Autonomous adaptive data acquisition for scanning hyperspectral imaging.

Communications Biology
Elizabeth A HolmanPaul W Sternberg

Abstract

Non-invasive and label-free spectral microscopy (spectromicroscopy) techniques can provide quantitative biochemical information complementary to genomic sequencing, transcriptomic profiling, and proteomic analyses. However, spectromicroscopy techniques generate high-dimensional data; acquisition of a single spectral image can range from tens of minutes to hours, depending on the desired spatial resolution and the image size. This substantially limits the timescales of observable transient biological processes. To address this challenge and move spectromicroscopy towards efficient real-time spatiochemical imaging, we developed a grid-less autonomous adaptive sampling method. Our method substantially decreases image acquisition time while increasing sampling density in regions of steeper physico-chemical gradients. When implemented with scanning Fourier Transform infrared spectromicroscopy experiments, this grid-less adaptive sampling approach outperformed standard uniform grid sampling in a two-component chemical model system and in a complex biological sample, Caenorhabditis elegans. We quantitatively and qualitatively assess the efficiency of data acquisition using performance metrics and multivariate infrared spectral analysi...Continue Reading

References

Feb 11, 1994·Science·M ChalfieD C Prasher
Apr 15, 2006·Science·Ben N G GiepmansRoger Y Tsien
Jul 6, 2014·Nature Protocols·Matthew J BakerFrancis L Martin
May 9, 2019·Nature Communications·Mohammad H RohbanAnne E Carpenter

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Methods Mentioned

BETA
PCA
imaging techniques

Software Mentioned

Scipy package
MATLAB
PyQt
scipy
LUR
Windows
AADA
OMNIC
LOOCV

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