Intact Tissue Sections Research Articles

Full-length target sequences of GeoMx digital spatial profiling probes reveal that gene-promiscuity predicts probe sensitivity to EDTA tissue decalcification

GeoMx Digital Spatial Profiling (Nanostring) is a commercial spatial transcriptomics method to selectively analyze regions of interest within intact tissue sections. We show that decalcification with ethylene-diamine-tetra-acetic (EDTA) variably attenuates probe counts, while probes that are more resistant to this effect consequently appear overexpressed after quantile normalization. By determining the undisclosed full-length target sequences of probes used in the human whole transcriptome panel, hereby updating target transcripts and genes, we find that the gene-promiscuity of probes is an important factor that determines sensitivity to EDTA incubation.

Abstract 1140: A spatially mapped gene expression signature for intestinal stem-like cells identifies high-risk precursors of gastric cancer

Abstract Gastric intestinal metaplasia (GIM) is a precursor lesion for the intestinal subtype of gastric cancer (GC). A risk stratification tool is the Operative Link on GIM (OLGIM), a system that relies on histopathologic annotation of gastric biopsies. Advanced OLGIM (stages III and IV) have the highest risk of progression to GC. Molecular biomarkers for advanced OLGIM are lacking. We explored transcriptomics biomarkers for advanced OLGIM to aid risk assessment efforts among GC precursors.We used clinical and genomic data from four cohorts: 1) GAPS, a cohort of OLGIM-staged patients (N=303); 2) a subset of confirmed intestinal-type GC from The Cancer Genome Atlas (TCGA, N=198); 3) a compilation of scRNA-seq data (N=40), and 4) a spatial transcriptomics cohort (N=5) with annotated H&E-stained histopathology slides from GC or OLGIM-staged patients.We separated the GAPS bulk RNAseq gene expression dataset into independent discovery (N=88) and validation (N=215) sets. Through a combination of differential expression analysis and co-expression analysis, we identified a discrete set of 100 significantly upregulated genes characterizing advanced OLGIM that were confirmed in the validation set. Importantly, these genes were overexpressed both in the corpus and antrum of the stomach. We leveraged spatial transcriptomics assays to refine this signature to genes overexpressed specifically in GIM foci. We interrogated the TCGA dataset and confirmed these genes to be also expressed in gastric tumors. The final high-risk signature included 26 genes. Single-cell RNAseq analysis revealed these genes to be expressed by aberrant intestinal-like lineages. Further, reference-mapping identified cells resembling mature enterocytes and goblet cells that expressed 16 of these genes, whereas 10 genes from the high-risk signature pinpoint immature intestinal stem-like cells at varying stages of differentiation (duodenum stem cells, duodenum differentiating stem cells, and duodenum transit amplifying cells). Finally, we confirmed expression of 12 of these genes by single-molecule fluorescence in situ hybridization (smFISH), that distinguish differentiated from stem-like lineages in metaplasia and are absent in normal gastric glands in intact tissue sections from GC precursors.Our study identified as signature of 26 genes that are: i) characteristically over-expressed in advanced OLGIM stages, ii) localize specifically to metaplasia, iii) are expressed in gastric tumors, and iv) distinguish differentiated from stem-like cells in GIM. These results provide transcriptomics biomarkers that indicate the establishment and expansion of intestinal stem-like cells in advanced OLGIM, with profound translational implications, as these cells may harbor and expand mutations that predispose advanced OLGIM patients to GC. Citation Format: Ignacio A. Wichmann, Robert J. Huang, Andrew Su, Anuja Sathe, Miranda V. Shum, Susan M. Grimes, Rithika Meka, Alison Almeda, Xiangqi Bai, Jeanne Shen, Quan Nguyen, Manuel Amieva, Joo Ha Hwang, Hanlee P. Ji. A spatially mapped gene expression signature for intestinal stem-like cells identifies high-risk precursors of gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1140.

The evolution of immune profiling: will there be a role for nanoparticles?

Immune profiling provides insights into the functioning of the immune system, including the distribution, abundance, and activity of immune cells. This understanding is essential for deciphering how the immune system responds to pathogens, vaccines, tumors, and other stimuli. Analyzing diverse immune cell types facilitates the development of personalized medicine approaches by characterizing individual variations in immune responses. With detailed immune profiles, clinicians can tailor treatment strategies to the specific immune status and needs of each patient, maximizing therapeutic efficacy while minimizing adverse effects. In this review, we discuss the evolution of immune profiling, from interrogating bulk cell samples in solution to evaluating the spatially-rich molecular profiles across intact preserved tissue sections. We also review various multiplexed imaging platforms recently developed, based on immunofluorescence and imaging mass spectrometry, and their impact on the field of immune profiling. Identifying and localizing various immune cell types across a patient's sample has already provided important insights into understanding disease progression, the development of novel targeted therapies, and predicting treatment response. We also offer a new perspective by highlighting the unprecedented potential of nanoparticles (NPs) that can open new horizons in immune profiling. NPs are known to provide enhanced detection sensitivity, targeting specificity, biocompatibility, stability, multimodal imaging features, and multiplexing capabilities. Therefore, we summarize the recent developments and advantages of NPs, which can contribute to advancing our understanding of immune function to facilitate precision medicine. Overall, NPs have the potential to offer a versatile and robust approach to profile the immune system with improved efficiency and multiplexed imaging power.

Abstract P1185: Spatial Transcriptome Profiling In The Mouse Heart With Single-Cell Resolution

Background: The spatial organization of the diverse cells in the heart shapes biological function. For instance, during the inflammatory and fibrotic processes that are central to heart disease, many cells act mainly within their local microenvironment. Recent studies using single-cell RNA sequencing (scRNA-seq) have provided a detailed list of the cell types and states involved in development, homeostasis and in cardiovascular diseases. However, scRNA-seq requires tissue dissociation, which removes spatial information and thereby limits our ability to determine how cells interact with each other to maintain health or cause disease. As a consequence, the precise spatial organization of cell types and states in the mammalian heart remains unknown. We sought to address this knowledge gap by using multiplexed error-robust fluorescence in situ hybridization (MERFISH), a method that enables simultaneous identification and localization of single transcripts from hundreds of different genes in intact tissue sections. Methods/Results: We adapted MERFISH and developed a new single-cell image segmentation method to creating a single-cell atlas of the mouse heart. First, we designed and constructed a 128-gene MERFISH probe library including cell type marker genes and also genes covering a broad range of cardiac signaling pathways. We then optimized sample preparation, including cell boundary staining and tissue clearing. Once optimized, we used this approach to image 10 μm-thick sections from healthy mouse heart, resolving tens of thousands of cells and millions of transcripts. Our data thus constitute the first single-cell spatial transcriptome atlas of the mouse heart. Conclusion: Through our newly developed cardiac MERFISH platform, we identified tens of thousands of cells and labeled millions of individual RNA transcripts in intact sections of mouse heart. Our cellular transcriptome profile maps enable new insights into how the organization of the heart shapes its biological function.

Abstract 3116: Spatial single-cell atlas of stage III colorectal cancer

Abstract Background: Spatial heterogeneity and multicellular interactions within the tumor microenvironment drive tumor progression and response to therapy, yet, tumor molecular profiling often requires dissociation of cells, losing the spatial context. Spatial proteomic technologies enable capturing single-cell information with the spatial context allowing us to link cancer-causing mutations to outcomes in cell signaling, responses and survival that will aid in diagnosis, prognostication, and treatment of cancer. We aim to capture single cells in intact tissue sections of stage III colorectal tumors from 52 patients to spatially characterize cell types, interactions, and organization of the tumor microenvironment in relation to genetic alterations and clinical outcomes. Methods: Using Hyperion Imaging Mass Cytometry (IMC), we profiled 16 protein markers across 170 tissue regions, capturing molecular signatures of tissue architecture, tumor cells, and immune cells. We developed an analysis pipeline to quantify single-cell protein expression and identify cell phenotypes. We preserved spatial information to characterize the interactions between neighboring cells and identify multicellular communities. We also generated whole exome sequencing data, RNAseq data and histopathological images from sections of the same tissue blocks. We compared cellular and spatial features between tumors harboring prognostic genomic evens such as TP53 deletion and MSI and between patients stratified by clinical outcomes such as survival and recurrence. Results: We spatially profiled over 800,000 cells to identify 10 tumor, immune and stromal cell phenotypes, and validated identified cell types against matched histopathology and CIBERSORTx deconvoluted RNAseq. To quantify cell-cell interactions, we measured the distance between neighboring cells and performed neighborhood enrichment analysis. We characterized the multicellular organization of the tumor microenvironment to identify 7 consistent cell neighborhoods of up to 10 adjacent cells. We quantified p53 expression in individual tumor cells and found that TP53 deletion (all heterozygous) had no significant association with p53 expression or p53+ tumor cell abundance, suggesting spatial proteomics reveals additional information absent in traditional sequencing. We find that MSI is exclusive of TP53 deletion and that MSI positive tumors had few p53+ tumor cells, increased CD8+ T cell composition and B cells that were further away from p53+ tumor cells compared to MSI negative tumors. Conclusions: Using Hyperion IMC to acquire a spatial single-cell atlas of stage III colorectal cancer enabled us to characterize the tumor microenvironment by defining cell types, quantifying cell interactions, and identifying multicellular communities. These features enable the linkage of prognostic genetic alternations and clinical outcomes with changes in the tumor microenvironment. Citation Format: Andrew Su, Minh Tran, HoJoon Lee, Anuja Sathe, Xiangqi Bai, Richard Cruz, Lance Pflieger, Quan Nguyen, Hanlee P. Ji, Terence Rhodes. Spatial single-cell atlas of stage III colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3116.

VistoSeg: Processing utilities for high-resolution images for spatially resolved transcriptomics data.

Spatially resolved transcriptomics (SRT) is a growing field that links gene expression to anatomical context. SRT approaches that use next-generation sequencing (NGS) combine RNA sequencing with histological or fluorescent imaging to generate spatial maps of gene expression in intact tissue sections. These technologies directly couple gene expression measurements with high-resolution histological or immunofluorescent images that contain rich morphological information about the tissue under study. While broad access to NGS-based spatial transcriptomic technology is now commercially available through the Visium platform from the vendor 10× Genomics, computational tools for extracting image-derived metrics for integration with gene expression data remain limited. We developed VistoSeg as a MATLAB pipeline to process, analyze and interactively visualize the high-resolution images generated in the Visium platform. VistoSeg outputs can be easily integrated with accompanying transcriptomic data to facilitate downstream analyses in common programing languages including R and Python. VistoSeg provides user-friendly tools for integrating image-derived metrics from histological and immunofluorescent images with spatially resolved gene expression data. Integration of this data enhances the ability to understand the transcriptional landscape within tissue architecture. VistoSeg is freely available at http://research.libd.org/VistoSeg/.

Novel insights from spatial transcriptome analysis in solid tumors.

Since its first application in 2016, spatial transcriptomics has become a rapidly evolving technology in recent years. Spatial transcriptomics enables transcriptomic data to be acquired from intact tissue sections and provides spatial distribution information and remedies the disadvantage of single-cell RNA sequencing (scRNA-seq), whose data lack spatially resolved information. Presently, spatial transcriptomics has been widely applied to various tissue types, especially for the study of tumor heterogeneity. In this review, we provide a summary of the research progress in utilizing spatial transcriptomics to investigate tumor heterogeneity and the microenvironment with a focus on solid tumors. We summarize the research breakthroughs in various fields and perspectives due to the application of spatial transcriptomics, including cell clustering and interaction, cellular metabolism, gene expression, immune cell programs and combination with other techniques. As a combination of multiple transcriptomics, single-cell multiomics shows its superiority and validity in single-cell analysis. We also discuss the application prospect of single-cell multiomics, and we believe that with the progress of data integration from various transcriptomics, a multilayered subcellular landscape will be revealed.

High-resolution Slide-seqV2 spatial transcriptomics enables discovery of disease-specific cell neighborhoods and pathways

SummaryHigh-resolution spatial transcriptomics enables mapping of RNA expression directly from intact tissue sections; however, its utility for the elucidation of disease processes and therapeutically actionable pathways remains unexplored. We applied Slide-seqV2 to mouse and human kidneys, in healthy and distinct disease paradigms. First, we established the feasibility of Slide-seqV2 in tissue from nine distinct human kidneys, which revealed a cell neighborhood centered around a population of LYVE1+ macrophages. Second, in a mouse model of diabetic kidney disease, we detected changes in the cellular organization of the spatially restricted kidney filter and blood-flow-regulating apparatus. Third, in a mouse model of a toxic proteinopathy, we identified previously unknown, disease-specific cell neighborhoods centered around macrophages. In a spatially restricted subpopulation of epithelial cells, we discovered perturbations in 77 genes associated with the unfolded protein response. Our studies illustrate and experimentally validate the utility of Slide-seqV2 for the discovery of disease-specific cell neighborhoods.

Spatial genomics enables multi-modal study of clonal heterogeneity in tissues.

The state and behaviour of a cell can be influenced by both genetic and environmental factors. In particular, tumour progression is determined by underlying genetic aberrations1-4 as well as the makeup of the tumour microenvironment5,6. Quantifying the contributions of these factors requires new technologies that can accurately measure the spatial location of genomic sequence together with phenotypic readouts. Here we developed slide-DNA-seq, a method for capturing spatially resolved DNA sequences from intact tissue sections. We demonstrate that this method accurately preserves local tumour architecture and enables thede novo discovery of distinct tumour clones and their copy number alterations. We then apply slide-DNA-seq to a mouse model ofmetastasis and a primary human cancer, revealing that clonal populations are confined to distinct spatial regions. Moreover, through integration with spatial transcriptomics, we uncover distinct sets of genes that are associated with clone-specific genetic aberrations, the local tumour microenvironment, or both. Together, this multi-modal spatial genomics approach provides a versatile platform for quantifying how cell-intrinsic and cell-extrinsic factors contribute to gene expression, protein abundance and other cellular phenotypes.

Understanding the tumour immune microenvironment of stage III colorectal cancer using multiplexed imaging mass cytometry

Background/Aim: The tumour immune microenvironment is spatially heterogenous and plays a crucial role in cancer progression and response to therapy. Current histopathological examination cannot comprehensively assess the molecular heterogeneity in the tumour microenvironment. We aim to capture tissue morphology and single cell protein expression of cancer and immune cells within intact tissue sections of stage III colorectal tumours from 11 patients. Methods: Using Hyperion Image Mass Cytometry (IMC), we simultaneously profiled 16 protein markers from FFPE samples, including cancer (Ki-67, p53) and immune (CD3, CD4, CD8a, CD45RO, CD20, CD68, Granzyme B) cell markers. We integrated this with matched histopathological imaging and whole-exome-sequencing data. Results: We identified seven cancer, immune and stromal cell types and preserved their spatial organisation within the microenvironment. These cell types were validated on matched H&E histopathology. We found significant interactions of cancer cells with NK-cells and B-cells. Furthermore, we found that tumours with 17p deletion had significantly fewer NK-cells. Conclusion: Using IMC to acquire rich spatial data at single-cell resolution enabled us to assess the heterogeneity of tumour tissue by defining cell types, immune infiltration, and cancer-immune cell interaction within intact tissue sections. Integration of this data with H&E images could assist pathologists in cancer diagnosis.

Venom Gland Mass Spectrometry Imaging of Saw-Scaled Viper, Echis carinatus sochureki, at High Lateral Resolution.

The snake venom gland is the place for the synthesis, storage, and secretion of a complex mixture of proteins and peptides, i.e., the venom. The morphology of the gland has been revealed by classical histology and microscopic studies. However, knowledge about the gland's cellular secretory and functional processes is still incomplete and has so far been neglected by the omics disciplines. We used autofocusing atmospheric-pressure matrix-assisted laser desorption/ionization (AP-SMALDI) mass spectrometry imaging (MSI) to investigate endogenous biomolecular distributions in the venom glands of the saw-scaled viper, Echis carinatus sochureki, employing different sample preparation methods. Fresh-freezing and formalin-fixation were tested for the gland to obtain intact tissue sections. Subsequently, MSI was conducted with 12 μm pixel resolution for both types of preparations, and the lateral distributions of the metabolites were identified. Experiments revealed that lipids belonging to the classes of PC, SM, PE, PS, PA, and TG are present in the venom gland. PC (32:0) and SM (36:1) were found to be specifically located in the areas where cells are present. The snake venom metalloprotease inhibitor pEKW (m/z 444.2233) was identified in the venom by top-down LC-MS/MS and localized by MALDI-MSI in the gland across secretory epithelial cells. The peptide can inhibit the venom's enzymatic activity during long-term storage within the venom gland. With a high degree of spectral similarities, we concluded that formalin-fixed tissue, in addition to its high ability to preserve tissue morphology, can be considered as an alternative method to fresh-frozen tissue in the case of lipid and peptide MS imaging in venom gland tissues.

Effect of MALDI matrices on lipid analyses of biological tissues using MALDI-2 postionization mass spectrometry.

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) allows for highly multiplexed, untargeted detection of many hundreds of analytes from tissue. Recently, laser postionization (MALDI-2) has been developed for increased ion yield and sensitivity for lipid IMS. However, the dependence of MALDI-2 performance on the various lipid classes is largely unknown. To understand the effect of the applied matrix on MALDI-2 analysis of lipids, samples including an equimolar lipid standard mixture, various tissue homogenates, and intact rat kidney tissue sections were analyzed using the following matrices: α-cyano-4-hydroxycinnamic acid, 2',5'-dihydroxyacetophenone, 2',5'-dihydroxybenzoic acid (DHB), and norharmane (NOR). Lipid signal enhancement of protonated species using MALDI-2 technology varied based on the matrix used. Although signal improvements were observed for all matrices, the most dramatic effects using MALDI-2 were observed using NOR and DHB. For lipid standards analyzed by MALDI-2, NOR provided the broadest coverage, enabling the detection of all 13 protonated standards, including nonpolar lipids, whereas DHB gave less coverage but gave the highest signal increase for those lipids recorded. With respect to tissue homogenates and rat kidney tissue, mass spectra were compared and showed that the number and intensity of neutral lipids tentatively identified with MALDI-2 using NOR increased significantly (e.g., fivefold intensity increase for triacylglycerol). In the cases of DHB with MALDI-2, the number of protonated lipids identified from tissue homogenates doubled with 152 on average compared with 76 with MALDI alone. High spatial resolution imaging (~20 μm) of rat kidney tissue showed similar results using DHB with 125 lipids tentatively identified from MALDI-2 spectra versus just 72 using standard MALDI. From the four matrices tested, NOR provided the greatest increase in sensitivity for neutral lipids (triacylglycerol, diacylglycerol, monoacylglycerol, and cholesterol ester), and DHB provided the highest overall number of lipids detected using MALDI-2 technology.

Quantitative Profiling of the Human Substantia Nigra Proteome from Laser-capture Microdissected FFPE Tissue

Laser-capture microdissection (LCM) allows the visualization and isolation of morphologically distinct subpopulations of cells from heterogeneous tissue specimens. In combination with formalin-fixed and paraffin-embedded (FFPE) tissue it provides a powerful tool for retrospective and clinically relevant studies of tissue proteins in a healthy and diseased context. We first optimized the protocol for efficient LCM analysis of FFPE tissue specimens. The use of SDS containing extraction buffer in combination with the single-pot solid-phase-enhanced sample preparation (SP3) digest method gave the best results regarding protein yield and protein/peptide identifications. Microdissected FFPE human substantia nigra tissue samples (∼3,000 cells) were then analyzed, using tandem mass tag (TMT) labeling and LC-MS/MS, resulting in the quantification of >5,600 protein groups. Nigral proteins were classified and analyzed by abundance, showing an enrichment of extracellular exosome and neuron-specific gene ontology (GO) terms among the higher abundance proteins. Comparison of microdissected samples with intact tissue sections, using a label-free shotgun approach, revealed an enrichment of neuronal cell type markers, such as tyrosine hydroxylase and alpha-synuclein, as well as proteins annotated with neuron-specific GO terms. Overall, this study provides a detailed protocol for laser-capture proteomics using FFPE tissue and demonstrates the efficiency of LCM analysis of distinct cell subpopulations for proteomic analysis using low sample amounts.

De novo discovery of metabolic heterogeneity with immunophenotype-guided imaging mass spectrometry

BackgroundImaging mass spectrometry enables in situ label-free detection of thousands of metabolites from intact tissue samples. However, automated steps for multi-omics analyses and interpretation of histological images have not yet been implemented in mass spectrometry data analysis workflows. The characterization of molecular properties within cellular and histological features is done via time-consuming, non-objective, and irreproducible definitions of regions of interest, which are often accompanied by a loss of spatial resolution due to mass spectra averaging.Methods: We developed a new imaging pipeline called Spatial Correlation Image Analysis (SPACiAL), which is a computational multimodal workflow designed to combine molecular imaging data with multiplex immunohistochemistry (IHC). SPACiAL allows comprehensive and spatially resolved in situ correlation analyses on a cellular resolution. To demonstrate the method, matrix-assisted laser desorption-ionization (MALDI) Fourier-transform ion cyclotron resonance (FTICR) imaging mass spectrometry of metabolites and multiplex IHC staining were performed on the very same tissue section of mouse pancreatic islets and on human gastric cancer tissue specimens. The SPACiAL pipeline was used to perform an automatic, semantic-based, functional tissue annotation of histological and cellular features to identify metabolic profiles. Spatial correlation networks were generated to analyze metabolic heterogeneity associated with cellular features.Results: To demonstrate the new method, the SPACiAL pipeline was used to identify metabolic signatures of alpha and beta cells within islets of Langerhans, which are cell types that are not distinguishable via morphology alone. The semantic-based, functional tissue annotation allows an unprecedented analysis of metabolic heterogeneity via the generation of spatial correlation networks. Additionally, we demonstrated intra- and intertumoral metabolic heterogeneity within HER2/neu-positive and -negative gastric tumor cells.Conclusions: We developed the SPACiAL workflow to provide IHC-guided in situ metabolomics on intact tissue sections. Diminishing the workload by automated recognition of histological and functional features, the pipeline allows comprehensive analyses of metabolic heterogeneity. The multimodality of immunohistochemical staining and extensive molecular information from imaging mass spectrometry has the advantage of increasing both the efficiency and precision for spatially resolved analyses of specific cell types. The SPACiAL method is a stepping stone for the objective analysis of high-throughput, multi-omics data from clinical research and practice that is required for diagnostics, biomarker discovery, or therapy response prediction.

Mixed Multiplex Staining: Automated RNAscope™ and OPAL™ for Multiple Targets.

The application of multiplex immunofluorescent detection of proteins, mRNAs, or both in combination has contributed to our understanding of biomarker expression and distribution within normal and pathological diseased tissues. In combination with whole-slide scanning these techniques permit the visualization of multiple biomarkers, their distribution within normal and pathological tissues, and their spatial and proximal distribution to each other in intact tissue sections. The expression of multiple biomarkers can now be used to accurately phenotype specific cell types, e.g., immune cells which rely on the expression of multiple biomarkers for precise identification. The availability of antibody reagents to detect specific protein targets, range of targets in specific technology platforms, and tissue fixation may mean that one technology may not be suitable for visualizing all biomarkers of interest. Here I demonstrate that a mixed multiplex approach for multiple protein and mRNA can be delivered on automated platforms suitable for high-throughput environments, and predict that mixed multiplex approaches will be important in future to compensate for tissue fixation differences, biomarker availability, and technology platform limitations.

Imaging with mass spectrometry, the next frontier in sphingolipid research? A discussion on where we stand and the possibilities ahead.

In the last ten years, mass spectrometry (MS) has become the favored analytical technique for sphingolipid (SPL) analysis and measurements. Indeed MS has the unique ability to both acquire sensitive and quantitative measurements and to resolve the molecular complexity characteristic of SPL molecules, both across the different SPL families and within the same SPL family. Currently, two complementary MS-based approaches are used for lipid research: analysis of lipid extracts, mainly by infusion electrospray ionization (ESI), and mass spectrometry imaging (MSI) from a sample surface (i.e. intact tissue sections, cells, model membranes, thin layer chromatography plates) (Fig. 1). The first allows for sensitive and quantitative information about total lipid molecular species from a given specimen from which lipids have been extracted and chromatographically separated prior to the analysis; the second, albeit generally less quantitative and less specific in the identification of molecular species due to the complexity of the sample, allows for spatial information of lipid molecules from biological specimens. In the field of SPL research, MS analysis of lipid extracts from biological samples has been commonly utilized to implicate the role of these lipids in specific biological functions. On the other hand, the utilization of MSI in SPL research represents a more recent development that has started to provide interesting descriptive observations regarding the distribution of specific classes of SPLs within tissues. Thus, it is the aim of this review to discuss how MSI technology has been employed to extend the study of SPL metabolism and the type of information that has been obtained from model membranes, single cells and tissues. We envision this discussion as a complementary compendium to the excellent technical reviews recently published about the specifics of MSI technologies, including their application to SPL analysis (Fuchs et al., 2010; Berry et al., 2011; Ellis et al., 2013; Eberlin et al., 2011; Kraft and Klitzing, 2014).

Barcoded solid-phase RNA capture for Spatial Transcriptomics profiling in mammalian tissue sections.

Spatial resolution of gene expression enables gene expression events to be pinpointed to a specific location in biological tissue. Spatially resolved gene expression in tissue sections is traditionally analyzed using immunohistochemistry (IHC) or in situ hybridization (ISH). These technologies are invaluable tools for pathologists and molecular biologists; however, their throughput is limited to the analysis of only a few genes at a time. Recent advances in RNA sequencing (RNA-seq) have made it possible to obtain unbiased high-throughput gene expression data in bulk. Spatial Transcriptomics combines the benefits of traditional spatially resolved technologies with the massive throughput of RNA-seq. Here, we present a protocol describing how to apply the Spatial Transcriptomics technology to mammalian tissue. This protocol combines histological staining and spatially resolved RNA-seq data from intact tissue sections. Once suitable tissue-specific conditions have been established, library construction and sequencing can be completed in ~5-6 d. Data processing takes a few hours, with the exact timing dependent on the sequencing depth. Our method requires no special instruments and can be performed in any laboratory with access to a cryostat, microscope and next-generation sequencing.

Abstract 202: Low Mitochondrial Membrane Potential Identified a Novel Population of Human Cardiac Progenitor Cells with the Capacity for Myocardial Repair

As resident cardiac stem/progenitor cells (CPCs) stay in the hypoxia niche, have less metabolic activity and lower mitochondrial membrane potential, the aim of this study was to characterize a new subset of CPCs with lower mitochondrial membrane potential and examine their potential for myocardial repair. Following the live cell staining with a lipophilic cationic dye tetramethylrhodamine methyl ester (TMRM) at a final concentration of 25 nm, a novel subset of hCPCs with the lowest ~3% TMRM fluorescence were sorted from right atrial appendage derived cells (RAACs) in patients with cardiomyopathy, and named as ΔΨ m low -hCPCs, which are self-renewable, and characterized as positive (>90%) for CD90, CD44, CD140b, CD172a, however, negative (<5%) for CD34, CD45, CD54, CD117, CD133. The increased expression of pluripotent genes (LIN28, NANOG, & OCT3/4) and cardiac lineage genes (GATA4, MEF2C, MYOCD, & NKX2.5), as well as the decreased expression of cell cycle inhibitor (CDKN2A & CDKN1A) were observed in ΔΨ m low -hCPCs comparing with the non-sorted RAACs. Interestingly, ΔΨ m low -hCPCs exclusively express heme oxygenase-1 (HO-1). And CPCs positive for HO-1 and NKX2.5/GATA4 were found in the intact human heart tissue sections. ΔΨ m low -hCPCs are able to differentiate into all three cardiac lineage cells in both in vitro and in vivo conditions, and display superior ability for survival, proliferation and release of cytoprotective cytokines compared to the RAACs. At 35 days, compared with the vehicle and control cell groups, transplantation of ΔΨ m low -hCPCs significantly improve the cardiac structure and function following myocardial infarction in the immunodeficient mouse model, as indicated by the increased left ventricular (LV) ejection fraction, increased positive and negative LV dP/dt, reduced LV remodeling, and reduced infarct size. The immunohistochemistry studies showed the significant increased cardiac proliferation, newly formed cardiomyocytes, angiogenesis, and decreased cell apoptosis in the ΔΨ m low -hCPCs treated group. In summary, we identified a new hCPC population that might represent a primitive progenitor cell population within human hearts. The existence of these cells opens new opportunities for myocardial repair.

Abstract 4622: Deciphering gene subsets associated with clinical outcomes of early stage breast carcinoma patients treated with Tamoxifen

Abstract NSABP Protocol B-09 reported primary breast carcinomas expressing estrogen (ER) and progestin receptors (PR) exhibited increased likelihood of response to Tamoxifen. However, some patients with early stage lesions relapse within 10 years. We identified a 5-gene model from 14 candidate expression profiles that improved differentiation of these patients (Kerr & Wittliff, Horm Canc 2:261-71, 2011). Goals are to examine relationships of clinical outcomes of patients whose carcinomas express individual genes and subsets of the models and externally validate their clinical utility. Procedures: De-identified primary breast carcinomas from 342 patients treated with Tamoxifen were used to assess clinical relevance of 14 candidate genes. Laser capture microdissection (LCM) was used previously to isolate carcinoma cells and assess 22,000 genes by microarray. qPCR validated expression of 14 candidate genes. Univariable Cox regression analyses were performed and Kaplan-Meier plots were constructed to assess relationships between either progression-free (PFS) or overall survival (OS) and expression of each gene using R Studio 1.0.143. Relationships of gene expression were segregated by ER/PR (quantified by EIA or radio-ligand binding) with clinical outcomes. SurvExpress evaluated performance externally of individual gene expression and signatures. Results: Univariable Cox regression of microarray results for 14 candidate genes from LCM-procured cancer cells of 247 biopsies without regard to ER/PR status indicated that RERG, ERRB4 and ESR1 were related to PFS while these 3 genes and EGFR and CAXII expression were associated with OS (adjusted p value < 0.30). Validation of individual gene expression of 274 intact tissue sections by qPCR revealed that 4 of these genes (exception of EGFR) as well as PGR, BCL2, SLC39A6, EDG-1, and CD34 were associated with PFS. Expression levels of each of these 9 candidate genes as well as PTGDS, SDF and NQO-1 were independently associated with OS. Kaplan-Meier plots assessed ability of each gene expression level to predict clinical outcomes (PFS/OS). Multivariate Cox regression, performed with backward conditional selection using either microarray data or qPCR results, revealed clinically relevant genes subsets for PFS and OS. SurvExpress, an online tool, was used to externally validate molecular signatures. Conclusions: We externally validated the 5-gene model composed of PGR, BCL2, ERBB4, RERG and CD34 that identified a subset of early stage, ER+ /PR+ breast cancers patients treated with Tamoxifen that exhibited high recurrence rates. ER, PR and HER2 proteins with gene expression were correlated with prediction of clinical outcomes. Clinically relevant molecular signatures identified gene candidates for development of companion diagnostics to their protein products to improve breast cancer management and prediction of risk of recurrence. Citation Format: Zohair Riaz Hameed, Michael W. Daniels, D. Alan Kerr II, James L. Wittliff. Deciphering gene subsets associated with clinical outcomes of early stage breast carcinoma patients treated with Tamoxifen [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4622.

Matrix-assisted laser desorption/ionization imaging mass spectrometry of intraperitoneally injected danegaptide (ZP1609) for treatment of stroke-reperfusion injury in mice.

This work focuses on direct matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) detection of intraperitoneally (IP)-injected dipeptide ZP1609 in mouse brain tissue. Direct analysis of drug detection in intact tissue sections provides distribution information that can impact drug development. MALDI-IMS capabilities of uncovering drug transport across the blood-brain barrier are demonstrated. Successful peptide detection using MALDI-IMS was achieved using a MALDI TOF/TOF system. Upon optimization of sample preparation procedures for dipeptide ZP1609, an additional tissue acidification procedure was found to greatly enhance signal detection. The imaging data acquired was able to determine successful transport of ZP1609 across the blood-brain barrier. Data obtained from MALDI-IMS can help shape our understanding of biological functions, disease progression, and effects of drug delivery. Direct detection of ZP1609 throughout the brain tissue sections was observed from MALDI-MS images. However, in cases where there was induction of stroke, a peak of lower signal intensity was also detected in the target m/z region. Although distinct differences in signal intensity can be seen between control and experimental groups, fragments and adducts of ZP1609 were investigated using MALDI-IMS to verify detection of the target analyte. Overall, the data reveals successful penetration of ZP1609 across the blood-brain barrier. The benefits of tissue acidification in the enhancement of detection sensitivity for low-abundance peptides were demonstrated. MALDI-IMS has been shown to be a useful technique in the direct detection of drugs within intact brain tissue sections.