Special Issue in SLAS Discovery – completed manuscripts accepted until August 31, 2026.
The Special Issue welcomes high-quality short- or full-length research reports, reviews, and perspectives related to high-content screening (HCS), imaging (HCI), analysis (HCA) and Informatics.
Guest Editors:
Evgeny Shlevkov, PhD
Lieber Institute for Brain Development and Johns Hopkins School of Medicine
Paul A. Johnston, PhD
University of Pittsburgh
John Moffat, PhD
Genentech
The Society for Biomolecular Imaging and Informatics (SBI2) is an international community of leaders, scientists, and students that promotes technological advancement, discovery, and education to quantitatively interrogate biological models and provide high-context information at the cellular level. SBI2 is collaborating with SLAS Discovery to publish the 4th joint Special Issue featuring high-quality short- or full-length research reports, reviews, and perspectives on high-content screening (HCS), imaging (HCI), analysis (HCA) and Informatics. Manuscript submissions are welcome from academia, industry and technology providers.
Areas of interest include, but are not limited to HCS/HCA/HCI applications for:
Keywords:
Lead generation, phenomics, cell painting, live cell kinetics, bright field imaging & analysis, machine learning analysis, AI-based Image analysis.
Submit your manuscript before August 31, 2026. All submitted papers will be subject to peer review to ensure scientific rigor, clarity of expression, and integration with other SLAS Discovery Special Issue contributions.
Questions? Please e-mail SLAS Publishing Manager Jenny Cunningham, MS.
Special Issue in SLAS Technology – completed manuscripts accepted until October 31, 2026.
This Special Issue explores transformative new forms of cell- and organoid-based assays being enabled by advancements in high-resolution and flexible electrophysiological interfaces
Guest editors:
Chris Puleo, PhD
Rensselaer Polytechnic Institute
Feng Guo, PhD
Indiana University Bloomington
Xiao Yang, PhD
Johns Hopkins University
New applications involving electrophysiological systems that both stimulate and sense their biological environment are advancing rapidly. This is in part due to the emergence of new platforms that enable interaction with biological tissues at unprecedented spatial and temporal scales. Ultra-high density and CMOS integrated electrode arrays now provide coverage that enables axon-level electrical mapping and imaging in neuron cultures, and nanoelectrode arrays have even enabled intracellular electrophysiological imaging. Stretchable and neuron-like electrodes provide a new capability to integrate recording systems directly within organoids and living biological neural networks. While further integration of recording arrays with wireless data and power transfer systems has enabled the expansion of the number and types of technologies that can be implanted or worn for development and testing in clinical studies.
Applications of these advanced systems extend across fields, ranging from new tools and assays for disease phenotyping, drug discovery, and therapeutic testing to novel forms of biocomputers and bioelectronic medicine interfaces. However, technical, interdisciplinary, and translational challenges remain, including the need to simplify use for broader adoption across disciplines and fields, the development of new and advanced data compression techniques for real-time use, improving long-term stability of electrical-biological interfaces across biocomputing and bioelectronic fields, and pushing past proof-of-concept studies into the heavily regulated world of clinical and pharmaceutical applications.
To overcome these limitations and explore new opportunities, this Special Issue highlights emerging solutions and applications of high spatial and temporal resolution electrophysiological systems. These advances will include novel use of commercial systems, such as the use of integrated and high-density multi-electrode arrays (MEA) with iPSC-derived neural and cardiac organoids (for patient and application-specific drug discovery and screening) or with novel stimulation and training protocols (for transforming the capabilities of biological computers). The issue will also cover non-commercially available research technologies that continue to improve our ability to electrically interface with biological tissue, as well as new sample- and data-handling systems that will enable simplified use in pharmaceutical and clinical settings.
Finally, the issue will highlight altogether new forms of bioelectronic devices that interface with the body to obtain novel drug-like effects, new personalized diagnostic biomarkers, or novel ways of communicating with neurological and mechanical prosthetics and devices.
This issue will bring together contributions from leading researchers across the multidisciplinary fields of in vitro (e.g., organoid) models and assays, bioelectronic medicine, biocomputing, and bioinspired bioelectronic interfaces, based on their common need for and use of advanced electrophysiology systems. It will underscore their potential to reshape and have a profound impact on important fields such as drug development, clinical therapeutics/diagnostics, and computer science.
Keywords:
Submit your manuscript before October 31, 2026. All submitted papers will be subject to peer review to ensure scientific rigor, clarity of expression, and integration with other SLAS Technology Special Issue contributions.
Special Issue in SLAS Technology – completed manuscripts accepted until November 30, 2026.
We invite researchers, scientists, and industry experts to submit articles on work related to NexusXp: The Connected Lab, also known as the lab of the future, cloud lab or walk-away lab.
Guest Editors:
Kalpesh Gupta, PhD
Moderna
Mario Richter, PhD
Abbvie
Mohit Goel, MS
Modern
We are excited to announce a special edition on “NexusXp: The Connected Lab” in the field of lab automation. This edition aims to explore cutting-edge advancements, innovative technologies, and visionary concepts shaping the future of laboratories. We invite researchers, scientists, and industry experts to submit their original research papers, case studies and review articles.
Topics of interest include, but are not limited to:
Submit your manuscript before November 30, 2026. All submitted papers will be subject to peer review to ensure scientific rigor, clarity of expression, and integration with other SLAS Technology Special Issue contributions.
Questions? Please e-mail SLAS Publishing Manager Jenny Cunningham, MS.
Special Issue in SLAS Technology – completed manuscripts accepted until December 15, 2026.
This Special Issue will highlight emerging approaches and applications that leverage AI to enable autonomous and AI-guided experimental discovery across disciplines such as chemistry, biology, materials science and drug discovery.
Guest Editors:
Yao Fehlis, PhD
Kungfu.AI
Joseph Brown, PhD
University of Toronto
Artificial intelligence is rapidly reshaping the landscape of experimental science by enabling new paradigms for designing, executing, and interpreting experiments. A key development in this transformation is the emergence of self-driving laboratory systems that integrate machine learning, robotics, and automated instrumentation to enable closed-loop experimental workflows. In these systems, AI models propose new experiments based on prior data, automated platforms execute them with minimal human intervention, and the resulting data are used to continuously update predictive models. This dynamic feedback cycle significantly accelerates discovery by enabling more efficient exploration of complex experimental spaces.
Recent advances in agentic AI, active learning and foundation models for science are further expanding the capabilities of autonomous experimentation platforms. These technologies allow laboratory systems to reason about experimental goals, adapt strategies in real time and coordinate multiple instruments and data streams. As a result, researchers are beginning to realize laboratory environments that can autonomously iterate between hypothesis generation, experiment execution and data-driven model refinement.
This Special Issue will highlight emerging approaches and applications that leverage AI to enable autonomous and AI-guided experimental discovery across disciplines such as chemistry, biology, materials science, and drug discovery.
Topics of interest include, but are not limited to:
Article Types:
By bringing together advances in algorithms, instrumentation, and laboratory automation, this issue aims to showcase how AI-driven systems are transforming experimental workflows and accelerating scientific innovation.
Submit your manuscript before December 15, 2026. All submitted papers will be subject to peer review to ensure scientific rigor, clarity of expression, and integration with other SLAS Technology Special Issue contributions.
Questions? Please e-mail SLAS Publishing Manager Jenny Cunningham, MS.
