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Diagnostics Biochemistry and molecular biology, Technology and innovation

Future Trends in Spatial Biology

Pathologists are on the front lines of precision medicine, working to diagnose and treat diseases with ever-increasing accuracy – and, as our understanding of the human body improves, so must our diagnostic tools and techniques.

A new and developing area, spatial biology, holds promise for creating novel and someday clinically significant insights. In the past few decades, the study of cell morphology and molecular biology have followed separate paths – microscopy was used to examine cellular structure and dynamics, whereas genomics and transcriptomic methods were used to study gene expression in homogenized tumor samples containing millions of cells and lacking in situ context. Recent technological advancements have revolutionized our capacity to quantify cellular heterogeneity with spatial context, opening the doors to advanced study of the tumor microenvironment and treatment responses.

Spatial biology is a relatively new field of study in which cells and tissues are observed in more or less intact 2D or 3D surroundings. In the same way that GPS records location coordinates to generate a map and track specific targets of interest, cellular- and molecular-level applications can also follow a similar logic. We can use these techniques to help map out a cell’s spatial architecture and understand how it interacts with its surroundings, allowing us to see things that would be unobtainable using bulk sequencing or other technologies.

Going forward – and as technology matures – spatial biology will play an increasingly vital role in untangling the complexities of diseases. What does the near future hold? In 2022, I anticipate the following trends will make headway in the field.

1. Automation

Spatial biology may surprise those who are new to the technology because it involves a wide range of methodologies (such as cyclic immunostaining, in situ sequencing via barcodes, or imaging mass cytometry) and different target analytes (proteins, RNA, and more) (1). However, many current spatial biology approaches have stages that must be completed by hand, making them low-throughput, time-consuming, and unscalable.

In recent years, a few industry solution providers and academic research groups have focused on the end-to-end workflow needed for high-throughput, multi-omics spatial tissue profiling with minimum user input (2,3,4,5). The procedures for each of these automated solutions vary largely – from expensive, closed-system instruments, liquid-handling robots, and specialized equipment to open-source protocols that use existing laboratory equipment.

2. Resolution

There has been great demand for cellular – or even subcellular – spatial resolution for molecular targets in biological sciences, driven by both scientific curiosity and the potential to gain important new insights into subcellular components and biologically significant interactions between neighboring cells. It is worth noting that, in many cases, particularly those using spatial barcoding, high resolution is not required – in fact, research shows that significant discoveries were made with spatially barcoded technologies with 55–100-micron resolution, the equivalent of cellular “neighborhoods” (6,7). Nonetheless, spatial resolution has become a common metric for prospective users to compare the performances of different technologies and test developers are highly motivated to make improvements in this area.

In general, pathologists will favor single cell level resolution. Current spatially barcoded array techniques are at a disadvantage compared with image-based techniques in spatial resolution, but they are moving quickly to catch up – dropping the 100 µm resolution (6) to a few hundred nanometers (8).

Figure 1. The number of spatial multi-omics articles per year on PubMed. Search query: spatial multi-omics.

3. Multi-omics and multiplex

Individuals have unique genomic, transcriptomic, and proteomic profiles – all of which can play a role in disease progression and treatment response. Though studying the transcriptome provides gene expression data on a temporal snapshot of potentially labile RNA molecules present in a cell or tissue at a given point in time, proteomic detections provide more accurate phenotypic information about present and active proteins.

Studies have often demonstrated a disconnect between changes happening at the RNA level and those at the protein level. One explanation is that many post-transcriptional events, including translation, mRNA decay, and splicing, can affect gene expression. A multi-omic approach that integrates spatial proteomics data with spatial transcriptomics can provide a more comprehensive understanding of tissue biology. Additionally, more biomarkers can now be detected within their natural spatial confines, thanks to advanced technologies such as multiplex fluorescence, DNA, RNA, and metal isotope labeling.

The number of protein targets may be expanded by iterative techniques that include repeating rounds of antibody labeling and detection with a multiplex count of more than 50 targets (1), but such cyclic processes can be labor and time-intensive. This can be avoided by relying on secondary readouts such as deep sequencing, which could quantify up to 100 proteins in a single staining and scanning procedure. Sequencing-based spatial transcriptomic methods provide the highest level of multiplexing, demonstrating spatially resolved information on 10,000 or more genes (1).

4. Artificial intelligence (AI)

Spatial biology techniques generate a large amount of data, often in the form of images – but with the sheer volume and complexity of data comes a range of new analysis challenges. Multi-omics, multiplexing, and multimodal data integration (e.g., processing the same piece of tissue for single-cell RNA-seq and spatial biology in parallel) offer a wealth of information, but much of it is left untapped or underused.

There is an unmet need in the community for improved computational tools to extract quantitative information from images and sequencing data. The resulting data points and spatial features need to be first linked by tissue morphology, most commonly the H&E-stained tissue slide, and then with clinical information to produce new insights. In recent years, AI has been used in image analysis for various tasks such as classification, segmentation, and tracking. In particular, it is increasingly used in histopathology to help pathologists with disease detection and prognosis. The best part? AI can also be used for spatial data analysis.

5. Sample quality

One of the key challenges in spatial biology is tissue acquisition. To acquire high-quality tissue samples, we must pay attention to how the tissue is collected, processed, and stored. Advances in the multiplex analysis of proteins on FFPE sections are bringing patients one step closer to next-generation pathology, in which companion diagnostic tests suggest therapeutic actions for individualized medicine. However, moving nascent transcriptomic technologies from the lab to clinics has several drawbacks, including variations encountered in fixation and potential analyte degradation.

Under environmental conditions, RNA is an unstable molecule that is easily degraded by RNase. FFPE samples are typically fixed for a minimum of 24 hours in 10 percent neutral buffered formalin, which cross links and thus damages the molecule – and the high temperatures used during FFPE processing can further degrade it. The integrity of RNA can vary because clinical samples are frequently stored in fixative and the time between taking the biopsy and fixation (cold ischemic time) may differ from sample to sample. To be able to translate spatial omics methods to the clinic, sample quality must improve in terms of analyte preservation and workflow robustness.

6. Standardized diagnostic biomarkers

Thousands of biomarkers can be detected from the same tissue, but what does that mean to the pathologist who has to arrive at a diagnosis? It is important to remember that spatial data should not be viewed in isolation; rather, they should be considered in the context of clinical and complementary molecular information. The problem is that it often proves difficult to understand how biomarker data may be used in clinical applications due to the lack of cross-modality standardization of cell phenotype definitions. Keith Wharton, Vice President and Medical Director of Ultivue and a board-certified pathologist with a background in molecular biology, said, “Right now, everybody has a different opinion about the meaning, for example, of T cell exhaustion versus dysfunction versus other terms to indicate T effector cells that don’t kill the tumor,” he says, referring to the lack of consensus in T lymphocyte biomarkers and their clinical application. "Can we use multiple modalities, perhaps on samples from multiple species, to agree as a community on which standardized cell phenotypes should be measured and how?” Of potential relevance, the Partnership for Accelerating Cancer Therapies (PACT) collaborates with the pharmaceutical industry to facilitate systematic and uniform clinical testing of biomarkers to enable consistent generation of data, standardized assays to support data reproducibility and comparability of findings across studies, and the discovery and validation of new biomarkers.

Spatial biology is a powerful new tool that we are just beginning to harness for clinical benefit, but it has the potential to unlock many of the mysteries of disease and cellular interactions. Exciting progress has already been made in the field, and it’s clear that spatial biology is here to stay. Is there a role for it in clinical practice? “The connection is indirect,” said Richard Levenson, Professor of Pathology and Laboratory Medicine at UC Davis, who made significant contributions to the development of multiple spatial multiplexed imaging technologies. “The role of spatial biology for pathologists is on the research side, so they can understand disease and the biology behind it.” In pathology, the objective is to find practical, inexpensive solutions that lead to a diagnosis and prognosis – and, although spatial biology is heading in the right direction, it still has a long road ahead in its transition from bench to bedside.

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  1. SM Lewis et al., “Spatial omics and multiplexed imaging to explore cancer biology,” Nat Methods, 18, 997 (2021) PMID: 34341583.
  2. Bio-Techne Corporation, “Bio-Techne and Akoya Biosciences Partner to Deliver Automated Spatial Multiomics Workflow with Industry-Leading Speed and Resolution” (2022). Available at: https://prn.to/3OkfsTM
  3. S Vickovic et al., “SM-Omics is an automated platform for high-throughput spatial multi-omics,” Nat Commun, 13, 795 (2022). PMID: 35145087.
  4. E Berglund et al., “Automation of Spatial Transcriptomics library preparation to enable rapid and robust insights into spatial organization of tissues,” BMC Genomics, 21, 298 (2020). PMID: 32293264.
  5. D Farrell, “NanoString Expands Partnership with Leica Biosystems to Accelerate Spatial Discoveries with Automated Workflow” (2022). Available at: https://yhoo.it/3uZkOgn.
  6. PL Ståhl et al., “Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science,” Science, 353, 78 (2016). PMID: 27365449.
  7. MV Hunter et al., “Spatially resolved transcriptomics reveals the architecture of the tumor-microenvironment interface,” Nat Commun, 12, 6278 (2021). PMID: 34725363.
  8. A Chen et al., “Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays,” Cell, 185, 1777 (2022). PMID: 35512705.
About the Author
Tian Yu

Chief Scientific Officer of Truckee Applied Genomics, Nevada, USA. She is a molecular biologist experienced with life science and healthcare startups, currently focusing on leading research and development in Truckee Applied Genomics and developing tools to solve “Garbage In, Garbage out” problems in molecular pathology.

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