In a field where methods evolve faster than memory, reproducibility and collaboration aren’t just nice to have; they’re how you survive.
When I first started working with spatial transcriptomics, I’d already spent over a decade in bioinformatics. It felt like the natural next step, full of promise: mapping gene expression in a spatial context, preserving tissue architecture, and moving from gene lists to something more meaningful.
But the learning curve was steeper than I expected. The data was large, the image files proprietary, and the tools fragmented; one for alignment, another for clustering, a third for image overlays. None worked together seamlessly. There was no single source of truth; just GitHub issues and preprints. Every decision, like how to normalize or how to map clusters back to coordinates, had technical and biological consequences I couldn’t fully grasp right away.
I’d read the papers, installed the packages, even run example workflows. But when I tried to follow a tutorial on spatial clustering, everything spiraled: niche identification, PCA tuning, noise from misaligned regions. I filled pages with notes, but ended up with more questions than answers.
Other projects moved forward while I was stuck reprocessing the same few samples, trying to follow published workflows that gave inconsistent results.
Eventually, I had to admit it: I thought I was ready, but I wasn’t. Not from lack of effort, but because the ground had shifted. Spatial wasn’t just a new toolset. It was a new way of thinking.
Things started to shift when I asked for help and connected with others facing the same issues. And that’s when I realized I wasn’t alone.
At conferences, in webinars, and in casual conversations, I keep hearing the same thing. Whether it’s spatial, long-read, or single-cell, people feel overwhelmed.
They’re overwhelmed not just by the pace of new technologies, but by the fear that their hard-won work won’t hold up. A pipeline breaks, a tool gets deprecated, and suddenly months of analysis are unusable or out of date.
Everyone’s running at full force, but there’s no way to win the race because the finish line keeps moving.
More Data, More Tools, More Complexity
The pressure to innovate in life sciences is constant. Every year brings new experimental techniques designed to see deeper, resolve finer details, or measure more layers of biology at once. Each new assay generates new types of data. That means new file formats, new tools, and new questions to answer.
Pipelines evolve just to keep up with what the assays make possible. A workflow that worked last year may not support the resolution, structure, or scale of this year’s data.
Tool developers move fast, but not always in sync. Formats change, dependencies break, and documentation rarely keeps up. Preprints introduce new ideas, but long-term support is often missing.
And because research questions keep changing, so do the goals of your analysis. Specialized knowledge becomes outdated. Broad knowledge feels shallow. The more you try to keep up, the more awareness you have of how much the ground is shifting beneath your feet.
The Cost of Chasing Every Tool
When you’re always chasing the next tool, things start to slip.
Projects lose consistency. One team member uses one method, the next uses something else. Parameters change. File names change. People leave, and no one remembers what was done.
Even when work gets done, handing it off is frustrating. You spend hours retracing someone else's steps. Or worse, you have to redo everything from scratch.
Reproducibility suffers. Confidence takes a hit. You hesitate to share results, worried they might not hold up. And all of this adds up to stress, burnout, and the feeling that no matter how much you learn, you’re still behind.
What’s Actually Worth Learning
My experience as a bioinformatician (and a developer of a bioinformatics platform) taught me this: The answer isn’t learning every new tool. It’s creating a system that holds up, even as the tools change.
Start with stable infrastructure. Something you can build on, rather than rebuild every time.
Track everything: parameters, container versions, input files. Make your work reproducible by default, not just when you have time to clean things up.
And most importantly, use systems that let you evolve without losing your history. So when you switch methods or revisit a project, the thread isn’t lost.
How Via Foundry Helps You Keep Building Without Losing Your Place
That’s why we built Via Foundry the way we did.
It’s designed to support change, because change isn’t the exception in bioinformatics, it’s the rule.
With containerization and version control built in, you don’t have to worry about drift. Every run is tracked: inputs, parameters, software versions, and outputs.
That means when someone leaves, hands off a project, or comes back months later, the work is still usable.
It also means researchers can focus on the biological questions, such as planning comparisons or annotating differentially expressed genes, while Via Foundry takes care of tool selection and execution behind the scenes. That frees them from managing tool installs or chasing bugs from outdated environments.
Every tool comes with its own dependencies and database requirements. Even in containerized setups, installing and maintaining them can take up significant time and space. Via Foundry handles that too, so you can spend less time managing environments and more time planning your next project.
You keep evolving. But you stop losing track.
You’re Not Behind. You’re Building.
This field isn’t slowing down. It never has.
The real challenge goes beyond keeping up with the new developments, and includes not losing what you’ve already built.
As teams grow and methods multiply, the need for shared infrastructure and institutional memory becomes even more important.
Centralization and collaboration aren’t just nice to have. They’re how science scales.
Via Foundry gives teams the foundation to keep going, together.
Learn how to stay in the race without starting over every time. Schedule a demo here: https://www.viascientific.com/schedule-a-call
