Transforming lncRNA Analysis with VITA Platform
In 1957, Francis Crick introduced the central dogma of molecular biology, stating that information flows from DNA to RNA to proteins [1-2]. This concept laid the foundation for understanding gene expression and regulation. Initially, the discovery that less than 2% of the human genome codes for proteins led to the rest being labeled as "junk DNA." However, much of this DNA is transcribed into non-coding RNAs. Among these, long non-coding RNAs (lncRNAs) are crucial regulators of gene expression.
LncRNAs: Crucial Regulators of Gene Expression
LncRNAs, non-coding transcripts of more than 200 nucleotides, regulate gene expression by organizing chromatin, modifying histones, recruiting transcription factors, and stabilizing mRNA. Additionally, lncRNAs can influence gene expression in neighboring cells via extracellular vesicles (Figure 1).
Figure 1: Mechanisms of lncRNAs in gene expression regulation [3].
LncRNAs influence various physiological processes, including development and immune responses. For instance, lncRNA TUNA is essential for neural differentiation in embryonic stem cells [4]. In the immune system, lncRNAs like lincRNA-Cox2 modulate immune cell differentiation, activation, and function [5]. Moreover, lncRNAs play crucial roles in disease development, such as cancer progression. HOTAIR, an oncogenic lncRNA, is overexpressed in various cancers, promoting metastasis, proliferation, invasion, and resistance to apoptosis [6].
The Current State and Frontiers of lncRNA Analysis
Given the crucial roles of lncRNAs, their functional analysis is highly relevant but challenging due to their low and tissue-specific expression. Common methods for lncRNA analysis face limitations in sensitivity, throughput, and comprehensiveness.
Bulk RNA sequencing (RNA-seq) profiles lncRNA expression but often misses low-abundance lncRNAs and cannot dissect cell heterogeneity. Real-time quantitative PCR (qPCR) offers high sensitivity but is limited to targeted lncRNAs. In situ hybridization (ISH) and fluorescence in situ hybridization (FISH) pinpoint lncRNA localization but require extensive optimization and face challenges with low-expressed lncRNAs.
Single-cell RNA sequencing (scRNA-seq) opened new avenues for lncRNA analysis by revealing heterogeneity within cell populations. However, most scRNA-seq technologies face challenges detecting lncRNAs due to their low expression levels and complex secondary structures. Additionally, most scRNA-seq technologies rely on poly(A) capture, which biases lncRNA detection since lncRNAs can either possess or lack polyadenylation.
M20 Genomics‘s VITA platform offers a cutting-edge solution for analyzing lncRNA data in various sample types, including frozen and formalin-fixed paraffin-embedded (FFPE) samples. Leveraging random primers for RNA capture, the VITA platform captures the whole transcriptome independent of poly(A) tails and abundance, providing various hybridization options per fragment. By overcoming the limitations of current technologies, VITA platform empowers researchers to uncover the complex regulatory networks involving lncRNAs.
Advanced lncRNA Analysis Powered by VITA
To demonstrate the outstanding capabilities of the VITA platform in analyzing lncRNAs, we conducted comprehensive transcriptome profiling in both frozen and FFPE cancer tissues.
In our analysis, the VITA platform showcased its exceptional performance by capturing 11,078 cells and detecting a total of 34,044 genes in a frozen liver cancer sample. The median gene count for this sample was 2,999, and the median UMI count was 7,221. In an FFPE-preserved human lung cancer sample, the VITA platform captured 9,649 cells and detected a total of 34,611 genes. The median gene count for this sample was 1,107, while the median UMI count stood at 1,994 (Table 1).
Table 1: Sequencing metrics of VITA libraries.
Unsupervised clustering of the frozen liver cancer sample resulted in 19 cell clusters, which were further annotated as six major cell types (Figure 2).
Figure 2: UMAP analysis (left) and cell type annotation (right) in a frozen human liver cancer sample.
In the FFPE sample of human lung cancer, unsupervised clustering delineated 15 cell clusters, which were annotated as ten major cell types (Figure 3).
Figure 3: UMAP analysis (left) and cell type annotation (right) in a FFPE human lung cancer sample.
Comprehensive lncRNA Profiling with VITA
Utilizing an innovative random primer-based strategy for RNA capture, the VITA platform enables the capture of full transcriptomes, including diverse RNA biotypes such as lncRNA, snRNA, and miRNA. In our analysis, lncRNAs comprised 42% of the total RNA in the frozen liver cancer sample and 40% in the FFPE lung cancer sample (Figure 4).
Figure 4: Proportions of RNA biotypes in a frozen human liver cancer sample (left) and a FFPE human lung cancer sample (right) detected with VITA platform.
UMAP clustering and cell type annotation, utilizing differentially expressed lncRNAs from both samples, identified the same six and ten major cell types (Figure 5) as previously detected in the total transcriptome data analysis (Figures 2 and 3). This finding underscores the efficacy of lncRNAs in accurately delineating cell types.
Figure 5: Cell type annotation based on differentially expressed lncRNA in a frozen human liver cancer sample (left) and a FFPE human lung cancer sample (right).
Given the tissue-specific expression of many lncRNAs, they hold great promise as biomarkers for various health conditions. We quantified lncRNA expression levels in both samples, revealing distinct expression patterns across different cell types (Figure 6).
Figure 6: Cell-type specific expression of lncRNAs in a frozen human liver cancer sample (up) and a FFPE human lung cancer sample (down).
The cellular expression patterns of lncRNAs vary significantly by tissue type and disease context. In the liver cancer sample, the lncRNA ENSG0000028XXXX is highly expressed in endothelial cells, LINC0XXXX in B cells, and MSX-XXX in hepatic stellate cells. In the lung cancer sample, ENSG0000028YYYY shows high expression in macrophages, LINC0XXXX in T cells, and FEXXXX in endothelial cells (Figure 7).
Figure 7: Expression levels of lncRNAs in various cell types in a frozen human liver cancer sample (up) and a FFPE human lung cancer sample (down) (LncRNA names are masked to protect unpublished data).
LncRNAs play crucial roles in the spatial and temporal regulation of gene expression. By analyzing the association of lncRNAs and mRNAs, single-cell lncRNA analysis with VITA platform offers in-depth insights into the mechanisms of lncRNA mediated gene regulation in various cell types. This analysis revealed various associations of specific lncRNAs and mRNAs in certain cell types (Figure 8).
Figure 8: LncRNA-mRNA pair analysis in various cell types in a frozen human liver cancer sample (up) and a FFPE human lung cancer sample (down).
Unleashing the Power of Single-Cell lncRNA Analysis
Single-cell lncRNA analysis with the VITA platform offers precise subpopulation identification, cell type annotation and provides deep insights into the complex mechanisms by which lncRNAs influence gene expression. This comprehensive high-resolution data establishes VITA as a revolutionary tool in the field.
VITA is reshaping transcriptomic research, facilitating the precise delineation and functional analysis of lncRNAs in disease progression, immune responses, and cellular differentiation. This advancement opens avenues for the development of innovative diagnostic and therapeutic strategies. Join us to revolutionize research and decode the intricate mechanisms of health and disease.
*For further inquiries or more information about M20's innovative VITA platform, 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] Crick, F.H.C. (1958) On protein synthesis. Soc. Exp. Biol. 12, 138–163.
[2] Crick, F. (1970) Central dogma of molecular biology. Nature227, 561–563.
[3] Fernandes JCR, Acuña SM, Aoki JI, Floeter-Winter LM, Muxel SM. Long Non-Coding RNAs in the Regulation of Gene Expression: Physiology and Disease. Non-Coding RNA. 2019; 5(1):17.
[4] Lin, N., Chang, K. Y., Li, Z., Gates, K., Rana, Z. A., Dang, J., Zhang, D., Han, T., Yang, C. S., Cunningham, T. J., Head, S. R., Duester, G., Dong, P. D., & Rana, T. M. (2014). An evolutionarily conserved long noncoding RNA TUNA controls pluripotency and neural lineage commitment. Molecular cell, 53(6), 1005–1019.
[5] Carpenter, S., Aiello, D., Atianand, M. K., Ricci, E. P., Gandhi, P., Hall, L. L., Byron, M., Monks, B., Henry-Bezy, M., Lawrence, J. B., O'Neill, L. A., Moore, M. J., Caffrey, D. R., & Fitzgerald, K. A. (2013). A long noncoding RNA mediates both activation and repression of immune response genes. Science (New York, N.Y.), 341(6147), 789–792.
[6] Gupta, R., Shah, N., Wang, K. et al.Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature 464, 1071–1076 (2010).
