Unveiling Cellular Mysteries: The Power of Single-Cell Sequencing with FFPE Samples

Single-cell RNA sequencing (scRNA-seq) is transforming our comprehension of cellular mechanisms, and has become indispensable in clinical studies over the past decades ^[1-4]. However, the procurement of fresh samples, a commonly held requirement for most scRNA-Seq technologies, comes with significant challenges for clinical studies.

Formalin-fixed and paraffin-embedded (FFPE) samples, commonly used in clinical settings for long-term storage, have been largely incompatible with conventional single-cell transcriptome methods due to the extensive RNA cross-linking and fragmentation caused by the fixation process. As a result, the majority of single-cell technologies have been unable to effectively analyze FFPE samples, limiting researchers' ability to leverage the wealth of clinical specimens available for study.

The recent development of the novel single-cell transcriptome technology M20 Seq, alongside the VITA single-cell transcriptome product series, which are compatible with FFPE samples, provides a groundbreaking solution. This significant advancement allows researchers to leverage existing clinical archives for biomedical research, expanding the scope of clinical studies and enhancing our understanding of disease mechanisms.

FFPE Samples: The Hidden Treasure

FFPE samples have long served as a critical resource in clinical studies and diagnostics, widely collected and stored in clinical settings. Their long-term preservation capacity and widespread availability make them indispensable for retrospective and longitudinal studies across decades of medical history.

Figure 1: Formalin-fixed paraffin-embedded (FFPE) tissues.

In clinical studies, FFPE samples present significant advantages over fresh tissues. Their convenient storage conditions eliminate the requirement for immediate processing or specialized handling necessary for fresh samples. This not only reduces costs but also simplifies logistics in clinical settings. Furthermore, FFPE samples allow for retrospective analysis with known clinical outcomes. Unlike fresh samples, whose suitability for studies can only be determined after processing—often resulting in wasted resources on unsuitable samples—FFPE tissues are already associated with clinical characteristics, such as tumor subtype or key biomarkers. Therefore, FFPE samples provide a valuable resource for molecular research in clinical settings.

Recently, we've gained the capability to utilize FFPE samples for high-throughput single-cell transcriptome research using the VITA single-cell transcriptome products mentioned earlier. With these advancements, researchers can now unlock the potential of these archived specimens to unravel the complexities of disease biology at the single-cell level.

 Leveraging FFPE Samples For In-Depth Insights in Tumor Biology

Transcriptomic profiling of FFPE samples has revolutionized our understanding of tumor biology, unveiling molecular biomarkers critical for clinical advancements and deepening our comprehension of tumor dynamics. While bulk RNA-seq has long been the gold standard for gene expression analysis ^[5-9], there is a growing recognition of the necessity for more advanced methodologies, such as scRNA-Seq, which enables the elucidation of cellular heterogeneity within complex tissues in various clinical studies.

1. Tumor Classification

Tailoring treatments to individual patients relies on accurately categorizing tumor subtypes. Insights from FFPE samples have been crucial for discovering molecular biomarkers that enable precise tumor categorization. A study involving archival FFPE breast cancer specimens from 58 patients underscored the efficacy of gene set expression analysis in distinguishing between ER⁺ (estrogen receptor-positive) and ER^- (estrogen receptor-negative) breast cancers (Figure 2). Through the utilization of regulon analysis, researchers identified KDM4B as a molecular marker for ER⁺ cancers with prognostic significance ^[5].

Figure 2: Breast cancer subtype identification in FFPE samples utilizing clinically relevant gene panels.

While traditional bulk RNA sequencing provides valuable insights, it may obscure vital details by averaging and thresholding gene expression levels across different cell types and states.  In contrast, scRNA-Seq offers a solution by providing insights into the gene expression of individual cells. This approach not only enables the identification of unique signature genes in specific cell types but also unveils rare cell populations that may be missed by bulk RNA-seq. By overcoming the limitations of bulk RNA-seq, scRNA-Seq offers a more comprehensive understanding of cellular heterogeneity within FFPE samples, thus facilitating precise tumor classification and precision medicine.

2. Identification of Prognostic Markers

Molecular prognostic biomarkers are crucial for tailoring personalized treatment, yet validated markers with clinical significance remain limited. A recent study has identified prognostic markers based on gene expression signatures for uterine clear cell carcinomas (CCC), a rare subtype of uterine cancer. Through the analysis of tumor samples from 68 patients, researchers have correlated clinical outcomes with molecular profiles, revealing notable patterns such as low hormone receptor expression and frequent PI3K pathway alterations (Figure 3). Transcriptomic analysis has unveiled a distinct profile of heightened expression of immune response-associated genes in cases of uterine CCC with a favorable prognosis. These findings suggest that patients exhibiting this profile may represent promising candidates for immunotherapy ^[6].

Figure 3: Gene expression profile of uterine CCC according to the 786-gene panel ^[6].

Another study provided new insights into breast cancer brain metastasis (BCBM) in patients by analyzing changes in the expression levels of key biomarkers such as ER, PR, HER-2, and Ki-67. Remarkably, 45% of patients showed changes in at least one biomarker between primary and metastatic lesions. A notable prolongation of survival in patients with HER-2 expression discordance was observed (Figure 4) ^[7], underscoring the prognostic importance of biomarker variations in predicting outcomes and potentially guiding treatment strategies.

Figure 4: Survival curves of patients with positive-to-negative HER-2 expression versus negative-to-positive HER-2 expression ^[8].

While these studies utilized technologies such as bulk RNA-seq and immunohistochemistry to access gene expression in FFPE samples, scRNA-seq represents a paradigm shift by enabling the characterization of gene expression dynamics at the cellular level. This advantage enhances the identification of prognostic markers by providing a more granular view of cellular heterogeneity within tumor samples. Furthermore, scRNA-seq facilitates precise identification and functional characterization of immune cells and their interactions within the tumor microenvironment. Consequently, it offers novel insights into tumor immunology and potential therapeutic targets that may have been overlooked with conventional methods.

Moreover, applying scRNA-seq to paired FFPE samples from primary tumors and metastases enables the tracking of alterations in cell subtype/state and gene expression patterns associated with metastasis. This provides an in-depth understanding of the molecular mechanisms driving tumor metastasis and facilitates the identification of precise biomarkers associated with tumor progression.

3. Assessing Treatment Resistance

Transcriptomic analysis of FFPE samples has been crucial in identifying biomarkers contributing to predict treatment responses, as evidenced by a study on esophageal squamous cell carcinoma (ESCC). The analysis of 103 ESCC FFPE samples revealed an aggressive mesenchymal phenotype in treatment-naïve patients, highlighting PLEK2 and IFI6 as resistance biomarkers to neoadjuvant immunotherapy (Figure 5). Validating these biomarkers with immunohistochemistry highlights gene expression data's ability to identify novel markers ^[8].

Figure 5: High expression of PLEK2 and IFI6 predicts therapeutic efficacy and resistance to neoadjuvant immunotherapy in ESCC ^[9].

Bulk transcriptomic data provides fundamental insights into resistance biomarkers; however, it often obscures the molecular profiles of rare or low-abundance cell subtypes in the tumor microenvironment, potentially overlooking crucial contributors to resistance mechanisms. In contrast, scRNA-seq offers a solution by providing comprehensive insights into the transcriptional profiles of individual cells, regardless of their abundance. This approach enables the identification of rare cell populations in FFPE samples and the unique gene expression patterns of each cell population, thus offering a more nuanced understanding of drug resistance mechanisms.

M20 Seq: The Power of Random Primers for FFPE Sample Analysis

Historically, the use of single-cell RNA Sequencing was limited by a narrow range of sample types suitable for analysis. The reliance of most technologies on poly(A) tails for RNA capture prevents the use of FFPE samples, especially as these transcript regions are particularly prone to degradation.

Through M20 Seq and the associated VITA High-Throughput Single-Cell Transcriptome Platform (Figure 6), we've pioneered a groundbreaking innovation by harnessing random primers for RNA capture. Unlike traditional poly(T) primers, which target poly(A) tails, random primers bind to RNA molecules indiscriminately. This feature is especially advantageous for FFPE samples, where RNA molecules are often fragmented and heavily crosslinked with proteins. By binding to random regions of transcripts, these primers enable the capture of even degraded fragments. Furthermore, they facilitate the profiling of complete transcriptomes by capturing both messenger RNAs and non-coding RNAs, offering a more comprehensive understanding of molecular mechanisms at the transcriptional level.

Figure 6: VITA products for FFPE samples.

We are proud to stand at the forefront of a transformative journey, pushing the boundaries of single-cell technologies to access the wealth of information in archival FFPE samples. We are committed to enable breakthroughs and empower our customers to make new discoveries in biomedical research.

* For further inquiries or more information about M20's innovative products, please don't hesitate to reach out to us at info@m20genomics.com. We are committed to pioneering innovation and welcome opportunities to collaborate and contribute to advancing our understanding of human health and disease.

References

[1] Paik, D.T., Cho, S., Tian, L. et al. (2020). Single-cell RNA sequencing in cardiovascular development, disease and medicine. Nat Rev Cardiol, 17(7), 457–473.

[2] Piwecka, M., Rajewsky, N. & Rybak-Wolf, A. (2023). Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease. Nat Rev Neurol, 19(6), 346–362.

[3] Ratnasiri, K., Wilk, A. J., Lee, M. J., Khatri, P., & Blish, C. A. (2023). Single-cell RNA-seq methods to interrogate virus-host interactions. Semin Immunopathol, 45(1), 71–89.

[4] Huang, D., Ma, N., Li, X. et al. (2023). Advances in single-cell RNA sequencing and its applications in cancer research. J Hematol Oncol, 16(1), 98.

[5] Pennock, N.D., Jindal, S., Horton, W. et al. (2019). RNA-seq from archival FFPE breast cancer samples: molecular pathway fidelity and novel discovery. BMC Med Genomics, 12(1), 195.

[6] Nigon, E., Lefeuvre-Plesse, C., Martinez, A. et al. (2023). Clinical, pathological, and comprehensive molecular analysis of the uterine clear cell carcinoma: a retrospective national study from TMRG and GINECO network. J Transl Med, 21(1), 408.