3′ UTR functions as the availability of experimental approaches is often crucial for progress and discoveries. Compared with research on transcription or translation regulation, or on microRNA (miRNA)-mediated posttranscriptional regulation, research on miRNA-independent functions of 3′ UTRs lags far behind. Most functions of 3′ UTRs are mediated by RNA-binding proteins (RBPs). But, in contrast to miRNA target sites, the binding motifs for many RBPs are still not known (Baltz et al. 2012; Ray et al. 2013; Dominguez et al. 2018). And even if they are known, clear functional effects often require the motifs to be present several times. As the motifs are often spread out over large distances, functional motifs can often not be identified using deletion mutants (Besse et al. 2009; Kristjansdottir et al. 2015; Ma and Mayr 2018). Moreover, many RBPs bind to the same motif (Ray et al. 2013; Dominguez et al. 2018), and it is usually unknown whether they compete or cooperate (Hennig and Sattler 2015). Finally, more than half of human genes use alternative cleavage and polyadenylation to generate mRNA isoforms that differ only in their 3′ UTRs (Lianoglou et al. 2013). This makes research on 3′ UTR isoform–specific functions challenging as the amino acid sequences of the proteins generated from the alternative 3′ UTR isoforms are identical.
This review summarizes how research on 3′ UTR functions has been approached in the last 25 years, what has been learned, and how it could be addressed in the future. Several topics relevant for 3′ UTR biology are being reviewed elsewhere in this collection, including regulation by miRNAs, RNA editing, RNA structure, the identification of RBPs, and RNA–protein interactions. Also, several “noncanonical” functions of 3′ UTRs were reviewed recently and are not included here (Mayr 2017). Thus, this review is not comprehensive but tries to focus on experimental approaches and concepts to show how regulation by 3′ UTRs is accomplished.
Unveil
SeekSpace, an advanced spatial single-cell technology, independently developed by SeekGene.
✨ Key Features of SeekSpace:
- Precision Localization: Achieve accurate spatial localization of individual cells, enabling deeper insights into tissue architecture and cellular interactions.
- High Throughput: Process up to 30,000 cells per sample, making studies more feasible than ever.
- Robust Data Quality: With a median UMI count around 1,000, expect reliable and high-quality data you can trust.
- Simultaneous Detection: Conduct concurrent detection of single-cell gene expression and spatial information from the same tissue slice, streamlining your workflow.
- Simplified Analysis: Eliminate the need for complex permeability condition exploration or deconvolution analysis.
💡 SeekSpace is poised to empower researchers in unlocking the complexities of multicellular systems and advancing our understanding of biological processes at the single-cell level.
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Comparative study spacial scRna Seq
SeekSpace stands out among spatial single-cell RNA sequencing (scRNA-seq) technologies due to its unique combination of features. Here’s how it compares to other popular solutions like 10x Genomics’ Visium, Spatial Transcriptomics, and MERFISH:
1. Precision Localization
- SeekSpace: Offers precise spatial localization of individual cells, enhancing the ability to analyze tissue architecture and cell-cell interactions.
- Visium: Captures spatial gene expression data at the tissue section level but lacks single-cell resolution.
- MERFISH: Provides high spatial resolution at the single-cell level, but is limited in multiplexing capabilities compared to other platforms.
2. High Throughput
- SeekSpace: Processes up to 30,000 cells per sample, making it more feasible for large-scale studies.
- Visium: Typically processes fewer cells per sample; throughput is dependent on the number of capture spots, which can be a limiting factor for larger tissues.
- Spatial Transcriptomics: Throughput varies, but can struggle to match the high cell count capabilities of SeekSpace due to spot limitations.
- MERFISH: While high-resolution, the number of cells analyzed per experiment is often lower due to its imaging-based approach.
3. Robust Data Quality
- SeekSpace: Consistently high-quality data with a median UMI count of around 1,000, ensuring robust and reliable outputs.
- Visium: Provides good quality data, but UMI counts can vary based on tissue type and sample preparation.
- Spatial Transcriptomics: Often requires extensive pre-processing and quality control to match the data consistency seen with SeekSpace.
- MERFISH: Offers high specificity and sensitivity, but may require more complex data acquisition and analysis pipelines.
4. Simultaneous Detection
- SeekSpace: Allows for concurrent detection of gene expression and spatial data from the same tissue slice, streamlining workflow and reducing the need for separate assays.
- Visium: Primarily focuses on transcriptome data but does not offer direct simultaneous spatial detection at the single-cell level.
- Spatial Transcriptomics: Similar to Visium, it captures spatial gene expression data but often requires integration with other datasets for more comprehensive analysis.
- MERFISH: Can achieve spatial and gene expression detection, but the workflow is more complex and often less scalable than SeekSpace.
5. Simplified Analysis
- SeekSpace: Eliminates the need for complex permeability condition exploration or deconvolution analysis, reducing time and effort during data processing.
- Visium: Data analysis can require additional steps for deconvolution to infer single-cell information.
- Spatial Transcriptomics: Often necessitates complex deconvolution algorithms, which can complicate data interpretation.
- MERFISH: Requires sophisticated imaging analysis tools, which can be challenging to set up and optimize.
Overall Comparison
SeekSpace offers a more streamlined, high-throughput, and precise solution compared to existing technologies. While methods like MERFISH and Visium excel in certain areas (e.g., high resolution or transcriptome-wide profiling), they often require more complex workflows and may not match SeekSpace's efficiency in handling larger cell populations. Additionally, SeekSpace’s ability to simplify analysis and avoid cumbersome deconvolution steps makes it particularly user-friendly and scalable for research labs looking to delve into spatial biology without extensive technical barriers.
Summary Table
| Feature | SeekSpace | Visium | Spatial Transcriptomics | MERFISH |
|---|---|---|---|---|
| Precision Localization | High | Moderate | Moderate | High |
| High Throughput | Up to 30,000 cells | Limited | Limited | Variable |
| Robust Data Quality | Consistent UMI ~1,000 | Variable | Requires QC | High, but complex |
| Simultaneous Detection | Yes | No | No | Yes, complex |
| Simplified Analysis | Yes | Requires Deconvolution | Requires Deconvolution | Complex, Imaging |
SeekSpace is positioned to be a powerful tool for researchers aiming to gain comprehensive insights into the spatial dynamics of multicellular systems, combining ease of use, data robustness, and scalability.
Gene detection Growth
Genes attendus
time increase with SeekSoul
users of SeekSpace
SeekSpace projects deployed in Europe in 2025
New Transcriptome information turnover
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