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 analysis, respectively.

无创且无标记的光谱显微镜（spectromicroscopy）技术可以提供定量的生化信息，以补充基因组测序，转录组谱分析和蛋白质组学分析。但是，光谱显微镜技术会产生高维数据。单个光谱图像的采集范围可能从数十分钟到数小时不等，这取决于所需的空间分辨率和图像尺寸。这大大限制了可观察到的瞬时生物过程的时间尺度。为了解决这一挑战并将光谱学技术朝着高效的实时时空化学成像方向发展，我们开发了一种无网格的自主自适应采样方法。我们的方法大大减少了图像采集时间，同时在更陡峭的理化梯度区域中增加了采样密度。秀丽隐杆线虫。我们分别使用性能指标和多元红外光谱分析法定量和定性地评估数据采集的效率。