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Spatial learning in microorganisms

Spatial learning in microorganisms



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Has there ever been an experiment performed that demonstrated a form of 'spatial memory' in a unicellular organism? I'm imagining something analogous to the classic 'rat in maze' experiments, but obviously on a much smaller scale. Possibly even something as simple as following a concentration gradient, but choosing to go either upstream or downstream based on some sort of prior reward in a similar circumstance.


To build on busukxuan's answer, there are a lot of single-celled organism responses that kind of resemble memory:

Slime mold uses an externalized spatial 'memory' to navigate in complex environments - essentially, a slime mold leaves behind a trail, which it then avoids, allowing it to avoid where it has traveled in the past.

If you apply a periodic stimulus to Physarum, it will anticipate the next stimulus: Popular, openly accessible summary Paywalled journal article.

If the amoeba Dictyostelium sees an increasing cAMP gradient, it will follow that gradient - and then be less sensitive to a reversal in the gradient: Cellular memory in eukaryotic chemotaxis

In fact, even bacteria have some memory - they can figure out whether or not they are going up a chemoattractant gradient by making a comparison with the signal they observed a few seconds ago: Temporal comparisons in bacterial chemotaxis


AI Tools Will Help Us Make the Most of Spatial Biology

April 27, 2020 | We have heard a lot about cellular and tissue spatial biology lately, and for good reason. Tissues are heterogeneous mixtures of cells this is particularly important in disease. Cells are also the foundational unit of life, and they are shaped by those cells proximal to them. Not surprisingly, the research field sought to survey cellular and tissue heterogeneity. The last decade saw massive adoption of single-cell sequencing RNA. This approach requires that we disaggregate cells, leading to accounting and characterization of cell populations, but at the same time losing their spatial context such as their proximity to other cells or where they fit with traditional approaches such as histopathology.

Enter Spatial Genomics

That’s why we have welcomed spatial transcriptomics and a focus on mapping RNA transcripts to their location within a tissue. After all, understanding disease pathology requires that we understand not only the underlying genomics and transcriptomics but also the relationship between cells and their relative locations within a tissue. Along for the ride: new avenues for the study of cancer, immunology, and neurology, among many others. What’s changed is the emergence of new tools for resolving spatial heterogeneity. SeqFISH and MerFISH are novel approaches for mapping gene expression within model systems. Multiple companies such as 10x Genomics and NanoString are now democratizing access to spatial transcriptomics, introducing new technologies and assays. They are opening up the study of disease pathology.

AI & Deep Learning: Adding to Our Vocabulary

New experimental methods often start with historical analysis approaches. Let's consider the first step in analysis: finding clusters of spots/cells with similar gene expression and then visualizing by reducing dimensions. In single-cell RNA-seq, the tSNE projection and color-coding clustering may be the signature plot, much like the Manhattan plot was to the GWAS.

Yet, critically, we haven’t leveraged the underlying histopathology image—the foundation of diagnosis and study of disease. We haven’t leveraged the fact that two spots are neighboring. What happens when we do? What happens at the edges between two clusters? What happens when cell types intersperse or infiltrate, such as in immune response? Are there image analysis methods we aren’t considering that have a high potential impact?

Indeed, concepts such as convolutional neural networks (CNNs) and generative adversarial networks (GANs) have been instrumental in classifying features and underlying hidden layers. We can go beyond the tSNE in spatial transcriptomics—and the question should be about viewing the latent space (the representation of the data that drives classifying regions and the discovery of hidden biology). These terms and concepts are foundational when it comes to artificial intelligence and need to be front and center in spatial transcriptomics analysis.

Of course, the use of AI and deep learning terminology is ubiquitous. Getting away from the hype, from self-driving cars to the successes in image recognition (ImageNet Challenge), some of the most remarkable achievements leverage spatial and imaging data. Data matters and one then asks: should we consider a single spatial transcriptomics section as one experimental data point, or is it 4,000 images and 4,000 transcriptomes?

In spatial biology, we can anticipate that applying AI to cell-by-cell maps of gene or protein activity will pave the way for significant discoveries that we might never achieve on our own. Incorporating spatially-resolved data could be the next leap forward in our understanding of biology. There will be questions we never even knew to ask that may be answered by combining spatial transcriptomics and spatial proteomics. But to get there, we need to come together and work as a community to build up the training data sets and other resources that will be essential for giving AI the best chance at success.

We have yet to truly make the most of the spatial biology data that has been generated. If we do not address this limitation, we will continue to miss out even as we produce more and more of this information.


Key Terms

  • mutualism: Any interaction between two species that benefits both typically involves the exchange of substances or services.
  • parasitism: Interaction between two organisms, in which one organism (the parasite) benefits and the other (the host) is harmed.
  • commensalism: Describes a relationship between two living organisms where one benefits and the other is not significantly harmed or helped.

Spatial learning and memory impaired after infection of non-neurotropic influenza virus in BALB/c male mice

During the influenza pandemic or seasonal influenza outbreak, influenza infection can cause acute influenza-associated encephalopathy/encephalitis (IAE), even death. Patients with severe IAE will also have severe neurological sequelae. Neurologic disorders have been demonstrated in the mice treated with peripheral influenza viruses infection, whether neurotropic or non-neurotropic viruses. However, previous studies focused on the acute phase of infection, and rarely paid attention to a longer range of observations. Therefore, the long-term effect of non-neurotropic virus infection on the host is not very clear. In this study, adult mice were infected with influenza virus H1N1/PR8. Then, spontaneous behavior, body weight, expression of cytokines in brain, spatial learning ability and spatial memory ability were observed, until the complete recovery period. The results showed that cytokines in the brain were highly expressed in the convalescent phase (14 day post inoculation, dpi), especially BDNF, IBA1, CX3CL1 and CD200 were still highly expressed in the recovery phase (28 dpi). Otherwise the emotional and spatial memory ability of mice were impacted in the convalescent phase (14 dpi) and the recovery phase (28 dpi). In brief, BALB/c mice infected with non-neurotropic influenza virus H1N1, the weight and motor ability decreased in acute stage. During the recovery period, the body weight and activity ability were completely restored, whereas the emotion disordered, and the ability of spatial learning and memory were impacted in the infected mice. This long-term behavior impact may be the lag injury caused by non-neurotropic influenza infection.

Keywords: Behavior Infection Influenza virus H1N1 Motor Spatial learning Spatial memory.

Copyright © 2021 Elsevier Inc. All rights reserved.

Conflict of interest statement

Declaration of competing interest The authors declare that they have no competing interests.


New Experiences Enhance Learning by Resetting Key Brain Circuit

A study of spatial learning in mice shows that exposure to new experiences dampens established representations in the brain’s hippocampus and prefrontal cortex, allowing the mice to learn new navigation strategies. The study, published in Nature, was supported by the National Institutes of Health.

“The ability to flexibly learn in new situations makes it possible to adapt to an ever-changing world,” noted Joshua A. Gordon, M.D., Ph.D., a senior author on the study and director of the National Institute of Mental Health, part of NIH. “Understanding the neural basis of this flexible learning in animals gives us insight into how this type of learning may become disrupted in humans.”

Dr. Gordon co-supervised the research project with Joseph A. Gogos, M.D., Ph.D. , and Alexander Z. Harris, M.D., Ph.D. , both of Columbia University, New York City.

Whenever we encounter new information, that information must be consolidated into a stable, lasting memory for us to recall it later. A key mechanism in this memory consolidation process is long-term potentiation, which is a persistent strengthening of neural connections based on recent patterns of activity. Although this strengthening of neural connections may be persistent, it can’t be permanent, or we wouldn’t be able to update memory representations to accommodate new information. In other words, our ability to remember new experiences and learn from them depends on information encoding that is both enduring and flexible.

To understand the specific neural mechanisms that make this plasticity possible, the research team, led by Alan J. Park, Ph.D. , of Columbia, examined spatial learning in mice.

Spatial learning depends on a key circuit between the ventral hippocampus (a structure located in the middle of the brain) and the medial prefrontal cortex (located just behind the forehead). Connectivity between these brain structures strengthens over the course of spatial learning. If the connectivity remains at maximum strength, however, it impairs later adaptation to new tasks and rules. The researchers hypothesized that exposure to a new experience may serve as an environmental trigger that dampens established hippocampal-prefrontal connectivity, enabling flexible spatial learning.

In the first task, the researchers trained mice to navigate a maze in a certain way to receive a reward. Some of the mice were then allowed to explore a space they hadn’t seen before, while others explored a familiar space. The mice then engaged in a second spatial task, which required that they switch to a new navigation strategy to get a reward.

As expected, all of the mice favored their original navigation strategy at first. But the mice that had explored a new space gradually overcame this bias and successfully learned the new navigation strategy about halfway through the 40-trial training session. When the researchers tested a subset of the mice on the first task again, they found that the novelty-exposed mice were able to switch back to the original strategy, indicating that they updated and chose their strategy according to the task demands.

Additional findings showed that the effects of novelty extended beyond new spaces: Encountering new mice before the second task also enhanced learning of the new reward strategy.

Changes in brain activity throughout training revealed the neuronal mechanisms that drive this novelty-enhanced learning. In rodents, there is a well-defined firing pattern in the hippocampus known as the theta wave, which is thought to play a central role in learning and memory. When Park and coauthors examined recordings from the ventral hippocampus, they found that the theta wave became stronger during exploration of the novel arena and the hour that followed the theta wave decreased as the mice became familiar with the arena over the next two days. The researchers found that novelty exposure also disrupted encoding of the original navigation strategy, reorganizing the firing pattern of individual neurons in the ventral hippocampus to bring them in sync with the theta wave.

At the same time, neurons in the medial prefrontal cortex showed decreased theta wave synchrony, and correlations between hippocampal activity and prefrontal activity weakened. These and other findings suggest that novelty exposure dampened the synaptic connections between the ventral hippocampus and medial prefrontal cortex, resetting the circuit to allow for subsequent strengthening of connectivity associated with learning.

By triggering this reset, novelty appears to facilitate strategy updating in response to the task’s specific reward structure. Machine learning analyses indicated that, following novelty exposure, ventral hippocampal neurons switched encoding from a strategy that predicted reward on the first task to one that predicted reward on the second task. The task-specific information was then relayed to the medial prefrontal neurons, which updated encoding accordingly.

On a chemical level, the neurotransmitter dopamine acts as a key mediator of this plasticity. Several experiments showed that activating dopamine D1-receptors in the ventral hippocampus led to novelty-like effects, including dampened hippocampal-prefrontal connectivity and enhanced learning. Blocking D1-receptors prevented these novelty-induced effects.

Together, these findings shed light on some of the brain mechanisms that play a role in flexible information encoding.

“Our study points to novelty as one way to trigger the circuitry reset that facilitates spatial learning in mice,” said Park. “The next step is to build on these findings and explore whether novelty plays a similar role in human memory and learning.”


Discussion

The results of the experiments reported here show that spatial learning promotes the survival of adult-born neurons that are relatively more mature, induces the death of cells that are more immature, and finally, stimulates proliferation of precursors. Blocking learning-induced cell death has shown an interdependency of these events and their involvement in learning. Thus, blocking learning-induced apoptosis inhibits cell survival and cell proliferation, and impairs memory abilities. These results indicate that spatial learning could involve a cascade of events similar to the selective stabilization process by which neuronal networks are sculpted by adding and removing specific populations of cells as a function of their maturity and functional relevance.

Learning-induced apoptosis is a very specific phenomenon. It is selectively induced by a specific phase of spatial learning, the late phase, during which performances stabilize. In contrast, apoptosis of newborn neurons does not seem to be influenced by stress and/or physical activity: (1) animals were habituated to the pool before training in order to diminish its stressful component (but see also [28]) (2) learning did not induce cell death during the first 3 d of training, during which physical activity is at its highest and (3) no modification in apoptosis was observed in Yoked animals exposed to the pool for 6 or 8 d. Furthermore, apoptosis was not influenced by hippocampus-independent learning in the water maze, such as cued learning of the platform position. Finally, the learning-induced increase in cell death is correlated with spatial abilities, i.e., rats with the highest number of dying cells have the best memory performances. This observation confirms that spatial learning, and not training, physical activity, or stress, increases apoptosis.

Spatial learning-induced apoptosis targets a population of young newborn neurons that are within a specific time window. Indeed, learning did not promote the death of newly born cells that were younger than 5 d or older than 13 d at the time of the sacrifice. In contrast, it promotes the death of cells that are 7 and 9 d old at the time of the sacrifice. These results are consistent with recent studies showing that the selective regulation of survival/death by input activity or the response to experience-specific modifications of adult-born neurons occur at a critical period during an immature stage [29,30].

We also showed that the administration of the antiapoptotic agent zVAD induces deficits in spatial memory. This is consistent with an earlier observation showing that administration of anti-caspases impaired spatial memory [31]. Here, we show that spatial memory impairment after caspase inhibition is due to the blockade of learning-induced neuronal apoptosis. The implication of apoptosis in learning seems quite specific. Thus, when the caspase inhibitor zVAD was infused during learning, but outside the window of learning-induced apoptosis, no effects on spatial learning were observed. In addition, zVAD injections per se did not alter the neurophysiological responsiveness of the hippocampus in a non-neurogenic area. Altogether, these data show that it is the learning-induced apoptosis in the DG that is involved in spatial memory.

The relationship described here between learning-induced increases in survival, apoptosis, and proliferation of newborn cells provides a three-step picture of the relationship between neurogenesis and spatial learning (Figure 8). First, acquisition of the task induces an increase in the survival of newborn neurons generated 1 wk before the task and that consequently have reached an intermediate level of maturity. Second, once the task starts to be mastered, learning induces apoptosis of newborn neurons that are a few days younger than those for which survival has been increased. Third, learning-induced apoptosis is followed by an increase in cell proliferation that provides the hippocampus with a new pool of young neurons [16,20].

The early phase of learning in the water maze, characterized by a fast improvement in performance, increases the survival of newborn neurons that were produced 1 wk before exposure to the task. Once the task has begun to be mastered, during the late phase of learning, learning induces apoptosis of newborn neurons that are a few days younger than the ones for which survival has been increased. This wave of cell death is followed by an increase in cell proliferation. Learning-induced apoptosis plays a pivotal role in this intermingled chain of events since it is necessary for the survival of the older, newly born neurons, but also for the increase in cell proliferation occurring during the late phase of learning.

This homeostatic regulation of neurogenesis by learning is consistent with the selective stabilization theory according to which regressive events will stabilize a particular set of contacts among many others, thereby sculpting the precise circuits that are crucial for a given function [32]. It has been estimated that during development, after an initial proliferating phase during which a large number of newborn neurons are produced, at least half of the initial neuronal population is eliminated by apoptosis [33]. This neuronal elimination serves several functions, among which is the regulation of target innervation. Indeed, neural function depends upon a precise quantitative relationship between neurons: each axon innervates an appropriate number of target cells and each target cell is innervated by an appropriate number of axons. The decision for survival or death during development is governed by afferences and/or efferences [34,35].

In the case of hippocampal adult-born neurons, it might be hypothesized that those cells that are successfully connected, both in terms of efferent output and afferent input, are the ones that can be rescued by the stimuli generated in the course of learning. In favor of this hypothesis, it has been shown that enhanced synaptic activity enhances cell survival [36]. In contrast, apoptosis could constitute a trimming mechanism that suppresses more-immature neurons that have not been selected by learning. Their suppression could favor the integration of older cells that have been stabilized by activity-dependent stimuli generated in the course of learning. These regressive events could also, by clearing the network of nonspecific noise due to superfluous new neurons, enhance the signal-to-noise ratio. Supporting this idea, an improvement in the signal-to-noise ratio of motor cortex cells during motor skill learning has been linked to a practice-related improvement in behavioral performance [37].

The precise mechanisms by which learning promotes the survival or apoptosis of immature newborn neurons are currently unknown. However, analysis of the developmental pattern of newborn neurons provides a certain number of putative explanations. Newborn neurons follow a precise maturation of neuronal connectivity and function that requires about 1 mo. They extend their dendritic tree at variable times after mitosis, and by 3 wk, their dendritic arborization resembles that of mature neurons [12,38]. In addition, as soon as 10 d after birth, newly born cells extend axons into the CA3 subfield of the hippocampus [9,12]. After the first week of maturation, they also receive depolarizing GABA inputs [39–44]. Toward the end of the second week, GABA inputs become progressively hyperpolarizing, and the adult-born neurons begin receiving functional glutamatergic depolarizing afferents [11,29,40,41], a process that occurs in parallel with the formation of dendritic spines.

On the basis of this developmental pattern, it seems likely that newborn neurons that are younger than 5 d are not influenced by learning because they lack afferent inputs and have not yet reached projection territories. Neurons that are in the window during which learning induces apoptosis should have received functional depolarizing GABA inputs, although their dendritic tree would still be poorly developed, and these newborn cells should not have reached their target area. Thus, in response to learning-driven depolarization, this imbalance between input and output activity may impede the survival of these cells and lead to their death. Older neurons that survive as a consequence of learning have a more developed dendritic tree that receives depolarizing GABA inputs and starts to have some glutamatergic ones. Furthermore, these newborn neurons have also reached the CA3 subfield. It is then likely that these newborn neurons that have reached a higher stage of maturation, with balanced input/output connections, can benefit from the pro-differentiating effects of the activation by GABA and glutamatergic inputs by learning [45].

Whether or not the newly born neurons whose survival is increased by learning participate in the memory process remains an open question. Although newborn neurons need several weeks before reaching full functional maturation (for review see [45]), they may participate in the processing of memory at immature stages due to their high plasticity level [46,47]. These peculiar properties may explain why immature neurons are responsive to life experiences within a critical time period [30]. Surviving newly born neurons having similar birthdates may induce the formation of functional neuronal assemblies in the CA3 subfield, and the resulting new circuits may store memory traces [48]. Alternatively, addition of these new circuits could encode the time of new memories [49]. However, recent studies have shown that although spatial behaviors preferentially activated new neurons in the dentate gyrus [50,51], this recruitment did not occur until they were at least 4 wk old [50]. Thus, if the neurons whose survival is increased by learning are not recruited by the ongoing behavior, they may support a subsequent learning experience. Additional investigations are required to determine whether adult-born neurons exert a functional role in memory formation before or after reaching complete maturity

Our observations also show that spatial learning is not onlybased upon the addition of new neurons or synaptic connections, but also upon regressive events that culminate in the removal of neurons from the cellular network of the adult central nervous system. An interplay between the addition and removal of adult-born neurons as a mechanism that sustains learned behavior has already been reported for adult songbirds [52,53]. Interestingly, our results show that relationships between learning, neurogenesis, and apoptosis are quite different in mammals and in birds. In the adult male canary, for example, neurogenesis is triggered by a wave of apoptosis of adult neurons within the higher vocal center [54]. The current interpretation of these processes is that the death of older neurons and their substitution by new ones allows the canaries to forget the song repertoire learned the previous year and replace it with a new one [52]. In mammals, during the encoding of new information, it is the apoptosis of younger neurons that facilitates the survival of older ones. As a consequence, whereas apoptosis in birds subserves the substitution of older learning for new, in mammals, apoptosis seems to allow the efficient adding up of new information.

In conclusion, our results show that spatial learning involves a mechanism very similar to the selective stabilization process observed during brain development, in which the production of new neurons is followed by an active selection of some and removal of others. As a consequence, spatial learning is not only based upon additive processes, ranging from synaptic strengthening to the formation of new synapses and new neurons, but also upon regressive phenomena, such as neuronal apoptosis. This epigenetic specification of networks by removal of neurons in the adult brain provides evidence of an additional mechanism contributing to the establishment of memory formation in mammals.


Novel insights about your tissue. Visualized.

The relationship between cells and their relative locations within tissue is critical to understanding normal development and disease pathology. Spatial transcriptomics is a groundbreaking molecular profiling method that allows scientists to measure all the gene activity in a tissue sample and map where the activity is occurring. Already this technology is leading to new discoveries that are proving instrumental in helping scientists gain a better understanding of biological processes and disease.

Fuel your spatial discoveries with spatial capture technology

Spatial capture technology powers the Visium Spatial platform through the use of spatially barcoded mRNA-binding oligonucleotides. There are two methods for how mRNA molecules get a spatial barcode.

In fresh frozen tissues, the tissue is fixed and permeabilized to release RNA which binds to adjacent capture probes, allowing for the capture of gene expression information. cDNA is then synthesized from captured RNA and sequencing libraries prepared.

In FFPE tissues, the tissue is permeabilized to release ligated probe pairs that bind to adjacent capture probes on the slide, allowing for the capture of gene expression information. Pairs of probes specific to each gene in the protein-coding transcriptome are hybridized to their gene target and then ligated to one another. The probe pairs are extended to incorporate complements of the spatial barcodes and sequencing libraries prepared.

Examine gene expression in the context of the tissue microenvironment

Innovation in spatial transcriptomics methodologies is enabling scientists to get a holistic understanding of cells in their morphological context. In this presentation, you will hear first-hand from 10x Genomics scientists about groundbreaking improvements to the technology and exciting applications showcased by users.


Sexually dimorphic spatial learning in meadow voles Microtus pennsylvanicus and deer mice Peromyscus maniculatus.

L A Galea, M Kavaliers, K P Ossenkopp Sexually dimorphic spatial learning in meadow voles Microtus pennsylvanicus and deer mice Peromyscus maniculatus.. J Exp Biol 1 January 1996 199 (1): 195–200. doi: https://doi.org/10.1242/jeb.199.1.195

A number of studies examining developmental, neural and hormonal aspects of sexually dimorphic spatial learning (Morris water-maze) in meadow voles (Microtus pennsylvanicus) and deer mice (Peromyscus maniculatus) are described. We found that, in adult deer mice, female spatial performance decreased during the breeding season relative to the non-breeding season, whereas the reverse pattern was observed in male performance. There was a sex difference favouring males in spatial learning during the breeding season, but not during the non-breeding season. In adult meadow voles, females with low levels of oestradiol and males performed better in the water-maze than females with high levels of oestradiol. Postweaning voles (20 and 25 days after birth) acquired the water-maze task more quickly than preweaning voles (day 10). No sex difference in water-maze performance was evident at any of these juvenile ages. When these same voles were tested again as adults to investigate retention and re-acquisition of the water-maze, both males and females from male-biased litters re-acquired the task better than males and females from female-biased litters. Together, the results of these studies indicate that sexually dimorphic spatial ability is dependent on the organization (in utero) and activational effects of gonadal hormones. These studies provide the first demonstration of the influence of natural changes in reproductive status on spatial learning of deer mice and meadow voles. The results also demonstrate that spatial performance of males and females is differentially affected by changes in reproductive status and that group differences in the laboratory are associated with group differences in space utilization in the wild. These findings help to clarify previous apparently contradictory findings about sex differences in spatial ability.

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Advancing Science: A Spatial Biology Conference

Spatial technologies are enabling new discoveries that advance our understanding of a range of biology, including viral host immune response and the tumor immune microenvironment. This virtual conference brings together research professionals, scientists, and clinicians from around the world to learn about and discuss recent discoveries in spatial biology.

Scientific tracks include:

This free online event allows you to participate at the most convenient time for your location:

Europe: 9:00 AM-3:30 PM British Summer Time / 10:00 AM-4:30 PM Central European Time. September 15.

Americas: 11:00 AM-5:30 PM Eastern Time. September 15.

Asia Pacific: 11:00 AM- 5:30 PM Beijing Standard Time. September 16.

Plenary: Peter Sorger, PhD

Alexander Aivazidis, PhD

Omer Bayraktar, PhD

Alain Borczuk, MD

Christina Curtis, PhD

Z. Gordon Jiang, MD, PhD

Christopher E. Mason, PhD

Muh-Hwa Yang, MD, PhD

Karin Pelka, PhD

Laura Perin, PhD

Robert E. Schwartz, PhD

Sargis Sedrakyan, PhD

Åsa Segerstolpe, PhD

David T. Ting, MD

Ioannis Vlachos, PhD

Advancing Science: A Spatial Biology Conference

Exhibits Hall/Poster Viewing/Networking/Software Demonstrations

Spatial Analysis of Immune Micro-Environments In Cancer and COVID-19

Otto Krayer Professor of Systems Biology at Harvard Medical School, Head of the Harvard Program in Therapeutic Sciences (HiTS) and Director of its Laboratory of Systems Pharmacology Harvard Medical School

Transcriptomics of SARS-CoV2 Induced Lung Injury: A Spatial Transcriptomics Approach

Z. Gordon Jiang, MD, PhD, Assistant Professor of Medicine, Beth Israel Deaconess Medical Center

COVID-19 Respiratory Distress Syndrome: Dissecting the Root Cause of Its Severity

Robert Schwartz, MD, PhD, Assistant Professor of Medicine, Weill Cornell Medicine and Alain Borczuk, MD, Professor, Pathology and Laboratory Medicine, Weill Cornell Medicine

Spatial Insights Of Lung Pathology in COVID-19 Autopsies

Åsa Segerstolpe, PhD, Research Scientist, Broad Institute of MIT and Harvard

Temporal and Spatial Heterogeneity of Host Response to SARS-CoV-2 Pulmonary Infection

David Ting, MD, Associate Clinical Director for Innovation, MGH Cancer Center Assistant Professor of Medicine, Harvard Medical School

SPATIAL GENOMICS RESEARCH

Mechanisms of Alport Syndrome Pathogenesis

Laura Perin, PhD, Investigator, Research Urology, Assistant Professor of Research Surgery, Children&rsquos Hospital Los Angeles, The Saban Research Institute and Sargis Sedrakyan, PhD, Investigator, Research Urology Assistant Professor of Research Surgery, Children&rsquos Hospital Los Angeles, The Saban Research Institute

Cellular Elements and Spatially-Organized Immune Hubs in Colorectal Cancer

Karin Pelka, PhD, Post-Doctoral Fellow, Broad Institute of MIT and Harvard and Massachusetts General Hospital (MGH)

Spatial Evolution of Epithelial-Mesenchymal Program of Head and Neck Cancer

Muh-Hwa Yang, MD, PhD, Investigator, Urology Research

Assistant Professor, National Yang Ming University

SPATIAL GENOMICS DATA ANALYSIS

Shotgun Transcriptome and Spatial Profiling of SARS-CoV-2

Christopher E. Mason, PhD, Associate Professor Director, WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine

Spatial Characterization of The Tumor-Immune Microenvironment Through Therapy in Breast Cancer

Christina Curtis, PhD, Associate Professor, Medicine - Oncology

Associate Professor, Genetics, Stanford University

Whole Transcriptome in Situ Cell Atlas Reveals The Cellular Composition of The Developing Human Brain

Omer Bayraktar, PhD, Appointed Cellular Genetics Group Leader, Wellcome Sanger Institute and Alexander Aivazidis, PhD, student, Wellcome Sanger Institute

Spatial Transcriptomics and Single-Cell Integration for In-Depth Localized Tissue Interrogation

Ioannis Vlachos, PhD, Assistant Professor of Pathology, Beth Israel Deaconess Medical Center

Exhibits Hall/Poster Viewing/Networking/Software Demonstrations

Advancing Science: A Spatial Biology Conference

Start Time &ndash 9:00 am BST / 10:00 am CET

British Standard Time (BST)

Central European Time (CET)

Exhibits Hall/Poster Viewing/Networking/Software Demonstrations

Spatial Analysis of Immune Micro-Environments In Cancer and COVID-19

Otto Krayer Professor of Systems Biology at Harvard Medical School, Head of the Harvard Program in Therapeutic Sciences (HiTS) and Director of its Laboratory of Systems Pharmacology Harvard Medical School

Transcriptomics of SARS-CoV2 Induced Lung Injury: A Spatial Transcriptomics Approach

Z. Gordon Jiang, MD, PhD, Assistant Professor of Medicine, Beth Israel Deaconess Medical Center

COVID-19 Respiratory Distress Syndrome: Dissecting the Root Cause of Its Severity

Robert Schwartz, MD, PhD, Assistant Professor of Medicine, Weill Cornell Medicine and Alain Borczuk, MD, Professor, Pathology and Laboratory Medicine, Weill Cornell Medicine

Spatial Insights Of Lung Pathology in COVID-19 Autopsies

Åsa Segerstolpe, PhD, Research Scientist, Broad Institute of MIT and Harvard

Temporal and Spatial Heterogeneity of Host Response to SARS-CoV-2 Pulmonary Infection

David Ting, MD, Associate Clinical Director for Innovation, MGH Cancer Center Assistant Professor of Medicine, Harvard Medical School

SPATIAL GENOMICS RESEARCH

Mechanisms of Alport Syndrome Pathogenesis

Laura Perin, PhD, Investigator, Research Urology, Assistant Professor of Research Surgery, Children&rsquos Hospital Los Angeles, The Saban Research Institute and Sargis Sedrakyan, PhD, Investigator, Research Urology Assistant Professor of Research Surgery, Children&rsquos Hospital Los Angeles, The Saban Research Institute

Cellular Elements and Spatially-Organized Immune Hubs in Colorectal Cancer

Karin Pelka, PhD, Post-Doctoral Fellow, Broad Institute of MIT and Harvard and Massachusetts General Hospital (MGH)

Spatial Evolution of Epithelial-Mesenchymal Program of Head and Neck Cancer

Muh-Hwa Yang , MD, PhD, Investigator, Urology Research

Assistant Professor, National Yang Ming University

SPATIAL GENOMICS DATA ANALYSIS

Shotgun Transcriptome and Spatial Profiling of SARS-CoV-2

Christopher E. Mason, PhD, Associate Professor Director, WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine

Spatial Characterization of The Tumor-Immune Microenvironment Through Therapy in Breast Cancer

Christina Curtis, PhD, Associate Professor, Medicine - Oncology

Associate Professor, Genetics, Stanford University

Whole Transcriptome in Situ Cell Atlas Reveals The Cellular Composition of The Developing Human Brain

Omer Bayraktar, PhD, Appointed Cellular Genetics Group Leader, Wellcome Sanger Institute and Alexander Aivazidis, PhD, student, Wellcome Sanger Institute

Spatial Transcriptomics and Single-Cell Integration for In-Depth Localized Tissue Interrogation

Ioannis Vlachos, PhD, Assistant Professor of Pathology, Beth Israel Deaconess Medical Center

Exhibits Hall/Poster Viewing/Networking/Software Demonstrations

Advancing Science: A Spatial Biology Conference

Start Time &ndash Asia-Pacific: 10:30 am (Beijing Standard Time)

Exhibits Hall/Poster Viewing/Networking/Software Demonstrations

Spatial Analysis of Immune Micro-Environments In Cancer and COVID-19

Otto Krayer Professor of Systems Biology at Harvard Medical School, Head of the Harvard Program in Therapeutic Sciences (HiTS) and Director of its Laboratory of Systems Pharmacology Harvard Medical School

Transcriptomics of SARS-CoV2 Induced Lung Injury: A Spatial Transcriptomics Approach

Z. Gordon Jiang, MD, PhD, Assistant Professor of Medicine, Beth Israel Deaconess Medical Center

COVID-19 Respiratory Distress Syndrome: Dissecting the Root Cause of Its Severity

Robert Schwartz, MD, PhD, Assistant Professor of Medicine, Weill Cornell Medicine and Alain Borczuk, MD, Professor, Pathology and Laboratory Medicine, Weill Cornell Medicine

Spatial Insights Of Lung Pathology in COVID-19 Autopsies

Åsa Segerstolpe, PhD, Research Scientist, Broad Institute of MIT and Harvard

Temporal and Spatial Heterogeneity of Host Response to SARS-CoV-2 Pulmonary Infection

David Ting, MD, Associate Clinical Director for Innovation, MGH Cancer Center Assistant Professor of Medicine, Harvard Medical School

SPATIAL GENOMICS RESEARCH

Mechanisms of Alport Syndrome Pathogenesis

Laura Perin, PhD, Investigator, Research Urology, Assistant Professor of Research Surgery, Children&rsquos Hospital Los Angeles, The Saban Research Institute Sargis Sedrakyan, PhD, Investigator, Research Urology Assistant Professor of Research Surgery, Children&rsquos Hospital Los Angeles, The Saban Research Institute

Cellular Elements and Spatially-Organized Immune Hubs in Colorectal Cancer

Karin Pelka, PhD, Post-Doctoral Fellow, Broad Institute of MIT and Harvard and Massachusetts General Hospital (MGH)

Spatial Evolution of Epithelial-Mesenchymal Program of Head and Neck Cancer

Muh-Hwa Yang , MD, PhD, Investigator, Urology Research

Assistant Professor, National Yang Ming University

SPATIAL GENOMICS DATA ANALYSIS

Shotgun Transcriptome and Spatial Profiling of SARS-CoV-2

Christopher E. Mason, PhD, Associate Professor Director, WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine

Spatial Characterization of The Tumor-Immune Microenvironment Through Therapy in Breast Cancer

Christina Curtis, PhD, Associate Professor, Medicine - Oncology

Associate Professor, Genetics, Stanford University

Whole Transcriptome in Situ Cell Atlas Reveals The Cellular Composition of The Developing Human Brain

Omer Bayraktar, PhD, Appointed Cellular Genetics Group Leader, Wellcome Sanger Institute and Alexander Aivazidis, PhD, student, Wellcome Sanger Institute

Spatial Transcriptomics and Single-Cell Integration for In-Depth Localized Tissue Interrogation

Ioannis Vlachos, PhD, Assistant Professor of Pathology, Beth Israel Deaconess Medical Center

Exhibits Hall/Poster Viewing/Networking/Software Demonstrations

For information on becoming a sponsor or exhibitor, please click here.

As an innovator in reagents and tools, our purpose is to serve life science researchers globally to achieve their mission, faster. Providing the research and clinical communities with tools and scientific support, we offer highly validated biological binders and assays to address . important targets in critical biological pathways.

Leica Biosystems is a global leader in Anatomical Pathology solutions and automation, striving to advance cancer diagnostics to improve patients' lives. Leica Biosystems provides Pathologists, Histologists, and Researchers a comprehensive range of products for each step in the . Pathology process. From specimen preparation and staining to imaging and reporting, our solutions help increase workflow efficiencies meaning patients receive their results sooner. Slide after slide, Leica's easy-to-use and consistently reliable offerings provide you with the diagnostic clarity you need to give patients the results that they can trust.

For more than 20 years, Illumina has aspired to improve human health by unlocking the power of the genome. Now, through innovation and collaboration with pathologists, hematologists, and oncologists, we are enabling genomic breakthroughs in immunotherapy, biomarker discovery, and . therapy selection. As we move towards precision medicine, we have only just begun to discover the true impact of genomics. The opportunity to transform tumor profiling with innovative assays that enable comprehensive genomic profiling, new companion diagnostic development, and liquid biopsy inspires us to push boundaries and drive innovation.

Advance Cell Diagnostics - a Bio-Techne brand, based in Silicon Valley, ACD's products and pharma assay services are based on its proprietary RNAscope™ technology, the first multiplex fluorescent and chromogenic in situ hybridization platform, capable of detecting and . quantifying single molecules of RNA in situ. ACD has two product lines, namely RNAscope and BaseScope™ consisting of assay reagent kits and 15,000+ off-the-shelf probes in addition to a Pharma Assay Services business which allow customers to run assays for their unique targets rapidly. Since its first launch in 2011, the technology boasts 1200+ citations, a new publication each day now, for single, duplex and multiplex RNA analysis. ACD's products and services are based on its proprietary RNAscopeTechnology, the first multiplex fluorescent and chromogenic in situ hybridization platform capable of detecting and quantifying RNA biomarkers in situ at single molecule sensitivity. In addition to its ongoing efforts to develop proprietary diagnostic tests for cancer management, ACD also establishes partnerships with pharmaceutical and biotechnology companies to validate biomarkers for targeted therapeutic development

NANOSTRING GAMIFICATION

Announcing! All registered attendees will have the opportunity to participate in the NanoString Gamification activity. Upon entry into the virtual conference, attendees will earn points and compete for a chance to win a pair of Apple Air Pod Pro's and other awesome prizes! Collect points by attending session presentations, joining a live GeoMx Interactive Data Experience, engaging in the Virtual Lab, exploring the Exhibit Hall, interacting with booth reps, downloading posters and participating in social. Winners will be announced Friday, September 18th at 5pm ET!

Terms and Conditions for the NanoString Gamification Activity Prizes

in connection with

&ldquo Advancing Science - A Spatial Biology Conference&rdquo

Registered attendees of the NanoString Technologies Advancing Science - A Spatial Biology Conference who have participated in the Gamification Activity will have the opportunity to win the following prizes a pair of Apple AirPods Pro (three winners), NanoString branded face masks and wine glass (5 winners per region), and NanoString branded face masks and socks (25 winners per region).


Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment.

Keywords: artificial intelligence bioinformatics computer vision deep learning digital pathology immuno-oncology lymphocytes machine learning tumor microenvironment tumor-infiltrating lymphocytes.

Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Conflict of interest statement

Michael Seiler, Peter G. Smith, Ping Zhu, Silvia Buonamici, and Lihua Yu are employees of H3 Biomedicine, Inc. Parts of this work are the subject of a patent application: WO2017040526 titled “Splice variants associated with neo-morphic sf3b1 mutants.” Shouyoung Peng, Anant A. Agrawal, James Palacino, and Teng Teng are employees of H3 Biomedicine, Inc. Andrew D. Cherniack, Ashton C. Berger, and Galen F. Gao receive research support from Bayer Pharmaceuticals. Gordon B. Mills serves on the External Scientific Review Board of Astrazeneca. Anil Sood is on the Scientific Advisory Board for Kiyatec and is a shareholder in BioPath. Jonathan S. Serody receives funding from Merck, Inc. Kyle R. Covington is an employee of Castle Biosciences, Inc. Preethi H. Gunaratne is founder, CSO, and shareholder of NextmiRNA Therapeutics. Christina Yau is a part-time employee/consultant at NantOmics. Franz X. Schaub is an employee and shareholder of SEngine Precision Medicine, Inc. Carla Grandori is an employee, founder, and shareholder of SEngine Precision Medicine, Inc. Robert N. Eisenman is a member of the Scientific Advisory Boards and shareholder of Shenogen Pharma and Kronos Bio. Daniel J. Weisenberger is a consultant for Zymo Research Corporation. Joshua M. Stuart is the founder of Five3 Genomics and shareholder of NantOmics. Marc T. Goodman receives research support from Merck, Inc. Andrew J. Gentles is a consultant for Cibermed. Charles M. Perou is an equity stock holder, consultant, and Board of Directors member of BioClassifier and GeneCentric Diagnostics and is also listed as an inventor on patent applications on the Breast PAM50 and Lung Cancer Subtyping assays. Matthew Meyerson receives research support from Bayer Pharmaceuticals is an equity holder in, consultant for, and Scientific Advisory Board chair for OrigiMed and is an inventor of a patent for EGFR mutation diagnosis in lung cancer, licensed to LabCorp. Eduard Porta-Pardo is an inventor of a patent for domainXplorer. Han Liang is a shareholder and scientific advisor of Precision Scientific and Eagle Nebula. Da Yang is an inventor on a pending patent application describing the use of antisense oligonucleotides against specific lncRNA sequence as diagnostic and therapeutic tools. Yonghong Xiao was an employee and shareholder of TESARO, Inc. Bin Feng is an employee and shareholder of TESARO, Inc. Carter Van Waes received research funding for the study of IAP inhibitor ASTX660 through a Cooperative Agreement between NIDCD, NIH, and Astex Pharmaceuticals. Raunaq Malhotra is an employee and shareholder of Seven Bridges, Inc. Peter W. Laird serves on the Scientific Advisory Board for AnchorDx. Joel Tepper is a consultant at EMD Serono. Kenneth Wang serves on the Advisory Board for Boston Scientific, Microtech, and Olympus. Andrea Califano is a founder, shareholder, and advisory board member of DarwinHealth, Inc. and a shareholder and advisory board member of Tempus, Inc. Toni K. Choueiri serves as needed on advisory boards for Bristol-Myers Squibb, Merck, and Roche. Lawrence Kwong receives research support from Array BioPharma. Sharon E. Plon is a member of the Scientific Advisory Board for Baylor Genetics Laboratory. Beth Y. Karlan serves on the Advisory Board of Invitae.


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