Spatially exploring RNA biology in archival formalin-fixed paraffin-embedded tissues

Introduction: Spatial transcriptomics is revolutionizing our understanding of developmental biology, oncology, and disease pathology, mapping intricate gene expression patterns within their native tissue context. However, to date, the scope of the field primarily revolves around the analysis of messenger RNA (mRNA) expression. The transcriptome in eukaryotic cells is a myriad of all dynamic and diverse RNA molecules encompassing not only mature mRNAs encoding protein synthesis but also splicing variants, small RNAs, and other non-coding RNAs with regulatory functions. Thus, spatial profiling of different RNA species throughout their life cycle is imperative for dissecting the full picture of RNA biology in complex tissues. Furthermore, formalin-fixed paraffin-embedded (FFPE) tissues are essential in clinical practice, being the backbone of human disease histopathological diagnoses. Clinical pathology researchers have accrued vast collections of FFPE blocks over time, creating a rich, yet underutilized compendium of materials that, accompanied by comprehensive clinical data, stands as a treasure trove for human biology and translational research. Nevertheless, FFPE specimens pose certain challenges. The RNA within these samples is susceptible to fragmentation during the paraffin-embedding process and may further experience heightened degradation under suboptimal storage conditions. Consequently, options for spatially profiling RNA molecules in this challenging tissue type are limited. The capability to spatially explore RNA biology in FFPE tissues holds transformative potential for human biology research and clinical histopathology.

Materials and

Methods: In this evolving landscape, we present Pathology-compatible Deterministic Barcoding in Tissue (Patho-DBiT) not only enabling spatial full-coverage base-by-base whole transcriptome sequencing but also meticulously crafted to address the distinctive challenges of clinically archived FFPE tissues. Patho-DBiT integrates in situ polyadenylation, microfluidic barcoding, and computational innovations to navigate and decode the rich RNA biology inherent in FFPE samples. The platform adeptly capitalizes on RNA fragmentation naturally occurring in FFPE specimens and appends poly(A) tails to a broad spectrum of RNA species, thereby overcoming traditional barriers associated with FFPE samples. The Patho-DBiT method initiates with tissue section deparaffinization and heat-induced crosslink reversal, adhering to a standardized protocol (Figure.1A). After tissue permeabilization, enzymatic in situ polyadenylation enables detection of the full spectrum of RNAs, followed by cDNA strand synthesis by reverse transcription. Spatial barcoding is then achieved using a microfluidic device with two PDMS chips featuring 50 parallel microchannels. These channels sequentially deliver horizontal (A1-A50) and perpendicular (B1-B50) barcodes, creating a unique 2D barcode combination array. Post-imaging, the tissue undergoes digestion to extract barcoded cDNA to perform the downstream procedures including template switch and PCR amplification. By spatially barcoding intact mRNAs, fragmented mRNAs lacking poly(A) tails, various forms of large and small non-coding RNAs, splicing isoforms, and precursor RNAs carrying single nucleotide variations (SNVs), we now can gain a deeper appreciation of high-sensitivity transcriptomics, alternative splicing, genetic variation profile, microRNA-mRNA regulation, and RNA dynamics within complex tissues (Figure.1A).

Results, Conclusions, and Discussions: To benchmark Patho-DBiT, we applied it to mouse embryonic day 13 (E13) FFPE section. Unsupervised clustering revealed 20 clusters, closely aligned with the H&E histology (Figure.1B). Patho-DBiT detected an average of 5,480 genes and 15,381 UMIs per pixel, with notably high reproducibility of R=0.999 (Figure.1C,D). Compared to normal DBiT-seq without polyadenylation, Patho-DBiT exhibited notably increased coverage across the entire gene body, a higher percentage of reads mapping to the 5'UTR, and enhanced capture of multiple non-coding RNAs (Figure.1E,F). The integration with scRNA-seq reference dataset revealed a cohesive pattern (Figure.1G). We applied Patho-DBiT across diverse tissue types and spatial resolutions, yielding remarkable performance (Figure.1H). Its capability to simultaneously map gene expression, alternative splicing and A-to-I RNA editing was further demonstrated in the mouse FFPE coronal brain section (Figure.2). Patho-DBiT was employed to barcode a tissue section obtained from a patient diagnosed with angioimmunoblastic T-cell lymphoma (Figure.3A,B). This block had been stored at room temperature for over five years. Unsupervised clustering revealed 10 spatially organized clusters aligned with histological structures. The UMAP, with an average of 5,364 genes and 11,989 UMIs per pixel, delineated distinct cell types defined by canonical markers, further validated with the high-plex immunofluorescence (IF) spatial cell typing using the CODEX technology (Figure.3C-F). We conducted Patho-DBiT on another lymphoma biopsy stored for three years and validated its capability to capture rare cell types in specific regions using IF staining (Figure.4A-C). With genomic coverage capability superior to that of single-cell technologies (Figure.4D,E), genome-wide single nucleotide RNA variants can be captured to distinguish different malignant clones from non-malignant cells. The spatial heatmap of accumulated SNVs highlighted a notably higher SNV burden in the B cell region compared to other areas (Figure.4F). The tumor signature in these B cells was validated through immunohistochemistry staining of canonical markers (Figure.4G). Leveraging RNA-encoded variant information, unsupervised tumor discrimination was achieved (Figure.4H). Furthermore, Patho-DBiT also maps microRNA-mRNA regulatory networks and RNA splicing dynamics, decoding their roles in spatial tumorigenesis trajectory (Figure.5). Patho-DBiT represents a first-of-its-kind technology for exploring spatial RNA biology in clinically archived FFPE samples to study human tissue biology and aid in pathology evaluation.

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