Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer.

The presence of intratumoral tertiary lymphoid structures (TLS) is associated with positive clinical outcomes and responses to immunotherapy in cancer. Here, we used spatial transcriptomics to examine the nature of B cell responses within TLS in renal cell carcinoma (RCC). B cells were enriched in TLS, and therein, we could identify all B cell maturation stages toward plasma cell (PC) formation. B cell repertoire analysis revealed clonal diversification, selection, expansion in TLS, and the presence of fully mature clonotypes at distance. In TLS+ tumors, IgG- and IgA-producing PCs disseminated into the tumor beds along fibroblastic tracks. TLS+ tumors exhibited high frequencies of IgG-producing PCs and IgG-stained and apoptotic malignant cells, suggestive of anti-tumor effector activity. Therapeutic responses and progression-free survival correlated with IgG-stained tumor cells in RCC patients treated with immune checkpoint inhibitors. Thus, intratumoral TLS sustains B cell maturation and antibody production that is associated with response to immunotherapy, potentially via direct anti-tumor effects.

A Spatial Transcriptomics Browser for Discovering Gene Expression Landscapes across Microscopic Tissue Sections.

A crucial feature of life is its spatial organization and compartmentalization on the molecular, cellular, and tissue levels. Spatial transcriptomics (ST) technology has opened a new chapter of the sequencing revolution, emerging rapidly with transformative effects across biology. This technique produces extensive and complex sequencing data, raising the need for computational methods for their comprehensive analysis and interpretation. We developed the ST browser web tool for the interactive discovery of ST images, focusing on different functional aspects such as single gene expression, the expression of functional gene sets, as well as the inspection of the spatial patterns of cell-cell interactions. As a unique feature, our tool applies self-organizing map (SOM) machine learning to the ST data. Our SOM data portrayal method generates individual gene expression landscapes for each spot in the ST image, enabling its downstream analysis with high resolution. The performance of the spatial browser is demonstrated by disentangling the intra-tumoral heterogeneity of melanoma and the microarchitecture of the mouse brain. The integration of machine-learning-based SOM portrayal into an interactive ST analysis environment opens novel perspectives for the comprehensive knowledge mining of the organization and interactions of cellular ecosystems.

How spatial omics approaches can be used to map the biological impacts of stress in psychiatric disorders: a perspective, overview and technical guide.

Exposure to significant levels of stress and trauma throughout life is a leading risk factor for the development of major psychiatric disorders. Despite this, we do not have a comprehensive understanding of the mechanisms that explain how stress raises psychiatric disorder risk. Stress in humans is complex and produces variable molecular outcomes depending on the stress type, timing, and duration. Deciphering how stress increases disorder risk has consequently been challenging to address with the traditional single-target experimental approaches primarily utilized to date. Importantly, the molecular processes that occur following stress are not fully understood but are needed to find novel treatment targets. Sequencing-based omics technologies, allowing for an unbiased investigation of physiological changes induced by stress, are rapidly accelerating our knowledge of the molecular sequelae of stress at a single-cell resolution. Spatial multi-omics technologies are now also emerging, allowing for simultaneous analysis of functional molecular layers, from epigenome to proteome, with anatomical context. The technology has immense potential to transform our understanding of how disorders develop, which we believe will significantly propel our understanding of how specific risk factors, such as stress, contribute to disease course. Here, we provide our perspective of how we believe these technologies will transform our understanding of the neurobiology of stress, and also provided a technical guide to assist molecular psychiatry and stress researchers who wish to implement spatial omics approaches in their own research. Finally, we identify potential future directions using multi-omics technology in stress research.

Zika virus co-opts microRNA networks to persist in placental niches detected by spatial transcriptomics.

BACKGROUND: Zika virus congenital infection evades double-stranded RNA detection and may persist in the placenta for the duration of pregnancy without accompanying overt histopathologic inflammation. Understanding how viruses can persist and replicate in the placenta without causing overt cellular or tissue damage is fundamental to deciphering mechanisms of maternal-fetal vertical transmission. OBJECTIVE: Placenta-specific microRNAs are believed to be a tenet of viral resistance at the maternal-fetal interface. We aimed to test the hypothesis that the Zika virus functionally disrupts placental microRNAs, enabling viral persistence and fetal pathogenesis. STUDY DESIGN: To test this hypothesis, we used orthogonal approaches in human and murine experimental models. In primary human trophoblast cultures (n=5 donor placentae), we performed Argonaute high-throughput sequencing ultraviolet-crosslinking and immunoprecipitation to identify any significant alterations in the functional loading of microRNAs and their targets onto the RNA-induced silencing complex. Trophoblasts from same-donors were split and infected with a contemporary first-passage Zika virus strain HN16 (multiplicity of infection=1 plaque forming unit per cell) or mock infected. To functionally cross-validate microRNA-messenger RNA interactions, we compared our Argonaute high-throughput sequencing ultraviolet-crosslinking and immunoprecipitation results with an independent analysis of published bulk RNA-sequencing data from human placental disk specimens (n=3 subjects; Zika virus positive in first, second, or third trimester, CD45- cells sorted by flow cytometry) and compared it with uninfected controls (n=2 subjects). To investigate the importance of these microRNA and RNA interference networks in Zika virus pathogenesis, we used a gnotobiotic mouse model uniquely susceptible to the Zika virus. We evaluated if small-molecule enhancement of microRNA and RNA interference pathways with enoxacin influenced Zika virus pathogenesis (n=20 dams total yielding 187 fetal specimens). Lastly, placentae (n=14 total) from this mouse model were analyzed with Visium spatial transcriptomics (9743 spatial transcriptomes) to identify potential Zika virus-associated alterations in immune microenvironments. RESULTS: We found that Zika virus infection of primary human trophoblast cells led to an unexpected disruption of placental microRNA regulation networks. When compared with uninfected controls, Zika virus-infected placentae had significantly altered SLC12A8, SDK1, and VLDLR RNA-induced silencing complex loading and transcript levels (-22; adjusted P value <.05; Wald-test with false discovery rate correction q<0.05). In silico microRNA target analyses revealed that 26 of 119 transcripts (22%) in the transforming growth factor-ß signaling pathway were targeted by microRNAs that were found to be dysregulated following Zika virus infection in trophoblasts. In gnotobiotic mice, relative to mock controls, Zika virus-associated fetal pathogenesis included fetal growth restriction (P=.036) and viral persistence in placental tissue (P=.011). Moreover, spatial transcriptomics of murine placentae revealed that Zika virus-specific placental niches were defined by significant up-regulation of complement cascade components and coordinated changes in transforming growth factor-ß gene expression. Finally, treatment of Zika virus-infected mice with enoxacin abolished placental Zika virus persistence, rescued the associated fetal growth restriction, and the Zika virus-associated transcriptional changes in placental immune microenvironments were no longer observed. CONCLUSION: These results collectively suggest that (1) Zika virus infection and persistence is associated with functionally perturbed microRNA and RNA interference pathways specifically related to immune regulation in placental microenvironments and (2) enhancement of placental microRNA and RNA interference pathways in mice rescued Zika virus-associated pathogenesis, specifically persistence of viral transcripts in placental microenvironments and fetal growth restriction.

Discrete placental gene expression signatures accompany diabetic disease classifications during pregnancy.

BACKGROUND: Gestational diabetes mellitus affects up to 10% of pregnancies and is classified into subtypes gestational diabetes subtype A1 (GDMA1) (managed by lifestyle modifications) and gestational diabetes subtype A2 (GDMA2) (requiring medication). However, whether these subtypes are distinct clinical entities or more reflective of an extended spectrum of normal pregnancy endocrine physiology remains unclear. OBJECTIVE: Integrated bulk RNA-sequencing (RNA-seq), single-cell RNA-sequencing (scRNA-seq), and spatial transcriptomics harbors the potential to reveal disease gene signatures in subsets of cells and tissue microenvironments. We aimed to combine these high-resolution technologies with rigorous classification of diabetes subtypes in pregnancy. We hypothesized that differences between preexisting type 2 and gestational diabetes subtypes would be associated with altered gene expression profiles in specific placental cell populations. STUDY DESIGN: In a large case-cohort design, we compared validated cases of GDMA1, GDMA2, and type 2 diabetes mellitus (T2DM) to healthy controls by bulk RNA-seq (n=54). Quantitative analyses with reverse transcription and quantitative PCR of presumptive genes of significant interest were undertaken in an independent and nonoverlapping validation cohort of similarly well-characterized cases and controls (n=122). Additional integrated analyses of term placental single-cell, single-nuclei, and spatial transcriptomics data enabled us to determine the cellular subpopulations and niches that aligned with the GDMA1, GDMA2, and T2DM gene expression signatures at higher resolution and with greater confidence. RESULTS: Dimensional reduction of the bulk RNA-seq data revealed that the most common source of placental gene expression variation was the diabetic disease subtype. Relative to controls, we found 2052 unique and significantly differentially expressed genes (-22 thresholds; q<0.05 Wald Test) among GDMA1 placental specimens, 267 among GDMA2, and 1520 among T2DM. Several candidate marker genes (chorionic somatomammotropin hormone 1 [CSH1], period circadian regulator 1 [PER1], phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit beta [PIK3CB], forkhead box O1 [FOXO1], epidermal growth factor receptor [EGFR], interleukin 2 receptor subunit beta [IL2RB], superoxide dismutase 3 [SOD3], dedicator of cytokinesis 5 [DOCK5], suppressor of glucose, and autophagy associated 1 [SOGA1]) were validated in an independent and nonoverlapping validation cohort (q<0.05 Tukey). Functional enrichment revealed the pathways and genes most impacted for each diabetes subtype, and the degree of proximal similarity to other subclassifications. Surprisingly, GDMA1 and T2DM placental signatures were more alike by virtue of increased expression of chromatin remodeling and epigenetic regulation genes, while albumin was the top marker for GDMA2 with increased expression of placental genes in the wound healing pathway. Assessment of these gene signatures in single-cell, single-nuclei, and spatial transcriptomics data revealed high specificity and variability by placental cell and microarchitecture types. For example, at the cellular and spatial (eg, microarchitectural) levels, distinguishing features were observed in extravillous trophoblasts (GDMA1) and macrophages (GDMA2). Lastly, we utilized these data to train and evaluate 4 machine learning models to estimate our confidence in predicting the control or diabetes status of placental transcriptome specimens with no available clinical metadata. CONCLUSION: Consistent with the distinct association of perinatal outcome risk, placentae from GDMA1, GDMA2, and T2DM-affected pregnancies harbor unique gene signatures that can be further distinguished by altered placental cellular subtypes and microarchitectural niches.

Spatial transcriptomics: Technologies, applications and experimental considerations.

The diverse cell types of an organ have a highly structured organization to enable their efficient and correct function. To fully appreciate gene functions in a given cell type, one needs to understand how much, when and where the gene is expressed. Classic bulk RNA sequencing and popular single cell sequencing destroy cell structural organization and fail to provide spatial information. However, the spatial location of gene expression or of the cell in a complex tissue provides key clues to comprehend how the neighboring genes or cells cross talk, transduce signals and work together as a team to complete the job. The functional requirement for the spatial content has been a driving force for rapid development of the spatial transcriptomics technologies in the past few years. Here, we present an overview of current spatial technologies with a special focus on the commercially available or currently being commercialized technologies, highlight their applications by category and discuss experimental considerations for a first spatial experiment.

Comparison of the Illumina NextSeq 2000 and GeneMind Genolab M sequencing platforms for spatial transcriptomics.

BACKGROUND: The Illumina sequencing systems demonstrate high efficiency and power and remain the most popular platforms. Platforms with similar throughput and quality profiles but lower costs are under intensive development. In this study, we compared two platforms Illumina NextSeq 2000 and GeneMind Genolab M for 10x Genomics Visium spatial transcriptomics. RESULTS: The performed comparison demonstrates that GeneMind Genolab M sequencing platform produces highly consistent with Illumina NextSeq 2000 sequencing results. Both platforms have similar performance in terms of sequencing quality and detection of UMI, spatial barcode, and probe sequence. Raw read mapping and following read counting produced highly comparable results that is confirmed by quality control metrics and strong correlation between expression profiles in the same tissue spots. Downstream analysis including dimension reduction and clustering demonstrated similar results, and differential gene expression analysis predominantly detected the same genes for both platforms. CONCLUSIONS: GeneMind Genolab M instrument is similar to Illumina sequencing efficacy and is suitable for 10x Genomics Visium spatial transcriptomics.

Spatial Gene Expression Analysis Reveals Characteristic Gene Expression Patterns of De Novo Neuroendocrine Prostate Cancer Coexisting with Androgen Receptor Pathway Prostate Cancer.

Neuroendocrine prostate carcinoma (NEPC) accounts for less than 1% of prostate neoplasms and has extremely poorer prognosis than the typical androgen receptor pathway-positive adenocarcinoma of the prostate (ARPC). However, very few cases in which de novo NEPC and APRC are diagnosed simultaneously in the same tissue have been reported. We report herein a 78-year-old man of de novo metastatic NEPC coexisting with ARPC treated at Ehime University Hospital. Visium CytAssist Spatial Gene Expression analysis (10× genetics) was performed using formalin-fixed, paraffin-embedded (FFPE) samples. The neuroendocrine signatures were upregulated in NEPC sites, and androgen receptor signatures were upregulated in ARPC sites. TP53, RB1, or PTEN and upregulation of the homologous recombination repair genes at NEPC sites were not downregulated. Urothelial carcinoma markers were not elevated. Meanwhile, Rbfox3 and SFRTM2 levels were downregulated while the levels of the fibrosis markers HGF, HMOX1, ELN, and GREM1 were upregulated in the tumor microenvironment of NEPC. In conclusion, the findings of spatial gene expression analysis in a patient with coexisting ARPC and de novo NEPC are reported. The accumulation of cases and basic data will help with the development of novel treatments for NEPC and improve the prognosis of patients with castration-resistant prostate cancer.

spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data.

BACKGROUND: Spatially-resolved transcriptomics has now enabled the quantification of high-throughput and transcriptome-wide gene expression in intact tissue while also retaining the spatial coordinates. Incorporating the precise spatial mapping of gene activity advances our understanding of intact tissue-specific biological processes. In order to interpret these novel spatial data types, interactive visualization tools are necessary. RESULTS: We describe spatialLIBD, an R/Bioconductor package to interactively explore spatially-resolved transcriptomics data generated with the 10x Genomics Visium platform. The package contains functions to interactively access, visualize, and inspect the observed spatial gene expression data and data-driven clusters identified with supervised or unsupervised analyses, either on the user's computer or through a web application. CONCLUSIONS: spatialLIBD is available at https://bioconductor.org/packages/spatialLIBD . It is fully compatible with SpatialExperiment and the Bioconductor ecosystem. Its functionality facilitates analyzing and interactively exploring spatially-resolved data from the Visium platform.

Multiplexed spatial transcriptomics methods and the application of expansion microscopy.

While spatial transcriptomics has undeniably revolutionized our ability to study cellular organization, it has driven the development of a great number of innovative transcriptomics methods, which can be classified into in situ sequencing (ISS) methods, in situ hybridization (ISH) techniques, and next-generation sequencing (NGS)-based sequencing with region capture. These technologies not only refine our understanding of cellular processes, but also open up new possibilities for breakthroughs in various research domains. One challenge of spatial transcriptomics experiments is the limitation of RNA detection due to optical crowding of RNA in the cells. Expansion microscopy (ExM), characterized by the controlled enlargement of biological specimens, offers a means to achieve super-resolution imaging, overcoming the diffraction limit inherent in conventional microscopy and enabling precise visualization of RNA in spatial transcriptomics methods. In this review, we elaborate on ISS, ISH and NGS-based spatial transcriptomic protocols and on how performance of these techniques can be extended by the combination of these protocols with ExM. Moving beyond the techniques and procedures, we highlight the broader implications of transcriptomics in biology and medicine. These include valuable insight into the spatial organization of gene expression in cells within tissues, aid in the identification and the distinction of cell types and subpopulations and understanding of molecular mechanisms and intercellular changes driving disease development.

Integrating gene expression and imaging data across Visium capture areas with visiumStitched.

Background: Visium is a widely-used spatially-resolved transcriptomics assay available from 10x Genomics. Standard Visium capture areas (6.5mm by 6.5mm) limit the survey of larger tissue structures, but combining overlapping images and associated gene expression data allow for more complex study designs. Current software can handle nested or partial image overlaps, but is designed for merging up to two capture areas, and cannot account for some technical scenarios related to capture area alignment. Results: We generated Visium data from a postmortem human tissue sample such that two capture areas were partially overlapping and a third one was adjacent. We developed the R/Bioconductor package visiumStitched, which facilitates stitching the images together with Fiji (ImageJ), and constructing SpatialExperiment R objects with the stitched images and gene expression data. visiumStitched constructs an artificial hexagonal array grid which allows seamless downstream analyses such as spatially-aware clustering without discarding data from overlapping spots. Data stitched with visiumStitched can then be interactively visualized with spatialLIBD. Conclusions: visiumStitched provides a simple, but flexible framework to handle various multi-capture area study design scenarios. Specifically, it resolves a data processing step without disrupting analysis workflows and without discarding data from overlapping spots. visiumStiched relies on affine transformations by Fiji, which have limitations and are less accurate when aligning against an atlas or other situations. visiumStiched provides an easy-to-use solution which expands possibilities for designing multi-capture area study designs.

A systematic evaluation of state-of-the-art deconvolution methods in spatial transcriptomics: insights from cardiovascular disease and chronic kidney disease.

A major challenge in sequencing-based spatial transcriptomics (ST) is resolution limitations. Tissue sections are divided into hundreds of thousands of spots, where each spot invariably contains a mixture of cell types. Methods have been developed to deconvolute the mixed transcriptional signal into its constituents. Although ST is becoming essential for drug discovery, especially in cardiometabolic diseases, to date, no deconvolution benchmark has been performed on these types of tissues and diseases. However, the three methods, Cell2location, RCTD, and spatialDWLS, have previously been shown to perform well in brain tissue and simulated data. Here, we compare these methods to assess the best performance when using human data from cardiovascular disease (CVD) and chronic kidney disease (CKD) from patients in different pathological states, evaluated using expert annotation. In this study, we found that all three methods performed comparably well in deconvoluting verifiable cell types, including smooth muscle cells and macrophages in vascular samples and podocytes in kidney samples. RCTD shows the best performance accuracy scores in CVD samples, while Cell2location, on average, achieved the highest performance across all test experiments. Although all three methods had similar accuracies, Cell2location needed less reference data to converge at the expense of higher computational intensity. Finally, we also report that RCTD has the fastest computational time and the simplest workflow, requiring fewer computational dependencies. In conclusion, we find that each method has particular advantages, and the optimal choice depends on the use case.

Aging- and alcohol-associated spatial transcriptomic signature in mouse acute pancreatitis reveals heterogeneity of inflammation and potential pathogenic factors.

The rapidly aging population is consuming more alcohol, leading to increased alcohol-associated acute pancreatitis (AAP) with high mortality. However, the mechanisms remain undefined, and currently there are no effective therapies available. This study aims to elucidate aging- and alcohol-associated spatial transcriptomic signature by establishing an aging AAP mouse model and applying Visium spatial transcriptomics for understanding of the mechanisms in the context of the pancreatic tissue. Upon alcohol diet feeding and caerulein treatment, aging mice (18 months) developed significantly more severe AAP with 5.0-fold increase of injury score and 2.4-fold increase of amylase compared to young mice (3 months). Via Visium spatial transcriptomics, eight distinct tissue clusters were revealed from aggregated transcriptomes of aging and young AAP mice: five acinar, two stromal, and one islet, which were then merged into three clusters: acinar, stromal, and islet for the comparative analysis. Compared to young AAP mice, > 1300 differentially expressed genes (DEGs) and approximately 3000 differentially regulated pathways were identified in aging AAP mice. The top five DEGs upregulated in aging AAP mice include Mmp8, Ppbp, Serpina3m, Cxcl13, and Hamp with heterogeneous distributions among the clusters. Taken together, this study demonstrates spatial heterogeneity of inflammatory processes in aging AAP mice, offering novel insights into the mechanisms and potential drivers for AAP development. KEY MESSAGES: Mechanisms regarding high mortality of AAP in aging remain undefined. An aging AAP mouse model was developed recapturing clinical exhibition in humans. Spatial transcriptomics identified contrasted DEGs in aging vs. young AAP mice. Top five DEGs were Mmp8, Ppbp, Serpina3m, Cxcl13, and Hamp in aging vs. young AAP mice. Our findings shed insights for identification of molecular drivers in aging AAP.

Comparative Methods for Demystifying Spatial Transcriptomics.

Spatial Transcriptomics (ST), coined as the term for parallel RNA-Seq on cell populations ordered spatially on a histological tissue section, has recently become increasingly popular, especially in experiments where microfluidics-based single-cell sequencing fails, such as assays on neurons. ST platforms, like the 10x Visium technology investigated herein, therefore produce in a single experiment simultaneously thousands of RNA readouts, captured by an array of micrometer scale spots under the histological section. Therefore, a central challenge of analyzing ST experiments consists of analyzing the gene expression morphology of all spots to delineate clusters of similar cell mixtures, which are then compared to each other to identify up- or down-regulated marker genes. Moreover, another level of complexity in ST experiments, compared to traditional RNA-Seq, is imposed by staining the tissue section with protein markers of cells or cell components to identify spots providing relevant information afterward. The corresponding microscopy images need to be analyzed in addition to the RNA-Seq read mappings on the reference genome and transcriptome sequences. Focusing on the software suite provided by the Visium platform manufacturer, we break down the ST analysis pipeline into its four essential steps-the image analysis, the read alignment, the gene quantification, and the spot clustering-and compare results obtained when using reads from different subsets of spots and/or when employing alternative genome or transcriptome references. Our comparative analyses demonstrate the impact of spot selection and the choice of genome/transcriptome references on the analysis results when employing the manufacturer's pipeline.

PanIN and CAF transitions in pancreatic carcinogenesis revealed with spatial data integration.

This study introduces a new imaging, spatial transcriptomics (ST), and single-cell RNA-sequencing integration pipeline to characterize neoplastic cell state transitions during tumorigenesis. We applied a semi-supervised analysis pipeline to examine premalignant pancreatic intraepithelial neoplasias (PanINs) that can develop into pancreatic ductal adenocarcinoma (PDAC). Their strict diagnosis on formalin-fixed and paraffin-embedded (FFPE) samples limited the single-cell characterization of human PanINs within their microenvironment. We leverage whole transcriptome FFPE ST to enable the study of a rare cohort of matched low-grade (LG) and high-grade (HG) PanIN lesions to track progression and map cellular phenotypes relative to single-cell PDAC datasets. We demonstrate that cancer-associated fibroblasts (CAFs), including antigen-presenting CAFs, are located close to PanINs. We further observed a transition from CAF-related inflammatory signaling to cellular proliferation during PanIN progression. We validate these findings with single-cell high-dimensional imaging proteomics and transcriptomics technologies. Altogether, our semi-supervised learning framework for spatial multi-omics has broad applicability across cancer types to decipher the spatiotemporal dynamics of carcinogenesis.

Nextflow pipeline for Visium and H&E data from patient-derived xenograft samples.

We designed a Nextflow DSL2-based pipeline, Spatial Transcriptomics Quantification (STQ), for simultaneous processing of 10x Genomics Visium spatial transcriptomics data and a matched hematoxylin and eosin (H&E)-stained whole-slide image (WSI), optimized for patient-derived xenograft (PDX) cancer specimens. Our pipeline enables the classification of sequenced transcripts for deconvolving the mouse and human species and mapping the transcripts to reference transcriptomes. We align the H&E WSI with the spatial layout of the Visium slide and generate imaging and quantitative morphology features for each Visium spot. The pipeline design enables multiple analysis workflows, including single or dual reference genome input and stand-alone image analysis. We show the utility of our pipeline on a dataset from Visium profiling of four melanoma PDX samples. The clustering of Visium spots and clustering of H&E imaging features reveal similar patterns arising from the two data modalities.

Systematic Sampling of the Female Reproductive System for Molecular Characterization.

As part of the National Institutes of Health Human BioMolecular Atlas Program to develop a global platform to map the 37 trillion cells in the adult human body, we are generating a comprehensive molecular characterization of the female reproductive system. Data gathered from multiple single-cell/single-nucleus and spatial molecular assays will be used to build a 3D molecular atlas. Herein, we describe our multistep protocol, beginning with an optimized organ procurement workflow that maintains functional characteristics of the uterus, ovaries, and fallopian tubes by perfusing these organs with preservation solution. We have also developed a structured tissue sampling procedure that retains information on individual-level anatomic, physiologic, and individual diversity of the female reproductive system, toward full exploration of the function and structure of female reproductive cells. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Preparation and preservation of the female reproductive system (ovaries, fallopian tubes, and uterus) prior to procurement Basic Protocol 2: Removal of the female reproductive system en bloc Basic Protocol 3: Postsurgical dissection of ovaries Basic Protocol 4: Postsurgical dissection of fallopian tubes Basic Protocol 5: Postsurgical dissection of cervix Basic Protocol 6: Postsurgical dissection of uterine body Support Protocol 1: OCT-embedded tissue protocol Support Protocol 2: Tissue fixation protocol Support Protocol 3: Snap-frozen tissue protocol Basic Protocol 7: Tissue slice preparation for Visium analysis Support Protocol 4: Hematoxylin and eosin staining for 10X Visium imaging Basic Protocol 8: Manual tissue dissociation for Multiome analysis Basic Protocol 9: Tissue dissociation for Multiome analysis using S2 Singulator.

Spatial Transcriptomics in Kidney Tissue.

Unlike bulk and single-cell/single-nuclei RNA sequencing methods, spatial transcriptome sequencing (ST-seq) resolves transcriptome expression within the spatial context of intact tissue. This is achieved by integrating histology with RNA sequencing. These methodologies are completed sequentially on the same tissue section placed on a glass slide with printed oligo-dT spots, termed ST-spots. Transcriptomes within the tissue section are captured by the underlying ST-spots and receive a spatial barcode in the process. The sequenced ST-spot transcriptomes are subsequently aligned with the hematoxylin and eosin (H&E) image, giving morphological context to the gene expression signatures within intact tissue. We have successfully employed ST-seq to characterize mouse and human kidney tissue. Here, we describe in detail the application of Visium Spatial Tissue Optimization (TO) and Visium Spatial Gene Expression (GEx) protocols for ST-seq in fresh frozen kidney tissue.

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/.

Tissue RNA Integrity in Visium Spatial Protocol (Fresh Frozen Samples).

The transcriptome of a tissue can be acquired both by single-cell RNAseq (scRNA-seq) and by spatial transcriptomics (ST). The dissociation step, which is mandatory in scRNA-seq methods, might lead to the loss of fragile cells and of spatial information, thus limiting the acquisition of the tissue cellular organization. Spatial transcriptomics methods moderate the above-mentioned issues and provide single-cell transcripts detection over an intact fresh frozen tissue section. Visium platform, commercialized from 10× Genomics, provides a whole transcriptome spatial transcriptomics platform, which does not require dedicated instruments, other than those available in any pathology laboratory. In spatial transcriptomics, proper tissue handling is mandatory to preserve the morphological quality of the tissue sections and the integrity of mRNA transcripts. Proper tissue handling is critical for downstream library preparation and sequencing performance. In this chapter, we describe the most critical steps of Visium protocol on fresh frozen tissues and we provide indications on how to interpret the data obtained from the quality control analysis recommended during the workflow.