Spatial biology first entered the omics scene about a decade ago. Since then, the technology has generated significant buzz both inside and outside of the lab—beyond its contributions to science. The industry that surrounds spatial technologies has been full of litigious drama, with courtroom battles that have called into question the role of patent IP in the scientific enterprise.
Perhaps the legal contention has added to spatial biology’s mystique. (Would companies fight so hard over technologies that are not truly “game-changing”?) Regardless, many consider spatial worth the hype (and the legal bills). The ability to uncover new biology by applying spatial information is redefining how questions are asked in biology.
But this article is not about the legal battles, the drama, or even the technologies; it is about the science. Despite spatial’s growing popularity across the life sciences, one area seems to have benefitted more than others: cancer biology. Here, we explore how spatial technology is advancing the field of cancer research. What new questions are being asked that couldn’t be asked before? What new insights is spatial providing? And how will spatial advance in the clinic and make a difference for patients?
To answer these questions, GEN spoke with three researchers using spatial in their cancer research programs. Coincidentally, all three started their labs at the time that spatial biology was coming into existence. In turn, the research programs have grown together, and are now inextricably linked, with the technology.
Arutha Kulasinghe, PhD, associate professor & clinical-oMx group leader at The University of Queensland and founding scientific director of the Queensland Spatial Biology Centre, did his first spatial experiment in 2018, using a $10,000 grant. Describing that time as “the wild west” of spatial, he ran three 20 plex (analyzing 20 proteins) samples. Despite the small scale, when the data came back, Kulasinghe remembers, he thought “This is the future.”
Associate Professor Arutha Kulasinghe, PhD, (left) Founding Scientific Director of the Queens-land Spatial Biology Centre (QSBC) is pictured together with Meg Donovan, PhD, a research officer at the QSBC. Their work is pioneering spatial transcriptomics using digital spatial profiling approaches to study different types of cancer including lung and head and neck.
Fast forward to 2025: Kulasinghe is now director of a new spatial biology center in Queensland, Australia—located on a hospital campus with the premise of translating spatial technology to the clinic. The goal is to scale spatial, to build tissue atlases that can be mined with large data sets.
“Everyone can generate a beautiful picture using spatial,” notes Kulasinghe. But the biggest challenge right now for cancer and spatial, he adds, is the lack of clinical value. Because, he asserts, the variation seen in smaller studies is patient variation—not biological patterns in the data. But Kulasinghe is working to change that. A recent study from his lab and collaborators includes 600 lung cancer patients who have received immunotherapy—both responders and non-responders. This, he says, is the beginning of well statistically powered studies that will be able to answer meaningful questions.
The Kulasinghe lab is focused on computation. Because, as Kulasinghe says, it takes a month to generate a spatial data set. “Then, it’s about two years of analysis.”
Computational members of his team code in R, MATLAB, or Python and then work closely with leading immunologists to unpack the data at a cellular (or subcellular) level. The data reveal far more than cell types, however. Spatial analysis is no longer just proximity, he asserts. It’s functional, metabolic, distance, and single point-based statistics.
An almost endless series of questions can be asked like: What cell types are always found together? What cell types are never found close together? What is the subset of CD8+ T cells that is infiltrating? Are those cells exhausted? Are they moving away from the tumor? What are the distance metrics associated with that? Can we model that? What are the patterns at a cohort level between responders to a therapy versus non-responders?
Another exciting advance in cancer biology, made possible with large training data sets on different cancers, is the overlaying of bulk data from the cancer genome atlas with H&E information. H&E slides cost a dollar: spatial assays cost thousands. But when a multiomics dataset is imputed into the H&E at a pixel level, spatial information can be obtained in any lab where an H&E slide is scanned and uploaded—whether it’s in Boston or Botswana.
According to some colleagues, Carlos Fernandez-del Castillo, MD, Mass General Hospital, is one of the best pancreatic cancer surgeons in the world. His R0 resection rate (surgical removal of the entire tumor) is tremendously high according to his colleague, William Hwang, MD, PhD, a radiation oncologist in the Department of Radiation Oncology at Mass General Cancer Center.
William Hwang, MD, PhD
Mass General Cancer Center
But even Fernandez-del Castillo gets stumped sometimes. He has lamented to Hwang about the particularly challenging cases when the margin is negative (meaning the cancer is removed) in all places except in the nerve, which he cannot remove. In those cases, he is convinced that metastasis will follow. The cancer cells have not only found their way into the nerves, but they can also track along them.
In addition to being a physician, Hwang is also a scientist with a strong interest in GI cancer (and particularly pancreatic cancer). His lab focuses on cancer neuroscience, but not brain tumors. Rather, the idea is that tumor innervation plays unexplored critical roles in tumor biology and patient outcomes. The field is new, but Hwang notes that, since he started getting interested in it over the past five years, he has seen “a mini-revolution.” And, although the Hwang lab uses multiple tools, spatial is one of the most important tools that they use to answer questions in that space.
Cell-cell interactions are the lab’s “bread and butter”. They ask the question: How do the nerves interact with cancer cells? In some instances, it is electrical interactions that could not be addressed by spatial techniques. But there are other interactions—paracrine interactions, physical interactions, juxtracrine interactions—that can be captured by spatial omics. It is challenging to explore this area without using spatial techniques. “I mean, you can try,” asserts Hwang, “but it doesn’t work very well.”
Hwang tells GEN that he first got into the field because his patients kept having a terrible, visceral pain that was hard to treat. He met a pathologist who told him about perineural invasion and showed him images of cancer cells intimately wrapped around nerves inside the tumor. Hwang remembers thinking, “This doesn’t look like an accident.” When people started looking into the relationship between the two cell types, it became clear that the cancer cells derive advantages (metabolic and electrical advantages have both been noted) from recruiting nerves into the tumor and interacting with them. In addition, the nerves that are innervating the tumor modulate the immune response to cancer by secreting factors (for example, norepinephrine) that change the behavior and the phenotypic features of immune cells. Ultimately, Hwang thinks that one way to improve a patient’s response to immunotherapy is through modulating the nerve signaling to the tumor.
The current versions of spatial technology limit the research that can be done in this field today. For example, fibers or thick nerve bundles are visible in a section but it’s hard to know which cell body the fibers came from. Researchers can still learn a lot from the fibers at the RNA level, Hwang attests, and even more at the protein level. Ideally, they could trace the fibers using 3D technology. Some companies are moving in that direction: Singular Genomics offers a 3D reconstruction technology and a startup company, Stellaromics, has adapted technology from the lab of Karl Deisseroth, MD, PhD, at Stanford University to visualize cells in 100-micron sections in 3D.
Another technology on Hwang’s wish list is a target-agnostic protein spatial method. The currently available panels are not ideal; they are either tailored to cancer immunology or neuro panels tailored to the central nervous system. Hwang wants to do “spatial mass spec with high resolution” to discover new biology and answer the question of which proteins enable the intimate interactions between cancer cells and nerves. This could lead to the identification of truly novel cancer nerve interaction targets that could be brought into clinical trials.
Another innovation Hwang is keeping his eye on is the ability to have functional temporal readouts to try to understand which cell types are progenitors to other cell types, in the context of tissue. In other words, a method that keeps track of time and that could indicate which cells came first and how long they’ve been there, while still layering in the spatial information of which cells they are interacting with. Integrating that timing information into spatial is another frontier that will be really amazing, he says, because it will allow better functional experiments in a mouse.
“We try to innovate in the spatial space, both in terms of the experimental techniques that we’re applying and the analytical methods,” Hwang notes. They also work on their own analytical methods, recently having published a method called Spatially Constrained Optimal Transport Interaction Analysis (SCOTIA)—a Python package for inferring cell-cell interactions from imaging-based spatial omics data—in Nature Genetics.
Spatial meets gaming: Xbox ANZ (Australia and New Zealand) and Cure Cancer partnered with Kulasinghe to develop three custom Xbox Series X consoles. Inspired by the beauty of spatial images and the impact of spatial biology on cancer research, the exclusive consoles were auctioned off last year, with all proceeds supporting Cure Cancer.
At the time that spatial was taking off, Tullia Bruno, PhD, was working as a research assistant professor with Dario Vignali, PhD, professor and chair of the Department of Immunology at the University of Pittsburgh. Bruno moved into her own tenure track position after a few years (she is currently an assistant professor of Immunology at the University of Pittsburgh School of Medicine) and knew that spatial would be a key component to her work.
Tullia Bruno, PhD
University of Pittsburgh
Because the technology was so nascent when she started, some people asked Bruno if she should hold off on spatial experiments until the technology matured. But Bruno didn’t want to wait. So, she dove in headfirst. And now, she estimates that 70% of her lab is doing spatial experiments.
The tertiary lymphoid structure (TLS) field experienced a surge of excitement in 2020 when a series of papers, published in Nature, demonstrated that patients with B cells or TLS at baseline did better on immunotherapy. It was a game changer, Bruno says. She was already working in the TLS field and, around the same time, spatial was taking off. The perfect partnership was born because, as Bruno says, “You can’t study a tertiary lymphoid structure if you can’t visualize it in a tissue section.”
In order to understand why cancer is particularly well suited to spatial, it is important to understand how the tissue ends up in researchers’ hands. For example, when a researcher is receiving a fresh tissue resection from a lung cancer patient, the surgeon will send over a piece of a piece to the lab. Meaning, the researcher does not get the full resection, which can limit the experiment.
But spatial changes that. With spatial, a researcher could have access to, and cut slides from, all the different blocks that the specimens were put in, yielding a far more comprehensive picture of what’s going on. For TLS studies, this is incredibly important because the TLS often don’t reside within the tumor; they can reside on the border of the tumor—an area that may not be given to the researcher by the surgeon. Bruno thinks a lot about their collection methods and they have fine-tuned their lung cancer surgery collection for fresh tissue. They now get a piece of tumor inside, a peritumoral piece, and a normal adjacent piece of tissue, and they can compare all three of those sites locationally within the same patient.
Bruno’s recent paper in Cancer Cell illustrates the approach her lab is taking to using spatial to uncover cancer biology. Approaching the research using spatial identifies not only where the TLS are, but also what type of TLS are there. Spatial transcriptomics (using the GeoMX from Nanostring/Bruker) analyzes the composition in more detail and yields more robust and comprehensive gene signatures. In the end, this work could identify targets that could be leveraged to induce TLS function to be helpful in the microenvironment.
Bruno is starting to build spatial transcriptomic data sets that compare TLS across multiple solid tumors and ask, are these targets shared or are they different? Indeed, they have found shared targets between ovarian and lung cancer. Bruno says this is “really exciting” because now they can start to verify the results in model systems. “The things that we are doing now on patient samples would be difficult to do without these technologies,” she asserts. They simply could not have done it 10 years ago.
One of the premier multiomics conferences of the year is held annually in Australia. Last year, the conference was held in early December at the Brisbane Convention & Exhibition Centre. This infographic, created by Alice Edy (www.Livedrawing.com.au) summarizes a panel discussion from the meeting entitled, “Driving OMICS to the clinic.”
Spatial is not only about opening up biology. In this way, it is also about unlocking the right patient specimens that are needed to do the work. Because spatial can be done on FFPE samples, there are decades of data—comprehensive historical data sets—sitting in tissue banks across the world that can be explored. For example, two surgeons working on very rare skull-based chordomas approached Bruno to collaborate because obtaining fresh tissue to study such a rare condition would take years. However, they could search through the tissue that had been put in FFPE, go back 20, 30, or more years, and identity the tissue blocks with skull-based chordomas. In doing this, they built a comprehensive cohort.
Kulasinghe agrees, noting that because the technology is FFPE compatible from day one, it means “We just have to walk across to the hospital, open up some fridges, or look on their bench, and they’ve got thousands of samples. We can profile all of that. We can go back to the seventies, eighties, nineties.”
This aspect of spatial research makes Bruno particularly happy because the FFPE samples came from patients who donated tissue. It’s nice to see, she says, that their tissue is getting utilized to try to find new targets and to try to understand biology better from a spatial context.
And Bruno is not alone in her enthusiasm for spatial biology. She says that there is a lot of movement in the spatial direction in the cancer immunotherapy field and notes that most talks that she goes to have at least one piece of spatial data. The majority of labs in the field are doing spatial, she asserts. And if they are not, she adds, “they are thinking about it.”
Several cores of a tissue microarray feature different areas of a human pancreatic tumor analyzed with spatial multi-omics: cancer glands (green), stroma (magenta), and immune cells (cyan). [Dennis Gong / Hwang lab]
The post Spatial Biology Reveals Past, Present, and Future Cancer Biology appeared first on GEN - Genetic Engineering and Biotechnology News.
Perhaps the legal contention has added to spatial biology’s mystique. (Would companies fight so hard over technologies that are not truly “game-changing”?) Regardless, many consider spatial worth the hype (and the legal bills). The ability to uncover new biology by applying spatial information is redefining how questions are asked in biology.
But this article is not about the legal battles, the drama, or even the technologies; it is about the science. Despite spatial’s growing popularity across the life sciences, one area seems to have benefitted more than others: cancer biology. Here, we explore how spatial technology is advancing the field of cancer research. What new questions are being asked that couldn’t be asked before? What new insights is spatial providing? And how will spatial advance in the clinic and make a difference for patients?
To answer these questions, GEN spoke with three researchers using spatial in their cancer research programs. Coincidentally, all three started their labs at the time that spatial biology was coming into existence. In turn, the research programs have grown together, and are now inextricably linked, with the technology.
Going Big in Oz
Arutha Kulasinghe, PhD, associate professor & clinical-oMx group leader at The University of Queensland and founding scientific director of the Queensland Spatial Biology Centre, did his first spatial experiment in 2018, using a $10,000 grant. Describing that time as “the wild west” of spatial, he ran three 20 plex (analyzing 20 proteins) samples. Despite the small scale, when the data came back, Kulasinghe remembers, he thought “This is the future.”
Associate Professor Arutha Kulasinghe, PhD, (left) Founding Scientific Director of the Queens-land Spatial Biology Centre (QSBC) is pictured together with Meg Donovan, PhD, a research officer at the QSBC. Their work is pioneering spatial transcriptomics using digital spatial profiling approaches to study different types of cancer including lung and head and neck.
Fast forward to 2025: Kulasinghe is now director of a new spatial biology center in Queensland, Australia—located on a hospital campus with the premise of translating spatial technology to the clinic. The goal is to scale spatial, to build tissue atlases that can be mined with large data sets.
“Everyone can generate a beautiful picture using spatial,” notes Kulasinghe. But the biggest challenge right now for cancer and spatial, he adds, is the lack of clinical value. Because, he asserts, the variation seen in smaller studies is patient variation—not biological patterns in the data. But Kulasinghe is working to change that. A recent study from his lab and collaborators includes 600 lung cancer patients who have received immunotherapy—both responders and non-responders. This, he says, is the beginning of well statistically powered studies that will be able to answer meaningful questions.
The Kulasinghe lab is focused on computation. Because, as Kulasinghe says, it takes a month to generate a spatial data set. “Then, it’s about two years of analysis.”
Computational members of his team code in R, MATLAB, or Python and then work closely with leading immunologists to unpack the data at a cellular (or subcellular) level. The data reveal far more than cell types, however. Spatial analysis is no longer just proximity, he asserts. It’s functional, metabolic, distance, and single point-based statistics.
An almost endless series of questions can be asked like: What cell types are always found together? What cell types are never found close together? What is the subset of CD8+ T cells that is infiltrating? Are those cells exhausted? Are they moving away from the tumor? What are the distance metrics associated with that? Can we model that? What are the patterns at a cohort level between responders to a therapy versus non-responders?
Another exciting advance in cancer biology, made possible with large training data sets on different cancers, is the overlaying of bulk data from the cancer genome atlas with H&E information. H&E slides cost a dollar: spatial assays cost thousands. But when a multiomics dataset is imputed into the H&E at a pixel level, spatial information can be obtained in any lab where an H&E slide is scanned and uploaded—whether it’s in Boston or Botswana.
Targeting neurons
According to some colleagues, Carlos Fernandez-del Castillo, MD, Mass General Hospital, is one of the best pancreatic cancer surgeons in the world. His R0 resection rate (surgical removal of the entire tumor) is tremendously high according to his colleague, William Hwang, MD, PhD, a radiation oncologist in the Department of Radiation Oncology at Mass General Cancer Center.
William Hwang, MD, PhD
Mass General Cancer Center
But even Fernandez-del Castillo gets stumped sometimes. He has lamented to Hwang about the particularly challenging cases when the margin is negative (meaning the cancer is removed) in all places except in the nerve, which he cannot remove. In those cases, he is convinced that metastasis will follow. The cancer cells have not only found their way into the nerves, but they can also track along them.
In addition to being a physician, Hwang is also a scientist with a strong interest in GI cancer (and particularly pancreatic cancer). His lab focuses on cancer neuroscience, but not brain tumors. Rather, the idea is that tumor innervation plays unexplored critical roles in tumor biology and patient outcomes. The field is new, but Hwang notes that, since he started getting interested in it over the past five years, he has seen “a mini-revolution.” And, although the Hwang lab uses multiple tools, spatial is one of the most important tools that they use to answer questions in that space.
Cell-cell interactions are the lab’s “bread and butter”. They ask the question: How do the nerves interact with cancer cells? In some instances, it is electrical interactions that could not be addressed by spatial techniques. But there are other interactions—paracrine interactions, physical interactions, juxtracrine interactions—that can be captured by spatial omics. It is challenging to explore this area without using spatial techniques. “I mean, you can try,” asserts Hwang, “but it doesn’t work very well.”
Hwang tells GEN that he first got into the field because his patients kept having a terrible, visceral pain that was hard to treat. He met a pathologist who told him about perineural invasion and showed him images of cancer cells intimately wrapped around nerves inside the tumor. Hwang remembers thinking, “This doesn’t look like an accident.” When people started looking into the relationship between the two cell types, it became clear that the cancer cells derive advantages (metabolic and electrical advantages have both been noted) from recruiting nerves into the tumor and interacting with them. In addition, the nerves that are innervating the tumor modulate the immune response to cancer by secreting factors (for example, norepinephrine) that change the behavior and the phenotypic features of immune cells. Ultimately, Hwang thinks that one way to improve a patient’s response to immunotherapy is through modulating the nerve signaling to the tumor.
The current versions of spatial technology limit the research that can be done in this field today. For example, fibers or thick nerve bundles are visible in a section but it’s hard to know which cell body the fibers came from. Researchers can still learn a lot from the fibers at the RNA level, Hwang attests, and even more at the protein level. Ideally, they could trace the fibers using 3D technology. Some companies are moving in that direction: Singular Genomics offers a 3D reconstruction technology and a startup company, Stellaromics, has adapted technology from the lab of Karl Deisseroth, MD, PhD, at Stanford University to visualize cells in 100-micron sections in 3D.
Another technology on Hwang’s wish list is a target-agnostic protein spatial method. The currently available panels are not ideal; they are either tailored to cancer immunology or neuro panels tailored to the central nervous system. Hwang wants to do “spatial mass spec with high resolution” to discover new biology and answer the question of which proteins enable the intimate interactions between cancer cells and nerves. This could lead to the identification of truly novel cancer nerve interaction targets that could be brought into clinical trials.
Another innovation Hwang is keeping his eye on is the ability to have functional temporal readouts to try to understand which cell types are progenitors to other cell types, in the context of tissue. In other words, a method that keeps track of time and that could indicate which cells came first and how long they’ve been there, while still layering in the spatial information of which cells they are interacting with. Integrating that timing information into spatial is another frontier that will be really amazing, he says, because it will allow better functional experiments in a mouse.
“We try to innovate in the spatial space, both in terms of the experimental techniques that we’re applying and the analytical methods,” Hwang notes. They also work on their own analytical methods, recently having published a method called Spatially Constrained Optimal Transport Interaction Analysis (SCOTIA)—a Python package for inferring cell-cell interactions from imaging-based spatial omics data—in Nature Genetics.
Spatial meets gaming: Xbox ANZ (Australia and New Zealand) and Cure Cancer partnered with Kulasinghe to develop three custom Xbox Series X consoles. Inspired by the beauty of spatial images and the impact of spatial biology on cancer research, the exclusive consoles were auctioned off last year, with all proceeds supporting Cure Cancer.
Opening up a new TLS world
At the time that spatial was taking off, Tullia Bruno, PhD, was working as a research assistant professor with Dario Vignali, PhD, professor and chair of the Department of Immunology at the University of Pittsburgh. Bruno moved into her own tenure track position after a few years (she is currently an assistant professor of Immunology at the University of Pittsburgh School of Medicine) and knew that spatial would be a key component to her work.
Tullia Bruno, PhD
University of Pittsburgh
Because the technology was so nascent when she started, some people asked Bruno if she should hold off on spatial experiments until the technology matured. But Bruno didn’t want to wait. So, she dove in headfirst. And now, she estimates that 70% of her lab is doing spatial experiments.
The tertiary lymphoid structure (TLS) field experienced a surge of excitement in 2020 when a series of papers, published in Nature, demonstrated that patients with B cells or TLS at baseline did better on immunotherapy. It was a game changer, Bruno says. She was already working in the TLS field and, around the same time, spatial was taking off. The perfect partnership was born because, as Bruno says, “You can’t study a tertiary lymphoid structure if you can’t visualize it in a tissue section.”
In order to understand why cancer is particularly well suited to spatial, it is important to understand how the tissue ends up in researchers’ hands. For example, when a researcher is receiving a fresh tissue resection from a lung cancer patient, the surgeon will send over a piece of a piece to the lab. Meaning, the researcher does not get the full resection, which can limit the experiment.
But spatial changes that. With spatial, a researcher could have access to, and cut slides from, all the different blocks that the specimens were put in, yielding a far more comprehensive picture of what’s going on. For TLS studies, this is incredibly important because the TLS often don’t reside within the tumor; they can reside on the border of the tumor—an area that may not be given to the researcher by the surgeon. Bruno thinks a lot about their collection methods and they have fine-tuned their lung cancer surgery collection for fresh tissue. They now get a piece of tumor inside, a peritumoral piece, and a normal adjacent piece of tissue, and they can compare all three of those sites locationally within the same patient.
Bruno’s recent paper in Cancer Cell illustrates the approach her lab is taking to using spatial to uncover cancer biology. Approaching the research using spatial identifies not only where the TLS are, but also what type of TLS are there. Spatial transcriptomics (using the GeoMX from Nanostring/Bruker) analyzes the composition in more detail and yields more robust and comprehensive gene signatures. In the end, this work could identify targets that could be leveraged to induce TLS function to be helpful in the microenvironment.
Bruno is starting to build spatial transcriptomic data sets that compare TLS across multiple solid tumors and ask, are these targets shared or are they different? Indeed, they have found shared targets between ovarian and lung cancer. Bruno says this is “really exciting” because now they can start to verify the results in model systems. “The things that we are doing now on patient samples would be difficult to do without these technologies,” she asserts. They simply could not have done it 10 years ago.
One of the premier multiomics conferences of the year is held annually in Australia. Last year, the conference was held in early December at the Brisbane Convention & Exhibition Centre. This infographic, created by Alice Edy (www.Livedrawing.com.au) summarizes a panel discussion from the meeting entitled, “Driving OMICS to the clinic.”
History repeating itself
Spatial is not only about opening up biology. In this way, it is also about unlocking the right patient specimens that are needed to do the work. Because spatial can be done on FFPE samples, there are decades of data—comprehensive historical data sets—sitting in tissue banks across the world that can be explored. For example, two surgeons working on very rare skull-based chordomas approached Bruno to collaborate because obtaining fresh tissue to study such a rare condition would take years. However, they could search through the tissue that had been put in FFPE, go back 20, 30, or more years, and identity the tissue blocks with skull-based chordomas. In doing this, they built a comprehensive cohort.
Kulasinghe agrees, noting that because the technology is FFPE compatible from day one, it means “We just have to walk across to the hospital, open up some fridges, or look on their bench, and they’ve got thousands of samples. We can profile all of that. We can go back to the seventies, eighties, nineties.”
This aspect of spatial research makes Bruno particularly happy because the FFPE samples came from patients who donated tissue. It’s nice to see, she says, that their tissue is getting utilized to try to find new targets and to try to understand biology better from a spatial context.
And Bruno is not alone in her enthusiasm for spatial biology. She says that there is a lot of movement in the spatial direction in the cancer immunotherapy field and notes that most talks that she goes to have at least one piece of spatial data. The majority of labs in the field are doing spatial, she asserts. And if they are not, she adds, “they are thinking about it.”
Several cores of a tissue microarray feature different areas of a human pancreatic tumor analyzed with spatial multi-omics: cancer glands (green), stroma (magenta), and immune cells (cyan). [Dennis Gong / Hwang lab]
The post Spatial Biology Reveals Past, Present, and Future Cancer Biology appeared first on GEN - Genetic Engineering and Biotechnology News.