Download to read offline
Uploaded byOla Spjuth
Towards automated phenotypic cell profiling with high-content imaging
This document outlines an initiative at Uppsala University to enhance drug discovery through automated phenotypic cell profiling using high-content imaging and AI. The research aims to integrate automation and intelligent experimental design to speed up the hypothesis generation, testing, and data analysis processes in biological labs. Key technologies mentioned include automated labs, advanced imaging techniques, and a focus on data-driven science to optimize drug development and safety assessments.
More Related Content
Similar to Towards automated phenotypic cell profiling with high-content imaging
Towards automated phenotypic cell profiling with high-content imaging
- 1. Towards automated phenotypiccell profiling with high-content imaging Ola Spjuth Department of Pharmaceutical Biosciences, Uppsala University Scaleout Systems AB
- 2. Automation and roboticswill be increasingly important in biological labs
- 3. AI will havehigh impact in experimental design and hypothesis testing/generation
- 4. Use of live/temporal profilingwill increase
- 5. Use of live/temporalprofiling will increase Data velocity will increase Automated, continuous analytics and AI will be needed
- 7. Who are we? •Academic research group at Uppsala University • Background in computational pharmacology (data science, AI/ML) • Good at e-infrastructure, big data (data engineering) • Setting up an high-content imaging lab for cell profiling Research group website: http://pharmb.io
- 8. Accelerate drug discoveryusing AI, automation and intelligent design of experiments • Predict safety concerns • Explain drug mechanisms • Screen for new drugs Research objective
- 9. Hypothesis revise Insight • Iterative • Flexible •Mostly manual • Slow Experiments Analysis and interpretation Traditional hypothesis testing • Retrospective analysis • Hopefully predictive • Expensive • Limited for hypothesis testing more Predictive modeling Database Data generation Traditional Processing Stream Processing Data Data Query request response Real- T ime Analytics Data Results ModelPrediction Modeling and prediction
- 10. Data-driven science Data Hypothesis Scientist Data aditional ProcessingStream Processing Data Repository a Query request response Real- T ime Analytics Data Results Current fact finding Analyze data in motion – before it is stored Low latency paradigm, push model Data driven: bring data to the analytics al fact finding d analyze information stored on disk aradigm, pull model driven: submits queries to static data Model Insights
- 11. Considerations for “thenext experiment” • Quality over Quantity: Better data is often more useful than simply more data • Data collection may be expensive • Cost of time and materials for an experiment • Cheap vs. expensive data • Raw images vs. annotated images • Want to collect best data at minimal cost • Can machines (AI) learn with fewer training instances if they ask the right questions?
- 12. Intelligently designing experiments •Plan experiment under constrained resources • Vary factors and study response • Seek optimal design • DoE (Design of Experiments) • Example: Select X combinations of drugs to test (cannot measure all combinations due to costs, time etc.) ”…DECREASE, an efficient machine learning model that requires only a limited set of pairwise dose–response measurements for accurate prediction of drug combination synergy in a given sample.”
- 13. Active learning: Whichexperiment should be done next? Nature Model Data Passive Learning Nature ModelResponse Active Learning Query • Exploration: Could lead to better predictions in future • Exploitation: Make best predictions given current data • Tradeoff!
- 14. Our vision: Closed-loop (autonomous)experimentation Automation Informatics Data essing Stream Processing Query esponse Real- T ime Analytics Data Results Current fact finding Analyze data in motion – before it is storedstored on disk Continuous AI Results Intelligent design of experiments Experiments Scientist External data
- 16. Automation in lifescience • Varying degrees of automation! • Automated instrument: working with a microplate (or stack of microplates) • Robot: Liquid handling robot • Automated lab • A set of instruments, each working with microplates • A plate handling robot serving multiple instruments
- 17. Robot scientist 1. King,R. D. et al. Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427, 247–252 (2004) 2. King, RD et al. "The Automation of Science". (2009) Science. 324 (5923): 85–89 3. Williams, K. et al."Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases". (2015) Journal of the Royal Society Interface. 12 (104): 20141289. Adam1,2 is able to perform independent experiments to test hypotheses and interpret findings without human guidance: • hypothesizing to explain observations • devising experiments to test these hypotheses • physically running the experiments using laboratory robotics • interpreting the results from the experiments • repeating the cycle as required
- 18. Let’s go backto cell profiling with high content imaging!
- 19. High-throughput biology ThousandsThousandsDozens Robots vs. Disease: ”TacklingWorld Health Problems by Analyzing Cell Images” Anne E. Carpenter, PhD IMAGING PLATFORM
- 20. Genetic or chemical perturbations Experiments in multi- wellplates Imaging Features Hypotheses Convolutional Neural Network Predictions Cell painting: Imaging with multiplexed dyes Bray et al. (2016). “Cell Painting, a High-Content Image-Based Assay for Morphological Profiling Using Multiplexed Fluorescent Dyes.” Nature Protocols 11 (9): 1757–74.
- 21. Holographic live cellimaging • Quantitative phase-contrast microscopy • Holographic phase-shift imaging • Label-free, live cell imaging • Used inside incubator HoloMonitor system
- 22. Protein degradation Cholesterol-loweringDNA replication Microtubule stabilizer Actin disruptor Kinase inhibitor Classify images into biological mechanisms Kensert A, Harrison PJ, Spjuth O. Transfer learning with deep convolutional neural network for classifying cellular morphological changes. SLAS DISCOVERY: Advancing Life Sciences R&D. 24, 4 (2019)
- 23. •Fluorescent LNPs (lipids) •FluorescentCargo (mRNA) •Fluorescent Product (protein) No LNPs Partial LNP uptake LNP uptake and mRNA decoding
- 25. Make predictions using available data Externaldata Data warehouse Design new experimentsAI Modeling Publish data and models Manual wet lab Hypothesis Verify using external protocol Automated lab Carry out new experiments Analysis pipeline Aim: Intelligent system for drug/chemical profiling Hypothesis Hypothesis test generate
- 26. Fully automated cellpainting • Facilities, environmental control, ventilation • Instruments and control software • Automation system (dynamic scheduling) • Lab protocol • Compute and storage resources • Analysis pipelines
- 27. Automating our cell-basedlab Fixed setup (version 1) • ImageXpress XLS (Molecular Devices) • Plate robot (Preciseflex) • Plate incubator (Liconic), barcode reader • BioMek 4000 liquid handling (Beckman Coulter) • Green Button Go lab automation software (Biosero) Observations: • Quick to get up and running • Suitable for fixed protocols • Dependent on vendors to solve problems • Not easy to expand or configure for us Our priorities: • Flexibility to expand/adapt • Open source or good APIs • Low cost, serviceable by us • Configurable by us
- 28. Opentrons OT-2 Biotek MultiFloFX, Multi-Mode Dispenser Open source lab automation Universal Robots UR10e Biotek 405 LS, Washer Collaborators wanted!
- 30. Dealing with largescale data • High volume, relatively high velocity • Continuously process data, train models, serve models • Embrace scalable virtual infrastructures (cloud) and microservices (containers) GPU cluster CPU server Storage Cloud HPC Online processing
- 31. Robotized lab images Automating ourdata processing ImageDBImage viewer File system Metadata Files (images) https://github.com/pharmbio/imagedb Cold storage Hot storage Online, intelligent processing Cell profilesQC workflows Interestingness models HASTE CORE and Cell Profiler Pipeline https://github.com/HASTE-project/cellprofiler-pipeline Avoid storing uninteresting data
- 32. Robotized lab Data scientists Empoweringour data scientists ImageDB File system Metadata Files (images) Models CPU/GPU/HPC cloud Notebooks Data Models External users Services Public services Publish
- 33. Data is notstatic! • Public databases • Batch/continuous updates • In-house data • Batch/continuous updates Need to continuously re-train models.
- 34. AI modeling lifecycle Model Development ML studio ML workflow automation Package & Deploy Models Model Serving Model management Model serving Monitoring Explore Data and Develop Models Train at scale Register Model and Metadata for Serving Package and Publish Run in operations Monitor LoggingIntegrate Data scientist Data Engineer Data Engineer Promote Model Ship Model In collaboration with: https://github.com/leanaiorg/leanaistack Lean AI Stack
- 35. http://haste.research.it.uu.se/ Carolina Wählby OlaSpjuth Andreas Hellander
- 36. Relevant software wedevelop (and others could use) • Virtual Infrastructure with Kubernetes (IaaC) • Portable, scalable, resilient • ImageDB (projects, images, results etc.) • ImageViewer • Batch and Continuous Cell Profiler pipelines • Deep Learning notebooks • Open source lab automation system (in progress) • Design cell-based experiments (in progress) • Construct steering protocols for robotized lab • Compound annotation project (to be started) ImageDBImage viewer https://github.com/pharmbio
- 37. Integrate with ourother AI services Site-of-metabolism and reaction types http://ptp.service.pharmb.io/ https://metpred.service.pharmb.io/draw/ Target (safety) profiles
- 38. Data-driven science Data Hypothesis Scientist Data aditional ProcessingStream Processing Data Repository a Query request response Real- T ime Analytics Data Results Current fact finding Analyze data in motion – before it is stored Low latency paradigm, push model Data driven: bring data to the analytics al fact finding d analyze information stored on disk aradigm, pull model driven: submits queries to static data Insights Stream Processing Real- T ime Analytics Data Results Current fact finding Analyze data in motion – before it is stored Low latency paradigm, push model Data driven: bring data to the analytics isk ata Data Repository Data Query request response Real- T ime Analytics Data Results Current fact finding Analyze data in motion – before it is stored Low latency paradigm, push model Data driven: bring data to the analytics Historical fact finding Find and analyze information stored on disk Batch paradigm, pull model Query-driven: submits queries to static data
- 39. Some ongoing projects •Cell painting on combinations of 2-3 environmental compounds • Cell painting on 380 kinase inhibitors on U2OS and MCF7 cell lines • Cell painting and holographic imaging of 120 GPCR drugs • Exploring dynamics of drug delivery using LNPs via imaging • Deep Learning (CNN, RNN) and Cell Profiler features • Comparing Cell Morphology with Gene Expression (public data) • Several other projects in the pipeline…
- 40. We believe inOpen Science • All source code (software, notebooks) published online: https://github.com/pharmbio • Protocols published online • https://protocol-delivery.protocols.opentrons.com/protocol/1494-uppsala- university • All data will be made available online
- 41. Collaborations and funding IT-dept/UU AndreasHellander Salman Toor Carolina Wählby Ida-Maria Sintorn MedSci/UU Kim Kultima Stephanie Herman Payam Emami NGI/UGC Adam Ameur UUH/Clinical Genetics Lucia Cavelier AstraZeneca/Stena Line Lars Carlsson Ernst Ahlberg Prosilico AB Urban Fagerholm Sven Hellberg Karolinska Institutet/MEB Juni Palmgren Martin Eklund Jordi Carreras Puigvert Karolinska Institutet/IMM Roland Grafström Pekka Kohonen SciLifeLab Data center Johan Rung Hanna Kultima Funding:Consortia and involvements:
- 42. - Thank you- Email: ola.spjuth@farmbio.uu.se Web: https://pharmb.io