Monday, October 26, 2020
3:30 - 4:30 PM Pacific Time (PT)
Please view each participant's poster and visit their individual Zoom Links to discuss their poster!
3:30 - 4:30 PM Pacific Time (PT)
Please view each participant's poster and visit their individual Zoom Links to discuss their poster!
Participants
Katy Börner, PhD, Indiana University
Title: The Human Body Atlas: High-Resolution, Functional Mapping of Voxel, Vector, and Meta Datasets
Abstract: The ultimate goal of the HIVE Mapping effort is to develop a common coordinate framework (CCF) for the healthy human body that supports the cataloguing of different types of individual cells within anatomical structures, understanding the function and relationships between those cell types, and modeling their individual and collective function. In order to exploit human and machine intelligence, different visual interfaces are implemented in support of CCF data generation, exploration, and communication. The CCF and the interactive data visualizations are multilevel and multi-scale. They support the registration and exploration of diverse types of data—from single cell to whole body. In the initial two years, MC-IU ran user needs analyses with stakeholders, compiled an initial CCF ontology and associated 3D object library, developed novel CCF registration and exploration UIs, and explored using the vasculature as a coordinate system to map all cells in the human body, see https://hubmapconsortium.github.io/ccf.
View/Download Poster Here
Zoom Link
Passcode: 969619
Katy Börner, PhD, Indiana University
Title: The Human Body Atlas: High-Resolution, Functional Mapping of Voxel, Vector, and Meta Datasets
Abstract: The ultimate goal of the HIVE Mapping effort is to develop a common coordinate framework (CCF) for the healthy human body that supports the cataloguing of different types of individual cells within anatomical structures, understanding the function and relationships between those cell types, and modeling their individual and collective function. In order to exploit human and machine intelligence, different visual interfaces are implemented in support of CCF data generation, exploration, and communication. The CCF and the interactive data visualizations are multilevel and multi-scale. They support the registration and exploration of diverse types of data—from single cell to whole body. In the initial two years, MC-IU ran user needs analyses with stakeholders, compiled an initial CCF ontology and associated 3D object library, developed novel CCF registration and exploration UIs, and explored using the vasculature as a coordinate system to map all cells in the human body, see https://hubmapconsortium.github.io/ccf.
View/Download Poster Here
Zoom Link
Passcode: 969619
Zach Hettinger, PhD Student, University of Kentucky
Title: Single cell transcriptomics identifies age-related transcriptional heterogeneity within fibroadipose progenitor cells that can be reprogrammed by mechanical loading in old rats recovering from disuse atrophy
Abstract: Older adults suffer from an inability to fully recover muscle mass following disuse atrophy, predisposing older adults to age-related functional decline. Extracellular matrix (ECM) content is elevated with age and during the recovery period following disuse atrophy, which likely blunts the responsiveness of muscle to mechanical stimuli important for muscle regrowth. Understanding how age affects ECM deposition and if this process can be remodeled may give insight into therapeutic strategies for enhancing muscle regrowth in older adults.
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Artur Lazarian, PhD, Columbia University Irving Medical Center
Title: Lipidomic profiling of healthy and Alzheimer’s disease mouse brains by using DESI Imaging Mass Spectrometry (IMS): regional lipid dyshomeostasis in Alzheimer’s disease.
Abstract: Alzheimer’s disease (AD) is one of the biggest health challenges that we face worldwide, which causes millions of deaths annually and costs reach many billions. Yet, the cause of the disease is not fully understood and therefore no effective treatment is available up to date. Lipidomic data from autopsy brain, human plasma and animal models highlight severe lipid dyshomeostasis in AD. The importance of lipid metabolism in AD is supported by GWAS studies which have identified multiple lipid modifying enzymes and interacting proteins. Thus, the regional disruption of lipid metabolism and homeostasis are likely to play a crucial role in the development of Alzheimer’s pathology. It is clear that specific pathways in lipid metabolism underlie AD disease mechanisms leading to behavioral impairment. Specifically, previous studies have shown that the loss of polyunsaturated fatty acids among multiple phospholipid classes is common in AD affected human brain and mouse models. Therefore, it is of high importance to identify the highly specific and precise regionally dysregulated lipid species in both healthy and AD affected human and mouse brains. By applying Imaging Mass Spectrometry (IMS) using Desorption Electrospray Ionization (DESI) Synapt G2-Si (Waters), we have developed a workflow for high-content regionally specific lipidomics profiling for healthy and AD affected brains. We have shown regional dysregulation of specific lipid species in different brain areas including hippocampus, a brain region known to be vulnerable in early in AD. As supported by our preliminary data, the IMS will allow us to detect broad range of versatile lipid species by applying targeted and untargeted analysis, Ion Mobility Separation, and DESI Imaging. Such analysis will allow insight into disease susceptibility, progression and perhaps allow identification of novel therapeutic strategies.
View/Download Poster Here
Zoom Link
Passcode: 20201026
Title: Lipidomic profiling of healthy and Alzheimer’s disease mouse brains by using DESI Imaging Mass Spectrometry (IMS): regional lipid dyshomeostasis in Alzheimer’s disease.
Abstract: Alzheimer’s disease (AD) is one of the biggest health challenges that we face worldwide, which causes millions of deaths annually and costs reach many billions. Yet, the cause of the disease is not fully understood and therefore no effective treatment is available up to date. Lipidomic data from autopsy brain, human plasma and animal models highlight severe lipid dyshomeostasis in AD. The importance of lipid metabolism in AD is supported by GWAS studies which have identified multiple lipid modifying enzymes and interacting proteins. Thus, the regional disruption of lipid metabolism and homeostasis are likely to play a crucial role in the development of Alzheimer’s pathology. It is clear that specific pathways in lipid metabolism underlie AD disease mechanisms leading to behavioral impairment. Specifically, previous studies have shown that the loss of polyunsaturated fatty acids among multiple phospholipid classes is common in AD affected human brain and mouse models. Therefore, it is of high importance to identify the highly specific and precise regionally dysregulated lipid species in both healthy and AD affected human and mouse brains. By applying Imaging Mass Spectrometry (IMS) using Desorption Electrospray Ionization (DESI) Synapt G2-Si (Waters), we have developed a workflow for high-content regionally specific lipidomics profiling for healthy and AD affected brains. We have shown regional dysregulation of specific lipid species in different brain areas including hippocampus, a brain region known to be vulnerable in early in AD. As supported by our preliminary data, the IMS will allow us to detect broad range of versatile lipid species by applying targeted and untargeted analysis, Ion Mobility Separation, and DESI Imaging. Such analysis will allow insight into disease susceptibility, progression and perhaps allow identification of novel therapeutic strategies.
View/Download Poster Here
Zoom Link
Passcode: 20201026
Xinbei Li, PhD Student, Johns Hopkins University School of Medicine
Title: Understanding the post-transcriptional mechanism of neuropathic pain with single molecule detection
Abstract: Neuropathic pain is a chronic condition which can arise following damage to the somatosensory system. The molecular mechanisms of neuropathic pain remain incompletely understood but require enduring alterations in protein synthesis affecting neuronal signaling and excitability. We investigate the roles of non-coding RNA regulatory pathways in impacting hyperalgesia and determining the mRNA complement recruited during this protein synthesis response in neuropathic pain. Nerve injury alters the expression of many miRNAs, including the highly conserved Let-7 family miRNAs, which repress pro-growth mRNAs and are implicated in axon growth and brain circuit formation. The Lin28 RNA binding protein can prevent maturation of let-7 precursor RNAs; consequently, increased Lin28 signaling promotes pro-growth gene expression. The regulation and potential roles role of Lin28/Let-7 pathway in neuropathic pain remain unexplored. Using complementary mouse models of neuropathic pain, we evaluate molecular mechanisms underlying pain using single molecule detection and genetic manipulation. Sensitive RNA imaging assays, RNAScope in situ hybridization (ISH), amplify single RNA target signals in fixed tissues to allow mapping of the spatiotemporal patterns and cell type specificity of changes in non-coding RNA regulatory pathways. Digital PCR is used to provide sensitive and quantitative validation.
View/Download Poster Here
Zoom Link
Passcode: 886911
Title: Understanding the post-transcriptional mechanism of neuropathic pain with single molecule detection
Abstract: Neuropathic pain is a chronic condition which can arise following damage to the somatosensory system. The molecular mechanisms of neuropathic pain remain incompletely understood but require enduring alterations in protein synthesis affecting neuronal signaling and excitability. We investigate the roles of non-coding RNA regulatory pathways in impacting hyperalgesia and determining the mRNA complement recruited during this protein synthesis response in neuropathic pain. Nerve injury alters the expression of many miRNAs, including the highly conserved Let-7 family miRNAs, which repress pro-growth mRNAs and are implicated in axon growth and brain circuit formation. The Lin28 RNA binding protein can prevent maturation of let-7 precursor RNAs; consequently, increased Lin28 signaling promotes pro-growth gene expression. The regulation and potential roles role of Lin28/Let-7 pathway in neuropathic pain remain unexplored. Using complementary mouse models of neuropathic pain, we evaluate molecular mechanisms underlying pain using single molecule detection and genetic manipulation. Sensitive RNA imaging assays, RNAScope in situ hybridization (ISH), amplify single RNA target signals in fixed tissues to allow mapping of the spatiotemporal patterns and cell type specificity of changes in non-coding RNA regulatory pathways. Digital PCR is used to provide sensitive and quantitative validation.
View/Download Poster Here
Zoom Link
Passcode: 886911
Murugesan Raju, PhD, University of Missouri
Title: Discovering Potential Biomarkers for Uveal Melanoma Targeted Therapy
Abstract: Genotype-directed targeted therapy has been widely accepted in the health care regimes for personalized medicine. The identification of genetic alterations in genes is a powerful tool to achieve this goal. The protein-coding region of the human genome is called the exome. Sequencing the exome is an efficient way to identify coding variants across a genome in diseases versus healthy subject samples. Though the exome represents less than 2% of the genome, it contains ~85% of known disease-related variants, making the whole-exome sequence a critical tool to discover biomarkers for the pathological condition. Uveal (ocular) melanoma is a common intraocular tumor and the exact cause is unknown. In this study, we used publicly available exome databases such as the cancer genome atlas (TCGA) to investigate biomarkers for Uveal melanoma. We retrieved about 80 uveal melanoma tumor cases of their genomic and RNA seq data. We analyzed the data using TCGA workflow-Bioconductor in the R package. Several novel biomarkers/SNP were identified and found to be associated with Uveal melanoma, including SF3B1, BAP1, EIF1AX etc. The BAP1 mutated gene has been known to be associated with a unique global DNA methylation profile. This biomarker may be a potential gene-based targeted therapy for Uveal melanoma.
View/Download Poster Here
Zoom Link
Passcode: YMr80t
Title: Discovering Potential Biomarkers for Uveal Melanoma Targeted Therapy
Abstract: Genotype-directed targeted therapy has been widely accepted in the health care regimes for personalized medicine. The identification of genetic alterations in genes is a powerful tool to achieve this goal. The protein-coding region of the human genome is called the exome. Sequencing the exome is an efficient way to identify coding variants across a genome in diseases versus healthy subject samples. Though the exome represents less than 2% of the genome, it contains ~85% of known disease-related variants, making the whole-exome sequence a critical tool to discover biomarkers for the pathological condition. Uveal (ocular) melanoma is a common intraocular tumor and the exact cause is unknown. In this study, we used publicly available exome databases such as the cancer genome atlas (TCGA) to investigate biomarkers for Uveal melanoma. We retrieved about 80 uveal melanoma tumor cases of their genomic and RNA seq data. We analyzed the data using TCGA workflow-Bioconductor in the R package. Several novel biomarkers/SNP were identified and found to be associated with Uveal melanoma, including SF3B1, BAP1, EIF1AX etc. The BAP1 mutated gene has been known to be associated with a unique global DNA methylation profile. This biomarker may be a potential gene-based targeted therapy for Uveal melanoma.
View/Download Poster Here
Zoom Link
Passcode: YMr80t
Erik Segerdell, MS, University of Oregon
Title: Investigating the systems biology of aging in Caenorhabditis elegans at cellular resolution
Abstract: While the age-dependent prevalence of disease is one of humanity’s most important health concerns, the underlying biological cause of aging has proven difficult to determine. From a systems biology perspective, aging can be explained as a degradation in complex functional regulatory networks caused by either changes in a limited set of central components, or by heterogeneous failure across the network, ultimately resulting in failure upon crossing a critical frailty threshold.
Caenorhabditis elegans, a free-living nematode worm found in temperate soil environments, is an ideal model for systems-biology studies at the cellular, tissue, and inter-tissue levels. C. elegans has a short lifespan amenable to aging studies, and a precisely described cellular lineage and anatomy. We therefore measured cellular heterogeneity in gene expression in C. elegans by profiling the transcriptomes of individual cells using single cell RNA-seq (scRNA-seq), which allows the quantitative characterization of cell-to-cell variability in gene expression. scRNA-seq is a promising technology for investigating age-specific changes in gene regulatory networks at a cellular resolution.
We have developed a single-cell pipeline to collect a complete data set from cohorts of aging worms across their lifespan. Initial bioinformatics analysis suggests that animals on day 1 of adulthood are still undergoing developmental changes as they transition into adulthood and reproduction is beginning. This is observable as day 1 samples clustering away from all other ages and with more variance between day 1 samples being apparent relative to the within-day sample variance at later ages. The samples from day 2-onward show more within-day consistency while exhibiting a distinct aging progression from days 2-12, after which a more consistent aged state is maintained. Using advanced analytical methods, we integrated the sample datasets for cell cluster identification. We assigned cell identity using an innovative automated tissue enrichment analysis workflow based on WormBase expression data, and a preliminary inspection has found these assignments to be consistent with the cluster-specific expression of known tissue-specific markers. Initial analysis aiming to identify patterns of change with age indicates that cluster identity loses specificity at later ages as fewer genes per cluster are expressed. We are carrying out further analyses designed to pull out putative age-related genes which can tell us about the physiological state of the animal and have predictive power, to lay out potential hypotheses of how expression changes with age, and to facilitate comparative analysis with other model species.
View/Download Poster Here
Zoom Link
Title: Investigating the systems biology of aging in Caenorhabditis elegans at cellular resolution
Abstract: While the age-dependent prevalence of disease is one of humanity’s most important health concerns, the underlying biological cause of aging has proven difficult to determine. From a systems biology perspective, aging can be explained as a degradation in complex functional regulatory networks caused by either changes in a limited set of central components, or by heterogeneous failure across the network, ultimately resulting in failure upon crossing a critical frailty threshold.
Caenorhabditis elegans, a free-living nematode worm found in temperate soil environments, is an ideal model for systems-biology studies at the cellular, tissue, and inter-tissue levels. C. elegans has a short lifespan amenable to aging studies, and a precisely described cellular lineage and anatomy. We therefore measured cellular heterogeneity in gene expression in C. elegans by profiling the transcriptomes of individual cells using single cell RNA-seq (scRNA-seq), which allows the quantitative characterization of cell-to-cell variability in gene expression. scRNA-seq is a promising technology for investigating age-specific changes in gene regulatory networks at a cellular resolution.
We have developed a single-cell pipeline to collect a complete data set from cohorts of aging worms across their lifespan. Initial bioinformatics analysis suggests that animals on day 1 of adulthood are still undergoing developmental changes as they transition into adulthood and reproduction is beginning. This is observable as day 1 samples clustering away from all other ages and with more variance between day 1 samples being apparent relative to the within-day sample variance at later ages. The samples from day 2-onward show more within-day consistency while exhibiting a distinct aging progression from days 2-12, after which a more consistent aged state is maintained. Using advanced analytical methods, we integrated the sample datasets for cell cluster identification. We assigned cell identity using an innovative automated tissue enrichment analysis workflow based on WormBase expression data, and a preliminary inspection has found these assignments to be consistent with the cluster-specific expression of known tissue-specific markers. Initial analysis aiming to identify patterns of change with age indicates that cluster identity loses specificity at later ages as fewer genes per cluster are expressed. We are carrying out further analyses designed to pull out putative age-related genes which can tell us about the physiological state of the animal and have predictive power, to lay out potential hypotheses of how expression changes with age, and to facilitate comparative analysis with other model species.
View/Download Poster Here
Zoom Link
Sarah Van Dierdonck, PhD Student, Duke University
Title: Cell type specific BIRD Factor Interactions
Abstract: The development of specialized cell types within an organism requires extensive regulation of gene expression. Combinatorial regulation is an important mechanism by which a relatively small number of transcription factors precisely regulate a much larger pool of genes. Studying the roles of transcription factor complexes independent of the individual components requires great spatial specificity - a protein may take part in different complexes in different cells with resultant different roles. Arabidopsis thaliana is thus well suited as a model system. The radial organization of cell files and longitudinal progression of developmental stages facilitates cell
type identification.
My project focuses on transcription factors of the 16-member BIRD/INDETERMINATE DOMAIN (IDD) family. BIRD factors have roles in the development of the root cortex and endodermis tissue, as well as other tissue types. Expression of specific BIRD combinations characterizes a range of cell types and developmental stages. Additionally, interactions between BIRDs have been shown by yeast two-hybrid and by BiFC in transfected Arabidopsis cell cultures. I have used a single cell RNA root atlas developed by the Benfey lab (Shahan et al, 2020) to identify BIRD factor pairs with unique co-localization patterns. I will confirm the likely match of transcript and protein localization and confirm in planta interaction using Fluorescence Correlation Spectroscopy techniques. I will then use single cell RNA analysis of BIRD localization mutants to probe the specific regulatory roles of BIRD complexes and test my hypothesis: that the roles of BIRD transcription factors in the root of the model plant Arabidopsis thaliana are dependent on expression domain specific combinatorial regulation
View/Download Poster Here
Zoom Link
Title: Cell type specific BIRD Factor Interactions
Abstract: The development of specialized cell types within an organism requires extensive regulation of gene expression. Combinatorial regulation is an important mechanism by which a relatively small number of transcription factors precisely regulate a much larger pool of genes. Studying the roles of transcription factor complexes independent of the individual components requires great spatial specificity - a protein may take part in different complexes in different cells with resultant different roles. Arabidopsis thaliana is thus well suited as a model system. The radial organization of cell files and longitudinal progression of developmental stages facilitates cell
type identification.
My project focuses on transcription factors of the 16-member BIRD/INDETERMINATE DOMAIN (IDD) family. BIRD factors have roles in the development of the root cortex and endodermis tissue, as well as other tissue types. Expression of specific BIRD combinations characterizes a range of cell types and developmental stages. Additionally, interactions between BIRDs have been shown by yeast two-hybrid and by BiFC in transfected Arabidopsis cell cultures. I have used a single cell RNA root atlas developed by the Benfey lab (Shahan et al, 2020) to identify BIRD factor pairs with unique co-localization patterns. I will confirm the likely match of transcript and protein localization and confirm in planta interaction using Fluorescence Correlation Spectroscopy techniques. I will then use single cell RNA analysis of BIRD localization mutants to probe the specific regulatory roles of BIRD complexes and test my hypothesis: that the roles of BIRD transcription factors in the root of the model plant Arabidopsis thaliana are dependent on expression domain specific combinatorial regulation
View/Download Poster Here
Zoom Link
Lukas Weber, PhD, Johns Hopkins University
Title: Unsupervised analysis of transcriptome-scale spatial gene expression in human dorsolateral prefrontal cortex using spatial transcriptomics data
Abstract: Spatial transcriptomics (ST) refers to emerging technologies for measuring transcriptome-wide gene expression in a spatial context (e.g. two-dimensional tissue slides), with spatial resolution on the order of single cells, depending on the cell density of the tissue. The recent commercial release of the 10x Genomics Visium platform, which builds on the popular Chromium platform for single-cell RNA sequencing, has made these analyses more widely accessible. The Visium platform measures gene expression with a barcode at each spatial coordinate (spot), enabling the quantification of thousands of genes per spot, with thousands of spots per sample, at a spatial resolution of around 1-50 cells per spot, depending on the tissue type. However, a number of data analysis challenges remain. As part of an experimental collaboration, we recently developed an unsupervised analysis pipeline for exploratory analyses of spatially organized cell types within the dorsolateral prefrontal cortex (DLPFC) of the human brain, which exhibits distinct gene expression in the cortical layers. Using twelve samples from three neurotypical donors measured on the Visium platform, we compared our results to semisupervised
and supervised approaches, which made use of either known marker genes from mouse or manually annotated cortical layer information. Our unsupervised clustering-based approach successfully recovered known laminar structure and layer-enriched genes, while also
allowing novel or unexpected spatial organizations to be identified, without the need for laborintensive annotation of laminar clusters based on cytoarchitecture. We investigated several alternative clustering setups to improve performance. Our pipeline leveraged several
R/Bioconductor and Python packages, and in ongoing work, we are developing a more general analysis pipeline for unsupervised analyses of ST data.
View/Download Poster Here
Zoom Link
Title: Unsupervised analysis of transcriptome-scale spatial gene expression in human dorsolateral prefrontal cortex using spatial transcriptomics data
Abstract: Spatial transcriptomics (ST) refers to emerging technologies for measuring transcriptome-wide gene expression in a spatial context (e.g. two-dimensional tissue slides), with spatial resolution on the order of single cells, depending on the cell density of the tissue. The recent commercial release of the 10x Genomics Visium platform, which builds on the popular Chromium platform for single-cell RNA sequencing, has made these analyses more widely accessible. The Visium platform measures gene expression with a barcode at each spatial coordinate (spot), enabling the quantification of thousands of genes per spot, with thousands of spots per sample, at a spatial resolution of around 1-50 cells per spot, depending on the tissue type. However, a number of data analysis challenges remain. As part of an experimental collaboration, we recently developed an unsupervised analysis pipeline for exploratory analyses of spatially organized cell types within the dorsolateral prefrontal cortex (DLPFC) of the human brain, which exhibits distinct gene expression in the cortical layers. Using twelve samples from three neurotypical donors measured on the Visium platform, we compared our results to semisupervised
and supervised approaches, which made use of either known marker genes from mouse or manually annotated cortical layer information. Our unsupervised clustering-based approach successfully recovered known laminar structure and layer-enriched genes, while also
allowing novel or unexpected spatial organizations to be identified, without the need for laborintensive annotation of laminar clusters based on cytoarchitecture. We investigated several alternative clustering setups to improve performance. Our pipeline leveraged several
R/Bioconductor and Python packages, and in ongoing work, we are developing a more general analysis pipeline for unsupervised analyses of ST data.
View/Download Poster Here
Zoom Link
Bocheng Yin, PhD, University of Virginia
Title: Using automated Spatially Targeted Optical Micro Proteomics (autoSTOMP) to explore the effectory proteins near the parasitophorous vacuole membrane (PVM) of Toxoplasma
Abstract: The parasitophorous vacuole membrane (PVM) is the battlefield composed of secreted Toxoplasma components that recruit host nutrients and subvert an arsenal of host innate immune sensing components. Within PVM, the parasite proliferates while prevent severe inflammatory response causing host damage. This commensalism should maintain long enough to allow Toxoplasma entering a stage forming cyst that can survive to infect a next host. To identify components of the PVM involved in innate immune sensing, we perform a novel discovery proteomics technique called automated Spatially Targeted Optical Micro Proteomics (autoSTOMP) designed to identify the components of subcellular structures. In autoSTOMP immunofluorescence microscopy identifies structures of interest (SOI) and tag the SOI proteins with biotin tag. Proteins tagged in this way are then precipitated and identified by mass spectrometry. We validated autoSTOMP can selectively identify the distinct proteomic profiles from PVM vicinity regions of Toxoplasma infected primary mouse bone marrow-derived dendritic cells (mBMDCs). Next, we identify novel innate immune sensing components recruited to PVM in mBMDCs to study the alternations that occurred at PVM interface by comparing hostt-intrinsic immune responses triggered by the activation of toll-like receptor ligands (the inflammasome) or interferon-γ (IFN-γ regulated GTPases). We found that the PVM components are distinct between stimulating conditions and generated a list of host response proteins. We are validating a subset of these candidate proteins that serve in parasite clearance and host cell death and attempt to demonstrate that a balance between the parasite and host that regulated near the PVM.
View/Download Poster Here
Zoom Link
Passcode: 750117
Title: Using automated Spatially Targeted Optical Micro Proteomics (autoSTOMP) to explore the effectory proteins near the parasitophorous vacuole membrane (PVM) of Toxoplasma
Abstract: The parasitophorous vacuole membrane (PVM) is the battlefield composed of secreted Toxoplasma components that recruit host nutrients and subvert an arsenal of host innate immune sensing components. Within PVM, the parasite proliferates while prevent severe inflammatory response causing host damage. This commensalism should maintain long enough to allow Toxoplasma entering a stage forming cyst that can survive to infect a next host. To identify components of the PVM involved in innate immune sensing, we perform a novel discovery proteomics technique called automated Spatially Targeted Optical Micro Proteomics (autoSTOMP) designed to identify the components of subcellular structures. In autoSTOMP immunofluorescence microscopy identifies structures of interest (SOI) and tag the SOI proteins with biotin tag. Proteins tagged in this way are then precipitated and identified by mass spectrometry. We validated autoSTOMP can selectively identify the distinct proteomic profiles from PVM vicinity regions of Toxoplasma infected primary mouse bone marrow-derived dendritic cells (mBMDCs). Next, we identify novel innate immune sensing components recruited to PVM in mBMDCs to study the alternations that occurred at PVM interface by comparing hostt-intrinsic immune responses triggered by the activation of toll-like receptor ligands (the inflammasome) or interferon-γ (IFN-γ regulated GTPases). We found that the PVM components are distinct between stimulating conditions and generated a list of host response proteins. We are validating a subset of these candidate proteins that serve in parasite clearance and host cell death and attempt to demonstrate that a balance between the parasite and host that regulated near the PVM.
View/Download Poster Here
Zoom Link
Passcode: 750117
Questions?
Please contact Stephanie: [email protected] | 858-246-0949
Please contact Stephanie: [email protected] | 858-246-0949
