Gene Expression Analysis Market by Product Type (Instruments, Reagents & Consumables, Software & Services), Technology (In Situ Hybridization, Microarray, qPCR), Disease Type, Application, End User - Global Forecast 2025-2032
Description
The Gene Expression Analysis Market was valued at USD 4.30 billion in 2024 and is projected to grow to USD 4.65 billion in 2025, with a CAGR of 8.39%, reaching USD 8.20 billion by 2032.
A strategic orientation to modern gene expression analysis highlighting critical technologies, stakeholder convergence, and operational priorities for research and clinical labs
Gene expression analysis has become a foundational pillar spanning basic biology, translational research, and clinical diagnostics. Contemporary workflows integrate high-throughput sequencing, quantitative PCR platforms, microarray-based profiling, and emerging in situ modalities to map transcriptomic landscapes with unprecedented resolution. As a result, stakeholders from academic labs to commercial service providers are recalibrating capabilities to address the dual demands of deeper biological insight and more rapid clinical translation.
In practice, laboratories are balancing investments in instrumentation, reagents, and software with a growing dependency on advanced data analytics and regulatory-compliant workflows. Consequently, collaborations among instrument manufacturers, reagent suppliers, software developers, and end users are intensifying. This introduction frames the subsequent analysis by emphasizing technology convergence, the centrality of data interpretation, and the operational imperatives that shape procurement, experimental design, and long-term strategic planning for institutions engaged in gene expression research.
How technological convergence, data science integration, and regulatory evolution are reshaping gene expression workflows and competitive strategies across research and clinical sectors
The landscape of gene expression analysis is undergoing transformative shifts driven by technological maturation, computational advances, and evolving application demands. Single-cell transcriptomics and spatially resolved approaches have migrated from niche proof-of-concept experiments into routine components of discovery pipelines, enabling investigators to resolve cellular heterogeneity and microenvironmental context that were previously obscured. At the same time, long-read sequencing and hybrid approaches are expanding the detectable isoform complexity, which alters assay selection and downstream analysis priorities.
Concurrently, artificial intelligence and machine learning are becoming integral to extracting biologically actionable signals from expansive expression datasets. Cloud-native platforms and containerized workflows are streamlining collaboration and reproducibility, which in turn reshapes procurement decisions and service delivery models. Regulatory scrutiny and data governance considerations are also evolving, prompting laboratories and service providers to implement more rigorous validation and traceability practices. Together, these shifts demand adaptive strategies across R&D, clinical translation, and commercial operations to remain competitive and scientifically credible.
Assessing how tariff adjustments in the United States are influencing supply chain resilience, procurement strategies, and operational continuity for gene expression stakeholders
Recent tariff measures announced in the United States for 2025 have introduced additional complexity to global supply chains that support gene expression workflows. Tariffs on key components such as specialized reagents, instrument parts, and certain imported consumables create input cost pressure that reverberates through procurement, inventory management, and vendor negotiations. In response, many organizations have reassessed supplier diversity, sought alternative sourcing strategies, and accelerated qualification of domestic or regional vendors to mitigate supply risk and maintain continuity of critical experiments.
Beyond sourcing, the indirect effects include extended lead times and a heightened emphasis on inventory planning to avoid disruptions for time-sensitive studies. Service providers and reagent manufacturers have begun to revisit pricing models and contractual terms to apportion risk, while end users are weighing the trade-offs between cost, reliability, and regulatory compliance. Importantly, these dynamics are encouraging greater vertical integration and local manufacturing initiatives, which may reduce exposure to tariff volatility over time. Overall, the tariff environment necessitates more proactive procurement governance and scenario planning across institutions engaged in gene expression analysis.
Granular segmentation insights that clarify distinct technology stacks, application demands, and end-user requirements shaping procurement and product development priorities
Segmentation provides a lens to understand how different components of gene expression analysis attract distinct strategic priorities and investment patterns. Based on product type, the market spans instruments, reagents and consumables, and software and services; instruments encompass imaging systems, microarray scanners, qPCR instruments, and sequencers, while reagents and consumables include buffers and ancillary items, enzymes and master mixes, and kits, and software and services cover consulting, custom assays, and data analysis. This layered product taxonomy reveals where margins, innovation cycles, and customer support requirements diverge, with instruments demanding capital investment and service ecosystems, reagents requiring supply chain robustness, and software and services hinging on specialized expertise and repeatable analytical pipelines.
When viewed through application lenses, gene expression work supports academic research, diagnostics, drug discovery and development, and personalized medicine; academic workflows emphasize biomarker validation, gene function studies, and pathway analysis, diagnostics prioritize cancer, genetic disease, and infectious disease applications, drug discovery focuses on biomarker discovery, molecular screening, target identification, and toxicogenomics, and personalized medicine addresses clinical domains such as cardiology, neurology, and oncology. Technology segmentation distinguishes in situ hybridization, microarray, qPCR, and RNA sequencing as distinct methodological approaches, each with unique throughput, resolution, and validation demands. End users include academic and research institutes, contract research organizations, hospitals and diagnostic centers, and pharma and biotech companies, and disease-type segmentation captures cancer, cardiovascular diseases, infectious diseases, and neurological disorders. Synthesizing these segments highlights where convergent needs exist-such as high-throughput, validated assays for translational pipelines-and where niche expertise and tailored service models are required to meet specialized applications.
Regional dynamics and strategic implications for adoption, validation, and supply chain localization across major global clusters and research ecosystems
Geographic dynamics exert a significant influence on adoption patterns, regulatory expectations, and partnership models in gene expression analysis. In the Americas, advanced research infrastructure, a dense concentration of commercial service providers, and well-established diagnostic pathways foster rapid technology uptake and complex public-private collaborations. Conversely, Europe, the Middle East and Africa present a mosaic of regulatory frameworks and reimbursement environments that require nuanced regulatory strategies and localized validation to enable clinical integration and cross-border collaboration.
In the Asia-Pacific region, expansive investments in research capacity, growing domestic manufacturing capabilities, and a rising number of translational initiatives are accelerating demand for scalable workflows and regional service providers. These regional differences shape decisions on where to localize manufacturing, how to structure distribution networks, and which strategic partnerships to prioritize. Transitional trends such as harmonization of data standards, increased cross-border research consortia, and regional centers of excellence are further influencing how organizations allocate resources across these geographic clusters.
Competitive and collaborative company strategies emphasizing integration, validation, and service excellence as differentiators in gene expression workflows
Competitive dynamics in the gene expression domain are characterized by a mix of technology differentiation, service specialization, and strategic partnerships. Established instrument providers are reinforcing installed bases through service contracts, consumable ecosystems, and integrated software, while specialized reagent developers focus on kit robustness, lot-to-lot consistency, and certification pathways to secure clinical and research accounts. At the same time, software vendors and analytics providers are differentiating through user experience, reproducibility, and capabilities for multi-omic integration.
Collaborations between instrument manufacturers, reagent suppliers, and analytical service firms are increasingly common, driven by the need to deliver end-to-end validated solutions that reduce time-to-result for customers. Moreover, agile start-ups are carving out niches in spatial transcriptomics, single-cell analytics, and AI-driven interpretation, prompting incumbents to pursue targeted partnerships, licensing agreements, or selective internal development. Commercial strategies that emphasize transparent validation data, flexible business models, and post-sale scientific support tend to resonate most with institutional purchasers who require predictable performance and long-term reliability.
Actionable recommendations for leaders to align technology investments, supply chain strategies, and analytics capabilities for resilient and impactful gene expression programs
Leaders in both commercial and academic settings should adopt a multifaceted approach to capture value from evolving gene expression capabilities. First, invest in modular platforms that support a spectrum of technologies-qPCR, sequencing, spatial methods-so organizations can pivot as scientific requirements change. Second, prioritize partnerships that combine manufacturing reliability with local distribution and regulatory know-how to reduce exposure to tariff-driven and logistical disruptions. Third, embed data governance and reproducible pipelines early, ensuring analytical transparency and facilitating collaborations across institutions.
In addition, companies should align commercial models to emphasize service bundles that include assay validation and data interpretation support, thereby creating recurring value beyond one-time instrument sales. Procurement teams should re-evaluate inventory strategies and supplier contracts to balance cost efficiency with continuity of supply. Finally, training and workforce development must be integral to strategic planning; investing in bioinformatics capacity and user training helps maximize the return on complex platforms and accelerates translational impact.
A transparent and rigorous research methodology combining expert interviews, technical validation, and triangulated secondary sources to ensure credible and actionable insights
This research synthesizes primary qualitative engagements with subject-matter experts, technical leads, procurement specialists, and clinical stakeholders alongside rigorous secondary-source validation. Primary interviews were structured to capture operational realities, validation requirements, and procurement decision criteria across diverse end users. Secondary sources included peer-reviewed literature, regulatory guidance documents, and vendor technical specifications to establish benchmarking baselines for technology performance and assay validation practices.
Data synthesis relied on triangulation to reconcile divergent perspectives and ensure that conclusions reflect robust cross-validation. The methodology incorporated scenario analysis to test supply chain and regulatory sensitivities, and expert panels were convened to review draft interpretations and ensure technical accuracy. Quality controls included reproducibility checks for analytical workflows and independent peer review of methodology summaries. Together, these approaches ensure that findings are rooted in contemporary practice and reflect the practical considerations that shape adoption and implementation of gene expression technologies.
Concise synthesis of technological promise, operational realities, and strategic choices required to translate gene expression advances into reproducible research and clinical impact
In summary, gene expression analysis sits at a crossroads of rapid technological innovation and pragmatic operational considerations. Advances such as single-cell and spatial transcriptomics, combined with improved sequencing modalities and AI-driven analytics, are expanding what is scientifically possible, while supply chain dynamics, regulatory expectations, and the need for robust validation practices define how rapidly those advances can be adopted in routine workflows. Consequently, organizations must balance ambition with operational readiness when selecting technologies and partners.
The emergent picture calls for integrated strategies that link procurement, data governance, and scientific validation; institutions that cultivate flexible platforms, invest in analytics expertise, and secure resilient supply lines will be best positioned to translate novel capabilities into reproducible discoveries or clinical utility. Ultimately, thoughtful alignment of technology, people, and processes will determine who captures the most value from the gene expression landscape in research and clinical settings.
Note: PDF & Excel + Online Access - 1 Year
A strategic orientation to modern gene expression analysis highlighting critical technologies, stakeholder convergence, and operational priorities for research and clinical labs
Gene expression analysis has become a foundational pillar spanning basic biology, translational research, and clinical diagnostics. Contemporary workflows integrate high-throughput sequencing, quantitative PCR platforms, microarray-based profiling, and emerging in situ modalities to map transcriptomic landscapes with unprecedented resolution. As a result, stakeholders from academic labs to commercial service providers are recalibrating capabilities to address the dual demands of deeper biological insight and more rapid clinical translation.
In practice, laboratories are balancing investments in instrumentation, reagents, and software with a growing dependency on advanced data analytics and regulatory-compliant workflows. Consequently, collaborations among instrument manufacturers, reagent suppliers, software developers, and end users are intensifying. This introduction frames the subsequent analysis by emphasizing technology convergence, the centrality of data interpretation, and the operational imperatives that shape procurement, experimental design, and long-term strategic planning for institutions engaged in gene expression research.
How technological convergence, data science integration, and regulatory evolution are reshaping gene expression workflows and competitive strategies across research and clinical sectors
The landscape of gene expression analysis is undergoing transformative shifts driven by technological maturation, computational advances, and evolving application demands. Single-cell transcriptomics and spatially resolved approaches have migrated from niche proof-of-concept experiments into routine components of discovery pipelines, enabling investigators to resolve cellular heterogeneity and microenvironmental context that were previously obscured. At the same time, long-read sequencing and hybrid approaches are expanding the detectable isoform complexity, which alters assay selection and downstream analysis priorities.
Concurrently, artificial intelligence and machine learning are becoming integral to extracting biologically actionable signals from expansive expression datasets. Cloud-native platforms and containerized workflows are streamlining collaboration and reproducibility, which in turn reshapes procurement decisions and service delivery models. Regulatory scrutiny and data governance considerations are also evolving, prompting laboratories and service providers to implement more rigorous validation and traceability practices. Together, these shifts demand adaptive strategies across R&D, clinical translation, and commercial operations to remain competitive and scientifically credible.
Assessing how tariff adjustments in the United States are influencing supply chain resilience, procurement strategies, and operational continuity for gene expression stakeholders
Recent tariff measures announced in the United States for 2025 have introduced additional complexity to global supply chains that support gene expression workflows. Tariffs on key components such as specialized reagents, instrument parts, and certain imported consumables create input cost pressure that reverberates through procurement, inventory management, and vendor negotiations. In response, many organizations have reassessed supplier diversity, sought alternative sourcing strategies, and accelerated qualification of domestic or regional vendors to mitigate supply risk and maintain continuity of critical experiments.
Beyond sourcing, the indirect effects include extended lead times and a heightened emphasis on inventory planning to avoid disruptions for time-sensitive studies. Service providers and reagent manufacturers have begun to revisit pricing models and contractual terms to apportion risk, while end users are weighing the trade-offs between cost, reliability, and regulatory compliance. Importantly, these dynamics are encouraging greater vertical integration and local manufacturing initiatives, which may reduce exposure to tariff volatility over time. Overall, the tariff environment necessitates more proactive procurement governance and scenario planning across institutions engaged in gene expression analysis.
Granular segmentation insights that clarify distinct technology stacks, application demands, and end-user requirements shaping procurement and product development priorities
Segmentation provides a lens to understand how different components of gene expression analysis attract distinct strategic priorities and investment patterns. Based on product type, the market spans instruments, reagents and consumables, and software and services; instruments encompass imaging systems, microarray scanners, qPCR instruments, and sequencers, while reagents and consumables include buffers and ancillary items, enzymes and master mixes, and kits, and software and services cover consulting, custom assays, and data analysis. This layered product taxonomy reveals where margins, innovation cycles, and customer support requirements diverge, with instruments demanding capital investment and service ecosystems, reagents requiring supply chain robustness, and software and services hinging on specialized expertise and repeatable analytical pipelines.
When viewed through application lenses, gene expression work supports academic research, diagnostics, drug discovery and development, and personalized medicine; academic workflows emphasize biomarker validation, gene function studies, and pathway analysis, diagnostics prioritize cancer, genetic disease, and infectious disease applications, drug discovery focuses on biomarker discovery, molecular screening, target identification, and toxicogenomics, and personalized medicine addresses clinical domains such as cardiology, neurology, and oncology. Technology segmentation distinguishes in situ hybridization, microarray, qPCR, and RNA sequencing as distinct methodological approaches, each with unique throughput, resolution, and validation demands. End users include academic and research institutes, contract research organizations, hospitals and diagnostic centers, and pharma and biotech companies, and disease-type segmentation captures cancer, cardiovascular diseases, infectious diseases, and neurological disorders. Synthesizing these segments highlights where convergent needs exist-such as high-throughput, validated assays for translational pipelines-and where niche expertise and tailored service models are required to meet specialized applications.
Regional dynamics and strategic implications for adoption, validation, and supply chain localization across major global clusters and research ecosystems
Geographic dynamics exert a significant influence on adoption patterns, regulatory expectations, and partnership models in gene expression analysis. In the Americas, advanced research infrastructure, a dense concentration of commercial service providers, and well-established diagnostic pathways foster rapid technology uptake and complex public-private collaborations. Conversely, Europe, the Middle East and Africa present a mosaic of regulatory frameworks and reimbursement environments that require nuanced regulatory strategies and localized validation to enable clinical integration and cross-border collaboration.
In the Asia-Pacific region, expansive investments in research capacity, growing domestic manufacturing capabilities, and a rising number of translational initiatives are accelerating demand for scalable workflows and regional service providers. These regional differences shape decisions on where to localize manufacturing, how to structure distribution networks, and which strategic partnerships to prioritize. Transitional trends such as harmonization of data standards, increased cross-border research consortia, and regional centers of excellence are further influencing how organizations allocate resources across these geographic clusters.
Competitive and collaborative company strategies emphasizing integration, validation, and service excellence as differentiators in gene expression workflows
Competitive dynamics in the gene expression domain are characterized by a mix of technology differentiation, service specialization, and strategic partnerships. Established instrument providers are reinforcing installed bases through service contracts, consumable ecosystems, and integrated software, while specialized reagent developers focus on kit robustness, lot-to-lot consistency, and certification pathways to secure clinical and research accounts. At the same time, software vendors and analytics providers are differentiating through user experience, reproducibility, and capabilities for multi-omic integration.
Collaborations between instrument manufacturers, reagent suppliers, and analytical service firms are increasingly common, driven by the need to deliver end-to-end validated solutions that reduce time-to-result for customers. Moreover, agile start-ups are carving out niches in spatial transcriptomics, single-cell analytics, and AI-driven interpretation, prompting incumbents to pursue targeted partnerships, licensing agreements, or selective internal development. Commercial strategies that emphasize transparent validation data, flexible business models, and post-sale scientific support tend to resonate most with institutional purchasers who require predictable performance and long-term reliability.
Actionable recommendations for leaders to align technology investments, supply chain strategies, and analytics capabilities for resilient and impactful gene expression programs
Leaders in both commercial and academic settings should adopt a multifaceted approach to capture value from evolving gene expression capabilities. First, invest in modular platforms that support a spectrum of technologies-qPCR, sequencing, spatial methods-so organizations can pivot as scientific requirements change. Second, prioritize partnerships that combine manufacturing reliability with local distribution and regulatory know-how to reduce exposure to tariff-driven and logistical disruptions. Third, embed data governance and reproducible pipelines early, ensuring analytical transparency and facilitating collaborations across institutions.
In addition, companies should align commercial models to emphasize service bundles that include assay validation and data interpretation support, thereby creating recurring value beyond one-time instrument sales. Procurement teams should re-evaluate inventory strategies and supplier contracts to balance cost efficiency with continuity of supply. Finally, training and workforce development must be integral to strategic planning; investing in bioinformatics capacity and user training helps maximize the return on complex platforms and accelerates translational impact.
A transparent and rigorous research methodology combining expert interviews, technical validation, and triangulated secondary sources to ensure credible and actionable insights
This research synthesizes primary qualitative engagements with subject-matter experts, technical leads, procurement specialists, and clinical stakeholders alongside rigorous secondary-source validation. Primary interviews were structured to capture operational realities, validation requirements, and procurement decision criteria across diverse end users. Secondary sources included peer-reviewed literature, regulatory guidance documents, and vendor technical specifications to establish benchmarking baselines for technology performance and assay validation practices.
Data synthesis relied on triangulation to reconcile divergent perspectives and ensure that conclusions reflect robust cross-validation. The methodology incorporated scenario analysis to test supply chain and regulatory sensitivities, and expert panels were convened to review draft interpretations and ensure technical accuracy. Quality controls included reproducibility checks for analytical workflows and independent peer review of methodology summaries. Together, these approaches ensure that findings are rooted in contemporary practice and reflect the practical considerations that shape adoption and implementation of gene expression technologies.
Concise synthesis of technological promise, operational realities, and strategic choices required to translate gene expression advances into reproducible research and clinical impact
In summary, gene expression analysis sits at a crossroads of rapid technological innovation and pragmatic operational considerations. Advances such as single-cell and spatial transcriptomics, combined with improved sequencing modalities and AI-driven analytics, are expanding what is scientifically possible, while supply chain dynamics, regulatory expectations, and the need for robust validation practices define how rapidly those advances can be adopted in routine workflows. Consequently, organizations must balance ambition with operational readiness when selecting technologies and partners.
The emergent picture calls for integrated strategies that link procurement, data governance, and scientific validation; institutions that cultivate flexible platforms, invest in analytics expertise, and secure resilient supply lines will be best positioned to translate novel capabilities into reproducible discoveries or clinical utility. Ultimately, thoughtful alignment of technology, people, and processes will determine who captures the most value from the gene expression landscape in research and clinical settings.
Note: PDF & Excel + Online Access - 1 Year
Table of Contents
195 Pages
- 1. Preface
- 1.1. Objectives of the Study
- 1.2. Market Segmentation & Coverage
- 1.3. Years Considered for the Study
- 1.4. Currency
- 1.5. Language
- 1.6. Stakeholders
- 2. Research Methodology
- 3. Executive Summary
- 4. Market Overview
- 5. Market Insights
- 5.1. Integration of single-cell transcriptomics with spatial omics for precision diagnostics
- 5.2. Adoption of CRISPR-based gene editing integration with RNA sequencing workflows for functional genomic screening
- 5.3. Emergence of AI-driven predictive modeling for differential expression analysis in clinical trials
- 5.4. Expansion of long-read sequencing technologies in gene expression profiling to resolve transcript isoforms
- 5.5. Incorporation of multi-omics data integration platforms to enhance transcriptomic insights in drug discovery
- 5.6. Rapid deployment of portable nanopore RNA sequencing devices for point-of-care expression profiling in remote research settings
- 6. Cumulative Impact of United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. Gene Expression Analysis Market, by Product Type
- 8.1. Instruments
- 8.1.1. Imaging Systems
- 8.1.2. Microarray Scanners
- 8.1.3. qPCR Instruments
- 8.1.4. Sequencers
- 8.2. Reagents & Consumables
- 8.2.1. Buffers & Others
- 8.2.2. Enzymes & Master Mixes
- 8.2.3. Kits
- 8.3. Software & Services
- 8.3.1. Consulting
- 8.3.2. Custom Assays
- 8.3.3. Data Analysis
- 9. Gene Expression Analysis Market, by Technology
- 9.1. In Situ Hybridization
- 9.2. Microarray
- 9.3. qPCR
- 9.4. RNA Sequencing
- 10. Gene Expression Analysis Market, by Disease Type
- 10.1. Cancer
- 10.2. Cardiovascular Diseases
- 10.3. Infectious Diseases
- 10.4. Neurological Disorders
- 11. Gene Expression Analysis Market, by Application
- 11.1. Academic Research
- 11.1.1. Biomarker Validation
- 11.1.2. Gene Function Study
- 11.1.3. Pathway Analysis
- 11.2. Diagnostics
- 11.2.1. Cancer
- 11.2.2. Genetic Disease
- 11.2.3. Infectious Disease
- 11.3. Drug Discovery & Development
- 11.3.1. Biomarker Discovery
- 11.3.2. Molecular Screening
- 11.3.3. Target Identification
- 11.3.4. Toxicogenomics
- 11.4. Personalized Medicine
- 11.4.1. Cardiology
- 11.4.2. Neurology
- 11.4.3. Oncology
- 12. Gene Expression Analysis Market, by End User
- 12.1. Academic & Research Institutes
- 12.2. Contract Research Organizations
- 12.3. Hospitals & Diagnostic Centers
- 12.4. Pharma & Biotech Companies
- 13. Gene Expression Analysis Market, by Region
- 13.1. Americas
- 13.1.1. North America
- 13.1.2. Latin America
- 13.2. Europe, Middle East & Africa
- 13.2.1. Europe
- 13.2.2. Middle East
- 13.2.3. Africa
- 13.3. Asia-Pacific
- 14. Gene Expression Analysis Market, by Group
- 14.1. ASEAN
- 14.2. GCC
- 14.3. European Union
- 14.4. BRICS
- 14.5. G7
- 14.6. NATO
- 15. Gene Expression Analysis Market, by Country
- 15.1. United States
- 15.2. Canada
- 15.3. Mexico
- 15.4. Brazil
- 15.5. United Kingdom
- 15.6. Germany
- 15.7. France
- 15.8. Russia
- 15.9. Italy
- 15.10. Spain
- 15.11. China
- 15.12. India
- 15.13. Japan
- 15.14. Australia
- 15.15. South Korea
- 16. Competitive Landscape
- 16.1. Market Share Analysis, 2024
- 16.2. FPNV Positioning Matrix, 2024
- 16.3. Competitive Analysis
- 16.3.1. 10x Genomics
- 16.3.2. Agilent Technologies Inc.
- 16.3.3. Bio-Rad Laboratories Inc.
- 16.3.4. BioSpyder, Inc.
- 16.3.5. CD Genomics
- 16.3.6. Charles River Laboratories International, Inc.
- 16.3.7. Danaher Corporation
- 16.3.8. DiaSorin S.p.A.
- 16.3.9. Eurofins Scientific SE
- 16.3.10. F. Hoffmann-La Roche Ltd.
- 16.3.11. Illumina Inc.
- 16.3.12. Inotiv, Inc.
- 16.3.13. LGC Limited
- 16.3.14. Merck KGaA
- 16.3.15. Meridian Bioscience, Inc.
- 16.3.16. NanoString Technologies Inc.
- 16.3.17. New England Biolab
- 16.3.18. Oxford Gene Technology IP Limited
- 16.3.19. Oxford Nanopore Technologies plc
- 16.3.20. Promega Corporation
- 16.3.21. QIAGEN N.V.
- 16.3.22. Revvity, Inc.
- 16.3.23. Standard BioTools
- 16.3.24. Takara Bio, Inc.
- 16.3.25. Thermo Fisher Scientific Inc
- 16.3.26. Zymo Research Corporation
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