Reproducible and sensitive micro-tissue RNA sequencing from formalin-fixed paraffin-embedded tissues for spatial gene expression analysis

An optimized workflow for transcriptomic analysis from archival paraformaldehyde-fixed retinal tissues collected by laser capture microdissection

RNA sequencing (RNA-seq) coupled with laser capture microdissection (LCM) is a powerful tool for transcriptomic analysis in unfixed fresh-frozen tissues. Fixation of ocular tissues for immunohistochemistry commonly involves the use of paraformaldehyde (PFA) followed by embedding in Optimal Cutting Temperature (OCT) medium for long-term cryopreservation. However, the quality of RNA derived from such archival PFA-fixed/OCT-embedded samples is often compromised, limiting its suitability for transcriptomic studies. In this study, we aimed to develop a methodology to extract high-quality RNA from PFA-fixed canine eyes by utilizing LCM to isolate retinal tissue. We demonstrate the efficacy of an optimized LCM and RNA purification protocol for transcriptomic profiling of PFA-fixed retinal specimens. We compared four pairs of canine retinal tissues, where one eye was subjected to PFA-fixation prior to OCT embedding, while the contralateral eye was embedded fresh frozen (FF) in OCT without fixation. Since the RNA obtained from PFA-fixed retinas were contaminated with genomic DNA, we employed two rounds of DNase I treatment to obtain RNA suitable for RNA-seq. Notably, the quality of sequencing reads and gene sets identified from both PFA-fixed and FF tissues were nearly identical. In summary, our study introduces an optimized workflow for transcriptomic profiling from PFA-fixed archival retina. This refined methodology paves the way for improved transcriptomic analysis of preserved ocular tissue, bridging the gap between optimal sample preservation and high-quality RNA data acquisition.

A practical guide to spatial transcriptomics

Spatial transcriptomics is revolutionizing modern biology, offering researchers an unprecedented ability to unravel intricate gene expression patterns within tissues. From pioneering techniques to newly commercialized platforms, the field of spatial transcriptomics has evolved rapidly, ushering in a new era of understanding across various disciplines, from developmental biology to disease research. This dynamic expansion is reflected in the rapidly growing number of technologies and data analysis techniques developed and introduced. However, the expanding landscape presents a considerable challenge for researchers, especially newcomers to the field, as staying informed about these advancements becomes increasingly complex. To address this challenge, we have prepared an updated review with a particular focus on technologies that have reached commercialization and are, therefore, accessible to a broad spectrum of potential new users. In this review, we present the fundamental principles of spatial transcriptomic methods, discuss the challenges in data analysis, provide insights into experimental considerations, offer information about available resources for spatial transcriptomics, and conclude with a guide for method selection and a forward-looking perspective. Our aim is to serve as a guiding resource for both experienced users and newcomers navigating the complex realm of spatial transcriptomics in this era of rapid development. We intend to equip researchers with the necessary knowledge to make informed decisions and contribute to the cutting-edge research that spatial transcriptomics offers.

The cardiovascular pathologist in the aortic team

Aortic diseases require a multidisciplinary management for diagnosis, treatment and follow-up with better outcomes in referral centers using a team-based approach. The setting up of a multi-disciplinary aortic team for the discussion of complex cases has been already proposed; it is also supported by the ACC/AHA. Surgeons and radiologists, more or less other physicians such as cardiologists, geneticists, rheumatologists/internal medicine specialists and pathologists are involved into such a team. The role of the cardiovascular pathologist is to examine the aortic specimens, to diagnose and classify the aortic lesions. Herein, the role of the pathologist in the aortic team is discussed and the pathobiology of aortic diseases is reviewed for reference by pathologists. The aortic specimens are mainly obtained from emergency or elective surgical procedures on the thoracic aorta, less frequently from organ/tissue (including cardiac or heart valve) donors, post-mortem procedures or abdominal aortic surgery. In the last decade, together with the progress of medical sciences, the histological definitions and classifications of the aortic pathology are undergoing thorough revisions that are addressed to an etiopathogenetic approach because of possible clinico-pathological correlations, therapeutic and prognostic impact. Pathologists may also have an important role in research and teaching. Therefore, histological analyses of the aortic specimens require adequate sample processing and pathologist expertise because histology contributes to definite diagnosis, correct management of patients and even (in genetic diseases) families, but also to research in the challenging field of aortopathies.

Identification of target cells of human papillomavirus 18 using squamocolumnar junction organoids

Human papillomavirus 18 (HPV18) is a highly malignant HPV genotype among high-risk HPVs, characterized by the difficulty of detecting it in precancerous lesions and its high prevalence in adenocarcinomas. The cellular targets and molecular mechanisms underlying its infection remain unclear. In this study, we aimed to identify the cells targeted by HPV18 and elucidate the molecular mechanisms underlying HPV18 replication. Initially, we established a lentiviral vector (HPV18LCR-GFP vector) containing the HPV18 long control region promoter located upstream of EGFP. Subsequently, HPV18LCR-GFP vectors were transduced into patient-derived squamocolumnar junction organoids, and the presence of GFP-positive cells was evaluated. Single-cell RNA sequencing of GFP-positive and GFP-negative cells was conducted. Differentially expressed gene analysis revealed that 169 and 484 genes were significantly upregulated in GFP-positive and GFP-negative cells, respectively. Pathway analysis showed that pathways associated with cell cycle and viral carcinogenesis were upregulated in GFP-positive cells, whereas keratinization and mitophagy/autophagy-related pathways were upregulated in GFP-negative cells. siRNA-mediated luciferase reporter assay and HPV18 genome replication assay validated that, among the upregulated genes, ADNP, FHL2, and NPM3 were significantly associated with the activation of the HPV18 early promoter and maintenance of the HPV18 genome. Among them, NPM3 showed substantially higher expression in HPV-related cervical adenocarcinomas than in squamous cell carcinomas, and NPM3 knockdown of HPV18-infected cells downregulated stem cell-related genes. Our new experimental model allows us to identify novel genes involved in HPV18 early promoter activities. These molecules might serve as therapeutic targets in HPV18-infected cervical lesions.

Systematic benchmarking of imaging spatial transcriptomics platforms in FFPE tissues

Emerging imaging spatial transcriptomics (iST) platforms and coupled analytical methods can recover cell-to-cell interactions, groups of spatially covarying genes, and gene signatures associated with pathological features, and are thus particularly well-suited for applications in formalin fixed paraffin embedded (FFPE) tissues. Here, we benchmarked the performance of three commercial iST platforms on serial sections from tissue microarrays (TMAs) containing 23 tumor and normal tissue types for both relative technical and biological performance. On matched genes, we found that 10x Xenium shows higher transcript counts per gene without sacrificing specificity, but that all three platforms concord to orthogonal RNA-seq datasets and can perform spatially resolved cell typing, albeit with different false discovery rates, cell segmentation error frequencies, and with varying degrees of sub-clustering for downstream biological analyses. Taken together, our analyses provide a comprehensive benchmark to guide the choice of iST method as researchers design studies with precious samples in this rapidly evolving field.

Dual spatially resolved transcriptomics for human host-pathogen colocalization studies in FFPE tissue sections

Technologies to study localized host-pathogen interactions are urgently needed. Here, we present a spatial transcriptomics approach to simultaneously capture host and pathogen transcriptome-wide spatial gene expression information from human formalin-fixed paraffin-embedded (FFPE) tissue sections at a near single-cell resolution. We demonstrate this methodology in lung samples from COVID-19 patients and validate our spatial detection of SARS-CoV-2 against RNAScope and in situ sequencing. Host-pathogen colocalization analysis identified putative modulators of SARS-CoV-2 infection in human lung cells. Our approach provides new insights into host response to pathogen infection through the simultaneous, unbiased detection of two transcriptomes in FFPE samples.

Enhancing the sensitivity of bacterial single-cell RNA sequencing using RamDA-seq and Cas9-based rRNA depletion

Bacterial populations exhibit heterogeneity in gene expression, which facilitates their survival and adaptation to unstable and unpredictable environments through the bet-hedging strategy. However, unraveling the rare subpopulations and heterogeneity in gene expression using population-level gene expression analysis remains a challenging task. Single-cell RNA sequencing (scRNA-seq) has the potential to identify rare subpopulations and capture heterogeneity in bacterial populations, but standard methods for scRNA-seq in bacteria are still under development, mainly due to differences in mRNA abundance and structure between eukaryotic and prokaryotic organisms. In this study, we present a hybrid approach that combines random displacement amplification sequencing (RamDA-seq) with Cas9-based rRNA depletion for scRNA-seq in bacteria. This approach allows cDNA amplification and subsequent sequencing library preparation from low-abundance bacterial RNAs. We evaluated its sequenced read proportion, gene detection sensitivity, and gene expression patterns from the dilution series of total RNA or the sorted single Escherichia coli cells. Our results demonstrated the detection of more than 1000 genes, about 24% of the genes in the E. coli genome, from single cells with less sequencing effort compared to conventional methods. We observed gene expression clusters between different cellular proliferation states or heat shock treatment. The approach demonstrated high detection sensitivity in gene expression analysis compared to current bacterial scRNA-seq methods and proved to be an invaluable tool for understanding the ecology of bacterial populations and capturing the heterogeneity of bacterial gene expression.

Integrated spatial analysis of gene mutation and gene expression for understanding tumor diversity in formalin-fixed paraffin-embedded lung adenocarcinoma

Introduction

A deeper understanding of intratumoral heterogeneity is essential for prognosis prediction or accurate treatment plan decisions in clinical practice. However, due to the cross-links and degradation of biomolecules within formalin-fixed paraffin-embedded (FFPE) specimens, it is challenging to analyze them. In this study, we aimed to optimize the simultaneous extraction of mRNA and DNA from microdissected FFPE tissues (φ = 100 µm) and apply the method to analyze tumor diversity in lung adenocarcinoma before and after erlotinib administration.

Method

Two magnetic beads were used for the simultaneous extraction of mRNA and DNA. The decross-linking conditions were evaluated for gene mutation and gene expression analyses of microdissected FFPE tissues. Lung lymph nodes before treatment and lung adenocarcinoma after erlotinib administration were collected from the same patient and were preserved as FFPE specimens for 4 years. Gene expression and gene mutations between histologically classified regions of lung adenocarcinoma (pre-treatment tumor in lung lymph node biopsies and post-treatment tumor, normal lung, tumor stroma, and remission stroma, in resected lung tissue) were compared in a microdissection-based approach.

Results

Using the optimized simultaneous extraction of DNA and mRNA and whole-genome amplification, we detected approximately 4,000-10,000 expressed genes and the epidermal growth factor receptor (EGFR) driver gene mutations from microdissected FFPE tissues. We found the differences in the highly expressed cancer-associated genes and the positive rate of EGFR exon 19 deletions among the tumor before and after treatment and tumor stroma, even though they were collected from tumors of the same patient or close regions of the same specimen.

Conclusion

Our integrated spatial analysis method would be applied to various FFPE pathology specimens providing area-specific gene expression and gene mutation information.