From AI-driven discovery to tightening regulatory scrutiny, the life sciences are entering a period of rapid, uneven change that will be felt first at the lab bench. Over the next year, laboratory scientists will be asked to do more with increasingly complex data, more automated instruments, and greater pressure to deliver reproducible, translational results faster than ever.
As 2025 comes to a close, experts at Revvity have shared their predictions on key trends that may shape the future of the industry in 2026 and beyond. Read how today’s decisions in the lab can determine tomorrow’s discoveries.
Cell and gene therapy
Michelle Fraser, Head of Cell and Gene Therapy, Revvity
In the five years since the pioneers of CRISPR/Cas9 received the Nobel Prize in Chemistry, the field of cell and gene editing has made remarkable progress. Among these advances is the ongoing evolution of base editing, a technology that enables precise, single-base modifications to correct disease-causing mutations or silence genes without inducing the double-stranded DNA breaks characteristic of CRISPR/Cas9. This significantly reduces DNA damage, leading to a higher yield of healthier cells. Contemporary base editing platforms are highly flexible and modular, allowing for fine-tuning, simultaneous multi-gene edits, and even concurrent knock-ins, supporting complex, multiplexed genetic engineering. Recently, Revvity partnered with Profluent to incorporate AI-designed enzymes into base editors, vastly expanding the potential for optimizing these tools. The future promises base editing as a versatile toolbox for tackling both simple and complex genetic disorders.
Companion diagnostics
Madhuri Hegde, SVP and chief scientific officer, Revvity
In the year ahead, we anticipate more targeted and personalized therapies being used to treat chronic and rare conditions, due in part to the proliferation of multiomics in companion diagnostics (CDx) and the need for diagnostic assays for newer FDA approved therapeutics. By integrating various omics disciplines (e.g., genomics, proteomics, metabolomics), researchers are able to develop a more comprehensive approach to understand the molecular basis of disease – moving beyond single biomarker models to more robust, multi-layered systems. This integrated approach can lead to improvements in target validation, patient selection and reduce attrition in clinical trials. It’s also encouraging to see that regulators are seemingly more open to accelerating the approval process for new drugs that address major public health issues or treat large, unmet medical needs. For example, Sanofi’s Tzield is one of the first recipients of a national priority voucher from the U.S. FDA, which could expedite its approval for use in stage 3 T1D individuals. Our team expects continued technological advances in 2026 and collaboration between pharma companies working on the same therapeutic approaches spanning oncology, immunology, rare and chronic diseases and complex neurological disorders like ALS and Alzheimer’s.
Immunotherapy
Chris Lowe, LentiBOOST business leader at Revvity
The field of immunotherapy is rich with strategies for leveraging the natural capability of a person’s immune system to fight disease. The efficacy, scalability, cost and safety of these strategies, however, isn’t always certain, particularly as the complexity of therapeutic payloads increases and the range of target cell types expands. To improve the therapeutic response of certain cell therapies and to lower costs, drug development teams are increasingly turning to solutions that can boost lentiviral transduction efficiency – i.e., to improve gene delivery and expression. This can be the difference between a therapy making it to the clinic or not. When successfully integrated into clinical trials, these solutions could result in more effective cell therapies for a wide range of diseases.
Innovations in standardization for clinical research
Miguel Tam, director of product management, life science reagents at BioLegend – part of Revvity
In clinical research, accurate and reproducible product performance is essential for successful translation into diagnostic applications. As research continues to advance the understanding, diagnosis, and treatment of rare and complex conditions - such as type 1 diabetes, cancer, allergies, and rare deficiencies like paroxysmal nocturnal hemoglobinuria (PNH) - the need for well-defined, biologically relevant controls has steadily increased. From modified lyophilized human cells to carefully engineered cell lines, complex positive controls that mimic a patient’s condition or serve as biomarkers are enhancing confidence in diagnostic tools. Beyond improving reproducibility and accuracy, higher standardization across diverse disease areas is moving clinical research and diagnostics towards greater ease of use as well as reduced costs by increasing the efficiency of diagnostic tests - critical elements in advancing healthcare solutions worldwide.
Newborn screening
Anna Godenhjelm, general manager, Reproductive Health at Revvity
Newborn screening is steadily evolving, driven by rapid advances in therapeutics, technology, and global health priorities. Advances in cell and gene therapies will continue driving panel expansions as new treatments are made available. Next-generation sequencing (NGS) will gain more traction worldwide, enabling earlier and more comprehensive detection of rare genetic diseases. While ethical, legal, and cost concerns remain, over 60 feasibility studies are actively addressing these challenges, signaling the integration of NGS into public health programs. International organizations, including the World Health Organization, will signal the importance of neonatal and infant health and advocate for universal, equitable screening programs. Collaborative efforts will establish new screening programs, aiming to ensure that more newborns benefit from early diagnosis and improved health outcomes.
AI-augmented molecular design and federated learning
David Gosalvez, PhD, Chief Strategy Officer, Revvity Signals
In 2026, life-science R&D will cross a meaningful inflection point as AI-augmented molecular design becomes not just a promising capability but the default mode of early discovery. The winners will be the organizations that deliver the predictive power of models directly into scientific context—embedded into electronic notebooks, analysis platforms, and design workflows—so chemists and biologists can act on high-confidence insights without leaving their workspace. At the same time, the industry will increasingly recognize that the true competitive advantage lies not in algorithms but in the training data behind them. As the benefits of collaborative model improvement begin to outweigh long-held concerns over data sovereignty, the industry will shift toward Federated Learning: a framework where pharma companies can benefit from collective intelligence without ever sharing raw data. By the end of 2026, federated approaches will move from pilots to standard practice, enabling secure cross-company model refinement and accelerating how the industry innovates.