information Science and Technology Beijing 100084 China

Optical recording of intricate molecular dynamics is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. This creates major computational challenges to extract and quantify biologically meaningful spatiotemporal patterns embedded within complex and rich data sources, many of which cannot be captured with existing methods. Here, we introduce activity quantification and analysis (AQuA2), a fast, accurate, and versatile data analysis platform built upon advanced machine-learning techniques. It decomposes complex live-imaging-based datasets into elementary signaling events, allowing accurate and unbiased quantification of molecular activities and identification of consensus functional units. We demonstrate applications across a wide range of biosensors, cell types, organs, animal models, microscopy techniques, and imaging approaches. As exemplar findings, we show how AQuA2 identified drug-dependent interactions between neurons and astroglia, as well as distinct sensorimotor signal propagation patterns in the mouse spinal cord. © 2025 The Author(s)

关键词： AQuA2 astrocytes cell interaction analysis functional units glial cells image analysis machine learning molecular spatiotemporal signals neurons time-lapse imaging

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Fast sequential clustering in riemannian manifolds for dynamic and time-series-annotated multilayer networks

TechRxiv

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TechRxiv 2020年

作者： Ye, Cong Slavakis, Konstantinos Nakuci, Johan Muldoon, Sarah F. Medaglia, John NY14260 United States State University of New York at Buffalo China The Neuroscience Program UB SUNY United States The Department of Mathematics and the Computational and Data-Enabled Science and Engineering Program UB SUNY United States The Department of Psychology Drexel University PA19104 United States The Perelman School of Medicine University of Pennsylvania PA19104 United States

This work exploits Riemannian manifolds to build a sequential-clustering framework able to address a wide variety of clustering tasks in dynamic multilayer (brain) networks via the information extracted from their nodal time-series. The discussion follows a bottom-up path, starting from feature extraction from time-series and reaching up to Riemannian manifolds (feature spaces) to address clustering tasks such as state clustering, community detection (a.k.a. network-topology identification), and subnetwork-sequence tracking. Kernel autoregressive-moving-average modeling and kernel (partial) correlations serve as case studies of generating features in the Riemannian manifolds of Grassmann and positive-(semi)definite matrices, respectively. Feature point-clouds form clusters which are viewed as submanifolds according to Riemannian multi-manifold modeling. A novel sequential-clustering scheme of Riemannian features is also established: feature points are first sampled in a non-random way to reveal the underlying geometric information, and, then, a fast sequential-clustering scheme is brought forth that takes advantage of Riemannian distances and the angular information of tangent spaces. By virtue of the landmark points and the sequential processing of the Riemannian features, the computational complexity of the framework is rendered free from the length of the available time-series data. The effectiveness and computational efficiency of the proposed framework are validated by extensive numerical tests against several state-of-the-art manifold-learning and brain-network-clustering schemes on synthetic as well as real functional-magnetic-resonance-imaging (fMRI) and electroencephalogram (EEG) data. © 2020, CC BY.

关键词： Time series

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Integrating spatially-resolved transcriptomics data across tissues and individuals: challenges and opportunities

arXiv

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arXiv 2024年

作者： Guo, Boyi Ling, Wodan Kwon, Sang Ho Panwar, Pratibha Ghazanfar, Shila Martinowich, Keri Hicks, Stephanie C. Department of Biostatistics Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States Division of Biostatistics Department of Population Health Sciences Weill Cornell Medicine NY United States Lieber Institute for Brain Development Johns Hopkins Medical Campus BaltimoreMD United States Solomon H. Snyder Department of Neuroscience Johns Hopkins School of Medicine BaltimoreMD United States Biochemistry Cellular and Molecular Biology Graduate Program Johns Hopkins School of Medicine BaltimoreMD United States School of Mathematics and Statistics The University of Sydney NSW2006 Australia Sydney Precision Data Science Centre University of Sydney NSW2006 Australia Charles Perkins Centre The University of Sydney NSW2006 Australia Department of Psychiatry and Behavioral Sciences Johns Hopkins School of Medicine BaltimoreMD United States Johns Hopkins Kavli Neuroscience Discovery Institute BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins University BaltimoreMD United States Center for Computational Biology Johns Hopkins University BaltimoreMD United States Malone Center for Engineering in Healthcare Johns Hopkins University BaltimoreMD United States

Advances in spatially-resolved transcriptomics (SRT) technologies have propelled the development of new computational analysis methods to unlock biological insights. As the cost of generating these data decreases, these technologies provide an exciting opportunity to create large-scale atlases that integrate SRT data across multiple tissues, individuals, species, or phenotypes to perform population-level analyses. Here, we describe unique challenges of varying spatial resolutions in SRT data, as well as highlight the opportunities for standardized preprocessing methods along with computational algorithms amenable to atlas-scale datasets leading to improved sensitivity and reproducibility in the future. © 2024, CC BY-NC-ND.

关键词： Biotic

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Approaching maximum resolution in structured illumination microscopy via accurate noise modeling

Npj imaging

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Npj imaging 2025年第1期3卷 5页

作者： Ayush Saurabh Peter T Brown J Shepard Bryan Iv Zachary R Fox Rory Kruithoff Cristopher Thompson Comert Kural Douglas P Shepherd Steve Pressé Center for Biological Physics Arizona State University Tempe AZ USA. Department of Physics Arizona State University Tempe AZ USA. Computational Science and Engineering Division Oak Ridge National Laboratory Oak Ridge TN USA. Department of Physics The Ohio State University Columbus OH USA. Interdisciplinary Biophysics Graduate Program The Ohio State University Columbus OH USA. School of Molecular Sciences Arizona State University Tempe AZ USA.

Biological images captured by microscopes are characterized by heterogeneous signal-to-noise ratios (SNRs) due to spatially varying photon emission across the field of view convoluted with camera noise. State-of-the-art unsupervised structured illumination microscopy (SIM) reconstruction methods, commonly implemented in the Fourier domain, often do not accurately model this noise. Such methods therefore suffer from high-frequency artifacts, user-dependent choices of smoothness constraints making assumptions on biological features, and unphysical negative values in the recovered fluorescence intensity map. On the other hand, supervised algorithms rely on large datasets for training, and often require retraining for new sample structures. Consequently, achieving high contrast near the maximum theoretical resolution in an unsupervised, physically principled manner remains an open problem. Here, we propose Bayesian-SIM (B-SIM), a Bayesian framework to quantitatively reconstruct SIM data, rectifying these shortcomings by accurately incorporating known noise sources in the spatial domain. To accelerate the reconstruction process, we use the finite extent of the point-spread-function to devise a parallelized Monte Carlo strategy involving chunking and restitching of the inferred fluorescence intensity. We benchmark our framework on both simulated and experimental images, and demonstrate improved contrast permitting feature recovery at up to 25% shorter length scales over state-of-the-art methods at both high- and low SNR. B-SIM enables unsupervised, quantitative, physically accurate reconstruction without the need for labeled training data, democratizing high-quality SIM reconstruction and expands the capabilities of live-cell SIM to lower SNR, potentially revealing biological features in previously inaccessible regimes.

关键词： Biophysics Image processing Imaging and sensing Super-resolution microscopy

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The Waiting-Time Paradox

arXiv

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arXiv 2020年

作者： Masuda, Naoki Porter, Mason A. Department of Mathematics State University of New York at Buffalo BuffaloNY United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY United States Department of Mathematics University of California Los Angeles Los AngelesCA United States

Suppose that you’re going to school and arrive at a bus stop. How long do you have to wait before the next bus arrives? Surprisingly, it is longer — possibly much longer — than what the bus schedule suggests intuitively. This phenomenon, called the waiting-time paradox, has a purely mathematical origin. Different buses arrive with different intervals, leading to this paradox. In this article, we look at the waiting-time paradox, explain why it happens, and explore some of its implications (beyond the possibility of being late for school). Copyright © 2020, The Authors. All rights reserved.

关键词： Buses

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BPS2025 - Structural basis of potassium selectivity in kalium channelrhodopsins

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Biophysical Journal 2025年第3期124卷 64a-65a页

作者： Seiya Tajima Yoon Seok Kim YoungJu Jo Peter Y. Wang Joseph M. Paggi Charu Ramakrishnan Shunki Takaramoto Takashi Nagata Masae Konno Kota Katayama Hideki Kandori Ron O. Dror Keiichi Inoue Karl Deisseroth Hideaki Kato Research Center for Advanced Science and Technology The University of Tokyo Tokyo Japan Department of Bioengineering Stanford University Stanford CA USA Department of Computer Science Stanford University Stanford CA USA Cracking the Neural Code Program Stanford University Stanford CA USA The Institute for Solid State Physics The University of Tokyo Kashiwa Japan PRESTO Japan Science and Technology Agency Kawaguchi Japan Department of Life Science and Applied Chemistry Nagoya Institute of Technology Nagoya Japan OptoBioTechnology Research Center Nagoya Institute of Technology Nagoya Japan Institute for Computational and Mathematical Engineering Stanford University Stanford CA USA Howard Hughes Medical Institute Stanford University Stanford CA USA Graduate School of Arts and Sciences The University of Tokyo Tokyo Japan

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Author Correction: Supervised learning of high-confidence phenotypic subpopulations from single-cell data

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Nature Machine Intelligence 2023年 676页

作者： Tao Ren Ling-Yun Wu Canping Chen Susan Liu Shunyi Du Zheng Xia Alexey V. Danilov Xiwei Wu Xiangnan Guan Mara H. Sherman Lisa M. Coussens Paul T. Spellman Andrew C. Adey Gordon B. Mills Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China Computational Biology Program Oregon Health & Science University Portland OR USA Department of Biomedical Engineering Oregon Health & Science University Portland OR USA Knight Cancer Institute Oregon Health & Science University Portland OR USA City of Hope National Medical Center Duarte CA USA Department of Oncology Biomarker Development Genentech Inc South San Francisco CA USA Department of Cell Developmental & Cancer Biology Oregon Health & Science University Portland OR USA Cancer Biology & Genetics Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Molecular and Medical Genetics Oregon Health & Science University Portland OR USA Division of Oncological Sciences Knight Cancer Institute Oregon Health & Science University Portland OR USA

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An integrative systems-biology approach defines mechanisms of Alzheimer’s disease neurodegeneration

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Nature Communications 2025年第1期16卷 1-21页

作者： Leventhal, Matthew J. Zanella, Camila A. Kang, Byunguk Peng, Jiajie Gritsch, David Liao, Zhixiang Bukhari, Hassan Wang, Tao Pao, Ping-Chieh Danquah, Serwah Benetatos, Joseph Nehme, Ralda Farhi, Samouil Tsai, Li-Huei Dong, Xianjun Scherzer, Clemens R. Feany, Mel B. Fraenkel, Ernest MIT Ph.D. Program in Computational and Systems Biology Cambridge MA United States Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA United States Broad Institute of Harvard and MIT Cambridge MA United States Department of Pathology Brigham and Women’s Hospital and Harvard Medical School Boston MA United States Spatial Technology Platform Broad Institute of Harvard and MIT Cambridge MA United States Precision Neurology Program Brigham and Women’s Hospital and Harvard Medical school Boston MA United States APDA Center for Advanced Parkinson’s Disease Research Brigham and Women’s Hospital and Harvard Medical School Boston MA United States Picower Institute for Learning and Memory Massachusetts Institute of Technology Cambridge MA United States Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA United States Stanley Center for Psychiatric Research Broad Institute of Harvard and MIT Cambridge MA United States School of Computer Science Northwestern Polytechnical University Xi’an China Stephen and Denise Adams Center of Yale School of Medicine New Haven CT United States

Despite years of intense investigation, the mechanisms underlying neuronal death in Alzheimer’s disease, remain incompletely understood. To define relevant pathways, we conducted an unbiased, genome-scale forward genetic screen for age-associated neurodegeneration in Drosophila. We also measured proteomics, phosphoproteomics, and metabolomics in Drosophila models of Alzheimer’s disease and identified Alzheimer’s genetic variants that modify gene expression in disease-vulnerable neurons in humans. We then used a network model to integrate these data with previously published Alzheimer’s disease proteomics, lipidomics and genomics. Here, we computationally predict and experimentally confirm how HNRNPA2B1 and MEPCE enhance toxicity of the tau protein, a pathological feature of Alzheimer’s disease. Furthermore, we demonstrated that the screen hits CSNK2A1 and NOTCH1 regulate DNA damage in Drosophila and human stem cell-derived neural progenitor cells. Our study identifies candidate pathways that could be targeted to ameliorate neurodegeneration in Alzheimer’s disease. © The Author(s) 2025.

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Metrics for benchmarking and uncertainty quantification: Quality, applicability, and a path to best practices for machine learning in chemistry

arXiv

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arXiv 2020年

作者： Vishwakarma, Gaurav Sonpal, Aditya Hachmann, Johannes Department of Chemical and Biological Engineering University at Buffalo State University of New York BuffaloNY14260 United States Computational and Data-Enabled Science and Engineering Graduate Program University at Buffalo State University of New York BuffaloNY14260 United States New York State Center of Excellence in Materials Informatics BuffaloNY14203 United States

This review aims to draw attention to two issues of concern when we set out to make machine learning work in the chemical and materials domain, i.e., statistical loss function metrics for the validation and benchmarking of data-derived models, and the uncertainty quantification of predictions made by them. They are often overlooked or underappreciated topics as chemists typically only have limited training in statistics. Aside from helping to assess the quality, reliability, and applicability of a given model, these metrics are also key to comparing the performance of different models and thus for developing guidelines and best practices for the successful application of machine learning in chemistry. Copyright © 2020, The Authors. All rights reserved.

关键词： Benchmarking

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On Performance Prediction of Big Data Transfer in High-performance Networks

On Performance Prediction of Big Data Transfer in High-perfo...

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IEEE International Conference on Communications (ICC)

作者： Wuji Liu Daqing Yun Chase Q. Wu Nageswara S.V. Rao Aiqin Hou Wei Shen Department of Computer Science New Jersey Institute of Technology Newark NJ USA Computer and Information Sciences Program Harrisburg University Harrisburg PA USA Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge TN USA School of Information Science and Technology Northwest University Xi’an Shaanxi China School of Informatics Science and Technology Zhejiang Sci-Tech University Hangzhou Zhejiang China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Big data generated by large-scale scientific and industrial applications need to be transferred between different geographical locations for remote storage, processing, and analysis. High-speed dedicated connections provisioned in High-performance Networks (HPNs) are increasingly utilized to carry out such big data transfer. HPN management highly relies on an important capability of performance (mainly throughput) prediction to reserve sufficient bandwidth and meanwhile avoid over-provisioning that may result in unnecessary resource waste. This capability is critical to improving the resource (mainly bandwidth) utilization of dedicated connections and meeting various user requests for data transfer. Conventional methods conduct performance prediction by fitting prior observed transfer history with predefined loss functions, without considering unobservable latent factors such as competing loads on end hosts. Such latent factors also have a significant impact on the application-level data transfer performance, which may result in an inaccurate prediction model. In this paper, we first investigate the impact of latent factors and propose a clustering-based method to eliminate their negative impact on performance prediction. We then develop a robust machine learning-based performance predictor by: i) incorporating the proposed latent factor elimination method into data preprocessing, and ii) adopting a customized domain guided loss function. Extensive experimental results show that our predictor achieves significantly higher prediction accuracy than several other state-of-the-art methods.

关键词： Data transfer Big Data Throughput Bandwidth Measurement Predictive models Protocols

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