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If “Spatial Biology” conjures images of a science conducted in zero gravity aboard the International Space Station, think again. As one of the most rapidly advancing areas in biomedical research and precision medicine, spatial biology probes the inner landscape of intact cells and tissues down to single-molecule resolution. By preserving location and organization, instead of dissociating tissue into component parts, this discipline maps fine architecture, cellular neighborhoods, and individual molecules.

Maintaining that context is driving new ways of characterizing disease progression, identifying biomarkers, advancing therapeutics, and monitoring treatment responses. Fueling these advances is an expanding toolbox that encompasses high-plex imaging, spatial transcriptomics and proteomics, multiomic profiling, and increasingly sophisticated computational analyses. We consulted industry leaders about how the field is progressing, barriers that remain, and their vision for what is needed to propel spatial biology into its next phase.

Scaling spatial discovery

While genomics and single-cell sequencing profile molecular features and cellular states, spatial biology answers questions that neither can address, such as where those features and cells are located and how they are organized.

“When you dissociate a tissue into individual cells, you lose something fundamental: where those cells were and how they were organized relative to one another,” observes Kamila Belhocine, PhD, senior director of spatial biology at 10x Genomics. She continues: “Spatial biology puts that context back. In cancer, for example, knowing that a particular immune cell population is present within a tumor sample is useful. But knowing whether those cells have infiltrated the tumor core, are sitting at its margin, or are excluded from it altogether can tell you something very different about the biology, and potentially, about response to treatment. The same principle applies to understanding tissue architecture in cases like development and cell differentiation, and disease progression.”

Several barriers have prevented spatial biology from going mainstream in the biopharma/biotech industry. Belhocine says, “I think about four things: speed, reliability, cost, and interpretability. Spatial needs to be fast enough to fit into routine research workflows, robust enough to generate consistent results at scale, affordable enough to use across large studies and, ultimately, straightforward enough to turn the resulting data into biological insight. The first three are fundamentally technology and engineering challenges. Interpretability is harder because it also requires accumulating evidence and validating which spatial signatures are truly meaningful and actionable.”

However, according to Belhocine, single-cell technologies had to address similar issues. “A decade ago, single-cell analysis was largely a specialized technique. Continued innovation made it easier, more scalable, more robust, and dramatically more cost-effective, opening it to a much broader community and enabling much larger studies. We think spatial is following a similar trajectory.”

Belhocine says the company’s newest spatial instrument, Atera, which will be shipping in the second half of the year, is an important step in that progression. “This technology combines whole-transcriptome spatial profiling with single-cell sensitivity and subcellular resolution at a massive scale designed for much larger studies.”

Looking ahead, Belhocine predicts, “As spatial technologies become more scalable, robust and cost-effective, researchers can apply them across much larger patient cohorts to identify biomarkers and signatures of treatment response. Additionally, AI will undoubtedly accelerate that trajectory even further.”

Spatial multiomics

Gary Latham, PhD, CTO, Bio-Techne, provides another perspective on the role of integrating protein analysis and gene expression in spatial biology. “A fuller understanding of tissue biology requires seeing where proteins and genes are expressed, how they relate, and how they interact within the tissue architecture.”

This molecular multiplexing can provide a more complete view of tissue biology. “Spatial biology is unique from other technologies because it takes us from seeing what is there to understanding what is where,” says Latham. He continues, “This is critical because biology can’t be understood as a parts list. Instead, it is a dynamic system of interactions and networks where location defines meaning. While single-cell sequencing can tell you which cell types exist and their molecular states, spatial biology tells you how those cells are organized, how they may be interacting, and how these relationships may influence disease biology and clinical outcomes.”

Latham indicates that the field is working to overcome several challenges. “There is a need for reproducible, standardized automation for today’s biomarkers and tomorrow’s multiomic precision medicine. Many spatial biology workflows remain exploratory and manual during early discovery phases. As programs move toward translational research, the focus shifts to robustness, reproducibility, and standardized, validated workflows. Ultimately, clinical adoption requires validated panels, automation, and throughput suited to routine testing.”

According to Latham, Bio-Techne is helping address these barriers in several ways. Its COMET Suite provides an end-to-end automated platform for hyperplex immunofluorescence and multiomics staining, imaging, and analysis of tissue samples. Additionally, its RNAscope portfolio enables same-section multiomics, with 80,000 RNA probes spanning 450 species and compatibility with multiple automated staining instruments.

Latham believes the spatial multiomics approach can connect gene and protein expression with tissue architecture within the same cells and microenvironment, revealing biological relationships and linking state to function that no single modality can uncover.

He says several areas are seeing the fastest adoption of spatial biology. “Oncology and immuno-oncology continue to dominate spatial biology applications, while immunology and neuroscience represent the next-most prominent areas of focus. Biodistribution of gene therapies is an especially high-impact application: spatial multiomics can localize an AAV vector, transgene mRNA, or oligonucleotide therapeutics such as ASOs and siRNAs to specific cells, then measure changes in the target or downstream gene expression and the associated protein phenotype.”

Integrated discovery & translation

Spatial biology is increasingly moving beyond simply identifying where cells are located to understanding how cellular neighborhoods, cell–cell interactions, and biological pathways shape disease biology. As Thomas Campbell, PhD, senior director of product management for Bruker Spatial Biology, explains, “Biology is inherently multi-dimensional, and no single spatial technology can or will capture every aspect of that complexity. The field needs multiple fit-for-purpose approaches that allow researchers to ask different questions and connect insights across DNA, RNA, proteins, and cellular function.” This ability to examine biology from complementary perspectives is becoming increasingly important as spatial research moves from discovery toward translation.

According to Campbell, understanding these context-dependent differences is driving the field. “The next phase of spatial biology will be about using high-plex discovery to develop focused, quantitative assays that can be deployed across large cohorts. One of the biggest challenges in translational biopharma is the discovery-to-translation gap. A study may identify dozens or hundreds of potentially relevant biomarkers, but a translational or clinically deployable assay demands a much smaller, highly reproducible panel.”

Thus, defining a clinically relevant spatial signature requires insights across the genome, transcriptome, and proteome. Campbell says Bruker Spatial Biology’s portfolio comprises platforms spanning multiple omic layers—including DNA, RNA, proteins, post-translational modifications, miRNA and live cell function—“because we recognize that no single modality can fully resolve the biological complexity that drives disease.”

That integrated approach is reflected in several of the company’s platforms. CosMx^® Spatial Molecular Imager (SMI) provides a view of the whole transcriptome at subcellular resolution, while enabling the simultaneous measurement of RNA and protein within the same tissue section. The CellScape^™ XR platform provides quantitative spatial proteomics using flexible assay chemistry. Meanwhile, the GeoMx^® Digital Spatial Profiler (DSP) profiles the whole transcriptome and more than 1,200 proteins from distinct tissue compartments. Campbell says that, together, these three platforms “provide complementary views of biology and, when used together, enable a more complete understanding of cellular function across multiple omic layers.”

Campbell sees this transition from discovery to translation as one of the most important opportunities for spatial biology. “Whether it’s in oncology, immunology, or neuroscience applications, the future of spatial biology is not simply just about generating more data; it’s translating increasingly sophisticated spatial discoveries into assays that can answer focused biological and clinical questions across large numbers of samples,” he says. “That requires a portfolio of complementary technologies. High-plex discovery platforms can reveal the complexity and heterogeneity of disease, while more focused and quantitative approaches can translate those findings into reproducible biomarkers and clinically relevant assays. Ultimately, the value of spatial biology will be measured by how effectively we can connect discovery to clinically actionable insights.”

From plex to precision

“Historically, spatial biology involved a fundamental tradeoff between plex, sensitivity, and resolution—researchers had to sacrifice one to gain another,” suggests Mike Miller, PhD, CTO and CPO at Quanterix. He says that is now shifting. “With today’s tools, those tradeoffs have narrowed considerably. The current challenge becomes building a platform that delivers reproducibility, rigor, and clinical-grade integrity at scale.”

The company is addressing those needs in several ways. Miller reports: “Our Akoya PhenoCycler-Fusion platform enables researchers to visualize and quantify 100+ proteins in a single tissue section at single-cell resolution.” The platform uses molecular barcoding chemistry. Miller elaborates: “Molecular barcoding is a way of identifying and measuring multiple proteins in a tissue while avoiding many of the limitations of traditional immunohistochemistry and conventional multiplexing approaches.”

This approach combines three main components: an antibody that recognizes the target of interest; a unique molecular barcode, consisting of a short oligonucleotide sequence attached to the antibody; and a complementary oligonucleotide carrying a fluorescent reporter that binds to the barcode, enabling target visualization and quantification. “The key advantage is that the fluorescent reporter can be reversibly hybridized and removed without removing the antibody or repeatedly staining and stripping the tissue. This enables a cyclical imaging workflow.”

The company also recently introduced its PhenoCode2.0 chemistry as part of its new molecular barcoding kit, “which lets researchers reliably barcode virtually any antibody of their choice.” Miller continues: “This means researchers can easily expand and customize their biomarker panels to address new hypotheses, incorporate emerging biomarkers, and scale between unbiased discovery and targeted validation, accelerating the path to new insights.”

Beyond expanding what can be measured, Miller believes the next phase of spatial biology will focus on translating high-plex discoveries into larger, clinically relevant studies. “That’s ultimately what will separate platforms going forward, bridging the gap between generating a striking image in discovery and generating a measurement that holds up in a clinical trial or diagnostic setting.”

Next-generation sequencing (NGS) has matured into an essential part of modern drug development and diagnostic laboratories, and its steadily dropping costs are making it affordable for many facilities that a few years ago couldn’t justify the expense.

The coming years will build on that foundation by extracting more and different types of data from samples, leveraging AI both upstream and downstream, and streamlining workflow. The entire workflow—not just the sequencing itself—will become more cost effective, industry experts tell GEN.

The next wave

“The next major breakthrough in sequencing technology will likely come not from the sequencer alone, but from the ability to extract more meaningful biological insights from every sample,” Karen B. Chapman, PhD, associate VP, R&D Genomics, Agilent Technologies, says.

She envisions “the convergence of improved long-read sequencing, intelligent assay design, AI-enabled analysis, and integrated automation across sample preparation, target enrichment, quality control, sample tracking, and data interpretation.”

Multiomics assays will likely be the next big thing.

“Sequencing has traditionally focused on the four canonical bases, but DNA molecules contain much more information, including methylation, damage, and secondary structure, that can reveal different layers of biology,” Mark Van Oene, president and CEO, PacBio, points out. “We see sequencing moving toward a rapid test that can deliver comprehensive, highly accurate results within hours, enabling scientists and clinicians to make decisions faster.”

The experts at New England Biolabs (NEB) concur. “That’s one of the next big opportunities in sequencing: to gather different data types and provide them in a way that allows them to be interpreted usefully in the context of the human genome,” Andrew Barry, associate director, product portfolio management, NEB, says.

Such multiomics assays will “enable richer and more comprehensive biological insights,” Lakmal Jayasinghe, PhD, CSO, Oxford Nanopore Technologies, says. “The next major breakthrough will be the ability to analyze DNA, RNA, and proteins on a common sensing platform along with epigenetics, epitranscriptomics, and post-translational modifications of proteins. The next frontier is understanding how that code is regulated and executed in real biological systems. We see proteomics as a key part of that future.”

As the industry pushes current boundaries, it is also working to improve sequencing performance through parallel workflows and automation that can accommodate a broad range of samples. Opportunities exist “to increase throughput, improve fidelity, and simplify workflows,” Jayasinghe adds.

To that end, Keerthana “Kit” Krishnan, director, NGS applications and product development, NEB, points out: “Library prep may need a bit more work.” On the upstream side, she identifies sample extraction and sample prep as areas with opportunities for improvement. Downstream opportunities, she says, are in data analysis.

For NGS to become a routine clinical tool, however, those analyses must be clinically meaningful and linked to follow-up actions, she says. She envisions NGS assay kits that are tailored to specific clinical applications.

AI adds value today

“The real breakthrough,” Chapman of Agilent Technologies points out, “will be…in delivering deeper, more actionable biological insights in a way that is economically viable, operationally practical, and accessible to a broad range of laboratories and users. AI will play a critical role in enabling this vision, unlocking new possibilities that lower computational and labor costs while enhancing accuracy and increasing operational speed.”

Across the board, instrument developers are leveraging AI both upstream and down. For example:

• PacBio uses AI upstream to transform raw sequencing data into HiFi reads and to detect DNA modifications. Downstream applications include tertiary analysis, which Van Oene says can be used “to identify and prioritize genetic variants associated with rare disease.”
• NEB’s bioinformatics team deploys AI for coding support, while the automation team is exploring its application to streamline multi-instrument automation processes for developers and for field application scientists to anticipate pitfalls customers may face. AI is also becoming an important tool in enzyme discovery.
• Agilent uses AI to learn from large datasets and develop ways to improve probe placement and target-enrichment design to enhance specificity, coverage and uniformity. Other applications may include “improving base calling, variant detection, structural variant characterization, methylation assessment and clinical interpretations,” Chapman says.
• Oxford Nanopore uses AI to help build more accurate models and to directly detect DNA and RNA modifications. Looking ahead, “AI will become increasingly important in helping scientists, clinicians, and biopharma companies interpret complex multiomics datasets,” Jayasinghe says.
• Illumina has embedded AI tools in its secondary analysis pipeline and throughout its software and informatics portfolio. It has also created the peer-reviewed Primate, Splice and Promoter AI genomic models.

Future AI value

Algorithms are moving “beyond simple rule-based design and increasingly learn from large datasets to optimize enrichment performance,” Chapman says. “Over time, these approaches could evolve into fully-AI-driven panel design systems that automatically optimize probe selection, predict assay performance, minimize sequencing costs, and continuously improve…as more data becomes available.”

“Looking ahead, the most transformative role for AI may be guiding researchers toward the right biological signals to measure, rather than merely processing larger volumes of data,” she continues. “AI can help to identify the most relevant genomic regions, uncover intricate molecular patterns, integrate genomic and methylation information, and bridge genomics discoveries with protein, cellular, and metabolic biology.”

As AI becomes increasingly ubiquitous, explainable AI will become a requirement, these experts agree. The ability to interrogate, assess, and validate the AI’s reasoning is imperative not only for on-instrument applications such as base-calling, but especially for tertiary analysis where understanding the evidence behind a result is critical.

As Barry says, “To use AI effectively, we have to know what’s going on underneath the hood.”

Continuing, overall cost reduction

The lab cost of genome sequencing broke $100 two years ago, and some labs are advertising 30x whole-genome sequencing and analysis for about $500. And, Van Oene from PacBio says, “There is potential to drive the cost of whole-genome sequencing down by another order of magnitude.”

For example, Van Oene says that increasing sample throughput offers a major opportunity “to reduce instrument amortization by spreading the cost of the instrument across many more genomes.” Other opportunities are emerging through computing advances that speed processing and analysis as well as through improvements in compute logistics, such as data compression, which reduces storage requirements, and cloud computing, which reduces hardware requirements and expands access to data.

“The sequencing part of it is becoming a smaller part of the total cost of a workflow,” Fiona Kaper, PhD, VP, head of assay R&D, Illumina, points out.

So, rather than focusing solely on reducing the cost of sequencing itself, NGS innovators increasingly are targeting efficiencies across the entire workflow, from improving sequencing performance and optimizing library preparation to expanding multiplexing capabilities that generate more insight from every run. “Ultimately,” Chapman, Agilent Technologies, says, “the industry’s focus will shift from the cost of sequencing a genome to the cost of delivering a clinically meaningful answer.”

“There’s still some meat left on the bone in terms of how you can improve some workflows,” Barry, NEB, observes.

Scalability is one example. The challenge of sequencing and analyzing a genome varies by size, and solutions need to address that. Therefore, scaling the upstream environment to meet the need is a key consideration for some instrument makers, along with logistical enhancements, such as lyophilization and assay formats, to improve ease of use globally.

Multiomics and multimodal assays two other examples. “We see a lot more customers starting to layer in additional information like proteomics, transcriptomics, and spatial and single cell analysis,” Kaper says.

This trend suggests a future, modular, environment in which scientists can select and optimize the assays that best address their questions. “Having a black box [solution]…really doesn’t work for everyone,” Krishnan underscores.

Further cost reductions, these experts agree, will stem from automating and consolidating workflows, effectively multiplexing assays and developing multimodal assays to generate more actionable information with each run. Such simplification extends to library preparation, which has expanded from DNA and RNA to include proteomics and data from spatial and single-cell analysis.

Instruments developers also are reducing the quantities of reagents needed. Consequently, Van Oene says, “Reducing reagent volumes in library preparation and sequencing will lower consumable costs as well as shipping and storage requirements—which people don’t usually think about when they think about the cost of a genome.”

“Time is also a form of cost,” Kaper says. The industry recognizes that by increasing data quality, capabilities, and sequencing speed to help its users do more, better, and faster.

“The goal,” as Jayasinghe of Oxford Nanopore Technologies says, “is to help customers derive meaningful answers faster, with fewer steps and greater efficiency.”

Ubiquitous sequencing

Five to ten years from now, NGS may well be the core technology for research and clinical labs both, Kaper, Illumina, predicts. “Anything that has to do with the genome, like oncology, could really benefit from technologies that interrogate the genome and also the epigenome, proteome, etc.,” she says.

“Ultimately, the goal is not simply a cheaper genome, but a more informative genome, at a much lower cost,” Van Oene concludes.