scRNAseq Demands You to Be a Researcher, Coder, and Innovator

Via Foundry makes it possible to excel in all three without the complexity

Single-cell RNA sequencing (scRNAseq) has brought a new level of detail to biological research. However, this breakthrough comes with its own set of challenges. Scientists in this field find themselves wearing multiple hats—they need to know the science, and they also need to be able to run complex bioinformatics pipelines.

First, scientists have to wrangle the intricacies of the analysis itself. Then comes the task of making sure their analysis is scalable, reusable, and reproducible. Fortunately, solutions have been emerging that eliminate both of these pressure points.

The Goal of Single-Cell RNA Sequencing

The goals of scRNAseq can vary depending on the research question at hand. Broadly speaking, researchers use scRNAseq to:

1. Identify and Characterize Cell Types and Subtypes: scRNAseq allows for the detection of distinct cell populations within a heterogeneous tissue, enabling the discovery of new cell types and subtypes based on their unique gene expression profiles.
2. Understand Cellular Heterogeneity: By analyzing gene expression at the single-cell level, researchers can explore the diversity of cells within a sample, which is crucial for understanding complex tissues like the brain or tumors.
3. Study Developmental Processes and Lineage Relationships: scRNAseq helps in mapping the trajectories of cell differentiation and development by capturing snapshots of cells at various stages, revealing how cells evolve over time.
4. Investigate Cellular Responses to Stimuli or Treatments: Researchers can assess how individual cells respond to specific stimuli, drugs, or environmental changes, providing insights into cellular functions and mechanisms of action.
5. Explore Disease Mechanisms: In diseases like cancer, scRNAseq can uncover molecular changes at the single-cell level, aiding in the understanding of disease progression, metastasis, and drug resistance.
6. Analyze the Tumor Microenvironment: scRNAseq enables the study of interactions between cancer cells and surrounding stromal or immune cells, which is essential for developing targeted therapies.
7. Advance Personalized Medicine: By identifying unique cellular signatures in patients, scRNAseq can help tailor treatments to individual genetic profiles, potentially improving therapeutic outcomes and reducing side effects.

But the process isn’t always straightforward. Each experiment generates massive amounts of data, and finding patterns within that data is as much about experimentation as it is about applying the right tools. 

In our experience, doing this well requires a highly adaptable sandbox environment where scientists and bioinformaticians can iterate and optimize different parameters, test various algorithms, and adjust other settings on the fly to extract the insights from the native complexity of single-cell analytics. 

The Challenge for Scientists

In RNA therapeutics, researchers are deeply specialized in their field, focusing on complex biological processes and discovering cellular mechanisms. But scRNAseq requires more than just knowledge of RNA. It demands a level of skill in coding and bioinformatics that can be hard to acquire. You aren’t just running data through a machine and collecting results; you’re working through the data, adjusting parameters, testing ideas, and figuring out the bigger picture.

Many scientists face a bottleneck at this point–when it’s time to process and analyze the single-cell data they generate. They likely know the exact questions they want to ask of the data, but still have to rely on bioinformaticians to process it, and shape the data so their questions can be quickly answered.

The Challenge for Bioinformaticians

For bioinformaticians in labs with established scRNAseq pipelines, the challenge shifts from developing pipelines, to managing and scaling them. 

Ensuring that all the data is running through the approved pipelines, tracking the necessary metadata across a growing number of samples, and handling the substantial memory and processing power required for industrial scRNAseq analysis at scale places a significant burden on your bioinformaticians. 

In addition to that, to effectively interpret and visualize these complex results, researchers increasingly rely on interactive applications, which enable dynamic exploration and analysis of single-cell data.

Via Foundry: Removing the Bottlenecks While Enhancing Control

Via Foundry eliminates the challenges endemic to single cell analytics. It gives scientists direct access to powerful tools for exploring their data, without the need for advanced programming. Instead of waiting for a bioinformatician to run an analysis, you can control the process yourself. Via Foundry lets you directly explore different clustering techniques, adjust parameters and algorithms, and experiment with your data in a low-code/no-code environment. 

This not only speeds up the research process but also enables a more hands-on approach to customizing your analysis, making it easier for you to spot the patterns and insights that matter most, while keeping a detailed audit trail of your analytical journey.

Let’s dig into how this is all done:

Running Single Cell RNA Sequencing in Via Foundry

As demonstrated in the video above, running a single-cell RNA sequencing pipeline using Via Foundry is straightforward. Below, we’ll guide you through the process step-by-step, providing an overview of the pipeline and the tools Via Foundry offers to help you make sense of your data.

Step 1: Setting Up Your Run Environment

The first step is to select the appropriate run environment. Via Foundry allows you to seamlessly execute single-cell RNA seq on the correct infrastructure.

To get started, select the Via Foundry Run Environment, which ensures that everything is set up correctly to process your data with maximum efficiency.

Step 2: Inputting Your Sequencing Reads

Next, you’ll need to input your sequencing reads. You can either provide the raw BCL folder directly from your storage or upload pre-processed FASTQ files. If you’re working with public data from NCBI/GEO, importing the data is straightforward using an accession ID.

Via Foundry offers flexibility in how you manage your data. You can check out the platform’s documentation for step-by-step instructions on creating a data source and importing it for processing.

Step 3: Choosing the Genome Build

After the reads are uploaded, the next step is to choose the reference genome that will guide the alignment process. If you are working with human samples, the HG38 reference genome is often the most appropriate option for human scRNAseq experiments.

Via Foundry offers a range of pre-built genome options, but if you’re working with custom genomes, you can also modify the inputs to align with your research needs.

Step 4: Tuning the Analysis Settings

While Via Foundry optimizes most settings for typical use cases, you can adjust additional input parameters if necessary. These include options like ambient RNA removal, which helps reduce contamination. For most users, the default settings work well, but the platform gives you the flexibility to fine-tune your analysis if needed.

Step 5: Running the Single-Cell RNA Analysis

With the data in place and parameters set, it’s time to run the analysis. By setting the run scRNA Analysis option to true, Via Foundry will take care of the heavy lifting, processing your data through clustering, quality control, and identifying marker genes.

Advanced users can access further customizations by adjusting the settings through the wrench icon, allowing for deeper control over their analysis if specific experimental needs arise.

Step 6: Reviewing the Results

Once your analysis is complete, you’ll be able to dive into the results. Via Foundry generates a detailed report that includes:

• Count Metrics and QC Reports: This includes crucial metrics like the barcode rank plot, which helps distinguish real cells from background noise, and the Mean Reads per Cell, giving insight into the depth of sequencing coverage.
• Clustering Reports: Via Foundry allows you to assess the stability and accuracy of your clustering results. You can explore tSNE and UMAP visualizations, which help display cell populations based on gene expression patterns, and adjust clustering resolutions to find the optimal setup for your data.
• Gene Markers: Using differential expression analysis, Via Foundry identifies the top gene markers for each cluster, giving you insights into the biology of each cell type. Heatmaps and other visualization tools allow you to explore these relationships in detail.
• Interactive Apps: Researchers can conveniently launch applications like scViewer, iSEE, and Cellxgene within Via Foundry, enabling dynamic exploration and thorough analysis of single-cell data. If coding is needed to create custom plots or implement non-standard analysis methods, tools like Jupyter Notebooks and RStudio are also available.

Conclusion

With Via Foundry, running and analyzing a single-cell RNA sequencing experiment doesn’t have to be a bottleneck. The platform’s low-code/no-code environment empowers both scientists and bioinformaticians to take control of their workflows, from setting up and running pipelines to interpreting and visualizing results.

By simplifying the process and making it more interactive, Via Foundry ensures that researchers can focus on the science, not the technology, unlocking faster insights and deeper understanding of cellular biology.

If you’re ready to transform how you approach scRNAseq analysis, Via Foundry is the tool that puts the power of advanced bioinformatics directly into your hands. Request a demo to see it in action today.

About Via Scientific Inc.

Via Scientific Inc., a Cambridge-based tech and AI company, has launched Via Foundry, a multi-omics accelerator platform designed to advance scientific breakthroughs. Via Foundry automates complex data tasks with features like drag-and-drop pipelines and customizable analytics, ensuring data is shareable, reusable, and reproducible, allowing researchers to focus on scientific insights instead of code. Via Scientific supports biotech, pharma, research institutes, and universities.