Terabyte-Scale Biological Imaging

GPU-accelerated analysis and visualization for cutting-edge biological research

Quick Takeaways

Challenge: Scientists acquiring terabytes of impressive but uninformative data—state-of-the-art microscopes without hypothesis-driven design

Solution: Outcome-driven experimental design framework—ensure teams build what’s needed at every decision point, not just initial planning

Impact: 170+ international scientists annually, publications in Nature (2017, 2021), Nature Communications, framework published in Journal of Cell Science (2020)

Role: Data Scientist at an elite research institution’s Advanced Imaging Center

Philosophy: Not all quantitative data are informative—prevent wasted effort by ensuring experiments can test the hypothesis

Key Insight: Continuously return to “what does success look like?” throughout execution—not just upfront planning

Technical Contribution: GPU-accelerated pipelines for real-time visualization and terabyte-scale feature extraction

Learn the framework → | Publications →

The Challenge: When Technology Outpaces Understanding

The research institution hosts some of the world’s most advanced biological imaging facilities. Scientists from around the globe come to use cutting-edge microscopes that generate terabyte-scale time-lapse datasets over critical 2-week experimental windows.

But state-of-the-art microscopes create a dangerous trap: biologists can spend considerable time and resources acquiring huge amounts of data without proper planning, only to realize later that the data cannot appropriately address their biological question.

The real challenge wasn’t technical - it was conceptual. How do you help researchers design experiments that produce informative data, not just quantitative data?

The Core Problem:

  • Modern microscopes will always generate quantifiable data (a digital image is intrinsically a data map)
  • But not all quantifiable data are biologically meaningful
  • Researchers often get side-tracked by new observations or start experiments without hypothesis-driven design
  • Descriptive semantics like “analyze the spatial-temporal dynamics of an organelle” don’t translate into measurable experimental variables

Why This Matters: Even accurate, quantitative datasets generated with best practices won’t necessarily yield biologically meaningful results. An image can be quantified, but those measurements are only informative when they’re pertinent to the hypothesis.

My role was to transform vague biological queries into hypothesis-driven experiments that produced data capable of challenging those hypotheses.

The Solution: Outcome-Driven Experimental Design

As Data Scientist for the Advanced Imaging Center, I pioneered an outcome-driven approach to quantitative microscopy - a methodology I later published in Journal of Cell Science (2020) as “Hypothesis-driven quantitative fluorescence microscopy: the importance of reverse-thinking in experimental design.”

The Philosophy: Start at the End

Conventional experimental workflow moves forward: Hypothesis → Experimental planning → Sample preparation → Image acquisition → Processing → Results

I reversed it: Hypothesis → What informative results would test this? → What data would produce those results? → What parameters are needed? → Which microscope fits?

This ensures the hypothesis remains central to every decision and that experiments yield information capable of challenging the hypothesis.

Why Outcome-Driven Discipline Works:

The essence of efficient experimental design is continuously returning to “what would answer my hypothesis?” at every decision point. Without this discipline, experiments quickly become too ambitious and unnecessarily complex—acquiring impressive but uninformative data.

Key Insight: Microscopy isn’t a single assay - it’s a collection of assays that vary depending on experimental design. You can measure molecular abundance, spatial location, movement behavior, morphological changes, structural features, molecular association, enzymatic activity, and more. The challenge is knowing which measurements answer your hypothesis.

From Descriptive to Quantitative Semantics

I taught researchers to translate vague descriptions into measurable analytical metrics:

  • “Membrane 3D dynamics” → filopodial angular deflection, membrane surface curvature, turnover rate
  • “Mitochondrial morphology changes” → sphericity, volume, fission/fusion event rates
  • “Organelle dynamics” → velocity, directionality, persistence, diffusion constant
  • “Protein localization changes” → co-occurrence coefficient, correlation coefficient, image ratio

Once you define the necessary metrics, the required imaging parameters become clear: temporal resolution, spatial resolution, phototoxicity tolerance, field of view, imaging depth, multiplexing capacity.

Technical Implementation

Armed with this philosophy, I built infrastructure to support hypothesis-driven experiments:

  • DirectX and CUDA pipelines for real-time visualization enabling iterative experimental refinement during live imaging sessions
  • Feature extraction and tracking workflows for terabyte-scale 4D/5D datasets, translating raw images into analytical metrics (velocity, directionality, morphology)
  • Scalable processing infrastructure spanning laptops to multi-GPU HPC clusters, ensuring tools worked regardless of researcher’s computational resources
  • Signal processing algorithms optimized for biological imaging, ensuring accurate measurements that represent biological truth
  • Interactive visualization tools bridging observation and quantification - letting researchers explore data while maintaining analytical rigor

Collaborative Consulting Process

For 170+ international scientists annually, I guided experimental design through the reverse-logic framework:

  1. Clarify the hypothesis: Translate descriptive working models into testable, negatable statements with defined experimental variables
  2. Define informative results: What analytical metrics would quantitatively test the hypothesis? (Not “desired outcomes” but metrics that could support or negate the hypothesis)
  3. Identify required data: What needs to be captured by the microscope to produce those metrics?
  4. Determine experimental parameters: What imaging parameters (resolution, speed, depth, phototoxicity tolerance) does the data demand?
  5. Select appropriate microscope: Which instrument aligns with these parameters? (Often NOT the latest super-resolution technology, but the tool best suited to the specific analytical metrics)
  6. Establish rigorous controls: Define experimental baselines, validation standards, and comparative controls to ensure measurements accurately represent biological truth

This iterative process - re-evaluating each step during experimentation - prevented researchers from acquiring massive datasets that couldn’t answer their questions.

The Impact

Publications in Top Journals: Work contributed directly to publications in Nature (2017, 2021), Nature Communications, Molecular Biology of the Cell, and Journal of Experimental Botany

Global Scientific Enablement: Supported 170+ international research teams annually, enabling discoveries that wouldn’t have been possible without computational infrastructure

Reproducible Science: Built analysis pipelines adopted as standards across multiple research groups

Performance Breakthroughs:

  • 10x speedups for typical visualization workflows
  • Sub-minute processing for datasets that previously took hours
  • Real-time feedback during live experiments

Key Publications:

Technical Stack

GPU Computing: CUDA, DirectX, OpenGL for real-time processing Languages: C++, MATLAB, Python High-Performance Computing: Distributed computing with OpenMP, cluster scheduling Imaging Analysis: ITK, custom algorithms for segmentation, tracking, lineaging, colocalization Visualization: Custom rendering pipelines, VTK integration Data Systems: HDF5, TIFF stacks, custom formats for multi-dimensional imaging